@ARTICLE{Vegter2023171,
author={Vegter, D. and van Hillegersberg, J. and Olthaar, M.},
title={Performance measurement system for circular supply chain management},
journal={Sustainable Production and Consumption},
year={2023},
volume={36},
pages={171-183},
doi={10.1016/j.spc.2023.01.003},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146298283&doi=10.1016%2fj.spc.2023.01.003&partnerID=40&md5=f38578f5a7ed0f52d696c03f047697dd},
affiliation={University of Twente, Faculty of Behavioural, Management and Social Sciences, Hallenweg 17, Enschede, 7522 NH, Netherlands; Jheronimus Academy of Data Science, Sint Janssingel 92, 's-Hertogenbosch, 5211 DA, Netherlands; NHL Stenden University, Academy of Economics and Logistics, van Schaikweg 94, Emmen, 7811 KL, Netherlands},
abstract={Circular supply chain management is characterized by multiple interdependencies between various performance objectives for circularity, economic, environmental and social performance. A performance measurement system which takes these interdependencies into account is currently not available, however, is needed to identify effective actions, involve stakeholders and prevent unintended consequences of actions. The objective of this paper is to develop this performance measurement system. Using a methodology of multiple case studies, we identify the interdependencies between the various performance objectives and combine these in a causal loop diagram and a system dynamics model. We then evaluate the usability of these models in two companies which are in transition to circular supply chain management. The companies confirm the relevance of performance objectives, their interdependencies and the validity of the outputs. This results in the following contributions: Service lifetime - the time period of use, recovery and reuse until incineration - is as relevant to circularity as the much-mentioned product lifetime. The maturity of circularity follows four phases: virgin materials only, combination, recovered materials only, deterioration. Shortening the supply chain leads to a rebound effect and increases the environmental impact. The circular premium can relate to shareholders as well as to customers. © 2023 The Authors},
author_keywords={Circular economy;  Circular supply chain;  Performance measurement;  Performance measurement system;  Sustainability;  System dynamics},
keywords={Combustion;  Deterioration;  Economic and social effects;  Environmental impact;  Sustainable development;  System theory, Circular economy;  Circular supply chain;  Economic performance;  Environmental performance;  Management IS;  Performance measurement system;  Performance measurements;  Performance objective;  Social performance;  System Dynamics, Supply chain management},
references={Alamerew, Y.A., Brissaud, D., Modelling reverse supply chain through system dynamics for realizing the transition towards the circular economy: a case study on electric vehicle batteries (2020) J. Clean. Prod., 254; Appolloni, A., Jabbour, C.J.C., D'Adamo, I., Gastaldi, M., Settembre-Blundo, D., Green recovery in the mature manufacturing industry: the role of the green-circular premium and sustainability certification in innovative efforts (2022) Ecol. Econ., 193; Aronson, D., Angelakis, D., Step-by-step stocks and flows: converting from causal loop diagrams (2018) Systems Thinker, 10; ASCM, Supply Chain Operations Reference Model SCOR. Version 14.0 (2022), https://scor.ascm.org/processes/introduction, Retrieved 21 September 2022 from; Ayati, M.S., Shekarian, E., Majava, J., Wæhrens, B.V., Toward a circular supply chain: understanding barriers from the perspective of recovery approaches (2022) J. Clean. Prod., , 131775; Barlas, Y., Formal aspects of model validity and validation in system dynamics (1996) Syst. Dyn. Rev., 12 (3), pp. 183-210; Bassi, A.M., Bianchi, M., Guzzetti, M., Pallaske, G., Tapia, C., Improving the understanding of circular economy potential at territorial level using systems thinking (2021) Sustain. Prod. Consum., 27, pp. 128-140; Bauwens, T., Are the circular economy and economic growth compatible? A case for post-growth circularity (2021) Resour. Conserv. Recycl., 175; Berkhout, P.H., Muskens, J.C., Velthuijsen, J.W., Defining the rebound effect (2000) Energy Policy, 28 (6-7), pp. 425-432; Blum, N.U., Haupt, M., Bening, C.R., Why “Circular” doesn't always mean “Sustainable” (2020) Resour. Conserv. Recycl., 162; Bocken, N.M., De Pauw, I., Bakker, C., Van Der Grinten, B., Product design and business model strategies for a circular economy (2016) J. Ind. Prod. Eng., 33 (5), pp. 308-320; Bressanelli, G., Perona, M., Saccani, N., Challenges in supply chain redesign for the circular economy: a literature review and a multiple case study (2019) Int. J. Prod. Res., 57 (23), pp. 7395-7422; Cagno, E., Negri, M., Neri, A., Giambone, M., One Framework to Rule Them All: An Integrated, Multi-level and Scalable Performance Measurement Framework of Sustainability, Circular Economy and Industrial Symbiosis. Sustainable Production and Consumption (2022); Campuzano, F., Mula, J., Supply Chain Simulation: A System Dynamics Approach for Improving Performance (2011), Springer Science & Business Media; Castro, C.G., Trevisan, A.H., Pigosso, D.A., Mascarenhas, J., The rebound effect of circular economy: definitions, mechanisms and a research agenda (2022) J. Clean. Prod., , 131136; Colasante, A., D'Adamo, I., The circular economy and bioeconomy in the fashion sector: emergence of a “sustainability bias” (2021) J. Clean. Prod., 329; Cooper, T., Beyond Recycling: The Longer Life Option (1994), New Economics Foundation London; D'Adamo, I., Lupi, G., Sustainability and resilience after COVID-19: a circular premium in the fashion industry (2021) Sustainability, 13 (4), p. 1861; Eisenhardt, K.M., Building theories from case study research (1989) Acad. Manag. Rev., 14 (4), pp. 532-550; Elgazzar, S., Tipi, N., Jones, G., Key characteristics for designing a supply chain performance measurement system (2019) Int. J. Product. Perform. Manag., 68 (2), pp. 296-318; Towards a Circular Economy—Economic and Business Rationale for an Accelerated Transition (2013), Ellen MacArthur Foundation Cowes, UK; Circular Consumer Electronics (2018), Ellen MacArthur Foundation Cowes, UK; https://ellenmacarthurfoundation.org/topics/circular-economy-introduction/examples, Ellen Mac Arthur Foundation, n.d.Ellen Mac Arthur Foundation. (nd). Circular economy examples and case studies. Retrieved 16 August 2022 from; Farooque, M., Zhang, A., Thürer, M., Qu, T., Huisingh, D., Circular supply chain management: a definition and structured literature review (2019) J. Clean. Prod., 228, pp. 882-900; Geissdoerfer, M., Morioka, S.N., de Carvalho, M.M., Evans, S., Business models and supply chains for the circular economy (2018) J. Clean. Prod., 190, pp. 712-721; Kazancoglu, Y., Ekinci, E., Mangla, S.K., Sezer, M.D., Kayikci, Y., Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics (2021) Bus. Strateg. Environ., 30 (1), pp. 71-91; Kim, D.H., (1999) Introduction to Systems Thinking, 16. , Pegasus Communications Waltham, MA; Kirchherr, J., Reike, D., Hekkert, M., Conceptualizing the circular economy: an analysis of 114 definitions (2017) Resour. Conserv. Recycl., 127, pp. 221-232; Lahane, S., Kant, R., Shankar, R., Circular supply chain management: a state-of-art review and future opportunities (2020) J. Clean. Prod., 258; Lim, M.K., Lai, M., Wang, C., Lee, Y., Circular economy to ensure production operational sustainability: a green-lean approach (2022) Sustain. Prod. Consum., 30, pp. 130-144; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., Measuring supply chain performance: a lifecycle framework and a case study (2018) Int. J. Oper. Prod. Manag., 38 (4), pp. 934-956; McElroy, M.W., Making materiality determinations: a context-based approach (No. 2019-6) (2019) UNRISD Working Paper; Oladimeji, O.O., Keathley-Herring, H., Cross, J.A., System dynamics applications in performance measurement research: a systematic literature review (2020) Int. J. Product. Perform. Manag., 69 (7), pp. 1541-1578; Potting, J., Hekkert, M.P., Worrell, E., Hanemaaijer, A., Circular economy: measuring innovation in the product chain (2017) Planbureau voor de Leefomgeving, 2544; Rebs, T., Brandenburg, M., Seuring, S., System dynamics modeling for sustainable supply chain management: a literature review and systems thinking approach (2019) J. Clean. Prod., 208, pp. 1265-1280; Roci, M., Salehi, N., Amir, S., Shoaib-ul-Hasan, S., Asif, F.M., Mihelič, A., Rashid, A., Towards circular manufacturing systems implementation: a complex adaptive systems perspective using modelling and simulation as a quantitative analysis tool (2022) Sustain. Prod. Consum., 31, pp. 97-112; Roy, T., Garza-Reyes, J.A., Kumar, V., Kumar, A., Agrawal, R., Redesigning traditional linear supply chains into circular supply chains–A study into its challenges (2022) Sustain. Prod. Consum., 31, pp. 113-126; Sacco, P., Vinante, C., Borgianni, Y., Orzes, G., Circular economy at the firm level: a new tool for assessing maturity and circularity (2021) Sustainability, 13 (9), p. 5288; Sehnem, S., Vazquez-Brust, D., Pereira, S.C.F., Campos, L.M., Circular economy: benefits, impacts and overlapping (2019) Supply Chain Manag. Int. J., 24 (6), pp. 784-804; Senge, P.M., Forrester, J.W., Tests for building confidence in system dynamics models. System dynamics (1980) TIMS Studies in Management Sciences, 14, pp. 209-228; Taghikhah, F., Voinov, A., Shukla, N., Extending the supply chain to address sustainability (2019) J. Clean. Prod., 229, pp. 652-666; Towill, D.R., Industrial dynamics modelling of supply chains (1996) Int. J. Phys. Distrib. Logist. Manag., 26 (2), pp. 23-42; Uhrenholt, J.N., Kristensen, J.H., Rincón, M.C., Adamsen, S., Jensen, S.F., Waehrens, B.V., Maturity model as a driver for circular economy transformation (2022) Sustainability, 14 (12), p. 7483; https://www.un.org/sustainabledevelopment/sustainable-development-goals/, United Nations, n.d.United Nations. (nd). Sustainable development goals. Retrieved 8 December 2022 from; van der Velden, N.M., Vogtländer, J.G., Monetisation of external socio-economic costs of industrial production: a social-LCA-based case of clothing production (2017) J. Clean. Prod., 153, pp. 320-330; Vegter, D., van Hillegersberg, J., Olthaar, M., Supply chains in circular business models: processes and performance objectives (2020) Resour. Conserv. Recycl., 162; Vegter, D., Hillegersberg, J.V., Olthaar, M., Performance measurement Systems for Circular Supply Chain Management: current state of development (2021) Sustainability, 13 (21), p. 12082; Vogtländer, J.G., Brezet, H.C., Hendriks, C.F., The virtual eco-costs ‘99 a single LCA-based indicator for sustainability and the eco-costs-value ratio (EVR) model for economic allocation (2001) Int. J. Life Cycle Assess., 6 (3), pp. 157-166; Circular Transition Indicators V3.0. Metrics for Business, by Business (2022); Zink, T., Geyer, R., Circular economy rebound (2017) J. Ind. Ecol., 21 (3), pp. 593-602},
correspondence_address1={Vegter, D.; University of Twente, Hallenweg 17, Netherlands; email: d.a.vegter@utwente.nl},
publisher={Elsevier B.V.},
issn={23525509},
language={English},
abbrev_source_title={Sustain. Prod. Consum.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Saleheen2023,
author={Saleheen, F. and Habib, M.M.},
title={Embedding attributes towards the supply chain performance measurement},
journal={Cleaner Logistics and Supply Chain},
year={2023},
volume={6},
doi={10.1016/j.clscn.2022.100090},
art_number={100090},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144564636&doi=10.1016%2fj.clscn.2022.100090&partnerID=40&md5=5af2501924b30f41a07b7f05e24666e3},
affiliation={Faculty of Business, Higher Colleges of Technology (HCT), United Arab Emirates; School of Business and Entrepreneurship, Independent University – Bangladesh (IUB), Dhaka, Bangladesh},
abstract={Dependency on the Balanced Scorecard (BSC) and the Supply Chain Operations Reference (SCOR) model is inadequate in the context of sustainability, environment, and other multiple factors along with the changes in the market dynamics. As a result, performance evaluation through a holistic approach in the manufacturing industry is not comprehensive. Therefore, a Supply Chain Performance Measurement (SCPM) is required to assess the supply chain's effectiveness. This research formulates Integrated Supply Chain Performance Measurement (ISCPM) model through the supply chain performance attributes in the outlook of input-process-output considering the BSC and the SCOR model at three decisions levels with an application of quantitative techniques to bring synergic effect to all stakeholder issues. A gap analysis was performed through systematic literature reviews. The integrated supply chain performance measurement model incorporates ten supply chain performance measurement attributes supplier relationship management (SRM), internal supply chain management (ISCM), and customer relationship management (CRM). The model was then tested from the various sectors in the manufacturing industry of Bangladesh, through a simple random sampling. The ISCPM model furnishes stakeholders of the manufacturing supply chain with the appropriate strategies to review and appraise their performance toward fulfillment of the ultimate goals. This model is suggested to be implemented in manufacturing industries to improve their operational performance. The study contributes to academia developing the ISCPM model with ten performance measurement attributes that enrich the literature on SCPM. It also contributes to the industry for practitioners by bringing synergy into the framework and performance measurement attributes which eventually upgrade the prosperity of the manufacturing industry and eventually the society. © 2022 The Author(s)},
author_keywords={Balanced scorecard model;  Integrated supply chain performance measurement;  Supply chain operations reference;  Supply chain performance measurement},
references={Ali, Z., Gongbing, B., Mehreen, A., Does supply chain finance improve SMEs performance? The moderating role of trade digitization (2018) Bus. Process. Manag. J.; Alora, A., Barua, M., Barrier analysis of supply chain finance adoption in manufacturing companies (2019) BIJ, 26 (7), pp. 2122-2145; Antony, J., Sony, M., An empirical study into the limitations and emerging trends of Six Sigma in manufacturing and service organizations (2019) International Journal of Quality & Reliability Management, 37 (3), pp. 470-493; Bacoup, P., Michel, C., Habchi, G., Pralus, M., From a Quality Management System (QMS) to a Lean Quality Management System (LQMS) (2018) The TQM Journal, 30 (1), pp. 20-42; Ballou, R.H., (1992), (1978). Basic Business Logistics. Englewood Cliffs NJ: Prentice-Hall. Ballou, R.H; Ballou, R.H., The evaluation and future of logistics and supply chain management (2007) Eur. Bus. Rev., 19 (4), pp. 332-348; Barratt, M., (2004), 9, pp. 30-42. , Understanding the meaning of collaboration in the supply chain. Int. J. Emerald Group Publishing Limited, (1), Doi. 10.1108/13598540410517566; Baymout, M., Financial Supply Chain Management (2015) Int. J. Sci. Eng. Res., 6 (5), pp. 222-232; Beamon, B.M., Measuring Supply Chain Performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; (2019), https://www.balancedscorecard.org/BSC-Basics/About-the-Balanced-, BSC. Balanced Scorecard Institute. Retrieved from Scorecard; Chen, H., Supply chain risk's impact on corporate financial performance (2018) Int. J. Oper. Prod. Manage., 38 (3), pp. 713-731; Chopra, S., Meindl, P., Supply chain management: Strategy, Planning, and Operation (2015), fourth ed. Pearson Education Boston, MA; Chopra, S., Meindl, P., Supply chain management: Strategy, planning, and operation (2016), sixth ed. Pearson Harlow, Essex; Ciampi, F., The co-evolutionary relationship between digitalization and organizational agility: Ongoing debates, theoretical developments, and future research perspectives (2022) Technol. Forecast. Soc. Chang., 176, p. 12138; Creswell, W.J., Creswell, D.J., Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (2018), fifth ed. SAGE Publications Inc. Los Angeles; Dohale, V., Gunasekaran, A., Akarte, M.M., Verma, P., 52 Years of Manufacturing Strategy: An Evolutionary Review of Literature (1969–2021) (2022) Int. J. Prod. Res., 60 (2), pp. 569-594; Fosso, W.S., Akter, S., Understanding supply chain analytics capabilities and agility for data-rich environments (2019) Int. J. Oper. Prod. Manage., 39 (6/7/8), pp. 887-912; Garay-Rondero, C., Martinez-Flores, J., Smith, N., Caballero Morales, S., Aldrette- Malacara, A., Digital supply chain model in Industry 4.0 (2019) J. Manuf. Technol. Manage.; Gulliksen, H., Tukey, J.W., Reliability for the law of comparative judgment (1952) Psychometrika, 23, pp. 95-110; Hoque, Z., 20 years of studies on the balanced scorecard: Trends, accomplishments (2014) Gaps and opportunities for future research”. The British accounting review, 46 (1), pp. 33-59; Kaplan, R.S., Norton, D.P., The Balanced Scorecard-Measures that Drive Performance (1992) Harv. Bus. Rev.; Kaplan, R.S., Norton, D.P., Alignment: Using the Balanced Scorecard to Create Corporate Synergies (2006), Harvard Business School Press Boston, MA; Kothari, C.R.G., (2019), p. 8. , Research Methodology. Methods and Techniques, Ch I. (4th ed.) New Delhi: New Age International Limited; (2019), S. Kottala K. Herbert empirical investigation of supply chain operations reference model practices and supply chain performance: Evidence from manufacturing sector“ International Journal of Productivity and Performance Management Vol. ahead-of-print No. ahead-of-print.”An 10.1108/IJPPM-09-2018-0337; Kumar, R., Research Methodology: A Step-by-Step Guide for Beginners (2014), fourth ed. SAGE Publications Inc. London; Kumar, G., Goswami, M., Sustainable supply chain performance, its practice and impact on barriers to collaboration (2019) Int. J. Product. Perform. Manage., 68 (8), pp. 1434-1456; Kurien, G.P., Qureshi, M.N., House of sustainable waste management: an implementation framework (2018) Int. J. Sustain. Manuf., 4 (1), pp. 79-96; Maghsoudi, A., Pazirandeh, A., Visibility, resource sharing and performance in supply chain relationships: insights from humanitarian practitioners (2016) Supply Chain Manage., 21 (1), pp. 125-139; Miraz, H.M., Saleheen, F., Almunawar, N.M., Sumi, R.F., Hasan, T., (2022), Green, Circular, and Digital Economies as Tools for Recovery and Sustainability, Factors Affecting on IoT Base Business Management: in Post, Pandemic Period, IGI Global Publisher of Timely Knowledge, 701 E. Chocolate Ave. Hershey, PA 17033, USA, DOI: 10.4018/978-1-7998-9664-7; Miraz, H.M., Habib, M.M., Saleheen, F., ICT Integration in Management for Public Educational Institutions in Bangladesh (2016) Open J. Adv. Bus. Manage. (OJABM), 1 (1), pp. 01-10; Molina-Besch, K., Prioritization guidelines for green food packaging development (2016) Br. Food J., 118, pp. 2512-2533; Monczka, R.M., Handfield, R.B., Giunipero, L.C., Patterson, J.L., Purchasing and Supply chain management (2016), sixth ed. Cengage Boston; Panahifar, F., Byrne, P., Salam, M., Heavey, C., Supply chain collaboration and firm's performance: The critical role of information sharing and trust (2018) J. Enterp. Inf. Manage., 31 (3), pp. 358-379; Paul, J., Criado, A.R., The art of writing the literature review: What do we know and what do we need to know? (2020) Int. Bus. Rev., Elsevier, 29 (4); Manzur Hossain Khan, S.A.M., Mustaffa, N.A., Mamun Habib, M.D., Miraz, M.H., Saleheen, F., Rahman, M., Change Management for Information System in Public Service (2020) Int. Supply Chain Technol. J., 6 (8); Sachin, S.K., Gunasekaran, A., Big data-driven supply chain performance measurement system: a review and framework for implementation (2019) Int. J. Prod. Res., 58 (1); Saleheen, F., Habib, M.M., (2017), p. 651. , Performance measurement framework of supply chain management: A multi-criteria decision analysis (MCDA), AHP approach. Proceedings from International Conference on Business and Management (ICBM), ISBN 978-984-34-2360-3, Dhaka, Bangladesh, 21-22 September; Saleheen, F., Habib, M.M., (2022), 15 (2). , Integrated Supply Chain Performance Measurement Model for the Manufacturing Industry, Journal of Service Science and Management, Scientific Research Publishing, ISSN Print: 1940-9893, ISSN Online: 1940-9907, April; Saleheen, F., Habib, M.M., (2022), 10 (3). , Constructing Performance Measurement Index for the Manufacturing Industry: An Empirical Study, Open Journal of Social Sciences, Scientific Research Publishing, ISSN Print: 2327-5952, ISSN Online: 2327-5960, March; Saleheen, F., Habib, M.M., Hanafi, Z., (2014), 3 (6-1), pp. 51-56. , Demand and Supply Planning in Retail Operations. International Journal of Business and Economics Research. Special Issue: Supply Chain Management: Its Theory and Applications. ISSN: 2328-7543 (Print), 2328-756X (Online), Science Publishing Group, New York, USA, DOI: 10.11648/j.ijber.s.2014030601.18; Saleheen, F., Habib, M.M., Hanafi, Z., (2018), 7, pp. 2051-3771. , An Empirical Study on Supply Chain Management Performance Measurement through AHP. International Journal of Supply Chain Management (IJSCM) (Scopus), ExcelingTech Publisher, UK, Number 6, ISSN 2050-7399 (Online); Saleheen, F., Habib, M.M., Hanafi, Z., (2018), Supply Chain Performance Measurement Model: A Literature Review. Int. J. Supply Chain Manage. (IJSCM), 7(3), 70-78, ExcelingTech Publishers; Saleheen, F., Habib, M., Constructing Performance Measurement Index for the Manufacturing Industry: An Empirical Study (2022) Open J. Soc. Sci., 10, pp. 137-151; Saleheen, F., Habib, M.M., (2022), https://doi.org/10.52305/FBAX6283, “What to Know about Supply Chain Management”, Supply Chain Performance Measurement for Manufacturing Industry, Nova Science Publishers, USA, DOI:; Saleheen, F., Habib, M.M., (2022), Supply Chain Resiliency, Efficiency, and Visibility in the Post-Pandemic Era, Supply Chain Practice towards Resilience. IGI Global, DOI: 10.4018/978-1-7998-9506-0, April; Saleheen, F., Habib, M.M., (2022), 11 (1). , http://excelingtech.co.uk/, Supply Chain Performance Measurement Models: A Comparative Study. International Journal of Supply Chain Management (IJSCM), ExcelingTech Pub, UK, ISSN: 2050-7399 (Online), 2051-3771 (Print), Page 74; Saleheen, F., Habib, M., Global Supply Chain Disruption Management Post Covid 19 (2022) Am. J. Ind. Bus. Manage., 12, pp. 376-389; Saleheen, F., Habib, M.M., Hanafiz, Z., (2018), 7, pp. 2051-3771. , Supply Chain Performance Measurement Model: A Literature Review. Int. J. Supply Chain Manage. (IJSCM), ExcelingTech Publisher, UK, Number 3, ISSN 2050-7399 (Online); Saleheen, F., Habib, M.M., Hanafi, Z., An Implementation of Balanced Scorecard on Supply Chain Performance Measurement in Manufacturing Industry (2019) Proceedings From 2nd International Conference on Business and Management (ICBM), 320, , Brac University ISBN 978-984-344-3540; (2012), www.supply-chain.org, SCOR 10. Supply Chain Council, Inc. Supply Chain Reference Model. ISBN 0-615-20259-4, USA. Retrieved from:; Singh, A., Misra, S., Ordering drivers of green supply chain management practices in the Indian construction industry: An impact assessment framework (2020) Int. J. Qual. Reliab. Manage.; Soosay, C., Hyland, P., A decade of supply chain collaboration and directions for future research (2015) Supply Chain Manage., 20 (6), pp. 613-630; Zhu, J., Liu, W., A tale of two databases: the use of Web of Science and Scopus in academic papers (2020) Scientometrics, 123 (1), pp. 321-335},
correspondence_address1={Saleheen, F.; Faculty of Business, United Arab Emirates; email: fsaleheen@hct.ac.ae},
publisher={Elsevier Ltd},
issn={27723909},
language={English},
abbrev_source_title={Clean. Logist. Supply Chain},
document_type={Article},
source={Scopus},
}

@ARTICLE{Qazi2023241,
author={Qazi, A. and Simsekler, M.C.E. and Formaneck, S.},
title={Supply chain risk network value at risk assessment using Bayesian belief networks and Monte Carlo simulation},
journal={Annals of Operations Research},
year={2023},
volume={322},
number={1},
pages={241-272},
doi={10.1007/s10479-022-04598-3},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126065326&doi=10.1007%2fs10479-022-04598-3&partnerID=40&md5=6c593db971e884a8e17945eed77504a2},
affiliation={School of Business Administration, American University of Sharjah, Sharjah, United Arab Emirates; Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates; Faculty of Management, Canadian University Dubai, Dubai, United Arab Emirates},
abstract={Several techniques have been proposed in supply chain risk management to capture causality among risks in a network setting and prioritize risks regarding their network-wide propagation impact. However, these techniques might be unable to capture the Risk Network Value at Risk (RNVaR), the maximum risk exposure expected at a given confidence level for a given timeframe, associated with individual supply chain performance measures within a network setting. With an exclusive focus on using point estimates in existing techniques, there is a risk of overlooking tail distributions and ignoring critical risks. In this paper, we aim to address this research gap by introducing new risk metrics and a new process theoretically grounded in Bayesian Belief Network and Monte Carlo Simulation frameworks. Integrating these two techniques helps establish the RNVaR that is associated with different performance measures and the relative importance of individual risks for resource allocation. We demonstrate the application of the proposed process through a real case study in the telecommunications industry and compare the results of this study with existing approaches. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.},
author_keywords={Bayesian belief network;  Monte Carlo simulation;  Performance measures;  Risk metrics;  Risk network value at risk;  Supply chain risk management},
references={Ackermann, F., Howick, S., Quigley, J., Walls, L., Houghton, T., Systemic risk elicitation: Using causal maps to engage stakeholders and build a comprehensive view of risks (2014) European Journal of Operational Research, 238 (1), pp. 290-299; Adedipe, T., Shafiee, M., Zio, E., Bayesian network modelling for the wind energy industry: An overview (2020) Reliability Engineering & System Safety, 202. , &, (,).,., https://doi.org/10.1016/j.ress.2020.107053; Aloini, D., Dulmin, R., Mininno, V., Ponticelli, S., Supply chain management: A review of implementation risks in the construction industry (2012) Business Process Management Journal, 18 (5), pp. 735-761; Aqlan, F., Lam, S.S., A fuzzy-based integrated framework for supply chain risk assessment (2015) International Journal of Production Economics, 161, pp. 54-63. , &, (,).,., https://doi.org/10.1016/j.ijpe.2014.11.013; Aven, T., Risk assessment and risk management: Review of recent advances on their foundation (2016) European Journal of Operational Research, 253 (1), pp. 1-13. , (, a,).,., (,)., https://doi.org/10.1016/j.ejor.2015.12.023; Aven, T., Supplementing quantitative risk assessments with a stage addressing the risk understanding of the decision maker (2016) Reliability Engineering & System Safety, 152, pp. 51-57; Aven, T., Improving risk characterisations in practical situations by highlighting knowledge aspects, with applications to risk matrices (2017) Reliability Engineering & System Safety, 167, pp. 42-48; Badurdeen, F., Shuaib, M., Wijekoon, K., Brown, A., Faulkner, W., Amundson, J., Quantitative modeling and analysis of supply chain risks using Bayesian theory (2014) Journal of Manufacturing Technology Management, 25 (5), pp. 631-654; Berger, P.D., Gerstenfeld, A., Zeng, A.Z., How many suppliers are best? A decision-analysis approach (2004) Omega, 32 (1), pp. 9-15. , &, (,).,., (,)., https://doi.org/10.1016/j.omega.2003.09.001; Boutselis, P., McNaught, K., Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context (2019) International Journal of Production Economics, 209, pp. 325-333; Cao, S., Bryceson, K., Hine, D., An ontology-based Bayesian network modelling for supply chain risk propagation (2019) Industrial Management and Data Systems, 119 (8), pp. 1691-1711. , &, (,).,., (,)., https://doi.org/10.1108/IMDS-01-2019-0032; Chaudhuri, A., Mohanty, B.K., Singh, K.N., Supply chain risk assessment during new product development: A group decision making approach using numeric and linguistic data (2013) International Journal of Production Research, 51 (10), pp. 2790-2804; Chiu, C.-H., Choi, T.-M., Supply chain risk analysis with mean-variance models: A technical review (2016) Annals of Operations Research, 240, pp. 489-507; Choi, T.-M., Risk analysis in logistics systems: A research agenda during and after the COVID-19 pandemic (2021) Transportation Research Part e: Logistics and Transportation Review, 145; Chopra, S., Sodhi, M.S., Managing risk to avoid supply-chain breakdown (2004) MIT Sloan Management Review, 46 (1), pp. 53-61. , https://sloanreview.mit.edu/article/managing-risk-to-avoid-supplychain-breakdown/; Diabat, A., Govindan, K., Panicker, V.V., Supply chain risk management and its mitigation in a food industry (2012) International Journal of Production Research, 50 (11), pp. 3039-3050; Dogan, I., Aydin, N., Combining Bayesian networks and total cost of ownership method for supplier selection analysis (2011) Computers and Industrial Engineering, 61 (4), pp. 1072-1085; Dong, Q., Cooper, O., An orders-of-magnitude AHP supply chain risk assessment framework (2016) International Journal of Production Economics, 182, pp. 144-156; Dubey, R., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Blome, C., Luo, Z., Antecedents of resilient supply chains: An empirical study (2019) IEEE Transactions on Engineering Management, 66 (1), pp. 8-19; Duijm, N.J., Recommendations on the use and design of risk matrices (2015) Safety Science, 76, pp. 21-31; Fahimnia, B., Tang, C.S., Davarzani, H., Sarkis, J., Quantitative models for managing supply chain risks: A review (2015) European Journal of Operational Research, 247 (1), pp. 1-15; Fan, H., Cheng, T.C.E., Li, G., Lee, P.K.C., The effectiveness of supply chain risk information processing capability: An information processing perspective (2016) IEEE Transactions on Engineering Management, 63 (4), pp. 414-425; Fenton, N.E., Noguchi, T., Neil, M., An extension to the noisy-or function to resolve the ‘Explaining Away’ deficiency for practical Bayesian network problems (2019) IEEE Transactions on Knowledge and Data Engineering, 31 (12), pp. 2441-2445; Garvey, M.D., Carnovale, S., Yeniyurt, S., An analytical framework for supply network risk propagation: A Bayesian network approach (2015) European Journal of Operational Research, 243 (2), pp. 618-627; Gaudenzi, B., Borghesi, A., Managing risks in the supply chain using the AHP method (2006) International Journal of Logistics Management, 17 (1), pp. 114-136; Gaudenzi, B., Qazi, A., Assessing project risks from a supply chain quality management (SCQM) perspective (2021) International Journal of Quality & Reliability Management, 38 (4), pp. 908-931. , &, (,).,., (,)., https://doi.org/10.1108/IJQRM-01-2020-0011; GeNIe 2.0 The Decision Systems Laboratory, Genie and SMILE, , http://genie.sis.pitt.edu/about.html, Retrieved June 5, 2015, from; Ghadge, A., Dani, S., Chester, M., Kalawsky, R., A systems approach for modelling supply chain risks (2013) Supply Chain Management: An International Journal, 18 (5), pp. 523-538; Heckmann, I., Comes, T., Nickel, S., A critical review on supply chain risk—Definition, measure and modeling (2015) Omega, 52, pp. 119-132; Ho, W., Zheng, T., Yildiz, H., Talluri, S., Supply chain risk management: A literature review (2015) International Journal of Production Research, 53 (16), pp. 5031-5069; Hossain, N.U.I., Amrani, S.E., Jaradat, R., Marufuzzaman, M., Buchanan, R., Rinaudo, C., Modeling and assessing interdependencies between critical infrastructures using Bayesian network: A case study of inland waterway port and surrounding supply chain network (2020) Reliability Engineering & System Safety, 198; Hosseini, S., Al Khaled, A., Sarder, M.D., A general framework for assessing system resilience using Bayesian networks: A case study of sulfuric acid manufacturer (2016) Journal of Manufacturing Systems, 41, pp. 211-227; Hosseini, S., Barker, K., A Bayesian network model for resilience-based supplier selection (2016) International Journal of Production Economics, 180, pp. 68-87; Hosseini, S., Ivanov, D., A new resilience measure for supply networks with the ripple effect considerations: A Bayesian network approach (2019) Annals of Operations Research, , https://doi.org/10.1007/s10479-019-03350-8; Hosseini, S., Ivanov, D., Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review (2020) Expert Systems with Applications, 161; Hosseini, S., Ivanov, D., Blackhurst, J., Conceptualization and measurement of supply chain resilience in an open-system context (2020) IEEE Transactions on Engineering Management; Hosseini, S., Ivanov, D., Dolgui, A., Review of quantitative methods for supply chain resilience analysis (2019) Transportation Research Part e: Logistics and Transportation Review, 125, pp. 285-307; Hosseini, S., Ivanov, D., Dolgui, A., Ripple effect modelling of supplier disruption: Integrated Markov chain and dynamic Bayesian network approach (2020) International Journal of Production Research, 58 (11), pp. 3284-3303. , &, (, b,).,., (,)., https://doi.org/10.1080/00207543.2019.1661538; Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M.D., Barker, K., Khaled, A.A., Resilient supplier selection and optimal order allocation under disruption risks (2019) International Journal of Production Economics, 213, pp. 124-137; Jensen, F.V., Nielsen, T.D., (2007) Bayesian networks and decision graphs, , Springer; Jiang, M., Lu, J., The analysis of maritime piracy occurred in Southeast Asia by using Bayesian network (2020) Transportation Research Part e: Logistics and Transportation Review, 139; Käki, A., Salo, A., Talluri, S., Disruptions in supply networks: A probabilistic risk assessment approach (2015) Journal of Business Logistics, 36 (3), pp. 273-287; Kardes, I., Ozturk, A., Cavusgil, S.T., Cavusgil, E., Managing global megaprojects: Complexity and risk management (2013) International Business Review, 22 (6), pp. 905-917; Kjaerulff, U.B., Madsen, A.L., (2013) Bayesian Networks and Influence diagrams : A Guide to Construction and Analysis, , Springer; Knemeyer, A.M., Zinn, W., Eroglu, C., Proactive planning for catastrophic events in supply chains (2009) Journal of Operations Management, 27 (2), pp. 141-153; Kull, T.J., Talluri, S., A supply risk reduction model using integrated multicriteria decision making (2008) IEEE Transactions on Engineering Management, 55 (3), pp. 409-419; Lee, C.K.M., Yeung, Y.C., Hong, Z., An integrated framework for outsourcing risk management (2012) Industrial Management & Data Systems, 112 (4), pp. 541-558; Li, L., Wang, J., Leung, H., Zhao, S., A Bayesian method to mine spatial data sets to evaluate the vulnerability of human beings to catastrophic risk (2012) Risk Analysis, 32 (6), pp. 1072-1092; Lin, P., Neil, M., Fenton, N., Improved high dimensional discrete Bayesian network inference using triplet region construction (2020) Journal of Artificial Intelligence Research, 69, pp. 231-295. , &, (,).,., https://doi.org/10.1613/jair.1.12198; Liu, Z., Lai, M., Zhou, T., Zhou, Y., A supply chain risk assessment model based on multistage influence diagram (2009) 6Th International Conference on Service Systems and Service Management, pp. 72-75. , IEEE; Lockamy, A., McCormack, K., Analysing risks in supply networks to facilitate outsourcing decisions (2010) International Journal of Production Research, 48 (2), pp. 593-611. , &, (,).,., (,)., https://doi.org/10.1080/00207540903175152; Lockamy, A., McCormack, K., Modeling supplier risks using Bayesian networks (2012) Industrial Management & Data Systems, 112 (2), pp. 313-333; Luo, L., Zhang, L., Wu, G., Bayesian belief network-based project complexity measurement considering causal relationships (2020) Journal of Civil Engineering and Management, 26 (2), pp. 200-215. , &, (,).,., (,)., https://doi.org/10.3846/jcem.2020.11930; Manuj, I., Mentzer, J.T., Global supply chain risk management (2008) Journal of Business Logistics, 29 (1), pp. 133-155; Mengshoel, O.J., Poll, S., Kurtoglu, T., Developing large-scale Bayesian networks by composition: Fault diagnosis of electrical power systems in aircraft and spacecraft (2009) The IJCAI-09 Workshop on Self-* and Autonomous Systems: Reasoning and Integration Challenges (SAS-09), , Pasadena, California, USA; Mogre, R., Talluri, S., Amico, F.D., A decision framework to mitigate supply chain risks: An application in the offshore-wind industry (2016) IEEE Transactions on Engineering Management, 63 (3), pp. 316-325; Nadkarni, S., Shenoy, P.P., A causal mapping approach to constructing Bayesian networks (2004) Decision Support Systems, 38 (2), pp. 259-281; Nagurney, A., Matsypura, D., Global supply chain network dynamics with multicriteria decision-making under risk and uncertainty (2005) Transportation Research Part e: Logistics and Transportation Review, 41 (6), pp. 585-612; Neil, M., Chen, X., Fenton, N., Optimizing the calculation of conditional probability tables in hybrid Bayesian networks using binary factorization (2012) IEEE Transactions on Knowledge and Data Engineering, 24 (7), pp. 1306-1312; Neil, M., Fenton, N., Nielson, L., Building large-scale Bayesian networks (2000) Knowledge Engineering Review, 15 (3), pp. 257-284; Nepal, B., Yadav, O.P., Bayesian belief network-based framework for sourcing risk analysis during supplier selection (2015) International Journal of Production Research, 53 (20), pp. 6114-6135. , &, (,).,., (,)., https://doi.org/10.1080/00207543.2015.1027011; Norrington, L., Quigley, J., Russell, A., Van der Meer, R., Modelling the reliability of search and rescue operations with Bayesian belief networks (2008) Reliability Engineering & System Safety, 93 (7), pp. 940-949; Ojha, R., Ghadge, A., Tiwari, M.K., Bititci, U.S., Bayesian network modelling for supply chain risk propagation (2018) International Journal of Production Research, 56 (17), pp. 5795-5819; Pavlov, A., Ivanov, D., Dolgui, A., Sokolov, B., Hybrid fuzzy-probabilistic approach to supply chain resilience assessment (2018) IEEE Transactions on Engineering Management, 65 (2), pp. 303-315; Pitchforth, J., Mengersen, K., A proposed validation framework for expert elicited Bayesian networks (2013) Expert Systems with Applications, 40 (1), pp. 162-167; Qazi, A., Akhtar, P., Risk matrix driven supply chain risk management: Adapting risk matrix based tools to modelling interdependent risks and risk appetite (2020) Computers & Industrial Engineering, 139. , &, (,).,., https://doi.org/10.1016/j.cie.2018.08.002; Qazi, A., Dickson, A., Quigley, J., Gaudenzi, B., Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks (2018) International Journal of Production Economics, 196, pp. 24-42; Qazi, A., Quigley, J., Dickson, A., Ekici, Ş.Ö., Exploring dependency based probabilistic supply chain risk measures for prioritising interdependent risks and strategies (2017) European Journal of Operational Research, 259 (1), pp. 189-204. , https://doi.org/10.1016/j.ejor.2016.10.023; Qazi, A., Quigley, J., Dickson, A., Kirytopoulos, K., Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects (2016) International Journal of Project Management, 34 (7), pp. 1183-1198; Qazi, A., Simsekler, M.C.E., Worst Expected Best method for assessment of probabilistic network expected value at risk: Application in supply chain risk management (2022) International Journal of Quality & Reliability Management, 39 (1), pp. 155-175. , &, (,).,., (,)., https://doi.org/10.1108/IJQRM-07-2020-0238; Rodgers, M., Oppenheim, R., Ishikawa diagrams and Bayesian belief networks for continuous improvement applications (2019) The TQM Journal, 31 (3), pp. 294-318. , &, (,).,., (,)., https://doi.org/10.1108/TQM-11-2018-0184; Rodgers, M., Singham, D., A framework for assessing disruptions in a clinical supply chain using Bayesian belief networks (2020) Journal of Pharmaceutical Innovation, 15, pp. 467-481. , &, (,).,., https://doi.org/10.1007/s12247-019-09396-2; Ruiz-Benítez, R., López, C., Real, J.C., The lean and resilient management of the supply chain and its impact on performance (2018) International Journal of Production Economics, 203, pp. 190-202; Sarker, S., Engwall, M., Trucco, P., Feldmann, A., Internal visibility of external supplier risks and the dynamics of risk management silos (2016) IEEE Transactions on Engineering Management, 63 (4), pp. 451-461; Segal, S., (2011) Corporate Value of Enterprise Risk Management: The Next Step in Business Management, , Wiley; Sener, A., Barut, M., Dag, A., Yildirim, M.B., Impact of commitment, information sharing, and information usage on supplier performance: A Bayesian belief network approach (2021) Annals of Operations Research, 303, pp. 125-158. , &, (,).,., https://doi.org/10.1007/s10479-019-03504-8; Shad, M.K., Lai, F.-W., Fatt, C.L., Klemeš, J.J., Bokhari, A., Integrating sustainability reporting into enterprise risk management and its relationship with business performance: A conceptual framework (2019) Journal of Cleaner Production, 208, pp. 415-425; Sharma, S., Routroy, S., Modeling information risk in supply chain using Bayesian networks (2016) Journal of Enterprise Information Management, 29 (2), pp. 238-254; Sharma, S.K., Sharma, S., Developing a Bayesian network model for supply chain risk assessment (2015) Supply Chain Forum: An International Journal, 16 (4), pp. 50-72. , https://doi.org/10.1080/16258312.2015.11728693; Simsekler, M.C.E., Qazi, A., Adoption of a data-driven Bayesian belief network investigating organizational factors that influence patient safety (2020) Risk Analysis, , https://doi.org/10.1111/risa.13610; Sinha, P.R., Whitman, L.E., Malzahn, D., Methodology to mitigate supplier risk in an aerospace supply chain (2004) Supply Chain Management: An International Journal, 9 (2), pp. 154-168; (2015) Society for Risk Analysis Glossary, , https://www.sra.org/risk-analysis-introduction/risk-analysis-glossary/; Sweeting, P., (2017) Financial enterprise risk management, , Cambridge University Press; Talluri, S., Kull, T.J., Yildiz, H., Yoon, J., Assessing the efficiency of risk mitigation strategies in supply chains (2013) Journal of Business Logistics, 34 (4), pp. 253-269; Tang, C.S., Zimmerman, J.D., Nelson, J.I., Managing new product development and supply chain risks: The Boeing 787 case (2009) Supply Chain Forum: An International Journal, 10 (2), pp. 74-86. , &, (,).,., (,)., https://doi.org/10.1080/16258312.2009.11517219; Tokdemir, O.B., Erol, H., Dikmen, I., Delay risk assessment of repetitive construction projects using line-of-balance scheduling and Monte Carlo simulation (2019) Journal of Construction Engineering and Management, 145 (2), p. 04018132; Tong, R., Cheng, M., Zhang, L., Liu, M., Yang, X., Li, X., The construction dust-induced occupational health risk using Monte-Carlo simulation (2018) Journal of Cleaner Production, 184, pp. 598-608; Trkman, P., McCormack, K., Supply chain risk in turbulent environments—A conceptual model for managing supply chain network risk (2009) International Journal of Production Economics, 119 (2), pp. 247-258; Tse, Y.K., Zhang, M., Tan, K.H., Pawar, K., Fernandes, K., Managing quality risk in supply chain to drive firm’s performance: The roles of control mechanisms (2019) Journal of Business Research, 97, pp. 291-303. , &, (,).,., https://doi.org/10.1016/j.jbusres.2018.01.029; Tummala, R., Schoenherr, T., Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP) (2011) Supply Chain Management: An International Journal, 16 (6), pp. 474-483; Wan, C., Yan, X., Zhang, D., Qu, Z., Yang, Z., An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks (2019) Transportation Research Part e: Logistics and Transportation Review, 125, pp. 222-240; Werner, C., Bedford, T., Cooke, R.M., Hanea, A.M., Morales-Nápoles, O., Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions (2017) European Journal of Operational Research, 258 (3), pp. 801-819; Wilson, M.C., The impact of transportation disruptions on supply chain performance (2007) Transportation Research Part e: Logistics and Transportation Review, 43 (4), pp. 295-320; Wu, T., Huang, S., Blackhurst, J., Zhang, X., Wang, S., Supply chain risk management: An agent-based simulation to study the impact of retail stockouts (2013) IEEE Transactions on Engineering Management, 60 (4), pp. 676-686; Zhang, G., Thai, V.V., Law, A.W.-K., Yuen, K.F., Loh, H.S., Zhou, Q., Quantitative risk assessment of seafarers’ nonfatal injuries due to occupational accidents based on bayesian network modeling (2020) Risk Analysis, 40 (1), pp. 8-23; Zhang, Y., Selecting risk response strategies considering project risk interdependence (2016) International Journal of Project Management, 34 (5), pp. 819-830},
correspondence_address1={Qazi, A.; School of Business Administration, United Arab Emirates; email: aeroactuary@gmail.com},
publisher={Springer},
issn={02545330},
language={English},
abbrev_source_title={Ann. Oper. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Israel202317,
author={Israel, B. and Mahuwi, L. and Mwenda, B.},
title={A review of financial and non-financial measures of supply chain performance},
journal={International Journal of Production Management and Engineering},
year={2023},
volume={11},
number={1},
pages={17-29},
doi={10.4995/ijpme.2023.18797},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85152405758&doi=10.4995%2fijpme.2023.18797&partnerID=40&md5=39a64027c793d19616ab542b8b4eece4},
affiliation={Department of Procurement and Supply Management, College of Business Education, Post Box 3810, Mbeya, Tanzania; Department of Accountancy, College of Business Education, Post Box 3810, Mbeya, Tanzania},
abstract={This study ascertains the pertinent measures and metrics of supply chain performance through an extensive literature review. A total of 53 international peer-reviewed journal articles published between 2012-2021 were retrieved from Scopus, Emerald insight, Elsevier and Springer databases for review. The articles reviewed were selected and categorised based on the approach, scope and contribution in assessing and ascertaining the measures and metrics of supply chain performance. Limited to the review of literature, this study provides a benchmark and approaches for ascertaining the level of supply chain performance from financial and non-financial perspectives. Throughout the review, the study found inventory turnover ratio, supply chain and logistics costs and cash flows reflected in terms of ROI, ROE, ROA, profit margin, working capital and assets as the prime financial measures of supply chain performance. Moreover, the study found customer satisfaction, delivery performance, quality services, enhanced relationships and competitive advantages as the key non-financial measures of supply chain performance. The study concludes and recommends the financial and non-financial measures as the appropriate approaches for ascertaining the level of supply chain performance. In order to appropriately ascertain the level of supply chain performance, performance measurement should be accounted for by all actors at each node of the supply chain, using the same approach, measures and metrics. Additionally, further studies are required to address the benefits and issues that impede supply chain performance and measurements across different sectors of the economy. © 2023 Authors.},
author_keywords={financial measures;  non-financial measures;  performance measurement;  supply chain key performance indicator;  Supply chain performance;  supply chain performance measures},
references={Agrell, J., Marbini, A., Frontier-based performance analysis models for supply chain management: State of the art and research directions (2013) Computers and industrial engineering, 66 (3), pp. 567-583. , https://doi.org/10.1016/j.cie.2013.02.014; Ali, R., Exploring antecedents of extended supply chain performance measures (2015) Benchmarking: An international journal, 22 (5), pp. 752-772. , https://doi.org/10.1108/BIJ-04-2013-0040; Anand, N., Grover, N., Measuring retail supply chain performance: Theoreticalmodel using key performance indicators (KPIs) (2015) Benchmarking: An international journal, 22 (1), pp. 1-16. , https://doi.org/10.1108/BIJ-05-2012-0034; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply chain management: An international journal, 17 (1), pp. 78-92. , https://doi.org/10.1108/13598541211212221; Baumann, E., Genoulaz, V., A framework for sustainable performance assessment of supply chain management practices (2014) Computers and industrial engineering, 76, pp. 138-147. , https://doi.org/10.1016/j.cie.2014.07.029; Benzidia, S., Makaoui, N., Improving SMEs performance through supply chain flexibility and market agility: IT orchestration perspective (2020) Supply chain forum: An international journal, 21 (3), pp. 173-184. , https://doi.org/10.1080/16258312.2020.1801108; Bhattacharya, K., Kumar, S., A robust two-layer green supply chain modelling under performance based fuzzy game theoretic approach (2020) Computers and industrial engineering, 152, p. 107005. , https://doi.org/10.1016/j.cie.2020.107005; Bjorklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply chain management: An international journal, 17 (1), pp. 29-39. , https://doi.org/10.1108/13598541211212186; Burritt, S., Measuring and managing sustainability performance of supply chains (2014) Supply chain management: An international journal, 19 (3), pp. 232-241. , https://doi.org/10.1108/SCM-02-2014-0061; Chand, P., Thakkar, J., Ghosh, K., Analysis of supply chain performance metrics for Indian mining & earthmoving equipment manufacturing companies using hybrid MCDM model (2020) Resources Policy, 68, p. 101742. , https://doi.org/10.1016/j.resourpol.2020.101742; Charkha, P., Jaju, S., Identification of performance measures for supply chain performance measurement in textile enterprise (2019) Smart Technologies for Energy, Environment and Sustainable Development. Lecture Notes on Multidisciplinary Industrial Engineering, pp. 607-617. , https://doi.org/10.1007/978-981-13-6148-7_58, Kolhe, M., Labhasetwar, P., Suryawanshi, H. (eds) Springer, Singapore; Dhaigude, A., Kapoor, R., The mediation role of supply chain agility on supply chain orientation-supply chain performance link (2017) Journal of decision systems, 26 (3), pp. 275-293. , https://doi.org/10.1080/12460125.2017.1351862; Elgazzar, S., Tipi, N., Jones, G., Key characteristics for designing a supply chain performance measurement system (2019) International journal of productivity and performance management, 68 (2), pp. 296-318. , https://doi.org/10.1108/IJPPM-04-2018-0147; Elrod, C., Murray, S., Bande, S., A review of performance metrics for supply chain Management (2013) Engineering Management Journal, 25 (3), pp. 39-50. , https://doi.org/10.1080/10429247.2013.11431981; Frederico, G., Garza-Reyes, J., Kumar, A., Kumar, V., Performance measurement for supply chains in the Industry 4.0 era: a balanced scorecard approach (2021) International journal of productivity and performance management, 70 (4), pp. 789-807. , https://doi.org/10.1108/IJPPM-08-2019-0400; Galankashi, M., Rafiei, F., Financial performance measurement of supply chains: A review (2021) International journal of productivity and performance management, 71 (5), pp. 1674-1707. , https://doi.org/10.1108/IJPPM-11-2019-0533; Gawankar, S., Gunasekaran, A., Kamble, S., A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context (2019) International journal of production research, 58 (5), pp. 1574-1593. , https://doi.org/10.1080/00207543.2019.1668070; Gopal, P., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International journal of productivity and performance management, 61 (5), pp. 518-547. , https://doi.org/10.1108/17410401211232957; Govindan, K., Mangla, S., Luthra, S., Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies (2017) Production planning and control, 28 (6), pp. 552-573. , https://doi.org/10.1080/09537287.2017.1309716; Han, W., Huang, Y., Macbeth, D., Performance measurement of cross-culture supply chain partnership: a case study in the Chinese automotive industry (2017) International journal of production research, 56 (7), pp. 2437-2451. , https://doi.org/10.1080/00207543.2017.1377357; Huang, C., Su, C., Chen, P. K., An empirical study of the impact of knowledge creation and sharing on supply chain practice with competitive performance (2013) Journal of statistics and management systems, 13 (5), pp. 921-936. , https://doi.org/10.1080/09720510.2010.10701511; Huatuco, L., Torres, J., Calinescu, N., Performance measurement of sustainable supply chains (2013) International journal of productivity and performance management, 62 (8). , https://doi.org/10.1108/IJPPM-07-2013-0131; Hsu, B., Hsu, L., Shu, M., Evaluation of supply chain performance using delivery-time performance analysis chart approach (2013) Journal of statistics and management systems, 16 (1), pp. 73-87. , https://doi.org/10.1080/09720510.2013.777568; Israel, B., Kazungu, I., Mchopa, A., Centralised medical supplies procurement and health service delivery in Arusha and Kilimanjaro regions Tanzania (2019) East African Journal of Social and Applied Sciences, 1 (1), pp. 70-79; Israel, B., Enhancing customer retention in manufacturing SMEs through supply chain innovative practices (2022) Management dynamics in the knowledge economy, 10 (3), pp. 272-286; Israel, B., Joint ventures for SMEs competitiveness and inclusive growth: A comparative analysis of SMEs in Mbeya, Tanzania (2022) E-Journal of Humanities, Arts and Social Sciences, 3 (8), pp. 337-353. , https://doi.org/10.38159/ehass.2022385; Israel, B., The role of co-operative societies in supply chain of agricultural products: a review of literature (2022) Journal of international trade, logistics and law, 8 (2), pp. 69-77; Jain, R., Verma, M., Block chain: a pathway to improve performance measures of supply chain (2021) International journal of services operations and informatics, 11 (2), pp. 300-314. , https://doi.org/10.1504/IJSOI.2021.117258; Jin, H., Jeong, S., Kim, S., A Linkage model of supply chain operation and financial performance for economic sustainability of firm (2017) Sustainability, 9 (1), p. 139. , https://doi.org/10.3390/su9010139; Junior, F., Carpinetti, L., Quantitative models for supply chain performance evaluation: a literature review (2017) Computers and industrial engineering, 113, pp. 333-346. , https://doi.org/10.1016/j.cie.2017.09.022; Junior, F., Cesar, L., An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics (2019) Computers and industrial engineering, 139, p. 106191. , https://doi.org/10.1016/j.cie.2019.106191; Kailash, R., Goyal, S., Weighted interpretive structural modelling approach for exploration of performance measures of benchmarking of internal supply chain management (2021) International journal of agile systems and management, 14 (1), pp. 119-139. , https://doi.org/10.1504/IJASM.2021.114911; Kamble, S., Gunasekaran, A., Big data-driven supply chain performance measurement system: a review and framework for implementation (2019) International journal of production research, 58 (1), pp. 65-86. , https://doi.org/10.1080/00207543.2019.1630770; Karamouz, S., Ahmadi, R., Ghafournia, M., Supply chain quality management performance measurement: Systematic review (2020) International journal of quality and reliability management, 38 (2), pp. 484-504. , https://doi.org/10.1108/IJQRM-03-2019-0073; Katiyar, R., Barua, M., Meena, P., Modelling the measures of supply chain performance in the Indian automotive industry (2015) Benchmarking: An international journal, 22 (4), pp. 665-696. , https://doi.org/10.1108/BIJ-09-2014-0091; Laihonen, H., Pekkola, S., Impacts of using a performance measurement system in supply chain management: a case study (2016) International journal of production research, 54 (18), pp. 5607-5617. , https://doi.org/10.1080/00207543.2016.1181810; Lakri, S., Dallery, Y., Jemai, Z., Measurement and management of supply chain performance: Practices in today’s large companies (2015) Supply chain forum: An international journal, 16 (4), pp. 16-30. , https://doi.org/10.1080/16258312.2015.11728691; Lehyani, F., Zouari, A., Ghorbel, A., Tollenaere, M., Defining and Measuring Supply Chain Performance: A Systematic Literature Review (2021) Engineering management journal, 33 (4), pp. 283-313. , https://doi.org/10.1080/10429247.2020.1834309; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., Measuring supply chain performance: A lifecycle framework and a case study (2018) International journal of operations and production management, 38 (4), pp. 934-956. , https://doi.org/10.1108/IJOPM-07-2015-0455; Malviya, R., Developing integrated framework to measure performance of green supply chain management: A comparative case analysis (2019) Benchmarking: An International Journal, 27 (2), pp. 634-665. , https://doi.org/10.1108/BIJ-01-2019-0016; Mastos, T., Gotzamani, K., Kafetzopoulos, D., Development and validation of a measurement instrument for sustainability in food supply chains (2021) Sustainability, 14 (9), p. 5203. , https://doi.org/10.3390/su14095203; Matsoso, M., Benedict, O., Non-financial performance measures in small medium enterprises’ supply chain management (2014) Journal of economics, 5 (3), pp. 247-257. , https://doi.org/10.1080/09765239.2014.11885001; Matsoso, M., Benedict, O., Financial Performance Measures of Small Medium Enterprises in the 21st Century (2017) Journal of economics, 7 (2-3), pp. 144-160. , https://doi.org/10.1080/09765239.2016.11907829; Menhat, M., Yusuf, Y., Factors influencing the choice of performance measures for the oil and gas supply chain – exploratory study (2018) IOP Conference Series: Materials Science and Engineering, 342 (1), p. 012091. , https://doi.org/10.1088/1757-899X/342/1/012091, IOP Publishing; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking: An international journal, 25 (3), pp. 932-967. , https://doi.org/10.1108/BIJ-08-2017-0224; Moazzam, M., Akhtar, P., Garnevska, E., Marr, N., Measuring agri-food supply chain performance and risk through a new analytical framework: a case study of New Zealand dairy (2018) Production planning and control, 29 (15), pp. 1258-1274. , https://doi.org/10.1080/09537287.2018.1522847; Moreira, M., Tjahjono, B., Applying performance measures to support decision-making in supply chain operations: A case of beverage industry (2016) International journal of production research, 54 (8), pp. 2345-2365. , https://doi.org/10.1080/00207543.2015.1076944; Mwenda, B., Pastory, D., Financial Performance of Firms Before and After Listing on Dar es Salaam Stock Exchange, Tanzania (2021) The Second Business and Economic Development Conference (BEDC, 2021), , Held on 16th 17th November, 2021 in Dodoma, Tanzania; Mwenda, B., Ndiege, B., Pastory, D., Non-financial information disclosure and performance of firms listed at Dar es salaam Stock Exchange, Tanzania: is there a link? (2021) Journal of Co-operative and Business Studies, 6 (2), pp. 47-58. , https://doi.org/10.1007/978-3-030-68836-3_25; Najmi, A., Makui, A., A conceptual model for measuring supply chain’s performance (2012) Production planning and control, 23 (9), pp. 694-706. , https://doi.org/10.1080/09537287.2011.586004; Patil, A., Shardeo, V., Madaan, J., Modelling performance measurement barriers of humanitarian supply chain (2021) International journal of productivity and performance management, 70 (8), pp. 1972-2000. , https://doi.org/10.1108/IJPPM-01-2020-0031; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: an exploratory study (2015) International journal of productivity and performance management, 64 (8), pp. 1068-1109. , https://doi.org/10.1108/IJPPM-04-2014-0064; Ramezankhani, M., Torabi, S.A., Vahidi, F., Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach (2018) Computers and industrial engineering, 126, pp. 531-548. , https://doi.org/10.1016/j.cie.2018.09.054; Samaranayake, P., Laosirihongthong, T., Configuration of supply chain integration and delivery performance: Unitary structure model and fuzzy approach (2016) Journal of modelling in management, 11 (1), pp. 43-74. , https://doi.org/10.1108/JM2-01-2014-0005; Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) International journal of productivity and performance management, 64 (6), pp. 784-810. , https://doi.org/10.1108/IJPPM-11-2013-0185; Schaltegger, S., Burritt, R., Measuring and managing sustainability performance of supply chains (2014) Supply Chain Management: An internationaljournal, 19 (3). , https://doi.org/10.1108/SCM-02-2014-0083; Sillanpaa, I., Empirical study of measuring supply chain performance (2015) Benchmarking: An International Journal, 22 (2), pp. 290-308. , https://doi.org/10.1108/BIJ-01-2013-0009; Tigga, A., Kannabiran, G., Arumugam, V., Exploring relationships among IT advancement, IT assimilation, supply chain capabilities and supply chain performance (2021) Journal of decision systems, 30 (4), pp. 414-438. , https://doi.org/10.1080/12460125.2021.1873022; Yu, W., The effect of IT-enabled supply chain integration on performance (2015) Production planning and control, 26 (12), pp. 945-957. , https://doi.org/10.1080/09537287.2014.1002021; Zaman, K., Ahsan, A., Lean supply chain performance measurement (2014) International journal of productivity and performance management, 63 (5), pp. 588-612. , https://doi.org/10.1108/IJPPM-05-2013-0092},
correspondence_address1={Israel, B.; Department of Procurement and Supply Management, Post Box 3810, Tanzania; email: isbara03@gmail.com},
publisher={Universidad Politecnica de Valencia.},
issn={23405317},
language={English},
abbrev_source_title={Int. J. Prod. Manag. Eng.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Fares2023,
author={Fares, N. and Lloret, J.},
title={Barriers to supply chain performance measurement during disruptions such as the COVID-19 pandemic},
journal={International Journal of Quality and Reliability Management},
year={2023},
doi={10.1108/IJQRM-03-2022-0095},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151946131&doi=10.1108%2fIJQRM-03-2022-0095&partnerID=40&md5=9d2e91edbd1914227092b4e818709f8a},
affiliation={Universitat Politecnica de Valencia, Campus de Gandia, Grao De Gandia, Spain},
abstract={Purpose: Amid the outbreak of coronavirus disease 2019 (COVID-19), supply chains have faltered. This has influenced operational and financial performance and lead to uncertainty in supply and distribution. Therefore, systems measuring supply chain risk and disruption management performance have gained interest. This study explores barriers to supply chain performance measurement during disruptions such as COVID-19. Design/methodology/approach: Based on a survey and literature review, the authors formalise the barriers and rank them using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) fuzzy Višekriterijumsko kompromisno rangiranje (VIKOR) methodology. A total of 14 experts in 11 countries were surveyed. Performance measurement is based on a balanced scorecard. Findings: Fifteen barriers are identified. Based on DEMATEL analysis, the cause–effect relationships among the four scorecard aspects are explored. The customer axis is revealed as the cause, while the financial, internal business and learning/development are identified as the effect of the supply chain performance measurement. Fuzzy VIKOR calculations show that uncertainty of investment, disrupted cash flows and the bullwhip effect are the most critical barriers to measure supply chain performance during outbreaks such as the COVID-19 pandemic. Research limitations/implications: The study identifies and ranks general barriers; additional research is required to differentiate barriers in specific industrial sectors. Practical implications: The findings may help develop proactive, resilient supply chain performance strategies to overcome disruptions. Social implications: Policy-makers and decision-makers in industrial and service firms can explore these findings to inform strategies for robust supply chains that can resist disruption in risky environments. Originality/value: This research addresses a knowledge gap in barriers to measure supply chain performance in post-pandemic areas. It is unclear how far firms will measure supply chain performance in terms of learning from disruption patterns, managing financial and customer demand processes in light of COVID-19. This study contributes by explaining the influences among the barriers and exploring them, offering insights from multiple stakeholders. © 2023, Emerald Publishing Limited.},
author_keywords={Barrier;  COVID-19;  Disruption;  Measurement;  Performance management;  Supply chain},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: review and classification (2012) Journal of Organizational Management Studies, 2012; Al-Mansour, J.F., Al-Ajmi, S.A., Coronavirus’ COVID-19’-supply chain disruption and implications for strategy, economy, and management (2020) The Journal of Asian Finance, Economics, and Business, 7 (9), pp. 659-672; Alarifi, A.A., Husain, K.S., The influence of Internet banking services quality on e-customers’ satisfaction of Saudi banks: comparison study before and during COVID-19 (2021) International Journal of Quality and Reliability Management, 40 (2), pp. 496-516; Aliakbari Nouri, F., Shafiei Nikabadi, M., Olfat, L., Developing the framework of sustainable service supply chain balanced scorecard (SSSC BSC) (2019) International Journal of Productivity and Performance Management, 68 (1), pp. 148-170; Alkasasbeh, O.M.A., Al-kasasbeh, S.M., Supply chains and COVID-19: impact on Jordan’s countermeasures and post-COVID-19 era (2022) Journal of Social Sciences and Management Studies, 1 (1), pp. 15-22; Arunmozhi, M., Persis, J., Sreedharan, V.R., Chakraborty, A., Zouadi, T., Khamlichi, H., Managing the resource allocation for the COVID-19 pandemic in healthcare institutions: a pluralistic perspective (2022) International Journal of Quality and Reliability Management, 39 (9), pp. 2184-2204; Atayah, O.F., Dhiaf, M.M., Najaf, K., Frederico, G.F., Impact of COVID-19 on financial performance of logistics firms: evidence from G-20 countries (2021) Journal of Global Operations and Strategic Sourcing, 15 (2), pp. 172-196; Atayah, O.F., Najaf, K., Subramaniam, R.K., Chin, P.N., The ascension of executives’ tenure, corporate risk-taking and corporate performance: evidence from Malaysia (2021) Asia-Pacific Journal of Business Administration, 14 (1), pp. 101-123; Autry, C.W., Williams, B.D., Golicic, S., Relational and process multiplexity in vertical supply chain triads: an exploration in the US restaurant industry (2014) Journal of Business Logistics, 35 (1), pp. 52-70‏; Balaji, M., Dinesh, S.N., Kumar, P.M., Ram, K.H., Balanced scorecard approach in deducing supply chain performance (2021) Materials Today: Proceedings, 47, pp. 5217-5222‏; Barman, A., Das, R., De, P.K., Impact of COVID-19 in food supply chain: disruptions and recovery strategy (2021) Current Research in Behavioral Sciences, 2, p. 100017‏; Basheer, M., Siam, M., Awn, A., Hassan, S., Exploring the role of TQM and supply chain practices for firm supply performance in the presence of information technology capabilities and supply chain technology adoption: a case of textile firms in Pakistan (2019) Uncertain Supply Chain Management, 7 (2), pp. 275-288; Belhadi, A., Kamble, S., Jabbour, C.J.C., Gunasekaran, A., Ndubisi, N.O., Venkatesh, M., Manufacturing and service supply chain resilience to the COVID-19 outbreak: lessons learned from the automobile and airline industries (2021) Technological Forecasting and Social Change, 163, p. 120447‏; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers andIndustrial Engineering, 53 (1), pp. 43-62‏; Bode, C., Wagner, S.M., Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions (2015) Journal of Operations Management, 36, pp. 215-228‏; Braga, I.F., Ferreira, F.A., Ferreira, J.J., Correia, R.J., Pereira, L.F., Falcão, P.F., A DEMATEL analysis of smart city determinants (2021) Technology in Society, 66, p. 101687‏‏; Butt, A.S., Supply chains and COVID-19: impacts, countermeasures and post-COVID-19 era (2021) The International Journal of Logistics Management, , doi:, ahead-of-print; Camilleri, M.A., E-commerce websites, consumer order fulfilment and after-sales service satisfaction: the customer is always right, even after the shopping cart check-out (2021) Journal of Strategy and Management, 15 (3), pp. 377-396; Chandra, D., Kumar, D., Evaluating the effect of key performance indicators of vaccine supply chain on sustainable development of mission indradhanush: a structural equation modeling approach (2021) Omega, 101, p. 102258‏; Chen, Y., Ran, Y., Huang, G., Xiao, L., Zhang, G., A new integrated MCDM approach for improving QFD based on DEMATEL and extended MULTIMOORA under uncertainty environment (2021) Applied Soft Computing, 105, p. 107222‏; Chen, J., Wang, H., Zhong, R.Y., A supply chain disruption recovery strategy considering product change under COVID-19 (2021) Journal of Manufacturing Systems, 60, pp. 920-927‏; Chenarides, L., Manfredo, M., Richards, T.J., COVID‐19 and food supply chains (2021) Applied Economic Perspectives and Policy, 43 (1), pp. 270-279‏; Chowdhury, P., Paul, S.K., Kaisar, S., Moktadir, M.A., COVID-19 pandemic related supply chain studies: a systematic review (2021) Transportation Research Part E: Logistics and Transportation Review, 148, p. 102271‏; Craighead, C.W., Blackhurst, J., Rungtusanatham, M.J., Handfield, R.B., The severity of supply chain disruptions: design characteristics and mitigation capabilities (2007) Decision Sciences, 38 (1), pp. 131-156‏; Dhiaf, M.M., Najaf, K., Marashdeh, H., Atayah, O.F., Frederico, G.F., The role of project’s initiatives focused on the reduction of environmental footprints during COVID-19: evidence from the United States firms (2021) Operations Management Research, 15 (1), pp. 413-427; Diptyanusa, A., Zablon, K.N., Addressing budget reduction and reallocation on health-related resources during COVID-19 pandemic in malaria-endemic countries (2020) Malaria Journal, 19 (1), pp. 1-4‏; Eger, L., Komárková, L., Egerová, D., Mičík, M., The effect of COVID-19 on consumer shopping behaviour: generational cohort perspective (2021) Journal of Retailing and Consumer Services, 61, p. 102542‏; El Baz, J., Ruel, S., Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era (2021) International Journal of Production Economics, 233, p. 107972‏; Fares, N., Lloret, J., An integrated SWOT-AHP-fuzzy TOPSIS approach for maturity management following the COVID-19 outbreak: lessons learned from fast fashion (2022) Journal of Global Operations and Strategic Sourcing, , doi:, ahead-of-print; Fares, N., Lebbar, M., Optimization of fast fashion retail supply chain processes: overall literature review and future research challenges (2019) International Journal of Engineering Research in Africa, 45, pp. 205-220; Fares, N., Lebbar, M., Sbihi, N., Quick response in fast fashion retail: an optimization supply chain responsiveness model (2018) 2018 4th International Conference on Optimization and Applications (ICOA), pp. 1-5‏. , IEEE; Fares, N., Lloret, J., Kumar, V., Frederico, G.F., Kumar, A., Garza-Reyes, J.A., Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs (2022) Benchmarking: An International Journal, , doi:, ahead-of-print; Farooque, M., Zhang, A., Liu, Y., Barriers to circular food supply chains in China (2019) Supply Chain Management: An International Journal, 24 (5), pp. 677-696; Frederico, G.F., Towards a supply chain 4.0 on the post-COVID-19 pandemic: a conceptual and strategic discussion for more resilient supply chains (2021) Rajagiri Management Journal, 15 (2), pp. 94-104; Free, C., Hecimovic, A., Global supply chains after COVID-19: the end of the road for neoliberal globalisation? (2021) Accounting, Auditing and Accountability Journal, 34 (1), pp. 58-84; Goel, R.K., Saunoris, J.W., Goel, S.S., Supply chain performance and economic growth: the impact of COVID-19 disruptions (2021) Journal of Policy Modeling, 43 (2), pp. 298-316‏; Golan, M.S., Jernegan, L.H., Linkov, I., Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic (2020) Environment Systems and Decisions, 40, pp. 222-243; Guo, D., Li, M., Ling, S., Zhong, R.Y., Rong, Y., Huang, G.Q., Synchronization-oriented reconfiguration of FPAI under graduation intelligent manufacturing system in the COVID-19 pandemic and beyond (2021) Journal of Manufacturing Systems‏, 60, pp. 893-902; Gupta, R., Rathore, B., Biswas, B., Impact of COVID-19 on supply chains: lessons learned and future research directions (2021) International Journal of Quality and Reliability Management, , doi:, ahead-of-print; Handfield, R.B., Graham, G., Burns, L., Coronavirus, tariffs, trade wars, and supply chain evolutionary design (2020) International Journal of Operations and Production Management‏, 40 (10), pp. 1649-1660; Herold, D.M., Nowicka, K., Pluta-Zaremba, A., Kummer, S., COVID-19 and the pursuit of supply chain resilience: reactions and ‘lessons learned’ from logistics service providers (LSPs) (2021) Supply Chain Management: An International Journal‏, 26 (6), pp. 702-714; Hofmann, V., Stokburger-Sauer, N.E., Wanisch, A., Hebborn, H., Masked smiles matter–Employees’ verbal expertise and emotion display during COVID-19 (2021) The Service Industries Journal, 41 (1-2), pp. 107-137‏; Ivanov, D., Supply chain viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies (2021) International Journal of Production Research, 59 (12), pp. 3535-3552; Jæger, B., Menebo, M.M., Upadhyay, A., Identification of environmental supply chain bottlenecks: a case study of the Ethiopian healthcare supply chain (2021) Management of Environmental Quality: An International Journal, 32 (6), pp. 1233-1254; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Karmaker, C.L., Ahmed, T., Ahmed, S., Ali, S.M., Moktadir, M.A., Kabir, G., Improving supply chain sustainability in the context of COVID-19 pandemic in an emerging economy: exploring drivers using an integrated model (2021) Sustainable Production and Consumption, 26, pp. 411-427; Kazancoglu, Y., Sagnak, M., Mangla, S.K., Sezer, M.D., Pala, M.O., A fuzzy based hybrid decision framework to circularity in dairy supply chains through big data solutions (2021) Technological Forecasting and Social Change, 170; Koca, G., Egilmez, O., Akcakaya, O., Evaluation of the smart city: applying the DEMATEL technique (2021) Telematics and Informatics, 62; Kouhizadeh, M., Saberi, S., Sarkis, J., Blockchain technology and the sustainable supply chain: theoretically exploring adoption barriers (2021) International Journal of Production Economics, 231; Kumar, N., Brint, A., Shi, E., Upadhyay, A., Ruan, X., Integrating sustainable supply chain practices with operational performance: an exploratory study of Chinese SMEs (2019) Production Planning and Control, 30 (5-6), pp. 464-478; Kumar, A., Mangla, S.K., Kumar, P., Song, M., Mitigate risks in perishable food supply chains: learning from COVID-19 (2021) Technological Forecasting and Social Change, 166, p. 120643‏; Kusrini, N., Maswadi, M., The performance improvement of sustainable palm oil supply chain management after COVID-19: priority indicators using F-AHP (2021) Uncertain Supply Chain Management, 9 (2), pp. 227-236‏; Lemghari, R., Sarsri, D., Okar, C., Es-satty, A., Supply chain performance measurement in the automotive sector: a structured content analysis (2019) Uncertain Supply Chain Management, 7 (4), pp. 567-588‏; Lu, D., Ding, Y., Asian, S., Paul, S.K., From supply chain integration to operational performance: the moderating effect of market uncertainty (2018) Global Journal of Flexible Systems Management, 19 (1), pp. 3-20‏; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Maryono, D., Ambarwati, R., Evaluation of hospital supply chain performance during the covid-19 pandemic: balanced scorecard approach (2022) Transitioning from Globalized to Localized and Self-Reliant Economies, pp. 95-110. , IGI Global, Hershey, PA; Masudin, I., Ramadhani, A., Restuputri, D.P., Amallynda, I., The effect of traceability system and managerial initiative on Indonesian food cold chain performance: a Covid-19 pandemic perspective (2021) Global Journal of Flexible Systems Management, 22 (4), pp. 331-356; Minondo, A., Impact of COVID-19 on the trade of goods and services in Spain (2021) Applied Economic Analysis, 29 (85), pp. 58-76; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Mondal, A., Roy, S.K., Multi-objective sustainable opened-and closed-loop supply chain under mixed uncertainty during COVID-19 pandemic situation (2021) Computers and Industrial Engineering, 159, p. 107453‏; Moretto, A., Caniato, F., Can supply chain finance help mitigate the financial disruption brought by COVID-19? (2021) Journal of Purchasing and Supply Management, 27 (4), p. 100713‏; Muhammad, N., Upadhyay, A., Kumar, A., Gilani, H., Achieving operational excellence through the lens of lean and Six Sigma during the COVID-19 pandemic (2022) The International Journal of Logistics Management, 33 (3), pp. 818-835; Mukhuty, S., Upadhyay, A., Rothwell, H., Strategic sustainable development of Industry 4.0 through the lens of social responsibility: the role of human resource practices (2022) Business Strategy and the Environment.‏, 31 (5), pp. 2068-2081; Nagurney, A., Supply chain game theory network modeling under labor constraints: applications to the Covid-19 pandemic (2021) European Journal of Operational Research, 293 (3), pp. 880-891; Najaf, K., Atayah, O.F., Understanding governance compliance for RegTech (2021) Artificial Intelligence and Islamic Finance: Practical Applications for Financial Risk Management, , ISBN:, Routledge, eBook, 9781003171638; Najaf, K., Atayah, O., Devi, S., Ten years of journal of accounting in emerging economies: a review and bibliometric analysis (2021) Journal of Accounting in Emerging Economies, 1 (2), pp. 182-196; Najaf, K., Chin, A., Najaf, R., Conceptualising the corporate governance issues of fintech firms (2021) The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success, 21 (1), pp. 187-197; Najaf, K., Haj Khalifa, A., Obaid, S.M., Rashidi, A.A., Ataya, A., Does sustainability matter for Fintech firms? Evidence from United States firms (2023) Competitiveness Review: An International Business Journal, 33 (1), pp. 161-180; Najaf, K., Mostafiz, M.I., Najaf, R., Fintech firms and banks sustainability: why cybersecurity risk matters? (2021) International Journal of Financial Engineering, 8 (2); Najaf, K., Subramaniam, R.K., Atayah, O.F., Understanding the implications of FinTech Peer-to-Peer (P2P) lending during the COVID-19 pandemic (2022) Journal of Sustainable Finance and Investment, 12 (1), pp. 87-102; Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., Vasilakis, C., Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions (2021) European Journal of Operational Research, 290 (1), pp. 99-115‏; Nikookar, E., Varsei, M., Wieland, A., Gaining from disorder: making the case for antifragility in purchasing and supply chain management (2021) Journal of Purchasing and Supply Management, 27 (3), p. 100699‏; Oh, S., Moon, H.C., Zhong, Y., Contingency management and supply chain performance in Korea: a COVID-19 pandemic approach (2020) Sustainability, 12 (23), p. 9823‏; Opricovic, S., Fuzzy VIKOR with an application to water resources planning (2011) Expert Systems with Applications, 38 (10), pp. 12983-12990; Orlando, B., Tortora, D., Pezzi, A., Bitbol-Saba, N., The disruption of the international supply chain: firm resilience and knowledge preparedness to tackle the COVID-19 outbreak (2021) Journal of International Management, 28 (1); Patil, A., Shardeo, V., Madaan, J., Modelling performance measurement barriers of humanitarian supply chain (2021) International Journal of Productivity and Performance Management, 70 (8), pp. 1972-2000; Paul, S.K., Chowdhury, P., Moktadir, M.A., Lau, K.H., Supply chain recovery challenges in the wake of COVID-19 pandemic (2021) Journal of Business Research, 136, pp. 316-329; Ramavandi, B., Darabi, A.H., Omidvar, M., Risk assessment of hot and humid environments through an integrated fuzzy AHP-VIKOR method (2021) Stochastic Environmental Research and Risk Assessment, 35 (12), pp. 2425-2438; Ruiz-Benítez, R., López, C., Real, J.C., The lean and resilient management of the supply chain and its impact on performance (2018) International Journal of Production Economics, 203, pp. 190-202‏; Safa, M., Sharma, N., Zelbst, P., Information-driven supply chain during a catastrophic event: COVID-19 pandemic (2021) Journal of Operations and Strategic Planning, 4 (1), pp. 27-51‏; Sara, J., Stikkelman, R.M., Herder, P.M., Assessing relative importance and mutual influence of barriers for CCS deployment of the ROAD project using AHP and DEMATEL methods (2015) International Journal of Greenhouse Gas Control, 41, pp. 336-357; Schleper, M.C., Gold, S., Trautrims, A., Baldock, D., Pandemic-induced knowledge gaps in operations and supply chain management: COVID-19’s impacts on retailing (2021) International Journal of Operations and Production Management, 41 (3), pp. 193-205; Shahed, K.S., Azeem, A., Ali, S.M., Moktadir, M., A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk (2021) Environmental Science and Pollution Research, pp. 1-16‏; Sharma, A., Adhikary, A., Borah, S.B., Covid-19’s impact on supply chain decisions: strategic insights from NASDAQ 100 firms using Twitter data (2020) Journal of Business Research, 117, pp. 443-449‏; Sharma, M., Luthra, S., Joshi, S., Kumar, A., Developing a framework for enhancing survivability of sustainable supply chains during and post-COVID-19 pandemic (2020) International Journal of Logistics Research and Applications, pp. 1-21‏; Sharma, M., Luthra, S., Joshi, S., Kumar, A., Accelerating retail supply chain performance against pandemic disruption: adopting resilient strategies to mitigate the long-term effects (2021) Journal of Enterprise Information Management‏, 34 (6), pp. 1844-1873; Siagian, H., Tarigan, Z.J.H., Jie, F., Supply chain integration enables resilience, flexibility, and innovation to improve business performance in COVID-19 era (2021) Sustainability, 13 (9), p. 4669‏; Singh, S., Kumar, R., Panchal, R., Tiwari, M.K., Impact of COVID-19 on logistics systems and disruptions in food supply chain (2021) International Journal of Production Research, 59 (7), pp. 1993-2008‏; Srivastava, D.K., Kumar, V., Ekren, B.Y., Upadhyay, A., Tyagi, M., Kumari, A., Adopting Industry 4.0 by leveraging organisational factors (2022) Technological Forecasting and Social Change, 176; Tordecilla, R.D., Juan, A.A., Montoya-Torres, J.R., Quintero-Araujo, C.L., Panadero, J., Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: a review (2021) Simulation Modelling Practice and Theory, 106, p. 102166‏; Tsolas, S.D., Hasan, M.F., Survivability‐aware design and optimization of distributed supply chain networks in the post-COVID-19 era (2021) Journal of Advanced Manufacturing and Processing, 3 (3); Udofia, E.E., Adejare, B.O., Olaore, G.O., Udofia, E.E., Supply disruption in the wake of COVID-19 crisis and organisational performance: mediated by organisational productivity and customer satisfaction (2021) Journal of Humanities and Applied Social Sciences‏, 3 (5), pp. 319-338; Upadhyay, A., Antecedents of green supply chain practices in developing economies (2021) Management of Environmental Quality: An International Journal, 32 (6), pp. 1150-1165; Upadhyay, A., Akter, S., Adams, L., Kumar, V., Varma, N., Investigating ‘circular business models’ in the manufacturing and service sectors (2019) Journal of Manufacturing Technology Management, 30 (3), pp. 590-606; Upadhyay, A., Ayodele, J.O., Kumar, A., Garza-Reyes, J.A., A review of challenges and opportunities of blockchain adoption for operational excellence in the UK automotive industry (2020) Journal of Global Operations and Strategic Sourcing, 14 (1), pp. 7-60. , ‏; Upadhyay, A., Hernandez, M.J.P., Balodi, K.C., Covid-19 disaster relief projects management: an exploratory study of critical success factors (2022) Operations Management Research, pp. 1-12; Upadhyay, A., Kumar, A., Akter, S., An analysis of UK retailers’ initiatives towards circular economy transition and policy-driven directions (2022) Clean Technologies and Environmental Policy, 24 (4), pp. 1209-1217; Upadhyay, A., Mukhuty, S., Kumari, S., Garza-Reyes, J.A., Shukla, V., A review of lean and agile management in humanitarian supply chains: analysing the pre-disaster and post-disaster phases and future directions (2022) Production Planning and Control, 33 (6-7), pp. 641-654; Vinodh, S., Varadharajan, A.R., Subramanian, A., Application of fuzzy VIKOR for concept selection in an agile environment (2013) The International Journal of Advanced Manufacturing Technology, 65 (5), pp. 825-832; Wang, W.C., Lin, Y.H., Lin, C.L., Chung, C.H., Lee, M.T., DEMATEL-based model to improve the performance in a matrix organization (2012) Expert Systems with Applications, 39 (5), pp. 4978-4986‏; White, G.R., Cicmil, S., Upadhyay, A., Subramanian, N., Kumar, V., Dwivedi, A., The soft side of knowledge transfer partnerships between universities and small to medium enterprises: an exploratory study to understand process improvement (2019) Production Planning and Control, 30 (10-12), pp. 907-918; Yao, X., Azma, M., Do cloud-based enterprise resource planning systems affect the productivity of human resources in the COVID-19 era? (2022) Kybernetes, 51 (6), pp. 1967-1990; Yiwei, W., Najaf, K., Frederico, G.F., Atayah, O.F., Influence of COVID-19 pandemic on the tourism sector: evidence from China and United States stocks (2021) Current Issues in Tourism, 25 (23), pp. 3783-3798; Zhao, C., Liu, Z., Ding, Y., How COVID-induced uncertainty influences Chinese firms’ OFDI binary margins (2020) Emerging Markets Finance and Trade, 56 (15), pp. 3613-3625‏; Zhao, G., Ahmed, R.I., Ahmad, N., Yan, C., Usmani, M.S., Prioritizing critical success factors for sustainable energy sector in China: a DEMATEL approach (2021) Energy Strategy Reviews, 35, p. 100635‏; Zighan, S., Managing the great bullwhip effects caused by COVID-19 (2022) Journal of Global Operations and Strategic Sourcing, 15 (1), pp. 28-47},
correspondence_address1={Fares, N.; Universitat Politecnica de Valencia, Campus de Gandia, Grao De Gandia, Spain; email: naila.fares@gmail.com},
publisher={Emerald Publishing},
issn={0265671X},
language={English},
abbrev_source_title={Int. J. Qual. Reliab. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sangwa2023,
author={Sangwa, N.R. and Sangwan, K.S. and Paidipati, K.K. and Shah, B.},
title={Lean performance measurement system for an Indian automotive supply chain},
journal={International Journal of Quality and Reliability Management},
year={2023},
doi={10.1108/IJQRM-03-2022-0113},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85150611436&doi=10.1108%2fIJQRM-03-2022-0113&partnerID=40&md5=a116817da277233e57a618464fa75df7},
affiliation={Area of Operations and Supply Chain Management, Indian Institute of Management Sirmaur, Paonta Sahib, India; Mechanical Engineering Department, Birla Institute of Technology and Science Pilani, Pilani, India; Area of Decision Sciences, Indian Institute of Management Sirmaur, Paonta Sahib, India},
abstract={Purpose: This paper aims to present a simple and innovative fuzzy methodology-based lean performance measurement system (L-PMS) for an Indian automotive supply chain. The paper also enlightens the influence of coronavirus disease 2019 (COVID-19) on supply chains and the practical implications of the unprecedented disruptions on the performance measurement systems. Design/methodology/approach: The L-PMS is divided into three phases. In the first phase, the key performance indicator (KPI) list, as deemed fit by the organization, is prepared using literature and suggestions from the case organization. The list contains 61 KPIs measuring 24 performance dimensions in seven functional areas of the supply chain. In the second phase, the KPI performance data (actual, best and worst) are collected using the enterprise resource planning (ERP) system. In the last phase, the leanness score of the case organization is calculated at four levels – KPI, dimension, functional area and overall organization. Findings: The overall leanness score of the case organization is 60%. The case organization uses KPIs from all seven functional areas, but it needs to improve the number of KPIs in administration and supplier management functional areas. The case organization uses only quantitative KPIs. However, the performance dimensions at the middle level are adequate. The leanness level of the case organization in different areas is highly variable (ranges from 45% to 91%). Research limitations/implications: The major limitation of the study is that the case study is done at a single organization. Practical implications: The managers at the different levels of the hierarchy can use the lean performance measurement score to leverage the better performing areas/dimensions/KPIs and improve poor performing areas/dimensions/KPIs. The lean performance measurement at functional area level can help leadership to give responsibility to different people for the improvement of leanness with respect to different dimensions/functional areas. The disruptive impact of COVID-19 should clearly be understood by the managers to make appropriate decisions based on the severity as measured at different levels. Originality/value: According to the authors' best knowledge, this is the first lean performance measurement application at the four hierarchical levels (KPI, performance dimension, functional area and overall organization). © 2023, Emerald Publishing Limited.},
author_keywords={Fuzzy methodology;  Key performance indicators (KPIs);  Lean organizations;  Lean performance measurement;  Organizational functional areas},
references={Abdollahi, M., Arvan, M., Razmi, J., An integrated approach for supplier portfolio selection: lean or agile? (2015) Expert Systems with Applications, 42 (1), pp. 679-690; Afonso, T., Alves, A.C., Carneiro, P., Lean thinking, logistic and ergonomics: synergetic triad to prepare shop floor work systems to face pandemic situations (2021) International Journal of Global Business and Competitiveness, 16, pp. 62-76. , Su; Afshan, N., The performance outcomes of dimensions of supply chain integration: a conceptual framework (2013) Business: Theory and Practice/Verslas: Teorija Ir Praktika, 14 (4), pp. 323-331; Al-Ashaab, A., Golob, M., Urrutia, U.A., Gourdin, M., Petritsch, C., Summers, M., El-Nounu, A., Development and application of lean product development performance measurement tool (2016) International Journal of Computer Integrated Manufacturing, 29 (3), pp. 342-354; Alles, M.G., Gray, G.L., Will the medium become the message? A framework for understanding the coming automation of the audit process (2020) Journal of Information Systems, 34 (2), pp. 109-130; Alshater, M.M., Atayah, O.F., Khan, A., What do we know about business and economics research during COVID-19: a bibliometric review (2022) Economic Research-Ekonomska Istrazivanja, 35 (1), pp. 1884-1912; Alsmadi, M., Almani, A., Jerisat, R., A comparative analysis of Lean practices and performance in the UK manufacturing and service sector firms (2012) Total Quality Management and Business Excellence, 23 (4), pp. 381-396; Amin, M.A., Karim, M.A., A time-based quantitative approach for selecting lean strategies for manufacturing organisations (2013) International Journal of Production Research, 51 (4), pp. 1146-1167; Anvari, A., Zulkifli, N., Yusuff, R.M., A dynamic modeling to measure lean performance within lean attributes (2013) International Journal of Advanced Manufacturing Technology, 66 (5-8), pp. 663-677; Arif-Uz-Zaman, K., Ahsan, A.M.M.N., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612; Assef, F., Scarpin, C.T., Steiner, M.T., Confrontation between techniques of time measurement (2018) Journal of Manufacturing Technology Management, 29 (5), pp. 789-810; Atayah, O.F., Dhiaf, M.M., Najaf, K., Frederico, G.F., Impact of COVID-19 on financial performance of logistics firms: evidence from G-20 countries (2022) Journal of Global Operations and Strategic Sourcing, 15 (2), pp. 172-196; Atayah, O.F., Najaf, K., Subramaniam, R.K., Chin, P.N., The ascension of executives' tenure, corporate risk-taking and corporate performance: evidence from Malaysia (2022) Asia-Pacific Journal of Business Administration, 14 (1), pp. 101-123; Aydin, O., Pakdil, F., Fuzzy SERVQUAL analysis in airline services (2008) Organizacija, 41 (3), pp. 108-115; Ayeni, P., Ball, P., Baines, T., Towards the strategic adoption of lean in aviation Maintenance Repair and Overhaul (MRO) industry: an empirical study into the industry's lean status (2015) Journal of Manufacturing Technology Management, 27 (1), pp. 38-61; Azadeh, A., Zarrin, M., Abdollahi, M., Noury, S., Farahmand, S., Leanness assessment and optimization by fuzzy cognitive map and multivariate analysis (2015) Expert Systems with Applications, 42 (15-16), pp. 6050-6064; Azevedo, S.G., Carvalho, H., Cruz-Machado, V., LARG index: a benchmarking tool for improving the leaness, agility and greenness of the automotive supply chain (2016) Benchmarking: An International Journal, 23 (6), pp. 1472-1499; Bamber, G.J., Stanton, P., Bartram, T., Ballardie, R., Human resource management, Lean processes and outcomes for employees: towards a research agenda (2014) The International Journal of Human Resource Management, 25 (21), pp. 2881-2891; Bayou, M.E., de Korvin, A., Measuring the leanness of manufacturing systems-A case study of Ford Motor Company and general Motors (2008) Journal of Engineering and Technology Management, 25 (4), pp. 287-304; Behrouzi, F., Wong, K.Y., Lean performance evaluation of manufacturing systems: a dynamic and innovative approach (2011) Procedia Computer Science, 3, pp. 388-395; Belhadi, A., Touriki, F.E., Elfezazi, S., Evaluation of critical success factors (CSFs) to implement lean implementation in SMEs using AHP: a case study (2019) International Journal of Lean Six Sigma, 10 (3), pp. 803-829; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bhamu, J., Kumar, J.V.S., Sangwan, K.S., Productivity and quality improvement through value stream mapping: a case study of Indian automotive industry (2012) International Journal of Productivity and Quality Management, 10 (3), pp. 288-306; Bhamu, J., Sangwan, K.S., Lean manufacturing: literature review and research issues (2014) International Journal of Operations & Production Management, 34 (7), pp. 876-940; Bhasin, S., Prominent obstacles to lean (2012) International Journal of Productivity and Performance Management, 61 (4), pp. 403-425; Bhasin, S., Burcher, P., Lean viewed as a philosophy (2006) Journal of Manufacturing Technology Management, 17 (1), pp. 56-72; Bidhendi, S., Goh, S., Wandel, A., Development of a weighted leanness measurement method in modular construction companies (2019) Journal of Industrial Engineering International, 15 (4), pp. 603-625; Bonavia, T., Marin-Garcia, J.A., Integrating human resource management into lean production and their impact on organizational performance (2011) International Journal of Manpower, 32 (8), pp. 923-938; Bortolotti, T., Boscari, S., Danese, P., Successful lean implementation: organizational culture and soft lean practices (2015) International Journal of Production Economics, 160, pp. 182-201; Bortolotti, T., Romano, P., Martínez-Jurado, P.J., Moyano-Fuentes, J., Towards a theory for lean implementation in supply networks (2016) International Journal of Production Economics, 175, pp. 182-196; Bubber, D., Jain, R.K., Babber, G., Shashi, Transforming product development and production to be lean for improving business performance (2022) Benchmarking: An International Journal, , doi:, (ahead-of-print; Centobelli, P., Cerchione, R., Singh, R., The impact of leanness and innovativeness on environmental and financial performance: insights from Indian SMEs (2019) International Journal of Production Economics, 212, pp. 111-124; Choudhary, K., Sangwa, N.R., Sangwan, K.S., Singh, R.K., Impact of lean and quality management practices on green supply chain performance: an empirical study on ceramic enterprises (2022) Quality Management Journal, 29 (3), pp. 193-211; Das, K., Integrating lean systems in the design of a sustainable supply chain model (2018) International Journal of Production Economics, 198, pp. 177-190; Dhiaf, M.M., Najaf, K., Marashdeh, H., Atayah, O.F., Frederico, G.F., The role of project’s initiatives focused on the reduction of environmental footprints during COVID-19: evidence from the United States firms (2022) Operations Management Research, 15 (1-2), pp. 413-427; Dora, M., Kumar, M., Gellynck, X., Determinants and barriers to lean implementation in food-processing SMEs – a multiple case analysis (2016) Production Planning and Control, 27 (1), pp. 1-23; Elnadi, M., Shehab, E., Product-service system leanness assessment model: study of a UK manufacturing company (2021) International Journal of Lean Six Sigma, 12 (5), pp. 1046-1072; Fares, N., Lloret, J., Kumar, V., Frederico, G.F., Kumar, A., Garza-Reyes, J.A., Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs (2022) Benchmarking: An International Journal; Frederico, G.F., Towards a Supply Chain 4.0 on the post-COVID-19 pandemic: a conceptual and strategic discussion for more resilient supply chains (2021) Rajagiri Management Journal, 15 (2), pp. 94-104; Frederico, G.F., Kumar, V., Garza-Reyes, J.A., Impact of the strategic sourcing process on the supply chain response to the COVID-19 effects (2021) Business Process Management Journal, 27 (6), pp. 1775-1803; Garza-Reyes, J.A., Betsis, I.E., Kumar, V., Radwan Al-Shboul, M.D.A., Lean readiness – the case of the European pharmaceutical manufacturing industry (2018) International Journal of Productivity and Performance Management, 67 (1), pp. 20-44; Gupta, V., Acharya, P., Patwardhan, M., A strategic and operational approach to assess the lean performance in radial tyre manufacturing in India (2013) International Journal of Productivity and Performance Management, 62 (6), pp. 634-651; Gurumurthy, A., Kodali, R., Application of benchmarking for assessing the lean manufacturing implementation (2009) Benchmarking: An International Journal, 16 (2), pp. 274-308; Ivanov, D., Dolgui, A., Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak (2020) International Journal of Production Research, 58 (10), pp. 2904-2915; Jasti, N.V.K., Kodali, R., An empirical study for implementation of lean principles in Indian manufacturing industry (2016) Benchmarking: An International Journal, 23 (1), pp. 183-207; Kang, N., Zhao, C., Li, J., Horst, J.A., A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems (2016) International Journal of Production Research, 54 (21), pp. 6333-6350; Karim, A., Arif-Uz-Zaman, K., A methodology for effective implementation of lean strategies and its performance evaluation in manufacturing organizations (2013) Business Process Management Journal, 19 (1), pp. 169-196; Khalfallah, M., Lakhal, L., The impact of lean manufacturing practices on operational and financial performance: the mediating role of agile manufacturing (2021) International Journal of Quality and Reliability Management, 38 (1), pp. 147-168; Khoury, R.E., Nasrallah, N., Atayah, O.F., Dhiaf, M.M., Frederico, G.F., The impact of green supply chain management practices on environmental performance during COVID-19 period : the case of discretionary companies in the G-20 countries (2022) Benchmarking: An International Journal; Kilic, H.S., Yurdaer, P., Aglan, C., A leanness assessment methodology based on neutrosophic DEMATEL (2021) Journal of Manufacturing Systems, 59, pp. 320-344; Matawale, C.R., Datta, S., Mahapatra, S.S., Leanness estimation procedural hierarchy using interval-valued fuzzy sets (IVFS) (2014) Benchmarking: An International Journal, 21 (2), pp. 150-183; Narayanamurthy, G., Gurumurthy, A., Leanness assessment: a literature review (2016) International Journal of Operations and Production Management, 36 (10), pp. 1115-1160; Narayanamurthy, G., Gurumurthy, A., Systemic leanness an index for facilitating continuous improvement of lean implementation (2016) Journal of Manufacturing Technology Management, 27 (8), pp. 1014-1053; Nawanir, G., Lim, K.T., Othman, S.N., Adeleke, A.Q., Developing and validating lean manufacturing constructs: an SEM approach (2018) Benchmarking: An International Journal, 25 (5), pp. 1382-1405; Oleghe, O., Salonitis, K., Leanness assessment tools and frameworks (2018) Progress in Lean Manufacturing, Management and Industrial Engineering, pp. 1-39. , Davim, J.P. (Ed.), 1st ed., Springer, Cham; Pakdil, F., Leonard, K.M., Criteria for a lean organisation: development of a lean assessment tool (2014) International Journal of Production Research, 52 (15), pp. 4587-4607; Pakdil, F., Leonard, K., Implementing and sustaining lean processes: the dilemma of societal culture effects (2017) International Journal of Production Research, 55 (3), pp. 700-717; Pakdil, F., Toktaş, P., Leonard, K.M., Validation of qualitative aspects of the lean assessment tool (LAT) (2018) Journal of Manufacturing Technology Management, 29 (7), pp. 1094-1114; Papadopoulou, T.C., Özbayrak, M., Leanness: experiences from the journey to date (2005) Journal of Manufacturing Technology Management, 16 (7), pp. 784-807; Patidar, A., Sharma, M., Agrawal, R., Sangwan, K.S., Supply chain resilience and its key performance indicators: an evaluation under Industry 4.0 and sustainability perspective (2022) Management of Environmental Quality, , doi:, (ahead-of-print; Pietro, L.D., Mugion, R.G., Renzi, M.F., An integrated approach between Lean and customer feedback tools: an empirical study in the public sector (2013) Total Quality Management and Business Excellence, 24 (8), pp. 899-917; Psomas, E., Antony, J., Bouranta, N., Assessing lean adoption in food SMEs: evidence from Greece (2018) International Journal of Quality and Reliability Management, 35 (1), pp. 64-81; Ruben, R.B., Vinodh, S., Asokan, P., Implementation of lean six sigma framework with environmental considerations in an Indian automotive component manufacturing firm: a case study (2017) Production Planning and Control, 28 (15), pp. 1193-1211; Sahoo, S., Lean practices and operational performance: the role of organizational culture (2022) International Journal of Quality and Reliability Management, 39 (2), pp. 428-467; Sahoo, S., Yadav, S., Lean implementation in small- and medium-sized enterprises (2018) Benchmarking: An International Journal, 25 (4), pp. 1121-1147; Sangwa, N.R., Sangwan, K.S., Development of an integrated performance measurement framework for lean organizations (2018) Journal of Manufacturing Technology Management, 29 (1), pp. 41-84; Sangwa, N.R., Sangwan, K.S., Leanness assessment of organizational performance: a systematic literature review (2018) Journal of Manufacturing Technology Management, 29 (5), pp. 768-788; Santos Bento, G.D., Tontini, G., Developing an instrument to measure lean manufacturing maturity and its relationship with operational performance (2018) Total Quality Management and Business Excellence, 29 (9-10), pp. 977-995; Sharma, H., Sohani, N., Yadav, A., Comparative analysis of ranking the lean supply chain enablers: an AHP, BWM and fuzzy SWARA based approach (2022) International Journal of Quality and Reliability Management, 39 (9), pp. 2252-2271; Sheetal, S., Kumar, R., Shashi, S., Export competitiveness and concentration analysis of major sugar economies with special reference to India (2020) Journal of Agribusiness in Developing and Emerging Economies, 10 (5), pp. 687-715; Shen, H., Fu, M., Pan, H., Yu, Z., Chen, Y., The impact of the COVID-19 pandemic on firm performance (2020) Emerging Markets Finance and Trade, 56 (10), pp. 2213-2230; Singh, R., Centobelli, P., Cerchione, R., Evaluating partnerships in sustainability-oriented food supply chain: a five-stage performance measurement model (2018) Energies, 11 (12), p. 3473; Singh, B., Garg, S.K., Sharma, S.K., Development of index for measuring leanness: study of an Indian auto component industry (2010) Measuring Business Excellence, 14 (2), pp. 46-53; Sreedharan, V., R., Raju, R., Sunder M, V., Antony, J., Assessment of Lean Six Sigma Readiness (LESIRE) for manufacturing industries using fuzzy logic (2019) International Journal of Quality and Reliability Management, 36 (2), pp. 137-161; Susilawati, A., Tan, J., Bell, D., Sarwar, M., (2012) A multiple criteria decision making based performance measurement and improvement model for lean manufacturing activities, pp. 171-177. , International Conference on Mechanical, Automobile and Robotics Engineering, Singapore; Susilawati, A., Tan, J., Bell, D., Sarwar, M., Develop a framework of performance measurement and improvement system for lean manufacturing activity (2013) Lean Thinking, 4 (1), pp. 51-64; Susilawati, A., Tan, J., Bell, D., Sarwar, M., Fuzzy logic based method to measure degree of lean activity in manufacturing industry (2015) Journal of Manufacturing Systems, 34, pp. 1-11; Tavčar, J., Demšar, I., Duhovnik, J., Engineering change management maturity assessment model with lean criteria for automotive supply chain (2018) Journal of Engineering Design, 29 (4-5), pp. 235-257; Tiwari, A., Turner, C., Sackett, P., A framework for implementing cost and quality practices within manufacturing (2007) Journal of Manufacturing Technology Management, 18 (6), pp. 731-760; Tortorella, G.L., Fogliatto, F.S., Espôsto, K.F., Vergara, A.M.C., Vassolo, R., Mendoza, D.T., Narayanamurthy, G., Effects of contingencies on healthcare 4.0 technologies adoption and barriers in emerging economies (2020) Technological Forecasting and Social Change, 156; Vidyadhar, R., Sudeep Kumar, R., Vinodh, S., Antony, J., Application of fuzzy logic for leanness assessment in SMEs: a case study (2016) Journal of Engineering, Design and Technology, 14 (1), pp. 78-103; Wahab, A.N.A., Mukhtar, M., Sulaiman, R., A conceptual model of lean manufacturing dimensions (2013) Procedia Technology, 11, pp. 1292-1298; Wong, W.P., Ignatius, J., Soh, K.L., What is the leanness level of your organisation in lean transformation implementation? An integrated lean index using ANP approach (2014) Production Planning and Control, 25 (4), pp. 273-287; Yadav, G., Seth, D., Desai, T.N., Application of hybrid framework to facilitate lean six sigma implementation: a manufacturing company case experience (2018) Production Planning and Control, 29 (3), pp. 185-201; Yin, R.K., (2003) Case Study Research: Design and Methods, , Sage Publications, CA; Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8, pp. 338-353},
correspondence_address1={Shah, B.; Area of Operations and Supply Chain Management, India; email: bhavin.shah@iimsirmaur.ac.in},
publisher={Emerald Publishing},
issn={0265671X},
language={English},
abbrev_source_title={Int. J. Qual. Reliab. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gobachew2023,
author={Gobachew, A.M. and Haasis, H.-D. and Berhan, E.},
title={Case assessment and identification of pharmaceutical supply chain performance measures and metrics},
journal={African Journal of Science, Technology, Innovation and Development},
year={2023},
doi={10.1080/20421338.2022.2153981},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146714934&doi=10.1080%2f20421338.2022.2153981&partnerID=40&md5=c03a9c3add7054b1fe0e7e1ea67c18f6},
affiliation={University of Bremen, Bremen, Germany; School of Mechanical and Industrial Engineering, Addis Ababa University, Addis Ababa, Ethiopia},
abstract={A lot of attention has been paid to performance measurement during the past few decades. There are several reasons for utilizing performance measurement in a business, but the most important one is that, when done right, it will increase productivity. Productivity is crucial to a business’s capacity to compete and generate long-term profitability while providing required customer service. A firm will not last in the fierce business world of today if it cannot use its resources effectively to provide value for its consumers. This is especially important for public companies with limited public investments. The creation of adequate and completely usable performance assessment systems, or sets of measures, has, nevertheless, proven to be a very difficult process. This research has focused on the identification of key performance measures and metrics, on which there is still no common understanding. It focuses on identifying the outbound logistical aspect of the pharmaceutical supply chain’s performance measures and metrics. The study is mainly based on previous studies and opinions of the case company’s management staff. Based on a review of previous studies on and discussions with the management staff of the case company, performance measures for a pharmaceutical supply chain (focusing on inventory management and distribution activities) are identified and categorized into quality, time, financial, and productivity measures. Quality measures are observed to be the most crucial metric while assessing pharmaceutical supply chain performance. The case company’s management can utilize the study as a base to develop their own performance measurement system. © 2023 African Journal of Science, Technology, Innovation and Development.},
author_keywords={performance measurement;  pharmaceutical supply chain;  pharmaceutical supply chain performance},
references={Abiye, Z., Tesfaye, A., Hawaze, S., Barriers to Access: Availability and Affordability of Essential Drugs in a Retail Outlet of a Public Health Center in South Western Ethiopia (2013) Journal of Applied Pharmaceutical Science, 3 (10), pp. 101-105; Supply Chain Operations Reference Model (1997) Logistics Information Management, 10, pp. 62-67; (2020), http://www.apics.org/apics-for-business/benchmarking/scormark-process/scor-metrics, Supply Chain Metrics | SCOR Metrics | APICS. 2020. Accessed at; Azzone, G., Masella, C., Bertelè, U., Design of Performance Measures for Time-Based Companies (1991) International Journal of Operations & Production Management, 11 (3), pp. 77-85; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of Supply Chain Performance Measurement Systems: 1998–2015 (2016) Computers in Industry, 82 (October), pp. 135-150; Beamon, B.M., Designing the Green Supply Chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Birhanu, D., Krishnanand, L., Neelakanteswara Rao, A., Comparison of Select Financial Parameters of Ethiopian Consumer Goods Supply Chains (2017) Benchmarking: An International Journal, 24 (1), pp. 102-117; Cameron, A., Ewen, M., Ross-Degnan, D., Ball, D., Laing, R., Medicine Prices, Availability, and Affordability in 36 Developing and Middle-Income Countries: A Secondary Analysis (2008) Lancet, 373 (9659), pp. 240-249; Candan, G., Reşit Yazgan, H., A Novel Approach for Inventory Problem in the Pharmaceutical Supply Chain (2016) DARU Journal of Pharmaceutical Sciences, 24 (1), pp. 1-16; Carasso, B.S., Lagarde, M., Tesfaye, A., Palmer, N., Availability of Essential Medicines in Ethiopia: An Efficiency-Equity Trade-Off? (2009) Tropical Medicine and International Health, 14 (11), pp. 1394-1400; FEWES: Stop Stockouts (2018) Ethiopian Pharmaceutical Supply Agency, , Addis Ababa: EPSA; Pharmaceuticals Procurement List (2018) Pharmaceuticals Fund and Supply Agency, , Addis Ababa: EPSA,. 1st ed; Gebregeorgise, D.T., (2016), https://www.researchgate.net/publication/325216570_Detailed_countries_case_study-_Ethiopia’s_health_supply_chain_system, Detailed Countries Case StudyEthiopia’s Health Supply Chain System. Accessed at; Gobachew, A.M., (2022), Pharmaceutical Supply Chain Performance Optimization under Demand Uncertainty. Unpublished dissertation, University of Bremen; Gobachew, A.M., Kitaw, D., Berhan, E., Haasis, H.-D., ABC/XYZ Analysis for Kanban System Implementation in Pharmaceutical Supply Chain: A Case of Ethiopian Pharmaceutical Supply Agency (2021) International Journal of Information Systems and Supply Chain Management, 14 (3), pp. 63-78; Gunasekaran, A., Kobu, B., Performance Measures and Metrics in Logistics and Supply Chain Management: A Review of Recent Literature (1995-2004) for Research and Applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Kaplan, R.S., Norton, D.P., The Balanced Scorecard: Measures That Drive Performance (1992) Harvard Business Review, 83, pp. 71-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are Your Performance Measures Obsolete? (1989) Management Accounting, 70, pp. 45-50; Koç, Ç., An Evolutionary Algorithm for Supply Chain Network Design with Assembly Line Balancing (2017) Neural Computing and Applications, 28 (11), pp. 3183-3195; Kozarević, S., Puška, A., Use of Fuzzy Logic for Measuring Practices and Performances of Supply Chain (2018) Operations Research Perspectives, 5 (March), pp. 150-160; Kurien, G.P., Qureshi, M.N., Study of Performance Measurement Practices in Supply Chain Management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Lynch, R.L., Cross, K.F., (1990) Measure up! Yardsticks for Continuous Improvement, , Oxford: Basil Blackwell; Moons, K., Waeyenbergh, G., Pintelon, L., Measuring the Logistics Performance of Internal Hospital Supply Chains–A Literature Study (2019) Omega (United Kingdom), 82, pp. 205-217; Mousazadeh, M., Torabi, S.A., Zahiri, B., A Robust Possibilistic Programming Approach for Pharmaceutical Supply Chain Network Design (2015) Computers and Chemical Engineering, 82, pp. 115-128; (2000), https://mthink.com/what-about-measuring-supply-chain-performance/, What About Measuring Supply Chain Performance?; Neely, A., Adams, C., Crowe, P., The Performance Prism in Practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-1116; Nematollahi, M., Hosseini-motlagh, S.-M., Ignatius, J., Goh, M., Sagha, M., Coordinating a Socially Responsible Pharmaceutical Supply Chain Under Periodic Review Replenishment Policies (2018) Journal of Cleaner Production, 172, pp. 2876-2891; Purbey, S., Mukherjee, K., Bhar, C., Performance Measurement System for Healthcare Processes (2007) International Journal of Productivity and Performance Management, 56 (3), pp. 241-251; Sabri, Y., Micheli, G.J.L., Nuur, C., How Do Different Supply Chain Configuration Settings Impact on Performance Trade-Offs? (2017) International Journal of Logistics Systems and Management, 26 (1), pp. 34-56; Settanni, E., Harrington, T.S., Srai, J.S., Pharmaceutical Supply Chain Models: A Synthesis from a Systems View of Operations Research (2017) Operations Research Perspectives, 4, pp. 74-95; Shepherd, C., Günter, H., Measuring Supply Chain Performance: Current Research and Future Directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Tarimoradi, M., Fazel Zarandi, M.H., Zaman, H., Turksan, I.B., Evolutionary Fuzzy Intelligent System for Multi-Objective Supply Chain Network Designs: An Agent-Based Optimization State of the Art (2017) Journal of Intelligent Manufacturing, 28 (7), pp. 1551-1579; (2020) macrotrends, , https://www.macrotrends.net/countries/ETH/ethiopia/healthcare-spending, Ethiopia Healthcare Spending 2000–2020. Accessed at; (2020), https://www.worldometers.info/world-population/ethiopia-population/, Ethiopia Population; Zahiri, B., Jula, P., Tavakkoli-Moghaddam, R., Design of a Pharmaceutical Supply Chain Network Under Uncertainty Considering Perishability and Substitutability of Products (2018) Information Sciences, 423, pp. 257-283; Zahiri, B., Zhuang, J., Mohammadi, M., Toward an Integrated Sustainable-Resilient Supply Chain: A Pharmaceutical Case Study (2017) Transportation Research Part E: Logistics and Transportation Review, 103 (2017), pp. 109-142},
correspondence_address1={Gobachew, A.M.; University of BremenGermany; email: yemekonnen@gmail.com},
publisher={Routledge},
issn={20421338},
language={English},
abbrev_source_title={Afr. J. Sci. Technol. Innov. Dev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Fantozzi2023,
author={Fantozzi, I.C. and Di Luozzo, S. and Schiraldi, M.M.},
title={Industrial Performance Measurement Systems Coherence: A Comparative Analysis of Current Methodologies, Validation and Introduction to Key Activity Indicators},
journal={Applied Sciences (Switzerland)},
year={2023},
volume={13},
number={1},
doi={10.3390/app13010235},
art_number={235},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145900814&doi=10.3390%2fapp13010235&partnerID=40&md5=1301bd9223bca29b81e7bd6cc54a697b},
affiliation={Department of Enterprise Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, Rome, 00133, Italy},
abstract={This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM. © 2022 by the authors.},
author_keywords={decision systems;  industrial performance measurement and management;  key activity indicators;  key performance indicators;  operations and supply chain management;  systems optimization},
references={Bourne, M., Franco-Santos, M., Micheli, P., Pavlov, A., Performance measurement and management: A system of systems perspective (2017) Int. J. Prod. Res, 56, pp. 2788-2799; Hanson, J., Melnyk, S.A., Calantone, R.A., Defining and measuring alignment in performance management (2011) Int. J. Oper. Prod. Manag, 31, pp. 1089-1114; Micheli, P., Manzoni, J.-F., Strategic Performance Measurement: Benefits, Limitations and Paradoxes (2010) Long Range Plan, 43, pp. 465-476; Rodríguez-Rodríguez, R., Alfaro-Saiz, J.-J., Carot, J.M., A dynamic supply chain BSC-based methodology to improve operations efficiency (2020) Comput. Ind, 122, p. 103294; Pérez-Álvarez, J.M., Maté, A., Gómez-López, M.T., Trujillo, J., Tactical Business-Process-Decision Support based on KPIs Monitoring and Validation (2018) Comput. Ind, 102, pp. 23-39; Dekker, H.C., Groot, T., Schoute, M., A Balancing Act? The Implications of Mixed Strategies for Performance Measurement System Design (2012) J. Manag. Account. Res, 25, pp. 71-98; Micheli, P., Mura, M., Executing strategy through comprehensive performance measurement systems (2017) Int. J. Oper. Prod. Manag, 37, pp. 423-443; Wibisono, D., A Framework of Performance Measurement System for Manufacturing Company (2011) South East Asian J. Manag, 5, p. 1; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res, 25, pp. 173-186; Murata, K., Katayama, H., AN EVALUATION OF FACTORY PERFORMANCE UTILIZED KPI/KAI WITH DATA ENVELOPMENT ANALYSIS(<Special Issue>Operations Research for Performance Evaluation) (2009) J. Oper. Res. Soc. Jpn, 52, pp. 204-220; A Melnyk, S., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) J. Oper. Manag, 22, pp. 209-218; Kaganski, S., Paavel, M., Lavin, J., Selecting Key Performance Indicators with Support of Enterprise Analyze Model Proceedings of the 9th International DAAAM Baltic Conference Industrial Engineering, , Tallinn, Estonia, 24–26 April 2014; Kaganski, S., Majak, J., Karjust, K., Toompalu, S., Implementation of Key Performance Indicators Selection Model as Part of the Enterprise Analysis Model (2017) Procedia CIRP, 63, pp. 283-288; Alexander, A., Kumar, M., Walker, H., A decision theory perspective on complexity in performance measurement and management (2018) Int. J. Oper. Prod. Manag, 38, pp. 2214-2244; Ittner, C.D., Larcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Account. Organ. Soc, 28, pp. 715-741; Villalba-Diez, J., (2017) The Hoshin Kanri Forest, , Lean Strategic Organizational Design, Productivity Press, New York, NY, USA; Cherni, J., Martinho, R., Ghannouchi, S.A., Towards Improving Business Processes based on preconfigured KPI target values, Process Mining and Redesign Patterns (2019) Procedia Comput. Sci, 164, pp. 279-284; Torres-Salgado, M.K., Indicadores de desempeño de procesos médicos con alineamiento estratégico en la atención al paciente (2021) Cir. Cir, 89, pp. 403-410; Joppen, R., von Enzberg, S., Gundlach, J., Kühn, A., Dumitrescu, R., Key performance indicators in the production of the future (2019) Procedia CIRP, 81, pp. 759-764; Azzouz, M., Boukhedouma, S., Alimazghi, Z., Impact of Strategic Alignment on Company Performance: An approach based on performance indicators system design Proceedings of the 2020 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2020, pp. 233-240. , Depok, Indonesia, 17–18 October 2020; Suwignjo, P., Bititci, U., Carrie, A., Quantitative models for performance measurement system (2000) Int. J. Prod. Econ, 64, pp. 231-241; Di Luozzo, S., Del Beato, F., Schiraldi, M.M.M., Measuring coherence of performance measurement indicators in complex and changing environments (2021) Int. J. Prod. Perform. Manag; Lee, S., Costello, F.J., Lee, K.C., Hierarchical balanced scorecard-based organizational goals and the efficiency of controls processes (2021) J. Bus. Res, 132, pp. 270-288; Wannes, A., Ghannouchi, S.A., KPI-Based Approach for Business Process Improvement (2019) Procedia Comput. Sci, 164, pp. 265-270; Del-Río-Ortega, A., Resinas, M., Ruiz-Cortés, A., Challenges to Support A PPI Management Lifecycle (2010) Actas Talleres Jorn. Ing. Softw. Bases Datos, 4, pp. 8-13; Del-Río-Ortega, A., Resinas, M., Towards Modelling and Tracing Key Performance Indicators in Business Processes (2009) Actas Talleres Jorn. Ing. Softw. Bases Datos, 3, pp. 57-67; Schmidt, C., Li, W., Thiede, S., Kornfeld, B., Kara, S., Herrmann, C., Implementing Key Performance Indicators for Energy Efficiency in Manufacturing (2016) Procedia CIRP, 57, pp. 758-763; Aracıoğlu, B., Zalluhoğlu, A.E., Candemir, C., Measuring and Evaluating Performance within the Strategic Management Perspective: A Study on Performance Measurement of a Seafood Company (2013) Procedia Soc. Behav. Sci, 99, pp. 1026-1034; Kang, N., Zhao, C., Li, J., Horst, J.A., A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems (2016) Int. J. Prod. Res, 54, pp. 6333-6350; Di Luozzo, S., Keegan, R., Liolli, R., Schiraldi, M., Key Activity Indicators: Critical review and proposal of implementation criteria (2022) Int. J. Product. Perform. Manag; Battista, C., Schiraldi, M.M., The Logistic Maturity Model: Application to a Fashion Company (2013) Int. J. Eng. Bus. Manag, 5, p. 29},
correspondence_address1={Fantozzi, I.C.; Department of Enterprise Engineering, Via del Politecnico 1, Italy; email: italo.cesidio.fantozzi@uniroma2.it},
publisher={MDPI},
issn={20763417},
language={English},
abbrev_source_title={Appl. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Le2023,
author={Le, T.T.},
title={How do food supply chain performance measures contribute to sustainable corporate performance during disruptions from the COVID-19 pandemic emergency?},
journal={International Journal of Quality and Reliability Management},
year={2023},
doi={10.1108/IJQRM-03-2022-0089},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145696138&doi=10.1108%2fIJQRM-03-2022-0089&partnerID=40&md5=1dbed3fac02dab90dc558086bd798e33},
affiliation={Ho Chi Minh City University of Economics and Finance, Ho Chi Minh City, Viet Nam},
abstract={Purpose: This study aims to the development of the scale of supply chain performance measures (SCPMs), food supply chain resilience (FSCS) and sustainable corporate performance (SCP) in small- and medium-sized enterprises (SMEs) in an emerging market. Based on this purpose, the study examines the relationships between SCPMs and SCP by exploring the mediating role of FSCS in emerging markets. Design/methodology/approach: Based on a comprehensive literature review on the SCPMs, FSCS and SCP, the author evaluates the nexus of these constructs on disruptions during the COVID-19 pandemic emergency in an emerging market. The article follows a quantitative approach. A total of 567 valid responses from managers at senior and middle levels were received and used for data analysis. The Smart PLS version 3.3.2 was employed to analyse Structural Equation Modelling (SEM) to investigate the relationships between constructs and latent variables. Findings: This study provides some theoretical contributions to expand the extant literature on the domain of SCPMs. First, the findings determine that multidimensional measures of flexibility, diversity, agility, inventory efficiency, redundancy and robustness are appropriate for measuring food SC performance in disruptions during the COVID-19 emergency. Besides, this study enriches the existing literature on SC disruption by providing extensive empirical evidence on SCPMs in disruptions during the COVID-19 emergency. Finally, this research provides an integrated empirical model that explores the link between the identified food SCPMs to FSCS and SCP. Originality/value: The contributions may be of interest to business practitioners, business leaders and academics. In addition, this study provides empirical evidence to demonstrate that food SC performance, as measured by these measures, is strongly related to the firm's food supply chain resilience. This is the novel contribution of this study to the current literature on food SC management. Furthermore, this study provides further empirical evidence demonstrating the partial mediating role of the firm's food supply chain resilience in the nexus between food SC performance and SCP. The unique contribution of this study is an extension of the body of knowledge of SC management literature from a comprehensive approach by providing a proven set of performance measures of SC management to which it can drive SC resilience and SCP for food manufacturing SMEs in an emerging economy that hardly found in the current literature. © 2022, Emerald Publishing Limited.},
author_keywords={COVID-19 pandemic;  Disruption situations;  Emergency;  Food supply chain management;  Food supply chain performance measures;  Food supply chain resilience},
references={Abbas, J., Impact of total quality management on corporate green performance through the mediating role of corporate social responsibility (2020) Journal of Cleaner Production, 242, pp. 1-12; Abdallah, A.B., Al-Ghwayeen, W.S., Green supply chain management and business performance: the mediating roles of environmental and operational performances (2020) Business Process Management Journal, 26 (2), pp. 489-512; Adian, I., Doumbia, D., Gregory, N., Ragoussis, A., Reddy, A., Timmis, J., (2020) Small and medium enterprises in the pandemic: impact, responses and the role of development finance, , Policy Research Working Paper 9414, World Bank, Washington, DC; Alban, L., Häsler, B., Nielsen, L., Rüegg, S., Resilience in the pork supply chain from the food safety perspective (2017) Foz do Iguaçu - Brazil: 12th International Symposium on the Epidemiology and Control of Biological, Chemical and Physical Hazards in Pigs and Pork; Alinaghian, L., Qiu, J., Razmdoost, K., The role of network structural properties in supply chain sustainability: a systematic literature review and agenda for future research (2021) Supply Chain Management, 26 (2), pp. 192-211; Allam, D., Elseify, E., Youssef, A., Khourshed, N., The relationship between green supply chain management and profitability (2021) Open Access Library Journal, 8; Andres, L., (2012) Designing and Doing Surveys, , SAGE Publications, London; Aramyan, L.H., Oude Lansink, A.G.J.M., van der Vorst, J.G.A.J., van Kooten, O., Performance measurement in agri‐food supply chains: a case study (2007) Supply Chain Management, 12 (4), pp. 304-315; Asbjornslett, B.E., Assess the vulnerability of your production system (1999) Production Planning and Control, 10 (3), pp. 219-229; Baliga, R., Raut, R.D., Kamble, S.S., Sustainable supply chain management practices and performance: an integrated perspective from a developing economy (2020) Management of Environmental Quality, 31 (5), pp. 1147-1182; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 275-292; Becerra, P., Mula, J., Sanchis, R., Green supply chain quantitative models for sustainable inventory management: a review (2021) Journal of Cleaner Production, 328, p. 2021; Betts, T., Tadisina, S.K., Supply chain agility, collaboration, and performance: how do they relate (2009) Proceedings of annual conference of the production and operations management society, 20. , POMS 20th Annual Conference, Orlando, FL; Biemer, P., Lyberg, P., (2003) Introduction to Survey Quality, , John Wiley & Sons; Blackhurst, J., Dunn, K.S., Craighead, C.W., An empirically derived framework of global supply resiliency (2011) Journal of Business Logistics, 32 (4), pp. 374-391; Chakraborty, I., Maity, P., COVID-19 outbreak: migration, effects on society, global environment and prevention (2020) Science of the Total Environment, 728; Chang, U.A., Talpur, A.B., Chang, A., Hashmi, T., Sustainable supply chain management practices and firm performance: evidence from small and medium enterprises of Pakistan (2020) 2020 IEEE 13th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA); Ching, P.L., Mutuc, J.E., Jose, J.A., Assessment of the quality and sustainability implications of FIFO and LIFO inventory policies through system dynamics (2019) Advances in Science, Technology and Engineering Systems, 4, pp. 69-81; Chowdhury, M.M.H., Quaddus, M., Agarwal, R., Supply chain resilience for performance: role of relational practices and network complexities (2019) Supply Chain Management, 24 (5), pp. 659-676; Das, S., Hassan, H.M.K., Impact of sustainable supply chain management and customer relationship management on organizational performance (2022) International Journal of Productivity and Performance Management, 71 (6), pp. 2140-2160; Davis, K.F., Downs, S., Gephart, J.A., Towards food supply chain resilience to environmental shocks (2021) Nat Food, 2, pp. 54-65; Deconinck, K., Avery, E., Jackson, L.A., Food supply chains and covid‐19: impacts and policy lessons (2020) EuroChoices, 19 (3), pp. 34-39; Diamantopoulos, A., Siguaw, J.A., Formative versus reflective indicators in organisational measure development: a comparison and empirical illustration (2006) British Journal of Management, 17 (4), pp. 263-282; Dillman, D.A., (2007) Mail and Internet Surveys — the Tailored Design Method, , Wiley, New York; Duttagupta, S., Bhanja, S.N., Dutta, A., Sarkar, S., Chakraborty, M., Ghosh, A., Mondal, D., Impact of covid-19 lockdown on availability of drinking water in the arsenic-affected ganges river basin (2021) International Journal of Environmental Research and Public Health, 18 (6), p. 2832; Elleuch, H., Dafaoui, E., Elmhamedi, A., Chabchoub, A., Resilience and vulnerability in supply chain: literature review (2016) IFAC-PapersOnLine, 49 (12), pp. 1448-1453; Elmqvist, T., Folke, C., Nystrom, M., Peterson, G., Bengtsson, J., Walker, B., Norberg, J., Response diversity, ecosystem change, and resilience response diversity and ecosystem resilience (2003) Frontiers in Ecology and the Environment, 1, pp. 488-494; Fabeil, N.F., Pazim, K.H., Langgat, J., The impact of covid-19 pandemic crisis on micro-enterprises: entrepreneurs' perspective on business continuity and recovery strategy (2020) Journal of Economics and Business, 3 (2), pp. 837-844; Falk, R.F., Miller, N.B., (1992) A Primer for Soft Modeling, , University of Akron Press; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., Performance measurement in service businesses (1991) Management Accounting, 69, pp. 34-36; Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L., Holling, C.S., Regime shifts, resilience, and biodiversity in ecosystem management (2004) Annual Review of Ecology, Evolution, and Systematics, 35 (1), pp. 557-581; Fornell, C.D., Lacker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18, pp. 39-50; Freeman, R.E., (1984) Strategic Management: A Stakeholder Approach, , Pitman, Boston; Fu, Q., Abdul Rahman, A.A., Jiang, H., Abbas, J., Comite, U., Sustainable supply chain and business performance: the impact of strategy, network design, information systems, and organizational structure (2022) Sustainability, 14 (3), p. 1080; Garnett, P., Doherty, B., Heron, T., Vulnerability of the United Kingdom's food supply chains exposed by COVID-19 (2020) Nat Food, 1, pp. 315-318; Guersola, M., Pinheiro de Lima, E., Arns Steiner, M.T., Supply chain performance measurement: a systematic literature review (2018) International Journal of Logistics Systems and Management, 31 (1), pp. 109-131; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45, pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Research, 87, pp. 333-347; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2014) Multivariate Data Analysis, , Pearson Education; Hair, J.F., Jr., Sarstedt, M., Hopkins, L., Kuppelwieser, V.G., Partial least squares structural equation modeling (PLS-SEM): an emerging tool in business research (2014) European Business Review, 26 (2), pp. 106-121; Hair, J.F., Risher, J.J., Sarstedt, M., Ringle, C.M., When to use and how to report the results of PLS-SEM (2019) European Business Review, 31 (1), pp. 2-24; Hashmi, S.D., Akram, S., Impact of green supply chain management on financial and environmental performance: mediating role of operational performance and the moderating role of external pressures (2021) LogForum, 17 (3), pp. 359-371; Henseler, J., Hubona, G., Ray, P.A., Using PLS path modeling in new technology research: updated guidelines (2016) Industrial Management and Data Systems, 116 (1), pp. 2-20; Himanen, S.J., Rikkonen, P., Kahiluoto, H., Codesigning a resilient food system (2016) Ecology and Society, 21 (4), p. 41; Hodbod, J., Eakin, H., Adapting a social-ecological resilience framework for food systems (2015) Journal of Environmental Studies and Sciences, 5, pp. 474-484; Holling, C.S., Resilience and stability of ecological systems (1973) Annual Review of Ecology and Systematics, 4 (1), pp. 1-23; Hossain, M., The effect of the covid-19 on sharing economy activities (2020) Journal of Cleaner Production, 280, pp. 1-9; Hourneaux, F., Jr., Gabriel, M., Gallardo-Vázquez, D.A., Triple bottom line and sustainable performance measurement in industrial companies (2018) Revista de Gestão, 25 (4), pp. 413-429; Hu, L.T., Bentler, P.M., Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives (1999) Structural Equation Modeling, 6 (1), pp. 1-55; Jamaludin, M., The influence of supply chain management on competitive advantage and company performance (2021) Uncertain Supply Chain Management, 9 (3), pp. 696-704; Jha, M.K., Rangarajan, K., Analysis of corporate sustainability performance and corporate financial performance causal linkage in the Indian context (2020) Asian Journal of Sustainability and Social Responsibility, 5, p. 10; Jüttner, U., Maklan, S., Supply chain resilience in the global financial crisis: an empirical study (2011) International Journal of Supply Chain Management, 16 (4), pp. 246-259; Kamble, S.S., Gunasekaran, A., Gawankar, S.A., Achieving sustainable performance in a data-driven agriculture supply chain: a review for research and applications (2020) International Journal of Production Economics, 219, pp. 179-194; (1990) Measures for Manufacturing Excellence, , Kaplan, R.S. (Ed.) (, Harvard Business School Press, Boston, MA; Kaseva, J., Himanen, S.J., Kahiluoto, H., Managing diversity for food system resilience (2019) Advances in Food Security and Sustainability, 4, pp. 1-32; Kazancoglu, Y., Ozbiltekin-Pala, M., Sezer, M.D., Ekren, B.Y., Kumar, V., Assessing the impact of COVID-19 on sustainable food supply chains (2021) Sustainability, 14 (1), p. 143; Kock, N., Common method bias in PLS-SEM: a full collinearity assessment approach (2015) International Journal of E-Collaboration, 11 (4), pp. 1-10; Krenzke, T., Van de Kerckhove, W., Westat, L.M., Identifying and reducing non-response bias throughout the survey process (2006) Proceedings of the American Sociological Association: Survey Research Methods, , Philadelphia; Kumar, N., Tyagi, M., Sachdeva, A., Kazancoglu, Y., Ram, M., Impact analysis of COVID-19 outbreak on cold supply chains of perishable products using a SWARA based MULTIMOORA approach (2022) Operations Management Research, 15 (3), pp. 1-25; Lee, K., Romzi, P., Hanaysha, J., Alzoubi, H., Alshurideh, M., Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: an empirical study in Malaysia (2022) Uncertain Supply Chain Management, 10 (2), pp. 537-550; Lin, B.B., Resilience in agriculture through crop diversification: adaptive management for environmental change (2011) BioScience, 61, pp. 183-193; Maaz, M.A.M., Ahmad, R., Impact of supply chain performance on organizational performance mediated by customer satisfaction: a study of dairy industry (2022) Business Process Management Journal, 28 (1), pp. 1-22; Margherita, A., Heikkilä, M., Business continuity in the COVID-19 emergency: a framework of actions undertaken by world-leading companies (2021) Business Horizons, 64 (5), pp. 683-695; Mishra, D., Gunasekaran, A., Rameshwar, D., Papadopoulos, T., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Monostori, J., Supply chains robustness: challenges and opportunities (2018) Procedia CIRP, 67, pp. 110-115; Mu, W., van Asselt, E.D., van der Fels-Klerx, H.J., Towards a resilient food supply chain in the context of food safety (2021) Food Control, 125, pp. 1-8; Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, R., The socio-economic implications of the coronavirus pandemic (COVID-19): a review (2020) International Journal of Surgery, 78, pp. 185-193; (2020), https://www.oecd.org/coronavirus/policy-responses/food-supply-chains-and-covid-19-impacts-and-policy-lessons-71b57aea/; (2021) SME and Entrepreneurship Policy in Viet Nam, OECD Studies on SMEs and Entrepreneurship, , OECD Publishing, Paris; Oliver, T.H., Boyd, E., Balcombe, K., Benton, T.G., Bullock, J.M., Donovan, D., Overcoming undesirable resilience in the global food system (2018) Global Sustainability, 1; Orengo Serra, K.L., Sanchez-Jauregui, M., Food supply chain resilience model for critical infrastructure collapses due to natural disasters (2022) British Food Journal, 124 (13), pp. 14-34; Perdana, T., Onggo, B.S., Sadeli, A.H., Chaerani, D., Achmad, A.L.H., Hermiatin, F.R., Gong, Y., Food supply chain management in disaster events: a systematic literature review (2022) International Journal of Disaster Risk Reduction, 79; Ponomarov, Y.S., Holcomb, C.M., Understanding the concept of SC resilience (2009) International Journal of Logistics Management, 20 (1), pp. 124-143; Qin, X., Godil, D.I., Khan, M.K., Sarwat, S., Alam, S., Janjua, L., Investigating the effects of COVID-19 and public health expenditure on global supply chain operations: an empirical study (2021) Operations Management Research, 15 (1), pp. 11-13; Ram, M., Madu, C.N., Xing, L., Dohi, T., Guest editorial: recent communications in system reliability, quality and supply chain management (2022) International Journal of Quality and Reliability Management, 39 (3), pp. 673-674; Raza, J., Liu, Y., Zhang, J., Zhu, N., Hassan, Z., Gul, H., Hussain, S., Sustainable supply management practices and sustainability performance: the dynamic capability perspective (2021) SAGE Open, 11 (1), pp. 1-14; Reddy, K.J.M., Rao, A.N., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30, pp. 40-47; Santos, J.B., Brito, L.A.L., Toward a subjective measurement model for firm performance (2012) BAR-Brazilian Administration Review, 9 (SPE), pp. 95-117; Singh, C.S., Soni, G., Badhotiya, G.K., Performance indicators for supply chain resilience: review and conceptual framework (2019) Journal of Industrial Engineering International, 15, pp. 105-117; Smith, K., Lawrence, G., MacMahon, A., Muller, J., Brady, M., The resilience of long and short food chains: a case study of flooding in Queensland, Australia (2016) Agriculture and Human Values, 33 (1), pp. 45-60; Sohrabi, C., Alsafi, Z., O'Neill, N., Khan, M., Kerwan, A., Al-Jabir, A., Agha, R., World health organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19) (2020) International Journal of Surgery, 76, pp. 71-76; Stevenson, M., Spring, M., Flexibility from a supply chain perspective: definition and review (2007) International Journal of Operations and Production Management, 27 (7), pp. 685-713; Stone, J., Rahimifard, S., Resilience in agri-food supply chains: a critical analysis of the literature and synthesis of a novel framework (2018) Supply Chain Management: International Journal, 23 (3), pp. 207-238; Suryaningtyas, D., Sudiro, A., Troena Eka, A., Irawanton Dody, W., Organizational resilience and organizational performance: examining the mediating roles of resilient leadership and organizational culture (2019) Academy of Strategic Management Journal, 18 (2), pp. 1-7; Svajdova, L., Consumer behaviour during pandemic of COVID-19 (2021) Journal of International Business Research and Marketing, 6 (3), pp. 34-37; Teece, D.J., Pisano, G., Shuen, A., Dynamic capabilities and strategic management (1997) Strategic Management Journal, 18 (7), pp. 509-533; Tendall, D.M., Joerin, J., Kopainsky, B., Edwards, P., Shreck, A., Le, Q.B., Kruetli, P., Six, J., Food system resilience: defining the concept (2015) Global Food Security, 6, pp. 17-23; Toepoel, V., Schonlau, M., Dealing with nonresponse: strategies to increase participation and methods for postsurvey adjustments (2017) Mathematical Population Studies, 24 (2), pp. 79-83; Toussaint, M., Cabanelas, P., Muñoz-Dueñas, P., Social sustainability in the food value chain: what is and how to adopt an integrative approach? (2022) Quality and Quantity, 56, pp. 2477-2500; Umar, M., Wilson, M., Heyl, J., Food network resilience against natural disasters: a conceptual framework (2017) Sage Open, 7 (3); Von Bertalanffy, L., An outline of general system theory (1950) The British Journal for the Philosophy of Science, 1 (2), pp. 134-165; Wang, C.-H., How organizational green culture influences green performance and competitive advantage: the mediating role of green innovation (2019) Journal of Manufacturing Technology Management, 30 (4), pp. 666-683; Wang, J., Muddada, R.R., Wang, H., Ding, J., Lin, Y., Liu, C., Zhang, W., Toward a resilient holistic supply chain network system: concept, review and future direction (2016) IEEE Systems Journal, 10 (2), pp. 410-421; Wang, X., Rodrigues, V.S., Demir, E., Managing your supply chain pantry: food waste mitigation through inventory control (2019) IEEE Engineering Management Review, 47, pp. 97-102; Wetzels, M., Odekerken-Schröder, G., Van Oppen, C., Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration (2009) MIS Quarterly, 33 (1), pp. 177-195; Xie, Y., Yin, Y., Xue, W., Shi, H., Chong, D., Intelligent supply chain performance measurement in Industry 4.0 (2020) Systems Research and Behavioral Science, 37 (4), pp. 711-718; Yontar, E., Ersöz, S., Investigation of food supply chain sustainability performance for Turkey's food sector (2020) Frontiers in Sustainable Food Systems, 4, pp. 1-21},
correspondence_address1={Le, T.T.; Ho Chi Minh City University of Economics and FinanceViet Nam; email: tieplt@uef.edu.vn},
publisher={Emerald Publishing},
issn={0265671X},
language={English},
abbrev_source_title={Int. J. Qual. Reliab. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cagno202355,
author={Cagno, E. and Negri, M. and Neri, A. and Giambone, M.},
title={One Framework to Rule Them All: An Integrated, Multi-level and Scalable Performance Measurement Framework of Sustainability, Circular Economy and Industrial Symbiosis},
journal={Sustainable Production and Consumption},
year={2023},
volume={35},
pages={55-71},
doi={10.1016/j.spc.2022.10.016},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145432636&doi=10.1016%2fj.spc.2022.10.016&partnerID=40&md5=50b74f81c0136a411f72b92144abd60b},
affiliation={Politecnico di Milano, Department of Management, Economics and Industrial Engineering, Milan, Italy},
abstract={There is an overall established agreement on the need for the industry to transition towards a more ecological and ethical approach to business. Numerous ways to engage into this transition exist, such as Sustainability, Circular Economy and Industrial Symbiosis; yet the proliferation of alternative paradigms creates confusion and generates limited applications in industries. The measurement of performance appears indispensable to foster the implementation of improving actions, benchmark results and effectively communicate them to different stakeholders. Despite the emerging interest, a proper system for the measurement of performance related to the ecological and ethical transition is still missing. By means of selection mechanisms specifically developed, indicators retrieved from the extant literature have been organized in a novel integrated, multi-level and scalable framework of performance measurement systems. The framework presents some features of great relevance, in particular: i) integration of Sustainability, Circular Economy and Industrial Symbiosis paradigms; ii) possibility to be applied at different levels of application, i.e. the single firm, the supply chain or the district; iii) adaptability to firms with different characteristics (firm size and awareness), thanks to the development of two different (Full and Core) scalable systems. The framework has been then validated in four manufacturing firms against its capacity to represent each paradigm, its usefulness, and its ease of use. Besides the positive feedbacks obtained considering the three axes of analysis, the framework was appreciated for its capability to overcome tensions among the three paradigms and encourage firms to consider performance beyond their own boundaries, including the supply chain or district, and its scalability as for different sizes. This research advances the knowledge on the ecological and ethical transition and on the related paradigms, contributing on theoretical and practical levels and offering several insights for future research, especially empirical ones. © 2022 Institution of Chemical Engineers},
author_keywords={Circular Economy;  Indicators;  Industrial Symbiosis;  Performance Measurement System;  Sustainability},
keywords={Benchmarking;  Ecology;  Economic and social effects;  Philosophical aspects;  Supply chains, Circular economy;  Industrial symbiosis;  Measurements of;  Multilevels;  Performance;  Performance measurement frameworks;  Performance measurement system;  Scalable performance;  Selection mechanism;  Still missing, Sustainable development},
references={Acerbi, F., Taisch, M., A literature review on circular economy adoption in the manufacturing sector (2020) J. Clean. Prod., 273; Acharya, A.S., Prakash, A., Saxena, P., Nigam, A., Sampling: why and how of it? (2013) Indian J. Med. Special., 4, pp. 3-7; Adams, W.C., Conducting semi-structured interviews (2015) Handbook of Practical Program Evaluation, pp. 492-505; Ahmad, S., Wong, K.Y., Rajoo, S., Sustainability indicators for manufacturing sectors: a literature survey and maturity analysis from the triple-bottom line perspective (2019) J. Manuf. Technol. Manag., 30, pp. 312-334; Ahmed, A.A., Nazzal, M.A., Darras, B.M., Deiab, I.M., A comprehensive multi-level circular economy assessment framework (2022) Sustain. Prod. Consum., 32, pp. 700-717; Al-Thani, N.A., Al-Ansari, T., Comparing the convergence and divergence within industrial ecology, circular economy, and the energy-water-food nexus based on resource management objectives (2021) Sustain. Prod. Consum., 27, pp. 1743-1761; Alarcón, F., Cortés-Pellicer, P., Pérez-Perales, D., Sanchis, R., Sustainability vs. Circular economy from a disposition decision perspective: a proposal of a methodology and an applied example in SMEs (2020) Sustainability (Switzerland), 12, pp. 1-26; Alkhuzaim, L., Zhu, Q., Sarkis, J., Evaluating emergy analysis at the nexus of circular economy and sustainable supply chain management (2021) Sustain. Prod. Consum., 25, pp. 413-424; Angioletti, C.M., Despeisse, M., Rocca, R., Product circularity assessment methodology Cecilia (2017) IFIP Adv. Inf. Commun. Technol., 514; Ansari, Z.N., Kant, R., A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management (2017) J. Clean. Prod., 142, pp. 2524-2543; Arena, M., Azzone, G., A process-based operational framework for sustainability reporting in SMEs (2012) J. Small Bus. Enterp. Dev., 19, pp. 669-686; Asheim, B.T., Industrial districts as “learning regions”: a condition for prosperity (1996) Eur. Plan. Stud., 4, pp. 379-400; Baldassarre, B., Schepers, M., Bocken, N., Cuppen, E., Korevaar, G., Calabretta, G., Industrial Symbiosis: towards a design process for eco-industrial clusters by integrating circular economy and industrial ecology perspectives (2019) J. Clean. Prod., 216, pp. 446-460; Balugani, E., Butturi, M.A., Chevers, D., Parker, D., Rimini, B., Empirical evaluation of the impact of resilience and sustainability on firms' performance (2020) Sustainability (Switzerland), 12, pp. 1-18; Barratt, M., Choi, T.Y., Li, M., Qualitative case studies in operations management: trends, research outcomes, and future research implications (2011) J. Oper. Manag., 29, pp. 329-342; Baškarada, S., Qualitative case study guidelines (2014) Qual. Rep., 19, pp. 1-18; Bititci, U.S., Garengo, P., Ates, A., Nudurupati, S.S., Value of maturity models in performance measurement (2015) Int. J. Prod. Res., 53, pp. 3062-3085; Bouckaert, G., Halligan, J., A framework for comparative analysis of performance management (2006) Conference of European Group of Public Administration, Università Bocconi, Milan, pp. 1-21; Cagno, E., Neri, A., Howard, M., Brenna, G., Trianni, A., Industrial sustainability performance measurement systems: a novel framework (2019) J. Clean. Prod., 230, pp. 1354-1375; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: moving toward new theory (2008) Int. J. Phys. Distrib. Logist. Manag., 38, pp. 360-387; Carter, C.R., Washispack, S., Mapping the path forward for sustainable supply chain management: a review of reviews (2018) J. Bus. Logist., 39, pp. 242-247; Cayzer, S., Griffiths, P., Beghetto, V., Design of Indicators for Measuring Product Performance in the Circular Design of Indicators for Measuring Product Performance in the Circular Economy (2017); Chertow, M.R., Industrial Symbiosis: literature and taxonomy (2000) Annu. Rev. Energy Environ.; Chertow, M.R., “Uncovering” industrial symbiosis (2007) J. Ind. Ecol., 11, pp. 11-30; Chiambaretto, P., Bengtsson, M., Fernandez, A.S., Näsholm, M.H., Small and large firms' trade-off between benefits and risks when choosing a coopetitor for innovation (2020) Long Range Plan., 53; Collins, A.J., Hester, P., Ezell, B., Horst, J., An improvement selection methodology for key performance indicators (2016) Environ. Syst. Decis., 36, pp. 196-208; Cooper, D.R., Schindler, P.S., Business research methods (2014), 12th ed. McGraw-Hill/Irwin New York, NY; Corona, B., Shen, L., Reike, D., Rosales Carreón, J., Worrell, E., Towards sustainable development through the circular economy—a review and critical assessment on current circularity metrics (2019) Resour. Conserv. Recycl., 151; Correia, E., Carvalho, H., Azevedo, S.G., Govindan, K., Maturity models in supply chain sustainability: a systematic literature review (2017) Sustainability (Switzerland), 9, pp. 1-26; Dey, P.K., Yang, G.L., Malesios, C., De, D., Evangelinos, K., Performance Management of Supply Chain Sustainability in Small and Medium-sized Enterprises Using a Combined Structural Equation Modelling and Data Envelopment Analysis, Computational Economics (2021); DiCicco-Bloom, B., Crabtree, B.F., The qualitative research interview (2006) Med. Educ., 40, pp. 314-321; Domenech, T., Bleischwitz, R., Doranova, A., Panayotopoulos, D., Roman, L., Mapping industrial symbiosis development in Europe_ typologies of networks, characteristics, performance and contribution to the circular economy (2019) Resour. Conserv. Recycl., 141, pp. 76-98; Elkington, J., Cannibals with Forks: Triple Bottom Line of 21st Century Business (1997), The Triple Bottom Line; Elkington, J., 25 Years Ago I Coined the Phrase “Triple Bottom Line.” Here's Why It's Time to Rethink It [WWW Document] (2018), https://hbr.org/2018/06/25-years-ago-i-coined-the-phrase-triple-bottom-line-heres-why-im-giving-up-on-it, Harv Bus Rev; Ellen MacArthur Foundation, Towards the Circular Economy: Opportunities for the Consumer Goods Sector (2013), https://www.ellenmacarthurfoundation.org/assets/downloads/publications/TCE_Report-2013.pdf; Towards a Circular Economy: Business Rationale for an Accelerated Transition (2015), Ellen MacArthur Foundation (EMF); Circularity Indicators: An Approach to Measuring Circularity (2015), 12. , Ellen MacArthur Foundation; Towards a Circular Economy: Business Rationale for an Accelerated Transition (2015), Ellen MacArthur Foundation (EMF); What is a circular economy? A framework for an economy that is restorative and regenerative by design [WWW Document] (2021), https://www.ellenmacarthurfoundation.org/circular-economy/overview/concept; Circulate products and materials [WWW Document] (2022), https://ellenmacarthurfoundation.org/circulate-products-and-materials; European Environment Agency, Industrial air pollution in Europe costs society €277–€433 billion [WWW Document] (2021), https://www.eea.europa.eu/highlights/industrial-air-pollution-in-europe; The Overlooked Side of the Ecological Transition (2019); Eurostat, Assessing Sustainable Development in the European Union - Monitoring Report on Progress Towards the SDGs in an EU Context (2019), Greener Management International Luxembourg; Eurostat, Manufacturing statistics [WWW Document]. Nace (2020), http://ec.europa.eu/eurostat/statistics-explained/index.php/Manufacturing_statistics_-_NACE_Rev._2; Fehrer, J.A., Wieland, H., A systemic logic for circular business models (2021) J. Bus. Res., 125, pp. 609-620; Feil, A.A., Schreiber, D., Haetinger, C., Strasburg, V.J., Barkert, C.L., Sustainability indicators for industrial organizations: systematic review of literature (2019) Sustainability (Switzerland), 11, pp. 1-15; Felicio, M., Amaral, D., Esposto, K., Gabarrell Durany, X., Industrial symbiosis indicators to manage eco-industrial parks as dynamic systems (2016) J. Clean. Prod., 118, pp. 54-64; Fraccascia, L., Giannoccaro, I., What, where, and how measuring industrial symbiosis: a reasoned taxonomy of relevant indicators (2020) Resour. Conserv. Recycl., 157; Fraccascia, L., Yazan, D.M., The supply chain implications of industrial symbiosis the supply chain implications of industrial symbiosis (2019) Procedia Environmental Science, Engineering, and Management; García-Quevedo, J., Jové-Llopis, E., Martínez-Ros, E., Barriers to the circular economy in European small and medium-sized firms (2020) Bus. Strateg. Environ., 29, pp. 2450-2464; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: a review for a research agenda (2005) Int. J. Manag. Rev., 7, pp. 25-47; Geissdoerfer, M., Savaget, P., Bocken, N.M.P., Hultink, E.J., The circular economy – a new sustainability paradigm? (2017) J. Clean. Prod., 143, pp. 757-768; Govindan, K., Rajeev, A., Padhi, S.S., Pati, R.K., Supply chain sustainability and performance of firms: a meta-analysis of the literature (2020) Transport Res E Log, 137; Govindan, K., Shaw, M., Majumdar, A., Social sustainability tensions in multi-tier supply chain: a systematic literature review towards conceptual framework development (2021) J. Clean. Prod., 279; Harris, S., Martin, M., Diener, D., Circularity for circularity's sake? Scoping review of assessment methods for environmental performance in the circular economy (2021) Sustain. Prod. Consum., 26, pp. 172-186; Hibberts, M., Johnson, R.B., Hudson, K., Handbook of survey methodology for the social sciences (2012) Handbook of Survey Methodology for the Social Sciences, pp. 1-520. , Springer Science + Business Media New York; Howard, M., Hopkinson, P., Miemczyk, J., The regenerative supply chain: a framework for developing circular economy indicators (2019) Int. J. Prod. Res., 57, pp. 7300-7318; Tracking industry 2021 [WWW Document]. IEA (2021), https://www.iea.org/reports/tracking-industry-2021; Jacobs, M., Sustainable development as a contested concept (1999) Fairness and Futurity: Essays on Environmental Sustainability and Social Justice, pp. 21-46; Johansson, N., Henriksson, M., Circular economy running in circles? A discourse analysis of shifts in ideas of circularity in Swedish environmental policy (2020) Sustain. Prod. Consum., 23, pp. 148-156; Junaid, M., Zhang, Q., Syed, M.W., Effects of sustainable supply chain integration on green innovation and firm performance (2022) Sustain. Prod. Consum., 30, pp. 145-157; Kallio, H., Pietilä, A.M., Johnson, M., Kangasniemi, M., Systematic methodological review: developing a framework for a qualitative semi-structured interview guide (2016) J. Adv. Nurs., 72, pp. 2954-2965; Kalmykova, Y., Sadagopan, M., Rosado, L., Circular economy - from review of theories and practices to development of implementation tools (2018) Resour. Conserv. Recycl., 135, pp. 190-201; Kazancoglu, Y., Kazancoglu, I., Sagnak, M., A new holistic conceptual framework for green supply chain management performance assessment based on circular economy (2018) J. Clean. Prod., 195, pp. 1282-1299; Kennedy, E.N., Kundu, G.K., Green supply chain key practices: literature (2017) Int. J. Green Econ., 11, p. 165; Kirchherr, J., Reike, D., Hekkert, M., Conceptualizing the circular economy: an analysis of 114 definitions (2017) Resour. Conserv. Recycl., 127, pp. 221-232; Kohlbacher, F., The use of qualitative content analysis in case study research (2006) Forum Qual. Soc. Res., 7, pp. 1-30; Köhler, J., Geels, F.W., Kern, F., Markard, J., Onsongo, E., Wieczorek, A., Alkemade, F., Wells, P., An agenda for sustainability transitions research: state of the art and future directions (2019) Environ Innov Soc Transit, 31, pp. 1-32; Korhonen, J., Honkasalo, A., Seppälä, J., Circular economy: the concept and its limitations (2018) Ecol. Econ., 143, pp. 37-46; Kravchenko, M., Pigosso, D.C.A., McAloone, T.C., A procedure to support systematic selection of leading indicators for sustainability performance measurement of circular economy initiatives (2020) Sustainability (Switzerland), 12; Kristensen, H.S., Mosgaard, M.A., A review of micro level indicators for a circular economy – moving away from the three dimensions of sustainability? (2020) J. Clean. Prod., 243; Kumar, V., Sezersan, I., Garza-Reyes, J.A., Gonzalez, E.D.R.S., AL-Shboul, M.A., Circular economy in the manufacturing sector: benefits, opportunities and barriers (2019) Management Decision, 57, pp. 1067-1086; Kumar, V., Leng, J., Akberdina, V., Kuzmin, E., Digital transformation in industry (2022), 1st ed. Springer International Publishing Cham; Lee, S.Y., Hu, J., Lim, M.K., Maximising the circular economy and sustainability outcomes: an end-of-life Tyre recycling outlets selection model (2021) Int. J. Prod. Econ., 232; Li, Y., Shi, L., The resilience of interdependent industrial Symbiosis networks: a case of Yixing economic and technological development zone (2015) J. Ind. Ecol., 19, pp. 264-273; Liakos, N., Kumar, V., Pongsakornrungsilp, S., Garza-Reyes, J.A., Gupta, B., Pongsakornrungsilp, P., Understanding circular economy awareness and practices in manufacturing firms (2019) J. Enterp. Inf. Manag., 32, pp. 563-584; Lieder, M., Rashid, A., Towards circular economy implementation: a comprehensive review in context of manufacturing industry (2016) J. Clean. Prod.; Lin, J., Mills, A., Measuring the occupational health and safety performance of construction companies in Australia (2001) Facilities, 19, pp. 131-138; Liu, C., Côté, R.P., Zhang, K., Implementing a three-level approach in industrial symbiosis (2015) J. Clean. Prod., 87, pp. 318-327; Lombardi, D.R., Laybourn, P., Redefining industrial Symbiosis: crossing academic-practitioner boundaries (2012) J. Ind. Ecol., 16, pp. 28-37; Lozano, R., A holistic perspective on corporate sustainability drivers (2015) Corp. Soc. Responsib. Environ. Manag., 22 (1), pp. 32-44; Lütje, A., Wohlgemuth, V., Tracking sustainability targets with quantitative indicator systems for performance measurement of industrial symbiosis in industrial parks (2020) Adm. Sci., 10; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Malesios, C., Dey, P.K., Abdelaziz, F.B., Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling (2020) Ann. Oper. Res., 294, pp. 623-653; Maranesi, C., De Giovanni, P., Modern circular economy: corporate strategy, supply chain, and industrial symbiosis (2020) Sustainability (Switzerland), 12, pp. 1-25; Martin, M., Harris, S., Prospecting the sustainability implications of an emerging industrial symbiosis network (2018) Resour. Conserv. Recycl., 138, pp. 246-256; Martinho, V.J.P.D., Environmental and sustainability indicators insights into circular economy indicators: emphasizing dimensions of sustainability (2021) Environ. Sustain. Indic. J., 10; Massaroni, E., Cozzolino, A., Wankowicz, E., Sustainability in supply chain management - a literature review (2018) Sinergie Italian J. Manag., 331-355; Mengistu, A.T., Panizzolo, R., Tailoring sustainability indicators to small and medium enterprises for measuring industrial sustainability performance (2022) Meas. Bus. Excell.; Mengistu, A.T., Panizzolo, R., Analysis of indicators used for measuring industrial sustainability: a systematic review (2022) Environ. Dev. Sustain.; Mesa, J., Esparragoza, I., Maury, H., Developing a set of sustainability indicators for product families based on the circular economy model (2018) J. Clean. Prod.; Mies, A., Gold, S., Mapping the social dimension of the circular economy (2021) J. Clean. Prod., 321; Miles, M.B., Huberman, A.M., Saldaña, J., Qualitative Data Analysis - A Methods Sourcebook (2014), 3rd ed SAGE Publications. SAGE; Mirata, M., Experiences from early stages of a national industrial symbiosis programme in the UK: determinants and coordination challenges (2004) J. Clean. Prod., 12, pp. 967-983; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking; Mitchell, S., O'Dowd, P., Dimache, A., Manufacturing SMEs doing it for themselves: developing, testing and piloting an online sustainability and eco-innovation toolkit for SMEs (2020) Int. J. Sustain. Eng., 13, pp. 159-170; Molla, M.S., Ibrahim, Y., Ishak, Z., Corporate sustainability practices: A review on the measurements, relevant problems and a proposition (2019) Global Journal of Management and Business Research: D Accounting and Auditing, 19 (1). , https://www.researchgate.net/publication/330042197; Morales, M.E., Belmonte-Urena, L.J., Theoretical research on circular economy and sustainability trade-offs and synergies: a bibliometric analysis (2021) 2021 IEEE International Conference on Technology and Entrepreneurship, 2021. , ICTE; Mulia, P., Behura, A.K., Kar, S., Categorical imperative in defense of strong sustainability (2016) Problems Sustain. Dev., 11, pp. 29-36; Negri, M., Cagno, E., Colicchia, C., Sarkis, J., Integrating sustainability and resilience in the supply chain: a systematic literature review and a research agenda (2021) Bus Strategy Environ, 30, pp. 1-29; Negri, M., Neri, A., Cagno, E., Monfardini, G., Circular economy performance measurement in manufacturing firms: a systematic literature review with insights for small and medium enterprises and new adopters (2021) Sustainability (Switzerland), 13; Negri, M., Cagno, E., Colicchia, C., Building sustainable and resilient supply chains: a framework and empirical evidence on trade-offs and synergies in implementation of practices (2022) Prod. Plann. Control, pp. 1-24; Neri, A., Industrial sustainability performance measurement system—challenges for the development (2021) Methods Sustain. Sci., pp. 87-104; Neri, A., Cagno, E., Di Sebastiano, G., Trianni, A., Industrial sustainability: modelling drivers and mechanisms with barriers (2018) J. Clean. Prod., 194, pp. 452-472; Neri, A., Cagno, E., Lepri, M., Trianni, A., A triple bottom line balanced set of key performance indicators to measure the sustainability performance of industrial supply chains (2021) Sustain. Prod. Consum., 26, pp. 648-691; Neri, A., Cagno, E., Trianni, A., Barriers and drivers for the adoption of industrial sustainability measures in european SMEs: empirical evidence from chemical and metalworking sectors (2021) Sustain. Prod. Consum., 28, pp. 1433-1464; Neves, A., Godina, R., Azevedo, S.G., Matias, J.C.O., Current status, emerging challenges, and future prospects of industrial symbiosis in Portugal (2019) Sustainability (Switzerland), 11; Nikolaou, I.E., Tsagarakis, K.P., An introduction to circular economy and sustainability: some existing lessons and future directions (2021) Sustain. Prod. Consum., 28, pp. 600-609; Nikolaou, I.E., Tsalis, T.A., Evangelinos, K.I., A framework to measure corporate sustainability performance: a strong sustainability-based view of firm (2019) Sustain. Prod. Consum., 18, pp. 1-18; Nowlis, S.M., Kahn, B.E., Dhar, R., Coping with ambivalence: the effect of removing a neutral option on consumer attitude and preference judgments (2002) J. Consum. Res., 29, pp. 319-334; Oleinikova, A., Brovchenko, I., Thermodynamic properties of hydration water around solutes: effect of solute size and water-solute interaction (2012) J. Phys. Chem. B, 116, pp. 14650-14659; Opferkuch, K., Caeiro, S., Salomone, R., Ramos, T.B., Circular economy in corporate sustainability reporting: a review of organisational approaches (2021) Bus. Strateg. Environ., 30, pp. 4015-4036; Ormazabal, M., Prieto-Sandoval, V., Puga-Leal, R., Jaca, C., Circular economy in spanish SMEs: challenges and opportunities (2018) J. Clean. Prod., 185, pp. 157-167; Padilla-Rivera, A., Russo-Garrido, S., Merveille, N., Addressing the social aspects of a circular economy: a systematic literature review (2020) Sustainability (Switzerland), 12, pp. 1-17; Pandey, A.K., Prakash, R., Impact of industrial symbiosis on sustainability (2019) Open J. Energy Eff., 8, pp. 81-93; Parida, V., Burström, T., Visnjic, I., Wincent, J., Orchestrating industrial ecosystem in circular economy: a two-stage transformation model for large manufacturing companies (2019) J. Bus. Res., 101, pp. 715-725; Prieto-Sandoval, V., Ormazabal, M., Jaca, C., Viles, E., Key elements in assessing circular economy implementation in small and medium-sized enterprises (2018) Bus Strategy Environ; Purvis, B., Mao, Y., Robinson, D., Three pillars of sustainability: in search of conceptual origins (2019) Sustain. Sci., 14 (3), pp. 681-695; Rincón-Moreno, J., Ormazabal, M., Álvarez, M.J., Jaca, C., Shortcomings of transforming a local circular economy system through industrial symbiosis: a case study in Spanish SMEs (2020) Sustainability (Switzerland), 12, pp. 1-18; Rizos, V., Behrens, A., van der Gaast, W., Hofman, E., Ioannou, A., Kafyeke, T., Flamos, A., Topi, C., Implementation of circular economy business models by small and medium-sized enterprises (SMEs): barriers and enablers (2016) Sustainability (Switzerland), 8; Rossi, E., Bertassini, A.C., Ferreira, C.D.S., Neves do Amaral, W.A., Ometto, A.R., Circular economy indicators for organizations considering sustainability and business models: Plastic, textile and electro-electronic cases (2020) J. Clean Prod., 247; Rowley, J., Conducting research interviews (2012) Manag. Res. Rev., 35, pp. 260-271; Saidani, M., Yannou, B., Leroy, Y., Cluzel, F., How to assess product performance in the circular economy? Proposed requirements for the design of a circularity measurement framework (2017) Recycling, 2 (1); Saidani, M., Yannou, B., Leroy, Y., Cluzel, F., Kendall, A., A taxonomy of circular economy indicators (2019) J. Clean. Prod., 207, pp. 542-559; Saidani, M., Cluzel, F., Leroy, Y., Pigosso, D., Kravchenko, M., Kim, H., Nexus between life cycle assessment, circularity and sustainability indicators—part II: experimentations (2022) Circ. Econ. Sustain.; Salomone, R., Cecchin, A., Deutz, P., Raggi, A., Cutaia, L., Industrial Symbiosis for the Cicular Economy, Strategies for Sustainability (2020); Scheibehenne, B., Greifeneder, R., Todd, P.M., What moderates the too-much-choice effect? (2009) Psychol. Mark., 26, pp. 229-253; Schöggl, J.P., Stumpf, L., Baumgartner, R.J., The narrative of sustainability and circular economy - a longitudinal review of two decades of research (2020) Resour. Conserv. Recycl., 163; Simão, L.E., Somensi, K., Dávalos, R.V., Rodriguez, C.M.T., Measuring supply chain performance: the triple E model (2021) Int. J. Product. Perform. Manag.; Singh, S., Olugu, E.U., Fallahpour, A., Fuzzy-based sustainable manufacturing assessment model for SMEs (2014) Clean Techn. Environ. Policy, 16, pp. 847-860; Singh, S., Olugu, E.U., Musa, S.N., Development of sustainable manufacturing performance evaluation expert system for small and medium enterprises (2016) Procedia CIRP, 40, pp. 608-613; Stewart, R., Niero, M., Circular economy in corporate sustainability strategies: a review of corporate sustainability reports in the fast-moving consumer goods sector (2018) Bus Strategy Environ, 27, pp. 1005-1022; Sweeney, E., Grant, D.B., Mangan, D.J., The implementation of supply chain management theory in practice: an empirical investigation (2015) Supply Chain Manag., 20, pp. 56-70; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) Int. J. Prod. Res., 53, pp. 6473-6494; Critical Raw Materials Resilience: Charting a Path Towards Greater Security and Sustainability (2020), European Commission; Thurmond, V.A., The point of triangulation (2001) J. Nurs. Scholarsh., 33, pp. 253-258; Tonelli, F., Evans, S., Taticchi, P., Industrial sustainability: challenges, perspectives, actions (2013) Int. J. Bus. Innov. Res., 7, pp. 143-163; Trianni, A., Cagno, E., Neri, A., Modelling barriers to the adoption of industrial sustainability measures (2017) J. Clean. Prod., 168, pp. 1482-1504; Trianni, A., Cagno, E., Neri, A., Howard, M., Measuring industrial sustainability performance: empirical evidence from italian and german manufacturing small and medium enterprises (2019) J. Clean. Prod., 229, pp. 1355-1376; Triguero, Á., Cuerva, M.C., Sáez-Martínez, F.J., Closing the loop through eco-innovation by European firms: circular economy for sustainable development (2022) Bus. Strateg. Environ., 1-14; Tuni, A., Rentizelas, A., Chinese, D., An integrative approach to assess environmental and economic sustainability in multi-tier supply chains (2020) Prod. Plan. Control, 31, pp. 861-882; Resolution A/RES/70/1 ”transforming our word: the 2030 agenda for sustainable development” [WWW Document] (2015), https://www.un.org/en/development/desa/population/migration/generalassembly/docs/globalcompact/A_RES_70_1_E.pdf; Valenzuela-Venegas, G., Salgado, J.C., Díaz-Alvarado, F.A., Sustainability indicators for the assessment of eco-industrial parks: classification and criteria for selection (2016) J. Clean. Prod., 133, pp. 99-116; Vegter, D., van Hillegersberg, J., Olthaar, M., Performance measurement systems for circular supply chain management: current state of development (2021) Sustainability (Switzerland), 13; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) Int. J. Oper. Prod. Manag., 22, pp. 195-219; Walker, A.M., Opferkuch, K., Roos Lindgreen, E., Raggi, A., Simboli, A., Vermeulen, W.J.V., Caeiro, S., Salomone, R., What Is the Relation between Circular Economy and Sustainability? Answers From Frontrunner Companies Engaged With Circular Economy Practices. Circular Economy and Sustainability (2021); Walker, A.M., Opferkuch, K., Roos Lindgreen, E., Simboli, A., Vermeulen, W.J.V., Raggi, A., Assessing the social sustainability of circular economy practices: industry perspectives from Italy and the Netherlands (2021) Sustain. Prod. Consum., 27, pp. 831-844; Wen, Z., Meng, X., Quantitative assessment of industrial symbiosis for the promotion of circular economy: a case study of the printed circuit boards industry in China's Suzhou New District (2015) J. Clean. Prod., 90, pp. 211-219; Woodside, A.G., Wilson, E.J., Case study research methods for theory building (2003) J. Bus. Ind. Market., 18, pp. 493-508; Zainal, Z., Case study as a research method (2007) Jurnal Kemanusiaan, 5; Zink, T., Geyer, R., Circular economy rebound (2017) J. Ind. Ecol., 21, pp. 593-602},
correspondence_address1={Negri, M.; Politecnico di Milano, Italy; email: marta.negri@polimi.it},
publisher={Elsevier B.V.},
issn={23525509},
language={English},
abbrev_source_title={Sustain. Prod. Consum.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sanchez-Jimenez2023127,
author={Sanchez-Jimenez, L. and Salais-Fierro, T.E. and Saucedo-Martínez, J.A.},
title={A Model for the Control and Monitoring of Supply Chain Indicators},
journal={EAI/Springer Innovations in Communication and Computing},
year={2023},
pages={127-147},
doi={10.1007/978-3-031-07670-1_9},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145223096&doi=10.1007%2f978-3-031-07670-1_9&partnerID=40&md5=fc1fe31044a52bf3868ee3503cb4e8d1},
affiliation={Universidad Autónoma de Nuevo León, Ciudad Universitaria, Nuevo León, San Nicolás de los Garza, Mexico},
abstract={In the current competitive environment, companies are pushed to develop strategies to achieve operational excellence in pursuit of growth and profitability. A supply chain focuses primarily on reducing costs by optimizing its processes, achieving a service level that meets the required quality standards. Managing the success of the supply chain is considered an essential activity in any organization. Then, the effectiveness and efficiency of the supply chain can be determined through a performance measurement system focused especially on logistics processes. The proposal established in this research consists of a system that integrates auxiliary techniques in decision-making with the aim of establishing performance indicators within the supply logistics process. In addition, this system incorporates fuzzy logic in order to establish more realistic and robust metrics and with the ability to feed back indicators under uncertain environments or with a lack of information. The presented system is cyclical and adaptive, which includes techniques based on AHP, SCOR, and Fuzzy Logic, and they support the decision-maker in any environment, stage, or process of the supply chain by determining through projections if the objectives planted in the improvement plans have been achieved. Additionally, it identifies the attributes that impact on the supply chain and those that represent areas of opportunity to improve. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
author_keywords={Indicators;  Measurement;  Models;  Performance;  Supply chain;  Techniques},
keywords={Computer circuits;  Decision making;  Fuzzy logic, 'current;  Competitive environment;  Control and monitoring;  Fuzzy-Logic;  Growth and profitability;  Logistics process;  Operational excellence;  Performance;  Reducing costs;  Technique, Supply chains},
references={Ballou, R.H., (1998) Business Logistics Management, , 4th edn. (Prentice Hal, Hoboken; Kozlenkova, I.V., Hult, G.T.M., Lund, D.J., Mena, J.A., Kekec, P., The role of marketing channels in supply chain management (2015) J. Retail., 91 (4), pp. 586-609; Qi, Y., Huo, B., Wang, Z., Yeung, H.Y.J., The impact of operations and supply chain strategies on integration and performance (2017) Int. J. Product. Econ., 185, pp. 162-174; Jayaram, J., Dixit, M., Motwani, J., Supply chain management capability of small and medium sized family businesses in India: A multiple case study approach (2014) Int. J. Product. Econ., 147, pp. 472-485; Jagan Mohan Reddy, K., Neelakanteswara Rao, A., Krishnanand, L., A review on supply chain performance measurement systems (2019) Proc. Manuf., 30, pp. 40-47; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) Int. J. Product. Res., 45 (12), pp. 2819-2840; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logist., 21 (1), pp. 75-93; Frazelle, E.H., (2002) Supply Chain Strategy: The Logistics of Supply Chain Management, , 1st edn. (McGraw-Hill, New York; Christopher, M., (1999) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , 2nd edn. (Financial Times/Prentice Hall, London; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Comput. Ind. Eng., 113, pp. 333-346; Sundarakani, B., Razzak, H.A., Manikandan, S., Creating a competitive advantage in the global flight catering supply chain: A case study using SCOR model (2018) Int. J. Logist. Res. Appl., 21 (5), pp. 481-501; Lima-Junior, F.R., Carpinetti, L.C.R., An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCORⓇ metrics (2020) Comput. Ind. Eng., 139 (1-19); Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Comput. Ind., 82, pp. 135-150; Beamon, B.M., Supply chain design and analysis (1998) Models and Methods. Int. J. Product. Econ., 55 (3), pp. 281-294; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) Eur. J. Oper. Res., 144 (2), pp. 306-320; Cognitive Power Electronics 4.0-So knnten leistungselektronische Schaltungen entschei-den All-Electronics.De-Entwicklung, Ferti-Gung, Automatisierung, 29.03.2019. [Online], , https://www.all-electronics.de/so-koennten-leistungselektronische-schaltun-gen-entscheiden/; Sipahi, S., Timor, M., The analytic hierarchy process and analytic network process: An overview of applications (2010) Manage. Decis., 48 (5), pp. 775-808; Santos, J., Negasy, E., Cavique, L., Introduction to data envelopment analysis (2013) Efficiency Measures in the Agricultural Sector: With Applications, pp. 37-50. , Springer, Berlin, ), pp; Abukhousa, E., Al-Jaroodi, J., Lazarova-Molnar, S., Mohamed, N., Simulation and modeling efforts to support decision making in healthcare supply chain management (2014) Sci. World J., 3542; Belton, V., Stewart, T., (2002) Multiple Criteria Decision Analysis-An Integrated Approach, , Kluwer Academic Publishers, London; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance. J. Bus. Logist. 28(1) (2000) 75Pp; Vaidya, O.S., Analytic hierarchy process: An overview of applications (2006) Eur. J. Oper. Res., 169 (1), pp. 1-29; Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E.K., Nilashi, M., Zakuan, N., Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysis (2018) Ann. Oper. Res., 271, pp. 915-969; Delipinar, G.E., Kocaoglu, B., Using SCOR model to gain competitive advantage: A literature review (2016) Proc. Soc. Behav. Sci., 229, pp. 398-406; Elgazzar, S., Tipi, N., Jones, G., Key characteristics for designing a supply chain performance measurement system (2019) Int. J. Product. Perform. Manage., 68, pp. 296-318; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) J. Oper. Suppl. Chain Manage., 6, pp. 94-113; Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E.K., Antucheviciene, J., Supplier evaluation and selection in fuzzy environments: A review of MADM approaches (2017) Econ. Res.-Ekonomska Istraživanja, 30 (1), pp. 1073-1118; Aqlan, F., Lam, S.S., A fuzzy-based integrated framework for supply chain risk assessment (2015) Int. J. Prod. Econ., 161, pp. 54-63; Zanon, L., Munhoz Arantes, R., Del Rosso Calache, L., Ribeiro Carpinetti, L., A decision making model based on fuzzy inference to predict the impact of SCORⓇ indicators on customer perceived value (2020) Int. J. Product. Econ., 223 (1-17); Baily, P., Farmer, D., Jessop, D., Jones, D., (2005) Purchasing Principles and Management, , 9th edn. (Pearson, Boston; Govindan, K., Haq, A.N., Sasikumar, P., Arunachalam, S., Analysis and selection of green suppliers using interpretative structural modelling and analytic hierarchy process (2008) Int. J. Manage. Decis. Mak., 9 (2), pp. 163-182; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organizations (2013) Product. Plan. Control, 24 (8-9), pp. 702-720; Luthra, S., Garg, D., Haleem, A., Identifying and ranking of strategies to implement green supply chain management in Indian manufacturing industry using analytical hierarchy process (2013) J. Ind. Eng. Manage., 6 (4), pp. 930-962; Madaan, J., Mangla, S., Decision modeling approach for ecodriven flexible green supply chain (2015) Systemic Flexibility and Business Agility, pp. 343-364. , Springer, Delhi, ), pp; Ordoobadi, S.M., Application of AHP and taguchi loss functions in supply chain (2010) Ind. Manag. Data Syst., 110 (8), pp. 1251-1269; Abdullah, L., Fuzzy multi criteria decision making and its applications: A brief review of category (2013) Proc. Soc. Behav. Sci., 97, pp. 131-136; Farajpour, F., Taghavifard, M.T., Yousefli, A., Taghva, M.R., Information sharing assessment in supply chain: Hierarchical fuzzy rule-based system (2018) J. Inf. Knowl. Manage., 17 (1); Khan, A., Kusi-Sarpong, S., Kow Arhin, F., Kusi-Sarpong, H., Supplier sustainability performance evaluation and selection: A framework and methodology (2018) J. Clean. Product., 205, pp. 964-979; Segundo, U., Aldámiz-Echevarría, L., López-Cuadrado, J., Buenestado, D., Andrade, F., Pérez, T.A., Barrena, R., Pikatza, J.M., Improvement of newborn screening using a fuzzy inference system (2017) Exp. Syst. Appl., 78, pp. 301-318; Domínguez, E., Pérez, B., Rubio, Á.L., Zapata, M.A., A taxonomy for key performance indicators management (2018) Comput. Standards Interfaces, 64, pp. 24-40; Sellitto, M., Medeiros, G., Borchardt, M., Inácio, R., Viegas, C., A SCOR-based model for supply chain performance measurement: Application in the footwear industry (2015) International Journal of Production Research, 53 (16), pp. 4917-4926. , https://doi.org/10.1080/00207543. 2015.1005251; Akkawuttiwanich, P., Yenradee, P., Fuzzy QFD approach for managing SCOR performance indicators Computers and Industrial Engineering, 122, pp. 189-201. , https://doi.org/10.1016/j.cie. 2018.05.044},
correspondence_address1={Sanchez-Jimenez, L.; Universidad Autónoma de Nuevo León, Nuevo León, Mexico; email: loresanchez16@gmail.com},
editor={Torres-Guerrero F., Neira-Tovar L., Bacca-Acosta J.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={25228595},
isbn={9783031076695},
language={English},
abbrev_source_title={EAI/Springer Inno. Comm. Comp.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Wulandari2023187,
author={Wulandari, R. and Ridwan, A.Y. and Muttaqin, S.},
title={Halal Supply Chain Performance Measurement Model in Food Industry Using SCOR Model, AHP Method and OMAX},
journal={Lecture Notes in Mechanical Engineering},
year={2023},
pages={187-197},
doi={10.1007/978-981-19-3629-6_20},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137983502&doi=10.1007%2f978-981-19-3629-6_20&partnerID=40&md5=df1e2a054e4322dda69c6ec75e0a440e},
affiliation={Industrial Engineering Department, Telkom University, Bandung, Indonesia; Logistics Engineering Department, Faculty of Industrial Engineering, Telkom University, Bandung, Indonesia},
abstract={Indonesia is a country that has the largest Muslim population in the world, which makes Indonesia a large market share for Sharia products and services, especially Indonesia has potential in the halal food industry. There are several standards for the halal management system that must be considered, such as in SNI 99001: 2016 Clause 3.4.1 which contains criteria, methods, including measurements and related performance indicators needed to ensure effective operation, and inter-process control. Thus, the organization or company is needed to be able to establish company criteria, methods and measurement of performance indicators in order to ensure effectiveness in the process. The purpose of this study is to determine the criteria for measuring the performance of the halal supply chain in the planning process, to design a system and interface for measuring the performance of the halal supply chain in the planning process based on SNI 99001: 2016. In the process of completing this research using the SCOR model and the AHP, OMAX, and TLS method. The results of the performance measurement for the criteria and sub-criteria are obtained. In this study, three criteria were produced for each part of the planning, namely reliability, responsiveness, and cost. For planning the procurement of raw materials there are 6 sub-criteria reliability, 1 sub-criteria responsiveness, and 1 sub-criteria cost. For production planning, there are 5 reliability sub-criteria, 3 responsiveness sub-criteria, and 1 cost sub-criteria. For raw material procurement planning, there are 7 reliability sub-criteria, 3 responsiveness sub-criteria, and 1 cost sub-criteria. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.},
author_keywords={AHP;  Halal;  OMAX;  Performance measurement model;  SCOR;  SNI 99001:2016;  TLS},
keywords={Benchmarking;  Competition;  Costs;  Hierarchical systems;  Production control;  Supply chains, AHP;  Halal;  Indonesia;  Measurement model;  OMAX;  Performance measurement model;  Performance measurements;  SCOR;  SNI 99001:2016;  TLS, Reliability},
references={Tieman, M., van der Vorst, J.G., Ghazali, M.C., Principles in halal supply chain management (2012) J. Islamic Mark.; Fradinata, E., Kesuma, Z.M., Rusdiana, S., Support vector regression and adaptive neuro fuzzy to measure the Bullwhip effect in supply chain (2018) J. Phys. Conf. Ser.; Mefid, K.N., Ridwan, A.Y., Sari, W.P., Global industry perspective of halal cosmetics applying sales and distribution process based on enterprise resources planning (2019) Bull. Soc. Inform. Theory Appl., 3, pp. 61-68; Zahra, D.K., Ridwan, A.Y., Saputra, M., Halal SCM: How to monitor halal performance of procurement process for food industry in Indonesia by using ERP system (2020) In: ICORE, 5; Fauziyah, I., Ridwan, A., Muttaqin, P., Food production performance measurement system using halal supply chain operation reference (SCOR) model and analytical hierarchy process (AHP) (2020) IOP Conference Series: Materials Science and Engineering; Saaty, T.L., Decision making with the analytic hierarchy process (2008) Int. J. Serv. Sci., 1, pp. 83-98; Fradinata, E., Kesuma, Z.M., Rusdiana, S., Optimal lot-sizing algorithms on stochastic demand at the retailer (2018) Proceedings of Micoms 2017. Emerald Publishing Limited; Moharamkhani, A., Bozorgi-Amiri, A., Mina, H., Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS (2017) Int. J. Logist. Syst. Manag., 27, pp. 115-132; Fradinata, E., Kesuma, Z.M., Numeric model to predict the location of market demand and economic order quantity for retailers of supply chain (2018) IOP Conference Series: Materials Science and Engineering; Anugrah, S., Anggraini, Absor (2018) W., Fauzi, S.: Integrated Analytical Hierarchy Process and Objective Matrix in Balanced Scorecard Dashboard Model for Performance Measurement. Telkomnika, p. 16; Fradinata, E., Suthummanon, S., Sunthiamorntut, W., Comparison of hybrid ANN models: A case study of instant noodle industry in Indonesia (2017) Int. J. Adv. Appl. Sci, 4, pp. 19-28; Wahyuniardi, R., Syarwani, M., Anggani, R., Pengukuran Kinerja supply chain Dengan Pendekatan supply chain operation references (SCOR) (2017) Jurnal Ilmiah Teknik Industri, 16, pp. 123-132; Nusran, M., Triana, D., Raehana, S., Damis, H., Syarifudin, A., Wekke, I.S., Policy on halal slaughtering availability for halal chicken needs Makassar City Indonesia (2018) Int. J. Eng. Technol., 7, pp. 75-81; Nurhidayanti, S., Rusdi, N., Abdul, S., Yusuf, B., (2020) Analysing and Modelling Gripper Arm Using Shape Optimization of Fusion 360 and 3D Printing of PLA},
correspondence_address1={Wulandari, R.; Industrial Engineering Department, Indonesia; email: ratnawullandari@student.telkomuniversity.ac.id},
editor={Akhyar, Huzni S., Iqbal M.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={21954356},
isbn={9789811936289},
language={English},
abbrev_source_title={Lect. Notes Mech. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Abbas20221463,
author={Abbas, H. and Asim, Z. and Ahmed, Z. and Moosa, S.},
title={Exploring and establishing the barriers to sustainable humanitarian supply chains using fuzzy interpretive structural modeling and fuzzy MICMAC analysis},
journal={Social Responsibility Journal},
year={2022},
volume={18},
number={8},
pages={1463-1484},
doi={10.1108/SRJ-12-2020-0485},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114497512&doi=10.1108%2fSRJ-12-2020-0485&partnerID=40&md5=a3ccbc9a80ad7f85ede453ba6fea19e1},
affiliation={Department of Business Administration, College of Applied Sciences, University of Technology and Applied Sciences, Salalah, Oman; Faculty of Commerce and Management, SGT University, Gurugram, India; Department of Business Administration, Aligarh Muslim University, Aligarh, India},
abstract={Purpose: The continual onset of natural and manmade disasters propels the humanitarian supply chain (HSC) efforts (by organizations, groups and individuals) to always be on a stand-by mode with more and more sustainable solutions. Despite all the sincere and coordinated efforts from all the humanitarian agents and bodies, the likely sustainable outputs are hampered by certain barriers (impediments) which exist at different levels of the HSCs. A better understanding of such barriers and their mutual relationship is deemed helpful in improving the outcomes of humanitarian efforts. Thus, the purpose of this paper is to explore, refine, establish and classify these barriers which thwart the sustainable efforts of the HSCs individually as well as collectively. Design/methodology/approach: An extensive literature review is conducted to identify these barriers which were followed by soliciting the experts’ inputs to update, refine and retain the contextually relevant ones. The opinions about the nine identified and refined barriers are taken from eight experts based in the Northern India who are having at least five years of experience in humanitarian operations. Fuzzy interpretive structural modeling (FISM) is used to examine and establish a hierarchical relationship among these barriers, whereas fuzzy Matrice d’impacts croisés multiplication appliquée á un classment analysis is carried out to further classify these barriers into dependent, autonomous, linkage and dependent barriers. Findings: The analysis led to the formation of a FISM model where the operational challenges affecting the performance occupy the topmost position in the hierarchy. The results reveal that inconsistent motives, coordination and communication and operational challenges affecting the performance are the dependent, poor strategic planning, capacity-related challenges and poor performance measurement system are the autonomous, and financial challenges, locational challenges and lack of proper awareness are the independent barriers. Research limitations/implications: The focus of the researchers was to study and examine these barriers to sustainable HSCs with special reference to the epidemics and pandemics (especially COVID-19), and it sheds light particularly arising during and post disaster phases. Practical implications: The structural model contributed by this study is expected to be meaningful for practitioners besides enriching the body of literature. In the context of pandemics, it distinguishes itself from the other available frameworks. Social implications: As this research has been carried out in the context of the novel COVID-19, the framework is expected to assist policymakers in comprehending the issues impeding the sustainability of noble humanitarian efforts. Thus, ultimately it is expected to contribute to the ultimate cause of society at large. Originality/value: This research endeavor distinguishes itself from the other accessible published resources in terms of the specific context, the methodological approach and the nature of respondents. This paper concludes with the practical implications and directions for future research. © 2021, Emerald Publishing Limited.},
author_keywords={COVID-19;  Fuzzy interpretive structural modeling;  Fuzzy MICMAC analysis;  Humanitarian logistics;  Humanitarian supply chain;  Interpretive structural modeling;  Sustainable humanitarian logistics;  Sustainable humanitarian supply chain},
references={Al-Esmael, B., Talib, F., Faisal, M.N., Jabeen, F., Socially responsible supply chain management in small and medium enterprises in the GCC (2019) Social Responsibility Journal, 16 (3), pp. 369-386; Altay, N., Haavisto, I., Kovács, G., Spens, K., Editorial (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (2); Azmat, M., Kummer, S., Importance of key success factors for local and international NGOS in humanitarian supply chain (2019) Logforum, 15 (4), pp. 545-555; Bag, S., Humanitarian supply chain management: a bibliometric analysis of the literature (2016) AIMS International Journal of Management, 10 (3), pp. 175-202; Baharmand, H., Salvad, L.L., Comes, T., Lauras, M., On the literature divergences of the humanitarian supply chain (2015) International Conference on Information Systems for Crisis Response and Management in Mediterranean Countries, pp. 194-204. , Springer, Cham: October; Balcik, B., Bozkir, C.D.C., Kundakcioglu, O.E., A literature review on inventory management in humanitarian supply chains (2016) Surveys in Operations Research and Management Science, 21 (2), pp. 101-116; Bamel, N., Dhir, S., Sushil, S., Inter-partner dynamics and joint venture competitiveness: a fuzzy TISM approach (2019) Benchmarking: An International Journal, 26 (1); Banomyong, R., Julagasigorn, P., Varadejsatitwong, P., Piboonrungroj, P., The humanitarian supply chain assessment tool (HumSCAT) (2019) Journal of Humanitarian Logistics and Supply Chain Management, 9 (2), pp. 221-249; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Behl, A., Dutta, P., Gupta, S., Critical success factors for humanitarian supply chain management: a grey DEMATEL approach (2019) IFAC-PapersOnLine, 52 (13), pp. 159-164; Boostani, A., Jolai, F., Bozorgi-Amiri, A., Designing a sustainable humanitarian relief logistics model in pre-and postdisaster management (2020) International Journal of Sustainable Transportation, 15 (8), pp. 1-17; Caballero-Anthony, M., Cook, A.D., Chen, C., Knowledge management and humanitarian organisations in the Asia-Pacific: practices, challenges, and future pathways (2021) International Journal of Disaster Risk Reduction, 53, p. 102007; ChenVan Wassenhove, H.S., Cheng, T.C.E., (2020) Designing sustainable humanitarian supply chains, , SSRN 3601667; Cozzolino, A., Rossi, S., Conforti, A., Agile and lean principles in the humanitarian supply chain (2012) Journal of Humanitarian Logistics and Supply Chain Management, 2 (1), pp. 16-33; da Costa, S.R.A., Campos, V.B.G., de Mello Bandeira, R.A., Supply chains in humanitarian operations: cases and analysis (2012) Procedia – Social and Behavioral Sciences, 54, pp. 598-607; Dasaklis, T.K., Pappis, C.P., Rachaniotis, N.P., Epidemics control and logistics operations: a review (2012) International Journal of Production Economics, 139 (2), pp. 393-410; Dhamija, P., Gupta, S., Bag, S., Gupta, M.L., Humanitarian supply chain management: a systematic review and bibliometric analysis (2021) International Journal of Automation and Logistics, 3 (2), pp. 104-136; Dubey, R., Gunasekaran, A., The sustainable humanitarian supply chain design: agility, adaptability and alignment (2016) International Journal of Logistics Research and Applications, 19 (1), pp. 62-82; Dubey, R., Ali, S.S., Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis (2014) Global Journal of Flexible Systems Management, 15 (2), pp. 131-143; Estrada, M.A.R., Ndoma, A., The uses of unmanned aerial vehicles–UAV’s-(or drones) in social logistic: natural disasters response and humanitarian relief aid (2019) Procedia Computer Science, 149, pp. 375-383; Gorane, S.J., Kant, R., Modelling the SCM enablers: an integrated ISM-fuzzy MICMAC approach (2013) Asia Pacific Journal of Marketing and Logistics, 25 (2), pp. 263-286; Gutierrez, M.T.E., Mutuc, J.E.S., A model for humanitarian supply chain: an operation research approach (2018) Procedia Engineering, 212, pp. 659-666; Habib, M.S., Lee, Y.H., Memon, M.S., Mathematical models in humanitarian supply chain management: a systematic literature review (2016) Mathematical Problems in Engineering, 2016; Heaslip, G., Kovács, G., Grant, D.B., Servitization as a competitive difference in humanitarian logistics (2018) Journal of Humanitarian Logistics and Supply Chain Management, 8 (4), pp. 497-517; Hein, C., Behrens, F., Lasch, R., Insights on the costs of humanitarian logistics a case study analysis (2020) Logist. Res, 13 (1), pp. 1-17; Hezam, I.M., Nayeem, M.K., A systematic literature review on mathematical models of humanitarian logistics (2021) Symmetry, 13 (1), p. 11; Jabeen, F., Faisal, M.N., Katsioloudes, M., Localisation in an emerging Gulf economy: understanding the role of education, job attributes and analysing the barriers in its process (2018) Equality, Diversity and Inclusion: An International Journal, 37 (2), pp. 151-166; Jahre, M., Heigh, I., Does the current constraints in funding promote failure in humanitarian supply chains? (2008) Supply Chain Forum: An International Journal, 9 (2), pp. 44-54; Jain, A., Sharma, R., Ilavarasan, P.V., Total interpretive structural modelling of innovation measurement for Indian universities and higher academic technical institutions (2018) Flexibility in Resource Management, pp. 29-53; Jilani, A., Ali, Y., Khan, M.W., Greening of humanitarian supply chain with focus on logistics (2018) International Journal of Business Performance and Supply Chain Modelling, 10 (1), pp. 49-66; John, L., Ramesh, A., Modeling the barriers of humanitarian supply chain management in India (2016) Managing Humanitarian Logistics, pp. 61-82. , Springer, New Delhi; John, L., Ramesh, A., Sridharan, R., Humanitarian supply chain management: a critical review (2012) International Journal of Services and Operations Management, 13 (4), pp. 498-524; Kabra, G., Ramesh, A., Segmenting critical factors for enhancing the use of IT in humanitarian supply chain management (2015) Procedia – Social and Behavioral Sciences, 189, pp. 144-152; Kaur, H., Singh, S.P., Sustainable procurement and logistics for disaster resilient supply chain (2019) Annals of Operations Research, 283 (1-2), pp. 309-354; Keeble, B.R., The Brundtland report: ‘our common future’ (1988) Medicine and War, 4 (1), pp. 17-25; Khan, M., Sarmad, M., Ullah, S., Bae, J., Education for sustainable development in humanitarian logistics (2020) Journal of Humanitarian Logistics and Supply Chain Management, 10 (4), pp. 573-602; Khatwani, G., Singh, S.P., Trivedi, A., Chauhan, A., FuzzyTISM: a fuzzy extension of TISM for group decision making (2015) Global Journal of Flexible Systems Management, 16 (1), pp. 97-112; Kim, G.U., Kim, M.J., Ra, S.H., Lee, J., Bae, S., Jung, J., Kim, S.H., Clinical characteristics of asymptomatic and symptomatic patients with mild COVID-19 (2020) Clinical Microbiology and Infection, 26 (7); Klumpp, M., Loske, D., Long-term economic sustainability of humanitarian logistics – a multi-level and time-series data envelopment analysis (2021) International Journal of Environmental Research and Public Health, 18 (5), p. 2219; Kovács, G., Falagara Sigala, I., Lessons learned from humanitarian logistics to manage supply chain disruptions (2021) Journal of Supply Chain Management, 57 (1), pp. 41-49; Kunz, N., Gold, S., Sustainable humanitarian supply chain management–exploring new theory (2017) International Journal of Logistics Research and Applications, 20 (2), pp. 85-104; Kwon, I.W., Kim, S.H., Humanitarian supply chain/logistics: roadmap to effective relief effort (2018) Journal of International and Interdisciplinary Business Research, 5 (1), pp. 95-109; Maghsoudi, A., Advances in theory and their empirical application in humanitarian supply chain management research (2016) International Journal of Strategic Change Management, 7 (1), pp. 43-66; Mishra, N., Singh, A., Rana, N.P., Dwivedi, N.K., Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique (2017) Production Planning and Control, 28 (11-12), pp. 945-963; Mohaghar, A., Sahebi, I.G., Arab, A., Appraisal of humanitarian supply chain risks using best-worst method (2017) International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 11 (2), pp. 309-314; Moshtari, M., Altay, N., Heikkilä, J., Gonçalves, P., Procurement in humanitarian organizations: body of knowledge and practitioners’ challenge (2021) International Journal of Production Economics, 233; Muguruza, C.C., Everyday humanitarian diplomacy: experiences from border areas (2020) CMI Brief, 2020 (2); NadimGhosh, S.S., Chattopadhyay, J., (2020) Short-term predictions and prevention strategies for COVID-2019: a model based study; Oloruntoba, R., A planning and decision-making framework for sustainable humanitarian logistics in disaster response (2015) Humanitarian Logistics and Sustainability, pp. 31-48. , Springer, Cham; Pascucci, E., More logistics, less aid_ humanitarian-business partnerships and sustainability in the refugee camp (2021) World Development, 142, p. 105424; Pateman, H., Hughes, K., Cahoon, S., Humanizing humanitarian supply chains: a synthesis of key challenges (2013) The Asian Journal of Shipping and Logistics, 29 (1), pp. 81-102; Petrudi, S.H.H., Tavana, M., Abdi, M., A comprehensive framework for analyzing challenges in humanitarian supply chain management: a case study of the Iranian red crescent society (2020) International Journal of Disaster Risk Reduction, 42, p. 101340; Renteria, R., Chong, M., de Brito Junior, I., Luna, A., Quiliche, R., An entropy-based approach for disaster risk assessment and humanitarian logistics operations planning in Colombia (2021) Journal of Humanitarian Logistics and Supply Chain Management, 11 (3); Ruiz Estrada, M.A., (2020) The uses of drones in case of massive epidemics contagious diseases relief humanitarian aid_ Wuhan-COVID-19 crisis, , SSRN 3546547; Sabri, Y., Zarei, M.H., Harland, C., Using collaborative research methodologies in humanitarian supply chains (2019) Journal of Humanitarian Logistics and Supply Chain Management, 9 (3); Sahebi, I.G., Jafarnejad, A., Demand forecasting of emergency resource in humanitarian supply chain (2018) Proceedings of 103rd The IRES International Conference, pp. 129-136. , Zurich, Switzerland: 16th-17th February, 2018; Salvadó, L.L., Lauras, M., Comes, T., Sustainable performance measurement for humanitarian supply chain operations (2017) ISCRAM, pp. 775-783. , May; Salvadó, L.L., Lauras, M., Comes, T., A sustainability maturity assessment method for the humanitarian supply chain (2018) ISCRAM, , May; Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) International Journal of Productivity and Performance Management, 64 (6), pp. 784-810; Saxena, J.P., Sushil, Vrat, P., (2006) Policy and Strategy Formulation: An Application of Flexible Systems Methodology, , GIFT Pub; Schiffling, S., (2013) A customer perspective on performance measurement in humanitarian supply chains; Schiffling, S., (2013) takeholder salience in humanitarian supply chain management; Schiffling, S., Hannibal, C., Tickle, M., Fan, Y., The implications of complexity for humanitarian logistics: a complex adaptive systems perspective (2020) Annals of Operations Research, pp. 1-32; Schumann-Bölsche, D., Managing hazardous goods in humanitarian supply chains (2015) Humanitarian Logistics and Sustainability, pp. 151-168. , Springer, Cham; Singh, R.K., Gupta, A., Gunasekaran, A., Analysing the interaction of factors for resilient humanitarian supply chain (2018) International Journal of Production Research, 56 (21), pp. 6809-6827; Srivastava, A.K., Sushil, Modeling organizational and information systems for effective strategy execution (2015) Journal of Enterprise Information Management, 28 (4), pp. 556-578; Sushil, S., Interpreting the interpretive structural model (2012) Global Journal of Flexible Systems Management, 13 (2), pp. 87-106; Tabaklar, T., Halldórsson, Á., Kovács, G., Borrowing theories in humanitarian supply chain management (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (3), pp. 281-299; Wagner, S.M., Thakur-Weigold, B., Supporting collaboration in humanitarian supply chains–insights from a design science project (2018) Production Planning and Control, 29 (14), pp. 1130-1144; Wagner, S.M., Thakur-Weigold, B., Gatti, F., Stumpf, J., Measuring and improving the impact of humanitarian logistics consulting (2021) Production Planning and Control, 32 (2), pp. 83-103; Warfield, J.W., Developing interconnected metrices in structural modeling (1974) IEEE Transactions on Systems, Man, and Cybernetics, SMC-4 (1), pp. 51-81; Yadav, D.K., Barve, A., Modeling post-disaster challenges of humanitarian supply chains: a TISM approach (2016) Global Journal of Flexible Systems Management, 17 (3), pp. 321-340; Yadav, S., Singh, S.P., An integrated fuzzy-ANP and fuzzy-ISM approach using blockchain for sustainable supply chain (2020) Journal of Enterprise Information Management, 34 (1); Yang, F., Two-Stage robust counterpart model for humanitarian logistics management (2021) Discrete Dynamics in Nature and Society, 2021; Al Zaabi, H., Bashir, H., Modeling and analyzing project interdependencies in project portfolios using an integrated social network analysis-fuzzy TOPSIS MICMAC approach (2020) International Journal of System Assurance Engineering and Management, 11 (6), pp. 1-24; Dong, L., Chen, Y., Ge, Y., Xu, X., Structural analysis and comprehensive evaluation of economic index of distribution network (2020) E3S Web of Conferences, 213. , doi:, EDP Sciences; Jain, V.K., Jain, P.K., John, L., Ramesh, A., Humanitarian supply chain management in India: a SAP‐LAP framework (2012) Journal of Advances in Management Research, 9 (2), pp. 217-235},
correspondence_address1={Abbas, H.; Department of Business Administration, Oman; email: haideramu@gmail.com},
publisher={Emerald Publishing},
issn={17471117},
language={English},
abbrev_source_title={Soc. Responsib. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sharma2022,
author={Sharma, V. and Raut, R.D. and Hajiaghaei-Keshteli, M. and Narkhede, B.E. and Gokhale, R. and Priyadarshinee, P.},
title={Mediating effect of industry 4.0 technologies on the supply chain management practices and supply chain performance},
journal={Journal of Environmental Management},
year={2022},
volume={322},
doi={10.1016/j.jenvman.2022.115945},
art_number={115945},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136538634&doi=10.1016%2fj.jenvman.2022.115945&partnerID=40&md5=9c1815a7152808f9f3f8898128aa0ab2},
affiliation={Department of Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), NITIE, Vihar Lake, Powai, Maharashtra, Mumbai, 400087, India; Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Puebla, Mexico; Indian Institute of Management, Prabandh Vihar, Bodh Gaya, Bihar, 824234, India},
abstract={The ever-growing concern of sustainability and survivability attracts academicians and practitioners to develop strategies and supply chain capabilities that cater to the challenges and helps in achieving the sustainability development goals. There is a need to develop a holistic model that facilitates understanding the relationships among supply chain practices, industry 4.0 technologies, and supply chain performance measures. Thus, this study examines the mediating effect of industry 4.0 technologies on supply chain management practices and supply chain performance measures. A survey-based data was collected from manufacturing organizations across India, and 361 complete responses were obtained. Structural equation modeling (SEM) was utilized for data analysis. This study has multiple contributions. First, the results indicate that the supply chain management practices influence the industry 4.0 technologies adoption. Second, the results also revealed that the industry 4.0 technologies significantly positively affect supply chain performance measures. Finally, industry 4.0 technologies mediated the relations between supply chain management practices and supply chain performance measures. Furthermore, the findings offer important insights into understanding the underlying mechanisms in successfully adopting and effectively using industry 4.0 technologies. The implications for theory and practices are also discussed. © 2022 Elsevier Ltd},
author_keywords={Industry 4.0;  Industry 4.0 technologies;  Structural equation modeling (SEM);  Supply chain strategies;  Supply chain sustainability},
keywords={manufacturing;  modeling;  supply chain management;  sustainability, adoption;  article;  data analysis;  human;  India;  organization;  remission;  structural equation modeling;  commercial phenomena;  industry;  technology, India, Commerce;  India;  Industry;  Organizations;  Technology},
references={Acquaye, A., Feng, K., Oppon, E., Salhi, S., Ibn-Mohammed, T., Genovese, A., Hubacek, K., Measuring the environmental sustainability performance of global supply chains: a multi-regional input-output analysis for carbon, sulphur oxide and water footprints (2017) J. Environ. Manag., 187, pp. 571-585; Alicke, K., Rexhausen, D., Seyfert, A., Supply Chain 4.0 in consumer goods (2017), Mckinsey & Company 1(11); Al-Sheyadi, A., Muyldermans, L., Kauppi, K., The complementarity of green supply chain management practices and the impact on environmental performance (2019) J. Environ. Manag., 242, pp. 186-198; Beske, P., Land, A., Seuring, S., Sustainable supply chain management practices and dynamic capabilities in the food industry: a critical literature analysis (2014) Int. J. Prod. Econ., 152, pp. 131-143; Buer, S.V., Semini, M., Strandhagen, J.O., Sgarbossa, F., The complementary effect of lean manufacturing and digitalization on operational performance (2021) Int. J. Prod. Res., 59 (7), pp. 1976-1992; Buer, S.V., Strandhagen, J.O., Chan, F.T., The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda (2018) Int. J. Prod. Res., 56 (8), pp. 2924-2940; Cañas, H., Mula, J., Díaz-Madroñero, M., Campuzano-Bolarín, F., Implementing Industry 4.0 Principles (2021), p. 107379. , Computers & Industrial Engineering; Chandra, D., Kumar, D., Evaluating the effect of key performance indicators of the vaccine supply chain on sustainable development of mission indradhanush: a structural equation modeling approach (2021) Omega, 101; Cherrafi, A., Garza-Reyes, J.A., Kumar, V., Mishra, N., Ghobadian, A., Elfezazi, S., Lean, green practices and process innovation: a model for green supply chain performance (2018) Int. J. Prod. Econ., 206, pp. 79-92; Chiarini, A., Belvedere, V., Grando, A., Industry 4.0 strategies and technological developments. An exploratory research from Italian manufacturing companies (2020) Prod. Plann. Control, 31 (16), pp. 1385-1398; Choi, T.M., Wallace, S.W., Wang, Y., Big data analytics in operations management (2018) Prod. Oper. Manag., 27 (10), pp. 1868-1883; Ciano, M.P., Dallasega, P., Orzes, G., Rossi, T., One-to-one relationships between Industry 4.0 technologies and Lean Production techniques: a multiple case study (2021) Int. J. Prod. Res., 59 (5), pp. 1386-1410; Cifone, F.D., Hoberg, K., Holweg, M., Staudacher, A.P., Lean 4.0': how can digital technologies support lean practices? (2021) Int. J. Prod. Econ., 241; da Silva, C., Barbosa-Póvoa, A.P., Carvalho, A., Towards sustainable development: green supply chain design and planning using monetization methods (2021) Bus. Strat. Environ.; Ding, J., Chen, X., Sun, H., Yan, W., Fang, H., Hierarchical structure of a green supply chain (2021) Comput. Ind. Eng., 157; Fatorachian, H., Kazemi, H., Impact of Industry 4.0 on supply chain performance (2021) Prod. Plann. Control, 32 (1), pp. 63-81; Gardas, B.B., Raut, R.D., Narkhede, B., Identifying critical success factors to facilitate reusable plastic packaging towards sustainable supply chain management (2019) J. Environ. Manag., 236, pp. 81-92; Godinho Filho, M., Ganga, G.M.D., Gunasekaran, A., Lean manufacturing in Brazilian small and medium enterprises: implementation and effect on performance (2016) Int. J. Prod. Res., 54 (24), pp. 7523-7545; Govindan, K., Azevedo, S.G., Carvalho, H., Cruz-Machado, V., Impact of supply chain management practices on sustainability (2014) J. Clean. Prod., 85, pp. 212-225; Gunasekaran, A., Yusuf, Y.Y., Adeleye, E.O., Papadopoulos, T., Kovvuri, D., Geyi, D.A.G., Agile manufacturing: an evolutionary review of practices (2019) Int. J. Prod. Res., 57 (15-16), pp. 5154-5174; Gupta, H., Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS (2018) J. Environ. Manag., 226, pp. 201-216; Gupta, S., Chen, H., Hazen, B.T., Kaur, S., Gonzalez, E.D.S., Circular economy and big data analytics: a stakeholder perspective (2019) Technol. Forecast. Soc. Change, 144, pp. 466-474; Hair, J.F., Jr., Matthews, L.M., Matthews, R.L., Sarstedt, M., PLS-SEM or CB-SEM: updated guidelines on which method to use (2017) Int. J. Multivar. Data Anal., 1 (2), pp. 107-123; Hines, P., Holweg, M., Rich, N., Learning to evolve: a review of contemporary lean thinking (2004) Int. J. Oper. Prod. Manag., 24 (10), pp. 994-1011; Ivanov, D., Correction to: viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic (2021) Ann. Oper. Res., 1-2; Ivanov, D., Digital supply chain management and technology to enhance resilience by building and using end-to-end visibility during the COVID-19 pandemic (2021) IEEE Trans. Eng. Manag.; Ivanov, D., Dolgui, A., Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak (2020) Int. J. Prod. Res., 58 (10), pp. 2904-2915; Kamalahmadi, M., Parast, M.M., A review of the literature on the principles of enterprise and supply chain resilience: major findings and directions for future research (2016) Int. J. Prod. Econ., 171, pp. 116-133; Kamble, S., Gunasekaran, A., Dhone, N.C., Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies (2020) Int. J. Prod. Res., 58 (5), pp. 1319-1337; Khan, S.A.R., Godil, D.I., Jabbour, C.J.C., Shujaat, S., Razzaq, A., Yu, Z., Green data analytics, blockchain technology for sustainable development, and sustainable supply chain practices: evidence from small and medium enterprises (2021) Ann. Oper. Res., pp. 1-25; Khan, S.A.R., Piprani, A.Z., Yu, Z., Digital technology and circular economy practices: future of supply chains (2022) Oper. Manag. Res., pp. 1-13; Khan, S.A.R., Razzaq, A., Yu, Z., Miller, S., Industry 4.0 and circular economy practices: a new era business strategies for environmental sustainability (2021) Bus. Strat. Environ., 30 (8), pp. 4001-4014; Khan, S.A.R., Waqas, M., Honggang, X., Ahmad, N., Yu, Z., Adoption of innovative strategies to mitigate supply chain disruption: COVID-19 pandemic (2022) Oper. Manag. Res., pp. 1-19; Khan, S.A.R., Yu, Z., Sarwat, S., Godil, D.I., Amin, S., Shujaat, S., The role of block chain technology in circular economy practices to improve organisational performance (2022) Int. J. Logist. Res. Appl., 25 (4-5), pp. 605-622; Khan, S.A.R., Yu, Z., Umar, M., Tanveer, M., Green capabilities and green purchasing practices: a strategy striving towards sustainable operations (2022) Bus. Strat. Environ.; Kiraz, A., Canpolat, O., Özkurt, C., Taşkın, H., Analysis of the factors affecting the Industry 4.0 tendency with the structural equation model and an application (2020) Comput. Ind. Eng., 150; Compact SDG Industry Matrix: Energy, Natural Resources and Chemicals (2016), https://d306pr3pise04h.cloudfront.net/docs/publications%2F2017%2FSDG-industry-matrix-enrc.pdf; Compact SDG Industry Matrix: Industrial Manufacturing (2016), https://home.kpmg/content/dam/kpmg/xx/pdf/2017/05/sdg-industrial-manufacturing.pdf; Compact SDG Industry Matrix: Transportation Industry Highlights (2016), https://home.kpmg/content/dam/kpmg/xx/pdf/2017/05/sdg-transportation.pdf; Kühnen, M., Silva, S., Hahn, R., From negative to positive sustainability performance measurement and assessment? A qualitative inquiry drawing on framing effects theory (2022) Bus. Strat. Environ.; Küpper, D.A.J.D., Heidemann, A., Ströhle, J., Spindelndreier, D., Knizek, C., Industry 4.0: the Next Level of Operational Excellence (2017), https://www.bcg.com/publications/2017/lean-meets-industry-4.0, Boston Consulting Group Boston; Li, Y., Dai, J., Cui, L., The impact of digital technologies on economic and environmental performance in the context of industry 4.0: a moderated mediation model (2020) Int. J. Prod. Econ., 229; Lotfi, M., Saghiri, S., Disentangling resilience, agility and leanness: conceptual development and empirical analysis (2018) J. Manuf. Technol. Manag., 29 (1), pp. 168-197; Machado, C.G., Winroth, M.P., Ribeiro da Silva, E.H.D., Sustainable manufacturing in Industry 4.0: an emerging research agenda (2020) Int. J. Prod. Res., 58 (5), pp. 1462-1484; Manavalan, E., Jayakrishna, K., A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements (2019) Comput. Ind. Eng., 127, pp. 925-953; Mathiyazhagan, K., Agarwal, V., Appolloni, A., Saikouk, T., Gnanavelbabu, A., Integrating lean and agile practices for achieving global sustainability goals in Indian manufacturing industries (2021) Technol. Forecast. Soc. Change, 171; Nakagawa, E.Y., Antonino, P.O., Schnicke, F., Capilla, R., Kuhn, T., Liggesmeyer, P., Industry 4.0 reference architectures: state of the art and future trends (2021) Comput. Ind. Eng., p. 107241; Narayanamurthy, G., Tortorella, G., Impact of COVID-19 outbreak on employee performance–moderating role of industry 4.0 base technologies (2021) Int. J. Prod. Econ., 234; Núñez-Merino, M., Maqueira-Marín, J.M., Moyano-Fuentes, J., Martínez-Jurado, P.J., Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review (2020) Int. J. Prod. Res., 58 (16), pp. 5034-5061; Oliveira-Dias, D., Maqueira, J.M., Moyano-Fuentes, J., The link between information and digital technologies of industry 4.0 and agile supply chain: mapping current research and establishing new research avenues (2022) Comput. Ind. Eng., p. 108000; Qi, Y., Huo, B., Wang, Z., Yeung, H.Y.J., The impact of operations and supply chain strategies on integration and performance (2017) Int. J. Prod. Econ., 185, pp. 162-174; Qi, Y., Mao, Z., Zhang, M., Guo, H., Manufacturing practices and servitization: the role of mass customization and product innovation capabilities (2020) Int. J. Prod. Econ., 228; Raji, I.O., Shevtshenko, E., Rossi, T., Strozzi, F., Industry 4.0 technologies as enablers of lean and agile supply chain strategies: an exploratory investigation (2021) Int. J. Logist. Manag., 32 (4), pp. 1150-1189; Raut, R.D., Mangla, S.K., Narwane, V.S., Dora, M., Liu, M., Big data analytics as a mediator in lean, agile, resilient, and green (LARG) practices effects on sustainable supply chains (2021) Transport. Res. E Logist. Transport. Rev., 145; Rosin, F., Forget, P., Lamouri, S., Pellerin, R., Impacts of industry 4.0 technologies on lean principles (2020) Int. J. Prod. Res., 58 (6), pp. 1644-1661; Ruiz-Benítez, R., López, C., Real, J.C., The lean and resilient management of the supply chain and its impact on performance (2018) Int. J. Prod. Econ., 203, pp. 190-202; Ruiz-Benitez, R., López, C., Real, J.C., Achieving sustainability through the lean and resilient management of the supply chain (2019) Int. J. Phys. Distrib. Logist. Manag., 49 (2), pp. 122-155; Shah, R., Ward, P.T., Lean manufacturing: context, practice bundles, and performance (2003) J. Oper. Manag., 21 (2), pp. 129-149; Shah, R., Ward, P.T., Defining and developing measures of lean production (2007) J. Oper. Manag., 25 (4), pp. 785-805; Shah, R., Ward, P.T., Defining and developing measures of lean production (2007) J. Oper. Manag., 25 (4), pp. 785-805; Sharma, V., Raut, R.D., Mangla, S.K., Narkhede, B.E., Luthra, S., Gokhale, R., A systematic literature review to integrate lean, agile, resilient, green and sustainable paradigms in the supply chain management (2021) Bus. Strat. Environ., 30 (2), pp. 1191-1212; Shrouf, F., Miragliotta, G., Energy management based on Internet of Things: practices and framework for adoption in production management (2015) J. Clean. Prod., 100, pp. 235-246; Srivastava, S.K., Green supply-chain management: a state-of-the-art literature review (2007) Int. J. Manag. Rev., 9 (1), pp. 53-80; Stekelorum, R., Laguir, I., Gupta, S., Kumar, S., Green supply chain management practices and third-party logistics providers' performances: a fuzzy-set approach (2021) Int. J. Prod. Econ., 235; Thaher, Y.A., Jaaron, A.A., The impact of sustainability strategic planning and management on the organizational sustainable performance: a developing-country perspective (2022) J. Environ. Manag., 305; Tortorella, G.L., Fettermann, D., Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies (2018) Int. J. Prod. Res., 56 (8), pp. 2975-2987; Tortorella, G.L., Fogliatto, F.S., Cauchick-Miguel, P.A., Kurnia, S., Jurburg, D., Integration of industry 4.0 technologies into total productive maintenance practices (2021) Int. J. Prod. Econ., 240; Tortorella, G.L., Giglio, R., van Dun, D.H., Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement (2019) Int. J. Oper. Prod. Manag., 39, pp. 860-886. , 6/7/8; Touriki, F.E., Benkhati, I., Kamble, S.S., Belhadi, A., An integrated smart, green, resilient, and lean manufacturing framework: a literature review and future research directions (2021) J. Clean. Prod.; United Nations, The sustainable development goals (2019), https://www.un.org/sustainabledevelopment/sustainable-development-goals, [WWW Document]. UN; Wankhede, V.A., Vinodh, S., Analysis of industry 4.0 challenges using best worst method: a case study (2021) Comput. Ind. Eng., p. 107487; Wong, C.Y., Boon-itt, S., Wong, C.W., The contingency effects of internal and external collaboration on the performance effects of green practices (2021) Resour. Conserv. Recycl., 167; Yu, Y., Zhang, J.Z., Cao, Y., Kazancoglu, Y., Intelligent transformation of the manufacturing industry for Industry 4.0: seizing financial benefits from supply chain relationship capital through enterprise green management (2021) Technol. Forecast. Soc. Change, 172; Yu, Z., Khan, S.A.R., Umar, M., Circular economy practices and industry 4.0 technologies: a strategic move of automobile industry (2022) Bus. Strat. Environ., 31 (3), pp. 796-809; Yu, Z., Khan, S.A.R., Mathew, M., Umar, M., Hassan, M., Sajid, M.J., Identifying and analyzing the barriers of Internet-of-Things in sustainable supply chain through newly proposed spherical fuzzy geometric mean (2022) Comput. Ind. Eng., 169; Yu, Z., Umar, M., Rehman, S.A., Adoption of technological innovation and recycling practices in automobile sector: under the Covid-19 pandemic (2022) Oper. Manag. Res., 1-9; Yu, Z., Waqas, M., Tabish, M., Tanveer, M., Haq, I.U., Khan, S.A.R., Sustainable supply chain management and green technologies: a bibliometric review of literature (2022) Environ. Sci. Pollut. Control Ser., pp. 1-17; Yusuf, Y., Menhat, M.S., Abubakar, T., Ogbuke, N.J., Agile capabilities as necessary conditions for maximising sustainable supply chain performance: an empirical investigation (2020) Int. J. Prod. Econ., 222; Zhang, M., Lettice, F., Chan, H.K., Nguyen, H.T., Supplier integration and firm performance: the moderating effects of internal integration and trust (2018) Prod. Plann. Control, 29 (10), pp. 802-813},
correspondence_address1={Hajiaghaei-Keshteli, M.; Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Mexico; email: mostafahaji@tec.mx},
publisher={Academic Press},
issn={03014797},
coden={JEVMA},
pubmed_id={36041298},
language={English},
abbrev_source_title={J. Environ. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Patil2022,
author={Patil, A. and Madaan, J. and Chan, F.T.S. and Charan, P.},
title={Advancement of performance measurement system in the humanitarian supply chain},
journal={Expert Systems with Applications},
year={2022},
volume={206},
doi={10.1016/j.eswa.2022.117844},
art_number={117844},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132315492&doi=10.1016%2fj.eswa.2022.117844&partnerID=40&md5=4b5707993e7de8c2117d065d82a1a503},
affiliation={Department of Management Studies, Indian Institute of Technology, Delhi, India; Department of Decision Sciences, Macau University of Science and Technology, Taipa, Macau; Indian Institute of Management Raipur, Chhattisgarh, 493661, India},
abstract={Performance measurement activities (PMA) in the humanitarian supply chain (HSC) face several issues. Many of these issues are avoidable and inter-related, subsequently producing undesirable cascading effects. HSC Stakeholders are actively looking for means to reduce the complexities associated with PMA and improve its efficiency. In this paper, we identify and model the eleven strategies that can improve both PMA and the performance measurement system (PMS). Strategies were identified using detailed literature reviews and discussions with the experts and were modelled using the grey DEMATEL and m-TISM methodology. The results indicate that the most effective strategies are fundraising for PMA, Digital supply chain adoption, Top management support, Public awareness activities and Relief worker training. Four strategies can be grouped into the cause side and seven strategies on the effect side. The key recommendation is to rapidly adopt digital supply chain applications in the HSC. HSC stakeholders also need to pay increased attentions towards fundraising activities targeting PMS. This study advances the understanding of PMS in the context of HSC. The decision-maker can adopt the study's findings to reduce the complexities that occurred during PMA. © 2022 Elsevier Ltd},
author_keywords={Humanitarian supply chain;  Multi-criteria decision analysis;  Performance measurement system;  Relief work},
keywords={Decision making;  Human resource management, Cascading effects;  DEMATEL;  Digital supply chain;  Humanitarian supply chain;  Its efficiencies;  Literature reviews;  Multi-criteria decision analysis;  Performance measurement system;  Performance measurements;  Relief works, Supply chains},
references={Abidi, H., (2019), Performance Management in Supply Chains Applications to humanitarian and commercial supply chains; Abidi, H., de Leeuw, S., Dullaert, W., Performance management practices in humanitarian organisations (2020) Journal of Humanitarian Logistics and Supply Chain Management, 10 (2), pp. 125-168; Abidi, H., De Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: A systematic literature review (2014) Supply Chain Management, 19 (5-6), pp. 592-608; Agarwal, S., Kant, R., Shankar, R., Humanitarian supply chain management frameworks: A critical literature review and framework for future development (2019) Benchmarking: An International Journal, 26 (6), pp. 1749-1780; Anjomshoae, A., Banomyong, R., Mohammed, F., Kunz, N., A systematic review of humanitarian supply chains performance measurement literature from 2007 to 2021 (2022) International Journal of Disaster Risk Reduction, 72; Anjomshoae, A., Hassan, A., Wong, K.Y., An integrated AHP-based scheme for performance measurement in humanitarian supply chains (2019) International Journal of Productivity and Performance Management, 68 (5), pp. 938-957; Anjomshoae, A., Hassan, A., Yew, K., Banomyong, R., (2021), An integrated multi-stage fuzzy inference performance measurement scheme in humanitarian relief operations. International Journal of Disaster Risk Reduction, 61(July 2020), 102298; Asgary, R., Lawrence, K., Evaluating underpinning, complexity and implications of ethical situations in humanitarian operations: Qualitative study through the lens of career humanitarian workers (2020) BMJ Open, 10 (9), pp. 1-11; Atkinson, A., Waterhouse, J., Wells, R., A stakeholder approach to strategic performance measurement (1997) Sloan Management Review, 38 (3), pp. 25-37; Azmat, M., Atif, M., Kummer, S., Identification and prioritization of critical success factors in faith-based and non-faith-based organizations’ humanitarian supply chain (2019) Journal of International Humanitarian Action, 4 (1); Battista, P., Verhun, D., Customer relationship management: The promise and the reality (2000) CMA Magazine, 74 (4), p. 34; Beamon, B.M., Humanitarian relief chains: Issues and challenges (2004) Proceedings of the 34th International Conference on Computers and Industrial Engineering, pp. 77-82; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Bhanot, N., Rao, P.V., Deshmukh, S.G., An integrated approach for analysing the enablers and barriers of sustainable manufacturing (2017) Journal of Cleaner Production, 142, pp. 4412-4439; Blecken, A., Supply chain process modelling for humanitarian organizations (2010) International Journal of Physical Distribution and Logistics Management, 40 (8), pp. 675-692; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations and Production Management, 22 (11), pp. 1288-1310; Cardoso, B., de, F.O., Fontainha, T.C., (2020), & Leiras, A. Performance Indicators in Humanitarian Operations from the Beneficiary Perspective: A Systematic Literature Review. International Conference on Production and Operations Management Society, 283–292. Springer; Chang, B., Chang, C.W., Wu, C.H., Fuzzy DEMATEL method for developing supplier selection criteria (2011) Expert Systems with Applications, 38 (3), pp. 1850-1858; D'Haene, C., Verlinde, S., Macharis, C., Measuring while moving (humanitarian supply chain performance measurement – status of research and current practice) (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (2), pp. 146-161; Davidson, A., Key, L., (2006), Performance Indicators in Humanitarian Logistics; (2020), Development Initiatives. Global humanitarian assistance report. GHA Annual Report, 1–132; Fathalikhani, S., Hafezalkotob, A., Soltani, R., (2020), Government intervention on cooperation, competition, and coopetition of humanitarian supply chains. Socio-Economic Planning Sciences, 69(May 2019), 100715; Fikar, C., Gronalt, M., Hirsch, P., A decision support system for coordinated disaster relief distribution (2016) Expert Systems with Applications, 57, pp. 104-116; Fontainha, T.C., Leiras, A., de Bandeira, R.A.M., Scavarda, L.F., Public-private-people relationship stakeholder model for disaster and humanitarian operations (2017) International Journal of Disaster Risk Reduction, 22, pp. 371-386; Gardas, B.B., Raut, R.D., Cheikhrouhou, N., Narkhede, B.E., A hybrid decision support system for analyzing challenges of the agricultural supply chain (2019) Sustainable Production and Consumption, 18, pp. 19-32; Gutierrez, D.M., Scavarda, L.F., Fiorencio, L., Martins, R.A., Evolution of the performance measurement system in the logistics department of a broadcasting company: An action research (2015) International Journal of Production Economics, 160, pp. 1-12; Haavisto, I., Goentzel, J., Measuring humanitarian supply chain performance in a multi-goal context (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (3), pp. 300-324; (2019), Hashemi Petrudi, S. H., Tavana, M., & Abdi, M. A comprehensive framework for analyzing challenges in humanitarian supply chain management: A case study of the Iranian Red Crescent Society. International Journal of Disaster Risk Reduction, 42(September 2019), 101340; Jeble, S., Kumari, S., Venkatesh, V.G., Singh, M., Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain: Developing framework and future research directions (2019) Benchmarking, 27 (2), pp. 606-633; Ju-Long, D., Control problems of grey systems (1982) Systems and Control Letters, 1 (5), pp. 288-294; Kabra, G., Ramesh, A., Analyzing ICT issues in humanitarian supply chain management: A SAP-LAP Linkages framework (2015) Global Journal of Flexible Systems Management, 16 (2), pp. 157-171; Kabra, G., Ramesh, A., Information technology, mutual trust, flexibility, agility, adaptability: Understanding their linkages and impact on humanitarian supply chain management performance (2016) Risk, Hazards and Crisis in Public Policy, 7 (2), pp. 79-103; Kakha, G., Tabasi, S., Jami, M., Danesh Narooei, K., Evaluation of the impacting factors on sustainable mining development using the grey-decision making trial and evaluation laboratory approach (2019) International Journal of Engineering, Transactions A: Basics, 32 (10), pp. 1497-1505; Kelman, I., Ahmed, B., Esraz-Ul-Zannat, M., Saroar, M.M., Fordham, M., Shamsudduha, M., Warning systems as social processes for Bangladesh cyclones (2018) Disaster Prevention and Management: An International Journal, 27 (4), pp. 370-379; Kraft, P., Rennhak, C., Sauer, A., (2016), Humanitarian Supply Chain Performance Management: Development and Evaluation of a Comprehensive Performance Measurement Framework Based on the Balanced Scorecard. Munich Business School Working Paper, 1–34; Kumar, H., Singh, M.K., Gupta, M.P., Madaan, J., Moving towards smart cities: Solutions that lead to the Smart City Transformation Framework (2020) Technological Forecasting and Social Change, 153; Larrea, O., (2013), Key performance indicators in humanitarian logistics in Colombia. In IFAC Proceedings Volumes (IFAC-PapersOnline) (Vol. 6). IFAC; Lee, Y.C., Li, M.L., Yen, T.M., Huang, T.H., Analysis of adopting an integrated decision making trial and evaluation laboratory on a technology acceptance model (2010) Expert Systems with Applications; Leiras, A., de Brito, I., Queiroz Peres, E., Rejane Bertazzo, T., Yoshizaki, T.Y., H., Literature review of humanitarian logistics research: Trends and challenges (2014) Journal of Humanitarian Logistics and Supply Chain Management, 4 (1), pp. 95-130; Lu, Q., Goh, M., De Souza, R., A SCOR framework to measure logistics performance of humanitarian organizations (2016) Journal of Humanitarian Logistics and Supply Chain Management, 6 (2), pp. 222-239; Madaan, J., Chan, F.T.S., Niu, B., Strategies for evaluating performance of flexibility in product recovery system (2016) International Journal of Production Research, 54 (10), pp. 2895-2906; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literaturer review (1995) International Journal of Operations & Production Management, 15 (4), p. 35; Ocha, U.N., Global Humanitarian Overview 2021 (2020), UN Office for the Coordination of Humanitarian Affairs Geneva; Paciarotti, C., Valiakhmetova, I., Evaluating disaster operations management: An outcome-process integrated approach (2021) Production and Operations Management, 30 (2), pp. 543-562; Patil, A., Shardeo, V., Dwivedi, A., Madaan, J., An integrated approach to model the blockchain implementation barriers in humanitarian supply chain (2021) Journal of Global Operations and Strategic Sourcing, 14 (1), pp. 81-103; Patil, A., Shardeo, V., Madaan, J., Modelling performance measurement barriers of humanitarian supply chain (2020) International Journal of Productivity and Performance Management, 70 (8), pp. 1972-2000; Pettit, S., Beresford, A., Critical success factors in the context of humanitarian aid supply chains (2009) International Journal of Physical Distribution & Logistics Management, 39 (6), pp. 450-468; Queiroz, M.M., Pereira, S.C.F., Telles, R., Machado, M.C., Industry 4.0 and digital supply chain capabilities: A framework for understanding digitalisation challenges and opportunities (2019) Benchmarking; Raju, E., (2013), Housing Reconstruction in Disaster Recovery: A Study of Fishing Communities Post-Tsunami in Chennai, India. PLoS Currents, (APR 2013), 1–8; Reunanen, T., Performance measurement systems in humanitarian supply chain (2018), Fida International Case Study; Rodríguez-Espíndola, O., Chowdhury, S., Beltagui, A., Albores, P., The potential of emergent disruptive technologies for humanitarian supply chains: The integration of blockchain, Artificial Intelligence and 3D printing (2020) International Journal of Production Research, 58 (15), pp. 4610-4630; Sandvik, K.B., Gabrielsen Jumbert, M., Karlsrud, J., Kaufmann, M., Humanitarian technology: A critical research agenda (2014) International Review of the Red Cross, 96, pp. 219-242; Sawhill, J.C., Williamson, D., Mission impossible? Measuring success in nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 371-386; Schiffling, S., Piecyk, M., Performance measurement in humanitarian logistics: A customer-oriented approach (2014) Journal of Humanitarian Logistics and Supply Chain Management, 4 (2), pp. 198-221; Scholten, K., Sharkey Scott, P., Fynes, B., Scott, P.S., Fynes, B., Sharkey Scott, P., Fynes, B., (Le)agility in humanitarian aid (NGO) supply chains (2010) International Journal of Physical Distribution & Logistics Management, 40 (8-9), pp. 623-635; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Management Research News, 32 (11), pp. 1038-1049; Sharma, P., Joshi, A., Challenges of using big data for humanitarian relief: Lessons from the literature (2019) Journal of Humanitarian Logistics and Supply Chain Management, 10 (4), pp. 423-446; Tan Caroline, L.I.N., Smyrnios, K.X., How do australian fast-growth small-to-medium enterprises measure performance? (2011) Journal of Enterprising Culture, 19 (1), pp. 41-60; Tang, K., Wang, F., Liu, J., Jia, P., Liu, J., Water vapor permeability of leathers by grey system theory (2013) Reviews on Advanced Materials Science, 33 (4), pp. 373-382; Thomas, A.S., Kopczak, L., From logistics to supply chain management: The path forward in the humanitarian sector (2005) Fritz Institute, 15, pp. 1-15; Tseng, M.L., A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach (2009) Expert Systems with Applications, 36 (4), pp. 7738-7748; Van Der Laan, E.A., De Brito, M.P., Vergunst, D.A., Performance measurement in humanitarian supply chains (2009) International Journal of Risk Assessment and Management, 13 (1), pp. 22-45; Van Wassenhove, L.N., Blackett memorial lecture humanitarian aid logistics: Supply chain management in high gear (2006) Journal of the Operational Research Society, 57 (5), pp. 475-489; Vega, D., Case studies in humanitarian logistics research (2018) Journal of Humanitarian Logistics and Supply Chain Management, 8 (2), pp. 134-152; Wagner, S.M., Thakur-Weigold, B., Gatti, F., Stumpf, J., Measuring and improving the impact of humanitarian logistics consulting (2020) Production Planning and Control, pp. 1-21; Wang, Z., Zhang, J., (2019), Agent-based evaluation of humanitarian relief goods supply capability. International Journal of Disaster Risk Reduction, 36(December 2018), 101105; Wouters, M., Wilderom, C., Developing performance-measurement systems as enabling formalization: A longitudinal field study of a logistics department (2008) Accounting, Organizations and Society, 33 (4-5), pp. 488-516; Yadav, D.K., Barve, A., Analysis of critical success factors of humanitarian supply chain: An application of Interpretive Structural Modeling (2015) International Journal of Disaster Risk Reduction, 12, pp. 213-225; Younis, H., Sundarakani, B., The impact of firm size, firm age and environmental management certification on the relationship between green supply chain practices and corporate performance (2020) Benchmarking, 27 (1), pp. 319-346},
correspondence_address1={Chan, F.T.S.; Department of Decision Sciences, Macau; email: tschan@must.edu.mo},
publisher={Elsevier Ltd},
issn={09574174},
coden={ESAPE},
language={English},
abbrev_source_title={Expert Sys Appl},
document_type={Article},
source={Scopus},
}

@ARTICLE{IvodeCarvalho2022565,
author={Ivo de Carvalho, M. and Relvas, S. and Barbosa-Póvoa, A.P.},
title={A roadmap for sustainability performance assessment in the context of Agri-Food Supply Chain},
journal={Sustainable Production and Consumption},
year={2022},
volume={34},
pages={565-585},
doi={10.1016/j.spc.2022.10.001},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140299891&doi=10.1016%2fj.spc.2022.10.001&partnerID=40&md5=aeec6e023138728f4be832f405fc563b},
affiliation={Centre for Management Studies (CEG-IST), Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, Lisbon, 1049-001, Portugal},
abstract={Sustainability is today one of the most significant supply chain concerns. To guarantee the path towards sustainability, supply chains need to use Performance Measurement Systems (PMS) to assess and monitor their sustainable performance. The Agri-Food Supply Chain (AFSC) is not an exception. However, given this sector's complexity and specific requirements, more profound research is needed in the context of sustainable performance. So far, none of the existent literature studies focuses on proposing a PMS system that simultaneously incorporates the Triple Bottom Line (TBL) approach and promotes an integrated SC view. For that, the basis of a PMS system should be defined, which means establishing the performance areas that should be measured and monitored considering the sustainability goals. This paper aims two contribute to reduce this gap and the contributions are three-fold. First, define a standardized mapping of the Agri-Food Sustainable Supply Chain (AF-SSC) to be considered in a PMS, identifying appropriately the key stakeholders, material, information, and financials' direct and reverse flows. Second, identify the most critical sustainability-related Performance Areas (PA) (categories) that should be measured in any AF-SSC. Third, and for each PA proposed, detail the main subcategories (characteristics). A multimethod methodology comprising semi-structured interviews including sector key stakeholders and a content-based literature review method is employed. Validation of the results is performed through AFSC stakeholders. As a result, a framework to guide the sustainable measurement of different SC stakeholders is outlined, that includes 16 performance areas and 71 subcategories. Given the high heterogeneity of the performance measures used in the literature, this research establishes the standard to be followed and a roadmap to guide the companies in evaluating their sustainability performance, encompassing the TBL. © 2022 Institution of Chemical Engineers},
author_keywords={Agri-Food Supply Chain;  Performance measurement;  Sustainability},
keywords={Food supply;  Supply chains, Agri-food supply chains;  Performance assessment;  Performance measurement system;  Performance measurements;  Roadmap;  Sustainability performance;  Sustainable performance;  Sustainable supply chains;  Triple Bottom Line, Sustainable development},
references={Ahlström, F., Ferning, C., Cheniere, M.K., Sorooshian, S., Performance indicators of textile reverse logistics (2020) IOP Conf. Ser. Earth and Environ. Sci., 442 (1); Al-Haddad, B., Chuman, M., Kouki, R., Clearence Process and Its Effect on The Supply Chain Performance in Jeddah Port (2021) Palarch's J. Archaeol. Egypt/Egyptol., 18 (12), pp. 31-39; Almeida, A., Bastos, J., Francisco, R.D.P., Azevedo, A., Ávila, P., Sustainability assessment framework for (2016) Int. J. Ind. Syst. Eng., 24 (2), pp. 198-222; Aramyan, L.H., Ondersteijn, C.J.M., Kooten, O.V., Oude Lansink, A., Performance indicators in agri-food production chains (2006) Quantifying the Agri-Food Supply Chain, pp. 49-66; Aramyan, L.H., Lansink, A.G.J.M.O., van der Vorst, J.G.A.J., van Kooten, O., Performance measurement in agri-food supply chains : a case study (2007) Supply Chain Manag. Int. J., 12 (4), pp. 304-315; Azevedo, S.G., Pimentel, C.M.O., Alves, A.C., Matias, J.C.O., Support of advanced technologies in supply chain processes and sustainability impact (2021) Appl. Sci., 11 (7), p. 3026; Baba, A.A.M., Ma'aram, A., Ishak, F.I., Md Sirat, R., Kadir, A.Z.A., Key performance indicator of sustainability in the Malaysian food supply chain (2019) IOP Conference Series: Materials Science and Engineering, 697 (1); Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Manag., 19 (3), pp. 275-291; Bajec, P., Tuljak-Suban, D., Bajor, I., A warehouse social and environmental performance metrics framework (2020) Promet Traffic Traffico, 32 (4), pp. 513-526; Barbosa-Póvoa, A.P., da Silva, C., Carvalho, A., Opportunities and challenges in sustainable supply chain: an operations research perspective (2018) Eur. J. Oper. Res., 268 (2), pp. 399-431; Batista, L., Bourlakis, M., Smart, P., Maull, R., In search of a circular supply chain archetype–a content-analysis-based literature review (2018) Prod. Plan. Control, 29 (6), pp. 438-451; Bukeviciute, L., Dierx, A., Ilzkovitz, F., Roty, G., Price transmission along the food supply chain in the European Union Price transmission along the food supply chain in the European Union (2009) 113th EAAE Seminar “A Resilient European Food Industry and Food Chain in a Challenging World”, Chania, Crete, Greece, January, pp. 1-17; Butzer, S., Schötz, S., Petroschke, M., Steinhilper, R., Development of a performance measurement system for international reverse supply chains (2017) Procedia CIRP, 61, pp. 251-256; Callado, A.A.C., Jack, L., Relations between usage patterns of performance indicators and the role of individual firms in fresh fruit Agri-food supply chains journal of applied accounting research article information (2017) J. Appl. Acc. Res., 18 (3), pp. 375-398; Chopra, S., Laux, C., Schmidt, E., Rajan, P., Perception of performance indicators in an agri-food supply chain : a case study of India ’ s public distribution system (2017) Int. J. Food Syst. Dyn., 8 (2), pp. 130-145; Choudhary, A., De, A., Ahmed, K., Shankar, R., An integrated fuzzy intuitionistic sustainability assessment framework for manufacturing supply chain: a study of UK based firms (2021) Annals of Operations Research (Issue 0123456789), , Springer US; Dania, W.A.P., Xing, K., Amer, Y., Collaboration behavioural factors for sustainable agri-food supply chains: a systematic review (2018) J. Clean. Prod., 186, pp. 851-864; Demartini, M., Pinna, C., Aliakbarian, B., Tonelli, F., Terzi, S., Soft drink supply chain sustainability: a case based approach to identify and explain best practices and key performance indicators (2018) Sustainability (Switzerland), 10 (10); Genovese, A., Morris, J., Piccolo, C., Koh, S.C.L., Assessing redundancies in environmental performance measures for supply chains (2017) J. Clean. Prod., 167, pp. 1290-1302; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organizational resources: a literature review Stefan (2010) Corp. Soc. Responsib. Environ. Manag., 17, pp. 230-245; Gong, M., Simpson, A., Koh, L., Tan, K.H., Inside out: the interrelationships of sustainable performance metrics and its effect on business decision making: theory and practice (2018) Resour. Conserv. Recycl., 128, pp. 155-166; Gosling, J., Jia, F., Gong, Y., Brown, S., The role of supply chain leadership in the learning of sustainable practice: toward an integrated framework (2016) J. Clean. Prod., 137, pp. 1458-1469; Govindan, K., Aditi, Dhingra Darbari, J., Kaul, A., Jha, P.C., Structural model for analysis of key performance indicators for sustainable manufacturer–supplier collaboration: a grey-decision-making trial and evaluation laboratory-based approach (2021) Bus. Strateg. Environ., pp. 1-21. , May 2020; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Ilgin, M.A., Gupta, S.M., Environmentally conscious manufacturing and product recovery (ECMPRO): a review of the state of the art (2010) J. Environ. Manag., 91 (3), pp. 563-591; Izadikhah, M., Saen, R.F., Roostaee, R., How to assess sustainability of suppliers in the presence of volume discount and negative data in data envelopment analysis? (2018) Ann. Oper. Res., 269 (1-2), pp. 241-267; Jaffee, S., Siegel, P., Andrews, C., Agriculture and Rural Development Discussion Paper 47 Rapid Agricultural Supply Chain Risk Assessment: A Conceptual Framework (2010), 55. , http://www.worldbank.org/rural; Jamshed, S., Qualitative research method-interviewing and observation (2014) J. Basic Clin. Pharm., 5 (4), p. 87; Jeswani, H.K., Figueroa-Torres, G., Azapagic, A., The extent of food waste generation in the UK and its environmental impacts (2021) Sustain. Prod. Consum., 26, pp. 532-547; Jia, F., Lamming, R., Sartor, M., Orzes, G., Nassimbeni, G., Global purchasing strategy and international purchasing offices: evidence from case studies (2014) Int. J. Prod. Econ., 154 (2014), pp. 284-298; Kafetzopoulos, D., Vouzas, F., Skalkos, D., Developing and validating an innovation drivers' measurement instrument in the agri-food sector (2020) Br. Food J., 122 (4), pp. 1199-1214; Khirfan, L., Peck, M., Mohtat, N., Systematic content analysis: a combined method to analyze the literature on the daylighting (de-culverting) of urban streams (2020) MethodsX, 7; Kirchherr, J., Reike, D., Hekkert, M., Conceptualizing the circular economy: an analysis of 114 definitions (2017) Resour. Conserv. Recycl., 127 (April), pp. 221-232; Koh, L.S.C., Genovese, A., Acquaye, A.A., Barratt, P., Rana, N., Kuylenstierna, J., Gibbs, D., Decarbonising product supply chains: design and development of an integrated evidence-based decision support system-the supply chain environmental analysis tool (SCEnAT) (2013) Int. J. Prod. Res., 51 (7), pp. 2092-2109; Mangla, S.K., Luthra, S., Rich, N., Kumar, D., Rana, N.P., Dwivedi, Y.K., Enablers to implement sustainable initiatives in Agri-food supply chains (2018) Int. J. Prod. Econ., 203, pp. 379-393; Mayring, P., Qualitative Inhaltanalyse – Grundlagen und Techniken (Qualitative Content Analysis – Basics and Techniques) (2003), 8th ed. Beltz Verlag; Mobfood, The Portuguese Agri-Food Suplly Chain: Fruits and Vegetables and the Meat Sector Case Study (2021); Moktadir, A., Mahmud, Y., Banaitis, A., Sarder, T., Khan, M.R., Key performance indicators for adopting sustainability practices in (2021) E&M Econ. Manag., 24 (1), pp. 197-213; Moktadir, M.A., Dwivedi, A., Rahman, A., Chiappetta Jabbour, C.J., Paul, S.K., Sultana, R., Madaan, J., An investigation of key performance indicators for operational excellence towards sustainability in the leather products industry (2020) Bus. Strateg. Environ., 29 (8), pp. 3331-3351; Moreno-Miranda, C., Dries, L., Integrating coordination mechanisms in the sustainability assessment of agri-food chains: from a structured literature review to a comprehensive framework (2022) Ecological Economics, 192 (October 2021), p. 107265; Münch, C., von der Gracht, H.A., Hartmann, E., The future role of reverse logistics as a tool for sustainability in food supply chains : a Delphi-based scenario study (2021) Supply Chain Manag.l, , ahead-of-p(ahead-of-print); Neri, A., Cagno, E., Lepri, M., Trianni, A., A triple bottom line balanced set of key performance indicators to measure the sustainability performance of industrial supply chains (2021) Sustain. Prod. Consum., 26, pp. 648-691; Olugu, E.U., Wong, K.Y., An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry (2012) Expert Syst. Appl., 39 (1), pp. 375-384; Omolayo, Y., Feingold, B.J., Neff, R.A., Romeiko, X.X., Life cycle assessment of food loss and waste in the food supply chain (2021) Resour. Conserv. Recycl., 164 (September 2020), p. 105119; Pinna, C., Demartini, M., Tonelli, F., Terzi, S., How soft drink supply chains drive sustainability: key performance indicators (KPIs) identification (2018) Procedia CIRP, 72, pp. 862-867; Poponi, S., Arcese, G., Pacchera, F., Martucci, O., Resources, Conservation & Recycling Evaluating the transition to the circular economy in the agri-food sector: selection of indicators (2022) Resour. Conserv. Recycl., 176 (April 2021), p. 105916; Qorri, A., Mujkić, Z., Kraslawski, A., A conceptual framework for measuring sustainability performance of supply chains (2018) J. Clean. Prod., 189 (May), pp. 570-584; Ramos, E., Coles, P.S., Chavez, M., Hazen, B., Measuring Agri-food supply chain performance: insights from the peruvian kiwicha industry (2022) BIJ, 29 (5), pp. 1484-1512; Ramos, M.P., Custodio, E., Jiménez, S., Mainar-Causapé, A.J., Boulanger, P., Ferrari, E., Do Agri-food market incentives improve food security and nutrition indicators? A microsimulation evaluation for Kenya (2022) Food Secur., 14 (1), pp. 209-227; Rinaldi, M., Caterino, M., Fera, M., Manco, P., Macchiaroli, R., Technology selection in green supply chains - the effects of additive and traditional manufacturing (2021) J. Clean. Prod., 282; Rodrigues, A.P., Fernandes, M.L., Rodrigues, M.F.F., Bortoluzzi, S.C., Gouvea da Costa, S.E., Pinheiro de Lima, E., Developing criteria for performance assessment in municipal solid waste management (2018) J. Clean. Prod., 186, pp. 748-757; Ruiz-Almeida, A., Rivera-Ferre, M.G., Internationally-based indicators to measure Agri-food systems sustainability using food sovereignty as a conceptual framework (2019) Food Secur., 11, pp. 1321-1337; Saeed, M.A., Kersten, W., Supply chain sustainability performance indicators - a content analysis based on published standards and guidelines (2017) Logist. Res., 10 (1), pp. 1-19; Saeed, M.A., Kersten, W., Supply chain sustainability performance indicators-a systematic literature review (2020) Logist. Res., 13 (1), pp. 1-19; Saeed, M.A., Kersten, W., Sustainability performance assessment framework: a cross-industry multiple case study (2020) Int. J. Sust. Dev. World, 27 (6), pp. 496-514; Sangwan, K.S., Key activities, decision variables and performance indicators of reverse logistics (2017) Procedia CIRP, 61, pp. 257-262; Sangwan, K.S., Bhakar, V., Digalwar, A.K., A sustainability assessment framework for cement industry – a case study (2019) Benchmarking, 26 (2), pp. 470-497; Santos, D.D.A., Quelhas, O.L.G., Gomes, C.F.S., Zotes, L.P., França, S.L.B., de Souza, G.V.P., de Araújo, R.A., Santos, S.D.S.C., Proposal for a maturity model in sustainability in the supply chain (2020) Sustainability (Switzerland), 12 (22), pp. 1-37; Schreier, M., Qualitative Content Analysis in Practice (2012), SAGE Publications; Seuring, S., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Manag., 13 (2), pp. 128-137; Seuring, S., Gold, S., (2012) Conducting Content-analysis Based Literature Reviews in Supply Chain Management, 5, pp. 544-555. , (August 2011); Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod., 16 (15), pp. 1699-1710; Sgarbossa, F., Russo, I., Int. J. Production economics a proactive model in sustainable food supply chain : insight from a case study (2020) Int. J. Prod. Econ., 183 (2017), pp. 596-606; Shashi, Singh, R., Centobelli, P., Cerchione, R., Evaluating partnerships in sustainability-oriented food supply chain: a five-stage performance measurement model (2018) Energies, 11 (12), p. 3473; Silvestri, L., Silvestri, C., Forcina, A., Luca, C.D., A review of energy-based indicators for assessing sustainability and circular economy in the Agri-food production (2022) Procedia Comput. Sci., 200 (2019), pp. 1756-1765; Soosay, C.A., An empirical study of individual competencies in distribution centres to enable continuous innovation (2005) Creat. Innov. Manag., 14 (3), pp. 299-310; Tajbakhsh, A., Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2015) J. Clean. Prod., 105, pp. 74-85; Tajbakhsh, A., Hassini, E., Performance measurement of sustainable supply chains: a review and research questions (2015) Int. J. Product. Perform. Manag., 64 (6), pp. 744-783; Toop, T.A., Ward, S., Oldfield, T., Hull, M., Kirby, M.E., Theodorou, M.K., AgroCycle - developing a circular economy in agriculture (2017) Energy Procedia, 123, pp. 76-80; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review* (2003) Br. J. Manag., 14, pp. 207-222; Tripathi, S., Gupta, M., (2019) A Current Review of Supply Chain Performance Measurement Systems, , K. Shanker et al. (eds.) Springer Nature Advances i; Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T., Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy (2014) Biosyst. Eng., 120, pp. 47-64; Uysal, F., An integrated model for sustainable performance measurement in supply chain (2012) Procedia - Soc. Behav. Sci., 62 (1995), pp. 689-694; Valenzuela, L., Maturana, S., Designing a three-dimensional performance measurement system (SMD3D) for the wine industry: a chilean example (2016) Agric. Syst., 142, pp. 112-121; Van der Vorst, J.G.A., Kooten, O.V., Marcelis, W., (2007) Quality Controlled Logistics in Food Suply Chain Networks: Integrated Decision-Making on Quality and Logistics to Meet Advanced Customer Demands. The 14Th International Annual Euroma Conference, April, pp. 17-20; Verdouw, C.N., Beulens, A.J.M., Trienekens, J.H., Wolfert, J., Process modelling in demand-driven supply chains: a reference model for the fruit industry (2010) Comput. Electron. Agric., 73 (2), pp. 174-187; Yadav, V.S., Singh, A.R., Gunasekaran, A., Raut, R.D., Narkhede, B.E., A systematic literature review of the agro-food supply chain : challenges, network design, and performance measurement perspectives (2022) Sustain. Prod. Consum., 29, pp. 685-704; Yin, R.K., Case study research:design and methods (2003) Applied Social Research Methods Series, 5. , SAGE Publications Issue 3; Zhang, J.J., Joglekar, N., Verma, R., Pushing the frontier of sustainable service operations management: evidence from US hospitality industry (2012) J. Serv. Manag., 23 (3), pp. 377-399},
correspondence_address1={Ivo de Carvalho, M.; Centre for Management Studies (CEG-IST), Av. Rovisco Pais, Portugal; email: mafalda.i.de.carvalho@tecnico.ulisboa.pt},
publisher={Elsevier B.V.},
issn={23525509},
language={English},
abbrev_source_title={Sustain. Prod. Consum.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Varadejsatitwong2022,
author={Varadejsatitwong, P. and Banomyong, R. and Julagasigorn, P.},
title={A Proposed Performance-Measurement System for Enabling Supply-Chain Strategies},
journal={Sustainability (Switzerland)},
year={2022},
volume={14},
number={19},
doi={10.3390/su141911797},
art_number={11797},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139934098&doi=10.3390%2fsu141911797&partnerID=40&md5=2eb69e41a3c3ecdd1179f9bdfc4517ef},
affiliation={Thammasat Business School, Thammasat University, Bangkok, 10200, Thailand},
abstract={Existing performance-measurement systems in the supply-chain literature have been designed for specific supply chains. Without a business-appropriate performance-measurement system, practitioners devise strategies that are neither scientific nor supported by data. The objective of this paper was to propose a performance-measurement system to support the enabling of supply-chain strategies. The proposed performance-measurement system (PMS), combining the Plan–Do–Check–Act cycle (PDCA) with the evidence-based management (EBM) concept, served as the basis for developing a procedural framework. The PMS was validated using the case logistics service providers (LSPs). The PDCA methodology was used to develop a structural framework for fourth-party logistics (4PL). In the Plan step, the research team identified the business problems of fourth-party logistics (4PL). In the Do step, the LSP literature was reviewed, to identify five performance dimensions (‘Service Quality’; ‘Social and Environmental’; ‘Inter-organizational Relationship’; ‘Financial’; and ‘Efficiency and Effectiveness’), and key performance indicators (KPIs). The 4PL management team participated in the finalization of the five performance dimensions and the 53 KPIs, which were used to propose a preliminary version of the structural framework for the 4PL. In the Check step, the data collected from 478 subcontractors of the 4PL were analyzed, using confirmatory factor analysis and structural equation modeling, and were used to validate the proposed structural framework for the 4PL. The validated structural framework was further presented at an academic conference, and to the 4PL for feedback, and was used to formulate supply-chain strategies through brainstorming. The findings include a validated structural framework containing five performance dimensions and 32 KPIs. The research revealed that input performance (‘Service Quality’, ‘Social and Environmental’, and ‘Inter-organizational Relationship’) positively affected output performance (‘Financial’ and ‘Efficiency and Effectiveness’). Supply-chain strategies were further suggested to the 4PL. The validating case in this study demonstrated that the employed procedural framework (PDCA and EBM) was applicable, and could be used to derive a structural framework and supply-chain strategies for the 4PL. This study contributes to the literature, by proposing a PMS for supply-chain strategy development. The paper’s illustrative case provides a practical application of how to develop a performance-measurement system. © 2022 by the authors.},
author_keywords={evidence-based management;  logistics service providers;  performance-measurement system;  the PDCA cycle},
keywords={business;  factor analysis;  management practice;  performance assessment;  research work;  service quality;  strategic approach;  supply chain management},
references={Christopher, M., (2011) Logistics & Supply Chain Management, , 4th ed., Pearson, London, UK; Xia, L.X.X., Supply chain modelling and improvement in telecom industry: A case study Proceedings of the 2006 4th IEEE International Conference on Industrial Informatics, , Singapore, 16–18 August 2006; Farokhi, S., Roghanian, E., Samimi, Y., Quantitative target setting in balanced scorecard method using simultaneous equations system and goal programming (2019) Int. J. Product. Perform. Manag, 69, pp. 2089-2118; Lima-Junior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks (2019) Int. J. Prod. Econ, 212, pp. 19-38; Tahir, A.C., Darton, R.C., The process analysis method of selecting indicators to quantify the sustainability performance of a business operation (2010) J. Clean. Prod, 18, pp. 1598-1607; Shapiro, M.J., Morey, J.C., Small, S.D., Langford, V., Kaylor, C.J., Jagminas, L., Suner, S., Jay, G.D., Simulation based teamwork training for emergency department staff: Does it improve clinical team performance when added to an existing didactic teamwork curriculum? (2004) BMJ Qual Saf, 13, pp. 417-421; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Comput. Ind, 56, pp. 663-680; Qi, Y., Huo, B., Wang, Z., Yeung, H.Y.J., The impact of operations and supply chain strategies on integration and performance (2017) Int. J. Prod. Econ, 185, pp. 162-174; Schnetzler, M.J., Sennheiser, A., Schönsleben, P., A decomposition-based approach for the development of a supply chain strategy (2007) Int. J. Prod. Econ, 105, pp. 21-42; Jacobs, F.R., Chase, R.B., (2011) Operations and Supply Management, , 13th ed., McGraw-Hill/Iriwin, New York, NY, USA; Liu, W.B., Meng, I.W., Mingers, J., Tang, N., Wang, W., Developing a performance management system using soft systems methodology: A Chinese case study (2012) Eur. J. Oper. Res, 223, pp. 529-540; Heikkilä, M., Bouwman, H., Heikkilä, J., Solaimani, S., Janssen, W., Business model metrics: An open repository (2016) Inf. Syst. E-Bus. Manag, 14, pp. 337-366; Kumru, M., A balanced scorecard-based composite measuring approach to assessing the performance of a media outlet (2012) Serv. Ind. J, 32, pp. 821-843; Hradílek, J., Cat Model for Complex Evaluation of Organisational Maturity in Small and Medium Enterprises (2020) Cent. Eur. Bus. Rev, 9, p. 1; Kaplan, R., Norton, D., The Balanced Scorecard—Measures That Drive Performance (2005) Harv. Bus. Rev, 83, p. 172; Van Hoek, R., Measuring the unmeasurable—Measuring and improving performance in the supply chain (1998) Int. J. Supply Chain. Manag, 3, pp. 187-192; Beamon, B., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag, 19, pp. 275-292; Holmberg, S., A systems perspective on supply chain measurements (2000) Int. J. Phys. Distrib. Logist. Manag, 30, pp. 847-868; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag, 21, pp. 71-87; (2019) Supply Chain Operations Reference Model, , http://www.apics.org/apics-for-business/frameworks/scor, APICS Suly Chain Council, Chicago, IL, USA, available online; Chorfi, Z., Benabbou, L., Berrado, A., An integrated performance measurement framework for enhancing public health care supply chains (2018) Supply Chain Forum, 19, pp. 191-203; Reddy, J.M., Rao, N., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manuf, 30, pp. 40-47; Zizlavsky, O., The Balanced Scorecard: Innovative Performance Measurement and Management Control System (2014) J. Technol. Manag. Innov, 9, pp. 210-222; Van Veen-Dirks, P.M.G., Complementary choices and management control: Field research in a flexible production environment (2006) Manag. Account. Res, 17, pp. 72-105; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng, 53, pp. 43-62; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: Design and development guidelines (2013) Int. J. Product. Perform. Manag, 62, pp. 805-826; Nouri, F.A., Nikabadi, M.S., Olfat, L., Developing the framework of sustainable service supply chain balanced scorecard (SSSC BSC) (2019) Int. J. Product. Perform. Manag, 68, pp. 148-170; Li, L., Su, Q., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) Int. J. Prod. Res, 49, pp. 33-57; Sellitto, M.A., Pereira, G.M., Borchardt, M., Da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: Application in the footwear industry (2015) Int. J. Prod. Res, 53, pp. 4917-4926; Tang, L.C., Goh, T.N., Lam, S.W., Zhang, C.W., Fortification of Six Sigma: Expanding the DMAIC toolset (2007) Qual. Reliab. Eng. Int, 23, pp. 3-18; Lepori, E., Damand, D., Barth, B., Benefits and limitations of the SCOR model in warehousing (2013) IFAC Proc. Vol, 46, pp. 424-429; Axelsson, P., Frankel, J., (2014) Performance Measurement System for Warehouse Activities Based on the SCOR® Model, , Department of Industrial Management and Logistics, Lund University, Lund, Sweden; Rillo, M., Limitations of balanced scorecard Proceedings of the 2nd Scientific and Educational Conference, Business Administration: Business in a Globalizing Economy, , Parnu, Estonia, 16 July 2004; Thompson, K.R., Mathys, N.J., It’s time to add the employee dimension to the balanced scorecard (2013) Organ. Dyn, 2, pp. 135-144; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) Int. J. Oper. Prod. Manag, 25, pp. 1264-1277; Barber, E., How to measure the “value” in value chains (2008) Int. J. Phys. Distrib. Logist. Manag, 38, pp. 685-698; Shewhart, W.A., (1931) Economic Control of Quality of Manufactured Product, , Macmillan And Co., Ltd., New York, NY, USA; Deming, W.E., (2000) The New Economics, for Industry, Government, Education, , 2nd ed., MIT Press, Cambridge, MA, USA; Ribeiro, L.M.M., Cabral, S.J.A., A benchmarking methodology for metalcasting industry (2006) Benchmarking, 13, pp. 23-35; Du, Q.L., Cao, S.M., Ba, L.L., Cheng, J.M., Application of PDCA cycle in the performance management system Proceedings of the 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1-4. , Dalian, China, 12–14 October 2008; Realyvásquez-Vargas, A., Arredondo-Soto, K.C., Carrillo-Gutiérrez, T., Ravelo, G., Applying the Plan-Do-Check-Act (PDCA) cycle to reduce the defects in the manufacturing industry. A case study (2018) Appl. Sci, 8; Hervani, A.M., Helms, M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12, pp. 330-353; Pesonen, V., Development and Analysis of Performance Measurement Indicators Utilized by Third Party Logistics Service Providers (2021) Master’s Thesis, , Industrial Management, School of Technology and Innovations, University of Vaasa, Vaasa, Finland; Ghosh, S., Mandal, M.C., Ray, A., A PDCA based approach to evaluate green supply chain management performance under fuzzy environment (2022) Int. J. Sci. Manag. Eng, pp. 1-15; Sangpikul, A., Implementing academic service learning and the PDCA cycle in a marketing course: Contributions to three beneficiaries (2017) J. Hosp. Leis. Sports Tour. Educ, 21, pp. 83-87; Matsuo, M., Nakahara, J., The effects of the PDCA cycle and OJT on workplace learning (2013) Int. J. Hum. Resour. Manag, 24, pp. 195-207; Sarkis, J., Benchmarking for agility (2001) Benchmarking, 8, pp. 88-107; Bhutta, K.S., Huq, F., Benchmarking—Best practices: An integrated approach (1999) Benchmarking, 6, pp. 254-268; Pham Evans, M.T., Tisak, D.J., Williamson, D.F., Twenty-first century benchmarking: Searching for the next generation (2012) Benchmarking, 19, pp. 760-780; Kulikowski, K., The model of evidence-based benchmarking: A more robust approach to benchmarking (2021) Benchmarking, 28, pp. 721-736; Barends, E., Rousseau, D.M., (2018) Evidence-Based Management: How to Use Evidence to Make Better Organizational Decisions, , Kogan Page Publishers, New York, NY, USA; Briner, R.B., Denyer, D., Rousseau, D.M., Evidence-based management: Concept cleanup time? (2009) Acad. Manag. Perspect, 23, pp. 19-32; Banomyong, R., Cook, P., Kent, P., Formulating regional logistics development policy: The case of ASEAN (2008) Int. J. Logist. Res. Appl, 11, pp. 359-379; Rezaei, J., van Roekel, W.S., Tavasszy, L., Measuring the relative importance of the logistics performance index indicators using Best Worst Method (2018) Transp. Policy, 68, pp. 158-169; Mangiaracina, R., Perego, A., Seghezzi, A., Tumino, A., Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: A literature review (2019) Int. J. Phys. Distrib. Logist. Manag, 49, pp. 901-920; Gubrium, J.F., Holstein, J.A., (2001) Handbook of Interview Research: Context and Method, , Sage Publications, Thousand Oaks, CA, USA; Hemingway, P., Brereton, N., What Is a Systematic Review? (2009) Hayward Med. Commun, 2, pp. 1-8; Varadejsatitwong, P., Banomyong, R., Julagaasigorn, P., Developing a Performance Measurement Framework for Logistics Service Providers Proceedings of the 2021 Conference: The International Conference on Industrial Engineering and Operations Management (IEOM), pp. 994-1005. , Monterrey, Mexico, 3–5 November 2021; Ramos, T.B., Development of regional sustainability indicators and the role of academia in this process: The Portuguese practice (2009) J. Clean. Prod, 17, pp. 1101-1115; De Nooy, W., Mrvar, A., Batagelj, V., (2005) Exploratory Social Network Analysis with Pajek, , Cambridge University Press, Cambridge, UK; Hummon, N.P., Dereian, P., Connectivity in a citation network: The development of DNA theory (1989) Soc. Netw, 11, pp. 39-63; Kemp, P.A., Researching housing management performance (1995) Urban. Stud, 32, pp. 779-790; Southern, G., A systems approach to performance measurement in hospitality (1999) Int. J. Contemp. Hosp, 11, pp. 366-376; Straub, A., Koopman, M., van Mossel, H.J., Systems approach and performance measurement by social enterprises (2010) Facilities, 28, pp. 321-331; Sulek, J.M., Lind, M.R., A systems model for evaluating transit performance (2000) J. Public Trans, 3, p. 2; Banomyong, R., Julagasigorn, P., Varadejsatitwong, P., Piboonrungroj, P., The humanitarian supply chain assessment tool (HumSCAT) (2019) J. Humanit. Logist. Supply Chain Manag, 9, pp. 221-249; Brown, S.R., A primer on Q methodology (1993) Operant. Subj, 6, pp. 91-138; Hinkin, T.R., Tracey, J.B., Enz, C.A., Scale construction: Developing reliable and valid measurement instruments (1997) J. Hosp. Tour. Res, 21, pp. 100-120; Oppenheim, A.N., (2000) Questionnaire Design, Interviewing and Attitude Measurement, , Bloomsbury Publishing, London, UK; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2019) Multivariate Data Analysis, , 8th ed., Cengage Learning EMEA, London, UK; Srivastava, S., Singh, R.K., Exploring integrated supply chain performance in healthcare: A service provider perspective (2021) Benchmarking, 28, pp. 106-130; George, D., Mallery, P., (2003) SPSS for Windows Step by Step: A Simple Guide and Reference 11.0 Update, , 4th ed., Allyn & Bacon, Boston, MA, USA; Osborn, A.F., Principles and Procedures of Creative Thinking (1953) Applied Imagination, , Scribners, Hunter, NY, USA; Byrne, B.M., (1998) Structural Equation Modeling with LISREL, PRELIS, and SIMPLIS: Basic Concepts, Applications, and Programming, , Lawrence Erlbaum Associates, Mahwah, NJ, USA; De Jong, G., Causal loops in long-term supply relationships: Theory and evidence from the United States, Japan, and Europe (1999) Ph.D. Thesis, , SOM, University of Groningen, Groningen, The Netherlands; Bagozzi, R.P., Yi, Y., On the evaluation of structural equation models (1988) J. Acad. Mark. Sci, 16, pp. 74-94; Browne, M.W., Cudeck, R., Alternative ways of assessing model fit (1992) Sociol. Methods Res, 21, pp. 230-258; Fu, X., Dong, M., Liu, S., Han, G., Trust based decisions in supply chains with an agent (2016) Decis. Support Syst, 82, pp. 35-46; Panahifar, F., Byrne, P.J., Heavey, C., A hybrid approach to the study of CPFR implementation enablers (2015) Prod. Plan. Control, 26, pp. 1090-1109; Lorenzo Ochoa, O., Claes, B., Koryak, O., Diaz, A., Integration through orchestration: The interplay between enterprise systems and inventory management capabilities (2017) J. Enterp. Inf. Manag, 30, pp. 555-582; Tang, O., Musa, S.N., Identifying risk issues and research advancements in supply chain risk management (2011) Int. J. Prod. Econ, 133, pp. 25-34; Lyu, J., Ding, J.H., Chen, P.S., Coordinating replenishment mechanisms in supply chain: From the collaborative supplier and store-level retailer perspective (2010) Int. J. Prod. Econ, 123, pp. 221-234; Greening, D.W., Turban, D.B., Corporate social performance as a competitive advantage in attracting a quality workforce (2000) Bus. Soc, 39, pp. 254-280; Ting, I.W.K., Azizan, N.A., Bhaskaran, R.K., Sukumaran, S.K., Corporate social performance and firm performance: Comparative study among developed and emerging market firms (2019) Sustainability, 12; Hong, B., Li, Z., Minor, D., Corporate governance and executive compensation for corporate social responsibility (2016) J. Bus. Ethics, 136, pp. 199-213; Bansal, P., Roth, K., Why companies go green: A model of ecological responsiveness (2000) Acad. Manag. J, 43, pp. 717-736; Journeault, M., The influence of the eco-control package on environmental and economic performance: A natural resource-based approach (2016) Manag. Account. Res, 28, pp. 149-178; Ormazabal, M., Rich, E., Sarriegi, J.M., Viles, E., Environmental Management Evolution Framework (2017) Organ Environ, 30, pp. 27-50; Szymczyka, K., Kadłubek, M., Challenges in general cargo distribution strategy in urban logistics—Comparative analysis of the biggest logistics operators in EU (2019) Transp. Res. Proc, 39, pp. 525-533; Islam, M.Z., Jasimuddin, S.M., Hasan, I., Organizational culture, structure, technology infrastructure and knowledge sharing: Empirical evidence from MNCs based in Malaysia (2015) VINE J. Inf. Knowl. Manag. Syst, 45, pp. 67-88; Rosendaal, B., Frankema, K.B., Knowledge sharing within teams: Enabling and constraining factors (2013) Knowl. Manag. Res. Pract, 13, pp. 235-247; Rutten, W., Blaas-Franken, J., Martin, H., The impact of (low) trust on knowledge sharing (2016) J. Knowl. Manag, 20, pp. 199-214; Wang, C.L., Siu, N.Y.M., Barnes, B.R., The significance of trust and renqing in the long-term orientation of Chinese business-to-business relationships (2008) Ind. Mark. Manag, 37, pp. 819-824; Wang, C.L., Shi, Y., Barnes, B.R., The role of satisfaction, trust and contractual obligation on long-term orientation among Chinese and Western managers (2015) J. Bus. Res, 68, pp. 473-479; Vij, S., Farooq, R., Multi-group moderation analysis for relationship between knowledge sharing orientation and business performance (2014) J. Knowl. Manag, 10, pp. 36-53; Baba, M.D., Sha’ri, M.Y., Azhari, M.S., A benchmarking implementation framework for automotive manufacturing SMEs (2006) Benchmarking, 13, pp. 396-430},
correspondence_address1={Varadejsatitwong, P.; Thammasat Business School, Thailand; email: paitoon-var58@tbs.tu.ac.th},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Thekkoote20222359,
author={Thekkoote, R.},
title={Understanding big data-driven supply chain and performance measures for customer satisfaction},
journal={Benchmarking},
year={2022},
volume={29},
number={8},
pages={2359-2377},
doi={10.1108/BIJ-01-2021-0034},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118232318&doi=10.1108%2fBIJ-01-2021-0034&partnerID=40&md5=66d95ebcdbbe117e975acf6897b90b6f},
affiliation={University of Johannesburg, Johannesburg, South Africa},
abstract={Purpose: Supply chain analytics with big data capability are now growing to the next frontier in transforming the supply chain. However, very few studies have identified its different dimensions and overall effects on supply chain performance measures and customer satisfaction. The aim of this paper to design the data-driven supply chain model to evaluate the impact on supply chain performance and customer satisfaction. Design/methodology/approach: This research uses the resource-based view, emerging literature on big data, supply chain performance measures and customer satisfaction theory to develop the big data-driven supply chain (BDDSC) model. The model tested using questionnaire data collected from supply chain managers and supply chain analysts. To prove the research model, the study uses the structural equation modeling technique. Findings: The results of the study identify the supply chain performance measures (integration, innovation, flexibility, efficiency, quality and market performance) and customer satisfaction (cost, flexibility, quality and delivery) positively associated with the BDDSC model. Originality/value: This paper fills the significant gap in the BDDSC on the different dimensions of supply chain performance measures and their impacts on customer satisfaction. © 2021, Emerald Publishing Limited.},
author_keywords={Customer services quality;  Logistics;  Performance;  Quality improvement;  Supply chain management},
references={Akter, S., D'ambra, J., Ray, P., Service quality of mHealth platforms: development and validation of a hierarchical model using PLS (2010) Electronic Markets, 20, pp. 209-227; Askariazad, M.H., Babakhani, N., An application of European Customer Satisfaction Index (ECSI) in business to business (B2B) context (2015) Journal of Business and Industrial Marketing, 30 (1), pp. 17-31; Augier, M., Teece, D.J., Dynamic capabilities and the role of managers in business strategy and economic performance (2009) Organization Science, 20 (2), pp. 410-421; Bag, S., Big data and predictive analysis is key to superior supply chain performance: a South African experience (2017) International Journal of Information Systems and Supply Chain Management, 10 (2), pp. 66-84; Bag, S., Telukdarie, A., Pretorius, J.H.C., Gupta, S., Industry 4.0 and supply chain sustainability: framework and future research directions (2018) Benchmarking: An International Journal, 28 (5), pp. 1410-1450; Bag, S., Wood, L.C., Mangla, S.K., Luthra, S., Procurement 4.0 and its implications on business process performance in a circular economy (2020) Resources, Conservation and Recycling, 152, pp. 104-502; Bag, S., Wood, L.C., Xu, L., Dhamija, P., Kayikci, Y., Big data analytics as an operational excellence approach to enhance sustainable supply chain performance (2020) Resources, Conservation and Recycling, 153; Barney, J.B., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Becker, J.-M., Klein, K., Wetzels, M., Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models (2012) Long Range Planning, 45, pp. 359-394; Blome, C., Schoenherr, T., Eckstein, D., The impact of knowledge transfer and complexity on supply chain flexibility: a knowledge-based view (2014) International Journal of Production Economics, 147, pp. 307-316; Cao, M., Zhang, Q., Supply chain collaboration: impact on collaborative advantage and firm performance (2011) Journal of Operations Management, 29, pp. 163-180; Chang, H.H., An empirical study of evaluating supply chain management integration using the balanced score card in Taiwan (2009) The Service Industries Journal, 29 (2), pp. 185-202; Chavez, R., Yu, W., Feng, M., Wiengarten, F., The effect of customer-centric green supply chain management on operational performance and customer satisfaction (2014) Business Strategy and the Environment, 25 (3), pp. 205-220; Chavez, R., Yu, W., Jacobs, M., Fynes, B., Wiengarten, F., Lecuna, A., Internal lean practices and performance: the role of technological turbulence (2015) International Journal of Production Economics, 160 (2), pp. 157-171; Chen, D.Q., Preston, D.S., Swink, M., How the use of big data analytics affects value creation in S.C. Management (2015) Journal of Management Information Systems, 32 (4), pp. 4-39; Cheung, M.W.L., Meta-analysis: a structural equation modelling approach (2015) Wiley, , https://play.google.com/store/books/details/Mike_W_L_heung_Meta_Analysis?id=VHFuCAAAQBAJ, Chichester; Cheung, G.W., Lau, R.S., Testing mediation and suppression effects of latent variables: bootstrapping with structural equation models (2008) Organisational Research Methods, 11, pp. 296-325; Chutia, L.J., Baruah, P., Change initiatives in the retail sector of India: a literature study (2014) Global Journal of Commerce and Management Perspective, 3 (5), pp. 35-40; Cudeck, R., du Toit, S., Sörbom, D., Structural equation modelling: present and future: a festschrift in honor of Karl Jöreskog (2001) Scientific Software International, , https://books.google.co.uk/books/about/Structural_Equation_Modeling.html?id=AMLNoKmC_IC&redir_esc=y, Lincolnwood, IL; Deshpande, A., Supply chain management dimensions, supply chain performance, and organizational performance: an integrated framework (2012) International Journal of Business and Management, 7 (8), pp. 1833-1819; Dong, S., Ding, M., Grewal, R., Zhao, P., Functional forms of the satisfaction–loyalty relationship (2011) International Journal of Research in Marketing, 28 (1), pp. 38-50; Fawcett, S., Calantone, R., Roath, A., Meeting quality and cost imperatives in a global market (2000) International Journal of Physical Distribution and Logistics Management, 30 (6), pp. 472-499; Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘big data' can make big impact: findings from a systematic review and a longitudinal case study (2015) International Journal of Production Economics, 165, pp. 234-246; Frank, A.G., Dalenogare, L.S., Ayala, N.F., Industry 4.0 technologies: implementation patterns in manufacturing companies (2019) International Journal of Production Economics, 210, pp. 15-26; Frohlich, M.T., Westbrook, R., Arcs of integration: an international study of supply chain strategies (2001) Journal of Operations Management, 19 (2), pp. 185-200; Gawankar, S., Kamble, S., Raut, R., An investigation of relationship between Supply Chain Management Practices (SCMP) on Supply Chain Performance Measurement (SCMPM) of Indian retail chain using S.E.M (2017) Benchmarking: An International Journal, 24 (1), pp. 257-295; Grewal, R., Cote, J.A., Baumgartner, H., Multicollinearity and measurement error in structural equation models: implications for theory testing (2004) Marketing Science, 23, pp. 519-529; Gunasekaran, A., Irani, Z., Choy, K.-L., Filippi, L., Papadopoulos, T., Performance measures and metrics in outsourcing decisions: a review for research and applications (2015) International Journal of Production Economics, 161, pp. 153-166; Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S., Big data and predictive analytics for supply chain and organizational performance (2017) Journal of Business Research, 70, pp. 308-317; Gupta, S., Drave, V.A., Bag, S., Luo, Z., Leveraging smart supply chain and information system agility for supply chain flexibility (2019) Information Systems Frontiers, 21 (3), pp. 547-564; Gupta, S., Modgil, S., Gunasekaran, A., Big data in lean six sigma: a review and further research directions (2019) International Journal of Production Research, 58 (3), pp. 1-23; Hair, J., Black, W., Babin, B.Y.A., Anderson, R., Tatham, R., (2014) Multivariate Data Analysis. A Global Perspective, , Pearson Prentice Hall, New Delhi; Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A., Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications (2014) International Journal of Production Economics, 154, pp. 72-80; Hazen, B.T., Skipper, J.B., Ezell, J.D., Boone, C.A., Big data and predictive analytics for S.C. sustainability: a theory-driven research agenda (2016) Computers and Industrial Engineering, 101, pp. 592-598; Hill, T., (1993) Manufacturing Strategy: The Strategic Management of The Manufacturing Function, , MacMillan, London; Hofmann, E., Big data and supply chain decisions: the impact of volume, variety and velocity properties on the bullwhip effect (2017) International Journal of Production Research, 55 (17), pp. 5108-5126; Jain, S., Jain, N.K., Metri, B., Strategic framework towards measuring a circular supply chain management (2018) Benchmarking: An International Journal, 25 (8), pp. 3238-3252; Jeble, S., Dubey, R., Childe, S.J., Papadopoulos, T., Roubaud, D., Prakash, A., Impact of big data and predictive analytics capability on supply chain sustainability (2018) International Journal of Logistics Management, 29 (2), pp. 513-538; Kamble, S.S., Gunasekaran, A., Big data-driven supply chain performance measurement system: a review and framework for implementation (2019) International Journal of Production Research, 58 (1), pp. 1-22; Kamble, S.S., Gunasekaran, A., Parekh, H., Joshi, S., Modelling the Internet of Things adoption barriers in food retail supply chains (2019) Journal of Retailing and Consumer Services, 48, pp. 154-168; Kumar, V., Batista, L., Maull, R., The impact of operations performance on customer loyalty (2011) Service Science, 3 (2), pp. 158-171; Kuo, Y.H., Kusiak, A., From data to big data in production research: the past and future trends (2019) International Journal of Production Research, 57, pp. 4828-4853; Kwon, O., Lee, N., Shin, B., Data quality management, data usage experience and acquisition intention of big data analytics (2014) International Journal of Information Management, 34 (3), pp. 387-394; Lau Antonio, K.W., Yam, R., Tang, E., The impacts of product modularity on competitive capabilities and performance: an empirical study (2007) International Journal of Production Economics, 105 (1), pp. 1-20; Lee, J., Lapira, E., Bagheri, B., Kao, H.A., Recent advances and trends in predictive manufacturing systems in big data environment (2013) Manufacturing Letters, 1 (1), pp. 38-41; Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., (2011) Big Data: The Next Frontier for Innovation, Competition, and Productivity, , McKinsey Global Institute, San Francisco, CA; Martin, R.P., Patterson, J.W., On measuring company performance within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; Matthias, O., Fouweather, I., Gregory, I., Vernon, A., Making sense of big data – can it transform operations management? (2017) International Journal of Operations and Production Management, 37 (1), pp. 37-55; McAfee, A., Brynjolfsson, E., Investing in the I.T. that makes a competitive difference (2008) Harvard Business Review, 86 (7-8), pp. 98-107; McAfee, A., Brynjolfsson, E., Big data: the management revolution (2012) Harvard Business Review, 90 (10), pp. 60-68; Müller, O., Fay, M., vom Brocke, J., The effect of big data and analytics on firm performance: an econometric analysis considering industry characteristics (2018) Journal of Management Information Systems, 35 (2), pp. 488-509; Prahalad, C.K., Hamel, G., The core competence of the corporation (1990) Harvard Business Review, 68 (3), pp. 79-93; Rosenzweig, E., Roth, A., Dean, J.W., The influence of an integration strategy on competitive capabilities and business performance: an exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21 (4), pp. 437-456; Russom, P., (2011) ‘The Three vs. of Big Data: TDWI Research': Best Practice Report, , Fourth Quarter, TDWI, Renton, WA; Salam, M.A., The mediating role of supply chain collaboration on the relationship between technology, trust, and operational performance (2017) Benchmarking: An International Journal, 24 (2), pp. 298-317; Salam, M.A., Analyzing manufacturing strategies and Industry 4.0 supplier performance relationships from a resource-based perspective (2019) Benchmarking: An International Journal, 28 (4), pp. 1697-1716; Samal, A., Patra, S., Chatterjee, D., Impact of culture on organizational readiness to change: the context of bank M&A (2019) Benchmarking: An International Journal, 28 (5), pp. 1503-1523; Sanders, N.R., (2014) Big Data-Driven Supply Chain Management: A Framework for Implementing Analytics and Turning Information into Intelligence, , Pearson Financial Times, Upper Saddle River, NJ; Saunders, M., Lewis, P., Thornhill, A., (2003) Research Methods for Business Students, , Pearson Education, Harlow; Sony, M., Naik, S., Key ingredients for evaluating Industry 4.0 readiness for organizations: a literature review (2019) Benchmarking: An International Journal, 27 (7), pp. 2213-2232; Stanley, E.F., Cynthia, W., Chad, A., Gregory, M., Supply chain information-sharing b: benchmarking a proven path (2009) Benchmarking: An International Journal, 16 (2), pp. 222-246; Stefanovic, N., Collaborative predictive business intelligence model for spare parts inventory replenishment (2015) Computer Science and Information Systems, 12 (3), pp. 911-930; Sundarakani, B., Kamran, R., Maheshwari, P., Jain, V., Designing a hybrid cloud for a supply chain network of Industry 4.0: a theoretical framework (2019) Benchmarking: An International Journal, 28 (5), pp. 1524-1542; Swink, M., Narasimhan, R., Kim, S.W., Manufacturing practices and strategy integration: effects on cost efficiency, flexibility, and MarketBased performance (2005) Decision Sciences, 36 (3), pp. 427-457; Swink, M., Narasimhan, R., Wang, C., Managing beyond the factory walls: effects of four types of strategic integration on manufacturing plant performance (2007) Journal of Operations Management, 25 (1), pp. 148-164; Tan, K.H., Ji, G., Lim, C.P., Tseng, M.L., Using big data to make better decisions in the digital economy (2017) International Journal of Production Research, 55 (17), pp. 4998-5000; Tarhini, A., Yunis, M., El-Kassar, A.N., Innovative sustainable methodology for managing-house software development in S.M.E.s (2018) Benchmarking: An International Journal, 25 (3), pp. 1085-1103; Teece, D.J., The foundations of enterprise performance: dynamic and ordinary capabilities in an (economic) theory of firms (2014) Academy of Management Perspectives, 28 (4), pp. 328-352; Teoman, S., Ulengin, F., The impact of management leadership on quality performance throughout a supply chain: an empirical study (2017) Total Quality Management and Business Excellence, 29 (11-12), pp. 1427-1451; Tracey, M., Lim, J., Vonderembse, M., The impact of supply-chain management capabilities on business performance (2005) Supply Chain Management: An International Journal, 10 (3), pp. 179-191; Trkman, P., McCormack, K., de Oliveira, M.P.V., Ladeira, M.B., The impact of business analytics on supply chain performance (2010) Decision Support Systems, 49, pp. 318-327; Tseng, M.L., Tan, R.R., Chiu, A.S.F., Chien, C.F., Kuo, T.S., Circular economy meets Industry 4.0: can big data drive industrial symbiosis? (2018) Resources, Conservation and Recycling, 131, pp. 146-147; Tung, J., A study of product innovation on firm performance (2012) The International Journal of Organizational Innovation, 4 (3), pp. 84-97; Um, J., Lyons, A., Lamb, H.K.S., Cheng, T.C.E., Dominguez-Pery, C., Product variety management and supply chain performance: a capability perspective on their relationships and competitiveness implications (2017) International Journal of Production Economics, 187, pp. 15-26; Vanpoucke, E., Vereecke, A., Muylle, S., Leveraging the impact of supply chain integration through information technology (2017) International Journal of Operations and Production Management, 37 (4), pp. 510-530; Waller, M.A., Fawcett, S.E., Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management (2013) Journal of Business Logistics, 34 (2), pp. 77-84; Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R., Childe, S.J., Big data analytics and firm performance: effects of dynamic capabilities (2017) Journal of Business Research, 70, pp. 356-365; Wetzels, M., Odekerken-Schroder, G., Van Oppen, C., Using PLS path modeling for assessing hierarchical construct models: guidelines and empirical illustration (2009) MIS Quarterly, 33, p. 177; Wheel Wright, S.C., Manufacturing strategy: defining the missing link (1984) Strategic Management Journal, 5, pp. 77-91; Williams, L.J., Vandenberg, R.J., Edwards, J.R., 12 structural equation modelling in management research: a guide for improved analysis (2009) The Academy of Management Annals, 3, pp. 543-604; Yoo, S.H., Seo, Y.W., Effect of supply chain structure and power dynamics on R&D and market performances (2017) Journal of Business Economics and Management, 18 (3), pp. 487-504; Yu, W., The effect of IT-enabled supply chain integration on performance (2015) Production Planning and Control, 26 (12), pp. 945-957; Zaefarian, G., Forkmann, S., Mitrega, M., Henneberg, S.C., A capability perspective on relationship ending and its impact on product innovation success and firm performance (2017) Long Range Planning, 50 (2), pp. 184-199; Zhao, X., Sum, C.C., Qi, Y., Zhang, H., Lee, T.S., A taxonomy of manufacturing strategies in China (2006) Journal of Operations Management, 24 (5), pp. 621-636; Ivanov, D., Dolgui, A., Sokolov, B., The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics (2019) International Journal of Production Research, 57 (3), pp. 829-846; Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., Mehta, A., Impact of big data on supply chain management (2018) International Journal of Logistics Research and Applications, 21 (6), pp. 579-596; Singh, R.K., Kumar, P., Chand, M., Evaluation of supply chain coordination index in context to Industry 4.0 environment (2019) Benchmarking: An International Journal, 28 (5), pp. 1622-1637; Tracey, M., Vonderembse, M., Lim, J.S., Manufacturing technology and strategy formulation: Keys to enhancing competitiveness and improving performance (1999) Journal of Operations Management, 17 (4), pp. 411-428; Vickery, S., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships (2003) Journal of Operations Management, 21 (5), pp. 523-539; Wiengarten, F., Onofrei, G., Humphreys, P., Fynes, B., A supply chain view on certification standards: does supply chain certification improve performance outcomes? (2018) ISO 9001, ISO 14001, and New Management Standards. Measuring Operations Performance, pp. 193-214. , Heras-Saizarbitoria, I. (Ed.), Springer, Cham},
correspondence_address1={Thekkoote, R.; University of JohannesburgSouth Africa; email: tramadas@gmail.com},
publisher={Emerald Publishing},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Asrol2022,
author={Asrol, M. and Syahruddin},
title={Supply Chain Performance Measurement and Improvement for Forging Industry},
journal={International Journal of Industrial Engineering and Production Research},
year={2022},
volume={33},
number={3},
doi={10.22068/ijiepr.33.3.14},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138021141&doi=10.22068%2fijiepr.33.3.14&partnerID=40&md5=f25fbb6aa26548bcd0c3d312738370ac},
affiliation={Industrial Engineering Department, BINUS Graduate Program, Master of Industrial Engineering, Bina Nusantara University, Jakarta, 11480, Indonesia},
abstract={Forging Industry supply chain involves various actors and acts as Industry Intermediate providing various products for downstream industrial customers. This study aims to analyze supply chain performance and recommend improvement strategy at forging Industry. This study applied supply chain operation reference (SCOR) and analytical hierarchy process (AHP) to analyze supply chain performance. A SWOT analysis was applied to improve supply chain performance. The data was validated at ABC co. and XYZ co. as two focal company in supply chain operations of forging industry. Supply chain performance measurement at ABC co. show the performance was 99.42% (excellent) and 99.05% (excellent) in 2019 and 2020, respectively. Supply chain performance score at XYZ co. showed 96.60% (good) and 97.52% (good) in 2019 and 2020, respectively. This study has succeeded in formulating efforts to improve the supply chain performance with SWOT analysis that extracted from the performance measurement analysis, field observation and in-depth interview. The suggested strategies were producing quality goods according to domestic market specifications, maintaining good relations with suppliers or outsourcing, improving services using high technology. © Iran University of Science and Technology 2022.},
author_keywords={Analytical hierarchy process;  Forging industry;  Performance;  Strategy;  Supply chain},
references={Alamsjah, F., Asrol, M., Inter-island Logistics and the Role of an Agile Supply Chain to Achieve Supply Chain Performance: Initial Findings (2021) 2021 IEEE Int. Conf. Ind. Eng. Eng. Manag. IEEM, pp. 270-274; Gholampour, G., Rahim, A. R. B. A., Gholampour, F., A Qualitative Research on Strategic Performance of Supply Chain- A Case study in Automotive Industry (2018) Int. J. Ind. Eng. Prod. Res, 29 (4), pp. 497-513; Afianti, E. Z., Satriawan, K., Gede, W., Yoga, S., (2019) Production productivity analysis of PT. Mr. Bakery Badung Bali Production Productivity Analysis PT. Bakery Bakery Badung Bali; Nallusamy, S., Adil Ahamed, M. A., Implementation of Lean Tools in an Automotive Industry for Productivity Enhancement - A Case Study (2017) Int. J. Eng. Res. Africa, 29, pp. 175-185; Kassami, S., Designing a generic decision-making model for supply chain planning in an uncertain environment: viability mathematical model (2022) Int. J. Ind. Eng. Prod. Res, , http://ijiepr.iust.ac.ir/article-1-1492-en.html&sw=Supply+Chain, Accessed: [Online]Available; Ramayanti, G., Sastraguntara, G., Supriyadi, S., Productivity analysis with the Objective Matrix (OMAX) method on the production floor of a beverage bottle company (2020) J. INTECH Tek. Ind. Univ. Serang Raya, 6 (1), pp. 31-38; Djatna, T., SCOR-Based Information Modeling for Managing Supply Chain Performance of Palm Oil Industry at Riau and Jambi Provinces, Indonesia (2020) Int. J Sup. Chain. Mgt, 9 (5). , http://excelingtech.co.uk/, [Online]. Available; Darmawan, M. A., Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study (2018) J. Clean. Prod, 191, pp. 273-282; Azbari, M. E., Olfat, L., Amiri, M., Soofi, J. B., A Network Data Envelopment Analysis Model for Supply Chain Performance Evaluation: Real Case of Iranian Pharmaceutical Industry (2014) Int. J. Eng. Prod. Res, 25 (2), pp. 125-137; Heizer, J., Render, B., Munson, C., (2018) Operations Management: Sustainability and Supply Chain Management, , 12th Edition. Boston (US): Pearson; Asrol, M., Marimin, M., Machfud, M., Supply Chain Performance Measurement and Improvement for Sugarcane Agroindustry Supply Chain Performance Measurement and Improvement for Sugarcane Agro-industry (2017) Int. J. Supply Chain Manag, 6, pp. 8-21; Safaie, N., Piroozfar, S., Golrizgashti, S., Identifying and Ranking Supply Chain Management Damages Using Analytic Network Process (FMCG Case Study) (2019) Int. J. Ind. Eng. Prod. Res, 30 (3), pp. 313-327; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning and Operation, , 5th ed. New York (US): Pearson; Asrol, M., Marimin, M., Machfud, M., Yani, M., Taira, E., Risk Management for Improving Supply Chain Performance of Sugarcane Agroindustry (2021) Ind. Eng. Manag. Syst, 20 (1), pp. 9-26; (2012) Supply Chain Operations Reference Model Revision 11.0, , New York (US): Supply Chain Council; Pujawan, I. N., Geraldin, L. H., House of risk: a model for proactive supply chain risk management (2009) Bus. Process Manag. J, 15 (6), pp. 953-967; Marimin, M., Safriyana, S., Evaluation of palm oil supply chain’s performance, added value, and performance improvement: A case study at X Co (2018) IOP Conf. Ser. Earth Environ. Sci, 196 (1); Lemghari, R., Okar, C., Sarsri, D., Supply Chain Performance Measurement: A Case Study about Applicability of SCOR ® Model in Automotive Industry Firm (2018) MATEC Web Conf, 200, p. 00016; Kusrini, E., Caneca, V. I., Helia, V. N., Miranda, S., Supply Chain Performance Measurement Usng Supply Chain Operation Reference (SCOR) 12.0 Model: A Case Study in A A Leather SME in Indonesia (2019) IOP Conference Series: Materials Science and Engineering, 697 (1), p. 012023; Wang, C. N., Hoang Viet, V. T., Ho, T. P., Nguyen, V. T., Nguyen, V. T., Multicriteria decision model for the selection of suppliers in the textile industry (2020) Symmetry (Basel), 12 (6), p. 979; Kasi, V., Systemic Assessment of SCOR for Modeling Supply Chains (2005) Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pp. 1-10; Palma-mendoza, J. A., Analytical hierarchy process and SCOR model to support supply chain re-design (2014) Int. J. Inf. Manage, 34 (5), pp. 634-638; David, F. R., (2011) Strategic Management; Concepts and Cases, 43 (11). , Thirteen E. Boston (US): Prentice Hall; Saaty, T., (1980) The Analytic Hierarchy Process, , New York (US): McGraw-Hill; Saaty, T. L., Decision making with the analytic hierarchy process (2008) Int. J. Serv. Sci, 1 (1), pp. 83-96. , https://doi.org/10.1504/IJSSci.2008.01759; Marimin, M., Supply chain performance measurement and improvement of palm oil agroindustry: A case study at Riau and Jambi Province (2020) IOP Conf. Ser. Earth Environ. Sci, 443 (1), p. 012056; Fonseca, L. M., Lima, V. M., Impact of Supplier Management Strategies on the Organizational Performance of ISO 9001 Certified Organizations (2015) Qual. Innov. Prosper, 19 (2), pp. 32-54. , https://doi.org/10.12776/qip.v19i2.592; Asrol, M., Marimin, M., Machfud, M., Yani, M., Method and Approach Mapping of Fair and Balanced Risk and Value-added Distribution in Supply Chains: A Review and Future Agenda (2018) Int. J. Supply Chain Manag, 7 (5), pp. 74-95. , http://excelingtech.co.uk/, [Online]. Available; Ganeshkumar, C., Pachayappan, M., Madanmohan, G., Agri-food supply chain management: literature review (2017) Intell. Inf. Manag, 9, pp. 68-96; Asrol, M., Marimin, M., Machfud, M., Yani, M., Taira, E., Supply Chain Fair Profit Allocation Based on Risk and Value Added for Sugarcane Agro-industry (2020) Oper. Supply Chain Manag, 13 (2), pp. 150-165; Saaty, T. L., Making and validating complex decisions with the AHP/ANP (2005) J. Syst. Sci. Syst. Eng, 14 (1), pp. 1-36; Pei, F., Tong, Y., He, F., Li, D., Research on design of the smart factory for forging enterprise in the industry 4.0 environment (2017) Mechanika, 23 (1), pp. 146-152; Hamid, A. A., Supply chain integration and health firms’ operational performance: Implications for underdeveloped countries (2021) Int. J. Ind. Eng. Prod. Res, 32 (1), pp. 143-157},
correspondence_address1={Asrol, M.; Industrial Engineering Department, Indonesia; email: muhammad.asrol@binus.edu},
publisher={Iran University of Science and Technology},
issn={20084889},
language={English},
abbrev_source_title={Intl. J. Indust. Eng. Prod. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Govindan2022,
author={Govindan, K. and Kannan, D. and Jørgensen, T.B. and Nielsen, T.S.},
title={Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence},
journal={Transportation Research Part E: Logistics and Transportation Review},
year={2022},
volume={164},
doi={10.1016/j.tre.2022.102725},
art_number={102725},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134488149&doi=10.1016%2fj.tre.2022.102725&partnerID=40&md5=7ed57da7e2f35decd4f9d377f3f72440},
affiliation={China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai, 201306, China; Center for Sustainable Supply Chain Engineering, Department of Technology and Innovation, University of Southern Denmark, Campusvej 55, Odense M, Denmark; Danish Institute for Advanced Study, University of Southern Denmark, Campusvej 55, Odense M, Denmark; Yonsei Frontier Lab, Yonsei University, Seoul, South Korea},
abstract={Companies require a greater understanding of the Supply Chain (SC) benefits that can be gained from industry 4.0 (I4.0) and, more specifically, which technologies and concepts that can improve certain SC performance measures. A state-of-the-art systematic literature review (SLR) has been done on supply chain performance measurement linked with various industry 4.0 technologies. Based on the findings of the review through content analysis, this paper presents a framework for exploring the usage of I4.0 technologies to identify the potential supply chain performance measures. This framework includes the dimensions of Procurement 4.0, Manufacturing 4.0, Logistics 4.0, and Warehousing 4.0. As a scientific contribution, this study has validated the proposed framework through case studies, where the existing studies are limited. Finally, several fruitful future possible extensions have been discussed based on the proposed framework. © 2022 The Author(s)},
author_keywords={Concepts;  Content analysis;  Industry 4.0;  Supply Chain 4.0;  Supply Chain Performance;  Systematic Literature Review;  Technologies},
keywords={conceptual framework;  literature review;  performance assessment;  supply chain management;  technological change;  technological development;  transportation development},
references={Abidi, H., (2014), De Leeuw, S. and Klumpp, M. Humanitarian supply chain performance management: a systematic literature review. Supply Chain Manage.: Int. J; Ada, N., Kazancoglu, Y., Sezer, M.D., Ede-Senturk, C., Ozer, I., Ram, M., Analyzing barriers of circular food supply chains and proposing industry 4.0 solutions (2021) Sustainability, 13 (12), p. 6812; Adhitya, Y., Prakosa, S.W., Köppen, M., Leu, J.S., Feature Extraction for Cocoa Bean Digital Image Classification Prediction for Smart Farming Application (2020) Agronomy, 10 (11), p. 1642; Agrawal, R., Wankhede, V.A., Kumar, A., Luthra, S., Huisingh, D., (2021), Progress and trends in integrating Industry 4.0 within Circular Economy: A comprehensive literature review and future research propositions. Business Strategy and the Environment; Aheleroff, S., Xu, X., Zhong, R.Y., Lu, Y., Digital twin as a service (DTaaS) in industry 4.0: an architecture reference model (2021) Adv. Eng. Inf., 47; Ahmed, S., Kalsoom, T., Ramzan, N., Pervez, Z., Azmat, M., Zeb, B., Ur Rehman, M., Towards supply chain visibility using internet of things: A dyadic analysis review (2021) Sensors, 21 (12), p. 4158; Ahuett-Garza, H., Kurfess, T., (2018), https://doi.org/10.1016/j.mfglet.2018.02.011, A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing. Manuf. Lett. 15, 60–63. Available at:; Albini, A., Rajnai, Z., (2018), https://doi.org/10.1016/j.promfg.2018.03.074, General Architecture of Cloud Costing models for capacity optimization in Industry 4.0: Trade-off between used capacity and operational efficiency General Architecture of Cloud. Procedia Manuf., 22, 485–490. Available at:; Andaluz, E., (2017), The Process Digital Twin: A step toward operational excellence; (2018), https://leankit.com/learn/lean/industry-four-point-0/, Anon Industry 4.0 | LeanKit. Available at: [Accessed October 10, 2018]; Aoun, A., Ilinca, A., Ghandour, M., Ibrahim, H., A review of Industry 4.0 characteristics and challenges, with potential improvements using blockchain technology (2021) Comput. Ind. Eng., 162; Asakura, T., Industrial operations supporting industry 4.0 (2016) NEC Tech. J., 11 (1), pp. 31-34; Attaran, M., (2020), 21, pp. 158-172. , July. Digital technology enablers and their implications for supply chain management. In: Supply Chain Forum: An International Journal, No. 3. Taylor & Francis; Awan, U., Sroufe, R., Shahbaz, M., Industry 4.0 and the circular economy: A literature review and recommendations for future research (2021) Bus. Strategy Environ., 30 (4), pp. 2038-2060; Bag, S., Pretorius, J.H.C., Gupta, S., Dwivedi, Y.K., Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities (2020) Technol. Forecast. Soc. Chang.; Bag, S., Gupta, S., Kumar, S., Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development (2021) Int. J. Prod. Econ., 231; Bag, S., Pretorius, J.H.C., Gupta, S., Dwivedi, Y.K., Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities (2021) Technol. Forecast. Soc. Chang., 163; Bagula, A., Ajayi, O., Maluleke, H., Cyber physical systems dependability using cps-iot monitoring (2021) Sensors, 21 (8), p. 2761; Bahrin, M.A.K., Industry 4.0: A review on industrial automation and robotic (2016) Jurnal Teknologi, 78 (6-13), pp. 137-143; Bai, H., Li, S., Shepherd, R.F., Elastomeric Haptic Devices for Virtual and Augmented Reality (2021) Adv. Funct. Mater., 31 (39), p. 2009364; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Comput. Ind., 82, pp. 135-150; Bamakan, S.M.H., Faregh, N., ZareRavasan, A., Di-ANFIS: an integrated blockchain–IoT–big data-enabled framework for evaluating service supply chain performance (2021) J. Comput. Des. Eng., 8 (2), pp. 676-690; Bamunuarachchi, D., Georgakopoulos, D., Banerjee, A., Jayaraman, P.P., Digital Twins Supporting Efficient Digital Industrial Transformation (2021) Sensors, 21 (20), p. 6829; Banomyong, R., Varadejsatitwong, P., Oloruntoba, R., A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016 (2019) Ann. Oper. Res., 283 (1-2), pp. 71-86; Barata, J., Rupino Da Cuncha, P., Stal, J., Mobile supply chain management in the Industry 4.0 era: An annotated bibliography and guide for future research (2018) J. Enterprise Inform. Manage., 31 (1), pp. 173-192; Barreto, L., Amaral, A., Pereira, T., (2017), https://doi.org/10.1016/j.promfg.2017.09.045, Industry 4.0 implications in logistics: an overview. Procedia Manuf. 13, pp.1245–1252. Available at:; Beyca, O.F., Hancerliogullari, G., Yazici, I., (2018), pp. 217-234. , Additive Manufacturing Technologies and Applications. In Industry 4.0: Managing The Digital Transformation; Bhattacharya, S., Chatterjee, A., (2021), Digital project driven supply chains: a new paradigm. Supply Chain Manage.: Int. J; Bienhaus, F., Haddud, A., Procurement 4.0: factors influencing the digitisation of procurement and supply chains (2018) Bus. Process Manage. J.; Birkel, H., Müller, J.M., Potentials of industry 4.0 for supply chain management within the triple bottom line of sustainability–A systematic literature review (2021) J. Cleaner Prod., 289; Bisio, I., Exploiting context-aware capabilities over the internet of things for industry 4.0 applications (2018) IEEE Network, 32 (3), pp. 108-114; Biswal, A.K., Jenamani, M., Kumar, S.K., Warehouse efficiency improvement using RFID in a humanitarian supply chain: Implications for Indian food security system (2018) Transport. Res. Part E: Logist. Transport. Rev., 109, pp. 205-224; Bocken, N.M.P., Sustainable business models and structures for Industry 4.0. Journal of Security and Sustainability (2016) Issues, 2 (April), pp. 1-28; Bodrow, W., Impact of Industry 4.0 in service oriented firm (2017) Adv. Manuf., 5 (4), pp. 394-400; Brandín, R., Abrishami, S., Information traceability platforms for asset data lifecycle: blockchain-based technologies (2021) Smart Sustain. Built Environ., 10 (3), pp. 364-386; Brandín, R., Abrishami, S., (2021), Information traceability platforms for asset data lifecycle: blockchain-based technologies. Smart Sustain. Built Environ; Burnham, J.F., Scopus database: A review (2006) Biomed. Digital Libraries, 3, pp. 1-8; Caggiano, A., Teti, R., Digital factory technologies for robotic automation and enhanced manufacturing cell design (2018) Cogent Eng., 5 (1), pp. 1-14; Cagliano, A.C., Mangano, G., Rafele, C., (2021), 22, pp. 100-114. , April. Determinants of digital technology adoption in supply chain. An exploratory analysis. In: Supply Chain Forum: An International Journal, No. 2 Taylor & Francis; Calabrese, A., Levialdi Ghiron, N., Tiburzi, L., ‘Evolutions’ and ‘revolutions’ in manufacturers’ implementation of industry 4.0: a literature review, a multiple case study, and a conceptual framework (2020) Production Plann. Control, pp. 1-15; Chen, T., Lin, Y.C., Feasibility Evaluation and Optimization of a Smart Manufacturing System Based on 3D Printing: A Review (2017) Int. J. Intell. Syst., 32 (4), pp. 394-413; Chhetri, S.R., Manufacturing Supply Chain and Product Lifecycle Security in the Era of Industry 4.0. Journal of Hardware and Systems (2017) Security, pp. 51-68. , http://link.springer.com/10.1007/s41635-017-0031-0, Available at:; Choi, T.-M., Blockchain-technology-supported platforms for diamond authentication and certification in luxury supply chains (2019) Transport. Res. Part E: Logist. Transport. Rev., 128, pp. 17-29; Choi, T.-M., Guo, S., Luo, S., (2020), When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management? Transport. Res. Part E: Logist. Transport. Rev. 135, art. no. 101860; Choi, T.-M., Wen, X., Sun, X., Chung, S.-H., The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era (2019) Transport. Res. Part E: Logist. Transport. Rev., 127, pp. 178-191; Chopra, S., Meindl, P., Strategy, planning, and operation (2001) Supply Chain Manage., pp. 13-17. , http://catalogue.pearsoned.ca/assets/hip/ca/hip_ca_pearsonhighered/preface/0134731883.pdf; Cifone, F.D., Hoberg, K., Holweg, M., Staudacher, A.P., ‘Lean 4.0’: How can digital technologies support lean practices? (2021) Int. J. Prod. Econ., 241; Cohen, Y., (2017), https://doi.org/10.1016/j.ifacol.2017.08.2550, Assembly system configuration through Industry 4.0 principles: Assembly the system. IFAC-PapersOnLine, 50(1), pp.14958–14963. Available at:; Consoli, R., (2018), https://www.manufacturing.net/article/2018/05/using-big-data-analytics-improve-production, Using Big Data Analytics To Improve Production. Available at: [Accessed October 17, 2018]; Contents, T.O.F., A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing (2017) Manuf. Lett., 15, pp. 1-10; Corches, C., Daraban, M., Miclea, L., Availability of an RFID object-identification system in IoT environments (2021) Sensors, 21 (18), p. 6220; Cozmiuc, D., Petrisor, I., Industrie 4.0 by Siemens: steps made next (2018) J. Cases Inform. Technol. (JCIT), 20 (1), pp. 31-45; Dafflon, B., Moalla, N., Ouzrout, Y., The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: a literature review (2021) Int. J. Adv. Manuf. Technol., 113 (7), pp. 2395-2412; Dallasega, P., Simulation Based Validation of Supply Chain Effects through ICT enabled Real-time-capability in ETO Production Planning (2017) Procedia Manuf., 11 (June), pp. 846-853; Dallasega, P., Rauch, E., Linder, C., (2018), https://doi.org/10.1016/j.compind.2018.03.039, Industry 4.0 as an enabler of proximity for construction supply chains: A systematic literature review. Comput. Ind. 99(March), pp.205–225. Available at:; Dalmarco, G., Barros, A.C., (2018), pp. 303-319. , Adoption of Industry 4.0 Technologies in Supply Chains. In Innovation and Supply Chain Management; De Vass, T., Shee, H., Miah, S.J., Iot in supply chain management: a narrative on retail sector sustainability (2021) Int. J. Logist. Res. Appl., 24 (6), pp. 605-624; (2017), https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/cip/deloitte-cn-cip-industry-4-0-digital-twin-technology-en-171215.pdf, Deloitte Industry 4.0 and the digital twin Manufacturing meets its match A Deloitte series on Industry 4.0, digital manufacturing enterprises, and digital supply networks, Available at: [Accessed October 17, 2018]; Denyer, D., Pilbeam, C., (2013), Doing a literature review in business and management, (September), pp.1–57; Denyer, D., Tranfield, D., (2009), Producing a Systematic Review. The SAGE Handbook of Organizational Research Methods, pp.671–689; Demartini, M., Tonelli, F., Govindan, K., An investigation into modelling approaches for industrial symbiosis: A literature review and research agenda (2022) Clean. Logist. Suppl. Chain, , 100020; Dev, N.K., Shankar, R., Swami, S., Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system (2020) Int. J. Prod. Econ., 223, p. 107519; Dev, N.K., Shankar, R., Zacharia, Z.G., Swami, S., Supply chain resilience for managing the ripple effect in Industry 4.0 for green product diffusion (2021) Int. J. Phys. Distrib. Logist. Manageent.; Ding, B., Pharma Industry 4.0: literature review and research opportunities in sustainable pharmaceutical supply chains (2018) Process Saf. Environ. Prot., 119, pp. 115-130. , https://www.sciencedirect.com/science/article/pii/S0957582018303720, Available at:; Ding, Y., Jin, M., Li, S., Feng, D., Smart logistics based on the internet of things technology: an overview (2021) Int. J. Logist. Res. Appl., 24 (4), pp. 323-345; Dobrescu, R., Mocanu, S., Chenaru, O., Nicolae, M., Florea, G., Versatile edge gateway for improving manufacturing supply chain management via collaborative networks (2021) Int. J. Comput. Integr. Manuf., 34 (4), pp. 407-421; Dobrowolski, Z., Internet of things and other e-solutions in supply chain management may generate threats in the energy sector—The quest for preventive measures (2021) Energies, 14 (17), p. 5381; Dossou, P.E., Nachidi, M., (2017), Modeling Supply Chain Performance. Procedia Manuf. 11(June), pp.838–845; Druehl, C., Carrillo, J., Hsuan, J., (2018), Technological Innovations: Impacts on Supply Chains, pp.259–281; Duong, L.N., Al-Fadhli, M., Jagtap, S., Bader, F., Martindale, W., Swainson, M., Paoli, A., A review of robotics and autonomous systems in the food industry: From the supply chains perspective (2020) Trends Food Sci. Technol., 106, pp. 355-364; Durão, L.F.C.S., Additive manufacturing scenarios for distributed production of spare parts (2017) Int. J. Adv. Manuf. Technol., 93 (1-4), pp. 869-880; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things (2017) Ind. Manage. Data Syst., 117 (2), pp. 267-286; Ekren, B.Y., Mangla, S.K., Turhanlar, E.E., Kazancoglu, Y., Li, G., Lateral inventory share-based models for IoT-enabled E-commerce sustainable food supply networks (2021) Comput. Oper. Res., 130; Esmaeilian, B., Sarkis, J., Lewis, K., Behdad, S., Blockchain for the future of sustainable supply chain management in Industry 4.0 (2020) Resour. Conserv. Recycl., 163; Fatorachian, H., Kazemi, H., Impact of Industry 4.0 on supply chain performance (2021) Prod. Plann. Control, 32 (1), pp. 63-81; Fernández-Caramés, T.M., A fog computing based cyber-physical system for the automation of pipe-related tasks in the industry 4.0 shipyard (2018) Sensors (Switzerland), 18 (6); Fernández-Caramés, T.M., Blanco-Novoa, O., Froiz-Míguez, I., Fraga-Lamas, P., Towards an autonomous industry 4.0 warehouse: A UAV and blockchain-based system for inventory and traceability applications in big data-driven supply chain management (2019) Sensors, 19 (10), p. 2394; (2017), https://doi.org/10.1016/j.promfg.2017.09.039, Fernández-Miranda, S.S. The challenge of integrating Industry 4.0 in the degree of Mechanical Engineering. Procedia Manuf., 13, pp.1229–1236. Available at:; Ferrari, R., Writing Narrative Style Literature Review (2015) Medical Writing, 24 (4), pp. 230-235; Ford, S., Despeisse, M., Additive manufacturing and sustainability: an exploratory study of the advantages and challenges (2016) J. Cleaner Prod., 137, pp. 1573-1587; Franco, D., Ganga, G.M.D., de Santa-Eulalia, L.A., Godinho Filho, M., Consolidated and inconclusive effects of additive manufacturing adoption: A systematic literature review (2020) Comput. Ind. Eng., 148, p. 106713; Garrido-Hidalgo, C., Olivares, T., Ramirez, F.J., Roda-Sanchez, L., An end-to-end Internet of Things solution for reverse supply chain management in industry 4.0 (2019) Comput. Ind., 112; Geissbauer, R., Weissbarth, R., Wetzstein, J., Procurement 4.0: Are you ready for the digital revolution ? (2016) Strategy, 1, pp. 1-12; Geissbauer, R., (2018), https://www.strategyand.pwc.com/industry4-0, Global Digital Operations Study 2018. Digital Champions. How industry leaders build integrated operations ecosystems to deliver end-to-end customer solutions, Available at:; Ghadimi, P., Wang, C., Lim, M.K., Heavey, C., Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains (2019) Comput. Ind. Eng., 127, pp. 588-600; Ghobakhloo, M., Industry 4.0, digitization, and opportunities for sustainability (2020) J. Cleaner Prod., 252; Ghobakhloo, M., Fathi, M., Iranmanesh, M., Maroufkhani, P., Morales, M.E., Industry 4.0 ten years on: A bibliometric and systematic review of concepts, sustainability value drivers, and success determinants (2021) J. Cleaner Prod., 302; Glas, A.H., Kleemann, F.C., The Impact of Industry 4.0 on Procurement and Supply Management: A Conceptual and Qualitative Analysis (2016) Int. J. Bus. Manage. Invent., 5 (6), pp. 55-66; Goetschalckx, M., Supply Chain Engineering (2011), Springer US; Gorecki, S., Possik, J., Zacharewicz, G., Ducq, Y., Perry, N., A multicomponent distributed framework for smart production system modeling and simulation (2020) Sustainability, 12 (17), p. 6969; Govindan, K., Cheng, T.E., Mishra, N., Shukla, N., Big data analytics and application for logistics and supply chain management (2018) Transport. Res. Part E: Logist. Transport. Rev., 114, pp. 343-349; Govindan, K., (2018), ”Defining Industry 4.0: a critical review” – Working paper; Grieco, A., An Industry 4.0 Case Study in Fashion Manufacturing (2017) Procedia Manuf., 11 (June), pp. 871-877; Gruzauskas, V., Minimizing the trade-off between sustainability and cost effective performance by using autonomous vehicles (2018) J. Cleaner Prod., 184, pp. 709-717. , http://www.sciencedirect.com/science/article/pii/S0959652618306401, Available at:; Gu, D., Shi, X., Poprawe, R., Bourell, D.L., Setchi, R., Zhu, J., Material-structure-performance integrated laser-metal additive manufacturing (2021) Science, 372 (6545), p. p.eabg1487; Gunasekaran, A., (2015), http://dx.doi.org/10.1016/j.ijpe.2014.12.021, Performance measures and metrics in outsourcing decisions: A review for research and applications. Int. J. Prod. Econ. 161, pp.153–166. Available at:; Gunduz, M.A., Demir, S., Paksoy, T., Matching functions of supply chain management with smart and sustainable Tools: A novel hybrid BWM-QFD based method (2021) Comput. Ind. Eng., 162; Guo, S., Choi, T.M., Chung, S.H., Self-design fun: Should 3D printing be employed in mass customization operations? (2022) Eur. J. Oper. Res., 299 (3), pp. 883-897; Guo, D., Li, M., Lyu, Z., Kang, K., Wu, W., Zhong, R.Y., Huang, G.Q., Synchroperation in industry 4.0 manufacturing (2021) Int. J. Prod. Econ., 238, p. 108171; Gupta, H., Kumar, S., Kusi-Sarpong, S., Jabbour, C.J.C., Agyemang, M., Enablers to supply chain performance on the basis of digitization technologies (2020) Ind. Manage. Data Syst.; Haarman, M., Mulders, M., Vassiliadis, C., Predictive maintenance 4.0: predict the unpredictable (2017) PwC Mainnovation, 4; Hahn, J., (2020), pp. 52-70. , Student Engagement and Smart Spaces: Library Browsing and Internet of Things Technology. In: Emerging Trends and Impacts of the Internet of Things in Libraries. IGI Global; Halaška, M., Šperka, R., Performance of an automated process model discovery–the logistics process of a manufacturing company (2019) Eng. Manage. Prod. Services, 11 (2), pp. 106-118; Harrison, R., Vera, D., Ahmad, B., (2016), Engineering Methods and Tools for Cyber-Physical Automation Systems, 104(5), pp.973–985; Haverkort, B.R., Zimmermann, A., Smart Industry: How ICT Will Change the Game! (2017) IEEE Internet Comput., 21 (1), pp. 8-10; He, P., Li, K., Kumar, P.R., An enhanced branch-and-price algorithm for the integrated production and transportation scheduling problem (2021) Int. J. Prod. Res., pp. 1-16; Hitmar, M., (2014), https://www.industryweek.com/IoT-boosts-quality, Six Ways the “Internet of Things” Can Boost Quality | IndustryWeek. Available at: [Accessed October 17, 2018]; Ho, G.T.S., Tang, Y.M., Tsang, K.Y., Tang, V., Chau, K.Y., A blockchain-based system to enhance aircraft parts traceability and trackability for inventory management (2021) Expert Syst. Appl., 179; Hofmann, E., Rüsch, M., (2017), http://dx.doi.org/10.1016/j.compind.2017.04.002, Industry 4.0 and the current status as well as future prospects on logistics. Comput. Ind., 89, pp.23–34. Available at:; Huan, S.H., Sheoran, S.K., Wan, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Manage., 9 (1), pp. 23-29; Ivanov, D., Dolgui, A., A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2021) Prod. Plann. Control, 32 (9), pp. 775-788; Ivanov, D., Dolgui, A., A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0 (2021) Prod. Plann. Control, 32 (9), pp. 775-788; Ivanov, D., Sokolov, B., Dolgui, A., (2020), pp. 1-9. , Introduction to scheduling in industry 4.0 and cloud manufacturing systems. In: Scheduling in Industry 4.0 and Cloud Manufacturing. Springer, Cham; Ivanov, D., (2016), http://dx.doi.org/10.1080/00207543.2014.999958, A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. Int. J. Prod. Res. 54(2), pp.386–402. Available at:; James, C.D., Mondal, S., A review of machine efficiency in mass customization (2019) Benchmarking: Int. J., 26 (2), pp. 638-691; Jermsittiparsert and Boonratanakittiphumi, The supply chain management, enterprise resource planning systems and the organizational performance of thai manufacturing firms: Does the application of industry 4.0 matter? (2019) Int. J. Innov., Creativity Change, 8 (8), pp. 82-102; Jermsittiparsert and Boonratanakittiphumi, The role of additive manufacturing and supply chain management in improving the supply chain performance: A case of industry 4.0 in disk Thailand (2019) Int. J. Innov., Creativity Change, 8 (8), pp. 103-121; Jha, A.K., Agi, M.A., Ngai, E.W., A note on big data analytics capability development in supply chain (2020) Decis. Support Syst., 138, p. 113382; Jimeno-Morenilla, A., Azariadis, P., Molina-Carmona, R., Kyratzi, S., Moulianitis, V., Technology enablers for the implementation of Industry 4.0 to traditional manufacturing sectors: A review (2021) Comput. Ind., 125; Kaivo-oja, J., Kuusi, O., Knudsen, M.S., Lauraéus, I.T., Digital twin: current shifts and their future implications in the conditions of technological disruption (2020) Int. J. Web Eng. Technol., 15 (2), pp. 170-188; Kalsoom, T., Ramzan, N., Ahmed, S., Ur-Rehman, M., Advances in Sensor Technologies in the Era of Smart Factory and Industry 4.0 (2020) Sensors, 20 (23), p. 6783; Kamble, S.S., Gunasekaran, A., Big data-driven supply chain performance measurement system: a review and framework for implementation (2020) Int. J. Prod. Res., 58 (1), pp. 65-86; Kamble, S.S., Gunasekaran, A., Gawankar, S.A., Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives (2018) Process Saf. Environ. Prot., 117, pp. 408-425; Kang, H.S., Smart manufacturing: Past research, present findings, and future directions (2016) Int. J. Precision Eng. Manuf. - Green Technol., 3 (1), pp. 111-128; Karlsson, C., (2016), Research Methods for Operations Management Second Edi., New York; Kaur, J., Kaur, P.D., (2018), Electronic Commerce Research and Applications CE-GMS : A cloud IoT-enabled grocery management system, 28, pp.63–72; Kazancoglu, Y., Ozkan-Ozen, Y.D., Sagnak, M., Kazancoglu, I., Dora, M., (2021), Framework for a sustainable supply chain to overcome risks in transition to a circular economy through Industry 4.0. Prod. Plann. Control, pp.1-16; Kerin, M., Pham, D.T., A review of emerging industry 4.0 technologies in remanufacturing (2019) J. Cleaner Prod., 237; Khan, P.W., Byun, Y.C., Park, N., IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning (2020) Sensors, 20 (10), p. 2990; Kilubi, I., Haasis, H.D., Supply chain risk management research: avenues for further studies (2016) Int. J. Supply Chain Operations Resilience, 2 (1), p. 51. , http://www.inderscience.com/link.php?id=75899, Available at:; Kitzinger, J., Qualitative Research: Introducing focus groups (1995) BMJ, 311 (7000), p. 299; Koh, L., Orzes, G., Jia, F.J., The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management (2019) Int. J. Oper. Prod. Manage., 39 (6/7/8), pp. 817-828; Kouhizadeh, M., Saberi, S., Sarkis, J., Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers (2021) Int. J. Prod. Econ., 231; Kovacs, G., Kot, S., New Logistics and Production Trends As the Effect of Global Economy Changes (2016) Polish J. Manage. Stud., 14 (2), pp. 115-126. , http://pjms.zim.pcz.pl/files/New-Logistics-and-Production-Trends-as-the-Effect-of-Global-Economy-Changes.pdf, Available at:; Kriebitz, A., Lütge, C., Artificial intelligence and human rights: a business ethical assessment (2020) Bus. Human Rights J., 5 (1), pp. 84-104; Krugh, M., Measurement of Operator-machine Interaction on a Chaku-chaku Assembly Line (2017) Procedia Manuf., 10, pp. 123-135; Kusiak, A., (2018), https://doi.org/10.1080/00207543.2017.1351644, Smart manufacturing. Int. J. Prod. Res. 56(1–2), pp.508–517. Available at:; Kuznaz, T., Pfohl, H.-C., Yahsi, B., The impact of Industry 4.0 on the Supply Chain (2015) Innovations and Strategies for Logistics and Supply Chains, pp. 32-58; Lallie, H.S., Shepherd, L.A., Nurse, J.R., Erola, A., Epiphaniou, G., Maple, C., Bellekens, X., Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic (2021) Comput. Security, 105; Lebas, M.J., Performance measurement and performance management (1995) Int. J. Prod. Econ., 41 (1-3), pp. 23-35; Lee, C., Lim, C., From technological development to social advance: A review of Industry 4.0 through machine learning (2021) Technol. Forecast. Soc. Chang., 167; Lehyani, F., Zouari, A., Ghorbel, A., Tollenaere, M., Defining and Measuring Supply Chain Performance: A Systematic Literature Review (2021) Eng. Manage. J., 33 (4), pp. 283-313; Lerher, T., Warehousing 4.0 by using shuttlebased storage and retrieval systems (2018) FME Trans., 46 (3), pp. 381-385; Li, D., A big data enabled load-balancing control for smart manufacturing of Industry 4.0 (2017) Cluster Comput., 20 (2), pp. 1855-1864; Li, T.H.S., A Three-Dimensional Adaptive PSO-Based Packing Algorithm for an IoT-Based Automated e-Fulfillment Packaging System (2017) IEEE Access, 5, pp. 9188-9205; Li, X., A review of industrial wireless networks in the context of Industry 4.0 (2017) Wireless Netw., 23 (1), pp. 23-41; Li, Y., Dai, J., Cui, L., The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model (2020) Int. J. Prod. Econ.; Li, L., (2017), http://dx.doi.org/10.1016/j.apm.2017.03.031, An improved stochastic programming model for supply chain planning of MRO spare parts. Appl. Math. Modell., 47, pp.189–207. Available at:; Li, G., Hou, Y., Wu, A., Fourth Industrial Revolution: technological drivers, impacts and coping methods (2017) Chinese Geograph. Sci., 27 (4), pp. 626-637; Lianguang, M., Study on supply-chain of modern agricultural products based on IOT in order to guarantee the quality and safety (2014) Adv. J. Food Sci. Technol., 6 (4), pp. 520-526; Lim, C.H., Lim, S., How, B.S., Ng, W.P.Q., Ngan, S.L., Leong, W.D., Lam, H.L., A review of industry 4.0 revolution potential in a sustainable and renewable palm oil industry: HAZOP approach (2021) Renew. Sustain. Energy Rev., 135; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: a literature review (2017) Comput. Ind. Eng., 113, pp. 333-346; Lin, K.C., Shyu, J.Z., Ding, K., A cross-strait comparison of innovation policy under industry 4.0 and sustainability development transition (2017) Sustainability (Switzerland), 9 (5); Liu, B., De Giovanni, P., Green process innovation through Industry 4.0 technologies and supply chain coordination (2019) Ann. Oper. Res., pp. 1-36; Long, F., Zeiler, P., Bertsche, B., (2018), Realistic modelling of flexibility and dependence in production systems in Industry 4.0 for analysing their productivity and availability. Proc. Instit. Mech. Eng., Part O: J. Risk Reliab. 232(2), pp.174–184; Lu, Y., (2017), http://dx.doi.org/10.1016/j.jii.2017.04.005, Industry 4.0: A survey on technologies, applications and open research issues. J. Ind. Inform. Integration 6, pp.1–10. Available at:; Lugaresi, G., Matta, A., Automated manufacturing system discovery and digital twin generation (2021) J. Manuf. Syst., 59, pp. 51-66; Lyu, M., Biennier, F., Ghodous, P., Integration of ontologies to support Control as a Service in an Industry 4.0 context (2021) SOCA, 15 (2), pp. 127-140; Ma, J., Wang, Q., Zhao, Z., SLAE–CPS: Smart lean automation engine enabled by cyber-physical systems technologies (2017) Sensors (Switzerland), 17 (7); Maestrini, V., (2017), http://dx.doi.org/10.1016/j.ijpe.2016.11.005, Supply chain performance measurement systems: A systematic review and research agenda. Int. J. Prod. Econ. 183(November 2016), pp.299–315. Available at:; Mahroof, K., A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse (2019) Int. J. Inf. Manage., 45, pp. 176-190; Majeed, M.A.A., Rupasinghe, T.D., Internet of things (IoT) embedded future supply chains for industry 4.0: An assessment from an ERP-based fashion apparel and footwear industry (2017) Int. J. Supply Chain Manage., 6 (1), pp. 25-40. , https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016560680&partnerID=40&md5=3777467dbbf669c2f570e02870f3394e, Available at:; Manyika, J., Disruptive technologies: Advances that will transform life, business, and the global economy (2013) McKinsey Global Insitute, May, p. 163. , http://www.mckinsey.com/insights/business_technology/disruptive_technologies%5Cnhttp://www.chrysalixevc.com/pdfs/mckinsey_may2013.pdf, Available at:; Maslarić, M., Nikoličić, S., Mirčetić, D., Logistics Response to the Industry 4.0: The Physical Internet. Open (2016) Engineering, 6 (1), pp. 511-517; Mastos, T.D., Nizamis, A., Vafeiadis, T., Alexopoulos, N., Ntinas, C., Gkortzis, D., Papadopoulos, A., Tzovaras, D., Industry 4.0 sustainable supply chains: An application of an IoT enabled scrap metal management solution (2020) J. Cleaner Prod.; Moghaddam, M., Nof, S.Y., Collaborative service-component integration in cloud manufacturing (2018) Int. J. Prod. Res., 56 (1-2), pp. 677-691; Molka-Danielsen, J., Engelseth, P., Wang, H., (2018), https://doi.org/10.1016/j.jii.2018.02.001, Large scale integration of wireless sensor network technologies for air quality monitoring at a logistics shipping base. J. Ind. Inform. Integration, 10(November 2017), pp.20–28. Available at:; Moradlou, H., (2018), Reshoring and additive manufacturing. 7(3), pp.241–263; Moreira, A.C., Ferreira, L.M., Zimmermann, R., (2018), https://books.google.nl/books?id=wvhYDwAAQBAJ&pg=PA324&lpg=PA324&dq=The+main+benefit+of+blockchain+is+that+it+allows+significant+simplification+of+interaction+and+reduces+the+noise+in+communication+between+agents,+thus+allowing+them+to+communicate+directl, Innovation and Supply Chain Management: Relationship, Collaboration and Strategies, Available at:; Morella, P., Lambán, M.P., Royo, J., Sánchez, J.C., Ng Corrales, L.D.C., Development of a new green indicator and its implementation in a cyber–physical system for a green supply chain (2020) Sustainability, 12 (20), p. 8629; Mostafa, N., Hamdy, W., Alawady, H., Impacts of Internet of Things on supply chains: A framework for warehousing (2019) Social Sci., 8 (3), p. 84; Mrugalska, B., Ahmed, J., Organizational agility in industry 4.0: A systematic literature review (2021) Sustainability, 13 (15), p. 8272; Müller, C., Grunewald, M., Spengler, T.S., Redundant configuration of automated flow lines based on “Industry 4.0”-technologies. Journal of (2017) Bus. Econ., 87 (7), pp. 877-898; Murmura, F., Bravi, L., Additive manufacturing in the wood-furniture sector: Sustainability of the technology, benefits and limitations of adoption (2018) J. Manuf. Technol. Manage., 29 (2), pp. 350-371; Mustafee, N., Katsaliaki, K., Taylor, S.J., Distributed Approaches to Supply Chain Simulation: A Review (2021) ACM Trans. Model. Comput. Simul. (TOMACS), 31 (4), pp. 1-31; Nandi, S., Hervani, A.A., Helms, M.M., Sarkis, J., Conceptualising Circular economy performance with non-traditional valuation methods: Lessons for a post-Pandemic recovery (2021) Int. J. Logist. Res. Appl., pp. 1-21; Nascimento, D.L.M., Alencastro, V., Quelhas, O.L.G., Caiado, R.G.G., Garza-Reyes, J.A., Rocha-Lona, L., Tortorella, G., Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal (2018) J. Manuf. Technol. Manage., 30 (3), pp. 607-627; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) Int. J. Oper. Prod. Manag., 15 (4), pp. 80-116; Nia, A.R., Awasthi, A., Bhuiyan, N., Industry 4.0 and demand forecasting of the energy supply chain: A literature review (2021) Comput. Ind. Eng., 154; Nica, E., Cyber-physical production networks and advanced digitalization in Industry 4.0 manufacturing systems: Sustainable supply chain management, organizational resilience, and data-driven innovation. Journal of Self-Governance and Management (2019) Economics, 7 (3), pp. 27-33; Novak, A., Bennett, D., Kliestik, T., Product decision-making information systems, real-time sensor networks, and artificial intelligence-driven big data analytics in sustainable Industry 4.0. Economics (2021) Manage. Financial Markets, 16 (2), pp. 62-72; Oesterreich, T.D., Teuteberg, F., Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry (2016) Comput. Ind., 83, pp. 121-139; Oh, A.S., Development of a Smart Supply-Chain Management Solution Based on Logistics Standards Utilizing Artificial Intelligence and the Internet of Things (2019) J. Inform. Commun. Convergence Eng., 17 (3), pp. 198-204; Oku, K., Okada, K., Miyatsuji, H., NEC industrial IoT - Building the foundation for next-generation monozukuri (2015) NEC Tech. J., 10 (1), pp. 23-27; Oncioiu, I., Bunget, O.C., Türkeș, M.C., Căpușneanu, S., Topor, D.I., Tamaș, A.S., Rakoș, I.S., Hint, M., The impact of big data analytics on company performance in supply chain management (2019) Sustainability, 11 (18), p. 4864; Oztemel, E., Gursev, S., Literature review of Industry 4.0 and related technologies (2020) J. Intell. Manuf., 31 (1), pp. 127-182; Pagoropoulos, A., Maier, A., McAloone, T.C., Assessing transformational change from institutionalising digital capabilities on implementation and development of Product-Service Systems: Learnings from the maritime industry (2017) J. Cleaner Prod., 166, pp. 369-380; Pandey, S., Singh, R.K., Gunasekaran, A., Kaushik, A., Cyber security risks in globalized supply chains: conceptual framework (2020) J. Global Oper. Strategic Sourcing; Piccarozzi, M., Aquilani, B., (2018), Industry 4. 0 in Management Studies: A Systematic Literature Review. pp.1–24; Pivoto, D.G., de Almeida, L.F., da Rosa Righi, R., Rodrigues, J.J., Lugli, A.B., Alberti, A.M., Cyber-physical systems architectures for industrial internet of things applications in Industry 4.0: A literature review (2021) J. Manuf. Syst., 58, pp. 176-192; Pyun, J., Rha, J.S., Review of Research on Digital Supply Chain Management Using Network Text Analysis (2021) Sustainability, 13 (17), p. 9929; Qi, Q., Tao, F., Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison (2018) IEEE Access, 6, pp. 3585-3593; Radanliev, P., De Roure, D., Page, K., Nurse, J.R., Mantilla Montalvo, R., Santos, O., Maddox, L.T., Burnap, P., Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains (2020) Cybersecurity, 3, pp. 1-21; Rahman, M., Kamal, M.M., Aydin, E., Haque, A.U., Impact of Industry 4.0 drivers on the performance of the service sector: comparative study of cargo logistic firms in developed and developing regions (2022) Prod. Plann. Control, 33 (2-3), pp. 228-243; Rai, R., Tiwari, M.K., Ivanov, D., Dolgui, A., Machine learning in manufacturing and industry 4.0 applications (2021) Int. J. Prod. Res., 59 (16), pp. 4773-4778; Raji, I.O., Shevtshenko, E., Rossi, T., Strozzi, F., (2021), October. Modelling the relationship of digital technologies with lean and agile strategies. In Supply Chain Forum: An International Journal (Vol. 22, No. 4, pp. 323-346). Taylor & Francis; Rajput, S., Singh, S.P., Industry 4.0 model for integrated circular economy-reverse logistics network (2021) Int. J. Logist. Res. Appl., pp. 1-41; Rakyta, M., Proactive approach to smart maintenance and logistics as a auxiliary and service processes in a company (2016) J. Appl. Eng. Sci., 14 (4), pp. 433-442; Ranjan, J., Foropon, C., Big data analytics in building the competitive intelligence of organizations (2021) Int. J. Inf. Manage., 56; Ratna, V.V., Conceptualizing Internet of Things (IoT) model for improving customer experience in the retail industry (2020) Int. J. Manage., 11 (5); Reddy, G.R.K., Singh, H., Hariharan, S., Supply chain wide transformation of traditional industry to industry 4.0 (2016) ARPN J. Eng. Appl. Sci., 11 (18), pp. 11089-11097; Reed, J., (2018), The Industry 4.0 revolution. DAIRY INDUSTRIES international, (May), pp.40–42; Reitze, A., Roadmap for a Smart Factory: A Modular, Intelligent Concept for the Production of Specialty Chemicals (2018) Angewandte Chemie - International Edition, 57 (16), pp. 4242-4247; Rejeb, A., Keogh, J.G., Wamba, S.F., Treiblmaier, H., The potentials of augmented reality in supply chain management: a state-of-the-art review (2020) Manage. Rev. Quart., pp. 1-38; Rejeb, A., Keogh, J.G., Wamba, S.F., Treiblmaier, H., The potentials of augmented reality in supply chain management: A state-of-the-art review (2021) Manage. Rev. Quart., 71 (4), pp. 819-856; Reyes, M., Meier, R., Pereira, S., Silva, C.A., Dahlweid, F.M., Tengg-Kobligk, H.V., Summers, R.M., Wiest, R., On the interpretability of artificial intelligence in radiology: challenges and opportunities (2020) Radiol.: Artificial Intell., 2 (3), p. e190043; Rodič, B., Industry 4.0 and the New Simulation Modelling Paradigm (2017) Organizacija, 50 (3), pp. 193-207; Rødseth, H., Schjølberg, P., Marhaug, A., (2017), https://doi.org/10.1007/s40436-017-0202-9, Deep digital maintenance. Adv. Manuf. 5(4), pp.299–310. Available at:; Romero-Silva, R., De Leeuw, S., Learning from the past to shape the future: A comprehensive text mining analysis of OR/MS reviews (2021) Omega, 100; Ruppert, T., Abonyi, J., Software Sensor for Activity-Time Monitoring and Fault Detection in Production Lines (2018) Sensors, 18 (7), p. 2346. , http://www.mdpi.com/1424-8220/18/7/2346, Available at:; Sachdeva, N., Shrivastava, A.K., Chauhan, A., Modeling supplier selection in the era of Industry 4.0 (2021) Benchmarking: Int. J., 28 (5), pp. 1809-1836; Sanders, N.R., Boone, T., Ganeshan, R., Wood, J.D., Sustainable supply chains in the age of AI and digitization: research challenges and opportunities (2019) J. Bus. Logist., 40 (3), pp. 229-240; Sartal, A., Bellas, R., Mejías, A.M., García-Collado, A., (2020), The sustainable manufacturing concept, evolution and opportunities within Industry 4.0: A literature review. Adv. Mech. Eng. 12(5), p.1687814020925232; Saunders, M., Lewis, P., Thornhill, A., (2007), Research Methods for Business Students 5th ed; Sawangwong, A., Chaopaisarn, P., The impact of applying knowledge in the technological pillars of Industry 4.0 on supply chain performance (2021) Kybernetes.; Schniederjans, D.G., Curado, C., Khalajhedayati, M., Supply chain digitisation trends: An integration of knowledge management (2020) Int. J. Prod. Econ., 220; Schüter, F., Hetterscheid, E., Supply Chain Process Oriented Supply Chain Process Oriented Technology-Framwork for Industry 4.0 (2017) Proceeding of the Hamburg International Conference of Logistics (HICL); Schütze, A., Helwig, N., Schneider, T., Sensors 4.0 - Smart sensors and measurement technology enable Industry 4.0 (2018) J. Sens. Sens. Syst., 7 (1), pp. 359-371; Schwab, K., (2016), The Fourth Industrial Revolution. World Economic Forum; Serrano-Ruiz, J.C., Mula, J., Poler, R., Smart Master Production Schedule for the Supply Chain: A Conceptual Framework (2021) Computers, 10 (12), p. 156; Seuring, S., (2005), Conducting a literature review-The example of sustainability in supply chains. Research Methodologies in Supply Chain Management: In Collaboration with Magnus Westhaus, pp.91–106; Seyedghorban, Z., Tahernejad, H., Meriton, R., Graham, G., Supply chain digitalization: past, present and future (2020) Prod. Plann. Control, 31 (2-3), pp. 96-114; Shayganmehr, M., Gupta, S., Laguir, I., Stekelorum, R., Kumar, A., (2021), Assessing the role of industry 4.0 for enhancing swift trust and coordination in humanitarian supply chain. Ann. Oper. Res. pp.1-33; Shen, B., Dong, C., Ng, C.T., Technology-Driven Supply Chain Management with OR Applications in Industrial 4.0 Era (2021) Asia-Pacific J. Operational Res., p. 2102003; Shukri, S.E., Al-Sayyed, R., Hudaib, A., Mirjalili, S., Enhanced multi-verse optimizer for task scheduling in cloud computing environments (2021) Expert Syst. Appl., 168; Siafara, L.C., SAMBA – an architecture for adaptive cognitive control of distributed Cyber-Physical Production Systems based on its self-awareness (2018) Elektrotechnik und Informationstechnik, 135 (3), pp. 270-277. , https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048000269&doi=10.1007%2Fs00502-018-0614-7&partnerID=40&md5=f457c9ba346e6dc0dbc11d4d303ac1c2, Available at:; Sim, S., Cho, M., Convergence model of AI and IoT for virus disease control system (2021) Pers. Ubiquit. Comput., pp. 1-11; Simchi-Levi, D., Wu, M.X., Powering retailers’ digitization through analytics and automation (2018) Int. J. Prod. Res., 56 (1-2), pp. 809-816; Spieske, A., Birkel, H., (2021), Improving supply chain resilience through industry 4.0: a systematic literature review under the impressions of the COVID-19 pandemic. Comput. Ind. Eng. 158, p.107452; (2016), http://dx.doi.org/10.1080/00207543.2016.1192302, Srai, J.S. Distributed manufacturing: scope, challenges and opportunities. Int. J. Prod. Res. 54(23), pp.6917–6935. Available at:; Stanisławski, R., Szymonik, A., Impact of Selected Intelligent Systems in Logistics on the Creation of a Sustainable Market Position of Manufacturing Companies in Poland in the Context of Industry 4.0 (2021) Sustainability, 13 (7), p. 3996; (2015), http://dx.doi.org/10.1080/00207543.2015.1030466, Staudt, F.H. Warehouse performance measurement: A literature review. Int. J. Prod. Res. 53(18), pp.5524–5544. Available at:; Steinhaeusser, T., Reinhart, G., (2017), http://dx.doi.org/10.1016/j.procir.2016.11.198, Ensuring Time-saving and Effective Production Planning by Prioritizing Activities based on Company-specific Validation Success Rates. Procedia CIRP, 61, pp.505–510. Available at:; Stock, R.J., Boyer, L.S., Developing a consensus definition of supply chain management: a qualitative study (2009) Int. J. Phys. Distrib. Logist. Manage., 39 (8), pp. 690-711; Strandhagen, J.O., Logistics 4.0 and emerging sustainable business models (2017) Adv. Manuf.cturing, 5 (4), pp. 359-369; (2017), https://doi.org/10.1007/s40436-017-0200-y, Strandhagen, J.W. The fit of Industry 4.0 applications in manufacturing logistics: a multiple case study. Adv. Manuf. 5(4), pp.344–358. Available at:; Strozzi, F., (2017), https://doi.org/10.1080/00207543.2017.1326643, Literature review on the ‘smart factory’ concept using bibliometric tools. Int. J. Prod. Res. 55(22), pp.1–20. Available at:; Subramanian, N., Abdulrahman, M.D., Zhou, X., Integration of logistics and cloud computing service providers: Cost and green benefits in the Chinese context (2014) Transport. Res. Part E: Logist. Transport. Rev., 70 (1), pp. 86-98; Sun, L., Wang, Y., Hua, G., Cheng, T.C.E., Dong, J., Virgin or recycled? Optimal pricing of 3D printing platform and material suppliers in a closed-loop competitive circular supply chain (2020) Resour. Conserv. Recycl., 162; Sundarakani, B., Kamran, R., Maheshwari, P., Jain, V., Designing a hybrid cloud for a supply chain network of Industry 4.0: a theoretical framework (2019) Benchmarking: Int. J.; Tang, H., CASOA: An Architecture for Agent-Based Manufacturing System in the Context of Industry 4.0 (2017) IEEE Access, 6, pp. 12746-12754; Tang, D., Using autonomous intelligence to build a smart shop floor (2018) Int. J. Adv. Manuf. Technol., 94 (5-8), pp. 1597-1606; Tang, C.S., Veelenturf, L.P., The strategic role of logistics in the industry 4.0 era (2019) Transport. Res. Part E: Logist. Transport. Rev., 129, pp. 1-11; Thiebes, S., Lins, S., Sunyaev, A., Trustworthy artificial intelligence (2021) Electronic Markets, 31 (2), pp. 447-464; Thoben, K.-D., Industrie 4.0 and Smart Manufacturing - A review of research issues and application examples (2017) Article in Int. J. Automation Technol., , https://www.researchgate.net/profile/Thorsten_Wuest/publication/312069858_Industrie_40_and_Smart_Manufacturing_-_A_Review_of_Research_Issues_and_Application_Examples/links/586e0bb108aebf17d3a73562.pdf; Tibazarwa, A., Strategic Integration for Hardware and Software Convergence Complexity (2021) IEEE Eng. Manage. Rev., 49 (3), pp. 92-102; Tjahjono, B., Espluges, C., (2017), https://doi.org/10.1016/j.promfg.2017.09.193, What doesIndustry 4.0 mean to Supply Chain? Procedia Manuf., 13, pp.1183–1190. Available at:; Tjahjono, B., Esplugues, C., (2017), https://doi.org/10.1016/j.promfg.2017.09.191, What does Industry 4.0 mean to Supply Chain? Procedia Manuf., 13, pp.1175–1182. Available at:; Toka, A., (2013), Cloud Computing in Supply Chain Management: An Overview. E-Logistics and E-Supply Chain Management, (January), pp.218–231; Tran-Dang, H., Kim, D.S., The Physical Internet in the Era of Digital Transformation: Perspectives and Open Issues (2021) IEEE Access, 9, pp. 164613-164631; Tranfield, D., Denyer, D., Smart, P., Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review (2003) Br. J. Manag., 14 (3), pp. 207-222. , http://doi.wiley.com/10.1111/1467-8551.00375, Available at:; Trappey, A.J.C., Trappey, C.V., (2017), http://dx.doi.org/10.1016/j.aei.2016.11.007, Hareesh Govindarajan, U., A review of essential standards and patent landscapes for the Internet of Things: A key enabler for Industry 4.0. Adv. Eng. Inform. 33, 208–229. Available at:; Trappey, A.J.C., Trappey, C.V., Fan, C.Y., IoT patent roadmap for smart logistic service provision in the context of Industry 4.0. Journal of the Chinese Institute of Engineers (2017) Trans. Chinese Instit. Eng., Series A/Chung-kuo Kung Ch'eng Hsuch K'an, 40 (7), pp. 593-602; Trstenjak, M., Cosic, P., Process Planning in Industry 4.0 Environment (2017) Procedia Manuf., 11 (June), pp. 1744-1750; Tsuchiya, A., http://www.jiem.org/index.php/jiem/article/view/2534, 2018a. Software defined networking firewall for industry 4.0 manufacturing systems. J. Ind. Eng. Manage. 11(2), p.318. Available at:; Tsuchiya, A., 2018b. Software defined networking firewall for industry 4.0 manufacturing systems. J. Ind. Eng. Manage. 11(2), p.318; Tupa, J., Simota, J., Steiner, F., Aspects of Risk Management Implementation for Industry 4.0 (2017) Procedia Manuf., 11 (June), pp. 1223-1230; Tuptuk, N., Hailes, S., Security of smart manufacturing systems (2018) J. Manuf. Syst., 47 (February), pp. 93-106; Tuzkaya, U.R., A Single Side Priority Based GA Approach for 3D Printing Center Integration to Spare Part Supply Chain in Automotive Industry (2021) Tehnički vjesnik, 28 (3), pp. 836-844; Ud Din, F., Paul, D., Ryan, J., Henskens, F., Wallis, M., AOSR 2.0: A Novel Approach and Thorough Validation of an Agent-Oriented Storage and Retrieval WMS Planner for SMEs, under Industry 4.0 (2021) Future Internet, 13 (6), p. 155; Ustundag, A., Tanyas, M., The impacts of Radio Frequency Identification (RFID) technology on supply chain costs (2009) Transport. Res. Part E: Logist. Transport. Rev., 45 (1), pp. 29-38; Vanderroost, M., Ragaert, P., Verwaeren, J., (2017), http://dx.doi.org/10.1016/j.compind.2017.02.002, De Meulenaer, B., The digitization of a food package's life cycle: Existing and emerging computer systems in the logistics and post-logistics phase. Comput. Ind. 87, pp.15–30. Available at:; Vanderroost, M., Ragaert, P., Verwaeren, J., Meulenaer, B., (2017), http://dx.doi.org/10.1016/j.compind.2017.02.002, De, Computers in Industry The digitization of a food package ’ s life cycle: Existing and emerging computer systems in the pre-logistics phase. Comput. Ind., 87, pp.1–14. Available at:; Wagire, A.A., Rathore, A.P.S., Jain, R., Analysis and synthesis of Industry 4.0 research landscape (2019) J. Manuf. Technol. Manage.; (2017), http://dx.doi.org/10.1016/j.promfg.2017.02.094, Waibel, M.W. Investigating the Effects of Smart Production Systems on Sustainability Elements. Procedia Manuf. 8(October 2016), pp.731–737. Available at:; Wang, S., Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination (2016) Comput. Netw., 101, pp. 158-168. , https://ac.els-cdn.com/S1389128615005046/1-s2.0-S1389128615005046-main.pdf?_tid=3c0e75f6-ce9b-11e7-805c-00000aacb361&acdnat=1511255358_ea06e74d398925ac317a9a5de38adbb7, Available at:; Wang, Y., (2017), https://doi.org/10.1007/s40436-017-0204-7, Industry 4.0: a way from mass customization to mass personalization production. Adv. Manuf. 5(4), 311–320. Available at:; Wang, J., Lim, M.K., Zhan, Y., Wang, X., (2020), An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Transport. Res. Part E: Logist. Transport. Rev., 135, art. no. 101886; Wang, C., Jiang, P., Lu, T., Production events graphical deduction model enabled real-time production control system for smart job shop (2017) Proc. Instit. Mech. Eng., Part C: J. Mech. Eng. Sci., 232 (28), pp. 2803-2820; Weyer, S., Schmitt, M., Ohmer, M., Gorecky, D., (2015), http://dx.doi.org/10.1016/j.ifacol.2015.06.143, Towards industry 4.0 - Standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC-PapersOnLine, 28(3), pp.579–584. Available at:; Weyer, S., Schmitt, M., Ohmer, M., Gorecky, D., (2015), http://dx.doi.org/10.1016/j.ifacol.2015.06.143, Standardization as as the the crucial crucial challenge challenge Towards Standardization as the crucial challenge for highly production systems for highly modular, multi-vendor production systems for highly modular, multi-vendor production. IFAC-PapersOnLine, 48(3), pp.579–584. Available at:; Xue, L., Ray, G., Sambamurthy, V., The impact of supply-side electronic integration on customer service performance (2013) J. Oper. Manage., 31 (6), pp. 363-375; Xue, L., Zhang, C., Risk Mitigation in Supply Chain Digitization: System Modularity and Information Technology Governance (2013) J. Manage. Inform. Syst.ms, 30 (1), pp. 325-352. , http://www.tandfonline.com/doi/full/10.2753/MIS0742-1222300110, Available at:; Yan, J., Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance (2017) IEEE Access, 5, pp. 23484-23491; Yavas, V., (2020), Ozkan-Ozen, Y.D. Logistics centers in the new industrial era: A proposed framework for logistics center 4.0. Transport. Res. Part E: Logist. Transport. Rev., 135, art. no. 101864; Zahedi, A., Salehi-Amiri, A., Smith, N.R., Hajiaghaei-Keshteli, M., Utilizing IoT to design a relief supply chain network for the SARS-COV-2 pandemic (2021) Appl. Soft Comput., 104; Zheng, T., Ardolino, M., Bacchetti, A., Perona, M., Zanardini, M., The impacts of Industry 4.0: a descriptive survey in the Italian manufacturing sector (2020) J. Manuf. Technol. Manage., 31 (5), pp. 1085-1115; Zheng, T., Ardolino, M., Bacchetti, A., Perona, M., The applications of Industry 4.0 technologies in manufacturing context: a systematic literature review (2021) Int. J. Prod. Res., 59 (6), pp. 1922-1954; Zheng, M., Wu, K., Smart spare parts management systems in semiconductor manufacturing (2017) Ind. Manage. Data Syst., 117 (4), pp. 754-763; Zhong, R.Y., Intelligent Manufacturing in the Context of Industry 4.0: A Review (2017) Engineering, 3, pp. 616-630; Zhu, Q., Sarkis, J., Lai, K.-H., Supply chain-based barriers for truck-engine remanufacturing in China (2014) Transport. Res. Part E: Logist. Transport. Rev., 68, pp. 103-117; Zonta, T., da Costa, C.A., da Rosa Righi, R., de Lima, M.J., da Trindade, E.S., Li, G.P., Predictive maintenance in the Industry 4.0: A systematic literature review (2020) Comput. Ind. Eng., 150, p. 106889},
correspondence_address1={Kannan, D.; Center for Sustainable Supply Chain Engineering, Campusvej 55, Denmark; email: deka@iti.sdu.dk},
publisher={Elsevier Ltd},
issn={13665545},
language={English},
abbrev_source_title={Transp. Res. Part E Logist. Transp. Rev.},
document_type={Article},
source={Scopus},
}

@BOOK{Girardi2022137,
author={Girardi, A.},
title={Performance Measurement Systems Threatened by Pandemic Opportunities in Retail: How Managers Struggled to Balance Growing Sales With Unexpectedly Inadequate Supply Chain KPIs},
journal={Handbook of Research on Digital Innovation and Networking in Post-COVID-19 Organizations},
year={2022},
pages={137-151},
doi={10.4018/978-1-6684-6762-6.ch007},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137095757&doi=10.4018%2f978-1-6684-6762-6.ch007&partnerID=40&md5=e553211ab35e4ea86fbbc84b12598b6e},
affiliation={University of Modena and Reggio Emilia, Italy},
abstract={Performance measurement systems such as KPIs are necessary managerial tools to set targets and monitor performances. During the outbreak of COVID-19, some companies experienced threats to traditional and consolidated business lines. The retail sector saw no decline in turnover. The aim of the chapter is to uncover and analyse the challenges imposed by COVID-19 to the retail sector as well as possible solutions implemented in order to overcome such difficulties. The chapter reports the case study of the largest retailer in Italy during the COVID-19 pandemic. Although seeing sales increase, the managers had many difficulties on the side of procurement and distribution of goods. The performance monitoring system was suddenly insufficient to manage the emergency. The solutions concerned the adjustment of the algorithms with dynamic components addition to KPIs and the conversion of some efficiency targets into effective ones. The chapter suggests insights on how performance measurement systems can be designed and developed in advance to allow more dynamism during emergencies. © 2022 by IGI Global. All rights reserved.},
references={Aberbach, J.D., Rockman, B.A., Conducting and Coding Elite Interviews (2002) PS, Political Science & Politics, 35 (4), pp. 673-676; Aguinis, H., Burgi-Tian, J., Talent management challenges during COVID-19 and beyond: Performance management to the rescue (2021) BRQ Business Research Quarterly, 24 (3), pp. 233-240; Almeida, A., Azevedo, A., A multi-perspective performance approach for complex manufacturing environments (2016) Journal of Innovation Management, 4 (2), pp. 125-155; Barykin, S.Y., Kapustina, I.V., Kalinina, O.V., Dubolazov, V.A., Esquivel, C.A.N., Alyarovna, N.E., Sharapaev, P., The sharing economy and digital logistics in retail chains: Opportunities and threats (2021) Academy of Strategic Management Journal, 20 (2), pp. 1-14; Bazeley, P., (2014) Qualitative data analysis with nvivo + the coding manual for qualitative researchers, , Sage Publications; Burgos, D., Ivanov, D., Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions (2021) Transportation Research Part E, Logistics and Transportation Review, 152, pp. 1-20. , PMID:34934397; Chan, A.P.C., Chan, A.P.L., Key performance indicators for measuring construction success (2004) Benchmarking, 11 (2), pp. 203-221; Cowling, M., Brown, R., Rocha, A., Did you save some cash for a rainy COVID-19 day? The crisis and SMEs (2020) International Small Business Journal: Researching Entrepreneurship, 38 (7), pp. 593-604. , PMID:35125601; De Vito, A., Gómez, J.-P., Estimating the COVID-19 cash crunch: Global evidence and policy (2020) Journal of Accounting and Public Policy, 39 (2), pp. 1-14; Drever, E., (2006) Using semi-structured interviews in small-scale research: A teacher's guide, , The SCRE Centre; Hartmann, N.N., Lussier, B., Managing the sales force through the unexpected exogenous COVID-19 crisis (2020) Industrial Marketing Management, 88, pp. 101-111; Hohenstein, N.-O., Supply chain risk management in the COVID-19 pandemic: Strategies and empirical lessons for improving global logistics service providers' performance (2022) The International Journal of Logistics Management; Ivanov, D., Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case (2020) Transportation Research Part E, Logistics and Transportation Review, 136, pp. 1-14. , PMID:32288597; Ivanov, D., Das, A., Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: A research note (2020) International Journal of Integrated Supply Management, 13 (1), pp. 90-102; Kim, J., Kim, J., Wang, Y., Uncertainty risks and strategic reaction of restaurant firms amid COVID-19: Evidence from China (2021) International Journal of Hospitality Management, 92, pp. 1-21. , PMID:33223596; Kim, R.Y., The Impact of COVID-19 on Consumers: Preparing for Digital Sales (2020) IEEE Engineering Management Review, 48 (3), pp. 212-218; Marshall, C., Rossman, G.B., (2016) Designing Qualitative Research, , Sage (Atlanta, Ga.); Merchant, K.A., der Stede, W.A., (2017) Management control systems performance measurement, evaluation, and incentives, , Pearson; O'Dwyer, B., Qualitative data analysis: Illuminating a process for transforming a 'messy' but 'attractive' 'nuisance (2004) The real life guide to accounting research, pp. 391-407. , Elsevier; Parisi, C., Bekier, J., Assessing and managing the impact of COVID-19: A study of six European cities participating in a circular economy project (2022) Accounting, Auditing & Accountability Journal, 35 (1), pp. 97-107; Parmenter, D., (2020) Key performance indicators: Developing, implementing, and using winning KPIs, , Wiley; Paul, S.K., Chowdhury, P., A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19 (2021) International Journal of Physical Distribution & Logistics Management, 51 (2), pp. 104-125; Rahman, T., Moktadir, M.A., Paul, S.K., Key performance indicators for a sustainable recovery strategy in health-care supply chains: COVID-19 pandemic perspective (2021) Journal of Asia Business Studies; Robinson, K., Accounting Numbers As "Inscription": Action At a Distance and the Development of Accounting (1992) Accounting, Organizations and Society, 17 (7), pp. 685-708; Schmidt, C., The analysis of semi-structured interviews (2004) A Companion to Qualitative Research, pp. 253-258. , Sage; Smith, J., (1972) Interviewing in market and social research, , Routledge & Kegan Paul; Van Gorp, J.C., Using key performance indicators to manage energy costs (2005) Strategic Planning for Energy and the Environment, 25 (2), pp. 9-25; Yin, R.K., (1983) The Case Study Method: An Annotated Bibliography, , Sage; Yuan, J., Zeng, A.Y., Skibniewski, M.J., Li, Q., Selection of performance objectives and key performance indicators in public-private partnership projects to achieve value for money (2009) Construction Management and Economics, 27 (3), pp. 253-270},
correspondence_address1={Girardi, A.; University of Modena and Reggio EmiliaItaly},
publisher={IGI Global},
isbn={9781668467633; 9781799888567},
language={English},
abbrev_source_title={handb. of res. on Digital Innovation and netw. in Post-COVID-19 Organizations},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Singh2022,
author={Singh, N. and Ashish, D.K. and Dixit, A.},
title={A strategy to enhance the resource utilization for construction supply chain in rural India},
journal={AIP Conference Proceedings},
year={2022},
volume={2413},
doi={10.1063/5.0089300},
art_number={030017},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133496377&doi=10.1063%2f5.0089300&partnerID=40&md5=08dea8b2d1d6514a8236181be0615d4e},
affiliation={Department Of Civil Engineering, Maharaja Agrasen University, HP, India; Sj Mehta School Of Management, Indian Institute Of Technology, Bombay, India},
abstract={The government supported construction projects especially in rural India are experiencing extensive delays and continuously increasing operational cost during the project. Due to a dramatic shift in the capacity and volume of the rural Indian constructions over the last decade, it has been observed that the performance of the Construction Supply Chain in rural India needs to be improved in various dimensions. Therefore, all government supported construction schemes in rural India is studied and analyzed in this current study. The identified critical factors which play a crucial role for efficient CSC are lack of local population involvement, poor planning, inadequate survey, natural disaster, proper maintenance, fund crisis, inefficient real time monitoring system, delay in payment, lack of commitment, inefficient site management, poor site coordination, improper planning, lack of clarity in project scope, lack of communication, and substandard contract. The use of various techniques such as preferred supplier status, analytical procurement model, 5S practice and performance measurement system is explored to develop a strategy for improved resource utilization. © 2022 Author(s).},
author_keywords={Construction Supply Chain;  Government supported construction schemes;  Resource utilization;  Rural;  Strategic fit},
references={Tatari, O., Kucukvar, M., Sustainability assessment of U.S. Construction sectors: ecosystems perspective (2012) J Construct Eng Manag, 138, pp. 918-922; Global Construction, Oxford Economics. Global construction 2020: a global forecast for the construction industry over the next decade to 2020 (2011) Final Rep, p. 3; McCarthy, P., (2011) International comparison program; Onat, N.C., Kucukvar, M., Carbon footprint of construction industry: A global review and supply chain analysis (2020) Renewable and Sustain Energy Reviews, p. 124; Goel, A., Ganesh, L.S., Kaur, A., Deductive content analysis of research on sustainable construction in India: current progress and future directions (2019) J Clean Prod., 226, pp. 142-158; Farooqui, R., Ahmed, S., Assessment of Pakistani construction industry: current performance and the way forward (2008) J Adv Perform Inform Value., 1 (1), pp. 51-72; Nawi, M.N.M., Baluch, N., Bahaudin, A.Y., (2014) Impact of fragmentation issue in construction industry: an overview, , Building surveying, facilities management and engineering conference; Eriksson, P.E., Improving construction supply chain collaboration and performance: a lean construction pilot project (2010) Supply Chain Management: An Int J, 15 (5), pp. 394-403; Le, P.L., Elmughrabi, W., Thien-My, D., Chaabane, A., Present focuses and future directions of decision-making in construction supply chain management: a systematic review (2020) Int J of Construction Management, 20, pp. 490-509; Azambuja, M., O'Brien, W., Construction supply chain modeling: issues and perspectives (2009) Construction Supply Chain Management Handbook, , Boca Raton (FL): CRC Press; Abas, M., Khattak, S.B., Habib, T., Nadir, U., Assessment of critical risk and success factors in construction supply chain: a case of Pakistan (2020) Int J of Construction Management; Segerstedt, A., Olofsson, T., Supply chains in the construction industry (2010) Supp Chain Manage, 15 (5), pp. 347-353; Cheng, J.C., Law, K.H., Bjornsson, H., Jones, A., Sriram, R., A service oriented framework for construction supply chain integration (2019) Autom Constr, 19 (2), pp. 245-260; Chen, Q., Hall, D.M., Adey, B.T., Haas, C.T., Identifying enablers for coordination across construction supply chain processes: a systematic literature review Engineering (2020) Construction and Architectural Management; Mojumder, A., Singh, A., An exploratory study of the adaptation of green supply chain management in construction industry: The case of Indian Construction Companies (2021) J of Cleaner Production, p. 295; Singh, A., Misra, S.C., Ordering drivers of green supply chain management practices in Indian construction industry An impact assessment framework (2020) Int J of Quality & Reliability Management; Manikandan, S., Sundarakani, B., Pereira, V., Skill Development: Role of Industry-Academia Dyadic Collaboration for Sustaining the Construction Supply Chain in Rural India (2020) Sustainable Business Practices for Rural Development The Role of Intellectual Capital, , Palgrave Macmillan; https://pmgsy.nic.in; O'Neill, P., Petts, R., Beusch, A., Improved Asset Management Climbing Out of the Road Sector Pothole! (2020) Grameen Sampark, Pradhan Mantri Gram Sadak Yojana, pp. 40-47; Khorramshahgol, R., Al-Husain, R., A multi-objective approach to design strategic supply chains and develop responsiveness-efficiency frontiers (2018) Int. J. Logist. Manage., 29, pp. 365-386; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning, and Operations., , fifth ed, Prentice-Hall, Upper Saddle River, NJ; Tamayo-Torres, I., Gutiérrez-Gutiérrez, L.J., Llorens-Montes, F.J., Llorens-Montes, F.J., Organizational learning and innovation as sources of strategic fit (2016) Ind. Manage. Data Syst., 116, pp. 1445-1467; Johnson, M., Critical success factors for B2B e-markets: a strategic fit perspective (2013) Market. Intell. Plann., 31, pp. 337-366; Roh, J., Hong, P., Min, H., Implementation of a responsive supply chain strategy in global complexity: the case of manufacturing firms (2014) Int. J. Prod. Econ., 147, pp. 198-210; Dixit, A., Routroy, S., Dubey, S.K., A Strategy to Improve Resource Utilization: Case Study of Generic Drug Distribution in Rajasthan (2020) Materials Today: Proceedings, 18 (2), pp. 562-567; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Rev, 75, pp. 105-117; Prasad, K.V., Vasugi, V., Venkatesan, R., Bhat, N.S., Critical causes of time overrun in Indian construction projects and mitigation measures (2019) Int J of Construction Education and Research, 15 (3), pp. 216-238; Doloi, H., Sawhney, A., Iyer, K.C., Rentala, S., Analysing factors affecting delays in Indian construction projects (2012) Int J of Project Management, 30 (4), pp. 479-489; Dixit, A., Routroy, S., Dubey, S.K., An Efficient Drug Warehouse Operations: An Application of 5S (2019) AIP Conference Proceedings, pp. 1-8},
correspondence_address1={Singh, N.; Department Of Civil Engineering, HP, India; email: cgcnavdeep@gmail.com},
editor={Dixit A., Prakash S.O., Sahu P., Gautam A.V.},
publisher={American Institute of Physics Inc.},
issn={0094243X},
isbn={9780735443389},
language={English},
abbrev_source_title={AIP Conf. Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{RahiminezhadGalankashi20221674,
author={Rahiminezhad Galankashi, M. and Mokhatab Rafiei, F.},
title={Financial performance measurement of supply chains: a review},
journal={International Journal of Productivity and Performance Management},
year={2022},
volume={71},
number={5},
pages={1674-1707},
doi={10.1108/IJPPM-11-2019-0533},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100835975&doi=10.1108%2fIJPPM-11-2019-0533&partnerID=40&md5=e9c26930e1bbb58f2543765d0a1df75a},
affiliation={Department of Industrial, Mechanical and Aerospace Engineering, Buein Zahra Technical University, Buein Zahra, Iran; Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran},
abstract={Purpose: This study provides a systematic review on performance measurement of supply chains from a financial perspective. Design/methodology/approach: This study systematically reviews the financial performance measures of supply chains. More specifically, this research reviews a total of 100 papers published in more than 50 peer-reviewed journals. The reviewed papers are categorized into three major areas of engineering, business and management. Additionally, the papers are investigated based on country, journal frequency, applied methods, publication date and research type (application or developmental). Findings: According to the obtained results, cost, return on assets (ROA), sales, asset turnover, return on investment (ROI), market share, inventory turnover, profit margin, revenue growth, economic value added (EVA) and cash-to-cash cycle are the most common metrics of financial performance measurement. Next, a framework is developed based on different categories of performance measurement and decision levels of the supply chain. Finally, some research directions are suggested to be further investigated by other scholars. Originality/value: Although available studies on supply chain performance measurement are very vast and comprehensive, the majority of the studies have neglected to highlight the importance of financial measures. In other words, with the advent of nonfinancial measures, however, the majority of supply chain managers still prefer to consider financial issues in their performance assessment process. © 2022, Emerald Publishing Limited.},
author_keywords={Financial performance measurement;  Performance metrics;  Review;  Supply chain},
references={Abolghasemi Kordestani, A., Farhat, F., (2009) Supply Chain Maturity and Financial Performance: Study of Swedish SMEs, , Student thesis, University of Gävle, Gävle, Sweden; Agarwal, V., Taffler, R., Comparing the performance of market-based and accounting-based bankruptcy prediction models (2008) Journal of Banking and Finance, 32 (8), pp. 1541-1551; Ahmad, K., Mohamed, S., The application of non-financial performance measurement in Malaysian manufacturing firms (2016) Procedia Economics and Finance, 35 (October), pp. 476-484. , doi:, 2015; Ahmad, K., Zabri, S.M., The deployment of performance measurement system under the supply chain management environment: the case of Malaysian manufacturing companies (2018) Management and Production Engineering Review, 9 (1), pp. 3-12; Aissa Fantazy, K., Kumar, V., Kumar, U., An empirical study of the relationships among strategy, flexibility, and performance in the supply chain context (2009) Supply Chain Management: An International Journal, 14 (3), pp. 177-188; Algren, C., Kotzab, H., State of the art of supply chain performance measurement in Danish industrial companies (2011) 23rd Annual NOFOMA Conference in Harstad, pp. 1-17. , Norway: 2011; Almasi, M., Khoshfetrat, S., Galankashi, M.R., Sustainable supplier selection and order allocation under risk and inflation condition (2019) IEEE Transactions on Engineering Management; Altman, E.I., Predicting financial distress of companies: revisiting the Z-score and ZETA® models (2013) Handbook of Research Methods and Applications in Empirical Finance, , Edward Elgar Publishing; Aramyan, L.H., Lansink, A.G.O., van der Vorst, J.G., van Kooten, O., Performance measurement in agri-food supply chains : a case study (2007) Supply Chain Management: An International Journal, , doi:, 12 4, p. 304, 315; Aramyan, L.H., Oude Lansink, A.G., van der Vorst, J.G., van Kooten, O., Performance measurement in agri-food supply chains: a case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Arif-Uz-Zaman, K., Ahsan, A.N., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, , 63 5, p. 588; Arnold, M.F., (2008) Non-financial performance metrics for corporate responsibility reporting revised, , A Doughty Centre for Corporate Responsibility Occasional Paper; Baier, C., Hartmann, E., Moser, R., Strategic alignment and purchasing efficacy: an exploratory analysis of their impact on financial performance (2008) Journal of Supply Chain Management, 44 (4), pp. 36-52; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Barker, R.C., (1995) Financial performance measurement: not a total solution, 33 (2), pp. 31-39; Bascom, W.O., The economics of financial reform in developing Countries (2016) Topics Development Economics, , doi:, Palgrave Macmillan, XII, 204; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), p. 275; Behrouzi, F., Wong, K.Y., An integrated stochastic-fuzzy modeling approach for supply chain leanness evaluation (2013) The International Journal of Advanced Manufacturing Technology, 68 (5-8), pp. 1677-1796; Berman, O., Wang, Q., Inbound logistic planning: minimizing transportation and inventory cost (2006) Transportation Science, 40 (3), pp. 287-299; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning and Control, 18 (8), pp. 666-680; Bichou, K., Review of port performance approaches and a supply chain framework to port performance benchmarking (2006) Research in Transportation Economics, 17, pp. 567-598; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Brainard, W.C., Tobin, J., Pitfalls in financial model building (1968) The American Economic Review, 58 (2), pp. 99-122; Brewer, P.C., Using the balanced scorecard to measure supply chain performance Peter C Brewer; Thomas WSpeh (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bullinger, H.J., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Cai, S., The importance of customer focus for organizational performance: a study of Chinese companies (2009) International Journal of Quality and Reliability Management, 26 (4), pp. 369-379; Camerinelli, E., Cantu, A., Measuring the value of the supply chain: a framework (2006) Supply Chain Practice, 8 (1), pp. 40-59; Carr, A.S., Smeltzer, L.R., The relationship of strategic purchasing to supply chain management (1999) European Journal of Purchasing and Supply Management, 5 (1), pp. 43-51; Carter, J.R., Narasimhan, R., Is purchasing really strategic? (1996) International Journal of Purchasing and Materials Management, , 32 4, 20-28; Cavinato, J.L., A total cost/value model for supply chain competitiveness (1992) Journal of Business Logistics, 13 (2), pp. 285-291; Chae, B.K., Developing key performance indicators for supply chain: an industry perspective (2009) Supply Chain Management, , 14 6, p. 422; Chan, F.T., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: International Journal; Chan, F.T., Qi, H.J., Feasibility of performance measurement system for supply chain: a process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T., Qi, H.J., Chan, H.K., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) Management Decision, , 41 7, p. 635; Chan, F.T., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Chehbi-gamoura, S., Derrouiche, R., Damand, D., Barth, M., Derrouiche, R., Damand, D., The Management of Operations Insights from big Data Analytics in supply chain management : an all-inclusive literature review using the SCOR model Insights from big Data Analytics in supply chain management : an all-inclusive literature review using the SCOR model (2019) Production Planning and Control, pp. 1-27; Chen, I.J., Paulraj, A., Lado, A.A., Strategic purchasing, supply management, and firm performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers and Industrial Engineering, 62 (3), pp. 801-818; Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, Planning and Operations, , Prentice Hall, New Jersey; Christensen, W.J., Germain, R.N., Birou, L., Variance vs average: supply chain lead-time as a predictor of financial performance (2007) Supply Chain Management: An International Journal, 12 (5), pp. 349-357; Davis, S., Albright, T., An investigation of the effect of balanced scorecard implementation on financial performance (2004) Management Accounting Research, 15 (2), pp. 135-153; Dehning, B., Richardson, V.J., Zmud, R.W., The financial performance effects of IT-based supply chain management systems in manufacturing firms (2007) Journal of Operations Management, 25 (4), pp. 806-824; Dissanayake, C.K., Cross, J.A., International Journal of Production Economics Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM (2018) International Journal of Production Economics, 201 (March), pp. 102-115; Dossi, A., Patelli, L., You learn from what you measure: financial and non-financial performance measures in multinational companies (2010) Long Range Planning, 43 (4), pp. 498-526; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things: toward more practical SCPMS (2017) Industrial Management and Data Systems, , doi:, 117 2, 267-286; Elgazzar, S., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain operations' performance to the company's financial strategy: a case study of an Egyptian natural bottled water company (2011) Proceedings of the 16th International Symposium on Logistics (ISL) 2011, p. 173181. , p., Centre for Concurrent Enterprise Nottingham University Business School, Nottingham; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes' performance to a company's financial strategic objectives (2012) European Journal of Operational Research, , 223 1, 276-289; Ellinger, A.D., Ellinger, A.E., Yang, B., Howton, S.W., The relationship between the learning organization concept and firms' financial performance: an empirical assessment (2002) Human Resource Development Quarterly, 13 (1), pp. 5-22; Engblom, J., Solakivi, T., Töyli, J., Ojala, L., Multiple-method analysis of logistics costs (2012) International Journal of Production Economics, 137 (1), pp. 29-35; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Fabbe-Costes, N., Jahre, M., Supply chain integration and performance: a review of the evidence (2008) The International Journal of Logistics Management, 19 (2), pp. 130-154; Fawcett, S.E., Wallin, C., Allred, C., Magnan, G., Supply chain information-sharing: benchmarking a proven path (2009) Benchmarking: An International Journal, , 16 2, 222-246; Field, J.M., Meile, L.C., Supplier relations and supply chain performance in financial services processes (2008) International Journal of Operations & Production Management, , 28 2, p. 185; Fitzgerald, L., Johnston, R., Brignall, T.J., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, 69. , Chartered Institute of Management Accountants (CIMA), London: 1991; Friemann, F., Wandfluh, M., Schönsleben, P., Alard, R., Improving the application of financial measures in supply chain management (2012) IFIP International Conference on Advances in Production Management Systems, pp. 584-591. , Springer, Berlin, Heidelberg; Fullerton, R.R., Wempe, W.F., Lean manufacturing, non-financial performance measures, and financial performance (2009) International Journal of Operations and Production Management, , 29 3, p. 214; Gabler, S., Gabler wirtschaftslexikon. schlagworte: kooperation, fokales unternehmen (2014) Neue Institutionenökonomik, Institution, Transaktionskostenökonomik; Galankashi, M.R., Helmi, S.A., Assessment of lean manufacturing practices: an operational perspective (2017) International Journal of Services and Operations Management, 28 (2), pp. 163-184; Galankashi, M.R., Fallahiarezoudar, E., Moazzami, A., Yusof, N.M., Helmi, S.A., Performance evaluation of a petrol station queuing system: a simulation-based design of experiments study (2016) Advances in Engineering Software, 92, pp. 15-26; Galankashi, M.R., Fallahiarezoudar, E., Moazzami, A., Helmi, S.A., Rohani, J.M., Yusof, N.M., An efficient integrated simulation–Taguchi approach for sales rate evaluation of a petrol station (2018) Neural Computing and Applications, 29 (4), pp. 1073-1085; Galankashi, M.R., Helmi, S.A., Hisjam, M., Rahim, A.R.A., Leanness assessment in automotive industry: case study approach (2018) International Journal of Value Chain Management, 9 (1), pp. 70-88; Galankashi, M.R., Helmi, S.A., Rahim, A., Rahman, A., Rafiei, F.M., Agility assessment in manufacturing companies (2019) Benchmarking: An International Journal, , 26 7, 2081-2104; Galant, A., Cadez, S., Corporate social responsibility and financial performance relationship : a review of measurement approaches (2017) Economic Research-Ekonomska Istraživanja, 30 (1), pp. 1-18; Gawankar, S.A., Gunasekaran, A., Kamble, S., A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context (2020) International Journal of Production Research, 58 (5), pp. 1574-1593; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations and Production Management, 16 (8), pp. 63-80; Gomm, M.L., Supply chain finance: applying finance theory to supply chain management to enhance finance in supply chains (2010) International Journal of Logistics: Research and Applications, , 13 2, 133-142; Green, K.W., Jr., McGaughey, R., Casey, K.M., Does supply chain management strategy mediate the association between market orientation and organizational performance? (2006) Supply Chain Management: An International Journal, 11 (5), pp. 407-414; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, , doi:, 87 3, 333-347; Haghighi, S.M., Torabi, S.A., Ghasemi, R., An integrated approach for performance evaluation in sustainable supply chain networks (with a case study) (2016) Journal of Cleaner Production, 137, pp. 579-597; Harrington, H.J., (1991) Business Process Improvement: The Breakthrough Strategy for Total Quality, Productivity, and Competitiveness, , McGraw Hill Professional, New York; Hernaus, T., Bach, M.P., Vuksic, V.B., Influence of strategic approach to BPM on financial and non‐financial performance (2012) Baltic Journal of Management, , 7 4, p. 376; Hofmann, E., Supply chain finance: some conceptual insights (2005) Beiträge Zu Beschaffung Und Logistik, pp. 203-214; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: a case study from the packaging industry (2009) Production Planning and Control, , 20 1, 68-81; Hwang, Y.D., Lin, Y.C., Lyu, J., The performance evaluation of SCOR sourcing process —The case study of Taiwan's TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Hwang, Y., Wen, Y., Chen, M., (2010) Total Quality Management and Business Excellence A study on the relationship between the PDSA cycle of green purchasing and the performance of the SCOR model, pp. 37-41. , doi:, April 2014; Hwang, G., Han, S., Jun, S., Park, J., Operational performance metrics in manufacturing Process (2014) Based on SCOR Model and RFID Technology, 5 (1); Ittner, C.D., Larcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting, Organizations and Society, 28 (7-8), pp. 715-741; Jahera, J.S., Jr., Lloyd, W.P., Additional evidence on the validity of ROI as a measure of business performance (1992) Mid-Atlantic Journal of Business, 28 (2), pp. 105-113; Jaimes, W.A., Serna, M.D.A., Buritica, N.C., Key performance measures for supply chain management from the Colombian shipyard (2011) Maritime Logistics in the Global Economy: Current Trends and Approaches, , Lohmar-Koln, Auflage, 237-257; Jakub, S., Viera, B., Eva, K., Economic Value Added as a measurement tool of financial performance (2015) Procedia Economics and Finance, 26, pp. 484-489; Jansen, E.P., Merchant, K.A., van der Stede, W.A., National differences in incentive compensation practices : the differing roles of financial performance measurement in the United States and The Netherlands q (2009) Accounting, Organizations and Society, 34 (1), pp. 58-84; Jin, S., Jeong, S., Kim, K., A linkage model of supply chain operation and financial performance for economic sustainability of firm (2017) Sustainability, 9 (1), pp. 139-162; Johnson, M., Templar, S., (2007) The influence of supply chains on a company's financial performance, , https://dspace.lib.cranfield.ac.uk/bitstream/handle/1826/4375/The_influence_of_sup?sequence=1, accessed, 2 December 2019; Johnson, M., Templar, S., The relationships between supply chain and firm performance: the development and testing of a unified proxy (2011) International Journal of Physical Distribution and Logistics Management, 41 (2), pp. 88-103; Jordão, R.V.D., Almeida, V.R.D., Performance measurement, intellectual capital and financial sustainability (2017) Journal of Intellectual Capital, 18 (3), pp. 643-666; Kamau, N., (2014) Measuring and Evaluating Performance in Integrated Supply Chain Management, , Doctoral dissertation, United States International University-Africa, Nairobi, Kenya; Kaplan, R.S., (1988) One cost system isn't enough (pp. 61-6). Harvard Business Review; Kaplan, R.S., Cooper, R., (1998) Cost & Effect: Using Integrated Cost Systems to Drive Profitability and Performance, , Harvard Business Press, Boston; Karl, A.A., Micheluzzi, J., Leite, L.R., Pereira, C.R., Supply chain resilience and key performance indicators: a systematic literature review (2018) Production, 28, pp. 1-16; Kasi, V., Systemic assessment of SCOR for modeling supply chains (2005) Proceedings of the 38th Annual Hawaii International Conference on System Sciences, , IEEE, p. 87b; Khoshfetrat, S., Rahiminezhad Galankashi, M., Almasi, M., Sustainable supplier selection and order allocation: a fuzzy approach (2020) Engineering Optimization, 52 (9), pp. 1494-1507; Kim, S.W., An investigation on the direct and indirect effect of supply chain integration on firm performance (2009) International Journal of Production Economics, 119 (2), pp. 328-346; Kocaoğlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, pp. 113-132. , doi:, 24 1; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Lambert, D.M., Burduroglu, R., Measuring and selling the value of logistics (2000) The International Journal of Logistics Management, 11 (1), pp. 1-18; Lawton, T.C., Solomko, S., When being the lowest cost is not enough: building a successful low-fare airline business model in Asia (2005) Journal of Air Transport Management, 11 (6), pp. 355-362; Lee, C.S., Wong, K.Y., Advances in intellectual capital performance measurement: a state-of-the-art review (2019) The Bottom Line, 32 (2), pp. 118-134; Lehmann, S., Optimizing urban material flows and waste streams in urban development through principles of zero waste and sustainable consumption (2011) Sustainability, 3 (1), pp. 155-183; Lenny Koh, S.C., Demirbag, M., Bayraktar, E., Tatoglu, E., Zaim, S., The impact of supply chain management practices on performance of SMEs (2007) Industrial Management and Data Systems, 107 (1), pp. 103-124; Li, X., Wu, Q., Holsapple, C.W., Goldsby, T., An empirical examination of firm financial performance along dimensions of supply chain resilience (2017) Management Research Review, , 40 3, 254-269; Lima-junior, F.R., Cesar, L., Carpinetti, R., International Journal of Production Economics Combining SCOR s model and fuzzy TOPSIS for supplier evaluation and management (2016) International Journal of Production Economics, 174, pp. 128-141; Lockamy, A., III, McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278; Lord, R.K., Mayhew, C.R., Korupolu, R., Mantheiy, E.C., Friedman, M.A., Palmer, J.B., Needham, D.M., ICU early physical rehabilitation programs: financial modeling of cost savings (2013) Critical Care Medicine, 41 (3), pp. 717-724; Lu, G., Shang, G., Impact of supply base structural complexity on financial performance: roles of visible and not-so-visible characteristics (2017) Journal of Operations Management, 53, pp. 23-44; Lynch, J.P., Forman, S.A., Graff, S., Gunby, M.C., High-risk population health management--achieving improved patient outcomes and near-term financial results (2000) American Journal of Managed Care, 6 (7), pp. 781-791; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Mandal, S., Supply chain performance: review of empirical literature (2012) Romanian Review of Social Sciences, (3), pp. 24-34; Matsoso, M.L., Benedict, O.H., Non-financial performance measures in small medium enterprises' supply chain management (2014) Journal of Economics, 5 (3), pp. 247-257; McMullan, A., Supply chain management practices in Asia Pacific today (1996) International Journal of Physical Distribution and Logistics Management, 26 (10), pp. 79-95; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Murray, J.Y., Kotabe, M., Wildt, A.R., Strategic and financial performance implications of global sourcing strategy: a contingency analysis (1995) Journal of International Business Studies, 26 (1), pp. 181-202; Narasimhan, R., Das, A., The impact of purchasing integration and practices on manufacturing performance (2001) Journal of Operations Management, 19 (5), pp. 593-609; Nasiri, M., Ukko, J., Saunila, M., Rantala, T., Rantanen, H., Digital-related capabilities and financial performance: the mediating effect of performance measurement systems (2020) Technology Analysis and Strategic Management, , 32 12, 1393-1406; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: a structured approach (1997) International Journal of Operations and Production Management, , 17 11, p. 1131; Ntabe, E.N., Lebel, L., Munson, A.D., Santa-eulalia, L.A., International Journal of Production Economics A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues (2015) International Journal of Production Economics, 169, pp. 310-332; Otley, D., Measuring Performance: the accounting perspective (2002) Business Performance Measurement: Theory and Practice, , Neely, A. (Ed.), Cambridge University Press, Cambridge; Ou, C.S., Liu, F.C., Hung, Y.C., Yen, D.C., A structural model of supply chain management on firm performance (2010) International Journal of Operations and Production Management, 30 (5), pp. 526-545; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management and Data Systems, 110 (9), pp. 1297-1318; Paulraj, A., Chen, I.J., Flynn, J., Levels of strategic purchasing: impact on supply integration and performance (2006) Journal of Purchasing and Supply Management, 12 (3), pp. 107-122; Persson, F., Araldi, M., The development of a dynamic supply chain analysis tool – integration of SCOR and discrete event simulation (2009) International Journal of Production Economics, 121 (2), pp. 574-583; Phillips, J.J., (2012) Return on Investment in Training and Performance Improvement Programs, , https://www.amazon.com/Investment-Training-Performance-Improvement-Improving-ebook/dp/B001UN2WCA, 2nd ed. Routledge; Rahiminezhad Galankashi, M., Helmi, S.A., Assessment of hybrid Lean-Agile (Leagile) supply chain strategies (2016) Journal of Manufacturing Technology Management, 27 (4), pp. 470-482; Rai, A., Patnayakuni, R., Seth, N., Firm performance impacts of digitally enabled supply chain integration capabilities (2006) MIS Quarterly, 30 (2), pp. 225-246; Randall, W.S., Theodore Farris, M., Supply chain financing: using cash-to-cash variables to strengthen the supply chain (2009) International Journal of Physical Distribution and Logistics Management, 39 (8), pp. 669-689; Reichardt, E., Nichols, L.J., SCOR your ISO certification (2003) Quality, 42 (2), pp. 44-47; Ritchie, W.J., Kolodinsky, R.W., Nonprofit organization financial performance measurement: an evaluation of new and existing financial performance measures (2003) Nonprofit Management and Leadership, 13 (4), pp. 367-381; Røge, K.M., Lennon, N.J., A study on the criteria of internal transparency, efficiency and effectiveness in measuring local government performance (2018) Financial Accountability and Management, 34 (4), pp. 392-409; Sahay, B.S., Supply chain collaboration: the key to value creation (2003) International Journal of Productivity and Performance Management, , 52 2-3, p. 76; Saranga, H., Moser, R., Performance evaluation of purchasing and supply management using value chain DEA approach (2010) European Journal of Operational Research, 207 (1), pp. 197-205; Seiler, A.C., (2016) Measuring Performance in Supply Chain Networks, , Doctoral dissertation, University of Salford; Shah, J., Singh, N., Benchmarking internal supply chain performance: development of a framework (2001) Journal of Supply Chain Management, 37 (4), pp. 37-47; Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2011) Behavioral Operations in Planning and Scheduling, pp. 105-121; Shi, M., Yu, W., Supply chain management and financial performance: literature review and future directions (2013) International Journal of Operations and Production Management, , 33 10, p. 1283; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking, , doi:, 17 3, p. 320; Simpson, W.G., Kohers, T., The link between corporate social and financial performance: evidence from the banking industry (2002) Journal of Business Ethics, 35 (2), pp. 97-109; Škrinjar, R., Bosilj-Vukšić, V., Indihar-Štemberger, M., The impact of business process orientation on financial and non-financial performance (2008) Business Process Management Journal, 14 (5), pp. 738-754; Sroufe, R., Gopalakrishna-Remani, V., Management, social sustainability, reputation, and financial performance relationships: an empirical examination of US firms (2019) Organization and Environment, 32 (3), pp. 331-362; Söderberg, L., Bengtsson, L., Supply chain management maturity and performance in SMEs (2010) Operations Management Research, 3 (1-2), pp. 90-97; Swinehart, K.D., Smith, A.E., Internal supply chain performance measurement: a health care continuous improvement implementation (2005) International Journal of Health Care Quality Assurance, 18 (6-7), p. 533; Tan, Q., Sousa, C.M., Why poor performance is not enough for a foreign exit: the importance of innovation capability and international experience (2019) Management International Review, 59 (3), pp. 465-498; Tan, K.C., Kannan, V.R., Handfield, R.B., Ghosh, S., Supply chain management: an empirical study of its impact on performance (1999) International Journal of Operations and Production Management, 19 (10), pp. 1034-1052; Tangen, S., Performance measurement: from philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) International Journal of Production Research, 53 (21), pp. 6473-6494; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal; Thirumalai, S., Sinha, K.K., Customer satisfaction with order fulfillment in retail supply chains: implications of product type in electronic B2C transactions (2005) Journal of Operations Management, 23 (3-4), pp. 291-303; Tippayawong, K.Y., Tiwaratreewit, T., Sopadang, A., Positive influence of green supply chain operations on Thai electronic firms (2015) Financial Performance, 118, pp. 683-690; Tobin, J., A general equilibrium approach to monetary theory (1969) Journal of Money, Credit and Banking, 1 (1), pp. 15-29; Turley, G., Robbins, G., Mcnena, S., A framework to measure the financial performance of local governments (2015) Local Government Studies, , doi:, 41 3; Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships (2003) Journal of Operations Management, 21 (5), pp. 523-539; Wagner, S.M., Grosse-Ruyken, P.T., Erhun, F., The link between supply chain fit and financial performance of the firm (2012) Journal of Operations Management, 30 (4), pp. 340-353; Wang, W.Y., Chan, H.K., Pauleen, D.J., Aligning business process reengineering in implementing global supply chain systems by the SCOR model (2010) International Journal of Production Research, , 48 19, 5647-5669; Wibowo, M.A., Nur, M., The analysis of supply chain performance measurement at construction project (2015) Procedia Engineering, 125, pp. 25-31; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), p. 361; Wu, F., Yeniyurt, S., Kim, D., Cavusgil, S.T., The impact of information technology on supply chain capabilities and firm performance: a resource-based view (2006) Industrial Marketing Management, 35 (4), pp. 493-504; Xie, Y., Yin, Y., Xue, W., Shi, H., Chong, D., Intelligent supply chain performance measurement in Industry 4.0 (2020) Systems Research and Behavioral Science, , doi:, 37 4, 711-718; Yu, W., Jacobs, M.A., Salisbury, W.D., Enns, H., The effects of supply chain integration on customer satisfaction and financial performance: an organizational learning perspective (2013) International Journal of Production Economics, 146 (1), pp. 346-358; Bosilj‐Vukšić, V., Indihar‐Štemberger, M., The impact of business process orientation on financial and non‐financial performance (2008) Business Process Management Journal, , 14 5, p. 738; Christopher, M.L., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London; Cooper, M.C., Ellram, L.M., Characteristics of supply chain management and the implications for purchasing and logistics strategy (1993) International Journal of Logistics Management, 4 (2), pp. 13-24; Jin, B., Performance implications of information technology implementation in an apparel supply chain (2006) Supply Chain Management: An International Journal, 11 (4), pp. 309-316; La Londe, B.J., Masters, J.M., Emerging logistics strategies: blueprints for the next century (1994) International Journal of Physical Distribution and Logistics Management, 24 (7), pp. 35-47; Lambert, D.M., Stock, J.R., Ellram, L.M., (1998) Fundamentals of Logistics Management (The Irwin/McGraw-Hill Series in Marketing), , McGraw-Hill, Irwin, California; Li, L., Su, Q., (2011) International Journal of Production Ensuring supply chain quality performance through applying the SCOR model, pp. 37-41. , doi:, November 2014; Narasimhan, R., Nair, A., The antecedent role of quality, information sharing and supply chain proximity on strategic alliance formation and performance (2005) International Journal of Production Economics, 96 (3), pp. 301-313; Shepherd, C., (2011) Measuring supply chain performance : current research and future directions; Vickery, S.N., Calantone, R., Dröge, C., Supply chain flexibility: an empirical study (1999) Journal of Supply Chain Management, 35 (2), pp. 16-24; Zhou, H., Benton, W.C., Schilling, D.A., Milligan, G.W., (2011) Supply Chain Integration and the SCOR Model, 32 (4), pp. 332-344},
correspondence_address1={Rahiminezhad Galankashi, M.; Department of Industrial, Iran; email: Masoud.rahiminejad@yahoo.com},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Ramos20221484,
author={Ramos, E. and Coles, P.S. and Chavez, M. and Hazen, B.},
title={Measuring agri-food supply chain performance: insights from the Peruvian kiwicha industry},
journal={Benchmarking},
year={2022},
volume={29},
number={5},
pages={1484-1512},
doi={10.1108/BIJ-10-2020-0544},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112305965&doi=10.1108%2fBIJ-10-2020-0544&partnerID=40&md5=fffc870be48630e3cd8c3a3ba2d6235e},
affiliation={Programa de Ingenieria Industrial, Universidad Peruana de Ciencias Aplicadas, Lima, Peru; Decision and Technology Analytics, College of Business, Lehigh University, Bethlehem, PA, United States; Universidad Nacional de Ingenieria, Lima, Peru; Logistikum, University of Applied Sciences Upper Austria – Campus Steyr, Steyr, Austria},
abstract={Purpose: Agri-food firms face many challenges when assessing and managing their performance. The purpose of this research is to determine important factors for an integrated agri-food supply chain performance measurement system. Design/methodology/approach: This research uses the Peruvian kiwicha supply chain as a meaningful context to examine critical factors affecting agri-food supply chain performance. The research uses interpretative structural modelling (ISM) with fuzzy MICMAC methods to suggest a hierarchical performance measurement model. Findings: The resulting kiwicha supply chain performance management model provides insights for managers and academic theory regarding managing competing priorities within the agri-food supply chain. Originality/value: The model developed in this research has been validated by cooperative kiwicha associations based in Puno, Peru, and further refined by experts. Moreover, the results obtained through ISM and fuzzy MICMAC methods could help decision-makers from any agri-food supply chain focus on achieving high operational performance by integrating key performance measurement factors. © 2021, Edgar Ramos, Phillip S. Coles, Melissa Chavez and Benjamin Hazen.},
author_keywords={Agri-food supply chain;  Kiwicha;  Metrics;  Performance measurement;  Supply chain management},
references={Agustin, W., Dania, P., Xing, K., Amer, Y., Collaboration behavioural factors for sustainable agri-food supply chains: a systematic review (2018) Journal of Cleaner Production, 186, pp. 851-864; Ahumada, O., Villalobos, J.R., Application of planning models in the agri-food supply chain: a review (2009) European Journal of Operational Research, 196 (1), pp. 1-20; Akhtar, P., Tse, Y.K., Khan, Z., Rao-Nicholson, R., Data-driven and adaptive leadership contributing to sustainability: global agri-food supply chains connected with emerging markets (2016) International Journal of Production Economics, 181, pp. 392-401; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: a literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Alfalla-Luque, R., Medina-Lopez, C., Dey, P.K., Supply chain integration framework using literature review (2013) Production Planning and Control, 24 (8-9), pp. 800-817; Alora, A., Barua, M.K., An integrated structural modelling and MICMAC analysis for supply chain disruption risk classification and prioritisation in India (2019) International Journal of Value Chain Management, 10 (1), pp. 1-25; Aramyan, L.H., Lansink, A.G.J.M.O., Van Der Vorst, J.G.A.J., Kooten, O.V., Performance measurement in agri-food supply chains: a case study (2007) Supply Chain Management, 12 (4), pp. 304-315; Arif-Uz-Zaman, K., Ahsan, A.M.M.N., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612; Balon, V., Sharma, A.K., Barua, M.K., Assessment of barriers in green supply chain management using ISM: a case study of the automobile industry in India (2016) Global Business Review, 17 (1), pp. 116-135; Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I.E., Omid, M., Green supplier selection using fuzzy group decision making methods: a case study from the agri-food industry (2018) Computers and Operations Research, 89, pp. 337-347; Banasik, A., Kanellopoulos, A., Claassen, G.D.H., Bloemhof-Ruwaard, J.M., van der Vorst, J.G.A.J., Closing loops in agricultural supply chains using multi-objective optimization: a case study of an industrial mushroom supply chain (2017) International Journal of Production Economics, 183, pp. 409-420; Bedoya-Perales, N.S., Pumi, G., Mujica, A., Talamini, E., Padula, A.D., Quinoa expansion in Peru and its implications for land use management (2018) Sustainability, 10 (2), p. 532; Bhat, A., Kumar Sharma, S., Supply chain risks: development of model and empirical evidence (2014) International Journal of Applied Management Science, 6 (1), pp. 45-64; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2014) Production Planning and Control, 25 (8), pp. 698-714; Bhosale, V.A., Kant, R., An integrated ISM fuzzy MICMAC approach for modelling the supply chain knowledge flow enablers (2016) International Journal of Production Research, 54 (24), pp. 7374-7399; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Birhanu, D., Lanka, K., Rao, A.N., Supply chain performance metrics framework: literature review and research issues (2016) International Journal of Supply Chain and Inventory Management, 1 (3), pp. 185-211; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Production Management, 20 (6), pp. 692-704; Bottani, E., Bigliardi, B., Supply chain performance measurement: a literature review and pilot study among Italian manufacturing companies (2014) International Journal of Engineering, Science and Technology, 6 (May), pp. 1-16; Bulsara, H.P., Qureshi, M.N., Patel, H., Green supply chain performance measurement: an exploratory study (2016) International Journal of Logistics Systems and Management, 23 (4), pp. 476-498; Cappelletti, G.M., Grilli, L., Nicoletti, G.M., Russo, C., Innovations in the olive oil sector: a fuzzy multicriteria approach (2017) Journal of Cleaner Production, 159, pp. 95-105; Chalyvidis, C.E., Ogden, J.A., Johnson, A.W., Using supply chain interoperability as a measure of supply chain performance (2013) Supply Chain Forum: An International Journal, 14 (3), pp. 52-73; Chandra, D., Kumar, D., A fuzzy MICMAC analysis for improving supply chain performance of basic vaccines in developing countries (2018) Expert Review of Vaccines, 17 (3), pp. 263-281; Chang, W., Ellinger, A.E., Kim, K.K., Franke, G.R., Supply chain integration and firm financial performance: a meta-analysis of positional advantage mediation and moderating factors (2016) European Management Journal, 34 (3), pp. 282-295; Chen, J.K., An extension of importance-performance analysis method: integrated with fuzzy MICMAC (2018) International Journal of Services Operations and Informatics, 9 (1), pp. 83-99; Chowdhury, N.A., Ali, S.M., Mahtab, Z., Rahman, T., Kabir, G., Paul, S.K., A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry (2019) Journal of Retailing and Consumer Services, 51 (June 2019), pp. 102-113; Coelho, L.M., Silva, P.M., Martins, J.T., Pinheiro, A.C., Vicente, A.A., Emerging opportunities in exploring the nutritional/functional value of amaranth (2018) Food and Function, 9 (11), pp. 5499-5512; Cunha Callado, A.A., Jack, L., Relations between usage patterns of performance indicators and the role of individual firms in fresh fruit agri-food supply chains (2017) Journal of Applied Accounting Research, 18 (3), pp. 375-398; Dalvi, M.V., Kant, R., Modelling supplier development enablers: an integrated ISM–FMICMAC approach (2018) International Journal of Management Science and Engineering Management, 13 (2), pp. 75-83; De, A., Singh, S.P., Analysis of fuzzy applications in the agri-supply chain: a literature review (2021) Journal of Cleaner Production, 283; De Mori, C., Batalha, M.O., Alfranca, O., A model for measuring technology capability in the agrifood industry companies (2016) British Food Journal, 118 (6), pp. 1422-1461; Doukidis, G.I., Matopoulos, A., Vlachopoulou, M., Manthou, V., Manos, B., A conceptual framework for supply chain collaboration: empirical evidence from the agri-food industry (2007) Supply Chain Management: An International Journal, 12 (3), pp. 177-186; Dube, A.S., Gawande, R.R., ISM-fuzzy MICMAC approach for analysis of GSCM enablers (2016) International Journal of Logistics Systems and Management, 24 (4), p. 426; Elrod, C., Murray, S., Bande, S., A review of performance metrics for supply chain management (2013) Engineering Management Journal, 25 (3), pp. 39-50; Faber, N., Organizing warehouse management (2013) International Journal of Operations and Production Management, 33 (9), pp. 1230-1256; (2019) FAOSTAT 2019: Countries by Commodity, , Food and Agriculture Organization of the United Nations, Rome; Fayezi, S., Zutshi, A., O'Loughlin, A., Understanding and development of supply chain agility and flexibility: a structured literature review (2017) International Journal of Management Reviews, 19 (4), pp. 379-407; Frederico, G.F., Garza-Reyes, J.A., Kumar, A., Kumar, V., Performance measurement for supply chains in the Industry 4.0 era: a balanced scorecard approach (2020) International Journal of Productivity and Performance Management, 70 (4), pp. 789-807; Gaitán-Cremaschi, D., Meuwissen, M.P.M., Oude, A.G.J.M.L., Total factor productivity: a framework for measuring agri-food supply chain performance towards sustainability (2017) Applied Economic Perspectives and Policy, 39 (2), pp. 259-285; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) International Journal of Production Economics, 134 (1), pp. 177-187; Gardas, B.B., Raut, R.D., Narkhede, B., Evaluating critical causal factors for post-harvest losses (PHL) in the fruit and vegetables supply chain in India using the DEMATEL approach (2018) Journal of Cleaner Production, 199, pp. 47-61; Gazdecki, M., Factors of business relationships change in agribusiness input distribution channel: the case of Polish market (2018) IMP Journal, 12 (3), pp. 567-582; Gorane, S.J., Kant, R., Supply chain management: modelling the enablers using ISM and fuzzy MICMAC approach (2013) International Journal of Logistics Systems and Management, 16 (2), pp. 147-166; Govindan, K., Sustainable consumption and production in the food supply chain: a conceptual framework (2018) International Journal of Production Economics, 195, pp. 419-431; Govindan, K., Mangla, S.K., Luthra, S., Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies (2017) Production Planning and Control, 28 (6-8), pp. 552-573; Grekova, K., Calantone, R.J., Bremmers, H.J., Trienekens, J.H., Omta, S.W.F., How environmental collaboration with suppliers and customers influences firm performance: evidence from Dutch food and beverage processors (2016) Journal of Cleaner Production, 112, pp. 1861-1871; Guardián Sedano, J.E., Trujillo Velásquez, I.A., Cadena de suministros para la exportación de granos andinos a Estados Unidos (2019) Ingeniería Industrial, (37), pp. 15-31; Guersola, M., Pinheiro de Lima, E., Arns Steiner, M.T., Supply chain performance measurement: a systematic literature review (2018) International Journal of Logistics Systems and Management, 31 (1), pp. 109-131; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hachicha, W., Elmsalmi, M., An integrated approach based-structural modeling for risk prioritization in supply network management (2014) Journal of Risk Research, 17 (10), pp. 1301-1324; Hajimirzajan, A., Vahdat, M., Sadegheih, A., Shadkam, E., Bilali, H.E., An integrated strategic framework for large-scale crop planning: sustainable climate-smart crop planning and agri-food supply chain management (2021) Sustainable Production and Consumption, 26 (4), pp. 709-732; Handfield, R.B., Cousins, P.D., Lawson, B., Petersen, K.J., How can supply management really improve performance? A knowledge-based model of alignment capabilities (2015) Journal of Supply Chain Management, 51 (3), pp. 3-17; Huang, M., Yen, G.F., Liu, T.-C., Reexamining supply chain integration and the supplier's performance relationships under uncertainty (2014) Supply Chain Management: An International Journal, 19 (1), pp. 64-78; Iijima, M., Azuma, K., High customer service quality in the beauty service design (2015) International Journal of Services, Technology and Management, 21 (1-3), pp. 72-82; Iqbal, C.M., Shalij, P.R., Supply chain performance measurement: a conceptual framework for ornamental fish supply chain (2016) International Journal of Supply Chain and Operations Resilience, 2 (4), pp. 267-288; Jagan Mohan, R., Neelakanteswara, R., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30, pp. 40-47; Jüttner, U., Peck, H., Christopher, M., Supply chain risk management: outlining an agenda for future research (2003) International Journal of Logistics: Research and Applications, 6 (4), pp. 197-210; Kafetzopoulos, D., Vouzas, F., Skalkos, D., Developing and validating an innovation drivers' measurement instrument in the agri-food sector (2020) British Food Journal, 122 (4), pp. 1199-1214; Katiyar, R., Barua, M.K., Meena, P.L., Analysing the interactions among the barriers of supply chain performance measurement: an ISM with fuzzy MICMAC approach (2018) Global Business Review, 19 (1), pp. 48-68; Khan, U., Haleem, A., Smart organisations: modelling of enablers using an integrated ISM and fuzzy-MICMAC approach (2013) International Journal of Intelligent Enterprise, 1 (3-4), p. 248; Kozarevic, S., Puska, A., Use of fuzzy logic for measuring practices and performances of supply chain (2018) Operations Research Perspectives, 5, pp. 150-260; Kühne, B., Lefebvre, V., Vermeire, B., Gellynck, X., Measuring innovation capacity in the agrifood sector: from single companies to value chains (2010) Journal on Chain and Network Science, 10 (3), pp. 145-157; Kumar, S., Routroy, S., Improving supply chain performance by Supplier Development program through enhanced visibility (2018) Materials Today: Proceedings, 5 (2), pp. 3629-3638; Kumar, A., Singh, R.K., Modgil, S., Exploring the relationship between ICT, SCM practices and organizational performance in agri-food supply chain (2020) Benchmarking, 27 (3), pp. 1003-1041; Kyllönen, H., Helo, P., SCOR based food supply chain's sustainable performance evaluation model (2012) Advanced Materials Research, 488-489, pp. 1039-1045. , Vols; Laihonen, H., Pekkola, S., Impacts of using a performance measurement system in supply chain management: a case study (2016) International Journal of Production Research, 20 (7543), pp. 1-11; Larrea‐Gallegos, G., Vázquez‐Rowe, I., Wiener, H., Kahhat, R., Applying the technology choice model in consequential life cycle assessment: a case study in the Peruvian agricultural sector (2019) Journal of Industrial Ecology, 23 (3), pp. 601-614; Lees, N., Nuthall, P., Wilson, M.M.J., Relationship quality and supplier performance in food supply chains (2020) International Food and Agribusiness Management Review, 23 (3), pp. 425-445; Lin, L.C., Li, T.S., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Software Quality Journal, 18 (3), pp. 387-406; Mangla, S.K., Luthra, S., Rich, N., Kumar, D., Rana, N.P., Dwivedi, Y.K., Enablers to implement sustainable initiatives in agri-food supply chains (2018) International Journal of Production Economics, 203 (April 2017), pp. 379-393; Martinez-Lopez, A., Millan-Linares, M.C., Rodriguez-Martin, N.M., Millan, F., Montserrat-de la Paz, S., Nutraceutical value of kiwicha (Amaranthus caudatus L.) (2020) Journal of Functional Foods, 65 (December 2019); Mazzawi, R., Alawamleh, M., The impact of supply chain performance drivers and value chain on companies: a case study from the food industry in Jordan (2013) International Journal of Networking and Virtual Organisations, 12 (2), pp. 122-132; Meena, P., Katiyar, R., Barua, M.K., Analysing the interactions among the barriers of supply chain performance measurement: an ISM with fuzzy MICMAC approach (2017) Global Business Review, 19 (1), pp. 1-21; Mishra, D., Gunasekaran, A., Rameshwar, D., Papadopoulos, T., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Moazzam, M., Akhtar, P., Garnevska, E., Marr, N.E., Measuring agri-food supply chain performance and risk through a new analytical framework: a case study of New Zealand dairy (2018) Production Planning and Control, 29 (15), pp. 1258-1274; Modgil, S., Sharma, S., Information systems, supply chain management and operational performance: tri-linkage—an exploratory study on pharmaceutical industry of India (2017) Global Business Review, 18 (3), pp. 652-677; Mohanty, M., Assessing sustainable supply chain enablers using total interpretive structural modeling approach and fuzzy-MICMAC analysis (2018) Management of Environmental Quality: An International Journal, 29 (2), pp. 216-239; Mora-Monge, C., Quesada, G., Gonzalez, M.E., Davis, J.M., Trust, power and supply chain integration in Web-enabled supply chains (2019) Supply Chain Management, 24 (4), pp. 524-539; Moreira, M., Tjahjono, B., Applying performance measures to support decision- making in supply chain operations: a case of beverage industry (2015) International Journal of Production Research, (August), pp. 1-21; Mura, M., Longo, M., Micheli, P., Bolzani, D., The evolution of sustainability measurement research (2018) International Journal of Management Reviews, 20 (3), pp. 661-695; Najmi, A., Makui, A., A conceptual model for measuring supply chain's performance (2012) Production Planning and Control, 23 (9), pp. 694-706; Oreja-Rodriguez, J.R., Yanes-Estevez, V., Garcia-Perez, A.M., Perceived environmental uncertainty in the agrifood supply chain (2010) British Food Journal, 112 (7), pp. 688-709; Panjehfouladgaran, H., Yusuff, R., Fuzzy performance measurement for supply chain management in Malaysian rubber glove manufacturer (2016) International Journal of Logistics Systems and Management, 24 (2), pp. 178-199; Patidar, R., Agrawal, S., A mathematical model formulation to design a traditional Indian agri-fresh food supply chain: a case study problem (2020) Benchmarking: An International Journal, 27 (8), pp. 2341-2363; Peano, C., Girgenti, V., Baudino, C., Giuggioli, N.R., Blueberry supply chain in Italy: management, innovation and sustainability (2017) Sustainability, 9 (261), pp. 1-17; Priya, T.S., Vivek, N., Restructuring the agricultural supply chain (2015) International Journal of Business Innovation and Research, 10 (1), pp. 135-148; Punniyamoorthy, M., Thamaraiselvan, N., Assessment of supply chain risk: scale development and validation (2013) Benchmarking: An International Journal, 20 (1), pp. 79-105; Puška, A., Kozarević, S., Okičić, J., Investigating and analyzing the supply chain practices and performance in agro-food industry (2020) International Journal of Management Science and Engineering Management, 15 (1), pp. 9-16; Quang, H.T., Hara, Y., Risks and performance in supply chain: the push effect (2017) International Journal of Production Research, 7543 (August), pp. 1-20; Quesada, H., Gazo, R., Sanchez, S., Critical factors affecting supply chain management: a case study in the US pallet industry (2012) Pathways to Supply Chain Excellence, pp. 33-56; Rajaguru, R., Matanda, M.J., Role of compatibility and supply chain process integration in facilitating supply chain capabilities and organizational performance (2019) Supply Chain Management: An International Journal, 24 (2), pp. 301-316; Ralston, P.M., Blackhurst, J., Cantor, D.E., Crum, M.R., A structure – conduct – performance perspective of how strategic supply chain integration affects firm performance (2015) Journal of Supply Chain Management, (April), pp. 47-65; Repo-Carrasco-Valencia, R., Hellström, J.K., Pihlava, J.M., Mattila, P.H., Flavonoids and other phenolic compounds in Andean indigenous grains: quinoa (Chenopodium quinoa), kañiwa (Chenopodium pallidicaule) and kiwicha (Amaranthus caudatus) (2010) Food Chemistry, 120 (1), pp. 128-133; Rogers, H., Srivastava, M., Pawar, K.S., Shah, J., Supply chain risk management in India ‐ practical insights (2016) International Journal of Logistics Research and Applications, 19 (4), pp. 278-299; Sahoo, S., Aligning operational practices to competitive strategies to enhance the performance of Indian manufacturing firms (2020) Benchmarking: An International Journal, 28 (1), pp. 131-165; Salin, V., Information technology in agri-food supply chains (1998) International Food and Agribusiness Management Review, 1 (3), pp. 329-334; Saputri, V.H.L., Sutopo, W., Hisjam, M., Ma'aram, A., Sustainable agri-food supply chain performance measurement model for GMO and Non-GMO using data envelopment analysis method (2019) Applied Sciences (Switzerland), 9 (6), pp. 1-11; Septiani, W., Marimin, Herdiyeni, Y., Haditjaroko, L., Risk dependency chain model of dairy agro-industry supply chain using fuzzy logic approach (2016) Supply Chain Forum, 17 (4), pp. 218-230; Shalij, P.R., Iqbal, C.M., Supply chain performance measurement: a conceptual framework for ornamental fish supply chain (2016) International Journal of Supply Chain and Operations Resilience, 2 (4), p. 267; Sharma, V., Dewangan, D.K., Agrawal, R., Enablers of eco-innovation to enhance the competitiveness of the Indian manufacturing sector: an integrated ISM-fuzzy MICMAC approach (2017) International Journal of Business Innovation and Research, 13 (4), p. 475; Shashi, Singh, R., Centobelli, P., Cerchione, R., Evaluating partnerships in sustainability-oriented food supply chain: a five-stage performance measurement model (2018) Energies, 11 (12). , 3473; Shohan, S., Ali, S.M., Kabir, G., Ahmed, S.K.K., Suhi, S.A., Haque, T., Green supply chain management in the chemical industry: structural framework of drivers (2019) International Journal of Sustainable Development and World Ecology, 26 (8), pp. 752-768; Siddh, M.M., Soni, G., Jain, R., Sharma, M.K., Structural model of perishable food supply chain quality (PFSCQ) to improve sustainable organizational performance (2018) Benchmarking, 25 (7), pp. 2272-2317; Sillanpää, I., Empirical study of measuring supply chain performance (2015) Benchmarking: An International Journal, 22 (2), pp. 290-308; Singh, R., Sandhu, H.S., Dev, G.N., Metri, B.A., Kaur, R., Technology, I., Modeling supply chain performance: a structural equation Approach (2013) International Journal of Information Systems and Supply Chain Management, 6 (4), pp. 18-41; Srinivasan, R., Swink, M., Leveraging supply chain integration through planning comprehensiveness: an organizational information processing (2015) Decision Sciences, 46 (5), pp. 823-861; Swanson, D., Goel, L., Francisco, K., Stock, J., An analysis of supply chain management research by topic (2018) Supply Chain Management, 23 (2), pp. 100-116; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain issues in SMEs: select insights from cases of Indian origin (2013) Production Planning and Control: The Management of Operations, 24 (1), pp. 47-71; Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T., Agrifood supply chain management: a comprehensive hierarchical decision-making framework and a critical taxonomy (2013) Biosystems Engineering, 120, pp. 47-64; Vinrald Samuel, M., Shah, M., Sahay, B.S., An insight into agri-food supply chains: a review (2012) International Journal of Value Chain Management, 6 (2), pp. 115-143; Vlajic, J.V., Van Lokven, S.W.M., Haijema, R., Van Der Vorst, J.G.A.J., Using vulnerability performance indicators to attain food supply chain robustness (2013) Production Planning and Control, 24 (8-9), pp. 785-799; Xiao, Y., Flexibility measure analysis of supply chain (2015) International Journal of Production Research, 53 (10), pp. 3161-3174; Yeboah Nyamah, E., Jiang, Y., Feng, Y., Enchill, E., Agri-food supply chain performance: an empirical impact of risk (2017) Management Decision, 55 (5), pp. 872-891; Zhao, G., Liu, S., Lopez, C., Chen, H., Lu, H., Mangla, S.K., Elgueta, S., Risk analysis of the agri-food supply chain: a multi-method approach (2020) International Journal of Production Research, 58 (16), pp. 4851-4876},
correspondence_address1={Hazen, B.; Logistikum, Austria; email: hazenscm@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Shashidharan2022263,
author={Shashidharan, M. and Mahajan, Y. and Anwar, S.},
title={Enhancement of Supply Chain Management through Educating Key Processes with the Hub and Spoke Model Based on Multi-Adaptive Spider Monkey Optimization Algorithm},
journal={World Journal of English Language},
year={2022},
volume={12},
number={3},
pages={263-276},
doi={10.5430/wjel.v12n3p263},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141113384&doi=10.5430%2fwjel.v12n3p263&partnerID=40&md5=15530567007bea1f6d82856ddd737c6f},
affiliation={AMET (Academy of Maritime Education and Training) University, India; Symbiosis centre for management and HRD, Pune, India},
abstract={Supply chain performance improvement has become a critical issue for gaining a competitive edge for companies. Over the last decade of the evolution of supply chain management (SCM), a steady stream of research and articles dealing with the theory and practice of SCM have been published, but the topic of performance measurement has not received adequate consideration. However, many critical drawbacks prevent the existing performance measurement systems from making a significant contribution to the development and improvement of SCM and thus, several researches is still needed in this area. So, we present a unique multi-adaptive spider monkey optimization algorithm (MASMOA) to enhance the ability of hub and spoke model to educate the key process throughout the SCM. Initially, for the pre-processing step, we use a normalizing technique to remove unwanted noises from the raw dataset, and then we use principal component analysis (PCA) to extract the unwanted features from the pre-processed data. The information was then encrypted and fed into the hub-and-spoke model for data protection purpose. The proposed method is then used to optimize the hub and spoke model's transport process over hubs. The optimized data is then decoded using a decryption technique to transform the encrypted information into actual format. The Rivest–Shamir–Adleman (RSA) algorithm was used for the encryption and decryption operations. Finally, performance measures such as encryption and decryption execution time, security rate, and scalability level are compared to comparable techniques. Using the MATLAB environment, the results are shown. Copyright for this article is retained by the author(s),},
author_keywords={Decryption;  Encryption;  Hub and Spoke model;  MATLAB tool;  Multi-Adaptive Spider Monkey Optimization Algorithm (MASMOA);  Rivest–Shamir–Adleman (RSA);  Supply Chain Management (SCM)},
references={Chen, S., Liu, X., Yan, J., Hu, G., Shi, Y., Processes, benefits, and challenges for adoption of blockchain technologies in food supply chains: A thematic analysis (2021) Information Systems and e-Business Management, 19 (3), pp. 909-935. , https://doi.org/10.1007/s10257-020-00467-3; Esmizadeh, Y., Mellat Parast, M., Logistics and supply chain network designs: Incorporating competitive priorities and disruption risk management perspectives (2021) International Journal of Logistics Research and Applications, 24 (2), pp. 174-197. , https://doi.org/10.1080/13675567.2020.1744546; Guerrero Campanur, A., Olivares-Benitez, E., Miranda, P. A., Perez-Loaiza, R. E., Ablanedo-Rosas, J. H., Design of a logistics nonlinear system for a complex, multiechelon, supply chain network with uncertain demands (2018) Complexity, 2018, pp. 1-16. , https://doi.org/10.1155/2018/4139601; Kayvanfar, V., Moattar Husseini, S. M., Sajadieh, M. S., Karimi, B., A multi-echelon multi-product stochastic model to supply chain of small-and-medium enterprises in industrial clusters (2018) Computers and Industrial Engineering, 115, pp. 69-79. , https://doi.org/10.1016/j.cie.2017.11.003; Li, L., Wang, B., Cook, D. P., Enhancing green supply chain initiatives via empty container reuse (2014) Transportation Research Part E, 70, pp. 190-204. , https://doi.org/10.1016/j.tre.2014.06.018; Lu, H., Li, L., Zhao, X., Cook, D., A model of integrated regional logistics hub in supply chain (2018) Enterprise Information Systems, 12 (10), pp. 1308-1335. , https://doi.org/10.1080/17517575.2018.1533588; Madhwal, Y., Panfilov, P. B., Blockchain and supply chain management: aircrafts’parts’business case (2017) DAAAM Proceedings, 28, pp. 1051-1056. , https://doi.org/10.2507/28th.daaam.proceedings.146; Mondal, S., Wijewardena, K. P., Karuppuswami, S., Kriti, N., Kumar, D., Chahal, P., Blockchain inspired RFID-based information architecture for food supply chain (2019) IEEE Internet of Things Journal, 6 (3), pp. 5803-5813. , https://doi.org/10.1109/JIOT.2019.2907658; Nabhani, F., Uhl, C., Kauf, F., Shokri, A., Supply chain process optimisation via the management of variance (2018) Journal of Management Analytics, 5 (2), pp. 136-153. , https://doi.org/10.1080/23270012.2018.1424571; Nakandala, D., Lau, H., Zhao, L., Development of a hybrid fresh food supply chain risk assessment model (2017) International Journal of Production Research, 55 (14), pp. 4180-4195. , https://doi.org/10.1080/00207543.2016.1267413; Nasr, N., Niaki, S. T. A., Hussenzadek Kashan, A., Seifbarghy, M., An efficient solution method for an agri-fresh food supply chain: Hybridization of Lagrangian relaxation and genetic algorithm (2021) Environmental Science and Pollution Research International, pp. 1-19. , https://doi.org/10.1007/s11356-021-13718-8; Patidar, R., Venkatesh, B., Pratap, S., Daultani, Y., A sustainable vehicle routing problem for Indian agri-food supply chain network design (2018) 2018 international conference on production and operations management society (POMS), pp. 1-5. , (December) IEEE Publications; Rahimi, M., Asef-Vaziri, A., Harrison, R., An inland port location-allocation model for a regional intermodal goods movement system (2008) Maritime Economics and Logistics, 10 (4), pp. 362-379. , https://doi.org/10.1057/mel.2008.17; Soltani, M., Mohammadi Zanjani, M., A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation (2021) International Journal of Industrial Engineering and Production Research, 32 (2), p. 0. , 0; Son, D., Kim, S., Jeong, B., Sustainable part consolidation model for customized products in closed-loop supply chain with additive manufacturing hub (2021) Additive Manufacturing, 37, p. 101643. , https://doi.org/10.1016/j.addma.2020.101643; Wang, J., Yue, H., Food safety pre-warning system based on data mining for a sustainable food supply chain (2017) Food Control, 73, pp. 223-229. , https://doi.org/10.1016/j.foodcont.2016.09.048; Yan, C., Wang, X., Yang, K., Fuzzy reliability optimization for 2-hub center problem with cluster-based policy and application in cross-border supply chain network design using TS Algorithm (2020) Complexity, 2020, pp. 1-12. , https://doi.org/10.1155/2020/5065640; Yang, A., Li, Y., Liu, C., Li, J., Zhang, Y., Wang, J., Research on logistics supply chain of iron and steel enterprises based on block chain technology (2019) Future Generation Computer Systems, 101, pp. 635-645. , https://doi.org/10.1016/j.future.2019.07.008},
correspondence_address1={Shashidharan, M.; AMET (Academy of Maritime Education and Training) UniversityIndia; email: shashi.amet@gmail.com},
publisher={Sciedu Press},
issn={19250703},
language={English},
abbrev_source_title={World J. English Lang.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Anjomshoae2022,
author={Anjomshoae, A. and Banomyong, R. and Mohammed, F. and Kunz, N.},
title={A systematic review of humanitarian supply chains performance measurement literature from 2007 to 2021},
journal={International Journal of Disaster Risk Reduction},
year={2022},
volume={72},
doi={10.1016/j.ijdrr.2022.102852},
art_number={102852},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124732905&doi=10.1016%2fj.ijdrr.2022.102852&partnerID=40&md5=145ca7042a36eed5e06e0680bec004a1},
affiliation={Thammasat Business School, Thammasat University, 2 Prachan Road, Pranakorn District, Bangkok, 10200, Thailand; Department of Materials, Manufacturing & Industrial Engineering, School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi, Malaysia, Johor Bahru, Malaysia; Coggin College of Business, University of North Florida, Jacksonville, United States},
abstract={Performance measurement has been an integral component of decision-making concerning the efficiency, effectiveness, and transparency of disaster relief operations. Recently, there has been an increasing amount of research in humanitarian relief supply chain performance measurement, which necessitates a thorough analysis of recent developments. To this end, this paper provides a systematic review of humanitarian supply chain performance measurement studies, through a categorical analysis of research articles. The authors identified and categorized research articles according to seven structural dimensions: disaster type, context of operation, disaster management phase, data analysis technique, level of analysis, performance measurement system development process, and performance measurement theme. This review intends to link the trends and theories in the broader body of performance measurement to the specific issues encountered in humanitarian supply chain performance measurement. A systematic literature review was developed by employing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology. The authors identified and reviewed 83 peer-reviewed journal articles that have appeared since 2007, the year in which the first journal article on humanitarian supply chain performance measurement was published. The review reveals that while there is a growing literature on humanitarian supply chain performance measurement, there is limited empirical evidence about performance measurement system implementation. This paper identifies two key research streams: contextual-based performance measurement and simulation and predictive performance measurement. This review contributes to theory by developing detailed categories of performance measurement studies and future research directions. The results are limited to peer-reviewed journals focusing on supply chain and operations management literature, and hence do not cover the literature from humanitarian organizations technical reports, independent evaluations and other academic fields such as organizational studies or accounting literature. © 2022 Elsevier Ltd},
author_keywords={Humanitarian logistics;  Humanitarian supply chain;  Literature review;  Performance measurement;  PRISMA},
references={UNHCR global trends - forced displacement in 2020 (2020), https://www.unhcr.org/flagship-reports/globaltrends/, (Accessed 22 September 2021); Coronavirus and aid: what we're watching (2020), https://www.thenewhumanitarian.org/news/2020/06/11/coronavirus-humanitarian-aid-response, (Accessed 10 June 2021); A record number of people will need help worldwide during 2020: UN Humanitarian Overview (2019), https://news.un.org/en/story/2019/12/1052731, (Accessed 10 June 2021); Besiou, M., Pedraza‐Martinez, A.J., Van Wassenhove, L.N., (2021) Humanitarian Operations and the UN Sustainable Development Goals. Production and Operations Management, 13579. , poms; Cardoso, B., Fontainha, T., Leiras, A., Cardoso, P.A., Performance evaluation in humanitarian operations based on the beneficiary perspective (2021) Int. J. Prod. Perform. Manag. Ahead-of-print, p. 475; Paciarotti, C., Valiakhmetova, I., Evaluating disaster operations management: an outcome‐process integrated approach (2021) Prod. Oper. Manag., 30, pp. 543-562; Dufour, C., Geoffroy, V., Maury, H., Grünewald, F., Rights, standards and quality in a complex humanitarian space: is Sphere the right tool? (2004) Disasters, 28, pp. 124-141; Anjomshoae, A., Hassan, A., Kunz, N., Wong, K.Y., de Leeuw, S., Toward a dynamic balanced scorecard model for humanitarian relief organizations’ performance management (2017) J. Humanit. Logist. Supply Chain Manag., 7, pp. 194-218; Anjomshoae, A., Hassan, A., Wong, K.Y., Banomyong, R., An integrated multi-stage fuzzy inference performance measurement scheme in humanitarian relief operations (2021) Int. J. Disaster Risk Reduc., p. 102298; ALNAP, Breaking the Mould: Alternative Approaches to Monitoring and Evaluation (2019), https://www.alnap.org/help-library/breaking-the-mould-alternative-approaches-to-monitoring-and-evaluation, ALNAP (Accessed 14 June 2021); Lu, Q., Goh, M., de Souza, R., A SCOR framework to measure logistics performance of humanitarian organizations (2016) J. Humanit. Logist. Supply Chain Manag., 6, pp. 222-239; Agarwal, S., Kant, R., Shankar, R., Humanitarian supply chain management frameworks: a critical literature review and framework for future development (2019) Benchmarking, 26, pp. 1749-1780; Apte, A., Understanding readiness metrics for the humanitarian operations through literature review (2020) J. Humanit. Logist. Supply Chain Manag., 10, pp. 321-345; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Manag. Res. News, 32, pp. 1038-1049; Patil, A., Shardeo, V., Madaan, J., Modelling performance measurement barriers of humanitarian supply chain (2020) Int. J. Prod. Perform. Manag. Ahead-of-print, p. 31; Kovács, G., Tatham, P., Humanitarian logistics performance in the light of gender (2009) Int. J. Prod. Perform. Manag., 58, pp. 174-187; Kaewkitipong, L., Chen, C.C., Ractham, P., A community-based approach to sharing knowledge before, during, and after crisis events: a case study from Thailand (2016) Comput. Hum. Behav., 54, pp. 653-666; Kunz, N., An automated quantitative content analysis process for humanitarian logistics research (2019) J. Humanit. Logist. Supply Chain Manag., 9, pp. 475-491; Clarke, M., Parris, B.W., Understanding disasters: managing and accommodating different worldviews in humanitarian response (2019) Int. J. Humanitarian Action, 4; HELiX, HELiX Focus Session C3 - how to measure satisfaction of humanitarian efforts (2021), https://www.youtube.com/watch?v=ph0aqhTcdLk, (Accessed 10 June 2021); Haavisto, I., Goentzel, J., Measuring humanitarian supply chain performance in a multi-goal context (2015) J. Humanit. Logist. Supply Chain Manag., 5, pp. 300-324; Rongier, C., Lauras, M., Galasso, F., Gourc, D., Towards a crisis performance-measurement system (2013) Int. J. Comput. Integrated Manuf., 26, pp. 1087-1102; Ramalingam, B., Mitchell, J., Counting what counts: performance and effectiveness in the humanitarian sector (2009) 8th Review of Humanitarian Action: Performance, Impact and Innovation, pp. 1-90; Davidson, A., Key Performance Indicators in Humanitarian Logistics (2006), Master Thesis USA; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) Int. J. Public Sect. Manag., 21, pp. 4-25; Schiffling, S., Piecyk, M., Performance measurement in humanitarian logistics: a customer-oriented approach (2014) J. Humanit. Logist. Supply Chain Manag., 4, pp. 198-221; Fontainha, T.C., Silva, L.D.O., de Lira, W.M., Leiras, A., Bandeira, R.A.D.M., Scavarda, L.F., Reference process model for disaster response operations (2020) Int. J. Logist. Res. Appl., pp. 1-26; Jeble, S., Kumari, S., Venkatesh, V.G., Singh, M., Influence of big data and predictive analytics and social capital on performance of humanitarian supply chain (2019) Benchmark Int. J., 27, pp. 606-633; Swaminathan, J.M., Big data analytics for rapid, impactful, sustained, and efficient (RISE) humanitarian operations (2018) Prod. Oper. Manag., 27, pp. 1696-1700; Dubey, R., Gunasekaran, A., Childe, S.J., Roubaud, D., Fosso Wamba, S., Giannakis, M., Foropon, C., Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain (2019) Int. J. Prod. Econ., 210, pp. 120-136; Abidi, H., de Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: a systematic literature review (2014) Supply Chain Manag.: Int. J., 19, pp. 592-608; Banomyong, R., Varadejsatitwong, P., Oloruntoba, R., A systematic review of humanitarian operations, humanitarian logistics and humanitarian supply chain performance literature 2005 to 2016 (2017) Ann. Oper. Res., 175, p. 475; Pettit, S., Beresford, A., Critical success factors in the context of humanitarian aid supply chains (2009) Int. J. Phys. Distrib. Logist. Manag., 39, pp. 450-468; Higgins, J.P.T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., Welch, V.A., Cochrane Handbook for Systematic Reviews of Interventions (2019), John Wiley & Sons; Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L.A., Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement (2015) Syst. Rev., 4, p. 1; Page, M.J., Moher, D., Evaluations of the uptake and impact of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement and extensions: a scoping review (2017) Syst. Rev., 6, p. 263; van Eck, N.J., Waltman, L., Software survey: VOSviewer, a computer program for bibliometric mapping (2010) Scientometrics, 84, pp. 523-538; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod., 16, pp. 1699-1710; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: a literature review (2010) Int. J. Prod. Res., 48, pp. 5137-5155; Van Wassenhove, L.N., Humanitarian aid logistics: supply chain management in high gear (2006) J. Oper. Res. Soc., 57, pp. 475-489; Beamon, B., Kotleba, S., Inventory modelling for complex emergencies in humanitarian relief operations (2006) Int. J. Logist., 9, pp. 1-18; Kunz, N., Reiner, G., A meta‐analysis of humanitarian logistics research (2012) J. Humanit. Logist. Supply Chain Manag., 2, pp. 116-147; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: a literature review (2017) Comput. Ind. Eng., 113, pp. 333-346; Abidi, H., de Leeuw, S., Dullaert, W., Performance management practices in humanitarian organisations (2020) J. Humanit. Logist. Supply Chain Manag., 10, pp. 125-168; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, Implementing and updating performance measurement systems (2000) Int. J. Op. Prod. Manag., 20, pp. 754-771; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced scorecard for natural disaster management projects, Disaster Prevention and Management (2007) Int. J., 16, pp. 785-806; Small, H., Co-citation in the scientific literature: a new measure of the relationship between two documents (1973) J. Am. Soc. Inf. Sci., 24, pp. 265-269; van Eck, N.J., Waltman, L., (2013) VOSviewer Manual, 1, pp. 1-53. , Univeristeit Leiden Leiden; Zhou, Q., Huang, W., Zhang, Y., Identifying critical success factors in emergency management using a fuzzy DEMATEL method (2011) Saf. Sci., 49, pp. 243-252; Zhou, X., Shi, Y., Deng, X., Deng, Y., D-DEMATEL: a new method to identify critical success factors in emergency management (2017) Saf. Sci., 91, pp. 93-104; Yadav, D.K., Barve, A., Analysis of critical success factors of humanitarian supply chain: an application of Interpretive Structural Modeling (2015) Int. J. Disaster Risk Reduc., 12, pp. 213-225; Blecken, A., Supply chain process modelling for humanitarian organizations (2010) Int. J. Phys. Distrib. Logist. Manag., 40, pp. 675-692; Perkins, M., Grey, A., Remmers, H., What do we really mean by “Balanced Scorecard” (2014) Int. J. Prod. Perform. Manag., 63, pp. 148-169; Ferreira, L.M.D.F., Silva, C., Azevedo, S.G., An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM) (2016) Benchmarking, 23, pp. 1398-1422; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) Int. J. Prod. Res., 53, pp. 4917-4926; Bhusiri, N., Tay, H.L., Developing performance measurement tool for slow-onset humanitarian supply chain operation (2021) Proceedings of the 11th Annual International Conference on Industrial Engineering and Operations Management; Blecken, A., Hellingrath, B., Dangelmaier, W., Schulz, S.F., A humanitarian supply chain process reference model (2009) Int. J. Serv. Technol. Manag., 12, p. 391; da Costa, N.D.B.O.L., Fontainha, T.C., Leiras, A., Brazilian Air Force operations in disaster response – a process analysis (2017) Disaster Prev. Manag., 26, pp. 479-498; D'Haene, C., Verlinde, S., Macharis, C., Measuring while moving (humanitarian supply chain performance measurement – status of research and current practice) (2015) J. Humanit. Logist. Supply Chain Manag., 5, pp. 146-161; Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) Int. J. Prod. Perform. Manag., 64, pp. 784-810; Berenguer, G., Modeling approaches and metrics to evaluate nonprofit operations (2016) Advances in Managing Humanitarian Operations, pp. 9-31. , C.W. Zobel N. Altay M.P. Haselkorn Springer International Publishing Cham; Acimovic, J., Goentzel, J., Models and metrics to assess humanitarian response capacity (2016) J. Oper. Manag., 45, pp. 11-29; Nath, R., Shannon, H., Kabali, C., Oremus, M., Investigating the Key Indicators for Evaluating Post-disaster Shelter, Disasters (2016); Izadikhah, M., Azadi, M., Kahi, V.S., Saen, R.F., Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains (2018) Int. J. Prod. Res., 2, pp. 1-21; Schön, A.-M., Al-Saadi, S., Grubmueller, J., Schumann-Bölsche, D., Developing a camp performance indicator system and its application to Zaatari, Jordan (2018) J. Humanit. Logist. Supply Chain Manag., 5, p. 17; Wagner, S.M., Thakur-Weigold, B., Gatti, F., Stumpf, J., Measuring and improving the impact of humanitarian logistics consulting (2021) Prod. Plann. Control, 32, pp. 83-103; Oloruntoba, R., An analysis of the Cyclone Larry emergency relief chain: some key success factors (2010) Int. J. Prod. Econ., 126, pp. 85-101; Abidi, H., Klumpp, M., de Leeuw, S., Modelling impact of key success factors in humanitarian logistics (2015) Logistics Management: Products, Actors, Technology — Proceedings of the German Academic Association for Business Research, Bremen, 2013, pp. 427-443. , J. Dethloff H.-D. Haasis H. Kopfer H. Kotzab J. Schönberger Springer Cham; Celik, E., Gumus, A.T., Alegoz, M., A trapezoidal type-2 fuzzy MCDM method to identify and evaluate critical success factors for humanitarian relief logistics management (2014) J. Intell. Fuzzy Syst., 27, pp. 2847-2855; Han, Y., Deng, Y., An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors (2018) Soft Comput., 22, pp. 5073-5090; Dangi, H.K., Bardhan, A.K., Some critical success factors in planning humanitarian relief operations in India (2014) Int. J. Emerg. Manag., 10, p. 103; Azmat, M., Atif, M., Kummer, S., Identification and prioritization of critical success factors in faith-based and non-faith-based organizations' humanitarian supply chain (2019) J. Int. Humanitarian Action, 4, p. 592; Rajakaruna, S., Wijeratne, A.W., Effectiveness of logistics skills to individual performance: challenges to Sri Lankan humanitarian sector (2019) Int. J. Learn. Change, 11, p. 324; Vaillancourt, A., Haavisto, I., Country logistics performance and disaster impact (2016) Disasters, 40, pp. 262-283; Salem, M., van Quaquebeke, N., Besiou, M., Meyer, L., Intergroup leadership: how leaders can enhance performance of humanitarian operations (2019) Prod. Oper. Manag., 28, pp. 2877-2897; Maghsoudi, A., Pazirandeh, A., Visibility, resource sharing and performance in supply chain relationships: insights from humanitarian practitioners (2016) Supply Chain Manag.: Int. J., 21, pp. 125-139; Dube, N., van der Vaart, T., Teunter, R.H., Van Wassenhove, L.N., Host government impact on the logistics performance of international humanitarian organisations (2016) J. Oper. Manag.; Urrea, G., Villa, S., Gonçalves, P., Exploratory analyses of relief and development operations using social networks (2016) Soc. Econ. Plann. Sci.; Villa, S., Gonçalves, P., Villy Odong, T., Understanding the contribution of effective communication strategies to program performance in humanitarian organizations (2017) J. Humanit. Logist. Supply Chain Manag., 7, pp. 126-151; Talwar, S., Kaur, P., Fosso Wamba, S., Dhir, A., Big Data in operations and supply chain management: a systematic literature review and future research agenda (2021) Int. J. Prod. Res., 3, pp. 1-26; Jerving, S., A strategic mindset shift is needed by NGOs to fully embrace technology (2018), https://www.devex.com/news/a-strategic-mindset-shift-is-needed-by-ngos-to-fully-embrace-technology-92749, (Accessed 14 September 2021); Gupta, S., Altay, N., Luo, Z., Big data in humanitarian supply chain management: a review and further research directions (2019) Ann. Oper. Res., 283, pp. 1153-1173; Kumar, P., Singh, R.K., Application of Industry 4.0 technologies for effective coordination in humanitarian supply chains: a strategic approach (2021) Ann. Oper. Res., 2, p. 31; Khan, M., Imtiaz, S., Parvaiz, G.S., Hussain, A., Bae, J., Integration of internet-of-things with blockchain technology to enhance humanitarian logistics performance (2021) IEEE Access, 9, pp. 25422-25436; Kunz, N., Gold, S., Sustainable humanitarian supply chain management – exploring new theory (2017) Int. J. Logist. Res. Appl., 20 (2), pp. 85-104; Muhammad, S., Kullapa, S., Lean readiness assessment model – a tool for Humanitarian Organizations' social and economic sustainability (2020) J. Humanitarian Logist. Supply Chain Manag. Ahead-of-print; Laguna-Salvadó, L., Lauras, M., Okongwu, U., Comes, T., A multicriteria Master Planning DSS for a sustainable humanitarian supply chain (2019) Ann. Oper. Res., 283, pp. 1303-1343; Bag, S., Luthra, S., Venkatesh, V.G., Yadav, G., Towards understanding key enablers to green humanitarian supply chain management practices (2020) Manag. Environ. Qual. Int. J., 31, pp. 1111-1145; Burton, H.V., Miles, S.B., Kang, H., Integrating performance-based engineering and urban simulation to model post-earthquake housing recovery (2018) Earthq. Spectra, 34, pp. 1763-1785; Debacker, M., van Utterbeeck, F., Ullrich, C., Dhondt, E., Hubloue, I., A discrete-event simulation model for testing responses to mass casualty incidents (2016) J. Med. Syst., 40, pp. 1-15; Wang, Z., Zhang, J., Agent-based evaluation of humanitarian relief goods supply capability (2019) Int. J. Disaster Risk Reduc., 36, p. 101105; Allahi, F., Fateh, A., Revetria, R., Cianci, R., The COVID-19 epidemic and evaluating the corresponding responses to crisis management in refugees: a system dynamic approach (2021) J. Humanitarian Logist. Supply Chain Manag. Ahead-of-print, p. 510; Banomyong, R., Sopadang, A., Using Monte Carlo simulation to refine emergency logistics response models: a case study (2010) Int. J. Phys. Distrib. Logist. Manag., 40, pp. 709-721; Besiou, M., Pedraza-Martinez, A.J., Van Wassenhove, L.N., Vehicle supply chains in humanitarian operations: decentralization, operational mix, and earmarked funding (2014) Prod. Oper. Manag., 23, p. 1950. , 1965; Besiou, M., Stapleton, O., Van Wassenhove, L.N., System dynamics for humanitarian operations (2011) J. Humanit. Logist. Supply Chain Manag., 1, pp. 78-103; Kunz, N., Reiner, G., Gold, S., Investing in disaster management capabilities versus pre-positioning inventory: a new approach to disaster preparedness (2014) Int. J. Prod. Econ., 157, pp. 261-272; Oladimeji, O., Cross, J., Keathley-Herring, H., System Dynamics Applications in Performance Measurement Research: Progress and Challenges, Management Decision Ahead-Of-Print (2020), p. 931; Mochizuki, J., Toyasaki, F., Sigala, I.F., Toward resilient humanitarian cooperation: examining the performance of horizontal cooperation among humanitarian organizations using an agent-based modeling (ABM) approach (2015) J. Nat. Disaster Sci., 36, pp. 35-52; Sahebjamnia, N., Torabi, S.A., Mansouri, S.A., A hybrid decision support system for managing humanitarian relief chains (2017) Decis. Support Syst., 95, pp. 12-26; Folan, P., Browne, J., A review of performance measurement: towards performance management (2005) Comput. Ind., 56, pp. 663-680; Van der Laan, E.A., Brito, M.D., Vergunst, D.A., Performance measurement in humanitarian supply chains (2009) Int. J. Risk Assess. Manag., 13, p. 22; Chehbi-Gamoura, S., Derrouiche, R., Damand, D., Barth, M., Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model (2020) Prod. Plann. Control, 31, pp. 355-382; Venkatesh, V.G., Zhang, A., Deakins, E., Luthra, S., Mangla, S., A Fuzzy AHP-TOPSIS Approach to Supply Partner Selection in Continuous Aid Humanitarian Supply Chains (2018), Annals of Operations Research; Harpring, R., Maghsoudi, A., Fikar, C., Piotrowicz, W., Heaslip, G., An analysis of compounding factors of epidemics in complex emergencies: a system dynamics approach (2021) J. Humanit. Logist. Supply Chain Manag.; Bianchi, C., Dynamic Performance Management (2016), Springer International Publishing Cham; Vega, D., Case studies in humanitarian logistics research (2018) J. Humanit. Logist. Supply Chain Manag., 8, pp. 134-152; Kunz, N., Van Wassenhove, L.N., Besiou, M., Hambye, C., Kovács, G., Relevance of humanitarian logistics research: best practices and way forward (2017) Int. J. Op. Prod. Manag., 175; Tofighi, S., Torabi, S.A., Mansouri, S.A., Humanitarian logistics network design under mixed uncertainty (2016) Eur. J. Oper. Res., 250, pp. 239-250; Liberatore, F., Pizarro, C., de Blas, C.S., Ortuño, M.T., Vitoriano, B., Uncertainty in humanitarian logistics for disaster management: a review (2013) Decision Aid Models for Disaster Management and Emergencies, pp. 45-74. , B. Vitoriano Atlantis Press Amsterdam; Azmat, M., Kummer, S., Importance of key success factors for local and international ngos in humanitarian supply chain (2019) Logforum, 15, pp. 545-555; Prakash, C., Besiou, M., Charan, P., Gupta, S., Organization theory in humanitarian operations:: a review and suggested research agenda (2020) J. Humanit. Logist. Supply Chain Manag., 10, pp. 261-284; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Ind. Manag. Data Syst., 110, pp. 1297-1318; Ladhari, R., A review of twenty years of SERVQUAL research (2009) Int. J. Qual. Serv. Sci., 1, pp. 172-198; Cronin, J.J., Taylor, S.A., Servperf versus servqual: reconciling performance-based and perceptions-minus-expectations measurement of service quality (1994) J. Market., 58, pp. 125-131; Anjomshoae, A., Banomyong, R., Kunz, N., Maghsoudi, A., A qualitative system dynamics model for humanitarian supply chain resilience (2021) The 25th International Symposium on Logistics (ISL) 2021: Building Resilience for Supply Chains; Bhusiri, N., Banomyong, R., Julagasigorn, P., Varadejsatitwong, P., Dhami, N., A purchasing portfolio model for humanitarian supply chain resilience: perspectives from a development aid context (2021) J. Humanit. Logist. Supply Chain Manag.; Han, Y., Chong, W.K., Li, D., A systematic literature review of the capabilities and performance metrics of supply chain resilience (2020) Int. J. Prod. Res., 58, pp. 4541-4566; Azmat, M., Kummer, S., Potential applications of unmanned ground and aerial vehicles to mitigate challenges of transport and logistics-related critical success factors in the humanitarian supply chain (2020) Asian J. Sustain. Soc. Responsib., 5, p. 20; Celik, E., Taskin Gumus, A., An Assessment Approach for Non-governmental Organizations in Humanitarian Relief Logistics and an Application in Turkey (2015), pp. 1-26. , Technological and Economic Development of Economy; Celik, E., Taskin Gumus, A., An outranking approach based on interval type-2 fuzzy sets to evaluate preparedness and response ability of non-governmental humanitarian relief organizations (2016) Comput. Ind. Eng., 101, pp. 21-34; Anjomshoae, A., Hassan, A., Wong, K.Y., An integrated AHP-based scheme for performance measurement in humanitarian supply chains (2019) Int. J. Prod. Perform. Manag., 68, pp. 938-957; Agarwal, S., Kant, R., Shankar, R., Evaluating solutions to overcome humanitarian supply chain management barriers: a hybrid fuzzy SWARA - fuzzy WASPAS approach (2020) Int. J. Disaster Risk Reduc., 51, p. 101838; Ataseven, C., Nair, A., Ferguson, M., The role of supply chain integration in strengthening the performance of not-for-profit organizations: evidence from the food banking industry (2020) J. Humanit. Logist. Supply Chain Manag., 10, pp. 101-123; Dangi, H., Bardhan, A.K., Narag, A.S., Humanitarian relief logistics: an exploratory study for need and importance of performance measurement system (2012) Int. J. Logist. Syst. Manag., 13, p. 1; Fu, J.S., Lai, C.-H., Determinants of organizational performance in disaster risk reduction: a preliminary study of global humanitarian NGOs (2020) J. Contingencies Crisis Manag., , n/a; Heyse, L., Morales, F.N., Wittek, R., Evaluator perceptions of NGO performance in disasters: meeting multiple institutional demands in humanitarian aid projects (2021) Disasters, 45, pp. 324-354; Khan, M., Lee, H., Bae, J., The role of transparency in humanitarian logistics (2019) Sustainability, 11, p. 2078; Namagembe, S., Enhancing service delivery in humanitarian relief chains: the role of relational capital (2020) J. Humanit. Logist. Supply Chain Manag., 10, pp. 169-203; Rajakaruna, S., Wijeratne, A., Mann, T., Yan, C., Effect of individual skills and performance on humanitarian organisations: a structural equation model (2017) Logistics, 1, p. 7; Bardhan, A.K., Dangi, H.K., Drivers and indicators of performance in relief chain: an empirical study (2016) Global Bus. Rev., 17, pp. 88-104; Diedrichs, D.R., Phelps, K., Isihara, P.A., Quantifying communication effects in disaster response logistics (2016) J. Humanit. Logist. Supply Chain Manag., 6, pp. 24-45; Gu, L., Ryzhov, I.O., Eftekhar, M., The facts on the ground: evaluating humanitarian fleet management policies using simulation (2021) Eur. J. Oper. Res., 293, pp. 681-702; Hwang, S., Park, M., Lee, H.-S., Lee, S., Hybrid simulation framework for immediate facility restoration planning after a catastrophic disaster (2016) J. Construct. Eng. Manag., 142, p. 4016026; Krejci, C.C., Hybrid simulation modeling for humanitarian relief chain coordination (2015) J. Humanit. Logist. Supply Chain Manag., 5, pp. 325-347; Mishra, V., Sharma, M.G., Understanding humanitarian supply chain through causal modelling (2020) South Asian J. Bus. Manag. Cases, 9, pp. 317-329; Peng, M., Peng, Y., Chen, H., Post-seismic supply chain risk management: a system dynamics disruption analysis approach for inventory and logistics planning (2014) Comput. Oper. Res., 42, pp. 14-24},
correspondence_address1={Anjomshoae, A.; Thammasat Business School, 2 Prachan Road, Pranakorn District, Thailand; email: anjomshoae.a@tbs.tu.ac.th},
publisher={Elsevier Ltd},
issn={22124209},
language={English},
abbrev_source_title={Int. J. Disaster Risk Reduct.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Aman2022,
author={Aman, S. and Seuring, S.},
title={Interestingly it's innovation: Reviewing sustainability performance management in the base of the pyramid (BoP)},
journal={Technovation},
year={2022},
volume={112},
doi={10.1016/j.technovation.2021.102394},
art_number={102394},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116722273&doi=10.1016%2fj.technovation.2021.102394&partnerID=40&md5=e6f4a7ba6a8b91caa66814ab39086348},
affiliation={Supply Chain Management, University of Kassel, Kleine Rosenstraße 1 – 3, Kassel, 34117, Germany},
abstract={While the relevance of business and supply chain processes in base of the pyramid context is much discussed, the link to performance measurement (PM) has not been clarified. Focusing on sustainable supply chain performance measurement (SSCPM), this paper provides a literature review to determine which PM tools, instruments, and indicators (PIs) have been used in the base-of-the-pyramid (BoP) literature and how they are associated with sustainability performance outcomes. Exploring SSCPM allows identifying key PIs (KPIs) considering all three dimensions of sustainability: social, environmental, and economic. A structured content analysis on 91 selected papers was performed, and the results interpreted using frequencies and contingency analysis. The core factor driving performance was found to be innovation. The findings suggest that the PIs used in the analysed BoP literature are primarily conventional, leaving a gap in the understanding of the sustainability perspective. The BoP literature also lacks an appropriate PM instrument and discusses predominantly specific indicators of these instruments, showing that research at the intersection of sustainable supply chain management (SSCM) and SCPM demands further development. Nevertheless, incorporating innovation into the PM system informs BoP policymakers to understand better how performance objectives can be achieved in BoP contexts. © 2021 Elsevier Ltd},
author_keywords={Base of the pyramid;  BoP;  Performance management;  Performance measurement;  Supply chain management;  Sustainability},
keywords={Automobile drivers;  Research and development management;  Sustainable development, Base of the pyramids;  IT innovation;  Measurements instruments;  Performance management;  Performance measurements;  Supply chain performance measurements;  Supply chain process;  Sustainability performance;  Sustainable supply chains, Supply chain management},
references={indicates that the paper was a part of the sample analysed for the extant study. A full list can be obtained from the first author; Aall, C., The manifold history of eco‐auditing and the case of municipal eco‐auditing in Norway (1999) Eco-Manage. Audit. J. Corp. Environ. Manage., 6 (4), pp. 151-157; Adegbile, A., Sarpong, D., Disruptive innovation at the base-of-the-pyramid (2018) Crit. Perspect. Int. Bus., 14 (2-3), pp. 111-138. , ∗; Agnihotri, A., Doing good and doing business at the bottom of the pyramid (2013) Bus. Horiz., 56, pp. 591-599. , ∗; Aguinis, H., Performance Management (2009), second ed. Pearson Upper Saddle River, NJ; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) J. Clean. Prod., 86, pp. 360-377; Ahlstrom, D., Innovation and growth: how business contributes to society (2010) Acad. Manag. Perspect., 24 (3), pp. 11-24; Ahrens, F., Dobrzykowski, D., Sawaya, W., Addressing mass-customisation trade-offs in bottom of the pyramid markets (2019), 49 (5), pp. 451-472. , ∗; Al-Saa'da, R.J., Taleb, Y.K.A., Al Abdallat, Al-Mahasneh, R.A.A., Nimer, N.A., Al-Weshah, G.A., Supply chain management and its effect on health care service quality: quantitative evidence from Jordanian private hospitals (2013) J. Manag. Strat., 4 (2), pp. 42-51; Anderson, J., Markides, C., Strategic innovation at the base of the pyramid (2007) MIT Sloan Manag. Rev., 49 (1), pp. 83-88; Ansari, S., Munir, K., Gregg, T., Impact at the ‘bottom of the pyramid’: the role of social capital in capability development and community empowerment (2012) J. Manag. Stud., 49 (4), pp. 813-842. , ∗; Aras, G., Crowther, D., Corporate sustainability reporting: a study in disingenuity? (2009) J. Bus. Ethics, 87 (1), p. 279; Arnold, D.G., Valentin, A., Corporate social responsibility at the base of the pyramid (2013) J. Bus. Res., 66, pp. 1904-1914. , ∗; Arnold, D.G., Williams, L., The paradox at the base of the pyramid: environmental sustainability and market-based poverty alleviation (2012) Int. J. Technol. Manag., 60 (1-2), pp. 44-59. , ∗; Arzu, A.G., Erman, E.T., Supply chain performance measurement: a literature review (2010) Int. J. Prod. Res., 48 (17), pp. 5137-5155; Azmat, F., Ha, H., ‘Corporate social responsibility, customer trust, and loyalty – perspectives from a developing country’ (2013) Int. Bus. Rev., 55 (3), pp. 253-270; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Manag.: Int. J., 17 (1), pp. 78-92; Bals, L., Tate, W.L., Sustainable supply chain design in social businesses: advancing the Theory of Supply Chain (2018) J. Bus. Logist., 39 (1), pp. 57-79; Bardy, R., Drew, S., Kennedy, T.F., Foreign investment and ethics: how to contribute to social responsibility by doing business in less- developed countries (2012) J. Bus. Ethics, 106, pp. 267-282. , ∗; Beckmann, M., Hielscher, S., Pies, I., Commitment strategies for sustainability: how business firms can transform tradeoffs into win-win outcomes (2014) Bus. Strat. Environ., 23 (1), pp. 18-37; Beckett, J.R., Chmielewski, D.A., Dembek, K., Taking the Time to Understand Time at the Bottom/base of the Pyramid (2020), pp. 1-32. , Business & Society; Bendul, J.C., Rosca, E., Pivovarova, D., Sustainable supply chain models for base of the pyramid (2017) J. Clean. Prod., 162, pp. S107-S120. , ∗; Benoît, C., Norris, G.A., Valdivia, S., Ciroth, A., Moberg, A., Bos, U., The guidelines for social life cycle assessment of products: just in time! (2010) Int. J. Life Cycle Assess., 15 (2), pp. 156-163; Berger, R., Choi, C.J., Kim, J.B., Responsible leadership for multinational enterprises in bottom of pyramid countries: the knowledge of local managers (2011) J. Bus. Ethics, 101 (4), pp. 553-561; Berger, E., Nakata, C., Implementing technologies for financial service innovations in base of the pyramid markets (2013) J. Prod. Innovat. Manag., 30 (6), pp. 1199-1211. , ∗; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., ‘20 years of performance measurement in sustainable supply chain management – what has been achieved?’ (2015) Supply Chain Manag.: Int. J., 20 (6), pp. 664-680; Borchardt, M., Pereira, G., Ferreira, A.R., Soares, M., Sousa, J., Battaglia, D., Leveraging frugal innovation in micro-and small enterprises at the base of the pyramid in Brazil: an analysis through the lens of dynamic capabilities (2020) J. Entrepren. Emerg. Econom., 2053-4604, pp. 1-23. , ∗; Brewer, B., Ashenbaum, B., Blair, C.W., Cross‐functional influence and the supplier selection decision in competitive environments: who makes the call? (2019) J. Bus. Logist., 40 (2), pp. 105-125; Brix-Asala, C., Hahn, R., Seuring, S., Reverse logistics and informal valorisation at the Base of the Pyramid: a case study on sustainability synergies and trade-offs (2016) Eur. Manag. J., 34 (4), pp. 414-423. , ∗; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analysing iterative KPI accomplishment (2009) Decis. Support Syst., 46 (2), pp. 512-521; Calton, J.M., Werhane, P.H., Hartman, L.P., Bevan, D., Building partnerships to create social and economic value at the base of the global development pyramid (2013) J. Bus. Ethics, 117, pp. 721-733. , ∗; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Manag., 8 (3), pp. 209-223; Chenhall, R.H., Langfield-Smith, K., Multiple perspectives of performance measures (2007) Eur. Manag. J., 25 (4), pp. 266-282; Das, D., Sustainable supply chain management in Indian organisations: an empirical investigation (2018) Int. J. Prod. Res., 56 (17), pp. 5776-5794; Daly, A., Zannetti, P., Air pollution modeling–An overview (2007) Ambient. Air Pollut., pp. 15-28; Dembek, K., York, J., Singh, P.J., Creating value for multiple stakeholders: sustainable business models at the Base of the Pyramid (2018) J. Clean. Prod., 196, pp. 1600-1612. , ∗; Dewangan, V., Godse, M., Towards a holistic enterprise innovation performance measurement system (2014) Technovation, 34 (9), pp. 536-545; Duarte, A.L.D.C.M., Macau, F., e Silva, Sanches, L.M., Last mile delivery to the bottom of the pyramid in Brazilian slums (2019) Int. J. Phys. Distrib. Logist. Manag., 49 (5), pp. 473-491; Edmonds, E.V., Pavcnik, N., Child labor in the global economy (2005) J. Econ. Perspect., 19 (1), pp. 199-220; Elaydi, R., Harrison, C., Strategic motivations and choice in subsistence markets (2010) J. Bus. Res., 63 (6), pp. 651-655; Environmental pollution centers, https://www.environmentalpollutioncenters.org/noise-pollution/; Fairtrade International, https://www.fairtrade.net/standard/aims; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., ‘The sustainability balanced scorecard–linking sustainability management to business strategy’ (2002) Bus. Strat. Environ., 11 (5), pp. 269-284; Fink, A., Conducting Research Literature Reviews, from the Internet to Paper (2014), fourth ed. Sage London; Fleiss, J.L., Levin, B., Paik, M.C., Statistical Methods for Rates and Proportions (2013), John wiley & sons; Gilbert, D.U., Rasche, A., Waddock, S., Accountability in a global economy: The emergence of international accountability standards (2011) Bus. Ethics Q., 21 (1), pp. 23-44; Glover, J., Touboulic, A., ‘Tales from the countryside: unpacking “passing the environmental buck” as hypocritical practice in the food supply chain’ (2020) J. Bus. Res., 121, pp. 33-46; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organisational resources: a literature review (2010) Corp. Soc. Responsib. Environ. Manag., 17, pp. 230-245; Gold, S., Hahn, R., Seuring, S., ‘Sustainable supply chain management in ‘base of the pyramid’ food projects – a path to triple bottom line approaches for multinationals?’ (2013) Int. Bus. Rev., 22, pp. 784-799. , ∗; Goldstein, R.D., Child Abuse and Neglect: Cases and Materials (1999), p. 178. , West Group St. Paul, MN; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) Int. J. Prod. Perform. Manag., 61 (5), pp. 518-547; Graafland, J.J., Eijffinger, S.C., SmidJohan, H., Benchmarking of corporate social responsibility: methodological problems and robustness (2004) J. Bus. Ethics, 53 (1-2), pp. 137-152; Granstrand, O., Holgersson, M., Innovation ecosystems: a conceptual review and a new definition (2020) Technovation, 90, p. 102098; Gray, D.E., Theoretical Perspectives and Research Methodologies, Doing Research in the Real World (2014), third ed. Stage Publication London; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2005) Int. J. Prod. Econ., 87 (3), pp. 333-347; Hahn, R., Gold, S., ‘Resources and governance in “base of the pyramid”-partnerships: assessing collaborations between businesses and non-business actors’ (2014) J. Bus. Res., 67 (7), pp. 1321-1333. , ∗; Hald, K.S., Ellegaard, C., Supplier evaluation processes: the shaping and reshaping of supplier performance (2011) Int. J. Oper. Prod. Manag., 31 (8), pp. 888-910; Hall, J., Matos, S.V., Martin, M.J., Innovation pathways at the base of the pyramid: establishing technological legitimacy through social attributes (2014) Technovation, 34 (5-6), pp. 284-294. , ∗; Hall, J., Matos, S., Sheehan, L., Silvestre, B., Entrepreneurship and innovation at the base of the pyramid: a recipe for inclusive growth or social exclusion? (2012) J. Manag. Stud., 49 (4), pp. 785-812. , ∗; Halme, M., Lindeman, S., Linna, P., Innovation for inclusive business: intrapreneurial bricolage in multinational corporations (2012) J. Manag. Stud., 49 (4), pp. 743-784. , ∗; Hart, S.L., A natural-resource-based view of the firm (1995) Acad. Manag. Rev., 20 (4), pp. 986-1014; Hart, S.L., Christensen, C.M., The great leap: driving innovation from the base of the pyramid (2002) MIT Sloan Manag. Rev., 44 (1), p. 51. , ∗; Hernandez-Cazares, R., Lawson-Lartego, L., Mathiassen, L., Quinonez-Romandia, S., Strategising for the bottom of the pyramid: an action research into a Mexican agribusiness (2019) J. Bus. Ind. Market., 35 (10), pp. 1475-1489. , ∗; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmark Int. J., 12 (4), pp. 330-353; Heuër, A., Women-to-women entrepreneurial energy networks: a pathway to green energy uptake at the base of pyramid (2017) Sustain. Energy Technol. Assess., 22, pp. 116-123. , ∗; Hirmer, S., Cruickshank, H., Making the deployment of pico-PV more sustainable along the value chain (2014) Renew. Sustain. Energy Rev., 30, pp. 401-411. , ∗; Howell, R., van Beers, C., Doorn, N., Value capture and value creation: the role of information technology in business models for frugal innovations in Africa (2018) Technol. Forecast. Soc. Change, 131, pp. 227-239. , ∗; Huan, S., Sheoran, S., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Manag., 9 (1), pp. 23-29; Huber, S., Bassen, A., Towards a sustainability reporting guideline in higher education (2018) Int. J. Sustain. High Educ., 19 (2), pp. 218-232; Hult, G.T.M., Ferrell, O.C., Hurley, R.F., Global organisational learning effects on cycle time performance (2002) J. Bus. Res., 55 (5), pp. 377-387; https://www.igi-global.com/dictionary/gender-diversity-in-the-senior-management-of-large-technology-companies/49101, IGI Global; (1996) ISO 14000 Series in Environmental Management Systems, , International Organization for Standardization Geneva; Jia, F., Zuluaga-Cardona, L., Bailey, A., Rueda, X., Sustainable supply chain management in developing countries: an analysis of the literature (2018) J. Clean. Prod., 189, pp. 263-278. , ∗; Karamchandani, A., Kubzansky, M., Lalwani, N., Is the bottom of the pyramid really for you (2011) Harv. Bus. Rev., 89 (3), pp. 107-111; Kaplan, R.S., Norton, D.P., Rugelsjoen, B., Managing alliances with the balanced scorecard (2010) Harv. Bus. Rev., 88 (1), pp. 114-120; Kaplinsky, R., Schumacher meets Schumpeter: appropriate technology below the radar (2011) Res. Pol., 40 (2), pp. 193-203; Kaptein, M., Van Tulder, R., Toward effective stakeholder dialogue (2003) Bus. Soc. Rev., 108 (2), pp. 203-224; Karnani, A., The mirage of marketing to the bottom of the pyramid: how the private sector can help alleviate poverty? (2007) Calif. Manag. Rev., 49 (4), pp. 90-111; Khalid, R., Seuring, S., Analysing base-of-the-pyramid research from a (sustainable) supply chain perspective (2019) J. Bus. Ethics, 155, pp. 663-686; Khalid, R., Seuring, S., Beske, P., Land, A., Yawar, S.A., Wagner, R., Putting sustainable supply chain management into base of the pyramid research (2015) Supply Chain Manag.: Int. J., 20 (6), pp. 681-696. , ∗; Khalid, R.U., Seuring, S., Wagner, R., Evaluating supply chain constructs in the base of the pyramid environment (2020) J. Clean. Prod., p. 122415. , ∗; Kistruck, G.M., Webb, J.W., Sutter, C.J., Ireland, R.D., Microfranchising in base–of–the–pyramid markets: institutional challenges and adaptations to the franchise model (2011) Enterpren. Theor. Pract., 35 (3), pp. 503-531; Kolk, A., Rivera-Santos, M., Rufín, C., ‘Reviewing a decade of research on the “base/bottom of the pyramid” (BoP) concept’ (2014) Bus. Soc., 53 (3), pp. 338-377. , ∗; Koster, M., Vos, B., van der Valk, W., Drivers and barriers for adoption of a leading social management standard (SA8000) in developing economies (2019) Int. J. Phys. Distrib. Logist. Manag., 49 (5), pp. 534-551. , ∗; Lai, K.H., Wong, C.W.Y., Green logistics management and performance: some empirical evidence from Chinese manufacturing exporters (2012) Omega, 40 (3), pp. 267-282; Lim, C., Han, S., Ito, H., Capability building through innovation for unserved lower end mega markets (2013) Technovation, 33, pp. 391-404; Logan, D., Roy, D., Regelbrugge, L., Global Corporate Citizenship - Rationale and Strategies (1997), ∗ The Hitachi Foundation New York; London, T., Anupindi, R., Using the base-of-the-pyramid perspective to catalyse interdependence-based collaborations (2012) Proc. Natl. Acad. Sci. Unit. States Am., 109 (31), pp. 12338-12343. , ∗; London, T., Anupindi, R., Sheth, S., Creating mutual value: lessons learned from ventures serving the base of the pyramid producers (2010) J. Bus. Res., 63, pp. 582-594; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: an empirical analysis (2014) J. Purch. Supply Manag., 20 (2), pp. 113-129; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Mahapatra, S., William, W.S., Padhy, R., Alignment in the base of the pyramid producer supply chains: The case of the handloom sector in Odisha, India (2019) J. Bus. Logist., 40 (2), pp. 126-144; Marconatto, D.A.B., Barin-Cruz, L., Pozzebon, M., Poitras, J.E., Developing sustainable business models within BoP contexts: mobilising native capability to cope with government programs (2016) J. Clean. Prod., 129, pp. 735-748. , ∗; Mayring, P., Qualitative content analysis: theoretical background and procedures (2015) Approaches to Qualitative Research in Mathematics Education, pp. 365-380. , A. Bikner-Ahsbahs C. Knipping N.C. Presmeg Springer Dordrecht; Murphy, M., Perrot, F., Rivera-Santos, M., New perspectives on learning and innovation in cross-sector collaborations (2012) J. Bus. Res., 65, pp. 1700-1709. , ∗; Nahapiet, J., Ghoshal, S., Social capital, intellectual capital, and the organizational advantage (1998) Acad. Manag. Rev., 23 (2), pp. 242-266; Nakata, C., Weidner, K., Enhancing new product adoption at the base of the pyramid: a contextualised model (2012) J. Prod. Innovat. Manag., 29 (1), pp. 21-32. , ∗; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) Int. J. Oper. Prod. Manag., 15 (4), pp. 80-116; Nili, A., Tate, M., Barros, A., Johnstone, D., An approach for selecting and using a method of inter-coder reliability in information management research (2020) Int. J. Inf. Manag., 54, pp. 102-154; Nitkin, D., Brooks, L.J., Sustainability auditing and reporting: the Canadian experience (1998) J. Bus. Ethics, 17 (13), pp. 1499-1507; Owen, D.L., Swift, T.A., Humphrey, C., Bowerman, M., The new social audits: accountability, managerial capture or the agenda of social champions? (2000) Eur. Account. Rev., 9 (1), pp. 81-98; Pervez, T., Maritz, A., de Waal, A., Innovation and social entrepreneurship at the bottom of the pyramid – a conceptual framework (2013) S. Afr. J. Econ. Manag. Sci., 16 (Special Issue), pp. 54-66. , ∗; Prahalad, C.K., Bottom of the pyramid as a source of breakthrough innovations (2012) J. Prod. Innovat. Manag., 29 (1), pp. 6-12. , ∗; Prahalad, C.K., Hammond, A., Serving the world's poor, profitably (2002) Harv. Bus. Rev., pp. 1-11; Prahalad, C.K., Hart, S.L., The Fortune at the Bottom of the Pyramid (1998), pp. 2-14. , Strategy & Business; Ramachandran, J., Pant, A., Pani, S.K., Building the BoP producer ecosystem: the evolving engagement of Fabindia with Indian handloom artisans (2012) J. Prod. Innovat. Manag., 29 (1), pp. 33-51. , ∗; Ramani, S.V., Mukherjee, V., Can breakthrough innovations serve the poor (bop) and create reputational (CSR) value? Indian case studies (2014) Technovation, 34, pp. 295-305. , ∗; Ramani, S.V., SadreGhazi, S., Duysters, G., On the diffusion of toilets as bottom of the pyramid innovation: lessons from sanitation entrepreneurs (2012) Technol. Forecast. Soc. Change, 79, pp. 676-687. , ∗; Ray, S., Ray, P.K., Product innovation for the people's car in an emerging economy (2011) Technovation, 31 (5-6), pp. 216-227; Rebehy, P.C.P.W., Costa, A.L., Campello, C.A.G.B., Espinoza, D.F., Neto, M.J., Innovative social business of selective waste collection in Brazil (2017) J. Clean. Prod., 154, pp. 462-473. , ∗; Reficco, E., Márquez, P., Inclusive networks for building BoP markets (2012) Bus. Soc., 51 (3), pp. 512-554. , (*); Rosca, E., Arnold, M., Bendul, J.C., ‘Business models for sustainable innovation–an empirical analysis of frugal products and services’ (2017) J. Clean. Prod., 162, pp. S133-S145. , ∗; Rosca, E., Bendul, J.C., Value chain integration of base of the pyramid consumers: an empirical study of drivers and performance outcomes (2019) Int. Bus. Rev., 28 (1), pp. 162-176. , ∗; Rosca, G., Möllering, A., Rijal, J., Christine, B., Supply chain inclusion in base of the pyramid markets: a cluster analysis and implications for global supply chains (2019) Int. J. Phys. Distrib. Logist. Manag., 49 (5), pp. 575-598. , ∗; Saeed, M.A., Kersten, W., Supply chain sustainability performance indicators: a content analysis based on published standards and guidelines (2017) Logist. Res., 10 (12), pp. 1-19; Saul, C.J., Gebauer, H., Digital transformation as an enabler for advanced services in the sanitation sector (2018) Sustainability, 10 (3), p. 752; Schaltegger, S., Burritt, R., Measuring and managing sustainability performance of supply chains: review and sustainability supply chain management framework (2014) Supply Chain Manag.: Int. J., 19 (3), pp. 232-241; Schaltegger, S., Burritt, R., Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Manag.: Int. J., 19 (3), pp. 275-291; Schrader, C., Freimann, J., Seuring, S., Business strategy at the base of the pyramid (2012) Bus. Strat. Environ., 21, pp. 281-298. , ∗; Schuster, T., Holtbrügge, D., Market entry of multinational companies in markets at the bottom of the pyramid: a learning perspective (2012) Int. Bus. Rev., 21, pp. 817-830. , ∗; Schuster, T., Holtbrügge, D., Benefits of cross-sector partnerships in markets at the base of the pyramid (2014) Bus. Strat. Environ., 23, pp. 188-203. , ∗; Scott, I., A business model for success: enterprises serving the base of the pyramid with off-grid solar lighting (2017) Renew. Sustain. Energy Rev., 70, pp. 50-55. , ∗; Sesan, T., Raman, S., Clifford, M., Forbes, I., Corporate-led sustainable development and energy poverty alleviation at the bottom of the pyramid: the case of the Clean Cook Nigeria (2013) World Dev., 45, pp. 137-146. , ∗; Seuring, S., Brix-Asala, C., Khalid, R.U., Analysing base-of-the-pyramid projects through sustainable supply chain management (2019) J. Clean. Prod., 212 (1), pp. 1086-1097. , ∗; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod., 16 (15), pp. 1699-1710; Seuring, S., Yawar, S.A., Land, A., Khalid, R.U., Sauer, P.C., ‘The application of theory in literature reviews – illustrated with examples from supply chain management’ (2020) Int. J. Oper. Prod. Manag., 41 (1), pp. 1-20; Shivarajan, S., Srinivasan, A., The poor as suppliers of intellectual property: a social network approach to sustainable poverty alleviation (2013) Bus. Ethics Q., 23 (3), pp. 381-406. , ∗; Silvestre, B.S., Neto, R.S., Capability accumulation, innovation, and technology diffusion: lessons from a base of the pyramid cluster (2014) Technovation, 34, pp. 270-283. , ∗; Sinkovics, N., Sinkovics, R.R., Yamin, M., ‘The role of social value creation in business model formulation at the bottom of the pyramid – implications for MNEs?’ (2014) Int. Bus. Rev., 23, pp. 692-707. , ∗; Steers, R.M., Antecedents and outcomes of organizational commitment (1977) Adm. Sci. Q., pp. 46-56; Sutter, C.J., Kistruck, G.M., Morris, S., Adaptions to knowledge templates in base-of-the-pyramid markets: the role of social interaction (2014) Strat. Entrepren. J., 8, pp. 303-320. , ∗; https://www.hq.nasa.gov/iwgsdi/Waste_Poduction.html, Sustainable Development Indicator Group; Tanskanen, P., Management and recycling of electronic waste (2013) Acta Mater., 61 (3), pp. 1001-1011; Terziovski, M., Samson, D., Dow, D., The business value of quality management systems certification. Evidence from Australia and New Zealand (1997) J. Oper. Manag., 15 (1), pp. 1-18; https://www.un.org/en/sections/issues-depth/human-rights/, United Nations; Van den Waeyenberg, S., Hens, L., Overcoming institutional distance: expansion to base-of-the-pyramid markets (2012) J. Bus. Res., 65, pp. 1692-1699. , ∗; VanSandt, C.V., Sud, M., Poverty alleviation through partnerships: a road less travelled for business, governments, and entrepreneurs (2012) J. Bus. Ethics, 110, pp. 321-332. , ∗; Varadarajan, R., Kaul, R., Doing well by doing good innovations: alleviation of social problems in emerging markets through corporate social innovations (2017) J. Bus. Res., 86, pp. 225-233. , ∗; Varga, V., Rosca, E., Driving impact through base of the pyramid distribution models (2019) Int. J. Phys. Distrib. Logist. Manag., 49 (5), pp. 492-513; Viswanathan, M., Sridharan, S., Ritchie, R., Venugopal, S., Jung, K., Marketing interactions in subsistence marketplaces: a bottom-up approach to designing public policy (2012) J. Publ. Pol. Market., 31 (2), pp. 159-177. , ∗; Wan, F., Williamson, P.J., Yin, E., Antecedents and implications of disruptive innovation: evidence from China (2015) Technovation, 39, pp. 94-104; Wilding, R., Wagner, B., Seuring, S., Gold, S., ‘Conducting content‐analysis based literature reviews in supply chain management’ (2012) Supply Chain Manag.: Int. J., 7 (5), pp. 544-555; Yawar, S.A., Seuring, S., The role of supplier development in managing social and societal issues in supply chains (2018) J. Clean. Prod., 182, pp. 227-237; Zhu, Q., Sarkis, J., Lai, K.H., Choosing the right approach to green your supply chains (2019) Modern Supply Chain Res. Appl., 1 (1), pp. 54-67; Zhu, F., Wei, Z., Bao, Y., Zou, S., Base-of-the-Pyramid (BOP) orientation and firm performance: a strategy tripod view and evidence from China (2019) Int. Bus. Rev., 28 (6), p. 101594. , ∗},
correspondence_address1={Seuring, S.; Supply Chain Management, Kleine Rosenstraße 1 – 3, Germany; email: seuring@uni-kassel.de},
publisher={Elsevier Ltd},
issn={01664972},
coden={TNVTD},
language={English},
abbrev_source_title={Technovation},
document_type={Review},
source={Scopus},
}

@ARTICLE{Cavicchi20221551,
author={Cavicchi, C. and Vagnoni, E.},
title={The role of performance measurement in assessing the contribution of circular economy to the sustainability of a wine value chain},
journal={British Food Journal},
year={2022},
volume={124},
number={5},
pages={1551-1568},
doi={10.1108/BFJ-08-2021-0920},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115099542&doi=10.1108%2fBFJ-08-2021-0920&partnerID=40&md5=3f30b4d8f2c16d160afb126173faafed},
affiliation={Department of Economics and Management, University of Ferrara, Ferrara, Italy},
abstract={Purpose: This study aims to ascertain the extent to which a cooperative controlling a wine supply chain implements a performance measurement system (PMS) that monitors the effects of a circular economy (CE) strategy, developed through partnerships, on the economic and environmental sustainability of the value chain. Design/methodology/approach: The study analyzes the characteristics of the closed-loop business model and uses the PMS to assess the contribution of CE partnerships to the sustainability of the value chain. The case study is based on interviews conducted on the cooperative's top management and supplemented with an analysis of external reports, related documents and direct observations. Findings: The PMS was underpinned by enterprise resource planning (ERP), through which CE indicators control for the benefits generated on behalf of the cooperative and its CE partners. Originality/value: Given the paucity of the studies that address the performance measurement of CE at the supply chain level and its relation to sustainability, this study sheds light on the role that PMS can play in tracking the contribution of CE partnerships to the sustainability of a wine value chain operating in agro-waste valorization. Furthermore, the performance measurement of the CE strategy contributes to an assessment of the responsible production of sustainable development goals at the supply chain level. © 2021, Emerald Publishing Limited.},
author_keywords={Circular economy;  Partnerships;  Performance measurement system;  Sustainable development;  Wine value chain},
keywords={agricultural waste;  article;  environmental sustainability;  interview;  performance measurement system;  sustainable development;  valorization;  wine},
references={Abdous, M.H., He, W., Yen, C.J., Using data mining for predicting relationships between online question theme and final grade (2012) Educational Technology and Society, 15 (3), pp. 77-88; Alfiero, S., Christofi, M., Bonadonna, A., Street food traders, farmers and sustainable practice to reduce food waste in the Italian context (2020) British Food Journal, 122 (5), pp. 1361-1380; Amicarelli, V., Bux, C., Food waste measurement toward a fair, healthy and environmental-friendly food system: a critical review (2021) British Food Journal, 123 (8), pp. 2907-2935; Annunziata, E., Pucci, T., Frey, M., Zanni, L., The role of organizational capabilities in attaining corporate sustainability practices and economic performance: evidence from Italian wine industry (2018) Journal of Cleaner Production, 171, pp. 1300-1311; Baiano, A., An overview on sustainability in the wine production chain (2021) Beverages, 7 (1), p. 15; Berardi, P.C., Dias, J.M., How has the wine sector incorporated the premises of circular economy? (2019) Journal of Environmental Science and Engineering B, 8, pp. 108-117; Beske, P., Land, A., Seuring, S., Sustainable supply chain management practices and dynamic capabilities in the food industry: a critical analysis of the literature (2014) International Journal of Production Economics, 152, pp. 131-143; Brown, P., Bocken, N., Balkenende, R., Why do companies pursue collaborative circular oriented innovation? (2019) Sustainability, 11 (3), p. 635; Cantino, V., Giacosa, E., Cortese, D., A sustainable perspective in wine production for common-good management: the case of Fontanafredda biological ‘reserve’ (2019) British Food Journal, 121 (2), pp. 259-274; Ciccullo, F., Cagliano, R., Bartezzaghi, G., Perego, A., Implementing the circular economy paradigm in the agri-food supply chain: the role of food waste prevention technologies (2021) Resources, Conservation and Recycling, 164, p. 105114; Daou, A., Mallat, C., Chammas, G., Cerantola, N., Kayed, S., Saliba, N.A., The Ecocanvas as a business model canvas for a circular economy (2020) Journal of Cleaner Production, 258, p. 120938; Deng, L., Wang, R., Dong, T., Feng, J., Weisong, M., Assessing the table grape supply chain performance in China - a value chain analysis perspective (2016) British Food Journal, 118 (5), pp. 1129-1145; Donner, M., Gohier, R., de Vries, H., A new circular business model typology for creating value from agro-waste (2020) Science of the Total Environment, 716, p. 137065; Eisenhardt, K.M., Building theories from case study research (1989) The Academy of Management Review, 14 (4), pp. 532-550; Elia, V., Gnoni, M.G., Tornese, F., Measuring circular economy strategies through index methods: a critical analysis (2017) Journal of Cleaner Production, 142, pp. 2741-2751; Ellram, L.M., Tate, W.L., The use of secondary data in purchasing and supply management (P/SM) research (2016) Journal of Purchasing and Supply Management, 22 (4), pp. 250-254; Esposito, B., Sessa, M.R., Sica, D., Malandrino, O., Towards circular economy in the agri-food sector. A systematic literature review (2020) Sustainability, 12 (18), p. 7401; (2020) Farm to Fork Strategy, for a Fair, Healthy and Environmentally-Friendly Food System, , European Union, Brussels; (2011) Global Food Losses and Food Waste: Extent, Causes and Prevention, , FAO, Rome; (2021) Five reasons why a sustainable and circular global bioeconomy just makes sense, , http://www.fao.org/fao-stories/article/en/c/1330390/, FAO, Rome: :, accessed, 30 March 2021; Gromis di Trana, M., Bava, F., Pisoni, P., A sustainable value generator in the Italian wine industry: Casa E. di Mirafiore e Fontanafredda winery (2020) British Food Journal, 122 (5), pp. 1321-1340; Hillis, V., Lubell, M., Hoffman, M., Sustainability partnerships and viticulture management in California (2018) Journal of Environmental Management, 217, pp. 214-225; Howard, M., Hopkinson, P., Miemczyk, J., The regenerative supply chain: a framework for developing circular economy indicators (2019) International Journal of Production Research, 57 (23), pp. 7300-7318; Irvine, R.M., A conceptual study of value chain analysis as a tool for assessing a veterinary surveillance system for poultry in Great Britain (2015) Agricultural Systems, 135, pp. 143-158; Jonsen, K., Jehn, K.A., Using triangulation to validate themes in qualitative studies (2009) Qualitative Research in Organizations and Management: An International Journal, 4 (2), pp. 123-150; Jurgilevich, A., Birge, T., Kentala-Lehtonen, J., Korhonen-Kurki, K., Pietikäinen, J., Saikku, L., Schösler, H., Transition towards circular economy in the food system (2016) Sustainability, 8 (1), p. 69; Kalmykova, Y., Sadagopan, M., Rosado, L., Circular economy–From review of theories and practices to development of implementation tools (2018) Resources, Conservation and Recycling, 135, pp. 190-201; Kazancoglu, Y., Kazancoglu, I., Sagnak, M., A new holistic conceptual framework for green supply chain management performance assessment based on circular economy (2018) Journal of Cleaner Production, 195, pp. 1282-1299; Kristensen, H.S., Mosgaard, M.A., A review of micro level indicators for a circular economy–moving away from the three dimensions of sustainability? (2020) Journal of Cleaner Production, 243, p. 118531; Lubell, M., Hillis, V., Hoffman, M., Innovation, cooperation, and the perceived benefits and costs of sustainable agriculture practices (2011) Ecology and Society, 16 (4), p. 23; Marques, M.J., Bienes, R., Cuadrado, J., Ruiz‐Colmenero, M., Barbero‐Sierra, C., Velasco, A., Analyzing perceptions attitudes and responses of winegrowers about sustainable land management in Central Spain (2015) Land Degradation and Development, 26 (5), pp. 458-467; Merli, R., Preziosi, M., Acampora, A., Sustainability experiences in the wine sector: toward the development of an international indicators system (2018) Journal of Cleaner Production, 172, pp. 3791-3805; Mirabella, N., Castellani, V., Sala, S., Current options for the valorization of food manufacturing waste: a review (2014) Journal of Cleaner Production, 65, pp. 28-41; Montemayor, E., Bonmatí, A., Torrellas, M., Camps, F., Ortiz, C., Domingo, F., Riau, V., Antón, A., Environmental accounting of closed-loop maize production scenarios: Manure as fertilizer and inclusion of catch crops (2019) Resources, Conservation and Recycling, 146, pp. 395-404; Normann, R., Ramirez, R., From value chain to value constellation: designing interactive strategy (1993) Harvard Business Review, 71 (4), pp. 65-77; Pagotto, M., Halog, A., Towards a circular economy in Australian agri‐food industry: an application of input‐output oriented approaches for analyzing resource efficiency and competitiveness potential (2016) Journal of Industrial Ecology, 20 (5), pp. 1176-1186; Poltronieri, C.F., Ganga, G.M.D., Gerolamo, M.C., Maturity in management system integration and its relationship with sustainable performance (2019) Journal of Cleaner Production, 207, pp. 236-247; Pomarici, E., Vecchio, R., Mariani, A., Wineries' perception of sustainability costs and benefits: an exploratory study in California (2015) Sustainability, 7 (12), pp. 16164-16174; Porter, M., (1985) Competitive Advantage, , Free Press, New York, NY; Principato, L., Ruini, L., Guidi, M., Secondi, L., Adopting the circular economy approach on food loss and waste: the case of Italian pasta production (2019) Resources, Conservation and Recycling, 144, pp. 82-89; Pucci, T., Casprini, E., Galati, A., Zanni, L., The virtuous cycle of stakeholder engagement in developing a sustainability culture: Salcheto winery (2020) Journal of Business Research, 119, pp. 364-376; Saidani, M., Yannou, B., Leroy, Y., Cluzel, F., Kendall, A., A taxonomy of circular economy indicators (2019) Journal of Cleaner Production, 207, pp. 542-559; Salvioni, D.M., Almici, A., Transitioning toward a circular economy: the impact of stakeholder engagement on sustainability culture (2020) Sustainability, 12 (20), p. 8641; Santini, C., Cavicchi, A., Casini, L., Sustainability in the wine industry: key questions and research trends (2013) Agricultural and Food Economics, 1 (1), pp. 1-14; Sehnem, S., Ndubisi, N.O., Preschlak, D., Bernardy, R.J., Santos Junior, S., Circular economy in the wine chain production: maturity, challenges, and lessons from an emerging economy perspective (2020) Production Planning and Control, 31 (11-12), pp. 1-21; Sharma, Y.K., Mangla, S.K., Patil, P.P., Liu, S., When challenges impede the process: for circular economy-driven sustainability practices in food supply chain (2019) Management Decision, 57 (4), pp. 995-1017; Siepmann, L., Nicholas, K.A., German winegrowers' motives and barriers to convert to organic farming (2018) Sustainability, 10 (11), p. 4215; Szolnoki, G., A cross-national comparison of sustainability in the wine industry (2013) Journal of Cleaner Production, 53, pp. 243-251; Vaismoradi, M., Turunen, H., Bondas, T., Content analysis and thematic analysis: implications for conducting a qualitative descriptive study (2013) Nursing and Health Sciences, 15 (3), pp. 398-405; Yin, R.K., (2018) Case Study Research and Applications: Design and Methods, , Sage publications, Thousand Oaks; Zhu, Q., Jia, R., Lin, X., Building sustainable circular agriculture in China: economic viability and entrepreneurship (2019) Management Decision, 57 (4), pp. 1108-1122},
correspondence_address1={Cavicchi, C.; Department of Economics and Management, Italy; email: caterina.cavicchi@unife.it},
publisher={Emerald Group Holdings Ltd.},
issn={0007070X},
language={English},
abbrev_source_title={Br. Food J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Agarwal2022,
author={Agarwal, S. and Kant, R. and Shankar, R.},
title={Exploring sustainability balanced scorecard for performance evaluation of humanitarian organizations},
journal={Cleaner Logistics and Supply Chain},
year={2022},
volume={3},
doi={10.1016/j.clscn.2021.100026},
art_number={100026},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131442314&doi=10.1016%2fj.clscn.2021.100026&partnerID=40&md5=4cb0031a4eec9efae4c27e7ef30decee},
affiliation={Department of Mechanical Engineering, S. V. National Institute of Technology, Gujarat, Surat, 395007, India; Department of Management Studies, Indian Institute of Technology, New Delhi, Delhi, 110 016, India},
abstract={Performance measures are vital in assessing the disaster relief operations and mitigate the losses that occur during disaster. It is imperative to benchmark and monitor the growth of humanitarian organizations (HOs). The purpose of this research is to explore the performance measures for HOs based on a sustainability balanced scorecard (SBSC). This study identifies and finalizes 31 performance measures through literature and brainstorming session conducted with the experts. Finalized performance measures are classified into five humanitarian supply chain management (HSCM) performance perspectives using SBSC. The best worst method (BWM) is applied to prioritize the performance measures, and the additive ratio assessment (ARAS) is applied to evaluate the performance of HOs. The outcome of this research reveals that “beneficiaries’ and donors’ perspectives” is the most significant HSCM performance perspectives. The findings help to identify the relative importance of each performance measure for performance evaluation of HOs. The proposed framework can help HOs on the aspects of performance improvement relevant to both donors and beneficiaries group that facilitates stakeholders in the benchmarking and network design of HOs. This study explores the SBSC in HSCM to analyze the performance measures and evaluate the performance of HOs that enable an integrated performance measurement system. HOs monitored their logistics performance effectively, which further lead to process improvement for betterment. © 2022 The Authors},
author_keywords={Humanitarian supply chain management;  Performance evaluation;  Performance measures;  Sustainability balanced scorecard},
references={Abidi, H., de Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: a systematic literature review (2014) Supply Chain Manage.: Int. J., 19 (5-6), pp. 592-608; Abidi, H., de Leeuw, S., Dullaert, W., Performance management practices in humanitarian organizations (2020) J. Humanit. Logist. Supply Chain Manage., 10 (2), pp. 125-168; Altay, N., Gunasekaran, A., Dubey, R., Childe, S.J., Agility and resilience as antecedents of supply chain performance under moderating effects of organizational culture within the humanitarian setting: a dynamic capability view (2018) Prod. Planning Control, 29 (14), pp. 1158-1174; Anjomshoae, A., Hassan, A., Kunz, N., Wong, K.Y., de Leeuw, S., Toward a dynamic balanced scorecard model for humanitarian relief organizations’ performance management (2017) J. Humanit. Logist. Supply Chain Manage., 7 (2), pp. 194-218; Anjomshoae, A., Hassan, A., Wong, K.Y., An integrated AHP-based scheme for performance measurement in humanitarian supply chains (2019) Int. J. Prod. Perform. Manage., 68 (5), pp. 938-957; Apte, A., Understanding readiness metrics for the humanitarian operations through literature review (2020) J. Humanit. Logist. Supply Chain Manage., 10 (3), pp. 321-345; Baffoe, B.O.K., Luo, W., Humanitarian relief sustainability: a framework of humanitarian logistics digital business ecosystem (2020) Transp. Res. Procedia, 48, pp. 363-387; Baharamand, H., Comes, T., Lauras, M., Defining and measuring the network flexibility of humanitarian supply chains: insights from the 2015 Nepal earthquake (2017) Ann. Oper. Res., 283 (1), pp. 961-1000; Balki, M.K., Erdoğan, S., Aydın, S., Sayin, C., The optimization of engine operating parameters via SWARA and ARAS hybrid method in a small SI engine using alternative fuels (2020) J. Cleaner Prod., 258; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) Int. J. Public Sect., 21 (1), pp. 4-25; Behl, A., Dutta, P., Humanitarian supply chain management: a thematic literature review and future directions of research (2019) Ann. Oper. Res., 283 (1), pp. 1001-1044; Burcu Balcik, D.R., Haavisto, I., Goentzel, J., Measuring humanitarian supply chain performance in a multi-goal context (2015) J. Humanit. Logist. Supply Chain Manag., 5 (3), pp. 300-324; Chae, B., Developing key performance indicators for supply chain: an industry perspective (2009) Supply Chain Manage.: Int. J., 14 (6), pp. 422-428; Chai, N., Sustainability performance evaluation system in government; a balanced scorecard approach towards sustainable development (2009), Springer New York; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains (2011) Int. J. Prod. Perform. Manage., 60 (6), pp. 583-602; D'Haene, C., Verlinde, S., Macharis, C., Measuring while moving (humanitarian supply chain performance measurement – status of research and current practice (2015) J. Humanit. Logist. Supply Chain Manage., 5 (2), pp. 146-161; Davidson, A.L., Key Performance Indicators in Humanitarian Logistics, Doctoral dissertation (2006) Massachusetts Inst. Technol.; De Leeuw, S., (2010) Towards a reference mission map for performance measurement in humanitarian supply chains, pp. 166-172. , Berlin Springer Verlag Berlin; De Moura, E.H., (2020), T.B.R. e Cruz, D.M.D.G., Chiroli, A framework proposal to integrate humanitarian logistics practices, disaster management and disaster mutual assistance: A Brazilian case, Safety Science, 132 104965; Delmonteil, F.-X., Rancourt, M.-È., The role of satellite technologies in relief logistics (2017) J. Humanit. Logist. Supply Chain Manage., 7 (1), pp. 57-78; Dubey, R., Gunasekaran, A., The sustainable humanitarian supply chain design: agility, adaptability and alignment (2016) Int. J. Logist. Res. Appl., 19 (1), pp. 62-82; Duran, S., Gutierrez, M.A., Keskinocak, P., Pre-Positioning of Emergency Items for CARE International (2011) Interfaces, 41 (3), pp. 223-237; Elgazzar, S., Tipi, N., Jones, G., Key characteristics for designing a supply chain performance measurement system (2019) Int. J. Prod. Perform. Manage., 68 (2), pp. 296-318; Erbeyoğlu, G., Bilge, Ü., A robust disaster preparedness model for effective and fair disaster response (2020) Eur. J. Oper. Res., 280 (2), pp. 479-494; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard–linking sustainability management to business strategy (2002) Business Strategy Environment, 11 (5), pp. 269-284; Frennesson, L., Kembro, J., de Vries, H., Van Wassenhove, L., Jahre, M., Localisation of logistics preparedness in international humanitarian organisations (2021) J. Humanit. Logist. Supply Chain Manage., 11 (1), pp. 81-106; Ghenai, C., Albawab, M., Bettayeb, M., Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method (2020) Renewable Energy, 146, pp. 580-597; Glenn Richey, R., Pettit, S., Beresford, A., Critical success factors in the context of humanitarian aid supply chains (2009) Int. J. Phys. Distrib. Logist. Manag., 39 (6), pp. 450-468; Goldschmidt, K.H., Kumar, S., Humanitarian operations and crisis/disaster management: a retrospective review of the literature and framework for development (2016) Int. J. Disaster Risk Reduct., 20, pp. 1-13; Govindan, K., Shankar, K.M., Kannan, D., Achieving sustainable development goals through identifying and analyzing barriers to industrial sharing economy: A framework development (2020) Int. J. Prod. Econ., 227; Haavisto, I., Kovács, G., Perspectives on sustainability in humanitarian supply chains” (2014) Disaster Prevention and Management, 23 (5), pp. 610-631; Habib, M.S., Lee, Y.H., Memon, M.S., Mathematical Models in Humanitarian Supply Chain Management: A Systematic Literature Review (2016) Mathematical Problems in Engineering, 2016, pp. 1-20; Izadikhah, M., Azadi, M., Shokri Kahi, V., R., Farzipoor Saen, Developing a new chance constrained NDEA model to measure the performance of humanitarian supply chains (2019) Int. J. Prod. Res., 57 (3), pp. 662-682; Jahre, M., Kovács, G., Spens, K.M., Humanitarian logistics in disaster relief operations, Int. J. Phys. Distrib (2007) Logist. Manage., 37 (2), pp. 99-114; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance (1992) Global Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Strategic learning & the balanced scorecard (1996) Strategy & Leadership, 24 (5), pp. 18-24; Kaplan, R.S., Norton, D.P., Having trouble with your strategy? (2000) Then map it, Harvard Business Review, 78 (5), pp. 167-176; Kovács, G., Tatham, P., Humanitarian logistics performance in the light of gender (2009) Int. J. Prod. Perform. Manage., 58 (2), pp. 174-187; Kovács, G., Tatham, P., Larson, P.D., What skills are needed to be a humanitarian logistician? (2012) J. Bus. Logist., 33 (3), pp. 245-258; Kumar, S., Xu, C., Ghildayal, N., Chandra, C., Yang, C.M., Social media effectiveness as a humanitarian response to mitigate influenza epidemic and COVID-19 pandemic (2021) Ann. Oper. Res., pp. 1-29; Kunz, N., Gold, S., Sustainable humanitarian supply chain management–exploring new theory (2017) Int. J. Logist. Res. Appl., 20 (2), pp. 85-104; Kunz, N., Reiner, G., Gold, S., Investing in Disaster Management Capabilities versus Pre-Positioning Inventory: A New Approach to Disaster Preparedness (2014) Int. J. Prod. Econ., 157, pp. 261-272; L'Hermitte, C., Tatham, P., Brooks, B., Bowles, M., Supply chain agility in humanitarian protracted operations (2016) J. Humanit. Logist. Supply Chain Manag., 6 (2), pp. 173-201; Liao, H., Wen, Z., Liu, L., Integrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section (2019) Technol. Econ. Dev. Econ., 25 (6), pp. 1188-1212; Lu, Q., Goh, M., de Souza, R., A SCOR framework to measure logistics performance of humanitarian organizations (2016) J. Humanit. Logist. Supply Chain Manage., 6 (2), pp. 222-239; Lu, M.T., Hsu, C.C., Liou, J.J., Lo, H.W., A hybrid MCDM and sustainability-balanced scorecard model to establish sustainable performance evaluation for international airports (2018) J. Air Transp. Manage., 71, pp. 9-19; Malek, J., Desai, T.N., A framework for prioritizing the solutions to overcome sustainable manufacturing barriers (2021) Cleaner Logist. Supply Chain, 1; Mari Ainikki Anttila, U., Human security and learning in crisis management (2014) J. Humanit. Logist. Supply Chain Manage., 4 (1), pp. 82-94; Medina-Borja, A., Pasupathy, K.S., Triantis, K., Large-scale data envelopment analysis (DEA) implementation: a strategic performance management approach” (2007) J. Oper. Res. Soc., 58 (8), pp. 1084-1098; Meredith, J., Theory building through conceptual methods” (1993) Int. J. Oper. Prod. Manage., 13 (5), pp. 3-11; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced scorecard for natural disaster management projects (2007) Disaster Prevent. Manage.: Int. J., 16 (5), pp. 785-806; Mohammadi, M., Rezaei, J., Bayesian best-worst method: A probabilistic group decision making model (2019) Omega (United Kingdom), pp. 1-8; Mollah, A.K., Sadhukhan, S., Das, P., Anis, M.Z., A cost optimization model and solutions for shelter allocation and relief distribution in flood scenario (2018) Int. J. Disaster Risk Reduct., 31, pp. 1187-1198; Moshtari, M., Altay, N., Heikkilä, J., Gonçalves, P., Procurement in Humanitarian Organizations: Body of Knowledge and Practitioner's Challenges” (2021) Int. J. Prod. Econ.; Mwambo, F.M., Fürst, C., Nyarko, B.K., Borgemeister, C., Martius, C., Maize production and environmental costs: Resource evaluation and strategic land use planning for food security in northern Ghana by means of coupled emergy and data envelopment analysis (2020) Land Use Policy, 95; Nandi, S., Sarkis, J., Hervani, A.A., Helms, M.M., Redesigning supply chains using blockchain-enabled circular economy and COVID-19 experiences (2021) Sustain. Prod. Consump., 27, pp. 10-22; Neely, A., Mike, G., Platts, K., Performance measurement system design: a literature review and research agenda (1995) Int. J. Oper. Prod. Manage., 15 (4), pp. 80-116; Nikkhoo, F., Bozorgi-Amiri, A., Heydari, J., Coordination of relief items procurement in humanitarian logistic based on quantity flexibility contract (2018) Int. J. Disaster Risk Reduct., 31, pp. 331-340; Noori, N.S., Weber, C., Dynamics of coordination-clusters in long-term rehabilitation (2016) J. Humanit. Logist. Supply Chain Manage., 6 (3), pp. 296-328; Oktari, R.S., Munadi, K., Idroes, R., Knowledge management practices in disaster management: Systematic review (2020) Int. J. Disaster Risk Reduct.; Oloruntoba, R., An analysis of the Cyclone Larry emergency relief chain: Some key success factors” (2010) Int. J. Prod. Econ., 126 (1), pp. 85-101; Patil, A., Shardeo, V., Dwivedi, A., Madaan, J., Varma, N., Barriers to sustainability in humanitarian medical supply chains (2021) Sustain. Prod. Consump., 27, pp. 1794-1807; Patil, A., Shardeo, V., Madaan, J., Modelling performance measurement barriers of humanitarian supply chain (2021) Int. J. Prod. Perform. Manage., 70 (8), pp. 1972-2000; Pomponi, F., Moghayedi, A., Alshawawreh, L., D'Amico, B., Windapo, A., Sustainability of post-disaster and post-conflict sheltering in Africa: What matters? (2019) Sustainable Production and Consumption, 20, pp. 140-150; Rabbani, A., Zamani, M., Yazdani-Chamzini, A., Zavadskas, E.K., Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies (2014) Expert Syst. Appl., 41 (16), pp. 7316-7327; Rajakaruna, S., Wijeratne, A.W., Mann, T.S., Yan, C., Identifying key skill sets in humanitarian logistics: developing a model for Sri Lanka (2017) Int. J. Disaster Risk Reduct., 24, pp. 58-65; Ramezanali, A.K., Feizi, F., Jafarirad, A., Lotfi, M., Application of Best-Worst method and Additive Ratio Assessment in mineral prospectivity mapping: A case study of vein-type copper mineralization in the Kuhsiah-e-Urmak Area (2020) Iran, Ore Geology Reviews, 117; Rezaei, J., Best-worst multi-criteria decision-making method (2015) Omega (United Kingdom), 53, pp. 49-57; Rezaei, J., Best-worst multi-criteria decision-making method: Some properties and a linear model (2016) Omega (United Kingdom), 64, pp. 126-130; Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) Int. J. Prod. Perform. Manage., 64 (6), pp. 784-810; (2010), SCC., Supply chain operations reference model, version 10.0, Supply Chain Council available at: supply-chain.org/scor/10.0 (accessed 29 April 2013); Schiffling, S., Piecyk, M., Performance measurement in humanitarian logistics: a customer-oriented approach (2014) J. Humanit. Logist. Supply Chain Manag., 4 (2), pp. 198-221; Schön, A.-M., Al-Saadi, S., Grubmueller, J., Schumann-Bölsche, D., Developing a camp performance indicator system and its application to Zaatari, Jordan (2018) J. Humanit. Logist. Supply Chain Manage., 8 (3), pp. 346-373; Shamout, S., Boarin, P., Wilkinson, S., The shift from sustainability to resilience as a driver for policy change: A policy analysis for more resilient and sustainable cities in Jordan (2021) Sustain. Prod. Consump., 25, pp. 285-298; Stanujkic, D., Extension of the ARAS method for decision-making problems with interval-valued triangular fuzzy numbers (2015) Informatica, 26, pp. 335-355; Tatham, P., Blecken, A., Supply chain process modelling for humanitarian organizations (2010) Int. J. Phys. Distrib. Logist. Manage., 40 (8-9), pp. 675-692; Tatham, P.H., Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Manage. Res. News, 32 (11), pp. 1038-1049; Tatham, P., Spens, K., Towards a humanitarian logistics knowledge management system (2011) Disaster Prevent. Manage., 20 (1), pp. 6-26; Van der Laan, E.A., de Brito, M.P., Vergunst, D.A., Performance measurement in humanitarian supply chains (2009) Int. J. Risk Assess. Manage., 13 (1), pp. 22-45; Van Wassenhove, L.N., Humanitarian aid logistics: supply chain management in high gear (2006) J. Oper. Res. Soc., 57 (5), pp. 475-489; Wagner, S.M., Thakur-Weigold, B., Gatti, F., Stumpf, J., Measuring and improving the impact of humanitarian logistics consulting (2021) Prod. Plann. Control, 32 (2), pp. 83-103; Whetten, D.A., What constitutes a theoretical contribution? (1989) Acad. Manag. Rev., 14 (4), pp. 490-495; Yazdani, M., Tavana, M., Pamučar, D., Chatterjee, P., A rough based multi-criteria evaluation method for healthcare waste disposal location decisions (2020) Comput. Ind. Eng., 143; Zavadskas, E.K., Turskis, Z., A new additive ratio assessment (ARAS) method in multicriteria decision-making (2010) Technol. Econ. Dev. Econ., 16 (2), pp. 159-172},
correspondence_address1={Agarwal, S.; Department of Mechanical Engineering, Gujarat, India; email: engineer.sachinagarwal@gmail.com},
publisher={Elsevier Ltd},
issn={27723909},
language={English},
abbrev_source_title={Clean. Logist. Supply Chain},
document_type={Article},
source={Scopus},
}

@ARTICLE{Maaz20221,
author={Maaz, M.A.M. and Ahmad, R.},
title={Impact of supply chain performance on organizational performance mediated by customer satisfaction: a study of dairy industry},
journal={Business Process Management Journal},
year={2022},
volume={28},
number={1},
pages={1-22},
doi={10.1108/BPMJ-05-2021-0292},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124079797&doi=10.1108%2fBPMJ-05-2021-0292&partnerID=40&md5=d85e3ef9ee013cee4be15ee5e3de5c66},
affiliation={Department of Agricultural Economics and Business Management, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh, India},
abstract={Purpose: The aim of this study is to empirically test the relationship between supply chain performance and organizational performance mediated by customer satisfaction. Design/methodology/approach: Respondents of this study include all the supply chain stakeholders, managers working in the dairy industry, bulk milk chillers/coolers (BMCs), farmer producers, wholesalers, transporters, retailers and consumers. The final sample has 67 firms and their forward and backward linkages, taking the total sample size to 1,139. Mediation analysis was performed using SPSS AMOS v26 to analyze the impact of supply chain performance on organizational performance mediated by customer satisfaction. Findings: The findings of this study reveal that supply chain performance positively impacts organizational performance, and customer satisfaction partially mediates the relationship between them. Research limitations/implications: This study provides an opportunity for future researchers to test this model in different locations and different industries, incorporating other mediating variables, such as risk, coordination, supply chain orientation etc., as antecedents and operational, economic and marketing performance as consequences. Practical implications: The study suggests that enhanced organizational performance requires supply chain management to be treated as a strategic function. Managers working at the processing/manufacturing level must focus on improving supply chain performance indicators through collaboration with supply chain stakeholders. Originality/value: The present study adds to the existing literature by adopting a new supply chain performance measurement approach. The current literature has focused primarily on supply chain practices to study their impact on customer satisfaction and organizational performance. In contrast, the present study demonstrates how improvement in supply chain performance can enhance organizational performance through customer satisfaction. © 2022, Emerald Publishing Limited.},
author_keywords={Customer satisfaction;  Dairy industry;  Mediation analysis;  Organizational performance;  Supply chain performance},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: review and classification (2012) Journal of Organizational Management Studies, 2012, pp. 1-20; Ahire, S.L., Golhar, D.Y., Waller, M.A., Development and validation of TQM implementation constructs (1996) Decision Sciences, 27 (1), pp. 23-56; Alfalla-Luque, R., Marin-Garcia, J.A., Medina-Lopez, C., An analysis of the direct and mediated effects of employee commitment and supply chain integration on organizational performance (2015) International Journal of Production Economics, 162, pp. 242-257; Aramyan, L., Ondersteijn, C., Kooten, O.V., Lansink, A.O., Performance indicators in agri-food production chains (2006) Quantifying the Agri-Food Supply Chain, pp. 47-64. , Ondersteijn, C., Wijnands, J., Huirne, R. and Kooten, O.V., and,(Eds), Springer; Aramyan, L.H., Lansink, A.G., Vorst, J.G., Kooten, O.V., Performance measurement in agri-food supply chains: a case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Azar, A., Kahnali, R.A., Allahvirdi, Relationship between supply chain quality management practices and their effects on organizational performance (2010) Singapore Management Review, 32 (1), pp. 45-69; Bagozzi, R.P., (1980) Causal Models in Marketing, , Wiley, New York; Bagozzi, R.P., Philips, L.W., Representing and testing organizational theories: a holistic construal (1982) Administrative Science Quarterly, 27 (3), pp. 459-489; Baihaqi, I., Sohal, A.S., The impact of information sharing in supply chains on organisational performance: an empirical study (2013) Production Planning and Control, 24 (8-9), pp. 743-758; Ballou, R., (1978) Basic Business Logistics, , Prentice-Hall, New Jersey; Baron, R.M., Kenny, V.A., The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations (1986) Journal of Personality and Social Psychology, 51 (6), pp. 1173-1182; Bartuševičienė, I., Šakalytė, E., Organizational assessment: effectiveness vs. efficiency (2013) Social Transformations in Contemporary Society, 1 (1), pp. 43-53; Beamon, B.M., Performance measures in supply chain management (1996) Proceedings of the 1996 Conference on Agile and Intelligent Manufacturing System, 23. , Rensselaer Polytechnic Institute, New York; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bijman, J., (2002) Essays on Agricultural Cooperatives, , Erasmus University, Rotterdam; Bourne, M., Neely, A., Implementing performance measurement systems: a literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Carrión, G.C., Nitzl, C., Roldán, J.L., Mediation analyses in partial least squares structural equation modeling: guidelines and empirical examples (2017) Partial Least Squares Path Modeling, pp. 173-195. , Springer, Cham; Chan, F.T., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chopra, S., Laux, C., Schmidt, E., Rajan, P., Perception of performance indicators in an agri-food supply chain: a case study of India's public distribution system (2017) International Journal on Food System Dynamics, 8 (20), pp. 130-145; Chopra, S., Meindel, P., (2003) Supply Chain Management: Strategy, Planning and Operation, , Pearson Education, New Delhi; Chopra, S., Meindel, P., (2013) Supply Chain Management: Strategy, Planning and Operation, , 5th ed., Pearson Education, New Delhi; Christopher, M.L., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London; Christopher, M.L., (2005) Logistics and Supply Chain Management: Creating Value-Adding Networks, , FT Prentice Hall, London; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: more than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-14; Crosby, P.B., (1979) Quality Is Free: The Art of Making Quality Certain, , New American Library, New York; Dinu, M.D., Supply chain performance within agri food sector (2016) Economics of Agriculture, 63 (3), pp. 919-928; Dost, M.K., Rehman, C.A., Gilaninia, S., Ismail, K.B., Akram, M.W., The impact of knowledge management's practices on supply chain performance of the dairy sector in Central Punjab: a mediating role of decentralization (2018) Economic Research-Ekonomska Istraživanja, 31 (1), pp. 290-312; Ellinger, A., Shin, H., Northington, W.M., Adams, F.G., The influence of supply chain management competency on customer satisfaction and shareholder value (2012) Supply Chain Management: An International Journal, 17 (3), pp. 249-262; Fornell, C., Johnson, M.D., Anderson, E.W., Cha, J., Bryant, B.E., The American customer satisfaction index: nature, purpose, and findings (1996) Journal of Marketing, 60 (4), pp. 7-18; Golovkova, A., Eklof, J., Malova, A., Podkorytova, O., Customer satisfaction index and financial performance: a European cross-country study (2019) International Journal of Bank Marketing, 37 (2), pp. 479-491; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: a framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Gorane, S., Kant, R., Supply chain practices and organizational performance- an empirical investigation of Indian manufacturing organizations (2017) The International Journal of Logistics Management, 28 (1), pp. 75-101; Govindan, K., Mangla, S.K., Luthra, S., Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies (2017) Production Planning and Control, 28 (6), pp. 552-573; Green, K.W., Inman, R.A., Using a just-in-time selling strategy to strengthen supply chain linkages (2005) International Journal of Production Research, 43 (16), pp. 3437-3453; Green, K.W., Whitten, D., Inman, R.A., The impact of timely information on organisational performance in a supply chain (2007) Production Planning and Control, 18 (4), pp. 274-282; Green, K.W., Whitten, D., Inman, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply Chain Management: An International Journal, 13 (4), pp. 317-327; Gronroos, C., (2000) Service Management and Marketing: A Customer Relationship Approach, , 2nd ed., John wiley, Chichester; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 2 (1-2), pp. 71-87; Hair, J.F., Anderson, R., Tatham, R., William, C., (1995) Multivariate Data Analysis, , 4th ed., Prentice-Hall, New Jersey; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2014) Multivariate Data Analysis, , 7th ed., Pearson Education, Essex; Hausman, W.H., Supply chain performance metrics (2002) The Practice of Supply Chain Management, pp. 61-73. , Billington, C., Harrison, T., Lee, H. and Neale, J.,(Eds), Kluwer Academic Publishers; Heskett, J., Ivie, R., Glaskowsky, N., (1964) Business Logistics: Management of Physical Supply and Distribution, , Ronald Press, New York; Huang, P.-L., Lee, B.C., Chen, C.-C., The influence of service quality on customer satisfaction and loyalty in B2B technology service industry (2019) Total Quality Management and Business Excellence, 30 (13-14), pp. 1449-1465; Kaplan, R.S., Bruns, W.J., (1987) Accounting and Management: Field Study Perspectives, , Harvard Business School Press, Boston; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance (1992) Harvard Business Review, 83 (7), pp. 71-79; Kim, D., Lee, R.P., Systems collaboration and strategic collaboration: their impacts on supply chain responsiveness and market performance (2010) Decision Sciences, 41 (4), pp. 955-981; Kotler, P., Armstrong, G., (2012) Principles of Marketing, , 14th ed., Prentice-Hall, New Jersey; Kurien, G.P., Qureshi, M., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Lai, K., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Journal of Transportation Research: Part E, 38 (6), pp. 439-456; Lambert, D.M., Harrington, T.C., Measuring nonresponse bias in customer service mail surveys (1990) Journal of Business Logistics, 11 (2), pp. 5-25; Lambert, D.M., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lambert, D.M., Stock, J.R., Ellram, L., (1998) Fundamentals of Logistics Management, , McGraw-Hill/Irwin, Boston; Lin, J., Wu, C., The role of expected future use in relationship-based service retention (2011) Managing Service Quality, 21 (5), pp. 535-551; Londe, B.J., Masters, J.M., Emerging logistics strategies: blueprints for the next century (1994) International Journal of Physical Distribution and Logistics Management, 24 (7), pp. 35-47; Low, J., The value creation index (2000) Journal of Intellectual Capital, 1 (3), pp. 252-262; Maaz, M.A., Abad, A., Ahmad, R., Service quality and customer satisfaction in organised and unorganised food service sector (2019) International Journal of Commerce and Business Management, 12 (2), pp. 58-66; Maaz, M.A., Ahmad, R., Abad, A., Factors influencing purchase of milk through organized and unorganized channels (2020) International Journal of Management, 11 (10), pp. 1186-1197; Madhavan, M., Krishnakumar, K., Karpagam, K., Performance of dairy industry in India: an analysis (2020) Journal of Xian University of Architecture and Technology, 12 (6), pp. 1610-1620; Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Moazzam, M., Akhtar, P., Garnevska, E., Marr, N.E., Measuring agri-food supply chain performance and risk through a new analytical framework: a case study of New Zealand dairy (2018) Production Planning and Control, 29 (15), pp. 1258-1274; Mor, R.S., Bhardwaj, A., Singh, S., A structured literature review of the supply chain practices in dairy industry (2018) Journal of Operations and Supply Chain Management, 11 (1), pp. 14-25; Mor, R.S., Bhardwaj, A., Singh, S., Arora, V.K., Exploring the factors affecting supply chain performance in dairy industry using exploratory factor analysis technique (2020) International Journal of Industrial and Systems Engineering, 36 (2), pp. 248-265; Myerson, P.A., (2015) Supply Chain and Logistics Management, , Pearson Education, New Jersey; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-117; Oliver, R.K., Webber, M.D., Supply-chain management: logistics catches up with strategy (1992) Logistics: The Strategic Issues, , Christopher, M. (Ed.), Chapman and Hall, London; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Otto, A.S., Szymanski, D.M., Varadarajan, R., Customer satisfaction and firm performance: insights from over a quarter century of empirical research (2019) Journal of the Academy of Marketing Science, 48 (3), pp. 543-564; Prajapati, H., Kant, R., Gorane, S., Impact study of supply chain practices on organisational performance for Indian chemical industries (2018) International Journal of Logistics Systems and Management, 31 (1), pp. 20-38; Prakash, G., Pant, R.R., Performance measurement of a dairy supply chain: a balance scorecard perspective (2013) IEEE International Conference on Industrial Engineering and Engineering Management, pp. 196-200. , IEEE; Raza, S.A., Javaid, S.T., Hasan, A., Internet banking and customer satisfaction (2015) Pakistan Qualitative Research in Financial Markets, pp. 24-36; Sanchez, A.M., Perez, M.P., Supply chain flexibility and firm performance (2005) International Journal of Operations and Production Management, 25 (7), pp. 681-700; Schonsleben, P., (2003) Integral Logistics Management: Planning and Control of Comprehensive Supply Chains, , St Lucie Press, Boca Raton; Sel, Ç., Bilgen, B., Quantitative models for supply chain management within dairy industry: a review and discussion (2015) European Journal of Industrial Engineering, 9 (5), pp. 561-594; Singh, S.P., Tegegne, F., Ekenem, E., The food processing industry in India: challenges and opportunities (2012) Journal of Food Distribution Research, 43 (1), pp. 81-89; Stern, J., Stewart, B., Chew, D., The EVA financial management system (1995) Journal of Applied Corporate Finance, 8 (2), pp. 32-46; Susanty, A., Bakhtiar, A., Purwaningsih, R., Dewanti, D.F., Milk in a dairy supply chain: a balanced supply chain management scorecard approach (2017) Performance Measurement of the Relationship between Farmers-Cooperatives-Industrial Processing, 2017 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1387-1391. , IEEE; Tomic, B., Spasojevic Brkic, V.K., Customer satisfaction and ISO 9001 improvement requirements in the supply chain (2019) The TQM Journal, 31 (2), pp. 222-238; Toni, A.D., Tonchia, S., Performance measurement systems models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 46-70; Van der Vorst, J., (2000) Effective food supply chains: generating, modelling and evaluating supply chain scenarios, , Doctoral thesis, Wageningen University; Vickery, S., Calantone, R., Dröge, C., Supply chain flexibility: an empirical study (1999) The Journal of Supply Chain Management, 35 (2), pp. 16-24; Widyaningrum, D., Masruroh, N.A., Development of the sea fishery supply chain performance measurement system: a case study (2012) International Journal of Supply Chain Management, 1 (3), pp. 20-32; Williams, B., Onsman, A., Brown, T., Exploratory factor analysis: a five-step guide for novices (2010) Australasian Journal of Paramedicine, 8 (3), pp. 1-13; Wood, R.E., Goodman, J.S., Beckmann, N., Cook, A., Mediation testing in management research: a review and proposals (2008) Organizational Research Methods, 11 (2), pp. 270-295; Wouters, M., A developmental approach to performance measures – results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78; Yu, W., Jacobs, M.A., Salisbury, W.D., Enns, H., The effects of supply chain integration on customer satisfaction and financial performance: an organizational learning perspective (2013) International Journal of Production Economics, 146 (1), pp. 346-358; Zaiţ, A., Bertea, P.E., Methods for testing discriminant validity (2011) Management and Marketing Journal, 9 (2), pp. 217-224; Cscmp, (2013) CSCMP Supply Chain Management Definitions and Glossary, , https://cscmp.org/CSCMP/Educate/SCM_Definitions_and_Glossary_of_Terms.aspx, accessed, 5 February 2019},
correspondence_address1={Maaz, M.A.M.; Department of Agricultural Economics and Business Management, India; email: mamaaz@myamu.ac.in},
publisher={Emerald Group Holdings Ltd.},
issn={14637154},
language={English},
abbrev_source_title={Bus. Process Manage. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Patrucco2022207,
author={Patrucco, A. and Frattini, F. and Di Benedetto, A.},
title={Characteristics of supplier performance measurement systems in collaborative innovation projects: the role of the purchasing department},
journal={Supply Chain Management},
year={2022},
volume={27},
number={2},
pages={207-231},
doi={10.1108/SCM-11-2020-0551},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107831112&doi=10.1108%2fSCM-11-2020-0551&partnerID=40&md5=9ac62173c9400294ad31561c0d8f86c8},
affiliation={Department of Marketing and Logistics – College of Business, Florida International University, Miami, FL, United States; School of Management, Politecnico di Milano, Milan, Italy; Department of Marketing and Supply Chain Management – Fox School of Business, Temple University, Philadelphia, PA, United States},
abstract={Purpose: In the wake of the growing popularity of the open innovation approach, leveraging suppliers as external sources of innovation has attracted increasing interest from scholars and practitioners. Successful supplier involvement largely depends on an effective performance measurement process, but both supply chain management and innovation management literature have paid limited attention to this aspect. This paper aims to fill this gap by illustrating how companies measure the performance of the suppliers involved in their innovation projects and what role is played by the purchasing department. Design/methodology/approach: This study interviews project stakeholders from nine different organizations acting as focal companies in the supply chains of various industries. This paper complements this on-field information with a vast amount of data collected from secondary project documents. Structured data coding and analysis allow us to discuss how companies redesign their performance measurement systems to ease the collaboration with suppliers in innovation and what factors underly these decisions. Findings: The findings show that, in many cases, supplier performance measurement systems deviate from their typical characteristics to support collaboration in innovation projects. They integrate quantitative and qualitative measures, include contributions from different project stakeholders and are oriented toward high visibility and transparency with suppliers. A more substantial redesign of these systems is favored when purchasing is assigned to strategic project responsibilities and possesses higher absorptive capacity. Originality/value: The results complement the knowledge for the supply chain management field, where supplier performance measurement systems have been discussed in the context of traditional buyer-supplier relationships, but not comprehensively in innovation projects and not considering the role of purchasing. Findings also contribute to the innovation management literature, which has mostly focused on what aspects need to be measured for innovation partners, rather than how to manage the performance measurement process in practice. © 2021, Emerald Publishing Limited.},
author_keywords={Absorptive capacity;  Buyer-supplier relationships;  Open innovation;  Performance measurement;  Purchasing;  Supplier involvement;  Supplier-manufacturer relationships},
references={Arvidsson, A.P., Melander, L., The multiple levels of trust when selecting suppliers–insights from an automobile manufacturer (2020) Industrial Marketing Management, 87, pp. 138-149; Barney, J.B., Purchasing, supply chain management and sustained competitive advantage: the relevance of resource‐based theory (2012) Journal of Supply Chain Management, 48 (2), pp. 3-6; Bastl, M., Johnson, M., Choi, T.Y., Who's seeking whom? Coalition behavior of a weaker player in buyer–supplier relationships (2013) Journal of Supply Chain Management, 49 (1), pp. 8-28; Bellamy, M.A., Ghosh, S., Hora, M., The influence of supply network structure on firm innovation (2014) Journal of Operations Management, 32 (6), pp. 357-373; Bogers, M., Zobel, A.K., Afuah, A., Almirall, E., Brunswicker, S., Dahlander, L., Hagedoorn, J., The open innovation research landscape: established perspectives and emerging themes across different levels of analysis (2017) Industry and Innovation, 24 (1), pp. 8-40. , …; Bourne, M., Franco-Santos, M., Micheli, P., Pavlov, A., Performance measurement and management: a system of systems perspective (2018) International Journal of Production Research, 56 (8), pp. 2788-2799; Bunduchi, R., Trust, partner selection and innovation outcome in collaborative new product development (2013) Production Planning & Control, 24 (2-3), pp. 145-157; Carr, A.S., Kaynak, H., Communication methods, information sharing, supplier development and performance: an empirical study of their relationships (2007) International Journal of Operations & Production Management, 27 (4), pp. 346-370; Cassiman, B., Valentini, G., Open innovation: are inbound and outbound knowledge flows really complementary? (2016) Strategic Management Journal, 37 (6), pp. 1034-1046; Castaldi, C., Ten Kate, C., Den Braber, R., Strategic purchasing and innovation: a relational view (2011) Technology Analysis & Strategic Management, 23 (9), pp. 983-1000; Chen, I.J., Paulraj, A., Lado, A.A., Strategic purchasing, supply management, and firm performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Cheng, C.C., Krumwiede, D., Enhancing the performance of supplier involvement in new product development: the enabling roles of social media and firm capabilities (2018) Supply Chain Management: An International Journal, 23 (3), pp. 171-187; Chesbrough, H.W., (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, , Harvard Business Press; Choy, K.L., Lee, W.B., Lo, V., An enterprise collaborative management system – a case study of supplier relationship management (2004) Journal of Enterprise Information Management, 17 (3), pp. 191-207; Cohen, W.M., Levinthal, D.A., Absorptive capacity: a new perspective on learning and innovation (1990) Administrative Science Quarterly, 35 (1), pp. 128-152; Cooper, R.G., The drivers of success in new-product development (2019) Industrial Marketing Management, 76, pp. 36-47; Cousins, P.D., Lawson, B., The effect of socialization mechanisms and performance measurement on supplier integration in new product development (2007) British Journal of Management, 18 (3), pp. 311-326; De Toni, A., Nassimbeni, G., A method for the evaluation of suppliers’ co-design effort (2001) International Journal of Production Economics, 72 (2), pp. 169-180; Dey, P.K., Bhattacharya, A., Ho, W., Clegg, B., Strategic supplier performance evaluation: a case-based action research of a UK manufacturing organisation (2015) International Journal of Production Economics, 166, pp. 192-214; Dowlatshahi, S., Designer–buyer–supplier interface: theory versus practice (2000) International Journal of Production Economics, 63 (2), pp. 111-130; Dulmin, R., Mininno, V., Supplier selection using a multicriteria decision aid method (2003) Journal of Purchasing and Supply Management, 9 (4), pp. 177-187; Ellram, L.M., The supplier selection decision in strategic partnerships (1990) Journal of Purchasing and Materials Management, 26 (4), pp. 8-14; Emden, Z., Calantone, R.J., Droge, C., Collaborating for new product development: selecting the partner with maximum potential to create value (2006) Journal of Product Innovation Management, 23 (4), pp. 330-341; Gentles, S.J., Charles, C., Ploeg, J., McKibbon, K.A., Sampling in qualitative research: insights from an overview of the methods literature (2015) The Qualitative Report, 20 (11), pp. 1772-1789; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Gibbert, M., Ruigrok, W., Wicki, B., What passes as a rigorous case study? (2008) Strategic Management Journal, 29 (13), pp. 1465-1474; Goffin, K., Lemke, F., Szwejczewski, M., An exploratory study of ‘close’ supplier–manufacturer relationships (2006) Journal of Operations Management, 24 (2), pp. 189-209; Gonzalez-Benito, J., A theory of purchasing's contribution to business performance (2007) Journal of Operations Management, 25 (4), pp. 901-917; Gosling, J., Purvis, L., Naim, M.M., Supply chain flexibility as a determinant of supplier selection (2010) International Journal of Production Economics, 128 (1), pp. 11-21; Hald, K.S., Ellegaard, C., Supplier evaluation processes: the shaping and reshaping of supplier performance (2011) International Journal of Operations & Production Management, 31 (8), pp. 888-910; Handfield, R.B., Ragatz, G.L., Petersen, K.J., Monczka, R.M., Involving suppliers in new product development (1999) California Management Review, 42 (1), pp. 59-82; Hennink, M.M., Kaiser, B.N., Marconi, V.C., Code saturation versus meaning saturation: how many interviews are enough? (2017) Qualitative Health Research, 27 (4), pp. 591-608; Hoegl, M., Wagner, S.M., Buyer-supplier collaboration in product development project (2005) Journal of Management, 31 (4), pp. 530-548; Hong, Y., Hartley, J.L., Managing the supplier–supplier interface in product development: the moderating role of technological newness (2011) Journal of Supply Chain Management, 47 (3), pp. 43-62; Humphreys, P., Huang, G., Cadden, T., McIvor, R., Integrating design metrics within the early supplier selection process (2007) Journal of Purchasing and Supply Management, 13 (1), pp. 42-52; Jajja, M.S.S., Kannan, V.R., Brah, S.A., Hassan, S.Z., Linkages between firm innovation strategy, suppliers, product innovation, and business performance: insights from resource dependence theory (2017) International Journal of Operations & Production Management, 37 (8), pp. 1054-1075; Johnsen, T.E., Supply network delegation and intervention strategies during supplier involvement in new product development (2011) International Journal of Operations & Production Management, 31 (6), pp. 686-708; Kahn, K.B., Understanding innovation (2018) Business Horizons, 61 (3), pp. 453-460; Kauppi, K., Brandon-Jones, A., Ronchi, S., van Raaij, E.M., Tools without skills: exploring the moderating effect of absorptive capacity on the relationship between e-purchasing tools and category performance (2013) International Journal of Operations & Production Management, 33 (7), pp. 828-857; Koufteros, X.A., Cheng, T.E., Lai, K.H., ‘Black-box’ and ‘gray-box’ supplier integration in product development: antecedents, consequences and the moderating role of firm size (2007) Journal of Operations Management, 25 (4), pp. 847-870; Koufteros, X., Vickery, S.K., Dröge, C., The effects of strategic supplier selection on buyer competitive performance in matched domains: does supplier integration mediate the relationships? (2012) Journal of Supply Chain Management, 48 (2), pp. 93-115; Kraljic, P., Purchasing must become supply management (1983) Harvard Business Review, 61 (5), pp. 109-117; Lawson, B., Potter, A., Determinants of knowledge transfer in inter-firm new product development projects (2012) International Journal of Operations & Production Management, 32 (10), pp. 1228-1247; Lawson, B., Cousins, P.D., Handfield, R.B., Petersen, K.J., Strategic purchasing, supply management practices and buyer performance improvement: an empirical study of UK manufacturing organisations (2009) International Journal of Production Research, 47 (10), pp. 2649-2667; Le Dain, M.A., Calvi, R., Cheriti, S., Measuring supplier performance in collaborative design: proposition of a framework (2011) R&D Management, 41 (1), pp. 61-79; Lu, P., Yuan, S., Wu, J., The interaction effect between intra-organizational and inter-organizational control on the project performance of new product development in open innovation (2017) International Journal of Project Management, 35 (8), pp. 1627-1638; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: an empirical analysis (2014) Journal of Purchasing and Supply Management, 20 (2), pp. 113-129; McCutcheon, D.M., Grant, R.A., Hartley, J., Determinants of new product designers' satisfaction with suppliers' contributions (1997) Journal of Engineering and Technology Management, 14 (3-4), pp. 273-290; Maestrini, V., Luzzini, D., Caniato, F., Ronchi, S., Effects of monitoring and incentives on supplier performance: an agency theory perspective (2018) International Journal of Production Economics, 203, pp. 322-332; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., The impact of supplier performance measurement systems on supplier performance: a dyadic lifecycle perspective (2018) International Journal of Operations & Production Management, 38 (11), pp. 2040-2061; Maestrini, V., Martinez, V., Neely, A., Luzzini, D., Caniato, F., Maccarrone, P., The relationship regulator: a buyer-supplier collaborative performance measurement system (2018) International Journal of Operations & Production Management, 38 (11), pp. 2022-2039; Melander, L., Tell, F., Uncertainty in collaborative NPD: effects on the selection of technology and supplier (2014) Journal of Engineering and Technology Management, 31, pp. 103-119; Mikkelsen, O.S., Johnsen, T.E., Purchasing involvement in technologically uncertain new product development projects: challenges and implications (2019) Journal of Purchasing and Supply Management, 25 (3), p. 100496; Miles, M.B., Huberman, A.M., (1994) Qualitative Data Analysis: An Expanded Sourcebook, , Sage Publications, Thousands Oaks, CA; Najafi-Tavani, S., Najafi-Tavani, Z., Naudé, P., Oghazi, P., Zeynaloo, E., How collaborative innovation networks affect new product performance: product innovation capability, process innovation capability, and absorptive capacity (2018) Industrial Marketing Management, 73, pp. 193-205; Narasimhan, R., Narayanan, S., Perspectives on supply network–enabled innovations (2013) Journal of Supply Chain Management, 49 (4), pp. 27-42; Nassimbeni, G., Battain, F., Evaluation of supplier contribution to product development: fuzzy and neuro-fuzzy based approaches (2003) International Journal of Production Research, 41 (13), pp. 2933-2956; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Pagell, M., Wu, Z., Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars (2009) Journal of Supply Chain Management, 45 (2), pp. 37-56; Patrucco, A.S., Moretto, A., Knight, L., Does relationship control hinder relationship commitment? The role of supplier performance measurement systems in construction infrastructure projects (2020) International Journal of Production Economics, 233, p. 108000; Pekkola, S., Ukko, J., Designing a performance measurement system for collaborative network (2016) International Journal of Operations & Production Management, 36 (11), pp. 1410-1434; Petersen, K.J., Handfield, R.B., Ragatz, G.L., Supplier integration into new product development: coordinating product, process and supply chain design (2005) Journal of Operations Management, 23 (3-4), pp. 371-388; Pisano, G.P., You need an innovation strategy (2015) Harvard Business Review, 93 (6), pp. 44-54; Potter, A., Lawson, B., Help or hindrance? Causal ambiguity and supplier involvement in new product development teams (2013) Journal of Product Innovation Management, 30 (4), pp. 794-808; Prahinski, C., Benton, W.C., Supplier evaluations: communication strategies to improve supplier performance (2004) Journal of Operations Management, 22 (1), pp. 39-62; Prahinski, C., Fan, Y., Supplier evaluations: the role of communication quality (2007) The Journal of Supply Chain Management, 43 (3), pp. 16-28; Revilla, E., Knoppen, D., Building knowledge integration in buyer-supplier relationships: the critical role of strategic supply management and trust (2015) International Journal of Operations & Production Management, 35 (10), pp. 1408-1436; Revilla, E., Sáenz, M.J., Knoppen, D., Towards an empirical typology of buyer-supplier relationships based on absorptive capacity (2013) International Journal of Production Research, 51 (10), pp. 2935-2951; Ritala, P., Hurmelinna‐Laukkanen, P., Incremental and radical innovation in coopetition – the role of absorptive capacity and appropriability (2013) Journal of Product Innovation Management, 30 (1), pp. 154-169; Rosell, D.T., Lakemond, N., Melander, L., Integrating supplier knowledge in new product development projects: decoupled and coupled approaches (2017) Journal of Knowledge Management, 21 (5), pp. 1035-1052; Saenz, M.J., Revilla, E., Knoppen, D., Absorptive capacity in buyer–supplier relationships: empirical evidence of its mediating role (2014) Journal of Supply Chain Management, 50 (2), pp. 18-40; Salvador, F., Villena, V.H., Supplier integration and NPD outcomes: conditional moderation effects of modular design competence (2013) Journal of Supply Chain Management, 49 (1), pp. 87-113; Schiele, H., How to distinguish innovative suppliers? Identifying innovative suppliers as new task for purchasing (2006) Industrial Marketing Management, 35 (8), pp. 925-935; Schiele, H., Supply-management maturity, cost savings and purchasing absorptive capacity: testing the procurement–performance link (2007) Journal of Purchasing and Supply Management, 13 (4), pp. 274-293; Schiele, H., Early supplier integration: the dual role of purchasing in new product development (2010) R&D Management, 40 (2), pp. 138-153; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Sharma, S., Development of supplier relationship including cost of defectives in the cyclic production (2013) Production Planning & Control, 24 (8-9), pp. 759-768; Søgaard, B., Skipworth, H.D., Bourlakis, M., Mena, C., Wilding, R., Facing disruptive technologies: aligning purchasing maturity to contingencies (2019) Supply Chain Management: An International Journal, 24 (1), pp. 147-169; Song, M., Di Benedetto, C.A., Supplier’s involvement and success of radical new product development in new ventures (2008) Journal of Operations Management, 26 (1), pp. 1-22; Soosay, C.A., Hyland, P., A decade of supply chain collaboration and directions for future research (2015) Supply Chain Management: An International Journal, 20 (6), pp. 613-630; Soosay, C.A., Hyland, P.W., Fodrer, M., Supply chain collaboration: capabilities for continuous innovation (2008) Supply Chain Management: An International Journal, 13 (2), pp. 160-169; Suurmond, R., Wynstra, F., Dul, J., Unraveling the dimensions of supplier involvement and their effects on NPD performance: a meta‐analysis (2020) Journal of Supply Chain Management, 56 (3), pp. 26-46; Tsai, K.H., Hsu, T.T., Cross-functional collaboration, competitive intensity, knowledge integration mechanisms, and new product performance: a mediated moderation model (2014) Industrial Marketing Management, 43 (2), pp. 293-303; Van Echtelt, F.E., Wynstra, F., Van Weele, A.J., Duysters, G., Managing supplier involvement in new product development: a multiple‐case study (2008) Journal of Product Innovation Management, 25 (2), pp. 180-201; Von Krogh, G., Netland, T., Wörter, M., Winning with open process innovation (2018) MIT Sloan Management Review, 59 (2), pp. 53-56; Voss, C., Case research in operations management (2010) Researching Operations Management, pp. 176-209. , Routledge; Wagner, S.M., Tapping supplier innovation (2012) Journal of Supply Chain Management, 48 (2), pp. 37-52; Wagner, S.M., Hoegl, M., Involving suppliers in product development: insights from R&D directors and project managers (2006) Industrial Marketing Management, 35 (8), pp. 936-943; West, J., Bogers, M., Leveraging external sources of innovation: a review of research on open innovation (2014) Journal of Product Innovation Management, 31 (4), pp. 814-831; Wetzstein, A., Hartmann, E., Benton, W.C., Jr., Hohenstein, N.O., A systematic assessment of supplier selection literature – state-of-the-art and future scope (2016) International Journal of Production Economics, 182, pp. 304-323; Winter, S., Lasch, R., Recommendations for supplier innovation evaluation from literature and practice (2016) International Journal of Operations & Production Management, 36 (6), pp. 643-664; Wynstra, F., Weggeman, M., Van Weele, A., Exploring purchasing integration in product development (2003) Industrial Marketing Management, 32 (1), pp. 69-83; Yan, T., Dooley, K., Buyer–supplier collaboration quality in new product development projects (2014) Journal of Supply Chain Management, 50 (2), pp. 59-83; Yan, T., Dooley, K.J., Communication intensity, goal congruence, and uncertainty in buyer–supplier new product development (2013) Journal of Operations Management, 31 (7-8), pp. 523-542; Yan, T., Nair, A., Structuring supplier involvement in new product development: a China–US study (2016) Decision Sciences, 47 (4), pp. 589-627; Yoon, B., Song, B., A systematic approach of partner selection for open innovation (2014) Industrial Management & Data Systems, 114 (7), pp. 1068-1093; Zahra, S.A., George, G., Absorptive capacity: a review, reconceptualization, and extension (2002) Academy of Management Review, 27 (2), pp. 185-203; Zhao, Y., Cavusgil, E., Cavusgil, S.T., An investigation of the black-box supplier integration in new product development (2014) Journal of Business Research, 67 (6), pp. 1058-1064; Zimmermann, R.D.F., Ferreira, L.M., Carrizo Moreira, A., The influence of supply chain on the innovation process: a systematic literature review (2016) Supply Chain Management: An International Journal, 21 (3), pp. 289-304; Zimmermann, R., Ferreira, L.M.D.F., Moreira, A.C., An empirical analysis of the relationship between supply chain strategies, product characteristics, environmental uncertainty and performance (2020) Supply Chain Management: An International Journal, 25 (3), pp. 375-391},
correspondence_address1={Patrucco, A.; Department of Marketing and Logistics – College of Business, United States; email: andreastefano.patrucco@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Nazari-Ghanbarloo2022576,
author={Nazari-Ghanbarloo, V.},
title={A dynamic performance measurement system for supply chain management},
journal={International Journal of Productivity and Performance Management},
year={2022},
volume={71},
number={2},
pages={576-597},
doi={10.1108/IJPPM-01-2020-0023},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098498982&doi=10.1108%2fIJPPM-01-2020-0023&partnerID=40&md5=7caed8cb0d4202b034deb4847b062568},
affiliation={Faculty of Engineering, Kharazmi University, Tehran, Iran},
abstract={Purpose: This paper aims to propose a dynamic model for measurement supply chain performance (SCP) based on a dynamic balanced scorecard (DBSC). Balanced scorecard (BSC) can be defined as a popular performance measurement method that can translate the strategy into a set of performance indicators and manage the status of implementing the various strategies. However, BSC is unable to simulate the complicated environment and the dynamic behavior of performance metrics. Therefore, the author combines BSC with system dynamics (SD) to explore a more efficient tool for measurement SCP. Design/methodology/approach: A dynamic causal model is proposed based on the causal hypotheses. The developed DBSC enables managers to evaluate and measure the SCP in a much-balanced way. Using DBSC makes it possible that different SCP metrics to be reviewed and distributed into the four above-mentioned perspectives. It also enables supply chain (SC) managers to evaluate different strategies to improve SCP. Findings: investigates two strategies to improve SCP as follows: (1) competitive strategy and (2) harvesting big data and using data mining techniques to determine the customer's expectations and then compares the results of these two strategies based on the four perspectives of DBSC and introduce the best strategy. Finally, harvesting big data and data mining is selected as the best strategy. Originality/value: This study proposes a novel strategic management tool for measurement SCP and simulation of the complicated environment and the dynamic behavior of performance metrics. The proposed DBSC model enables managers to compare different strategies and select the best strategy. © 2020, Emerald Publishing Limited.},
author_keywords={Big data;  Data mining;  Dynamic balanced scorecard;  Strategy;  Supply chain performance;  System dynamics},
references={Akkermans, H.A., van Oorschot, K.E., Relevance assumed: a case study of balanced scorecard development using system dynamics (2005) Journal of the Operational Research Society, 56 (8), pp. 931-941; Anjomshoae, A., Hassan, A., Kunz, N., Wong, K.Y., Leeuw, S.D., Toward a dynamic balanced scorecard model for humanitarian relief organizations' performance management (2017) Journal of Humanitarian Logistics and Supply Chain Management, 7 (2), pp. 194-218; Balfaqih, H., Mohd, N.Z., Saibani, N.T., Al-Nory, M., Review of supply chain performance measurement systems: 1998– 2015 (2016) Computers in Industry, 82, pp. 135-150; Barlas, Y., Formal aspects of model validity and validation in system dynamics (1996) System Dynamics Review, 12 (3), pp. 183-210; Barnabe, F., A ‘system dynamics-based balanced scorecard’ to support strategic decision making: insights from a case study (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 446-473; Barnabe, F., Busco, C., The causal relationships between performance drivers and outcomes reinforcing balanced scorecards' implementation through system dynamics models (2012) Journal of Accounting and Organizational Change, 8 (4), pp. 528-538; Bhagwat, M., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers and Industrial Engineering, 53, pp. 43-62; Bhagwat, M., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning and Control, 20 (8), pp. 678-690; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2014) Production Planning and Control, 25 (8), pp. 698-714; Bianchi, C., Montemaggiore, G.B., Enhancing strategy design and planning in public utilities through ‘dynamic’ balanced scorecards: insights from a project in a city water company (2008) System Dynamics Review, 24 (2), pp. 175-213; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21, pp. 75-94; Capelo, C., Dias, J.F., A system dynamics-based simulation experiment for testing mental model and performance effects of using the balanced scorecard (2009) System Dynamics Review, 25 (1), pp. 1-34; Ceresia, F., Montemaggiore, G.B., Applying the goal setting practice in the dynamic balance scorecard learning and growth perspective (2010) Proceeding of 2010 International System Dynamics Conference, pp. 110-122. , Seoul, South Korea: July 25–29, 2010; Coyle, R.G., (1996) System Dynamics Modelling: A Practical Approach, , Chapman and Hall, London; Drury, C., (2002) Management and Cost Accounting, , 5th ed., Thomson, London; Fisher, M., Raman, A., Using data and big data in retailing (2017) Production and Operations Management, 27 (9), pp. 1665-1669; Forrester, J.W., Senge, P.M., Tests for building confidence in system dynamics models (1980) TIMS Studies in the Management Sciences, 14, pp. 209-228; Garrison, R.H., Noreen, E.W., (2000) Managerial Accounting, , 9th ed., McGraw Hill, New York, NY; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Project Organisation and Management, 21, pp. 71-87; Gunasekaran, A., Patel, C., Ronald, E., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-348; Jagan, K., Neelakanteswara Rao, A., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30, pp. 40-47; Kache, F., Seuring, S., Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management (2017) International Journal of Operations and Production Management, 37 (1), pp. 10-36; Kaplan, R., Norton, D., The balanced scorecard: measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-99; Kaplan, R., Atkinson, A., (1998) Advanced Management Accounting, , Prentice Hall International, Englewood Cliffs, NJ; Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Kaplan, R., Norton, D., Linking the balanced scorecard to strategy (1996) California Management Review, 39, pp. 53-79; Kaplan, R., Norton, D., Having trouble with your strategy? Then map it (2000) Harvard Business Review, 78, pp. 167-176; Kaplan, R., Norton, D., (2001) The Strategy Focused Organization. How Balanced Scorecard Companies Thrive in the New Business Environment, , HBS Press, Boston; Khakbaz, S.B., Hajiheydari, N., Proposing a basic methodology for developing balanced scorecard by system dynamics approach (2015) Kybernetes, 44, pp. 1049-1066; Kunc, M., Using systems thinking to enhance strategy maps (2008) Management Decision, 46 (5), pp. 761-778; Kunc, M., O'Brien, F.A., The role of business analytics in supporting strategy processes: opportunities and limitations (2018) Journal of the Operational Research Society, 70 (6), pp. 974-985; Mooraj, S., Oyon, D., Hostettler, D., The balanced scorecard: a necessary good or an unnecessary evil? (1999) European Management Journal, 17 (5), pp. 481-491; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Nielsen, S., Nielsen, E.H., System dynamics modelling for a balanced scorecard: computing the influence of skills, customers, and work in process on the return on capital employed (2008) Management Research News, 31 (3), pp. 169-188; Norreklit, H., The balance on the balanced scorecard: a critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Opresnik, D., Taisch, M., The value of big data in servitization (2015) International Journal of Production Economics, 165, pp. 174-184; Sanders, N.R., (2014) Big Data Driven Supply Chain Management: A Framework for Implementing Analytics and Turning Information into Intelligence, , Pearson Education, Upper Saddle River, New Jersey; Schoeneborn, F., (2003) Linking Balanced Scorecard to System Dynamics, Proceeding of 2003 International Conference, , System Dynamics Society, New York; Shafiee, M., Lotfi, F.H., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Applied Mathematical Modelling, 38 (21), pp. 5092-5112; Sterman, J.D., (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World, , Irwin/McGraw-Hill, Boston; Swaminathan, J.M., Big data analytics for rapid, impactful, sustained and efficient (RISE) humanitarian operations (2017) Production and Operations Management, 27 (9), pp. 1696-1700; Tambe, P., Big data investment, skills, and firm value (2014) Management Science, 60 (6), pp. 1452-1469; Tan, K.H., Zhan, Y., Improving new product development using big data: a case study of an electronics company (2017) R & D Management, 47 (4), pp. 570-582; Tsalis, A.T., Nikolaou, E.I., Grigoroudis, E., Tsagarakis, P.K., A dynamic sustainability Balanced Scorecard methodology as a navigator for exploring the dynamics and complexity of corporate sustainability strategy (2015) Civil Engineering and Environmental Systems, 32 (4), pp. 281-300; Wagner, B.A., Fillis, I., Johansson, U., E-business and E-supply chain strategy in small and medium sized businesses (SMEs) (2003) Supply Chain Management – An International Journal, 8 (4), pp. 343-354; Waller, M.A., Fawcett, S.E., Click here for a data scientist: big data, predictive analytics, and theory development in the era of a maker movement supply chain (2013) Journal of Business Logistics, 34 (4), pp. 249-252; Zhan, Y., Hua Tan, K., An analytic infrastructure for harvesting big data to enhance supply chain performance (2018) European Journal of Operational Research, 281 (3), pp. 559-574},
correspondence_address1={Nazari-Ghanbarloo, V.; Faculty of Engineering, Iran; email: vahid77nazari@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Abdeen2022248,
author={Abdeen, F.N. and Sandanayake, Y.G. and Ramachandra, T.},
title={A framework for facilities management supply chain performance evaluation: case study of hotel buildings},
journal={Facilities},
year={2022},
volume={40},
number={3-4},
pages={248-267},
doi={10.1108/F-12-2020-0125},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121485100&doi=10.1108%2fF-12-2020-0125&partnerID=40&md5=953f1d8b7c4197e10bf67113dd2997a1},
affiliation={Department of Building Economics, University of Moratuwa, Moratuwa, Sri Lanka},
abstract={Purpose: The performance of the facilities management supply chain (FMSC) in the hotel sector is challenged by the diverse nature of parties involved, their relationships and the flows of services and products. Although performance measurement systems have been endorsed by researchers worldwide as a mechanism to evaluate and improve performance, there seems lack of mechanisms to evaluate the FMSC performance in hotels. Hence, this paper aims to develop a framework that would enable to evaluate FMSC performance in hotels. Design/methodology/approach: A case study strategy was used, where 3 five-star hotels in Sri Lanka were studied for the purpose. The data was collected through semi-structured interviews conducted with 21 professionals involved in FMSC and through document reviews. A content analysis was performed and the framework was developed. This was validated with 3 subject matter experts in the field. Findings: The study findings revealed that the FMSC process is different from manufacturing and service supply chain (SC) processes as it comprises both product and service elements and incorporates internal, as well as external customers. The developed FMSC process comprises seven sub-processes as follows: delivery of products, delivery of services, sourcing, make/fulfil, delivery of FM services and products, receipt of FM services and receipt of products by customers. Based on the derived FMSC process and the key activities, 38 key performance indicators were developed and used in the framework to evaluate the performance of FMSC. Originality/value: The developed performance evaluation framework is expected to facilitate performance measurement of the SC and enhance its performance. Further, it would enhance cooperation among FMSC partners and assist in achieving FMSC excellence. © 2021, Emerald Publishing Limited.},
author_keywords={Facilities management supply chain;  Facilities management supply chain processes;  Hotels;  Performance evaluation},
references={Adeyemi, T.O., (2015) Integration of facilities management in hotels, , A case study of selected hotelsjos; Akkaranggoon, S., (2010) 'Supply Chain Management Practices in the Hotel Industry: An Examination of Hotel', , University of Exeter, Exeter; Al-Aomar, R., Hussain, M., Exploration and prioritization of lean techniques in a hotel supply chain (2019) International Journal of Lean Six Sigma, 10 (1); Amaratunga, D., Baldry, D., Assessment of facilities management performance in higher education properties (2000) Facilities, 18 (7-8), pp. 293-301; Amaratunga, R.D.G., Baldry, D., (2002) Performance measurement in facilities management organisations: transition from measurement to management; Anand, N., Grover, N., Measuring retail supply chain performance: theoretical model using key performance indicators (KPIs) (2015) Benchmarking: An International Journal, 22 (1), pp. 135-166; Ayyildiz, E., Gumus, A.T., Interval-valued pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0 (2021) Complex and Intelligent Systems, 7 (1), pp. 559-576; Barnard, J., (2006) A multi-view framework for defining the services supply chain using object oriented methodology, , Eletronics Thesis and Dissertation, University of Florida; Bauer, K., KPIs: Not all metrics are created equal (2004) Information Management. SourceMedia, 14 (12), p. 42. , No, p; Supply chains and supply networks: distinctions and overlaps (2013) Supply Chain Management: An International Journal, 18 (6), pp. 644-652. , doi:, Braziotis, C., Bourlakis, M., Rogers, H. and Tannock, J; Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521. , Cai, J., Liu, X., Xiao, Z. and Liu, J; Campbell, L., Finch, E., Customer satisfaction and organisational justice (2004) Facilities, 22 (7-8), pp. 178-189; Chae, B., Developing key performance indicators for supply chain: an industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Durodola, O.D., Ayedun, C.A., Adedoyin, A.O., Beneficial application of facilities management in hotel organizations in South-Western Nigeria (2012) Mediterranean Journal of Social Sciences, 3 (1), p. 413; Ewurum, N.I., Fidelis-Umeh, D., Emoh, F., Effect of facilities management on hotel service delivery in South East Nigeria' (2015) Nigerian Journal of Management Research, 10 (1), pp. 112-121; Gong, K., Yan, H., Performance measurement of logistics service supply chain using bijective soft set (2015) Journal of Advanced Manufacturing Systems, 14 (1), pp. 23-40; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , No; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Heijden, P.R.J., Facility management contrasts between office and hotel industries (2014) Facility Management Studies, 1; Jones, C., Facilities management in medium-sized UK hotels (2002) International Journal of Contemporary Hospitality Management, 14 (2), pp. 72-80; Kocaoğlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Kok, H.B., Mobach, M.P., Omta, O.S.W.F., The added value of facility management in the educational environment (2011) Journal of Facilities Management, 9 (4), pp. 249-265; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Lapide, L., What about measuring supply chain performance’ (2000) Achieving Supply Chain Excellence through Technology, 2 (2), pp. 287-297; Lima-Junior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks (2019) International Journal of Production Economics, 212, pp. 19-38; McCormack, K., Bronzo Ladeira, M., Paulo Valadares de Oliveira, M., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Madritsch, T., Ebinger, M., Performance measurement in facility management the environment management maturity model BEM3 (2011) Research Journal of Economics, Business and ICT, 2, pp. 4-10; Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315. , Maestrini, V., Luzzini, D., Maccarrone, P. and Caniato, F; Mohd Yusoff, Y., Ashari, H., Salleh, M.N., The impact of supply chain management as mediator between strategic orientations and supply chain performance (2016) International Journal of Supply Chain Management, 5 (2), pp. 101-107; Nelson, M.L., Alexander, K., ‘The emergence of supply chain management as a strategic facilities management tool’ (2002) Proceedings of the Euro FM Research Symposium in Facilities Management, , The University of Salford, Salford; A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues (2015) International Journal of Production Economics, 169, pp. 310-332. , Ntabe, E.N., LeBel, L., Munson, A.D. and Santa-Eulalia, L.-A; Okoroh, M.I., Jones, C., Ilozor, B.D., FM application in the hospitality sector (2002) Facilities, 20 (7-8), pp. 239-250; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Penny, W.Y.K., The use of environmental management as a facilities management tool in the Macao hotel sector (2007) Facilities, 1; Pasanen, S.R., (2015), Internal supply chain: process and performance measurement development, Helsinki Metropolia University of Applied Sciences, Finland, 1-94; Designing and managing the optimum strategic FM supply chain (2014) Journal of Facilities Management, 12 (4), pp. 330-336. , doi:, Pitt, M., Chotipanich, S., Amin, R. and Issarasak, S; Priyangika, K.D.T., Perera, B., Wickremanayake Karunaratne, T.L., Facilities management roles in the hotel industry: the skills and competencies required (2020) Journal of Quality Assurance in Hospitality and Tourism, 21 (4), pp. 454-473; Ren, T., ‘Application of supply chain performance measurement based on SCOR model (2008) Wireless Communications, Networking and Mobile Computing, 2008. WiCOM’08. 4th International Conference on. IEEE, pp. 1-4; Sari, K., Suslu, M., A modeling approach for evaluating green performance of a hotel supply chain (2018) Technological Forecasting and Social Change, 137, pp. 53-60; (2018) Tourism industry report, , www.sltda.lk/sites/default/files/industry-report-Q2-2018.pdf, (accessed 12 May 2019; Sundarakani, B., Abdul Razzak, H., Manikandan, S., Creating a competitive advantage in the global flight catering supply chain: a case study using SCOR model (2018) International Journal of Logistics Research and Applications, 21 (5), pp. 481-501; Toni, A., Montagner, M., (2009), A PMS for facility services supply chain: a casethe healthcare sector, POMS 20th Annual Conference Orlando, Florida U.S.A, 1-35; Tucker, M., Pitt, M., Customer performance measurement in facilities management: a strategic approach (2009) International Journal of Productivity and Performance Management, 58 (5), pp. 407-422; Vanichkobchinda, P., (2010) Applying logistics and supply chain management strategy to improving operation efficiency in facility management and maintenance, , UTCC Engineering Research Papers, University of the Thai Chamber of Commerce; Weyers, M., (2017) An Application of the Supply Chain Operations Reference Model for the Service Supply Chain for Standardised Back Office Services, , Stellenbosch University, Stellenbosch},
correspondence_address1={Abdeen, F.N.; Department of Building Economics, Sri Lanka; email: nisharaabdeen04@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={02632772},
language={English},
abbrev_source_title={Facilities},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jha202266,
author={Jha, A. and Sharma, R.R.K. and Kumar, V. and Verma, P.},
title={Designing supply chain performance system: a strategic study on Indian manufacturing sector},
journal={Supply Chain Management},
year={2022},
volume={27},
number={1},
pages={66-88},
doi={10.1108/SCM-05-2020-0198},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101270300&doi=10.1108%2fSCM-05-2020-0198&partnerID=40&md5=71b00107d3ee34f294434f7c0480e014},
affiliation={Department of Transportation Management, School of Business, University of Petroleum and Energy Studies, Dehradun, India; Department of Industrial and Management Engineering, Indian Institute of Technology Kanpur, Kanpur, India; Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan},
abstract={Purpose: A well-designed supply chain performance measurement system, should account for not only the capabilities and performance attributes of the focal firm but also its supply chain partners. The purpose of this paper is to help design a system that strikes a balance between the strategic objectives of the focal firm and its supply partners vis-à-vis the requirements of supply chain performance (cost, quality, speed and customer taste). Design/methodology/approach: A theoretical framework on the strategic supply chain performance measurement system is developed based on existing literature and subsequently tested using a survey on 136 successful manufacturing organizations in India. The organizations were clustered into three strategy types and compared using analysis of variance on ranks to look for differences in preference for performance parameters. Findings: The study examined the five dimensions of the supply chain practices, namely, strategic supply/distribution network, customer relationship, internal operations, information sharing and social and environmental responsiveness. The empirical results demonstrate the inclusion of business strategy orientation in designing today’s supply chain and hence its performance measurement system. Not supported hypotheses were addressed in the light of contextual factors. Research limitations/implications: The study is confined to finding preferences of non-financial aspects of supply chain performance and tier-1 suppliers. The research helps better design and benchmark supply chain performance metrics, based on the strategic choice of the firm. Originality/value: This paper highlights the shortcomings in the existing performance measurement and gaps in the existing literature in the supply chain context. Further, it gives a holistic view of strategic supply chain performance measurement design. © 2021, Emerald Publishing Limited.},
author_keywords={Business strategy;  Key performance indicators;  Miles and snow;  Performance management;  Supply chain},
references={Agarwal, A., Shankar, R., Tiwari, M.K., Modeling agility of supply chains (2007) Industrial Marketing Management, 36 (4), pp. 443-457; Ambe, I.M., Badenhorst-Weiss, J.A., South African automotive industry: trends and challenges in the supply chain (2011) Journal of Contemporary Management, 8, pp. 337-362; Amir, F., Significance of lean, agile and leagile decoupling point in supply chain management (2011) Journal of Economics and Behavioral Studies, 3 (5), pp. 287-295; Anthony, R.N., (1965) Planning and Control Systems: A Framework for Analysis. Graduate School of Business Administration, , Harvard University, Boston, MA; Aslam, H., Blome, C., Roscoe, S., Azhar, T.M., Determining the antecedents of dynamic supply chain capabilities (2020) Supply Chain Management: An International Journal, 25 (4), pp. 427-442; Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A stakeholder approach to strategic performance measurement (1997) MIT Sloan Management Review, 38 (3), pp. 25-37; Avittathur, B., Shah, J., Gupta, O.K., Distribution centre location modelling for differential sales tax structure (2005) European Journal of Operational Research, 16 (1), pp. 191-205; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bortoluzzi, D.A., Lunkes, R.J., dos Santos, E.A., Mendes, A.C.A., Effect of online hotel reviews on the relationship between defender and prospector strategies and management controls (2020) International Journal of Contemporary Hospitality Management, pp. 1-25; Brush, T.H., Bromiley, P., Hendrickx, M., The free cash flow hypothesis for sales growth and firm performance (2000) Strategic Management Journal, 21 (4), pp. 455-472; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Carr, A.S., Pearson, J.N., Strategically managed buyer–seller relationships and performance outcomes (1999) Journal of Operations Management, 17 (5), pp. 497-519; Carroll, S.J., Gillen, D.I., Are the classical management functions useful in describing managerial work? (1987) Academy of Management Review, 12 (1), pp. 38-51; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Chatterjee, D., Sharma, R.R.K., Shanker, K., Important differences in management control systems, cultural dimensions and management practices of innovators, prospectors and defenders (2008) 3rd European Conference on Entrepreneurship and Innovation, pp. 71-82. , The University of Winchester, Winchester: 15-16 Sept, 2008; Chi, M., Huang, R., George, J.F., Collaboration in demand-driven supply chain: based on a perspective of governance and IT-business strategic alignment (2020) International Journal of Information Management, 52, p. 102062; Chopra, S., Meindl, P., (2010) Supply Chain Management: Strategy, Planning and Operation, , 5th ed, Pearson, Upper Saddle River, NJ; Costello, A.M., Mitigating incentive conflicts in inter-firm relationships: evidence from long-term supply contracts (2013) Journal of Accounting and Economics, 56 (1), pp. 19-39; Coughlan, A., Erin, A., Louis, S., Adel, E.-A., (2006) Marketing Channels, , 7th ed, Prentice Hall, Upper Saddle River, NJ; Curbelo, A.D., Delgado, F.M., SCOR model and the balanced scorecard, a powerful combination for business management assets (2013) Visión de Futuro, 18 (1), pp. 58-75; Day, G.S., The capabilities of market-driven organizations (1994) Journal of Marketing, 58 (4), pp. 37-52; Do, T.N., Kumar, V., Do, H.M., Prioritize the key factors of Vietnamese coffee industries for sustainability (2020) International Journal of Productivity and Performance Management, 69 (6), pp. 1153-1176; Dobroszek, J., Supply chain and logistics controller–two promising professions for supporting transparency in supply chain management (2020) Supply Chain Management: An International Journal, 25 (5), pp. 505-519; Duarte, S., Machado, V.C., Manufacturing paradigms in supply chain management (2011) International Journal of Management Science and Engineering Management, 6 (5), pp. 328-342; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes’ performance to a company’s financial strategic objectives (2012) European Journal of Operational Research, 223 (1), pp. 276-289; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Forslund, H., Jonsson, P., Obstacles to supply chain integration of the performance management process in buyer‐supplier dyads: the buyers' perspective (2009) International Journal of Operations & Production Management, 29 (1), pp. 77-95; Fosso Wamba, S., Kala Kamdjoug, J.R., Epie Bawack, R., Keogh, J.G., Bitcoin, blockchain and fintech: a systematic review and case studies in the supply chain (2020) Production Planning & Control, 31 (2-3), pp. 115-142; Frazier, G.L., Organizing and managing channels of distribution (1999) Journal of the Academy of Marketing Science, 27 (2), pp. 226-240; Gattorna, J.L., (1998) Strategic Supply Chain Alignment: best Practice in Supply Chain Management, , Gower Publishing, Ltd, Hampshire; Giunipero, L., Handfield, R.B., Eltantawy, R., Supply management’s evolution: key skill sets for the supply manager of the future (2006) International Journal of Operations & Production Management, 26 (7), pp. 822-844; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000–2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Grant, D., Managing sustainability risks in fashion supply chains (2020) Proceedings of the 19th logistics research network annual conference, , 1-8. 3rd-5th September, 2014, Huddersfield; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and application (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Lai, K.-H., Cheng, T.C.E., Responsive supply chain: a competitive strategy in a networked economy (2008) International Journal of Management Science, 36 (4), pp. 549-564; Gunasekaran, A., Patelb, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gurumurthy, A., Kodali, R., Application of benchmarking for assessing the lean manufacturing implementation (2009) Benchmarking: An International Journal, 16 (2), pp. 274-308; Hair, J.F., Anderson, R.E., Tahtam, R.L., Black, W.C., (1998) Multivariate Data Analysis with Readings, , PrenticeHall, Englewood Cliffs, NJ; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2010) Multivariate Data Analysis, , 7th ed, Prentice Hall, Englewood Cliffs, NJ; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hausman, W.H., Supply chain performance metrics (2004) The Practice of Supply Chain Management: Where Theory and Application Converge, pp. 61-73. , Springer, Boston, MA; Hu, W., Shou, Y., Kang, M., Park, Y., Risk management of manufacturing multinational corporations: the moderating effects of international asset dispersion and supply chain integration (2019) Supply Chain Management, An International Journal, 25 (1), pp. 61-76; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Huang, S.H., Liu, P., Mokasdar, A., Hou, L., Additive manufacturing and its societal impact: a literature review (2013) The International Journal of Advanced Manufacturing Technology, 67 (5-8), pp. 1191-1203; Ip, W.H., Chan, S.L., Lam, C.Y., Modeling supply chain performance and stability (2011) Industrial Management & Data Systems, 111 (8), pp. 1332-1354; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review (January-February), pp. 71-79; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California Management Review, 39 (1), pp. 53-79; Kocaoğlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Kumar, V., Sharma, R.R.K., Exploring critical success factors for TQM implementation using interpretive structural modeling approach: extract from case studies (2017) International Journal of Productivity and Quality Management, 21 (2), pp. 203-228; Kumar, V., Sharma, R.R.K., An empirical investigation of critical success factors influencing the successful TQM implementation for firms with different strategic orientation (2017) International Journal of Quality & Reliability Management, 34 (9), pp. 1530-1550; Kumar, V., Sharma, R.R.K., Relating management problem solving styles of leaders to TQM focus: an empirical study (2017) The TQM Journal, 29 (2), pp. 218-239; Kumar, V., Sharma, R.R.K., Leadership styles and their relationship with TQM focus for Indian firms: an empirical investigation (2018) International Journal of Productivity and Performance Management, 67 (6), pp. 1063-1088; Kumar, V., Sharma, R.R.K., Verma, P., Lai, K.K., Chang, Y.H., Mapping the TQM implementation: an empirical investigation of the cultural dimensions with different strategic orientation in Indian firms (2018) Benchmarking: An International Journal, 25 (8), pp. 3081-3116; Kumar, V., Verma, P., Mangla, S.K., Mishra, A., Dababrata, C., Hsu, S.C., Lai, K.K., Barriers to total quality management for sustainability in Indian organizations (2020) International Journal of Quality & Reliability Management, 37 (6-7), pp. 1007-1031; Kumar, V., Verma, P., Sharma, R.R.K., Khan, A.F., Conquering in emerging markets: critical success factors to enhance supply chain performance (2017) Benchmarking: An International Journal, 24 (3), pp. 570-593; Kumar, V., (2017) Relating critical success factors and culture to strategic orientation and leadership for TQM implementation in Indian firms: developing a theoretical framework and its empirical investigation, , PhD dissertation submitted at Department of Industrial and Management Engineering, Indian Institute of Technology Kanpur; Kumar, V., Sharma, R.R.K., Identifying critical success & failure factors for TQM implementation: extract from real case studies (2015) Industrial Engineering and Engineering Management (IEEM), 2015 IEEE International Conference on, pp. 16-20. , 6-9 Dec, IEEE, Singapore; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-20; Lima-Junior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks (2019) International Journal of Production Economics, 212, pp. 19-38; Lima-Junior, F.R., Carpinetti, L.C.R., An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics (2020) Computers & Industrial Engineering, 139, p. 106191; Lin, C., Chiu, Y.W., Chen, W.C., Ting, S.F., Exploring differences in competitive performance based on Miles and Snow's strategy typology for the semiconductor industry (2020) Industrial Management & Data Systems, 120 (6), pp. 1125-1148; Liu, C., Kong, D., Business strategy and sustainable development: evidence from China (2020) Business Strategy and the Environment, pp. 1-14; Malhotra, N., Dash, S., (2011) Marketing Research: An Applied Orientation, , 6th ed., Dorling Kindersley (India) Pvt. Ltd, Licensees of Pearson EducationSouth Asia, New Delhi; Mason-Jones, R., Naylor, B., Towill, D.R., Engineering the leagile supply chain (2000) International Journal of Agile Management Systems, 2 (1), pp. 54-61; Miles, R.E., Snow, C.C., (1978) Organizational Strategy, Structure, and Process, , McGraw-Hill, New York, NY; Miles, R.E., Snow, C.C., Meyer, A.D., Coleman, H.J., Jr., Organizational strategy, structure, and process (1978) Academy of Management Review, 3 (3), pp. 546-562; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Naylor, J.B., Naim, M.M., Berry, D., Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 107-118; Nunnally, J.L., (1978) Psychometric Theory, , 2nd ed, McGraw-Hill, New York, NY; Osorio‐Londoño, A.A., Naranjo‐Valencia, J.C., Calderón‐Hernández, G., Training and its influence on competitive strategy implementation (2020) Human Resource Development Quarterly, 31 (2), pp. 149-172; Panda, S., Sharma, R., Do changes in patent policy influence firms’ technology strategy? Evidence from manufacturing in India (2020) Journal of Policy Modeling, pp. 1-14; Parast, M.M., A learning perspective of supply chain quality management: empirical evidence from US supply chains (2019) Supply Chain Management: An International Journal, 25 (1), pp. 17-34; Pathak, D.K., Verma, A., Kumar, V., Performance variable of GSCM for sustainability in Indian automobile organizations using TOPSIS method (2020) Business Strategy & Development, 3 (4), pp. 590-602; Peng, C.W., The role of business strategy and CEO compensation structure in driving corporate social responsibility: linkage towards a sustainable development perspective (2020) Corporate Social Responsibility and Environmental Management, 27 (2), pp. 1028-1039; Prahinksi, C., Benton, W.C., Supplier evaluations: communication strategies to improve supplier performance (2004) Journal of Operations Management, 22 (1), pp. 39-62; Preuss, L., Rhetoric and reality of corporate greening: a view from the supply chain management function (2005) Business Strategy and the Environment, 14 (2), pp. 123-139; Qi, Y., Boyer, K.K., Zhao, X., Supply chain strategy, product characteristics, and performance impact: evidence from Chinese manufacturers (2009) Decision Sciences, 40 (4), pp. 667-695; Romano, P., Co-ordination and integration mechanisms to manage logistics processes across supply networks (2003) Journal of Purchasing and Supply Management, 9 (3), pp. 119-134; Sehgal, V., (2010) Supply Chain as Strategic Asset: The Key to Reaching Business Goals, 22. , John Wiley and Sons; Sharma, N.K., Govindan, K., Lai, K.K., Chen, W.K., Kumar, V., The transition from linear economy to circular economy for sustainability among SMEs: a study on prospects, impediments, and prerequisites (2020) Business Strategy and the Environment, pp. 1-20; Sharma, R.R.K., Shrotriya, S., Behera, A., Seliger, G., Eggenstein, M., Relating objectives to manufacturing decisions in dynamic environments: implications of an exploratory study of Indian and German manufacturing firms (2003) International Journal of Manufacturing Technology and Management, 5 (5-6), pp. 472-491; Shen, H., Mei, N., Gao, Y., Matching entrepreneurial orientation and operations strategy for manufacturing firms in China (2020) Operations Management Research, 13 (1-2), pp. 39-52; Stadtler, H., Supply chain management and advanced planning––basics, overview and challenges (2005) European Journal of Operational Research, 163 (3), pp. 575-588; Tang, O., Musa, S.N., Identifying risk issues and research advancements in supply chain risk management (2011) International Journal of Production Economics, 133 (1), pp. 25-34; van Kalsbeek, R., Broekhuis, M., Roodbergen, K.J., Controlling and enabling practices to manage supply in online service triads (2020) Supply Chain Management: An International Journal; Varadarajan, P., The sustainable growth model: a tool for evaluating the financial feasibility of market share strategies (1983) Strategic Management Journal, 4 (4), pp. 353-367; Verma, P., Sharma, R.R.K., The linkages between horizontal strategy, person-environment fit and horizontal fit: an empirical study (2018) International Journal of Industrial and Systems Engineering, 28 (2), pp. 216-239; Verma, P., Sharma, R.R.K., Kumar, V., Hsu, S.C., Lai, K.K., Identifying organizational variables to the implementation of horizontal strategy in conglomerates (2020) Benchmarking: An International Journal; Verma, P., Kumar, V., Jha, A., Muthukumaar, V., Do, H.M., Sharma, N.K., Gupta, S., The degree of collaboration between business units in diversified corporation: a strategic perspective (2020) Business Strategy & Development, 3 (4), pp. 449-460; Vilanova, M.R.N., Magalhães Filho, P., Balestieri, J.A.P., Performance measurement and indicators for water supply management: review and international cases (2015) Renewable and Sustainable Energy Reviews, 43, pp. 1-12; Walton, S.V., Handfield, R.B., Melnyk, S.A., The green supply chain: integrating suppliers into environmental management processes (1998) International Journal of Purchasing and Materials Management, 34 (1), pp. 2-11; Wang, W.Y., Chan, H.K., Pauleen, D.J., Aligning business process reengineering in implementing global supply chain systems by the SCOR model (2010) International Journal of Production Research, 48 (19), pp. 5647-5669; Wang, W.Y., Heng, M.S., Chau, P.Y., Implementing supply chain management in the new era: a replenishment framework for the supply chain operation reference model (2007) Supply Chain Management: Issues in the New Era of Collaboration and Competition, pp. 1-22. , IGI Global; Wu, S.I., Hung, J.M., The performance measurement of cause-related marketing by balance scorecard (2007) Total Quality Management & Business Excellence, 18 (7), pp. 771-791; Zimmermann, R., Ferreira, L.M.D., Moreira, A.C., An empirical analysis of the relationship between supply chain strategies, product characteristics, environmental uncertainty and performance (2020) Supply Chain Management: An International Journal, 25 (3), pp. 375-391; Akerlof, G., The market for ‘lemons’: qualitative uncertainty and the market mechanism (1970) The Quarterly Journal of Economics, 84 (3), pp. 488-500; El-Tawy, N., Gallear, D., Leanness and agility as means for improving supply chains: a case study on Egypt (2011) paper read at European, Mediterranean and Middle Eastern Conference on Information Systems, pp. 809-841. , Athens: May 30-31; Roscoe, S., Eckstein, D., Blome, C., Goellner, M., Determining how internal and external process connectivity affect supply chain agility: a life-cycle theory perspective (2020) Production Planning & Control, 31 (1), pp. 78-91},
correspondence_address1={Kumar, V.; Department of Information Management, Taiwan; email: vimaljss91@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ozbiltekin-Pala2022,
author={Ozbiltekin-Pala, M. and Koçak, A. and Kazancoglu, Y.},
title={A proposed circular-SCOR model for supply chain performance measurement in manufacturing industry during COVID-19},
journal={International Journal of Quality and Reliability Management},
year={2022},
doi={10.1108/IJQRM-03-2022-0101},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85144069032&doi=10.1108%2fIJQRM-03-2022-0101&partnerID=40&md5=d0e7feda1f37a098936ccdfc12f97a76},
affiliation={Department of Logistics Management, Yasar University, Izmir, Turkey; Faculty of Economics and Administrative Sciences, Ege University, Izmir, Turkey},
abstract={Purpose: COVID-19 is a global event affecting supply chain operations and human health. With COVID-19, many issues in business models, business processes and supply chains, especially in the manufacturing industry, have had to change. The ability to analyze supply chain performances and ensure circularity in supply chains has become one of the factors whose importance has increased rapidly with COVID-19. Therefore, it aims to determine which supply chain performance criteria come to the fore for the company under consideration to accelerate the transformation into high performance and circularity in supply chains. Design/methodology/approach: In this study, a new circular-SCOR model is proposed, and 17 supply chain performance measurement criteria are prioritized for a manufacturing company in the context of circular economy principles during COVID-19 by using stepwise weight assessment ratio analysis and analytical hierarchy process method, separately. Findings: As a result, for both methods, in the case study discussed, the demand fulfillment rate is determined as the most prominent criterion in line with the circular economy principles in the COVID-19 period in manufacturing supply chains. Originality/value: It is expected that this study will contribute to managers and policy makers as it addresses the “new normal” that started after COVID-19 and the criteria to be considered in supply chain performance measurement and emphasizes the need to adopt circular supply chains, especially in manufacturing industries. © 2022, Emerald Publishing Limited.},
author_keywords={Circular-SCOR model;  COVID-19;  Performance measurement;  Supply chain},
references={Ada, E., Sagnak, M., Kazancoglu, Y., Luthra, S., Kumar, A., A framework for evaluating information transparency in supply chains (2021) Journal of Global Information Management, 29 (6), pp. 1-22; Agarwal, S., Kant, R., Shankar, R., Exploring sustainability balanced scorecard for performance evaluation of humanitarian organizations (2022) Cleaner Logistics and Supply Chain, 3 (March 2022); Agrawal, D., Minocha, S., Namasudra, S., Gandomi, A.H., A robust drug recall supply chain management system using hyperledger blockchain ecosystem (2022) Computers in Biology and Medicine, 140; Agrawal, R., Wankhede, V.A., Kumar, A., Upadhyay, A., Garza-Reyes, J.A., Nexus of circular economy and sustainable business performance in the era of digitalization (2021) International Journal of Productivity and Performance Management, 71 (3), pp. 748-774; Ahmed, M.N., Yas, A.H., Hussein, I.G., Evaluation of construction companies performance by using stepwise weight assessment ratio analysis (2022) Periodicals of Engineering and Natural Sciences, 10 (1), pp. 253-267; Akhmetzyanov, B.Z., Egizbaev, S.R., Opportunities and prospects for the transition to post-COVID circular economy (2022) Post-COVID Economic Revival, 2, pp. 35-50. , Palgrave Macmillan, Cham; Akram, K., Saeed, A., Bresciani, S., Rehman, S.U., Ferraris, A., Factors affecting environmental performance during the covid-19 period in the leather industry: a moderated-mediation approach (2022) Journal of Competitiveness, 14 (1), pp. 5-22; Alkhuzaim, L., Zhu, Q., Sarkis, J., Evaluating emergy analysis at the nexus of circular economy and sustainable supply chain management (2021) Sustainable Production and Consumption, 25, pp. 413-424; Almatarneh, Z., Jarah, B., Jarrah, M., The role of management accounting in the development of supply chain performance in logistics manufacturing companies (2022) Uncertain Supply Chain Management, 10 (1), pp. 13-18; Alzoubi, H.M., Elrehail, H., Hanaysha, J.R., Al-Gasaymeh, A., Al-Adaileh, R., The role of supply chain integration and agile practices in improving Lead time during the COVID-19 crisis (2022) International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 13 (1), pp. 1-11; Appolloni, A., Jabbour, C.J.C., D'Adamo, I., Gastaldi, M., Settembre-Blundo, D., Green recovery in the mature manufacturing industry: the role of the green-circular premium and sustainability certification in innovative efforts (2022) Ecological Economics, 193; Asamoah, D., Nuertey, D., Agyei-Owusu, B., Acquah, I.N., Antecedents and outcomes of supply chain security practices: the role of organizational security culture and supply chain disruption occurrence (2022) International Journal of Quality and Reliability Management, 39 (4), pp. 1059-1082; Astanti, R.D., Daryanto, Y., Dewa, P.K., Low-Carbon supply chain model under a vendor-managed inventory partnership and carbon cap-and-trade policy (2022) Journal of Open Innovation: Technology, Market, and Complexity, 8 (1), p. 30; Ayyildiz, E., Gumus, A.T., Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0 (2021) Complex and Intelligent Systems, 7 (1), pp. 559-576; Azbari, K.E., Ashofteh, P.S., Golfam, P., Loáiciga, H.A., Ranking of wastewater reuse allocation alternatives using a variance-based weighted aggregated sum product assessment method (2022) Environment, Development and Sustainability, 24 (2), pp. 2497-2513; Azcarate-Aguerre, J.F., Klein, T., Konstantinou, T., Veerman, M., Facades-as-a-Service: the role of technology in the circular servitisation of the building envelope (2022) Applied Sciences, 12 (3), p. 1267; Bag, S., Dhamija, P., Bryde, D.J., Singh, R.K., Effect of eco-innovation on green supply chain management, circular economy capability, and performance of small and medium enterprises (2022) Journal of Business Research, 141, pp. 60-72; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management – what has been achieved? (2015) Supply Chain Management: An International Journal, 20 (6), pp. 664-680; Bouguerra, A., Hughes, M., Cakir, M.S., Tatoglu, E., Linking entrepreneurial orientation to environmental collaboration: a stakeholder theory and evidence from multinational companies in an emerging market (2022) British Journal of Management, (2022), pp. 1-25; Brewer, P., Speh, T., Using the Balanced Scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (14), pp. 75-93; Bruyninckx, H., Karadeniz, G., Martin, J., The European Green Deal: opportunities and prospects after the Covid-19 crisis (2021) Social Policy in the European Union: State of Play, 2021, p. 61; Calzolari, T., Genovese, A., Brint, A., Circular Economy indicators for supply chains: a systematic literature review (2022) Environmental and Sustainability Indicators, 13; Chaouni Benabdellah, A., Zekhnini, K., Cherrafi, A., Sustainable and resilience improvement through the design for circular digital supply chain (2021) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, 633. , Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G. and Romero, D., and,(Eds), doi:, Springer, Cham; Chhatwani, M., Mishra, S.K., Varma, A., Rai, H., Psychological resilience and business survival chances: a study of small firms in the USA during COVID-19 (2022) Journal of Business Research, 142, pp. 277-286; Choi, T.M., Shi, X., On-demand ride-hailing service platforms with hired drivers during coronavirus (COVID-19) outbreak: can blockchain help? (2022) IEEE Transactions on Engineering Management, pp. 1-16; Das, D., Verma, P., Tanksale, A.N., Designing a closed-loop supply chain for reusable packaging materials: a risk-averse two-stage stochastic programming model using CVaR (2022) Computers and Industrial Engineering, 167 (May 2022); Dharani, M., Hassan, M.K., Rabbani, M.R., Huq, T., Does the Covid-19 pandemic affect faith-based investments? Evidence from global sectoral indices (2022) Research in International Business and Finance, 59; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things: toward more practical SCPMS (2017) Industrial Management and Data Systems, 117 (2), pp. 267-286; Dwivedi, A., Paul, S.K., A Framework for digital supply chains in the era of circular economy: implications on environmental sustainability (2022) Business Strategy and the Environment, 31 (4), pp. 1249-1274; Dzogbewu, T.C., Afrifa Jnr, S., Amoah, N., Fianko, S.K., de Beer, D., Additive manufacturing interventions during the COVID-19 pandemic: south Africa (2022) Applied Sciences, 12 (1), p. 295; Ebrahimi, S., Esmaeili, S.A.H., Sobhani, A., Szmerekovsky, J., Renewable jet fuel supply chain network design: application of direct monetary incentives (2022) Applied Energy, 310; Fargnoli, M., Haber, N., Tronci, M., Case study research to foster the optimization of supply chain management through the PSS approach (2022) Sustainability, 14 (4), p. 2235; Feizabadi, J., Machine learning demand forecasting and supply chain performance (2022) International Journal of Logistics Research and Applications, 25 (2), pp. 119-142; Fernandes, A.C., Vilhena, E., Oliveira, R., Sampaio, P., Carvalho, M.S., Supply chain quality management impact on organization performance: results from an international survey (2022) International Journal of Quality and Reliability Management, 39 (2), pp. 630-646; Fu, X., Qiang, Y., Liu, X., Jiang, Y., Cui, Z., Zhang, D., Wang, J., Will multi-industry supply chains’ resilience under the impact of COVID-19 pandemic be different? A perspective from China’s highway freight transport (2022) Transport Policy, 118 (March), pp. 165-178; Gebhardt, M., Spieske, A., Birkel, H., The future of the circular economy and its effect on supply chain dependencies: empirical evidence from a Delphi study (2022) Transportation Research Part E: Logistics and Transportation Review, 157; Govindan, K., Dhingra Darbari, J., Kaul, A., Jha, P.C., Structural model for analysis of key performance indicators for sustainable manufacturer–supplier collaboration: a grey‐decision‐making trial and evaluation laboratory‐based approach (2021) Business Strategy and the Environment, 30 (4), pp. 1702-1722; Hahm, J.B., Byon, K.K., Hyun, Y.A., Hahm, J., The show must go on: the mediating role of self-evaluation in the relationship between performers’ technology acceptance and satisfaction level with remote performances in Korea during the COVID-19 pandemic (2022) Technology in Society, 68 (February 2022); Hazen, B.T., Russo, I., Confente, I., Pellathy, D., Supply chain management for circular economy: conceptual framework and research agenda (2020) The International Journal of Logistics Management, 32 (2), pp. 510-537; Henke, N., Puri, A., Saleh, T., (2020) Accelerating Analytics to Navigate COVID-19 and the Next Normal, , Mckinsey, London; Hervani, A.A., Nandi, S., Helms, M.M., Sarkis, J., A performance measurement framework for socially sustainable and resilient supply chains using environmental goods valuation methods (2022) Sustainable Production and Consumption, 30, pp. 31-52; Hohenstein, N.O., Supply chain risk management in the COVID-19 pandemic: strategies and empirical lessons for improving global logistics service providers’ performance (2022) The International Journal of Logistics Management, 33 (4), pp. 1336-1365; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Hwihanus, H., Wijaya, O., Nartasari, D., The role of supply chain management on Indonesian small and medium enterprise competitiveness and performance (2022) Uncertain Supply Chain Management, 10 (1), pp. 109-116; Iftikhar, R., Khan, M.S., Social media big data analytics for demand forecasting: development and case implementation of an innovative framework (2022) Research Anthology on Big Data Analytics, Architectures, and Applications, pp. 902-920. , IGI Global; Ivanov, D., Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/Sars-CoV-2) case (2020) Transportation Research Part E: Logistics and Transportation Review, 136 (2020), pp. 1-14; Jæger, B., Menebo, M.M., Upadhyay, A., Identification of environmental supply chain bottlenecks: a case study of the Ethiopian healthcare supply chain (2021) Management of Environmental Quality: An International Journal, 14 (1); Jaeger, B., Upadhyay, A., Understanding barriers to circular economy: cases from the manufacturing industry (2020) Journal of Enterprise Information Management, 33 (4), pp. 729-745; Jafari-Sadeghi, V., Mahdiraji, H.A., Devalle, A., Pellicelli, A.C., Somebody is hiding something: disentangling interpersonal level drivers and consequences of knowledge hiding in international entrepreneurial firms (2022) Journal of Business Research, 139, pp. 383-396; Jardioui, M., Garengo, P., Semma, E.A., How organizational culture influences performance measurement systems in SMEs (2019) International Journal of Productivity and Performance Management, 69 (2), pp. 217-235; Javaid, M., Haleem, A., Suman, R., Major advancements of industry 4.0 to overcome challenges in manufacturing during the COVID-19 pandemic (2022) Cyber-Physical Systems: Solutions to Pandemic Challenges, pp. 157-182. , CRC Press; Joshi, S., Sharma, M., Impact of sustainable supply chain management on performance of SMEs amidst COVID-19 pandemic: an Indian perspective (2022) International Journal of Logistics Economics and Globalisation, 9 (3), pp. 248-276; Jraisat, L., Upadhyay, A., Ghalia, T., Jresseit, M., Kumar, V., Sarpong, D., Triads in sustainable supply-chain perspective: why is a collaboration mechanism needed? (2021) International Journal of Production Research, ahead-of-print, pp. 1-17; Juliana, J., Nagoya, R., Bangkara, B., Purba, J., Fachrurazi, F., The role of supply chain on the competitiveness and the performance of restaurants (2022) Uncertain Supply Chain Management, 10 (2), pp. 445-452; Jwijati, I., Bititci, U.S., Caldwell, N., Garengo, P., Dan, W., Impact of national culture on performance measurement systems in manufacturing firms (2022) Production Planning and Control, 440, pp. 1-16; Kamble, S.S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., Sharma, R., Digital twin for sustainable manufacturing supply chains: current trends, future perspectives, and an implementation framework (2022) Technological Forecasting and Social Change, 176; Kazancoglu, I., Ozbiltekin-Pala, M., Mangla, S.K., Kazancoglu, Y., Jabeen, F., Role of flexibility, agility and responsiveness for sustainable supply chain resilience during COVID-19 (2022) Journal of Cleaner Production, 362 (2022); Kharub, M., Mor, R.S., Rana, S., Mediating role of manufacturing strategy in the competitive strategy and firm performance: evidence from SMEs (2022) Benchmarking: An International Journal, 29 (10), pp. 3275-3301; Khokhar, M., Zia, S., Islam, T., Sharma, A., Iqbal, W., Irshad, M., Going green supply chain management during covid-19, assessing the best supplier selection criteria: a triple bottom line (tbl) approach (2022) Problemy Ekorozwoju, 17 (1), pp. 36-51; Kiers, J., Seinhorst, J., Zwanenburg, M., Stek, K., Which strategies and corresponding competences are needed to improve supply chain resilience: a COVID-19 based review (2022) Logistics, 6 (1), p. 12; Kirbac, A., Burmaoglu, S., Gungor, D.O., Technological intelligence for circular supply chain: a co-citation analysis approach (2022) Foresight; Kirchoff, J.F., Falasca, M., Environmental differentiation from a supply chain practice view perspective (2022) International Journal of Production Economics, 244; Krishnan, R., Yen, P., Agarwal, R., Arshinder, K., Bajada, C., Collaborative innovation and sustainability in the food supply chain-evidence from farmer producer organisations (2021) Resources, Conservation and Recycling, 168; Kumar, N., Brint, A., Shi, E., Upadhyay, A., Ruan, X., Integrating sustainable supply chain practices with operational performance: an exploratory study of Chinese SMEs (2019) Production Planning and Control, 30 (5-6), pp. 464-478; Kumar, A., Mangla, S.K., Kumar, P., Song, M., Mitigate risks in perishable food supply chains: learning from COVID-19 (2021) Technological Forecasting and Social Change, 166; Kumar, P., Sharma, D., Pandey, P., Coordination mechanisms for digital and sustainable textile supply chain (2022) International Journal of Productivity and Performance Management, , ahead-of-print ahead-of-print; Laing, T., Upadhyay, A., Mohan, S., Subramanian, N., Environmental improvement initiatives in the coal mining industry: maximisation of the triple bottom line (2019) Production Planning and Control, 30 (5-6), pp. 426-436; Lee, K., Romzi, P., Hanaysha, J., Alzoubi, H., Alshurideh, M., Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: an empirical study in Malaysia (2022) Uncertain Supply Chain Management, 10 (2), pp. 537-550; Lim, M.K., Lai, M., Wang, C., Lee, Y., Circular economy to ensure production operational sustainability: a green-lean approach (2022) Sustainable Production and Consumption, 30, pp. 130-144; Lin, K., Musa, S.N., Yap, H.J., Vehicle routing optimization for pandemic containment: a systematic review on applications and solution approaches (2022) Sustainability, 14 (4), p. 2053; Liu, Z., Rotary Kiln thermal simulation model and smart supply chain logistics transportation monitoring management (2022) Journal of Advanced Transportation, 2022, pp. 1-13; López, A.T., Cruz, D.L.G., The contingency approach to COVID-19 of subsistence businesses (2022) Research in Administrative Sciences Under COVID-19, pp. 31-47. , Emerald Publishing; Long, L.N.B., Cuong, T.N., Kim, H.S., You, S.S., (2022) Sustainability and robust decision-support strategy for multi-echelon supply chain system against disruptions, , SSRN 4020779; Luo, S., Hu, W., Liu, W., Liu, Z., Huang, Q., Chen, Z., Flexibility enhancement measures under the COVID-19 pandemic–A preliminary comparative analysis in Denmark, The Netherlands, and Sichuan of China (2022) Energy, 239; Maaz, M.A.M., Ahmad, R., Impact of supply chain performance on organizational performance mediated by customer satisfaction: a study of dairy industry (2022) Business Process Management Journal, 28 (1), pp. 1-22; Macchion, L., Da Giau, A., Caniato, F., Caridi, M., Danese, P., Rinaldi, R., Vinelli, A., Strategic approaches to sustainability in fashion supply chain management (2018) Production Planning and Control, 29 (1), pp. 9-28; Markaki, O., Askounis, D., Assessing the operational and economic efficiency benefits of dynamic manufacturing networks through fuzzy cognitive maps: a case study (2021) Operational Research, 21 (2), pp. 925-950; Maryono, D., Ambarwati, R., Evaluation of hospital supply chain performance during the COVID-19 pandemic: balanced score card approach (2022) Transitioning from Globalized to Localized and Self-Reliant Economies, pp. 95-110. , IGI Global; Mayanti, B., Helo, P., Closed-loop supply chain potential of agricultural plastic waste: economic and environmental assessment of bale wrap waste recycling in Finland (2022) International Journal of Production Economics, 244; Mishra, R., Singh, R.K., Rana, N.P., Developing environmental collaboration among supply chain partners for sustainable consumption and production: insights from an auto sector supply chain (2022) Journal of Cleaner Production, 338 (March); Mollenkopf, D.A., Ozanne, L.K., Stolze, H.J., A transformative supply chain response to COVID-19 (2020) Journal of Service Management, 32 (2), pp. 190-202; Moretto, A., Caniato, F., Can Supply Chain Finance help mitigate the financial disruption brought by Covid-19? (2021) Journal of Purchasing and Supply Management, 27 (4); Moyer, J.W., Ruene, M.E., (2020) Demand for Online Ordering Leaves Grocery Stores Scrambling, Customers Waiting, , https://www. washingtonpost.com/local/demand- for- online- ordering- leaves- grocery- stores- scrambling- customers- waiting/2020/03/27/8c246b48- 6ed7- 11ea- 96a0- df4c5d9284af _ story.html, The Washington Post, March 27: accessed, 30 April 2021; Muhammad, N., Upadhyay, A., Kumar, A., Gilani, H., Achieving operational excellence through the lens of lean and Six Sigma during the COVID-19 pandemic (2022) The International Journal of Logistics Management, 2022, pp. 1-19; Mukhsin, M., Suryanto, T., The effect of sustainable supply chain management on company performance mediated by competitive advantage (2022) Sustainability, 14 (2), p. 818; Murshed, S.M., Consequences of the Covid-19 pandemic for economic inequality (2022) Covid-19 and International Development, pp. 59-70. , Springer, Cham; Nag, U., Sharma, S.K., Govindan, K., Investigating drivers of circular supply chain with product-service system in automotive firms of an emerging economy (2021) Journal of Cleaner Production, 319; Nagao, T., Ijuin, H., Yamada, T., Nagasawa, K., Zhou, L., COVID-19 disruption strategy for redesigning global supply chain network across TPP countries (2022) Logistics, 6 (1), p. 2; Narayanan, A., Ishfaq, R., Impact of metric-alignment on supply chain performance: a behavioral study (2022) The International Journal of Logistics Management, 33 (1), pp. 365-384; Nguyen, D., Nguyen, T., Nguyen, X., Do, T., Ngo, H., The effect of supply chain finance on supply chain risk, supply chain risk resilience, and performance of Vietnam SMEs in global supply chain (2022) Uncertain Supply Chain Management, 10 (1), pp. 225-238; Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., Vasilakis, C., Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions (2021) European Journal of Operational Research, 290 (1), pp. 99-115; Nouri, F.A., Nikabadi, M.S., Olfat, L., Developing the framework of sustainable service supply chain balanced scorecard (SSSC BSC) (2019) International Journal of Productivity and Performance Management, 68 (1), pp. 148-170; Oh, S., Moon, H.C., Zhong, Y., Contingency management and supply chain performance in Korea: a covid-19 pandemic approach (2020) Sustainability, 12 (23), p. 9823; Ozdemir, D., Sharma, M., Dhir, A., Daim, T., Supply chain resilience during COVID 19 pandemic (2022) Technology in Society, 68 (5); Ozkanlisoy, O., The covid-19 outbreaks effects and new inclinations in terms of logistics and supply chain activities: a conceptual framework (2021) Journal of Management Marketing and Logistics, 8 (2), pp. 76-88; Panwar, R., Pinkse, J., De Marchi, V., The future of global supply chains in a post-COVID-19 world (2022) California Management Review, 64 (2), pp. 5-23; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14 (4), pp. 335-346; Pathak, S.K., Sharma, V., Chougule, S.S., Goel, V., Prioritization of barriers to the development of renewable energy technologies in India using integrated Modified Delphi and AHP method (2022) Sustainable Energy Technologies and Assessments, 50; Pathan, A.I., Girish Agnihotri, P., Said, S., Patel, D., AHP and TOPSIS based flood risk assessment-a case study of the Navsari City, Gujarat, India (2022) Environmental Monitoring and Assessment, 194 (7), pp. 1-37; Patti, A., Acierno, D., Towards the sustainability of the plastic industry through biopolymers: properties and potential applications to the textiles world (2022) Polymers, 14 (4), p. 692; Paul, S.K., Chowdhury, P., Moktadir, M.A., Lau, K.H., Supply chain recovery challenges in the wake of COVID-19 pandemic (2021) Journal of Business Research, 136, pp. 316-329; Pereira, V., Bamel, U., Charting the managerial and theoretical evolutionary path of AHP using thematic and systematic review: a decadal (2012–2021) study (2022) Annals of Operations Research, ahead-of-print, pp. 1-17; Pereira, V., Nandakumar, M.K., Sahasranamam, S., Bamel, U., Malik, A., Temouri, Y., An exploratory study into emerging market SMEs' involvement in the circular Economy: evidence from India's indigenous Ayurveda industry (2022) Journal of Business Research, 142, pp. 188-199; Pimenta, M.L., Cezarino, L.O., Piato, E.L., da Silva, C.H.P., Oliveira, B.G., Liboni, L.B., Supply chain resilience in a Covid-19 scenario: mapping capabilities in a systemic framework (2022) Sustainable Production and Consumption, 29, pp. 649-656; Rahman, T., Moktadir, M.A., Paul, S.K., Key performance indicators for a sustainable recovery strategy in health-care supply chains: COVID-19 pandemic perspective (2021) Journal of Asia Business Studies, 16 (3), pp. 472-494; Reddy, K.N., Kumar, A., Capacity investment and inventory planning for a hybrid manufacturing–re-manufacturing system in the circular economy (2021) International Journal of Production Research, 59 (8), pp. 2450-2478; Rodrigues, A.P., Fernandes, M.L., Rodrigues, M.F.F., Bortoluzzi, S.C., da Costa, S.G., de Lima, E.P., Developing criteria for performance assessment in municipal solid waste management (2018) Journal of Cleaner Production, 186, pp. 748-757; Roy, T., Garza-Reyes, J.A., Kumar, V., Kumar, A., Agrawal, R., Redesigning traditional linear supply chains into circular supply chains–A study into its challenges (2022) Sustainable Production and Consumption, 31, pp. 113-126; Rutitis, D., Smoca, A., Uvarova, I., Brizga, J., Atstaja, D., Mavlutova, I., Sustainable value chain of industrial biocomposite consumption: influence of COVID-19 and consumer behaviour (2022) Energies 2022, 15, p. 466; Şahin, Y., Bozkurt, Y., Multi-criteria decision support with SWARA and TOPSIS methods to the digital transformation process in the iron and steel industry (2022) Digitizing Production Systems, pp. 309-322. , Springer, Cham; Saini, G., Budhwar, V., Choudhary, M., Review on people’s trust on home use medical devices during Covid-19 pandemic in India (2022) Health and Technology, 12 (2), pp. 1-20; Sakdiyah, K., Financial performance assessment analysis using economic value added (EVA) method (2022) NUsantara Islamic Economic Journal, 1 (1), pp. 54-64; Saleheen, F., Habib, M.M., Hanafi, Z., Supply chain performance measurement model: a literature review (2018) International Journal of Supply Chain Management (IJSCM), 7 (3), pp. 70-78; Sardi, A., Garengo, P., Bititci, U., Measurement and management of competences by enterprise social networking (2019) International Journal of Productivity and Performance Management, 68 (1), pp. 109-126; Sarfraz, A., Sarfraz, Z., Sarfraz, M., Sarfraz, M., Abdul Razzack, A., Bano, S., Singh Makkar, S., Cherrez-Ojeda, I., Industry 4.0 technologies for the manufacturing and distribution of COVID-19 vaccines (2022) Journal of Primary Care and Community Health, 13. , 21501319211068638; Shahed, K.S., Azeem, A., Ali, S.M., Moktadir, M., A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk (2021) Environmental Science and Pollution Research, ahead-of-print, pp. 1-16; Sharma, M., Alkatheeri, H., Jabeen, F., Sehrawat, R., Impact of COVID-19 pandemic on perishable food supply chain management: a contingent Resource-Based View (RBV) perspective (2022) The International Journal of Logistics Management; Sharma, M., Kumar, A., Luthra, S., Joshi, S., Upadhyay, A., The impact of environmental dynamism on low‐carbon practices and digital supply chain networks to enhance sustainable performance: an empirical analysis (2022) Business Strategy and the Environment; Siagian, H., Tarigan, Z.J.H., Jie, F., Supply chain integration enables resilience, flexibility, and innovation to improve business performance in COVID-19 era (2021) Sustainability, 13 (9), p. 4669; Singagerda, F., Fauzan, A., Desfiandi, A., The role of supply chain visibility, supply chain flexibility, supplier development on business performance of logistics companies (2022) Uncertain Supply Chain Management, 10 (2), pp. 463-470; Singh, R., Centobelli, P., Cerchione, R., Digitization of information sharing to minimize the impact of COVID-19 in the food supply chain (2022) Handbook of Research on the Platform Economy and the Evolution of E-Commerce, pp. 251-272. , IGI Global; Sivageerthi, T., Bathrinath, S., Uthayakumar, M., Bhalaji, R.K.A., A SWARA method to analyze the risks in coal supply chain management (2022) Materials Today: Proceedings, 50, pp. 935-940; Soares, J.C., Sousa, S., Tereso, A., Industry practices on the rework of defective products: survey results (2020) The TQM Journal, 32 (6), pp. 1177-1196; Srivastava, D.K., Kumar, V., Ekren, B.Y., Upadhyay, A., Tyagi, M., Kumari, A., Adopting Industry 4.0 by leveraging organisational factors (2022) Technological Forecasting and Social Change, 176; Stalmachova, K., Chinoracky, R., Strenitzerova, M., Changes in business models caused by digital transformation and the COVID-19 pandemic and possibilities of their measurement—case study (2022) Sustainability, 14 (1), p. 127; Taghizadeh-Yazdi, M., Farrokhi, Z., Mohammadi-Balani, A., An integrated inventory model for multi-echelon supply chains with deteriorating items: a price-dependent demand approach (2020) Journal of Industrial and Production Engineering, 37, pp. 87-96; Tasnim, Z., Hamid, A.B.A., Dwivedi, Y.K., Shareef, M.A., Sustainable disaster supply chain management for relief operations in Bangladesh (2022) Journal of Humanitarian Logistics and Supply Chain Management, 12 (2), pp. 285-304; Taylor, A., Taylor, M., Factors influencing effective implementation of performance measurement systems in small and medium-sized enterprises and Large firms: a perspective from contingency theory (2014) International Journal of Production Research, 52 (3), pp. 847-866; Torkashvand, M., Neshat, A., Javadi, S., Yousefi, H., DRASTIC framework improvement using stepwise weight assessment ratio analysis (SWARA) and combination of genetic algorithm and entropy (2021) Environmental Science and Pollution Research, 28 (34), pp. 46704-46724; Tseng, M.L., Bui, T.D., Lim, M.K., Fujii, M., Mishra, U., Assessing data-driven sustainable supply chain management indicators for the textile industry under industrial disruption and ambidexterity (2022) International Journal of Production Economics, 245; Upadhyay, A., Antecedents of green supply chain practices in developing economies (2020) Management of Environmental Quality: An International Journal, ahead-of-print; Upadhyay, A., Akter, S., Adams, L., Kumar, V., Varma, N., Investigating “circular business models” in the manufacturing and service sectors (2019) Journal of Manufacturing Technology Management, 30 (3), pp. 590-606; Upadhyay, A., Mukhuty, S., Kumar, V., Kazancoglu, Y., Blockchain technology and the circular economy: implications for sustainability and social responsibility (2021) Journal of Cleaner Production, 293; Upadhyay, A., Laing, T., Kumar, V., Dora, M., Exploring barriers and drivers to the implementation of circular economy practices in the mining industry (2021) Resources Policy, 72; Upadhyay, A., Kumar, A., Akter, S., An analysis of UK retailers' initiatives towards circular economy transition and policy-driven directions (2022) Clean Technologies and Environmental Policy, 24 (4), pp. 1209-1217; Vali-Siar, M.M., Roghanian, E., Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption (2022) Sustainable Production and Consumption, 30, pp. 278-300; Van Hoek, R., Research opportunities for a more resilient post-COVID-19 supply chain – closing the gap between research findings and industry practice (2020) International Journal of Operations and Production Management, 40 (4), pp. 341-355; Vegter, D., van Hillegersberg, J., Olthaar, M., Supply chains in circular business models: processes and performance objectives (2020) Resources, Conservation and Recycling, 162; Vu, T., Nguyen, D., Luong, T., Nguyen, T., Doan, T., The impact of supply chain financing on SMEs performance in Global supply chain (2022) Uncertain Supply Chain Management, 10 (1), pp. 255-270; Werner, M.J.E., Yamada, A.P.L., Domingos, E.G.N., Leite, L.R., Pereira, C.R., Ex- ploring organizational resilience through key performance indicators (2021) Journal of Industrial and Production Engineering, 38 (1), pp. 51-65; Whicker, L., Bernon, M., Templar, S., Mena, C., Understanding the relationships between time and cost to improve supply chain performance (2009) International Journal of Production Economics, 121 (2), pp. 641-650; Wicaksono, T., Illés, C.B., From resilience to satisfaction: defining supply chain solutions for agri-food SMEs through quality approach (2022) Plos One, 17 (2); Xuan, H.A., Trinh, V.V., Techato, K., Phoungthong, K., Use of hybrid MCDM methods for site location of solar-powered hydrogen production plants in Uzbekistan (2022) Sustainable Energy Technologies and Assessments, 52; Yadav, V.S., Singh, A.R., Gunasekaran, A., Raut, R.D., Narkhede, B.E., A systematic literature review of the agro-food supply chain: challenges, network design, and performance measurement perspectives (2022) Sustainable Production and Consumption, 29, pp. 685-704; Yadav, S., Garg, D., Luthra, S., Ranking of performance indicators in an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC) (2022) International Journal of Quality and Reliability Management, 39 (3), pp. 777-803; Ye, F., Liu, K., Li, L., Lai, K.H., Zhan, Y., Kumar, A., Digital supply chain management in the COVID-19 crisis: an asset orchestration perspective (2022) International Journal of Production Economics, 245; Yoo, S.H., Cheong, T., Inventory model for sustainable operations of a closed-loop supply chain: role of a third-party refurbisher (2021) Journal of Cleaner Production, 315 (September 2021); Yuliana, M., Santoso, S.P., Putro, J.N., Wijaya, C.J., Lunardi, V.B., Environmental impact, health hazards and waste management of medical textile products (2022) Medical Textiles from Natural Resources, pp. 829-864. , Woodhead Publishing; Zaman, M., Vo-Thanh, T., Hasan, R., Shams, S.R., Vukovic, D.B., How can hotels create sustainable competitive advantages? A resource-based view (2022) Journal of Strategic Marketing, ahead-of-print, pp. 1-16; Zhang, A., Alvi, M.F., Gong, Y., Wang, J.X., Overcoming barriers to supply chain decarbonization: Case studies of first movers (2022) Resources, Conservation and Recycling, 186; Zhao, N., Liu, X., Wang, Q., Zhou, Z., Information technology-driven operational decisions in a supply chain with random demand disruption and reference effect (2022) Computers and Industrial Engineering, 171},
correspondence_address1={Kazancoglu, Y.; Department of Logistics Management, Turkey; email: yigit.kazancoglu@yasar.edu.tr},
publisher={Emerald Publishing},
issn={0265671X},
language={English},
abbrev_source_title={Int. J. Qual. Reliab. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Ghanname2022,
author={Ghanname, O. and Imrani, O.E. and Babounia, A. and Chrayah, M.},
title={Supply Chain Management Control System: A literature review},
journal={2022 IEEE 14th International Conference of Logistics and Supply Chain Management, LOGISTIQUA 2022},
year={2022},
doi={10.1109/LOGISTIQUA55056.2022.9938093},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143086482&doi=10.1109%2fLOGISTIQUA55056.2022.9938093&partnerID=40&md5=f9c0a29c9dab8e30600dc00ff9d3aa06},
affiliation={Ibn Tofail University, Kenitra, Morocco; Abdelmalek Essaadi University, Tetouan, Morocco},
abstract={The aim of this paper is to provide a literature review of the Supply Chain Management Control Systems as a discipline that converges two fields: The MCS and the SCM. This convergence has as goal the enhance of the organization's performance. Faced with the critical situation and the crisis that the world is experiencing due to the Covid-19 pandemic, companies are today, more than ever, forced to ensure the efficiency of their SCs as a pledge to improve their overall performance. Therefore, the purpose of this article is to provide a review of the role of the MCS in managing and assuring the resilience and the performance of the SC. To get a solid grasp of the structure of MCS research in SCM and study the link between, we conducted a literature review of the articles published between 2016 and 2021. The aim is to provide a view of the recent researches covering the subject of the SCMCS. © 2022 IEEE.},
author_keywords={Management Control Systems;  Supply chain management;  Supply chain management control system;  supply chain performance measurement},
keywords={Control systems, Literature reviews;  Management control system;  Organization performance;  Performance;  Recent researches;  Supply chain management control system;  Supply chain performance measurements, Supply chain management},
references={Buyurgan, N., Zhang, S., Okyay, H.K., Methodical analysis of inventory discrepancy under conditions of uncertainty in supply chain management (2019) IJLSM, 32, p. 272; Cobb, B.R., Inventory control for returnable transport items in a closed-loop supply chain (2016) Transportation Research Part E: Logistics and Transportation Review, 86, pp. 53-68; Darom, M., Plant, E., (2020) The Development of A Performance Measurement System for Indirect Procurement: A Delphi Study, , MBE a-of-print; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things: Toward more practical SCPMS (2017) IMDS, 117, pp. 267-286; El-Garaihy, W.H., (2021) Analysis of Supply Chain Operations Reference (SCOR] and Balanced Scorecard (BSC] in Measuring Supply Chains Efficiency Using DEMATEL and DEA Techniques, , JGOSS a-of-print; Fauzi, T.H., (2021) Impact of Enterprise Resource Planning Systems on Management Control Systems and Firm Performance, pp. 745-754; Fernandes, A.C., Vilhena, E., Oliveira, R., Sampaio, P., Carvalho, M.S., (2021) Supply Chain Quality Management Impact on Organization Performance: Results from An International Survey, , IJQRM a-of-print; Frederico, G.F., Garza-Reyes, J.A., Kumar, A., Kumar, V., Performance measurement for supply chains in the Industry 4.0 era: A balanced scorecard approach (2020) IJPPM, 70, pp. 789-807; Göksu, A., Kocamaz, U.E., Uyaroglu, Y.Y., Synchronization and control of chaos in supply chain management (2015) Computers & Industrial Engineering, 86, pp. 107-115; Hill, C.A., Zhang, G.P., Miller, K.E., Collaborative planning, forecasting, and replenishment & firm performance: An empirical evaluation (2018) International Journal of Production Economics, 196, pp. 12-23; Köhler, P.N., Möller, M.A., Pannek, J., Allgöwer, F., Distributed economic model predictive control for cooperative supply chain management using customer forecast information (2021) IFAC Journal of Systems and Control, 15, p. 100125; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: A bibliometric study (2018) BIJ, 25, pp. 932-967; Naimi, M.A., Faisal, N., Mohd, R., Sobh, R., Prioritization of Supply Chain Reconfiguration Variables using Balanced Score Card and Analytic Network Process (2020) Ijoqm, 26, p. 95; Nazari-Ghanbarloo, V., (2020) A Dynamic Performance Measurement System for Supply Chain Management, , IJPPM a-of-print; Norouzinav, H., Jahedmotlagh, M.R., Makui, A., Robust controlling of chaotic behavior in supply chain networks (2017) Journal of the Operational Research Society, 68, pp. 711-724; Rasolofo-Distler, F., Distler, F., Using the balanced scorecard to manage service supply chain uncertainty: Case studies in French real estate services (2018) Knowl Process Manag, 25, pp. 129-142; Reusen, E., Stouthuysen, K., Misaligned control: The role of management control system imitation in supply chains (2017) Accounting, Organizations and Society, 61, pp. 22-35; Suzan, L., Mulyani, S., Sukmadilaga, C., Farida, I., Empirical Testing of the Implementation of Supply Chain Management and Successful Supporting Factors of Management Accounting Information Systems (2019) International Journal of Supply Chain Management, 8 (4); Xu, X., Lee, S.-D., Kim, H.-S., You, S.-S., Management and optimisation of chaotic supply chain system using adaptive sliding mode control algorithm (2021) International Journal of Production Research, 59, pp. 2571-2587; Balfaqih, H., Nopiah .mohd, Z., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems 1998-2015 (2016) Computers in Industry, 82, pp. 135-150; Dreyer, H.C., Alfnes, E., Strandhagen, J.O., Thomassen, M.K., Global supply chain control systems: A conceptual framework for the global control centre (2009) Production Planning & Control, 20, pp. 147-157; Jeschonowski, D.P., Schmitz, J., Wallenburg, C.M., Weber, J., Management control systems in logistics and supply chain management: A literature review (2009) Logist. Res, 1, pp. 113-127; Seuring, S.A., Supply chain controlling: Summarizing recent developments in German literature (2006) Supp Chain Mnagmnt, 11, pp. 10-14},
editor={Jawab F., El Farouk I.I.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781665474184},
language={English},
abbrev_source_title={IEEE Int. Conf. Logist. Supply Chain Manag., LOGISTIQUA},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Bottani20222437,
author={Bottani, E. and Bigliardi, B. and Rinaldi, M.},
title={Development and proposal of a LARG (lean, agile, resilient, green) performance measurement system for a food supply chain},
journal={IFAC-PapersOnLine},
year={2022},
volume={55},
number={10},
pages={2437-2444},
doi={10.1016/j.ifacol.2022.10.074},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142929611&doi=10.1016%2fj.ifacol.2022.10.074&partnerID=40&md5=bfb5e5cea1d03e3d5b1a6ebb17a6d0be},
affiliation={Department of Engineering and Architecture, University of Parma, viale delle Scienze 181/A, Parma, 43124, Italy; Department of Engineering, University of Campania Luigi Vanvitelli, via Roma 29, Aversa, 81031, Italy},
abstract={The study aims to delineate a framework for the measurement and evaluation of supply chain performance in the food sector. The proposed approach grounds on the well-known LARG (lean, agile, resilient, and green) perspectives, for which the relating literature is analysed, and the available metrics are reviewed with a specific focus on the food sector. This allows for the development of an appropriate performance measurement system for the food supply chain. Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license.},
author_keywords={framework;  LARG (lean, agile, resilient, and green);  literature review;  performance measurement system},
keywords={Agile manufacturing systems;  Food supply, Food sector;  Food supply chain;  Framework;  Lean, agile, resilient, and green;  Literature reviews;  Performance measurement system;  Supply chain performance, Supply chains},
references={Accorsi, R., Cholette, S., Manzini, M., Pini, C., Penazzi, S., The land-network problem: ecosystem carbon balance in planning sustainable agro-food supply chains (2016) Journal of Cleaner Production, 112, pp. 158-171; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling agility of supply chain (2007) Industrial Marketing Management, 36 (4), pp. 443-457; Ahumada, O., Villalobos, J.R., Application of planning models in the agri-food supply chain: A review (2009) European Journal of Operational Research, 196 (1), pp. 1-20; Aliev, R.A., Fazlollahi, B., Guirimov, B.G., Aliev, R.R., Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management (2007) Information Sciences, 177, pp. 4241-4255; Aramyan, L.H., Lansink, A.G.J.M.O., Van Der Vorst, J.G.A.J., Kooten, O.V., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management, 12 (4), pp. 304-315; Arif-Uz-Zaman, K., Ahsan, A.M.M.N., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612; Azevedo, S.G., Carvalho, H., Cruz-Machado, V., A proposal of LARG supply chain management practices and a performance measurement system (2011) International Journal of e-Education, e-Business, e-Management and e-Learning, 1 (1), pp. 7-14; Azevedo, S.G., Carvalho, H., Cruz-Machado, V., LARG index: A benchmarking tool for improving the leanness, agility, resilience and greenness of the automotive supply chain (2016) Benchmarking, 23 (6), pp. 1472-1499; Behrouzi, F., Wong, K.Y., Lean performance evaluation of manufacturing systems: A dynamic and innovative approach (2011) Procedia Computer Science, 3, pp. 388-395; Bhasin, S., Lean and performance measurement (2008) Journal of Manufacturing Technology Management, 19 (5), pp. 670-684; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Bigliardi, B., Bottani, E., Supply chain performance measurement: a literature review and pilot study among Italian manufacturing companies (2014) International Journal of Engineering, Science and Technology, 6 (3), pp. 1-16; Bottani, E., Murino, T., Schiavo, M., Akkerman, R., Resilient food supply chain design: Modelling framework and metaheuristic solution approach (2019) Computers & Industrial Engineering, 135, pp. 177-198; Cabral, I., Grilo, A., Cruz-Machado, V., A decision-making model for lean, agile, resilient and green supply chain management (2012) International Journal of Production Research, 50 (17), pp. 4830-4845; Callado, A.A.C., Jack, L., Balanced scorecard metrics and specific supply chain roles (2015) International Journal of Productivity and Performance Management, 64 (2), pp. 288-300; Cardoso, S.R., Paula Barbosa-Póvoa, A., Relvas, S., Novais, A.Q., Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty (2015) Omega, 56, pp. 53-73; Carlsson-Kanyama, A., Ekström, M.P., Shanahan, H., Food and life cycle energy inputs: Consequences of diet and ways to increase efficiency (2003) Ecological Economics, 44 (2-3), pp. 293-307; Carvalho, H., Azevedo, S.G., Duarte, S., Cruz-Machado, V., Green and lean paradigms influence on sustainable business development of manufacturing supply chains (2011) International Journal of Green Computing, 2 (2), pp. 45-62; Casino, F., Kanakaris, V., Dasaklis, T.K., Moschuris, S., Stachtiaris, S., Pagoni, M., Rachaniotis, N.P., Blockchain-based food supply chain traceability: A case study in the dairy sector (2021) International Journal of Production Research, 59 (19), pp. 5758-5770; Christopher, M., Peck, H., Building the resilient supply chain (2004) The International Journal of Logistics Management, 15 (2), pp. 1-14; Dora, M., Kumar, A., Mangla, S.K., Pant, A., Kamal, M.M., Critical success factors influencing artificial intelligence adoption in food supply chains (2021) International Journal of Production Research; Elrod, C., Murray, S., Bande, S., A review of performance metrics for supply chain management (2013) EMJ - Engineering Management Journal, 25 (3), pp. 39-50; Eshlaghy, T.R., Ghatari, A.R., Nikoomaram, H., Zandhessami, H., Process based agile supply chain model (2009) Contemporary Engineering Sciences, pp. 117-138; Fawcett, S.E., Using strategic assessment to increase the value-added capabilities of manufacturing and logistics (1995) Production and Inventory Management Journal, 36 (2), pp. 33-38; Garcia, F.A., Marchetta, M.G., Camargo, M., Morel, L., Forradellas, R.Q., A framework for measuring logistics performance in the wine industry (2012) International Journal of Production Economics, 135 (1), pp. 284-298; Gerbens-Leenes, P.W., Nonhebel, S., Ivens, W.P.M.F., A method to determine land requirements relating to food consumption patterns (2002) Agriculture, Ecosystems and Environment, 90 (1), pp. 47-58; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Heller, M.C., Keoleian, G.A., Assessing the sustainability of the US food system: A life cycle perspective (2003) Agricultural Systems, 76 (3), pp. 1007-1041; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353; Hoffman, D., (2004) The hierarchy of supply chain metrics: diagnosing your supply chain health, , Gartner Research, Boston US; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers and Industrial Engineering, 48 (2), pp. 377-394; Jha, P.K., Ghorai, S., Jha, R., Datt, R., Sulapu, G., Singh, S.P., Forecasting the impact of epidemic outbreaks on the supply chain: Modelling asymptomatic cases of the COVID-19 pandemic (2021) International Journal of Production Research, , press; Juttner, U., Peck, H., Christopher, M., Supply chain risk management: outlining an agenda for future research (2003) International Journal of Logistics: Research and Applications, pp. 197-210; Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., Diabat, A., Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain (2013) Journal of Cleaner Production, 47, pp. 355-367; Li, H., Li, D., Jiang, D., Optimising the configuration of food supply chains (2021) International Journal of Production Research, 59 (12), pp. 3722-3746; Lin, C., Chiu, H., Tseng, Y., Agility evaluation using fuzzy logic (2006) International Journal of Production Economics, 101 (2), pp. 353-368; Lotfi, M., Saghiri, S., Disentangling resilience, agility and leanness: Conceptual development and empirical analysis (2018) Journal of Manufacturing Technology Management, 29 (1), pp. 168-197; Maskell, B., (1991) Performance measurement for world class manufacturing, , Productivity Press, Cambridge US; Parker, C., Performance measurement (2000) Work Study, 49 (2), pp. 63-66; Parveen, M., Rao, T.V.V.L.N., An integrated approach to design and analysis of lean manufacturing system: A perspective of lean supply chain (2009) International Journal of Services and Operations Management, 5 (2), pp. 175-208; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: an exploratory study (2015) International Journal of Productivity and Performance Management, 64 (8), pp. 1068-1091; Prakash, G., Pant, R.R., Performance measurement of a dairy supply chain: A balance scorecard perspective (2013) Proceedings of the 2013 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 196-200; Shah, R., Ward, P.T., Defining and developing measures of lean production (2007) Journal of Operations Management, 25 (4), pp. 785-805; Soriano-Meier, H., Forrester, P.L., A model for evaluating the degree of leanness of manufacturing firms (2002) Integrated Manufacturing Systems, 13 (2), pp. 104-109; Srivastava, S., Green supply management: a state-of-the art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Stranieri, S., Varacca, A., Casati, M., Capri, E., Soregaroli, C., Adopting environmentally-friendly certifications: Transaction cost and capabilities perspectives within the Italian wine supply chain (2021) Supply Chain Management, , press; Swafford, P.M., Ghosh, S., Murthy, N., Achieving supply chain agility through IT integration and flexibility (2008) International Journal of Production Economics, 116 (2), pp. 288-297; Van der Vorst, J., Performance measurement in agri-food supply chain networks. An overview (2005) Quantifying the agri-food supply chain - ch.2, pp. 13-24. , C.J.M. Ondersteijn, J.H.M. Wijnands, R.B.M. Huirne and O. van Kooten eds, Springer, The Netherlands; Van Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 126-147; Vinodh, S., Devadasan, S.R., Vimal, K.E.K., Kumar, D., Design of agile supply chain assessment model and its case study in an Indian automotive components manufacturing organization (2013) Journal of Manufacturing Systems, 32 (4), pp. 620-631; Vlajic, J.V., Van Der Vorst, J.G.A.J., Haijema, R., A framework for designing robust food supply chains (2012) International Journal of Production Economics, 137 (1), pp. 176-189; Walker, H., Di Sisto, L., McBain, D., Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 69-85; Womack, J.J., (1990) The Machine that Changed the World: The Story of Lean Production, , Harper Collins Publishers, New York US; Womack, J.J., Jones, P., (1996) Lean Thinking: Banish Waste and Create Wealth in Your Corporation, , Simon & Schuster, New York US; Wu, T., Blackhurst, J., Chidambaram, V., A model for inbound supply risk analysis (2006) Computers in Industry, 57 (4), pp. 350-365; Wu, X., Zhong, X., Song, S., Wu, C., Study on risk analysis of supply chain enterprises (2006) Journal of Systems Engineering and Electronics, 17 (4), pp. 781-787; Yusuf, Y.Y., Gunasekaran, A., Adeleye, E.O., Sivayoganathan, K., Agile supply chain capabilities: Determinants of competitive objectives (2004) European Journal of Operational Research, 159, pp. 379-392},
correspondence_address1={Bottani, E.; Department of Engineering and Architecture, viale delle Scienze 181/A, Italy; email: eleonora.bottani@unipr.it},
editor={Bernard A., Dolgui A., Benderbal H.H., Ivanov D., Lemoine D., Sgarbossa F.},
publisher={Elsevier B.V.},
issn={24058963},
language={English},
abbrev_source_title={IFAC-PapersOnLine},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Bottani2022,
author={Bottani, E. and Bigliardi, B. and Rinaldi, M. and Montanari, R.},
title={Evaluation of the LARG (lean, agile, resilient, green) performance in a food supply chain: a case study in Italy},
journal={Proceedings of the Summer School Francesco Turco},
year={2022},
page_count={6},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142801566&partnerID=40&md5=4af2c68ea38dd7ecba581996192454a9},
affiliation={Dipartimento di Ingegneria e Architettura, University of Parma, Viale delle Scienze 181/A, Parma, 43124, Italy; Dipartimento di Ingegneria, University of Campania “Luigi Vanvitelli”, via Roma 29, Aversa, 81031, Italy},
abstract={The study proposes an evaluation of the LARG (lean, agile, resilient, and green) performance of a real food system, whose focal company is a main manufacturer of food products, based in the North of Italy. In a previous study by some of the authors of the present paper, a framework for evaluating the LARG performance of food supply chains was delineated, and appropriate key performance indicators (KPIs) were identified, on the basis of a review of the available literature on supply chain performance measurement and food supply chains. In this study, the application of that framework is presented along with the presentation of a case study. Direct interviews with the company’s managers and the usage of other available sources allowed first to acquire a detailed knowledge about the context in which the company operates, so as to evaluate the relative importance of the various KPIs, and second, to gather the relevant data for deriving a score against these KPIs for the targeted company. From an analysis of the outcomes obtained and from the detailed description of the case study, some insights can be derived for the whole food supply chain. © 2022, AIDI - Italian Association of Industrial Operations Professors. All rights reserved.},
author_keywords={agile;  and green);  case study;  food supply chain;  framework;  LARG (lean;  performance measurement system;  resilient},
references={Azevedo, S.G., Carvalho, H., Cruz-Machado, V., A proposal of LARG supply chain management practices and a performance measurement system. International Journal of e-Education, e-Business, e-Management and (2011) E-Learning, 1 (1), pp. 7-14; Bottani, E., Bigliardi, B., Rinaldi, M., (2022) Development and Proposal of a LARG (Lean, Agile, Resilient, Green) Performance Measurement System for a Food Supply Chain. Proceedings of the MIM 2022 International Conference, Nantes, France, , 22-24 June; Carvalho, H., Azevedo, S.G., Duarte, S., Cruz-Machado, V., Green and lean paradigms influence on sustainable business development of manufacturing supply chains (2011) International Journal of Green Computing, 2 (2), pp. 45-62; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Jha, P.K., Ghorai, S., Jha, R., Datt, R., Sulapu, G., Singh, S.P., Forecasting the impact of epidemic outbreaks on the supply chain: Modelling asymptomatic cases of the COVID-19 pandemic (2021) International Journal of Production Research, , in press; Kolence, K.W., The Software Empiricist (1973) ACM Sigmetrics Performance Evaluation Review, 2 (2), pp. 31-36; Lotfi, M., Saghiri, S., Disentangling resilience, agility and leanness: Conceptual development and empirical analysis (2018) Journal of Manufacturing Technology Management, 29 (1), pp. 168-197; Maskell, B., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Cambridge (US); Narasimhan, R., Swink, M., Kim, S.W., Disentangling leanness and agility: An empirical investigation (2006) Journal of Operations Management, 24 (5), pp. 440-457; Parker, C., Performance measurement (2000) Work Study, 49 (2), pp. 63-66; Parveen, M., Rao, T.V.V.L.N., An integrated approach to design and analysis of lean manufacturing system: A perspective of lean supply chain (2009) International Journal of Services and Operations Management, 5 (2), pp. 175-208; Rinaldi, M., Murino, T., Gebennini, E., Morea, D., Bottani, E., A literature review on quantitative models for supply chain risk management: Can they be applied to pandemic disruptions? (2022) Computers and Industrial Engineering, 170},
publisher={AIDI - Italian Association of Industrial Operations Professors},
issn={22838996},
language={English},
abbrev_source_title={Proc. Summer Sch. Francesco Turco},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Gera2022624,
author={Gera, R. and Yadav, R. and Khera, G.S. and Saxena, A. and Chadha, P. and Dixit, S. and Yuliya Sergeevna, L.},
title={A systematic literature review of supply chain management practices and performance},
journal={Materials Today: Proceedings},
year={2022},
volume={69},
pages={624-632},
doi={10.1016/j.matpr.2022.10.203},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141918438&doi=10.1016%2fj.matpr.2022.10.203&partnerID=40&md5=d49b03495f0b37b0b2720861221fd76b},
affiliation={School of Management and Commerce, K R Mangalam University, Sohna Rd, Haryana, Sohna Rural, 122103, India; School of Management and Liberal Studies, Northcap University, Haryana, Gurugram, 12207, India; School of Management and Commerce, Manav Rachna University, Haryana, Faridabad, 121004, India; 1 Division of Research and Innovation, Uttranchal University Uttrakhand248007, India; Departments of Economics and Management, Vladivostok State University of Economics and Service, Gogol, Vladivostok, 690014, Russian Federation},
abstract={The study is a systematic literature review of current state of research on the dyadic relationship of supply chain management practices with supply chain performance measures published in empirical research articles in literature. Forty-three empirical research papers published in high quality Scopus and WoS indexed Journals between 2018 and 2022 were selected for this study through systematic approach for categorization and synthesis of findings on the subject. The findings were categorized within the themes of operational, environmental, economic, firm and supply chain performance and evaluated within the structure-conduct-performance paradigm. “Benchmarking an International Journal” is the leading channel for publishing on this topic and partial least square structural equation modeling methods are the most employed statistical technique for analysis of data. Organizational theories like resource based view; institutional theory and stakeholder's theory are the dominant theoretical perspective adopted by the selected art. Studies are geographically concentrated in South Asia, Europe, USA and some countries of Africa and Middle East. Most studies are focused on manufacturing industries and very few service industries like food or grocery retailers have been researched. Internal and external Specific supply chain (SCM) practices affect different measures of supply chain performance through various pathways. Green supply chain management practices, supply chain quality practices, innovative management, lean management practices and industry 4.0 technologies are gaining prominence while market and financial performance outcomes of SCM practices have not been adequately researched.Conclusions followed by theoretical and practical implications are drawn and future research directions identified. © 2022},
author_keywords={Content analysis;  Economic performance;  Environmental performance;  Firm performance;  Operational performance;  Supply chain management practices, supply chain performance},
keywords={Economic analysis;  Environmental management;  Supply chain management, Content analysis;  Economic performance;  Environmental performance;  Firm Performance;  Operational performance;  Performance;  Supply chain management practice, supply chain performance;  Supply chain management practices;  Supply chain performance;  Systematic literature review, Least squares approximations},
references={Jüttner, U., Christopher, M., The role of marketing in creating a supply chain orientation within the firm (2013) Int. J. Logist. Res. Appl., 16 (2), pp. 99-113; Terziovski, M., Morgan, J.P., Management practices and strategies to accelerate the innovation cycle in the biotechnology industry (2006) Technovation, 26 (5-6), pp. 545-552; Flynn, B.B., Schroeder, R.G., Sakakibara, S., The impact of quality management practices on performance and competitive advantage (1995) Decision Sci., 26 (5), pp. 659-691; Fening, F.A., Pesakovic, G., Amaria, P., Relationship between quality management practices and the performance of small and medium size enterprises (SMEs) in Ghana (2008) Int. J. Qual. Reliab. Manag.; Powell, T.C., Total quality management as competitive advantage: a review and empirical study (1995) Strateg. Manag. J., 16 (1), pp. 15-37; Kaynak, H., The relationship between total quality management practices and their effects on firm performance (2003) J. Oper. Manage., 21 (4), pp. 405-435; De Cerio, J.M.D., Factors relating to the adoption of quality management practices: an analysis for Spanish manufacturing firms (2003) Total Qual. Manag. Business Excell., 14 (1), pp. 25-44; Ghosh, S., Mandal, M.C., Ray, A., Green supply chain management framework for supplier selection: an integrated multi-criteria decision-making approach (2021) Int. J. Manag. Sci. Eng. Manag., pp. 1-15; Al Zaabi, S., Al Dhaheri, N., Diabat, A., Analysis of interaction between the barriers for the implementation of sustainable supply chain management (2013) Int. J. Adv. Manuf. Technol., 68 (1), pp. 895-905; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: moving toward new theory (2008) Int. J. Phys. Distrib. Logist. Manage., 38 (5), pp. 360-387; Flynn, B.B., Sakakibara, S., Schroeder, R.G., Bates, K.A., Flynn, E.J., Empirical research methods in operations management (1990) J. Oper. Manage., 9 (2), pp. 250-284; Croom, S., Romano, P., Giannakis, M., Supply chain management: an analytical framework for critical literature review (2000) Eur. J. Purchas. Supply Manage., 6 (1), pp. 67-83; Xu, L., Mathiyazhagan, K., Govindan, K., Haq, A.N., Ramachandran, N.V., Ashokkumar, A., Multiple comparative studies of green supply chain management: pressures analysis (2013) Resour. Conserv. Recycl., 78, pp. 26-35; Ho, D.C., Au, K.F., Newton, E., Empirical research on supply chain management: a critical review and recommendations (2002) Int. J. Prod. Res., 40 (17), pp. 4415-4430; Gammelgaard, B., Schools in logistics research? a methodological framework for analysis of the discipline (2004) Int. J. Phys. Distrib. Logist. Manage., 34 (6), pp. 479-491; Halldórsson, Á., Arlbjørn, J.S., Research methodologies in supply chain management—What do we know? (2005) Research Methodologies in Supply Chain Management, pp. 107-122. , Physica-Verlag HD; Carter, C.R., Ellram, L.M., Thirty-rve years of the journal of supply chain management: where have we been and where are we going? (2003) J. Supply Chain Manag., 39 (1), pp. 27-39; Wolf, J., The nature of supply chain management research: insights from a content analysis of international supply chain management literature from 1990 to 2006 (2008), Springer Science & Business Media; Fabbe-Costes, N., Jahre, M., Supply chain integration and performance: a review of the evidence (2008) Int. J. Logist. Manag.; Giunipero, L.C., Hooker, R.E., Joseph-Matthews, S.A.C.H.A., Yoon, T.E., Brudvig, S., A decade of SCM literature: past, present and future implications (2008) J. Supply Chain Manag., 44 (4), pp. 66-86; Seuring, S., Müller, M., Westhaus, M., Morana, R., Conducting a literature review—the example of sustainability in supply chains (2005) Res. Methodol. Supply Chain Manag., pp. 91-106; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) Br. J. Manag., 14 (3), pp. 207-222; Jabbour, C.J.C., Santos, F.C.A., The central role of human resource management in the search for sustainable organizations (2008) Int. J. Hum. Resour. Manag., 19 (12), pp. 2133-2154; Xu, J., Yu, Y., Wu, Y., Zhang, J.Z., Liu, Y., Cao, Y., Eachempati, P., Green supply chain management for operational performance: anteceding impact of corporate social responsibility and moderating effects of relational capital (2022) J. Enterprise Information Manag.; Abbas, T.M., Hussien, F.M., The effects of green supply chain management practices on firm performance: empirical evidence from restaurants in Egypt (2021) Tour. Hospital. Res., 21 (3), pp. 358-373; Zaid, A., Sleimi, M., Saleh, M.W., Othman, M., The mediating roles of knowledge transfer and supply chain quality management capabilities on organisational performance (2021) VINE J. Information Knowledge Manag. Syst.; Yang, X., Wang, J., (2022), The relationship between sustainable supply chain management and enterprise economic performance: does firm size matter?. Journal of Business & Industrial Marketing, (ahead-of-print); Riglietti, G., Avatefipour, A., Trucco, P., The impact of business continuity management on the components of supply chain resilience: a quantitative analysis (2021) J. Business Continuity Emerg. Plann., 15 (2), pp. 182-195; Sharma, S., Modgil, S.T., (2019), SCM and operational performance: an empirical study of Indian pharmaceutical industry. Business Process Management Journal; Phan, A.C., Nguyen, H.A., Trieu, P.D., Nguyen, H.T., Matsui, Y., (2019), Impact of supply chain quality management practices on operational performance: empirical evidence from manufacturing companies in Vietnam. Supply Chain Management: An International Journal; Qorri, A., Gashi, S., Kraslawski, A., (2021), pp. 194-216. , Performance outcomes of supply chain practices for sustainable development: A meta‐analysis of moderators. Sustainable Development, 29(1); Phan, A., Nguyen, H., Pham, T., Relationship between service recovery, customer satisfaction and customer loyalty: empirical evidence from e-retailing (2021) Uncertain Supply Chain Manag., 9 (1), pp. 1-10; Hong, J., Zhou, Z., Li, X., Lau, K.H., (2020), Supply chain quality management and firm performance in China's food industry—the moderating role of social co-regulation. International Journal of Logistics Management, The; Hussain, M., Khan, M., Ajmal, M., Khan, B.A., Supply chain quality management and organizational performance: empirical evidence from telecom industry in the UAE (2019), An International Journal Benchmarking; Trujillo-Gallego, M., Sarache, W., (2022), de Sousa Jabbour, A. B. L. Digital technologies and green human resource management: Capabilities for GSCM adoption and enhanced performance. International Journal of Production Economics, 108531; Siddh, M.M., Kumar, S., Soni, G., Jain, V., Chandra, C., Jain, R., Kazancoglu, Y., Impact of agri-fresh food supply chain quality practices on organizational sustainability (2021) Oper. Manag. Res., pp. 1-20; Petljak, K., Zulauf, K., Štulec, I., Seuring, S., Wagner, R., Green supply chain management in food retailing: survey-based evidence in Croatia (2018), An International Journal Supply Chain Management; Das, C., Jharkharia, S., Effects of low carbon supply chain practices on environmental sustainability: an empirical study on Indian manufacturing firms (2019) South Asian J. Business Stud., 8 (1), pp. 2-25; Singh, T., Tripathi, S., Dwivedi, A., Acevedo-Duque, Á., Post-COVID green supply chain management of used products: a study towards awareness for vaccination (2022) Environ. Sci. Pollut. Res., pp. 1-15; Agyabeng-Mensah, Y., Ahenkorah, E., Afum, E., Agyemang, A.N., Agnikpe, C., Rogers, F., Examining the influence of internal green supply chain practices, green human resource management and supply chain environmental cooperation on firm performance (2020), An International Journal Supply Chain Management; Wang, C., Zhang, Q., Zhang, W., Corporate social responsibility, Green supply chain management and firm performance: the moderating role of big-data analytics capability (2020) Res. Transport. Business Manag., 37, p. 100557; Kumar, A., Singh, R.K., (2022), Supply chain management practices, retail outlets attributes and organisational performance: a case of organised food retailers in India. Journal of Global Operations and Strategic Sourcing, (ahead-of-print); Cheng, T.C.E., Choy, P.W.C., Wong, S.W., Virtual manufacturing: critical capabilities and their organizational performance implications (EMR-22-0098-engineering management review) (2022) IEEE Eng. Manage. Rev.; Sturm, S., Hohenstein, N.O., Birkel, H., Kaiser, G., Hartmann, E., Empirical research on the relationships between demand-and supply-side risk management practices and their impact on business performance (2021), An International Journal Supply Chain Management; Singh, R.K., Kumar, R., Strategic issues in supply chain management of Indian SMEs due to globalization: an empirical study (2020), An International Journal Benchmarking; Kumar, A., Singh, R.K., Modgil, S., Influence of data-driven supply chain quality management on organizational performance: evidences from retail industry (2020) TQM J.; Umar, M., Khan, S.A.R., Yusliza, M.Y., Ali, S., Yu, Z., Industry 4.0 and green supply chain practices: an empirical study (2021) Int. J. Prod. Perform. Manag.; Nguyen, M.H., Phan, A.C., Matsui, Y., Supply chain integration and economic performance: empirical evidence from a developing country (2021), An International Journal Benchmarking; Younis, H., Sundarakani, B., (2019), pp. 319-346. , The impact of firm size, firm age and environmental management certification on the relationship between green supply chain practices and corporate performance. Benchmarking: An International Journal, 27(1); Aalirezaei, A., Esfandi, N., Noorbakhsh, A., Evaluation of relationships between GSCM practices and SCP using SEM approach: an empirical investigation on Iranian automobile industry (2018) J. Remanuf., 8 (1), pp. 51-80; Chen, Y., Chen, I.J., (2019), Mixed sustainability motives, mixed results: The role of compliance and commitment in sustainable supply chain practices. Supply Chain Management: An International Journal; Sahi, G.K., Gupta, M.C., Cheng, T.C.E., Mantok, S., (2021), Mitigating the tension in pursuit of operational ambidexterity: The roles of knowledge development and bricolage. International Journal of Production Economics, 239, 108201; Okoumba, W.V.L., Mafini, C., Bhadury, J., Supply chain management and organizational performance: Evidence from SMEs in South Africa (2020) Afr. J. Manag., 6 (4), pp. 295-326; Lambourdiere, E., Corbin, E., Blockchain and maritime supply-chain performance: dynamic capabilities perspective (2020) Worldwide Hospital. Tour. Themes, 12 (1), pp. 24-34; Parast, M.M., A learning perspective of supply chain quality management: empirical evidence from US supply chains (2019), An International Journal Supply Chain Management; Sharma, M., Alkatheeri, H., Jabeen, F., Sehrawat, R., Impact of COVID-19 pandemic on perishable food supply chain management: a contingent resource-based view (RBV) perspective (2022) Int. J. Logist. Manag.; Golrizgashti, S., Bidhandi, H.M., Safari, M., Hodaei, H., A causal structure between total quality management, organisational culture, knowledge management, supplier integration and supply chain performance-an FMCG case study (2022) Int. J. Integr. Supply Manag., 15 (2), pp. 206-231; Choudhary, K., Sangwa, N.R., Sangwan, K.S., Singh, R.K., Impact of lean and quality management practices on green supply chain performance: an empirical study on ceramic enterprises (2022) Qual. Manag. J., 29 (3), pp. 193-211; Kirchoff, J.F., Falasca, M., Environmental differentiation from a supply chain practice view perspective (2022) Int. J. Prod. Econ., 244, p. 108365; Yang, J., Wang, Y., Gu, Q., Xie, H., The antecedents and consequences of green purchasing: an empirical investigation (2021) Benchmarking: Int. J., 29 (1), pp. 1-21; Hong, J., Liao, Y., Zhang, Y., Yu, Z., The effect of supply chain quality management practices and capabilities on operational and innovation performance: evidence from Chinese manufacturers (2019) Int. J. Prod. Econ., 212, pp. 227-235; Choudhary, K., Sangwan, K.S., Adoption of green practices throughout the supply chain: an empirical investigation (2019), An International Journal Benchmarking; Duong, B.A.T., Truong, H.Q., Sameiro, M., Sampaio, P., Fernandes, A.C., Vilhena, E., Bui, L.T.C., Yadohisa, H., Supply chain management and organizational performance: the resonant influence (2019) Int. J. Qual. Reliab. Manag., 36 (7), pp. 1053-1077; Choudhary, K., Sangwan, K.S., Benchmarking Indian ceramic enterprises based on green supply chain management pressures, practices and performance (2018), An International Journal Benchmarking; Ahmed, W., Najmi, A., Developing and analyzing framework for understanding the effects of GSCM on green and economic performance: perspective of a developing country (2018) Manag. Environ. Qual.: Int. J.},
correspondence_address1={Gera, R.; School of Management and Commerce, },
publisher={Elsevier Ltd},
issn={22147853},
language={English},
abbrev_source_title={Mater. Today Proc.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Nair2022,
author={Nair, P.R. and Anbuudayasankar, S.P. and Devanathan, S.R. and Raghuram, R.P.},
title={Empirical Investigation to Assess the Impact of ICT Deployment in SCM Using SEM},
journal={International Journal of Information Systems and Supply Chain Management},
year={2022},
volume={15},
number={1},
doi={10.4018/IJISSCM.287135},
art_number={287135},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140918456&doi=10.4018%2fIJISSCM.287135&partnerID=40&md5=f408120679543529a7625f729cd34e60},
affiliation={Amrita School of Engineering, Coimbatore, India},
abstract={The adoption of information and communication technology (ICT) is becoming increasingly ubiquitous, and it is prominent in supply chain too. The objective of this paper is to perform an empirical investigation to assess the impact of ICT in supply chain management (SCM) using structural equation modeling (SEM). A survey questionnaire was administered and collected from 200+ SCM professionals in sectors such as manufacturing, services, MSMEs, international companies, as well as SCM/ERP professionals working as domain experts in IT and service companies. Confirmatory factor analysis was used to validate the suggested empirical model and hypothesis based on the impact of ICT deployment on SCOR Level I metrics/indicators as constructs. This research appends to the literature on supply chain performance measures and addresses a recognized gap in terms of rubrics, constructs, assessment frameworks, and metrics of these ICT benefits and capabilities in SCM. This framework can be used by any enterprise irrespective of the geography or country, vertical or domain, manufacturing or services. © 2022 Authors. All rights reserved.},
author_keywords={Empirical Investigation;  Information and Communication Technology;  Structural Equation Modeling;  Supply Chain Management},
keywords={Factor analysis;  Manufacture;  Surveys, Domain experts;  Empirical investigation;  Information and Communication Technologies;  International company;  IT companies;  Management professionals;  Manufacturing service;  Service companies;  Structural equation models;  Technology deployment, Supply chain management},
references={(2015) Supply Chain Operations Reference Model, Revision 11.0, , https://docs.huihoo.com/scm/supply-chain-operations-reference-model-r11.0.pdf, Retrieved from; (2017) Supply Chain Operations Reference Model, Revision 12.0, , http://www.apics.org/docs/default-source/scor-training/scor-v12-0-framework-introduction.pdf?sfvrsn=2, Retrieved from; Bagozzi, R. P., Yi, Y., Specification, evaluation, and interpretation of structural equation models (2012) Journal of the Academy of Marketing Science, 40 (1), pp. 8-34; Birkel, H. S., Hartmann, E., Impact of IoT challenges and risks for SCM (2019) Supply Chain Management, 24 (1), pp. 39-61; Cheng, J.-H., Chen, M.-C., Influence of Transactional and Moral Orientations on Innovation Performance in Supply Chains (2016) International Journal of Information Systems and Supply Chain Management, 9 (2), pp. 61-77; Christopher, M., (2016) Logistics & Supply Chain Management, , Pearson; de Barros, A. P., Ishikiriyama, C. S., Peres, R. C., Gomes, C. F. S., Processes and benefits of the application of information technology in supply chain management: An analysis of the literature (2015) Procedia Computer Science, 55, pp. 698-705; Dong, S., Xu, S. X., Zhu, K. X., Information technology in supply chains: The value of IT-enabled resources under competition (2009) Information Systems Research, 20 (1), pp. 18-32; Ferrantino, M. J., Koten, E. E., Understanding Supply Chain 4.0 and its potential impact on global value chains (2019) Global Value Chain Development Report, 2019, p. 103. , https://www.wto.org/english/res_e/booksp_e/gvc_dev_report_2019_e_ch5.pdf, Retrieved from; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Subramanian, N., Papadopoulos, T., Information technology for competitive advantage within logistics and supply chains: A review (2017) Transportation Research Part E, Logistics and Transportation Review, 99, pp. 14-33; Gunasekharan, A., Ngai, E. W. T., Virtual Supply Chain Management (2004) Production Planning and Control, 15 (6), pp. 584-595; Hasibuan, A., Arfah, M., Parinduri, L., Hernawati, T., Harahap, B., Sibuea, S. R., Sulaiman, O. K., Performance analysis of Supply Chain Management with Supply Chain Operation reference model (2018) Journal of Physics: Conference Series, 1007 (1), p. 012029. , (April); Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Katiyar, R., Barua, M. K., Meena, P. L., Analyzing the Interactions Among the Barriers of Supply Chain Performance Measurement: An ISM with Fuzzy MICMAC Approach (2017) Global Business Review, 19 (1), pp. 48-68; Khan, S. A., Chaabane, A., Dweiri, F., A knowledge-based system for overall supply chain performance evaluation: A multi-criteria decision making approach (2019) Supply Chain Management, 24 (3), pp. 377-396. , Advance online publication; Khan, S. A. R., Yu, Z., Assessing the eco-environmental performance: An PLS-SEM approach with a practice-based view (2021) International Journal of Logistics Research and Applications, 24 (3), pp. 303-321; Khan, S. A. R., Yu, Z., Golpira, H., Sharif, A., Mardani, A., A state-of-the-art review and meta-analysis on sustainable supply chain management: Future research directions (2021) Journal of Cleaner Production, 278, p. 123357. , Advance online publication; Khan, S.A.R., Yu, Z., Sarwat, S., Godil, D.I., Amin, S., Shuaat, S., The role of block chain technology in circular economy practices to improve organizational performance (2021) International Journal of Logistics Research and Applications; Koot, M., Mes, M. R. K., Jacob, M. E., A systematic literature review of supply chain decision making supported by the Internet of Things and Big Data Analytics (2021) Computers & Industrial Engineering, 154, p. 107076. , Advance online publication; Kritchanchai, D., Hoeur, S., Engelseth, P., Develop a strategy for improving healthcare logistics performance, Supply Chain Forum (2017) International Journal (Toronto, Ont.), 19 (1), pp. 55-69; Kumar, A., Singh, R. K., Modgil, S., Exploring the relationship between ICT, SCM practices and organizational performance in agri-food supply chain (2020) Benchmarking, 27 (3), pp. 1003-1041; Lima-Junior, F. R., Carpinetti, L. C. R., Quantitative models for supply chain performance evaluation: A literature review (2017) Computers & Industrial Engineering, 113, pp. 333-346; Mihardjo, L. W. W., Sasmoko, S., Alamsyah, F., Elidjen, E., Maximizing co-creation strategy through integration of distinctive capabilities and customer experiences in supply chain management (2020) Uncertain Supply Chain Management, 8 (1), pp. 187-196; Muñuzuri, J., Onieva, L., Cortés, P., Guadix, J., Using IoT data and applications to improve port-based intermodal supply chains (2020) Computers & Industrial Engineering, 139, p. 105668; Nair, P. R., Anbuudayasankar, S. P., An investigation on the benefits of ICT deployment in supply chain management (SCM) (2016) Indian Journal of Science and Technology, 9 (30), pp. 1-7; Nazempour, R., Yang, J., Waheed, A., An Empirical Study to Understand the Effect of Supply Chain Agility on Organizational Operational Performance: SC Agility and Organizational Performance (2018) International Journal of Information Systems and Supply Chain Management, 11 (4), pp. 1-20; Pérez-López, R. J., Olguín-Tiznado, J. E., García-Alcaraz, J. L., Mojarro-Magaña, M., Camargo-Wilson, C., López-Barreras, J. A., Integrating and controlling ICT implementation in the supply chain: The SME experience from baja California (2021) Mathematics, 9 (11), p. 1234; Prajogo, D., Olhager, J., Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration (2012) International Journal of Production Economics, 135 (1), pp. 514-522; Raghuram, P., Saleeshya, P. G., Assessing the responsiveness of supply chain - SEM based approach (2016) International Journal of Logistics Systems and Management, 25 (4), pp. 558-579; Raghuram, P., Saleeshya, P. G., Responsiveness Model of Textile Supply Chain – A Structural Equation Modeling based Investigation (2021) International Journal of Services and Operations Management, 38 (3), pp. 419-440; Sardana, G. D., Exploring the performance of a responsive supply chain (2009) Supply Chain Forum: An International Journal, 10 (2), pp. 38-39; Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., King, J., Reporting structural equation modeling and confirmatory factor analysis results: A review (2006) The Journal of Educational Research, 99 (6), pp. 323-338; Singh, A., Teng, J. T. C., Enhancing supply chain outcomes through Information Technology and Trust (2016) Computers in Human Behavior, 54, pp. 290-300; Singh, R., Sandhu, S., Metri, B. A., Kaur, R., Modeling Supply Chain Performance: A Structural Equation Approach (2013) International Journal of Information Systems and Supply Chain Management, 6 (4), pp. 18-41; Singh, R., Sandhu, S., Metri, B. A., Kaur, R., Supply Chain Management Practices, Competitive Advantage and Organizational Performance: A Confirmatory Factor Model (2014) International Journal of Information Systems and Supply Chain Management, 7 (2), pp. 22-46; Sudarshan, S. V., Priyadarshuan, A., Anbuudayasankar, S. P., Analysis of the barriers in implementing green supply chain management (GSCM) practices: A hybrid approach. IOP Conference Series (2019) Materials Science and Engineering, 577 (1), p. 012005; Van Hoek, R., Aronsson, H., Auramo, J., Kauremaa, J., Tanskanen, K., Benefits of IT in supply chain management: An explorative study of progressive companies (2005) International Journal of Physical Distribution & Logistics Management, 35 (2), pp. 82-100; Wu, F., Yeniyurt, S., Kim, D., Cavusgil, S. T., The impact of information technology on supply chain capabilities and firm performance: A resource-based view (2006) Industrial Marketing Management, 35 (4), pp. 493-504; Xue, J., Li, G., Li, N., Does green and sustainable engagement benefit online platforms in supply chains? The role of green and public concern (2021) International Journal of Logistics Research and Applications, , do i},
publisher={IGI Global},
issn={19355726},
language={English},
abbrev_source_title={Int. J. Inf. Syst. Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Narassima2022265,
author={Narassima, M.S. and Nair, P.R. and Anbuudayasankar, S.P. and Ganesh, K.},
title={Information and communication technology application in supply chain management: a literature review},
journal={International Journal of Enterprise Network Management},
year={2022},
volume={13},
number={3},
pages={265-285},
doi={10.1504/ijenm.2022.125809},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140639060&doi=10.1504%2fijenm.2022.125809&partnerID=40&md5=efe6f78dc3fd3b351389873a33ac3454},
affiliation={Great Lakes Institute of Management, Chennai, 603102, India; Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India; Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India; Center of Competence, McKinsey and Company Inc., Chennai, 600113, India},
abstract={A systematic review of the literature on the impact of information and communication technology (ICT) in supply chain management (SCM) has been presented in this study. The benefits of ICT deployment for SCM, the supply chain performance measures and constructs and SCM process reference models developed by supply chain council and global supply chain forum (GSCF) are presented. Several ICT tools that are widely deployed and emerging tools have been discussed. Also, the impacts of these ICT tools across various facets and echelons of the supply chain have been presented. Though it is well-known that ICT positively affects SCM performance and improves supply chain capabilities, the dearth of literature in the quantification of the impacts and a need for a generic framework for performance measurement of ICT deployment for SCM open avenues for further research. Copyright © 2022 Inderscience Enterprises Ltd.},
author_keywords={ICT;  information and communication technology;  SCM;  SCOR;  supply chain management;  supply chain metrics;  supply chain operations reference;  supply chain performance measurement},
references={Anderson, D.L., Britt, F.E., Favre, D.J., The seven principles of supply chain management, logistics management (1997) Supply Chain Management Review, 11 (3), pp. 41-46; (2011), http://www.apics.org/docs/default-source/industry-content/apics-2011supply-chain-strategy-executive-summary.pdf, [online] (accessed 20 May 2020); (2012) Supply Chain Operations Reference Model, , https://docs.huihoo.com/scm/supply-chain-operations-reference-model-r11.0.pdf, Revision 11, [online] (accessed 20 May 2020); Auramo, J., (2006) Implications of Supply Chain Visibility: Benefits in Transaction Execution and Resource Network Management, , https://aaltodoc.aalto.fi/handle/123456789/2805, Doctoral dissertation, Helsinki University of Technology Laboratory of Industrial Management [online]; Auramo, J., Aminoff, A., Punakivi, M., Research agenda for e-business logistics based on professional opinions (2002) International Journal of Physical Distribution and Logistics Management, 32 (7), pp. 513-531; Auramo, J., Kauremaa, J., Tanskanen, K., Benefits of IT in supply chain management: an explorative study of progressive companies (2005) International Journal of Physical Distribution and Logistics Management, 35 (2), pp. 82-100; Barnard, J.H., (2006) Multi-View Framework for Defining the Services Supply Chain Using Object-Oriented Methodology, , Doctoral dissertation, University of Central Florida, ProQuest; Barros, A.P.D., Ishikiriyama, C.S., Cordeiro, R., Peres, C.F.S.G., Processes and benefits of the application of information technology in supply chain management: an analysis of the literature (2015) Information Technology and Quantitative Management (ITQM) Procedia Computer Science, 55 (1), pp. 698-705; Bendoly, E., Rosenzweig, E.D., Stratman, J.K., Performance metric portfolios: a framework and empirical analysis (2007) Production and Operations Management, 16 (2), pp. 257-276; Bennett, N., Lemione, G.J., What VUCA really means for you (2014) Harvard Business Review, 92 (1). , Nos. /2; Bhagwat, R., Sharma, M.K., Management of information system in Indian SMEs: an exploratory study (2006) International Journal of Enterprise Network Management, 1 (1), pp. 99-125; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bharadwaj, A.S., A resource-based perspective on information technology capability and firm performance: an empirical investigation (2000) MIS Quarterly, 24 (1), pp. 169-196; Birkel, H.S., Hartmann, E., Impact of IoT challenges and risks for SCM (2019) Supply Chain Management: An International Journal, 24 (1), pp. 39-61; Boulesnane, S., Bouzidi, L., The mediating role of information technology in the decision-making context (2013) Logistics Information Management, 26 (4), pp. 387-399; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for managing supply chain risks (2014) European Journal of Operational Research, 233 (2), pp. 299-312; Büyüközkan, G., Göçer, F., Digital supply chain: literature review and a proposed framework for future research (2018) Computers in Industry, 97 (1), pp. 157-177; Carr, N.G., IT doesn't matter (2003) Harvard Business Review, pp. 1-29. , https://www.classes.cs.uchicago.edu/archive/2014/fall/51210-1/required.reading/ITDoesntMatter.pdf, May, [online]; Cederlund, J., Kohli, R., Sherer, S., Yao, O., How Motorola put CPFR into action (2007) Supply Chain Management Review, 11 (7), pp. 28-35; Chithambaranathan, P., Subramanian, N., Gunasekaran, A., Palaniappan, P.L.K., Service supply chain environmental performance evaluation using grey based hybrid MCDM approach (2015) International Journal of Production Economics, 166, pp. 163-176; Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, Planning and Operation, , Prentice-Hall, New Jersey; Christopher, M., (2016) Logistics and Supply Chain Management, , Pearson, Harlow; Coltman, T., Devinney, T.M., Midgley, D.F., Customer relationship management and firm performance (2011) Journal of Information Technology, 26 (3), pp. 205-219; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Dong, S., Xu, S.X., Zhu, K.X., Information technology in supply chains: the value of it-enabled resources under competition (2009) Information Systems Research, 20 (1), pp. 18-32; Dweiri, F., Khan, S.A., Jain, V., Production planning forecasting method selection in a supply chain: a case study (2015) International Journal of Applied Management Science, 7 (1), pp. 38-58; Fatorachian, H., Kazemi, H., Impact of industry 4.0 on supply chain performance (2021) Production Planning and Control, 32 (1), pp. 63-81; Fawcett, S.E., Wallin, C., Allred, C., Fawcett, A.M., Magnan, G.M., Information technology as an enabler of supply chain collaboration: a dynamic capabilities perspective (2011) Journal of Supply Chain Management, 47 (1), pp. 38-59; Ferrantino, M.J., Koten, E.E., (2019) Understanding Supply Chain 4.0 and its Potential Impact on Global Value Chains, Global Value Chain Development Report, , https://www.wto.org/english/res_e/booksp_e/gvc_dev_report_2019_e_ch5.pdf, World Trade Organization (WTO) [online]; Fosso-Wamba, S., Akter, S., Coltman, T., Ngai, E.W.T., Guest editorial: information technology-enabled supply chain management (2015) Production Planning and Control, 26 (12), pp. 933-944; Gamme, N., Johansson, M., (2015) Measuring Supply Chain Performance Through KPI IDENTIFICation and Evaluation, , Master thesis, Chalmers University of Technology, ProQuest; Gordon, S.R., Tarafdar, M., How do a company's information technology competences influence its ability to innovate? (2007) Logistics Information Management, 20 (3), pp. 271-290; Grover, V., Kohli, R., Co creating IT value: new capabilities and metrics for multiform environments (2012) MIS Quarterly, 36 (1), pp. 225-232; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Ngai, E.W.T., Virtual Supply Chain Management (2004) Production Planning and Control, 15 (6), pp. 584-595; Gunasekaran, A., Irani, Z., Choy, K-L., Filippi, L., Papadopoulos, T., Performance measures and metrics in outsourcing decisions: a review for research and applications (2015) International Journal of Production Economics, 161 (C), pp. 153-166; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Subramanian, N., Papadopoulos, T., Information technology for competitive advantage within logistics and supply chains: a review (2017) Transportation Research Part E, 99 (C), pp. 14-33; Gunter, H., Grote, G., Thees, O., Information technology in supply networks: does it lead to better collaborative planning? (2006) Logistics Information Management, 19 (5), pp. 540-550; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hazen, B.T., Byrd, T.A., Toward creating competitive advantage with logistics information technology (2012) International Journal of Physical Distribution and Logistics Management, 42 (1), pp. 8-35; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hofmann, E., Rüsch, M., Industry 4.0 and the current status as well as future prospects on logistics (2017) Computers in Industry, 89 (1), pp. 23-34; Huo, B., Zhang, C., Zhao, X., The effect of IT and relationship commitment on supply chain coordination: a contingency and configuration approach (2015) Information and Management, 52 (6), pp. 728-740; Irfan, D., Xiaofei, X., Chun, D.S., A SCOR reference model of the supply chain management system in an enterprise (2008) International Arab Journal of Information Technology, 5 (3), pp. 288-295; Jharkharia, S., Shankar, R., IT-enablement of supply chains: understanding the barriers (2005) Journal of Enterprise Information Management, 18 (1), pp. 1-2; Joyce, R.K., (2006) Successful Implementation of Decision Support Systems for Supply Chain Management within Manufacturing Companies, , Doctoral dissertation, Nova Southeastern University, ProQuest; Kamble, S.S., Gunasekaran, A., Sharma, R., Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry (2018) Computers in Industry, 101 (1), pp. 107-119; Katiyar, R., Barua, M.K., Meena, P.L., Analyzing the interactions among the barriers of supply chain performance measurement: an ISM with fuzzy MICMAC approach (2017) Global Business Review, 19 (1), pp. 48-68; Kauffman, R.J., Kumar, A., (2005) A Critical Assessment of the Capabilities of Five Measures for ICT Development, , http://misrc.umn.edu/workingpapers/fullpapers/2005/0506_031805.pdf, Working paper series, Management Information Systems Research Center, University of Minnesota [online] (accessed 12 May 2020); Ketchen, D.J., Hult, G.T.M., Bridging organization theory and supply chain management: the case of best value supply chains (2007) Journal of Operations Management, 25 (2), pp. 573-580; Khan, S.A., Chaabane, A., Dweiri, F., A knowledge-based system for overall supply chain performance evaluation: a multi-criteria decision making approach (2019) Supply Chain Management: An International Journal, , https://doi.org/10.1108/SCM-06-2017-0197, [online] (accessed 12 May 2020); Kim, S.M., (2004) An Empirical Investigation of the Impact of Electronic Commerce on Supply Chain Management: A Study in the Health Care Industry, , Doctoral dissertation, University of Nebraska, ProQuest; Kirkpatrick, D.L., Kirkpatrick, J.D., (2007) Implementing the Four Levels, , Berrett-Koehler Publishers, Oakland; Kritchanchai, D., Hoeur, S., Engelseth, P., Develop astrategy for improving healthcare logistics performance (2017) Supply Chain Forum: An International Journal, 19 (1), pp. 55-69; Kumar, A., Singh, R.K., Modgil, S., Exploring the relationship between ICT, SCM practices and organizational performance in agri-food supply chain (2020) Benchmarking, 27 (3), pp. 1003-1041; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-113; Lima-Junior, F.R., Carpinetti, L.C.R, Quantitative models for Supply chain performance evaluation (2017) Computers and Industrial Engineering, 113 (C), pp. 333-346; Liu, Z., Prajogo, D., Oke, A., Supply chain technologies: linking adoption, utilization and performance (2016) Journal of Supply Chain Management, 52 (4), pp. 22-41; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: a case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Malhotra, A., Gosain, S., El Sawy, O.A., Leveraging standard electronic business interfaces to enable adaptive supply chain partnerships (2007) Information Systems Research, 18 (3), pp. 260-279; (2019) Digital India: Technology to transform a connected nation, , https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Digital%20India%20Technology%20to%20transform%20a%20connected%20nation/Digital-India-technology-to-transform-a-connected-nation-Full-report.ashx, [online] (accessed 22 July 2020); Mihardjo, L.W.W., Sasmoko, S., Alamsyah, F., Elidjen, E., Maximising co-creation strategy through integration of distinctive capabilities and customer experiences in supply chain management (2020) Uncertain Supply Chain Management, 8 (1), pp. 187-196; Mishra, S., Modi, S.B., Animesh, A., The relationship between information technology capability, inventory efficiency and shareholder wealth: a firmlevel empirical analysis (2013) Journal of Operations Management, 31 (6), pp. 298-312; Nabavi, M., (2006) Integrating Information Resources in Supply Chains; A Simulation Analysis of Delivery Performance and Resource Utilization, , Doctoral dissertation, University of Nebraska, ProQuest; Nagayama, K., Weill, P., (2004) Seven-Eleven Japan Co., Ltd.: Reinventing the Retail Business Model, , MIT Sloan CISR Working Paper 338, MIT Sloan WP 4485-04, January; Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: a case study of an auto industry supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 593-603; Nair, P.R., Anbuudayasankar, S.P., Tackling supply chain through RFID: opportunities and challenges (2016) Advances in Intelligent Systems and Computing, 438, pp. 467-475. , Springer, Singapore; Nair, P.R., Raju, V., Anbuudayashankar, S.P., Overview of information technology tools for supply chain management (2009) CSI Communications, 33 (9), pp. 20-27; Nair, P.R., Sahay, M., Anbuudayasankar, S.P., Investigation through system dynamics for the benefits of ICT intervention in distribution (2017) Prabandhan: Indian Journal of Management, 10 (8), pp. 42-51; Ngai, E., Chau, D., Chan, T., Information technology, operational and management competencies for supply chain agility: findings from case studies (2011) Journal of Strategic Information Systems, 20 (3), pp. 232-249; Paulraj, A., Chen, I.J., Strategic buyer-supplier relationships, information technology and external logistics integration (2007) Journal of Supply Chain Management, 43 (2), pp. 2-14; Peng, X.D., Liu, G., Heim, G.R., Impacts of information technology on mass customization capability of manufacturing plants (2011) International Journal of Operations and Production Management, 31 (10), pp. 1022-1047; Prajogo, D., Olhager, J., Supply chain integration and performance: the effects of long-term relationships, information technology and sharing and logistics integration (2012) International Journal of Production Economics, 135 (1), pp. 514-522; Qrunfleh, S., Tarafdar, M., Lean and agile supply chain strategies and supply chain responsiveness: the role of strategic supplier partnership and postponement (2013) Supply Chain Management: An International Journal, 18 (6), pp. 571-582; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Proceedings of 2nd International Conference on Emerging Trends in Engineering and Technology, ICETET-09, pp. 802-807; Russell, D.M., Hoag, A.M., People and information technology in the supply chain: social and organizational influences on adoption (2004) International Journal of Physical Distribution and Logistics Management, 34 (2), pp. 102-122; Sahay, B.S., Ranjan, J., Real time business intelligence in supply chain analytics (2008) Information Management and Computer Security, 16 (1), pp. 28-48; Sanders, N.R., Premus, R., Modeling the relationship between firm IT capability, collaboration and performance (2005) Journal of Business Logistics, 26 (1), pp. 1-23; Sardana, G.D., Exploring the performance of a responsive supply chain (2009) Supply Chain Forum, 10 (2), pp. 38-51; Sarode, A.D, Prakash, M.K., A framework for performance measurement system of supply chain management (2011) International Journal of Advanced Engineering Technology, 2 (4), pp. 182-190; Setia, P., Sambamurthy, V., Closs, D.J., Realizing business value of agile IT applications: antecedents in the supply chain networks (2007) Information Technology and Management, 9 (1), pp. 5-19; Shore, B., Information sharing in global supply chains (2001) Journal of Global Information Technology Management, 4 (3), pp. 27-46; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Managing The Supply Chain: The Definitive Guide For The Business Professional, , McGraw Hill, New York; Singh, A., Teng, J.T.C., Enhancing supply chain outcomes through information technology and trust (2016) Computers in Human Behavior, 54 (C), pp. 290-300; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , Pitman Publishing, London; Srinivasan, K., Kekre, S., Mukhopadhyay, T., Impact of electronic data interchange technology on JIT shipments (1994) Management Science, 40 (10), pp. 1291-1304; Stufflebeam, D.L., The CIPP model for evaluation (2003) International Handbook of Educational Evaluation, pp. 31-62. , Springer, Dordrecht; Subramani, M., How do suppliers benefit from information technology use in supply chain relationships? (2004) MIS Quarterly, 28 (1), pp. 45-73; Towill, D., The seamless supply chain - the predator's strategic advantage (1997) International Journal of Technology Management, 13 (1), pp. 37-56; Tsen, M.L., Wu, K.J., Nguyen, T.T, Information technology in supply chain management: a case study (2011) Procedia - Social and Behavioral Sciences, 25, pp. 257-272; Vaio, A.D., Varriale, L., Blockchain technology in supply chain management for sustainable performance: evidence from the airport industry (2020) International Journal of Information Management, 52 (1), p. 102014; Vickery, S.K., Droge, C., Seita, P., Sambamurthy, V., Supply chain information technologies and organisational initiatives: complementary versus independent effects on agility and firm performance (2010) International Journal of Production Research, 48 (23), pp. 7025-7042; Vineesh, D., Anbuudayasankar, S.P., Narassima, M.S., Enhancing dairy manufacturing through customer feedback: a statistical approach (2018) IOP Conference Series: Materials Science and Engineering, 310 (1), p. 12097. , February; Wamba, S.F., Queiroz, M.M., Blockchain in the operations and supply chain management: benefits, challenges and future research opportunities (2020) International Journal of Information Management, 52 (1), p. 102064; Wu, F., Yeniyurt, S., Kim, D., Cavusgil, S.T., The impact of information technology on supply chain capabilities and firm performance: a resource-based view (2006) Industrial Marketing Management, 35 (4), pp. 493-504; Yadav, S., Singh, S.P., Blockchain critical success factors for sustainable supply chain (2020) Resources, Conservation and Recycling, 152 (1), p. 104505; Yao, Y., (2002) Measuring Impacts of Electronic Commerce on Supply Chain Management, , Doctoral dissertation, University of Maryland, ProQuest; Zhang, C., Dhaliwal, J., An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management (2009) International Journal of Production Economics, 120 (1), pp. 252-269; Zhang, M., Qin, J., Wei, T., Capacity coordination in a service supply chain (2017) International Journal of Applied Management Science, 9 (1), pp. 19-37; Zhang, X., (2012) Role of ICT in Supply Chains (SC), , https://www.rug.nl/research/portal/files/10544028/193569-L-bw-Zhang1.pdf, Doctoral dissertation, University of Groningen, [online] (accessed 20 June 2020)},
correspondence_address1={Anbuudayasankar, S.P.; Department of Mechanical Engineering, India; email: sp_anbu@cb.amrita.edu},
publisher={Inderscience Publishers},
issn={17481252},
language={English},
abbrev_source_title={Int. J. Enterp. Network Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{AmoozadMahdiraji2022,
author={Amoozad Mahdiraji, H. and Yaftiyan, F. and Abbasi Kamardi, A.A. and Garza-Reyes, J.A. and Razavi Hajiagha, S.H.},
title={The role of Industry 4.0 technologies on performance measurement systems of supply chains during global pandemics: an interval-valued intuitionistic hesitant fuzzy approach},
journal={International Journal of Quality and Reliability Management},
year={2022},
doi={10.1108/IJQRM-03-2022-0094},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139916754&doi=10.1108%2fIJQRM-03-2022-0094&partnerID=40&md5=50b4aab66b4c520e52058a6af592e89b},
affiliation={School of Business, University of Leicester, Leicester, United Kingdom; University of Tehran, Tehran, Iran; Centre for Supply Chain Improvement, The University of Derby, Derby, United Kingdom; Khatam University, Tehran, Iran},
abstract={Purpose: This paper aims to investigate Supply Chain (SC) Performance Measurement Systems (PMSs) (SCPMSs) that are suitable and applicable to evaluate SC performance during unexpected events such as global pandemics. Furthermore, the contribution of Industry 4.0 Disruptive Technologies (IDTs) to implement SCPMSs during such Black Swan events is investigated in this study. Design/methodology/approach: The research methodology is based upon a novel qualitative and quantitative mixed-method. A Systematic Literature Review (SLR) was initially employed to identify two complete lists of SCPMSs and IDTs. Then, a novel Interval-Valued Intuitionistic Hesitant-Fuzzy (IVIHF)-Delphi method was firstly developed in this paper to screen the extracted SCPMSs. Afterward, the Propriety, Economic, Acceptable, Resource, Legal (PEARL) indicator of the Hanlon method was innovatively applied to prioritize the identified IDTs for each finalized SCPMS. Findings: Two high-score SCPMSs including the SC operations reference (SCOR) model and sustainable SCPMS were recommended to improve measuring the performance of the pharmaceutical SC of emerging economies such as Iran in which the societal, biological and economic issues were undeniable, particularly during unexpected events. Employing nine IDTs such as simulation, big data analytics, cloud technologies, etc., would facilitate implementing sustainable SCPMS from distinct perspectives. Originality/value: This is one of the first papers to provide in-depth insights into determining the priority of contribution of IDTs in applying different SCPMSs during global pandemics. Proposing a novel multi-layer mixed-methodology involving SLR, IVIHF-Delphi, and the PEARL indicator of the Hanlon method is another originality offered by this paper. © 2022, Emerald Publishing Limited.},
author_keywords={Global pandemics;  IDTs;  IVIHF-Delphi;  PEARL indicator;  PMSs;  SCPMSs},
references={Atanassov, K.T., Intuitionistic fuzzy sets (1999) Intuitionistic Fuzzy Sets. Studied in Fuzziness and Soft Computing, 35, pp. 1-137. , Physica, Heidelberg; Bai, C., Dallasega, P., Orzes, G., Sarkis, J., Industry 4.0 technologies assessment: a sustainability perspective (2020) International Journal of Production Economics, 229; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998-2015 (2016) Computers in Industry, 82, pp. 135-150; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Belton, I., MacDonald, A., Wright, G., Hamlin, I., Improving the practical application of the Delphi method in group-based judgment: a six-step prescription for a well-founded and defensible process (2019) Technological Forecasting and Social Change, 147, pp. 72-82; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management–what has been achieved? (2015) Supply Chain Management: An International Journal, 20 (6), pp. 664-680; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: a development guide (1997) International Journal of Operations and Production Management, 17 (5), pp. 522-534; Büyüközkan, G., Göçer, F., Digital supply chain: literature review and a proposed framework for future research (2018) Computers in Industry, 97, pp. 157-177; Cai, M., Luo, J., Influence of COVID-19 on manufacturing industry and corresponding countermeasures from supply chain perspective (2020) Journal of Shanghai Jiaotong University (Science), 25 (4), pp. 409-416; Castillo, O., Melin, P., A review on the design and optimization of interval type-2 fuzzy controllers (2012) Applied Soft Computing, 12 (4), pp. 1267-1278; Chand, P., Thakkar, J.J., Ghosh, K.K., Analysis of supply chain performance metrics for Indian mining and earthmoving equipment manufacturing companies using hybrid MCDM model (2020) Resources Policy, 68; Chennell, A., Dransfield, S., Field, J., Fisher, N., Saunders, I., Shaw, D., OPM: a system for organisational performance measurement (2000) Proceedings of the Performance Measurement–Past, Present and Future Conference, pp. 19-21. , Cambridge; Choi, B.C., Maza, R.A., Mujica, O.J., The Pan American Health Organization-adapted Hanlon method for prioritization of health programs (2019) Revista Panamericana de Salud Pública, 43, p. 61; De Toni, A., Tonchia, S., Performance measurement systems-models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 46-71; Dolgui, A., Ivanov, D., Exploring supply chain structural dynamics: new disruptive technologies and disruption risks (2020) International Journal of Production Economics, 229; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Farhadinia, B., A series of score functions for hesitant fuzzy sets (2014) Information Sciences, 277, pp. 102-110; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard–linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11 (5), pp. 269-284; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, 69. , Chartered Institute of Management Accountants, London; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: a review of their consequences and a framework for research (2012) Management Accounting Research, 23 (2), pp. 79-119; Frederico, G.F., Garza-Reyes, J.A., Kumar, A., Kumar, V., Performance measurement for supply chains in the Industry 4.0 era: a balanced scorecard approach (2020) International Journal of Productivity and Performance Management, 70 (4), pp. 789-807; Garay-Rondero, C.L., Martinez-Flores, J.L., Smith, N.R., Morales, S.O.C., Aldrette-Malacara, A., Digital supply chain model in Industry 4.0 (2020) Journal of Manufacturing Technology Management, 31 (5), pp. 887-933; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: a review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Goel, R.K., Saunoris, J.W., Goel, S.S., Supply chain performance and economic growth: the impact of COVID-19 disruptions (2021) Journal of Policy Modeling, 43 (2), pp. 298-316; Gold, T., The lunar surface (1955) Monthly Notices of the Royal Astronomical Society, 115 (6), pp. 585-604; Goodman, C.M., The Delphi technique: a critique (1987) Journal of Advanced Nursing, 12 (6), pp. 729-734; Grida, M., Mohamed, R., Zaied, A.N.H., Evaluate the impact of COVID-19 prevention policies on supply chain aspects under uncertainty (2020) Transportation Research Interdisciplinary Perspectives, 8; Hajiagha, S.H.R., Alaei, S., Mahdiraji, H.A., Yaftiyan, F., International collaboration formation in entrepreneurial food industry: evidence of an emerging economy (2021) British Food Journal, 124 (7), pp. 2012-2038; Hald, K.S., Mouritsen, J., The evolution of performance measurement systems in a supply chain: a longitudinal case study on the role of interorganisational factors (2018) International Journal of Production Economics, 205, pp. 256-271; Hanlon, J.J., The design of public health programs for underdeveloped countries (1954) Public Health Reports, 69 (11), p. 1028; Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Gu, X., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China (2020) The Lancet, 395 (10223), pp. 497-506; Jafari-Sadeghi, V., Mahdiraji, H.A., Bresciani, S., Pellicelli, A.C., Context-specific micro-foundations and successful SME internationalisation in emerging markets: a mixed-method analysis of managerial resources and dynamic capabilities (2021) Journal of Business Research, 134, pp. 352-364; Joshi, D., Kumar, S., Interval-valued intuitionistic hesitant fuzzy Choquet integral based TOPSIS method for multi-criteria group decision making (2016) European Journal of Operational Research, 248 (1), pp. 183-191; Ka, J.M.R., Ab, N.R., Lb, K., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30, pp. 40-47; Kaplan, R.S., Norton, D.P., The balance scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Strategic Finance, 70 (12), p. 45; Koh, L., Orzes, G., Jia, F.J., The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management (2019) International Journal of Operations and Production Management, 39 (7-8), pp. 817-828. , 6; Laitinen, E.K., (1996) Framework for Small Business Performance Measurement: Towards Integrated PM Systems, , Vaasan Yliopisto, Vaasa; Lee, K.M., Lee, K.M., Cios, K.J., Comparison of interval-valued fuzzy sets, intuitionistic fuzzy sets, and bipolar-valued fuzzy sets (2001) Computing and Information Technologies: Exploring Emerging Technologies, pp. 433-439; Love, D.C., Allison, E.H., Asche, F., Belton, B., Cottrell, R.S., Froehlich, H.E., Gephart, J.A., Nussbaumer, E.M., Emerging COVID-19 impacts, responses, and lessons for building resilience in the seafood system (2021) Global Food Security, 28; Lu, M.T., Hsu, C.C., Liou, J.J., Lo, H.W., A hybrid MCDM and sustainability-balanced scorecard model to establish sustainable performance evaluation for international airports (2018) Journal of Air Transport Management, 71, pp. 9-19; Macdonald, J.R., Corsi, T.M., Supply chain disruption management: severe events, recovery, and performance (2013) Journal of Business Logistics, 34 (4), pp. 270-288; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Maestrini, V., Maccarrone, P., Caniato, F., Luzzini, D., Supplier performance measurement systems: communication and reaction modes (2018) Industrial Marketing Management, 74, pp. 298-308; Mahdiraji, H.A., Sedigh, M., Hajiagha, S.H.R., Garza-Reyes, J.A., Jafari-Sadeghi, V., Dana, L.P., A novel time, cost, quality and risk tradeoff model with a knowledge-based hesitant fuzzy information: an RandD project application (2021) Technological Forecasting and Social Change, 172; Masteika, I., Čepinskis, J., Dynamic capabilities in supply chain management (2015) Procedia-Social and Behavioral Sciences, 213, pp. 830-835; McNair, C.J., Lynch, R.L., Cross, K.F., Do financial and nonfinancial performance measures have to agree? (1990) Strategic Finance, 72 (5), p. 28; Narayanamoorthy, S., Geetha, S., Rakkiyappan, R., Joo, Y.H., Interval-valued intuitionistic hesitant fuzzy entropy based VIKOR method for industrial robots selection (2019) Expert Systems with Applications, 121, pp. 28-37; Neely, A., The performance measurement revolution: why now and what next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Neiger, B.L., Thackeray, R., Fagen, M.C., Basic priority rating model 2.0: current applications for priority setting in health promotion practice (2011) Health Promotion Practice, 12 (2), pp. 166-171; Peng, X., Selvachandran, G., Pythagorean fuzzy set: state of the art and future directions (2019) Artificial Intelligence Review, 52 (3), pp. 1873-1927; Peng, J.J., Wang, J.Q., Zhang, H.Y., Chen, X.H., An outranking approach for multi-criteria decision-making problems with simplified neutrosophic sets (2014) Applied Soft Computing, 25, pp. 336-346; Pickett, G., Hanlon, J.J., (1990) Public Health: Administration and Practice, , Times Mirror/Mosby College, St. Louis; Power, D., Supply chain management integration and implementation: a literature review (2005) Supply Chain Management: An International Journal, 10 (4), pp. 252-263; Tat, R., Heydari, J., Avoiding medicine wastes: introducing a sustainable approach in the pharmaceutical supply chain (2021) Journal of Cleaner Production, 320; Thanki, S., Thakkar, J., A quantitative framework for lean and green assessment of supply chain performance (2018) International Journal of Productivity and Performance Management, 67 (2), pp. 366-400; Tirivangani, T., Alpo, B., Kibuule, D., Gaeseb, J., Adenuga, B.A., Impact of COVID-19 pandemic on pharmaceutical systems and supply chain–a phenomenological study (2021) Exploratory Research in Clinical and Social Pharmacy, 2; Torra, V., Hesitant fuzzy sets (2010) International Journal of Intelligent Systems, 25 (6), pp. 529-539; Wamba, S.F., Queiroz, M.M., Trinchera, L., Dynamics between blockchain adoption determinants and supply chain performance: an empirical investigation (2020) International Journal of Production Economics, 229; Wang, C.Y., Chen, S.M., An improved multiattribute decision making method based on new score function of interval-valued intuitionistic fuzzy values and linear programming methodology (2017) Information Sciences, 411, pp. 176-184; Xu, S., Zhang, X., Feng, L., Yang, W., Disruption risks in supply chain management: a literature review based on bibliometric analysis (2020) International Journal of Production Research, 58 (11), pp. 3508-3526; Zadeh, L.A., A note on prototype theory and fuzzy sets (1996) Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh, pp. 587-593; Zadeh, L.A., A note on Z-numbers (2011) Information Sciences, 181 (14), pp. 2923-2932; Zhang, Z., Gao, J., Gao, Y., Yu, W., Two-sided matching decision making with multi-granular hesitant fuzzy linguistic term sets and incomplete criteria weight information (2021) Expert Systems with Applications, 168},
correspondence_address1={Amoozad Mahdiraji, H.; School of Business, United Kingdom; email: ham26@leicester.ac.uk},
publisher={Emerald Publishing},
issn={0265671X},
language={English},
abbrev_source_title={Int. J. Qual. Reliab. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Achetoui202225,
author={Achetoui, Z. and Mabrouki, C. and Mousrij, A.},
title={A BALANCED PERFORMANCE MEASUREMENT SYSTEM for AUTOMOTIVE SPARE PARTS SUPPLY CHAIN},
journal={Scientific Journal of Silesian University of Technology. Series Transport},
year={2022},
volume={116},
pages={25-55},
doi={10.20858/sjsutst.2022.116.2},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137185975&doi=10.20858%2fsjsutst.2022.116.2&partnerID=40&md5=068854fbbe60e3216c841adb4abfd65b},
affiliation={Laboratory of Engineering, Industrial Management and Innovation, Hassan First University, Faculty of Science and Technology, Km. 3, B.P. 577, Settat, Morocco},
abstract={This paper aims to provide a performance measurement system for the automotive spare parts supply chain. We focus on an independent distributor belonging to an independent channel. The framework encompasses different performance aspects for a high supply chain visibility, as well as the required categories, subcategories and key performance indicators for the automotive spare parts supply chain performance measurement. The framework is the first contribution that advances the performance measurement in the automotive aftermarket and allows assessing the performance of the automotive spare parts supply chain as a whole. © 2022 Faculty of Transport and Aviation Engineering, Silesian University of Technology. All Rights Reserved.},
author_keywords={automotive aftermarket;  performance measurement;  spare parts;  supply chain management},
references={Andrea, Bacchetti, Nicola, Saccani, Spare part classification and demand forecasting for stock control: Investigating the gap between research and practice (2012) Omega, 40, pp. 722-737; Michalski, R., Wierzbicki, S., An analysis of degradation of vehicles in operation (2008) Eksploatacja i Niezawodnosc – Maintenance and Reliability, 1, pp. 30-32; Andries, Botha, Grobler, Jacomine, Sarma Yadavalli, V.S., System dynamics comparison of three inventory management models in an automotive parts supply chain (2017) Journal of Transport and Supply Chain Management, 11 (a281); Jozef, Matuszek, Seneta, Tomasz, Dulina, Luboslav, Bigosova, Eleonora, Manufacturability Assessment of the Product Assembly Processes Design in the Automotive Industry (2020) Communications - Scientific Letters of the University of Zilina, 22 (4), pp. 112-120; Ryma, Berrehal, Benissaad, S., Determining the optimal periodicity for preventive replacement of mechanical spare parts (2016) Mechanika, 22 (2), pp. 156-161; Ivan, Dyakov, Prentkovskis, Olegas, Optimization problems in designing automobiles (2008) Transport, 23 (4), pp. 316-322; Chieux, T, Guillaneuf, V, Whiteman, J., (2005) Aftermarket Parts: Changing Channels Under 1400/02, , Solihull. UK: ICDP; Parikshit, Charan, Supply chain performance issues in an automobile company: a SAP‐LAP analysis (2012) Measuring Business Excellence, 16 (1), pp. 67-86; (2002) Global Study on Spare Parts Logistics, , Munich; De Leeuw Sander, Loek, Beekman, Supply chain-oriented performance measurement for automotive spare parts (2008) International Journal of Automotive Technology and Management, 8 (1), pp. 56-70; Sabrina, Grimaldi, Carlo, Rafele, Current applications of a reference framework for the supply chain performance measurement (2007) International Journal of Business Performance Management, 9 (2), pp. 206-225; Paolo, Gaiardelli, Nicola, Saccani, Lucrezia, Songini, Performance measurement systems in after-sales service: an integrated framework (2007) International Journal of Business Performance Management, 9 (2), pp. 145-171; Leonard, FS, Sasser, WE., The Incline of Quality (1982) Harvard Business Review, 60 (5), pp. 163-171; Berry Leonard, L, Bennett David, R, Brown Carter, W., (1989) Service quality: A profit strategy for financial institutions, , Irwin Professional Pub; Ananthanarayanan, Parasuraman, Reflections on gaining competitive advantage through customer value (1997) Journal of the Academy of Marketing Science, 25 (2), pp. 154-161; Jensen, Hans Rask, Antecedents and consequences of consumer value assessments: implications for marketing strategy and future research (2001) Journal of Retailing and Consumer Services, 8 (6), pp. 299-310; Alexandre, Dolgui, Proth, Jean-Marie, Warehouse Management and Design (2010) Supply Chain Engineering – Useful Methods and Techniques, pp. 419-447. , Chap. 11 in London: Springer; Tomas, Kucera, Application of the Activity-Based Costing to the Logistics Cost Calculation for Warehousing in the Automotive Industry (2019) Communications - Scientific Letters of the University of Zilina, 21 (4), pp. 35-42; De Koster, MBM, Warffemius, PMJ., American, Asian and third‐party international warehouse operations in Europe: A performance comparison (2005) International Journal of Operations & Production Management, 25 (8), pp. 762-780; Van den Berg, Jeroen. P., Zijm, Willem HM., Models for warehouse management: Classification and examples (1999) International journal of production economics, 59 (1-3), pp. 519-528; Yifan, Wu, Ming, Dong, Combining multi-class queueing networks and inventory models for performance analysis of multi-product manufacturing logistics chains (2008) The International Journal of Advanced Manufacturing Technology, 37 (5-6), pp. 564-575; Keebler, James S., Plank, Richard E., Logistics performance measurement in the supply chain: a benchmark (2009) Benchmarking: An International Journal, 16 (6), pp. 785-798; Armstrong, Michael, (2009) Armstrong’s Handbook of Human Resource Management Practice, , London and Philadelphia. Kogan Page},
publisher={Faculty of Transport and Aviation Engineering, Silesian University of Technology},
issn={02093324},
language={English},
abbrev_source_title={Sci. J. Sil. Univ. Technol., Series Transp.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Thang20221024,
author={Thang, T.D. and Uyen, D.M. and Huy, T.Q. and Paulo, A.S. and Carvalho, M.S. and An, D.T.B.},
title={DIRECT AND INDIRECT EFFECTS OF RISKS ON SERVICE-ORIENTED SUPPLY CHAIN: A COVID-19 PANDEMIC PERSPECTIVE},
journal={International Conference on Quality Engineering and Management},
year={2022},
pages={1024-1038},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85137071544&partnerID=40&md5=aa167b6a7289170613212c1975918661},
affiliation={School of Business & Management, RMIT University, Viet Nam; Production and Systems Department, Minho University, Braga, Portugal; CIRTech Institute, HUTECH University, Ho Chi Minh City, Viet Nam},
abstract={Purpose - A solid service-oriented foundation is required to make supply chain management a competitive advantage, especially in this Covid-19 pandemic. A well-established service-oriented supply chain becomes more adaptable to changing client expectations. This study aims at analysing the direct and indirect impact of risks on the service-oriented supply chain from a pandemic perspective. Design/methodology/approach - The Q-sort method is applied with the participation of nine top-level managers to initially review the reliability, validity, and unidimensionality of research concepts. Then a questionnaire containing these measuring variables is developed to obtain the opinions of those who are experienced in logistics and supply chain management. These empirical data are analysed based on Structural Equation Modelling (SEM) to evaluate direct and indirect effects of risks on supply chain performance. Findings - The risk is inherent in service-oriented supply chains, affecting both direct and indirect performance. The proposed risk model explains 33.6 percent of Supplier performance, 46.4 percent of Operational performance, 47.1 percent of Customer satisfaction, and 46.5 percent of Finance variation. We found that service-oriented supply chains effectively monitor demand risk. External risk has the smallest impact on supply chain performance measures, whereas demand risk has the smallest effect. That a service-oriented supply chain is focused on meeting customer demand and managing demand-related risks is reinforced by these findings. Research limitations/implications - In the literature on supply chain risk management, resilience studies and disruption management receive less attention than studies on risk assessment and risk mitigation (Katsaliaki et al., 2021). Future supply chain risk management research should differentiate between risk-as an event and/or risk-as a process since they have different periodic effects on response management and resilience. Originality/value - This is a pioneering study looking at the risk side of service-oriented supply chain. The data using in this research is from a large-scale survey supported by Japanese Government to promote ASEAN sustainable socio-economic development. This dataset collected during the Covid-19 pandemic to validate our models is an interesting and topical point of this study. © 2022 Universidade do Minho. All rights reserved.},
author_keywords={Covid-19 pandemic;  Risk;  Service dominant logic;  Service-oriented supply chain;  Supply chain performance;  Supply chain risk management},
keywords={Competition;  Customer satisfaction;  Risk assessment;  Supply chain management;  Surveys, Covid-19 pandemic;  Demand risks;  Indirect effects;  Risks management;  Service Oriented;  Service-dominant Logic;  Service-oriented supply chain;  Supply chain performance;  Supply chain risk management;  Supply-chain risks, Risk management},
references={Barichello, R., The COVID-19 pandemic: Anticipating its effects on Canada's agricultural trade (2020) Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 68 (2), pp. 219-224. , https://doi.org/1.1111/cjag.12244; Boyer, K. K., Hult, G. T., Frohlich, M., An exploratory analysis of extended grocery supply chain operations and home delivery (2003) Integrated Manufacturing Systems, 14 (8), pp. 652-663. , https://doi.org/1.1108/09576060310503465; Carranza, E. J. M., (2008) Geochemical anomaly and mineral prospectivity mapping in GIS, , Elsevier; Child, J., Organizational structure, environment and performance: The role of strategic choice (1972) Sociology, 6 (1), pp. 1-22. , https://doi.org/1.1177/003803857200600101; Chopra, S., Sodhi, M., Managing risk to avoid supply-chain breakdown (2012) MIT Sloan Management Review (Fall 2004), 46 (1), pp. 53-61; Christopher, M., Peck, H., Building the resilient supply chain (2004) The international journal of logistics management, 15 (2), pp. 1-14. , http://dx.doi.org/1.1108/09574090410700275; Comfort, L. K., Risk and resilience: inter-organizational learning following the Northridge earthquake of 17 January 1994 (1994) Journal of Contingencies and Crisis management, 2 (3), pp. 157-17. , https://doi.org/1.1111/j.1468-5973.1994.tb00038.x; Dreher, A., Gassebner, M., Greasing the wheels? The impact of regulations and corruption on firm entry (2013) Public Choice, 155 (3), pp. 413-432. , https://doi.org/1.1007/s11127-011-9871-2; Duncan, R. B., Characteristics of organizational environments and perceived environmental uncertainty (1972) Administrative science quarterly, 17 (3), pp. 313-327. , https://doi.org/1.2307/2392145; Duong, B. A. T., Truong, H. Q., Sameiro, M., Sampaio, P., Fernandes, A. C., Vilhena, E., Bui, L. T. C., Yadohisa, H., Supply chain management and organizational performance: the resonant influence (2019) International Journal of Quality & Reliability Management, 36 (7), pp. 1053-1077. , https://doi.org/1.1108/IJQRM-11-2017-0245; Felfel, H., Yahia, W. B., Ayadi, O., Masmoudi, F., Stochastic multi-site supply chain planning in textile and apparel industry under demand and price uncertainties with risk aversion (2018) Annals of Operations Research, 271 (2), pp. 551-574. , https://doi.org/1.1007/s10479-018-2980-2; Fernandes, A. C., Sampaio, P., Sameiro, M., Truong, H. Q., Supply chain management and quality management integration: A conceptual model proposal (2017) International Journal of quality & reliability management, 34 (1), pp. 53-67. , https://doi.org/1.1108/IJQRM-03-2015-0041; Flint, D. J., Lusch, R. F., Vargo, S. L., The supply chain management of shopper marketing as viewed through a service ecosystem lens (2014) International Journal of Physical Distribution & Logistics Management, 44 (1), pp. 23-38. , https://doi.org/1.1108/IJPDLM-12-2012-0350, (/2); Gamil, Y., Alhagar, A., The Impact of Pandemic Crisis on the Survival of Construction Industry: A Case of COVID-19 (2020) Mediterranean Journal of Social Sciences, 11 (4), pp. 122-122. , https://doi.org/1.36941/mjss-2020-0047; George, A. Z., Lisa, M. E., Joseph, R. C., Joseph, L. C., An analysis of supply risk assessment techniquesnull (2004) International journal of physical distribution & logistics management, 34 (5), pp. 397-413. , https://doi.org/1.1108/09600030410545445; Hailu, G., Economic thoughts on COVID-19 for Canadian food processors (2020) Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, 68 (2), pp. 163-169. , https://doi.org/1.1111/cjag.12241; Hair, J. F., Anderson, R. E., Tatham, R. L., Black, W. C., (1995) Multivariate data analysis with readings, , (4th ed). New Jersy; Hançerlioğulları, G., Şen, A., Aktunç, E. A., Demand uncertainty and inventory turnover performance: an empirical analysis of the US retail industry (2016) International Journal of Physical Distribution & Logistics Management, 46 (6), pp. 681-708. , http://dx.doi.org/1.1108/IJPDLM-12-2014-0303, (/7); Hansen, E., Nybakk, E., Panwar, R., Firm performance, business environment, and outlook for social and environmental responsibility during the economic downturn: findings and implications from the forest sector (2013) Canadian Journal of Forest Research, 43 (12), pp. 1137-1144. , https://doi.org/1.1139/cjfr-2013-0215; Heizer, J. H., Render, B., Weiss, H. J., (2008) Principles of Operations Management, , http://books.google.pt/books?id=044rAQAAMAAJ, Pearson Prentice Hall; Hendricks, K. B., Singhal, V. R., The effect of supply chain glitches on shareholder wealth (2003) Journal of operations management, 21 (5), pp. 501-522. , https://doi.org/1.1016/j.jom.2003.02.003; Hendricks, K. B., Singhal, V. R., An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm (2005) Production and Operations management, 14 (1), pp. 35-52. , https://doi.org/1.1111/j.1937-5956.2005.tb00008.x; Ho, W., Zheng, T., Yildiz, H., Talluri, S., Supply chain risk management: a literature review (2015) International Journal of Production Research, 53 (16), pp. 5031-5069. , https://doi.org/1.1080/00207543.2015.1030467; Ivanov, D., Exiting the COVID-19 pandemic: after-shock risks and avoidance of disruption tails in supply chains (2021) Annals of Operations Research, , https://doi.org/1.1007/s10479-021-04047-7; Jüttner, U., Supply chain risk management: Understanding the business requirements from a practitioner perspective (2005) The International Journal of Logistics Management, 16 (1), pp. 120-141. , https://doi.org/1.1108/09574090510617385; Kaplan, R. S., Conceptual foundations of the balanced scorecard (2009) Handbooks of management accounting research, 3, pp. 1253-1269. , https://doi.org/1.1016/S1751-3243(07)03003-9; Kaplan, R. S., Norton, D., The Balanced Scorecard: Measures that Drive Performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kate, S., (2013) Health and Safety Executive - Annual Statistics Report for Great Britain 2012/13, , http://www.hse.gov.uk/statistics/overall/hssh1213.pdf, Retrieved 07 June 2016 from; Katsaliaki, K., Galetsi, P., Kumar, S., Supply chain disruptions and resilience: a major review and future research agenda (2021) Annals of Operations Research, , https://doi.org/1.1007/s10479-020-03912-1; Kaynak, H., Hartley, J. L., A replication and extension of quality management into the supply chain (2008) Journal of operations management, 26 (4), pp. 468-489. , http://dx.doi.org/1.1016/j.jom.2007.06.002; Khan, O., Christopher, M., Burnes, B., The impact of product design on supply chain risk: a case study (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 412-432. , https://doi.org/1.1108/09600030810882834; Krause, D., Ellram, L. M., The effects of the economic downturn on interdependent buyer–supplier relationships (2014) Journal of Business Logistics, 35 (3), pp. 191-212. , https://doi.org/https://doi.org/1.1111/jbl.12053; Li, S., Rao, S. S., Ragu-Nathan, T., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of operations management, 23 (6), pp. 618-641. , https://doi.org/1.1016/j.jom.2005.01.002; Lin, Y., Zhou, L., The impacts of product design changes on supply chain risk: a case study (2011) International Journal of Physical Distribution & Logistics Management, 41 (2), pp. 162-186. , https://doi.org/1.1108/09600031111118549; Lusch, R. F., Reframing supply chain management: a service-dominant logic perspective (2011) Journal of supply chain management, 47 (1), pp. 14-18. , https://doi.org/1.1111/j.1745-493X.201.03211.x; Meixell, M. J., Gargeya, V. B., Global supply chain design: A literature review and critique (2005) Transportation Research Part E: Logistics and Transportation Review, 41 (6), pp. 531-555. , http://dx.doi.org/1.1016/j.tre.2005.06.003; Prasetyanti, L. A., Simatupang, T. M., A framework for service-based supply chain (2015) Procedia Manufacturing, 4, pp. 146-154. , https://doi.org/1.1016/j.promfg.2015.11.025; Quang, H. T., Hara, Y., The push effect of risks on supply chain performance: service-oriented firms (2019) Business Process Management Journal, 25 (7), pp. 1734-1758. , https://doi.org/1.1108/BPMJ-12-2017-0343; Quang, H. T., Sampaio, P., Carvalho, M. S., Fernandes, A. C., An, D. T. B., Vilhenac, E., An extensive structural model of supply chain quality management and firm performance (2016) International Journal of Quality & Reliability Management, 33 (4), pp. 444-464. , https://doi.org/doi:1.1108/IJQRM-11-2014-0188; Queiroz, M. M., Ivanov, D., Dolgui, A., Wamba, S. F., Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review (2020) Annals of operations research, , https://doi.org/1.1007/s10479-020-03685-7; Roberton, T., Carter, E. D., Chou, V. B., Stegmuller, A. R., Jackson, B. D., Tam, Y., Sawadogo-Lewis, T., Walker, N., Early estimates of the indirect effects of the COVID-19 pandemic on maternal and child mortality in low-income and middle-income countries: a modelling study (2020) The Lancet Global Health, 8 (7), pp. 901-908. , https://doi.org/1.1016/S2214-109X(20)30229-1; Schiele, H., How to distinguish innovative suppliers? Identifying innovative suppliers as new task for purchasing (2006) Industrial Marketing Management, 35 (8), pp. 925-935. , https://doi.org/1.1016/j.indmarman.2006.05.003; Sengupta, K., Heiser, D. R., Cook, L. S., Manufacturing and service supply chain performance: a comparative analysis (2006) Journal of supply chain management, 42 (4), pp. 4-15. , https://doi.org/1.1111/j.1745-493X.2006.00018.x; Shareef, M. A., Dwivedi, Y. K., Kumar, V., Hughes, D. L., Raman, R., Sustainable supply chain for disaster management: structural dynamics and disruptive risks (2020) Annals of Operations Research, , https://doi.org/1.1007/s10479-020-03708-3; Singh, S., Kumar, R., Panchal, R., Tiwari, M. K., Impact of COVID-19 on logistics systems and disruptions in food supply chain (2021) International Journal of Production Research, 59 (7), pp. 1993-2008. , https://doi.org/1.1080/00207543.202.1792000; Svensson, G., Aspects of sustainable supply chain management (SSCM): conceptual framework and empirical example (2007) Supply chain management: An international journal, 12 (4), pp. 262-266. , https://doi.org/1.1108/13598540710759781; Thun, J.-H., Hoenig, D., An empirical analysis of supply chain risk management in the German automotive industry (2011) International journal of production economics, 131 (1), pp. 242-249. , https://doi.org/1.1016/j.ijpe.2009.1.010; Truong, H. Q., Hara, Y., Supply chain risk management: manufacturing-and service-oriented firms (2018) Journal of Manufacturing Technology Management, 29 (2), pp. 218-239. , https://doi.org/1.1108/JMTM-07-2017-0145; Truong, H. Q., Sameiro, M., Fernandes, A. C., Sampaio, P., Duong, B. A. T., Supply chain management practices and firms’ operational performance (2017) International Journal of Quality & Reliability Management, 34 (2), pp. 176-193. , https://doi.org/1.1108/IJQRM-05-2015-0072; Truong Quang, H., Hara, Y., Risks and performance in supply chain: the push effect (2018) International Journal of Production Research, 56 (4), pp. 1369-1388. , https://doi.org/1.1080/00207543.2017.1363429; Vargo, S. L., Lusch, R. F., Evolving to a new dominant logic for marketing (2004) Journal of marketing, 68 (1), pp. 1-17. , https://doi.org/1.1509/jmkg.68.1.1.24036; Vargo, S. L., Lusch, R. F., From goods to service (s): Divergences and convergences of logics (2008) Industrial marketing management, 37 (3), pp. 254-259. , https://doi.org/1.1016/j.indmarman.2007.07.004; Vickery, S. K., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships (2003) Journal of operations management, 21 (5), pp. 523-539. , https://doi.org/1.1016/j.jom.2003.02.002; Wagner, S. M., Bode, C., An empirical examination of supply chain performance along several dimensions of risk (2008) Journal of business logistics, 29 (1), pp. 307-325. , https://doi.org/1.1002/j.2158-1592.2008.tb00081.x; Wu, L.-C., Wu, L.-H., Improving the global supply chain through service engineering: A services science, management, and engineering-based framework (2015) Asia Pacific Management Review, 20 (1), pp. 24-31. , https://doi.org/1.1016/j.apmrv.2014.12.002; Yan, X., Du, S., Hu, L., Supply chain performance for a risk inequity averse newsvendor (2020) Annals of Operations Research, 290 (1), pp. 897-921. , https://doi.org/1.1007/s10479-018-3038-1; Yeung, A. C. L., Strategic supply management, quality initiatives, and organizational performance (2008) Journal of operations management, 26 (4), pp. 490-502. , https://doi.org/1.1016/j.jom.2007.06.004},
correspondence_address1={An, D.T.B.; CIRTech Institute, Viet Nam; email: dtb.an@hutech.edu.vn},
editor={Sampaio P., Domingues P., Costa A., Casadesus M., Marimon F., Pires A.R., Saraiva P.},
publisher={Universidade do Minho},
issn={21843481},
isbn={9789895491117},
language={English},
abbrev_source_title={Internat. Conf. Qual. Eng. Manag.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Zulkarnaen2022,
author={Zulkarnaen, I. and Siswanto, N. and Vanany, I.},
title={Modeling of beef supply chain performance measurement information system based SCOR},
journal={IOP Conference Series: Earth and Environmental Science},
year={2022},
volume={1063},
number={1},
doi={10.1088/1755-1315/1063/1/012030},
art_number={012030},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136793519&doi=10.1088%2f1755-1315%2f1063%2f1%2f012030&partnerID=40&md5=63c9aa83329b3bfc1e836aeda8d11df4},
affiliation={Industrial Engineering, Bhayangkara Jakarta Raya University, Bekasi, Indonesia; Industrial and System Enginering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia},
abstract={Performance Measurement Supply chain is a challenge for every fresh meat industry. Providing timely performance reports across all components is very important, considering that performance reports are a benchmark for customer orders. The purpose of this study is to create a performance appraisal information system model using SCOR (Supply Chain Operation Reference) to meet the requirements of customers or industry players along the fresh beef supply chain. This study uses a modeling framework and business process notation (BPMN) based on Rapid Application Development (RAD). This research will integrate this required model process with a cloud-based front-end with SCOR-based database operations connected to mobile and desktop networks. Supply chain performance will be visualized and display the performance measurements' results into graphs, histograms, and line charts. © Published under licence by IOP Publishing Ltd.},
references={Zissis, D, Ioannou, G, Burnetas, A, Supply Chain Coordination Under Discrete Information Asymmetries and Quantity Discounts (2015) United Kingdom Omega, 53, pp. 21-29; Asrol, M, Marimin Machfud Yani, M, Method and approach mapping of fair and balanced risk and value-added distribution in supply chains: A review and future agenda (2018) Int. J. Supply Chain Manag, 7, pp. 74-95; Chopra, S, Meindl, P, (2016) Supply Chain Managment: Strategy, Planning, and Operation 7 Kepos Pearson; Paduloh, P, Purba, H, Analysis of productivity based on KPI case study automotive paint industry (2020) J. Eng. Manag. Ind. Syst, 8, p. 112; Heckmann, I, Comes, T, Nickel, S, A critical Review on Supply Chain Risk-Definition (2015) Measure and Modeling United Kingdom Omega, 52, pp. 119-132; Paduloh, P, Rosihan, R I., Sumanto 2020 Analysis of reverse supply chain performance with the supply chain operation reference method in beef industry J. Tek. Ind. Pert, 30, pp. 329-337. , 2020; Van Der Vorst, J G. A. J., Ossevoort, R, Keizer, M D., Woensel, T V., Verdouw, C N., Wnink, E, Koppes, R, Willegen, R V., (2015) DAVINC3I: Towards Collaborative Responsive Logistics Networks in Floriculture, , Springer International Publishing; Akkawuttiwanich, P, Yenradee, P, Fuzzy QFD approach for managing SCOR performance indicators (2018) Comput. Ind. Eng, 122, pp. 189-201; Delipinar, G E., Kocaoglu, B, Using SCOR model to gain competitive advantage: A Literature Review (2016) Procedia-Soc. Behav. Sci, 229, pp. 398-406; Wang, C N., Huang, Y F., Cheng, I F., Nguyen, V T., A Multi-Criteria Decision-Making (MCDM) approach using hybrid SCOR Metrics, AHP, and TOPSIS for supplier evaluation and selection in the gas and oil industry (2018) Processes, 6, p. 12; Saaty, T L., Decision making with the analytic hierarchy process (2008) Int. J. Serv. Sci, 1, pp. 83-98; Raisaro, J L., SCOR: A secure international informatics infrastructure to investigate COVID-19 (2020) J. Am. Med. Informatics Assoc, 1, p. 6. , 0},
correspondence_address1={Zulkarnaen, I.; Industrial Engineering, Indonesia; email: iskandar.zulkarnaen@dsn.ubharajaya.ac.id},
publisher={Institute of Physics},
issn={17551307},
language={English},
abbrev_source_title={IOP Conf. Ser. Earth Environ. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Boonsothonsatit2022427,
author={Boonsothonsatit, G. and Chaopaisarn, P.},
title={Order penetration point-based supply chain performance optimisation for SMEs (OPPO)},
journal={International Journal of Logistics Systems and Management},
year={2022},
volume={42},
number={3},
pages={427-439},
doi={10.1504/IJLSM.2022.124192},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134747325&doi=10.1504%2fIJLSM.2022.124192&partnerID=40&md5=284e93dcfec81b85df8b7b168549ddc1},
affiliation={Graduate School of Management and Innovation, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand; Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand},
abstract={This paper aims to develop order penetration point-based supply chain performance optimisation for SMEs (OPPO). OPPO supports identifying critical performance measures and their root-causes, figuring out their intricate interrelations, and optimising their conflicts in consideration of their relative preferences. It provides the best-fitted solution for Pareto-optimal results. The OPPO development covered nine performance measures which were causally linked for identifying a few of critical performance measures and their root-causes. Their conflicts in consideration of their relative preferences were optimised using weighted max-min fuzzy goal programming (WMM-FGP) for providing Pareto optimality. As a result, supply chain cost and customer lead time were identified as critical performance measures. Their Pareto optimality in consideration of their relative preferences suggests the best-fitted product lot-size and position of order penetration point (OPP). © 2022 Inderscience Enterprises Ltd.. All rights reserved.},
author_keywords={optimisation;  order penetration point;  small and medium-sized enterprises;  supply chain performance measurement},
references={Amid, A., Ghodsypour, S.H., O'Brien, C., Fuzzy multiobjective linear model for supplier selection in a supply chain (2006) International Journal of Production Economics, 104 (2), pp. 394-407; Andersson, J., (2011) A Survey of Multiobjective Optimization in Engineering Design, , Linköping University; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Arvis, J.F., Saslavsky, D., Ojala, L., Shepherd, B., Busch, C., Raj, A., (2014) Connecting to Complete: Trade Logistics in the Global Economy, , The international Bank for Reconstruction and Development/The World Bank, Washington DC; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Beamon, E.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2014) Production Planning and Control, 25 (8), pp. 698-714; Bolstorff, P., Rosenbaum, R., (2003) Supply Chain Excellence: A Handbook for Dramatic Improvement using the SCOR Model, , Amacom, New York; Boonsothonsatit, G., Supeekit, T., CausaL Linkages to Systematically evaluate supply chain performance (CALLS) for SMEs in Thailand (2017) International Conference on Industrial Technology and Management, , 7-10 March, Cambridge, UK; Boonsothonsatit, K., Kara, S., Ibbotson, S., Kayis, B., Development of a generic decision support system based on multi-objective optimisation for green supply chain network design (GOOG) (2015) Journal of Manufacturing Technology Management, 26 (7), pp. 1069-1084; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3-4), pp. 209-223. , Nos; Charan, P., Shankar, R., Baisya, R., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; (2004) Supply Chain Management Process Standards, , CSCMP Council of Supply Chain Management Professionals, Oak Brook, IL; Donk, D.P., Doorne, R., The impact of the customer order decoupling point on type and level of supply chain integration (2015) International Journal of Production Research, 54 (9), pp. 2572-2584; Foggin, J., Mentzer, J., Monroe, C., A supply chain diagnostic tool (2004) International Journal of Physical Distribution and Logistics Management, 34 (10), pp. 827-855; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Huang, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Liberatore, M.J., Miller, T., A framework for integrating activity-based costing and the balanced scorecard into the logistics strategy development and monitoring process (1998) Journal of Business Logistics, 19 (2), pp. 131-154; Lin, C.C., A weighted max-min model for fuzzy goal programming (2004) Fuzzy Sets and Systems, 142 (3), pp. 407-420; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: an exploratory study (2004) International Journal of Operations and Production Management, 24 (11), pp. 1192-1218. , Nos. /12; Masud, A.M., Ravindran, A., (2008) Multiple Criteria Decision Making, , CRC Press, Florida; Miettinen, K., (2004) Nonlinear Multiobjective Optimization, , Kluwer Academic, Massachusetts; Naim, M., Childerhouse, P., Disney, S., Towill, D., A supply chain diagnostic methodology: determining the vector of change (2002) Computers and Industrial Engineering, 43 (1), pp. 135-157. , Nos. /2; Najmi, A., Makui, A., A conceptual model for measuring supply chain's performance (2012) Production Planning and Control, 23 (9), pp. 694-706; Neely, A., The evolution of performance measurement research: developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Noor-E-Alam, M., Hasin, M.A.A., Sharif Ullah, A.M.M., Lipi, T.F., Supplier evaluation with GD-based multi criteria decision making (2008) International Journal of Industrial and Systems Engineering, 3 (3), pp. 368-381; Ramdas, K., Spekman, R.E., Chain or shackles: understanding what drives supply chain performance (2000) Interfaces, 30 (4), pp. 3-21; Rangaiah, G.P., (2008) Multi-Objective Optimization: Techniques and Applications in Chemical Engineering, , World Scientific Publishing Co. Pte. Ltd, Toh Tuck Link; Saaty, T.L., A scaling method for priorities in hierarchical structures (1977) Journal of Mathematical Psychology, 15 (3), pp. 234-281; Saaty, T.L., (1996) Decision Making with Dependence and Feedback: The Analytic Network Process, , RWS Publications, Pittsburgh, Pennsylvania; Shamsuzzaman, M., Sharif Ullah, A.M.M., Bohez, E.L.J., Applying linguistic criteria in FMS Selection: fuzzy-set-AHP approach (2003) Journal of Integrated Manufacturing Systems, 14 (3), pp. 247-254; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3), pp. 242-258. , Nos. /4; Sinha, R.K., Babu, A.S., Quality of customer service in supply chain system: a diagnostic study (1998) International Journal of Quality and Reliability Management, 15 (8), pp. 844-859. , Nos. /9; Tiwari, R.N., Dharmar, S., Rao, J.R., Fuzzy goal programming - an additive model (1987) Fuzzy Sets and Systems, 24 (1), pp. 27-34; Yu, R., Tzeng, G.H., A soft computing method for multi-criteria decision making with dependence and feedback (2006) Applied Mathematics and Computation, 180 (1), pp. 63-75; Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8 (3), pp. 338-353; Zimmermann, H.J., Fuzzy programming and linear programming with several objective functions (1978) Fuzzy Sets and Systems, 1 (1), pp. 45-55},
correspondence_address1={Boonsothonsatit, G.; Graduate School of Management and Innovation, Thailand; email: drkanda@gmail.com},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gopal2022,
author={Gopal, P.R.C. and Rana, N.P. and Krishna, T.V. and Ramkumar, M.},
title={Impact of big data analytics on supply chain performance: an analysis of influencing factors},
journal={Annals of Operations Research},
year={2022},
doi={10.1007/s10479-022-04749-6},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130797821&doi=10.1007%2fs10479-022-04749-6&partnerID=40&md5=b33dbb502a6df64efdb3ec84e25fb8e0},
affiliation={School of Management, National Institute of Technology Warangal, Telangana, Warangal, 506004, India; College of Business and Economics, Qatar University, P.O. Box 2713, Doha, Qatar; Operations Management & Quantitative Techniques Group, Indian Institute of Management Raipur, Cheriya, Chhattisgarh, Naya Raipur, 493661, India},
abstract={This paper aims to understand the impact of big data analytics on the retail supply chain. For doing so, we set our context to select the best big data practices amongst the available alternatives based on retail supply chain performance. We have applied TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) for the selection of the best big data analytics tools among the identified nine practices (data science, neural networks, enterprise resource planning, cloud computing, machine learning, data mining, RFID, Blockchain and IoT and Business intelligence) based on seven supply chain performance criteria (supplier integration, customer integration, cost, capacity utilization, flexibility, demand management, and time and value). One of the intriguing understandings from this paper is that most of the retail firms are in a dilemma between customer loyalty and cost while implementing the big data practices in their organization. This study analyses the dominance of the big data practices at the retail supply chain level. This helps the newly emerging retail firms in evaluating the best big data practice based on the importance and dominance of supply chain performance measures. © 2022, The Author(s).},
author_keywords={Big data practices;  Multi criteria decision making;  Retail supply chains;  TODIM},
references={Akter, S., Michael, K., Uddin, M.R., McCarthy, G., Rahman, M., Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics (2020) Annals of Operations Research, 308, pp. 7-39; Aloysius, J.A., Hoehle, H., Goodarzi, S., Venkatesh, V., Big data initiatives in retail environments: Linking service process perceptions to shopping outcomes (2018) Annals of Operations Research, 270 (1), pp. 25-51; Angeles, R., RFID technologies: Supply-chain applications and implementation issues (2005) Information Systems Management, 22 (1), pp. 51-65; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Arunachalam, D., Kumar, N., Kawalek, J.P., Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice (2018) Transportation Research Part e: Logistics and Transportation Review, 114, pp. 416-436; Banerjee, M., Mishra, M., Retail supply chain management practices in India: A business intelligence perspective (2017) Journal of Retailing and Consumer Services, 34, pp. 248-259; Barba-Romero, S., Pomerol, J.C., (2000) Multicriterion Decision in Management: Principles And Practice, , Kluwer Academic Publishers; Barometer, G.R.T., Global retail theft barometer (2015) Loss Prevention Magazine, , https://losspreventionmedia.com/the-global-retail-theft-barometer/; Bouyssou, D., Some remarks on the notion of compensation in MCDM (1986) European Journal of Operational Research, 26 (1), pp. 150-160; Brans, J.P., Mareschal, B., The PROMETHEE methods for MCDM; the PROMCALC, GAIA and BANKADVISER software (1990) Readings in Multiple Criteria Decision Aid, pp. 216-252. , C.A.B. Costa, Springer, Berlin, Heidelberg; Chae, B., Olson, D., Sheu, C., The impact of supply chain analytics on operational performance: A resource-based view (2014) International Journal of Production Research, 52 (16), pp. 4695-4710; Clemen, R.T., Reilly, T., Making hard decisions with decision tools (2013) Cengage Learning, , Pacific Grove, Duxbury; Davenport, T., O’dwyer, J., Tap into the power of analytics (2011) Supply Chain Quarterly, 4, pp. 28-31; Gangadharan, G.R., Swami, S.N., Business intelligence systems: Design and implementation strategies (2004) 26Th International Conference on Information Technology Interfaces, pp. 139-144; Gawankar, S.A., Kamble, S., Raut, R., An investigation of the relationship between supply chain management practices (SCMP) on supply chain performance measurement (SCPM) of Indian retail chain using SEM (2017) Benchmarking: An International Journal, 24 (1), pp. 257-295; Gawankar, S.A., Gunasekaran, A., Kamble, S., A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context (2020) International Journal of Production Research, 58 (5), pp. 1574-1593. , &; Gomes, L.F.A.M., Lima, M.M.P.P., TODIM: Basics and application to multicriteria ranking of projects with environmental impacts (1992) Foundations of Computing and Decision Sciences, 16 (4), pp. 113-127; Gomes, L.F.A.M., Lima, M.M.P.P., From modelling individual preferences to multicriteria ranking of discrete alternatives: A look at Prospect Theory and the additive difference model (1992) Foundations of Computing and Decision Sciences, 17 (3), pp. 171-184; Gupta, S., Altay, N., Luo, Z., Big data in humanitarian supply chain management: A review and further research directions (2019) Annals of Operations Research, 283 (1), pp. 1153-1173; Handfield, R.B., Nichols, E.L., Jr., Key issues in global supply base management (2004) Industrial Marketing Management, 33 (1), pp. 29-35; Hazen, B.T., Skipper, J.B., Boone, C.A., Hill, R.R., Back in business: Operations research in support of big data analytics for operations and supply chain management (2018) Annals of Operations Research, 270 (1), pp. 201-211; Helms, M.M., Cengage, G., Distribution and distribution requirements planning [electronic version] (2006) Encyclopedia of Management, , http://www.enotes.com/management-encyclopedia/distribution-distributionrequirements-planning; Helo, P., Hao, Y., Blockchains in operations and supply chains: A model and reference implementation (2019) Computers & Industrial Engineering, 136, pp. 242-251; Hollinger, R.C., Davis, J.L., National retail security survey, Report, Department of Sociology and the Centre for Studies in Criminology and Law (2001) University of Florida; Hüner, K.M., Schierning, A., Otto, B., Österle, H., Product data quality in supply chains: The case of Beiersdorf (2011) Electronic Markets, 21 (2), p. 141; Katal, A., Wazid, M., Goudar, R.H., Big data: Issues, challenges, tools and good practices. In: 2013 Sixth international conference on contemporary computing (IC3) (2013) Pp 404–409, , (, August); Keeney, R.L., Raiffa, H., (1993) Decisions with multiple objectives: Preferences and value trade-offs, , Cambridge University Press; Koçoğlu, İ., İmamoğlu, S.Z., İnce, H., Keskin, H., The effect of supply chain integration on information sharing: Enhancing the supply chain performance (2011) Procedia-Social and Behavioural Sciences, 24, pp. 1630-1649; Kshetri, N., 1 Blockchain’s roles in meeting key supply chain management objectives (2018) International Journal of Information Management, 39, pp. 80-89; Kumar, Y., Sood, K., Kaul, S., Vasuja, R., Big data analytics and its benefits in healthcare (2020) Big data analytics in healthcare, , Kulkarni AJ, Siarry P, Singh PK, Abraham A, Zhang M, Zomaya A, Baki F, (eds), Springer; Kumar, G., Subramanian, N., Arputham, R.M., Missing link between sustainability collaborative strategy and supply chain performance: Role of dynamic capability (2018) International Journal of Production Economics, 203, pp. 96-109; Kusiak, A., Data mining: Manufacturing and service applications (2006) International Journal of Production Research, 44 (18-19), pp. 4175-4191; LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N., Big data, analytics and the path from insights to value (2011) MIT Sloan Management Review, 52 (2), pp. 21-32; Li, L., Chi, T., Hao, T., Yu, T., Customer demand analysis of the electronic commerce supply chain using Big Data (2018) Annals of Operations Research, 268 (1), pp. 113-128; Liberatore, M.J., Luo, W., The analytics movement: Implications for operations research (2010) Interfaces, 40 (4), pp. 313-324; Loureiro, A.L., Miguéis, V.L., da Silva, L.F., Exploring the use of deep neural networks for sales forecasting in fashion retail (2018) Decision Support Systems, 114, pp. 81-93; Manupati, V.K., Jedidah, S.J., Gupta, S., Bhandari, A., Ramkumar, M., Optimization of a multi-echelon sustainable production-distribution supply chain system with lead time consideration under carbon emission policies (2019) Computers & Industrial Engineering, 135, pp. 1312-1323; Manupati, V.K., Schoenherr, T., Ramkumar, M., Panigrahi, S., Sharma, Y., Mishra, P., Recovery strategies for a disrupted supply chain network: Leveraging blockchain technology in pre-and post-disruption scenarios (2022) International Journal of Production Economics, 245, p. 108389; Manupati, V.K., Schoenherr, T., Ramkumar, M., Wagner, S.M., Pabba, S.K., Inder Raj Singh, R., A blockchain-based approach for a multi-echelon sustainable supply chain (2020) International Journal of Production Research, 58 (7), pp. 2222-2241; Manupati, V.K., Schoenherr, T., Subramanian, N., Ramkumar, M., Soni, B., Panigrahi, S., A multi-echelon dynamic cold chain for managing vaccine distribution (2021) Transportation Research Part e: Logistics and Transportation Review, 156, p. 102542; Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., (2011) Big Data: The Next Frontier for Innovation, Competition, and Productivity, , McKinsey Global Institute; Mareschal, B., Brans, J.P., The PROMETHEE methods for MCDM: The PROMCALC, GAIA and BANK ADVISER software (No. 2013/9337) (1990) Ulb--Universite Libre De Bruxelles; Martin, A.J., (1995) DRP: Distribution resource planning: The gateway to true quick response and continuous replenishment, , Wiley; McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J., Barton, D., Big data: The management revolution (2012) Harvard Business Review, 90 (10), pp. 60-68; Mishra, D., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Big Data and supply chain management: A review and bibliometric analysis (2018) Annals of Operations Research, 270 (1), pp. 313-336; Ngare, M., ERP systems: Optimizing business benefits (2007) Tampere Polytechnic University of Applied Sciences, pp. 1-52. , International Business Degree Program Thesis; Ozu, A., Kasuga, N., Morikawa, H., Cloud computing and its impact on the Japanese macroeconomy–its oligopolistic market characteristics and social welfare (2020) Telecommunications Policy, 44 (1), p. 101852; Rai, A., Patnayakuni, R., Seth, N., Firm performance impacts of digitally enabled supply chain integration capabilities (2006) MIS Quarterly, 30, pp. 225-246; Raj, P.V.R.P., Jauhar, S.K., Ramkumar, M., Pratap, S., Procurement, traceability and advance cash credit payment transactions in supply chain using blockchain smart contracts (2022) Computers & Industrial Engineering, 167; Raman, A., DeHoratius, N., Ton, Z., Execution: The missing link in retail operations (2001) California Management Review, 43 (3), pp. 136-152; Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., Mehta, A., Impact of big data on supply chain management (2018) International Journal of Logistics Research and Applications, 21 (6), pp. 579-596; Ramkumar, M., Jenamani, M., E-procurement service provider selection—An analytic network process-based group decision-making approach (2012) Service Science, 4 (3), pp. 269-294; Ramkumar, M., Schoenherr, T., Jenamani, M., Risk assessment of outsourcing e-procurement services: Integrating SWOT analysis with a modified ANP-based fuzzy inference system (2016) Production Planning & Control, 27 (14), pp. 1171-1190; Ranjan, J., Business justification with business intelligence (2008) VINE, 38 (4), pp. 461-475; Ranjan, J., Business intelligence: Concepts, components, techniques and benefits (2009) Journal of Theoretical and Applied Information Technology, 9 (1), pp. 60-70; Reyes, P.M., Li, S., Visich, J.K., Determinants of RFID adoption stage and perceived benefits (2016) European Journal of Operational Research, 254 (3), pp. 801-812; Roux, D., (2002) Nobel en E ́conomie, , 2, Economica; Roy, B., Bouyssou, D., (1993) Aide multicritère à la décsion: Méthodes et cas, , Economica; Ruehle, F., Data science applications to string theory (2020) Physics Reports, 839, pp. 1-117; (2015) Latest Global Retail Theft Barometer Study Finds U.S. Retail Shrink Up, , https://e-proof.sps.co.in/springer/Online_PDF_Corr.asp; Sener, A., Barut, M., Oztekin, A., Avcilar, M.Y., Yildirim, M.B., The role of information usage in a retail supply chain: A causal data mining and analytical modeling approach (2019) Journal of Business Research, 99, pp. 87-104; Shafiq, A., Ahmed, M.U., Mahmoodi, F., Impact of supply chain analytics and customer pressure for ethical conduct on socially responsible practices and performance: An exploratory study (2020) International Journal of Production Economics, 225, p. 107571; Shah, J., (2016) Supply chain management: Text and Cases, , Pearson Education India; Sharma, R., Kamble, S.S., Gunasekaran, A., Kumar, V., Kumar, A., A systematic literature review on machine learning applications for sustainable agriculture supply chain performance (2020) Computers & Operations Research, 119, p. 104926; Srivastava, S.K., Radio frequency identification technology in retail outlets: Indian scenario (2007) International Journal of Manufacturing Technology and Management, 10 (1), pp. 71-91; Sultana, S., Akter, S., Kyriazis, E., How data-driven innovation capability is shaping the future of market agility and competitive performance? (2022) Technological Forecasting and Social Change, 174, p. 121260; Sultana, S., Akter, S., Kyriazis, E., Wamba, S.F., Architecting and Developing Big Data-Driven Innovation (DDI) in the Digital Economy (2021) Journal of Global Information Management, 29 (3), pp. 165-187; Tarei, P.K., Kumar, G., Ramkumar, M., A Mean-Variance robust model to minimize operational risk and supply chain cost under aleatory uncertainty: A real-life case application in petroleum supply chain (2022) Computers & Industrial Engineering, 166; Tiwari, S., Wee, H.M., Daryanto, Y., Big data analytics in supply chain management between 2010 and 2016: Insights to industries (2018) Computers & Industrial Engineering, 115, pp. 319-330; Trkman, P., McCormack, K., De Oliveira, M.P.V., Ladeira, M.B., The impact of business analytics on supply chain performance (2010) Decision Support Systems, 49 (3), pp. 318-327; Trotta, L.T.F., Nobre, F.F., Gomes, L.F.A.M., Multi-criteria decision making – An approach to setting priorities in health care (1999) Statistics in Medicine, 18, pp. 3345-3354; Tsai, M.C., Lee, W., Wu, H.C., Determinants of RFID adoption intention: Evidence from Taiwanese retail chains (2010) Information & Management, 47 (5-6), pp. 255-261; Tversky, A., Intransitivity of preferences (1969) Psychological Review, 76 (1), p. 31; Waller, M.A., Fawcett, S.E., Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management (2013) Journal of Business Logistics, 34 (2), pp. 77-84; Wamba, S.F., Gunasekaran, A., Dubey, R., Ngai, E.W., Big data analytics in operations and supply chain management (2018) Annals of Operations Research, 270 (1), pp. 1-4; Whicker, L., Bernon, M., Templar, S., Mena, C., Understanding the relationships between time and cost to improve supply chain performance (2009) International Journal of Production Economics, 121 (2), pp. 641-650; Zhu, Y., Li, Y., Wang, W., Chen, J., What leads to post-implementation success of ERP? An empirical study of the Chinese retail industry (2010) International Journal of Information Management, 30 (3), pp. 265-276},
correspondence_address1={Rana, N.P.; College of Business and Economics, P.O. Box 2713, Qatar; email: nrananp@gmail.com},
publisher={Springer},
issn={02545330},
language={English},
abbrev_source_title={Ann. Oper. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yusriza2022,
author={Yusriza, F.A. and Abdul Rahman, N.A. and Jraisat, L. and Upadhyay, A.},
title={Airline catering supply chain performance during pandemic disruption: a Bayesian network modelling approach},
journal={International Journal of Quality and Reliability Management},
year={2022},
doi={10.1108/IJQRM-01-2022-0027},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130409498&doi=10.1108%2fIJQRM-01-2022-0027&partnerID=40&md5=fae4ff08de541e8515e113005a3f40df},
affiliation={Malaysian Institute of Aviation Technology (UniKL MIAT), Universiti Kuala Lumpur, Selangor, Malaysia; Talal Abu-Ghazaleh University College for Innovation (TAGUCI), Amman, Jordan; UiS Business School, University of Stavanger, Stavanger, Norway},
abstract={Purpose: The supply chain (SC) encompasses all actions related to meeting customer requests and transferring materials upstream to meet those demands. Organisations must operate towards increasing SC efficiency and effectiveness to meet SC objectives. Although most businesses expected the coronavirus disease 2019 (COVID-19) pandemic to severely negatively impact their SCs, they did not know how to model disruptions or their effects on performance in the event of a pandemic, leading to delayed responses, an incomplete understanding of the pandemic's effects and late deployment of recovery measures. Therefore, this study aims to consider the impact of implementing Bayesian network (BN) modelling to measure SC performance in the airline catering context. Design/methodology/approach: This study presents a method for modelling and quantifying SC performance assessment for airline catering. In the COVID-19 context, the researchers proposed a BN model to measure SC performance and risk events and quantify the consequences of pandemic disruptions. Findings: The study simulates and measures the impact of different triggers on SC performance and business continuity using forward and backward propagation analysis, among other BN features, enabling us to combine various SC perspectives and explicitly account for pandemic scenarios. Originality/value: This study's findings offer a fresh theoretical perspective on the use of BNs in pandemic SC disruption modelling. The findings can be used as a decision-making tool to predict and better understand how pandemics affect SC performance. © 2022, Emerald Publishing Limited.},
author_keywords={Airline catering sector;  Bayesian network;  COVID-19;  Logistic;  Performance measurement system;  Supply chain management;  Variables},
references={Abolbashari, M.H., Chang, E., Hussain, O.K., Saberi, M., Smart Buyer: a Bayesian Network Modelling approach for measuring and improving procurement performance in organisations (2018) Knowledge-Based Systems, 142, pp. 127-148; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Badulescu, Y., Hameri, A.P., Cheikhrouhou, N., Evaluating demand forecasting models using multi-criteria decision-making approach (2021) Journal of Advances in Management Research, 18 (5), pp. 661-683; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3); Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Beck, P., Hofmann, E., Multiple criteria decision making in supply chain management – currently available methods and possibilities for future research (2012) Die Unternehmung, 66 (2), pp. 180-213; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management – what has been achieved? (2015) Supply Chain Management, 20 (6), pp. 664-680; Bode, C., Wagner, S.M., Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions (2015) Journal of Operations Management, 36, pp. 215-228; Cao, S., Bryceson, K., Hine, D., An Ontology-based Bayesian Network Modelling for supply chain risk propagation (2019) Industrial Management and Data Systems, 119 (8), pp. 1691-1711; Chains, F.S., Comparing crises: great lockdown versus great recession (2020) Comparing Crises: Great Lockdown Versus Great Recession, pp. 1-11. , doi:, June; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: a framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; El Amrani, S., Ibne Hossain, N.U., Karam, S., Jaradat, R., Nur, F., Hamilton, M.A., Ma, J., Modelling and assessing sustainability of a supply chain network leveraging multi Echelon Bayesian Network (2021) Journal of Cleaner Production, 302; Euchi, J., Bouzidi, D., Bouzid, Z., Interpretive structural modeling technique to analyze the interactions between the factors influencing the performance of the reverse logistics chain (2018) Global Journal of Flexible Systems Management, 20 (1), pp. 43-55; Feng, N., Wang, H.J., Li, M., A security risk analysis model for information systems: causal relationships of risk factors and vulnerability propagation analysis (2014) Information Sciences, 256, pp. 57-73; Fonseca, L., Azevedo, A., COVID- 19: outcomes for global supply chains (2020) Management and Marketing. Challenges for the Knowledge Society, 15 (Special Issue), pp. 424-438; Frederico, G.F., Garza-Reyes, J.A., Kumar, A., Kumar, V., Performance measurement for supply chains in the Industry 4.0 era: a balanced scorecard approach (2021) International Journal of Productivity and Performance Management, 70 (4), pp. 789-807; Galankashi, M.R., Rafiei, F.M., Financial performance measurement of supply chains: a review (2021) International Journal of Productivity and Performance Management, 1 (1), pp. 1-22; Galankashi, M.R., Fallahiarezoudar, E., Moazzami, A., Helmi, S.A., Rohani, J.M., Yusof, N.M., An efficient integrated simulation–Taguchi approach for sales rate evaluation of a petrol station (2018) Neural Computing and Applications, 29 (4), pp. 1073-1085; Galankashi, M.R., Helmi, S.A., Hisjam, M., Rahim, A.R.A., Leanness assessment in automotive industry: case study approach (2018) International Journal of Value Chain Management, 9 (1), pp. 70-88; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Heckerman, D., Bayesian networks for data mining (1997) Data Mining and Knowledge Discovery, 1, pp. 79-119; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: a literature review (2010) European Journal of Operational Research, 202 (1), pp. 16-24; Hosseini, S., Ivanov, D., A new resilience measure for supply networks with the ripple effect considerations: a Bayesian network approach (2019) Annals of Operations Research, 2, pp. 1-27; Hosseini, S., Ivanov, D., A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic (2021) International Journal of Production Research, 59 (21), pp. 6370-6391; Huang, L., Cai, G., Yuan, H., Chen, J., A hybrid approach for identifying the structure of a Bayesian network model (2019) Expert Systems with Applications, 131 (814), pp. 308-320; Jensen, K.L., Toftum, J., Friis-Hansen, P., A Bayesian Network approach to the evaluation of building design and its consequences for employee performance and operational costs (2009) Building and Environment, 44 (3), pp. 456-462; Kamble, S.S., Gunasekaran, A., Big data-driven supply chain performance measurement system: a review and framework for implementation (2019) International Journal of Production Research, pp. 1-22; Kang, L., Chu, Y., Leng, K., Van Nieuwenhuyse, I., Construction of fast retrieval model of e-commerce supply chain information system based on Bayesian network (2020) Information Systems and E-Business Management, 18 (4), pp. 705-722; Khan, S.A., Chaabane, A., Dweiri, F.T., Multi-criteria decision-making methods application in supply chain management: a systematic literature review (2018) Multi-Criteria Methods and Techniques Applied to Supply Chain Management, 16 (1-2), pp. 1-31; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Majercak, P., Design of a supply chain performance monitoring system for a company in the context of the COVID-19 pandemic (2021) SHS Web of Conferences, 92; Maleki, M., Cruz-Machado, V., Supply chain performance monitoring using Bayesian network (2013) International Journal of Business Performance and Supply Chain Modelling, 5 (2), pp. 177-197; Musharraf, M., Smith, J., Khan, F., Veitch, B., MacKinnon, S., Assessing offshore emergency evacuation behavior in a virtual environment using a Bayesian Network approach (2016) Reliability Engineering and System Safety, 152, pp. 28-37; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , reprintedInternational Journal of Operations and Production Management, 25 12, 2005, 1228-1263; Neely, A., The evolution of performance measurement research: developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Nguyen, T.T.H., Bekrar, A., Le, T.M., Abed, M., Supply chain performance measurement using SCOR model: a case study of the coffee supply chain in Vietnam (2021) 2021 1st International Conference on Cyber Management And Engineering (CyMaEn), pp. 1-7. , IEEE; Nouri, A., Nikabadi, M.S., Olfat, L., Developing the framework of sustainable service supply chain balanced scorecard (SSSC BSC) (2019) International Journal of Productivity and Performance Management, 68 (1), pp. 148-170; Ojha, R., Ghadge, A., Tiwari, M.K., Bititci, U.S., Bayesian Network Modelling for supply chain risk propagation (2018) International Journal of Production Research, 56 (17), pp. 5795-5819; Parthiban, P., Zubar, H.A., Garge, C.P., A multi criteria decision making approach for suppliers selection (2012) Procedia Engineering, 38, pp. 2312-2328; Paul, A., Shukla, N., Paul, S.K., Trianni, A., Sustainable supply chain management and multi-criteria decision-making methods: a systematic review (2021) Sustainability, 13 (13); Pochampally, K.K., Gupta, S.M., Govindan, K., Metrics for performance measurement of a reverse/closed-loop supply chain (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (1), pp. 8-32; Punniyamoorthy, M., Murali, R., Balanced score for the balanced scorecard: a benchmarking tool (2008) Benchmarking: An International Journal, 15 (4), pp. 420-443; Rabbi, M., Ali, S.M., Kabir, G., Mahtab, Z., Paul, S.K., Green supply chain performance prediction using a Bayesian belief network (2020) Sustainability, 12 (3), pp. 1-19; Raval, S.J., Kant, R., Shankar, R., Benchmarking the Lean Six Sigma performance measures: a balanced score card approach (2019) Benchmarking: An International Journal, 26 (6), pp. 1921-1947; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: design and development guidelines (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 805-826; Saraswati, C.L., Strategic procurement practices through deep supplier relationships and exchange of valuable knowledge At Pt Aerofood Indonesia: a study at Aerofood ACS Unit Denpasar (2018) Jurnal Administrasi Bisnis, 58 (2), pp. 11-20; Seiler, A.C., (2016) Measuring performance in supply chain networks, , Doctoral dissertation, University of Salford, Salford; Singh, S., Kumar, R., Panchal, R., Manoj, K., Tiwari, M.K., Impact of COVID-19 on logistics systems and disruptions in food supply chain (2021) International Journal of Production Research, 59 (7); Sivakumar, G., Almehdawe, E., Kabir, G., Developing a decision-making framework to improve healthcare service quality during a pandemic (2022) Applied System Innovation, 5 (1), pp. 1-21; Sudan, T., Taggar, R., Recovering supply chain disruptions in post-COVID-19 pandemic through transport intelligence and logistics systems: India's experiences and policy options (2021) Frontiers in Future Transportation, 2; Tang, H., Liu, S., Basic theory of fuzzy Bayesian networks and its application in machinery fault diagnosis (2007) Proceedings - Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, 4, pp. 132-137. , doi:, Fskd; Tangen, S., Performance measurement: from philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Tse, H., Chow, K.P., Kwan, M., Reasoning about evidence using Bayesian networks (2012) IFIP Advances in Information and Communication Technology, 383, pp. 99-113. , doi:, AICT; Van Hoek, R.I., Measuring the immeasurable' - measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Zaim, H., Ramdani, M., Haddi, A., Multi-criteria analysis approach based on consumer satisfaction to rank B2C E-commerce websites (2016) SITA 2016-11th International Conference on Intelligent Systems: Theories and Applications; Zhou, Y., Li, X., Yuen, K.F., Holistic risk assessment of container shipping service based on Bayesian Network Modelling (2022) Reliability Engineering and System Safety, 220, p. 108305; Žic, J., Žic, S., Multi-criteria decision making in supply chain management based on inventory levels, environmental impact and costs (2020) Advances in Production Engineering and Management, 15 (2), pp. 151-163},
correspondence_address1={Upadhyay, A.; UiS Business School, Norway; email: arvind.upadhyay@uis.no},
publisher={Emerald Group Holdings Ltd.},
issn={0265671X},
language={English},
abbrev_source_title={Int. J. Qual. Reliab. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sardanelli2022,
author={Sardanelli, D. and Bittucci, L. and Mirone, F. and Marzioni, S.},
title={An integrative framework for supply chain rating: from financial-based to ESG-based rating models},
journal={Total Quality Management and Business Excellence},
year={2022},
doi={10.1080/14783363.2022.2069557},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130151644&doi=10.1080%2f14783363.2022.2069557&partnerID=40&md5=a2e790cbfba1750e03db33bce168cf86},
affiliation={Department of Management, University of Rome, La Sapienza”, Roma, Italy; Department of Management, Mercatorum University, Roma, Italy; Department of Economics and Finance, Luiss “Guido Carli” University, Roma, Italy},
abstract={Credit risk rating systems are frequently criticized because of their short-term focus and their narrow view of the firm as an entity isolated from its network of relationships. This paper aims at overcoming these limitations of traditional rating systems by proposing an integrated model of supply chain rating, based on both financial and non-financial variables and including environmental, social and governance (ESG) indicators to embrace a long-term orientation of value creation. Enlarging the scope of evaluation from the single company to its supply chain, we realize an integrated ESG–financial model which evaluates both financial and sustainability aspects of organizations, based on the supply chain they belong to. This model is especially useful for evaluating the potential growth of small and medium enterprises which, differently from large corporations, are usually more opaque to financial markets, and present lower disclosure intensity. Therefore, this measurement system can be adopted by both financial institutions, to evaluate potential borrowers, and individual companies, to make internal assessments or to evaluate the performance of their downstream and upstream partners. © 2022 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={ESG indicators;  supply chain finance;  supply chain management;  supply chain performance measurement;  supply chain quality management;  supply chain rating;  sustainable supply chain management},
references={Acquaye, A., Ibn-Mohammed, T., Genovese, A., Afrifa, G.A., Yamoah, F.A., Oppon, E., A quantitative model for environmentally sustainable supply chain performance measurement (2018) European Journal of Operational Research, 269 (1), pp. 188-205; Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: Review and classification (2012) Journal of Organizational Management Studies, 2012, pp. 1-20; Alhaddi, H., Triple bottom line and sustainability: A literature review (2015) Business and Management Studies, 1 (2), pp. 6-10; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Bastas, A., Liyanage, K., Sustainable supply chain quality management: A systematic review (2018) Journal of Cleaner Production, 181, pp. 726-744; Brandenburg, M., Rebs, T., Sustainable supply chain management: A modeling perspective (2015) Annals of Operations Research, 229 (1), pp. 213-252; Cheah, E.T., Chan, W.L., Chieng, C.L.L., The corporate social responsibility of pharmaceutical product recalls: An empirical examination of US and UK markets (2007) Journal of Business Ethics, 76 (4), pp. 427-449; Ciampi, F., Giannozzi, A., Marzi, G., Altman, E.I., Rethinking SME default prediction: A systematic literature review and future perspectives (2021) Scientometrics, 126, pp. 1-48; Ciasullo, M.V., Cardinali, S., Cosimato, S., A strenuous path for sustainable supply chains in the footwear industry: A business strategy issue (2017) Journal of Global Fashion Marketing, 8 (2), pp. 143-162; Ciasullo, M.V., Montera, R., Cucari, N., Polese, F., How an international ambidexterity strategy can address the paradox perspective on corporate sustainability: Evidence from Chinese emerging market multinationals (2020) Business Strategy and the Environment, 29 (5), pp. 2110-2129; Ciasullo, M.V., Montera, R., Douglas, A., Sostenibilità ambientale e indipendenza del Consiglio di Amministrazione: Quali effetti sull’innovazione ambidestra? (2020) Corporate Governance And Research & Development Studies, (1), pp. 41-63; Ciasullo, M.V., Polese, F., Montera, R., Carrubbo, L., A digital servitization framework for viable manufacturing companies (2021) The Journal of Business & Industrial Marketing, 36 (13), pp. 142-160; Clift, R., Metrics for supply chain sustainability (2004) Technological Choices for Sustainability, pp. 239-253; Cucari, N., Esposito de Falco, S., Orlando, B., Diversity of board of directors and environmental social governance: Evidence from Italian listed companies (2018) Corporate Social Responsibility and Environmental Management, 25 (3), pp. 250-266; Delmas, M., Blass, V.D., Measuring corporate environmental performance: The trade-offs of sustainability ratings (2010) Business Strategy and the Environment, 19 (4), pp. 245-260; Edwards, B., (2004) Credit management handbook, , Gower Publishing, Ltd; El Ghoul, S., Guedhami, O., Kwok, C.C., Mishra, D.R., Does corporate social responsibility affect the cost of capital? (2011) Journal of Banking & Finance, 35 (9), pp. 2388-2406; Elkington, J., The triple bottom line (1997) Environmental Management: Readings and Cases, 2, pp. 49-66; Escrig-Olmedo, E., Fernández-Izquierdo, M.Á., Ferrero-Ferrero, I., Rivera-Lirio, J.M., Muñoz-Torres, M.J., Rating the raters: Evaluating how ESG rating agencies integrate sustainability principles (2019) Sustainability, 11 (3), p. 915; Esteban-Sanchez, P., de la Cuesta-Gonzalez, M., Paredes-Gazquez, J.D., Corporate social performance and its relation with corporate financial performance: International evidence in the banking industry (2017) Journal of Cleaner Production, 162, pp. 1102-1110; (2014), Supply chain finance—EBA European market guide; (2018), Action Plan: Financing Sustainable Growth COM/2018/097 final; (2022), Proposal for a Directive on corporate sustainability due diligence and annex; Fahimnia, B., Sarkis, J., Davarzani, H., Green supply chain management: A review and bibliometric analysis (2015) International Journal of Production Economics, 162, pp. 101-114; Ferri, M.A., Simone, C., D’Angelo, E., Introduzione: dall'Agenda 2030 alla CSR: una visione integrata dell'innovazione sostenibile (2020) Introduzione: dall'Agenda 2030 alla CSR: una visione integrata dell'innovazione sostenibile, 2, pp. 9-14; Foster, S.T., Jr., Towards an understanding of supply chain quality management (2008) Journal of Operations Management, 26 (4), pp. 461-467; Foster, S.T., Jr., Wallin, C., Ogden, J., Towards a better understanding of supply chain quality management practices (2011) International Journal of Production Research, 49 (8), pp. 2285-2300; Giannakis, M., Papadopoulos, T., Supply chain sustainability: A risk management approach (2016) International Journal of Production Economics, 171, pp. 455-470; Gomm, M.L., Supply chain finance: Applying finance theory to supply chain management to enhance finance in supply chains (2010) International Journal of Logistics Research and Applications, 13 (2), pp. 133-142; Goss, A., Roberts, G.S., The impact of corporate social responsibility on the cost of bank loans (2011) Journal of Banking & Finance, 35 (7), pp. 1794-1810; Gualandris, J., Longoni, A., Luzzini, D., Towards supply chain transparency: Linking supply chain structure to collective ESG disclosure (2019) Academy of Management Proceedings (Vol. 2019, No. 1, p. 16503), , July,Briarcliff Manor, NY: Academy of Management, &, 10510; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: A literature review (2010) European Journal of Operational Research, 202 (1), pp. 16-24; Hong, J., Liao, Y., Zhang, Y., Yu, Z., The effect of supply chain quality management practices and capabilities on operational and innovation performance: Evidence from Chinese manufacturers (2019) International Journal of Production Economics, 212, pp. 227-235; Jia, F., Zhang, T., Chen, L., Sustainable supply chain Finance: Towards a research agenda (2020) Journal of Cleaner Production, 243, p. 118680; Karamouz, S.S., Kahnali, R.A., Ghafournia, M., Supply chain quality management performance measurement: Systematic review (2020) International Journal of Quality & Reliability Management, 38 (2), pp. 484-504; Klapper, L., The role of factoring for financing small and medium enterprises (2006) Journal of Banking Finance, 30 (11), pp. 3111-3130; Klimkiewicz, K., The role of ESG–based compensation in sustaining the supply chain (2017) Journal of Reverse Logistics, 3, pp. 12-17; Kuei, C.H., Madu, C.N., Lin, C., The relationship between supply chain quality management practices and organizational performance (2001) International Journal of Quality & Reliability Management, 18 (3), pp. 864-872; Kuei, C.H., Madu, C.N., Lin, C., Implementing supply chain quality management (2008) Total Quality Management & Business Excellence, 19 (11), pp. 1127-1141; Kusi-Sarpong, S., Gupta, H., Sarkis, J., A supply chain sustainability innovation framework and evaluation methodology (2019) International Journal of Production Research, 57 (7), pp. 1990-2008; Laengle, S., Merigó, J.M., Miranda, J., Słowiński, R., Bomze, I., Borgonovo, E., Dyson, R.G., Teunter, R., Forty years of the European Journal of operational research: A bibliometric overview (2017) European Journal of Operational Research, 262 (3), pp. 803-816; Lim, A.F., Lee, V.H., Foo, P.Y., Ooi, K.B., Tan, G.W.H., Unfolding the impact of supply chain quality management practices on sustainability performance: An artificial neural network approach (2021) Supply Chain Management: An International Journal; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: An empirical analysis (2014) Journal of Purchasing and Supply Management, 20 (2), pp. 113-129; Martins, C.L., Pato, M.V., Supply chain sustainability: A tertiary literature review (2019) Journal of Cleaner Production, 225, pp. 995-1016; McGuinness, G., Hogan, T., Powell, R., European trade credit use and SME survival (2018) Journal of Corporate Finance, 49, pp. 81-103; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: A bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Moretto, A., Grassi, L., Caniato, F., Giorgino, M., Ronchi, S., Supply chain finance: From traditional to supply chain credit rating (2019) Journal of Purchasing and Supply Management, 25 (2), pp. 197-217. , –,. B. Edwards, Credit Management Handbook (4th ed.), Gower Publishing, Hampshire (1997; Noci, G., Designing ‘green’vendor rating systems for the assessment of a supplier's environmental performance (1997) European Journal of Purchasing & Supply Management, 3 (2), pp. 103-114; Pal, R., Torstensson, H., Mattila, H., Antecedents of organizational resilience in economic crises—an empirical study of Swedish textile and clothing SMEs (2014) International Journal of Production Economics, 147, pp. 410-428; Pasetto, A., Rotondi, Z., (2015), Ruolo delle banche a sostegno dello sviluppo delle regioni: sistemi competitivi territoriali, filiere produttive globali e credito; Perera, S., Harrison, G., Poole, M., Customer-focused manufacturing strategy and the use of operations-based non-financial performance measures: A research note (1997) Accounting, Organizations and Society, 22 (6), pp. 557-572; Punniyamoorthy, M., Mathiyalagan, P., Parthiban, P., A strategic model using structural equation modeling and fuzzy logic in supplier selection (2011) Expert Systems with Applications, 38 (1), pp. 458-474; Robinson, C.J., Malhotra, M.K., Defining the concept of supply chain quality management and its relevance to academic and industrial practice (2005) International Journal of Production Economics, 96 (3), pp. 315-337; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Soares, A., Soltani, E., Liao, Y.Y., The influence of supply chain quality management practices on quality performance: An empirical investigation (2017) Supply Chain Management, 22 (2), pp. 22-144; Song, H., Turson, R., Ganguly, A., Yu, K., Evaluating the effects of supply chain quality management on food firms’ performance: The mediating role of food certification and reputation (2017) International Journal of Operations & Production Management, 37 (10), pp. 1541-1562; Talib, F., Rahman, Z., Qureshi, M.N., A study of total quality management and supply chain management practices (2011) International Journal of Productivity and Performance Management, 60 (3), pp. 268-288; Tamayo-Torres, I., Gutierrez-Gutierrez, L., Ruiz-Moreno, A., Boosting sustainability and financial performance: The role of supply chain controversies (2019) International Journal of Production Research, 57 (11), pp. 3719-3734; Wuttke, D.A., Blome, C., Foerstl, K., Henke, M., Managing the innovation adoption of supply chain finance—empirical evidence from six European case studies (2013) Journal of Business Logistics, 34 (2), pp. 148-166; Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., Xu, Y., Supply chain finance: A systematic literature review and bibliometric analysis (2018) International Journal of Production Economics, 204, pp. 160-173; Zhang, M., Hu, H., Zhao, X., Developing product recall capability through supply chain quality management (2020) International Journal of Production Economics, 229, p. 107795; Zhao, L., Huchzermeier, A., (2018) Supply chain finance integrating operations and finance in Global supply chains, , Springer; Zhu, Y., Zhou, L., Xie, C., Wang, G.J., Nguyen, T.V., Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach (2019) International Journal of Production Economics, 211, pp. 22-33},
correspondence_address1={Sardanelli, D.; Department of Management, La Sapienza”, Italy; email: domenico.sardanelli@uniroma1.it},
publisher={Routledge},
issn={14783363},
coden={TQMAE},
language={English},
abbrev_source_title={Total Qual. Manage. Bus. Excellence},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bhardwaj20223,
author={Bhardwaj, D. and Kinra, A.},
title={The Linkage Between Macro Logistics Capabilities and Micro Firm Performance Towards Framework Development for Supply Chain Performance Measurement},
journal={Lecture Notes in Logistics},
year={2022},
pages={3-14},
doi={10.1007/978-3-031-05359-7_1},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85129721519&doi=10.1007%2f978-3-031-05359-7_1&partnerID=40&md5=a8ece504543d22eb8e0f72cde4f9f836},
affiliation={Professorship for Global Supply Chain Management, Universität Bremen, Bremen, Germany},
abstract={Technological advancements, increased globalization, political, social, and environmental concerns are changing the world we live in. Firms applying a global strategic management approach to enter a new country have to dynamically assess and align their performance objectives and micro capabilities with the changing macro logistics higher-order capabilities and resources at the regional or country level, and vice versa. Higher-order capabilities are present in a specific location and emerge over time as a result of systematic interactions across firms and institutions, whereas micro capabilities and performance objectives are firm-wide. Previous research on logistics and supply chain performance measurement hasn’t precisely addressed this macro-micro perspective with a global strategic management view. Furthermore, this aspect associating macro and micro capabilities and performance has been only projected with the help of case study-based empirical investigation within the previous research. To investigate the magnitude of this association, this research uses an integrative literature review strategy that includes a qualitative content analysis of the most important/cited supply chain performance measurement literature. With the help of a macro-micro supply chain performance measurement framework adapted from the supply chain performance measurement literature, the findings bring out the link between various macro logistics higher-order resources and capability elements (input) such as collaboration, knowledge sharing between logistics partners, transportation mode and energy efficiency and the strategic firm performance objectives and dynamic micro capabilities (output) such as sustainability and process innovation. It also extends framework propositions to guide future supply chain performance measurement research with a particular emphasis on global supply chain decision-making. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
author_keywords={Higher-order capabilities;  Macro logistics capabilities;  Micro firm performance;  Supply chain performance measurement},
references={Akhtar, P., Ullah, S., Amin, S.H., Kabra, G., Shaw, S., Dynamic capabilities and environmental sustainability for emerging economies’ multinational enterprises (2020) Int. Stud. Manag. Organ., 50 (1), pp. 27-42; Aksnes, D.W., Citation rates and perceptions of scientific contribution (2006) J. Am. Soc. Inform. Sci. Technol., 57 (2), pp. 169-185; Barney, J.B., Clark, D.N.: Resource-based theory.: In Resource Based Theory: Creating and Sustaining Competitive Advantage. Oxford University Press on Demand (2007); Barney, J.B., Ketchen, D.J., Wright, M., Resource-Based theory and the value creation framework (2021) J. Manag., 47 (7), pp. 1936-1955; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3), pp. 275-292; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) Int. J. Public Sect. Manag., 21 (1), pp. 4-25; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20 (6), pp. 692-704; Bleady, A., Ali, A.H., Ibrahim, S.B., Dynamic capabilities theory: Pinning down a shifting concept (2018) Acad. Acc. Financ. Stud. J., 22 (2), pp. 1-16; Bookbinder, J.H., Tan, C.S., Comparison of Asian and European logistics systems (2003) Int. J. Phys. Distrib. Logist. Manag., 33 (1), pp. 36-58; Bowersox, D.J., Calantone, R.J., Rodrigues, A.M., Estimation of global logistics expenditures using neural networks (2003) J. Bus. Logist., 24 (2), pp. 21-36; Chiou, T.Y., Chan, H.K., Lettice, F., Chung, S.H., The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan (2011) Transp. Res. Part E: Logistic. Transp. Rev., 47 (6), pp. 822-836; Cooper, H.M., (1989) Integrating Research: A Guide for Literature Reviews, , Sage Publications, Inc; Dörnhöfer, M., Schröder, F., Günthner, W.A., Logistics performance measurement system for the automotive industry (2016) Logist. Res., 9 (1), pp. 1-26; Foss, N.J., Higher-order industrial capabilities and competitive advantage (1996) J. Ind. Stud., 3 (1), pp. 1-20; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Hasegan, M.F., Nudurupati, S.S., Childe, S.J., Predicting performance – a dynamic capability view (2018) Int. J. Oper. Prod. Manag., 38 (11), pp. 2192-2213; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Irfani, D.P., Wibisono, D., Basri, M.H., Design of a logistics performance management system based on the system dynamics model (2019) Meas. Bus. Excell., 23 (3), pp. 269-291; Kerr, S., Accounting, budgeting and control systems in their organizational context: Comments by the discussant (1983) Acc. Organ. Soc., 8 (2-3), pp. 171-174; Kinra, A., Hald, K.S., Mukkamala, R.R., Vatrapu, R., An unstructured big data approach for country logistics performance assessment in global supply chains (2020) Int. J. Oper. Prod. Manag., 40 (4), pp. 439-458; McIvor, R., Understanding the manufacturing location decision: The case for the transaction cost and capability perspectives (2013) J. Supply Chain Manag., 49 (2), pp. 23-26; Mollenkopf, D., Stolze, H., Tate, W.L., Ueltschy, M., Green, lean, and global supply chains (2010) Int. J. Phys. Distrib. Logist. Manag., 40 (1-2), pp. 14-41; Nudurupati, S.S., Garengo, P., Bititci, U.S., Impact of the changing business environment on performance measurement and management practices (2021) Int. J. Prod. Econ., 232 (1-15); Nyaga, G.N., Whipple, J.M., Lynch, D.F., Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ? (2010) J. Oper. Manag., 28 (2), pp. 101-114; Ojo, T.K., Quality of public transport service: an integrative review and research agenda (2019) Transp. Lett., 11 (2), pp. 104-116; Pe’Er, A., Vertinsky, I., King, A., Who enters, where and why? The influence of capabilities and initial resource endowments on the location choices of de novo enterprises (2008) Strategic Organ, 6 (2), pp. 119-149; Rouse, P., Putterill, M., An integral framework for performance measurement (2003) Manag. Decis., 41 (8), pp. 791-805; Roy, V., Schoenherr, T., Implications of Sectoral Logistical Capabilities for Export Competitiveness: A Public Policy Perspective for Interventions in the Logistics Sector (2020) IEEE Transactions on Engineering Management, pp. 1-14; Staudt, F.H., Alpan, G., Di Mascolo, M., Rodriguez, C.M.T., Warehouse performance measurement: A literature review (2015) Int. J. Prod. Res., 53 (18), pp. 5524-5544; Teece, D.J., Pisano, G., Shuen, A., Dynamic capabilities and strategic management (1997) Strateg. Manag. J., 18 (7), pp. 509-533; Teece, D.J., A capability theory of the firm: An economics and (strategic) management perspective (2019) N. Z. Econ. Pap., 53 (1), pp. 1-43; Wiengarten, F., Pagell, M., Ahmed, M.U., Gimenez, C., Do a country’s logistical capabilities moderate the external integration performance relationship? (2014) J. Oper. Manag., 32 (1-2), pp. 51-63; Wilkinson, A., Hill, M., Gollan, P., The sustainability debate (2001) Int. J. Oper. Prod. Manag., 21 (12), pp. 1492-1502},
correspondence_address1={Bhardwaj, D.; Professorship for Global Supply Chain Management, Germany; email: bhardwaj@uni-bremen.de},
publisher={Springer Science and Business Media B.V.},
issn={21948917},
language={English},
abbrev_source_title={Lect. Notes Logist.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Chayma2022,
author={Chayma, F. and Badr, T. and Fadwa, F. and Ahmed, M.},
title={Development of a conceptual model of multidimensional measurement of sustainable performance for road freight transport},
journal={2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, IRASET 2022},
year={2022},
doi={10.1109/IRASET52964.2022.9738425},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127938229&doi=10.1109%2fIRASET52964.2022.9738425&partnerID=40&md5=b8136ee73248fb7ea1ae3bf11214f912},
affiliation={Hassan 1er University, Faculty of Sciences and Technologies, Department of Mechanical and Electrical Engineering, Settat, Morocco; Mohammed v University, Faculty of Legal, Economic and Social Sciences - Souissi, Department of Economics and Management Sciences, Rabat, Morocco},
abstract={This study proposes a unique methodology for assessing road freight transport's sustainable performance level, which can be used to get an overall assessment of performance in terms of five separate dimensions: economic, environmental, social, operational, and a stakeholders' component. The study's goal is to define the dimensions required for a representative evaluation of the long-term performance of road transport, allowing for improved control of the logistics and supply chain. The article examines a large variety of current literature models and discusses various modes of transportation before settling on the most encompassing model as a starting point. The authors' perspective is to discover the most lucrative model among the papers analyzed that provide several models assessing performance in the transportation industry in order to construct a new performance assessment model that is even more efficient. The base model chosen has double originality: on one hand, it is multidimensional and allows you therefore to have a single performance measurement system to evaluate the overall performance of the organization. And in another hand, while it is suitable for measuring the performance of road transport companies, it is a generic model that can be used to measure the performance of any other sector. © 2022 IEEE.},
author_keywords={Model;  Multidimensional sustainable performance;  Performance measurement;  Road Freight Transport;  Sustainable performance},
keywords={Freight transportation;  Motor transportation;  Roads and streets;  Sustainable development, Conceptual model;  Long term performance;  Measurements of;  Multidimensional sustainable performance;  Performance;  Performance measurements;  Performance:level;  Road freight transport;  Sustainable performance, Supply chains},
references={Dey, A., LaGuardia, P., Srinivasan, M., Building sustainability in logistics operations: a research agenda (2011) Management Research Review, 34 (11), pp. 1237-1259. , https://doi.org/10.1108/01409171111178774; Pålsson, H., Johansson, O., Reducing transportation emissions: Company intentions, barriers and discriminating factors (2016) Benchmarking, 23 (3), pp. 674-703. , https://doi.org/10.1108/BIJ-03-2014-0025; Bretzke, W.R., Sustainable logistics: in search of solutions for a challenging new problem (2011) Logist. Res., 3, pp. 179-189. , https://doi.org/10.1007/s12159-011-0059-4; Dekker, R., Bloemhof, J., Mallidis, I., Operations Research for green logistics-An overview of aspects, issues, contributions and challenges (2012) European Journal of Operational Research, 219 (3), pp. 671-679; Litman, T., Burwell, D., Issues in sustainable transportation (2006) Int. J. Glob Environ. Issues, 6 (4), p. 331e347; (2017) CO2 emissions from fuel combustion: highlights, , http://www.iea.org/publications/freepublications/publication/CO2EmissionsfromFuelCombustionHighlights2017.pdf, International Energy Agency 06/10/2017; Fast Facts on Transportation Greenhouse Gas Emissions (2016) United States Environmental protection Agency: Green Vehicle Guide, , https://www.epa.gov/greenvehicles/fast-factstransportation-greenhouse-gas-emissions, USEPA, 10/07/2017; (2016) Analysis of GHG emissions in Brazil (1970-2014) and its implications for public policies and the Brazilian contribution to the Paris agreement [Analise das emissoes de GEE Brasil (1970-2014) e suas implicaçoes para políticas públicas e a contribuiçao brasileira para o acordo de Paris], , http://seeg.eco.br/wpcontent/uploads/2016/09/WIP-16-09-02-RelatoriosSEEG-Sintese.pdf, SEEG Sistema de Estimativas de Emissoes de Gases de Efeito Estufa (10/07/2017). (In Portuguese); (2015) Global Status Report on Road Safety, , http://www.who.int/violence_injury_prevention/road_safety_status/2015/en/, World Health Organization, Geneva, 15/06/2017; (2017) The World Bank in Transport, , http://www.worldbank.org/en/topic/transport/overview, World Bank Group 20/06/2017; (2016) Global 3PL Market Estimates, , http://www.3plogistics.com/3pl-market-info-resources/3pl-marketinformation/global3pl-market-size-estimates/, Armstrong & Associates Inc, 16/10/2017; Dobranskyte-Niskota, A., Perujo, A., Pregl, M., Indicators to assess sustainability of transport activities (2007) European Comission, Joint Research Centre; Cottrill, C.D., Derrible, S., Leveraging Big Data for the Development of Transport Sustainability Indicators (2015) Journal of Urban Technology, 22 (1), pp. 45-64; Wang, G., Bai, S., Ogden, J.M., Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data (2009) Transportation Research Part D: Transport and Environment, 14 (3), pp. 168-179; (2016) China Energy Development Report, , China Energy Research Association; Wang, H., Ou, X., Zhang, X., Mode, technology, energy consumption, and resulting CO 2 emissions in China's transport sector up to 2050 (2017) Energy Policy, 109, pp. 719-733; (2016) CO2 Emissions from Fuel Combustion Highlights, , IEA (International Energy Agency) Paris; Replogle, M.A., Sustainable Transportation Strategies for Third World Development (1988) 67th Annual Meeting of Transportation Research Board, , Washington, DC; Zegras, C., Sustainable transport indicators and assessment methodologies (2006) Biannual conference and exhibit of the clean air initiative for Latin American cities, 1, pp. 25-27. , July; Jeon, C.M., Amekudzi, A., Addressing sustainability in transportation systems: definitions, indicators, and metrics (2005) J. Infrastruct. Syst., 11 (1), p. 31e50; (1991) a report on Canada's progress towards a national set of environmental indicators, 91 (1). , Canada. Environment Canada. Indicators Task Force, & Canada. State of the Environment Reporting. Environment Canada; (2016) Moving on Sustainable Transportation, , www.tc.gc.ca/envaffairs/most; Ki-Moon, B., (2016) Mobilizing Sustainable Transport for Development, , United Nation Official Publication: New York, NY, USA; Ki-Moon, B., (2015) Transport for sustainable development: the case of inland transport, , United Nations Publications New York and Geneva; Elkington, J., ACCOUNTING FOR THE TRIPLE BOTTOM LINE (1998) Measuring Business Excellence, 2 (3), pp. 18-22. , https://doi.org/10.1108/eb025539; Fauzi, H., Svensson, G., Rahman, A.A., Triple Bottom Line "as "Sustainable Corporate Performance": A Proposition for the Future (2010) Sustainability, 2 (5), pp. 1345-1360; Wu, H., Dunn, S.C., Environmentally responsible logistics systems (1995) International Journal of Physical Distribution & Logistics Management, 25 (2), pp. 20-38. , https://doi.org/10.1108/09600039510083925; Rogers, M., Weber, W.L., Evaluating CO2 emissions and fatalities tradeoffs in truck transport (2011) International Journal of Physical Distribution & Logistics Management, 41 (8), pp. 750-767. , https://doi.org/10.1108/09600031111166410; Richardson, B., Toward a Policy on a Sustainable Transportation System (1999) Transportation Research Record: Journal of the Transportation Research Board, 1670, pp. 27-34; Black, W.R., Toward a measure of transport sustainability (2000) Transportation Research Board Meeting, Conference Preprints, , January, Transportation Research Board, Washington, DC; Touzi, B., Mabrouki, C., Farchi, A., Development of a multidimensional sustainability performance measurement model for road freight transport (2015) International Journal of Current Research, 7 (12), pp. 24526-24534; Pathak, D.K., Shankar, R., Choudhary, A., Performance assessment framework based on competitive priorities for sustainable freight transportation systems (2021) Transportation Research Part D: Transport and Environment, 90, p. 102663; Butzer, S., Schötz, S., Petroschke, M., Steinhilper, R., Development of a Performance Measurement System for International Reverse Supply Chains (2017) Procedia CIRP, 61, pp. 251-256; Olfat, L., Amiri, M., Bamdad Soufi, J., Pishdar, M., a dynamic network efficiency measurement of airports performance considering sustainable development concept: a fuzzy dynamic network-DEA approach (2016) Journal of Air Transport Management, 57, pp. 272-290; Awasthi, A., Chauhan, S.S., Omrani, H., Application of fuzzy TOPSIS in evaluating sustainable transportation systems (2011) Expert Systems with Applications, 38 (10), pp. 12270-12280; Zhao, Y., Triantis, K., Murray-Tuite, P., Edara, P., Performance measurement of a transportation network with a downtown space reservation system: a network-DEA approach (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 1140-1159; To, W.M., Lee, P.K.C., Yu, B.T.W., Sustainability assessment of an urban rail system-The case of Hong Kong (2020) Journal of Cleaner Production, 253, p. 119961; Rajak, S., Parthiban, P., Dhanalakshmi, R., Sustainable transportation systems performance evaluation using fuzzy logic (2016) Ecological Indicators, 71, pp. 503-513; Zope, R., Vasudevan, N., Arkatkar, S.S., Joshi, G., Benchmarking: a tool for evaluation and monitoring sustainability of urban transport system in metropolitan cities of India (2019) Sustainable Cities and Society, 45, pp. 48-58; Feizi, A., Joo, S., Kwigizile, V., Oh, J.-S., a pervasive framework toward sustainability and smart-growth: Assessing multifaceted transportation performance measures for smart cities (2020) Journal of Transport & Health, 19, p. 100956; Kumar, A., Anbanandam, R., Assessment of environmental and social sustainability performance of the freight transportation industry: An index-based approach (2020) Transport Policy; Wang, Z., Song, W.-K., Sustainable airport development with performance evaluation forecasts: a case study of 12 Asian airports (2020) Journal of Air Transport Management, 89, p. 101925; Rao, S.-H., Transportation synthetic sustainability indices: a case of Taiwan intercity railway transport (2021) Ecological Indicators, 127, p. 107753; Lu, M.-T., Hsu, C.-C., Liou, J.J.H., Lo, H.-W., a hybrid MCDM and sustainability-balanced scorecard model to establish sustainable performance evaluation for international airports (2018) Journal of Air Transport Management, 71, pp. 9-19; Ignaccolo, M., Inturri, G., Giuffrida, N., Torrisi, V., Cocuzza, E., Sustainability of Freight Transport through an Integrated Approach: the Case of the Eastern Sicily Port System (2020) Transportation Research Procedia, 45, pp. 177-184; Djordjevic, B., Mane, A.S., Krmac, E., Analysis of dependency and importance of key indicators for railway sustainability monitoring: a new integrated approach with DEA and Pearson correlation (2021) Research in Transportation Business & Management, p. 100650; Karjalainen, L., Juhola, S., Framework for Assessing Public Transportation Sustainability in Planning and Policy-Making (2019) Sustainability, 11 (4), p. 1028; Fürst, E., Oberhofer, P., Greening road freight transport: evidence from an empirical project in Austria (2012) Journal of Cleaner Production, 33, pp. 67-73; Lee, M.-K., Yoo, S.-H., The role of transportation sectors in the Korean national economy: An input-output analysis (2016) Transportation Research Part A: Policy and Practice, 93, pp. 13-22; Pesqueux, Y., (2002) Organisations: modèles et représentations, Presses Universitaires de France, collection Gestion, , Paris; Guimarães Da, L.J.S., Salomon, V.A.P., ANP Applied to the Evaluation of Performance Indicators of Reverse Logistics in Footwear Industry (2015) Procedia Computer Science, 55, pp. 139-148; Lambert, S., Riopel, D., Abdul-Kader, W., a reverse logistics decisions conceptual framework (2011) Computers & Industrial Engineering, 61 (3), pp. 561-581; Yang, J., On the construction and implementation methods for performance measurement of reverse supply chain (2010) 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery; Morioka, S.N., Carvalho, M.M., Measuring sustainability in practice: exploring the inclusion of sustainability into corporate performance systems in Brazilian case studies (2016) Journal of Cleaner Production, 136, pp. 123-133; Hamurcu, M., Eren, T., An Application of Multicriteria Decision-making for the Evaluation of Alternative Monorail Routes (2019) Mathematics, 7, p. 16. , https://doi.org/10.3390/math7010016; Saaty, T.L., Ozdemir, M.S., Why the magic number seven plus or minus two (2003) Mathematical and Computer Modelling, 38 (3-4), pp. 233-244},
editor={Benhala B., Mansouri K., Raihani A., Qbadou M.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781665422093},
language={English},
abbrev_source_title={Int. Conf. Innov. Res. Appl. Sci., Eng. Technol., IRASET},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Alcalde2022,
author={Alcalde, R. and de Armiño, C.A. and García, S.},
title={Analysis of the Economic Sustainability of the Supply Chain Sector by Applying the Altman Z-Score Predictor},
journal={Sustainability (Switzerland)},
year={2022},
volume={14},
number={2},
doi={10.3390/su14020851},
art_number={851},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122897571&doi=10.3390%2fsu14020851&partnerID=40&md5=4c796371c6a09870ccec97c1061e1972},
affiliation={Departamento de Economía y Administración de Empresas, Facultad de Ciencias Económicas y Empresariales, Universidad de Burgos, Pza. de la Infanta Dña. Elena S/N, Burgos, 09001, Spain; Departamento de Ingeniería de Organización, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria S/N, Burgos, 09006, Spain},
abstract={This paper fills the gap in the financial perspective of supply chain performance measure-ment, related to the lack of a bankruptcy probability indicator, and proposes a predictor which is the eighth-model of the Altman Z-Score Logistic Regression. Furthermore, a bankruptcy probability ranking is established for the companies’ supply chains, according to the industry to which they belong. Moreover, the values are set to establish three categories of companies according to predictor. The probability of bankruptcy is analysed and studied for the supply chain of different industries. The building industry is revealed to have the highest probability of bankruptcy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.},
author_keywords={Altman’s Z-score;  Bankruptcy;  Risk prediction;  Supply chain},
keywords={bankruptcy;  economic conditions;  supply chain management;  sustainability},
references={Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining Supply Chain Management (2001) J. Bus. Logist, 22, pp. 1-25. , [CrossRef]; Min, S., Zacharia, Z.G., Smith, C.D., Defining Supply Chain Management: In the Past, Present, and Future (2019) J. Bus. Logist, 40, pp. 44-55. , [CrossRef]; Akyuz, G.A., Erkan, T.E., Supply Chain Performance Measurement: A Literature Review (2009) Int. J. Prod. Res, 48, pp. 5137-5155. , [CrossRef]; Gopal, P., Thakkar, J., A Review on Supply Chain Performance Measures and Metrics: 2000–2011 (2012) Int. J. Prod. Perform. Manag, 61, pp. 518-547. , [CrossRef]; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply Chain Performance Measurement Systems: A Systematic Review and Research Agenda (2017) Int. J. Prod. Econ, 183, pp. 299-315. , [CrossRef]; Khan, S.A.R., Yu, Z., Golpira, H., Sharif, A., Mardani, A., A State-of-the-Art Review and Meta-Analysis on Sustainable Supply Chain Management: Future Research Directions (2021) J. Clean. Prod, 278, p. 123357. , [CrossRef]; Santos, D.D.A., Quelhas, O.L.G., Gomes, C.F.S., Zotes, L.P., França, S.L.B., De Souza, G.V.P., De Araújo, R.A., Santos, S.D.S.C., Proposal for a Maturity Model in Sustainability in the Supply Chain (2020) Sustainability, 12, p. 9655. , [CrossRef]; Santos, D.D.A., Quelhas, O.L.G., Gomes, C.F.S., Filho, J.R.D.F., Theoretical Proposal for an Integrated Sustainability Performance Measurement System in the Supply Chain (2021) Front. Sustain. Food Syst, 2, p. 78. , [CrossRef]; Jahani, H., Alavifard, F., Ivanov, D., Ghasemishabankareh, B., Managing the risk of supply chain bankruptcy in supply chain network redesign (2019) IFAC-PapersOnLine, 52, pp. 2431-2436. , [CrossRef]; Shi, Y., Li, X., An Overview of Bankruptcy Prediction Models for Corporate Firms: A Systematic Literature Review (2019) Intang. Cap, 15, pp. 114-127. , [CrossRef]; Yang, S.A., Birge, J., Parker, R., The Supply Chain Effects of Bankruptcy (2015) Manag. Sci, 61, pp. 2320-2338. , [CrossRef]; Craighead, C.W., Hult, G.T.M., Ketchen, D.J., The Effects of Innovation-Cost Strategy, Knowledge, and Action in the Supply Chain on Firm Performance (2009) J. Oper. Manag, 27, pp. 405-421. , [CrossRef]; Prusak, B., Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries (2018) Int. J. Financ. Stud, 6, p. 60. , [CrossRef]; Karan, M.B., Ulucan, A., Kaya, M., Credit Risk Estimation Using Payment History Data: A Comparative Study of Turkish Retail Stores (2012) Cent. Eur. J. Oper. Res, 21, pp. 479-494. , [CrossRef]; Zhang, T., Zhang, C.Y., Pei, Q., Misconception of Providing Supply Chain Finance: Its Stabilising Role (2019) Int. J. Prod. Econ, 213, pp. 175-184. , [CrossRef]; Alikhani, R., Torabi, S.A., Altay, N., Strategic Supplier Selection under Sustainability and Risk Criteria (2019) Int. J. Prod. Econ, 208, pp. 69-82. , [CrossRef]; Xu, X., Sun, Y., Hua, Z., Reducing the Probability of Bankruptcy Through Supply Chain Coordination (2009) IEEE Trans. Syst. Man Cybern. Part. C Appl. Rev, 40, pp. 201-215. , [CrossRef]; Gelsomino, L.M., Mangiaracina, R., Perego, A., Tumino, A., Supply Chain Finance: A Literature Review (2016) Int. J. Phys. Distrib. Logist. Manag, 46, pp. 348-366. , [CrossRef]; Yan, N., He, X., Optimal Trade Credit with Deferred Payment and Multiple Decision Attributes in Supply Chain Finance (2020) Comput. Ind. Eng, 147, p. 106627. , [CrossRef]; Huang, R., Ren, S., Do Credit Ratings Incorporate Business Linkages Along the Supply Chain? (2017) SSRN Electron. J, , [CrossRef]; Wang, Y., Wallace, S.W., Shen, B., Choi, T.-M., Service Supply Chain Management: A Review of Operational Models (2015) Eur. J. Oper. Res, 247, pp. 685-698. , [CrossRef]; Liu, W., Bai, E., Liu, L., Wei, W., A Framework of Sustainable Service Supply Chain Management: A Literature Review and Research Agenda (2017) Sustainability, 9, p. 421. , [CrossRef]; Alcalde-Delgado, R., Sáiz-Bárcena, L., Olmo, R., Pérez, C.A.D.A., Empirical Study of the Business Growth Strategy Related to the Added Value by Intellectual Capital (2020) Int. J. Prod. Manag. Eng, 8, pp. 1-12. , [CrossRef]; DuHadway, S., Carnovale, S., Hazen, B., Understanding Risk Management for Intentional Supply Chain Disruptions: Risk Detection, Risk Mitigation, and Risk Recovery (2019) Ann. Oper. Res, 283, pp. 179-198. , [CrossRef]; FitzPatrick, P.J., A Comparison of the Ratios of Successful Industrial Enterprises With Those of Failed Companies (1932) Certif. Public Account, 12, pp. 598-729; Elwell, F.H., Changes in the Financial Structure of Unsuccessful Industrial Corporations (Book) (1936) Account. Rev, 11, pp. 87-88; Beaver, W.H., Financial Ratios As Predictors of Failure (1966) J. Account. Res, 4, pp. 71-111. , [CrossRef]; Altman, E.I., Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy (1968) J. Financ, 23, pp. 589-609. , [CrossRef]; Altman, E.I., Applications of Distress Prediction Models: What Have We Learned After 50 Years from the Z-Score Models? (2018) Int. J. Financ. Stud, 6, p. 70. , [CrossRef]; Campillo, J.P., Serer, G.L., Ferrer, E.V., Validez de la Información Financiera en los Procesos de Insolvencia. Un Estudio de la Pequeña Empresa Española (2013) Cuad. Econ. Dir. Empresa, 16, pp. 29-40. , [CrossRef]; Du Jardin, P., A Two-Stage Classification Technique for Bankruptcy Prediction (2016) Eur. J. Oper. Res, 254, pp. 236-252. , [CrossRef]; Mai, F., Tian, S., Lee, C., Ma, L., Deep Learning Models for Bankruptcy Prediction Using Textual Disclosures (2019) Eur. J. Oper. Res, 274, pp. 743-758. , [CrossRef]; Ellinger, A.E., Natarajarathinam, M., Adams, F.G., Gray, J.B., Hofman, D., O’Marah, K., Supply Chain Management Competency and Firm Financial Success (2011) J. Bus. Logist, 32, pp. 214-226. , [CrossRef]; Calandro, J., Considering the Utility of Altman’s Z-Score as a Strategic Assessment and Performance Management Tool (2007) Strat. Leadersh, 35, pp. 37-43. , [CrossRef]; Ashraf, S., Félix, E.G.S., Serrasqueiro, Z., Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress? (2019) J. Risk Financ. Manag, 12, p. 55. , [CrossRef]; Altman, E.I., (1993) Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting & Avoiding Distress and Profiting from Bankruptcy, , 2nd ed.; John Wiley & Sons, Inc.: New York, NY, USA, ISBN 978-04-7155-253-6; Altman, E.I., Hotchkiss, E., (2011) Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt, , 3rd ed.; John Wiley & Sons Inc.: New York, NY, USA, ISBN 978-04-7169-189-1; Siddiqui, S.A., Business Bankruptcy Prediction Models: A Significant Study of the Altman’s Z-Score Model (2012) Asian J. Manag. Res, 3, pp. 212-219. , [CrossRef]; Panigrahi, A., Validity of Altman’s ‘Z’ Score Model in Predicting Financial Distress of Pharmaceutical Companies (2019) J. Econ. Public Policy, 4, pp. 65-73; Alali, M.S., Examining the Effect of Altman’s Zeta Model Score on the Share Price of Healthcare Companies Listed at Kuwait Stock Exchange (2019) Int. J. Econ. Financ, 11, p. 25. , [CrossRef]; Csikósová, A., Janošková, M., Čulková, K., Prediction of Developments in the Textile and Clothing Industry in Slovakia by Selected Indicators of Financial Analysis (2019) Fibres Text. East. Eur, 27, pp. 9-16. , [CrossRef]; Nandi, A., Sengupta, P.P., Dutta, A., Diagnosing the Financial Distress in Oil Drilling and Exploration Sector of India through Discriminant Analysis (2019) Vis. J. Bus. Perspect, 23, pp. 364-373. , [CrossRef]; Altman, E.I., Iwanicz-Drozdowska, M., Laitinen, E.K., Suvas, A., Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model (2014) Soc. Sci. Electron. Publ, pp. 1-48. , [CrossRef]; González-Martín, J.M., Sánchez-Medina, A.J., Alonso, J.B., González-Martín, J.M., Optimización de la Predicción de Problemas Financieros en Empresas Sanitarias Privadas Españolas Aplicando Algoritmos Genéticos (2018) Gac. Sanit, 33, pp. 462-467. , [CrossRef]; Hosaka, T., Bankruptcy Prediction Using Imaged Financial Ratios and Convolutional Neural Networks (2019) Expert Syst. Appl, 117, pp. 287-299. , [CrossRef]; Muñoz-Izquierdo, N., Laitinen, E.K., Camacho-Miñano, M., Pascual-Ezama, D., Does Audit Report Information Improve Financial Distress Prediction over Altman’s Traditional Z-Score Model? (2019) J. Int. Financ. Manag. Account, 31, pp. 65-97. , [CrossRef]; Alfaro, L., Asis, G., Chari, A., Panizza, U., Corporate Debt, Firm Size and Financial Fragility in Emerging Markets (2019) J. Int. Econ, 118, pp. 1-19. , [CrossRef]; Barboza, F., Kimura, H., Altman, E., Machine Learning Models and Bankruptcy Prediction (2017) Expert Syst. Appl, 83, pp. 405-417. , [CrossRef]; Smith, M., Liou, D., Industrial Sector and Financial Distress (2007) Manag. Audit. J, 22, pp. 376-391. , [CrossRef]; Altman, E.I., Hotchkiss, E., Wang, W., (2019) Corporate Financial Distress, Restructuring, and Bankruptcy, , 4th ed.; John Wiley & Sons, Inc.: New York, NY, USA, ISBN 978-11-1954-192-9; Lin, H., Default Prediction Model for SME’s: Evidence from UK Market Using Financial Ratios (2015) Int. J. Bus. Manag, 10, p. 81. , [CrossRef]; Ferguson, D.F., Hadar, E., Optimizing the IT Business Supply Chain Utilizing Cloud Computing (2011) Proceedings of the 2011 8th International Conference & Expo on Emerging Technologies for a Smarter World, pp. 1-6. , Long Island, NY, USA, 2–3 November; Reyes, P., Raisinghani, M.S., Singh, M., Global Supply Chain Management in the Telecommunications Industry: The Role of Information Technology in Integration of Supply Chain Entities (2002) J. Glob. Inf. Technol. Manag, 5, pp. 48-67. , [CrossRef]; Chen, Y.J., Tsai, H., Liu, Y.-F., Supply Chain Finance Risk Management (2018) Tour. Econ, 24, pp. 593-614. , [CrossRef]; Alberca, P., Parte, L., Operational Efficiency Evaluation of Restaurant Firms (2018) Int. J. Contemp. Hosp. Manag, 30, pp. 1959-1977. , [CrossRef]; van Bergen, M., Steeman, M., Reindorp, M., Gelsomino, L., Supply Chain Finance Schemes in the Procurement of Agricultural Products (2019) J. Purch. Supply Manag, 25, pp. 172-184. , [CrossRef]; Ye, H., Hu, X., Xie, M., Research on Supply Chain Finance Model based on Agricultural Logistics Park Information Platform (2018) Proceedings of the 2018 International Symposium on Social Science and Management Innovation (SSMI 2018), pp. 226-230. , Xi’an, China, 15–16 December Atlantis Press: Xi’an, China, 2019; Sun, Y., Liu, Z., Yang, H., How Does Suppliers’ Fairness Affect the Relationship Quality of Agricultural Product Supply Chains? (2018) J. Food Qual, 2018, pp. 1-15. , [CrossRef]; Xing, Y., Zhongkai, W., Supply Chain Financing Model under a New Mechanism of Bankruptcy Guarantee (2020) Econ. Comput. Econ. Cybern. Stud. Res, 54, pp. 243-262. , [CrossRef]; Lin, Q., Xiao, Y., Retailer Credit Guarantee in a Supply Chain with Capital Constraint under Push & Pull Contract (2018) Comput. Ind. Eng, 125, pp. 245-257. , [CrossRef]; Ameyaw, E.E., Chan, A.P.C., Owusu-Manu, D.-G., Edwards, D.J., Dartey, F., A Fuzzy-Based Evaluation of Financial Risks in Build–Own–Operate–Transfer Water Supply Projects (2017) J. Infrastruct. Syst, 23, p. 04017033. , [CrossRef]; Jiang, Z.-Z., He, N., Xiao, L., Sheng, Y., Government Subsidy Provision in Biomass Energy Supply Chains (2019) Enterp. Inf. Syst, 13, pp. 1367-1391. , [CrossRef]; Ye, F., Xie, Z., Cai, Z., Lin, Q., Optimization of the Biofuel Supply Chain With Capital-Constrained Farmers Under Government Subsidies (2019) IEEE Access, 8, pp. 8178-8192. , [CrossRef]; Truong, H.Q., Hara, Y., Supply Chain Risk Management: Manufacturing-and Service-Oriented Firms (2018) J. Manuf. Technol. Manag, 29, pp. 218-239. , [CrossRef]; Benedettini, O., Swink, M., Neely, A., Examining the Influence of Service Additions on Manufacturing Firms’ Bankruptcy Likelihood (2017) Ind. Mark. Manag, 60, pp. 112-125. , [CrossRef]; Shojaei, P., Haeri, S.A.S., Development of Supply Chain Risk Management Approaches for Construction Projects: A Grounded Theory Approach (2019) Comput. Ind. Eng, 128, pp. 837-850. , [CrossRef]; Panova, Y., Hilletofth, P., Managing Supply Chain Risks and Delays in Construction Project (2018) Ind. Manag. Data Syst, 118, pp. 1413-1431. , [CrossRef]; Aloini, D., Dulmin, R., Mininno, V., Ponticelli, S., Supply Chain Management: A Review of Implementation Risks in the Construction Industry (2012) Bus. Process. Manag. J, 18, pp. 735-761. , [CrossRef]; Giroux, G., Cassell, C., Changing Audit Risk Characteristics in the Public Client Market (2011) Res. Account. Regul, 23, pp. 177-183. , [CrossRef]; Chanos, J., Short-Lived Lessons from an Enron Short, , https://www.wsj.com/articles/SB114894232503965715, (accessed on 10 May 2021); Swalih, M.M., Vinod, M.S., Application Of Altman Z Score on BSE-Greenex Companies (2017) J. Appl. Manag. Invest, 6, pp. 205-215; Srebro, B., Mavrenski, B., Arsić, V.B., Knežević, S., Milašinović, M., Travica, J., Bankruptcy Risk Prediction in Ensuring the Sustainable Operation of Agriculture Companies (2021) Sustainability, 13, p. 7712. , [CrossRef]; Laitinen, E.K., Suvas, A., International Applicability of Corporate Failure Risk Models Based on Financial Statement Information: Comparisons across European Countries (2013) J. Financ. Econ, 1, pp. 1-26. , [CrossRef]},
correspondence_address1={Alcalde, R.; Departamento de Economía y Administración de Empresas, Pza. de la Infanta Dña. Elena S/N, Spain; email: radelgado@ubu.es},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Mahadevan2022,
author={Mahadevan, K. and Elias, A. and Samaranayake, P.},
title={Supply chain performance measurement through collaborative effectiveness: an Asia–Pacific perspective},
journal={International Journal of Productivity and Performance Management},
year={2022},
doi={10.1108/IJPPM-05-2021-0274},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122815867&doi=10.1108%2fIJPPM-05-2021-0274&partnerID=40&md5=2ee861ced931efe6f5710266d4cb1fd5},
affiliation={School of Business and Law, Central Queensland University, Cairns, Australia; Wellington School of Business and Government, Victoria University of Wellington, Wellington, New Zealand; School of Business, Western Sydney University, Sydney, Australia},
abstract={Purpose: The main purpose of this research is to investigate the supply chain performance through collaborative effectiveness, from the organisations of Asia–Pacific region perspective, compared with that of global organisations based on collaborative supply chain practices. Design/methodology/approach: The survey research methodology was adopted, involving the collection of survey data from 223 participants across 10 different industries and 6 geographic regions. Data included 103 organisations in the Asia–Pacific region including Australia, India, China, Malaysia, Singapore, Indonesia and Thailand. Findings: From a collaborative effectiveness perspective, the supply chain performance of Asia–Pacific organisations is different when compared with that of global organisations. It was also found that global organisations are driven by sales performance, while Asia–Pacific organisations are focussing mainly on the cost in terms of the number of employees, and therefore productivity. The metal industry stands out to be the best performing industry from a collaborative effectiveness perspective in the Asia–Pacific region and globally. Practical implications: The research findings can be used as a guide by industry practitioners to develop benchmarks for collaborative supply chain practices by industry and region of operations. This research has been limited to a few industries such as manufacturing, logistics, service and retail industries. Originality/value: This research generates new knowledge on the relationship between the collaborative effectiveness and supply chain performance, from the perspectives of both Asia–Pacific and global organisations, based on a collaborative effectiveness framework. The study proposes an innovative approach of assessing supply chain performance through collaborative effectiveness from the perspectives of geographical boundaries and industry types. © 2021, Emerald Publishing Limited.},
author_keywords={Asia–Pacific;  Collaborative effectiveness;  Supply chain performance},
references={Abdallah, A.B., Obeidat, B.Y., Aqqad, N., The Impact of supply chain management practices on supply chain performance in Jordan: the moderating effect of competitive intensity (2014) International Business Research, 7 (3), pp. 13-27; Al-Odeh, M., Supply chain information systems technologies and management strategies in Northern Minnesota (2016) Journal of Supply Chain Management Systems, 5 (2), pp. 22-37; Autry, C., (2013) Adversarial to Collaborative Relationships, Game Changing Trends in Supply Chain, , Global Supply Chain Institute and Ernst & Young, University of Tennessee, Knoxville, TN; Baah, C., Acquah, I.S.K., Ofori, D., Exploring the influence of supply chain collaboration on supply chain visibility, stakeholder trust, environmental and financial performances: a partial least square approach (2021) Benchmarking: An International Journal, , doi:, ahead of print; Bae, H.S., The interaction effect of strategic information systems on performance of international freight forwarders: an analysis of gaps in performance (2019) The Asian Journal of Shipping and Logistics, 35 (2), pp. 108-117; Bagchi, P.K., Chun Ha, B., Skjoett-Larsen, T., Sorensen, L.B., Supply Chain integration: a European survey (2005) Journal of Logistics Management, 16 (2), pp. 275-294; Barratt, M., Understanding the meaning of collaboration in the supply chain (2004) Supply Chain Management: An International Journal, 9 (1), pp. 3-42; Berkerly, A., Letzing, J., (2019) Tracing the global rise of China's tech giants, , Industry 4.0, World Economic Forum; Blumberg, B., Donald, R.C., Schindler, P.S., (2001) Business Research Methods, , McGraw Hill Education, Berkshire, London; Busi, M., Bititci, S., Collaborative performance management: present gaps and future research (2006) International Journal of Productivity and Performance Management, 55 (1), pp. 7-25; Caridi, M., Crippa, L., Perego, A., Sianesi, A., Tumino, A., Measuring visibility to improve supply chain performance: a quantitative approach (2010) Benchmarking: An International Journal, 17 (4), pp. 593-615; Cavana, R., Delahaye, B., Sekaran, U., (2001) Applied Business Research: Qualitative and Quantitative Methods, , Wiley, Brisbane, Australia; Chakraborty, A., Mandal, P., Understanding challenges of supply chain sustainability in Asia (2014) International Journal of Process Management and Benchmarking, 4 (1), pp. 51-68; Chapman, R.L., Chapman, G., Sloan, T.R., Up and Down the supply chain - how company size and strategy relate to the level of integration and its benefits (2007) Quality innovation knowledge: Proceedings of the Eighth International Research Conference on Quality Innovation and Knowledge Management, p. 1. , Sohal, A. (Ed.), p., New Delhi: India Habitat Centre; Chen, L., De Lombaerde, P., ASEAN between globalization and regionalization (2019) Asia Pacific Business Review, 25 (5), pp. 729-750; Childerhouse, P., Deakins, E., Böhme, T., Towill, D., Disney, S., Banomyong, R., Supply chain integration: an international comparison of maturity (2011) Asia Pacific Journal of Marketing and Logistics, 23 (4), pp. 531-552; (2010) The global manufacturing sector: current issues, , https://www.cimaglobal.com/Documents/Thought_leadership_docs/Global_manufacturing_report.pdf, accessed, 20 June 2020; Dejoux, C., Thévenet, M., The shift in talent management for French MNCs in Malaysia (2012) Revue de Gestion des Ressources Humaines, 4, pp. 36-51; Dubey, R., Atlay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., Childe, S.J., Supply chain agility, adaptability and alignment: empirical evidence from the Indian auto components industry (2018) International Journal of Operations and Production Management, 38 (1), pp. 129-148; Elias, A.A., Systems thinking and modelling for stakeholder management (2017) IIM Kozhikode Society and Management Review, 6 (2), pp. 123-131; Elias, A.A., Strategy development through stakeholder involvement: a New Zealand study (2019) Global Journal of Flexible Systems Management, 20 (4), pp. 313-322; Elias, A.A., Donadelli, F., Paiva, E.L., Araujo, P.P.B., Analysing the complexities of sustainable wood supply chain in the Amazon: a systems thinking approach (2021) The International Journal of Logistics Management, 32 (4), pp. 1481-1505; Elrod, C., Murray, S., Bande, S., A review of performance metrics for supply chain management (2013) Engineering Management Journal, 25 (3), pp. 39-50; Fabbe-Costes, N., Jahre, M., Roussat, C., Supply chain integration: the role of logistics service providers (2009) International Journal of Productivity and Performance Management, 58 (1), pp. 71-91; Fawcett, S.E., McCarter, M.W., Fawcett, A.M., Webb, G.S., Magnan, G.M., Why supply chain collaboration fails: the socio-structural view of resistance to relational strategies (2015) Supply Chain Management: An International Journal, 20 (6), pp. 648-663; Felsberger, A., Qaiser, F.H., Choudhary, A., Reiner, G., The impact of Industry 4.0 on the reconciliation of dynamic capabilities: evidence from the European manufacturing industries (2020) Production Planning and Control, , doi:, ahead of print; Fernandes, A.C., Vilhena, E., Oliveira, R., Sampaio, P., Carvalho, M.S., Supply chain quality management impact on organization performance: results from an international survey (2021) International Journal of Quality and Reliability Management, , ahead- of-print ahead-of-print; Goharshenasan, A., Sheikh Aboumasoudi, A., Shahin, A., Ansari, A., Prioritizing the economic indicators of SSC: an integrative QFD approach of performance prism and BSC (2021) Benchmarking: An International Journal, ahead-of-print (ahead-of-print); Haque, M., Islam, R., Impact of supply chain collaboration and knowledge sharing on organizational outcomes in pharmaceutical industry of Bangladesh (2018) Journal of Global Operations and Strategic Sourcing, 11 (3), pp. 301-320; Heaney, R., (2013) Supply chain visibility: a Critical Strategy to optimise cost and service, , https://www.gs1.org/sites/default/files/supply_chain_visibility_aberdeen_report.pdf, accessed, 20 June 2020; Hertel, T., Hummels, D., Walmsley, T.L., The vulnerability of the Asian supply chain to localized disasters (2014) Asia and Global Production Networks, pp. 16-18. , doi:, Edward Elgar Publishing, Cheltenham; Janvier-James, A.M., Benchmarking framework for supply chain collaboration: a data envelopment analysis (DEA) application (2011) International Journal of Business Administration, 2 (3), pp. 19-31; Kalakota, R., Robinson, M., (2001) e-Business 2.0: Roadmap for Success, , Addison – Wesley; Karuranga, E., Frayret, J.M., D'Amours, S., E-business in the Quebec forest industry: use, perception and projects (2005) Journal of Forest Products Business Research, 2 (1), pp. 1-18; Khan, S.A.R., Yu, Z., Golpira, H., Sharif, A., Mardani, A., A state-of-the-art review and meta-analysis on sustainable supply chain management: future research directions (2021) Journal of Cleaner Production, 278, p. 123357; Kumar, G., Banerjee, R.B., Supply chain collaboration index: an instrument to measure the depth of collaboration (2014) Benchmarking: An International Journal, 2 (12), pp. 184-204; Kumar, G., Banerjee, R.N., Meena, P.L., Ganguly, K.K., Joint planning and problem solving roles in supply chain collaboration (2017) IIMB Management Review, 29 (1), pp. 45-57; Kumar, V., Chibuzob, E.N., Garza-Reyesc, J.A., Kumaria, A., Rocha-Lonad, L., Lopez-Torrese, G.C., The impact of supply chain integration on performance: evidence from the UK food sector (2017) Procedia Manufacturing, 11, pp. 814-821; Kumar, G., Nachiappan, S., Ramkumar, M.A., Missing link between sustainability collaborative strategy and supply chain performance: role of dynamic capability (2018) International Journal of Production Economics, 203 (C), pp. 96-109; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega, 34 (2), pp. 107-124; Lu, Y., Industry 4.0: a survey on technologies, applications and open research issues (2017) Journal of Industrial Information Integration, 6, pp. 1-10; Lucato, W.C., Júnior, M.V., Vanalle, R.M., Salles, J.A., Model to measure the degree of competitiveness for auto parts manufacturing companies (2012) International Journal of Production Research, 50 (19), pp. 5508-5522; Ma, S., Hao, L., Aloysius, J.A., Women are an advantage in supply chain collaboration and efficiency (2021) Production and Operations Management, 30 (5), pp. 1427-1441; Mahadevan, K., (2013) Investigation of collaborative supply chain practices through integration, visibility and information sharing: theoretical and industry perspective, , https://researchdirect.westernsydney.edu.au/islandora/object/uws%3A18864, accessed, 20 June 2020; Mahadevan, K., Measuring collaborative effectiveness: a conceptual approach (2017) International Journal of Productivity and Performance Management, 66 (8), pp. 1105-1127; Mahadevan, K., Demystifying the dabbawallahs: India's lean food delivery operations explained with operations management practices (2021) Operations and Supply Chain Management: An International Journal, 14 (3), pp. 277-288; Mahadevan, K., Bäumler, I., Kotzab, H., Frame conditions, mindsets and skills in supply chain management from Taylorism to industry 4.0 (2021) World Review of Intermodal Transportation Research, 10 (4), pp. 305-324; McMullan, W.E., Kenworthy, T.P., (2015) Creativity and Entrepreneurial Performance: A General Scientific Theory, , Springer International Publishing, Switzerland; Myers, M.B., Cheung, M.S., Sharing global supply chain knowledge (2008) Sloan Management Review, 1 (49), pp. 67-73; Nagarajan, V., Savitskie, K., Ranganathan, S., Sen, S., Alexandrov, A., The effect of environmental uncertainty, information quality, and collaborative logistics on supply chain flexibility of small manufacturing firms in India (2013) Asia Pacific Journal of Marketing and Logistics, 25 (5), pp. 784-802; Panahifar, F., Byrne, P.J., Heavey, C., ISM analysis of CPFR implementation barriers (2014) International Journal of Production Research, 52, pp. 5255-5272; Panahifar, F., Byrne, P.J., Salam, M.A., Heavey, C., Supply chain collaboration and firm's performance: the critical role of information sharing and trust (2018) Journal of Enterprise Information Management, 31 (3), pp. 358-337; Pellathy, D.A., Mollenkopf, D.A., Stank, T.P., Autry, C.W., Cross‐functional integration: concept clarification and scale development (2019) Journal of Business Logistics, 40 (2), pp. 81-104; Petersen, K., Ragatz, G., Monczka, R., An examination of collaborative planning effectiveness and supply chain performance (2005) Journal of Supply Chain Management, 41 (2), pp. 14-25; Pongsathornwiwat, A., Jeenanunta, C., Theeramunkong, T., Huynh, V.N., (2015) Supply chain collaboration and service innovation in Thai hotel industry, , https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7228113, accessed, 20 June 2020; Popa-Anica, I., Food traceability systems and information sharing in food supply chain (2012) Management and Marketing Challenges for Knowledge Society, 7 (4), pp. 749-758; Ralston, P.M., Richey, R.G., Grawe, S.J., The past and future of supply chain collaboration: a literature synthesis and call for research (2017) The International Journal of Logistics Management, 28 (2), pp. 508-530; Salam, M.A., The mediating role of supply chain collaboration on the relationship between technology, trust and operational performance: an empirical investigation (2017) Benchmarking: An International Journal, 24 (2), pp. 298-317; Samaranayake, P., A conceptual framework for supply chain management: a structural integration (2005) Supply Chain Management: An International Journal, 10 (1), pp. 47-59; Sezen, B., Relative effects of design, integration and information sharing on supply chain performance (2008) Supply Chain Management: An International Journal, 13 (3), pp. 233-240; Shan, H., Li, Y., Shi, J., Influence of supply chain collaborative innovation on sustainable development of supply chain: a study on Chinese enterprises (2020) Sustainability, 12 (7), p. 2978; Shore, B., Venkatachalam, A., Evaluating the information sharing capabilities of supply chain partners: a fuzzy logic model (2003) International Journal of Physical Distribution and Logistics Management, 33 (9), pp. 804-824; Simatupang, T.M., Sridharan, R., The collaboration index: a measure for supply chain collaboration (2005) International Journal of Physical Distribution and Logistics Management, 35 (1), pp. 44-62; Singh, P.J., Power, D., The nature and effectiveness of collaboration between firms, their customers and suppliers: a supply chain perspective (2009) Supply Chain Management: An International Journal, 14 (3), pp. 189-200; Smaros, J., Lehtonen, J.M., Appelqvist, P., Holmstrom, J., The impact of increasing demand visibility on production and inventory control efficiency (2003) International Journal of Physical Distribution and Logistics Management, 33 (4), pp. 336-355; Soosay, C.A., Hyland, P., A decade of supply chain collaboration and directions for future research (2015) Supply Chain Management: An International Journal, 20 (6), pp. 613-630; Stank, T.P., Keller, S.B., Closs, D.J., Performance benefits of supply chain logistical integration (2001) Transportation Journal, 41 (2-3), pp. 32-46; Sukati, I., Hamida, A.B., Baharuna, R., Md Yusoffa, R., The study of supply chain management strategy and practices on supply chain performance (2012) Procedia-Social and Behavioral Sciences, 40, pp. 225-233; Svensson, G., The transparency of SCM ethics: conceptual framework and empirical illustrations (2009) Supply Chain Management: An International Journal, 14 (4), pp. 259-269; Thi Mai Anh, N., Hui, L., Khoa, V.D., Mehmood, S., Relational capital and supply chain collaboration for radical and incremental innovation: an empirical study in China (2019) Asia Pacific Journal of Marketing and Logistics, 31 (4), pp. 1076-1094; Tsang, R., Boutot, P.H., Guarraia, P., Athanassiou, M., (2015) Rethinking supply chains in the Asia-pacific region for global growth six priorities for Asia-pacific supply chains, , https://www.bain.com/insights/rethinking-supply-chains-in-the-asia-pacific-region-for-global-growth/, accessed, 20 June 2020; Tukamuhabwa, B., Stevenson, M., Busby, J., Supply chain resilience in a developing country context: a case study on the interconnectedness of threats, strategies and outcomes (2017) Supply Chain Management: An International Journal, 22 (6), pp. 468-505; Wiengarten, F., Humphreys, P., Cao, G., Fynes, B., McKittrick, A., Collaborative supply chain practices and performance: exploring the key role of information quality (2010) Supply Chain Management: An International Journal, 15 (6), pp. 463-473; Won Lee, C., Kwon, I.W.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer (2007) Supply Chain Management: An International Journal, 12 (6), pp. 444-452; Wu, L., Chiu, M.L., Examining supply chain collaboration with determinants and performance impact: social capital, justice, and technology use perspectives”, International (2018) Journal of Information Management, 39, pp. 5-19; Yang, Y.S., Kull, T.J., Nahm, A.Y., Li, B., Attitudes toward supplier integration: the USA vs China (2016) International Journal of Operations and Production Management, 37 (8), pp. 1094-1116; Yu, W., Chavez, R., Jacobs, M., Wong, C.Y., Yuan, C., Environmental scanning, supply chain integration, responsiveness, and operational performance: an integrative framework from an organizational information processing theory perspective (2019) International Journal of Operations & Production Management, 39 (5), pp. 787-814; Zacharia, Z.G., Nix, N.W., Lusch, R.F., An analysis of supply chain collaborations and their effect on performance outcomes (2009) Journal of Business Logistics, 30 (2), pp. 101-120},
correspondence_address1={Mahadevan, K.; School of Business and Law, Australia; email: K.Mahadevan@cqu.edu.au},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kristianto20221768,
author={Kristianto, I. and Rimawan, E. and Saroso, D.S.},
title={Analysis of the Effectiveness of the Machining Equipment Supply Chain Network Using the Supply Chain Operation Reference (SCOR) and Analytical Hierarchy Process (AHP) Method at Industrial Equipment Supplier Companies},
journal={International Journal of Mechanical Engineering},
year={2022},
volume={7},
number={1},
pages={1768-1777},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122383712&partnerID=40&md5=7499cede62e60efb7fc86bc80fc95e91},
affiliation={Master of Industrial Engineering Program, Universitas Mercu Buana Jakarta, Indonesia; Institut Teknologi dan Bisnis BRI (BRI Institute), Indonesia},
abstract={Background: Supply chain is very important for the sustainable development of an industry. At all scales, they must be able to run and develop their business supply chains well to achieve good sustainability. Because operations at all scales are unrestricted and the structure is complex, the complexity of implementing the supply chain model is also very complex. Supply chain performance measurement will greatly assist supply chain operations and development. The purpose of this study is to analyze the causes of supply chain flow obstruction and improve supply chain performance. Methods: The method used in measuring supply chain performance is the SCOR and AHP methods with the help of expert choice software. The measurement of supply chain performance obtained in the study is that there are 43 indicators. Result: Based on the research results, the company's supply chain performance indicators that need improvement and are prioritized are the indicators that get the lowest score. From the results of data processing, the level of importance of the existing criteria is at level 1 which is deliver with a weight of 1.89, level 2 is cost with a weight of 1.46, and level 3 is DC1 (message cost management) with a weight of 0.75. Based on the results of the analysis, the advice given is to better manage the management of each order cost that will be ordered for the entire supply chain chain to be more effective. © Kalahari Journals.},
author_keywords={AHP;  SCOR;  Software Expert Choice;  Supply Chain Management},
references={Natalia, C., Astuario, R., Penerapan Model Green SCOR untuk Pengukuran Kinerja Green Supply Chain (2015) Jurnal Metris, 16, p. 97106; Nicolae, L.I., Tanasescu, D., Popa, V., Customer Expectations Management in Store Management (2012) Supply Chain Management Journal, 3 (2), p. 5875; Nisa, K., Pengukuran Kinerja Perusahaan Menggunakan Analytic Network Process (ANP) pada Balanced Scorecard (BSC) dengan Pendekatan Fuzzy (2019) Jurnal Sistem Dan Teknologi Informasi (JUSTIN), 7 (3), p. 184. , https://doi.org/10.26418/justin.v7i3.33196; Nurhandayani, A., Noor, A.M., Pengukuran Kinerja Rantai Pasok Cv. Vio Burger Dengan Menggunakan Model Supply Chain Operation Reference (Scor) Dan Metode Analytical Hierarchy Process (Ahp) (2018) Jurnal Ilmiah Teknologi Dan Rekayasa, 23 (3), pp. 206-219. , https://doi.org/10.35760/tr.2018.v23i3.2470; Pujawan, I., Mahendrawathi, E.R., (2010) Supply Chain Management, , 2ndSurabaya: Guna Widya; Ramadhan, R.F., Ridwan, A.Y., Santosa, B., Designing System Monitoring of Halal Supply Chain Performance in Food Procurement and (Ahp) Method: A Mixed Between Indonesian Council of Religious (Mui) and Supply Chain Operations Reference (Scor) (2019) International Conference on Rural Development and Entrepreneurship, 5 (1), p. 11861196; Rimantho, D., Rachel, M., Cahyadi, B., Kurniawan, Y., Aplikasi Analytical Hierarchy Process Pada Pemilihan Metode Analisis Zat Organik Dalam Air (2016) Jurnal Ilmiah Teknik Industri, 15 (1), p. 47. , https://doi.org/10.23917/jiti.v15i1.1603; Saaty, T.L., (1993) Pengambilan Keputusan Bagi Para Pemimpin, , Jakarta: PT. Pustaka Binaman Pressindo; Setiawan, A., Pulansari, F., Sumiati, S., Pengukuran Kinerja Dengan Metode Supply Chain Operations Reference (Scor) (2020) Juminten, 1 (1), pp. 55-66. , https://doi.org/10.33005/juminten.v1i1.14; Urgensi Penelitian Manajemen Rantai Pasok: Pemetaan Isu, Objek, Dan Metodologi (2018) Jurnal Manajemen Maranatha, 17 (2), p. 147. , https://doi.org/10.28932/jmm.v17i2.800; Trasewicz, R., Integrated Approach to Supply Chain Performance Meaurement – Result of Study on Polish Market (2016) Transportation Reseach Prosedia, 14, p. 1431442},
publisher={Kalahari Journals},
issn={09745823},
language={English},
abbrev_source_title={Intern. J. Mech. Eng.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Piotrowicz2022,
author={Piotrowicz, W.D. and Ryciuk, U. and Szymczak, M.},
title={Lean and agile metrics. Literature review and framework for measuring leagile supply chain},
journal={International Journal of Productivity and Performance Management},
year={2022},
doi={10.1108/IJPPM-10-2020-0560},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122205673&doi=10.1108%2fIJPPM-10-2020-0560&partnerID=40&md5=4995e9030d16ea2a6b02379cae4bc517},
affiliation={Department of Marketing, Supply Chain Management and Social Responsibility, Hanken School of Economics, Helsinki, Finland; Faculty of Engineering Management, Białystok University of Technology, Białystok, Poland; Institute of International Business and Economics, Poznań University of Economics and Business, Poznań, Poland},
abstract={Purpose: The aim of this paper is to review metrics and develop a framework for measuring leagile supply chain. Metrics that are applicable in the lean, agile and leagile strategies are identified in the literature and are then combined into a framework that can reflect both agile and lean strategies – the leagile supply strategy. Design/methodology/approach: This work is based on the systematic literature review. Literature was collected, then lean and agile metrics were extracted, analysed, counted and grouped into the framework. Findings are compared against literature on leagile supply chain. Findings: Findings indicate that there are sets of metrics specific to lean strategy, such as are process-focused, cost, productivity, inventory and delivery-based metrics, and specific to agile such as flexibility, responsiveness, information sharing and cooperation. There are also metrics common for both strategies; they are related to time, quality and customer satisfaction. Lean measures are tangible and focused on internal processes and products, while agile measures are targeted at external environment. Practical implications: The framework could be used by practitioners as a starting point for performance system design. Originality/value: There is a need to stop looking at lean and agile as separate and distinct supply strategies. Results of this research indicate that lean and agile are interlinked, both are focusing on customer satisfaction and quality. Applying a proposed set of metrics enables to design supply chain measurement system that reflects both strategies to measure leagile supply chain. The framework could be used by practitioners as a starting point for performance system design. © 2021, Wojciech Domink Piotrowicz, Urszula Ryciuk and Maciej Szymczak.},
author_keywords={Leagile supply chain;  Lean and agile supply chain;  Performance metrics;  Strategy;  Supply chain performance measurement;  Supply chain strategy},
references={Afonso, H., do Rosário Cabrita, M., Developing a lean supply chain performance framework in a SME: a perspective based on the balanced scorecard (2015) Procedia Engineering, 131, pp. 270-279; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Ahn, H.J., Chiderhouse, P., Vossen, G., Lee, H., Rethinking XML-enabled agile supply chains (2012) International Journal of Information Management, 32 (1), pp. 17-23; Aitken, J., Childerhouse, P., Christopher, M., Towill, D., Designing and managing multiple pipelines (2005) Journal of Business Logistics, 26, pp. 73-96; Aitken, J., Childerhouse, P., Towill, D., The impact of product life cycle on supply chain strategy (2003) International Journal of Production Economics, 85 (2), pp. 127-140; Akyuz, G.K., Erkan, T.E., Supply chain performance measurement: a literature review (2009) International Journal of Production Research, 48 (17), pp. 1-19; Al Kahtani, M., Rehman, A.U., Al-Zabidi, A., Choudhary, A., Agile supply chain assessment: an empirical study on concepts, research and issues (2019) Arabian Journal for Science and Engineering, 44, pp. 2551-2565; Arif-Uz-Zaman, K., Nazmul Ahsan, A.M.M., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612; Arlbjørn, J.S., Freytag, P.V., Evidence of lean: a review of international peer-reviewed journal articles (2013) European Business Review, 25 (2), pp. 174-205; Aronsson, H., Abrahamsson, M., Spens, K., Developing lean and agile health care supply chains (2011) Supply Chain Management, 16 (3), pp. 176-183; Backhouse, C., Burns, N., Agile value chains for manufacturing-implications for performance measures (1999) International Journal of Agile Management Systems, 1 (2), pp. 76-82; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Computers in Industry, 82, pp. 135-150; Banerjee, A., Ganjeizadeh, F., Modeling a leagility index for supply chain sustenance (2017) Procedia Manufacturing, 11, pp. 996-1003; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3-4), pp. 275-292; Bernardes, E.S., Hanna, M.D., A theoretical review of flexibility, agility and responsiveness in the operations management literature: toward a conceptual definition of customer responsiveness (2009) International Journal of Operations and Production Management, 29 (1), pp. 30-53; Birhanu, D., Lanka, K., Rao, A.N., A survey of classifications in supply chain strategies (2014) Procedia Engineering, 97, pp. 2289-2297; Borgström, B., Hertz, S., Supply chain strategies: changes in customer order‐based production (2011) Journal of Business Logistics, 32, pp. 361-373; Braunscheidel, M.J., Suresh, N.C., The organizational antecedents of a firm's supply chain agility for risk mitigation and response (2009) Journal of Operations Management, 27 (2), pp. 119-140; Brown, J.L., Agnew, N.M., Corporate agility (1982) Business Horizons, 25 (2), pp. 29-33; Bruce, M., Daly, L., Towers, N., Lean or agile: a solution for supply chain management in the textiles and clothing industry? (2004) International Journal of Operations and Production Management, 24 (2), pp. 151-170; Carvalho, H., Cruz-Machado, V., Integrating lean, agile, resilience and green paradigms in supply chain management (LARG_SCM) (2011) Supply Chain Management, pp. 27-48. , IntechOpen; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Christopher, M., The agile supply chain: competing in volatile markets (2000) Industrial Marketing Management, 29, pp. 37-44; Christopher, M., Lowson, R., Peck, H., Creating agile supply chains in the fashion industry (2004) International Journal of Retail and Distribution Management, 32 (8), pp. 367-376; Christopher, M., Peck, H., Towill, D., A taxonomy for selecting global supply chain strategies (2006) The International Journal of Logistics Management, 17 (2), pp. 277-287; Christopher, M., Towill, D.R., Supply chain migration from lean and functional to agile and customised (2000) Supply Chain Management: An International Journal, 5 (4), pp. 206-213; Christopher, M., Towill, D.R., An integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution and Logistics Management, 31 (4), pp. 235-246; Ciccullo, F., Pero, M., Caridi, M., Gosling, J., Purvis, L., Integrating the environmental and social sustainability pillars into the lean and agile supply chain management strategies: a literature review and future research directions (2018) Journal of Cleaner Production, 172, pp. 2336-2350; Costantino, N., Dotoli, M., Falagario, M., Fanti, M.P., Mangini, A.M., A model for supply management of agile manufacturing supply chains (2012) International Journal of Production Economics, 135 (1), pp. 451-457; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: a framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Eckstein, D., Goellner, M., Blome, C., Henke, M., The performance impact of supply chain agility and supply chain adaptability: the moderating effect of product complexity (2015) International Journal of Production Research, 53 (10), pp. 3028-3046; Elrod, C., Murray, S.P.E., Bande, S., A review of performance metrics for supply chain management (2013) Engineering Management Journal, 25 (3), pp. 39-50; Fadaki, M., Rahman, S., Chan, C., Quantifying the degree of supply chain leagility and assessing its impact on firm performance (2019) Asia Pacific Journal of Marketing and Logistics, 31 (1), pp. 246-264; Fayezi, S., Zutshi, A., O’Loughlin, A., Understanding and development of supply chain agility and flexibility: a structured literature review (2017) International Journal of Management Reviews, 19 (4), pp. 379-407; Fening, F.A., Pesakovic, G., Amaria, P., Relationship between quality management practices and the performance of small and medium size enterprises (SMEs) in Ghana (2008) International Journal of Quality and Reliability Management, 25 (7), pp. 694-708; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75, pp. 105-117; Frazzon, E., Tortorella, G.L., Dávalos, R., Holtz, T., Coelho, L., Simulation-based analysis of a supplier-manufacturer relationship in lean supply chains (2017) International Journal of Lean Six Sigma, 8 (3), pp. 262-274; Fullerton, R.R., Wempe, W.F., Lean manufacturing, non-financial performance measures, and financial performance (2009) International Journal of Operations and Production Management, 29 (3), pp. 214-240; Gligor, D.M., Esmark, C.L., Holcomb, M.C., Performance outcomes of supply chain agility: when should you be agile? (2015) Journal of Operations Management, 33-34, pp. 71-82. , Vols; Gligor, D.M., Holcomb, M.C., Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 438-453; Gligor, D., Holcomb, M., Stank, T., A multidisciplinary approach to supply chain agility: conceptualization and scale development (2013) Journal of Business Logistics, 34 (2), pp. 94-108; Goldsby, T.J., Griffis, S.E., Roath, A.S., Modeling lean, agile, and leagile supply chain strategies (2006) Journal of Business Logistics, 27 (1), pp. 57-80; Gopal, P., Thakkar, J., A review on supply chain performance measures and metrics: 2000–2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Govindan, K., Azevedo, S.G., Carvalho, H., Cruz-Machado, V., Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach (2015) International Journal of Environmental Science and Technology, 12, pp. 15-34; Gunasekaran, A., Agile manufacturing: a framework for research and development (1999) International Journal of Production Economics, 62 (1), pp. 87-105; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gurahoo, N., Salisbury, R.H., Lean and agile in small- and medium-sized enterprises: complementary or incompatible (2018) South African Journal of Business Management, 49 (1); Hines, P., Holweg, M., Rich, N., Learning to evolve: a review of contemporary lean thinking (2004) International Journal of Operations and Production Management, 24 (10), pp. 994-1011; Holweg, M., The genealogy of lean production (2007) Journal of Operations Management, 25 (2), pp. 420-437; Huang, Y.-Y., Li, S.-J., Tracking the evolution of research issues on agility (2009) Asia Pacific Management Review, 14 (1), pp. 107-129; Jain, V., Benyoucef, L., Deshmukh, S.G., What’s the buzz about moving from ‘lean’to ‘agile’integrated supply chains? A fuzzy intelligent agent-based approach (2008) International Journal of Production Research, 46 (23), pp. 6649-6677; Jasti, N.V.K., Kodali, R., A critical review of lean supply chain management frameworks: proposed framework (2015) Production Planning and Control, 26 (13), pp. 1051-1068; Jin-Hai, L., Anderson, A.R., Harrison, R.T., The evolution of agile manufacturing (2003) Business Process Management Journal, 9 (2), pp. 170-189; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Khalili-Damghani, K., Tavana, M., A new fuzzy network data envelopment analysis model for measuring the performance of agility in supply chains (2013) The International Journal of Advanced Manufacturing Technology, 69, pp. 291-318; Khan, K.A., Bakkappa, B., Metri, B.A., Sahay, B.S., Impact of agile supply chains' delivery practices on firms' performance: cluster analysis and validation (2009) Supply Chain Management, 14 (1), pp. 41-48; Kim, K.K., Umanath, N.S., Kim, J.Y., Ahrensc, F., Kim, B., Knowledge complementarity and knowledge exchange in supply channel relationships (2012) International Journal of Information Management, 32, pp. 35-49; Kisperska-Moron, D., De Haan, J., Improving supply chain performance to satisfy final customers: ‘leagile’ experiences of a polish distributor (2011) International Journal of Production Economics, 133 (1), pp. 127-134; Kisperska-Moron, D., Swierczek, A., The agile capabilities of polish companies in the supply chain: an empirical study (2009) International Journal of Production Economics, 118, pp. 217-224; Krafcik, J.F., Triumph of the lean production system (1988) Sloan Management Review, 30 (1), pp. 41-51; Kuhlang, P., Edtmayr, T., Sihn, W., Methodical approach to increase productivity and reduce lead time in assembly and production-logistic processes (2011) CIRP Journal of Manufacturing Science and Technology, 4 (1), pp. 24-32; Kumar, M., Garg, D., Agarwal, A., Cause and effect analysis of inventory management in leagile supply chain (2019) Journal of Management Information and Decision Sciences, 22, p. 67; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Li, X., Chung, C., Goldsby, T.J., Holsapple, C.W., A unified model of supply chain agility: the work-design perspective (2008) International Journal of Logistics Management, 19 (3), pp. 408-435; Li, X., Goldsby, T.J., Holsapple, C.W., Supply chain agility: scale development (2009) International Journal of Logistics Management, 20 (2), pp. 408-424; Lin, C.-T., Chiu, H., Chu, P.-Y., Agility index in the supply chain (2006) International Journal of Production Economics, 100 (2), pp. 285-299; Madhani, P.M., Supply chain strategy selection: a multi-criteria decision-making approach (2017) The IUP Journal of Supply Chain Management, 14 (2), pp. 38-56; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., Measuring supply chain performance: a lifecycle framework and a case study (2018) International Journal of Operations and Production Management, 38 (4), pp. 934-956; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Malmbrandt, M., Åhlström, P., An instrument for assessing lean service adoption (2013) International Journal of Operations and Production Management, 33 (9), pp. 1131-1165; Marodin, G.A., Tortorella, G.L., Frank, A.G., Godinho Filho, M., The moderating effect of lean supply chain management on the impact of lean shop floor practices on quality and inventory (2017) Supply Chain Management, 22 (6), pp. 473-485; Mason-Jones, R., Naylor, B., Towill, D.R., Engineering the leagile supply chain (2000) International Journal of Agile Management Systems, 2 (1), pp. 54-61; Mason-Jones, R., Naylor, B., Towill, D.R., Lean, agile or leagile? Matching your supply chain to the marketplace (2000) International Journal of Production Research, 38 (17), pp. 4061-4070; Matawale, C.R., Datta, S., Mahapatra, S.S., Supplier selection in agile supply chain: application potential of FMLMCDM approach in comparison with fuzzy-TOPSIS and fuzzy-MOORA (2016) Benchmarking: An International Journal, 23 (7), pp. 2027-2060; Meade, D., Kumar, S., White, B., Analysing the impact of the implementation of lean manufacturing strategies on profitability (2010) Journal of the Operational Research Society, 61 (5), pp. 858-871; Mehralian, G., Zarenezhad, F., Rajabzadeh Ghatari, A., Developing a model for an agile supply chain in pharmaceutical industry (2015) International Journal of Pharmaceutical and Healthcare Marketing, 9 (1), pp. 74-91; Melnyk, A.S., Steward, D.M., Swink, M., Metrics and performance measurement in operations management: dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-217; Melton, T., The benefits of lean manufacturing: what lean thinking has to offer the process industries (2005) Chemical Engineering Research and Design, 83 (6), pp. 662-673; Mirghafoori, S.H., Andalib, D., Keshavarz, P., Developing green performance through supply chain agility in manufacturing industry: a case study approach (2017) Corporate Social Responsibility and Environmental Management, 24 (5), pp. 368-381; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Moyano-Fuentes, J., Sacristán-Díaz, M., Learning on lean: a review of thinking and research (2012) International Journal of Operations and Production Management, 32 (5), pp. 551-582; Naim, M.M., Gosling, J., On leanness, agility and leagile supply chains (2011) International Journal of Production Economics, 131 (1), pp. 342-354; Narasimhan, R., Swink, M., Kim, S.W., Disentangling leanness and agility: an empirical investigation (2006) Journal of Operations Management, 24 (5), pp. 440-457; Naylor, J.B., Naim, M.M., Berry, D., Leagility: integrating the lean and agile manufacturing strategies in the total supply chain (1999) International Journal of Production Economics, 62 (1), pp. 107-118; Neely, A., The evolution of performance measurement research. Developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (1), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Okoli, C., A guide to conducting a standalone systematic literature review (2015) Communications of the Association for Information Systems, 37, pp. 879-910; Packowski, J., Francas, D., Lean SCM : a paradigm shift in supply chain management (2013) Journal of Business Chemistry, 10 (3), pp. 131-213; Patel, B.S., Samuel, C., Sharma, S.K., Evaluation of agility in supply chains: a case study of an Indian manufacturing organization (2017) Journal of Manufacturing Technology Management, 28 (2), pp. 212-231; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: an exploratory study (2015) International Journal of Productivity and Performance Management, 64 (8), pp. 1068-1091; Qi, Y., Boyer, K.K., Zhao, X., Supply chain strategy, product characteristics, and performance impact: evidence from Chinese manufacturers (2009) Decision Sciences, 40, pp. 667-695; Rahimi, A., Raad, A., Alem Tabriz, A., Motameni, A., Providing an interpretive structural model of agile supply chain practices (2020) Journal of Modelling in Management, 15 (2), pp. 661-684; Rahiminezhad Galankashi, M., Helmi, S.A., Assessment of hybrid lean-agile (leagile) supply chain strategies (2016) Journal of Manufacturing Technology Management, 27 (4), pp. 470-482; Rimienė, K., Supply chain agility concept evolution (1990–2010) (2011) Economics and Management, 16, pp. 892-899; Saeed, K.A., Malhotra, M.K., Abdinnour, S., How supply chain architecture and product architecture impact firm performance: an empirical examination (2019) Journal of Purchasing and Supply Management, 25 (1), pp. 40-52; Sangwa, N.R., Sangwan, K.S., Development of an integrated performance measurement framework for lean organizations (2018) Journal of Manufacturing Technology Management, 29 (1), pp. 41-84; Schmitz, J., Platts, K., Supplier logistics performance measurement: indications from a study in the automotive industry (2004) International Journal of Production Economics, 89 (2), pp. 231-243; Shepherd, C., Gunter, H., Measuring supply chain: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Simatupang, T.M., Sridharan, R., Design for supply chain collaboration (2008) Business Process Management Journal, 14 (3), pp. 401-418; Stone, K.B., Four decades of lean: a systematic literature review (2012) International Journal of Lean Six Sigma, 3 (2), pp. 112-132; Sukwadi, R., Wee, H.-M., Yang, C.-C., Supply chain performance based on the lean–agile operations and supplier–firm partnership: an empirical study on the garment industry in Indonesia (2013) Journal of Small Business Management, 51, pp. 297-311; Swafford, P.M., Ghosh, S., Murthy, N., Achieving supply chain agility through IT integration and flexibility (2008) International Journal of Production Economics, 116 (2), pp. 288-297; Szymczak, M., Ryciuk, U., Leończuk, D., Piotrowicz, W., Witkowski, K., Nazarko, J., Jakuszewicz, J., Key factors for information integration in the supply chain – measurement, technology and information characteristics (2018) Journal of Business Economics and Management, 19 (5), pp. 759-776; Tarafdar, M., Qrunfleh, S., Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility (2017) International Journal of Production Research, 55 (4), pp. 925-938; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Thanki, S., Thakkar, J., A quantitative framework for lean and green assessment of supply chain performance (2018) International Journal of Productivity and Performance Management, 67 (2), pp. 366-400; Tortorella, G.L., Giglio, R., Limon-Romero, J., Supply chain performance: how lean practices efficiently drive improvements (2018) Journal of Manufacturing Technology Management, 29 (5), pp. 829-845; Ugochukwu, P., Engström, J., Langstrand, J., Lean in the supply chain: a literature review (2012) Management and Production Engineering Review, 3 (4), pp. 87-96; van Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 126-148; Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteria decision-making methodology (2004) International Journal of Production Economics, 91 (1), pp. 1-15; Wee, H.M., Wu, S., Lean supply chain and its effect on product cost and quality: a case study on Ford Motor Company (2009) Supply Chain Management, 14 (5), pp. 335-341; Womack, J.P., Jones, D.T., Roos, D., (1990) The Machine that Changed the World, , Simon and Schuster, New York; Wu, I.-L., Chuang, C.-H., Hsu, C.-H., Information sharing and collaborative behaviors in enabling supply chain performance: a social exchange perspective (2014) International Journal of Production Economics, 148, pp. 122-132; Wu, K.-J., Tseng, M.-L., Chiu, A.S.F., Lim, M.K., Achieving competitive advantage through supply chain agility under uncertainty: a novel multi-criteria decision-making structure (2017) International Journal of Production Economics, 190, pp. 96-107; Yang, Z., Zhang, L., Information sharing in supply chain: a review (2013) Journal of Digital Information Management, 11 (2), pp. 125-130; Yusuf, Y., Adeleye, E., Sivayoganathan, K., Volume flexibility: the agile manufacturing conundrum (2003) Management Decision, 41 (7), pp. 613-624; Yusuf, Y.Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N.M., Cang, S., A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry (2014) International Journal of Production Economics, 147, pp. 531-543; Yusuf, Y.Y., Sarhadi, M., Gunasekaran, A., Agile manufacturing: the drivers, concepts and attributes (1999) International Journal of Production Economics, 62 (1), pp. 33-43; Zhang, Z., Sharifi, H., A methodology for achieving agility in manufacturing organisations (2000) International Journal of Operations and Production Management, 20 (4), pp. 496-513},
correspondence_address1={Piotrowicz, W.D.; Department of Marketing, Finland; email: wojciech.piotrowicz@hanken.fi},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yadav2022685,
author={Yadav, V.S. and Singh, A.R. and Gunasekaran, A. and Raut, R.D. and Narkhede, B.E.},
title={A systematic literature review of the agro-food supply chain: Challenges, network design, and performance measurement perspectives},
journal={Sustainable Production and Consumption},
year={2022},
volume={29},
pages={685-704},
doi={10.1016/j.spc.2021.11.019},
note={cited By 30},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120347441&doi=10.1016%2fj.spc.2021.11.019&partnerID=40&md5=191ca22049fa8572624d030a738c1e6e},
affiliation={Department of Mechanical Engineering, National Institute of Technology Raipur, India; School of Business Administration, Penn State Harrisburg, 777 West Harrisburg Pike, Middletown, PA  17057-4846, United States; Department of Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Vihar Lake, NITIE, Powai, Mumbai, Maharashtra  Pin- 400087, India},
abstract={The present agriculture food supply chain (AFSC) faces numerous challenges about the growing demand for food items and consumer preference regarding food safety. In addition to growing concerns about sustainability, stringent government regulation, food security, and traceability issues force the managers, industries, and practitioners working in AFSC to adopt new tools, techniques, and methodology to model current food supply chain issues and correspondingly design the food logistics network. To this effect, the purpose of this article is threefold: the first focus is to identify the various challenges in AFSC, second is to review the research contribution in the field of designing agro-food supply chain network (AFSCN), while the third aspect is to investigate performance measurement system of AFSC through various performance indicators. For this, 108 articles are reviewed, and the main research findings are discussed in different aspects. Technology adoption and integration in design AFSCN are exciting prospects for future AFSC research. In addition, the digital transformation of AFSC is gaining wide acceptance across all agro-stakeholders and practitioners. Similarly, other research trends are further emphasized, and possible future challenges are also identified. © 2021 Institution of Chemical Engineers},
author_keywords={Agriculture food supply chain;  Network design;  Performance indicators;  Sustainability},
keywords={Agriculture;  Food supply;  Supply chains;  Sustainable development, Agriculture food supply chain;  Agro foods;  Design measurements;  Food supply chain;  Growing demand;  Network design;  Performance indicators;  Performance measurements;  Supply chain network;  Systematic literature review, Benchmarking},
references={Abdi, A., Abdi, A., Akbarpour, N., Amiri, A.S., Hajiaghaei-Keshteli, M., Innovative approaches to design and address green supply chain network with simultaneous pick-up and split delivery (2020) J. Clean. Prod., 250; Accorsi, R., Baruffaldi, G., Manzini, R., Tufano, A., On the design of cooperative vendors’ networks in retail food supply chains: a logistics-driven approach (2018) Int. J. Logist. Res. Appl., 21 (1), pp. 35-52; Accorsi, R., Baruffaldi, G., Manzini, R., A closed-loop packaging network design model to foster infinitely reusable and recyclable containers in food industry (2020) Sustain. Prod. Consumpt., 24, pp. 48-61; Accorsi, R., Cascini, A., Cholette, S., Manzini, R., Mora, C., Economic and environmental assessment of reusable plastic containers: a food catering supply chain case study (2014) Int. J. Prod. Econ., 152, pp. 88-101; Accorsi, R., Cholette, S., Manzini, R., Pini, C., Penazzi, S., The land-network problem: ecosystem carbon balance in planning sustainable agro-food supply chains (2016) J. Clean. Prod., 112, pp. 158-171; Akkerman, R., Farahani, P., Grunow, M., Quality, safety and sustainability in food distribution: a review of quantitative operations management approaches and challenges (2010) Or Spectrum, 32 (4), pp. 863-904; Allaoui, H., Guo, Y., Choudhary, A., Bloemhof, J., Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach (2018) Comput. Oper. Res., 89, pp. 369-384; Apaiah, R.K., Hendrix, E.M., Design of a supply chain network for pea-based novel protein foods (2005) J. Food Eng., 70 (3), pp. 383-391; Aramyan, L.H., Oude Lansink, A.G., Van Der Vorst, J.G., Van Kooten, O., Performance measurement in agri-food supply chains: a case study (2007) Supply Chain Manag., 12 (4), pp. 304-315; Aras, N., Bilge, Ü., Robust supply chain network design with multi-products for a company in the food sector (2018) Appl. Math. Model., 60, pp. 526-539; Ashby, A., Leat, M., Hudson-Smith, M., Making connections: a review of supply chain management and sustainability literature (2012) Supply Chain Manag., 17 (5), pp. 497-516; Azadnia, A.H., Saman, M.Z.M., Wong, K.Y., Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process (2015) Int. J. Prod. Res., 53 (2), pp. 383-408; Baghalian, A., Rezapour, S., Farahani, R.Z., Robust supply chain network design with service level against disruptions and demand uncertainties: a real-life case (2013) Eur. J. Oper. Res., 227 (1), pp. 199-215; Bai, X., Liu, Y., Robust optimization of supply chain network design in fuzzy decision system (2016) J. Intell. Manuf., 27 (6), pp. 1131-1149; Banasik, A., Kanellopoulos, A., Claassen, G.D.H., Bloemhof-Ruwaard, J.M., van der Vorst, J.G., Closing loops in agricultural supply chains using multi-objective optimization: a case study of an industrial mushroom supply chain (2017) Int. J. Prod. Econ., 183, pp. 409-420; Barbosa-Povoa, A.P., Mota, B., Carvalho, A., How to design and plan sustainable supply chains through optimization models? (2018) Pesquisa Operacional, 38 (3), pp. 363-388; Bastian, J., Zentes, J., Supply chain transparency as a key prerequisite for sustainable agri-food supply chain management (2013) Int. Rev. Retail Distrib. Consum. Res., 23 (5), pp. 553-570; Behzadi, G., O'Sullivan, M.J., Olsen, T.L., Zhang, A., Agribusiness supply chain risk management: a review of quantitative decision models (2018) Omega), 79, pp. 21-42; Beske, P., Land, A., Seuring, S., Sustainable supply chain management practices and dynamic capabilities in the food industry: a critical analysis of the literature (2014) Int. J. Prod. Econ., 152, pp. 131-143; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Borodin, V., Bourtembourg, J., Hnaien, F., Labadie, N., Handling uncertainty in agricultural supply chain management: a state of the art (2016) Eur. J. Oper. Res., 254 (2), pp. 348-359; Bortolini, M., Faccio, M., Gamberi, M., Pilati, F., Multi-objective design of multi-modal fresh food distribution networks (2016) Int. J. Logist. Syst. Manag., 24 (2), pp. 155-177; Bortolini, M., Galizia, F.G., Mora, C., Botti, L., Rosano, M., Bi-objective design of fresh food supply chain networks with reusable and disposable packaging containers (2018) J. Clean. Prod., 184, pp. 375-388; Bourlakis, M., Maglaras, G., Gallear, D., Fotopoulos, C., Examining sustainability performance in the supply chain: the case of the Greek dairy sector (2014) Ind. Market. Manag., 43 (1), pp. 56-66; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for sustainable supply chain management: developments and directions (2014) Eur. J. Oper. Res., 233 (2), pp. 299-312; Brundtland, G.H., Report of the World Commission On Environment and Development: ‘Our Common Future’. United Nations (1987), Oxford University Press Oxford; Chao, C., Zhihui, T., Baozhen, Y., Optimization of two-stage location–routing–inventory problem with time-windows in food distribution network (2019) Ann. Oper. Res., 273 (1-2), pp. 111-134; Chen, H., Theoretical foundations for cyber-physical systems: a literature review (2017) J. Indus. Integr. Manag., 2 (3); Chopra, S., Laux, C., Schmidt, E., Rajan, P., Perception of performance indicators in an agri-food supply chain: a case study of India's public distribution system (2017) Int. J. Food Syst. Dyn., 8 (2), p. 130; Cohen, M.A., Lee, H.L., Designing the right global supply chain network (2020) Manuf. Serv. Oper. Manag., 22 (1), pp. 15-24; Colicchia, C., Creazza, A., Dallari, F., Melacini, M., Eco-efficient supply chain networks: development of a design framework and application to a real case study (2016) Prod. Plann. Control, 27 (3), pp. 157-168; Corrado, S., Sala, S., Food waste accounting along global and European food supply chains: state of the art and outlook (2018) Waste Manage., 79, pp. 120-131; Cunha Callado, A.A., Jack, L., Relations between usage patterns of performance indicators and the role of individual firms in fresh fruit agri-food supply chains (2017) J. Appl. Account. Res., 18 (3), pp. 375-398; Dabbene, F., Gay, P., Tortia, C., Traceability issues in food supply chain management: a review (2014) Biosyst. Eng., 120, pp. 65-80; Dabbene, F., Gay, P., Food traceability systems: performance evaluation and optimization (2011) Comput. Electron. Agric., 75 (1), pp. 139-146; Dabbene, F., Gay, P., Sacco, N., Optimisation of fresh-food supply chains in uncertain environments, Part I: background and methodology (2008) Biosyst. Eng., 99 (3), pp. 348-359; Dabbene, F., Gay, P., Sacco, N., Optimisation of fresh-food supply chains in uncertain environments, Part II: a case study (2008) Biosyst. Eng., 99 (3), pp. 360-371; Das, K., Integrating lean, green, and resilience criteria in a sustainable food supply chain planning model (2019) Int. J. Math. Eng. Manag. Sci., 4 (2), pp. 259-275; Davis, K.F., Downs, S., Gephart, J.A., Towards food supply chain resilience to environmental shocks (2021) Nat. Food, 2 (1), pp. 54-65; De, A., Singh, S.P., Analysis of fuzzy applications in the agri-supply chain: a literature review (2021) J. Clean. Prod.; De Keizer, M., Akkerman, R., Grunow, M., Bloemhof, J.M., Haijema, R., van der Vorst, J.G., Logistics network design for perishable products with heterogeneous quality decay (2017) Eur. J. Oper. Res., 262 (2), pp. 535-549; De Oliveira, M.M., Lago, A., Dal'Magro, G.P., Food loss and waste in the context of the circular economy: a systematic review (2021) J. Clean. Prod., 294; De Steur, H., Wesana, J., Dora, M.K., Pearce, D., Gellynck, X., Applying value stream mapping to reduce food losses and wastes in supply chains: a systematic review (2016) Waste Manage., 58, pp. 359-368; Ding, S.B., α-Cost Minimization Model of Grain Supply Chain (2011) Key Engineering Materials, 474, pp. 50-53. , Trans Tech Publications; Djekic, I., Pojić, M., Tonda, A., Putnik, P., Bursać Kovačević, D., Režek-Jambrak, A., Tomasevic, I., Scientific challenges in performing life-cycle assessment in the food supply chain (2019) Foods, 8 (8), p. 301; Dobbs, R., Oppenheim, J., Thompson, F., Brinkman, M., Zornes, M., Resource Revolution: Meeting the World's Energy, Materials, Food, and Water Needs (2011), McKinsey Global Institute, McKinsey & Company; Dwivedi, A., Jha, A., Prajapati, D., Sreenu, N., Pratap, S., Meta-heuristic algorithms for solving the sustainable agro-food grain supply chain network design problem (2020) Modern Supply Chain Res. Appl., 2 (3), pp. 161-177; Eklington, J., Cannibals With Forks: The Triple Bottom Line of the 21st Century (1998), Oxford Press Oxford; Eriksson, M., Bartek, L., Löfkvist, K., Malefors, C., Olsson, M.E., Environmental Assessment of Upgrading Horticultural Side Streams—The Case of Unharvested Broccoli Leaves (2021) Sustainability, 13 (10), p. 5327; Eriksson, M., Spångberg, J., Carbon footprint and energy use of food waste management options for fresh fruit and vegetables from supermarkets (2017) Waste Manage., 60, pp. 786-799; Etemadnia, H., Hassan, A., Goetz, S., Abdelghany, K., Wholesale hub locations in food supply chains (2013) Transp. Res. Rec., 2379 (1), pp. 80-89; Facchini, E., Iacovidou, E., Gronow, J., Voulvoulis, N., Food flows in the United Kingdom: the potential of surplus food redistribution to reduce waste (2018) J. Air Waste Manage. Assoc., 68 (9), pp. 887-899; Fang, Y., Jiang, Y., Sun, L., Han, X., Design of green cold chain networks for imported fresh agri-products in belt and road development (2018) Sustainability, 10 (5), p. 1572; Rome Declaration On World Food Security and World Food Summit Plan of Action (1996), http://www.fao.org/3/w3613e/w3613e00.htm, World Food Summit Rome 13-17 November Accessed on 15/07/2021; (2021), http://www.fao.org/3/cb4474en/online/cb4474en.html#chapter-51, FAO. “Transforming food systems for food security, improved nutrition and affordable healthy diets for all”, The state of food security and nutrition in the world. Accessed on 15/07/2021; Farooque, M., Zhang, A., Liu, Y., Barriers to circular food supply chains in China (2019) Supply Chain Manag., 24 (5), pp. 677-696; Fassam, L., Dani, S., A conceptual understanding of criminality and integrity challenges in food supply chains (2017) Br. Food J., 119 (1), pp. 67-83; Fattahi, F., Nookabadi, A.S., Kadivar, M., A model for measuring the performance of the meat supply chain (2013) Br. Food J., 115 (8), pp. 1090-1111; Fink, A., Conducting Literature Research reviews: from Paper to the Internet (1998), Sage Publications, Inc Thousand Oaks, CA; About the Food Loss and Waste Accounting and Reporting Standard (2019), https://flwprotocol.org/wp-content/uploads/2019/04/About-The-FLW-Standard.pdf, Accessed on 22/10/2021; Gantz, J., Reinsel, D., Extracting value from chaos (2011) IDC iview, 1142 (2011), pp. 1-12; Gardas, B.B., Raut, R.D., Cheikhrouhou, N., Narkhede, B.E., A hybrid decision support system for analyzing challenges of the agricultural supply chain (2019) Sustain. Prod. Consump., 18, pp. 19-32; Gardas, B., Raut, R., Jagtap, A.H., Narkhede, B., Exploring the key performance indicators of green supply chain management in agro-industry (2019) J. Modell. Manag., 14 (1), pp. 260-283; Gellynck, X., Molnár, A., Aramyan, L., Supply chain performance measurement: the case of the traditional food sector in the EU (2008) J. Chain Netw. Sci., 8 (1), pp. 47-58; Ghadimi, P., Wang, C., Lim, M.K., Sustainable supply chain modeling and analysis: past debate, present problems and future challenges (2019) Resour. Conserv. Recycl., 140, pp. 72-84; Ghezavati, V.R., Hooshyar, S., Tavakkoli-Moghaddam, R., A Benders’ decomposition algorithm for optimizing distribution of perishable products considering postharvest biological behavior in agri-food supply chain: a case study of tomato (2017) Central Eur. J. Oper. Res., 25 (1), pp. 29-54; Gholamian, M.R., Taghanzadeh, A.H., Integrated network design of wheat supply chain: a real case of Iran (2017) Comput. Electron. Agric., 140, pp. 139-147; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organizational resources: a literature review (2010) Corpor. Social Responsibil. Environ. Manag., 17 (4), pp. 230-245; Gold, S., Seuring, S., Jeff, A.N.N., Barriers of food supply chains in Africa–a Delphi study (2012) Int. J. Agric. Resour. Govern. Ecol., 9 (3-4), pp. 228-246; Govindan, K., Jafarian, A., Khodaverdi, R., Devika, K., Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food (2014) Int. J. Prod. Econ., 152, pp. 9-28; Govindan, K., Soleimani, H., Kannan, D., Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future (2015) Eur. J. Oper. Res., 240 (3), pp. 603-626; Granillo-Macías, R., Logistics optimization through a social approach for food distribution (2020) Socioecon. Plann. Sci.; Gustavsson, J., Cederberg, C., Sonesson, U., Van Otterdijk, R., Meybeck, A., Global Food Losses and Food Waste (2011), pp. 1-38. , FAO Rome; Gunders, D., Wasted: How America is Losing up to 40 Percent of its Food from Farm to Fork to Landfill (2012), http://www.indianasna.org/content/indianasna/documents/NRDC_Wasted_Food_Report.pdf, Natural Resources Defense Council (NRDC) Issue Paper:12-06-B; Harrison, B., Foley, C., Edwards, D., Donaghy, G., Outcomes and challenges of an international convention centre's local procurement strategy (2019) Tour. Manag., 75, pp. 328-339; Hermann, M., Pentek, T., Otto, B., Design principles for industrie 4.0 scenarios (2016) 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928-3937. , IEEE; Higgins, A.J., Miller, C.J., Archer, A.A., Ton, T., Fletcher, C.S., McAllister, R.R.J., Challenges of operations research practice in agricultural value chains (2010) J. Oper. Res. Soc., 61 (6), pp. 964-973; Hosseini-Motlagh, S.M., Samani, M.R.G., Saadi, F.A., A novel hybrid approach for synchronized development of sustainability and resiliency in the wheat network (2020) Comput. Electron. Agric., 168; Hosseini-Motlagh, S.M., Samani, M.R.G., Saadi, F.A., Strategic optimization of wheat supply chain network under uncertainty: a real case study (2019) Oper. Res., pp. 1-41; (2018), https://www.insightsonindia.com/wp-content/uploads/2018/03/Insights-IAS-Summary-of-Economic-Survey-2017-2018.pdf, Indian Economic Survey. accessed on date 15/12/2020; Iakovou, E., Bochtis, D., Vlachos, D., Aidonis, D., Sustainable Agrifood supply chain management (2015) Supply Chain Manag. Sustain. Food Netw., pp. 1-39; Jiang, W.X., Hu, Z.X., Liang, Y., Chen, Y.Q., Modeling and optimization of food cold-chain intelligent logistics distribution network (2015) Adv. J. Food Sci. Technol., 7 (8), pp. 573-578; Jonkman, J., Barbosa-Póvoa, A.P., Bloemhof, J.M., Integrating harvesting decisions in the design of agro-food supply chains (2019) Eur. J. Oper. Res., 276 (1), pp. 247-258; Kamble, S.S., Gunasekaran, A., Gawankar, S.A., Achieving sustainable performance in a data-driven agriculture supply chain: a review for research and applications (2020) Int. J. Prod. Econ., 219, pp. 179-194; Kambli, A., McGarvey, R.G., Network design for local agriculture using robust optimization (2020) Inf. Process. Agric.; Kaminski, J., Christiaensen, L., Post-harvest loss in Sub-Saharan Africa: what do farmers say? (2014) Glob. Food Sec., 3, pp. 149-158; Kaplan, A., Norton, R.S., The balanced scorecard – measures that drive performance (1992) Harv. Bus. Rev., 70 (1), pp. 71-91; Kataike, J., Aramyan, L.H., Schmidt, O., Molnár, A., Gellynck, X., Measuring chain performance beyond supplier–buyer relationships in agri-food chains (2019) Supply Chain Manag.; Khalili-Damghani, K., Tavana, M., Amirkhan, M., A fuzzy bi-objective mixed-integer programming method for solving supply chain network design problems under ambiguous and vague conditions (2014) Int. J. Adv. Manuf. Technol., 73 (9-12), pp. 1567-1595; Khanh Nguyen, H.T., Nang Thu, T.T., Lebailly, P., Azadi, H., Economic challenges of the export-oriented aquaculture sector in Vietnam (2019) J. Appl. Aquacult., 31 (4), pp. 367-383; Kucukvar, M., Samadi, H., Linking national food production to global supply chain impacts for the energy-climate challenge: the cases of the EU-27 and Turkey (2015) J. Clean. Prod., 108, pp. 395-408; Kummu, M., De Moel, H., Porkka, M., Siebert, S., Varis, O., Ward, P.J., Lost food, wasted resources: global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use (2012) Sci. Total Environ., 438, pp. 477-489; Kwag, S.I., Ko, Y.D., Optimal design for the Halal food logistics network (2019) Transport. Res. Part E, 128, pp. 212-228; Leat, P., Revoredo-Giha, C., Building collaborative agri-food supply chains: the challenge of relationship development in the Scottish red meat chain (2008) Br. Food J., 110 (4-5), pp. 395-411; Lorentz, H., Production locations for the internationalising food industry: case study from Russia (2008) Br. Food J., 110 (3), pp. 310-334; Lorentz, H., Kittipanya-ngam, P., Srai, J.S., Emerging market characteristics and supply network adjustments in internationalising food supply chains (2013) Int. J. Prod. Econ., 145 (1), pp. 220-232; Luo, J., Ji, C., Qiu, C., Jia, F., Agri-food supply chain management: bibliometric and content analyses (2018) Sustainability, 10 (5), p. 1573; Mahapatra, M.S., Mahanty, B., India's national food security programme: a strategic insight (2018) Sādhanā, 43 (12), p. 194; Maiyar, L.M., Thakkar, J.J., A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approach (2017) Comput. Ind. Eng., 110, pp. 30-42; Maiyar, L.M., Thakkar, J.J., Modelling and analysis of intermodal food grain transportation under hub disruption towards sustainability (2019) Int. J. Prod. Econ., 217, pp. 281-297; Maiyar, L.M., Thakkar, J.J., Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions (2020) Int. J. Prod. Res., 58 (18), pp. 5651-5675; Mangla, S.K., Luthra, S., Rich, N., Kumar, D., Rana, N.P., Dwivedi, Y.K., Enablers to implement sustainable initiatives in agri-food supply chains (2018) Int. J. Prod. Econ., 203, pp. 379-393; Mangla, S.K., Sharma, Y.K., Patil, P.P., Yadav, G., Xu, J., Logistics and distribution challenges to managing operations for corporate sustainability: study on leading Indian diary organizations (2019) J. Clean. Prod., 238; Manning, L., Monaghan, J., Integrity in the fresh produce supply chain: solutions and approaches to an emerging issue (2019) J. Horticult. Sci. Biotechnol., 94 (4), pp. 413-421; Martinez, S., del Mar Delgado, M., Marin, R.M., Alvarez, S., The environmental footprint of an organic peri-urban orchard network (2018) Sci. Total Environ., 636, pp. 569-579; Martins, C.L., Melo, M.T., Pato, M.V., Redesigning a food bank supply chain network in a triple bottom line context (2019) Int. J. Prod. Econ., 214, pp. 234-247; Mayring, P., Qualitative Inhaltanalyse – Grundlagen Und Techniken (Qualitative Content Analysis – Basics and Techniques) (2003), 8th Edition Beltz Verlag Weinheim; McCullough, E., Stamoulis, K., Pingali, P., (2010) The Transformation of Agri-Food systems: Globalization, Supply Chains and Smallholder Farmers, , Earthscan London; Meredith, J., Theory building through conceptual methods (1993) Int. J. Oper. Prod. Manag., 13 (5), pp. 3-11; Messner, R., Johnson, H., Richards, C., From surplus-to-waste: a study of systemic overproduction, surplus and food waste in horticultural supply chains (2021) J. Clean. Prod., 278; Miranda-Ackerman, M.A., Azzaro-Pantel, C., Aguilar-Lasserre, A.A., A green supply chain network design framework for the processed food industry: application to the orange juice agrofood cluster (2017) Comput. Ind. Eng., 109, pp. 369-389; Moazzam, M., Akhtar, P., Garnevska, E., Marr, N.E., Measuring agri-food supply chain performance and risk through a new analytical framework: a case study of New Zealand dairy (2018) Prod. Plann. Control, 29 (15), pp. 1258-1274; Mogale, D.G., Cheikhrouhou, N., Tiwari, M.K., Modelling of sustainable food grain supply chain distribution system: a bi-objective approach (2020) Int. J. Prod. Res., 58 (18), pp. 5521-5544; Mogale, D.G., Dolgui, A., Kandhway, R., Kumar, S.K., Tiwari, M.K., A multi-period inventory transportation model for tactical planning of food grain supply chain (2017) Comput. Ind. Eng., 110, pp. 379-394; Mogale, D.G., Ghadge, A., Kumar, S.K., Tiwari, M.K., Modelling supply chain network for procurement of food grains in India (2020) Int. J. Prod. Res., 58 (21), pp. 6493-6512; Mogale, D.G., Kumar, M., Kumar, S.K., Tiwari, M.K., Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network (2018) Transport. Res. Part E, 111, pp. 40-69; Mogale, D.G., Kumar, S.K., Márquez, F.P.G., Tiwari, M.K., Bulk wheat transportation and storage problem of public distribution system (2017) Comput. Ind. Eng., 104, pp. 80-97; Mogale, D.G., Kumar, S.K., Tiwari, M.K., Green food supply chain design considering risk and post-harvest losses: a case study (2020) Ann. Oper. Res., 295, pp. 257-284; Mohammed, A., Wang, Q., Developing a meat supply chain network design using a multi-objective possibilistic programming approach (2017) Br. Food J., 119 (3), pp. 690-706; Mohammed, A., Wang, Q., The fuzzy multi-objective distribution planner for a green meat supply chain (2017) Int. J. Prod. Econ., 184, pp. 47-58; Mohammadi, Z., Barzinpour, F., Teimoury, E., Designing sustainable supply chain network by considering direct and indirect shipment: evidence from food industry (2020) Decis. Sci. Lett., 9 (3), pp. 323-336; Mohebalizadehgashti, F., Zolfagharinia, H., Amin, S.H., Designing a green meat supply chain network: a multi-objective approach (2020) Int. J. Prod. Econ., 219, pp. 312-327; Motevalli-Taher, F., Paydar, M.M., Emami, S., Wheat sustainable supply chain network design with forecasted demand by simulation (2020) Comput. Electron. Agric., 178; Murthy, D.S., Sudha, M., Gajanana, T.M., Marketing and post-harvest losses in fruits: its implications on availability and economy (2009) Indian J. Agric. Econ., 64 (2), pp. 259-275; Naik, G., Suresh, D.N., Challenges of creating sustainable agri-retail supply chains (2018) IIMB Manag. Rev., 30 (3), pp. 270-282; Nahman, A., de Lange, W., Costs of food waste along the value chain: evidence from South Africa (2013) Waste Manage., 33 (11), pp. 2493-2500; Ogier, M., Cung, V.D., Boissière, J., Service network design in short and local fresh food supply chain (2013) RAIRO-Oper. Res., 47 (4), pp. 445-464; Oglethorpe, D., Optimising economic, environmental, and social objectives: a goal-programming approach in the food sector (2010) Environ. Plan. A., 42 (5), pp. 1239-1254; Omolayo, Y., Feingold, B.J., Neff, R.A., Romeiko, X.X., Life cycle assessment of food loss and waste in the food supply chain (2021) Resour. Conserv. Recycl., 164; Orgut, I.S., Ivy, J., Uzsoy, R., Wilson, J.R., Modeling for the equitable and effective distribution of donated food under capacity constraints (2016) IIE Trans., 48 (3), pp. 252-266; Orjuela-Castro, J.A., Orejuela-Cabrera, J.P., Adarme-Jaimes, W., Last mile logistics in mega-cities for perishable fruits (2019) J. Ind. Eng. Manag., 12 (2), pp. 318-327; Paciarotti, C., Torregiani, F., The logistics of the short food supply chain: a literature review (2021) Sustain. Prod. Consumpt., 26, pp. 428-442; Pan, S., Ballot, E., Fontane, F., Hakimi, D., Environmental and economic issues arising from the pooling of SMEs’ supply chains: case study of the food industry in western France (2014) Flexible Serv. Manuf. J., 26 (1-2), pp. 92-118; Pant, K., Yadav, V.S., Singh, A.R., Design of multi-tier multi-time horizon closed-loop supply chain network with sustainability under uncertain environment for Indian paper industry (2021) Int. J. Sustain. Eng., 14 (2), pp. 107-122; Parfitt, J., Barthel, M., Macnaughton, S., Food waste within food supply chains: quantification and potential for change to 2050 (2011) Philos. Trans. R. Soc. B, 365, pp. 3065-3081; Patidar, R., Agrawal, S., Pratap, S., Development of novel strategies for designing sustainable Indian agri-fresh food supply chain (2018) Sādhanā, 43 (10), p. 167; Patidar, R., Agrawal, S., A mathematical model formulation to design a traditional Indian agri-fresh food supply chain: a case study problem (2020) Benchmarking, 27 (8), pp. 2341-2363; Patidar, R., Agrawal, S., Restructuring the Indian agro-fresh food supply chain network: a mathematical model formulation (2020) Clean Technol. Environ. Policy, 22 (10), pp. 2053-2077; Pourmohammadi, F., Teimoury, E., Gholamian, M., A scenario-based stochastic programming approach for designing and planning wheat supply chain (A case study) (2020) Decis. Sci. Lett.s, 9 (4), pp. 537-546; Rahbari, M., Hajiagha, S.H.R., Dehaghi, M.R., Moallem, M., Dorcheh, F.R., Modeling and solving a five-echelon location–inventory–routing problem for red meat supply chain (2020) Kybernetes, 50 (1), pp. 66-99; Randeree, K., Challenges in halal food ecosystems: the case of the United Arab Emirates (2019) Br. Food J., 121 (5), pp. 1154-1167; Raut, R.D., Luthra, S., Narkhede, B.E., Mangla, S.K., Gardas, B.B., Priyadarshinee, P., Examining the performance oriented indicators for implementing green management practices in the Indian agro sector (2019) J. Clean. Prod., 215, pp. 926-943; Rocco, C.D., Morabito, R., Robust optimisation approach applied to the analysis of production/logistics and crop planning in the tomato processing industry (2016) Int. J. Prod. Res., 54 (19), pp. 5842-5861; Rohmer, S.U.K., Gerdessen, J.C., Claassen, G.D.H., Sustainable supply chain design in the food system with dietary considerations: a multi-objective analysis (2019) Eur. J. Oper. Res., 273 (3), pp. 1149-1164; Rong, A., Akkerman, R., Grunow, M., An optimization approach for managing fresh food quality throughout the supply chain (2011) Int. J. Prod. Econ., 131 (1), pp. 421-429; Routroy, S., Bahera, A., Agriculture supply chain: a systematic review of literature and implications for future research (2007) J. Agribus. Dev. Emerg. Econ., 7 (3), pp. 275-302; Roy, P., Nei, D., Okadome, H., Nakamura, N., Orikasa, T., Shiina, T., Life cycle inventory analysis of fresh tomato distribution systems in Japan considering the quality aspect (2008) J. Food Eng., 86 (2), pp. 225-233; Sanjika, T.M., Bezuidenhout, C.N., Driving factors-based approach for identifying performance indicators in sugarcane supply and processing systems (2015) Br. Food J., 117 (6), pp. 1652-1669; Saputri, V.H.L., Sutopo, W., Hisjam, M., Ma'aram, A., Sustainable agri-food supply chain performance measurement model for GMO and Non-GMO using data envelopment analysis method (2019) Appl. Sci., 9 (6), p. 1199; Sarjono, H., Sanny, L., Melati, I., Supply chain design of potato commodity in wonosobo regency, Central Java – Indonesia (2018) Int. J. Eng. Technol., 7 (4), pp. 42-45; Scherhaufer, S., Moates, G., Hartikainen, H., Waldron, K., Obersteiner, G., Environmental impacts of food waste in Europe (2018) Waste Manage. (Oxford), 77, pp. 98-113; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod., 16 (15), pp. 1699-1710; Sgarbossa, F., Russo, I., A proactive model in sustainable food supply chain: insight from a case study (2017) Int. J. Prod. Econ., 183, pp. 596-606; Sharma, V.K., Chandna, P., Bhardwaj, A., Green supply chain management related performance indicators in agro industry: a review (2017) J. Clean. Prod., 141, pp. 1194-1208; Sharma, Y.K., Mangla, S.K., Patil, P.P., Liu, S., When challenges impede the process: for circular economy-driven sustainability practices in food supply chain (2019) Manag. Decis., 57 (4), pp. 995-1017; Shekarian, S., Amin, S.H., Shah, B., Tosarkani, B.M., Design and optimisation of a soybean supply chain network under uncertainty (2020) Int. J. Bus. Perform. Supply Chain Modell., 11 (2), pp. 176-200; Shukla, M., Jharkharia, S., Agri-fresh produce supply chain management: a state-of-the-art literature review (2013) Int. J. Oper. Prod. Manag., 33 (2), pp. 114-158; Shaabani, H., Kamalabadi, I.N., An efficient population-based simulated annealing algorithm for the multi-product multi-retailer perishable inventory routing problem (2016) Comput. Ind. Eng., 99, pp. 189-201; Song, B.D., Ko, Y.D., A vehicle routing problem of both refrigerated-and general-type vehicles for perishable food products delivery (2016) J. Food Eng., 169, pp. 61-71; Soto-Silva, W.E., Nadal-Roig, E., González-Araya, M.C., Pla-Aragones, L.M., Operational research models applied to the fresh fruit supply chain (2016) Eur. J. Oper. Res., 251 (2), pp. 345-355; Soysal, M., Bloemhof-Ruwaard, J.M., Haijema, R., van der Vorst, J.G., Modeling a green inventory routing problem for perishable products with horizontal collaboration (2018) Comput. Oper. Res., 89, pp. 168-182; Sufiyan, M., Haleem, A., Khan, S., Khan, M.I., Evaluating food supply chain performance using hybrid fuzzy MCDM technique (2019) Sustain. Prod. Consumpt., 20, pp. 40-57; Taghikhah, F., Voinov, A., Shukla, N., Filatova, T., Anufriev, M., Integrated modeling of extended agro-food supply chains: a systems approach (2021) Eur. J. Oper. Res., 288 (3), pp. 852-868; Tasca, A.L., Nessi, S., Rigamonti, L., Environmental sustainability of agri-food supply chains: an LCA comparison between two alternative forms of production and distribution of endive in northern Italy (2017) J. Clean. Prod., 140, pp. 725-741; Thomas-Francois, K., von Massow, M., Joppe, M., Service-oriented, sustainable, local food value chain–a case study (2017) Ann. Tour. Res., 65, pp. 83-96; Tsolakis, N., Anastasiadis, F., Srai, J., Sustainability performance in food supply networks: insights from the UK industry (2018) Sustainability, 10 (9), p. 3148; (2021), https://www.unep.org/resources/report/unep-food-waste-index-report-2021, UNEP Report. Food Waste Index Report 2021. Accessed on 15/07/2021; Utomo, D.S., Onggo, B.S., Eldridge, S., Applications of agent-based modelling and simulation in the agri-food supply chains (2018) Eur. J. Oper. Res., 269 (3), pp. 794-805; Validi, S., Bhattacharya, A., Byrne, P.J., A case analysis of a sustainable food supply chain distribution system—A multi-objective approach (2014) Int. J. Prod. Econ., 152, pp. 71-87; Van Der Vorst, J.G., Tromp, S.O., Zee, D.J.V.D., Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics (2009) Int. J. Prod. Res., 47 (23), pp. 6611-6631; Viet, N.Q., Behdani, B., Bloemhof, J., Data-driven process redesign: anticipatory shipping in agro-food supply chains (2020) Int. J. Prod. Res., 58 (5), pp. 1302-1318; Viswanadham, N., Can India be the food basket for the world? (2006) Achieving Rural and Global Supply Chain Excellence: The Indian Way, pp. 9-22. , N. Vishwanadham GLAMS Hyderabad; Viswanadham, N., Kameshwaran, S., The Supply Chain Ecosystem Framework (2013), pp. 17-44. , World Scientific Book Chapters; Vitali, A., Grossi, G., Martino, G., Bernabucci, U., Nardone, A., Lacetera, N., Carbon footprint of organic beef meat from farm to fork: a case study of short supply chain (2018) J. Sci. Food Agric., 98 (14), pp. 5518-5524; Vlachos, I.P., Key performance indicators of the impact of radio frequency identification technologies on supply chain management (2013) Int. J. RF Technol., 4 (2), pp. 127-146; Vrat, P., Gupta, R., Bhatnagar, A., Pathak, D.K., Fulzele, V., Literature review analytics (LRA) on sustainable cold-chain for perishable food products: research trends and future directions (2018) OPSEARCH, 55 (3-4), pp. 601-627; Wei, C., Asian, S., Ertek, G., Hu, Z.H., Location-based pricing and channel selection in a supply chain: a case study from the food retail industry (2018) Ann. Oper. Res., 291, pp. 959-984; Wognum, P.N., Bremmers, H., Trienekens, J.H., van der Vorst, J.G., Bloemhof, J.M., Systems for sustainability and transparency of food supply chains–Current status and challenges (2011) Adv. Eng. Inf., 25 (1), pp. 65-76; Wu, P.J., Huang, P.C., Business analytics for systematically investigating sustainable food supply chains (2018) J. Clean. Prod., 203, pp. 968-976; Yadav, V.S., Singh, A.R., Raut, R.D., Cheikhrouhou, N., Blockchain drivers to achieve sustainable food security in the Indian context (2021) Ann. Oper. Res., , In press; Yadav, V.S., Singh, A.R., Raut, R.D., Govindarajan, U.H., Blockchain technology adoption barriers in the Indian agricultural supply chain: an integrated approach (2020) Resour. Conserv. Recycl., 161; Yadav, V.S., Singh, A.R., Use of blockchain to solve select issues of Indian farmers (2019) AIP Conf. Proceed., 2148 (1). , AIP Publishing LLC; Yakavenka, V., Mallidis, I., Vlachos, D., Iakovou, E., Eleni, Z., Development of a multi-objective model for the design of sustainable supply chains: the case of perishable food products (2020) Ann. Oper. Res., 294 (1), pp. 593-621; Yakovleva, N., Measuring the sustainability of the food supply chain: a case study of the UK (2007) J. Environ. Plann. Policy Manage., 9 (1), pp. 75-100; Yakovleva, N., Sarkis, J., Sloan, T., Sustainable benchmarking of supply chains: the case of the food industry (2012) Int. J. Prod. Res., 50 (5), pp. 1297-1317; Yurt, O., Kota, L., Jarmai, K., Aglamaz, E., Analysis and optimisation of an olive oil supply chain: a case from Turkey (2019) Int. J. Sustain. Agric. Manag. Inform., 5 (1), pp. 59-79; Zambon, I., Cecchini, M., Egidi, G., Saporito, M.G., Colantoni, A., Revolution 4.0: industry vs. agriculture in a future development for SMEs (2019) Processes, 7 (1), p. 36; Zanoni, S., Mazzoldi, L., Ferretti, I., Eco-efficient cold chain networks design (2019) Int. J. Sustain. Eng., 12 (5), pp. 349-364; Zhu, Z., Chu, F., Dolgui, A., Chu, C., Zhou, W., Piramuthu, S., Recent advances and opportunities in sustainable food supply chain: a model-oriented review (2018) Int. J. Prod. Res., 56 (17), pp. 5700-5722},
correspondence_address1={Singh, A.R.; Department of Mechanical Engineering, India; email: arsingh.mech@nitrr.ac.in},
publisher={Elsevier B.V.},
issn={23525509},
language={English},
abbrev_source_title={Sustain. Prod. Consum.},
document_type={Review},
source={Scopus},
}

@CONFERENCE{Sardjono2021109,
author={Sardjono, W. and Johan, G.N. and Utomo, H. and Sukardi, S. and Rahmasari, A.},
title={SUPPLY CHAIN MEASUREMENT IN THE INDUSTRY 4.0 USING BALANCED SCORECARD},
journal={ACM International Conference Proceeding Series},
year={2021},
pages={109-114},
doi={10.1145/3512576.3512598},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128635752&doi=10.1145%2f3512576.3512598&partnerID=40&md5=03fe8356a87ce116b73f31f6e42b453f},
affiliation={Information Systems Management Department, Binus Graduate Program, Bina Nusantara University, Indonesia; School of Information Information, Systems Management, BINA NUSANTARA University, Indonesia; Faculty of Economy and Business, Department of Management, Gajah Mada University, Indonesia; Faculty of Economy and Business, Graduate Program, University of Indonesia, Indonesia; International Relations Department, Faculty of Humanities, Bina Nusantara University, Indonesia},
abstract={Industry 4.0 is a topic that attracts attention for practitioners and researchers because it has not been explored much. Technological developments that occur have disrupted existing business processes in the company. The supply chain is part of the company's business activities that are affected by technological developments of existing technology. Therefore, there is a need for realignment between technological developments and supply chain so that measurements can be made regarding supply chain performance in a company. This research provides a navigation that can be used by practitioners to measure performance in supply chains that have been integrated with industry 4.0. In addition, to develop research related to the Supply Chain performance measurement method using the balanced scorecard in the industrial era 4.0. © 2021 ACM.},
author_keywords={Balanced Scorecard;  Industry 4.0;  Performance;  Supply Chain},
keywords={Industrial research;  Industry 4.0, Balanced scorecards;  Business activities;  Business Process;  Measurement methods;  Performance;  Supply chain performance;  Supply chain performance measurements;  Technological development, Supply chains},
references={Mittal, S., Muztoba, A.K., Wuest, T., A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs (2018) Journal of Manufacturing, 49, pp. 194-214; Pacchini, A.P.T., Lucato, W.C., Mummolo, G., The degree of readiness for the implementation of Industry 4.0 (2019) Computers in Industry, 113. , Article 103125; Adeyeri, M., From Industry 3.0 to Industry 4.0: Smart Predictive Maintenance System as Platform for Leveraging (2018) Arctic, 71, pp. 64-81; Mahmood, T., Mubarik, M.S., Balancing innovation and exploitation in the fourth industrial revolution: Role of intellectual capital and technology absorptive capacity (2020) Technological Forecasting and Social Change, 160. , Article 120248; Kaivo-Oja, R.J., Lauraeus, T., Human factors and ergonomics in manufacturing in the industry 4.0 context-A scoping review (2021) Technology in Society, 65. , Article 101572; Yuanchun Zhang, Z., Montenegro-Marin, C.E., Vicente, G.D., Holistic cognitive conflict chain management framework in supply chain management (2021) Environmental Impact Assessment Review, 88. , Article 106564; Kumar, S., Tiwari, P., Zymbler, M., Internet of Things is a revolutionary approach for future technology enhancement: A review (2019) Journal of Big Data, 6, p. 1; Liao, A.Y., Nattuthurai, P., Li, B., The emerging big data analytics and IoT in supply chain management: A systematic review (2020) Journal of Supply Chain Management, 25 (2), pp. 141-156; Nikookar, E., Varsei, M., Wieland, A., Gaining from disorder: Making the case for antifragility in purchasing and supply chain management (2021) Journal of Purchasing and Supply Management, 27 (3). , Article 100699; Morgane, M.F., Salomée, R., Rainer, H., Sustainability management in supplay chains: The role of familiness (2021) Technological Forecasting and Social Change, 173. , Article 121078; LeMay, S., Helms, M.M., Kimball, B., McMahon, D., Supply chain management: The elusive concept & definition (2017) International Journal of Logistics Management, 28 (4), pp. 1425-1453; Carter, C.R., Rogers, D.S., Choi, T.Y., Toward the theory of the supply chain (2015) Journal of Supply Chain Management, 51 (2), pp. 89-97; Jorn-Henrik, T., Angles of Integration: An Empirical Analysis of the Alignment of Internet-Based Information Technology and Global Supply Chain Integration (2010) Journal of Supply Chain Management, 46, p. 2; Ellram, L.M., Cooper, M.C., Supply chain management: It's all about the journey, not the destination (2014) Journal of Supply Chain Management, 50 (1), pp. 8-20; Zachary, R.A., Gerald, R.F., David, M.S., Overqualified human resources, career development experiences, and work outcomes: Leverage an underutilized resource with political skill (2016) Human Resource Management Review, 26 (2), pp. 125-135; Tian, X., Joseph, S., Expanding Expanding green supply chain performance measurement through emergy accounting and analysis (2019) International Journal of Production Economics, 225. , Article 107576; Chao, F., Shanlin, Y., The combination of dependence-based interval-valued evidential reasoning approach with balanced scorecard for performance assessment (2021) Expert Systems with Applications, 39 (3), pp. 3717-3730; Sangjae Lee, L., Costello, F.J., Chang, L.K., Hierarchical balanced scorecardbased organizational goals and the efficiency of controls processes (2021) Journal of Business Research, 132, pp. 270-288; Guix, M., Xavier, F., The Materiality Balanced Scorecard: A framework for stakeholder-led integration of sustainable hospitality management and reporting (2020) International Journal of Hospitality Management, 91. , Article 102634; Centobelli, S.P., Myriam, E., Agile supply chain management: Where did it come from and where will it go in the era of digital transformation? (2020) Industrial Marketing Management, 90, pp. 324-345; Navin, K.D., Shankar, R., Jingxin, D., Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture (2018) Computers & Industrial Engineering, 128, pp. 1076-1087; Lima-Junior, F.R., Carpinetti, L.C.R., An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCORRmetrics (2019) Computers & Industrial Engineering, 139. , Article 106191},
publisher={Association for Computing Machinery},
isbn={9781450384971},
language={English},
abbrev_source_title={ACM Int. Conf. Proc. Ser.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Putridewi2021,
author={Putridewi, A. and Rizal, R. and Sari, D.S.},
title={Performance Analysis of the Cement Industry Based on Green Supply Chain Management},
journal={E3S Web of Conferences},
year={2021},
volume={328},
doi={10.1051/e3sconf/202132808001},
art_number={08001},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147036046&doi=10.1051%2fe3sconf%2f202132808001&partnerID=40&md5=4ee8eef2c53b7cc497808192ea1de837},
affiliation={Department of Industrial Engineering, Faculty of Engineering, Universitas Pembangunan Nasional Veteran Jakarta, Indonesia},
abstract={This study aims to provide information to companies regarding the latest performance conditions of environmentally friendly supply chain management through the design of the green supply operation reference and analytical hierarchy process (AHP) performance measurement system model, as well as to assess the company's GSCM performance which is then used to formulate recommendations for improving performance indicators that are considered inadequate good. The results of this study identified 18 performance indicators from 5 appropriate processes used in measuring the performance of PT XYZ's GSCM. Overall, the company's GSCM performance got a score of 77 which was included in the good category, but there were five indicators that had poor performance scores and recommendations for improvement were needed. © The Authors, published by EDP Sciences.},
author_keywords={AHP;  GSCM;  Performance Indicators},
references={Cash, R., Wilkerson, T., (2003) GreenSCOR: Developing A Green Supply Chain Analytical Tool; Davies, J., Hochman, S., The greening of the supply chain (2007) Supply Chain Management Review, 11 (5), pp. 13-14. , 2007; (2017) Development of State of The Art-Techniques in Cement Manufacturing: Trying to Look Ahead, , European Cement Research Academy; Cement Sustainability Initiative, Ed. CSI/ECRA-Technology Papers 2017; Govidan, K., Soleimani, H., Kannan, D., Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future (2015) European Journal of Operational Research, 603-626 (3), p. 240; Jayant, A., Azhar, M., Analysis of the barriers for implementing green supply chain management (GSCM) practices: An interpretive structural modelling (ISM) approach (2014) Procedia Engineering, 97; Rizal, R., Analysis of the ambient air environment of the steam power plant industry (2021) IOP Conference Series: Materials Science and Engineering, 12086 (1), p. 1125; Saaty, T.L., (1994) Fundamental of Decision Making and Priority Theory with The Analytic, , Pittburgh: RWS Publication; Scc, S.C.C., (2010) Supply Chain Operations Reference Model SCOR Version 10.0, , The Supply Chain Council, Inc. SCOR: The Supply Chain Reference ISBN; Schrödl, H., Simkin, P., (2013) A SCOR Perspective on Green SCM; Sumiati, E., Suryaningsih, E., Perbaikan Produksi Jamur Tiram dengan Modifikasi Bahan Baku Utama Media Bibit (2006) Jurnal Hortikultura, 16 (2); Srivastava, S.K., Green supply chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, Supply Chain Management Function, Business Strategy and The Environment, 14, pp. 123-139; Trienekens, J.H., Hvolby, H.H., Performance measurement and improvement in supply chains (2000) Proceedings of The Third CINET Conference; CI 2000 From Improvement to Innovation: CINET Conference: CI 2000 From Improvement to Innovation, pp. 399-409. , Aalborg, September 18-19, 2000; Walker, H., Sistob, L., McBainc, D., Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors (2008) J. of Purchasing & Supply Management, 14, pp. 69-85; Zhu, Q., Sarkis, J., Lai, K.H., Confirmation of measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, pp. 261-273},
correspondence_address1={Putridewi, A.; Department of Industrial Engineering, Indonesia; email: almaashputridewi@upnvj.ac.id},
editor={Wahab I.H.A., Mardian R., Bayuseno A.P.},
publisher={EDP Sciences},
issn={25550403},
language={English},
abbrev_source_title={E3S Web Conf.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kasparis2021,
author={Kasparis, E. and Huang, Y. and Lin, W. and Vasilakis, C.},
title={Improving timeliness in the neglected tropical diseases preventive chemotherapy donation supply chain through information sharing: A retrospective empirical analysis},
journal={PLoS Neglected Tropical Diseases},
year={2021},
volume={15},
number={11},
doi={10.1371/journal.pntd.0009523},
art_number={e0009523},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120609816&doi=10.1371%2fjournal.pntd.0009523&partnerID=40&md5=224340904584a7f19da12590089df963},
affiliation={Department for Health, University of Bath, Bath, United Kingdom; Trinity Business School, Trinity College Dublin, Dublin, Ireland; School of Management, University of Bath, Bath, United Kingdom},
abstract={Background Billions of doses of medicines are donated for mass drug administrations in support of the World Health Organization’s “Roadmap to Implementation,” which aims to control, eliminate, and eradicate Neglected Tropical Diseases (NTDs). The supply chain to deliver these medicines is complex, with fragmented data systems and limited visibility on performance. This study empirically evaluates the impact of an online supply chain performance measurement system, “NTDeliver,” providing understanding of the value of information sharing towards the success of global health programs. Methods Retrospective secondary data were extracted from NTDeliver, which included 1,484 shipments for four critical medicines ordered by over 100 countries between February 28, 2006 and December 31, 2018. We applied statistical regression models to analyze the impact on key performance metrics, comparing data before and after the system was implemented. Findings The results suggest information sharing has a positive association with improvement for two key performance indicators: purchase order timeliness (β = 0.941, p = 0.003) and—most importantly—delivery timeliness (β = 0.828, p = 0.027). There is a positive association with improvement for three variables when the data are publicly shared: shipment timeliness (β = 2.57, p = 0.001), arrival timeliness (β = 2.88, p = 0.003), and delivery timeliness (β = 2.82, p = 0.011). Conclusions Our findings suggest that information sharing between the NTD program partners via the NTDeliver system has a positive association with supply chain performance improvements, especially when data are shared publicly. Given the large volume of medicine and the significant number of people requiring these medicines, information sharing has the potential to provide improvements to global health programs affecting the health of tens to hundreds of millions of people. © 2021 Kasparis et al.},
keywords={Article;  chemotherapy;  evidence based medicine;  global health;  health care delivery;  health care system;  health program;  human;  human experiment;  information processing;  key performance indicator;  neglected disease;  performance measurement system;  retrospective study;  timeliness;  total quality management;  tropical disease;  chemoprophylaxis;  information dissemination;  neglected disease;  tropical medicine, Chemoprevention;  Humans;  Information Dissemination;  Neglected Diseases;  Retrospective Studies;  Tropical Medicine},
references={A Roadmap for Implementation: Accelerating work to overcome the global impact of neglected tropical diseases 2012, , http://www.who.int/neglected_diseases/NTD_RoadMap_2012_Fullversion.pdf; Bradley, M, Taylor, R, Jacobson, J, Guex, M, Hopkins, A, Jensen, J, Medicine donation programmes supporting the global drive to end the burden of neglected tropical diseases (2021) Transactions of The Royal Society of Tropical Medicine and Hygiene, 115 (2), pp. 136-144. , https://doi.org/10.1093/trstmh/traa167, PMID: 33452881; Souza, AA, Holloway, C, Williams, T., The NTD Supply Chain Forum—Strengthening the backbone of NTD programs (2020) PLOS Neglected Tropical Diseases, 14 (11). , https://doi.org/10.1371/journal.pntd.0008818, PMID: 33152744; https://unitingtocombatntds.org/reports/5th-report/ntds-and-uhc/, Uniting to Combat NTDs; Linehan, M, Baker, M, Hanson, C, Ottesen, EA, Torres, S, Sankara, D, Integrated Implementation of Programs Targeting Neglected Tropical Diseases through Preventive Chemotherapy: Proving the Feasibility at National Scale (2011) The American Journal of Tropical Medicine and Hygiene, 84 (1), pp. 5-14. , https://doi.org/10.4269/ajtmh.2011.10-0411, PMID: 21212194; (2017) Weekly Epidemiological Record, 92, pp. 145-164. , https://www.who.int/wer/2017/wer9213/en/, 31 March 13 –). World Health Organization. World Health Organization; 2017; Koporc, KM, Strunz, E, Holloway, C, Addiss, DG, Lin, W., Assessing “first mile” supply chain factors affecting timeliness of school-based deworming interventions: Supply and logistics performance indicators (2015) PLOS Neglected Tropical Diseases, 9 (12). , https://doi.org/10.1371/journal.pntd.0004115, PMID: 26657842; Sankar, G., Partners come together to solve supply chain challenges in global health (2019) Children Without Worms, , https://childrenwithoutworms.org/partners-come-together-to-solve-supply-chain-challenges-in-global-health/; Kumar, RS, Pugazhendhi, S., Information sharing in supply chains: An overview (2012) Procedia Engineering, 38, pp. 2147-2154; Li, S, Lin, B., Accessing information sharing and information quality in supply chain management (2006) Decision Support Systems, 42 (3), pp. 1641-1656; Lotfi, Z, Mukhtar, M, Sahran, S, Zadeh, AT., Information sharing in supply chain management (2013) Procedia Technology, 11, pp. 298-304; Hart, P, Saunders, C., Power and trust: Critical factors in the adoption and use of electronic data interchange (1997) Organization Science, 8 (1), pp. 23-42; Ghosh, A, Fedorowicz, J., The role of trust in supply chain governance (2008) Business Process Management Journal, 14 (4), pp. 453-470; Kembro, J, Selviaridis, K., Exploring information sharing in the extended supply chain: an interdependence perspective (2015) Supply Chain Management: An International Journal, 20 (4), pp. 455-470; Abidi, H, de Leeuw, S, Klumpp, M., Humanitarian supply chain performance management: a systematic literature review (2014) Supply Chain Management: An International Journal, 19 (5), pp. 592-608. , (/6); Cosgriff, CV, Ebner, DK, Celi, LA., Data sharing in the era of COVID-19 (2020) The Lancet Digital Health, 2 (5). , https://doi.org/10.1016/S2589-7500(20)30082-0, May 1; PMID: 32373785; Gardner, L, Ratcliff, J, Dong, E, Katz, A., A need for open public data standards and sharing in light of COVID-19 (2021) The Lancet Infectious Diseases, 21 (4). , https://doi.org/10.1016/S1473-3099(20)30635-6, Aug 10; PMID: 32791042; Newton, PN, Bond, KC, Adeyeye, M, Antignac, M, Ashenef, A, Awab, GR, COVID-19 and risks to the supply and quality of tests, drugs, and vaccines (2020) The Lancet Global Health, 8 (6); Al-Mandhari, AS, Brennan, RJ, Abubakar, A, Hajjeh, R., Tackling COVID-19 in the Eastern Mediterranean Region (2020) The Lancet, 396 (10265), pp. 1786-1788. , https://doi.org/10.1016/S0140-6736(20)32349-7, Nov 19; PMID: 33220856; Sharma, A, Gupta, P, Jha, R., COVID-19: Impact on health supply chain and lessons to be learnt (2020) Journal of Health Management, 22 (2), pp. 248-261. , Aug 11; Sharma, A, Adhikary, A, Borah, SB., Covid-19’s impact on supply chain decisions: Strategic insights from NASDAQ 100 firms using Twitter data (2020) Journal of Business Research, 117, pp. 443-449. , https://doi.org/10.1016/j.jbusres.2020.05.035, Jun 12; PMID: 32834209; Secure Data Kit, , https://www.securedatakit.com/casestudy/Real-Time-Tracking-of-Donated-Supplies-Anywhere-in-the-World; (2013) Joint Request for Selected Preventive Chemotherapy Medicines and Joint Reporting Form: A User Guide, , https://www.who.int/neglected_diseases/preventive_chemotherapy/reporting/JointForms_User_Guide.pdf, World Health Organization; (2010) Monitoring drug coverage for preventive chemotherapy–Manual, , https://www.who.int/neglected_diseases/preventive_chemotherapy/resources/9789241599993/en/, World Health Organization. World Health Organization; Bornbusch, A, Bates, J., Multiplicity in public health supply systems: a learning agenda (2013) Global Health: Science and Practice, 1 (2), pp. 154-159. , https://doi.org/10.9745/GHSP-D-12-00042, PMID: 25276528; Home | Logistics Performance Index, , https://lpi.worldbank.org/, The World Bank; [cited 2021Oct26]; Casson, RJ, Farmer, LDM., Understanding and checking the assumptions of linear regression: a primer for medical researchers (2014) Clinical and Experimental Ophthalmology, 42 (6), pp. 590-596. , https://doi.org/10.1111/ceo.12358, PMID: 24801277; Lee, SJ, Leipzig, RM, Walter, LC., Incorporating Lag Time to Benefit into Prevention Decisions for Older Adults (2013) JAMA, 310 (24), p. 2609. , https://doi.org/10.1001/jama.2013.282612, PMID: 24322396; Campbell, D. T., Stanley, J. C., (1963) Experimental and quasi-experimental designs for research, , Boston: Houghton Mifflin. (Knapp, 2016); Privett, N, Gonsalvez, D., The top ten global health supply chain issues: Perspectives from the field (2014) Operations Research for Health Care, 3 (4), pp. 226-230; Harris, AD, McGregor, JC, Perencevich, EN, Furuno, JP, Zhu, J, Peterson, DE, The use and interpretation of quasi-experimental studies in medical informatics (2006) Journal of the American Medical Informatics Association, 13 (1), pp. 16-23. , https://doi.org/10.1197/jamia.M1749, PMID: 16221933; Wickremasinghe, D, Gautham, M, Umar, N, Berhanu, D, Schellenberg, J, Spicer, N., "It’s about the idea hitting the bull’s eye": How aid effectiveness can catalyse the scale-up of health innovations (2018) International Journal of Health Policy and Management, 7 (8), pp. 718-727. , https://doi.org/10.15171/ijhpm.2018.08, PMID: 30078292; (2020) COVID-19: WHO issues interim guidance for implementation of NTD programmes, , https://www.who.int/neglected_diseases/news/COVID19-WHO-interim-guidance-implementation-NTD-programmes/en/, World Health Organization. World Health Organization; (2020) Ending the neglect to attain the Sustainable Development Goals: A road map for neglected tropical diseases 2021–2030, , https://www.who.int/publications/i/item/9789240010352, World Health Organization. World Health Organization; Rajkotia, Y., Beware of the success cartel: a plea for rational progress in global health (2018) BMJ Global Health, 3 (6). , https://doi.org/10.1136/bmjgh-2018-001197, PMID: 30498593},
correspondence_address1={Kasparis, E.; Department for Health, United Kingdom; email: elena.kasparis@bath.edu},
publisher={Public Library of Science},
issn={19352727},
pubmed_id={34843476},
language={English},
abbrev_source_title={PLoS. Negl. Trop. Dis.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Vegter2021,
author={Vegter, D. and van Hillegersberg, J. and Olthaar, M.},
title={Performance measurement systems for circular supply chain management: Current state of development},
journal={Sustainability (Switzerland)},
year={2021},
volume={13},
number={21},
doi={10.3390/su132112082},
art_number={12082},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118534908&doi=10.3390%2fsu132112082&partnerID=40&md5=aa844462c9cc4462f892eab41ae6fa65},
affiliation={Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, 7522 NB, Netherlands; Academy of Economics and Logistics, NHL Stenden University of Applied Sciences, Leeuwarden, 8917 DD, Netherlands},
abstract={With circular supply chain management, the need arises to measure circularity as well as economic, environmental, and social performance. This places higher demands on performance measurement systems for circular supply chain management. The current paper studies the extent to which performance measurement systems for circular supply chain management can meet these higher demands. To gain this insight, a systematic literature review is conducted. Moreover, existing definitions of circular supply chain management are reviewed and an improved definition of circular supply chain management is developed. The current paper finds, among other things, that the literature on performance measurement systems for circular supply chain management is scarce. Moreover, most of the available performance measurement systems for circular supply chain management have not been tested in practice and are therefore still in an early phase of development. Furthermore, none of the available performance measurement systems for circular supply chain management meet the requirements for effective performance measurement systems. More specifically, including interdependencies between performance measures is a requirement that none of the performance measurement systems for circular supply chain management can meet. Including interdependencies would better support the complex decision making that arises in circular supply chain management and enable one to challenge strategic assumptions about the improvement of circularity, economic, environmental, and social performance in parallel. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.},
author_keywords={Circular economy;  Circular supply chain;  Performance measurement;  Performance measurement system;  Supply chain;  Sustainability},
keywords={decision making;  literature review;  management;  supply chain management;  support},
references={(2017) Supply Chain Operations Reference (SCOR) Model: Version 12.0, , APICS. APICS: Chicago, IL, USA; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ, 183, pp. 299-315. , [CrossRef]; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) Int. J. Oper. Prod. Manag, 25, pp. 1228-1263. , [CrossRef]; Bentahar, O., Benzidia, S., Sustainable supply chain management: Trends and challenges (2018) Transp. Res. Part E Logist. Transp. Rev, 119, pp. 202-204. , [CrossRef]; Farooque, M., Zhang, A., Thürer, M., Qu, T., Huisingh, D., Circular supply chain management: A definition and structured literature review (2019) J. Clean. Prod, 228, pp. 882-900. , [CrossRef]; Govindan, K., Hasanagic, M., A systematic review on drivers, barriers, and practices towards circular economy: A supply chain perspective (2018) Int. J. Prod. Res, 56, pp. 278-311. , [CrossRef]; Vegter, D., van Hillegersberg, J., Olthaar, M., Supply chains in circular business models: Processes and performance objectives (2020) Resour. Conserv. Recycl, 162, p. 105046. , [CrossRef]; Kirchherr, J., Reike, D., Hekkert, M., Conceptualizing the circular economy: An analysis of 114 definitions (2017) Resour. Conserv. Recycl, 127, pp. 221-232. , [CrossRef]; Ghisellini, P., Cialani, C., Ulgiati, S., A review on circular economy: The expected transition to a balanced interplay of environmental and economic systems (2016) J. Clean. Prod, 114, pp. 11-32. , [CrossRef]; Batista, L., Bourlakis, M., Smart, P., Maull, R., In search of a circular supply chain archetype–a content-analysis-based literature review (2018) Prod. Plan. Control, 29, pp. 438-451. , [CrossRef]; Kravchenko, M., Pigosso, D.C., McAloone, T.C., Towards the ex-ante sustainability screening of circular economy initiatives in manufacturing companies: Consolidation of leading sustainability-related performance indicators (2019) J. Clean. Prod, 241, p. 118318. , [CrossRef]; Sehnem, S., Vazquez-Brust, D., Pereira, S.C.F., Campos, L.M., Circular economy: Benefits, impacts and overlapping (2019) Supply Chain Manag. Int. J, 24, pp. 784-804. , [CrossRef]; Pagell, M., Shevchenko, A., Why research in sustainable supply chain management should have no future (2014) J. Supply Chain Manag, 50, pp. 44-55. , [CrossRef]; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management– what has been achieved? (2015) Supply Chain Manag. Int. J, 20, pp. 664-680. , [CrossRef]; Moraga, G., Huysveld, S., Mathieux, F., Blengini, G.A., Alaerts, L., Van Acker, K., De Meester, S., Dewulf, J., Circular economy indicators: What do they measure? (2019) Resour. Conserv. Recycl, 146, pp. 452-461. , [CrossRef] [PubMed]; Saidani, M., Yannou, B., Leroy, Y., Cluzel, F., Kendall, A., A taxonomy of circular economy indicators (2019) J. Clean. Prod, 207, pp. 542-559. , [CrossRef]; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) Int. J. Prod. Res, 53, pp. 6473-6524. , [CrossRef]; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) Int. J. Product. Perform. Manag, 60, pp. 583-602. , [CrossRef]; Gopal, P., Thakkar, J., A review on supply chain performance measures and metrics: 2000–2011 (2012) Int. J. Product. Perform. Manag, 61, pp. 518-547. , [CrossRef]; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Comput. Ind, 82, pp. 135-150. , [CrossRef]; Ka, J.M.R., Ab, N.R., Lb, K., A review on supply chain performance measurement systems (2019) Procedia Manuf, 30, pp. 40-47; MacArthur, E., (2013) Towards the Circular Economy, Economic and Business Rationale for an Accelerated Transition, pp. 21-34. , Ellen MacArthur Foundation: Cowes, UK; Morseletto, P., Restorative and regenerative: Exploring the concepts in the circular economy (2020) J. Ind. Ecol, 24, pp. 763-773. , [CrossRef]; Potting, J., Hekkert, M.P., Worrell, E., Hanemaaijer, A., (2017) Circular Economy: Measuring Innovation in the Product Chain, , PBL Publishers: Sydney, Australia; Braun, G., Som, C., Schmutz, M., Hischier, R., Environmental Consequences of Closing the Textile Loop—Life Cycle Assessment of a Circular Polyester Jacket (2021) Appl. Sci, 11, p. 2964. , [CrossRef]; Fraccascia, L., Giannoccaro, I., Yazan, D.M., Choi, T., Ivanov, D., Managing supply chain operations in industrial symbiosis networks (2019) Resour. Conserv. Recycl, 151, p. 104323; Bressanelli, G., Perona, M., Saccani, N., Challenges in supply chain redesign for the Circular Economy: A literature review and a multiple case study (2019) Int. J. Prod. Res, 57, pp. 7395-7422. , [CrossRef]; Howard, M., Hopkinson, P., Miemczyk, J., The regenerative supply chain: A framework for developing circular economy indicators (2019) Int. J. Prod. Res, 57, pp. 7300-7318. , [CrossRef]; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) Int. J. Phys. Distrib. Logist. Manag, 38, pp. 360-387. , [CrossRef]; Blum, N.U., Haupt, M., Bening, C.R., Why “Circular” doesn’t always mean “Sustainable” (2020) Resour. Conserv. Recycl, 162, p. 105042. , [CrossRef]; Elgazzar, S., Tipi, N., Jones, G., Key characteristics for designing a supply chain performance measurement system (2019) J. Product. Perform. Manag, 68, pp. 296-318. , [CrossRef]; Negri, M., Neri, A., Cagno, E., Monfardini, G., Circular Economy Performance Measurement in Manufacturing Firms: A Systematic Literature Review with Insights for Small and Medium Enterprises and New Adopters (2021) Sustainability, 13, p. 9052. , [CrossRef]; Cagno, E., Neri, A., Howard, M., Brenna, G., Trianni, A., Industrial sustainability performance measurement systems: A novel framework (2019) J. Clean. Prod, 230, pp. 1354-1375. , [CrossRef]; Kravchenko, M., Pigosso, D.C., McAloone, T.C., A procedure to support systematic selection of leading indicators for sustainability performance measurement of circular economy initiatives (2020) Sustainability, 12, p. 951. , [CrossRef]; Guide to Key Performance Indicators, , https://www.pwc.com/gx/en/audit-services/corporate-reporting/assets/pdfs/uk_kpi_guide.pdf, PriceWaterhouseCoopers. (accessed on 21 September 2021); Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manag, 20, pp. 754-771. , [CrossRef]; Papulová, Z., Gažová, A., Šlenker, M., Papula, J., Performance Measurement System: Implementation Process in SMEs (2021) Sustainability, 13, p. 4794. , [CrossRef]; Cetinkaya, B., (2010) Developing a Sustainable Supply Chain Strategy; Sustainable Supply Chain Management, pp. 17-55. , Springer: Berlin/Heidelberg, Germany; Hansen, E.G., Schaltegger, S., Sustainability balanced scorecards and their architectures: Irrelevant or misunderstood? (2018) J. Bus. Ethics, 150, pp. 937-952. , [CrossRef]; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., Measuring supply chain performance: A lifecycle framework and a case study (2018) Int. J. Oper. Prod. Manag, 38, pp. 934-956. , [CrossRef]; Denyer, D., Tranfield, D., Producing a systematic review (2009) The Sage Handbook of Organizational Research Methods, pp. 671-689. , Buchanan, D.A., Bryman, A., Eds.; Sage Publications Ltd.: New York, NY, USA; Azevedo, S.G., Godina, R., Matias, J.C.D.O., Proposal of a sustainable circular index for manufacturing companies (2017) Resources, 6, p. 63. , [CrossRef]; Brown, P.J., Bajada, C., An economic model of circular supply network dynamics: Toward an understanding of performance measurement in the context of multiple stakeholders (2018) Bus. Strategy Environ, 27, pp. 643-655. , [CrossRef]; Edmondson, A.C., McManus, S.E., Methodological fit in management field research (2007) Acad. Manag. Rev, 32, pp. 1246-1264. , [CrossRef]; Ansari, Z.N., Kant, R., Shankar, R., Remanufacturing supply chain: An analysis of performance indicator areas (2020) J. Product. Perform. Manag, , preprint. [CrossRef]; Jain, S., Jain, N.K., Metri, B., Strategic framework towards measuring a circular supply chain management (2018) Benchmarking Int. J, 25, pp. 3238-3252. , [CrossRef]; Kazancoglu, Y., Kazancoglu, I., Sagnak, M., A new holistic conceptual framework for green supply chain management performance assessment based on circular economy (2018) J. Clean. Prod, 195, pp. 1282-1299. , [CrossRef]; Haghighi, S.M., Torabi, S.A., Ghasemi, R., An integrated approach for performance evaluation in sustainable supply chain networks (with a case study) (2016) J. Clean. Prod, 137, pp. 579-597. , [CrossRef]; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour. Conserv. Recycl, 55, pp. 567-579. , [CrossRef]; Bach, V., Berger, M., Henßler, M., Kirchner, M., Leiser, S., Mohr, L., Rother, E., Tikana, L., Integrated method to assess resource efficiency–ESSENZ (2016) J. Clean. Prod, 137, pp. 118-130. , [CrossRef]; Butzer, S., Schötz, S., Petroschke, M., Steinhilper, R., Development of a performance measurement system for international reverse supply chains (2017) Procedia Cirp, 61, pp. 251-256. , [CrossRef]; Wu, Z., Zhai, S., Hong, J., Zhang, Y., Shi, K., Building sustainable supply chains for organizations based on QFD: A case study (2018) Int. J. Environ. Res. Public Health, 15, p. 2834. , [CrossRef]; Beiler, B.C., de Arruda Ignácio, P.S., Júnior, A.C.P., Anholon, R., Rampasso, I.S., Reverse logistics system analysis of a Brazilian beverage company: An exploratory study (2020) J. Clean. Prod, 274, p. 122624. , [CrossRef]; Bracquené, E., Dewulf, W., Duflou, J.R., Measuring the performance of more circular complex product supply chains (2020) Resour. Conserv. Recycl, 154, p. 104608. , [CrossRef]; Genovese, A., Acquaye, A.A., Figueroa, A., Koh, S.L., Sustainable supply chain management and the transition towards a circular economy: Evidence and some applications (2017) Omega, 66, pp. 344-357. , [CrossRef]; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental performance indicators for integrated supply chains: The case of Xerox Ltd (1998) Supply Chain. Manag. Int. J, 3, pp. 149-156. , [CrossRef]},
correspondence_address1={Vegter, D.; Faculty of Behavioural, Netherlands; email: d.a.vegter@utwente.nl},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Review},
source={Scopus},
}

@ARTICLE{Lopes20211912,
author={Lopes, M.A. and Martins, R.A.},
title={Mapping the impacts of industry 4.0 on performance measurement systems},
journal={IEEE Latin America Transactions},
year={2021},
volume={19},
number={11},
pages={1912-1923},
doi={10.1109/TLA.2021.9475625},
art_number={9475625},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112166501&doi=10.1109%2fTLA.2021.9475625&partnerID=40&md5=f3eabfcada676813569e55ec56d5af3e},
affiliation={Departamento de Engenharia de Produção, Universidade Federal de São Carlos (UFSCar), São Paulo, São Carlos, Brazil},
abstract={Industry 4.0 technologies have the potential to enhance performance measurement systems dramatically. Although there are many reviews on Industry 4.0, there are none with attention on the interplay between Industry 4.0 and performance measurement systems. Thus, this article presents a scientific map on Industry 4.0 impacts on performance measurement systems. We applied bibliometric analysis and content analysis in a sample of 325 documents gathered from the Web of Science scientific index. R Bibliometrix package and VOSviewer software supported the data processing and analysis. The major results point out that scientific production has been rising since 2015, but it still is in the initial maturity stage without a few top productive authors and with high productivity of technological areas compared to business administration and operations management areas. This latest evidence calls attention to more research on how to use Industry 4.0 technologies in management systems. The results disclose positive impacts of Industry 4.0 technologies on performance measurement systems. The significant trend is the development of intelligent systems that provide adequate information for decision-making in real time with an extension to the entire supply chain. However, this green field calls for an interdisciplinary research effort from technological and management domains to apprehend the proper value to all stakeholders. © 2003-2012 IEEE.},
author_keywords={Bibliometric Analisys;  Industry 4.0;  Performance Measurement Systems;  Performance Measures;  Scientific Mapping;  Supply Chain Management},
keywords={Data handling;  Decision making;  Industry 4.0;  Intelligent systems;  Supply chains, Bibliometric analysis;  Business administration;  Data processing and analysis;  Interdisciplinary research;  Management domains;  Management systems;  Operations management;  Performance measurement system, Real time systems},
references={Bourne, M., Franco-Santos, M., Micheli, P., Pavlov, A., Performance measurement and management: A system of systems perspective (2018) Int. J. Prod. Res., 56 (8), pp. 2788-2799; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) Int. J. Oper. Prod. Manag., 25 (12), pp. 1228-1263; Mello, R., Martins, R.A., Can big data analytics enhance performance measurement systems? (2019) IEEE Eng. Manag. Rev., 47 (1), pp. 52-57; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) Int. J. Bus. Perform. Manag., 5 (1), pp. 1-24; Demchenko, Y., Grosso, P., De Laat, C., Membrey, P., Addressing big data issues in scientific data infrastructure (2013) 2013 International Conference on Collaboration Technologies and Systems (CTS), pp. 48-55; Zikopoulos, P., Eaton, C., Deroos, D., Deutsch, T., Lapis, G., (2012) Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, , McGraw-Hill; Mello, R., Leite, L.R., Martins, R.A., Is big data the next big thing in performance measurement systems? (2014) IIEE Annual Conference and Expo 2014, pp. 1837-1846; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) Int. J. Manag. Rev., 14 (3), pp. 305-327; Genovese, A., Lenny Koh, S.C., Kumar, N., Tripathi, P.K., Exploring the challenges in implementing supplier environmental performance measurement models: A case study (2014) Prod. Plan. Control, 25 (13-14), pp. 1198-1211; Jung, K., Choi, S.S., Kulvatunyou, B., Cho, H., Morris, K.C., A reference activity model for smart factory design and improvement (2017) Prod. Plan. Control, 28 (2), pp. 108-122; Trotta, D., Garengo, P., Industry 4. 0 key research topics: A bibliometric review (2018) 2018 7th International Conference on Industrial Technology and Management (ICITM), pp. 113-117; Ferreira, I.V., Bigheti, J.A., Godoy, E.P., Development of a wireless gateway for industrial internet of things applications (2019) IEEE Lat. Am. Trans., 17 (10), pp. 1637-1644; Nudurupati, S.S., Tebboune, S., Hardman, J., Contemporary performance measurement and management (pmm) in digital economies (2016) Prod. Plan. Control, 27 (3), pp. 226-235; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Comput. Ind. Eng., 60 (2), pp. 279-290; Lebas, M., Euske, K., A conceptual and operational delineation of performance (2002) Business Performance Measurement: Theory and Practice, pp. 65-79. , Cambridge, United Kingdom: Cambridge University Press; Berrah, L., Mauris, G., Vernadat, F., Information aggregation in industrial performance measurement: Rationales, issues and definitions (2004) Int. J. Prod. Res., 42 (20), pp. 4271-4293; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: A review of their consequences and a framework for research (2012) Manag. Account. Res., 23 (2), pp. 79-119; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) Int. J. Oper. Prod. Manag., 31 (12), pp. 1287-1310; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) Int. J. Oper. Prod. Manag., 23 (2), pp. 213-229; Schwab, K., (2016) The Fourth Industrial Revolution, , 1st ed. Cologny/Geneva, Switzerland: World Economic Forum; Drath, R., Horch, A., Industrie 4. 0: Hit or hype? (2014) IEEE Industrial Electronics Magazine, IEEE, pp. 56-58; Lerher, T., Warehousing 4. 0 by using shuttlebased storage and retrieval systems (2018) FME Trans., 46 (3), pp. 381-385; Öberg, C., Graham, G., How smart cities will change supply chain management: A technical viewpoint (2016) Prod. Plan. Control, 27 (6), pp. 529-538; Muhuri, P.K., Shukla, A.K., Abraham, A., Industry 4. 0: A bibliometric analysis and detailed overview (2019) Eng. Appl. Artif. Intell., 78, pp. 218-235; Zawadzki, P., Zywicki, K., Smart product design and production control for effective mass customization in the industry 4. 0 concept (2016) Manag. Prod. Eng. Rev., 7 (3), pp. 105-112; Li, F., Nucciarelli, A., Roden, S., Graham, G., How smart cities transform operations models: A new research agenda for operations management in the digital economy (2016) Prod. Plan. Control, 27 (6), pp. 514-528; Geissbauer, R., Vedso, J., Schrauf, S., (2016) Industry 4. 0: Building the Digital Enterprise, , https://www.pwc.com/id/en/CIPS/assets/industry-4.0-building-your-digital-enterprise.pdf; Hofmann, E., Rüsch, M., Industry 4. 0 and the current status as well as future prospects on logistics (2017) Comput. Ind., 89, pp. 23-34; Rüßmann, M., (2015) Industry 4. 0: The Future of Productivity and Growth in Manufacturing, , https://www.bcg.com/pt-br/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries; Hu, B., Kostamis, D., Managing supply disruptions when sourcing from reliable and unreliable suppliers (2015) Prod. Oper. Manag., 24 (5), pp. 808-820; Maddern, H., Smart, P.A., Maull, R.S., Childe, S., End-to-end process management: Implications for theory and practice (2014) Prod. Plan. Control, 25 (16), pp. 1303-1321; Hwang, G., Lee, J., Park, J., Chang, T.W., Developing performance measurement system for internet of things and smart factory environment (2017) Int. J. Prod. Res., 55 (9), pp. 2590-2602; Babiceanu, R.F., Seker, R., Big data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook (2016) Comput. Ind., 81, pp. 128-137; Kumaraguru, S., Kulvatunyou, B., Morris, K.C., Integrating real-time analytics and continuous performance management in smart manufacturing systems (2014) IFIP International Conference on Advances in Production Management Systems (APMS), 440, pp. 175-182; Silva, F.A.R., Analytical intelligence in processes: Data science for business (2018) IEEE Lat. Am. Trans., 16 (8), pp. 2240-2247; Isasi Gimenez, N.K., Morosini Frazzon, E., Uriona, M., Big data and business analytics in the supply chain: A review of the literature (2015) IEEE Lat. Am. Trans., 13 (10), pp. 3382-3391; Bonci, A., Pirani, M., Longhi, S., Robotics 4. 0: Performance improvement made easy (2017) IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, pp. 1-8; Kipper, L.M., Furstenau, L.B., Hoppe, D., Frozza, R., Iepsen, S., Scopus scientific mapping production in industry 4. 0 (2011-2018): A bibliometric analysis (2020) Int. J. Prod. Res., 58 (6), pp. 1605-1627; Chueke, G., Amatucci, M., O que é bibliometria? uma introdução ao fórum (2015) Rev. Eletrônica Negócios Int., 10 (2), pp. 1-5; Caviggioli, F., Ughetto, E., A bibliometric analysis of the research dealing with the impact of additive manufacturing on industry, business and society (2019) Int. J. Prod. Econ., 208, pp. 254-268; Zupic, I., Ater, T., Bibliometric methods in management and organization (2015) Organ. Res. Methods, 18 (3), pp. 429-472; Aria, M., Cuccurullo, C., Bibliometrix: An r-tool for comprehensive science mapping analysis (2017) J. Informetr., 11 (4), pp. 959-975; Morris, S.A., Martens Veer Der, B.Van, Mapping research specialties (2008) Annual Review of Information Science and Technology, 42, pp. 213-295; Matloff, N., (2011) The Art of R Programming: A Tour of Statistical Software Design, , São Francisco, USA: No Starch Press; Van Eck, N.J., Waltman, L., Software survey: Vosviewer, a computer program for bibliometric mapping (2010) Scientometrics, 84 (2), pp. 523-538; He, Q., Knowledge discovery through co-word analysis (1999) Libr. Trends, 48 (1), pp. 133-159; Leung, X.Y., Sun, J., Bai, B., Bibliometrics of social media research: A co-citation and co-word analysis (2017) Int. J. Hosp. Manag., 66, pp. 35-45; Guedes, V.L.S., Borschiver, S., Bibliometria: Uma ferramenta estatística para a gestão da informação e do conhecimento, em sistemas de informação e de comunicação e de avaliação científica e tecnológica (2005) Encontro Nacional de Ciência da Informação, pp. 1-18; Lucas, E.O., Garcia-Zorita, J.C., Sanz-Casado, E., Evolução histórica de investigação em informetria: Ponto de vista espanhol (2013) Liinc em Rev., 9 (1), pp. 255-270; Smiraglia, R.P., Isko 11's diverse bookshelf: An editorial (2011) Knowl. Organ., 38 (3), pp. 179-186; Xu, X., Chen, X., Jia, F., Brown, S., Gong, Y., Xu, Y., Supply chain finance: A systematic literature review and bibliometric analysis (2018) Int. J. Prod. Econ., 204, pp. 160-173; Bellardo, T., The use of co-citations to study science (1980) Libr. Res., 2 (3), pp. 231-237; Leite, L.R., Systematic literature review on performance measurement and sustainability (2012) American Society for Engineering Management (ASEM), pp. 869-878; Davis, J., Edgar, T., Porter, J., Bernaden, J., Sarli, M., Smart manufacturing, manufacturing intelligence and demand-dynamic performance (2012) Comput. Chem. Eng., 47, pp. 145-156; Zhou, J., Qingyang Hu, R., Qian, Y., Scalable distributed communication architectures to support advanced metering infrastructure in smart grid (2012) IEEE Trans. Parallel Distrib. Syst., 23 (9), pp. 1632-2173; Wu, Q., Cognitive internet of things: A new paradigm beyond connection (2014) IEEE Internet Things J., 1 (2), pp. 129-143; Haas, H., Yin, L., Wang, Y., Chen, C., What is lifi? (2016) J. Light. Technol., 34 (6), pp. 1533-1544; Sharma, P.K., Chen, M.Y., Park, J.H., A software defined fog node based distributed blockchain cloud architecture for iot (2018) IEEE Access, 6, pp. 115-124; Leimkuhler, F., Chen, Y., A relationship between lotka ' s law, bradford ' s law, and zipf ' s law (1986) J. Am. Soc. Inf. Sci., 37 (5), pp. 307-314; Osareh, F., Mostafavi, E., Lotka's law and authorship distribution in computer science using web of science (wos) during 1986-2009 (2011) Collnet J. Sci. Inf. Manag., 5 (2), pp. 171-183; Van Eck, N.J., Waltman, L., Visualizing bibliometric networks (2014) Measuring Scholarly Impact: Methods and Practice, pp. 285-320. , Springer; Atzori, L., Iera, A., Morabito, G., The internet of things: A survey (2010) Comput. Networks, 54 (15), pp. 2787-2805; Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M., Internet of things (iot): A vision, architectural elements, and future directions (2013) Futur. Gener. Comput. Syst., 29 (7), pp. 1645-1660; Wollschlaeger, M., Sauter, S., Jasperneite, J., The future of industrial communication: Automation networks in the era of the internet of things and industry 4. 0 (2017) IEEE Industrial Electronics Magazine, 11 (1), pp. 17-27; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, p. 14; Neely, A., The performance measurement revolution: Why now and what next? (1999) Int. J. Oper. Prod. Manag., 19 (2), pp. 205-228; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3), pp. 275-292; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res., 25 (2), pp. 173-186; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Meas. Bus. Excell., 14 (1), pp. 4-18; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20 (7), pp. 754-771; LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N., Big data, analytics and the path from insights to value (2011) MIT Sloan Management Review, 52 (2), pp. 21-31; Chen, H., Chiang, R.H.L., Storey, V.C., Business intelligence and analytics: From big data to big impact (2012) MIS Q., 36 (4), pp. 1165-1188; McAfee, A., Brynjolfsson, E., Big data: The management revolution (2012) Harvard Business Review, 90 (10), p. 4; Mayer-Schönberger, V., Cukier, K., (2013) Big Data: A Revolution That Will Transform How We Live, Work, and Think, , New York, USA: Houghton Mifflin Harcourt; Davenport, T.H., How strategists use 'big data' to support internal business decisions, discovery and production (2014) Strateg. Leadersh., 42 (4), pp. 45-50; Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How 'big data' can make big impact: Findings from a systematic review and a longitudinal case study (2015) Int. J. Prod. Econ., 165, pp. 234-246; Wang, G., Gunasekaran, A., Ngai, E.W.T., Papadopoulos, T., Big data analytics in logistics and supply chain management: Certain investigations for research and applications (2016) Int. J. Prod. Econ., 176, pp. 98-110; Waller, M.A., Fawcett, S.E., Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management (2013) J. Bus. Logist., 34 (2), pp. 77-84; Gunasekaran, A., Big data and predictive analytics for supply chain and organizational performance (2017) J. Bus. Res., 70, pp. 308-317; Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J., How to improve firm performance using big data analytics capability and business strategy alignment? (2016) Int. J. Prod. Econ., 182, pp. 113-131; Wamba, S.F., Gunasekaran, A., Akter, S., Fan Ren, S.J., Dubey, R., Childe, S.J., Big data analytics and firm performance: Effects of dynamic capabilities (2017) J. Bus. Res., 70, pp. 356-365; Gupta, M., George, J.F., Toward the development of a big data analytics capability (2016) Inf. Manag., 53 (8), pp. 1049-1064; Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A., Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications (2014) Int. J. Prod. Econ., 154, pp. 72-80; Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M., Industry 4. 0 (2014) Bus. Inf. Syst. Eng., 6 (4), pp. 239-242; Schmidt, R., Möhring, M., Härting, R.C., Reichstein, C., Neumaier, P., Jozinovic, P., Industry 4. 0-potentials for creating smart products: Empirical research results (2015) 18th International Conference on Business Information Systems, 208, pp. 16-27; Lee, J., Kao, H.A., Yang, S., Service innovation and smart analytics for industry 4. 0 and big data environment (2014) Procedia CIRP, 16, pp. 3-8; Kagermann, H., Wahlster, W., Helbig, J., Recommendations for implementing the strategic initiative industrie 4. 0 (2013); Lee, J., Bagheri, B., Kao, H.A., A cyber-physical systems architecture for industry 4. 0-based manufacturing systems (2015) Manuf. Lett., 3, pp. 18-23; Hermann, M., Pentek, T., Otto, B., Design principles for industrie 4. 0 scenarios (2016) 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928-3937; Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., Ivanova, M., A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4. 0 (2016) Int. J. Prod. Res., 54 (2), pp. 386-402; Sanders, A., Elangeswaran, C., Wulfsberg, J., Industry 4. 0 implies lean manufacturing: Research activities in industry 4. 0 function as enablers for lean manufacturing (2016) J. Ind. Eng. Manag., 9 (3), pp. 811-833; Liao, Y., Deschamps, F., De Loures, F.R.E., Ramos, L.F.P., Past, present and future of industry 4. 0-a systematic literature review and research agenda proposal (2017) Int. J. Prod. Res., 55 (12), pp. 3609-3629; Kamble, S.S., Gunasekaran, A., Big data-driven supply chain performance measurement system: A review and framework for implementation (2020) Int. J. Prod. Res., 58 (1), pp. 65-86; Appelbaum, D., Kogan, A., Vasarhelyi, M., Yan, Z., Impact of business analytics and enterprise systems on managerial accounting (2017) Int. J. Account. Inf. Syst., 25, pp. 29-44; Krishnamoorthi, S., Mathew, S.K., Business analytics and business value: A comparative case study (2018) Inf. Manag., 55 (5), pp. 643-666; Raffoni, A., Visani, F., Bartolini, M., Silvi, R., Business performance analytics: Exploring the potential for performance management systems (2018) Prod. Plan. Control, 29 (1), pp. 51-67; Jeble, S., Dubey, R., Childe, S.J., Papadopoulos, T., Roubaud, D., Prakash, A., Impact of big data and predictive analytics capability on supply chain sustainability (2018) Int. J. Logist. Manag., 29 (2), pp. 513-538; Gravili, G., Benvenuto, M., Avram, A., Viola, C., The influence of the digital divide on big data generation within supply chain management (2018) Int. J. Logist. Manag., 29 (2), pp. 592-628; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things (2017) Ind. Manag. Data Syst., 117 (2), pp. 267-286; Rezaei, M., Shirazi, M.A., Karimi, B., Iot-based framework for performance measurement (2017) Ind. Manag. Data Syst., 117 (4), pp. 688-712},
publisher={IEEE Computer Society},
issn={15480992},
language={Portuguese},
abbrev_source_title={IEEE. Lat. Am. Trans.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Negri20212858,
author={Negri, M. and Cagno, E. and Colicchia, C. and Sarkis, J.},
title={Integrating sustainability and resilience in the supply chain: A systematic literature review and a research agenda},
journal={Business Strategy and the Environment},
year={2021},
volume={30},
number={7},
pages={2858-2886},
doi={10.1002/bse.2776},
note={cited By 90},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104127582&doi=10.1002%2fbse.2776&partnerID=40&md5=dc2e8cd9442bda3877ebadac2c70b3c5},
affiliation={Department of Management Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy; Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, United States},
abstract={Sustainability has emerged as an important industrial strategic outlook expanding beyond organizational boundaries to include the supply chain. Simultaneously, the industry has also been faced with supply chain resilience concerns. Research on the intersection of supply chain sustainability and resilience is nascent and is a consequence of their observed mutual influences. However, confusion about concepts, implementation methods, and measurements of sustainable and resilient supply chains remains. This study completes a systematic literature review that critically examines several major observations and directions. We find the concept of sustainable supply chains is more established, and general agreement on its theoretical foundations exists. Supply chain resilience is relatively less mature. The nexus and relationships between the two topics are often incoherent: there is confusion on sustainable and resilient supply chains establishment; there is no clarity on what practices could jointly advance both areas. A major conflict exists since sustainability generally focuses on efficiency, while resilience seeks effectiveness. We recommend studies to analyze implementation relationships and impact. We also observe that performance measurement systems should be developed to assess supply chain sustainability and resilience performance taking with explicit consideration time horizons considered in these measures. © 2021 The Authors. Business Strategy and The Environment published by ERP Environment and John Wiley & Sons Ltd.},
author_keywords={resilience;  supply chain;  supply chain resilience;  sustainability;  sustainable supply chain},
keywords={literature review;  research;  supply chain management;  sustainability},
references={Abdallah, A.B., Al-Ghwayeen, W.S., Green supply chain management and business performance: The mediating roles of environmental and operational performances (2019) Business Process Management Journal, 26 (2), pp. 489-512. , https://doi.org/10.1108/BPMJ-03-2018-0091; Abdel-Basset, M., Mohamed, R., A novel plithogenic TOPSIS- CRITIC model for sustainable supply chain risk management (2020) Journal of Cleaner Production, 247 (2020), p. 119586. , https://doi.org/10.1016/j.jclepro.2019.119586; Abrahamsson, M., Christopher, M., Stensson, B.-I., Mastering supply chain management in an era of uncertainty at SKF (2015) Journal of Organizational Behavior, 34 (6), pp. 6-17. , https://doi.org/10.1002/joe.21631; (1997) Environmental reporting: An approach to good practice, , http://www.enviroreporting.com/others/acbe_guidelines.htm, Retrieved from; Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) Journal of Cleaner Production, 52, pp. 329-341. , https://doi.org/10.1016/j.jclepro.2013.02.018; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) Journal of Cleaner Production, 86, pp. 360-377. , https://doi.org/10.1016/j.jclepro.2014.08.005; Akcan, S., Taş, M.A., Green supplier evaluation with SWARA-TOPSIS integrated method to reduce ecological risk factors (2019) Environmental Monitoring and Assessment, 191 (12), pp. 1-22. , https://doi.org/10.1007/s10661-019-7884-3; Ali, A., Mahfouz, A., Arisha, A., Analysing supply chain resilience: Integrating the constructs in a concept mapping framework via a systematic literature review (2017) Supply Chain Management, 22 (1), pp. 16-39. , https://doi.org/10.1108/SCM-06-2016-0197; Ali, I., Gölgeci, I., Where is supply chain resilience research heading? A systematic and co-occurrence analysis (2019) International Journal of Physical Distribution and Logistics Management, 49 (8), pp. 793-815. , https://doi.org/10.1108/IJPDLM-02-2019-0038; Ali, I., Nagalingam, S., Gurd, B., Building resilience in SMEs of perishable product supply chains: Enablers, barriers and risks (2017) Production Planning and Control, 28 (15), pp. 1236-1250. , https://doi.org/10.1080/09537287.2017.1362487; Almasi, M., Khoshfetrat, S., Rahiminezhad Galankashi, M., Sustainable supplier selection and order allocation under risk and inflation condition (2019) IEEE Transactions on Engineering Management, pp. 1-15. , https://doi.org/10.1109/TEM.2019.2903176; Almeida, A., Bastos, J., Francisco, R.D.P., Azevedo, A., Ávila, P., Sustainability assessment framework for (2016) International Journal of Industrial and Systems Engineering, 24 (2), pp. 198-222; Ambulkar, S., Blackhurst, J., Grawe, S., Firm's resilience to supply chain disruptions: Scale development and empirical examination (2015) Journal of Operations Management, 33-34, pp. 111-122. , https://doi.org/10.1016/j.jom.2014.11.002; (2004) The UN human rights norms for business, , http://www.amnesty.org.uk/uploads/documents/doc_15550.pdf, Retrieved from; Anderson, D.R., Anderson, K.E., Sustainability risk management (2009) Risk Management, 12 (1), pp. 25-38; Ansari, Z.N., Kant, R., A state-of-art literature review reflecting 15 years of focus on sustainable supply chain management (2017) Journal of Cleaner Production, 142, pp. 2524-2543. , https://doi.org/10.1016/j.jclepro.2016.11.023; Arabsheybani, A., Paydar, M.M., Safaei, A.S., An integrated fuzzy MOORA method and FMEA technique for sustainable supplier selection considering quantity discounts and supplier's risk (2018) Journal of Cleaner Production, 190, pp. 577-591. , https://doi.org/10.1016/j.jclepro.2018.04.167; Ashby, A., Leat, M., Hudson-Smith, M., Making connections: A review of supply chain management and sustainability literature (2012) Supply Chain Management, 17 (5), pp. 497-516. , https://doi.org/10.1108/13598541211258573; Asokan, V.A., Yarime, M., Esteban, M., Introducing flexibility to complex, resilient socio-ecological systems: A comparative analysis of economics, flexible manufacturing systems, evolutionary biology, and supply chain management (2017) Sustainability (Switzerland), 9 (7), pp. 1-17. , https://doi.org/10.3390/su9071091; Awasthi, A., Govindan, K., Gold, S., Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach (2018) International Journal of Production Economics, 195, pp. 106-117. , https://doi.org/10.1016/j.ijpe.2017.10.013, (March 2016; Azevedo, S.G., Carvalho, H., Cruz-Machado, V., LARG index: A benchmarking tool for improving the leanness, agility, resilience and greenness of the automotive supply chain (2016) Benchmarking, 23 (6), pp. 1472-1499. , https://doi.org/10.1108/BIJ-07-2014-0072; Azevedo, S.G., Govindan, K., Carvalho, H., Cruz-Machado, V., Ecosilient index to assess the greenness and resilience of the upstream automotive supply chain (2013) Journal of Cleaner Production, 56, pp. 131-146. , https://doi.org/10.1016/j.jclepro.2012.04.011; Azzone, G., Noci, G., Manzini, R., Welford, R., Young, C.W., Defining environmental performance indicators: An integrated framework (1996) Business Strategy and the Environment, 5 (2), pp. 69-80. , https://doi.org/10.1002/(sici)1099-0836(199606)5:2<69::aid-bse48>3.0.co;2-w; Bag, S., Gupta, S., Foropon, C., Examining the role of dynamic remanufacturing capability on supply chain resilience in circular economy (2019) Management Decision, 57 (4), pp. 863-885. , https://doi.org/10.1108/MD-07-2018-0724; Bak, O., Shaw, S., Colicchia, C., Kumar, V., A systematic literature review of supply chain resilience in Small&Medium Enterprises (SMEs): A call for further research (2020) IEEE Transactions on Engineering Management, 58 (2), pp. 81-89. , https://doi.org/10.1109/TEM.2020.3016988; Bansall, P., Roth, K., Why companies go green: A model of ecological responsiveness Author (s): Pratima Bansal and Kendall Roth Source: The Academy of Management Journal (2000) Academy of Management Journal, 43 (4), pp. 717-736. , https://doi.org/10.1117/1.2220527, (Aug., 2000), Published by Academy of Management Stable URL http; Barreto, L.V., Anderson, H.C., Anglin, A., Tomovic, C.L., Product lifecycle management in support of green manufacturing: Addressing the challenges of global climate change (2010) International Journal of Manufacturing Technology and Management, 19 (3-4), pp. 294-305. , https://doi.org/10.1504/IJMTM.2010.031374; Behzadi, G., O'Sullivan, M.J., Olsen, T.L., On metrics for supply chain resilience (2020) European Journal of Operational Research, 287 (1), pp. 145-158. , https://doi.org/10.1016/j.ejor.2020.04.040; Beske, P., Seuring, S., Putting sustainability into supply chain management (2014) Supply Chain Management, 19 (3), pp. 322-331. , https://doi.org/10.1108/SCM-12-2013-0432; Bhamra, R., Dani, S., Burnard, K., Resilience: The concept, a literature review and future directions (2011) International Journal of Production Research, 49 (18), pp. 5375-5393. , https://doi.org/10.1080/00207543.2011.563826; Bianchi, R., Noci, G., “Greening” SMEs' competitiveness (1998) Small Business Economics, 11, pp. 269-281; Blair, M.E., Bugg-Levine, A.J., Rippin, T.M., (2004) The UN's role in corporate social responsibility, 4, pp. 21-23. , New York, US, The McKinsey Quarterly; Brandenburg, M., A hybrid approach to configure eco-efficient supply chains under consideration of performance and risk aspects (2017) Omega (United Kingdom), 70, pp. 58-76. , https://doi.org/10.1016/j.omega.2016.09.002; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for sustainable supply chain management: Developments and directions (2014) European Journal of Operational Research, 233 (2), pp. 299-312. , https://doi.org/10.1016/j.ejor.2013.09.032; Brinkley, C., The smallworld of the alternative food network (2018) Sustainability (Switzerland), 10 (8), p. 2921. , https://doi.org/10.3390/su10082921; Brusset, X., Teller, C., Supply chain capabilities, risks, and resilience (2017) International Journal of Production Economics, 184 (June 2016), pp. 59-68. , https://doi.org/10.1016/j.ijpe.2016.09.008; Cabral, I., Grilo, A., Cruz-Machado, V., A decision-making model for Lean, Agile, Resilient and Green supply chain management (2012) International Journal of Production Research, 50 (17), pp. 4830-4845. , https://doi.org/10.1080/00207543.2012.657970; Cano, J.A., Ayala, C.J., Research opportunities for supplier selection: An analysis from literature reviews (2019) IBIMA Business Review, 2019, p. 828922. , https://doi.org/10.5171/2019.828922; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) International Journal of Physical Distribution and Logistics Management, 41 (1), pp. 46-62. , https://doi.org/10.1108/09600031111101420; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution and Logistics Management, 38 (5), pp. 360-387. , https://doi.org/10.1108/09600030810882816; Carter, C.R., Washispack, S., Mapping the path forward for sustainable supply chain management: A review of reviews (2018) Journal of Business Logistics, 39 (4), pp. 242-247. , https://doi.org/10.1111/jbl.12196; Carvalho, H., Duarte, S., Machado, V.C., Lean, agile, resilient and green: Divergencies and synergies (2011) International Journal of Lean Six Sigma, 2 (2), pp. 151-179. , https://doi.org/10.1108/20401461111135037; Chavan, R.N., Patil, R.N., Chavan, S.T., Kulkarni, N., Chavan, S.S., Relative reliability risk index for green supply chain management (2018) International Journal of Mechanical Engineering and Technology, 9 (13), pp. 1264-1273; Chen, Y., Wang, S., Yao, J., Li, Y., Yang, S., Socially responsible supplier selection and sustainable supply chain development: A combined approach of total interpretive structural modeling and fuzzy analytic network process (2018) Business Strategy and the Environment, 27 (8), pp. 1708-1719. , https://doi.org/10.1002/bse.2236; Chowdhury, M.M.H., Quaddus, M., Supply chain resilience: Conceptualization and scale development using dynamic capability theory (2017) International Journal of Production Economics, 188, pp. 185-204. , https://doi.org/10.1016/j.ijpe.2017.03.020; Christopher, M., (2011) Logistics and supply chain management, , 4th ed., UK, Pearson Uk; Christopher, M., Peck, H., Building the resilient supply chain (2004) International Journal of Logistics Management, 15 (2), pp. 1-13; Chung, K., Chu, C., Green supply chain management of risk analysis in the aerospace technology industry (2016) Journal of Testing and Evaluation, 44 (3), pp. 1430-1441; Closs, D.J., Speier, C., Meacham, N., Sustainability to support end-to-end value chains: The role of supply chain management (2011) Journal of the Academy of Marketing Science, 39 (1), pp. 101-116. , https://doi.org/10.1007/s11747-010-0207-4; Colicchia, C., Dallari, F., Melacini, M., Increasing supply chain resilience in a global sourcing context (2010) Production Planning and Control, 21 (7), pp. 680-694. , https://doi.org/10.1080/09537280903551969; Cousins, P.D., Lamming, R.C., Bowen, F., The role of risk in environment-related supplier initiatives (2004) International Journal of Operations and Production Management, 24 (5-6), pp. 554-565. , https://doi.org/10.1108/01443570410538104; Cruz, J.M., Mitigating global supply chain risks through corporate social responsibility (2013) International Journal of Production Research, 51 (13), pp. 3995-4010. , https://doi.org/10.1080/00207543.2012.762134; Darom, N.A., Hishamuddin, H., Ramli, R., Mat Nopiah, Z., An inventory model of supply chain disruption recovery with safety stock and carbon emission consideration (2018) Journal of Cleaner Production, 197, pp. 1011-1021. , https://doi.org/10.1016/j.jclepro.2018.06.246; Das, D., Development and validation of a scale for measuring sustainable supply chain management practices and performance (2017) Journal of Cleaner Production, 164, pp. 1344-1362. , https://doi.org/10.1016/j.jclepro.2017.07.006; Das, D., The impact of sustainable supply chain management practices on firm performance: Lessons from Indian organizations (2018) Journal of Cleaner Production, 203, pp. 179-196. , https://doi.org/10.1016/j.jclepro.2018.08.250; Das, K., Integrating lean, green, and resilience criteria in a sustainable food supply chain planning model (2019) International Journal of Mathematical, Engineering and Management Sciences, 4 (2), pp. 259-275. , https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061796762%26partnerID=40%26md5=02b118af4c5369916856b9ad5a1754c4, Retrieved from; Datta, P.P., Christopher, M., Allen, P., Agent-based modelling of complex production/distribution systems to improve resilience (2007) International Journal of Logistics Research and Applications, 10 (3), pp. 187-203. , https://doi.org/10.1080/13675560701467144; de Oliveira, U.R., Espindola, L.S., da Silva, I.R., da Silva, I.N., Rocha, H.M., A systematic literature review on green supply chain management: Research implications and future perspectives (2018) Journal of Cleaner Production, 187, pp. 537-561. , https://doi.org/10.1016/j.jclepro.2018.03.083; de Souza, V., Bloemhof-Ruwaard, J., Borsato, M., Exploring ecosystem network analysis to balance resilience and performance in sustainable supply chain design (2019) International Journal of Advanced Operations Management, 11 (1-2), pp. 26-45. , https://doi.org/10.1504/IJAOM.2019.098525; de Souza, V., Bloemhof-Ruwaard, J., Borsato, M., Towards regenerative supply networks: A design framework proposal (2019) Journal of Cleaner Production, 221, pp. 145-156. , https://doi.org/10.1016/j.jclepro.2019.02.178; Denyer, D., Tranfield, D., Producing a systematic review (2009) The Sage Handbook of Organizational Research Methods, pp. 671-689. , Sage Publications Ltd; Derissen, S., Quaas, M.F., Baumgärtner, S., The relationship between resilience and sustainability of ecological-economic systems (2011) Ecological Economics, 70 (6), pp. 1121-1128. , https://doi.org/10.1016/j.ecolecon.2011.01.003; Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Shibin, K.T., Wamba, S.F., Sustainable supply chain management: Framework and further research directions (2017) Journal of Cleaner Production, 142, pp. 1119-1130. , https://doi.org/10.1016/j.jclepro.2016.03.117; Edgeman, R., Wu, Z., Supply chain criticality in sustainable and resilient enterprises (2016) Journal of Modelling in Management, 11 (4), pp. 869-888. , https://doi.org/10.1108/JM2-10-2014-0078; Elkington, J., Accounting for the triple bottom line (1998) Measuring Business Excellence, 2, pp. 18-22; Elleuch, H., Dafaoui, E., Elmhamedi, A., Chabchoub, H., Resilience and vulnerability in supply chain: Literature review (2016) IFAC-PapersOnLine, 49 (12), pp. 1448-1453. , https://doi.org/10.1016/j.ifacol.2016.07.775; Eltantawy, R., Towards sustainable supply management: Requisite governance and resilience capabilities (2015) Journal of Strategic Marketing, 24 (2), pp. 118-130. , https://doi.org/10.1080/0965254X.2015.1011201; Epstein, M.J., (2007) Measuring corporate environmental performance: Best practices for costing and managing an effective environmental strategy, 16, pp. 389-403. , 6th ed; Erickson, J.D., Gowdy, J.M., (2007) Frontiers in ecological economic theory and application, , Northampton, MA, Edward Elgar Publishing; Fahimnia, B., Jabbarzadeh, A., Marrying supply chain sustainability and resilience: A match made in heaven (2016) Transportation Research Part E: Logistics and Transportation Review, 91, pp. 306-324. , https://doi.org/10.1016/j.tre.2016.02.007; Fahimnia, B., Jabbarzadeh, A., Sarkis, J., Greening versus resilience: A supply chain design perspective (2018) Transportation Research Part E: Logistics and Transportation Review, 119, pp. 129-148. , https://doi.org/10.1016/j.tre.2018.09.005; Fahimnia, B., Sarkis, J., Davarzani, H., Green supply chain management: A review and bibliometric analysis (2015) International Journal of Production Economics, 162, pp. 101-114. , https://doi.org/10.1016/j.ijpe.2015.01.003; Fahimnia, B., Sarkis, J., Talluri, S., Editorial design and Management of Sustainable and Resilient Supply Chains (2019) IEEE Transactions on Engineering Management, 66 (1), pp. 2-7. , https://doi.org/10.1109/TEM.2018.2870924; Fahimnia, B., Tang, C.S., Davarzani, H., Sarkis, J., Quantitative models for managing supply chain risks: A review (2015) European Journal of Operational Research, 247 (1), pp. 1-15. , https://doi.org/10.1016/j.ejor.2015.04.034; Fazli-Khalaf, M., Mirzazadeh, A., Pishvaee, M.S., A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network (2017) Human and Ecological Risk Assessment, 23 (8), pp. 2119-2149. , https://doi.org/10.1080/10807039.2017.1367644; Fernando, Y., Walters, T., Ismail, M.N., Seo, Y.W., Kaimasu, M., Managing project success using project risk and green supply chain management: A survey of automotive industry (2018) International Journal of Managing Projects in Business, 11 (2), pp. 332-365. , https://doi.org/10.1108/IJMPB-01-2017-0007; Fiksel, J., Designing resilient, sustainable systems (2003) Environmental Science and Technology, 37 (23), pp. 5330-5339. , https://doi.org/10.1021/es0344819; Fiksel, J., Sustainability and resilience: Toward a systems approach (2006) Sustainability: Science, Practice and Policy, 2 (2), pp. 14-21. , https://doi.org/10.1080/15487733.2006.11907980; Fiksel, J., Polyviou, M., Croxton, K.L., Pettit, T.J., From risk to resilience: Learning to deal with disruption (2015) MIT Sloan Management Review, 56 (2), pp. 79-86; Foroozesh, N., Tavakkoli-Moghaddam, R., Mousavi, S.M., Sustainable-supplier selection for manufacturing services: A failure mode and effects analysis model based on interval-valued fuzzy group decision-making (2018) International Journal of Advanced Manufacturing Technology, 95 (9-12), pp. 3609-3629. , https://doi.org/10.1007/s00170-017-1308-8; Freise, M., Seuring, S., Social and environmental risk management in supply chains: A survey in the clothing industry (2015) Logistics Research, 8 (1), pp. 1-12. , https://doi.org/10.1007/s12159-015-0121-8; Furman, C.A., Papavasiliou, F., Scale and affect in the local food movement (2018) Food, Culture and Society, 21 (2), pp. 180-195. , https://doi.org/10.1080/15528014.2018.1427926; Giannakis, M., Papadopoulos, T., Supply chain sustainability: A risk management approach (2016) International Journal of Production Economics, 171, pp. 455-470. , https://doi.org/10.1016/j.ijpe.2015.06.032; Gimenez, C., Tachizawa, E.M., Extending sustainability to suppliers: A systematic literature review (2012) Supply Chain Management, 17 (5), pp. 531-543. , https://doi.org/10.1108/13598541211258591; Göçer, A., Fawcett, S.E., Tuna, O., What does the sustainability-risk interaction look like? Exploring nuanced relationships in emerging economy sustainability initiatives (2018) Sustainability (Switzerland), 10 (8), p. 2716. , https://doi.org/10.3390/su10082716; Göçer, A., Jin, Y.H., Fawcett, S.E., How does the contingent sustainability-risk-cost relationship affect the viability of CSR? An emerging economy perspective (2019) Sustainability (Switzerland), 11 (19), p. 5435. , https://doi.org/10.3390/su11195435; Golini, R., Moretto, A., Caniato, F., Caridi, M., Kalchschmidt, M., Developing sustainability in the Italian meat supply chain: An empirical investigation (2017) International Journal of Production Research, 55 (4), pp. 1183-1209. , https://doi.org/10.1080/00207543.2016.1234724; Gouda, S.-K., Saranga, H., Sustainable supply chains for supply chain sustainability: Impact of sustainability efforts on supply chain risk (2018) International Journal of Production Research, 56 (17), pp. 5820-5835. , https://doi.org/10.1080/00207543.2018.1456695; Govindan, K., Azevedo, S.G., Carvalho, H., Cruz-Machado, V., Lean, green and resilient practices influence on supply chain performance: Interpretive structural modeling approach (2015) International Journal of Environmental Science and Technology, 12 (1), pp. 15-34. , https://doi.org/10.1007/s13762-013-0409-7; Govindan, K., Seuring, S., Zhu, Q., Azevedo, S.G., Accelerating the transition towards sustainability dynamics into supply chain relationship management and governance structures (2016) Journal of Cleaner Production, 112, pp. 1813-1823. , https://doi.org/10.1016/j.jclepro.2015.11.084; Grant, D.B., Trauritrims, A., Wong, C.Y., (2017) Sustainable logistics and supply chain management—Principles and practices for sustainable operations and management, , Great Britain, Kogan Page; Gualandris, J., Kalchschmidt, M., Supply risk management and competitive advantage: A misfit model (2015) The International Journal of Logistics Management, 26 (3), pp. 459-478; Hallikas, J., Lintukangas, K., Kähkönen, A.K., The effects of sustainability practices on the performance of risk management and purchasing (2020) Journal of Cleaner Production, 263, p. 121579. , https://doi.org/10.1016/j.jclepro.2020.121579; Ham, Y., Chong, W.K., Li, D., A systematic literature review of the capabilities and performance metrics of supply chain resilience (2020) International Journal of Production Research, 58 (15), pp. 4541-4566. , https://doi.org/10.1080/00207543.2020.1785034; Harms, D., Hansen, E.G., Schaltegger, S., Strategies in sustainable supply chain management: An empirical investigation of large German companies (2013) Corporate Social Responsibility and Environmental Management, 20 (4), pp. 205-218. , https://doi.org/10.1002/csr.1293; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82. , https://doi.org/10.1016/j.ijpe.2012.01.042; He, L., Wu, Z., Xiang, W., Goh, M., Xu, Z., Song, W., Wu, X., A novel Kano-QFD-DEMATEL approach to optimise the risk resilience solution for sustainable supply chain (2020) International Journal of Production Research, pp. 1-22. , https://doi.org/10.1080/00207543.2020.1724343; Higgins, A.J., Miller, C.J., Archer, A.A., Ton, T., Fletcher, C.S., McAllister, R.R.J., Challenges of operations research practice in agricultural value chains (2010) Journal of the Operational Research Society, 61 (6), pp. 964-973. , https://doi.org/10.1057/jors.2009.57; Hofmann, H., Busse, C., Bode, C., Henke, M., Sustainability-related supply chain risks: Conceptualization and management (2014) Business Strategy and the Environment, 23 (3), pp. 160-172. , https://doi.org/10.1002/bse.1778; Holling, C.S., Resilience and stability of ecological systems (1973) Annual Review of Ecology and Systematics, 4 (1), pp. 1-23; Hollnagel, E., Woods, D.D., Leveson, N., (2006) Resilience engineering: Concepts and precepts, , Great Britain, Ashgate Publishing, Ltd; Horne, J.F., III, The coming age of organizational resilience (1997) Business Forum—California State University, Los Angeles, School of Business and Economics, 22 (2-3), p. 24; Hosseini, S., Barker, K., A Bayesian network model for resilience-based supplier selection (2016) International Journal of Production Economics, 180, pp. 68-87. , https://doi.org/10.1016/j.ijpe.2016.07.007; Hunkeler, D., Rebitzer, G., The future of life cycle assessment (2005) Journal LCA, 10, pp. 305-308; (2002) Beyond voluntarism: Human rights and the developing international legal obligations of companies, , ICHRP., Versoix, Switzerland, International Council on Human Rights Policy; Synthesis report (2007) Contribution of working groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, , Cambridge, UK, Cambridge University Press; (2009) Risk management—Vocabulary, , https://www.iso.org/obp/ui/#iso:std:iso:guide:73:ed-1:v1:en, Retrieved from; (2004) ISO 14000 essentials; Ivanov, D., Revealing interfaces of supply chain resilience and sustainability: A simulation study (2018) International Journal of Production Research, 56 (10), pp. 3507-3523. , https://doi.org/10.1080/00207543.2017.1343507; Jabbarzadeh, A., Fahimnia, B., Sabouhi, F., Resilient and sustainable supply chain design: Sustainability analysis under disruption risks (2018) International Journal of Production Research, 56 (17), pp. 5945-5968. , https://doi.org/10.1080/00207543.2018.1461950; Jain, V., Kumar, S., Soni, U., Chandra, C., Supply chain resilience: Model development and empirical analysis (2017) International Journal of Production Research, 55 (22), pp. 6779-6800. , https://doi.org/10.1080/00207543.2017.1349947; Ji, G., Ecological supply chains performance evaluation and disruption risk management strategies (2009) Human and Ecological Risk Assessment, 15 (2), pp. 351-370. , https://doi.org/10.1080/10807030902761346; Jørgensen, A., Le Bocq, A., Nazarkina, L., Hauschild, M., Methodologies for social life cycle assessment (2008) International Journal of Life Cycle Assessment, 13 (2), pp. 96-103. , https://doi.org/10.1065/lca2007.11.367; Juettner, U., Windler, K., Podleisek, A., Gander, M., Meldau, S., Implementing supplier management strategies for supply chain sustainability risks in multinational companies (2020) TQM Journal, 32 (5), pp. 923-938. , https://doi.org/10.1108/TQM-05-2019-0136; Jüttner, U., Maklan, S., Supply chain resilience in the global financial crisis: An empirical study (2011) Supply Chain Management, 16 (4), pp. 246-259. , https://doi.org/10.1108/13598541111139062; Jüttner, U., Peck, H., Christopher, M., Supply chain risk management: Outlining an agenda for future research (2003) International Journal of Logistics Research and Applications, 6 (4), pp. 197-210. , https://doi.org/10.1080/13675560310001627016; Kache, F., Seuring, S., Linking collaboration and integration to risk and performance in supply chains via a review of literature reviews (2014) Supply Chain Management, 19 (June), pp. 664-682. , https://doi.org/10.1108/SCM-12-2013-0478; Kamalahmadi, M., Parast, M.M., A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research (2016) International Journal of Production Economics, 171, pp. 116-133. , https://doi.org/10.1016/j.ijpe.2015.10.023; Kaur, H., Singh, S.P., Sustainable procurement and logistics for disaster resilient supply chain (2019) Annals of Operations Research, 283 (1), pp. 309-354. , https://doi.org/10.1007/s10479-016-2374-2; Kaur, H., Singh, S.P., Garza-Reyes, J.A., Mishra, N., Sustainable stochastic production and procurement problem for resilient supply chain (2020) Computers and Industrial Engineering, 139 (December 2018), p. 105560. , https://doi.org/10.1016/j.cie.2018.12.007; Kellner, F., Lienland, B., Utz, S., An a posteriori decision support methodology for solving the multi-criteria supplier selection problem (2019) European Journal of Operational Research, 272 (2), pp. 505-522. , https://doi.org/10.1016/j.ejor.2018.06.044; Kim, M., Chai, S., Implementing environmental practices for accomplishing sustainable green supply chain management (2017) Sustainability (Switzerland), 9 (7), p. 1192. , https://doi.org/10.3390/su9071192; Klewitz, J., Hansen, E.G., Sustainability-oriented innovation of SMEs: A systematic review (2014) Journal of Cleaner Production, 65, pp. 57-75. , https://doi.org/10.1016/j.jclepro.2013.07.017; Klibi, W., Martel, A., Guitouni, A., The design of robust value-creating supply chain networks: A critical review (2010) European Journal of Operational Research, 203 (2), pp. 283-293. , https://doi.org/10.1016/j.ejor.2009.06.011; Korhonen, J., Seager, T.P., Beyond eco-efficiency: A resilience perspective (2008) Business Strategy and the Environment, 17 (7), pp. 411-419. , https://doi.org/10.1002/bse.635; Leppelt, T., Foerstl, K., Reuter, C., Hartmann, E., Sustainability management beyond organizational boundaries-sustainable supplier relationship management in the chemical industry (2013) Journal of Cleaner Production, 56, pp. 94-102. , https://doi.org/10.1016/j.jclepro.2011.10.011; Levy, Y., Ellis, T.J., A systems approach to conduct an effective literature review in support of information systems research (2006) Informing Science Journal, 9, pp. 558-562. , https://doi.org/10.1109/IEEM.2012.6837801; Li, W.Y., Choi, T.M., Chow, P.S., Risk and benefits brought by formal sustainability programs on fashion enterprises under market disruption (2015) Resources, Conservation and Recycling, 104, pp. 348-353. , https://doi.org/10.1016/j.resconrec.2014.08.005; Lintukangas, K., Kähkönen, A.K., Ritala, P., Supply risks as drivers of green supply management adoption (2016) Journal of Cleaner Production, 112, pp. 1901-1909. , https://doi.org/10.1016/j.jclepro.2014.10.089; Lücker, F., Seifert, R.W., Building up resilience in a pharmaceutical supply chain through inventory, dual sourcing and agility capacity (2017) Omega (United Kingdom), 73, pp. 114-124. , https://doi.org/10.1016/j.omega.2017.01.001; Maditati, D.R., Munim, Z.H., Schramm, H.J., Kummer, S., A review of green supply chain management: From bibliometric analysis to a conceptual framework and future research directions (2018) Resources, Conservation and Recycling, 139 (December 2017), pp. 150-162. , https://doi.org/10.1016/j.resconrec.2018.08.004; Malek, A., Ebrahimnejad, S., Tavakkoli-Moghaddam, R., An improved hybrid grey relational analysis approach for green resilient supply chain network assessment (2017) Sustainability (Switzerland), 9 (8), p. 1433. , https://doi.org/10.3390/su9081433; Mäler, K.G., Sustainable development and resilience in ecosystems (2008) Environmental and Resource Economics, 39 (1), pp. 17-24. , https://doi.org/10.1007/s10640-007-9175-7; Mangla, S.K., Kumar, P., Barua, M.K., A flexible decision framework for building risk mitigation strategies in green supply chain using SAP-LAP and IRP approaches (2014) Global Journal of Flexible Systems Management, 15 (3), pp. 203-218. , https://doi.org/10.1007/s40171-014-0067-8; Mangla, S.K., Kumar, P., Barua, M.K., Flexible decision approach for analysing performance of sustainable supply chains under risks/uncertainty (2014) Global Journal of Flexible Systems Management, 15 (2), pp. 113-130. , https://doi.org/10.1007/s40171-014-0059-8; Mangla, S.-K., Kumar, P., Barua, M.K., Flexible decision Modeling for evaluating the risks in green supply chain using fuzzy AHP and IRP methodologies (2015) Global Journal of Flexible Systems Management, 16 (1), pp. 19-35. , https://doi.org/10.1007/s40171-014-0081-x; Mangla, S.-K., Kumar, P., Barua, M.K., Prioritizing the responses to manage risks in green supply chain: An Indian plastic manufacturer perspective (2015) Sustainable Production and Consumption, 1 (May), pp. 67-86. , https://doi.org/10.1016/j.spc.2015.05.002; Mangla, S.-K., Kumar, P., Barua, M.K., Risk analysis in green supply chain using fuzzy AHP approach: A case study (2015) Resources, Conservation and Recycling, 104, pp. 375-390. , https://doi.org/10.1016/j.resconrec.2015.01.001; Mangla, S.-K., Kumar, P., Barua, M.K., A fuzzy DEMATEL-based approach for evaluation of risks in green initiatives in supply chain (2016) International Journal of Logistics Systems and Management, 24 (2), pp. 226-243. , https://doi.org/10.1504/IJLSM.2016.076483; Mangla, S.-K., Kumar, P., Barua, M.K., An integrated methodology of FTA and fuzzy AHP for risk assessment in green supply chain (2016) International Journal of Operational Research, 25 (1), pp. 77-99. , https://doi.org/10.1504/IJOR.2016.073252; Mangla, S.-K., Luthra, S., Jakhar, S., Benchmarking the risk assessment in green supply chain using fuzzy approach to FMEA: Insights from an Indian case study (2018) Benchmarking, 25 (8), pp. 2660-2687. , https://doi.org/10.1108/BIJ-04-2017-0074; Mani, V., Delgado, C., Hazen, B.T., Patel, P., Mitigating supply chain risk via sustainability using big data analytics: Evidence from the manufacturing supply chain (2017) Sustainability (Switzerland), 9 (4), p. 608. , https://doi.org/10.3390/su9040608; Mani, V., Gunasekaran, A., Delgado, C., Supply chain social sustainability: Standard adoption practices in Portuguese manufacturing firms (2018) International Journal of Production Economics, 198 (July 2017), pp. 149-164. , https://doi.org/10.1016/j.ijpe.2018.01.032; Manuj, I., Mentzer, J.T., Global supply chain risk management (2008) Journal of Business Logistics, 29 (1), pp. 133-155. , https://doi.org/10.1002/j.2158-1592.2008.tb00072.x; Marco-Ferreira, A., Jabbour, C.J.C., Relating maturity levels in environmental management by adopting green supply chain management practices: Theoretical convergence and multiple case study (2019) Gestao e Producao, 26 (1). , https://doi.org/10.1590/0104-530X1822-19; Mari, S.I., Lee, Y.H., Memon, M.S., Sustainable and resilient supply chain network design under disruption risks (2014) Sustainability (Switzerland), 6 (10), pp. 6666-6686. , https://doi.org/10.3390/su6106666; Mari, S.I., Lee, Y.H., Memon, M.S., Sustainable and resilient garment supply chain network design with fuzzy multi-objectives under uncertainty (2016) Sustainability (Switzerland), 8 (10), p. 1038. , https://doi.org/10.3390/su8101038; Marteel, A.E., Davies, J.A., Olson, W.W., Abraham, M.A., Green chemistry and engineering: Drivers, metrics, and reduction to practice (2003) Annual Review of Environment and Resources, 28 (1), pp. 401-428; Martins, C.L., Pato, M.V., Supply chain sustainability: A tertiary literature review (2019) Journal of Cleaner Production, 225, pp. 995-1016. , https://doi.org/10.1016/j.jclepro.2019.03.250; Mathiyazhagan, K., Govindan, K., NoorulHaq, A., Geng, Y., An ISM approach for the barrier analysis in implementing green supply chain management (2013) Journal of Cleaner Production, 47, pp. 283-297. , https://doi.org/10.1016/j.jclepro.2012.10.042; Maxwell, J., Rothenberg, S., Briscoe, F., Marcus, A., Green schemes: Corporate environmental strategies and their implementation (1997) California Management Review, 3, pp. 118-134; Meixell, M.J., Luoma, P., Stakeholder pressure in sustainable supply chain management: A systematic review (2015) International Journal of Physical Distribution & Logistics Management, 45 (1-2), pp. 69-89. , https://doi.org/10.1108/IJPDLM-05-2013-0155; Miemczyk, J., Luzzini, D., Achieving triple bottom line sustainability in supply chains: The role of environmental, social and risk assessment practices (2019) International Journal of Operations and Production Management, 39 (2), pp. 238-259. , https://doi.org/10.1108/IJOPM-06-2017-0334; Miles, M., Covin, J.G., Environmental marketing: A source of reputational competitive and financial advantage (2000) Journal of Business Ethics, 23, pp. 299-311. , https://doi.org/10.2307/25074246; Min, H., Kim, I., Green supply chain research: Past, present, and future (2012) Logistics Research, 4 (1-2), pp. 39-47. , https://doi.org/10.1007/s12159-012-0071-3; Mohammed, A., Towards ‘gresilient’ supply chain management: A quantitative study (2020) Resources, Conservation and Recycling, 155, p. 104641. , https://doi.org/10.1016/j.resconrec.2019.104641; Mohammed, A., Harris, I., Soroka, A., Nujoom, R., A hybrid MCDM-fuzzy multi-objective programming approach for a G-resilient supply chain network design (2019) Computers and Industrial Engineering, 127, pp. 297-312. , https://doi.org/10.1016/j.cie.2018.09.052; Mohammed, A., Harris, I., Soroka, A., Naim, M., Ramjaun, T., Yazdani, M., Gresilient supplier assessment and order allocation planning (2020) Annals of Operations Research, 296 (1), pp. 335-362. , https://doi.org/10.1007/s10479-020-03611-x; Moreno-Camacho, C.A., Montoya-Torres, J.R., Jaegler, A., Gondran, N., Sustainability metrics for real case applications of the supply chain network design problem: A systematic literature review (2019) Journal of Cleaner Production, 231, pp. 600-618. , https://doi.org/10.1016/j.jclepro.2019.05.278; Neri, A., Cagno, E., Di Sebastiano, G., Trianni, A., Industrial sustainability: Modelling drivers and mechanisms with barriers (2018) Journal of Cleaner Production, 194, pp. 452-472. , https://doi.org/10.1016/j.jclepro.2018.05.140; Noci, G., Verganti, R., Managing “green” product innovation in small firms (1999) R&D Management, 29 (1), pp. 3-15. , https://doi.org/10.1111/1467-9310.00112; Panigrahi, S.S., Bahinipati, B., Jain, V., Sustainable supply chain management: A review of literature and implications for future research (2019) Management of Environmental Quality: An International Journal, 30 (5), pp. 1001-1049. , https://doi.org/10.1108/MEQ-01-2018-0003; Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S.J., Fosso-Wamba, S., The role of big data in explaining disaster resilience in supply chains for sustainability (2017) Journal of Cleaner Production, 142, pp. 1108-1118. , https://doi.org/10.1016/j.jclepro.2016.03.059; Perrings, C., Resilience and sustainable development (2006) Environment and Development Economics, 11 (4), pp. 417-427. , https://doi.org/10.1017/S1355770X06003020; Pettit, T.J., Croxton, K.L., Fiksel, J., Ensuring supply chain resilience: Development and implementation of an assessment tool (2013) Journal of Business Logistics, 34 (1), pp. 46-76. , https://doi.org/10.1111/jbl.12009; Pettit, T.J., Fiksel, J., Croxton, K.L., Ensuring supply chain resilience: Development of a conceptual framework (2010) Journal of Business Logistics, 31 (1), pp. 1-21. , https://doi.org/10.1002/j.2158-1592.2010.tb00125.x; Pinto, L., Green supply chain practices and company performance in Portuguese manufacturing sector (2020) Business Strategy and the Environment, 29 (5), pp. 1832-1849. , https://doi.org/10.1002/bse.2471; Ponomarov, S.Y., Holcomb, M.C., Understanding the concept of supply chain resilience (2009) The International Journal of Logistics Management, 20 (1), pp. 124-143. , https://doi.org/10.1108/09574090910954873; Qiu, R., Shi, S., Sun, Y., A p-robust green supply chain network design model under uncertain carbon price and demand (2019) Sustainability (Switzerland), 11 (21), p. 5928. , https://doi.org/10.3390/su11215928; Rajesh, R., On sustainability, resilience, and the sustainable–resilient supply networks (2018) Sustainable Production and Consumption, 15, pp. 74-88. , https://doi.org/10.1016/j.spc.2018.05.005; Rajesh, R., A fuzzy approach to analyzing the level of resilience in manufacturing supply chains (2019) Sustainable Production and Consumption, 18, pp. 224-236. , https://doi.org/10.1016/j.spc.2019.02.005; Rajesh, R., Social and environmental risk management in resilient supply chains: A periodical study by the Grey-Verhulst model (2019) International Journal of Production Research, 57 (11), pp. 3748-3765. , https://doi.org/10.1080/00207543.2019.1566656; Ramezankhani, M.J., Torabi, S.A., Vahidi, F., Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach (2018) Computers and Industrial Engineering, 126, pp. 531-548. , https://doi.org/10.1016/j.cie.2018.09.054; Ramos, T.B., de Melo, J.J., Developing and implementing an environmental performance index for the Portuguese military (2006) Business Strategy and the Environment, 15 (2), pp. 71-86. , https://doi.org/10.1002/bse.440; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916. , https://doi.org/10.1108/01443570510613956; Rao, P., Singh, A.K., La O'Castillo, O., Intal, P.S., Sajid, A., A metric for corporate environmental indicators for small and medium enterprises in the Philippines (2009) Business Strategy and the Environment, 18 (1), pp. 14-31. , https://doi.org/10.1002/bse.555; Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Pennington, D.W., Life cycle assessment part 1: Framework, goal and scope definition, inventory analysis, and applications (2004) Environment International, 30 (5), pp. 701-720. , https://doi.org/10.1016/j.envint.2003.11.005; Rice, J.B., Caniato, F., Building a secure and resilience supply chain (2003) Supply Chain Management Review, 5, pp. 22-30. , Pdf., (September/October); Rostamzadeh, R., Ghorabaee, M.K., Govindan, K., Esmaeili, A., Nobar, H.B.K., Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS-CRITIC approach (2018) Journal of Cleaner Production, 175, pp. 651-669. , https://doi.org/10.1016/j.jclepro.2017.12.071; Ruiz-Benitez, R., López, C., Real, J.C., Environmental benefits of lean, green and resilient supply chain management: The case of the aerospace sector (2017) Journal of Cleaner Production, 167, pp. 850-862. , https://doi.org/10.1016/j.jclepro.2017.07.201; Ruiz-Benitez, R., López, C., Real, J.C., Achieving sustainability through the lean and resilient management of the supply chain abstract (2019) International Journal of Physical Distribution and Logistics Management, 49 (2), pp. 122-155. , https://doi.org/10.1108/IJPDLM-10-2017-0320; (2008) Social Accountability International Standards; Sajjad, A., Eweje, G., Tappin, D., Managerial perspectives on drivers for and barriers to sustainable supply chain management implementation: Evidence from New Zealand (2020) Business Strategy and the Environment, 29 (2), pp. 592-604. , https://doi.org/10.1002/bse.2389; Sarkis, J., A strategic decision framework for green supply chain management (2003) Journal of Cleaner Production, 11 (4), pp. 397-409. , https://doi.org/10.1016/S0959-6526(02)00062-8; Sarkis, J., Current issues in the greening of the industry: A'sustainableì polemic (2007) Business Strategy and the Environment, 16 (3). , https://doi.org/10.1002/bse.566, 2476–2247; Sarkis, J., A boundaries and flows perspective of green supply chain management (2012) Supply Chain Management, 17 (2), pp. 202-216. , https://doi.org/10.1108/13598541211212924; Sarkis, J., Cohen, M.J., Dewick, P., Schröder, P., A brave new world: Lessons from the COVID-19 pandemic for transitioning to sustainable supply and production (2020) Resources, Conservation and Recycling, 159. , https://doi.org/10.1016/j.resconrec.2020.104894, (April; Sarkis, J., Zhu, Q., Lai, K.H., An organizational theoretic review of green supply chain management literature (2011) International Journal of Production Economics, 130 (1), pp. 1-15. , https://doi.org/10.1016/j.ijpe.2010.11.010; Sawyerr, E., Harrison, C., Developing resilient supply chains: Lessons from high-reliability organisations (2020) Supply Chain Management, 25 (1), pp. 77-100. , https://doi.org/10.1108/SCM-09-2018-0329; Sen, D.K., Datta, S., Mahapatra, S.S., On evaluation of supply chain's ecosilient (g-resilient) performance index: A fuzzy embedded decision support framework (2018) Benchmarking, 25 (7), pp. 2370-2389. , https://doi.org/10.1108/BIJ-03-2017-0038; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decision Support Systems, 54 (4), pp. 1513-1520. , https://doi.org/10.1016/j.dss.2012.05.053; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710. , https://doi.org/10.1016/j.jclepro.2008.04.020; Shafiq, A., Johnson, P.F., Klassen, R.D., Awaysheh, A., Exploring the implications of supply risk on sustainability performance (2017) International Journal of Operations and Production Management, 37 (10), pp. 1386-1407. , https://doi.org/10.1108/IJOPM-01-2016-0029; Shahin, A., Gunasekaran, A., Ebrahimi, S., Analysing the influence of supply chain risk on supply chain sustainability using structural equation modelling—With a case study in the home appliance industry (2019) World Review of Science, Technology and Sustainable Development, 15 (3), pp. 230-251. , https://doi.org/10.1504/WRSTSD.2019.102115; Sharma, S., Henriques, I., Stakeholder influences on sustainability practices in the Canadian forest products industry (2005) Strategic Management Journal, 26 (2), pp. 159-180. , https://doi.org/10.1002/smj.439; Shea, B.J., Grimshaw, J.M., Wells, G.A., Boers, M., Andersson, N., Hamel, C., Bouter, L.M., Development of AMSTAR: A measurement tool to assess the methodological quality of systematic reviews (2007) BMC Medical Research Methodology, 7 (10), pp. 7-10; Sheffi, Y., Rice, J.B., Fleck, J.M., Caniato, F., Supply chain response to global terrorism: A situation scan (2003) EurOMA POMS joint international conference, pp. 1-6; Shin, N., Park, S., Evidence-based resilience management for supply chain sustainability: An interpretive structural modelling approach (2019) Sustainability (Switzerland), 11 (2), p. 484. , https://doi.org/10.3390/su11020484; Shrivastava, P., Ecocentric management for a risk society (1995) Academy of Management Review, 20 (1), pp. 118-137; Simpson, M., Taylor, N., Barker, K., Environmental responsibility in SMEs: Does it deliver competitive advantage? (2004) Business Strategy and the Environment, 13 (3), pp. 156-171. , https://doi.org/10.1002/bse.398; Song, W., Ming, X., Liu, H.C., Identifying critical risk factors of sustainable supply chain management: A rough strength-relation analysis method (2017) Journal of Cleaner Production, 143, pp. 100-115. , https://doi.org/10.1016/j.jclepro.2016.12.145; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80. , https://doi.org/10.1111/j.1468-2370.2007.00202.x; Stiller, S., Gold, S., Socially sustainable supply chain management practices in the Indian seed sector: A case study (2014) Supply Chain Forum, 15 (1), pp. 52-67. , https://doi.org/10.1080/16258312.2014.11517333; Stindt, D., A generic planning approach for sustainable supply chain management—How to integrate concepts and methods to address the issues of sustainability? (2017) Journal of Cleaner Production, 153, pp. 146-163. , https://doi.org/10.1016/j.jclepro.2017.03.126; Suifan, T., Alazab, M., Alhyari, S., Trade-off among lean, agile, resilient and green paradigms: An empirical study on pharmaceutical industry in Jordan using a TOPSIS-entropy method (2019) International Journal of Advanced Operations Management, 11 (1-2), pp. 69-101. , https://doi.org/10.1504/IJAOM.2019.098493; Swanson, D., Goel, L., Francisco, K., Stock, J., An analysis of supply chain management research by topic (2018) Supply Chain Management, 23 (2), pp. 100-116. , https://doi.org/10.1108/SCM-05-2017-0166; Syed, M.W., Li, J.Z., Junaid, M., Ye, X., Ziaullah, M., An empirical examination of sustainable supply chain risk and integration practices: A performance-based evidence from Pakistan (2019) Sustainability (Switzerland), 11 (19), p. 5334. , https://doi.org/10.3390/su11195334; Tang, O., Musa, S.N., Identifying risk issues and research advancements in supply chain risk management (2011) International Journal of Production Economics, 133 (1), pp. 25-34. , https://doi.org/10.1016/j.ijpe.2010.06.013; Teuscher, P., Grüninger, B., Ferdinand, N., Risk Management in Sustainable Lessons Learnt from the case of (2006) Corporate Social Responsibility and Environmental Management, 10 (13), pp. 1-10. , https://doi.org/10.1002/csr; Thomé, A.M.T., Scavarda, L.F., Scavarda, A.J., Conducting systematic literature review in operations management (2016) Production Planning and Control, 27 (5), pp. 408-420. , https://doi.org/10.1080/09537287.2015.1129464; Tonelli, F., Evans, S., Taticchi, P., Industrial sustainability: Challenges, perspectives, actions (2013) International Journal of Business Inovation and Research, 7 (2), pp. 143-163; Torres-Ruiz, A., Ravindran, A., Multiobjective optimisation model for the selection of critical suppliers integrating sustainability criteria (2018) International Journal of Operational Research, 33 (2), pp. 208-238. , https://doi.org/10.1504/IJOR.2018.095198; Touboulic, A., Walker, H., Theories in sustainable supply chain management: A structured literature review (2015) International Journal of Physical Distribution and Logistics Management, 45, pp. 16-42. , https://doi.org/10.1108/IJPDLM-05-2013-0106; Trianni, A., Cagno, E., Neri, A., Modelling barriers to the adoption of industrial sustainability measures (2017) Journal of Cleaner Production, 168, pp. 1482-1504. , https://doi.org/10.1016/j.jclepro.2017.07.244; Tseng, M.L., Islam, M.S., Karia, N., Fauzi, F.A., Afrin, S., A literature review on green supply chain management: Trends and future challenges (2019) Resources, Conservation and Recycling, 141 (June 2018), pp. 145-162. , https://doi.org/10.1016/j.resconrec.2018.10.009; (2000) United Nations global compact; (2005) United Nations international strategy for disaster reduction, , New York, NY; Wagner, S.M., Bode, C., An empirical examination of supply chain performance along several dimensions of risk (2008) Journal of Business Logistics, 29 (1), pp. 307-325. , https://doi.org/10.1002/j.2158-1592.2008.tb00081.x; Walker, B., Salt, D., (2012) Resilience practice: Building capacity to absorb disturbance and maintain function, , Washington, DC, Island Press; Walton, S.V., Handfield, R.B., Melnyk, S.A., Green supply chain: Integrating suppliers into environmental management processes (1998) Proceedings—Annual Meeting of the Decision Sciences Institute, 3, pp. 1114-1116. , https://doi.org/10.1111/j.1745-493x.1998.tb00042.x; Wei, C.-T., Zuo, H., Jiang, C.B., Li, S.F., Modeling multilevel supplier selection problem based on weighted-directed network and its solution (2017) Discrete Dynamics in Nature and Society, 2017, p. ID 8470147. , https://doi.org/10.1155/2017/8470147; Weick, K.E., Sutcliffe, K.M., Obstfeld, D., Organizing for high reliability: Processes of collective mindfulness (2008) Crisis Management, 3 (1), pp. 81-123; Wetzstein, A., Feisel, E., Hartmann, E., Benton, W.C., Uncovering the supplier selection knowledge structure: A systematic citation network analysis from 1991 to 2017 (2019) Journal of Purchasing and Supply Management, 25 (4), p. 100519. , https://doi.org/10.1016/j.pursup.2018.10.002; Winter, M., Knemeyer, A.M., Exploring the integration of sustainability and supply chain management: Current state and opportunities for future inquiry (2013) International Journal of Physical Distribution and Logistics Management, 43 (1), pp. 18-38. , https://doi.org/10.1108/09600031311293237; Report of the World Commission on Environment and Development: “Our Common Future” (1987) Medicine and War, 4. , https://doi.org/10.1080/07488008808408783; Xu, M., Cui, Y., Hu, M., Xu, X., Zhang, Z., Liang, S., Qu, S., Supply chain sustainability risk and assessment (2019) Journal of Cleaner Production, 225, pp. 857-867. , https://doi.org/10.1016/j.jclepro.2019.03.307; Yun, G., Yalcin, M.G., Hales, D.N., Kwon, H.Y., Interactions in sustainable supply chain management: A framework review (2019) International Journal of Logistics Management, 30 (1), pp. 140-173. , https://doi.org/10.1108/IJLM-05-2017-0112; Zahiri, B., Zhuang, J., Mohammadi, M., Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study (2017) Transportation Research Part E: Logistics and Transportation Review, 103 (2017), pp. 109-142. , https://doi.org/10.1016/j.tre.2017.04.009; Zhu, Q., Sarkis, J., The moderating effects of institutional pressures on emergent green supply chain practices and performance (2007) International Journal of Production Research, 45 (18-19), pp. 4333-4355. , https://doi.org/10.1080/00207540701440345; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: Pressures, practices and performance (2005) International Journal of Operations and Production Management, 25 (5), pp. 449-468. , https://doi.org/10.1108/01443570510593148},
correspondence_address1={Negri, M.; Department of Management Economics and Industrial Engineering, Italy; email: marta.negri@polimi.it},
publisher={John Wiley and Sons Ltd},
issn={09644733},
language={English},
abbrev_source_title={Bus. Strategy Environ.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dey2021573,
author={Dey, P.K. and Yang, G.-L. and Malesios, C. and De, D. and Evangelinos, K.},
title={Performance Management of Supply Chain Sustainability in Small and Medium-Sized Enterprises Using a Combined Structural Equation Modelling and Data Envelopment Analysis},
journal={Computational Economics},
year={2021},
volume={58},
number={3},
pages={573-613},
doi={10.1007/s10614-019-09948-1},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075387308&doi=10.1007%2fs10614-019-09948-1&partnerID=40&md5=f039e00b1ac79384aea49a1689fef803},
affiliation={Aston Business School, Aston University, Birmingham, B4 7ET, United Kingdom; Institutes of Science and Development, Chinese Academy of Sciences, Beijing, 100190, China; Department of Environment, University of the Aegean, Mytilene, 81100, Greece},
abstract={Although the contribution of small and medium-sized enterprises (SMEs) to economic growth is beyond doubt, they collectively affect the environment and society negatively. As SMEs have to perform in a very competitive environment, they often find it difficult to achieve their environmental and social targets. Therefore, making SMEs sustainable is one of the most daunting tasks for both policy makers and SME owners/managers alike. Prior research argues that through measuring SMEs’ supply chain sustainability performance and deriving means of improvement one can make SMEs’ business more viable, not only from an economic perspective, but also from the environmental and social point of view. Prior studies apply data envelopment analysis (DEA) for measuring the performance of groups of SMEs using multiple criteria (inputs and outputs) by segregating efficient and inefficient SMEs and suggesting improvement measures for each inefficient SME through benchmarking it against the most successful one. However, DEA is limited to recommending means of improvement solely for inefficient SMEs. To bridge this gap, the use of structural equation modelling (SEM) enables developing relationships between the criteria and sub-criteria for sustainability performance measurement that facilitates to identify improvement measures for every SME within a region through a statistical modelling approach. As SEM suggests improvements not from the perspective of individual SMEs but for the totality of SMEs involved, this tool is more suitable for policy makers than for individual company owners/managers. However, a performance measurement heuristic that combines DEA and SEM could make use of the best of each technique, and thereby could be the most appropriate tool for both policy makers and individual SME owners/managers. Additionally, SEM results can be utilized by DEA as inputs and outputs for more effective and robust results since the latter are based on more objective measurements. Although DEA and SEM have been applied separately to study the sustainability of organisations, according to the authors’ knowledge, there is no published research that has combined both the methods for sustainable supply chain performance measurement. The framework proposed in the present study has been applied in two different geographical locations—Normandy in France and Midlands in the UK—to demonstrate the effectiveness of sustainable supply chain performance measurement using the combined DEA and SEM approach. Additionally, the state of the companies’ sustainability in both regions is revealed with a number of comparative analyses. © 2019, The Author(s).},
author_keywords={Benchmarking;  DEA;  Performance measurement;  SEM;  SMEs;  Supply chain;  Sustainability},
references={Abdul-Rashid, S.H., Sakundarini, N., Raja Ghazilla, R.A., Thurasamy, R., The impact of sustainable manufacturing practices on sustainability performance: Empirical evidence from Malaysia (2017) International Journal of Operations & Production Management, 37 (2), pp. 182-204; Acquaye, A., Feng, K., Oppon, E., Salhi, S., Ibn-Mohammed, T., Genovese, A., Hubacek, K., Measuring the environmental sustainability performance of global supply chains: A multi-regional input–output analysis for carbon, sulphur oxide and water footprints (2017) Journal of Environmental Management, 187, pp. 571-585; Ali, A.I., Seiford, L.M., Translation invariance in data envelopment analysis (1990) Operations Research Letters, 9, pp. 403-405; Arbuckle, J.L., (2014) Amos 23.0 user’s guide, , IBM SPSS, Chicago; Ates, A., Garengo, P., Cocca, P., Bititci, U., The development of SME managerial practice for effective performance management (2013) Journal of Small Business and Enterprise Development, 20 (1), pp. 28-54; Azadia, M., Jafarianb, M., Saen, R.F., Mirhedayatian, S.M., A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context (2015) Computer and Operations Research, 54, pp. 274-285; Banker, R.D., Maximum likelihood, consistency and data envelopment analysis: A statistical foundation (1993) Management Science, 39 (10), pp. 1265-1273; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Management Science, 30 (9), pp. 1078-1092; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2013) Production Planning & Control: SI - The Management of Operations; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), p. 305; Bjorklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Bollen, K.A., (1989) Structural equations with latent variables, , Wiley-Interscience, New York; Büyüközkan, G., Karabulut, Y., Sustainability performance evaluation: Literature review and future directions (2018) Journal of Environmental Management, 217, pp. 253-267; Cao, J., Chen, G., Khoveyni, M., Eslami, R., Yang, G., Specification of a performance indicator using the evidential-reasoning approach (2016) Knowledge-Based Systems, 92, pp. 138-150; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Chay, T., Xu, Y., Tiwari, A., Chay, F., Towards lean transformation: The analysis of lean implementation frameworks (2015) Journal of Manufacturing Technology Management, 26 (7), pp. 1031-1052; Devins, D., Johnson, S., Sutherland, J., Employer characteristics and employee training outcomes in UK SMEs: A multivariate analysis (2004) Journal of Small Business and Enterprise Development, Bradford, 11 (4), pp. 449-457; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (2012) Production Planning and Control: The Management of Operations, 24 (8-9), pp. 702-720; Dey, P.K., Cheffi, W., Managing supply chain integration: Contemporary approaches and scope for further research (2013) Production Planning and Control: The Management of Operations, 24 (8-9), pp. 653-657; Dey, P.K., Malesios, C., De, D., Chowdhury, S., Abdelaziz, F.B., Could lean practices and process innovation enhance supply chain sustainability of small and medium sized enterprises? (2018) Business Strategy and the Environment; Fen Tseng, Y., Jim Wu, Y., Wu, W., Chen, C., Exploring corporate social responsibility education (2010) Management Decision, 48 (10), pp. 1514-1528; Fitzgerald, L., Johnson, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance measurement in service businesses, , CIMA, London; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Gavronski, I., Klassen, R.D., Vachon, S., do Nascimento, L.F.M., A resource-based view of green supply management (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 872-885; Groves, C., Frater, L., Lee, R., Stokes, H., Is there room at the bottom for CSR? Corporate social responsibility and nanotechnology in the UK (2011) Journal of Business Ethics, 101 (4), pp. 525-552; Gunasekaran, A., Parel, C., Mc Gaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Halkos, G.E., Evangelinos, K., Determinants of environmental management systems standards implementation: Evidence from Greek industry (2002) Business Strategy and the Environment, 11 (6), pp. 360-375; Halkos, G.E., Polemis, G., The impact of economic growth on environmental efficiency of the electricity sector: A hybrid window DEA methodology for the USA (2018) Journal of Environmental Management, 211, pp. 334-346; Halkos, G.E., Tzeremes, N.G., Kourtzidis, S.A., Regional sustainability efficiency index in Europe: An additive two-stage DEA approach (2015) Operational Research, 15 (1), pp. 1-23; Halkos, G.E., Tzeremes, N.G., Kourtzidis, S.A., Measuring sustainability efficiency using a two-stage data envelopment analysis approach (2015) Journal of Industrial Ecology, 20 (5), pp. 1159-1175; Hall, J., Environmental supply chain dynamics (2000) Journal of Cleaner Production, 8, pp. 455-471; Hanim, H., Bakri, M., Mohamed, N., Said, J., Mitigating asset misappropriation through integrity and fraud risk elements: Evidence emerging economies (2017) Journal of Financial Crime, 24 (2); Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hoejmose, S., Brammer, S., Millington, A., An empirical examination of the relationship between business strategy and socially responsible supply chain management (2013) International Journal of Operations & Production Management, 33 (5), pp. 589-621; Hu, Q., Mason, R., Williams, S.J., Found, P., Lean implementation within SMEs: A literature review (2015) Journal of Manufacturing Technology Management, 26 (7), pp. 980-1012; Jamali, D., Zanhour, M., Keshishian, T., Peculiar strengths and relational attributes of SMEs in the context of CSR (2009) Journal of Business Ethics, 87, pp. 355-367; Johnson, M.P., Sustainability management and small and medium-sized enterprises: Managers awareness and implementation of innovative tools (2015) Corporate Social Responsibility and Environmental Management, 22 (5), pp. 281-285; Johnson, M.P., Schaltegger, S., Two decades of sustainability management tools for SMEs: How far have we come? (2016) Journal of Small Business Management, 54 (2), pp. 481-505; Jöreskog, K.G., (1994) Structural equation modeling with ordinal variables. Multivariate analysis and its applications, pp. 297-310. , Institute of Mathematical Statistics, Hayward, CA; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) International Journal of Production Economics, 101, pp. 99-108; Kaplan, R.S., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Kim, J., Realff, M.J., Lee, J.H., Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty (2011) Computers & Chemical Engineering, 35 (9), pp. 1738-1751; Kleindorfer, P.-R., Singhal, K., Wassenhove, L.-N.-V., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Kneip, A., Simar, L., Wilson, P.W., Asymptotics and consistent bootstraps for DEA estimators in non-parametric frontier models (2008) Econometric Theory, 24, pp. 1663-1697; Koh, S.C.L., Simpson, M., Change and uncertainty in SME manufacturing environments using ERP (2005) Journal of Manufacturing Technology Management, 16 (5-6), pp. 629-653; Korostelev, A., Simar, L., Tsybakov, A.B., On estimation of monotone and convex boundaries (1995) Publicaitons De l’Institut De Statistique De l’Universit´e De Paris, 39 (1), pp. 3-18; Lee, S.Y., Klassen, R.D., Drivers and enablers that foster environmental management capabilities in small- and medium-sized suppliers in supply chains (2008) Production and Operations Management, 17 (6), pp. 573-586; Lewis, W.G., Pun, K.F., Lalla, T.R.M., Empirical investigation of the hard and soft criteria of TQM in ISO 9001 certified small and medium-sized enterprises (2006) The International Journal of Quality & Reliability Management, 23 (8), pp. 964-985; Lynch, R., Cross, K., (1991) Measure up! Yardsticks for continuous improvement, , Blackwell, Cambridge; Malesios, C., Dey, P.K., Abdelaziz, F.B., Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling (2018) Annals of Operations Research; Marr, B., Creelman, J., (2011) More with less: Maximizing value in the public sector, , Palgrave Macmilllan, Basingstoke; Mollenkopf, D., Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors (2008) Journal of Purchasing and Supply Management, 14, pp. 69-85; Neely, A., Adams, C., Kennerley, M., (2002) The performance prism: The scorecard for measuring and managing stakeholder relationship, , Prentice Hall, London; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nguyen, T.H., Waring, T.S., The adoption of customer relationship management (CRM) technology in SMEs: An empirical study (2013) Journal of Small Business and Enterprise Development, 20 (4), pp. 824-848; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Orsato, R.-J., Competitive Environmental strategies: When does it pay to be green? (2006) California Management Review, 48 (2), pp. 127-143; Pala, M., Edum-Fotwe, F., Ruikar, K., Doughty, N., Peters, C., Contractor practices for managing extended supply chain tiers (2014) Supply Chain Management, 19 (1), pp. 31-45; Patyal, V.S., Koilakuntla, M., Infrastructure and core quality practices in Indian manufacturing organizations: Scale development and validation (2015) Journal of Advances in Management Research, Bingley, 12 (2), pp. 141-175; Poister, T.H., (2003) Measuring performance in public and nonprofit organizations, , Jossey-Bass, San Francisco, CA; Quah, H.S., Udin, Z.M., Supply chain management from the perspective of value chain flexibility: An exploratory study (2011) Journal of Manufacturing Technology Management, 22 (4), pp. 506-526; Ramezankhani, M.J., Ali Torabi, S., Vahidi, F., Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach (2018) Computers & Industrial Engineering, 126, pp. 531-548; Santos, M., CSR in SMEs: strategies, practices, motivations and obstacles (2011) Social Responsibility Journal, 7 (3), pp. 490-508; Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16, pp. 1699-1710; Sharma, M.K., Bhagwat, R., Dangayach, G.S., Practice of performance measurement: Experience from Indian SMEs (2005) International Journal of Globalisation and Small Business, 1 (2), pp. 183-213; Shen, W., Zhang, D., Liu, W., Yang, G., Increasing discrimination of DEA evaluation by utilizing distances to anti-efficient frontiers (2016) Computers & Operations Research, 75, pp. 163-173; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2005) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shi, Q., Zuo, J., Zillante, G., Exploring the management of sustainable construction at the programme level—A Chinese case study (2012) Construction Management and Economics, 30 (6), pp. 425-440; Smith, P., Model misspecification in data envelopment analysis (1997) Annals of Operations Research, 73, pp. 233-252; Sullivan-Taylor, B., Branicki, L., Creating resilient SMEs: Why one size might not fit all (2011) International Journal of Production Research, 49 (18), pp. 5565-5579; Su-Yol, L., Drivers for the participation of small and medium-sized suppliers in green supply chain initiatives (2008) Supply Chain Management, 13 (3), pp. 185-198; Taticchi, P., Cagnazzo, L., Tonelli, F., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Taticchi, P., Tonelli, F., Cagnazzo, L., A decomposition and hierarchical approach for business performance measurement and management (2009) Measuring Business Excellence, 13 (4), pp. 47-57; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains. A literature review and a research agenda (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 782-804; Thanassoulis, E., Dey, P.K., Petridis, K., Goniadis, I., Georgiou, A.C., Evaluating higher education teaching performance using combined analytic hierarchy process and data envelopment analysis (2017) Journal of the Operational Research Society, 68, pp. 431-554; Thomas, D.J., Griffin, P.M., Co-ordinated supply chain management (1996) European Journal of Operational Research, 94 (3), pp. 1-15; Towers, N., Burnes, B., A composite framework of supply chain management and enterprise planning for small and medium-sized manufacturing enterprises (2008) Supply Chain Management, 13 (5), pp. 349-355; Vidal, J., (2013) UK Government failing Legal Duty on Air Pollution, , Supreme Court rules, Environment, guardian.co.uk, Guardian; Walker, H., Jones, N., Sustainable supply chain management across the UK private sector (2012) Supply Chain Management, 17 (1), pp. 15-28; West, J., Challenges of funding open innovation platforms: Lessons from Symbian Ltd (2014) New Frontiers in Open Innovation, pp. 29-49. , H. Chesbrough, W. Vanhaverbeke, J. West, Oxford, Oxford University Press; Whyman, P.B., Petrescu, A.I., Workplace flexibility practices in SMEs: Relationship with performance via redundancies, absenteeism, and financial turnover (2015) Journal of Small Business Management, 53 (4), p. 1097; Wolff, J.A., Pett, T.L., Small-firm performance: Modeling the role of product and process improvements (2006) Journal of Small Business Management, 44 (2), pp. 268-284; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modelling (2007) Industrial management & Data system, 107 (3), pp. 361-381; Wyld, J., Pugh, G., Tyrrall, D., Can powerful buyers “exploit” SME suppliers? (2012) Journal of Small Business and Enterprise Development, 19 (2), pp. 322-334; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) Journal of Operations Management, 22, pp. 265-289; Zhu, Q., Sarkis, J., Lai, K.-H., Green supply chain management: Pressures, practices and performance within the Chinese automobile industry (2007) Journal of Cleaner Production, 15 (11-12), pp. 1041-1052; Zhu, Q., Sarkis, J., Lai, K.-H., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) Journal of Environmental Management, 85 (1), pp. 179-189},
correspondence_address1={Malesios, C.; Aston Business School, United Kingdom; email: c.malesios@aston.ac.uk},
publisher={Springer},
issn={09277099},
language={English},
abbrev_source_title={Comput. Econ.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Anjomshoae2021,
author={Anjomshoae, A. and Hassan, A. and Wong, K.Y. and Banomyong, R.},
title={An integrated multi-stage fuzzy inference performance measurement scheme in humanitarian relief operations},
journal={International Journal of Disaster Risk Reduction},
year={2021},
volume={61},
doi={10.1016/j.ijdrr.2021.102298},
art_number={102298},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107053690&doi=10.1016%2fj.ijdrr.2021.102298&partnerID=40&md5=963e5ae5d8f5512312b7375aff3d9421},
affiliation={Thammasat Business School, Thammasat University, 2 Prachan Road, Pranakorn District, Bangkok, 10200, Thailand; Department of Manufacturing and Industrial Engineering, School of Mechanical Engineering, Universiti Teknologi Malaysia, Malaysia},
abstract={Performance based measurement models and systems in humanitarian supply chains require inputs derived from decision-makers’ and practitioners’ experience. Past knowledge related to performance indicator are generally qualitative and imprecise. Existing humanitarian performance measurement systems are largely theoretical and do not consider qualitative and imprecise information in their assessment of humanitarian relief operations. This paper aims to address a practical approach to the performance measurement of humanitarian operations by recognizing the need for multi-dimensional performance evaluation while dealing with imprecision and uncertainty in evaluation processes. A hierarchical multi-stage Fuzzy Inference System (FIS) was developed to address imprecise and uncertain performance indicators. Performance indicators were clustered into four Balanced Scorecard (BSC) type categories. A prototype was developed using MATLAB toolbox. The FIS scheme was tested and validated by practitioners from leading international humanitarian organizations in Malaysia. The scheme facilitated performance analysis and provided insights into operational areas that need further improvements. The proposed FIS scheme enable decision-makers to address imprecise and fuzzy information more objectively. © 2021 Elsevier Ltd},
author_keywords={Fuzzy inference system;  Humanitarian logistics;  Performance indicators;  Performance measurement},
references={Abidi, H., de Leeuw, S., Dullaert, W., Performance management practices in humanitarian organisations (2020) J. Humanit. Logist. Supply Chain Manag., 10 (2), pp. 125-168; Wang, Z., Zhang, J., Agent-based evaluation of humanitarian relief goods supply capability (2019) International Journal of Disaster Risk Reduction, 36, p. 101105; Blecken, A., Supply chain process modelling for humanitarian organizations (2010) Int. J. Phys. Distrib. Logist. Manag., 40 (8-9), pp. 675-692; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Manag. Res. News, 32 (11), pp. 1038-1049; Lu, Q., Goh, M., de Souza, R., A SCOR framework to measure logistics performance of humanitarian organizations (2016) Jrnl Hum Log and Sup Chn Mnage, 6 (2), pp. 222-239; Perkins, M., Grey, A., Remmers, H., What do we really mean by “Balanced Scorecard”? (2014) Int. J. Prod. Perform. Manag., 63 (2), pp. 148-169; Tofighi, S., Torabi, S.A., Mansouri, S.A., Humanitarian logistics network design under mixed uncertainty (2016) Eur. J. Oper. Res., 250 (1), pp. 239-250; Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning—I (1975) Inf. Sci., 8 (3), pp. 199-249; Anjomshoae, A., Hassan, A., Kunz, N., Wong, K.Y., de Leeuw, S., Toward a dynamic balanced scorecard model for humanitarian relief organizations' performance management (2017) J. Humanit. Logist. Supply Chain Manag., 7 (2), pp. 194-218; Anjomshoae, A., Hassan, A., Wong, K.Y., An integrated AHP-based scheme for performance measurement in humanitarian supply chains (2019) Int J Productivity & Perf Mgmt, 68 (5), pp. 938-957; Abidi, H., de Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: a systematic literature review (2014) Supply Chain Manag.: Int. J., 19 (5-6), pp. 592-608; Davidson, A., Key Performance Indicators in Humanitarian Logistics (2006), Master Thesis, USA; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced scorecard for natural disaster management projects (2007) Disaster Prev. Manag., 16 (5), pp. 785-806; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) Int. J. Public Sect. Manag., 21 (1), pp. 4-25; Gizaw, B.T., Gumus, A.T., Humanitarian relief supply chain performance evaluation: a literature review (2016) Int. J. Math. Stat., 8 (2), p. 105; Dube, N., van der Vaart, T., Teunter, R.H., Van Wassenhove, L.N., Host government impact on the logistics performance of international humanitarian organisations (2016) J. Oper. Manag.; Rajakaruna, S., Wijeratne, A., Mann, T., Yan, C., Effect of individual skills and performance on humanitarian organisations: a structural equation model (2017) Logistics, 1 (1), p. 7; Nath, R., Shannon, H., Kabali, C., Oremus, M., Investigating the key indicators for evaluating post-disaster shelter (2016) Disasters; Schiffling, S., Piecyk, M., Performance measurement in humanitarian logistics: a customer-oriented approach (2014) J. Humanit. Logist. Supply Chain Manag., 4 (2), pp. 198-221; De Leeuw, S., (2010) Towards a reference mission map for performance measurement in humanitarian supply chains, 336, pp. 181-188; Yadav, D.K., Barve, A., Analysis of critical success factors of humanitarian supply chain: an application of Interpretive Structural Modeling (2015) International Journal of Disaster Risk Reduction, 12, pp. 213-225; Schön, A.-M., Al-Saadi, S., Grubmueller, J., Schumann-Bölsche, D., Developing a camp performance indicator system and its application to Zaatari, Jordan (2018) J. Humanit. Logist. Supply Chain Manag., 5 (8), p. 17; Abidi, H., Scholten, K., Applicability of performance measurement systems to humanitarian supply chains (2015) Humanitarian Logistics and Sustainability, pp. 235-260. , M. Klumpp S. de Leeuw A. Regattieri R. de Souza Springer Cham; Hashemi Petrudi, S.H., Tavana, M., Abdi, M., A comprehensive framework for analyzing challenges in humanitarian supply chain management: a case study of the Iranian Red Crescent Society (2020) International Journal of Disaster Risk Reduction, 42, p. 101340; Ross, T.J., Fuzzy Logic with Engineering Applications (2010), John Wiley & Sons, Ltd Chichester, UK; Gutjahr, W.J., Nolz, P.C., Multicriteria optimization in humanitarian aid (2016) Eur. J. Oper. Res., 252 (2), pp. 351-366; Kazeminia, M., Naderi, H., Ahmadvand, A., A new approach for evacuation process in disaster relief with uncertainties (2016) IJLSM, 24 (4), p. 466; Celik, E., A cause and effect relationship model for location of temporary shelters in disaster operations management (2017) International Journal of Disaster Risk Reduction, 22, pp. 257-268; Venkatesh, V.G., Zhang, A., Deakins, E., Luthra, S., Mangla, S., A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains (2018) Ann. Oper. Res.; Jia, X., Morel, G., Martell-Flore, H., Hissel, F., Batoz, J.-L., Fuzzy logic based decision support for mass evacuations of cities prone to coastal or river floods (2016) Environ. Model. Software, 85, pp. 1-10; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: a literature review (2017) Comput. Ind. Eng., 113, pp. 333-346; Kahraman, C., Fuzzy Applications in Industrial Engineering, Springer Berlin Heidelberg (2006), Berlin, Heidelberg; Sivanandam, S.N., Sumathi, S., Deepa, S.N., Introduction to Fuzzy Logic Using MATLAB, Springer Berlin Heidelberg (2007), Berlin, Heidelberg; Nakandala, D., Samaranayake, P., Lau, H.C.W., A fuzzy-based decision support model for monitoring on-time delivery performance: a textile industry case study (2013) Eur. J. Oper. Res., 225 (3), pp. 507-517; Pourjavad, E., Shahin, A., The application of Mamdani fuzzy inference system in evaluating green supply chain management performance (2018) Int. J. Fuzzy Syst., 20 (3), pp. 901-912; Han, Y., Deng, Y., An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors (2018) Soft Comput, 22 (15), pp. 5073-5090; Kacprzyk, J., Pedrycz, W., Springer Handbook of Computational Intelligence, Springer Berlin Heidelberg (2015), Berlin, Heidelberg; Amindoust, A., Ahmed, S., Saghafinia, A., Bahreininejad, A., Sustainable supplier selection: a ranking model based on fuzzy inference system (2012) Appl. Soft Comput., 12 (6), pp. 1668-1677; Mamdani, E.H., Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller (1975) Int. J. Man Mach. Stud., 7 (1), pp. 1-13; Yeh, Z.-M., Li, K.-H., A systematic approach for designing multistage fuzzy control systems (2004) Fuzzy Set Syst., 143 (2), pp. 251-273; Yam, Y., Fuzzy approximation via grid point sampling and singular value decomposition, IEEE Trans. Syst. Man Cybern (1997) Biol. Cybern., 27 (6), pp. 933-951; Combs, W.E., Andrews, J.E., Combinatorial rule explosion eliminated by a fuzzy rule configuration (1998) IEEE Trans. Fuzzy Syst., 6 (1), pp. 1-11; Fu, X., Zeng, X.-J., Luo, X., Wang, D., Xu, D., Fan, Q.-L., Designing an intelligent decision support system for effective negotiation pricing: a systematic and learning approach (2017) Decis. Support Syst., 96, pp. 49-66; Klir, G.J., St Clair, U.H., Yuan, B., Fuzzy Set Theory: Foundations and Applications (1997), Prentice Hall; London Prentice-Hall International Upper Saddle River, N.J; Singh, S., Olugu, E.U., Musa, S.N., Mahat, A.B., Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework (2018) J. Intell. Manuf., 29 (1), pp. 1-18; Martilla, J.A., James, J.C., Importance-performance analysis (1977) J. Market., 41 (1), p. 77; (2014) MERCY Malaysia Annual Report, Annual Report, , https://www.mercy.org.my/annual-reports/, Mercy Malaysia (Accessed 26 January 2019); Wikipedia, 2018 Sulawesi Earthquake and Tsunami (2019), https://en.wikipedia.org/w/index.php?oldid=887039744, Wikipedia (Accessed 11 March 2019); Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) Int. J. Prod. Perform. Manag., 64 (6), pp. 784-810; Singh, S., Olugu, E.U., Fallahpour, A., Fuzzy-based sustainable manufacturing assessment model for SMEs (2014) Clean Technol. Environ. Policy, 16 (5), pp. 847-860; Ghadimi, P., Dargi, A., Heavey, C., Sustainable supplier performance scoring using audition check-list based fuzzy inference system: a case application in automotive spare part industry (2017) Comput. Ind. Eng., 105, pp. 12-27},
correspondence_address1={Anjomshoae, A.; Thammasat Business School, 2 Prachan Road, Pranakorn District, Thailand; email: anjomshoae.a@tbs.tu.ac.th},
publisher={Elsevier Ltd},
issn={22124209},
language={English},
abbrev_source_title={Int. J. Disaster Risk Reduct.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Patil20211794,
author={Patil, A. and Shardeo, V. and Dwivedi, A. and Madaan, J. and Varma, N.},
title={Barriers to sustainability in humanitarian medical supply chains},
journal={Sustainable Production and Consumption},
year={2021},
volume={27},
pages={1794-1807},
doi={10.1016/j.spc.2021.04.022},
note={cited By 22},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85105499342&doi=10.1016%2fj.spc.2021.04.022&partnerID=40&md5=5a1099d45a18b436783bc2c5f7f4d186},
affiliation={Department of Management Studies, Indian Institute of Technology, Delhi, India; Jindal Global Business School, O. P. Jindal Global University, Sonipat, Haryana, India; Anisfiled School of Business Ramapo College of New Jersey, Mahwah, NJ, United States},
abstract={Medical and pharmaceutical products (MPP) are critical elements in humanitarian relief efforts. Relief operations are often burdened with inappropriate and unusable MPP, which leads to the wastage of resources and causes harm to the environment. This study investigates MPP management and facilitates the adoption of sustainability practices in the humanitarian medical supply chain. The study identifies 20 potential barriers for sustainability and categorises them into six different issues (material, operational, logistics, human, funding, and exogenous). Fuzzy best-worst method and analytic network process methodologies were adopted to prioritise barriers according to their degree of influence. Findings from the study suggest that stakeholders should focus attention on the material, operations, and logistical issues. Poor compliance with the World Health Organisation guidelines is reported as the most significant barrier to sustainability. Long-term collaboration and promotion of cash-based donation are suggested as suitable alternatives to product donation. The key recommendations of the study are to sensitise donors and media regarding donation practices and disaster relief victim requirements, build local capacity, design coordination mechanisms, and develop a performance measurement system incorporating the triple bottom line of sustainability. The study outcome reveals connections between barriers to sustainability and sheds light on the socioenvironmental aspect of donations. The study lays a foundation for future research, exploring how to better enable sustainability in the relief supply chain. © 2021 Institution of Chemical Engineers},
author_keywords={Analytic network process;  Barrier;  Fuzzy best-worst method;  Humanitarian medical supply chain;  Pharmaceuticals;  Sustainability},
keywords={Disaster prevention;  Supply chains, Analytic network process;  Design coordination;  Humanitarian relief;  Performance measurement system;  Pharmaceutical products;  Potential barriers;  Sustainability practices;  Triple Bottom Line, Sustainable development},
references={Abidi, H., Klumpp, M., (2013), pp. 1-19. , Performance measurement in humanitarian logistics; Abrahams, D., The barriers to environmental sustainability in post-disaster settings: a case study of transitional shelter implementation in Haiti (2014) Disasters, 38, p. S25S49; Abualkhair, H., Lodree, E.J., Davis, L.B., Managing volunteer convergence at disaster relief centers (2019) Int. J. Prod. Econ., 107399; Agarwal, S., Kant, R., Shankar, R., Humanitarian supply chain management frameworks (2019) Benchmarking, , BIJ-08-2018-0245; Alem, D., Clark, A., Moreno, A., Stochastic network models for logistics planning in disaster relief (2016) Eur. J. Oper. Res., 255, pp. 187-206; Asgary, R., Lawrence, K., Evaluating underpinning, complexity and implications of ethical situations in humanitarian operations: qualitative study through the lens of career humanitarian workers (2020) BMJ Open, 10, pp. 1-11; Babatunde, S., Oloruntoba, R., Agho, K., Healthcare commodities for emergencies in Africa: review of logistics models, suggested model and research agenda (2020) J. Humanit. Logist. Supply Chain Manage., 10, pp. 371-390; Baffoe, B.O.K., Luo, W., Humanitarian relief sustainability: a framework of humanitarian logistics digital business ecosystem (2020) Transp. Res. Procedia, 48, pp. 363-387; Bag, S., Luthra, S., Venkatesh, V.G., Yadav, G., Towards understanding key enablers to green humanitarian supply chain management practices (2020) Manage. Environ. Qual.; Behl, A., Dutta, P., Humanitarian supply chain management: a thematic literature review and future directions of research (2018) Ann. Oper. Res., pp. 1-44; Ben-David, A., Comparison of classification accuracy using Cohen's Weighted Kappa (2008) Expert Syst. Appl., 34, pp. 825-832; Benaragamama, B., Fernandopulle, R., The Expectations, the Reality and the Burden of Drug Donation (2007), Ministry of Healthcare and Nutrition & Faculty of Medicine, University of Colombo Sri Lanka; Bero, L., Carson, B., Moller, H., Hill, S., To give is better than to receive: Compliance with WHO guidelines for drug donations during 2000–2008 (2010) Bull. World Health Organ., 88, pp. 922-929; Boxall, A.B.A., The environmental side effects of medication: how are human and veterinary medicines in soils and water bodies affecting human and environmental health? (2004) EMBO Rep., 5, pp. 1110-1116; Burkart, C., Besiou, M., Wakolbinger, T., The funding—humanitarian supply chain interface (2016) Surv. Oper. Res. Manage. Sci., 21, pp. 31-45; Cao, C., Li, C., Yang, Q., Liu, Y., Qu, T., A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters (2018) J. Cleaner Prod., 174, pp. 1422-1435; Cao, C., Li, C., Yang, Q., Zhang, F., Multi-objective optimisation model of emergency organisation allocation for sustainable disaster supply chain (2017) Sustainability (Switzerland), 9; (2011), Catholic Health Association CHA medical surplus donation study: how effective surplus donation can relieve human suffering; Chen, L., Zhao, X., Tang, O., Price, L., Zhang, S., Zhu, W., Supply chain collaboration for sustainability: a literature review and future research agenda (2017) Int. J. Prod. Econ., 194, pp. 73-87; Claycomb, J., (2013), https://www.drugtopics.com/view/issues-emergency-pharmacy, Issues in emergency pharmacy New Zealand : new protocols [WWW Document]. Drug Topics. URL (accessed 11.3.20); Das, D., Dutta, P., A system dynamics framework for integrated reverse supply chain with three way recovery and product exchange policy (2013) Comput. Ind. Eng., 66, pp. 720-733; Davis, L.B., Samanlioglu, F., Qu, X., Root, S., Inventory planning and coordination in disaster relief efforts (2013) Int. J. Prod. Econ., 141, pp. 561-573; Day, J.M., Melnyk, S.A., Larson, P.D., Davis, E.W., Whybark, D.C., Humanitarian and disaster relief supply chains: a matter of life and death (2012) J. Supply Chain Manage., 48, pp. 21-36; Diedrichs, D.R., Phelps, K., Isihara, P.A., Quantifying communication effects in disaster response logistics: a multiple network system dynamics model (2016) J. Humanit. Logist. Supply Chain Manage., 6, pp. 24-45; Dolinskaya, I., Besiou, M., Guerrero-Garcia, S., Humanitarian medical supply chain in disaster response (2018) J. Humanit. Logist. Supply Chain Manage., 8, pp. 199-226; Dubey, R., Gunasekaran, A., The sustainable humanitarian supply chain design: agility, adaptability and alignment (2016) Int. J. Logist. Res. Appl., 19, pp. 62-82; Dwivedi, A., Agrawal, D., Madaan, J., Sustainable manufacturing evaluation model focusing leather industries in India: A TISM approach (2019) sJ. Sci. Technol. Policy Manage., 10, pp. 319-359; Eriksson, C.B., Kemp, H.V., Gorsuch, R., Hoke, S., Foy, D.W., Trauma exposure and PTSD symptoms in international relief and development personnel (2001) J. Trauma Stress, 14, pp. 205-212; Fong, C.M., Luttmer, E.F.P., What determines giving to hurricane katrina victims? Experimental evidence on racial group loyalty (2009) Am. Econ. J., 1, pp. 64-87; Genovese, A., Acquaye, A.A., Figueroa, A., Koh, S.C.L., Sustainable supply chain management and the transition towards a circular economy: evidence and some applications (2017) Omega (United Kingdom), 66, pp. 344-357; Gonzalez, R.A., Developing a multi-agent system of a crisis response organisation (2010) Bus. Process Manage. J., 16, pp. 847-870; Guo, S., Zhao, H., Fuzzy best-worst multicriteria decision-making method and its applications (2017) Knowl.-Based Syst., 121, pp. 23-31; Haavisto, I., Kovacs, G., Supplement to the 2012 Annual Statistical Report on United Nations Procurement (2012); Haavisto, I., Kovács, G., Perspectives on sustainability in humanitarian supply chains (2014) Disaster Prevent. Manage., 23, pp. 610-631; Holguín-Veras, J., Jaller, M., Van Wassenhove, L.N., Pérez, N., Wachtendorf, T., Material convergence: important and understudied disaster phenomenon (2014) Nat. Hazard. Rev., 15, pp. 1-12; Holguín-Veras, J., Jaller, M., Van Wassenhove, L.N., Pérez, N., Wachtendorf, T., On the unique features of post-disaster humanitarian logistics (2012) J. Oper. Manage., 30, pp. 494-506; Igoumenidis, M., Kyriopoulos, I.I., Athanasakis, K., Drug and cash donations to developing countries: problems and proposals (2013) J. Dev. Soc., 29, pp. 287-303; Jahre, M., Heigh, I., Does the current constraints in funding promote failure in humanitarian supply chains? (2008) Supply Chain Forum, 9, pp. 44-54; Jeswani, H.K., Azapagic, A., Environmental impacts of healthcare and pharmaceutical products: Influence of product design and consumer behaviour (2020) J. Cleaner Prod., 253; John, L., Gurumurthy, A., Soni, G., Jain, V., Modelling the inter-relationship between factors affecting coordination in a humanitarian supply chain: a case of Chennai flood relief (2019) Ann. Oper. Res., 237, pp. 1227-1258; Kamba, P.F., Ireeta, M.E., Balikuna, S., Kaggwa, B., Threats posed by stockpiles of expired pharmaceuticals in low- and middle-income countries: a Ugandan perspective (2017) Bull. World Health Organ., 95, pp. 594-598; Klumpp, M., Leeuw, S., (2015), De, Regattieri, A., Souza, R. De Humanitarian logistics and sustainability; Kummerer, K., Significance of antibiotics in the environment (2003) J. Antimicrob. Chemother., 52. , 317–317; Kunz, N., Gold, S., Sustainable humanitarian supply chain management–exploring new theory (2017) Int. J. Logist. Res. Appl., 20, pp. 85-104; Laguna-Salvadó, L., Lauras, M., Okongwu, U., Comes, T., A multicriteria Master Planning DSS for a sustainable humanitarian supply chain (2018) Ann. Oper. Res., pp. 1-41; Li, C., Zhang, F., Cao, C., Liu, Y., Qu, T., Organisational coordination in sustainable humanitarian supply chain: an evolutionary game approach (2019) J. Cleaner Prod., 219, pp. 291-303; Marks, I.H., Thomas, H., Bakhet, M., Fitzgerald, E., Medical equipment donation in low-resource settings: a review of the literature and guidelines for surgery and anaesthesia in low-income and middle-income countries (2019) BMJ Glob. Health, 4; McDonald, S., Fabbri, A., Parker, L., Williams, J., Bero, L., Medical donations are not always free: an assessment of compliance of medicine and medical device donations with World Health Organization guidelines (2009–2017) (2019) Int. Health, 11, pp. 379-402; Moktadir, M.A., Dwivedi, A., Ali, S.M., Paul, S.K., Kabir, G., Madaan, J., Antecedents for greening the workforce: implications for green human resource management (2019) Int. J. Manpower; Moktadir, M.A., Dwivedi, A., Rahman, A., Chiappetta Jabbour, C.J., Paul, S.K., Sultana, R., Madaan, J., An investigation of key performance indicators for operational excellence towards sustainability in the leather products industry (2020) Bus. Strategy Environ., 29, pp. 3331-3351; Muller, A., Whiteman, G., Exploring the geography of corporate philanthropic disaster response: a study of fortune global 500 firms (2009) J. Bus. Ethics, 84, pp. 589-603; Narayana, S.A., Pati, R.K., Padhi, S.S., Market dynamics and reverse logistics for sustainability in the Indian pharmaceuticals industry (2019) J. Cleaner Prod., 208, pp. 968-987; Nicholson, A., Runnels, L., Claire, G., Brown, L., Scott Wollek, R., Medical product shortages during disasters: opportunities to predict, prevent, and respond (2018) Proceedings of a Workshop in Brief, in: Medical Product Shortages During Disasters; Nicoli, F., Paudel, D., Bresciani, G., Rodi, D., Siniscalchi, A., Donation programme of returned medicines: Role of donors and point of view of beneficiaries (2018) Int. Health, 10, pp. 133-136; Opoku, E.E.O., Boachie, M.K., The environmental impact of industrialisation and foreign direct investment (2020) Energy Policy, 137; Özpolat, K., Rilling, J., Altay, N., Chavez, E., Engaging donors in smart compassion: USAID CIDI's greatest good donation calculator (2015) J. Humanit. Logist. Supply Chain Manage., 5, pp. 95-112; Pathinathan, T., Ponnivalavan, K., Dison, E.M., Different types of fuzzy numbers and certain properties (2015) J. Comput. Math. Sci., 6, pp. 631-651; Patil, A., Shardeo, V., Dwivedi, A., Madaan, J.K., An integrated approach to model the Blockchain implementation barriers in humanitarian supply chain (2020) J. Glob. Oper. Strategic Sourcing; Patil, A., Shardeo, V., Madaan, J., Modelling performance measurement barriers of humanitarian supply chain (2020) Int. J. Prod. Perform. Manage.; Pinheiro, C.P., Drug donations: what lies beneath (2008) Bull. World Health Organ., 86, p. 580; Pomponi, F., Moghayedi, A., Alshawawreh, L., D'Amico, B., Windapo, A., Sustainability of post-disaster and post-conflict sheltering in Africa: what matters? (2019) Sustain. Prod. Consump., 20, pp. 140-150; Prakash, C., Barua, M.K., Integration of AHP-TOPSIS method for prioritising the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment (2015) J. Manuf. Syst., 37, pp. 599-615; Privett, N., Gonsalvez, D., The top ten global health supply chain issues: Perspectives from the field (2014) Oper. Res. Health Care, 3, pp. 226-230; Raila, E.M., Anderson, D.O., Healthcare waste management during disasters and its effects on climate change: lessons from 2010 earthquake and cholera tragedies in Haiti (2017) Waste Manage. Res., 35, pp. 236-245; Reich, M.R., Wagner, A.K., McLaughlin, T.J., Dumbaugh, K.A., Derai-Cochin, M., Pharmaceutical donations by the USA: An assessment of relevance and time-to-expiry (1999) Bull. World Health Organ., 77, pp. 675-680; Rezaei, J., Best-worst multicriteria decision-making method (2015) Omega, 53, pp. 49-57; Saaty, T.L., Decision Making with Dependence and Feedback: the Analytic Network Process (1996), RWS Publ; Sabri, Y., Zarei, M.H., Harland, C., Using collaborative research methodologies in humanitarian supply chains (2019) J. Humanit. Logist. Supply Chain Manage.; Sabuj, S.U., Ali, S.M., Hasan, K.W., Paul, S.K., Contextual relationships among key factors related to environmental sustainability: evidence from an emerging economy (2021) Sustain. Prod. Consump., 27, pp. 86-99; Salvadó, L.L., Lauras, M., Comes, T., A Sustainability maturity assessment method for the humanitarian supply chain (2018) Proceedings of the International ISCRAM Conference 2018-May, pp. 277-290; Sarkis, J., Spens, K.M., Kovács, G., A study of barriers to greening the relief supply chain (2013) IGI Global (Ed.), Supply Chain Management: Concepts, Methodologies, Tools, and Applications, pp. 1407-1417; Shardeo, V., Patil, A., Dwivedi, A., Madaan, J., Investigating the transport flexibility measures for freight transportation: a fuzzy best-worst method approach (2020) Int. J. Logist. Syst. Manage.; Shardeo, V., Patil, A., Madaan, J., Critical success factors for blockchain technology adoption in freight transportation using fuzzy ANP - modified TISM approach (2020) Int. J. Inf. Technol. Decis. Making, 19, pp. 1549-1580; Spiegel, P.B., Differences in world responses to natural disasters and complex emergencies (2005) J. Am. Med. Assoc., 293, pp. 1915-1918; Stenson, J., Disaster management as a tool for sustainable development: a case study of cyanide leaching in the gold mining industry (2006) J. Cleaner Prod., 14, pp. 230-233; Stoughton, A.M., Ludema, J., The driving forces of sustainability (2012) J. Organ. Change Manage., 25, pp. 501-517; Suzuki, Y., Impact of material convergence on last-mile distribution in humanitarian logistics (2019) Int. J. Prod. Econ., 107515; Van Wassenhove, L.N., Blackett memorial lecture humanitarian aid logistics: Supply chain management in high gear (2006) J. Oper. Res. Soc., 57, pp. 475-489; Viegas, C.V., Bond, A., Vaz, C.R., Bertolo, R.J., Reverse flows within the pharmaceutical supply chain: a classificatory review from the perspective of end-of-use and end-of-life medicines (2019) J. Cleaner Prod., 238; Wang, S., Liu, F., Lian, L., Hong, Y., Chen, H., Integrated post-disaster medical assistance team scheduling and relief supply distribution (2018) Int. J. Logist. Manage., 29, pp. 1279-1305; (2011), WHO Guidelines for Medicine Donations Revised 2010, WHO Library Cataloguing-in-Publication Data; Zarei, M.H., Carrasco-Gallego, R., Ronchi, S., On the role of regional hubs in the environmental sustainability of humanitarian supply chains (2019) Sustain. Dev., 27, pp. 846-859; Zhang, L., Wu, L., Tian, F., Wang, Z., Retrospection-simulation-Revision: Approach to the analysis of the composition and characteristics of medical waste at a disaster relief site (2016) PLoS One, 11, pp. 1-12},
correspondence_address1={Madaan, J.; Department of Management Studies, India; email: jmadaan@dms.iitd.ac.in},
publisher={Elsevier B.V.},
issn={23525509},
language={English},
abbrev_source_title={Sustain. Prod. Consum.},
document_type={Article},
source={Scopus},
}

@BOOK{Tatlici2021160,
author={Tatlici, G. and Sertyesilisik, B.},
title={Integrating performance measurement systems into the global lean and sustainable construction supply chain management: Enhancing sustainability performance of the construction industry},
journal={Research Anthology on Environmental and Societal Well-Being Considerations in Buildings and Architecture},
year={2021},
pages={160-177},
doi={10.4018/978-1-7998-9032-4.ch007},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125156939&doi=10.4018%2f978-1-7998-9032-4.ch007&partnerID=40&md5=3e8883ae009c114c5d5863034f8b4579},
affiliation={Istanbul Technical University, Turkey},
abstract={The construction industry is one of the industries that have a great impact on the economy. A construction supply chain (CSC) is an important process affecting the circular economy (CE) and sustainability in practice. In order to analyze the efficiency of CE and CSC, a performance measurement system (PMS) is needed. At such a point, adopting a PMS to a supply chain at relatively early stages of a project could lead to sustainable supply chain management (SCM). Collaboration between all parties such as designers and contractors will be advantageous to gain competitiveness in the sector. This chapter aims to determine the need of PMS adaptation to construction supply chain process for sustainable and lean construction supply chain management based on the literature review. This chapter is expected to be beneficial for academics, researchers in the relevant field as well as policy makers and professionals. © 2021 by IGI Global. All rights reserved.},
references={Akkucuk, U., SCOR Model and the Green Supply Chain (2016) Handbook of Research on Waste Management Techniques for Sustainability, pp. 108-124. , U. Akkucuk (Ed.), Hershey, PA: IGI Global; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; (2017) Circular Business Models for the Built Environment, , http://publications.arup.com/publications/c/circular_business_models_for_the_built_environment; Balfaqih, H., Nopiah, Z.M., Saibania, N., Al-Noryb, M.T., Review of supply chain performance measurement systems: 1998-2015 (2016) Computers in Industry, 82, pp. 135-150; (2013) Supply Chain Analysis into the Construction Industry: A Report for the Construction Industrial Strategy; Bititci, U.S., Modelling of performance measurement manufacturing enterprises (1995) International Journal of Production Economics, 42 (2), pp. 137-147; Bititci, U.S., Mendibil, K., Albores, P., Martinez, M., Measuring and managing performance in collaborative enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Bocken, N.M.P., Olivetti, E.A., Cullen, J., Potting, J., Lifset, R., Taking the circularity to the next level: A special issue on the circular economy (2017) Journal of Industrial Ecology, 21 (3), pp. 476-482; Boiral, O., Global Warming: Should Companies Adopt a Proactive Strategy? (2006) Long Range Planning, 39 (3), pp. 315-330; Caniels, M.C.J., Gehrsizt, M.H., Semeijn, J., Participation of suppliers in greening supply chains: An empirical analysis of German automotive suppliers (2013) Journal of Purchasing and Supply Management, 19 (3), pp. 134-143; Carter, C.R., Carter, J.R., Interorganizational determinants of environmental purchasing: Initial evidence from the consumer products industries (1998) Decision Sciences, 29 (3), pp. 659-684; Carvalho, H., Duarte, S., Cruz-Machado, V., Lean, agile, resilient and green: Divergencies and synergies (2011) International Journal of Lean Six Sigma, 2 (2), pp. 151-179; Cheng, J.C.P., Law, K.H., Bjornsson, H., Jones, A., Sriram, R.D., Modeling and monitoring of construction supply chains (2010) Advanced Engineering Informatics, 24 (4), pp. 435-455; Chin, C.P., (2009) SC collaborator: A service oriented framework for construction supply chain collaboration and monitoring, , Stanford University; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply Chain Management: More Than a New Name for Logistics (1997) International Journal of logistics Management, 8 (1), pp. 1-14; Crespin-Mazet, F., Dontenwill, E., Sustainable procurement: Building legitimacy in the supply network (2012) Journal of Purchasing and Supply Management, 18 (4), pp. 207-217; Dües, C.M., Tan, K.H., Lim, M., Green as the new Lean: How to use Lean practices as a catalyst to greening your supply chain (2013) Journal of Cleaner Production, 40, pp. 93-100; Durdyev, S., Ismail, S., Role of the construction industry in economic development of Turkmenistan (2012) Energy Education Science and Technology, Part A. Energy Science and Research, 29 (2), pp. 883-890; Ehrenfeld, J.R., Design for environment: A new framework for strategic decisions (1995) Environmental Quality Management, 4 (4), pp. 37-51; (2013) Towards the Circular Economy, , www.ellenmacarthurfoundation.org/business/reports; (2013) Towards the Circular Economy - Opportunities for the Consumer Goods Sector, , www.ellenmacarthurfoundation.org/business/reports; (2014) Towards the Circular Economy - Accelerating the Scale-up across Global Supply Chains, , Retrieved from ellenmacarthurfoundation.org/business/reports; Erdmenger, C., Eri, V., Fuhr, V., Lackner, B., Schmid, A., van der Grijp, N., (2001) The world buys green - international survey on national green procurement practices, , Freiburg, Germany: ICLEI; Erkan, T.E., Bac, U., Supply chain performance measurement: A case study about applicability of scor model in a manufacturing industry firm (2011) International journal of business and management studies, 3 (1), pp. 381-390; Estampe, D., Lamouri, S., Paris, J., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Fernie, S., Leiringer, R., Thorpe, T., Rethinking change in construction: A critical perspective (2006) Building Research and Information, 34 (2), pp. 91-103; Florida, R., Lean and green: The move to environmentally conscious manufacturing (1996) California Management Review, 39 (1), pp. 80-105; Foerstl, K., Reuter, C., Hartmann, E., Blome, C., Managing supplier sustainability risks in adynamically changing environment-Sustainable supplier management in the chemical industry (2010) Journal of Purchasing and Supply Management, 16 (2), pp. 118-130; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Frosch, D., Gallopoulos, N., Strategies for manufacturing (1989) Scientific American, 261 (3), pp. 144-152; Geissdoerfer, M., Savaget, P., Bocken, N.M.P., Hultink, E.J., The Circular Economy - A New Sustainability Paradigm? (2017) Journal of Cleaner Production, 143, pp. 757-768; Giunipero, L.C., Hooker, R.E., Denslow, D., Purchasing and supply management sustainability: Drivers and barriers (2012) Journal of Purchasing and Supply Management, 18 (4), pp. 258-269; Goger, A., The making of a 'business case' for environmental upgrading: Sri Lanka's eco-factories (2013) Geoforum, 47, pp. 73-83; Gordon, P.J., (2001) Lean and Green: Profit for Your Workplace and the Environment, , San Francisco, CA: Berrett-Koehler Publishers; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gupta, S., Czinkota, M., Melewar, T.C., Embedding knowledge and value of a brand into sustainability for differentiation (2013) Journal ofWorld Business, 48 (3), pp. 287-296; Hoejmose, S.U., Adrien-Kriby, A.J., Socially and environmentally responsible procurement: A literature review and future research agenda of a managerial issue in the 21st century (2012) Journal of Purchasing and Supply Management, 18 (4), pp. 232-242; Howell, G.A., What is lean construction? (1999) IGLC-7 proceedings, , University of California, Berkeley, CA, July 26-28; (2007) Costs and benefits of green public procurement in Europe; Khasreen, M.M., Banfill, P.F.G., Menzies, G.F., Life-cycle assessment and the environmental impact of buildings: A review (2009) Sustainability, 1 (3), pp. 674-701; Koplin, J., Seuring, S., Mesterharm, M., Incorporating sustainability into supply management in automotive industry - the case of the Volkswagen AG (2007) Journal of Cleaner Production, 15 (11-12), pp. 1053-1062; Korhonen, J., Honkasalo, A., Seppälä, J., Circular Economy: The Concept and its Limitations (2018) Ecological Economics, 143, pp. 37-46; I˙nşaat: Sektörel Bakış (2018) Klynveld Peat Marwick Goerdeler, , https://assets.kpmg.com/content/dam/kpmg/tr/pdf/2018/01/sektorel-bakis-2018-insaat.pdf; Lacy, P., Rutqvist, J., (2015) Waste to Wealth: The Circular Economy Advantage, , New York: Palgrave Macmillan; Leising, E., Quist, J., Bocken, N., Circular Economy in the building sector: Three cases and a collaboration tool (2018) Journal of Cleaner Production, 176, pp. 976-989; Lockamy, A., III, McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management, 9 (4), pp. 272-278; Lucon, O., Ürge-Vorsatz, D., Zain Ahmed, A., Akbari, H., Bertoldi, P., Cabeza, L.F., Vilarino, M.V., Buildings (2014) Climate Change 2014: Mitigation of Climate Change, , O. Edenhofer, R. Pichs-Madruga, & Y. Sokona et al. (Eds.), Cambridge, UK: Cambridge University Press; Maestrini, V., Luzzini, D., Maccarronea, P., Caniatoa, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Martínez-Jurado, P.J., Moyano-Fuentes, J., Lean Management, Supply Chain Management and Sustainability: A Literature Review (2014) Journal of Cleaner Production, 85, pp. 134-150; Mollenkopf, D., Stolze, H., Tate, W.L., Ueltschy, M., Green, lean, and global supply chain (2010) International Journal of Physical Distribution & Logistics Management, 40 (1-2), pp. 14-41; Moyano-Fuentes, J., Sacristán-Díaz, M., Learning on lean: A review of thinking and research (2012) International Journal of Operations & Production Management, 32 (5), pp. 551-582; Murphy, D.F., Bendell, J., Do-it-yourself or do-it-together? The implementation of sustainable timber purchasing policies by DIY retailers in the UK (1998) Greener purchasing. Opportunities and innovations, pp. 118-134. , T. Russel (Ed.), Sheffield, UK: Greenleaf Publishing; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) Journal of Operations Supply Chain Management, 6 (2), pp. 94-113; Nasir, M.H.A., Genovese, A., Acquaye, A.A., Koh, S.C.L., Yamoah, F., Comparing linear and circular supply chains: A case study from the construction industry (2017) International Journal of Production Economics, 183, pp. 443-457; Neely, A.D., Mills, J.F., Platts, K.W., Gregory, M.J., Richards, A.H., Realising strategy through measurement (1994) International Journal of Operations & Production Management, 14 (3), pp. 140-152; Ochoa, A., Erdmenger, C., (2003) Study contract to survey the state of play of green public procurement in the European Union. ICLEI European Secretariat, , EcoProcurement Programme; (2000) Greener public purchasing, , Issues and practical solutions; Pomponi, F., Moncaster, A., Circular Economy for the built environment: A research framework (2017) Journal of Cleaner Production, 143, pp. 710-718; Powell, J.C., Tinch, R., White, O., Peters, M., (2006) Successful approaches to sustainable procurement: A report to the Departmentfor Environment, Food and Rural Affaires, , London, UK: Environmental Futures Ltd; Preuss, L., In dirty chains? Purchasing and greener manufacturing (2001) Journal of Business Ethics, 34 (3-4), pp. 345-359; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations & Production Management, 25 (9), pp. 898-916; Rohitratana, K., SA 8000-a tool to improve quality of life (2002) Managerial Auditing Journal, 17 (1-2), pp. 60-64; Serpell, A., Heredia, B., Supply Chain Management In Construction: Diagnosis And Application Issues (2004) Conference: CIB W107 & CIB TG23 - International Symposium on Globalisation and Construction, pp. 455-466. , Rotterdam; Sertyesilisik, B., Embedding Sustainability Dynamics in the Lean Construction Supply Chain (2016) YBL Journal of Environment, 4 (1); Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Siemens, R., A review and critical evaluation of selected greener public purchasing programmes and policies (2003) The environmental performance of public procurement. Issues of policy coherence, pp. 51-95. , N. Johnstone (Ed.), Paris, France: OECD; Stahel, W., Reday, G., (1981) Jobs for Tomorrow, the Potential for Substituting Manpower for Energ, , N.Y.: Vantage Press; Varnas, A., Balfors, B., Faith-Ell, C., Environmental consideration in procurement of construction contracts: Current practice, problems and opportunities in green procurement in the Swedish construction (2009) Journal of Cleaner Production, 17 (13), pp. 1214-1222; Vrijhoef, R., Koskela, L., The four roles of supply chain management in construction (2000) European Journal of Purchasing & Supply Management, 6 (3-4), pp. 169-178; Walker, B.H., Abel, N., Anderies, J.M., Ryan, P., Resilience, adaptability, and transform-ability in the Goulburn-Broken Catchment, Australia (2009) Ecology and Society, 14 (1), p. 12; Wu, Z., Pagell, M., Balancing priorities: Decision-making in sustainable supply chain management (2011) Journal of Operations Management, 29 (6), pp. 577-590; Abreu, M.F., Alves, A.C., Moreira, F., Lean-Green models for eco-efficient and sustainable production (2017) Energy, 137, pp. 846-853; Adetunji, I., Price, A.D.F., Fleming, P., Achieving sustainability in the construction supply chain (2008) Proceedings of the Institution of Civil Engineers, Engineering Sustainability, pp. 161-172; Ahuja, R., Sawhney, A., Arif, M., Driving lean and green project outcomes using BIM: A qualitative comparative analysis (2017) International Journal of Sustainable Built Environment, 6 (1), pp. 69-80; Aziz, R.F., Hafez, S.M., Applying lean thinking in construction and performance improvement (2013) Alexandria Engineering Journal, 52 (4), pp. 679-695; Bakshana, A., Srour, I., Chehab, G., El-Fadel, M., A field based methodology for estimating waste generation rates at various stages of construction projects (2015) Resources, Conservation and Recycling, 100, pp. 70-80; Brindley, C., Oxborrow, L., Aligning the sustainable supply chain to green marketing needs: A case study (2014) Industrial Marketing Management, 43 (1), pp. 45-55; Dahlbo, H., Bacher, J., Lahtinen, K., Jouttijarvi, T., Suoheimo, P., Mattila, T., Saramaki, S., Construction and demolition waste management - a holistic evaluation of environmental performance (2015) Journal of Cleaner Production, 107, pp. 333-341; Das, K., Integrating lean systems in the design of a sustainable supply chain model (2018) International Journal of Production Economics, 198, pp. 177-190; Dave, B., Kubler, S., Främling, K., Koskela, L., Opportunities for enhanced lean construction management using Internet of Things standards (2016) Automation in Construction, 61, pp. 86-97; Dhingra, R., Kress, R., Upreti, G., Does lean mean green? (2014) Journal of Cleaner Production, 85, pp. 1-7; Froese, T.M., The impact of emerging information technology on project management for construction (2010) Automation in Construction, 19 (5), pp. 531-538; Garza-Reyes, J.A., Lean and green: A systematic review of the state of the art literature (2015) Journal of Cleaner Production, 102, pp. 18-29; Gerber, D.J., Becerik-Gerber, B., Kunz, A., Building information modeling and lean construction: Technology, methodology and advances from practice (2010) 18th Annual Conference, International Group for Lean Construction; Hwang, B.G., Ng, W.J., Project management knowledge and skills for green construction: Overcoming challenges (2013) International Journal of Project Management, 31 (2), pp. 27-284; Issa, U.H., Implementation of lean construction techniques for minimizing the risks effect on project construction time (2013) Alexandria Engineering Journal, 52 (4), pp. 697-704; Jadhava, A., Orr, S., Malik, M., (in press). The role of supply chain orientation in achieving supply chain sustainability (2019) International Journal of Production Economics; Jakhara, S.K., Rathore, H., Manglac, S.K., Is lean synergistic with sustainable supply chain? An empirical investigation from emerging economy (2018) Resources, Conservation and Recycling, 139, pp. 262-269; Jones, B., Integrated project delivery (IPD) for maximizing design and construction considerations regarding sustainability (2014) Procedia Engineering, 95, pp. 528-538; Lu, W., Peng, Y., Webster, C., Zuo, J., Stakeholders' willingness to pay for enhanced construction waste management: A Hong Kong study (2015) Renewable & Sustainable Energy Reviews, 47, pp. 233-240; Marhani, M.A., Jaapar, A., Bari, N.A.A., Lean Construction: Towards enhancing sustainable construction in Malaysia (2012) ASIA Pacific International Conference on Environment-Behaviour Studies. Procedia: Social and Behavioral Sciences, 68, pp. 87-98; Marhani, M.A., Jaapar, A., Bari, N.A.A., Zawawi, M., Sustainability through Lean Construction Approach: A literature review (2013) AMER International Conference on Quality of Life. Procedia: Social and Behavioral Sciences, 101, pp. 90-99; Martens, M.L., Carvalho, M.M., The challenge of introducing sustainability into project management function: Multiple-case studies (2016) Journal of Cleaner Production, 117, pp. 29-10; Rafindadi, A.D., Mikic, M., Kovacic, I., Cekic, Z., Global Perception of Sustainable Construction Project Risks (2014) 27th IPMA World Congress. Procedia: Social and Behavioral Sciences, 119, pp. 456-465; Reefkea, H., Sundaram, D., Sustainable supply chain management: Decision models for transformation and maturity (2018) Decision Support Systems, 113, pp. 56-72; Tsenga, M., Wub, K., Huc, J., Wang, C., Decision-making model for sustainable supply chain finance under uncertainties (2018) International Journal of Production Economics, 205, pp. 30-36; Verrier, B., Rose, B., Caillaud, E., Lean and Green strategy: The Lean and Green House and maturity deployment model (2016) Journal of Cleaner Production, 116, pp. 150-156; Wibowo, M.A., Koestalam, P., Identification and analyze of influence level on waste construction management of performance (2015) Procedia Engineering, 125, pp. 46-52; Yusof, N., Abidin, N.Z., Zailani, S.H.M., Govindan, K., Iranmanesh, M., Linking the environmental practice of construction firms and the environmental behaviour of practitioners in construction projects (2016) Journal of Cleaner Production, 121, pp. 64-71; Zuo, J., Zillante, G., Wilson, L., Davidson, K., Pullen, S., Sustainability policy of construction contractors: A review (2012) Renewable & Sustainable Energy Reviews, 16 (6), pp. 3910-3916},
correspondence_address1={Tatlici, G.; Istanbul Technical UniversityTurkey},
publisher={IGI Global},
isbn={9781799890331; 9781799890324},
language={English},
abbrev_source_title={Res. Anthol. on Environ. and Soc. Well-Being Consid. in Build. and Archit.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Nguyen2021,
author={Nguyen, T.T.H. and Bekrar, A. and Le, T.M. and Abed, M.},
title={Supply Chain Performance Measurement using SCOR Model: A Case Study of the Coffee Supply Chain in Vietnam},
journal={2021 1st International Conference On Cyber Management and Engineering, CyMaEn 2021},
year={2021},
doi={10.1109/CyMaEn50288.2021.9497309},
art_number={9497309},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112411954&doi=10.1109%2fCyMaEn50288.2021.9497309&partnerID=40&md5=e8eb6178c67e0464c427472f625d83fc},
affiliation={Polytechnic University of Hauts-de-France, Lamih, Umr Cnrs 8201, Valenciennes, France; Polytechnic University of Hauts-de-France, Criss, Valenciennes, France},
abstract={Coffee is one of the national agricultural commodities in Vietnam. Several problems have existed in the coffee supply chain that threatened Vietnamese coffee's competitiveness. Using the latest version of the Supply Chain Operations Reference (SCOR) model, the study proposes a standard coffee supply chain model with the process elements' detailed level. This proposal model facilitates the investigation of external and internal problems that decrease the coffee supply chain's efficiency in Kontum province, Vietnam. The article also applies the SCOR model for performance measurement in the coffee supply chain. The achieved performance for the whole coffee SC is 68.28 with an average category. Most processes, including Source, Make, Deliver, and Return, have a small performance value with an inferior class. Costs and Agility have the lowest score. The study proposes some recommendations to improve the coffee supply chain's efficiency in Kontum of Vietnam. © 2021 IEEE.},
author_keywords={coffee supply chain;  performance evaluation;  SCOR model},
keywords={Agricultural robots;  Agriculture;  Efficiency, Agricultural commodities;  Performance measurements;  Performance value;  Process elements;  SCOR models;  Supply chain modeling;  Supply chain operations reference models;  Supply chain performance measurements, Supply chains},
references={(2020) Production, Planted Area and Area Having Product of Main Perennial Crops in 2018, , https://www.gso.gov.vn/default-en.aspx?tabid=778, General Statistics Office of Vietnam; Nguyen, T.V., Nguyen, N.C., Bosch, O.J.H., Contribution of the systems thinking approach to reduce production cost and improve the quality of vietnamese coffee (2015) Int. J. Mark. Bus. Syst, 1 (1), pp. 53-67; Nguyen, G.N.T., Sarker, T., Sustainable coffee supply chain management: A case study in buon me thuot city, daklak, vietnam (2018) Int. J. Corp. Soc. Responsib, 3 (1), pp. 1-17; (2019) Statistical Yearbook of Kontum, 2018, , http://thongkekontum.gov.vn/, Kontum Statistics Office; Mentzer, J.T., Keebler, J.S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) J. Bus, 22 (2), pp. 1-25; Trkman, P., McCormack, K., De Oliveira, M.P.V., Ladeira, M.B., The impact of business analytics on supply chain performance (2010) Decis. Support Syst, 49 (3), pp. 318-327; Zailani, S., Rajagopal, P., Supply chain integration and performance: Us versus east asian companies (2005) Supply Chain Manag, 10 (5), pp. 379-393; Rao Tummala, V.M., Phillips, C.L.M., Johnson, M., Assessing supply chain management success factors: A case study (2006) Supply Chain Manag, 11 (2), pp. 179-192; Tarasewicz, R., Integrated approach to supply chain performance measurement-results of the study on polish market (2016) Transp. Res. Procedia, 14, pp. 1433-1442; Wibowo, M.A., Sholeh, M.N., The analysis of supply chain performance measurement at construction project (2015) Procedia Eng, 125, pp. 25-31; Cheng, J.C.P., Law, K.H., Bjornsson, H., Jones, A., Sriram, R.D., Modeling and monitoring of construction supply chains (2010) Adv. Eng. Informatics, 24 (4), pp. 435-455; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) 2009 2nd Int. Conf. Emerg. Trends Eng. Technol. Icetet 2009, pp. 802-807; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ, 142 (2), pp. 247-258; Jagan Mohan Reddy, K., Neelakanteswara Rao, A., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manuf, 30, pp. 40-47; Persson, F., Supply chain dynamics in the scor model-a simulation modeling approach (2012) Proc.-Winter Simul. Conf, (2011); Öztaysi, B., Sürer, O., Supply chain performance measurement using a scor based fuzzy vikor approach (2014) Supply Chain Manag. Under Fuzziness, 313, pp. 199-224; Lima-Junior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on scor ® metrics and multilayer perceptron neural networks (2019) Int. J. Prod. Econ, 212, pp. 19-38. , no. January; Lima-Junior, F.R., Carpinetti, L.C.R., An adaptive network-based fuzzy inference system to supply chain performance evaluation based on scor® metrics (2020) Comput. Ind. Eng, 139, p. 106191; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighbourhood rough sets (2012) Int. J. Prod. Res, 50 (9), pp. 2484-2500; Prasetya, W., Natalia, C., Dias, S., Elnath, B., Silalahi, A., Monique, K., Performance measurement and analysis of coffee supply chain with scor method (case study of north sumatera coffee) (2017) JRDO-Journal Bus. Manag, (11), pp. 1-17; (2017) Supply Chain Operations Reference Model, , American Production Inventory Control Society; Essajide, D., Rachidi, L., Planning and modelling of pharmaceuticals wholesale-distributors supply chain using scor model : A moroccan case study i (2017) Int. J. Manag. Sci. Bus. Res, 6 (3), pp. 35-51; Palomino, E.R., Meza, S.H., Raffo, D.R.M.F.S., Lo, W.-S., Organic coffee supply chain management in the san martin region of peru (2017) Int. J. Innov. Manag. Technol, pp. 9-16; Irfan, D., Xiaofei, X., Chun, D.S., A scor reference model of the supply chain management system in an enterprise (2008) Int. Arab J. Inf. Technol, 5 (3), pp. 288-295; Dissanayake, C.K., Cross, J.A., Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using scor model and sem (2018) Int. J. Prod. Econ, 201, pp. 102-115. , no. March; Bukhori, I.B., Widodo, K.H., Ismoyowati, D., Evaluation of poultry supply chain performance in xyz slaughtering house yogyakarta using scor and ahp method (2015) Agric. Agric. Sci. Procedia, 3, pp. 221-225; Sellitto, M.A., Pereira, G.M., Borchardt, M., Da Silva, R.I., Viegas, C.V., A scor-based model for supply chain performance measurement: Application in the footwear industry (2015) Int. J. Prod. Res, 53 (16), pp. 4917-4926; Moazzam, M., Akhtar, P., Garnevska, E., Marr, N.E., Measuring agri-food supply chain performance and risk through a new analytical framework: A case study of new zealand dairy (2018) Prod. Plan. Control, 29 (15), pp. 1258-1274; Kusrini, E., Caneca, V.I., Helia, V.N., Miranda, S., Supply chain performance measurement usng supply chain operation reference (scor) 12.0 model : A case study in a a leather sme in indonesia (2019) Iop Conf. Ser. Mater. Sci. Eng, 697 (1), pp. 0-10; Van Nguyen, T., Nguyen, N.C., Bosch, O.J.H., Identifying key sucess factors in supply chain management for increasing the competitive advantages of vietnamese coffee (2017) An Int. Bus. J, 27 (5), pp. 438-461; Ntabe, E.N., LeBel, L., Munson, A.D., Santa-Eulalia, L.A., A systematic literature review of the supply chain operations reference (scor) model application with special attention to environmental issues (2015) Int. J. Prod. Econ, 169, pp. 310-332; Banker, R.D., Mitra, S., Procurement models in the agricultural supply chain: A case study of online coffee auctions in india (2007) Electron. Commer. Res. Appl, 6 (3), pp. 309-321; (2019) Vietnam Coffee Annual 2019, , https://apps.fas.usda.gov/newgainapi/api/report/downloadreportbyfilename?filename=CoffeeAnnual-Hanoi-Vietnam-5-20-2019.pdf, USDA Foreign Agricultural Service; Leal, J.E., Ahp-express: A simplified version of the analytical hierarchy process method (2020) MethodsX, 7; (2020) Total Production, , http://www.ico.org/new-historical.asp, International Coffee Organization; (2020) Agriculture, Forestry and Fishery, , https://www.gso.gov.vn/default-en.aspx?tabid=780, General Statistics Office of Vietnam; Abolghasemi, M., Beh, E., Tarr, G., Gerlach, R., Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion (2019) Comput. Ind. Eng, 142 (2020), p. 106380. , July; Benkachcha, S., Benhra, J., El Hassani, H., Demand forecasting in supply chain: Comparing multiple linear regression and artificial neural networks approaches (2014) Int. Rev. Model. Simulations, 7 (2), pp. 279-286},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781665432009},
language={English},
abbrev_source_title={Int. Conf. Cyber Manag. Eng., CyMaEn},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Azevedo2021,
author={Azevedo, S.G. and Pimentel, C.M.O. and Alves, A.C. and Matias, J.C.O.},
title={Support of advanced technologies in supply chain processes and sustainability impact},
journal={Applied Sciences (Switzerland)},
year={2021},
volume={11},
number={7},
doi={10.3390/app11073026},
art_number={3026},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103623451&doi=10.3390%2fapp11073026&partnerID=40&md5=61394ad37058e0cb2832f0d1aae3ca8e},
affiliation={CeBER R&D Center, Faculty of Economics, University of Coimbra, Av Dias da Silva 165, Coimbra, 3004-512, Portugal; GOVCOPP R&D Center, Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, Aveiro, 3810-193, Portugal; ALGORITMI R&D Center, Department of Production and Systems, School of Engineering, University of Minho, Guimarães, 4800-058, Portugal},
abstract={This paper aims to present a study of the type of advanced technologies used across manufacturing supply chains in supporting the main processes of the supply chain operations reference model (SCOR). It also intends to identify a set of sustainable performance indicators (environmental, economic, and social) suitable to evaluate a supply chain 4.0 (SC4.0). To attain this objective, based on the literature review, a conceptual model is proposed. The multiple case study is used with a cross-case comparison to identify the type of advanced technologies more commonly used in SC4.0, and the performance indicators more suitable for assessing a SC4.0 sustainability performance. A sample of ten case studies was considered with companies belonging to different manufacturing SCs, from different countries, and belonging to different echelons. Main findings revealed that the level of adoption of advanced technologies in the Supply Chain SCOR processes varies amongst the case studies. Some technologies are quite commonly used among the several SCOR processes and companies while others are seldom applied. Some indicators were also identified that are regarded as very or extremely suitable to evaluate the sustainability performance of a SC4.0. The main contribution of this research to the body of knowledge is the empirical insights on the SC4.0 field and on Supply Chain Sustainability performance measurement. The results provide guidelines for the selection of advanced technologies to support SC processes and for the design of sustainable SC4.0 performance measurement systems. © 2021 by the authors.},
author_keywords={Advanced technologies;  Case study;  Cross-country comparison;  Manufacturing supply chain;  SCOR model;  Supply Chain 4.0;  Sustainability},
references={Kagermann, H., Wahlster, W., Helbig, J., (2013) Recommendations for Implementing the Strategic Initiative INDUSTRY 4.0: Securing the Future of German Manufacturing Industry, , National Academy of Science and Engineering: Munchen, Germany; Kumara, P., Singh, R.K., Kumarcd, V., Managing supply chains for sustainable operations in the era of industry 4.0 and circular economy: Analysis of barriers (2021) Resour. Conserv. Recycl, 164, p. 105215; Merli, R., Preziosi, M., Acampora, A., How do scholars approach the circular economy? A systematic literature review (2018) J. Clean. Prod, 178, pp. 703-722; Hofmann, E., Rüsch, M., Industry 4.0 and the current status as well as future prospects on logistics (2017) Comput. Ind, 89, pp. 23-34; Lasi, H., Fettke, P.D.P., Kemper, H.G., Feld, D.I.T., Hoffmann, D.H.M., Industry 4.0 (2014) Bus. Inf. Syst. Eng, 6, pp. 239-242; Gilchrist, A., (2016) Industry 4.0: The Industrial Internet of Things, , Apress: Berkeley, CA, USA; Nascimento, D.L.M., Alencastro, V., Quelhas, O.L.G., Gusmao Caiado, R.G., Garza-Reyes, J.A., Rocha-Lona, L., Tortorella, G., Exploring Industry 4.0 technologies to enable circular economy practices in a manufacturing context: A business model proposal (2019) J. Manuf. Technol. Manag, 30, pp. 607-627; Pieroni, M.P.P., McAloone, T.C., Pigosso, D.C.A., Business model innovation for circular economy and sustainability: A review of approaches (2019) J. Clean. Prod, 215, pp. 198-216; Shibin, K.T., Dubey, R., Gunasekaran, A., Luo, Z., Papadopoulos, T., Roubaud, D., Frugal innovation for supply chain sustainability in SMEs: Multi-method research design (2018) Prod. Plan. Control, 29, pp. 908-927; Bastas, A., Liyanage, K., Sustainable supply chain quality management: A systematic review (2018) J. Clean. Prod, 181, pp. 726-744; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management: What has been achieved? (2015) Supply Chain Manag, 20, pp. 664-680; Khalid, U.R., Seuring, S., Beske, P., Land, A., Yawar, S.A., Wagener, R., Putting sustainable supply chain management into base of the pyramid research (2015) Supply Chain Manag, 20, pp. 681-696; Bocken, N., Boons, F., Baldassarre, B., Sustainable business model experimentation by understanding ecologies of business models sustainable supply chain (2019) J. Clean. Prod, 208, pp. 1498-1512; Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman Publishing: London, UK; Wu, L., Yue, X., Jin, A., Yen, D.C., Smart supply chain management: A review and implications for future research (2016) Int. J. Logist. Manag, 27, pp. 395-417; Irani, Z., Kamal, M.M., Sharif, A., Love, P.E.D., Enabling sustainable energy futures: Factors influencing green supply chain collaboration (2017) Prod. Plan. Control, 28, pp. 684-705; Ghadimi, P., Wang, C., Lim, M.K., Sustainable supply chain modeling and analysis: Past debate, present problems and future challenges (2019) Resour. Conserv. Recycl, 140, pp. 72-84; Gopal, P., Thakkar, J., Sustainable supply chain practices: An empirical investigation on Indian automobile industry (2016) Prod. Plan. Control, 27, pp. 49-64; Bibby, L., Dehe, B., Defining and assessing industry 4.0 maturity levels: Case of the defence sector (2018) Prod. Plan. Control, 29, pp. 1030-1043; Robson, C., (2011) Real World Research: A Resource for Users of Social Research Methods in Applied Settings, , 3rd ed.; Wiley: Chichester, UK; Pagell, M., Wu, Z., Building a more complete Theory of Sustainable Supply Chain Management using Case Studies of 10 Exemplars (2009) J. Supply Chain Manag, 45, pp. 37-56; Zhong, R.Y., Xu, X., Klotz, E., Newman, S.T., Intelligent Manufacturing in the Context of Industry 4.0: A Review (2017) Engineering, 3, pp. 616-630; Fatorachian, H., Kazemi, H., A critical investigation of Industry 4.0 in manufacturing: Theoretical operationalisation framework (2018) Prod. Plan. Control, 29, pp. 633-644; Phelps, T., SCOR and benefits of using process reference models (2006) Proceedings of the 2006 Supply Chain International Conference, , Taipei, Taiwan, 12 January; Georgise, F.B., Thoben, K.D., Seifert, M., Adapting the SCOR Model to Suit the Different Scenarios: A Literature Review & Research Agenda (2012) Int. J. Bus. Manag, 7, p. 2; Fahimnia, B., Tang, C.S., Davarzani, H., Sarkis, J., Quantitative models for managing supply chain risks: A review (2015) Eur. J. Oper. Res, 247, pp. 1-15; Marchesini, M., Alcântara, R., Logistics activities in supply chain business process: A conceptual framework to guide their implementation (2016) Int. J. Logist. Manag, 27, pp. 6-30; Maruna, V., Mercer, T., Igor Zecevic, I., Perisic, B., Bjeljac, P., The Business Process Transformation Framework Implementation through Metamodel Extension Proceedings of the 6th International Conference on Information Society and Technology (ICIST), pp. 11-17. , Barcelona, Spain, 18–20 March 2016; Hansen, E.G., Schaltegger, S., The Sustainability Balanced Scorecard: A Systematic Review of Architectures (2016) J. Bus. Ethic, 133, pp. 193-221; Mantje, T., Mantje, T., Smit, T., David Sterk, D., Mens, J., Standardisation of Supporting Processes in Healthcare a Case Study of the APQC Healthcare Process Classification Framework (2016) Proceedings of the Bled eConference, , Bled, Slovenia, 19–22 June; (2021) SCOR Supply Chain Operations Reference Model, , Https://www.apics.org/apics-for-business/frameworks/scor, APICS. (accessed on 13 January 2021); Ntabe, E., LeBel, L., Munson, A., Santa-Eulalia, L., A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues (2015) Int. J. Prod. Econ, 169, pp. 310-332; Spanaki, K., Adams, R., Mulligan, C., Lupu, E., A Research Agenda on Data Supply Chains (2016) Proceedings of the British Academy of Management (BAM) Conference, , Newcastle, UK, 6–8 September; Chehbi-Gamoura, S., Derrouiche, R., Damand, D., Barth, M., Insights from big Data Analytics in supply chain management: An all-inclusive literature review using the SCOR model (2020) Prod. Plan. Control, 31, pp. 355-382; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) Int. J. Oper. Prod. Manag, 20, pp. 1119-1145; Saisana, M., Tarantola, S., (2002) State-of-the-Art Report on Current Methodologies and Practices for Composite Indicator Development, , European Commission, Joint Research Centre: Ispra, Italy; Globerson, S., Issues in developing a performance criteria system for an organization (1985) Int. J. Prod. Res, 23, pp. 639-646; Foxon, T.J., McIlkenny, G., Gilmour, D., Oltean-Dumbrava, C., Souter, N., Ashley, R., Butler, D., Moir, J., Sustainability Criteria for Decision Support in the UK Water Industry (2002) J. Environ. Plan. Manag, 45, pp. 285-301; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) J. Clean. Prod, 86, pp. 360-377; Handfield, R., Sroufe, R., Melnyk, S., Applying environmental criteria to supplier assessment: A study in the application of the Analytical Hierarchy Process (2002) Eur. J. Oper. Res, 141, pp. 70-87; de Sousa Jabbour, A.B.L., Jabbour, C.J.C., Foropon, C., Godinho Filho, M., When titans meet: Can industry 4.0 revolutionise the environmentally-sustainable manufacturing wave? The role of critical success factors (2018) Technol. Forecast. Soc. Chang, 132, pp. 18-25; Nara, E.O.B., Da Costa, M.B., Baierle, I.C., Schaefer, J.L., Benitez, G.B., Santos, L.M.A.L.D., Benitez, L.B., Expected impact of industry 4.0 technologies on sustainable development: A study in the context of Brazil’s plastic industry (2021) Sustain. Prod. Consum, 25, pp. 102-122; Nara, E.O.B., Sordi, D.C., Schaefer, J.L., Schreiber, J.N.C., Baierle, I.C., Sellitto, M.A., Furtado, J.C., Prioritization of OHS key performance indicators that affecting business competitiveness: A demonstration based on MAUT and Neural Networks (2019) Saf. Sci, 118, pp. 826-834; Khuntia, J., Saldanha, T.J.V., Mithas, S., Sambamurthy, V., Information Technology and Sustainability: Evidence from an Emerging Economy (2018) Prod. Oper. Manag, 27, pp. 756-773; Jang, Y.J., Zheng, T., Bosselman, R., Top managers’ environmental values, leadership, and stakeholder engagement in promoting environmental sustainability in the restaurant industry (2017) Int. J. Hosp. Manag, 63, pp. 101-111; Glavič, P., Lukman, R., Review of sustainability terms and their definitions (2007) J. Clean. Prod, 15, pp. 1875-1885; Choi, S., Ng, A., Environmental and Economic Dimensions of Sustainability and Price Effects on Consumer Responses (2011) J. Bus. Ethics, 104, pp. 269-282; Morelli, J., Environmental Sustainability: A Definition for Environmental Professionals (2011) J. Environ. Sustain, p. 1; Gimenez, C., Sierra, V., Rodon, J., Sustainable operations: Their impact on the triple bottom line (2012) Int. J. Prod. Econ, 140, pp. 149-159; Dempsey, N., Bramley, G., Power, S., Brown, C., The social dimension of sustainable development: Defining urban social sustainability (2009) Sustain. Dev, 19, pp. 289-300; Gold, S., Seuring, S., Beske, P., The constructs of sustainable supply chain management: A content analysis based on published case studies (2010) Prog. Ind. Ecol. Int. J, 7, p. 114; Beier, G., Niehoff, S., Ziems, T., Xue, B., Sustainability aspects of a digitalized industry: A comparative study from China and Germany (2017) Int. J. Precis. Eng. Manuf. Technol, 4, pp. 227-234; Liu, X., Bae, J., Urbanization and industrialization impact of CO2 emissions in China (2018) J. Clean. Prod, 172, pp. 178-186; Ohno, T., (1988) Toyota Production System: Beyond Large-Scale Production, , 3rd ed.; CRC Press: New York, NY, USA; Womack, J., Jones, D., Roos, D., (1990) The Machine That Changed the World: The Story of Lean Production, Toyota’s Secret Weapon in the Global Car Wars That Is Now Revolutionizing World Industry, , Free Press: New York, NY, USA; Pojasek, R.B., Framing your lean-to-green effort (2008) Environ. Qual. Manag, 18, pp. 85-93; Abreu, M.F., Alves, A.C., Moreira, F., Lean-Green models for eco-efficient and sustainable production (2017) Energy, 137, pp. 846-853; Moreira, F., Alves, A.C., Sousa, R.M., Towards Eco-efficient Lean Production Systems (2010) Lect. Notes Control Inf. Sci, 322, pp. 100-108; Azevedo, S.G., Govindan, K., Carvalho, H., Cruz-Machado, V., An integrated model to assess the leanness and agility of the automotive industry (2012) Resour. Conserv. Recycl, 66, pp. 85-94; Carvalho, H., Cruz-Machado, V., Lean, agile, resilient and green supply chain: A review (2009) Proceedings of the 3rd International Conference on Management Science and Engineering Management, pp. 66-76. , Bangkok, Thailand, 2–4 November; Govindan, K., Azevedo, S.G., Carvalho, H., Cruz-Machado, V., Lean, green and resilient practices influence on supply chain performance: Interpretive structural modeling approach (2015) Int. J. Environ. Sci. Technol, 12, pp. 15-34; Brougham, D., Haar, J., Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace (2017) J. Manag. Org, 24, pp. 239-257; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) Int. J. Prod. Perform. Manag, 55, pp. 242-258; Salvado, M.F., Azevedo, S.G., Matias, J.C.O., Ferreira, L.M., Proposal of a Sustainability Index for the Automotive Industry (2015) Sustain. J. Rec, 7, pp. 2113-2144; Saeed, M., Kersten, W., Supply chain sustainability performance indicators: A content analysis based on published standards and guidelines (2017) Logist. Res, 10, pp. 1-19; Abreu, M.F., Alves, A.C., Moreira, F., The Lean-Green BOPSE Indicator to Assess Efficiency and Sustainability (2019) Lean Engineering for Global Development, pp. 259-291. , Alves, A.C., Kahlen, F.J., Flumerfelt, S., Siriban-Manalang, A.B., Eds.; Springer International Publishing: Cham, Switzerland; Kamble, S.S., Gunasekaran, A., Gawankar, S.A., Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives (2018) Process Saf. Environ. Prot, 117, pp. 408-425; Ramanathan, R., Philpott, E., Duan, Y., Cao, G., Adoption of business analytics and impact on performance: A qualitative study in retail (2017) Prod. Plan. Control, 28, pp. 985-998; Peukert, B., Benecke, S., Clavell, J., Neugebauer, S., Nissen, N.F., Uhlmann, E., Lang, K.D., Finkbeiner, M., Addressing Sustainability and Flexibility in Manufacturing Via Smart Modular Machine Tool Frames to Support Sustainable Value Creation (2015) Procedia CIRP, 29, pp. 514-519; Herrmann, C., Schmidt, C., Kurle, D., Blume, S., Thiede, S., Sustainability in manufacturing and factories of the future (2014) Int. J. Precis. Eng. Manuf. Technol, 1, pp. 283-292; Gabriel, M., Pessel, E., Industry 4.0 and Sustainability Impacts: Critical Discussion of Sustainability Aspects with a Special Focus on Future of Work and Ecological Consequences (2016) Int. J. Eng, 1, pp. 131-136; Ahi, P., Searcy, C., Jaber, M.Y., Energy-related performance measures employed in sustainable supply chains: A bibliometric analysis (2016) Sustain. Prod. Consum, 7, pp. 1-15; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) Int. J. Phys. Distrib. Logist. Manag, 41, pp. 46-62; Agami, N., Saleh, M., Rasmy, M., Supply Chain Performance Measurement Approaches: Review and Classification (2012) J. Organ. Manag. Stud, pp. 1-20; Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) J. Clean. Prod, 52, pp. 329-341; Tortorella, G.L., Giglio, R., Van Dun, D.H., Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement (2019) Int. J. Oper. Prod. Manag, 39, pp. 860-886; Brockhaus, S., Kersten, W., Knemeyer, A.M., Where Do We Go From Here? Progressing Sustainability Implementation Efforts Across Supply Chains (2013) J. Bus. Logist, 34, pp. 167-182; Azapagic, A., Systems Approach to Corporate Sustainability: A General Management Framework (2003) Process Saf. Environ. Prot, 81, pp. 303-316; Azapagic, A., Perdan, S., Indicators of Sustainable Development for Industry: A General Framework (2000) Process Saf. Environ. Prot, 78, pp. 243-261; Castka, P., Balzarova, M.A., ISO 26000 and supply chains: On the diffusion of the social responsibility standard (2008) Int. J. Prod. Econ, 111, pp. 274-286; Butner, K., The smarter supply chain of the future (2010) Strat. Leadersh, 38, pp. 22-31; Schuster, E., Allen, S., Brock, D., (2007) Global RFID: The Value of the EPC Global Network for Supply Chain Management, , Springer International Publishing: Cambridge, UK; Ardito, L., Scuotto, V., Del Giudice, M., Petruzzelli, A.M., A bibliometric analysis of research on Big Data analytics for business and management (2019) Manag. Decis, 57, pp. 1993-2009; Kache, F., Seuring, S., Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management (2017) Int. J. Oper. Prod. Manag, 37, pp. 10-36; Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C., Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination (2016) Comput. Netw, 101, pp. 158-168; Tu, M., Lim, M.K., Yang, M.F., IoT-based production logistics and supply chain system-Part 1: Modeling IoT-based manufacturing supply chain (2018) Ind. Manag. Data Syst, 118, pp. 65-95; Ghobakhloo, M., Fathi, M., Corporate survival in Industry 4.0 era: The enabling role of lean-digitized manufacturing (2019) J. Manuf. Technol. Manag, 31, pp. 1-30; Schroeder, A., Zarco, C.G., Baines, T., Bigdeli, A.Z., Barriers to capturing the value of advanced services and digitization in the road transport industry Proceedings of the Spring Servitization Conference SSC2016, p. 9. , Manchester, UK, 16–17 May 2016; Frontoni, E., Rosetti, R., Paolanti, M., Alves, A., HATS project for lean and smart global logistic: A shipping company case study (2020) Manuf. Lett, 23, pp. 71-74; Moyano-Fuentes, J., Maqueira-Marín, J.M., Martínez-Jurado, P.J., Sacristán-Díaz, M., Extending lean management along the supply chain: Impact on efficiency (2020) J. Manuf. Technol. Manag, 32, pp. 63-84; Novais, L., Marín, J.M.M., Moyano-Fuentes, J., Lean Production implementation, Cloud-Supported Logistics and Supply Chain Integration: Interrelationships and effects on business performance (2020) Int. J. Logist. Manag, 31, pp. 629-663; Chaopaisarn, P., Woschank, M., Requirement Analysis for SMART Supply Chain Management for SMEs (2019) Proceedings of the International Conference on Industrial Engineering and Operations Management, , Bangkok, Thailand, 5–7 March; Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M., How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective (2014) Int. J. Mech. Aerosp. Ind. Mechatron. Manuf. Eng, 8, pp. 37-44; Tan, K.H., Zhan, Y., Ji, G., Ye, F., Chang, C., Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph (2015) Int. J. Prod. Econ, 165, pp. 223-233; Alicke, K., Rexhausen, D., Seyfert, A., (2017) Supply Chain 4.0 in Consumer Goods, , Https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/supply-chain-4-0-in-consumer-goods, (accessed on 13 February 2021); Shukla, M., Tiwari, M.K., Big-data analytics framework for incorporating smallholders in sustainable palm oil production (2017) Prod. Plan. Control, 28, pp. 1365-1377; Pedroso, M.C., Nakano, D., Knowledge and information flows in supply chains: A study on pharmaceutical companies (2009) Int. J. Prod. Econ, 122, pp. 376-384; Mittal, S., Khan, M.A., Romero, D., Wuest, T., Smart manufacturing: Characteristics, technologies and enabling factors (2019) Proc. Inst. Mech. Eng. Part B J. Eng. Manuf, 233, pp. 1342-1361; Zhu, X., Mukhopadhyay, S.K., Kurata, H., A review of RFID technology and its managerial applications in different industries (2012) J. Eng. Technol. Manag, 29, pp. 152-167; Sangari, S., Razmi, J., Business intelligence competence, agile capabilities, and agile performance in supply chain: An empirical study (2015) Int. J. Logist. Manag, 26, pp. 356-380; Perry, C., Processes of a case study methodology for postgraduate research in marketing (1998) Eur. J. Mark, 32, pp. 785-802; Rowley, J., Using case studies in research (2002) Manag. Res. News, 25, pp. 16-27; Yin, R., (2014) Case Study Research: Design and Methods, , 5th ed.; SAGE Publications: Thousand Oaks, CA, USA; Amabile, T.M., Patterson, C., Mueller, J., Wojcik, T., Odomirok, P.W., Marsh, M., Kramer, S.J., Academic-Practitioner Collaboration in Management Research: A Case of Cross-Profession Collaboration (2001) Acad. Manag. J, 44, pp. 418-431; Leonard-Barton, D.A., Dual Methodology for Case Studies: Synergistic Use of a Longitudinal Single Site with Replicated Multiple Sites (1990) Org. Sci, 1, pp. 248-266; Pettigrew, A., (1973) The Politics of Organizational Decision Making, , Tavistock: London, UK; Zainal, Z., Case study as a research method (2007) J. Kemanus, 9, pp. 1-6; Flyvbjerg, B., Five Misunderstandings About Case-Study Research (2006) Qual. Inq, 12, pp. 219-245; Hossieni, S., Dehkordi, J., Aghapour, A., Insights into case-study: A discussion on forgotten aspects of case research (2012) Int. J. Sci. Res. Publ, 2, pp. 53-69; Easton, G., Critical realism in case study research (2010) Ind. Mark. Manag, 39, pp. 118-128; Järvensivu, T., Törnroos, J.Å., Case study research with moderate constructionism: Conceptualization and practical illustration (2010) Ind. Mark. Manag, 39, pp. 100-108; Gibbert, M., Ruigrok, W., Wicki, B., What passes as a rigorous case study? (2008) Strat. Manag. J, 29, pp. 1465-1474; Behling, O., The Case for the Natural Science Model For Research in Organizational Behavior And Organization Theory (1980) Acad. Manag. Rev, 5, pp. 483-490; Cook, T.D., Campbell, D., (1979) Quasi-Experimental Design: Design and Analysis Issues for Field Settings, , McNally: Skokie, IL, USA; Campbell, D.T., Degrees of Freedom and the Case Study (1975) Comp. Political Stud, 8, pp. 178-193; Crozier, G., Denzin, N., Lincoln, Y., Handbook of Qualitative Research (1994) Br. J. Educ. Stud, 42, p. 409; Calder, B.J., Phillips, L.W., Tybout, A.M., The Concept of External Validity (1982) J. Consum. Res, 9, pp. 240-244; McGrath, J.E., Brinberg, D., External Validity and the Research Process: A Comment on the Calder/Lynch Dialogue (1983) J. Consum. Res, 10, pp. 115-124; Numagami, T., Perspective: The Infeasibility of Invariant Laws in Management Studies: A Reflective Dialogue in Defense of Case Studies (1998) Organ. Sci, 9, pp. 1-15; Eisenhardt, K.M., Building Theories from Case Study Research (1989) Acad. Manag. Rev, 14, pp. 532-550; Rosenzweig, P.M., Singh, J.V., Organizational Environments and the Multinational Enterprise (1991) Acad. Manag. Rev, 16, pp. 340-361; Zhi, H.L., A comparison of convenience sampling and purposive sampling (2014) PubMed, 61, pp. 105-111; Miles, M.B., Huberman, A.M., (1994) Qualitative Data Analysis, , 2nd ed.; Sage: London, UK; Bryman, A., Burgess, R.G., (1999) Qualitative Research, , Sage: London, UK; Chen, H., Chiang, R.H.L., Storey, V.C., Business Intelligence and Analytics: From Big Data to Big Impact (2012) MIS Q, 36, p. 1165; Flak, J., Technologies for Sustainable Biomass Supply: Overview of Market Offering (2020) Agronomy, 10, p. 798; Zangiacomi, A., Pessot, E., Fornasiero, R., Bertetti, M., Sacco, M., Moving towards digitalization: A multiple case study in manufacturing (2019) Prod. Plan. Control, 31, pp. 143-157; Oliveira, T., Maria, M., Literature Review of Information Technology Adoption Models at Firm Level (2011) Electron. J. Inf. Syst. Eval, 14, pp. 110-121; Tortorella, G.L., Fettermann, D., Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies (2018) Int. J. Prod. Res, 56, pp. 2975-2987; Veile, J.W., Kiel, D., Müller, J.M., Voigt, K.I., Lessons learned from Industry 4.0 implementation in the German manufacturing industry (2019) J. Manuf. Technol. Manag, 31, pp. 977-997; van Loon, P., Van Wassenhove, L.N., Assessing the economic and environmental impact of remanufacturing: A decision support tool for OEM suppliers (2018) Int. J. Prod. Res, 56, pp. 1662-1674; Ramadan, M., Al-Maimani, H., Noche, B., RFID-enabled smart real-time manufacturing cost tracking system (2016) Int. J. Adv. Manuf. Technol, 89, pp. 969-985; Stock, T., Obenaus, M., Kunz, S., Kohl, H., Industry 4.0 as enabler for a sustainable development: A qualitative assessment of its ecological and social potential (2018) Process. Saf. Environ. Prot, 118, pp. 254-267; Beier, G., Ullrich, A., Niehoff, S., Reißig, M., Habich, M., Industry 4.0: How it is defined from a sociotechnical perspective and how much sustainability it includes: A literature review (2020) J. Clean. Prod, 259, p. 120856},
correspondence_address1={Matias, J.C.O.; GOVCOPP R&D Center, Portugal},
publisher={MDPI AG},
issn={20763417},
language={English},
abbrev_source_title={Appl. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Frederico2021789,
author={Frederico, G.F. and Garza-Reyes, J.A. and Kumar, A. and Kumar, V.},
title={Performance measurement for supply chains in the Industry 4.0 era: a balanced scorecard approach},
journal={International Journal of Productivity and Performance Management},
year={2021},
volume={70},
number={4},
pages={789-807},
doi={10.1108/IJPPM-08-2019-0400},
note={cited By 52},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085140386&doi=10.1108%2fIJPPM-08-2019-0400&partnerID=40&md5=f241a80897e46f04c90d1689d327f54f},
affiliation={School of Management, Federal University of Paraná, Curitiba, Brazil; Centre for Supply Chain Improvement, Derby Business School, University of Derby, Derby, United Kingdom; Bristol Business School, University of the West of England, Bristol, United Kingdom},
abstract={Purpose: The purpose of this paper is to present a theoretical approach based on the balanced scorecard (BSC) with regard to performance measurement – PM in supply chains for the Industry 4.0 era. Design/methodology/approach: This paper combines the literature of PM and specifically the BSC with the literature related to the dimensions of supply chain in the context of Industry 4.0. Findings: Dimensions extracted from the literature based on supply chains within the context of Industry 4.0 showed a strong alignment with the four perspectives of the BSC, which make it suitable to be considered as a performance measurement system (PMS) for supply chains in this new context. Research limitations/implications: From theoretical perspective, this study contributes to the limited literature on PM for supply chains in Industry 4.0 era. The study proposes a supply chain 4.0 Scorecard and strongly support researchers to conduct future empirical researches in order to get a deeper understanding about PM in supply chains in the Industry 4.0 era. As limitations, the theoretical framework proposed needs further empirical research in other to validate it and obtain new insights over the investigation conducted and presented into this paper. Practical implications: Practitioners can use this study as a guide to develop more effective performance measurement systems – PMSs in their organizations. Originality/value: This research is unique as it addresses a significant knowledge gap related to PM in supply chains in the Industry 4.0 era. It brings a significant contribution in terms of understanding how to measure performance in supply chains in this new era. © 2020, Emerald Publishing Limited.},
author_keywords={Balanced scorecard;  Industry 4.0;  Performance measurement;  Supply chain;  Theoretical framework},
references={Alicke, K., Rexbausen, D., Seyfert, A., (2016) Supply Chain 4.0 in Consumer Good, , https://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/supply-chain-4-0-in-consumer-goods, Mckinsey & Compant, Berlim; Ante, G., Facchini, F., Mossa, G., Digiesi, S., Developing a key performance indicators tree for lean and smart production (2018) IFAC-PapersOnLine, 51 (11), pp. 13-18; (2019) Supply Chain Operations Reference Model, , http://www.apics.org/apics-for-business/frameworks/scor, APICS Supply Chain Council; Ardito, L., Petruzzelli, A.M., Panniello, U., Garavelli, A.C., Towards Industry 4.0: mapping digital technologies for supply chain management marketing-integration (2016) Business Process Management Journal, pp. 1-26. , doi:, press; Bauters, K., Cottyn, J., Claeys, D., Slembrouck, M., Veelaert, P., Van Ladeghem, H., Automated work cycle classification and performance measurement for manual work stations (2018) Robotics and Computer-Integrated Manufacturing, 51, pp. 139-157; Barreto, L., Amaral, A., Pereira, T., Industry 4.0 implications in logistics: an overview (2017) Procedia Manufacturing, 13, pp. 1245-1252; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers and Industrial Engineering, 53, pp. 43-62; Bienhaus, F., Haddud, A., Procurement 4.0: factors influencing the digitization of procurement and supply chains (2018) Business Process Management Journal, 24 (4), pp. 965-984; Bititci, U.S., Carrie, A.S., McDevitt, L., Techniques integrated performance measurement systems: an audit and development guide (1997) The TQM Magazine, 9 (1), pp. 46-53; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Productions Management, 20 (6), pp. 692-704; Branger, J., Pang, Z., From automated home to sustainable, healthy and manufacturing home: a new story enabled by the Internet-of-things and industry 4.0 (2015) Journal of Management Analytics, 2 (4), pp. 314-332; Brewer, P., Speh, T., Using the Balanced Scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Brinch, M., Understanding the value of big data in supply chain management and its business processes: towards a conceptual framework (2018) International Journal of Operations and Production Management, 38 (7), pp. 1589-1614; Bukova, B., Brumercikova, E., Cerna, L., Drozdziel, P., The position of industry 4.0 in the worldwide logistics chains (2018) Scientific Journal on Transport and Logistic, 9 (1), pp. 18-23; Büyüközkan, G., Göçer, F., Digital Supply Chain: literature review and a proposed framework for future research (2018) Computers in Industry, 97, pp. 157-177; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Dallasega, P., Raucha, E., Linderb, C., Industry 4.0 as an enabler of proximity for construction supply chains: a systematic literature review (2018) Computers in Industry, 99, pp. 205-225; Dolgui, A., Ivanov, D., Sethi, S.P., Sokolov, B., Architectures for enterprise integration and interoperability: past, present and future (2018) International Journal of Production Research, pp. 1-22. , doi:, press; Eccles, R.G., Measuring corporate performance: the performance manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Emmer, C., Hofmann, T.M., Schmied, T., Stjepandic, J., Strietzela, M., A neutral approach for interoperability in the field of 3D measurement data management (2018) Journal of Industrial Information Integration, 12, pp. 47-56; Frederico, G.F., Martins, R.A., Model for alignment between performance measurement systems and maturity of supply chain management (2012) Gestão and Produção, 19 (4), pp. 857-871; Frederico, G.F., Martins, R.A., Performance measurement systems for supply chain management: how to manage its maturity (2014) International Journal of Supply Chain Management, 3 (2), pp. 24-30. , https://ojs.excelingtech.co.uk/index.php/IJSCM/article/view/924/pdf; Frederico, G., Garza-Reyes, J.A., Anosike, A., Kumar, V., Supply chain 4.0: concepts, maturity and research agenda (2019) Supply Chain Management: An International Journal, , doi:, Press; Ghobakhloo, M., The future of manufacturing industry: a strategic roadmap toward Industry 4.0 (2018) Journal of Manufacturing Technology Management, 29 (6), pp. 910-936; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Gottge, S., Menzel, T., (2017) An Exploratory Multiple Case Study on the Purchasing Process Reshaped by Industry 4.0 in the Automotive Industry, , http://www.diva-portal.org/smash/get/diva2:1109682/FULLTEXT02.pdf, Master Thesis, Linnaeus University; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Haddud, A., DeSouza, A., Khare, A., Lee, H., Examining potential benefits and challenges associated with the Internet of things integration in supply chains (2017) Journal of Manufacturing Technology Management, 29 (1); Hazen, B.T., Skipper, J.B., Ezell, J.D., Boone, C.A., Big data and predictive analytics for supply chain sustainability (2016) Journal of Computers and Industrial Engineering, 101, pp. 592-598; Hofmann, E., Rüch, M., Industry 4.0 and the current status as well as future prospects on logistics (2017) Computers in Industry, 89, pp. 23-34; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Iddris, F., Digital supply chain: survey of the literature (2018) International Journal of Business Research and Management, 9 (1), pp. 47-61. , http://www.cscjournals.org/manuscript/Journals/IJBRM/Volume9/Issue1/IJBRM-257.pdf; Jabbour, A.B.L.S., Jabbour, C.J.C., Godinho Filho, M., Roubaud, D., Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations (2018) Annals of Operations Research, pp. 1-14. , doi:, press; Kache, F., Seuring, S., Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management (2017) International Journal of Operations and Production Management, 37 (1), pp. 10-36; Kagermann, H., Wahlster, W., Helbig, J., (2013) Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0, , https://www.acatech.de/Publikation/recommendations-for-implementing-the-strategic-initiative-industrie-4-0-final-report-of-the-industrie-4-0-working-group/, Acatech, Frankfurt; Kamble, S.S., Gunasekaran, A., Gawankar, S.A., Sustainable Industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives (2018) Process Safety and Environmental Protection, 117, pp. 408-425; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: part II (2001) Accounting Horizons, 15 (2), pp. 147-160; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Productions Management, 23 (2), pp. 213-229; Kolberg, D., Zühlke, D., Lean automation enabled by industry 4.0 technologies (2015) IFAC-PapersOnLine, 48 (3), pp. 1870-1875; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41, pp. 23-35; Li, L., Su, Q., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) International Journal of Production Research, 49 (1), pp. 33-57; Liao, Y.L., Deschamps, F., Rocha Loures, E.F., Ramos Pereira, F.P., Past, present and future of Industry 4.0 - a systematic literature review and research agenda proposal (2017) International Journal of Production Research, 55 (12), pp. 3609-3629; Lin, D., Lee, C.K.M., Lau, H., Yang, Y., Strategic response to Industry 4.0: an empirical investigation on the Chinese automotive industry (2018) Industrial Management and Data Systems, 118 (3), pp. 589-605; Lu, Y., Industry 4.0: a survey on technologies, applications and open research issues (2017) Journal of Industrial Information Integration, 6, pp. 1-10; Malina, M., Nørreklit, H., Selto, F., Relations among measures, climate of control, and performance measurement models (2007) Contemporary Accounting Research, 24 (3), pp. 935-982; Miragliotta, G., Sianesi, A., Convertini, E., Distante, R., Data driven management in Industry 4.0: a method to measure Data Productivity (2018) IFAC-PapersOnLine, 51 (11), pp. 19-24; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Moeuf, A., Pellerin, R., Lamouri, S., Tamayo-Giraldo, S., Barbaray, R., The industrial management of SMEs in the era of Industry 4.0 (2017) International Journal of Production Research, 56 (3), pp. 1118-1136; Mrugalska, B., Wyrwicka, M.K., Towards lean production in industry 4.0 (2017) Procedia Manufacturing, 182, pp. 466-473; Muthusami, S., Srinivsan, M., Supply chain 4.0: digital tranformation, disruptions and strategies (2018) Review of Business and Technology Research, 14 (2), pp. 32-35. , http://mtmi.us/rbtr/dec/dec2017/06-Senthil-Srinivsan-pp32-35.pdf; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations and Production Management, 14 (4), pp. 81-114; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Niven, P., (2002) Balanced Scorecard Step-by-step: Maximizing Performance and Maintaining Results, , John Wiley & Sons, New York, NY; Nørreklit, H., Nørreklit, L., Mitchell, F., Bjørnenak, T., The rise of the balanced scorecard! Relevance regained? (2012) Journal of Accounting and Organizational Change, 8 (4), pp. 490-510; Nouri, F.A., Nikabadi, M.S., Olfat, L., Developing the framework of sustainable service supply chain balanced scorecard (SSSC BSC) (2019) International Journal of Productivity and Performance Management, 68 (1), pp. 148-170; Oztemel, E., Gursev, S., Literature review of Industry 4.0 and related technologies (2018) Journal of Intelligent Manufacturing, pp. 1-56. , doi:, press; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14 (4), pp. 335-346; Pereira, A.C., Romero, F., A review of the meanings and the implications of the industry 4.0 concept (2017) Procedia Manufacturing, 13, pp. 1206-1214; Pfohl, H., Yahsi, B., Kurnaz, T., The Impact of Industry 4.0 on Supply Chain (2015) Proceedings of the Hamburg International Conference of Logistics, , https://hicl.org/publications/2015/20/31.pdf, Hamburg, Germany: 2015; Porter, M.E., Heppelmann, J.E., How smart, connected products are transforming competition (2014) Harvard Business Review, 92 (11), pp. 64-88. , https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition; Queiroz, M.M., Telles, R., Big data analytics in supply chain and logistics: an empirical approach (2018) The International Journal of Logistics Management, 29 (2), pp. 767-783; Reddy, J.M., Rao, N., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30, pp. 40-47; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: design and development guidelines (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 805-826; Rossit, D.A., Tohmé, F., Frutos, M., Industry 4.0: smart scheduling (2018) International Journal of Production Research, pp. 1-12. , doi:, press; Rüttimann, B.G., Stöckli, M.T., Lean and industry 4.0—twins, partners, or contenders? A due clarification regarding the supposed clash of two production systems (2016) Journal of Service Science and Management, 9, pp. 485-500; Sanders, A., Elangeswaran, C., Wulfsberg, J., Industry 4.0 implies lean manufacturing: research activities in industry 4.0 function as enablers for lean manufacturing (2016) Journal of Industrial Engineering and Management, 9 (3), pp. 811-833; Santos, K., Loures, E., Piechnicki, F., Canciglieri, O., Opportunities assessment of product development process in industry 4.0 (2017) Procedia Manufacturing, 11, pp. 1358-1365; Schuh, G., Gartzen, T., Rodenhauser, T., Marks, A., Promoting working-based learning through Industry 4.0 (2015) Procedia CIRP, 32, pp. 82-87; Scrauf, S., Berttram, P., (2016) How Digitization Makes the Supply Chain More Efficient, Agile, and Customer-Focused, , https://www.strategyand.pwc.com/reports/digitization-more-efficient, PWC; Sellitto, M.A., Pereira, G.M., Borchardt, M., Da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) International Journal of Production Research, 53 (16), pp. 4917-4926; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shin, W.S., Dahlgaard, J.J., Dahlgaard-Park, S.M., Kim, M.G., A quality scorecard for the era of industry 4.0 (2018) Total Quality Management and Business Excellence, 29 (9-10), pp. 82-87; Soderberg, M., Kalagnanam, S., Sheenan, N.T., Vaidyanathan, G., When is a balanced scorecard a balanced scorecard (2011) International Journal of Productivity and Performance Management, 60 (7), pp. 688-708; Stevens, G., Johnson, M., Integrating the supply chain … 25 years on (2016) International Journal of Physical Distribution and Logistics Management, 46 (1), pp. 19-42; Stock, T., Seliger, G., Opportunities of sustainable manufacturing in industry 4.0 (2016) Procedia CIRP, 40, pp. 536-541; Swanson, D., The impact of digitization on product offerings: using direct digital manufacturing in the supply chain (2017) Proceedings of the 50th Hawaii International Conference on System Sciences, , doi:, Hawaii: 2017; Theeranuphattana, A., Tang, J., A conceptual model of performance measurement for supply chains: alternative considerations (2008) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Tjahjono, B., Esplugues, C., Ares, E., Pelaez, G., What does Industry 4.0 mean to supply chain (2017) Procedia Manufacturing, 13, pp. 1175-1182; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic literature review (2003) British Journal of Management, 14, pp. 207-222; Tu, M., An exploratory study of Internet of things (IoT) adoption intention in logistics and supply chain management: a mixed research approach (2018) The International Journal of Logistics Management, 29 (1), pp. 131-151; Van Aken, E., Lettens, G., Coleman, G., Farris, J., Van Goubergen, D., Assessing maturity and effectiveness of enterprise performance measurement systems (2005) International Journal of Productivity and Performance Management, 54 (6), pp. 400-418; Van Hoek, R., Measuring the unmeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Voepel, S.C., Leibold, M., Eckhoff, R.A., The tyranny of the Balanced Scorecard in the innovation economy (2006) Journal of Intellectual Capital, 7 (1), pp. 43-60; Wang, G., Gunasekaran, A., Ngai, E.W.T., Papadoulos, T., Big data analytics in logistics and supply chain management: certain investigations for research and applications (2016) International Journal of Production Economics, 176, pp. 98-110; Webster, J., Watson, R.T., Analysing the past to prepare for the future (2002) MIS Quartely, 26 (2), pp. 8-23; Wettstein, T., Kueng, P., A maturity model for performance measurement systems (2002) Management Information Systems, pp. 113-122. , https://www.witpress.com/Secure/elibrary/papers/MIS02/MIS02011FU.pdf; Wilkesmann, M., Wilkesmann, U., Industry 4.0 – organizing routines or innovations? (2018) VINE Journal of Information and Knowledge Management System, 48 (2), pp. 238-254; Wu, L., Yue, X., Jin, A., Yen, D.C., Smart supply chain management: a review and implications for future research (2016) The International Journal of Logistics Management, 27 (2), pp. 395-417},
correspondence_address1={Frederico, G.F.; School of Management, Brazil; email: guilherme.frederico@ufpr.br},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Sholeh2021,
author={Sholeh, M.N. and Nurdiana, A. and Dharmo, B. and Suharjono},
title={Implementation of construction supply chain flow based on SCOR 12.0 performance standards},
journal={Journal of Physics: Conference Series},
year={2021},
volume={1833},
number={1},
doi={10.1088/1742-6596/1833/1/012012},
art_number={012012},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103540989&doi=10.1088%2f1742-6596%2f1833%2f1%2f012012&partnerID=40&md5=be110e2dc3d73b86c4d15a6da35490ab},
affiliation={Civil and Planning Department, Vocational School, Diponegoro University, Semarang, 50275, Indonesia},
abstract={Supply Chain Operations Reference (SCOR) is a supply chain performance measurement model that has been widely used in the manufacturing industry with the latest model version 12.0. This article presents the adoption and development of a construction supply chain performance measurement framework in Indonesia based on SCOR 12.0. The supply chain supply for ready mix concrete and steel as a case study consists of contracts, on-site inspection, material usage, and payment. Simulations were carried out on Project X with precast concrete supplied by ready mix concrete and steel with three suppliers each. Performance indicators from SCOR are analyzed and simulated to be implemented in the supply chain flow. The result is a contract phase with overall value at riks (OVAR), on-site inspection with perfect order fulfillment (POF) and order fulfillment cycle time (OFCT), use of materials with upside supply chain adaptability (USCA) and downside supply chain adaptability (DSCA), payment with total supply chain management costs (TSCMC) and the cost of goods sold (COGS), and overall flow with cash-to-cash cycle time (CCT), return on supply chain fixed assets (RSCFA), return on working capital (ROWC). © Published under licence by IOP Publishing Ltd.},
keywords={Precast concrete;  Supply chain management, Construction supply chain;  Manufacturing industries;  On-site inspection;  Performance indicators;  Performance standards;  Ready-mix concrete;  Supply chain operations references;  Supply chain performance measurements, Ready mixed concrete},
references={Khumalo, J., Nqojela, P., Njisane, Y., (2010) Fourth Annual Competition Commission, Competition Tribunal and Mandela Institute Conference on Competition Law, Economics and Policy in South Africa, Johannesburg Cover pricing in the construction industry: Understanding the practice within a competition context; Adida, E., DeMiguel, V., Supply chain competition with multiple manufacturers and retailers (2011) Oper. Res, 59, pp. 156-172; Liu, Z., Nagurney, A., Supply chain outsourcing under exchange rate risk and competition (2011) Omega, 39, pp. 539-549; Segerstedt, A., Olofsson, T., Eriksson, P. E., Improving construction supply chain collaboration and performance: a lean construction pilot project Supply Chain Manag (2010) An Int. J.; (2017) Supply Chain Operations Reference (SCOR) Version 12.0, , APICS; Kusrini, E., Caneca, V. I., Helia, V. N., Miranda, S., (2019) Supply Chain Performance Measurement Using Supply Chain Operation Reference (SCOR) 12.0 Model: A Case Study in AA Leather SME in Indonesia IOP Conference Series: Materials Science and Engineering, p. 012023; Wibowo, M. A., Sholeh, M. N., The analysis of supply chain performance measurement at construction project (2015) Procedia Eng, 125, pp. 25-31; Wibowo, M. A., Handayani, N. U., Sinaga, G., Sholeh, M. N., Ulkhaq, M. M., The performance of building construction supply chain : A Case study in building construction project (2019) IOP Conf. Ser. Mater. Sci. Eng, 673, p. 012048; Thunberg, M., Persson, F., Using the SCOR model's performance measurements to improve construction logistics (2014) Prod. Plan. Control, 25, pp. 1065-1078; Cheng, J. C. P., Law, K., Bjornsson, H., Jones, A., Sriram, R. D., Modeling and monitoring of construction supply chains (2010) Adv. Eng. Informatics, 24, pp. 435-455; Cheng, C. P., (2010) SC Collaborator: A service oriented framework for construction supply chain collaboration and monitoring, , (Stanford University); Taylor, P., Sellitto, M. A., Pereira, G. M., Borchardt, M., Inâcio, R., Viegas, C. V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) Int. J. Prod. Res, 53, pp. 4917-4926; Sipe, L. R., Developing conceptual categories in classroom descriptive research: Some problems and possibilities (2004) Anthropol. Educ. Q, 35, pp. 472-485; Basili, V. R., Briand, L. C., Melo, W. L., A validation of object-oriented design metrics as quality indicators (1996) IEEE Trans. Softw. Eng, 22, pp. 751-761; Crowley, A., Construction as a manufacturing process: Lessons from the automotive industry (1998) Comput. Struct, 67, pp. 389-400; Nawi, M., Nasrun, M., Baluch, N. H., Bahaudin, A. Y., (2014) Impact of fragmentation issue in construction industry: An overview MATEC web of conferences, p. 15; Brandes, y, Kurama, Y., Use of Recycled Concrete Aggregates in Precast (2016) Prestressed Concrete Procedia Eng, 145, pp. 1338-1345; Liu, Z., Zhang, Y., Li, M., Integrated scheduling of ready-mixed concrete production and delivery (2014) Autom. Constr, 48, pp. 31-43; Mena, C., Humphries, A., Choi, T. Y., Toward a theory of multi-tier supply chain management (2013) J. Supply ChainManag, 49, pp. 58-77; Afolabi, A., Fagbenle, O., Mosaku, T., (2017) World Conference on Information Systems and Technologies Characteristics of a web-based integrated material planning and control system for construction project delivery, pp. 20-30},
correspondence_address1={Sholeh, M.N.; Civil and Planning Department, Indonesia; email: mohnursholeh@live.undip.ac.id},
editor={Rahmawati F., Hariyanto D., Hakim M.L., Malik A., Hasbi H., Nurhadiyanto D., Suprapto, Siswanto I., Komariah K., Widiastuti, Badarudin R., Nurhidayat},
publisher={IOP Publishing Ltd},
issn={17426588},
language={English},
abbrev_source_title={J. Phys. Conf. Ser.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Maestrini202157,
author={Maestrini, V. and Luzzini, D. and Patrucco, A.},
title={Measuring performance beyond the company boundaries: More a need than a challenge},
journal={Rutgers Business Review},
year={2021},
volume={6},
number={1},
pages={57-70},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104645823&partnerID=40&md5=e3425e5bf1e83f0083eebd0609213578},
affiliation={Snam S.p.A., Italy; EADA Business School, Barcelona, Spain; Florida International University, College of Business, United States},
abstract={In modern companies, measuring and control suppliers’ performance is a difficult-but fundamental-task. The adoption of inappropriate supplier performance measurement systems can generate undesired supplier behaviors, that can negatively affect buyer (and supply chain) performance. This paper reviews and addresses the main challenges connected to supplier performance management activities, and proposes a framework – the Strategy, Technology, and Approach model – that prescribes the main aspects to be considered for an effective design, use, implementation, and review of these systems. © 2021, Rutgers Business School. All rights reserved.},
references={Kaplan, R. S., Norton, D. P., The balanced scorecard: measures that drive performance (2005) Harvard Business Review, 83 (7), p. 172; Melnyk, S. A., Bourne, M., Frayer, D., Rifkin, W., Are your performance measurements destroying your supply chain? (2020) Supply Chain Management Review, pp. 28-33; Li, S., Rao, S. S., Ragu-Nathan, T. S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Lee, H. L., Aligning supply chain strategies with product uncertainties (2002) California Management Review, 44 (3), pp. 105-119; Maestrini, V., Martinez, V., Neely, A., Luzzini, D., Caniato, F., Maccarrone, P., The relationship regulator: a buyer-supplier collaborative performance measurement system (2018) International Journal of Operations & Production Management, 38 (11), pp. 2022-2039; Lindgreen, A., Vanhamme, J., van Raaij, E. M., Johnston, W. J., Go configure: The mix of purchasing practices to choose for your supply base (2013) California Management Review, 55 (2), pp. 72-96; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Henri, J. F., Management control systems and strategy: a resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558; Kraljic, P., Purchasing must become supply management (1983) Harvard Business Review, 61 (5), pp. 109-117; Hesping, F. H., Schiele, H., Purchasing strategy development: A multi-level review (2015) Journal of Purchasing and Supply Management, 21 (2), pp. 138-150; (2018) Gartner magic quadrant for strategic sourcing application suites, , Gartner Research. (August 1). Gartner},
publisher={Rutgers Business School},
issn={24742376},
language={English},
abbrev_source_title={Rutg. Busi. Rev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Karamouz2021484,
author={Karamouz, S.S. and Ahmadi Kahnali, R. and Ghafournia, M.},
title={Supply chain quality management performance measurement: systematic review},
journal={International Journal of Quality and Reliability Management},
year={2021},
volume={38},
number={2},
pages={484-504},
doi={10.1108/IJQRM-03-2019-0073},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086471406&doi=10.1108%2fIJQRM-03-2019-0073&partnerID=40&md5=1831c0392a4fead2463dfda76e964005},
affiliation={Hormozgan University, Bandarabas, Iran; Department of Industrial Management, Hormozgan University, Bandarabas, Iran},
abstract={Purpose: This study aims at investigating the existing knowledge in the literature on quality management performance measurement in order to identify performance measures in the field of total quality management and classify measures according to the three levels of supplier, customer and company. Design/methodology/approach: A systematic literature review is conducted at the intersection of performance measurement and total quality management fields. Four databases including Web of Sciences, Scopus, Emerald and Google Scholar were searched up to 2018. A final sample of 24 articles was selected based on the inclusion/exclusion criteria that constitutes the knowledge base of the study. Findings: One of the problems that many organizations face is the lack of knowledge and resources on how to evaluate, improve and manage the quality performance. Through analysis and synthesis of the literature, the study revealed the measures for assessing quality performance at three levels of supplier, customer and company. Research limitations/implications: This study focuses only on four databases, which may have limited the number of the databases included and thus the scope of this investigation to a certain extent. Originality/value: The present study aimed to identify and classify different performance measures through a systematic literature review framework classifying these measures into three groups. The article brings together fragmented literature from multiple studies to categorize research output regarding performance measurement of supply chain quality. The paper shows the state of the performance measurement in total quality management in supply chain. © 2020, Emerald Publishing Limited.},
author_keywords={Performance measurement;  Supply chain;  Systematic review;  Total quality management},
references={Agus, A., Supply chain management, product quality and business performance (2011) International Conference on Sociality and Economics Development IPEDR, pp. 98-102; Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) Journal of Cleaner Production, 52, pp. 329-341; Anderson, J.C., Rungtusanatham, M., Schroeder, R.G., A theory of quality management underlying the DEMING management method (1994) Academy of Management Review, 19, pp. 472-509; Bastas, A., liyanage, K., Sustainable supply chain quality management: a systematic review (2018) Journal of Cleaner Production, 181, pp. 726-744; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28, pp. 249-260; Chang, H.H., Development of performance measurement systems in quality management organisations (2006) Service Industries Journal, 26, pp. 765-786; Chen, Z., The relationships among JIT, TQM and production operations performance: an empirical study from Chinese manufacturing firms (2015) Business Process Management Journal, 21 (2), pp. 1015-1039; Chenhall, R.H., Reliance on manufacturing performance measures, total quality management and organizational performance (1997) Management Accounting Research, 8 (2), pp. 187-206; Chibba, A., (2017) Supply chain quality management-exploring performance of manufacturing organizations, , Luleå tekniska universitet; Chow, C.W., Van Der Stede, W.A., The use and usefulness of nonfinancial performance measures (2006) Management Accounting Quarterly, 7 (3), pp. 1-18; De toni, A., Nassimbeni, G., Tonchia, S., An instrument for quality performance measurement (1995) International Journal of Production Economics, 38 (2-3), pp. 199-207; Estampe, D., (2014) Supply Chain Performance and Evaluation Models, pp. 53-81. , John Wiley & Sons, New York; Fernandes, A.C., Sampaio, P., Sameiro, M., Truong, H.Q., Supply chain management and quality management integration: a conceptual model proposal (2017) International Journal of Quality and Reliability Management, 34, pp. 53-67; Ganji Jamehshooran, B., Shaharoun, M., Norehan Haron, H., Assessing supply chain performance through applying the SCOR model (2015) International Journal of Supply Chain Management, 4, pp. 1-11; Gopal, P., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61, pp. 518-547; Hietschold, N., Reinhardt, R., Gurtner, S., Measuring critical success factors of TQM implementation successfully–a systematic literature review (2014) International Journal of Production Research, 52 (21), pp. 6254-6272; Hsieh, H.-F., Shannon, S.E., Three approaches to qualitative content analysis (2005) Qualitative Health Research, 15 (9), pp. 1277-1288; Hu, H.Y., Chiu, S.-I., Yen, T.-M., Cheng, C.-C., Assessment of supplier quality performance of computer manufacturing industry by using ANP and DEMATEL (2015) The TQM Journal, 27 (1), pp. 122-134; Huo, B., Ye, Y., Zhao, X., Zhu, A., Supply chain quality integration: A taxonomy perspective (2019) International Journal of Production Economics, 207, pp. 236-246; Jesson, J., Matheson, L., Lacey, F.M., (2011) Doing Your Literature Review: Traditional and Systematic Techniques, pp. 103-127. , Sage, London; Jochem, R., Menrath, M., Landgraf, K., Implementing a quality-based performance measurement system: a case study approach (2010) The TQM Journal, 22 (4), pp. 410-422; Juran, J.M., (1989) Juran On Leadership for Quality: An Executive Handbook, , Free Press, New York, NY; Kalpande, S., Gupta, R., Dandekar, M., Framework of TQM implementation in small businesses of an industrially backward region: a concept (2012) Performance Improvement, 51 (7), pp. 7-13; Kaynak, H., Hartley, J.L., A replication and extension of quality management into the supply chain (2008) Journal of Operations Management, 26 (4), pp. 468-489; Kerfai, N., Bejar Ghadhab, B., Malouche, D., Performance measurement and quality costing in Tunisian manufacturing companies (2016) The TQM Journal, 28 (4), pp. 588-596; Kuei, C.-H., Madu, C.N., Lin, C., The relationship between supply chain quality management practices and organizational performance (2001) International Journal of Quality and Reliability Management, 18, pp. 864-872; Kumar, V., De Grosbois, D., Choisne, F., Kumar, U., Performance measurement by TQM adopters (2008) The TQM Journal, 20 (3), pp. 209-222; Lockamy, A., III, Quality-focused performance measurement systems: a normative model (1998) International Journal of Operations and Production Management, 18 (8), pp. 740-766; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Mehralian, G., Nazari, J.A., Nooriparto, G., Rasekh, H.R., TQM and organizational performance using the balanced scorecard approach (2017) International Journal of Productivity and Performance Management, 66 (1), pp. 111-125; Najmi, M., Kehoe, F.D., The role of performance measurement systems in promoting quality development beyond ISO 9000 (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 159-172; Neyestani, B., Juanzon, J.B.P., Developing an appropriate performance measurement framework for total quality management in construction, and other industries (2016) International Journal of Technology and Engineering, 5 (2), pp. 32-44; Noshad, K., Awasthi, A., Supplier quality development: a review of literature and industry practices (2015) International Journal of Production Research, 53 (2), pp. 466-487; Noshad, K., Awasthi, A., Investigating critical criteria for supplier quality development (2018) International Journal of Management Science and Engineering Management, 13 (3), pp. 215-224; Oke, S., Ayomoh, M., Oyedokun, I., An approach to measuring the quality of maintenance performance (2007) IMA Journal of Management Mathematics, 18 (1), pp. 17-32; Omurgonulsen, M., A research on the measurement of quality costs in the Turkish food manufacturing industry (2009) Total Quality Management, 20 (5), pp. 547-562; Opengart, R., Supply chain management and learning organization: a merging of literatures (2015) International Journal of Commerce and Management, 25, pp. 183-195; Pimentel, L., Major, M.J., Quality management and a balanced scorecard as supporting frameworks for a new management model and organisational change (2014) Total Quality Management and Business Excellence, 25 (7-8), pp. 763-775; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: an exploratory study (2015) International Journal of Productivity and Performance Management, 64 (8), pp. 1068-1091; Quang, H.T., Sampaio, P., Carvalho, M.S., Fernandes, A.C., Binh An, D.T., Vilhenac, E., An extensive structural model of supply chain quality management and firm performance (2016) International Journal of Quality and Reliability Management, 33, pp. 444-464; Rashid, K., Haris Aslam, M., Business excellence through total supply chain quality management (2012) Asian Journal on Quality, 13, pp. 309-324; Sang Chin, K., Yeung, I.-K., Fai Pun, K., Development of an assessment system for supplier quality management (2006) International Journal of Quality and Reliability Management, 23 (7), pp. 743-765; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55, pp. 242-258; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2010) Behavioral Operations in Planning and Scheduling, pp. 105-121. , Springer, Berlin, Heidelberg; Sinclair, D., Zairi, M., Benchmarking best-practice performance measurement within companies: using total quality management (1995) Benchmarking for Quality Management and Technology, 2 (3), pp. 53-71; Sinclair, D., Zairi, M., Effective process management through performance measurement: part I–applications of total quality-based performance measurement (1995) Business Process Re-Engineering and Management Journal, 1 (1), pp. 75-88; Talib, F., Rahman, Z., Qureshi, M., Analysis of interaction among the barriers to total quality management implementation using interpretive structural modeling approach (2011) Benchmarking: An International Journal, 18, pp. 563-587; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) International Journal of Production Research, 53 (21), pp. 6473-6494; Uyar, A., An exploratory study on quality costs in Turkish manufacturing companies (2008) International Journal of Quality and Reliability Management, 25 (6), pp. 604-620; Uyar, A., Quality performance measurement practices in manufacturing companies (2009) The TQM Journal, 21 (1), pp. 72-86; Van Der Spiegel, M., Luning, P., Ziggers, G., Jongen, W., Evaluation of performance measurement instruments on their use for food quality systems (2005) Critical Reviews in Food Science and Nutrition, 44 (7-8), pp. 501-512; Wad, R.P., Kumar, K.V., Analysis of performance measures for total quality management (TQM) in manufacturing industries (2012) The 9th AIMS Conference on Dynamical Systems, Differential Equations and Applications Orlando, , Florida, USA: 2012; Wad, R.P., Kumar, K.V., Analysis of performance measures for Total Quality Management (TQM) in manufacturing industries (2015) Twelfth AIMS International Conference on Management; Wang, F.-K., Du, T., Li, E., Applying six-sigma to supplier development (2004) Total Quality Management and Business Excellence, 15 (9-10), pp. 1217-1229; Wei, J.-T., Chang, Y.W., Zhang, X., Wu, H.-H., Tang, Y.-T., Performance measurement systems, TQM and multi-level firm performance: a person–organisation fit perspective (2017) Total Quality Management and Business Excellence, 30, pp. 1578-1598; Xian, S., Qiu, D., Zhang, S., A fuzzy principal component analysis approach to hierarchical evaluation model for balanced supply chain scorecard grading (2013) Journal of Optimization Theory and Applications, 159, pp. 518-535; Xiao, Y., Watson, M., Guidance on conducting a systematic literature review (2017) Journal of Planning Education and Research, 39 (1), pp. 93-112; Zhang, L., Wang, S., Li, F., Wang, H., Wang, L., Tan, W., A few measures for ensuring supply chain quality (2011) International Journal of Production Research, 49, pp. 87-97; Zhang, M., Guo, H., Huo, B., Zhao, X., Huang, J., Linking supply chain quality integration with mass customization and product modularity (2019) International Journal of Production Economics, 207, pp. 227-235; Zhong, J., Ma, Y., Tu, Y., Li, X., Supply chain quality management: an empirical study (2016) International Journal of Contemporary Hospitality Management, 28, pp. 2446-2472},
correspondence_address1={Ahmadi Kahnali, R.; Department of Industrial Management, Iran; email: ahmadi@hormozgan.ac.ir},
publisher={Emerald Group Holdings Ltd.},
issn={0265671X},
language={English},
abbrev_source_title={Int. J. Qual. Reliab. Manage.},
document_type={Review},
source={Scopus},
}

@BOOK{Grant2021226,
author={Grant, D.B. and Shaw, S. and Chaisurayakarn, S. and Shenin, A.N.},
title={Adoption of environmental management systems: Perspectives from UK, Finland and Thailand},
journal={Handbook of Sustainability-Driven Business Strategies in Practice},
year={2021},
pages={226-243},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130123580&partnerID=40&md5=08f336539f8308fc689bcd4bf9991b80},
affiliation={Hanken School of Economics, Helsinki, Finland; Thammasat University, Bangkok, Thailand; Hull University Business School, United Kingdom; Faculty of Management Sciences, Kasetsart University, Sriracha Campus, Thailand; Unilever, Finland},
abstract={Environmental supply chain performance measurement and reporting using environmental management systems (EMS) are evolving as new sustainability and societal challenges emerge. However, little research has been conducted to assess the use of appropriate EMS or the adoption of extant standards to understand environmental performance measurement in the logistics and supply chain sectors, or within different country contexts within globalized supply chains. This chapter compares studies undertaken in the UK, Finnish and Thai logistics and supply chain sectors to curate the adoption and use of EMS in each and identify common key enablers and inhibitors. The two most popular EMS are ISO 14001 and the European Union’s Eco-Management Audit Scheme EMAS. However, they have been inconsistently adopted across the various sectors, for example many UK logistics practitioners have developed ‘own company designed’ reporting systems. Further, logistics and supply chain practitioners in all contexts indicated a lack of understanding of EMS with many small firms doing no reporting at all. Key enablers and benefits for adoption are financially linked to customer requirements, waste reduction and operational efficiency. Key inhibitors to effective implementation are a lack of standard measures and reporting systems and government direction for same, and supply chain complexity. Thus, there is currently ʼno one size fits all’ system for reporting environmental supply chain performance and a clear divergence between normative theory and practice. © Stefan Markovic, Cristina Sancha and Adam Lindgreen 2021.},
references={Anderson, L., Analytic autoethnography (2006) Journal of Contemporary Ethnography, 35 (4), pp. 373-395. , https://doi.org/10.1177/0891241605280449; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120. , https://doi.org/10.1177/014920639101700108; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management-what has been achieved? (2015) Supply Chain Management: An International Journal, 20 (6), pp. 664-680. , http://dx.doi.org/10.1108/SCM-06-2015-0216; Björklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Bowen, G.A., Document analysis as a qualitative research method (2009) Qualitative Research Journal, 9 (2), pp. 27-40. , https://www.researchgate.net/profile/Glenn_Bowen/publication/240807798_Document_Analysis_as_a_Qualitative_Research_Method/links/59d807d0a6fdcc2aad065377/Document-Analysis-as-a-Qualitative-Research-Method.pdf; (2019) BREAAM, , http://www.breeam.com, Retrieved 3 December 2019 from; Çankaya, S.Y., Sezen, B., Effects of green supply chain management practices on sustainability performance (2019) Journal of Manufacturing Technology Management, 30 (1), pp. 98-121. , https://doi.org/10.1108/JMTM-03-2018-0099; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387. , http://dx.doi.org/10.1108/09600030810882816; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 46-62. , http://dx.doi.org/10.1108/09600031111101420; Chaisurayakarn, S., Grant, D.B., Talas, R., The effect of green logistics service quality on Thai logistics service provider performance (2016) Proceedings of the 21st Annual Logistics Research Network (LRN) Conference, , September, University of Hull, Hull, UK; Delmas, M.A., Burbano, V.A., The drivers of greenwashing (2011) California Management Review, 54 (1), pp. 64-87. , https://doi.org/10.1525/cmr.2011.54.1.64; Diabat, A., Govindan, K., An analysis of the enablers affecting the implementation of green supply chain management (2011) Resources, Conservations and Recycling, 55 (6), pp. 659-667. , http://dx.doi.org/10.1016/j.resconrec.2010.12.002; (2020) European Commission Eco-Management and Audit Scheme, , http://ec.europa.eu/environment/emas/index_en.htm, Retrieved 10 July 2020 from; Field, A.P., Gillett, R., How to do a meta-analysis (2010) British Journal of Mathematical and Statistical Psychology, 63, pp. 665-694. , https://doi.org/10.1348/000711010X502733; Ghadge, A., Wurtmann, H., Seuring, S., Managing climate change risks in global supply chains: A review and research agenda (2020) International Journal of Production Research, 58 (1), pp. 44-64. , https://doi.org/10.1080/00207543.2019.1629670; Gold, S., Hahn, R., Seuring, S., Sustainable supply chain management in ‘Base of the Pyramid’ food projects-a path to triple bottom line approaches for multinationals? (2013) International Business Review, 22 (5), pp. 784-799. , https://doi.org/10.1016/j.ibusrev.2012.12.006; Grant, D.B., Shaw, S., Environmental or sustainable supply chain performance measurement standards and certifications (2019) Handbook on the Sustainable Supply Chain, pp. 357-376. , J. Sarkis (Ed.), Cheltenham, UK and Northampton, MA: Edward Elgar; Grant, D.B., Trautrims, A., Wong, C.Y., (2017) Sustainable Logistics and Supply Chain Management, , (2nd ed.). London: Kogan Page; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , http://dx.doi.org/10.1080/00207540600806513; Gunasekaran, A., Patel, C., McGaughey, R.F., Framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347. , http://dx.doi.org/10.1016/j.ijpe.2003.08.003; Hagelaar, G.J.L.F., van der Vorst, J.G.A.J., Environmental supply chain management, using life cycle assessment to structure supply chains (2001) The International Food and Agribusiness Management Review, 4 (4), pp. 399-412. , http://dx.doi.org/10.1016/S1096-7508(02)00068-X; Hart, S.L., A natural resource based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1014. , http://dx.doi.org/10.5465/AMR.1995.9512280033; Hart, S.L., Dowell, G., A natural resource based view of a firm: Fifteen years after (2011) Journal of Management, 37 (5), pp. 1464-1479. , https://dx.doi.org/10.1177/0149206310390219; Harvey, L.J., Myers, M.D., Scholarship and practice: The contribution of ethnographic research methods to bridging the gap (1995) Information Technology & People, 8 (3), pp. 13-27. , https://doi.org/10.1108/09593849510098244; Heras-Saizarbitoria, I., Landín, G.A., Molina-Azorín, J.F., Do enablers matter for the benefits of ISO 14001? (2011) International Journal of Operations & Production Management, 31 (2), pp. 192-216. , http://dx.doi.org/10.1108/01443571111104764; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353. , http://dx.doi.org/10.1108/14635770510609015; Hillary, R., Environmental management systems and the smaller enterprise (2004) Journal of Cleaner Production, 12, pp. 561-569. , http://dx.doi.org/10.1016/j.jclepro.2003.08.006; Holt, D., Ghobadian, A., An empirical study of green supply chain management practices amongst UK manufacturers (2009) Journal of Manufacturing Technology Management, 20 (7), pp. 933-956. , http://dx.doi.org/10.1108/17410380910984212; Iraldo, F., Testa, F., Frey, M., Is an environmental management system able to influence environmental and competitive performance? The case of the eco-management and audit scheme (EMAS) in the European Union (2009) Journal of Cleaner Production, 17, pp. 1444-1452. , http://dx.doi.org/10.1016/j.jclepro.2009.05.013; (2020) International Organization for Standardization (ISO) 9000-Quality Management, , http://www.iso.org/iso/home/standards/management-standards/iso_9000.htm, Retrieved 10 July 2020 from; (2020) International Organization for Standardization (ISO) 14000-Environmental Management, , http://www.iso.org/iso/iso14000, Retrieved 10 July 2020 from; (2020) International Organization for Standardization (ISO) 26000-Social Responsibility, , http://www.iso.org/iso/home/standards/iso26000.htm, Retrieved 10 July 2020 from; Johnstone, L., The construction of environmental performance in ISO 14001-certified SMEs (2020) Journal of Cleaner Production, p. 263. , https://doi.org/10.1016/j.jclepro.2020.121559; Khan, Z., Cleaner production: An economical option for ISO certification in developing countries (2008) Journal of Cleaner Production, 16, pp. 22-27. , http://dx.doi.org/10.1016/j.jclepro.2006.06.007; Kor, Y.Y., Mahoney, J.T., Edith Penrose’s (1959) contributions to the resource-based view of strategic management (2004) Journal of Management Studies, 41, pp. 183-191. , https://doi.org/10.1111/j.1467-6486.2004.00427.x; Krippendorf, K., (2004) Content Analysis: An Introduction to Its Methodology, , (2nd ed.). Thousand Oaks, CA: Sage Publications; Lee, S.M., Kim, S.T., Choi, D., Green supply chain management and organizational performance (2012) Industrial Management & Data Systems, 112 (8), pp. 1148-1180. , http://dx.doi.org/10.1108/02635571211264609; Lyon, T.P., Maxwell, J.W., Greenwash: Corporate environmental disclosure under threat of audit (2011) Journal of Economics & Management Strategy, 20 (1), pp. 3-41. , http://dx.doi.org/10.1111/j.1530-9134.2010.00282.x; Markley, M.J., Davis, L., Exploring future competitive advantage through sustainable supply chains (2007) International Journal of Physical Distribution & Logistics Management, 37 (9), pp. 763-774; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315. , https://doi.org/10.1108/09600030710840859; Metta, H., A Multi-Stage Decision Support Model for Coordinated Sustainable Product and Supply Chain Design (2011) University of Kentucky Doctoral Dissertations 137, , https://uknowledge.uky.edu/gradschool_diss/137, Retrieved 3 December 2019 from; Miles, M.B., Huberman, A.M., (1994) Qualitative Data Analysis, , (2nd ed.). London: Sage Publications; Mohd-Jamal, N., Tayles, M., Grant, D.B., Investigating the relationship between supply chain management and management accounting practices (2019) Journal of Supply Chain Management: Research & Practice, 13 (2), pp. 1-22. , https://jscm.au.edu/index.php/jscm/article/view/173; Mori, R., Jr., Franks, D.M., Ali, S.H., (2015) Designing Sustainability Certification for Impact: Analysis of the Design Characteristics of 15 Sustainability Standards in the Mining Industry, , Centre for Social Responsibility in Mining, University of Queensland, Brisbane, Australia; Mosgaard, M.A., Kristensen, H.S., Companies that discontinue their ISO14001 certification-Reasons, consequences and impact on practice (2020) Journal of Cleaner Production, p. 260. , https://doi.org/10.1016/j.jclepro.2020.121052; (2020) NACE Rev. 2: Statistical Classification of Economic Activities in the European Community, , http://ec.europa.eu/eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF, Retrieved 10 July 2020 from; Nawrocka, D., Brorson, T., Lindhqvist, T., ISO 14001 in environmental supply chain practices (2009) Journal of Cleaner Production, 17, pp. 1435-1443. , https://doi.org/10.1016/j.jclepro.2009.05.004; Penrose, E., (1959) The Theory of the Growth of the Firm, , Oxford, UK: Oxford University Press; Prajogo, D., Tang, A.K.Y., Lai, K.-H., Do firms get what they want from ISO 14001 adoption? An Australian perspective (2012) Journal of Cleaner Production, 33, pp. 117-126. , http://dx.doi.org/10.1016/j.jclepro.2012.04.019; Psomas, E.L., Fotopoulos, C.V., Kafetzopoulos, D.P., Motives, difficulties and benefits in implementing the ISO 14001 environmental management system (2011) Management of Environmental Quality: An International Journal, 22 (4), pp. 502-521. , http://dx.doi.org/10.1108/14777831111136090; Raţiu, P., Mortan, M., EMAS Implementation in SMES: Driving forces and barriers (2014) Managerial Challenges of the Contemporary Society Proceedings, 7 (2), pp. 73-79. , Babeş-Bolyai University, Cluj-Napoca, Romania; Saha, M., Darnton, G., Green companies or green con-panies: Are companies really green, or are they pretending to be? (2005) Business and Society Review, 110 (2), pp. 117-157. , http://dx.doi.org/10.1111/j.0045-3609.2005.00007.x; Sarkis, J., Zhu, Q., Lai, K.-H., An organizational theoretic review of green supply chain management literature (2011) International Journal of Production Economics, 130, pp. 1-15. , http://dx.doi.org10.1016/j.ijpe.2010.11.010; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339. , http://dx.doi.org/10.1108/14635771011049326; Shaw, S., Grant, D.B., Mangan, J., A supply chain practice-based view of enablers, inhibitors and benefits for environmental supply chain performance measurement (2021) Production Planning & Control, 32 (5), pp. 382-396; Shenin, N., Grant, D.B., Investigating the adoption of environmental management systems in the Finnish logistics sector (2015) Proceedings of the 20th Annual Logistics Research Network (LRN) Conference, , September, University of Derby, Derby, UK; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18. , http://dx.doi.org/10.1108/13683041011027418; To, W.M., Tang, M.N.F., The adoption of ISO 14001 environmental management systems in Macao SAR, China: Trend, motivations, and perceived benefits (2014) Management of Environmental Quality: An International Journal, 25 (2), pp. 244-256. , http://dx.doi.org/10.1108/MEQ-01-2013-0002; Walker, H., Di Sisto, L., McBain, D., Drivers and inhibitors to environmental supply chain management practices: Lessons from the public and private sectors (2008) Journal of Purchasing & Supply Management, 14 (1), pp. 69-85. , http://dx.doi.org/10.1016/j.pursup.2008.01.007; Wiengarten, F., Pagell, M., Fynes, B., ISO 14000 certification and investments in environmental supply chain management practices: Identifying differences in motivation and adoption levels between Western European and North American companies (2013) Journal of Cleaner Production, 56, pp. 18-28. , http://dx.doi.org/10.1016/j.jclepro.2012.01.021; Wong, C.Y., Wong, C.W., Boon-itt, S., Integrating environmental management into supply chains: A systematic literature review and theoretical framework (2015) International Journal of Physical Distribution & Logistics Management, 45 (1-2), pp. 43-68. , https://doi.org/10.1108/IJPDLM-05-2013-0110; Wong, C.W.Y., Wong, C.Y., Boon-itt, S., Environmental management systems, practices and outcomes: Differences in resource allocation between small and large firms (2020) International Journal of Production Economics, p. 228. , https://doi.org/10.1016/j.ijpe.2020.107734},
publisher={Edward Elgar Publishing Ltd.},
isbn={9781789908350; 9781789908343},
language={English},
abbrev_source_title={Handb. of Sustainability-Driven Bus. Strategies in Practice},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Zhang2021948,
author={Zhang, Y.D. and Yang, Y. and Pan, W. and Pan, M.},
title={Key Performance Indicators of Offsite Construction Supply Chains: A Review},
journal={Proceedings of the International Symposium on Automation and Robotics in Construction},
year={2021},
volume={2021-November},
pages={948-955},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127592058&partnerID=40&md5=206d84f0e3640af2f6ccaa9c8d30d1c2},
affiliation={Department of Civil Engineering, University of Hong Kong, Hong Kong, Hong Kong},
abstract={Offsite construction (OSC) has demonstrated various benefits, but its wide adoption is constrained by complex and dynamic supply chains. It is vital to establish a comprehensive performance measurement system for OSC supply chains, which however has seldom been explored in detail. This paper aims to identify the key performance indicators (KPIs) of OSC supply chains through a systematic review of the literature published from December 2000 to March 2021. A total of 65 KPIs were extracted from 84 articles, including 35 economic, 19 social, and 11 environmental KPIs. A consistent KPI framework was initially proposed to describe how the KPIs jointly assess OSC supply chains. The results indicate that previous research mainly focused on the economic aspect of OSC supply chains while social and environmental performance were largely overlooked. Under each aspect, several indicators were ascertained as frequently cited KPIs, while some others received relatively little attention. This paper contributes to a better understanding of OSC supply chain performance by investigating current measurement efforts from a multidimensional perspective. To practically develop a quantifiable assessment method, further research should build on the framework and pay more attention to the overall or total performance and the neglected KPIs, particularly in social and environmental aspects. © 2021 Proceedings of the International Symposium on Automation and Robotics in Construction. All rights reserved.},
author_keywords={Key Performance Indicators;  Offsite Construction;  Performance Measurement;  Supply Chain},
keywords={Environmental management;  Robotics;  Supply chains, Complex supply chain;  Comprehensive performance;  Construction supply chain;  Dynamic supply chains;  Key performance indicators;  Off-site construction;  Performance measurement system;  Performance measurements;  Social and environmental;  Systematic Review, Benchmarking},
references={Pan, W., Goodier, C., House-building business models and off-site construction take-up (2012) Journal of Architectural Engineering, 18 (2), pp. 84-93; Pan, W., Yang, Y., Zhang, Z., Chan, S., (2019) Modularisation for Modernisation: A strategy paper rethinking Hong Kong construction, , The University of Hong Kong, Hong Kong SAR; Wang, Z., Hu, H., Zhou, W., RFID enabled knowledge-based precast construction supply chain (2017) Computer-Aided Civil and Infrastructure Engineering, 32 (6), pp. 499-514; Yang, Y., Pan, M., Pan, W., Zhang, Z., Sources of uncertainties in offsite logistics of modular construction for high-rise building projects Journal of Management in Engineering, 37 (3), p. 2021; Hussein, M., Eltoukhy, A. E. E., Karam, A., Shaban, I. A., Zayed, T., Modelling in off-site construction supply chain management: A review and future directions for sustainable modular integrated construction (2021) Journal of Cleaner Production, 310, p. 127503; Arashpour, M., Bai, Y., Aranda-mena, G., Bab-Hadiashar, A., Hosseini, R., Kalutara, P., Optimizing decisions in advanced manufacturing of prefabricated products: Theorizing supply chain configurations in off-site construction (2017) Automation in Construction, 84, pp. 146-153; Wuni, I. Y., Shen, G. Q., Stakeholder management in prefabricated prefinished volumetric construction projects: benchmarking the key result areas (2020) Built Environment Project and Asset Management, 10 (3), pp. 407-421; Halman, J. I. M., Voordijk, J. T., Balanced framework for measuring performance of supply chains in house building (2012) Journal of Construction Engineering and Management, 138 (12), pp. 1444-1450; Masood, R., Lim, J.B.P., González, V.A., Performance of the supply chains for New Zealand prefabricated house-building (2021) Sustainable Cities and Society, 64, p. 102537; Ding, Z. K., Liu, S., Luo, L. W., Liao, L. H., A building information modeling-based carbon emission measurement system for prefabricated residential buildings during the materialization phase Journal of Cleaner Production, p. 2642020; Li, Z., Shen, G.Q., Xue, X., Critical review of the research on the management of prefabricated construction (2014) Habitat International, 43, pp. 240-249; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) Journal of Cleaner Production, 86, pp. 360-377; Pan, W., Zhang, Z., Xie, M., Ping, T., (2020) Modular integrated construction performance measurement guidebook, , The University of Hong Kong, Hong Kong SAR; lkin ton . years a o coined the phrase “triple bottom line.” Here’s why it’s time to rethink it (2018) Harvard Business Review, 25, pp. 2-5; Henriques, A., Richardson, J., (2004) The triple bottom line: Dose it all add up, , Routledge, London; Webster, J., Watson, R.T., Analyzing the past to prepare for the future: Writing a literature review (2002) Mis Quarterly, 26 (2), pp. xiii-xxiii; Pan, W., Zhang, Z., Yang, Y., (2020) A glossary of modular integrated construction, , The University of Hong Kong, Hong Kong SAR; Wohlin, C., Guidelines for snowballing in systematic literature studies and a replication in software engineering (2014) Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering, pp. 1-10. , New York, United States; Sutrisna, M., Cooper-Cooke, B., Goulding, J., Ezcan, V., Investigating the cost of offsite construction housing in Western Australia (2019) International Journal of Housing Markets and Analysis, 12 (1), pp. 5-24; Kim, Y.W., Han, S. H., Yi, J. S., Chang, S., Supply chain cost model for prefabricated building material based on time-driven activity-based costing (2016) Canadian Journal of Civil Engineering, 43 (4), pp. 287-293; Tam, V. W. Y., Tam, C. M., Zeng, S. X., Ng, W. C. Y., Towards adoption of prefabrication in construction (2007) Building and Environment, 42, pp. 3642-3654; Blismas, N., Wakefield, R., Drivers, constraints and the future of offsite manufacture in Australia (2009) Construction Innovation, 9 (1), pp. 72-83; Zhai, X., Reed, R., Mills, A., Factors impeding the offsite production of housing construction in China: an investigation of current practice (2014) Construction Management and Economics, 32 (1-2), pp. 40-52; Kritchanchai, D., MacCarthy, B. L., Responsiveness of the order fulfilment process (1999) International Journal of Operations & Production Management, 19 (8), pp. 812-833; Sooriyamudalige, N., Domingo, N., Shahzad, W., Childerhouse, P., Barriers and enablers for supply chain integration in prefabricated elements manufacturing in New Zealand (2020) International Journal of Construction Supply Chain Management, 10 (1), pp. 73-91; Khalfan, M.M.A., Maqsood, T., Current state of offsite manufacturing in Australia and Chinese residential construction (2014) Journal of Construction Engineering, 2014, pp. 1-5; Pero, M., Stosslein, M., Cigolini, R., Linking product modularity to supply chain integration in the construction and shipbuilding industries (2015) International Journal of Production Economics, 170, pp. 602-615; Liu, S., Mansoor, A., Bouferguene, A., Al-Hussein, M., The performance evaluation of different modular construction supply chain configurations using discrete event simulation (2020) Construction Research Congress 2020: Computer Applications, pp. 944-953. , Arizona; Durdyev, S., Ismail, S., Offsite manufacturing in the construction industry for productivity improvement (2019) Engineering Management Journal, 31 (1), pp. 35-46; Ekanayake, E.M.A.C., Shen, G., Kumaraswamy, M.M., Critical capabilities of improving supply chain resilience in industrialized construction in Hong Kong (2020) Engineering, Construction and Architectural Management, , ahead-of-print(ahead-of-print); Ekanayake, E., Shen, G., Kumaraswamy, M., Resilient supply chains in industrialised construction: A Hong Kong case study (2020) Construction Research Congress 2020: Project Management and Controls, Materials, and Contracts, pp. 311-319. , Arizona; Wang, M., Altaf, M. S., Al-Hussein, M., Ma, Y., Framework for an IoT-based shop floor material management system for panelized homebuilding (2020) International Journal of Construction Management, 20 (2), pp. 130-145; Wuni, I.Y., Shen, G.Q., Critical success factors for modular integrated construction projects: A review (2019) Building Research & Information, 48 (7), pp. 763-784; Wu, P., Low, S.P., Barriers to achieving green precast concrete stock management – A survey of current stock management practices in Singapore (2014) International Journal of Construction Management, 14 (2), pp. 78-89; Luo, L., Liang, X., Fang, C., Wu, Z., Wang, X., Wang, Y., How to promote prefabricated building projects through internet of things? A game theory-based analysis Journal of Cleaner Production, p. 2762020; Liu, K., Zhang, S., Assessment of sustainable development capacity of prefabricated residential building supply chain (2018) ICCREM 2018: Sustainable Construction and Prefabrication, pp. 45-58. , Reston, VA; Liu, K., Su, Y., Zhang, S., Evaluating supplier management maturity in prefabricated construction project-survey analysis in China (2018) Sustainability, 10 (9); Luo, L., Shen, G. Q., Xu, G., Liu, Y., Wang, Y., Stakeholder-associated supply chain risks and their interactions in a prefabricated building project in Hong Kong (2019) Journal of Management in Engineering, 35 (2); Pan, W., Hon, C.K., Briefing: Modular integrated construction for high-rise buildings (2020) Proceedings of the Institution of Civil Engineers - Municipal Engineer, pp. 64-68. , Thomas Telford; Wang, W.C., Weng, S. W., Wang, S. H., Chen, C. Y., Integrating building information models with construction process simulations for project scheduling support (2014) Automation in Construction, 37 (6), pp. 68-80; Shi, Q., Ding, X., Zuo, J., Zillante, G., Mobile Internet based construction supply chain management: A critical review (2016) Automation in Construction, 72, pp. 143-154; Vrijhoef, R., Koskela, L., The four roles of supply chain management in construction (2000) European journal of purchasing supply management, 6 (3-4), pp. 169-178; Shen, L.Y., Hao, J. L., Tam, V. W. Y., Yao, H., A checklist for assessing sustainability performance of construction projects (2007) Journal of Civil Engineering and Management, 13 (4), pp. 273-281; Goulding, J.S., Rahimian, F. P., Arif, M., Sharp, M.D., New offsite production and business models in construction: priorities for the future research agenda (2015) Architectural Engineering and Design Management, 11 (3), pp. 163-184; Pan, W., Yang, Y., Yang, L., High-rise modular building: Ten-year journey and future development (2018) Construction Research Congress: Sustainable Design and Construction and Education, pp. 523-532. , Louisiana; Pan, W., Garmston, H., Building regulations in energy efficiency: Compliance in England and Wales (2012) Energy Policy, 45, pp. 594-605; Sandanayake, M., Luo, W., Zhang, G., Direct and indirect impact assessment in off-site construction—A case study in China Sustainable Cities and Society, p. 482019},
editor={Feng C., Linner T., Brilakis I.},
publisher={International Association for Automation and Robotics in Construction (IAARC)},
issn={24135844},
isbn={9789526952413},
language={English},
abbrev_source_title={Proc. Int. Symp. Autom. Robot. Constr.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Aini2021,
author={Aini, Q. and Tamira, S. and Amin, F. and Ikhlayel, M.},
title={Determining Catering Supply Chain Performance using Supply Chain Operations Reference and Analytical Hierarchy Process},
journal={2021 9th International Conference on Cyber and IT Service Management, CITSM 2021},
year={2021},
doi={10.1109/CITSM52892.2021.9588875},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123233235&doi=10.1109%2fCITSM52892.2021.9588875&partnerID=40&md5=f8b1e1b90156af330938acea9911d9ae},
affiliation={UIN Syarif Hidayatullah, Dept. of Information Systems, Jakarta, Indonesia; Al-Quds Open University, Dept. of Information Technology and Communications},
abstract={PT. KSM Catering is a very experienced company in catering services, providing catering bakery needs for companies, individuals, agencies, and events held such as weddings, head-shaving of an infant event, thanksgiving event, etc. The current research aims to investigate the supply chain performance of PT. KSM Catering so that evaluation and improvement can be carried out, supply chain performance is expected to increase and benefit the company. A good supply chain performance assessment among persons that involved to support process business, including external and internal organizations, and clients can be checked using the supply chain performance measurement, i.e., supply chain operations reference (SCOR) and Analytical Hierarchy Process (AHP) method is used to deal weight to the level of importance of the indicators at each level of the measurement metrics according to the importance of the company's indicators. The supply chain performance value of PT KSM Catering is 74.77. Therefore, it could be categorized as Good. Although it is said good, it still needed improvements to gain PT KSM Catering's performance as a whole. © 2021 IEEE.},
author_keywords={AHP;  analytical hierarchy process;  catering;  SCOR;  supply chain operations reference;  supply chain performance},
keywords={Hierarchical systems, 'current;  Analytical Hierarchy Process;  Catering;  Evaluation and improvement;  Good supply;  Service-providing;  Supply chain operation references;  Supply chain performance, Supply chains},
references={Hanugrani, N., Setyanto, N.W., Efranto, R.Y., Measurement of supply chain performance using supply chain operation reference based on analytical hierarchy process and objective matrix" (Indonesian) (2013) J. Rekayasa Dan Manaj. Sist. Ind., 1 (1), pp. 163-172; Sellitto, M.A., Pereira, G.M., Borchardt, M., Da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: Application in the footwear industry International Journal of Production Research, 53 (16), pp. 4917-4926; Wibowo, M.A., Sholeh, M.N., The Analysis of Supply Chain Performance Measurement at Construction Project (2015) Procedia Engineering, 125, pp. 25-31; Anjani, F., Zhafari, M., Aini, Q., Evaluation of Supply Chain Management Performance at MSMEs using the SCOR Method (2020) INTENSIF, 4 (2), pp. 159-172. , Aug; Lepori, E., Damand, D., Barth, B., Benefits and limitations of the SCOR model in warehousing (2013) IFAC Proceedings Volumes, 46 (9), pp. 424-429; Sael, N., Hamim, T., Benabbou, F., Implementation of the Analytic Hierarchy Process for Student Profile Analysis (2019) International Journal of Emerging Technologies in Learning (IJET), [S.l.], 14 (15), pp. 78-93. , Aug; Aini, Q., Utami, D.P., Qomariah, T.J., Applying house of risk analysis for supply chain risk mitigation (case study) 2019 7th International Conference on Cyber and IT Service Management (CITSM, pp. 1-4; Sumangkut, A.A., Supply chain management performance and information strategy at PT. Multi Food Manado" (Indonesian) (2013) J. Emba, 1 (3), pp. 914-920; Ibrahim, S., Hamid, A., Supply Chain Management Practices and Supply Chain Performance Effectiveness (2015) International Journal of Science and Research (IJSR), 3, pp. 187-195; Paul, J., (2014) Guide to Implementing Supply Chain Transformation with the SCOR Model (Indonesian), , Jakarta: PPM; Bolstorff, R.R., (2011) P, Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , New York: AMACOM; (2006) Supply Chain Operation Reference Model SCOR Version 8.0. Supply-Chain Council, Inc., , Supply-Chain Council Team; Nurhandayani, A., Noor, A.M., Measurement of Supply Chain Performance CV. Vio Burger using Supply Chain Operation Reference Model and Analytical Hierarchy Process Method" (Indonesian) (2020) J. Ilm. Teknol. Dan Rekayasa, 23 (3), pp. 206-219; Wibisono, D., (2006) Management of Performance (Indonesian), , Jakarta: Erlangga; Rahimdel, M.J., Ataei, M., Application of analytical hierarchy process to selection of primary crusher (2014) International Journal of Mining Science and Technology, 24 (4), pp. 519-523; Utami, M.C., Rizkyana, A., Fetrina, E., Ramadhan, G.A., Wahyuningsih, M., Setiadi, D.I., Aini, Q., Using AHP method in determining hospital for caesarean section in south tangerang 2019 7th International Conference on Cyber and IT Service Management (CITSM, pp. 1-7},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781665435482},
language={English},
abbrev_source_title={Int. Conf. Cyber IT Serv. Manag., CITSM},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Aman2021,
author={Aman, S. and Seuring, S.},
title={Analysing developing countries approaches of supply chain resilience to COVID-19},
journal={International Journal of Logistics Management},
year={2021},
doi={10.1108/IJLM-07-2021-0362},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121774335&doi=10.1108%2fIJLM-07-2021-0362&partnerID=40&md5=810de39cc51d0fc248ca540155a4d326},
affiliation={Supply Chain Management, University of Kassel, Kassel, Germany},
abstract={Purpose: The Covid-19 pandemic has made it essential to explore the resilience factors specific to developing regions, not only because they pose threats of extreme poverty and offer a novel context but also because they play an important role in globalisation. Design/methodology/approach: A mixed-method approach was undertaken to address this novel pandemic situation. First, an open-ended structured questionnaire was developed, and data were collected from three neighbouring emerging economies: Pakistan, India and Iran. Experts' perspectives on vulnerabilities, response measures, resilience and restoration of supply chain activities, and the role of social capital were collected. Second, building upon the findings from phase one of the studies, a quantitative structured questionnaire using the supply chain operational reference (SCOR) model was used to collect data in a structured manner. This quantitative data were further analysed using frequency and contingency analysis. Findings: The findings from the first phase of the study inductively derive 36 resilience categories. Later, the contingency findings show that supply chain (SC) disruption is a major vulnerability for emerging economies, whereas solutions offered to combat it lay in the reconfiguration of resources, such as financial, technological, human, information and material. Additionally, supply network structure and social capital play an integral part in making SCs resilient against disruption. Research limitations/implications: The respondents comprise the academics/SC researchers, which make the findings interesting though they lack the industrial experts' perspectives, directly. Nevertheless, the propositions can be tested in industrial settings to see whether the results are limited to a specific industrial setting or are rather generalised. Practical implications: Similarly, practitioners and policy makers can incorporate the SCOR metrics/factors outlined in this study into their performance measurement systems and ensure continuous monitoring for firm's resilience. Originality/value: The study offers a holistic understanding of the developing regions' approaches to Covid-19. The paper also takes a social capital perspective to explain firms' resilience in these emerging economies. © 2021, Emerald Publishing Limited.},
author_keywords={Disruption risk;  Resilience;  Social capital;  Social network theory;  Supply chain},
references={Aalbers, H.L., Smit, A., Advice tie reallocation under organizational turmoil: a longitudinal intra-firm network study (2020) Academy of Management Proceedings, 1, p. 17541; Adobor, H., Supply chain resilience: an adaptive cycle approach (2020) The International Journal of Logistics Management, 31 (3), pp. 443-463; Ali, I., Gurd, B., Managing operational risks through knowledge sharing in food supply chains (2020) Knowledge and Process Management, 27 (4), pp. 322-331; Ali, A., Mahfouz, A., Arisha, A., Analysing supply chain resilience: integrating the constructs in a concept mapping framework via a systematic literature review (2017) Supply Chain Management: An International Journal, 22 (1), pp. 1-49; Aman, S., Seuring, S., Interestingly it's innovation: reviewing sustainability performance management in the base of the pyramid (BoP) (2021) Technovation; Ambulkar, S., Blackhurst, J., Grawe, S., Firm's resilience to supply chain disruptions: scale development and empirical examination (2015) Journal of Operations Management, 33, pp. 111-122; Ansari, S., Munir, K., Gregg, T., Impact at the ‘bottom of the pyramid’: the role of social capital in capability development and community empowerment (2012) Journal of Management Studies, 49 (4), pp. 813-842; Awan, U., Kraslawski, A., Huiskonen, J., Governing interfirm relationships for social sustainability: the relationship between governance mechanisms, sustainable collaboration, and cultural intelligence (2018) Sustainability, 10 (12), p. 4473; Awan, U., Khattak, A., Rabbani, S., Dhir, A., Buyer-driven knowledge transfer activities to enhance organisational sustainability of suppliers (2020) Sustainability, 12 (7), p. 2993; Baker, S.R., Bloom, N., Davis, S.J., Terry, S.J., (2020) Covid-induced Economic Uncertainty (No. W26983), p. 26949. , p., National Bureau of Economic Research, Cambridge, MA; Bellamy, M.A., Ghosh, S., Hora, M., The influence of supply network structure on firm innovation (2014) Journal of Operations Management, 32 (6), pp. 357-373; Blocher, E.J., Stout, D.E., Juras, P.E., Smith, S., (2019) Cost Management (A Strategic Emphasis) 8e, , McGraw-Hill Companies, NY; Brown, G.W., McLean, I., McMillan, A., (2018) The Concise Oxford Dictionary of Politics and International Relations, , Oxford University Press, Oxford; Butt, A.S., Strategies to mitigate the impact of COVID-19 on supply chain disruptions: a multiple case analysis of buyers and distributors (2021) The International Journal of Logistics Management; Carnabuci, G., Diószegi, B., Social networks, cognitive style, and innovative performance: a contingency perspective (2015) Academy of Management Journal, 58 (3), pp. 881-905; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: the constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Christopher, M.I., (2017) Logistics and Supply Chain Management, p. 276. , p., 4th ed., Financial Times Prentice Hall, Harlow: 2011; Christopher, M., Holweg, M., Supply Chain 2.0: managing supply chains in the era of turbulence (2011) International Journal of Physical Distribution and Logistics Management, 41 (1), pp. 63-82; Christopher, M., Peck, H., Building the resilient supply chain (2004) The International Journal of Logistics Management, 15 (2), pp. 1-13; Creswell, J.W., Clark, V.L.P., (2007) Designing and Conducting Mixed Methods Research, , Sage, Thousand Oaks, CA; Damodaran, A., (1996) Corporate Finance, , John Wiley & Sons, NY; Davis-Sramek, B., Ishfaq, R., Gibson, B.J., Defee, C., Examining retail business model transformation: a longitudinal study of the transition to omnichannel order fulfillment (2020) International Journal of Physical Distribution and Logistics Management, 50 (5), pp. 557-576; de Sá, M.M., de Souza Miguel, P.L., de Brito, R.P., Pereira, S.C.F., Supply chain resilience: the whole is not the sum of the parts (2019) International Journal of Operations and Production Management; El Baz, J., Ruel, S., Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era (2021) International Journal of Production Economics, 233. , doi:, 107972; Fan, Y., Stevenson, M., A review of supply chain risk management: definition (2018) International Journal of Physical Distribution and Logistics Management, 48 (3), pp. 205-230; Feizabadi, J., Gligor, D., Alibakhshi, S., Strategic supply chains: a configurational perspective (2021) The International Journal of Logistics Management; Fernandes, N., (2020) Economic Effects of Coronavirus Outbreak (COVID-19) on the World Economy; Fiksel, J., Sustainability and resilience: toward a systems approach (2006) Sustainability: Science, Practice and Policy, 2 (2), pp. 14-21; Fleiss, J.L., Levin, B., Paik, M.C., (2013) Statistical Methods for Rates and Proportions, , John Wiley & Sons, NY; Giannoccaro, I., Iftikhar, A., Mitigating ripple effect in supply networks: the effect of trust and topology on resilience (2020) International Journal of Production Research, pp. 1-18; Granovetter, M., Problems of explanation in economic sociology (1992) Networks and Organizations: Structure, Form and Action, 25, p. 56; Green, J., Caracelli, V., Crafting mixed-method evaluation designs (1997) New Directions for Evaluation, 74, pp. 5-17; Hohenstein, N.-O., Feisel, E., Hartmann, E., Giunipero, L., Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation (2015) International Journal of Physical Distribution and Logistics Management, 45 (1-2), pp. 90-117; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Iftikhar, A., Purvis, L., Giannoccaro, I., A meta-analytical review of antecedents and outcomes of firm resilience (2021) Journal of Business Research, 135, pp. 408-425; Ivanov, D., Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case (2020) Transportation Research. Part E, Logistics and Transportation Review, p. 136; Ivanov, D., Lean resilience: AURA (Active Usage of Resilience Assets) framework for post-COVID-19 supply chain management (2021) The International Journal of Logistics Management; Ivanov, D., Dolgui, A., A digital supply chain twin for managing the disruptions risks and resilience in the era of Industry 4.0 (2020) Production Planning and Control, pp. 1-14; Jajja, M.S.S., Asif, M., Montabon, F.L., Chatha, K.A., The influence of institutional pressures and organization culture on supplier social compliance management systems (2019) International Journal of Physical Distribution and Logistics Management, 49 (5), pp. 552-574; Kale, P., Singh, H., Perlmutter, H., Learning and protection of proprietary assets in strategic alliances: building relational capital (2000) Strategic Management Journal, 21 (3), pp. 217-237; Kanter, R.M., Commitment and social organisation: a study of commitment mechanisms in utopian communities (1968) American Sociological Review, 33 (4), pp. 499-517; Khan, O., Burnes, B., Risk and SCM: creating a research agenda (2007) The International Journal of Logistics Management, 18 (2), pp. 197-216; Kim, Y., Choi, T.Y., Yan, T., Dooley, K., Structural investigation of supply networks: a social network analysis approach (2011) Journal of Operations Management, 29 (3), pp. 194-211; Lang, L.D., Behl, A., Dong, N.T., Thu, N.H., Dewani, P.P., Social capital in agribusiness: an exploratory investigation from a supply chain perspective during the COVID-19 crisis (2021) The International Journal of Logistics Management; Li, Y., Zobel, C.W., Seref, O., Chatfield, D., Network characteristics and supply chain resilience under conditions of risk propagation (2020) International Journal of Production Economics, 223; Ling, R., Yen, D.C., Customer relationship management: an analysis framework and implementation strategies (2001) Journal of Computer Information Systems, 41 (3), pp. 82-97; Luo, Y., Determinants of local responsiveness: perspectives from foreign subsidiaries in an emerging market (2001) Journal of Management, 27 (4), pp. 451-477; Mathiyazhagan, K., Nangia, S., Senthilkumar, A., Elangovan, K., Sugathan, S.K., Modelling the interrelationship of risks for green supply chain management adoption: a DEMATEL approach (2020) International Journal of Logistics Systems and Management, 36 (3), pp. 414-440; McKibbin, W., Fernando, R., The economic impact of COVID-19 (2020) Economics in the Time of COVID-, 19 (10), pp. 45-1162; Melnyk, S.A., Davis, E.W., Spekman, R.E., Sandor, J., Outcome-driven supply chains (2010) MIT Sloan Management Review, 51 (2), pp. 34-38; Melnyk, S., Narasimhan, R., DeCampos, H., SC design: issues, challenges, frameworks and solutions (2014) International Journal of Production Research, 52 (7), pp. 1887-1896; Morse, J.M., Niehaus, L., (2009) Mixed Method Design: Principles and Procedures, , Routledge, London; Nahapiet, J., Ghoshal, S., Social capital, intellectual capital, and the organisational advantage (1998) Academy of Management Review, 23 (2), pp. 242-266; Paauwe, J., Boon, C., Strategic HRM: a critical review (2018) Human Resource Management, pp. 49-73. , Routledge, London; Parmigiani, A., Rivera-Santos, M., Sourcing for the base of the pyramid: constructing supply chains to address voids in subsistence markets (2015) Journal of Operations Management, 33-34 (1), pp. 60-70. , Vols; Paul, S.K., Chowdhury, P., Strategies for managing the impacts of disruptions during COVID-19: an example of toilet paper (2020) Global Journal of Flexible Systems Management, 21 (3), pp. 283-293; Persson, F., SCOR template—a simulation based dynamic supply chain analysis tool (2011) International Journal of Production Economics, 131 (1), pp. 288-294; Pettit, T.J., Fiksel, J.C., Keely, L., Ensuring supply chain resilience: chain resilience development of a conceptual framework (2010) Journal of Business Logistics, 31 (1), pp. 1-21; Pettit, T.J., Croxton, K.L., Fiksel, J., The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience (2019) Journal of Business Logistics, 40 (1), pp. 56-65; Pfohl, H.C., Köhler, H., Thomas, D., State of the art in supply chain risk management research: empirical and conceptual findings and a roadmap for the implementation in practice (2010) Logistics Research, 2 (1), pp. 33-44; Polyviou, M., Croxton, K.L., Knemeyer, A.M., Resilience of medium-sized firms to supply chain disruptions: the role of internal social capital (2019) International Journal of Operations and Production Management, 40 (2), pp. 68-91; Ponis, T.S., Koronis, E., Supply chain resilience: definition of concept and its formative elements (2012) The Journal of Applied Business Research, 28 (5), pp. 921-930; Ponomarov, Y.S., Holcomb, C.M., Understanding the concept of supply chain resilience (2009) The International Journal of Logistics Management, 20 (1), pp. 124-143; Prahalad, C.K., (2005) The Fortune at the Bottom of the Pyramid, , Wharton School Publishing/Pearson Education, Upper Saddle River, NJ; Rehman, A., Jajja, M.S.S., Khalid, R.U., Seuring, S., The impact of institutional voids on risk and performance in base-of-the-pyramid supply chains (2020) The International Journal of Logistics Management, 31 (4), pp. 829-863; Ruel, S., EI Baz, J., Ivanov, D., Das, A., Supply chain viability: conceptualization, measurement, and nomological validation (2021) Annals of Operations Research, pp. 1-30; Sajko, M., Boone, C., Buyl, T., CEO greed, corporate social responsibility, and organisational resilience to systemic shocks (2021) Journal of Management, 47 (4), pp. 957-992; Salvato, C., Sargiacomo, M., Amore, M.D., Minichilli, A., Natural disasters as a source of entrepreneurial opportunity: family business resilience after an earthquake (2020) Strategic Entrepreneurship Journal, 14 (4), pp. 594-615; Saunders, M., Lewis, P., Thornhill, A., (2009) Research Methods for Business Students, , Pearson Education, London; Shivarajan, S., Srinivasan, A., The poor as suppliers of intellectual property: a social network approach to sustainable poverty alleviation (2013) Business Ethics Quarterly, 23 (3), pp. 381-406; Silvestre, B.S., Sustainable supply chain management in emerging economies: environmental turbulence, institutional voids and sustainability trajectories (2015) International Journal of Production Economics, 167, pp. 156-169; Son, B.G., Chae, S., Kocabasoglu-Hillmer, C., Catastrophic supply chain disruptions and supply network changes: a study of the 2011 Japanese earthquake (2021) International Journal of Operations and Production Management, 41 (6), pp. 781-804; Sutter, C.J., Kistruck, G.M., Morris, S., Adaptations to knowledge templates in base‐of‐the‐pyramid markets: the role of social interaction (2014) Strategic Entrepreneurship Journal, 8 (4), pp. 303-320; Svensson, G., A conceptual framework of vulnerability in firms' inbound and outbound logistics flows (2002) International Journal of Physical Distribution and Logistics Management, 32 (2), pp. 110-134; Tashakkori, A., Teddlie, C., Teddlie, C.B., (1998) Mixed Methodology: Combining Qualitative and Quantitative Approaches, 46. , Sage, Thousand Oaks, CA; Tilson, D., Lyytinen, K., Sørensen, C., Research commentary-Digital infrastructures: the missing IS research agenda (2010) Information Systems Research, 21 (4), pp. 748-759; Tukamuhabwa, B.R., Stevenson, M., Busby, J., Zorzini, M., Supply chain resilience: definition, re-view and theoretical foundations for further study (2015) International Journal of Production Research, 53 (18), pp. 5592-5623; Villena, V.H., Revilla, E., Choi, T.Y., The dark side of buyer–supplier relationships: a social capital perspective (2011) The Journal of Operations Management, 29 (6), pp. 561-576; Westman, W.E., Measuring the inertia and resilience of ecosystems (1978) Bioscience, 28, pp. 705-710; Wiedenmann, M., Größler, A., Supply risk identification in manufacturing supply networks (2021) The International Journal of Logistics Management, 32 (2), pp. 650-672; Wieland, A., Durach, C.F., Two perspectives on supply chain resilience (2021) Journal of Business Logistics, 42 (3), pp. 315-322; Wong, C.W., Lirn, T.C., Yang, C.C., Shang, K.C., Supply chain and external conditions under which supply chain resilience pays: an organisational information processing theorisation (2020) International Journal of Production Economics, 226; Yin, R.K., Case study research (2017) Design and Methods, , Sage Publications, Thousand Oaks, CA; Yu, W., Jacobs, M.A., Chavez, R., Yang, J., Dynamism, disruption orientation, and resilience in the SC and the impacts on financial performance: a dynamic capabilities perspective (2019) International Journal of Production Economics, 218, pp. 352-362; Zhu, Q., Lai, K.H., Enhancing supply chain operations with extended corporate social responsibility practices by multinational enterprises: social capital perspective from Chinese suppliers (2019) International Journal of Production Economics, 213, pp. 1-12},
correspondence_address1={Aman, S.; Supply Chain Management, Germany; email: sadaf.aman@uni-kassel.de},
publisher={Emerald Group Holdings Ltd.},
issn={09574093},
language={English},
abbrev_source_title={Int. J. Logist. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Susanto20211928,
author={Susanto, N. and Purwaningsih, R. and Rumita, R. and Septia, E.},
title={Supply chain performance measurement with supply chain operation references approach (A case study in a batik company)},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2021},
pages={1928-1938},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121125743&partnerID=40&md5=a18651762c7b85d412252e513a9d9930},
affiliation={Industrial Engineering Department, Faculty of Engineering, Diponegoro University, Semarang, Indonesia},
abstract={In carrying out the business activities especially in procurement process, CV. PT experienced some problems, such as incompatibility the material specifications and broken stitches on cloth. This problem causes a decrease of company productivity. Therefore, an evaluation of supply chain performance is needed in order to identify problem indicators, and determine improvement strategies for problem indicators. In this study, the measurement of the company supply chain performance is using the Supply Chain Operation Reference (SCOR) approach. The initial hierarchy model of performance measurement is adjusted to the condition of the company to measure supply chain performance using KPI performance indicators. A Snorm De Boer then is utilized to serves and equalize the matrix values derived from company annual data. The final stage in this study is measuring the importance of performance indicators using pairwise comparison weighting. The results obtained in this study are the total supply chain performance of CV. PT is 69.983 included in the average category. There are several performance indicators included in the average and marginal categories, so it needs to be improved. The improvement is given based on the practices in SCOR 12.0. © IEOM Society International.},
author_keywords={Batik;  Procurement;  Supply Chain},
references={Amit, R. H., Zott, C., Business Model Innovation: Creating Value in Times of Change (2010) IESE Business School Working Paper, 870; Liputra, D. T., Santoso, S., Susanto, N. A., Pengukuran kinerja rantai pasok dengan model supply chain operations reference (SCOR) dan metode perbandingan berpasangan (2018) Jurnal Rekayasa Sistem Industri, 7 (2), pp. 119-125; Erkan, T. E., Bac, U., Supply chain performance measurement: A case study about applicabillity of SCOR model in a manufaturing industry firm (2011) International Journal of Bussiness and Management Studies, 3, pp. 381-390; Georgise, F. B., Thoben, K-D, Seifert, M., Adapting the SCOR model to suit the different scenarios: A literature review & research agenda (2012) International Journal of Business and Management, 7 (6); Giannakis, M., Management of service supply chains with a service‐oriented reference model: The case of management consulting (2011) Supply Chain Management, 16 (5), pp. 346-361; Golparvar, M., Seifbarghy, M., Application of SCOR model in an oil-producing company (2009) Journal of Industrial Engineering, 4, pp. 59-69; Huang, T., Chen, Z., Wang, S., Jiang, D., Efficiency evaluation of key ports along the 21st-century maritime silk road based on the DEA–SCOR model (2020) Maritime Policy & Management, 48 (3), pp. 378-390; Hwang, G., Han, S., Jun, S., Park, J., Operational performance metrics in manufacturing process: Based on SCOR model and RFID tecnology (2014) International Journal of Innovation, Management and Technology, 5 (1), pp. 50-55; Immawan, T., Pratama, C. Y., Pengukuran performansi rantai pasok pada industri batik tipe produksi make to stock dengan menggunakan model SCOR 11.0 dan pembobotan AHP (Studi kasus batik Gunawan Setiawan, Surakarta) (2016) Teknoin, 22 (1), pp. 68-79; Immawan, T., Marimin, Arkeman, Y., Maulana, A., Sustainable supply chain management for make to stock-make to order production typology case study: Batik industry in Solo Indonesia (2015) European Journal of Business and Management, 7 (11), pp. 94-106; Kasi, V., Systemic assessment of SCOR for modeling supply chains (2005) 38th Hawaii International Conference on System Sciences (HICSS-38 2005), , 3-6 January Big Island, HI, USA, 2005; Kusrini, (2007) Konsep dan Aplikasi Sistem Pendukung Keputusan, , 1st ed., Andi Offset, Yogyakarta; Lee, Tzong-Ru, Yi-Siang, S., Sivakumar, P., The applications of SCOR in manufacturing: Two cases in Taiwan (2012) Procedia Engineering, 38, pp. 2548-2563. , (Jiun-Shen), and; Lestari, F., Ismail, K., Hamid, A.A, Supriyanto, E., Yanti, N., Sutupo, W., Supply chain configuration using hybrid SCOR model and discrete event simulation (2014) Proceedings of the World Congress on Engineering, 2, pp. 967-972. , London; Likumahwa, F. M., Purwaningsih, R., Handayani, N. U., The Influence of green supply chain management on company’s performance and competitiveness in wood furniture industry: An overview of conceptual model (2019) IOP Conference Series: Materials Science and Engineering, 598, p. 012076; Mutakin, A., Hubeis, M., Pengukuran Kinerja Manajemen Rantai Pasokan dengan SCOR Model 9.0 (Studi Kasus di PT Indocement Tunggal Prakarsa Tbk) (2011) Jurnal of Management and Organization, 2 (3), pp. 89-103; Paul, J., (2014) Penerapan Transformasi Rantai Suplai Dengan Model SCOR 15 Tahun Aplikasi Praktis Lintas Industri, , PPM Manajemen, Jakarta, Indonesia; Persson, F., Bengtsson, J., Gustad, Ö., Construction logistics improvements using the SCOR model – tornet case (2010) Advances in Production Management Systems. New Challenges, New Approaches, IFIP Advances in Information and Communication Technology, 338. , Vallespir B., Alix T. (eds) Springer, Berlin, Heidelberg; Pujawan, I., Mahendrawathi, (2010) Supply Chain Management, , Guna Widya, Surabaya; Purwaningsih, R., Susanto, N., Yudha, M. C., Penilaian keberlanjutan UKM batik Kota Semarang dengan metode Product Service System (PSS) (2016) Jurnal Teknik Industri, 18 (1), pp. 31-42; Saaty, T. L., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; Saleh, C., Mubiena, G. F., Immawan, T., Hassan, A., Lean production in improving supply chain performance through hybrid model SCOR 11.0 - system dynamics (2016) IOP Conference Series: Materials Science and Engineering, 114, p. 012069; Vanany, I., Suwignjo, P., Yulianto, D., Design of supply chain performance measurement for lamp industry (2005) 1st International Conference on Operations and Supply Chain Management, pp. 78-86; Wahyuniardi, R., Syarwani, M., Anggani, R., Pengukuran kinerja supply chain dengan pendekatan supply chain operation references (SCOR) (2017) Jurnal Ilmiah Teknik Industri, 16 (2), pp. 123-132; Wayyum, R., Ahmad, S., Usman, M., Effects of SCOR on management of SC (2010) International Journal of Management and Organizational Studies, 2 (1), pp. 85-91; Wigaringtyas, L. D., (2013) Pengukuran Kinerja Supply Chain Management Dengan Pendekatan Supply Chain Operation Reference (SCOR), , (Studi Kasus: UKM Batik Sekar Arum, Pajang, Surakarta), Thesis (unpublished), Universitas Muhammadiyah Surakarta, Surakarta; Zhou, H., Benton, W.C., Schilling, D.A., Milligan, G.W., Supply chain integration and the SCOR model (2011) Journal of Business Logistics, 32 (4), pp. 332-344},
publisher={IEOM Society},
issn={21698767},
isbn={9781792361258},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Grant202116,
author={Grant, D.B. and Shaw, S.},
title={Logistics and Supply Chain Management Performance Measures},
journal={International Encyclopedia of Transportation: Volume 1-7},
year={2021},
volume={3},
pages={16-23},
doi={10.1016/B978-0-08-102671-7.10210-6},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118282590&doi=10.1016%2fB978-0-08-102671-7.10210-6&partnerID=40&md5=db92d33cc46e7a1a5e88607cb636c432},
affiliation={Supply Chain Management and Social Responsibility Group, Department of Marketing, Hanken School of Economics, Helsinki, Finland; Logistics and Management Systems Subject Group, Hull University Business School, University of Hull, Yorkshire, Hull, United Kingdom},
abstract={Performance measures are a set of metrics used to quantify the efficiency and/or effectiveness of an organizational action and are especially important in a supply chain management context where such measures are typically quantitative and relate to reducing overall costs and the time taken to implement an action. Performance measurement in the logistics and supply chain domain is difficult, due to multiple tiers and the number of organizations involved. Organizations should first determine which measures are appropriate to meet their strategic objectives and guidance is provided on criteria for measure selection. Organizations then need to select an appropriate performance measurement system or framework to operationalize the selected measures in concert with any certification or accreditation schemes for their particular sector. Finally, two special cases are presented, sustainable or environmental and humanitarian logistics and supply chain management, to illustrate potential applications of the Plan-Do-Study-Act Cycle and the Balanced Scorecard, respectively. © 2021 Elsevier Ltd. All rights reserved},
author_keywords={Balanced scorecard;  Environmental logistics;  Humanitarian logistics;  Key performance indicators;  Logistics;  Metrics;  Performance measurement;  Plan-do-study-act cycle;  Supply chain management;  Sustainable logistics;  Transportation},
references={Banomyong, R., Grant, D.B., Transport in Humanitarian Supply Chains (2016) Supply Chain Management for Humanitarians: Tools for Practice, pp. 191-208. , G. Kovács, K. Spens, I. Haavisto (Eds), London: Kogan Page; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) Int. J. Logist. Manag, 5, pp. 11-28; Deming, W.E., (1950) Elementary Principles of the Statistical Control of Quality, , Tokyo: Japanese Union of Scientists and Engineeers Conference; Grant, D.B., (2012) Logistics Management, , Harlow UK: Pearson Education; Grant, D.B., Elliott, M., A proposed interdisciplinary framework for the environmental management of water and air-borne emissions in maritime logistics (2018) Ocean Coast. Manag, 163, pp. 162-172; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmark.: Int. J, 12, pp. 330-353; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Bus. Rev, 70, pp. 71-79; https://hydra.hull.ac.uk/resources/hull:8108, Shaw, S., 2013. Developing and testing green performance measures for the supply chain. PhD thesis University of Hull, UK. Available from:; Shewhart, W.A., (1939) Statistical Method from the Viewpoint of Quality Control, , Dover: Department of Agriculture; Widera, A., Hellingrath, B., Making performance measurement work in humanitarian logistics: the case of an IT-supported balanced scorecard (2016) Supply Chain Management for Humanitarians: Tools for Practice, pp. pp339-352. , G. Kovács, K. Spens, I. Haavisto (Eds), London: Kogan Page; https://www.apics.org/apics-for-business/frameworks/scor, Association for Supply Chain Management, 2019. Supply Chain Operations Reference (SCOR) Model. Available from:; https://www.balancedscorecard.org/, Balanced Scorecard Institute, 2019. Available from:; Beske-Janssen, P., Johnson, P.M., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management – what has been achieved? (2015) Supply Chain Manag.:Int J, 20, pp. 664-680; Grant, D.B., Shaw, S., Environmental or sustainable supply chain performance measurement standards and certifications (2019) Handbook on the Sustainable Supply Chain, pp. 357-376. , J. Sarkis (Ed.), Northampton MA: Edward Elgar Publishers; Grant, D.B., Trautrims, A., Wong, C.Y., (2017) Sustainable Logistics and Supply Chain Management, , second ed., London: Kogan Page; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) Int. J.Prod. Res, 45, pp. 2819-2840; Kasilingam, R.G., (1998) Logistics and Transportation: Design and Planning, , Boston MA: Springer; Neely, A.D., The evolution of performance measurement, developments in the last decade and a research agenda for the next (2005) Int. J. Operat. Prod. Manag, 25, pp. 1264-1277; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmark.: Int. J, 17, pp. 320-339; https://deming.org/, The W. Edwards Deming Institute, 2019. Availabe from:},
publisher={Elsevier},
isbn={9780081026724; 9780081026717},
language={English},
abbrev_source_title={International Encyclopedia of Transportation: Volume 1-7},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Lee202177,
author={Lee, C.-L. and Shih, Y.-N.},
title={Collaboration, strategic performance measurement systems, and competitiveness},
journal={Advances in Management Accounting},
year={2021},
volume={33},
pages={77-95},
doi={10.1108/S1474-787120210000033003},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117238499&doi=10.1108%2fS1474-787120210000033003&partnerID=40&md5=143a884e524c078b1465a2258dec7f48},
abstract={Collaborative relationships (CRs) in supply chains have become central in international business. Strategic performance measurement systems (SPMSs) establish a causal chain of performance measures that can be aligned with strategic goals and can link performance measures with business processes and suppliers. This study investigates whether CRs in supply chains positively affect buyer competitiveness by using SPMSs. Firm competitiveness is described in terms of product cost-price, delivery, as well as flexibility. We demonstrate through the use of SPMSs that CRs indirectly and positively influence the competitiveness of delivery and flexibility. This chapter contributes to the growing literature on the role of SPMSs in linking the relationship between CRs and competitiveness. © 2021 by Emerald Publishing Limited of reproduction in any form reserved..},
author_keywords={Collaboration;  Competitiveness;  International business;  Strategic goals;  Strategic performance measurement systems;  Supply chains},
references={Armstrong, J.S., Overton, T.S., Estimating non-response bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Bagchi, P.K., Skjoett-Larsen, T., Supply chain integration: A survey (2005) The International Journal of Logistics Management, 16 (2), pp. 275-294; Baron, R.M., Kenny, D.A., The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations (1986) Journal of Personality and Social Psychology, 51 (6), pp. 1173-1182; Bauer, A.M., Henderson, D., Lynch, D.P., Supplier internal control quality and the duration of customer–supplier relationships (2018) The Accounting Review, 93 (3), pp. 59-82; Bruce, M., Leverick, F., Littler, D., Complexities of collaborative product development (1995) Technovation, 15 (9), pp. 535-552; Cannella, S., Ciancimino, E., On the bullwhip avoidance phase: Supply chain collaboration and order smoothing (2010) International Journal of Production Research, 48 (22), pp. 6739-6776; Cao, M., Zhang, Q., Supply chain collaboration: Impact on collaborative advantage and firm performance (2011) Journal of Operations Management, 29 (3), pp. 163-180; Chan, D., Functional relations among constructs in the same content domain at different levels of analysis: A typology of composition models (1998) Journal of Applied Psychology, 83, pp. 234-246; Chang, J., The effects of buyer–supplier’s collaboration on knowledge and product innovation (2017) Industrial Marketing Management, 65, pp. 129-143; Chen, C.J., Wu, H.L., Lin, B.W., Evaluating the development of high-tech industries: Taiwan’s science park (2006) Technological Forecasting & Social Change, 73 (4), pp. 452-465; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting, Organizations and Society, 30 (5), pp. 395-422; Chenhall, R.H., Langfield-Smith, K., Performance measurement and reward systems, trust, and strategic change (2003) Journal of Management Accounting Research, 15 (1), pp. 117-143; Chin, W., The partial least squares modeling approach to structural equation modeling (1998) Modern Methods for Business Research, pp. 295-336. , G. Marcoulides (Ed.), (pp. , Mahwah, NJ: Lawrence Erlbaum Associates; Chow, W.S., Madu, C.N., Kuei, C.H., Lu, M.H., Lin, C., Tseng, H., Supply chain management in the US and Taiwan: An empirical study (2008) Omega, 36 (5), pp. 665-679; Coletti, A.L., Sedatole, K.L., Towry, K.L., The effect of control systems on trust and cooperation in collaborative environments (2005) The Accounting Review, 80 (2), pp. 477-500; Cronbach, L.J., Coefficient alpha and the internal structure of tests (1951) Psychometrika, 16 (3), pp. 297-334; Das, T.K., Teng, B.S., Trust, control and risk in strategic alliances: An integrated framework (2001) Organization Studies, 22 (2), pp. 251-283; Dekker, H.C., Control of inter-organizational relationships: Evidence on appropriation concerns and coordination requirements (2004) Accounting, Organizations and Society, 29 (1), pp. 27-49; Dekker, H.C., On the boundaries between intrafirm and interfirm management accounting research (2016) Management Accounting Research, 31, pp. 86-99; Dillman, D., (2007) Mail and Internet Survey: The Tailored Design Method, , New York, NY: Wiley; Dyer, J.H., Singh, H., The relational view: Cooperative strategy and sources of interorganizational competitive advantage (1998) Academy of Management Review, 23 (4), pp. 660-679; Elbashir, M.Z., Collier, P.A., Sutton, S.G., The role of organizational absorptive capacity in strategic use of business intelligence to support integrated management control systems (2011) The Accounting Review, 86 (1), pp. 155-184; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Glick, W.H., Conceptualizing and measuring organizational and psychological climate: Pitfalls in multilevel research (1985) Academy of Management Review, 10, pp. 601-616; Gosain, S., Malhotra, A., El Sawy, O.A., Coordinating for flexibility in e-business supply chains (2004) Journal of Management Information Systems, 21 (3), pp. 7-45; Grafton, J., Lillis, A.M., Widener, S.K., The role of performance measurement and evaluation in building organizational capabilities and performance (2010) Accounting, Organizations and Society, 35 (7), pp. 689-706; Greve, H.R., Baum, J.A.C., Mitsuhashi, H., Rowley, T.J., Built to last but falling apart: Cohesion, friction, and withdrawal from interfirm alliances (2010) Academy of Management Journal, 53 (2), pp. 302-322; Gulati, R., Singh, H., The architecture of cooperation: Managing coordination costs and appropriation concerns in strategic alliances (1998) Administrative Science Quarterly, 43 (4), pp. 781-814; Heide, J.B., Interorganizational governance in marketing channels (1994) Journal of Marketing, 58 (1), pp. 71-86; Hendricks, K., Singhal, V., Association between supply chain glitches and operating performance (2005) Management Science, 51 (5), pp. 695-711; Hulland, J., Use of partial least squares in strategic management research: A review of four recent (1999) Strategic Management Journal, 20 (2), pp. 195-204; Huo, B., Wang, Q., Zhao, X., Schuh, S., Threats and benefits of power discrepancies between organisations: A supply chain perspective (2016) International Journal of Production Research, 54 (13), pp. 3870-3884; Ireland, R.D., Hitt, M.A., Vaidyanath, D., Alliance management as a source of competitive advantage (2002) Journal of Management, 28 (3), pp. 413-446; Ittner, C.D., Larcker, D.F., Randell, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting Organizations and Society, 28 (7-8), pp. 715-741; Kahn, K.B., Interdepartmental integration: A definition with implications for product development performance (1996) Journal of Product Innovation Management, 13 (2), pp. 137-151; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston, MA: Harvard Business School Press; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management (2001) Accounting Horizons, 15 (1), pp. 87-104; Kaplan, R.S., Norton, D.P., Rugelsjoen, B., Managing alliances with the balanced scorecard (2010) Harvard Business Review, 88, pp. 114-120; Kraus, K., Strömsten, T., Internal/inter-firm control dynamics and power: A case study of the Ericsson-Vodafone relationship (2016) Management Accounting Research, 33, pp. 61-72; Kuk, G., Effectiveness of vendor-managed inventory in the electronics industry: Determinants and outcomes (2004) Information & Management, 41 (5), pp. 645-654; Kulp, S.C., The effect of information precision and information reliability on manufacturer– retailer relationships (2002) The Accounting Review, 77 (3), pp. 653-677; Langfield-Smith, K., The relations between transactional characteristics, trust and risk in the start-up phase of a collaborative alliance (2008) Management Accounting Research, 19 (4), pp. 344-364; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega, 34 (2), pp. 107-124; Malhotra, D., Lumineau, F., Trust and collaboration in the aftermath of conflict: The effects of contract structure (2011) Academy of Management Journal, 54 (5), pp. 981-998; Minnaar, R.A., Vosselman, E.G.J., Shared service centres and management control structure change: Exploring the scope and limitations of a transaction cost economics approach (2013) Journal of Accounting & Organizational Change, 9 (1), pp. 74-98; Myhr, N., Spekman, R.E., Collaborative supply-chain partnerships built upon trust and electronically mediated exchange (2005) Journal of Business and Industrial Marketing, 20 (4-5), pp. 179-186; Narayanan, S., Narasimhan, R., Schoenherr, T., Assessing the contingent effects of collaboration on agility performance in buyer–supplier relationships (2015) Journal of Operations Management, 33-34, pp. 140-154; Nunally, J.C., (1978) Psychometric Theory, , New York, NY: McGraw Hill Book Company; O’Donnell, E., Mallin, M.L., Hu, M.Y., The impact of governance on the development of trust in buyer–seller relationships (2008) The Marketing Management Journal, 18 (2), pp. 76-92; Podsakoff, P.M., Mackenzie, S.B., Lee, J.Y., Podsakoff, N.P., Common method biases in behavioral research: A critical review of the literature and recommended remedies (2003) Journal of Applied Psychology, 88 (5), pp. 879-903; Porter, M.E., (1980) Competitive Strategy: Techniques for Analyzing Industries and Competitors, , New York, NY: Free Press; Rosenzweig, E.D., A contingent view of e-collaboration and performance in manufacturing (2009) Journal of Operations Management, 27 (6), pp. 462-478; Shrout, P.E., Fleiss, J.L., Intraclass correlations: Uses in assessing rater reliability (1979) Psychological Bulletin, (2), pp. 420-428; Simatupang, T.M., Sridharan, R., An integrative framework for supply chain collaboration (2005) International Journal of Logistics Management, 16 (2), pp. 257-274; Simatupang, T.M., Wright, A.C., Sridharan, R., The knowledge of coordination for supply chain integration (2002) Business Process Management Journal, 8 (3), pp. 289-308; Simons, R., (1995) Levers of Control, , Boston, MA: Harvard University Press; Speklé, R.F., Verbeeten, F.H.M., The use of performance measurement systems in the public sector: Effects on performance (2014) Management Accounting Research, 25 (2), pp. 131-146; Tomkins, C., Interdependencies, trust and information in relationships, alliances and networks (2001) Accounting, Organizations and Society, 26 (2), pp. 161-191; Tsai, B.H., Li, Y., Cluster evolution of IC industry from Taiwan to China (2009) Technological Forecasting & Social Change, 76 (8), pp. 1092-1104; van Veen-Dirks, P.M.G., Verdaasdonk, P.J.A., The dynamic relation between management control and governance structure in a supply chain context (2009) Supply Chain Management: An International Journal, 14 (6), pp. 466-478; Wallenberg, C.M., Schaffler, T., The interplay of relational governance and formal control in horizontal alliances: A social contract perspective (2014) Journal of Supply Chain Management, 50 (2), pp. 41-58; Wold, H., Systems analysis by partial least squares (1985) Measuring the Unmeasureable, pp. 221-251. , P. Nijkamp, L. Leitner, & N. Wrigley (Eds.), (pp. , Boston, MA: Martinus Nijhoff Publishers},
publisher={Emerald Group Holdings Ltd.},
issn={14747871},
language={English},
abbrev_source_title={Adv. Manage. Account.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Sanchez-Jimenez2021225,
author={Sanchez-Jimenez, L. and Salais-Fierro, T.E.},
title={A Predictive Performance Measurement System for Decision Making in the Supply Chain},
journal={Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
year={2021},
volume={393 LNICST},
pages={225-244},
doi={10.1007/978-3-030-87495-7_15},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116825347&doi=10.1007%2f978-3-030-87495-7_15&partnerID=40&md5=c9359970ac278f1931147b66d4173a59},
affiliation={Universidad Autónoma de Nuevo León, Pedro de Alba S/N, Ciudad Universitaria, San Nicolás de los Garza, Nuevo León, Mexico},
abstract={Increasing business competitiveness forces companies to develop strategies in search of operational excellence. The supply chain aims to increase its efficiency by reducing costs without neglecting quality and service levels. The implementation of predictive performance evaluation systems as a management practice has increased in recent years because in addition to measuring the efficiency and effectiveness of processes under certain scenarios, it includes artificial intelligence techniques that anticipate future events and allow taking advantage of behavioral patterns of historical data and current information to identify risks and opportunities. This paper proposes a fuzzy logic-based performance measurement system to help predict purchasing behavior through the impact of attributes of the SCOR supply chain operations reference model. The SCOR level 1 indicators are used as a standard for benchmarking against other supply chains. The proposed model is applied through an illustrative case and, according to the results obtained, it facilitates performance prediction and allows scenario analysis. In addition, it is adaptive to any industry and cyclical in search of the desired result, therefore, it helps decision makers to anticipate situations under uncertainty parameters and conditions by determining through simulations the performance attributes with the greatest impact on purchasing and facilitating decision making. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
author_keywords={Fuzzy logic techniques;  Logistics KPIs;  Models;  Predictive performance measurement;  Supply chain},
keywords={Competition;  Computer circuits;  Efficiency;  Fuzzy logic;  Information management;  Risk perception, Decisions makings;  Fuzzy logic techniques;  Its efficiencies;  Logistic KPI;  Operational excellence;  Performance measurement system;  Performance measurements;  Predictive performance;  Predictive performance measurement;  Reducing costs, Supply chains},
references={Ballou, R.H., (1998) Business Logistics Management, , 4th edn. Prentice Hall, USA; Kozlenkova, I.V., Hult, G.T.M., Lund, D.J., Mena, J.A., Kekec, P., The role of marketing channels in supply chain management (2015) J. Retail., 91 (4), pp. 586-609; Qi, Y., Huo, B., Wang, Z., Yeung, H.Y.J., The impact of operations and supply chain strategies on integration and performance (2017) Int. J. Prod. Econ., 185, pp. 162-174; Jayaram, J., Dixit, M., Motwani, J., Supply chain management capability of small and medium sized family businesses in India: A multiple case study approach (2014) Int. J. Prod. Econ., 147, pp. 472-485; Ka, J.M.R., Ab, N.R., Lb, K., A review on supply chain performance measurement systems (2019) Proc. Manuf., 30, pp. 40-47; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logist., 21 (1), pp. 75-93; Frazelle, E.H., (2002) Supply Chain Strategy: The Logistics of Supply Chain Management, , 1st Edition. McGraw-Hill Professional; Christopher, M., (1999) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , 2nd edn. Financial Times/Prentice Hall, London; Beamon, B.M., Measuring SC performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manage., 20 (7), pp. 754-771; Lapide, L., Predictive metrics (2010) J. Bus. Forecast., 29 (2), p. 23; Stefanovic, N., Collaborative predictive business intelligence model for spare parts inventory replenishment (2015) Comput. Sci. Inform. Syst., 12 (3), pp. 911-930; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Comput. Ind. Eng., 113, pp. 333-346; Sundarakani, B., Razzak, H.A., Manikandan, S., Creating a competitive advantage in the global flight catering supply chain: A case study using SCOR model (2018) Int. J. Logist. Res. Appl., 21 (5), pp. 481-501; Lima-Junior, F.R., Carpinetti, L.C.R., An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR R○ metrics (2020) Comput. Ind. Eng., 139; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Comput. Ind., 82, pp. 135-150; Beamon, B.M., Supply chain design and analysis (1998) Models and Methods. Int. J. Prod. Econ., 55 (3), pp. 281-294; APICS-Supply Chain Operations Reference Model, Version 12.0. http://www.logsuper.com/ueditor/php/upload/file/20190530/1559181653829933.pdf. Accessed 3 Mar 2017; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logist., 28 (1); Sipahi, S., Timor, M., The analytic hierarchy process and analytic network process: An overview of applications (2010) Manag. Decis., 48 (5), pp. 775-808; Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E.K., Nilashi, M., Zakuan, N., Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysis (2018) Ann. Oper. Res., 271, pp. 915-969; Delipinar, G.E., Kocaoglu, B., Using SCOR model to gain competitive advantage: A literature review (2016) Proc.-Soc. Behav. Sci., 229, pp. 398-406; Elgazzar, S., Tipi, N., Jones, G., Key characteristics for designing a supply chain performance measurement system (2019) Int. J. Prod. Perform. Manage., 68 (2), pp. 296-318; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) J. Oper. Supply Chain Manag., 6 (94-113); Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E.K., Antucheviciene, J., Supplier evaluation and selection in fuzzy environments: A review of MADM approaches (2017) Econ. Res.-Ekonomska Istraživanja, 30 (1), pp. 1073-1118; Aqlan, F., Lam, S.S., A fuzzy-based integrated framework for supply chain risk assessment (2015) Int. J. Prod. Econ., 161, pp. 54-63; Zanon, L., Munhoz Arantes, R., Del Rosso Calache, L., Ribeiro Carpinetti, L., A decision making model based on fuzzy inference to predict the impact of SCOR R○ indicators on customer perceived value (2020) Int. J. Prod. Econ., 223; Baily, P., Farmer, D., Jessop, D., Jones, D., (2005) Purchasing Principles and Management, , 9th edn. Pearson, Boston; Adenso-Díaz, B., Alvarez, N.G., Alba, J.A.L., A fuzzy AHP classification of container terminals (2020) Marit. Econ. Logist., 22, pp. 218-238; Ganesh, A.H., Shobana, A.H., Ramesh, R., Identification of critical path for the analysis of bituminous road transport network using integrated FAHP-FTOPSIS method (2021) Mater. Today: Proc. 37(2), pp. 193-206; Mamdani, E., Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller (1975) Int. J. Man-Mach. Stud., 7 (1), pp. 1-13; Yang, M., Khan, F.I., Sadiq, R., Prioritization of environmental issues in offshore oil and gas operations: A hybrid approach using fuzzy inference system and fuzzy analytic hierarchy process (2011) Process Saf. Environ. Protect., 89 (1), pp. 22-34; Liu, Y., Zhang, X., Evaluating the undergraduate course based on a fuzzy AHP-FIS model (2020) Int. J. Mod. Educ. Comput. Sci., 12, pp. 55-66},
correspondence_address1={Sanchez-Jimenez, L.; Universidad Autónoma de Nuevo León, Pedro de Alba S/N, Ciudad Universitaria, Mexico; email: loresanchez16@gmail.com},
editor={Marmolejo-Saucedo J.A., Vasant P., Litvinchev I., Rodriguez-Aguilar R., Saucedo-Martinez J.A.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={18678211},
isbn={9783030874940},
language={English},
abbrev_source_title={Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{NoAuthor2021,
title={5th EAI International Conference on Computer Science and Engineering in Health Services, COMPSE 2021},
journal={Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
year={2021},
volume={393 LNICST},
page_count={258},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116726322&partnerID=40&md5=eb336dc3de7b86be774b398e91aaa7dc},
abstract={The proceedings contain 17 papers. The special focus in this conference is on Computer Science and Engineering in Health Services. The topics include: Performance Evaluation of Healthcare Systems Using Data Envelopment Analysis; organizational Efficiency in the Implementation of 4.0 Technologies in Logistics Operators in the Colombian Caribbean Region; sustainability Model for the Livestock Sector in the Department of La Guajira - Colombia; design of a Logistics Network Using Analytical Techniques and Agent-Based Simulation; a Predictive Performance Measurement System for Decision Making in the Supply Chain; implementation of Intelligent Automation of Production Processes in the Company Espumados del Litoral in the City of Barranquilla; a Look at the Literature Review of the Impact of Industry 4.0 on the Logistics Processes of the Food Sector in Barranquilla; A Life Cycle Assessment (LCA) of Antibacterial Gel Production; An Alternative Model to Estimate Annual Budget Through TDABC Methodology in Hospitals; ARM: A Real-Time Health Monitoring Mobile Application; Network Coding and Dispersal Information with TCP for Content Delivery; sentiment Analysis Model on Twitter About Video Streaming Platforms in Mexico; an Asset Index Proposal for Households in Mexico Applying the Mixed Principal Components Analysis Methodology; a Data-Driven Study to Highlight the Correlations Between Ambient Factors and Emotion; telerehabilitation Prototype for Postural Disorder Monitoring in Parkinson Disease.},
editor={Marmolejo-Saucedo J.A., Vasant P., Litvinchev I., Rodriguez-Aguilar R., Saucedo-Martinez J.A.},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={18678211},
isbn={9783030874940},
language={English},
abbrev_source_title={Lect. Notes Inst. Comput. Sci. Soc. Informatics Telecommun. Eng.},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Santos20211,
author={Santos, P.H.A. and Martins, R.A.},
title={Food Waste And Performance Measurement Systems: A Systematic Review Of The Literature [Sistemas de medição de desempenho e desperdício de alimentos: Revisão sistemática da literatura] [Sistemas de medición de desempeño y desperdicio de alimentos: Una revisión sistemática de la literatura]},
journal={RAE Revista de Administracao de Empresas},
year={2021},
volume={61},
number={5},
pages={1-24},
doi={10.1590/S0034-759020210505x},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114438559&doi=10.1590%2fS0034-759020210505x&partnerID=40&md5=4e8147ccfc86b9d053fd56401f44a0ab},
affiliation={Universidade Federal de São Carlos, Departamento de Engenharia de Produção, São Carlos, SP, Brazil},
abstract={This paper presents an analysis of the scientific production that deals with performance measurement and food waste, and examines the trends and challenges in the field. Food waste increases food insecurity and misuses scarce natural and financial resources. It is, however, very difficult to measure waste at the aggregate level, and the various ways companies use do not adopt a systemic approach. A systematic literature review was conducted using bibliometrics to guide content analysis. The results indicate that few articles focus on performance measurement systems for the whole supply chain, with a particular emphasis on sustainability. The use of digital technologies in performance measurement systems is a trend that was observed. This is an opportunity for research aimed at quantifying food waste better and helping reduce food insecurity © RAE | São Paulo},
author_keywords={food loss;  food security;  Food waste;  literature review;  performance measurement systems},
references={Alamar, M. D. C., Falagán, N., Aktas, E., Terry, L. A., Minimising food waste: A call for multidisciplinary research (2018) Journal of the Science of Food and Agriculture, 98 (1), pp. 8-11; Aria, M., Cuccurullo, C., Bibliometrix: An R-tool for comprehensive science mapping analysis (2017) Journal of Informetrics, 11 (4), pp. 959-975; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Bititci, U., Nudurupati, S., Driving continuous improvement (2002) Manufacturing Engineer, 81 (5), pp. 230-235; Bititci, U. S., Bourne, M., Cross, J. A., Nudurupati, S. S., Sang, K., Towards a theoretical foundation for performance measurement and management (2018) International Journal of Management Reviews, 20 (3), pp. 653-660; Bititci, U. S., Carrie, A. S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Bititci, U. S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations & Production Management, 26 (12), pp. 1325-1350; Blenkinsop, S. A., Burns, N., Performance measurement revisited (1992) International Journal of Operations & Production Management, 12 (10), pp. 16-25; Bohtan, A., Mathiyazhagan, K., Vrat, P., Modeling the public distribution system: A PO-P approach (2019) OPSEARCH, 56 (3), pp. 1024-1066; Bourne, M., Franco-Santos, M., Micheli, P., Pavlov, A., Performance measurement and management: A system of systems perspective (2018) International Journal of Production Research, 56 (8), pp. 2788-2799; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Braz, R. G. F., Scavarda, L. F., Martins, R. A., Reviewing and improving performance measurement systems: An action research (2011) International Journal of Production Economics, 133 (2), pp. 751-760; Brown, D., Li, Y., Solid state anaerobic co-digestion of yard waste and food waste for biogas production (2013) Bioresource Technology, 127, pp. 275-280; Buzby, J. C., Hyman, J., Total and per capita value of food loss in the United States (2012) Food Policy, 37 (5), pp. 561-570; Chan, H. K., Supply chain systems: Recent trend in research and applications (2011) IEEE Systems Journal, 5 (1), pp. 2-5; Charlebois, S., Creedy, A., Massow, M., “Back of house” – Focused study on food waste in fine dining: The case of Delish restaurants (2015) International Journal of Culture, Tourism and Hospitality Research, 9 (3), pp. 278-291. , von; Cicatiello, C., Franco, S., Pancino, B., Blasi, E., The value of food waste: An exploratory study on retailing (2016) Journal of Retailing and Consumer Services, 30, pp. 96-104; Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., Herrera, F., Science mapping software tools: Review, analysis, and cooperative study among tools (2011) Journal of the American Society for Information Science and Technology, 62 (7), pp. 1382-1402; (2010) Preparatory study on food waste across EU 27, , https://ec.europa.eu/environment/eussd/pdf/bio_foodwaste_report.pdf, Comissão Europeia; Cousins, P. D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships (2008) International Journal of Operations & Production Management, 28 (3), pp. 238-258; Dal’Magro, G. P., Talamini, E., Estimating the magnitude of the food loss and waste generated in Brazil (2019) Waste Management & Research, 37 (7), pp. 706-716; Derqui, B., Fernandez, V., The opportunity of tracking food waste in school canteens: Guidelines for self-assessment (2017) Waste Management, 69, pp. 431-444; (2016) Transforming our world: The 2030 agenda for sustainable development, , https://sdgs.un.org/2030agenda, Department of Economic and Social Affairs, UN Retrieved from; Despoudi, S., Papaioannou, G., Saridakis, G., Dani, S., Does collaboration pay in agricultural supply chain? An empirical approach (2018) International Journal of Production Research, 56 (13), pp. 4396-4417; Duong, L. N., Wood, L. C., Wang, W. Y., Effects of consumer demand, product lifetime, and substitution ratio on perishable inventory management (2018) Sustainability, 10 (5), p. 1559; Eck, N. J. Van, Waltman, L., (2013) VOSviewer manual, , https://www.vosviewer.com/download/f-33t2.pdf, Retrieved from; ElMekawy, A., Srikanth, S., Vanbroekhoven, K., Wever, H. De, Pant, D., Bioelectro-catalytic valorization of dark fermentation effluents by acetate oxidizing bacteria in bioelectrochemical system (BES) (2014) Journal of Power Sources, 262, pp. 183-191; Feiz, R., Johansson, M., Lindkvist, E., Moestedt, J., Påledal, S. N., Svensson, N., Key performance indicators for biogas production: Methodological insights on the life-cycle analysis of biogas production from source-separated food waste (2020) Energy, 200, p. 117462; Ferreira, A., Otley, D., The design and use of performance management systems: An extended framework for analysis (2009) Management Accounting Research, 20 (4), pp. 263-282; (2008) An introduction to the basic concepts of food security, , https://www.fao.org/3/a-al936e.pdf, Food and Agriculture Organization of the United Nations. Retrieved from; (2013) Food wastage footprint: Impacts on natural resources, , https://www..fao.org/3/i3347e/i3347e.pdf, Food and Agriculture Organization of the United Nations. Retrieved from; (2014) Mitigation of food wastage: Societal costs and benefits, , https://www.fao.org/3/a-i3989e.pdf, Food and Agriculture Organization of the United Nations. Retrieved from; (2015) The state of food and agriculture, , https://www.fao.org/3/a-i4910e.pdf, Food and Agriculture Organization of the United Nations. Retrieved from; (2017) Save food: Global initiative on food loss and waste reduction, , https://www.fao.org/3/a-i7657e.pdf, Food and Agriculture Organization of the United Nations. Retrieved from; (2019) The state of food and agriculture 2019: Moving forward on food loss and waste reduction, , http://www.fao.org/3/ca6030en/ca6030en.pdf, Food and Agriculture Organization of the United Nations. Retrieved from; Franco‐Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Galanakis, C. M., Recovery of high added-value components from food wastes: Conventional, emerging technologies and commercialized applications (2012) Trends in Food Science & Technology, 26 (2), pp. 68-87; Garcia-Garcia, G., Stone, J., Rahimifard, S., Opportunities for waste valorisation in the food industry: A case study with four UK food manufacturers (2019) Journal of Cleaner Production, 211, pp. 1339-1356; Garengo, P., Nudurupati, S., Bititci, U., Understanding the relationship between PMS and MIS in SMEs: An organizational life cycle perspective (2007) Computers in Industry, 58 (7), pp. 677-686; Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., Toulmin, C., Food security: The challenge of feeding 9 billion people (2010) Science, 327 (5967), pp. 812-818; Gustavsson, J., Cederberg, C., Sonesson, U., Otterdijk, R. Van, Meybeck, A., (2011) Global food losses and food waste, , http://www.fao.org/3/i2697e/i2697e.pdf, Retrieved from; Gutierrez, D. M., Scavarda, L. F., Fiorencio, L., Martins, R. A., Evolution of the performance measurement system in the logistics department of a broadcasting company: An action research (2015) International Journal of Production Economics, 160, pp. 1-12; Hall, K. D., Guo, J., Dore, M., Chow, C. C., The progressive increase of food waste in America and its environmental impact (2009) PloS One, 4 (11), p. e7940; Helden, G. J. V. Van, Johnsen, Å., Vakkuri, J., The life-cycle approach to performance management: Implications for public management and evaluation (2012) Evaluation, 18 (2), pp. 159-175; Henri, J. F., Organizational culture and performance measurement systems (2006) Accounting, Organizations and Society, 31 (1), pp. 77-103; Hertog, M. L., Uysal, I., McCarthy, U., Verlinden, B. M., Nicolaï, B. M., Shelf life modelling for first-expired-first-out warehouse management (2014) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372 (2017), p. 20130306; Hirsch, J. E., An index to quantify an individual's scientific research output (2005) Proceedings of the National Academy of Sciences, 102 (46), pp. 16569-16572; Hopwood, A. G., An empirical study of the role of accounting data in performance evaluation (1972) Journal of Accounting Research, 10, pp. 156-182; Jedermann, R., Nicometo, M., Uysal, I., Lang, W., Reducing food losses by intelligent food logistics (2014) Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 372 (2017), p. 20130302; Kaipia, R., Dukovska‐Popovska, I., Loikkanen, L., Creating sustainable fresh food supply chains through waste reduction (2013) International Journal of Physical Distribution & Logistics Management, 43 (3), pp. 262-276; Kaplan, R. S., Norton, D. P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79. , https://www.hbs.edu/faculty/Pages/item.aspx?num=9161, Retrieved from; Kaplan, R. S., Norton, D. P., Putting the balanced scorecard to work (1995) Performance measurement, management, and appraisal sourcebook, 66, pp. 66-79. , D. G. Shaw, C. E. Schneier, R. W. Beatty, & L. S. Baird (Eds), Amherst, USA: Human Resource Development; Kiran, E. U., Trzcinski, A. P., Ng, W. J., Liu, Y., Bioconversion of food waste to energy: A review (2014) Fuel, 134, pp. 389-399; Kummu, M., Moel, H. De, Porkka, M., Siebert, S., Varis, O., Ward, P. J., Lost food, wasted resources: Global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use (2012) Science of the Total Environment, 438, pp. 477-489; Liboni, L. B., Cezarino, L. O., Jabbour, C. J. C., Oliveira, B. G., Stefanelli, N. O., Smart industry and the pathways to HRM 4.0: Implications for SCM (2019) Supply Chain Management: An International Journal, 24 (1), pp. 124-146; Lopes, M. A., Martins, R. A., Mapping the impacts of Industry 4.0 on performance measurement systems (2021) IEEE Latin America Transactions, 19 (11), pp. 1912-1923. , https://latamt.ieeer9.org/index.php/transactions/article/view/4807, Retrieved from; Lundqvist, J., Fraiture, C. de, Molden, D., (2008) Saving water: From field to fork: Curbing losses and wastage in the food chain, , https://www.siwi.org/wp-content/uploads/2015/09/PB_From_Filed_to_fork_2008.pdf, Stockholm, Sweden: Stockholm International Water Institute. Retrieved from; Luo, J., Ji, C., Qiu, C., Jia, F., Agri-food supply chain management: Bibliometric and content analyses (2018) Sustainability, 10 (5), p. 1573; Maestrini, V., Maccarrone, P., Caniato, F., Luzzini, D., Supplier performance measurement systems: Communication and reaction modes (2018) Industrial Marketing Management, 74, pp. 298-308; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: A survey in the mines (2006) Management Accounting Research, 17 (3), pp. 315-339; Melnyk, S. A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Menna, F. De, Dietershagen, J., Loubiere, M., Vittuari, M., Life cycle costing of food waste: A review of methodological approaches (2018) Waste Management, 73, pp. 1-13; Mirabella, N., Castellani, V., Sala, S., Current options for the valorization of food manufacturing waste: A review (2014) Journal of Cleaner Production, 65, pp. 28-41; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: A bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Morioka, S. N., Iritani, D. R., Ometto, A. R., Carvalho, M. M. D., Revisão sistemática da literatura sobre medição de desempenho de sustentabilidade corporativa: Uma discussão sobre contribuições e lacunas (2018) Gestão & Produção, 25 (2), pp. 284-303; Moustafa, G. Y., Galal, N. M., El-Kilany, K. S., Sustainable dynamic pricing for perishable food with stochastic demand (2018) IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), , (December) Bangkok, Thailand; Naidoo, M., Gasparatos, A., Corporate environmental sustainability in the retail sector: Drivers, strategies and performance measurement (2018) Journal of Cleaner Production, 203, pp. 125-142; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nudurupati, S. S., Bititci, U. S., Kumar, V., Chan, F. T., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Nudurupati, S. S., Tebboune, S., Hardman, J., Contemporary performance measurement and management (PMM) in digital economies (2016) Production Planning & Control, 27 (3), pp. 226-235; Oelofse, S. H., Nahman, A., Estimating the magnitude of food waste generated in South Africa (2013) Waste Management & Research, 31 (1), pp. 80-86; Orjuela-Castro, J. A., Orejuela-Cabrera, J. P., Adarme-Jaimes, W., Last mile logistics in mega-cities for perishable fruits (2019) Journal of Industrial Engineering and Management, 12 (2), pp. 318-327; Papargyropoulou, E., Lozano, R., Steinberger, J. K., Wright, N., Ujang, Z. bin., The food waste hierarchy as a framework for the management of food surplus and food waste (2014) Journal of Cleaner Production, 76, pp. 106-115; Parfitt, J., Barthel, M., Macnaughton, S., Food waste within food supply chains: Quantification and potential for change to 2050 (2010) Philosophical Transactions of the Royal Society B: Biological Sciences, 365 (1554), pp. 3065-3081; Pauer, E., Wohner, B., Heinrich, V., Tacker, M., Assessing the environmental sustainability of food packaging: An extended life cycle assessment including packaging related food losses and waste and circularity assessment (2019) Sustainability, 11 (3), p. 925; Pirani, S. I., Arafat, H. A., Reduction of food waste generation in the hospitality industry (2016) Journal of Cleaner Production, 132, pp. 129-145; Raak, N., Symmank, C., Zahn, S., Aschemann-Witzel, J., Rohm, H., Processing-and product-related causes for food waste and implications for the food supply chain (2017) Waste Management, 61, pp. 461-472; Rezaei, M., Shirazi, M. A., Karimi, B., IoT-based framework for performance measurement (2017) Industrial Management & Data Systems, 117 (4), pp. 688-712; Searchinger, T., Waite, R., Hanson, C., Ranganathan, J., Dumas, P., Matthews, E., (2018) Creating a sustainable food future: A menu of solutions to feed nearly 10 billion people by 2050, , https://www.wri.org/research/creating-sustainable-food-future, World Resources Institute. Retrieved from; Sel, Ç., Pınarbaşı, M., Soysal, M., Çimen, M., A green model for the catering industry under demand uncertainty (2017) Journal of Cleaner Production, 167, pp. 459-472; Simons, R., Russ-Eft, D., Preskill, H., Tejada, R. A., Negrini, S. D., Corrales, M. M., Román, I., (2000) Performance measurement and control systems for implementing strategy (N. D10 276), , Tegucigalpa, Honduras: IICA; Simons, T., Top management team consensus, heterogeneity, and debate as contingent predictors of company performance: The complimentarity of group structure and process (1995) Academy of Management Proceedings, 1995 (1), pp. 62-66; Soysal, M., Bloemhof-Ruwaard, J. M., Haijema, R., Vorst, J. G., Modeling a green inventory routing problem for perishable products with horizontal collaboration (2018) Computers & Operations Research, 89, pp. 168-182. , van der; Steur, H. De, Wesana, J., Dora, M. K., Pearce, D., Gellynck, X., Applying value stream mapping to reduce food losses and wastes in supply chains: A systematic review (2016) Waste Management, 58, pp. 359-368; Stuart, T., (2009) Waste: Uncovering the global food scandal, , W. W. Norton & Company. New York, Estados Unidos; Szymczak, M., Ryciuk, U., Leończuk, D., Piotrowicz, W., Witkowski, K., Nazarko, J., Jakuszewicz, J., Key factors for information integration in the supply chain–measurement, technology and information characteristics (2018) Journal of Business Economics and Management, 19 (5), pp. 759-776. , doi. org; Tscharntke, T., Clough, Y., Wanger, T. C., Jackson, L., Motzke, I., Perfecto, I., Whitbread, A., Global food security, biodiversity conservation and the future of agricultural intensification (2012) Biological Conservation, 151 (1), pp. 53-59; Waarts, Y. R., Eppink, M., Oosterkamp, E. B., Hiller, S. R. C. H., Sluis, A. A. Van Der, Timmermans, T., (2011) Reducing food waste: Obstacles experienced in legislation and regulations (N. 2011-059), , Landbouw-Economisch Institut (LEI), Wageningen University and Research Centre (WUR). Wageningen, Países Baixos; Wamba, S. F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study (2015) International Journal of Production Economics, 165, pp. 234-246; West, P. C., Gerber, J. S., Engstrom, P. M., Mueller, N. D., Brauman, K. A., Carlson, K. M., Siebert, S., Leverage points for improving global food security and the environment (2014) Science, 345 (6194), pp. 325-328; Xu, L., He, W., Li, S., Internet of things in industries: A survey (2014) IEEE Transactions on Industrial Informatics, 10 (4), pp. 2233-2243; Zupic, I., Čater, T., Bibliometric methods in management and organization (2015) Organizational Research Methods, 18 (3), pp. 429-472},
correspondence_address1={Santos, P.H.A.; Universidade Federal de São Carlos, Brazil; email: phas223@gmail.com},
publisher={Fundacao Getulio Vargas},
issn={00347590},
language={English},
abbrev_source_title={RAE Rev. Adm. Empresas},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Zahoor20215849,
author={Zahoor, S. and Hayat, K. and Shaker, M. and Zahoor, M.Z.},
title={A review on integrated supply chain performance measurement system concerning industry 4.0: A developing world perspective},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2021},
pages={5849-5859},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114262157&partnerID=40&md5=49c804abe12dcb40b7cf423038eaff1f},
affiliation={Department of Mechanical, Automotive, and Materials Engineering, University of Windsor, Windsor, N9B 3P4, Canada; Department of Industrial and Manufacturing Engineering, University of Engineering and Technology, Lahore, Pakistan; University of MichiganMI  48109, United States},
abstract={With regards to Industry 4.0, integrated supply chain performance measurement system (SCPMS) is paramount for an efficient supply chain management (SCM) at inter-organizational and beyond-the-boundary processes. The recent technological innovations (internet of things (IoTs), big data, and web-based communication systems) revamp the SCPMS through effective data collection, information sharing, and framework integration among the different SC partners in developed countries. However, the research addressing the obstacles facing by developing countries is limited. During this study, a systematic review of available academic literature about the SCPMS, framework approach/techniques, SCPMS and technology, and constraints faced during its adoption is performed. A set of 56 articles are selected spanning over one decade (2011-2020) to grasp the understanding of trends and recent developments in SCPMS globally. Moreover, future research areas are identified, and research agenda is presented for Pakistan industrial sector – a representation of an developing world. © IEOM Society International.},
author_keywords={Big data;  Industry 4.0;  Integrated supply chain performance measurement system;  IoTs},
references={Abbasi, M., Afzal, M., Impact of e-source application on key business strategies – evidence from automotive industry of Pakistan (2011) Journal of Quality and Technology Management, pp. 37-62; Abdel-Basset, M., Manogaran, G., Mohamed, M., Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems (2019) Future Generation Computer Systems; Accorsi, R., Bortolini, M., Baruffaldi, G., Pilati, F., Ferrari, E., Internet-of-Things paradigm in food supply chains control and management (2017) Procedia Manufacturing, 11, pp. 889-895; Addo-Tenkorang, R., Helo, P., Big Data Applications in Operations/Supply-Chain Management: A Literature Review (2016) Computers & Industrial Engineering, 101, pp. 528-543; Ahmed, R., Munir, R., Sameer, M., Impact of supply chain integration on firm performance: Evidence from the manufacturing sector of Pakistan (2020) International Journal of Management, 11, pp. 1499-1509. , 08; Alfalla-Luque, R., Medina-Lopez, C., Dey, P.K., Supply chain integration framework using literature review (2012) Production Planning & Control, 1-18; Amoako-Gyampah, K., Gyasi Boakye, K., Adaku, E., Famiyeh, S., Supplier relationship management and firm performance in Developing Economies: A moderated mediation analysis of flexibility capability and ownership structure (2019) International Journal of Production Economics, 208, pp. 160-170; Ashby, A., Leat, M., Hudson-Smith, M., Making connections: A review of supply chain management and sustainability literature (2012) Supply Chain Management: An International Journal, 17 (5), pp. 497-516; Bag, S., Supplier management and sustainable innovation in supply networks: An empirical study (2018) Global Business Review, 19 (3S), pp. 176S-195S; Basheer, M.F., Hafeez, M.H., Hassan, S.G., Haroon, U., Exploring the role of TQM and supply chain practices for firm supply performance in the presence of organizational learning capabilities: A case of textile firms in Pakistan (2018) A Research Journal of Commerce, Economics, and Social Sciences, 12 (2), pp. 172-178; Ben-Daya, M., Hassini, E., Bahroun, Z., Internet of things and supply chain management: A literature review (2017) International Journal of Production Research, 57 (15-16); Bienhaus, F., Haddud, A., Procurement 4.0: Factors influencing the digitization of procurement and supply chains (2018) Business Process Management Journal, 24 (4); Chan, F., Nayak, A., Raj, R., Chong, A., Manoj, T., An innovative supply chain performance measurement system incorporating Research and Development (R&D) and marketing policy (2014) Computers & Industrial Engineering, 69, pp. 64-70; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.k., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62, pp. 801-818; Charkh, P.G., Jaju, S.B., Designing an innovative framework for supply chain performance measurement in textile industry (2014) International Journal of Logistics Systems and Management, 18 (2); Doliński, D., Koliński, A., Estimation of suppliers as an important element of the rationalization of supply processes – case study (2011) Scientific Journal of Logistics, 7 (2); Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organizations (2013) Production Planning & Control, 24, pp. 8-9; De Vass, Shee, Miah, The effect of “Internet of Things” on supply chain integration and performance: An organizational capability perspective (2018) Australasian Journal of Information Systems, 22; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38, p. 668; Ferreira, L., Borenstein, D., A fuzzy-Bayesian model for supplier selection (2012) Expert Systems with Applications, 39, pp. 7834-7844; Ganeshkumar, C., Pachayappan, M., Madanmohan, G., Agri-food Supply Chain Management: Literature Review (2017) Intelligent Information Management, 9, pp. 68-96; Ghadimi, P., Dargi, A., Heavy, C., Sustainable supplier performance scoring using audition checklist based fuzzy inference system: A case application in automotive spare part industry (2017) Computers & Industrial Engineering, 105, pp. 12-27; Hashim, M., Baig, S.A., Amjad, F., Nazam, M., Akram, M.U., Impact of supply chain management practices on organizational performance and moderating role of innovation culture: A case of Pakistan textile industry (2019) International Conference on Management Science and Engineering Management, pp. 390-401. , 5-8 August; Ontario, ON, Canada; Imam, A., Manzoor, S., Raza, A., Effective Supplier Management in Logistic Industry: A Case Study of Pakistan (2017) Journal of Applied Environmental and Biological Sciences, 7 (2), pp. 180-187; Khan, S.A.R., Yu, Z., Qianli, D., Study on the supply chain integration: In the perspective of Pakistan (2018) Computational Intelligence and Sustainable Systems, pp. 255-265; Khokhar, M., Iqbal, W., Hou, Y., Abbas, M., Fatima, A., Assessing supply chain performance from the perspective of Pakistan’s Manufacturing industry through social sustainability (2020) Processes, 8 (9), p. 1064; Kisomi, M., Solimanpur, M., Doniavi, A., An integrated supply chain configuration model and procurement management under uncertainty: A set based robust optimization methodology (2016) Applied Mathematical Modelling, 40 (17-18), pp. 7928-7949; Kumar, D., Rahman, Z., Sustainability adoption through buyer supplier relationship across supply chain: A literature review and conceptual framework (2015) International Strategic Management Review, 3, pp. 110-127; Lee, G., Shin, G., Hwang, D.W., Kuperd, P., Kang, M., How manufacturers' long-term orientation toward suppliers influences outstanding performance (2019) Industrial Marketing Management; Lemghari, R., Sarsri, D., Okar, C., Es-satty, A., Supply chain performance measurement in the automotive sector: A structured content analysis (2019) Uncertain Supply Chain Management, 7 (4), pp. 567-588; Li, M., Zheng, X., Zhuang, G., Information technology-enabled interactions, mutual monitoring, and supplier-buyer cooperation: A network perspective (2017) Journal of Business Research, 78, pp. 268-276; Li, S., Kang, M., Haney, M.H., The effect of supplier development on outsourcing performance: the mediating roles of opportunism and flexibility (2017) Production Planning & Control, 28 (6-8), pp. 599-609; Manavalan, E., Jayakrishna, K., A review of Internet of Things (IoT) embedded Sustainable Supply Chain for Industry 4.0 requirements (2019) Computers & Industrial Engineering, 127, pp. 925-953; Mani, V., Gunasekaran, A., Delgado, C., Enhancing supply chain performance through supplier social sustainability: An emerging economy perspective (2018) International Journal of Production Economics, 195, pp. 259-272; Mansoor, H.A., Asim, M., Manzoor, S., Cloud network/computing impact on inter-organizational trust which effects performance of supply chain management: A study of Pakistan’s firms (2020) CenRaPS Journal of Social Sciences, 2 (1), pp. 134-150; Maestrini, V., Maccarrone, P., Caniato, F., Luzzini, D., Supplier performance measurement systems: Communication and reaction modes (2018) Industrial Marketing Management, 74, pp. 298-308; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Maestrini, V., Martinez, V., Neely, A., Luzzini, D., Caniato, F., Maccarrone, P., The relationship regulator: A buyer-supplier collaborative performance measurement system (2018) International Journal of Operations & Production Management, 38 (11); Maestrini, V., Luzzini, D., Caniato, F., Ronchi, S., Effects of monitoring and incentives on supplier performance: an agency theory perspective (2018) International Journal of Production Economics, 203, pp. 322-332; Molamohamadi, Z., Ismail, N., Leman, Z., Zulkifli, N., Supplier Selection in a Sustainable Supply Chain (2013) Journal of Advanced Management Science, 1 (3); Mokhtar, S., Bahri, P.A., Moayer, S., James, A., Supplier portfolio selection based on the monitoring of supply risk indicators (2019) Simulation Modelling Practice and Theory, 97, p. 101955; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: A bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Nazam, M., Hashim, M., Randhawa, M.A., Maqbool, A., Modeling the barriers of sustainable supply chain practices: A Pakistani perspective (2019) International Conference on Management Science and Engineering Management, pp. 348-364. , 5-8 August; Ontario, ON, Canada; Parkash, S., Kaushik, V., Supplier performance monitoring and improvement (SPMI) through sipoc analysis and pdca model to the iso 9001 QMS in sports goods manufacturing industry (2011) Scientific Journal of Logistics, 7 (3); Parkash, S., Kaushik, V., Simple, cost effective & result oriented framework for supplier performance measurement in sports goods manufacturing industry (2011) Scientific Journal of Logistics, 7 (3); Peng, X., Wang, X., Chan, K.C., Does supplier stability matter in initial public offering pricing (2019) International Journal of Production Economics, 225. , C; Rehman, S.T., Khan, S.A., Kusi-Sarpong, S., Hassan, S.M., Supply chain performance measurement and improvement system: A MCDA-DMAIC methodology (2018) Journal of Modelling in Management, 13 (3); Shahid, H.M., Waseem, W., Khan, H., Waseem, F., Hasheem, M.J., Shi, Y., Process innovation as a moderator linking sustainable supply chain management with sustainable performance in the manufacturing sector of Pakistan (2020) Sustainability, 12 (6), p. 2303; Suvittawat, A., Strategic Procurement in Supply Chain Management: 7 New Expectation Skills for Effective Procurement (2017) International Journal of Applied Business and Economic Research, 15 (3), pp. 69-74; Teller, C., Kotzab, H., Grant, D., Holweg, C., The importance of key supplier relationship management in supply chains (2016) International Journal of Retail & Distribution Management, 44 (2); Torres-Ruiza, A., Ravindran, A.R., Use of interval data envelopment analysis, goal programming and dynamic eco-efficiency assessment for sustainable supplier management (2019) Computers & Industrial Engineering, 131, pp. 211-226; Truong, H., Sampaio, P., Sameiro, M., Binh, A., Duong, A., Vilhenac, E., An extensive structural model of supply chain quality management and firm performance (2016) International Journal of Quality & Reliability Management, 33 (4); Wu, L., Yue, X., Jin, A., Yen, D.C., (2016) Smart supply chain management: A review and implications for future research, 27 (2); Yin, Y., Qin, S., A smart performance measurement approach for collaborative design in Industry 4.0 (2019) Advances in Mechanical Engineering, 11 (1), pp. 1-15; Zhu, Q., Krikke, H., Marjolein, C., Caniëls, J., Integrated supply chain risk management: A systematic review (2016) International Journal of Logistics Management, 28 (4); Zhang, Q., Pan, J., Jiang, Y., Feng, T., The impact of green supplier integration on firm performance: The mediating role of social capital accumulation (2020) Journal of Purchasing and Supply Management, 26 (2), p. 100579},
publisher={IEOM Society},
issn={21698767},
isbn={9781792361241},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Indah2021487,
author={Indah, P.N. and Setiawan, R.F. and Hendrarini, H. and Yektingsih, E. and Sunarsono, R.J.},
title={Agriculture supply chain performance and added value of cocoa: A study in kare village, Indonesia},
journal={Bulgarian Journal of Agricultural Science},
year={2021},
volume={27},
number={3},
pages={487-497},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108421700&partnerID=40&md5=1a21f39827558b48f836ec82eb0a2e90},
affiliation={University of Pembangunan National “Veteran” East Java, Department of Agriculture, Faculty of Agriculture, Surabaya city, 60294, Indonesia; Airlangga University, Department of Management, Faculty of Economics and Business, Airlangga, Surabaya city, 60115, Indonesia},
abstract={This study aims to determine the condition of the cocoa supply chain, the value added members of the supply chain and supply chain performance measurement, and to enhance the potential performance and added value in researched area. This study was conducted in Kare Village, Indonesia. The method of determining the location was done purposively. The method of determining the sample for cocoa supply chain actors was carried out by using the Simple Random Sampling method while for collectors, wholesalers and SMEs use Purposive Sampling method include 20 cocoa farmers, 1 trade-collector, 1 wholesaler, and 1 cocoa MSMEs. Data collection techniques were through interviews and expert opinion. The analytical method used is descriptive analysis, additional value analysis uses the Hayami method and the design of metric measurements for cocoa supply chain performance uses the SCOR (Supply Chain Operations Reference)-AHP (Analytic Hierarchy Process) method. The results presented that the supply chain conditions for Cocoa in Kare Village had not been in an optimal performance. The additional value analysis showed the proportion of the added value earned by farmers was diverse. The results of the supply chain performance measurement metric design, known that the determinants of supply chain performance in sustainable supply chain management according to experts in the actor cluster (SMEs), the performance Indicator cluster (companies) and the alternative strategy cluster are Increasing Income was diverse too. Increasing the additional value is able to be done by utilizing by-products cocoa processing such as cocoa pulp and cocoa pod husks. The cocoa pulp can be processed into nata products and cocoa juice, while the cocoa pods can be used as fertilizer. © 2021, Agricultural Academy, Bulgaria. All rights reserved.},
author_keywords={Additional Value;  Agriculture Supply Chain;  Analytic Hierarchy Process;  Cocoa;  Supply Chain Operations Reference},
references={Anindita, K., Ambarawati, I. G., Dewi, R. K., Supply chain performance at Madukismo sugar factory by method SCOR-AHP (Kinerja Rantai Pasok di Pabrik Gula Madukis-mo dengan Metode SCOR-AHP.) (2018) Jurnal Sosial Ekonomi Dan Kebijakan Pertanian, 7 (2), pp. 176-187. , (Ind); Asian, S., Nie, X., Coordination in supply chains with uncertain demand and disruption risks: Existence, analysis, and insights (2014) IEEE Transactions on Systems, Man, and Cybernet-ics: Systems, 44 (9), pp. 1139-1154; Azmiyati, S., Hidayat, S., Supply chain performance measurement at PT (2017) Louserindo Megah Permai using SCOR and FAHP models (Pengukuran Kinerja Rantai Pasok pada PT. Louserindo Megah Permai Menggunakan Model SCOR dan FAHP.) Jurnal Al-Azhar Indonesia Seri Saisns Dan Teknologi, 3 (4), pp. 163-170. , (Ind); Bailey, J., Pearson, S., Development of a tool for mea-suring and analyzing computer user satisfaction (1983) Management Science, 29 (5), pp. 530-545; Banasik, A., Kanellopoulos, A, Claassen, G. D. H., Bloem-hof-Ruwaard, J. M., Vorst, G. J. A. J., Closing loops in agricultural supply chains using multi-objective opti-mization: A case study of an industrial mushroom supply chain (2017) International Journal of Production Economics, 183, pp. 409-420; Barratt, M., Oke, A., Antecedents of supply chain visibil-ity in retail supply chains: a resource based theory perspective (2007) Journal of Operations Management, 25 (6), pp. 1217-1233; Burns, A. C., Bush, R. F., (2014) Marketing research, , 7th edition. International edition, Pearson Education Limited, England; Cai, X., Zhou, X., Optimal policies for perishable products when transportation to export market is disrupted (2014) Production and Operations Management, 23 (5), pp. 907-923; Chandrasekaran, N., Raghuram, G., (2014) Agribusiness supply chain management, p. 77. , CRC Press. Boca Raton, Florida; Cosic, R., Shanks, G., Maynard, S., A business analyt-ics capability framework (2015) Australasian Journal of Information Systems, 19, pp. 5-19; (2005) Supply Chain and Logistics Terms and Glossary, , https://cscmp.org/Default.asp, CSCMP (accessed March 23, 2020); der Vorst, J. V., (2006) Performance measurement in ari-food sup-ply-chain networks-An overview, pp. 13-24. , Netherlands, Springer; Gardas, B., Raut, R., Jagtap, A. H., Narkhede, B., Ex-ploring the key performance indicators of green supply chain management in agro-industry (2018) Journal of Modelling in Man-agement, 14 (1); Gustin, C. M., Daugherty, P. J., Stank, T. P., The effects of information availability on logistics integration (1995) Journal of Business Logistics, 16 (1), pp. 1-21; Hadinata, S., Marianti, M. M., Analysis of the downstream impact of the cocoa industry in Indonesia (Analisis Dampak Hilirisasi Industri Kakao di Indonesia.) (2020) Jurnal Akun-tansi Maranatha, 12 (1), pp. 99-108. , (Ind); Hayami, Y., Kawagoe, T., Morooka, Y., Siregar, M., (1987) Agricultural marketing and processing in upland Java. A Per-spective from a Sunda Village, , Bogor: The CPGRT Centre; Hidayati, S., Suroso, E., Setiawan, T., Septiyan, J., Kurni-awan, A., Analysis of added value of rubber finished goods agroindustry in Lampung Province (Analisis Nilai Tam-bah Agroindustri Barang Jadi Karet di Provinsi Lampung (2020) Te-knotan, 14 (1), pp. 1-6. , (Ind); Kaijun, L., Ya, B., Limbo, J., Han-Chi, F., Nieuwenhuyse, I. V., Research on agricultural supply chain system with dou-ble chain architecture based on blockchain technology (2018) Future Generation Computer Systems. Future Generation Computer Systems-The International Journal of Escience, 86, pp. 641-649. , 2018; Kamble, S., Gunasekaran, A., Gawankar, S. A., Achiev-ing sustainable performance in a data-driven agriculture supply chain: A review for research and applications (2020) International Journal of Production Economics, 219 (C), pp. 179-194; King, R. P., Venturini, L., Demand for quality drives changes in food supply chains (2005), 794. , www.ers.usda.gov, New directions in global food markets, A. Regmi and M. Gehlhar (eds). Agriculture Information Bulletin, Washington, DC: United States Department of Agriculture; Mardesci, H., Analysis of added value of coconut water candy (Analisi Nilai Tambah Permen Air Kelapa.) (2019) Jurnal Te-knologi Pertanian, 8 (2), pp. 112-116. , (Ind); Maulana, H., Miftah, H., Yusdiarti, A., Analysis of added value of processed palm sugar in Joint Business Group (Kub) Palm Ants Sugar (Gsa) (Analisis Nilai Tambah Olahan Gula Aren Di Kelompok Usaha Bersama (Kub) Gula Semut Aren (Gsa)) (2019) Jurnal Agribisains, 4 (2), pp. 8-14. , (Ind); Mohr, J., Sohi, R. S., Communication flows in distribu-tion channels: impact on assessments of communication quality and satisfaction (1995) Journal of Retailing, 71 (4), pp. 393-416; Noviantari, K., Ibrahim Hasyim, A., Rosanti, N., Supply chain analysis and value added of Luwak in Lampung Province (Analisis Rantai Pasok dan Nilai Tambah Agroindustri Kopi Luwak di Provinsi Lampung.) (2018) Jiia, 3 (1), pp. 10-17. , (Ind); Nuzuliyah, L., Added value analysis of rhizome product (2018) Jurnal Teknologi Dan Manajemen Agroindustri, 7 (1), pp. 31-38. , (Ind); Rakhman, A., Machfud, M., Arkeman, Y., Supply chain management performance using the Supply Chain Operation Reference Method Approach (SCOR) (Kinerja Mana-jemen Rantai Pasok dengan Menggunakan Pendekatan Metode Supply Chain Operation Reference) (SCOR) (2018) Jurnal Aplikasi Bisnis Dan Manajemen, 4 (1), pp. 106-118. , (Ind); (2019) Cocoa agroindustry to increase farmers income in Konawe District (Agroindustri Kakao Untuk Meningkatkan Pendapatan Masyarakat Petani di Kabupaten Konawe.) OPTI-MA, 3 (2), pp. 30-37. , Rayuddin (Ind); Routroy, S., Behera, A., Agriculture supply chain: A systematic review of literature and implications for future re-search (2017) Journal of Agribusiness in Developing and Emerging Economies, 7 (3), pp. 275-302; Saputra, I. K., Bafadal, A., Aida, S., Tampabulu, Poleang Utara District, Bombana Regency (Tampabulu Kecamatan Poleang Utara Kabupaten Bombana.) (2019) Jurnal Ilmiah Agribisnis, 4 (3), pp. 66-70. , (Ind); Septiaji, I. D., Cepriadi, C., Tety, E., Analysis of added value of agroindustry of cocoa downstream products (Case study of Mini Chocato Factory, Kapalo Koto Village, Payakum-buh Selatan District, Sumatera Barat) (Analisis Nilai Tambah Agroindustri Produk Hilir Kakao (Studi Kasus Pabrik Mini Chocato Kelurahan Kapalo Koto, Kecamatan Payakumbuh Se-latan, Sumatera Barat)) (2018) Jurnal Agribisnis, 19 (2), pp. 72-86. , (Ind); Sgarbossa, F., Russo, I., A proactive model in sustainable food supply chain: Insight from a case study (2017) International Journal of Production Economics, 183, pp. 596-606; Sharma, R., Shishodia, A., Kamble, S., Gunasekaran, A., Bel-hadi, A., Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners (2020) International Journal of Logistics Research and Applica-tions, pp. 1-27; Tanjung, W. N., Hidayat, S., Azmiyati, S., System sim-ulation to improve supply chain performance (Simulasi Sistem Untuk Meningkatkan Kinerja Rantai Pasok.) (2017) Jurnal Al-Azhar Indonesia Seri Saisns Dan Teknologi, 3 (4), pp. 171-177. , (Ind); Tsolakis, N. K., Keramydas, C. A., Toka, A. K., Aidonis, D. A., Iakovou, E. T., Agrifood supply chain management: A comprehensive hierarchical decision-making framework and a critical taxonomy (2014) Biosystems Engineering, 120 (1), pp. 47-64; Wiedenmann, S., Geldermann, J., Supply planning for processors of agricultural raw materials (2015) European Journal of Operational Research, 242 (2), pp. 606-619; Yan, B., Wu, X., Ye, B., Zhang, Y., Three-level supply chain coordination of fresh agricultural products in the Internet of Things (2017) Industrial Management & Data Systems, 117 (9), pp. 1842-1865; Yoga, P. I., Mulyani, S., Satriawan, I., Analysis of added value of Kintamani Bangli Arabika coffee processing (Analisis Nilai Tambah Pengolahan Kopi Arabika Kintamani Bangli.) (2017) Jurnal Rekayasa Dan Manajemen Agroindustri, 4 (4), pp. 33-42. , (Ind)},
correspondence_address1={Indah, P.N.; University of Pembangunan National “Veteran” East Java, Indonesia; email: Pawana_ni@upnjatim.ac.id},
publisher={Agricultural Academy, Bulgaria},
issn={13100351},
language={English},
abbrev_source_title={Bulg. J. Agric. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Susanawati2021202,
author={Susanawati and Akhmadi, H. and Fauzan, M. and Rozaki, Z.},
title={Supply chain efficiency of red chili based on the performance measurement system in Yogyakarta, Indonesia},
journal={Open Agriculture},
year={2021},
volume={6},
number={1},
pages={202-211},
doi={10.1515/opag-2021-0224},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103327916&doi=10.1515%2fopag-2021-0224&partnerID=40&md5=02823cc499e8283c2191f766a9f4b9b3},
affiliation={Department of Agribusiness, Universitas Muhammadiyah Yogyakarta, Yogyakarta, 55183, Indonesia},
abstract={This study aimed to describe the relationship structure of the red chili supply chain based on actors and their activities and to measure the efficiency of the red chili supply chain using the Performance Measuring System (PMS) framework. The distribution of the red chili supply chain in this study began with farmers in Kulon Progo, Yogyakarta, and extended to consumers in Jakarta, Indonesia. The sampling technique was carried out in several stages for 132 respondents consisting of red chili farmers and consumers. The supply chain relationship structure was analyzed descriptively, while the efficiency of the red chili supply chain was examined using the PMS framework, including indicators of production costs, transaction costs, profits, and return on investment. The results revealed that the relationship structure of the red chili supply chain was formed by three chains consisting of eight actors: farmers, middlemen, wholesalers at the regency level, auction markets, Kramat Jati Central Market or Pasar Induk Kramat Jati (PIKJ) dealers, PIKJ centeng, retailers, and consumers. According to production costs, transaction costs, profits, and the rate of return on investment, chain three was the most efficient with chain actors comprising farmers - auction markets - wholesalers at the regency level - PIKJ dealers - PIKJ centeng - retailers - consumers. © 2021 Susanawati et al., published by De Gruyter 2021.},
author_keywords={efficiency;  performance measurement system;  red chili;  supply chain},
references={Ahmad, K., Zabri, S.M., The development of performance measurement system under the supply chain management environment: The case of Malaysian manufacturing companis (2018) Manag Prod Eng Rev, 9 (1), pp. 3-12; Amaral, H., Situmeang, Y.P., Suarta, M., The effects of compost and biochar on the growth and yield of red chili plants (2019) J Phys Conf ser, 1402 (3), pp. 1-6; Anatan, L., Pengaruh Implementasi Praktik-Praktik Manajemen Rantai Pasokan Terhadap Kinerja Rantai Pasok Dan Keunggulan Kompetitif (2010) Jurnal Karisma, 4 (2), pp. 106-117; Aquilano, N.J., Operation management (2006) McGraw-Hill/Irwin. A Business Unit of the McGrawHill Companies, , 11th edn. New York Avenue of the Americans; Aramyan, L.H., (2007) Measuring Supply Chain Performance in the Agri-food Sector, , Wageningen Wageningen University; Assauri, S., (2011) Manajemen Produksi Dan Operasi, , Jakarta FEUI; Athaillah, T., Hamid, A.H., Analisis Efisiensi Kinerja Rantai Pasok Ikan Tuna di Bandar (2018) Marine Fishieries, 9 (2), pp. 169-181; Beamon, B.M., Measuring supply chain performance (1999) Int J Prod Res, 19 (3-4), pp. 275-292; Van J., C., Huang, W.C., Anindita, R., Chang, W.I., Yang, S.H., Price volatility of cayenne pepper and red chili pepper in Papua and Maluku Provinces, Indonesia (2017) Scholar J Econ Bus Manag, 4 (9), pp. 590-599; Chopra, S., Meidl, P., Strategy, planning, and operation (2004) Supply Chain Management, , London, England Pearson Education International; Devaux, A., Velasco, C., Jager, M., (2016) Integrating Agricultural Innovation and Inclusive Value Chain Development, pp. 159-173. , Washington, DC, USA International Food Policy Research Institute; Djuric, I., Götz, L., Export restrictions-do consumers really benefit? The wheat-to-bread supply chain in Serbia (2016) Food Policy, 63, pp. 112-123; Fiore, M., Galati, A., Gołȩbiewski, J., Drejerska, N., Stakeholders' involvement in establishing sustainable business models: The case of polish dairy cooperatives (2020) Br Food J, 122 (5), pp. 1671-1691; Giacomarra, M., Crescimanno, M., Sakka, G., Galati.a, Stakeholder engagement toward value co-creation in the F&B packaging industry (2019) EuroMed J Bus, 15 (3), pp. 315-331; Hamilton-Hart, N., Indonesia's quest for food self-sufficiency: A new agricultural political economy? (2019) J Contemp Asia, 49 (5), pp. 734-758; Hanafi, Mamduh, M., (2010) Manajemen Keuangan, , Yogyakarta BPFE; Heydari, J., Govindan, K., Jafari, A., Reverse and closed loop supply chain coordination by considering government role (2017) Transport Res Part D Transport Environ, 52, pp. 379-398; Kamble, S.S., Gunasekaran, A., Gawankar, S.A., Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications (2020) Int J Prod Econ, 219, pp. 179-194; Kamilaris, A., Fonts, A., Prenafeta-Bold, F.X., The rise of blockchain technology in agriculture and food supply chains (2019) Trends Food Sci Technol, 91, pp. 640-652; Karyani, T., Susanto, A., Djuwendah, E., Hapsari, H., Red chili agribusiness and the risks faced by the farmers (2020) IOP Conf ser Earth Environ Sci, 466 (1), pp. 1-8; Ketokivi, M., Mahoney, J.T., Transaction cost economics as a theory of supply chain efficiency (2020) Production and Operation Management, , New Jersey, USA Wiley; Kurniawan, M., Santoso, I., Kamal, M.A., Risk management of shallot supply chain using failure mode effect analysis and analytic network process (case study in Batu, East Java) (2019) IOP Conf ser Earth Environ Sci, 230 (1), p. 012055; Rusliyadi, M., Libin, W., Agriculture development programs for poverty reduction evidences from Indonesia and China - Comparative study (2018) Asian J Agric Rural Dev, 8 (2), pp. 104-118; Manders, J.H.M., Caniels, M.C.J., Ghijsen, P.W.T., Exploring supply chain flexibility in a FMCG food supply chain (2016) J Purch Supply Manag, 22 (3), pp. 181-195; Morgan, C., Structure, speed, and salience: Performance measurement in the supply chain (2004) Bus Process Manag J, 10 (5), pp. 522-536; Morley, S., Kennedy, A., Pradesha, A., Hadiwidjaja, G., (2019) The Role of Agriculture in the Structural Transformation of Indonesia, , Washington, DC, USA International Food and Policy Research Institute; Munawir, (2004) Analisis Laporan Keuangan, , Yogyakarta Liberty; Nurdiani, N., Teknik Sampling Snowball Dalam Penelitian Lapangan Architecture (2014) ComTech Comput Math Eng Appl, 5 (2), pp. 1110-1118; Perdana, T., Hermiatin, F.R., Pratiwi, A.S.N., Ginanjar, T., Lean production on chili pepper supply chain using value stream mapping (2018) MIMBAR: Jurnal Sosial Dan Pembangunan, 34 (2), pp. 311-320; Pohlmann, C.R., Scavarda, A.J., Alves, M.B., Korzenowski, A.L., The role of the focal company in sustainable development goals: A Brazilian food poultry supply chain case study (2020) J Clean Prod, 245, p. 118798; Pucci, T., Casprini, E., Galati, A., Zanni, L., The virtuous cycle of stakeholder engagement in developing a sustainability culture: Salcheto winery (2018) J Bus Res, 119, pp. 364-376; Pujawan, I., Mahendrawathi, (2017) Supply Chain Management, , Edisi 3 Yogyakarta Andi; Rahayu, R., Purnomo, E.P., Malawani, A.D., Using the 'return on investment' strategy to sustain logistic supply provider toward Indonesia's logistic policy (2020) J Govt Civil Soc, 4 (2), pp. 201-218; Romadani, A., Mg Wi Endang, N.P., Sulasmiyati, S., Analisis return on investment (ROI) residual income (RI) (Studi Pada Perusahaan Makanan Dan Minuman Yang Terdaftar di Bursa Efek Indonesia Tahun 2011-2014) (2016) Jurnal Administrasi Bisnis, 3, pp. 1-8; Romeida, A., Ganefianti, D.W., Barchia, M.F., Supanjani, Herawati, R., Plant growth regulator formulation for propagating red chili UNIB CH23 hybrid through stem cutting (2020) Int J Agric Technol, 16 (4), pp. 959-974; Sarsour, W.M., Sabri, S.R.M., Evaluating the investment in the Malaysian construction sector in the long-run using the modified internal rate of return: A Markov chain approach (2020) J Asian Financ Econ Bus, 7 (8), pp. 281-287; Soepatini, N., Isa, A.P., Syamsudin, M., Supply chain analysis and commodity marketing of chilli in Subosukowonosraten (2018) International Conference on Economics, Business and Economic Education 2018, pp. 957-973. , KnE Social Sciences; Solekhah, U., Aspiranti, T., Analisis Efisiensi Kinerja Manajemen Rantai Paok Komoditas Paprika di Desa Pasarlingu Cisarua Kabupaten Bandung Barat (Studi Kasus Pada UKM Paprici Segar Segar Barokah di Desa Pasarlingu Cisarua Kabupaten Bandung Barat) (2018) Prosiding Manajemen Bandung Program Studi Manajemen Fakultas Ekonomi Dan Bisnis Universitas Islam Bandung, pp. 188-196; Springer-Heinze, A., (2007) Valuelinks Manual: The Methodology of Value Chain Promotion, , Eschborn Deutsche Gesellschaft für Internationale Zusammenarbeit; Stranieri, S., Miranda, F.R., Meuwissen, P.M., Soregaroli.c, Exploring the impact of blockchain on the performance of agri-food supply chains (2021) Food Control, 119, p. 107495; Sufa, M.F., Wigaringtyas, L.D., Munawir, H., Strategi Peningkatan Kinerja Rantai Pasok UKM Batik Dengan Supply Chain Operation Reference (SCOR) (2016) IENACO (Industrial Engineering National Conference); Sugiyono, Metode Penelitian Bisnis (2017) Pendekatan Kuantitatif, Kualitatif, Kombinasi, Dan R&D, , Bandung CV Alfabeta; Sunny, J., Undralla, N., Pillai, V.M., Supply chain transparency through blockchain-based traceability: An overview with demonstration (2020) Comput Ind Eng, 150, p. 106895; Suwarsinah, H.K., Harwanti, N.F., Hastuti, Firdaus, M., The pricing system of red onion and red chili commodities (2018) Jurnal Management and Agribusiness, 15 (2), pp. 150-161; Syamsudin, L., (2011) Manajemen Keuangan Perusahaan, , Jakarta Rajagrafindo; Vorst, J., (2004) Supply Chain Management: Theory and Practice, , Wageningen, The Netherlands Elsevier; Webber, C.M., Labaste, P., (2009) Building Competitiveness in Africa's Agriculture: A Guide to Value Chain Concepts and Applications, , Washington World Bank; Yanuarti, Afsari, (2016) Profil Komoditas Barang Kebutuhan Pokok Dan Barang Penting "komoditas Cabai", , Jakarta Kementrian Perdagangan RI; Yildiz, K., Ahi, M.T., Innovative decision support model for construction supply chain performance management (2020) Prod Plann Control, pp. 1-13; Yuliarti, Y., Fitrani, M., Studi Eksplorasi Permasalahan di Pasar Induk Kramat Jati Jakarta (2011) Planesa, 2 (2), pp. 105-113; Yun, Y., Kurniawan, A., Supply Chain N Logistik Dalam Kaitannya Dengan Ketahanan Pangan di Pedesaan (2015) Prosiding Sembistek 2014, pp. 63-75; Zamrodah, Y., Pintakami, L.B., Pendapatan Dan Kelayakan Usahatani Cabai Rawit (2020) J Agric Soc Econ, 1 (1), pp. 48-53},
correspondence_address1={Susanawati; Department of Agribusiness, Indonesia; email: susanawati@umy.ac.id},
publisher={De Gruyter Open Ltd},
issn={23919531},
language={English},
abbrev_source_title={Open Agric.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sharma2021254,
author={Sharma, S.K. and George, S.A.},
title={A vendor recommendation model using neural networks},
journal={International Journal of Process Management and Benchmarking},
year={2021},
volume={11},
number={2},
pages={254-270},
doi={10.1504/IJPMB.2021.113743},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103094734&doi=10.1504%2fIJPMB.2021.113743&partnerID=40&md5=d5202f6e371b7b8ac71e0594809c455e},
affiliation={Department of Management, New Academic Building, BITS Pilani, Rajasthan, PIN-333031, India; SP Jain Institute of Management and Research, Bhavan's Campus, Munshi Nagar, Dadabhai Road, Andheri West, Mumbai, 400 058, India},
abstract={Suppliers today are considered as a logical extension to a firm that provide critical inputs to its processes. Supplier performance evaluation is vital for any firm to carry out supplier selection and to practice effective supply chain management. Further, the supplier performance measurement system should continuously evolve in a company as a process for improving the capabilities, execution effectiveness and efficiency. Though the supplier performance systems have been well investigated in the literature, very few studies have proposed an order-based supplier selection approach that takes into account the changing priorities of the customer. The research tries to address this gap by attempting to establish a dynamic linkage between customer order priorities and supplier characteristics. A neural network model for the order driven vendor recommendation system that ranks the suppliers as per the requirements of the order has been proposed. © 2021 Inderscience Enterprises Ltd.},
author_keywords={Case application;  Neural network;  Vendor selection},
references={Ahmad, M.T., Mondal, S., Dynamic supplier selection model under two-echelon supply network (2016) Expert Systems with Applications, 65 (3), pp. 255-270; Ahmadi, H.B., Petrudi, S.H.H., Wang, X., Integrating sustainability into supplier selection with analytical hierarchy process and improved grey relational analysis: a case of telecom industry (2017) The International Journal of Advanced Manufacturing Technology, 90 (9-12), pp. 2413-2427. , Nos; Aissaoui, N., Haouari, M., Hassini, E., Supplier selection and order lot sizing modeling: a review (2007) Computers and Operations Research, 34 (12), pp. 3516-3540; Amorim, P., Curcio, E., Almada-Lobo, B., Barbosa-Póvoa, A.P., Grossmann, I.E., Supplier selection in the processed food industry under uncertainty (2016) European Journal of Operational Research, 252 (3), pp. 801-814; Asadabadi, M.R., A customer-based supplier selection process that combines quality function deployment, the analytic network process and a Markov chain (2017) European Journal of Operational Research, 26 (3), pp. 1049-1062; Ávila, P., Mota, A., Pires, A., Putnik, G., Teixeira, J., Supplier's selection model based on an empirical study (2012) Procedia Technology, 5, pp. 625-634; Bhutta, K.S., Huq, F., Supplier selection problem: a comparison of the total cost of ownership and analytic hierarchy process approaches (2002) Supply Chain Management: an International Journal, 7 (3), pp. 126-135; Cagnin, F., Oliveira, M.C., Simon, A.T., Helleno, A.L., Vendramini, M.P., 'Proposal of a method for selecting suppliers considering risk management: an application at the automotive industry (2016) International Journal of Quality and Reliability Management, 33 (4), pp. 488-498; Cengiz, A.E., Aytekin, O., Ozdemir, I., Kusan, H., Cabuk, A., A multi-criteria decision model for construction material supplier selection (2017) Procedia Engineering, 196, pp. 294-301; Chai, J., Ngai, E.W., Multi-perspective strategic supplier selection in uncertain environments (2015) International Journal of Production Economics, 166 (7), pp. 215-225; Degraeve, Z., Labro, E., Roodhooft, F., An evaluation of vendor selection models from a total cost of ownership perspective (2000) European Journal of Operational Research, 125 (1), pp. 34-58; Erginel, N., Gecer, A., Fuzzy multi-objective decision model for calibration supplier selection problem (2016) Computers and Industrial Engineering, 102 (6), pp. 166-174; Ferreira, L., Borenstein, D., A fuzzy-Bayesian model for supplier selection (2012) Expert Systems with Applications, 39 (9), pp. 7834-7844; Ghodsypour, S.H., O'Brien, C., A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming (1998) International Journal of Production Economics, 56 (4), pp. 199-212; Guosheng, H., Guohong, Z., Comparison on neural networks and support vector machines in suppliers' selection (2008) Journal of Systems Engineering and Electronics, 19 (2), pp. 316-320; Hamdan, S., Cheaitou, A., Dynamic green supplier selection and order allocation with quantity discounts and varying supplier availability (2017) Computers and Industrial Engineering, 110 (5), pp. 573-589; Hashemi, S.H., Karimi, A., Tavana, M., An integrated green supplier selection approach with analytic network process and improved grey relational analysis (2015) International Journal of Production Economics, 159 (6), pp. 178-191; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: a literature review (2010) European Journal of Operational Research, 202 (1), pp. 16-24; Hosseini, S., Barker, K., A Bayesian network model for resilience-based supplier selection (2016) International Journal of Production Economics, 180 (7), pp. 68-87; Huang, Z., Chen, H., Hsu, C.J., Chen, W.H., Wu, S., Credit rating analysis with support vector machines and neural networks: a market comparative study (2004) Decision Support Systems, 37 (4), pp. 543-558; Jauhar, S.K., Pant, M., Integrating DEA with DE and MODE for sustainable supplier selection (2017) Journal of Computational Science, 21 (3), pp. 299-306; Kaur, P., Rachana, K.N.L., An intutionistic fuzzy optimization approach to vendor selection problem (2016) Perspectives in Science, 8 (2), pp. 348-350; Liao, Z., Rittscher, J., A multi-objective supplier selection model under stochastic demand conditions (2007) International Journal of Production Economics, 105 (1), pp. 150-159; Machado, M.J.C.V., Fortunato, J.A.G., Sustainable management methods: performance assessment in large companies (2018) International Journal of Business Innovation and Research, 16 (4), pp. 471-485; Maghsoodi, A.I., Kavian, A., Khalilzadeh, M., Brauers, W.K., CLUS-MCDA: a novel framework based on cluster analysis and multiple criteria decision theory in a supplier selection problem (2018) Computers and Industrial Engineering, 118 (3), pp. 409-422; Mandolini, M., Marilungo, E., Germani, M., A TCO model for supporting the configuration of industrial plants (2017) Procedia Manufacturing, 11 (4), pp. 1940-1949; Masi, D., Micheli, G.J., Cagno, E., A meta-model for choosing a supplier selection technique within an EPC company (2013) Journal of Purchasing and Supply Management, 19 (1), pp. 5-15; Mukherjee, S., Kar, S., A three phase supplier selection method based on fuzzy preference degree (2013) Journal of King Saud University-Computer and Information Sciences, 25 (2), pp. 173-185; Niknamfar, A.H., Niaki, S.T.A., A binary-continuous invasive weed optimization algorithm for a vendor selection problem (2018) Knowledge-Based Systems, 140 (4), pp. 158-172; Nydick, R.L., Hill, R.P., Using the analytic hierarchy process to structure the supplier selection procedure (1992) Journal of Supply Chain Management, 28 (2), pp. 31-36; Pae, J.H., Lee, H.J., How do value creation capabilities enhance new product creativity? (2017) International Journal of Business Innovation and Research, 13 (3), pp. 288-308; Park, S., Ok, C., Ha, C., A stochastic simulation-based holistic evaluation approach with DEA for vendor selection (2018) Computers and Operations Research, 100 (4), pp. 368-378; Parkouhi, S.V., Ghadikolaei, A.S., A resilience approach for supplier selection: using fuzzy analytic network process and grey VIKOR techniques (2017) Journal of Cleaner Production, 161 (7), pp. 431-451; Pourabdollahi, Z., Karimi, B., Mohammadian, K., Kawamura, K., A hybrid agent-based computational economics and optimization approach for supplier selection problem (2017) International Journal of Transportation Science and Technology, 6 (4), pp. 344-355; Ramanathan, R., Supplier selection problem: integrating DEA with the approaches of total cost of ownership and AHP (2007) Supply Chain Management: an International Journal, 12 (4), pp. 258-261; Rosenthal, E.C., Zydiak, J.L., Chaudhry, S.S., Vendor selection with bundling (1995) Decision Sciences, 26 (1), pp. 35-48; Ruiz-Torres, A.J., Mahmoodi, F., Zeng, A.Z., Supplier selection model with contingency planning for supplier failures (2013) Computers and Industrial Engineering, 66 (2), pp. 374-382; Simić, D., Kovačević, I., Svirčević, V., Simić, S., 50 years of fuzzy set theory and models for supplier assessment and selection: a literature review (2017) Journal of Applied Logic, 24 (1), pp. 85-96; Simić, D., Svirčević, V., Simić, S., A hybrid evolutionary model for supplier assessment and selection in inbound logistics (2015) Journal of Applied Logic, 13 (2), pp. 138-147; Sobhani, A., Wahab, M.I.M., Jaber, M.Y., The effect of working environment aspects on a vendor-buyer inventory model (2018) International Journal of Production Economics, , November; Sumant, M., Negi, A., Review of lean-green manufacturing practices in SMEs for sustainable framework (2018) International Journal of Business Innovation and Research, 17 (1), pp. 38-64; Tavana, M., Fallahpour, A., Di Caprio, D., Santos-Arteaga, F.J., A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection (2016) Expert Systems with Applications, 61 (4), pp. 129-144; Ting, S.C., Cho, D.I., An integrated approach for supplier selection and purchasing decisions (2008) Supply Chain Management: An International Journal, 13 (2), pp. 116-127; Valluri, A., Croson, D.C., Agent learning in supplier selection models (2005) Decision Support Systems, 39 (2), pp. 219-240; Weber, C.A., Current, J.R., Benton, W.C., Vendor selection criteria and methods (1991) European Journal of Operational Research, 50 (1), pp. 2-18; Wujek, B., Hall, P., Günes, F., (2016) Best Practices for Machine Learning Applications, , SAS Institute Inc, USA; Yang, C.C., Chen, B.S., Supplier selection using combined analytical hierarchy process and grey relational analysis (2006) Journal of Manufacturing Technology Management, 17 (7), pp. 926-941; Yu, C., Wong, T.N., An agent-based negotiation model for supplier selection of multiple products with synergy effect (2015) Expert Systems with Applications, 42 (1), pp. 223-237; Zerbini, F., Borghini, S., Release capacity in the vendor selection process (2015) Journal of Business Research, 68 (2), pp. 405-414; Zhang, R., Li, J., Wu, S., Meng, D., Learning to select supplier portfolios for service supply chain (2016) PloS One, 11 (5), p. e0155672},
correspondence_address1={Sharma, S.K.; Department of Management, India; email: satyendra.sharma1979@gmail.com},
publisher={Inderscience Publishers},
issn={14606739},
language={English},
abbrev_source_title={Int. J. Process Manage. Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lehyani2021283,
author={Lehyani, F. and Zouari, A. and Ghorbel, A. and Tollenaere, M.},
title={Defining and Measuring Supply Chain Performance: A Systematic Literature Review},
journal={EMJ - Engineering Management Journal},
year={2021},
volume={33},
number={4},
pages={283-313},
doi={10.1080/10429247.2020.1834309},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101472585&doi=10.1080%2f10429247.2020.1834309&partnerID=40&md5=297eb01b96b9dd6a42e4126c28322acd},
affiliation={Université de Sfax, Tunisia; University of Grenoble-Alpes, Tunisia},
abstract={Supply Chain Performance (SCP) is a critical issue in most industries. Organizations expect to enhance their SCP because it allows better competitiveness through minimizing useless resources activities, unsatisfactory delivery times, noncompliant quality, overproduction, etc. In this context, this paper provides an overview of the methods in use in Supply Chain Performance Measurement (SCPM) and investigates the most used methods for data collection, approaches, and data analysis techniques in order to measure SCP. This study was based on 180 of 682 related articles, published from 2008 to 2019, which were collected from the Crossref and Sciencedirect databases. Articles were reviewed in order to update methods and techniques which are the most used to define and analyze this performance, both in academic research or industrial case studies. Findings of this work can be very helpful to researchers and practitioners to know similitude and difference of each method, approach and tool exploited in the field of SCPM, in their particular area of activity. © 2021 American Society for Engineering Management.},
author_keywords={Data Analysis;  Data Collection;  Engineering Management Profession;  Organizational & Performance Assessment;  Performance Measurement Approaches;  Performance Measurement Methods;  Supply Chain Management;  Supply Chain Performance Measurement},
keywords={Industrial research, Academic research;  Area of activities;  Critical issues;  Data analysis techniques;  Industrial case study;  Supply chain performance;  Supply chain performance measurements;  Systematic literature review, Supply chains},
references={Agami, N., Saleh, M., Rasmy, M., An innovative fuzzy logic based approach for supply chain performance management (2014) IEEE Systems Journal, 8 (2), pp. 336-342; Aharonovitz, M.C.S., Vidal Vieira, J.G., Suyama, S.S., How logistics performance is affected by supply chain relationships (2018) International Journal of Logistics Management, 29 (1), pp. 284-307; Aigbedo, H., Assessment of the effect of location and financial variables on environmental management performance for industrial goods supply chains (2019) Journal of Environmental Management, 23615 April 2019, pp. 254-268; Ainapur, B., Singh, R.K., Vittal, P.P., Supply Chain performance enhancement using Toc–entropy–GP application (2012) The International Journal of Management, 1 (4), pp. 1-19; Alfalla-Luque, R., Marin-Garcia, J.A., Medina-Lopez, C., An analysis of the direct and mediated effects of employee commitment and supply chain integration on organisational performance (2015) International Journal of Production Economics, 162April 2015, pp. 242-257; Al-Hawari, T., Ahmed, A., Khrais, S., Mumani, A., Impact of assignment, inventory policies and demand patterns on supply chain performance (2013) International Journal of Simulation Modelling, 12 (3), pp. 164-177; Ambe, I.M., Key indicators for optimising supply chain performance: The case of light vehicle manufacturers in South Africa (2014) Journal of Applied Business Research, 30 (1), pp. 277-289; Anuar, A., (2017) The determinant of the supply chain practices that influence supply chain performance in Malaysia (pp. 1–14); Anvari, M.R.A., Nayeri, M.D., Razavi, S.M., How to measure supply chain performance (case study) (2011) International Review of Business Research Papers, 7 (2), pp. 230-244; Arif-Uz-Zaman, K., Nazmul Ahsan, A.M.M., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Ashrafuzzaman, M., Al-Maruf, A., Mahbubul, I.M., Malek, A.A., Mukaddes, A.M.M., Quality function deployment approach to measure supply chain performance: A case study on garments accessories industries (2016) International Journal of Industrial and Systems Engineering, 22 (1), pp. 96-120; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 115-128; Asrol, M., Marimin, M., Machfud, M., Supply chain performance measurement and improvement for sugarcane Agro-industry (2017) International Journal of Supply Chain Management, 6 (3), pp. 8-21; (2011), A benchmarking framework for supply chain collaboration: A Data Envelopment Analysis (DEA) application., International Journal of Business Administration, 2, (3), 19–31; Azadeh, A., Sheikhalishahi, M., An efficient taguchi approach for the performance optimization of health, safety, environment and ergonomics in generation companies (2015) Safety and Health at Work, 6 (2), pp. 77-84; Azbari, M.E., Olfat, L., Amiri, M., Soofi, J.B., A network data envelopment analysis model for supply chain performance evaluation: Real case of Iranian pharmaceutical industry (2014) International Journal of Industrial Engineering & Production Research, 25 (2), pp. 125-138; Badiezadeh, T., Saen, R.F., Samavati, T., Assessing sustainability of supply chains by double frontier network DEA: A big data approach (2017) Computers & Operations Research, 98October 2018, pp. 284-290; Badiezadeh, T., Saen, R.F., Samavati, T., Assessing sustainability of supply chains by double frontier network DEA: A big data approach (2018) Computers & Operations Research, 98, p. 290. , October 2018; Bakar, H.A., Lukman Hakim, A., Choy Chong, I., Lin, B., Measuring supply chain performance among public hospital laboratories (2009) International Journal of Productivity and Performance Management, 59 (1), pp. 75-97; Balakannan, K., Nallusamy, S., Chakraborty, P.S., Majumdar, G., Performance evaluation of supply chain and logistics management system using balanced score card for efficiency enhancement in Indian automotive industries (2016) Indian Journal of Science and Technology, 9 (35), pp. 2-9; Balfaqih, H., Al-Nory, M.T., Nopiah, Z.M., Saibani, N., Environmental and economic performance assessment of desalination supply chain (2017) Desalination, 406March 2017, pp. 2-9; Balfaqih, H., Nopiah, Z.M., Saibani, N., A conceptual framework for supply chain performance in desalination industry (2016) International Journal of Industrial Engineering and Management, 7 (2), pp. 95-101; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Computers in Industry, 82October 2016, pp. 135-150; Balfaqih, H., Yunus, B., Supply chain performance in electronics manufacturing industry (2014) Applied Mechanics and Materials, 554June 2014, pp. 633-637; Bedford, D.S., Bisbe, J., Sweeney, B., Performance measurement systems as generators of cognitive conflict in ambidextrous firms (2018) Accounting, Organizations and Society, 72January 2019, pp. 21-37; Bezerra, G.C., Gomes, C.F., Performance measurement practices in airports: multidimensionality and utilization patterns (2018) Journal of Air Transport Management, 70July 2018, pp. 113-125; Bhagwat, R., Chan, F.T., Sharma, M.K., Performance measurement model for supply chain management in SMEs (2008) International Journal of Globalisation and Small Business, 2 (4), pp. 428-445; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning and Control, 20 (8), pp. 678-690; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach (2014) Production Planning and Control, 25 (8), pp. 698-714; Bigliardi, B., Bottani, E., Supply chain performance measurement: A literature review and pilot study among Italian manufacturing companies (2014) International Journal of Engineering, Science and Technology, 6 (3), pp. 1-16; Bigliardi, B., Bottani, E., Amaratunga, D., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Bukhori, I.B., Widodo, K.H., Ismoyowati, D., Evaluation of poultry supply chain performance in XYZ slaughtering house Yogyakarta using SCOR and AHP method (2015) Agriculture and Agricultural Science Procedia, 32015, pp. 221-225; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) International Journal of Logistics Systems and Management, 18 (2), pp. 216-230; Charkha, P.G., Jaju, S.B., Identification of performance measures for textile supply chain: Case of small & medium size enterprise (2015) International Journal of Supply Chain Management, 4 (3), pp. 50-58; Chatterjee, K., Pamucar, D., Zavadskas, E.K., Evaluating the performance of suppliers based on using the R’AMATEL-MAIRCA method for green supply chain implementation in electronics industry (2018) Journal of Cleaner Production, 18420 May 2018, pp. 101-129; Chaudhary, T., Chanda, A., Evaluation and measurement of performance, practice and pressure of green supply chain in Indian manufacturing industries (2015) Uncertain Supply Chain Management, 3 (4), pp. 363-374; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Chorfi, Z., Benabbou, L., Berrado, A., An integrated performance measurement framework for enhancing public health care supply chains (2018) Supply Chain Forum: An International Journal, 19 (3), pp. 191-203; Clivillé, V., Berrah, L., Overall performance measurement in a supply chain: Towards a supplier-prime manufacturer based model (2012) Journal of Intelligent Manufacturing, 23 (6), pp. 2459-2469; Dev, N.K., Shankar, R., Gupta, R., Dong, J., Multi-criteria evaluation of real-time key performance indicators of supply chain with consideration of big data architecture (2019) Computers & Industrial Engineering, 128February 2019, pp. 1076-1087; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (2013) Production Planning & Control, 24 (8-9), pp. 702-720; Dinçer, H., Hacıoğlu, Ü., Yüksel, S., Balanced scorecard based performance measurement of European airlines using a hybrid multicriteria decision making approach under the fuzzy environment (2017) Journal of Air Transport Management, 63August 2017, pp. 17-33; Dissanayake, C.K., Cross, J.A., Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM (2018) International Journal of Production Economics, 201July 2018, pp. 102-115; Dörnhöfer, M., Schröder, F., Günthner, W.A., Logistics performance measurement system for the automotive industry (2016) Logistics Research, 9 (1), pp. 1-26; Drohomeretski, E., Da Costa, S.E.G., de Lima, E.P., de Oliveira Neves, T.R., The application of sustainable practices and performance measures in the automotive industry: A systematic literature review (2015) Engineering Management Journal, 27 (1), pp. 32-44; Drzymalski, J., Odrey, N.G., Wilson, G.R., Aggregating performance measures of a multi-echelon supply chain using the analytical network and analytical hierarchy process (2010) International Journal of Services Economics and Management, 2 (3), pp. 286-306; Du, L.Z., Tao, D.X., Yu, L.Q., Development and application of the performance evaluation system for integrated supply chain (2014) Advanced Materials Research, 1037October 2014, pp. 532-535; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things: Toward more practical SCPMS (2017) Industrial Management and Data Systems, 117 (2), pp. 267-286; Dwivedi, V.K., Pathak, P., Performance of supply chain in an uncertain environment using fuzzy logic (2015) International Journal on Design and Manufacturing Technologies, 9 (2), pp. 1-5; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38 (6), pp. 6681-6688; Eskafi, S., Roghanian, E., Jafari-Eskandari, M., Designing a performance measurement system for supply chain using balanced scorecard, path analysis, cooperative game theory and evolutionary game theory: A case study (2015) International Journal of Industrial Engineering Computations, 6 (2), pp. 157-172; Fabbe-Costes, N., Jahre, M., Supply chain integration and performance: A review of the evidence (2008) The International Journal of Logistics Management, 19 (2), pp. 130-154; Falsafi, M., Fornasiero, R., Terkaj, W., Performance evaluation of stochastic forward and reverse supply networks (2019) Procedia CIRP, pp. 1342-1347; Fancello, G., Schintu, A., Serra, P., An experimental analysis of Mediterranean supply chains through the use of cost KPIs (2018) Transportation Research Procedia, pp. 137-146; Galankashi, M.R., Memari, A., Anjomshoae, A., Ma’aram, A., Helmi, S.A., Selection of supply chain performance measurement frameworks in electrical supply chains (2014) International Journal of Industrial Engineering and Management, 3 (5), pp. 131-137. , http://www.iim.ftn.uns.ac.rs/ijiem_journal.php; Gallear, D., Ghobadian, A., Li, Y., O’Regan, N., Childerhouse, P., Naim, M., An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams (2014) Production Planning and Control, 2513-14, pp. 1182-1197; Gandhare, B.S., Akarte, M.M., Patil, P.P., Maintenance performance measurement–A case of the sugar industry (2018) Journal of Quality in Maintenance Engineering, 24 (1), pp. 79-100; Gashti, S.G., Seyedhosseini, S.M., Noorossana, R., Developing a framework for supply chain value measurement based on value index system: Real case study of manufacturing company (2012) African Journal of Business Management, 6 (44), pp. 11023-11034; Gawankar, S.A., Kamble, S., Raut, R., An investigation of the relationship between supply chain management practices (SCMP) on supply chain performance measurement (SCPM) of Indian retail chain using SEM (2017) Benchmarking: An International Journal, 24 (1), pp. 257-295; George, J., Pillai, V.M., Supply chain performance evaluation using spreadsheet simulation (2014) Applied Mechanics and Materials, 592July 2014, pp. 2699-2703; Ghadimi, P., Wang, C., Azadnia, A.H., Lim, M.K., Sutherland, J.W., Life cycle-based environmental performance indicator for the coal-to-energy supply chain: A Chinese case application (2019) Resources, Conservation and Recycling, 147August 2019, pp. 28-38; Golrizgashti, S., Supply chain value creation methodology under BSC approach (2014) Journal of Industrial Engineering International, 10 (3), pp. 1-15; Gong, K., Yan, H., Performance measurement of logistics service supply chain using bijective soft set (2015) Journal of Advanced Manufacturing Systems, 14 (1), pp. 23-40; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Govindan, K., Khodaverdi, R., Vafadarnikjoo, A., Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain (2015) Expert Systems with Applications, 42 (20), pp. 7207-7220; Green, J.K., W., Whitten, D., Inman, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply Chain Management: An International Journal, 13 (4), pp. 317-327; Guersola, M., Lima, E.P.D., Steiner, M.T.A., Supply chain performance measurement: A systematic literature review (2018) International Journal of Logistics Systems and Management, 31 (1), pp. 109-131; Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S., Big data and predictive analytics for supply chain and organizational performance (2017) Journal of Business Research, 70January 2017, pp. 308-317; Haavisto, I., Goentzel, J., Burcu Balcik, D., Measuring humanitarian supply chain performance in a multi-goal context (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (3), pp. 300-324; Haghighi, S.M., Torabi, S.A., Ghasemi, R., An integrated approach for performance evaluation in sustainable supply chain networks (with a case study) (2016) Journal of Cleaner Production, 13720 November 2016, pp. 579-597; Hammes, G., De Souza, E.D., Rodriguez, C.M.T., Millan, R.H.R., Herazo, J.C.M., Evaluation of the reverse logistics performance in civil construction (2020) Journal of Cleaner Production, p. 119212. , 248, (March 2020), ; Hasibuan, A., Arfah, M., Parinduri, L., Hernawati, T., Harahap, B., Sibuea, S.R., Sulaiman, O.K., Performance analysis of supply chain management with supply chain operation reference model (2018) Journal of Physics. Conference Series, 1007 (1), pp. 012029-012036. , IOP Publishing; Hassan, H., Nabil, E., Rady, M., A model for evaluating and improving supply chain performance (2015) International Journal of Computer Science and Software Engineering, 4 (11), pp. 283-302; Hemalatha, S., Rao, K.N., Rambabu, G., Venkatasubbaiah, K., Supply chain performance evaluation through AHM and Membership degree transformation (2017) Materials Today: Proceedings, 4 (8), pp. 7848-7858; Hong, Y., Zhong-Hua, Y., Supply chain dynamic performance measurement based on BSC and SVM (2013) International Journal of Computer Science Issues, 10 (1), pp. 271-277. , http://www.IJCSI.org; Huang, C.W., Assessing the performance of tourism supply chains by using the hybrid network data envelopment analysis model (2018) Tourism Management, 65April 2018, pp. 303-316; Huo, B., Flynn, B.B., Zhao, X., Supply chain power configurations and their relationship with performance (2017) Journal of Supply Chain Management, 53 (2), pp. 88-111; Hwang, Y.D., Lin, Y.C., Lyu, J.J., The performance evaluation of SCOR sourcing process—the case study of Taiwan’s TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Ibrahim, S.B., Hamid, A.A., Supply chain management practices and supply chain performance effectiveness (2012) International Journal of Science and Research, 3 (8), pp. 187-195; Islam, M.S., Tseng, M.L., Karia, N., Lee, C.H., Assessing green supply chain practices in Bangladesh using fuzzy importance and performance approach (2018) Resources, Conservation and Recycling, 131April 2018, pp. 134-145; Jabari, A., Kaabinezhad, A., Ghayem, H., Performance evaluation of the supply chain in Persian Gulf petrochemical holding company (2015) European Online Journal of Natural and Social Sciences, 2 (3s), pp. 3411-3418; Jaimes, W.A., Serna, M.D.A., Buritica, N.C., (2011) Key performance measures for supply chain management from the Colombian shipyard. In Thorsten, B., Carlos, J., & Wolfgang, K. (Eds.), , pp. 237-253). , Maritime logistics the global economy: Current trends and approaches, (. Josef Eul Verlag; Jauhar, S.K., Pant, M., Nagar, A.K., Sustainable educational supply chain performance measurement through DEA and differential evolution: A case on Indian HEI (2017) Journal of Computational Science, 19March 2017, pp. 138-152; Jiang, D.K., Ma, M.Z., Research on the supply chain performance appraisal system based on balanced scorecard (2014) Applied Mechanics and Materials, 668, pp. 1637-1640; Jothimani, D., Sarmah, S.P., Supply chain performance measurement for third party logistics (2014) Benchmarking: An International Journal, 21 (6), pp. 944-963; Kamakoty, J., A balanced score card approach to performance measurement of firms (2018) Industrial Engineering Journal, 11 (5), pp. 5-15; Kang, S., Moon, T., Impact of information exchange and supply chain integration on supply chain performance (2016) International Journal of u-and e-Service Science and Technology, 9 (7), pp. 237-246; Katiyar, R., Meena, P.L., Barua, M.K., Tibrewala, R., Kumar, G., Impact of sustainability and manufacturing practices on supply chain performance: Findings from an emerging economy (2018) International Journal of Production Economics, pp. 303-316; Kazancoglu, Y., Kazancoglu, I., Sagnak, M., Fuzzy DEMATEL-based green supply chain management performance: Application in cement industry (2018) Industrial Management and Data Systems, 118 (2), pp. 412-431; Khalili-Damghani, K., Taghavi-Fard, M., Abtahi, A.R., A fuzzy two-stage DEA approach for performance measurement: Real case of agility performance in dairy supply chains (2012) International Journal of Applied Decision Sciences, 5 (4), pp. 293-317; Khamseh, A., Zahmatkesh, D., Supply chain performance evaluation using robust data envelopment analysis (2015) Uncertain Supply Chain Management, 3 (3), pp. 311-320; Kozarević, S., Puška, A., Use of fuzzy logic for measuring practices and performances of supply chain (2018) Operations Research Perspectives, 52018, pp. 150-160; Kumar, A., Mukherjee, K., Adlakha, A., Dynamic performance assessment of a supply chain process: A case from pharmaceutical supply chain in India (2015) Business Process Management Journal, 21 (4), pp. 743-770; Kusrini, E., Subagyo, Masruroh, N.A., Good criteria for supply chain performance measurement (2014) International Journal of Engineering Business Management, 6(January 2014), pp. 9-16; Landström, A., Almström, P., Winroth, M., Andersson, C., Öberg, A.E., Kurdve, M., Zackrisson, M., A life cycle approach to business performance measurement systems (2018) Procedia Manufacturing, 25October 2018, pp. 126-133; Lauras, M., Lamothe, J., Pingaud, H., A business process oriented method to design supply chain performance measurement systems (2011) International Journal of Business Performance Management, 12 (4), pp. 354-376; Lee, A.H., Chen, W.C., Chang, C.J., A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan (2008) Expert Systems with Applications, 34 (1), pp. 96-107; Leksono, E.B., Suparno, S., Vanany, I., Development of performance indicators relationships on sustainable healthcare supply chain performance measurement using balanced scorecard and DEMATEL (2018) International Journal on Advanced Science Engineering Information Technology, 8 (1), pp. 115-122; Lenin, K., Measuring supply chain performance in the healthcare industry (2014) Science Journal of Business and Management, 2 (5), pp. 136-142; Li, G., Shao, S., Zhang, L., Green supply chain behavior and business performance: Evidence from China (2019) Technological Forecasting and Social Change, 144July 2019, pp. 445-455; Liang, Y.H., Performance measurement of interorganizational information systems in the supply chain (2015) International Journal of Production Research, 53 (18), pp. 5484-5499; Lima-Junior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on SCOR metrics and multilayer perceptron neural networks (2019) International Journal of Production Economics, 212June 2019, pp. 19-38; Lin, L.C., Li, T.S., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Software Quality Journal, 18 (3), pp. 387-406; Lin, Y., Tseng, M.L., Chiu, A.S., Wang, R., Implementation and performance evaluation of a firm’s green supply chain management under uncertainty (2014) Industrial Engineering and Management Systems, 13 (1), pp. 15-28; Liu, F.H.F., Liu, Y.C., A methodology to assess the supply chain performance based on gap-based measures (2017) Computers & Industrial Engineering, 110August 2017, pp. 550-559; Liu, L., Yang, H., Research on the enterprise performance management information system development and robustness optimization based on data regression analysis and mathematical optimization theory (2016) International Journal of Security and Its Applications, 10 (4), pp. 377-390; Liu, Y., Eckert, C., Yannou-Le Bris, G., Petit, G., A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain (2019) Computers & Industrial Engineering, 127January 2019, pp. 196-212; Lu, Q., Goh, M., De Souza, R., A SCOR framework to measure logistics performance of humanitarian organi-zations (2016) Journal of Humanitarian Logistics and Supply Chain Management, 6 (2), pp. 222-239; Lyu, H., Zhou, Z., Zhang, Z., Measuring knowledge management performance in organizations: An integrative framework of balanced scorecard and fuzzy evaluation (2016) Information, 7 (2), p. 29; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183January 2017, pp. 299-315; Mandal, S., An empirical investigation on integrated logistics capabilities, supply chain agility and firm performance (2016) International Journal of Services and Operations Management, 24 (4), pp. 504-530; Mani, V., Gunasekaran, A., Delgado, C., Enhancing supply chain performance through supplier social sustainability: An emerging economy perspective (2018) International Journal of Production Economics, 195January 2018, pp. 259-272; Manikandan, M., Chidambaranathan, S., Developing a two dimensional framework to review the supply chain performance measurement literature (2017) International Journal of Engineering Trends and Technology, 43 (2), pp. 86-96; Mathivathanan, D., Govindan, K., Haq, A.N., Exploring the impact of dynamic capabilities on sustainable supply chain firm’s performance using Grey-Analytical Hierarchy Process (2017) Journal of Cleaner Production, 14720 March 2017, pp. 637-653; Mathiyalagan, P., Mannan, K.T., Parthiban, P., Performance evaluation in supply chain using balanced scorecard (2014) International Journal of Advances in Mechanical and Automobile Engineering, 1 (1), pp. 4-10; Meng, K., Lou, P., Peng, X., Prybutok, V., A hybrid approach for performance evaluation and optimized selection of recoverable end-of-life products in the reverse supply chain (2016) Computers & Industrial Engineering, 98August 2016, pp. 171-184; Meng, Q., Li, Z., Liu, H., Chen, J., Agent-based simulation of competitive performance for supply chains based on combined contracts (2017) International Journal of Production Economics, 193November 2017, pp. 663-676; Mesic, Ž., Molnár, A., Cerjak, M., Assessment of traditional food supply chain performance using triadic approach: The role of relationships quality (2018) Supply Chain Management: An International Journal, 23 (5), pp. 396-411; Mokhtar, A.R.M., Genovese, A., Brint, A., Kumar, N., Improving reverse supply chain performance: The role of supply chain leadership and governance mechanisms (2019) Journal of Cleaner Production, 21610 April 2019, pp. 42-55; Moons, K., Waeyenbergh, G., Pintelon, L., Timmermans, P., De Ridder, D., Performance indicator selection for operating room supply chains: An application of ANP (2019) Operations Research for Health Care, 23December 2019, p. 100229; Morita, M., Machuca, J.A., Pérez Díez de Los Ríos, J.L., Integration of product development capability and supply chain capability: The driver for high performance adaptation (2018) International Journal of Production Economics, 200June 2018, pp. 68-82; Motadel, M.R., Kordestani, M., Presenting a model for performance evaluation of supply chain with value analysis approach (2013) Interdisciplinary Journal of Contemporary Research in Business, 5 (4), pp. 185-195; Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain. Resources (2011) Conservation and Recycling, 55 (6), pp. 593-603; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) Journal of Operations and Supply Chain Management, 6 (2), pp. 94-113; Ndonye, S.K., Influence of information technology on logistics performance in Kenya with reference to cargo transportation (2014) Researchjournali’s Journal of Supply Chain Management, 1 (2), pp. 1-18; Nikabadi, M.S., Shahrabi, M.A., A framework for evaluation criteria of supply chain performance in automotive industry: The case of the Iranian automotive supply chain (2015) International Journal of Automotive Technology and Management, 15 (4), pp. 358-380; Omar, A.S., Waweru, M., Rimiru, R., A literature survey: Fuzzy logic and qualitative performance evaluation of supply chain management (2015) The International Journal of Engineering and Science, 4 (5), pp. 56-63; Panjehfouladgaran, H., Yusuff, R., Fuzzy performance measurement for supply chain management in Malaysian rubber glove manufacturer (2016) International Journal of Logistics Systems and Management, 24 (2), pp. 178-199; Panov, Z., Ristova, E., Stefanovska Ceravolo, L., Supply Chain’s Performance Measurement System and Integrated Framework (2011) International Journal for Science, Techniques and Innovations for the Industry MTM (Machines, Technologies, Materials), 22011, pp. 22-26; Partiwi, S.G., Agusta, Y.R., Designing model of performance measurement system of sugar industry cluster based on integrated performance measurement system approach (2018) IPTEK Journal of Proceedings Series, 3 (3), pp. 50-54; Patil, K.S., Framework for supply chain performance evaluation-SCOR (2015) International Journal of Emerging Research in Management and Technology, 4 (6), pp. 114-121; Patil, S.K., Kant, R., Evaluating the impact of knowledge management adoption on supply chain performance by BSC-FANP approach: An empirical case study (2016) Tékhne, 14 (1), pp. 52-74; Peixoto, M.G.M., Musetti, M.A., Mendonça, M.C.A., Multivariate analysis techniques applied for the performance measurement of Federal university hospitals of Brazil (2018) Computers & Industrial Engineering, 126December 2018, pp. 16-29; Peters, M.D., Wieder, B., Sutton, S.G., Wakefield, J., Business intelligence systems use in performance measurement capabilities: Implications for enhanced competitive advantage (2016) International Journal of Accounting Information Systems, 21June 2016, pp. 1-17; Piriyakul, M., Kerdpitak, C., Mediation effects of logistics performance on collaboration and firm performance of palm oil companies: PLS path modeling (2011) Journal of Management and Sustainability, 1 (1), pp. 90-98; Pramod, V.R., Banwet, D.K., Performance measurement of SHER service supply chain: A balanced score card–ANP approach (2011) International Journal of Business Excellence, 4 (3), pp. 321-345; Prasad, C.V., A conceptual framework for measuring supply chain performance (2012) Journal of Business Management and Research, 2 (1), pp. 39-55; Pretorius, C., Ruthven, G.A., Von Leipzig, K., An empirical supply chain measurement model for a national egg producer based on the supply chain operations reference model (2013) Journal of Transport and Supply Chain Management, 7 (1), pp. 1-13; Pungchompoo, S., Dunyakul, Y., Effects of collaborative factors on supply chain performance measurement in Thai frozen shrimp supply chain (2017) 4th International Conference on Industrial Engineering and Applications (ICIEA’17), pp. 131-135. , Apr2123, Nagoya, Japan; Ramezankhani, M.J., Torabi, S.A., Vahidi, F., Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach (2018) Computers & Industrial Engineering, 126December 2018, pp. 531-548; Reddy, J.M., K., Neelakanteswara Rao, A., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30 (2019), pp. 40-47; Rezaei, A.H., Adressi, A., Supply chain performance evaluation using data envelopment analysis (A case study of tile industry) (2015) International Journal of Supply and Operations Management, 2 (2), pp. 748-758; Rezaei, J., van Roekel, W.S., Tavasszy, L., Measuring the relative importance of the logistics performance index indicators using Best Worst method (2018) Transport Policy, 68September 2018, pp. 158-169; Robb, D.J., Xie, B., Arthanari, T., Supply chain and operations practice and performance in Chinese furniture manufacturing (2008) International Journal of Production Economics, 112 (2), pp. 683-699; Ruiz-Benítez, R., López, C., Real, J.C., The lean and resilient management of the supply chain and its impact on performance (2018) International Journal of Production Economics, 203September 2018, pp. 190-202; Sahu, A.K., Datta, S., Mahapatra, S.S., Evaluation of performance index in resilient supply chain: A fuzzy-based approach (2017) Benchmarking: An International Journal, 24 (1), pp. 118-142; Sahu, M.S.K., Datta, S., Patel, S.K., Mahapatra, S.S., Decision-making scenario towards supply chain performance assessment in fuzzy context (2013) International Journal of Engineering Research and Technology, 6 (4), pp. 541-556; Sarode, A.D., Khodke, P.M., Performance measurement of supply chain management: A decision framework for evaluating and selecting supplier performance in a supply chain (2009) International Journal of Applied Management and Technology, 8 (1), pp. 1-21; Sarode, A.D., Khodke, P.M., A framework for performance measurement system of supply chain management (2011) International Journal of Advanced Engineering Technology, 2 (4), pp. 182-190; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: Application in the footwear industry (2015) International Journal of Production Research, 53 (16), pp. 4917-4926; Shafiee, M., Lotfi, F.H., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Applied Mathematical Modeling, 38 (21), pp. 5092-5112; Shaik, M.N., Abdul-Kader, W., A hybrid multiple criteria decision making approach for measuring comprehensive performance of reverse logistics enterprises (2018) Computers & Industrial Engineering, 123September 2018, pp. 9-25; Shashi, S., Singh, R., A key performance measures for evaluating cold supply chain performance in farm industry (2015) Management Science Letters, 5 (8), pp. 721-738; Shibin, K.T., Gunasekaran, A., Dubey, R., Explaining sustainable supply chain performance using a total interpretive structural modeling approach (2017) Sustainable Production and Consumption, 12October 2017, pp. 104-118; Sillanpää, I., Kess, P., The literature review of supply chain performance measurement in the manufacturing industry (2012) Management and Production Engineering Review, 3 (2), pp. 79-88; Simão, L.E., Gonçalves, M.B., Rodriguez, C.M.T., An approach to assess logistics and ecological supply chain performance using postponement strategies (2016) Ecological Indicators, 63, pp. 398-408. , April 2016; Singh, S.C., Pandey, S.K., Supply chain performance: A review of literature (2013) Journal of Supply Chain Management Systems, 2 (4), pp. 1-12; Sirsath, V.R., Dalu, R.S., Supply chain performance evaluation models: A study (2015) International Journal of Innovative Science Engineering & Technology, 2 (11), pp. 182-190; Ślusarczyk, B., Kot, S., Principles of the supply chain performance measurement (2012) Advanced Logistic Systems, 6 (1), pp. 17-24; Song, M.X., Morgan, X.Y., Leveraging core capabilities and environmental dynamism for food traceability and firm performance in a food supply chain: A moderated mediation model (2019) Journal of Integrative Agriculture, 18 (8), pp. 1820-1837; Stefanović, N., Stefanović, D., Supply chain performance measurement system based on scorecards and web portals (2011) Computer Science and Information Systems, 8 (1), pp. 167-192; Stefanovic, N., (2014) Proactive, , supply chain performance management with predictive analytics., The Scientific World Journal, 2014, (October), Article ID 528917, 17; Sufiyan, M., Haleem, A., Khan, S., Khan, M.I., Evaluating food supply chain performance using hybrid fuzzy MCDM technique (2019) Sustainable Production and Consumption, 20October 2019, pp. 40-57; Sun, J., Wang, C., Ji, X., Wu, J., Performance evaluation of heterogeneous bank supply chain systems from the perspective of measurement and decomposition (2017) Computers & Industrial Engineering, 113November 2017, pp. 891-903; Supeekit, T., Somboonwiwat, T., Kritchanchai, D., DEMATEL-modified ANP to evaluate internal hospital supply chain performance (2016) Computers & Industrial Engineering, 102December 2016, pp. 318-330; Taghipour, M., Bagheri, M., Khodarezaei, M., Farid, F., Supply chain performance evaluation in the IT industry (2015) International Journal of Research and Reviews in Applied Sciences, 23 (2), p. 144; Tajbakhsh, A., Hassini, E., Performance measurement of sustainable supply chains: A review and research questions (2015) International Journal of Productivity and Performance Management, 64 (6), pp. 744-783; Tang, K.H.D., Dawal, S.Z.M., Olugu, E.U., Integrating fuzzy expert system and scoring system for safety performance evaluation of offshore oil and gas platforms in Malaysia (2018) Journal of Loss Prevention in the Process Industries, 56November 2018, pp. 32-45; Tarasewicz, R., Integrated approach to supply chain performance measurement–results of the study on polish market (2016) Transportation Research Procedia, pp. 1433-1442; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) International Journal of Production Research, 53 (21), pp. 6473-6494; Tavana, M., Kaviani, M.A., Di Caprio, D., Rahpeyma, B., A two-stage data envelopment analysis model for measuring performance in three-level supply chains (2016) Measurement, 78, pp. 322-333. , January 2016; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Thanki, S., Thakkar, J., A quantitative framework for lean and green assessment of supply chain performance (2018) International Journal of Productivity and Performance Management, 67 (2), pp. 366-400; Toloo, M., Allahyar, M., A simplification generalized returns to scale approach for selecting performance measures in data envelopment analysis (2018) Measurement, 121June 2018, pp. 327-334; Trivedi, A., Rajesh, K., A framework for performance measurement in supply chain using balanced score card method: A case study (2013) International Journal of Recent Trends in Mechanical Engineering, 4 (1), pp. 20-23; Trkman, P., McCormack, K., De Oliveira, M.P.V., Ladeira, M.B., The impact of business analytics on supply chain performance (2010) Decision Support Systems, 49 (3), pp. 318-327; Tseng, M.L., Lim, M.K., Wong, W.P., Chen, Y.C., Zhan, Y., A framework for evaluating the performance of sustainable service supply chain management under uncertainty (2018) International Journal of Production Economics, 195January 2018, pp. 359-372; Tseng, M.L., Wu, K.J., Lim, M.K., Wong, W.P., Data-driven sustainable supply chain management performance: A hierarchical structure assessment under uncertainties (2019) Journal of Cleaner Production, 227August 2019, pp. 760-771; Tyagi, M., Kumar, P., Kumar, D., Assessment of critical enablers for flexible supply chain performance measurement system using fuzzy DEMATEL approach (2015) Global Journal of Flexible Systems Management, 16 (2), pp. 115-132; Uygun, Ö., Dede, A., Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques (2016) Computers & Industrial Engineering, 102December 2016, pp. 502-511; Vaidya, O., Hudnurkar, M., Multi-criteria supply chain performance evaluation: An Indian chemical industry case study (2013) International Journal of Productivity and Performance Management, 62 (3), pp. 293-316; Vanalle, R.M., Ganga, G.M.D., Godinho Filho, M., Lucato, W.C., Green supply chain management: An investigation of pressures, practices, and performance within the Brazilian automotive supply chain (2017) Journal of Cleaner Production, 151May 2017, pp. 250-259; Vanichchinchai, A., The relationship between employee involvement, partnership management and supply performance: Findings from a developing country (2012) International Journal of Productivity and Performance Management, 61 (2), pp. 157-172; Varma, S., Wadhwa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance: Application of analytical hierarchy process to balanced scorecard (2008) Asia Pacific Journal of Marketing and Logistics, 20 (3), pp. 343-356; Verdecho, M.J., Alfaro-Saiz, J.J., Rodriguez–Rodriguez, R., A performance measurement framework for monitoring supply chain sustainability (2014) 6th International Conference on Industrial Engineering and Industrial Management, , Vigo: July18-20, 2012, Annals of Industrial Engineering 2012: Industrial Engineering: overcoming the crisis, Prado-Prado J., García-Arca J. (eds), 331-338, Springer London, &, In; Wibowo, M.A., Sholeh, M.N., The analysis of supply chain performance measurement at construction project (2015) Procedia Engineering, pp. 25-31; Wittstruck, D., Teuteberg, F., (2011) Development and simulation of a balanced scorecard for sustainable supply chain management–a system dynamics approach, , February 16–18, &,. Bernstein, A., & Schwabe, G., (Eds.), Proceedings of the 10th international conference on Wirtschaftsinformatik, Zürich, Switzerland. WI 2.011, (2, 332–341; Wong, W.P., Performance evaluation of supply chain in stochastic environment: Using a simulation based DEA framework (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 203-228; Wong, W.P., Jaruphongsa, W., Lee, L.H., Supply chain performance measurement system: A Monte Carlo DEA-based approach (2008) International Journal of Industrial and Systems Engineering, 3 (2), pp. 162-188; Xu, J., Li, B., Wu, D., Rough data envelopment analysis and its application to supply chain performance evaluation (2009) International Journal of Production Economics, 122 (2), pp. 628-638; Xu, Y., Liu, J., Wu, J., Luo, C., Improving supply chain performance through industry standards use and community socialization: A perspective of standards consortia (2016) International Journal of Physical Distribution and Logistics Management, 46 (8), pp. 763-782; Yang, J., Integrative performance evaluation for supply chain system based on logarithm triangular fuzzy number-AHP method (2009) Kybernetes, 38 (10), pp. 1760-1770; Yee, R.W., Yeung, A.C., Cheng, T.E., An empirical study of employee loyalty, service quality and firm performance in the service industry (2010) International Journal of Production Economics, 124 (1), pp. 109-120; You, T., Jung, W.S., A system dynamics analysis of national R&D performance measurement system in Korea (2018) Industrial Engineering & Management Systems, 17 (4), pp. 833-839; Yu, W., Chavez, R., Jacobs, M.A., Feng, M., Data-driven supply chain capabilities and performance: A resource-based view. Transportation research part E (2018) Logistics and Transportation Review, 114June 2018, pp. 371-385; Yusuf, Y., Menhat, M.S., Abubakar, T., Ogbuke, N.J., Agile capabilities as necessary conditions for maximising sustainable supply chain performance: An empirical investigation (2020) International Journal of Production Economics, p. 107501. , 222, (April 2020), ; Yusuf, Y.Y., Gunasekaran, A., Musa, A., Dauda, M., El-Berishy, N.M., Cang, S., A relational study of supply chain agility, competitiveness and business performance in the oil and gas industry (2014) International Journal of Production Economics, 147Part B, January 2014, pp. 531-543; Zervopoulos, P.D., Brisimi, T.S., Emrouznejad, A., Cheng, G., Performance measurement with multiple interrelated variables and threshold target levels: Evidence from retail firms in the US (2016) European Journal of Operational Research, 250 (1), pp. 262-272; Zhai, X.Y., Ye, H.K., A novel evaluation indicator system and evaluation method for supply chain performance of food production (2015) Advance Journal of Food Science and Technology, 7 (4), pp. 255-259; Zhou, X., Wang, Y., Chai, J., Wang, L., Wang, S., Lev, B., Sustainable supply chain evaluation: A dynamic double frontier network DEA model with interval type-2 fuzzy data (2019) Information Sciences, 504December 2019, pp. 394-421; Zizlavsky, O., The balanced scorecard: Innovative performance measurement and management control system (2014) Journal of Technology Management and Innovation, 9 (3), pp. 210-222},
correspondence_address1={Zouari, A.; Institut Supérieur de Gestion Industrielle de Sfax, Tunisia; email: ala.zouari@isgis.usf.tn},
publisher={Taylor and Francis Ltd.},
issn={10429247},
coden={EMJOE},
language={English},
abbrev_source_title={EMJ Eng Manage J},
document_type={Article},
source={Scopus},
}

@ARTICLE{Shaw2021382,
author={Shaw, S. and Grant, D.B. and Mangan, J.},
title={A supply chain practice-based view of enablers, inhibitors and benefits for environmental supply chain performance measurement},
journal={Production Planning and Control},
year={2021},
volume={32},
number={5},
pages={382-396},
doi={10.1080/09537287.2020.1737977},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081395864&doi=10.1080%2f09537287.2020.1737977&partnerID=40&md5=05075b7cf2b2d237fc70d57c35970a33},
affiliation={Hull University Business School, University of Hull, Hull, United Kingdom; Supply Chain Management and Social Responsibility, Hanken School of Economics, Helsinki, Finland; Centre for Logistics Research, Thammasat University, Bangkok, Thailand; School of Marine Science and Technology, Newcastle University, Newcastle, United Kingdom},
abstract={Organisations face ever increasing pressure to deliver triple-bottom-line performance results in their supply chains. Yet despite the importance and complexity associated with environmental supply chain performance measurement (ESCPM), organisations struggle to achieve this. The purpose of this paper is to identify the important enablers, inhibitors and benefits to implementing ESCPM as a practice in firms. Data were collected from three focus groups and an industry survey of 388 UK supply chain professionals in a three-phase empirical study. Eighteen enablers, seventeen inhibitors and eleven benefits were identified and ranked in importance. A supply chain practice-based view was utilised as an overarching theoretical lens to conceptualise the study’s findings and propose nineteen antecedents, arranged in a hierarchical framework, to enable practitioners to make effective ESCPM decisions. This paper provides an up-to-date review of the factors which influence ESCPM practice, addressing the need for additional research in this area. © 2020 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={benefits;  enablers;  Environmental supply chain performance measurement;  inhibitors;  supply chain management},
keywords={Computer applications;  Corrosion inhibitors;  Industrial engineering;  Operations research, , inhibitor;  benefits;  Empirical studies;  enablers;  Environmental supply chains;  Industry surveys;  Supply chain practices;  Triple Bottom Line, Supply chain management},
references={Ahi, P., Searcy, C., An Analysis of Metrics Used to Measure Performance in Green and Sustainable Supply Chains (2015) Journal of Cleaner Production, 86, pp. 360-377; Barney, J., Firm Resources and Sustained Competitive Advantage (1991) Journal of Management, 17 (1), pp. 99-120; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 Years of Performance Measurement in Sustainable Supply Chain Management–What Has Been Achieved? (2015) Supply Chain Management: An International Journal, 20 (6), pp. 664-680; Sundar, B., Rajan Varadarajanjohn, P.F., Sustainable Competitive Advantage in Service Industries: A Conceptual Model and Research Propositions (1993) Journal of Marketing, 57 (4), pp. 83-99. , . doi; Bhattacharya, A., Mohapatra, V., Kumar, P.K., Dey, M., Brady, M.K., Tiwarinudurupati, S.S., Green Supply Chain Performance Measurement Using Fuzzy ANP-Based Balanced Scorecard: A Collaborative Decision-Making Approach (2014) Production Planning & Control, 25 (8), pp. 698-714; Björklund, M., Martinsenabrahamsson, M., Performance Measurements in the Greening of Supply Chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Busse, C., Doing Well by Doing Good? The Self‐Interest of Buying Firms and Sustainable Supply Chain Management (2016) Journal of Supply Chain Management, 52 (2), pp. 28-47; Carterliane Easton, C.R.P., Sustainable Supply Chain Management: Evolution and Future Directions (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 46-62; Carter, C.R., Kosmol, T., Kaufmann, L., Toward a Supply Chain Practice View (2017) Journal of Supply Chain Management, 53 (1), pp. 114-122; Carter, C.R., Rogers, D.S., A Framework of Sustainable Supply Chain Management: Moving toward New Theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Carter, C.R., Washispack, S., Mapping the Path Forward for Sustainable Supply Chain Management: A Review of Reviews (2018) Journal of Business Logistics, 39 (4), pp. 242-247; Chahalsharma, H.D., Implications of Corporate Social Responsibility on Marketing Performance: A Conceptual Framework (2006) Journal of Services Research, 6 (1), pp. 205-216; Chienshih, M.K.-H., An Empirical Study of the Implementation of Green Supply Chain Management Practices in the Electrical and Electronic Industry and Their Relation to Organizational Performances (2007) International Journal of Environmental Science and Technology, 4 (3), pp. 383-394; De Angelis, R., Howard, M., Miemczyk, J., Supply Chain Management and the Circular Economy: Towards the Circular Supply Chain (2018) Production Planning & Control, 29 (6), pp. 425-437; Deegan, C., Shelly, M., Corporate Social Responsibilities: Alternative Perspectives about the Need to Legislate (2014) Journal of Business Ethics, 121 (4), pp. 499-526; Diabatgovindan, A., An Analysis of the Drivers Affecting the Implementation of Green Supply Chain Management (2011) Resources Conservation and Recycling, 55 (6), pp. 659-667; Diabat, A., Kannan, D., Mathiyazhagan, K., Analysis of Enablers for Implementation of Sustainable Supply Chain Management–a Textile Case (2014) Journal of Cleaner Production, 83, pp. 391-403; DiMaggio, P.J., Powell, W.W., The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields (1983) American Sociological Review, 48 (2), pp. 147-160; Dües, C.M., Tan, K.H., Lim, M., Green as the New Lean: How to Use Lean Practices as a Catalyst to Greening Your Supply Chain (2013) Journal of Cleaner Production, 40, pp. 93-100; Eriksson, L., Kettaneh-Wold, N., Trygg, J., Wikström, C., Wold, S., (2006) Multi-and Megavariate Data Analysis: Part I: Basic Principles and Applications. 2nd Revised and Enlarged Edition, , Malmö, Sweden: Umetrics Academy; Mark, E., Childe, S.J., Is the Reliability of Remanufactured Second Life Electronic Products Better than That of New ?” (2008) Paper Presented at Proceeding Electronics Goes Green, pp. 231-235. , Berlin: Fraunhofer IRB; Ferreira, L.M.D.F., Cristóvao, S., Integrating Sustainability Metrics in the Supply Chain Performance Measurement System (2016) Multiple Helix Ecosystems for Sustainable Competitiveness, pp. 113-132. , edited by Marta, Peris-Ortiz, João J., Ferreira, Luís, Farinha, and Nuno O., Fernandes, Cham, CH, Springer International Publishing; Garza-Reyes, J.A., Lean and Green–a Systematic Review of the State of the Art Literature (2015) Journal of Cleaner Production, 102, pp. 18-29; Genovese, A.C., Lenny Koh, N., Kumartripathi, P.K., Exploring the Challenges in Implementing Supplier Environmental Performance Measurement Models: A Case Study (2014) Production Planning & Control, 25 (13-14), pp. 1198-1211; Golicic, S.L., Smith, C.D., A Meta-Analysis of Environmentally Sustainable Supply Chain Management Practices and Firm Performance (2013) Journal of Supply Chain Management, 49 (2), pp. 78-95; Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N., Barriers Analysis for Green Supply Chain Management Implementation in Indian Industries Using Analytic Hierarchy Process (2014) International Journal of Production Economics, 147, pp. 555-568; Grantshaw, D.B., Environmental or Sustainable Supply Chain Performance Measurement Standards and Certifications (2019) Handbook on the Sustainable Supply Chain, pp. 357-376. , Joseph, Sarkis, Northampton, MA, Edward Elgar Publishers; Gunasekaran, A., Hong, P., Fujimoto, T., Building Supply Chain System Capabilities in the Age of Global Complexity: Emerging Theories and Practices (2014) International Journal of Production Economics, 147, pp. 189-197; Angappa, G., Patel, C., McGaughey, R.E., A Framework for Supply Chain Performance Measurement.” (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Subramanian, N., Rahman, S., Supply Chain Resilience: Role of Complexities and Strategies (2015) International Journal of Production Research, 53 (22), pp. 6809-6819; Gunasekaran, A., Subramanian, N., Rahman, S., Improving Supply Chain Performance through Management Capabilities (2017) Production Planning & Control, 28 (6-8), pp. 473-477; Haigh, M., Jones, M.T., (2006), http://195.130.87.21:8080/dspace/bitstream/123456789/456/1/The%20drivers%20of%20corporate%20social%20responsibility%20a%20critical%20review.pdf, The Drivers of Corporate Social Responsibility: A Critical Review. Ashridge Business School Working Paper, Accessed February 15 2020; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1995) Multivariate Data Analysis with Readings, , 4th ed, Englewood Cliffs, NJ: Prentice Hall; Hart, S.L., A Natural-Resource-Based View of the Firm (1995) Academy of Management Review, 20 (4), pp. 986-1014; Hartdowell, S.L., Invited Editorial: A Natural-Resource-Based View of the Firm Fifteen Years After (2011) Journal of Management, 37 (5), pp. 1464-1479; Hassini, E., Surti, C., Searcy, C., A Literature Review and a Case Study of Sustainable Supply Chains with a Focus on Metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-315; Hearnshaw, E.J.S., Wilson, M.M.J., A Complex Network Approach to Supply Chain Network Theory (2013) International Journal of Operations & Production Management, 33 (4), pp. 442-469; Hervani, A.A., Helms, M.M., Sarkis, J., Performance Measurement for Green Supply Chain Management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Krueger, R.A., Casey, M., (2009) Focus Groups: A Practical Guide for Applied Research, , 4th ed, Thousand Oaks, CA: Sage Publications; Lee, S.M., Jin, S.R., Donghyun, C., Yonghwi, N., Pressures Affecting Green Supply Chain Performance (2013) Management Decision, 51 (8), pp. 1753-1768; Likert, R., A Technique for the Measurement of Attitudes (1932) Archives of Psychology No. 140, pp. 1-55. , edited by R. S., Woodworth,, New York, NY, New York University; Luthra, S., Garg, D., Haleem, A., An Analysis of Interactions among Critical Success Factors to Implement Green Supply Chain Management towards Sustainability: An Indian Perspective (2015) Resources Policy, 46, pp. 37-50; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply Chain Performance Measurement Systems: A Systematic Review and Research Agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Mangan, J., Lalwani, C., Gardner, B., Combining Quantitative and Qualitative Methodologies in Logistics Research (2004) International Journal of Physical Distribution & Logistics Management, 34 (7), pp. 565-578; Markman, G.D., Krause, D., Theory Building Surrounding Sustainable Supply Chain Management: Assessing What We Know, Exploring Where to Go (2016) Journal of Supply Chain Management, 52 (2), pp. 3-10; Marshall, D., McCarthy, L., Heavey, C., McGrath, P., Environmental and Social Supply Chain Management Sustainability Practices: Construct Development and Measurement (2015) Production Planning & Control, 26 (8), pp. 673-690; Masi, D., Kumar, V., Garza-Reyes, J.A., Godsell, J., Towards a More Circular Economy: Exploring the Awareness, Practices, and Barriers from a Focal Firm Perspective (2018) Production Planning & Control, 29 (6), pp. 539-550; Miles, M.B., Huberman, A.M., (1994) Qualitative Data Analysis: An Expanded Sourcebook, , 2nd ed, Thousand Oaks, CA: Sage Publications; Mishra, D., Gunasekaran, A., Papadopoulos, T., Hazen, B., Green Supply Chain Performance Measures: A Review and Bibliometric Analysis (2017) Sustainable Production and Consumption, 10, pp. 85-99; Mitra, S., Datta, P.P., Adoption of Green Supply Chain Management Practices and Their Impact on Performance: An Exploratory Study of Indian Manufacturing Firms (2014) International Journal of Production Research, 52 (7), pp. 2085-2107; Moranagonzalez-Feliu, J., A Sustainable Urban Logistics Dashboard from the Perspective of a Group of Operational Managers (2015) Management Research Review, 38 (10), pp. 1068-1085; (2015), http://www.naics.com/, The Complete Source of NAICS and SIC Products & Services. Accessed February 15 2017; Nawrocka, D., Brorson, T., Lindhqvist, T., ISO 14001 in Environmental Supply Chain Practices (2009) Journal of Cleaner Production, 17 (16), pp. 1435-1443; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of Key Performance Measures for the Automobile Green Supply Chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; (2017), https://en.oxforddictionaries.com/definition/us/enabler, Enabler. Accessed February 15 2020; Rao, P., Holt, D., Do Green Supply Chains Lead to Competitiveness and Economic Performance? (2005) International Journal of Operations & Production Management, 25 (9), pp. 898-916; Saha, M., Darnton, G., Green Companies or Green Con-Panies: Are Companies Really Green, or Are They Pretending to Be? (2005) Business and Society Review, 110 (2), pp. 117-157; Vasco, S.-R., Piecyk, M., Potter, A., McKinnon, A., Naim, M., Edwards, J., Assessing the Application of Focus Groups as a Method for Collecting Data in Logistics (2010) International Journal of Logistics Research and Applications, 13 (1), pp. 75-94; Sarkis, J., Zhu, Q., Lai, K.-H., An Organizational Theoretic Review of Green Supply Chain Management Literature (2011) International Journal of Production Economics, 130 (1), pp. 1-15; Schalteggerburritt, S., Measuring and Managing Sustainability Performance of Supply Chains: Review and Sustainability Supply Chain Management Framework (2014) Supply Chain Management: An International Journal, 19 (3), pp. 232-241; Seuring, S., Müller, M., From a Literature Review to a Conceptual Framework for Sustainable Supply Chain Management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Shaw, S.B., Grantmangan, J., Developing Environmental Supply Chain Performance Measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339; Srivastava, S.K., Green Supply-Chain Management: A State-of-the-Art Literature Review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Stock, J.R., Boyer, S.L., Harmon, T., Research Opportunities in Supply Chain Management (2010) Journal of the Academy of Marketing Science, 38 (1), pp. 32-41; Amit, S., Kumara, S., Greaves, M., Raghavan, U.N., Supply-Chain Networks: A Complex Adaptive Systems Perspective (2005) International Journal of Production Research, 43 (20), pp. 4235-4265; Tate, W.L., Ellram, L.M., Schoenherr, T., Petersen, K.J., Global Competitive Conditions Driving the Manufacturing Location Decision (2014) Business Horizons, 57 (3), pp. 381-390; Testa, F., Iraldo, F., Shadows and Lights of GSCM (Green Supply Chain Management): Determinants and Effects of These Practices Based on a Multi-National Study (2010) Journal of Cleaner Production, 18 (10-11), pp. 953-962; Tuni, A., Rentizelas, A., Duffy, A., Environmental Performance Measurement for Green Supply Chains: A Systematic Analysis and Review of Quantitative Methods (2018) International Journal of Physical Distribution & Logistics Management, 48 (8), pp. 765-793; Walker, H., Di Sisto, L., McBain, D., Drivers and Barriers to Environmental Supply Chain Management Practices: Lessons from the Public and Private Sectors (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 69-85; Yu, W., Chavez, R., Feng, M., Green Supply Management and Performance: A Resource-Based View (2017) Production Planning & Control, 28 (6-8), pp. 659-670; Zhu, Q., Geng, Y., Lai, K.-H., Circular Economy Practices among Chinese Manufacturers Varying in Environmental-Oriented Supply Chain Cooperation and the Performance Implications (2010) Journal of Environmental Management, 91 (6), pp. 1324-1331; Zhu, Q., Sarkis, J., Relationships between Operational Practices and Performance among Early Adopters of Green Supply Chain Management Practices in Chinese Manufacturing Enterprises (2004) Journal of Operations Management, 22 (3), pp. 265-289; Zhu, Q., Sarkis, J., Lai, K.-H., Initiatives and Outcomes of Green Supply Chain Management Implementation by Chinese Manufacturers (2007) Journal of Environmental Management, 85 (1), pp. 179-189; Zhu, Q., Sarkis, J., Lai, K.-H., Institutional-Based Antecedents and Performance Outcomes of Internal and External Green Supply Chain Management Practices (2013) Journal of Purchasing and Supply Management, 19 (2), pp. 106-117},
correspondence_address1={Shaw, S.; Hull University Business School, Cottingham Road, United Kingdom},
publisher={Taylor and Francis Ltd.},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{Manville2021869,
author={Manville, G. and Papadopoulos, T. and Garengo, P.},
title={Twenty-first century supply chain management: a multiple case study analysis within the UK aerospace industry},
journal={Total Quality Management and Business Excellence},
year={2021},
volume={32},
number={7-8},
pages={869-885},
doi={10.1080/14783363.2019.1642101},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070211170&doi=10.1080%2f14783363.2019.1642101&partnerID=40&md5=cec1a5c12549c9e5f19fdee9b30e75c1},
affiliation={Norwich Business School, University of East Anglia, Norwich, United Kingdom; Kent Business School, University of Kent, Canterbury, United Kingdom; Department of Industrial Engineering, University of Padua, Padua, Italy},
abstract={In recent years, to stimulate the development of Lean Supply Chain Management (SCM) in SMEs, the UK aerospace industry has developed a change programme named ‘Supply Chains for the twenty-first century’ (SC21). This programme promotes the use of a simple standard improvement framework, which defines performance goals and standardises the approaches and tools for continuous sustainable improvement. However, its effective impact on Lean SCM and performance measurement systems (PMS) has not been widely covered in the literature. Adopting a qualitative research methodology approach, this study investigates five significant organisations to explore and contribute to knowledge on Lean SCM and PMS challenges related to the adoption of the SC21 programme. From an academic point of view, the paper highlights the key role of SC21 in accelerating the competitiveness of the aerospace industry by fostering managerial development of supply chain partners. In particular, it highlights the importance of PMS as well as collaboration between supply chain partners for efficient and effective SCM. From a practitioner’s perspective, the SC21 performance award is highlighted as a very successful approach in bridging the gap of differing agendas between supply chain partners. SMEs, with support from their larger supply chain partners, can embrace performance measurement practices to improve their performance. More established SMEs with a headcount of more than 50 employees are capable of developing and documenting strategic plans and more sophisticated PMS. © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={lean;  management capability;  performance measurement;  SME;  supply chain management},
references={Aboelmaged, M.G., Reconstructing Six Sigma barriers in manufacturing and service organisations (2011) International Journal of Quality and Reliability Management, 28 (5), pp. 519-541; Algassem, F., Yang, Q.P., Au, J., Application of lean six sigma principles to food distribution SMEs (2014) American Academic and Scholarly Research Journal, 6 (4), pp. 251-258; Antony, J., A SWOT analysis on six sigma: Some perspectives from leading academics and practitioners (2012) International Journal of Productivity and Performance Management, 61 (6), pp. 691-698; Antony, J., Kumar, M., Labib, A., Gearing six sigma into UK manufacturing SMEs: Results from a pilot study (2008) Journal of the Operational Research Society, 59 (4), pp. 482-493; (2018), http://as9100store.com/what-is-ISO-10007-Configuration-Management.aspx, Retrieved from; Assarlind, M., Gremyr, I., Backman, K., Multi-faceted views on a lean Six sigma application (2013) International Journal of Quality and Reliability Management, 30 (4), pp. 387-402; Balakrishnan, A., Geunes, J., Collaboration and coordination in supply chain management and e-commerce (2004) Production and Operations Management, 13 (1), pp. 1-2; Bhat, S., Gijo, E.V., Jnanesh, N.A., Application of lean six sigma methodology in the registration process of a hospital (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 613-643; Bititci, U.S., Firat, S.U.O., Garengo, P., How to compare performances of firms operating in different sectors? (2013) Production Planning and Control, 24 (12), pp. 1032-1049; Bourne, M., Researching performance measurement system implementation: The dynamics of success and failure (2005) Production Planning and Control, 16 (2), pp. 101-113; Braziotis, C., Tannock, J.D.T., Bourlakis, M., Strategic and operational considerations for the extended enterprise: Insights from the aerospace industry (2017) Production Planning and Control, 28 (4), pp. 267-280; Browning, T.R., Heath, R.D., Reconceptualizing the effects of lean on production costs with evidence from the F-22 program (2009) Journal of Operations Management, 27 (1), pp. 23-44; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chakravorty, S.S., Shah, A.D., Lean six sigma (LSS): An implementation experience (2012) European Journal of Industrial Engineering, 6 (1), pp. 118-137; Collis, J., Hussey, R., (2003) Business research, , Basingstoke: Palgrave; Conti, T.A., A history and review of the European quality award model (2007) The TQM Magazine, 19 (2), pp. 112-128; Dahlgaard, J.J., Chen, C.K., Jang, J.Y., Banegas, L.A., Dahlgaard-Park, S.M., Business excellence models: Limitations, reflections and further development (2013) Total Quality Management & Business Excellence, 24 (5-6), pp. 519-538; Dissanayake, C.K., Cross, J.A., Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM (2018) International Journal of Production Economics, 201, pp. 102-115; Drohomeretski, E., da Costa, S.E.G., de Lima, E.P., de Rosa Garbuio, P.A., Lean, six sigma and lean six sigma: An analysis based on operations strategy (2014) International Journal of Production Research, 52 (3), pp. 804-824; (2018), http://www.efqm.org/, European foundation for quality management excellence model. Retrieved from; Eisenhardt, K.M., Building theories from case study research (1989) Academy of Management Review, 14 (4), pp. 532-550; Funo, K.A., Muniz, J., Silva Marins, F.A., Salomon, V.A.P., Supply chain risk management: An exploratory research in Brazilian aerospace industry (2011) International Journal of Value Chain Management, 5 (3-4), pp. 265-280; Garengo, P., Biazzo, S., From ISO quality standards to an integrated management system: An implementation process in SME (2013) Total Quality Management and Business Excellence, 24 (3-4), pp. 310-335; Garengo, P., Biazzo, S., Bititci, U., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Garengo, P., Bititci, U., Towards a contingency approach to performance measurement: An empirical study in Scottish SMEs (2007) International Journal of Operations & Production Management, 27 (8), pp. 802-825; Garrido-Vega, P., Sacristán-Díaz, M., Magaña-Ramírez, L.M., Six sigma in SMES with low production volumes. A successful experience in aeronautics (2016) Universia Business Review, 51, pp. 52-71; George, M.L., (2002) Lean six sigma: Combining six sigma quality with lean production speed, , New York: McGraw Hill Professional, : 978- 0071385213; Gunasekaran, A., Patel, C., McGayghey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hallam, C.R.A., Keating, J., Company self-assessment of lean enterprise maturity in the aerospace industry (2014) Journal of Enterprise Transformation, 4 (1), pp. 51-71; Hanson, J.D., Melnyk, S.A., Calantone, R.A., Defining and measuring alignment in performance management (2011) International Journal of Operations & Production Management, 31 (10), pp. 1089-1114; Haque, B., James-Moore, M., Performance measurement experiences in aerospace product development processes (2005) International Journal of Business Performance Management, 7 (1), pp. 100-122; Heinicke, A., Performance measurement systems in small and medium-sized enterprises and family firms: A systematic literature review (2018) Journal of Management Control, 28 (4), pp. 457-502; Hines, P., Holweg, M., Rich, N., Learning to evolve (2004) International Journal of Operations & Production Management, 24 (10), pp. 994-1011; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Jasti, N.V.K., Kodali, R., A critical review of lean supply chain management frameworks: Proposed framework (2015) Production Planning & Control, 26 (13), pp. 1051-1068; Jenkins, A., Vignoles, A., Wolf, A., Galindo-Rueda, F., The determinants and labour market effects of lifelong learning (2003) Applied Economics, 35 (16), pp. 1711-1721; Johnson, H.T., Kaplan, R.S., (1987) Relevance lost, , Boston: Harvard Business School Press; Kache, F., Seuring, S., Linking collaboration and integration to risk and performance in supply chains via a review of literature reviews (2014) Supply Chain Management: An International Journal, 19, pp. 664-682; Kaplan, R., Norton, D., (1996) Harvard Business Review, , Using the balanced scorecard as a strategic management system., January–February, 75–85; Kaushik, P., Khanduja, D., Mittal, K., Jaglan, P., Application of six sigma methodology in a small and medium -sized manufacturing enterprise (2012) TQM Journal, 24 (1), pp. 4-16; Knapp, S., Lean six sigma implementation and organizational culture (2015) International Journal of Health Care Quality Assurance, 28 (8), pp. 855-863; Kourti, I., Effective performance management of inter-organisational collaborations through the construction of multiple identities (2017) International Journal of Business Performance Management, 18 (2), pp. 236-251; Kumar, M., Antony, J., Comparing the quality management practices in UK SMEs (2008) Industrial Management & Data Systems, 108 (9), pp. 1153-1166; Kumar, M., Antony, J., Singh, R.K., Tiwari, M.K., Perry, D., Implementing the lean sigma framework in an Indian SME: A case study (2006) Production Planning & Control, 17 (4), pp. 407-423; Lamming, R., (1993) Beyond partnership: Strategies for innovation and lean supply, 94. , London: Prentice Hall; Lincoln, Y.S., Guba, E.G., (1985) Naturalistic inquiry, 75. , London: Sage; Machado, M.C., Mendes, E.F., Telles, R., Sampaio, P., Towards a new model for SME self-assessment: A Brazilian empirical study (2018) Total Quality Management and Business Excellence, pp. 1-19; Manville, G., Implementing a balanced scorecard framework in a not for profit SME (2007) International Journal of Productivity and Performance Management, 56 (2), pp. 162-169; Martínez-Jurado, P.J., Moyano-Fuentes, J., Key determinants of lean production adoption: Evidence from the aerospace sector (2014) Production Planning and Control, 25 (4), pp. 332-345; McAdam, R., Quality models in an SME context (2000) International Journal of Quality and Reliability Management, 17, pp. 305-323; Meredith, J., Building operations management theory through case and field research (1998) Journal of Operations Management, 16 (4), pp. 441-454; Miles, M.B., Huberman, A.M., (1994) Qualitative data analysis: An expanded sourcebook, , London: Sage; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations and Production Management, 19, pp. 205-228; Neely, A., Mills, J., Platts, J., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; O’Regan, N., Stainer, L., Sims, M., Training in SMEs and its relationship to profitability, international (2010) Journal of Human Resources Development and Management, 10 (2), pp. 166-181; Pande, P.S., Neuman, R.P., Cavanagh, R.R., (2014) The six sigma way: How to maximize the impact of your change and improvement efforts, , New York, NY: McGraw Hill, 978-0071497329; Pešalj, B., Pavlov, A., Micheli, P., The use of management control and performance measurement systems in SMEs (2018) International Journal of Operations and Production Management, 38 (11), pp. 2169-2191; Peters, M., Odenthal, B., Schlick, C.M., Design, implementation and evaluation of a performance measurement system for virtual enterprises in the aerospace industry (2008) Strategies and tactics in supply chain event management, pp. 85-102. , Ijioui R., Emmerich H., Ceyp M., (eds), Berlin: Springer, &,. (Eds; Powell, T.C., Total quality management as competitive advantage: A review and empirical study (1995) Strategic Management Journal, 16 (1), pp. 15-37; Prashar, A., Adoption of six sigma DMAIC to reduce cost of poor quality (2014) International Journal of Productivity and Performance Management, 63 (1), pp. 103-126; Quayle, M., A study of supply chain management practice in UK industrial SMEs (2003) Supply Chain Management: An International Journal, 8 (1), pp. 79-86; Ruiz-Benitez, R., López, C., Real, J.C., Environmental benefits of lean, green and resilient supply chain management: The case of the aerospace sector (2018) Journal of Cleaner Production, 167, pp. 850-862; Saleheen, F., Habib, M.M., Hanafi, Z., Supply chain performance measurement model: A literature review (2018) International Journal of Supply Chain Management, 7 (3), pp. 70-78; Sardi, A., Garengo, P., Bititci, U., Measurement and management of competences by enterprise social networking (2019) International Journal of Productivity and Performance Management, 68 (1), pp. 109-126; (2019), http://www.adsgroup.org.uk/pages/91430300.asp, Retrieved from; Shokri, A., Waring, T.S., Nabhani, F., Investigating the readiness of people in manufacturing SMEs to embark on lean Six sigma projects (2016) International Journal of Operations & Production Management, 36 (8), pp. 850-878; Smith, M., Bititci, U.S., Interplay between performance measurement and management, employee engagement and performance (2017) International Journal of Operations & Production Management, 37 (9), pp. 1207-1228; Spekman, R., Davis, E.W., The extended enterprise: A decade later (2016) International Journal of Physical Distribution & Logistics Management, 46 (1), pp. 43-61; Suh, T., Kwon, I.W.G., Matter over mind: When specific asset investment affects calculative trust in supply chain partnership (2006) Industrial Marketing Management, 35 (2), pp. 191-201; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain issues in SMEs: Select insights from cases of Indian origin (2013) Production Planning & Control, 24 (1), pp. 47-71; Thomas, A., Barton, R., Developing an SME based six sigma strategy (2006) Journal of Manufacturing Technology Management, 17 (4), pp. 417-434; Thomas, A.J., Francis, M., Fisher, R., Byard, P., Implementing lean six sigma to overcome the production challenges in an aerospace company (2016) Production Planning & Control, 27 (7-8), pp. 591-603; Thonemann, U.W., Bradley, J.R., The effect of product variety on supply-chain performance (2002) European Journal of Operational Research, 143, pp. 548-569; Timans, W., Antony, J., Ahaus, K., van Solingen, R., Implementation of lean six sigma in small- and medium-sized manufacturing enterprises in the Netherlands (2012) Journal of the Operational Research Society, 63, pp. 339-353; Wang, F.K., Chen, K., Application of lean six sigma to a panel equipment manufacturer (2012) Total Quality Management, 23 (4), pp. 417-429; Yin, R.K., (2014) Case study research: Design and methods, , 5th ed., Thousand Oaks: Sage},
correspondence_address1={Manville, G.; Norwich Business School, United Kingdom; email: G.Manville@uea.ac.uk},
publisher={Routledge},
issn={14783363},
coden={TQMAE},
language={English},
abbrev_source_title={Total Qual. Manage. Bus. Excellence},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Fauziyah2020,
author={Fauziyah, I.S. and Ridwan, A.Y. and Muttaqin, P.S.},
title={Food production performance measurement system using halal supply chain operation reference (SCOR) model and analytical hierarchy process (AHP)},
journal={IOP Conference Series: Materials Science and Engineering},
year={2020},
volume={909},
number={1},
doi={10.1088/1757-899X/909/1/012074},
art_number={012074},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098323055&doi=10.1088%2f1757-899X%2f909%2f1%2f012074&partnerID=40&md5=70a3415e6db613f6cbf25daf8c9f3c5f},
affiliation={School of Industrial and System Engineering, Telkom University, Bandung, 40287, Indonesia},
abstract={Food and beverage companies should be able to maintain and measure their performances to survive while the business competition condition is very dynamic. This study was conducted to design a performance measurement system for food production that combines the halal factors in its business process. Halal means authorized, legal, permitted, and allowed to any object or activity that is used or implemented in Islam. Therefore, designing and measure the performance in this study will use the Supply Chain Operation Reference (SCOR) model and Analytical Hierarchy Process (AHP). By using the SCOR model, we will get performance metrics that serve as indicators to observe and maintain the company's performance. Meanwhile, by using AHP, we will get the importance weight of each metric toward the company's performance score. The results of this study obtained 15 metrics with 3 halal metrics. The 15 metrics are divided into three performance attributes, which are 5 metrics in the reliability attribute, 9 metrics in the responsiveness attribute, and 1 metric in the cost attribute. The results of the overall performance score is 72.73. Those metrics are useful to maintain and improve the company's performance. © Published under licence by IOP Publishing Ltd.},
references={Adiarni, N, Fortunella, A, The analysis of halal assurance system implementation (HAS 23000) in fried chicken flour product: a case study on (2018) XXX brand, 149, pp. 57-61; Kusrini, E, Qurtubi, Q, Fathoni, N H, Design performance measurement model for retail services using halal supply chain operation reference (SCOR): a case study in a retail in indonesia (2018) J. Adv. Manag. Sci, 6, pp. 218-221; Chaudry, M M, Hussaini, M M, Jackson, M A, Riaz, M N, (2000) Halal industrial production standards Illinois, , J&M Food Products Company; (2019) Industri makanan dan minuman jadi sektor kampiun, , Perindustrian Kementerian; (2017) Industri makanan dan minuman masih jadi andalan, , Perindustrian Kementerian; Cocca, P, Alberti, M, A framework to assess performance measurement systems in SMEs (2010) Int. J. Product. Perform. Manag, 59, pp. 186-200; Sudaryanto, Bahri, R, Performance evaluation of supply chain using SCOR model: the case of PT (2007) Yuasa, Indonesia Proceeding, Int. Semin. Ind. Eng. Manag, pp. 49-55; (2019) Peraturan Pemerintah Republik Indonesia nomor 31 tahun 2019 tentang peraturan pelaksanaan Undang-Undang nomor 33 tahun 2014 tentang jaminan produk halal, pp. 1-32. , Republik Indonesia; Jiang, Y, Zhou, L J, The quantitative research on the index system of supply chain performance measurement based on SCOR (2011) Adv. Intell. Soft Comput, 112, pp. 375-382; Kuswandi, R Y, Yanuar Ridwan, A, El Hadi, R M, Development of monitoring reverse logistic system for leather tanning industry using scor model (2018) Proceeding 2018 12th Int. Conf. Telecommun. Syst. Serv. Appl. TSSA 2018; Mefid, K N, Ridwan, A Y, Sari, W P, Global industry perspective of halal cosmetics applying sales and distribution process based on enterprise resources planning (2019) Bull. Soc. Informatics Theory Appl, 3, pp. 61-68; Kocaoglu, B, Gülsün, B, Tanyas, M, A SCOR based approach for measuring a benchmarkable supply chain performance (2013) J. Intell. Manuf, 24, pp. 113-132; Harwati, Yunita Pettalolo, A N, Halal criteria in supply chain operations reference (SCOR) for performance measurement: a case study (2019) IOP Conf. Ser. Mater. Sci. Eng, 505; Akbarizan, Lestari F, Ismail, K, Hamid, A B, Azwar, B, Nazar, H, Managing halal foods through power collaboration between supply chain actors Innov (2020) Manag. Educ. Excell. Vis. 2020 Reg. Dev. to Glob. Econ. Growth; Nasaruddin, R R, Fuad, F, Mel, M, Jaswir, I, Hamid, H A, The importance of a standardized Islamic Manufacturing Practice (IMP) for food and pharmaceutical productions (2012) Adv. Nat. Appl. Sci, 6, pp. 588-595; Bolstroff, P, Resenbaum, R, (2012) Supply Chain Excellence; Gordon, Stewart, (2017) Supply Chain Operations Reference Model; Waaly, A N, Ridwan, A Y, Akbar, M D, Development of sustainable procurement monitoring system performance based on supply chain reference operation (SCOR) and analytical hierarchy process (AHP) on leather tanning industry (2018) MATEC Web Conf, p. 204; Taherdoost, H, Decision making using the analytic hierarchy process (AHP); a step by step approach (2018) Int. Journel Econ. Manag. Syst, 2, pp. 244-246; Labib, A, (2014) Introduction to the Analytic Hierarchy Process; Hasibuan, A, Arfah, M, Parinduri, L, Hernawati, T, Suliawati, Harahap B, Sibuea, S R, Sulaiman, O K, Purwadi, A, Performance analysis of supply chain management with supply chain operation reference model (2018) J. Phys. Conf. Ser, 1007},
correspondence_address1={Fauziyah, I.S.; School of Industrial and System Engineering, Indonesia; email: istisalmaf@gmail.com},
publisher={IOP Publishing Ltd},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{EzZouaq2020,
author={Ez Zouaq, R.},
title={Contribution to the design of a logistics chain performance measurement system: Case of a Moroccan SME group},
journal={2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020},
year={2020},
doi={10.1109/LOGISTIQUA49782.2020.9353874},
art_number={9353874},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102148454&doi=10.1109%2fLOGISTIQUA49782.2020.9353874&partnerID=40&md5=8e95615e276b525a1b70611f1ef52b6d},
affiliation={Research Laboratory: Ermot, Fsjes - Usmba, Fez, Morocco},
abstract={Our research problem is oriented towards the design of a system for measuring the logistics performance of a group of SMEs operating in several cities in Morocco (thanks to intervention research). Our general problem can then be stated: how to implement a logistics performance measurement system in the context of Moroccan SMEs. The objective of this research work is the design and implementation of a system for measuring the logistics performance of Moroccan SMEs using the GIMSI approach (Generalization, Information, Measurement, System, and Initiative). To achieve this objective, we have adopted a qualitative approach based on an abductive reasoning mode (back and forth between research work that addressed the question of the design of performance management indicators and our field of research (group of SMEs)). © 2020 IEEE.},
author_keywords={ERP;  financial and non-financial indicators;  GIMSI;  logistics;  SMEs;  SMP},
keywords={Abductive reasoning;  Design and implementations;  Intervention research;  Logistics performance measurement;  Performance management;  Performance measurement system;  Qualitative approach;  Research problems, Supply chain management},
references={Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution "amp; Logistics Management, 24 (1), pp. 17-28; Fernandez, A., (1999) Les Nouveaux Tableaux de Bord des Managers, le Projet Décisionnel dans Sa Totalité, , 2000, 2003)Editions d'Organisation-3eme édition; Otto, A., Kotzab, H., Does supply chain management really pay Six perspectives to measure the performance of managing a supply chain (2001) Proceedings of the 13th Annual NOFOMA Conference, Reykjavik, June, Disponible sur, , http://www.nofoma.org; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations "amp; Production Management, 21 (1-2), pp. 71-87; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers "amp; Industrial Engineering, 53 (1), pp. 43-62; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Griffis, S.E., Goldsby, T.J., Cooper, M., Closs, D.J., Aligning logistics performance measures to the information needs of the firm (2007) Journal of Business Logistics, 28 (2), pp. 35-56; Estampe, D., Harreguy, A., Maldes, V., Mamere, M., Nougaret, S., Truin, B., La performance supply chain des acteurs du secteur automobile en France (1999) Logistique "amp; Management, 7, pp. 15-22; Pimor, Y., (1998) Logistique: Techniques et Mise en Oeuvre, p. 575. , 2eme édition, Dunod, Paris; Prtm, C.S., Roussel, J., (2002) Avantage Supply Chain, p. 406. , Edition d'organisation, coll performance/ qualité, septembre; Morana, J., Paché, G., Supply chain management et tableau de bord prospectif : a la recherche de synergies (2000) Publié dans la Revue Logistique et Management, 8 (1), pp. 77-88; Stapleton, D., Hanna, J.B., Yagla, S., Johnson, J., Markussen, D., Measuring logistics performance using the strategic profit model (2002) International Journal of Logistics Management, 13 (1), pp. 89-107; Meyssonnier, F., Pourtier, F., (2006) Les ERP Changent-ils le Controle de Gestion, , Comptabilité-Controle-Audit, juin; Demeestere, R., Loriono, P., Mottis, N., (2006) Controle de Gestion et Pilotage de l'Entreprise, , 3eme édition, Dunod; Lorino, P., (2001) Méthodes et Pratiques de la Performance. le Pilotage Par les Processus et les Compétences, , (2eme édition) Paris : Editions d'Organisation},
correspondence_address1={Ez Zouaq, R.; Research Laboratory: Ermot, Morocco; email: Rachid.ezzouak@usmba.ac.ma},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781728198811},
language={English},
abbrev_source_title={Int. Colloq. Logist. Supply Chain Manag., LOGISTIQUA},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{NoAuthor2020,
title={2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020},
journal={2020 13th International Colloquium of Logistics and Supply Chain Management, LOGISTIQUA 2020},
year={2020},
page_count={481},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102111872&partnerID=40&md5=01e378cd3de00ba7c44f953f3b12c75b},
abstract={The proceedings contain 80 papers. The topics discussed include: measuring the competitiveness of Mediterranean ports via the principal component analysis and hierarchical clustering analysis methods; tolerance analysis with the 1d angular dimension chain: application; impact of electric mobility on the sustainable development of the country, case study in morocco; scheduling optimization in logistics 4.0; intermodal terminal location problem under fuzzy restrictions; assessing healthcare waste treatment technologies in Tunisia within fuzzy ARAS-H; contribution to the design of a logistics chain performance measurement system: case of a Moroccan SME group; toward a smart port congestion optimizing model; motivations related to the quality management system and benefits of its implementation in the company: state of the art; control system in the smart grid: state of the art and opportunities; and blockchain technology in supply chain management and loyalty programs: toward blockchain implementation in retail market.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781728198811},
language={English},
abbrev_source_title={Int. Colloq. Logist. Supply Chain Manag., LOGISTIQUA},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Kottala20201925,
author={Kottala, S.Y. and Herbert, K.},
title={An empirical investigation of supply chain operations reference model practices and supply chain performance: Evidence from manufacturing sector},
journal={International Journal of Productivity and Performance Management},
year={2020},
volume={69},
number={9},
pages={1925-1954},
doi={10.1108/IJPPM-09-2018-0337},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068774845&doi=10.1108%2fIJPPM-09-2018-0337&partnerID=40&md5=30854905678d977b98d152751afedba2},
affiliation={Faculty 7, LogDynamics, Bremen, Germany; Department of Operations, School of Business Management, SVKM’s NMIMS University, Hyderabad Campus, Hyderabad, India; Department of Business Studies and Economics, University of Bremen, Bremen, Germany},
abstract={Purpose: The purpose of this paper is to develop and validate a scale measurement of supply chain operations reference (SCOR)-related performance indicators and proposed constructs, SCOR-related performance indicators as practices within the Indian manufacturing sector. Design/methodology/approach: A literature-based model on SCOR processes with five constructs and respective performance indicators was empirically validated by using a structured questionnaire. A total of 155 respondents among Indian manufacturing sector participated in this research, and the returned questionnaires were analyzed by using structural equation modeling. Findings: The study established a relationship among the SCOR-related performance indicators and overall supply chain performance indicators (OSCPI). The moderation effect of demographic characteristics, namely, employee size, company age and type of company showed significant differences between SCOR-related performance indicators and overall supply chain indicators. Research limitations/implications: The scope of the study is limited to specific Indian manufacturing firms. The survey could not represent whole population of manufacturing sector. Practical implications: The findings assist managers/supply chain practitioners in improving the performance measures identified using the standard framework, i.e., SCOR processes, overall supply chain performance measures as standard practices for Indian manufacturing sector for a profitable and sustainable business growth in global environment. Originality/value: This research holds a value for suggested practices under SCOR processes and the proposed model for OSCPI, a path finder/performance measurement tool for supply chain professionals in the Indian context. © 2019, Emerald Publishing Limited.},
author_keywords={ANOVA;  CFA;  Moderation effect;  Multiple regression;  SCOR model;  SEM},
references={Abdullah, R., Hassan, M.G., Johari, N.A., Exploring the linkage of supply chain integration between green supply chain practices and sustainable performance: a conceptual link (2014) 2014 4th International Conference on Future Environment and Energy IPCBEE, 61, p. 22; Agarwal, V., Govindan, K., Darbari, J.D., Jha, P.C., An optimization model for sustainable solutions towards implementation of reverse logistics under collaborative framework (2016) International Journal of System Assurance Engineering and Management, 7 (4), pp. 480-487; Ahmad, S., Schroeder, R., The impact of electronic data interchange on delivery performance (2001) Production and Operations Management, 10 (1), pp. 16-30; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Benton, W.C., Jr., Push and pull production systems (2011) Encyclopedia of Operations Research and Management Science, , Cochran, J. (Ed.), January 14, Wiley and Sons, Hoboken, NJ; Benton, W.C., Shin, H., Manufacturing planning and control: the evolution of MRP and JIT integration (1998) European Journal of Operational Research, 110 (3), pp. 411-440; Bhattacherjee, A., Individual trust in online firms: scale development and initial test (2002) Journal of Management Information Systems, 19 (1), pp. 211-241; Birhanu, D., Krishnanand, A.L., Neelakanteswara, R., Supply chain strategies of manufacturers in Ethiopia (2018) International Journal of Productivity and Performance Management, 67 (2), pp. 318-340; Blackburn, J., (1991) Time-Based Competition, , Business One Irwin, Homewood, IL; Bolstorff, P., (2006) Balancing your value chain metrics, , www.scelimited.com/sitebuildercontent/sitebuilderfiles/balancingyourvaluechainmetrics.pdf, (accessed, January 16, 2017; Bower, J.L., Hout, T.M., Fast cycle capability for competitive power (1988) Harvard Business Review, 66 (6), pp. 110-118; Bowersox, D., Closs, D., Cooper, M., (2010) Supply Chain Logistics Management, , International ed., McGraw-Hill, New York, NY; Bragg, S.M., (2011) Inventory Best Practices, , 2nd ed., John Wiley & Sons, Hoboken, NJ; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Brown, T.A., (2015) Confirmatory Factor Analysis for Applied Research, , 2nd ed., The Guilford Press, New York, NY and London; Burns, T., Stalker, G., (1961) The Management of Innovation, , Tavistock, London; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analysing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Cankaya, S., Sezen, B., Effects of green supply chain management practices on sustainability performance (2019) Journal of Manufacturing Technology Management, 30 (1), pp. 98-121; Cao, M., Zhang, Q., Supply chain collaboration: impact on collaborative advantage and firm performance (2011) Journal of Operations Management, 29 (3), pp. 163-180; Carr, A., Pearson, J., Strategically managed buyer-supplier relationships and performance outcomes (1999) Journal of Operations Management, 17 (5), pp. 497-519; Carter, C., Ellram, L., Reverse logistics: a review of the literature and framework for future investigation (1998) Journal of Business Logistics, 19 (1), pp. 85-102; Cavinato, J.L., Total cost value model for supply chain competitiveness (1992) Journal of Business Logistics, 13 (2), pp. 285-301; Chan, F., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: the constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 19-150; Cheng, T.C.E., An economic order quantity model with demand dependent unit production cost and imperfect production processes (1991) IIE Transactions, 23 (1), pp. 23-28; Choi, T., Hartley, J., An exploration of supplier selection practices across the supply chain (1996) Journal of Operations Management, 14 (4), pp. 333-343; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning, and Operation, , 5th ed., Pearson Education, Boston Consulting Group, Upper Saddle River, NJ; Christopher, M., (1992) Logistics and Supply Chain Management, , Financial Times Pitman Publishing, London; Christopher, M., (2016) Logistics & Supply Chain Management, , 5th ed., Pearson Education Limited, Harlow; Chunhua, T., Chai, Y., Liu, Y., Ren, S., Performance management in supply chain (2003) IEEE International Conference on Systems, Man and Cybernetics, pp. 4934-4939; Churchill, G.A., Jr., A paradigm for developing better measures of marketing constructs (1979) Journal of Marketing Research, 16 (1), pp. 64-73; Cicchetti, D.V., Sparrow, S.A., Developing criteria for establishing inter rater reliability of specific items: applications to assessment of adaptive behavior (1990) American Journal of Mental Deficiency, 86 (2), pp. 127-137; Clawson, C.J., Donald, E.V., Human values: a historical and interdisciplinary analysis (1978) NA – Advances in Consumer Research Volume 05, pp. 396-402. , Hunt, K. and Abor, A. and,(Eds), Association for Consumer Research, MI; Coyle, J.J., Novack, R.A., Gibson, B., Bardi, E.J., (2016) Transportation: A Global Supply Chain Perspective, , 8th ed., Cengage Learning, Boston, MA; Cronbach, L.J., Coefficient alpha and the internal structure of tests (1951) Psychometrika, 16 (3), pp. 297-334; Cua, K., McKone, K., Schroeder, R., Relationships between implementation of TQM, JIT and TPM and manufacturing performance (2001) Journal of Operations Management, 19 (6), pp. 675-694; Dadzie, A.S., Uren, V., Miller, T., Abba-Dabo, A., Value-driven partner search for energy from waste projects (2018) SEMANTiCS 2018 14th International Conference on Semantic Systems, , Vienna: September 10–13; Daugherty, P.J., Autry, C.W., Ellinger, A.E., Reverse logistics: the relationship between resource commitment and program performance (2001) Journal of Business Logistics, 22 (1), pp. 107-123; Daugherty, P.J., Richey, R.G., Genchev, S.E., Chen, H., Reverse logistics: superior performance through focused resource commitments to information technology (2005) Transportation Research Part E: Logistics and Transportation Review, 41 (2), pp. 77-92; Day, G.S., (1990) Market Driven Strategy, , Free Press, New York, NY; Degroote, S.E., Marx, T.G., The impact of IT on supply chain agility and firm performance: an empirical investigation (2013) International Journal of Information Management, 33 (6), pp. 909-916; Delipinar, G.E., Kocaoglu, B., Using SCOR model to gain competitive advantage: a literature review (2016) Procedia – Social and Behavioral Sciences, 229, pp. 398-406; (2017) India expected to be among top 5 manufacturing nations by 2020, , www2.deloitte.com/in/en/pages/manufacturing/articles/global-manufacturing-competitiveness-index_msm_moved.html, report by Deloitte: (accessed, May 18, 2018; Dobler, D.W., Burt, D.N., (1996) Purchasing and Supply Management, , The McGraw-Hill Companies, New York, NY; Dong, Y., Xu, K., A supply chain model of vendor managed inventory (2002) Transportation Research Part E, Logistics & Transportation Review, 38 (2), pp. 75-95; Dong, Y., Carter, C., Dresner, M., JIT purchasing and performance: an exploratory analysis of buyer and supplier perspectives (2001) Journal of Operations Management, 19 (4), pp. 471-483; Dyer, J.H., Singh, H., The relational view: cooperative strategy and sources of interorganizational competitive advantage (1998) The Academy of Management Review, 23 (4), pp. 660-679; Elgazzar, S., Tipi, N., Jones, G., Key characteristics for designing a supply chain performance measurement system (2019) International Journal of Productivity and Performance Management, 68 (2), pp. 296-318; Ellram, L.M., A managerial guide for the development and implementation of purchasing partnerships (1991) International Journal of Purchasing and Materials Management, 27 (3), pp. 2-8; Erlenkotter, D., Ford Whitman Harris and the economic order quantity model (1990) Operations Research, 38 (6), pp. 937-946; Facchin, T., Sellitto, M.A., Measurement of work-in-process and manufacturing lead time by petri nets modeling and throughput diagram PETRI – Nets (2012) Manufacturing and Computer Science, pp. 95-106. , http://dx.doi.org/10.5772/50288; Fawcett, S.E., Wallin, C., Allred, C., Fawcett, A.M., Magnan, G.M., Information technology as an enabler of supply chain collaboration: a dynamic-capabilities perspective (2011) Journal of Supply Chain Management, 47 (1), pp. 38-59; Ferrari, R., Sourcing and planning need to converge (2001) Supply Chain Management Review, 5 (6), pp. 19-20; Fisher, L.M., (1997) What is the right supply chain for your product?, , Harvard Business Review, March–April, 105-116; Fleischmann, M., Krikke, H.R., Dekker, R., Flapper, S.D.P., A characterization of logistics networks for product recovery (2000) Omega, 28 (1), pp. 653-666; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: a contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Flynn, B.B., Schroeder, R.G., Flynn, E.J., World class manufacturing: an investigation of Hayes and Wheelwright’s foundation (1999) Journal of Operations Management, 17 (1), pp. 249-269; Fugate, B., Mentzer, T., Stank, T., Logistics performance: efficiency, effectiveness and differentiation (2010) Journal of Business Logistics, 31 (1), pp. 43-62; Fullerton, R.R., McWatters, C.S., The production performance benefits from JIT Implementation (2001) Journal of Operations Management, 19 (1), pp. 81-96; Fullerton, R.R., McWatters, C.S., Fawson, C., An Examination of the relationships between JIT and financial performance (2003) Journal of Operations Management, 21 (4), pp. 383-404; Ganeshkumar, C., Mathan Mohan, G., Nambirajan, T., Multi-group moderating effect of goods produced in the manufacturing industry: supply chain management context (2014) NMIMS Management Review, 25 (5), pp. 10-30; Garcia, J.M., Lozano, S., Canca, D., Coordinated scheduling of production and delivery from multiple plants (2004) Robotics & Computer-Integrated Manufacturing, 20 (3), pp. 191-198; Garson, G.D., (2011) Structural equation modelling, stat notes, , http://faculty.chass.ncsu.edu/garson/PA/765/Structur.html#AIC, (accessed, June 19, 2018; Gelders, L., Mannaerts, P., Maes, J., Manufacturing strategy, performance indicators and improvement programs (1994) International Journal of Production Research, 32 (4), pp. 797-805; Georgise, F.B., Wuest, T., Thoben, K.D., SCOR model application in developing countries: challenges & requirements (2017) Production Planning & Control, 28 (1), pp. 17-32; Gerwin, D., Manufacturing flexibility: a strategic perspective (1993) Management Science, 39 (4), pp. 395-410; Giffi, C., Roth, A., Seal, G., (1990) Competing in World Class Manufacturing: America’s 21st Century Challenge, , Business One Irwin, Homewood, IL; Goldsby, T.J., Stank, T.P., World class logistics performance and environmentally responsible logistics practices (2000) Journal of Business Logistics, 21 (2), pp. 187-208; Govindan, K., Soleimani, H., Kannan, D., Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future (2015) European Journal of Operational Research, 240 (3), pp. 603-626; Govindan, K., Palaniappan, M., Zhu, Q., Kannan, D., Analysis of third party reverse logistics provider using interpretive structural modeling (2012) International Journal of Production Economics, 140 (1), pp. 204-211; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) International Journal of Purchasing and Materials Management, 30 (4), pp. 13-18; Greasley, A., Using simulation to assess the introduction of technology in a continuous operations process (2008) Journal of Manufacturing Technology Management, 19 (8), pp. 979-984; Green, S.D., Fernie, S., Weller, S., Making sense of supply chain management: a comparative study of aerospace and construction (2005) Construction Management & Economics, 23 (6), pp. 579-593; Grönroos, C., The relationship marketing process: communication, interaction, dialogue, value (2004) Journal of Business & Industrial Marketing, 19 (2), pp. 99-113; Guide, L.D., Wassenhove, V., The evolution of closed-loop supply chain research (2009) Operations Research, 57 (1), pp. 10-18; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Williams, H.J., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Gurin, R., Online system to streamline Ford’s delivery process (2000) Frontline Solutions, 1 (4), pp. 1-3; Ha, A.Y., Li, L., Ng, S., Price and delivery logistics competition in a supply chain (2003) Management Science, 49 (9), pp. 1139-1153; Hahn, C., Pinto, P., Bragg, D., Just-in-time production and purchasing (1983) International Journal of Purchasing and Materials Management, 19 (3), pp. 2-10; Hair, J.F., Tatham, R.L., Anderson, R.E., (1998) Multivariate Data Analysis, , 5th ed., Prentice Hall, Englewood Cliffs, NJ; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2010) Multivariate Data Analysis, , 7th ed., Prentice Hall, Englewood Cliffs, NJ; Harrington, L., (1996) Untapped savings abound, , IndustryWeek, July 15, 53-58; Harris, F.M., How many parts to make at once (1913) Factory: The Magazine of Management, 10 (2), pp. 135-136; Hausman, W., Montgomery, D., Roth, A., Why should marketing and manufacturing work together? Some exploratory empirical results (2002) Journal of Operations Management, 20 (3), pp. 241-258; Hayes, R.H., Wheelwright, S.C., (1984) Restoring our Competitive Edge: Competing through Manufacturing, , John Wiley, New York, NY; Henig, M., Levin, N., Joint production planning and product delivery commitments with random yield (1992) Operations Research, 40 (2), pp. 404-410; Hill, T., (1994) Manufacturing Strategy: Text and Cases, , Richard D. Irwin, Blue Ridge, IL; Hill, T.J., Teaching manufacturing strategy (1987) International Journal of Operations & Production Management, 6 (3), pp. 10-20; Hines, P., (1994) Creating World Class Suppliers, , Pitman Publishing, London; Hoffman, D., (2004) The hierarchy of supply chain metrics: diagnosing your supply chain health, , AMR Research, Boston, MA; Holmes, G., (2013) What role do private protected areas have in conserving global biodiversity?, , SRI Working Papers 46, Leeds; Howard, J.A., Sheth, J.N., (1969) The Theory of Buyer Behaviour, , Wiley, New York, NY; Hsu, C.W., Hu, A.H., Chiou, C.Y., Chen, T.C., Using the FDM and ANP to construct a sustainability balanced scorecard for the semiconductor industry (2011) Expert Systems with Applications, 38 (10), pp. 12891-12899; Hu, L., Bentler, P.M., Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives (1999) Structural Equation Modeling, 6 (1), pp. 1-55; Huang, S.H., Keskar, H., Comprehensive and configurable metrics for supplier selection (2007) International Journal of Production Economics, 105 (2), pp. 510-523; Huang, S.H., Sheoran, S.K., Wang, G., A review and analysis of SCOR model (2004) International Journal of Supply Chain Management, 9 (1), pp. 23-29; Jääskeläinen, A., Thitz, O., Prerequisites for performance measurement supporting purchaser-supplier collaboration (2018) Benchmarking: An International Journal, 25 (1), pp. 120-137; Jahre, M., Household waste collection as reverse channels – a theoretical perspective (1995) International Journal of Physical Distribution & Logistics Management, 25 (2), pp. 39-55; Jamehshooran, B.G., Shaharoun, A.M., Haron, H.N., Assessing supply chain performance through applying the SCOR Model (2015) International Journal of Supply Chain Management, 4 (1), pp. 1-11; Kannan, V.R., Tan, K., Just-in-time, total quality management, and supply chain management: understanding their linkages and impact on business performance (2005) Omega, 33 (2), pp. 153-162; Karrer, M., Supply chain event management. Impulse zur ereignisorientierten Steuerung von Supply Chains (2003) Innovationen im E-Business, pp. 187S-197S. , Dangelmaier, W., Gajewski, T. and Kösters, C.,(Eds), Paderborn; Kasilingam, R.G., (1998) Logistics and Transportation: Design and Planning, , Springer, Boston, MA; Kaynak, H., Hartley, J.K., A replication and extension of quality management into the supply chain (2008) Journal of Operations Management, 26 (4), pp. 468-489; Keebler, J., Manrodt, K., Durtsche, D., Ledyard, D., (1999) Keeping Score: Measuring the Value of Logistics in the Supply Chain, , Council of Logistics Management, Chicago, IL; Khatibi, A.A., Ismail, H., Thyagarajan, V., What drives customer loyalty: an analysis from the telecommunications industry (2002) Journal of Targeting, Measurement and Analysis for Marketing, 11 (1), pp. 34-44; Kleindorfer, P.R., Singhal, K., Van Wassenhove, L.N., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Koh, S.C.L., Demirbag, M., Bayraktar, E., Tatoglu, E., Zaim, S., The impact of supply chain management practices on performance of SMEs (2007) Industrial Management & Data Systems, 107 (1), pp. 103-124; Kotler, P., Levy, S.J., Broadening the concept of marketing (1969) Journal of Marketing, 33 (1), pp. 10-16; Kuo, Y.F., Wu, C.M., Deng, W.J., The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services (2009) Computers in Human Behaviour, 25 (4), pp. 887-896; Kwon, I.-W.G., Suh, T., Factors affecting the level of trust and commitment in supply chain relationships (2004) The Journal of Supply Chain Management, 40 (2), pp. 4-14; Lai, K., Ngai, E.W., Cheng, T.C., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-456; Laia, K.H., Ngaib, E.W.T., Chenga, T.C.E., An empirical study of supply chain performance in transport logistics (2004) International Journal of Production Economics, 87 (3), pp. 321-331; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Landeros, R., Reck, R., Plank, R.E., Maintaining buyer supplier partnerships (1995) International Journal of Purchasing and Materials Management, 31 (3), pp. 3-11; Landrum, H., Prybutok, V.R., A service quality and success model for the information service industry (2004) European Journal of Operational Research, 156 (3), pp. 628-642; Laosirihongthong, T., Adebanjo, D., Choon Tan, K., Green supply chain management practices and performance (2013) Industrial Management & Data Systems, 113 (8), pp. 1088-1109; Lapide, L., True measures of supply chain performance (2000) Supply Chain Management Review, 4 (3), pp. 25-27; Lapinskaite, I., Kuckailyte, J., The impact of supply chain cost on the price of the final product (2014) Business, Management and Education, 12 (1), pp. 109-126; Law, J., (2009) A Dictionary of Business and Management, , Oxford University Press, Oxford; Lawrence, P., Lorsch, J., (1967) Organization and Environment, , Harvard Business School Division of Research, Boston, MA; Lebas, M., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Lee, H., Padmanabham, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lee, H.L., Billington, C., (1992) Managing supply chain inventory: pitfalls and opportunities, , Sloan Management Review, Spring, 65-73; Levy, D.L., (1997) Lean production in an international supply chain, , Sloan Management Review, Winter, 94-102; Li, L., Su, Q., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) International Journal of Production Research, 49 (1), pp. 33-57; Li, S., Ragu Nathan, B., Ragu-Nathan, T.S., Subba Rao, S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega, 34 (2), pp. 107-124; Li, S., Rao, S., Ragunathan, T., Ragunathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: an exploratory study (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218; Lowe, T.J., Schwarz, L.B., Parameter estimation for the EOQ lot-size model: minimax and expected-value choices (1983) Naval Research Logistics Quarterly, 30 (2), pp. 367-376; Luo, X., Homburg, C., Christian, satisfaction, complaint, and the stock value gap (2008) Journal of Marketing, 72 (4), pp. 29-43; Luo, Y., Liu, Y., Xue, J., Relationship investment and channel performance: an analysis of mediating forces (2009) Journal of Management Studies, 46 (7), pp. 1113-1137; McKone, K., Schroeder, R., The impact of total productive maintenance practices on manufacturing performance (2001) Journal of Operations Management, 19 (1), pp. 39-57; MacBeth, D.K., Ferguson, N., (1994) Partnership Sourcing: An Integrated Supply chain Management Approach, , Pitman Publishing, London; Mac-Callum, R.C., Browne, M.W., Sugawara, H.M., Power analysis and determination of Sample size for covariance structure modelling (1996) Psychological Methods, 1 (1), pp. 130-149; MacDuffie, J., Sethuraman, K., Fisher, M., Product variety and manufacturing performance: evidence from the international automotive assembly plant study (1996) Management Science, 42 (3), pp. 350-369; Mahindroo, A., Samalia, H.V., Verma, P., Information systems road map to enhance economic and operational reverse logistics performance (2018) International Journal of Logistics Systems and Management, 29 (2), pp. 215-240; Makatsoris, H., Chang, Y., Design of a demand driven collaborative supply-chain planning and fulfillment system for distributed enterprises (2004) Production Planning & Control, 15 (3), pp. 256-269; Maloni, M.J., Benton, W.C., Supply chain partnerships: opportunities for operations research (1997) European Journal of Operational Research, 101 (3), pp. 419-429; Mani, V., Gunasekaran, A., Delgado, C., Enhancing supply chain performance through supplier social sustainability: an emerging economy perspective (2018) International Journal of Production Economics, 195 (1), pp. 259-272; Mapes, J., New, C., Szwejczewski, M., Performance trade-offs in manufacturing plants (1997) International Journal of Operations and Production Management, 17 (10), pp. 1020-1033; Mason-Jones, R., Towill, D.R., Enlightening supplies (1997) Manufacturing Engineer, 76 (4), pp. 156-160; Mollenkopf, H., Marcellini, F., Ruoppila, I., Széman, Z., Tacken, M., (2005) Enhancing Mobility in Later Life: Personal Coping, Environmental Resources and Technical Support; The Out-of-home Mobility of Older Adults in Urban and Rural Regions of Five European Countries, pp. 257-277. , IOS Press, Amsterdam; Monroe, K.B., (1991) Pricing: Making Profitable Decisions, , 2nd ed., McGraw-Hill, New York, NY; Muller, M., (2011) Essentials of Inventory Management, , 2nd ed., American Management Association, New York, NY; Nair, A., Meta-analysis of the relationship between quality management practices and firm performance-implication for quality management theory development (2006) Journal of Operations Management, 24 (6), pp. 948-975; Nakajima, S., (1988) Introduction to TPM, , Productivity Press, Cambridge, MA; Narasimhan, R., Kim, S., Information system utilization strategy for supply chain integration (2001) Journal of Business Logistics, 22 (2), pp. 51-75; Nazim, R., Raja Yaacob, R.A.I., Criteria for supplier selection: an application of AHP-SCOR integrated model (2017) International Journal of Supply Chain Management, 6 (3), pp. 2051-3771; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Realizing strategy through measurement (1994) International Journal of Operations & Production Management, 14 (3), pp. 140-152; Neely, N., (2003) Business Performance Measurement, , Cambridge University Press, Cambridge; New, S.J., A framework for analyzing supply chain improvement (1996) International Journal of Operations and Production Management, 16 (4), pp. 19-34; Novich, N., (1990) Distribution strategy: are you thinking small enough?, , Sloan Management Review, Fall, 71-77; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operations Research, 144 (2), pp. 306-320; Özer, Ö., Zheng, Y., Trust and trustworthiness (2017) Handbook of Information Exchange in Supply Chain Management, pp. 489-524. , Ha, A.Y. and Tang, C.S. and,(Eds), Springer International Publishing, Hoboken, NJ; Palma-Mendoza, J.A., Neailey, K., Roy, R., Business process re-design methodology to support supply chain integration (2014) International Journal of Information Management, 34 (2), pp. 167-176; Pande, P.S., Neuman, R.P., Cavangh, R.R., (2000) The Six Sigma way: How GE, Motorola, and other top Companies are Honing their Performance, , McGraw-Hill, New York, NY; Patterson, P.G., Spreng, R.A., Modeling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business, services context: an empirical examination (1997) International Journal of Service Industry Management, 8 (5), pp. 414-434; Peter, J.P., Olson, J.C., (1993) Consumer Behavior and Marketing Strategy, , 3rd ed., Irwin, Homewood, IL; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: an exploratory study (2015) International Journal of Productivity and Performance Management, 64 (8), pp. 1068-1091; Powell, T., Total quality management as competitive advantage: a review and empirical study (1995) Strategic Management Journal, 16 (1), pp. 15-27; Prahinski, C., Benton, W.C., Supplier evaluations: communication strategies to improve supplier performance (2004) Journal of Operations Management, 22 (1), pp. 39-62; Prajogo, D., Oke, A., Olhager, J., Supply chain processes: linking supply logistics integration, supply performance, lean processes and competitive performance (2016) International Journal of Operations & Production Management, 36 (2), pp. 220-238; Pyke, D.F., Cohen, M.A., Multiproduct integrated production-distribution systems (1994) European Journal of Operational Research, 74 (1), pp. 18-49; Ramish, A., Aslam, H., Measuring supply chain knowledge management (SCKM) performance based on double/triple loop learning principle (2016) International Journal of Productivity and Performance Management, 65 (5), pp. 704-722; Rodrigues, A.M., Stank, T.P., Lynch, D.F., Linking strategy, structure, process, and performance in integrated logistics (2004) Journal of Business Logistics, 25 (2), pp. 65-94; Rogers, D.S., Tibben-Lembke, R., An examination of reverse logistics practices (2001) Journal of Business Logistics, 22 (2), pp. 129-148; Rogers, D.S., Lambert, D.M., Croxton, K.L., Garcia-Dastugue, S.J., The returns management process (2002) The International Journal of Logistics Management, 13 (2), pp. 1-18; Rushton, A., Oxley, J., (1991) Handbook of Logistics and Distribution Management, , Kogan Page, London; Santos, C.P., Fernandes, D., Antecedents and consequences of consumer trust in the context of service recovery context of service recovery (2008) Brazilian Administrative Review, 5 (3), pp. 225-244; (2010) Supply chain operations reference model, overview (Version 10.0), , Supply Chain Council, Chicago, IL: (accessed, June 22, 2017; Schonberger, R.J., (1990) Building a Chain of Customers, , The Free Press, London; Schreiber, J.B., Nora, A., Stage, F.K., Barlow, E.A., King, J., Reporting structural equation modelling and confirmatory factor analysis results: a review (2006) Journal of Educational Research, 99 (6), pp. 323-337; Schwarz, L.B., Economic order quantities for products with finite demand horizons (1972) AIIE Transactions, 4 (3), pp. 234-237; Shah, R., Ward, P.T., Lean manufacturing: context, practice bundles, and performance (2007) Journal of Operations Management, 21 (2), pp. 129-149; Shepherd, C., Guenter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shepherd, C., Guenter, H., Measuring supply chain performance: current research and future directions (2010) Behavioral Operations in Planning and Scheduling, pp. 105-121. , Fransoo, J., Waefler, T. and Wilson, J. and,(Eds), Springer, Berlin, Heidelberg; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , Pitman Publishing, London; Souza, G.C., Supply chain analytics (2014) Business Horizons, 57 (5), pp. 595-605; Sri Yogi, K., Performance evaluation of reverse logistics: a case of LPG agency (2015) Cogent Business & Management, 2 (1), pp. 1-17; Sri Yogi, K., Kotzab, H., The intellectual foundation of supply chain management performance models: a bibliometric analysis and syntheses (2019) International Journal of Comparative Management, 2 (1), pp. 67-91; Stalk, G., Evans, P., Schulman, L.E., Competing on capabilities: the new rules of corporate strategy (1992) Harvard Business Review, 70 (2), pp. 57-69; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Sukati, I., Hamid, A.B., Baharun, R., Mdyusoff, R., The study of supply chain management strategy and practices on supply chain performance (2012) Procedia – Social and Behavioral Sciences, 40 (185), pp. 225-233; (2005) Supply chain operations reference model, overview (Version 7.0). (Software), , www.supply-chain.org, Supply Chain Council, Chicago, IL: (accessed, June 21, 2018; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains: a literature review and a research agenda (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 782-804; Thomas, D.J., Griffin, P.M., Co-ordinated supply chain management (1996) European Journal of Operational Research, 94 (3), pp. 1-15; Toni, A.D., Nissimbeni, G., Tonchia, S., New trends in supply environment (1994) Logistics Information Management, 7 (4), pp. 41-50; Towill, D.R., The seamless supply chain the predator’s strategic advantage (1997) International Journal of Technology Management, 14 (1), pp. 37-55; Tracey, M., Transportation effectiveness and manufacturing firm performance (2004) The International Journal of Logistics Management, 15 (2), pp. 31-50; Treleven, M., Single sourcing: a management tool for the quality supplier (1987) International Journal of Purchasing and Materials Management, 23 (1), pp. 19-24; Van Hoek, R., Measuring the immeasurable’ – measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Wagner, S.M., Intensity and managerial scope of supplier integration (2003) Journal of Supply Chain Management, 39 (3), pp. 4-15; Wang, Y., Lo, H.P., Yang, Y., An integrated framework for service quality, customer value, satisfaction: evidence from China’s telecommunication industry (2004) Information Systems Frontiers, 6 (4), pp. 325-340; Wild, R., (1995) Production and Operations Management, , Cassell Educational Limited, London; Womack, J., Jones, D., Roos, D., (1990) The Machine That Changed the World, , Rawson Associates, New York, NY; Yang, Z., Peterson, R.T., Customer perceived value, satisfaction, and loyalty: the role of switching costs (2004) Psychology and Marketing, 21 (10), pp. 799-822; Zeithaml, V.A., Consumer perceptions of price, quality and value: a means-end model and synthesis of evidence (1988) Journal of Marketing, 52 (3), pp. 2-22; Zhou, H., Benton, W.C., Schilling, D.A., Milligan, G.W., Supply chain integration and the SCOR model (2011) Journal of Business Logistics, 32 (4), pp. 332-344; Ahumada, O., Villalobos, J., Efficiency measures for inbound logistics performance (2004) IIE Annual Conference Proceedings, pp. 1-6; Birhanu, D., Lanka, K., Rao, A.N., (2014) A survey of classifications in supply chain strategies, , 12th Global Congress on Manufacturing and Management, 97, 2289-2297; Daugherty, P.J., Richey, R.G., Roath, A.S., Min, S., Chen, H., Arndt, A.D., Genchev, S.E., Is collaboration paying off for firms? (2006) Business Horizons, 49 (1), pp. 61-70; Dhawan, R., Swaroop, G., Zainulbhai, A., (2012) Fulfilling the promise of India’s manufacturing sector, , www.mckinsey.com/business-functions/operations/our-insights/fulfilling-the-promise-of-indias-manufacturing-sector, (accessed, May 24, 2017; Fawcett, S.E., Jones, S.L., Fawcett, A.M., Supply chain trust: the catalyst for collaborative innovation (2012) Business Horizons, 55 (2), pp. 163-178; Hugos, M., (2003) Essentials of Supply Chain Management, , Wiley, New York, NY; (2007) India manufacturing barometer, , www.pwc.in/assets/pdfs/publications/2017/india-manufacturing-barometer-standing-strong.pdf, available:, (accessed, May 21, 2017; Siomkas, G., Shrivastava, P., Responding to product liability crises (1993) Long Range Planning, 26 (5), pp. 72-79; (2000) Supply chain operations reference model Version 5.0; Tajbakhsh, A., Hassini, E., Performance measurement of sustainable supply chains: a review and research questions (2015) International Journal of Productivity and Performance Management, 64 (6), pp. 744-783},
correspondence_address1={Kottala, S.Y.; Faculty 7, Germany; email: dr.sriyogi@outlook.com; Kottala, S.Y.; Department of Operations, Hyderabad Campus, India; email: dr.sriyogi@outlook.com},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Santos20201,
author={Santos, D.A. and Quelhas, O.L.G. and Gomes, C.F.S. and Zotes, L.P. and França, S.L.B. and de Souza, G.V.P. and de Araújo, R.A. and Santos, S.D.S.C.},
title={Proposal for a maturity model in sustainability in the supply chain},
journal={Sustainability (Switzerland)},
year={2020},
volume={12},
number={22},
pages={1-37},
doi={10.3390/su12229655},
art_number={9655},
note={cited By 17},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096328789&doi=10.3390%2fsu12229655&partnerID=40&md5=8616fc36d7646af941f19f1c80de2174},
affiliation={Department of Production Engineering, Fluminense Federal University, Niterói, 24210-240, Brazil; Celso Suckow da Fonseca Federal Center for Technological Education, Rio de Janeiro, 20271-204, Brazil},
abstract={Sustainability has become a relevant element for organizations due to several motivators: companies adopt sustainability practices in need for regulatory compliance, anticipation of regulatory changes, understanding of limited natural resources, and a desire to limit expenditure associated with resource consumption and waste disposal. Thus, this article aims to identify the gaps present in maturity models, present an integrator theoretical model that considers the characteristic elements of the models present in the literature and affects the level of maturity in sustainability. The construction of the integrator theoretical model was based on the following stages: research questions; theoretical framework on Supply Chain Management; sustainability and concept of maturity model; maturity models and performance measurement systems; interpretation/analysis of research; and construction of the integrator theoretical model. The literature review was carried out in the Scopus and Web of Science databases. The main conclusions: maturity models focus on only one aspect or are excessively broad and do not include in detail the elements necessary to measure the level of maturity in sustainability. The proposed model aims to mitigate these gaps with the addition of the cross-sectional dimension, which offers an integrated and holistic view in relation to the other dimensions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
author_keywords={Green supply chain;  Maturity model;  Performance measurement system},
keywords={holistic approach;  integrated approach;  literature review;  model;  natural resource;  supply chain management;  sustainability;  waste disposal, Scopus},
references={Okongwu, U., Morimoto, R., Lauras, M., The maturity of supply chain sustainability disclosure from a continuous improvement perspective (2013) Int. J. Prod. Perform. Manag, 62, pp. 827-855; Lopes, L.J., Pires, S.R.I., Green supply chain management in the automotive industry: A study in Brazil (2020) Bus. Strat. Environ, 29, pp. 2755-2769; Council of Supply Chain Management Professionals (CSCMP), , http://cscmp.org/, (accessed on 26 May 2020); Bertaglia, P.R., (2009) Logística e Gerenciamento da Cadeia de Abastecimento, pp. 32-33. , 2nd ed.; Saraiva: São Paulo, Brazil; Govindan, K., Soleimani, H., Kannan, D., Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future (2015) Eur. J. Oper. Res, 240, pp. 603-626; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: Int. J, 12, pp. 330-353; Uygun, Ö., Dede, A., Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques (2016) Comput. Ind. Eng, 102, pp. 502-511; Kurnia, S., Rahim, M.M., SAmson, D., Prakash, S., Sustainable supply chain management capability maturity: Framework development and initial evaluation (2014) Proceedings of the European Conference on Information Systems (ECIS), , Tel Aviv, Israel, 9–11 June; Kumar, R., Chandrakar, R., Overview of green supply chain management: Operation and environmental impact at different stages of the supply chain (2012) Int. J. Eng. Adv. Technol, 1, pp. 1-6; Correia, E., Carvalho, H., Azevedo, S.G., Govindan, K., Maturity Models in Supply Chain Sustainability: A Systematic Literature Review (2017) Sustainability, 9, p. 64; Roy, V., Schoenherr, T., Charan, P., The thematic landscape of literature in sustainable supply chain management (SSCM) (2018) Int. J. Oper. Prod. Manag, 38, pp. 1091-1124; Elkington, J., Enter the triple bottom line (2004) The Triple Bottom Line: Does It All Add Up?, , Henriques, A., Richardson, J., Eds.; Earthscan: London, UK; Baumgartner, R.J., Ebner, D., Corporate sustainability strategies: Sustainability profiles and maturity levels (2010) Sustain. Dev, 18, pp. 76-89; Hynds, E.J., Brandt, V., Burek, S., Knox, P., Parker, J.P., Zietlow, M., Schwartz, L., Jäger, W., A Maturity Model for Sustainability in New Product Development (2014) Res. Manag, 57, pp. 50-57; Srai, J.S., Alinaghian, L.S., Kirkwood, D.A., Understanding sustainable supply network capabilities of multinationals: A capability maturity model approach (2013) Proc. Inst. Mech. Eng. Part B J. Eng. Manuf, 227, pp. 595-615; Rudnicka, A., How to manage sustainable supply chain? The issue of maturity (2016) LogForum, 12, pp. 203-211; Golinska, P., Kuebler, F., The Method for Assessment of the Sustainability Maturity in Remanufacturing Companies (2014) Procedia CIRP, 15, pp. 201-206; Verrier, B., Rose, B., Caillaud, E., Lean and Green strategy: The Lean and Green House and maturity deployment model (2016) J. Clean. Prod, 116, pp. 150-156; Edgeman, R., Eskildsen, J., Modeling and Assessing Sustainable Enterprise Excellence (2014) Bus. Strat. Environ, 23, pp. 173-187; Standing, C., Jackson, P., An approach to sustainability for information systems (2007) J. Syst. Inf. Technol, 9, pp. 167-176; Patón-Romero, J.D., Baldassarre, M.T., Rodriguez, M., Piattini, M., Maturity model based on CMMI for governance and management of Green IT (2019) IET Softw, 13, pp. 555-563; Tchokogué, A., Nollet, J., Merminod, N., Paché, G., Goupil, V., Is Supply’s Actual Contribution to Sustainable Development Strategic and Operational? (2018) Bus. Strategy Environ, 27, pp. 336-358; Robinson, H., Anumba, C., Carrillo, P., Al-Ghassani, A., STEPS: A knowledge management maturity roadmap for corporate sustainability (2006) Bus. Process. Manag. J, 12, pp. 793-808; Batista, L., Dora, M., Toth, J., Molnár, A., Malekpoor, H., Kumari, S., Knowledge management for food supply chain synergies – a maturity level analysis of SME companies (2019) Prod. Plan. Control, 30, pp. 995-1004; Babin, R., Nicholson, B., How green is my outsourcer? Measuring sustainability in global IT outsourcing (2011) Strat. Outsourcing: Int. J, 4, pp. 47-66; Gong, R., Xue, J., Zhao, L., Zolotova, O., Ji, X., Xu, Y., A Bibliometric Analysis of Green Supply Chain Management Based on the Web of Science (WOS) Platform (2019) Sustain, 11, p. 3459; Zimon, D., Tyan, J., Sroufe, R., drivers of sustainable supply chain management: Practices to alignment with un sustainable development goals (2020) Int. J. Qual. Res, 14, pp. 219-236; Zimon, D., Tyan, J., Sroufe, R., Implementing Sustainable Supply Chain Management: Reactive, Cooperative, and Dynamic Models (2019) Sustainability, 11, p. 7227; Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) J. Clean. Prod, 52, pp. 329-341; Reefke, H., Ahmed, M.D., Sundaram, D., Sustainable Supply Chain Management—Decision Making and Support: The SSCM Maturity Model and System (2014) Glob. Bus. Rev, 15, pp. 1-12; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ, 142, pp. 247-258; Kovacheva, T., Todorov, N., Optimizing software development process: A case study for integrated Agile-CMMI process model Proceedings of the 2011 IEEE EUROCON—International Conference on Computer as a Tool, pp. 1-2. , Lisbon, Portugal, 27–29 April 2011; Pigosso, D.C.A., Rozenfeld, H., McAloone, T.C., Ecodesign maturity model: A management framework to support ecodesign implementation into manufacturing companies (2013) J. Clean. Prod, 59, pp. 160-173; Aboelmaged, M.G., Linking operations performance to knowledge management capability: The mediating role of innovation performance (2014) Prod. Plan. Control, 25, pp. 44-58; Bititci, U.S., Garengo, P., Ates, A., Nudurupati, S.S., Value of maturity models in performance measurement (2015) Int. J. Prod. Res, 53, pp. 3062-3085; Gouvinhas, R.P., Reyes, T., Naveiro, R.M., Perry, N., Filho, E.R., A proposed framework of sustainable self-evaluation maturity within companies: An exploratory study (2016) Int. J. Interact. Des. Manuf. (IJIDeM), 10, pp. 319-327; Machado, C.G., De Lima, E.P., Da Costa, S.E.G., Angelis, J.J., Mattioda, R.A., Framing maturity based on sustainable operations management principles (2017) Int. J. Prod. Econ, 190, pp. 3-21; Bastas, A., Liyanage, K., Setting a framework for organisational sustainable development (2019) Sustain. Prod. Consum, 20, pp. 207-229; Neely, A., The performance measurement revolution: Why now and what next? (1999) Int. J. Oper. Prod. Manag, 19, pp. 205-228; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) Int. J. Bus. Perform. Manag, 5, pp. 1-24; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) Int. J. Oper. Prod. Manag, 27, pp. 784-801; Hald, K.S., Mouritsen, J., The evolution of performance measurement systems in a supply chain: A longitudinal case study on the role of interorganisational factors (2018) Int. J. Prod. Econ, 205, pp. 256-271; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Eng. Costs Prod. Econ, 17, pp. 253-262; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply-chain management (1997) Logist. Inf. Manag, 10, pp. 62-67; Van Hoek, R.I., Measuring the unmeasurable”—Measuring and improving performance in the supply chain (1998) Supply Chain Manag. Int. J, 3, pp. 187-192; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag, 19, pp. 275-292; Holmberg, S., A systems perspective on supply chain measurements (2000) Int. J. Phys. Distrib. Logist. Manag, 30, pp. 847-868; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag, 21, pp. 71-87; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) Eur. J. Inf. Syst, 14, pp. 335-346; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng, 53, pp. 43-62; Thakkar, J., Kanda, A., Deshmukh, S., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking Int. J, 16, pp. 702-723; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: A case study from the packaging industry (2009) Prod. Plan. Control, 20, pp. 68-81; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: An empirical analysis (2014) J. Purch. Supply Manag, 20, pp. 113-129; Sillanpää, I., Empirical study of measuring supply chain performance (2015) Benchmarking Int. J, 22, pp. 290-308; Laihonen, H., Pekkola, S., Impacts of using a performance measurement system in supply chain management: A case study (2016) Int. J. Prod. Res, 54, pp. 5607-5617; Jääskeläinen, A., Thitz, O., Prerequisites for performance measurement supporting purchaser-supplier collaboration (2018) Benchmark. Int. J, 25, pp. 120-137; Frederico, G.F., Martins, R.A., Modelo para alinhamento entre a maturidade dos sistemas de medição de desempenho e a maturidade da gestão da cadeia de suprimentos (2012) Gest. Prod, 19, pp. 857-871; Neto, M.S., Pires, S.R.I., Medição de desempenho em cadeias de suprimentos: Um estudo na indústria automobilística (2012) Gest. Prod, 19, pp. 733-746; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Bus. Process. Manag. J, 10, pp. 522-536; Lockamy, A., McCormack, K., Iii, A.L., Linking SCOR planning practices to supply chain performance (2004) Int. J. Oper. Prod. Manag, 24, pp. 1192-1218; Ayers, J.B., Malmberg, D.M., Supply chain systems: Are you ready? (2002) Infor. Strategy Exec. J, 19, pp. 18-27; Bell, E., Bryman, A., Harley, B., (2018) Business Research Methods, pp. 20-25. , 5th ed.; Oxford University Press: New York, NY, USA; Tranfield, D., Denyer, D., Smart, P., Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review (2003) Br. J. Manag, 14, pp. 207-222; Briner, R.B., Denyer, D., (2012) Handbook of Evidence-Based Management: Companies, Classrooms and Research Systematic Review and Evidence Synthesis as a Practice and Scholarship Tool, pp. 112-129. , 1st ed.; Oxford University Press: New York, NY, USA; Rousseau, D.M., Manning, J., Denyer, D., Evidence in management and organizational science: Assembling the field’s full weight of scientific knowledge through syntheses (2008) Acad. Manag. Ann, 2, pp. 475-515; Yatskovskaya, E., Srai, J.S., Kumar, M., Integrated Supply Network Maturity Model: Water Scarcity Perspective (2018) Sustainability, 10, p. 896; Rauch, E., Unterhofer, M., Rojas, R.A., Gualtieri, L., Woschank, M., Matt, D.T., A Maturity Level-Based Assessment Tool to Enhance the Implementation of Industry 4.0 in Small and Medium-Sized Enterprises (2020) Sustainability, 12, p. 3559; Xavier, A., Reyes, T., Aoussat, A., Luiz, L., De Souza, L.M., Eco-Innovation Maturity Model: A Framework to Support the Evolution of Eco-Innovation Integration in Companies (2020) Sustainability, 12, p. 3773; Yang, J.Y., Roh, T., Open for Green Innovation: From the Perspective of Green Process and Green Consumer Innovation (2019) Sustainability, 11, p. 3234; Aarstad, J., Jakobsen, S.-E., Norwegian Firms’ Green and New Industry Strategies: A Dual Challenge (2020) Sustainability, 12, p. 361; Kusi-Sarpong, S., Gupta, H., Sarkis, J., A supply chain sustainability innovation framework and evaluation methodology (2019) Int. J. Prod. Res, 57, pp. 1990-2008; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) Int. J. Phys. Distrib. Logist. Manag, 38, pp. 360-387},
correspondence_address1={Santos, D.A.; Department of Production Engineering, Brazil; email: dasantos@id.uff.br},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Malesios2020623,
author={Malesios, C. and Dey, P.K. and Abdelaziz, F.B.},
title={Supply chain sustainability performance measurement of small and medium sized enterprises using structural equation modeling},
journal={Annals of Operations Research},
year={2020},
volume={294},
number={1-2},
pages={623-653},
doi={10.1007/s10479-018-3080-z},
note={cited By 40},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055861176&doi=10.1007%2fs10479-018-3080-z&partnerID=40&md5=9997e911fe5d8f21eaecf9aeab0c1a7d},
affiliation={Aston University, Birmingham, United Kingdom; Neoma Business School, Rouen, France},
abstract={Sustainability of small and medium sized enterprises (SMEs) is significant as SMEs contribute to GDP substantially in every economy. This research develops an innovative sustainable supply chain performance measurement model for SMEs. Prior researches predominantly use balanced score card (BSC) approach that presume causal relationship of criteria and Data Envelopment Analysis (DEA), which derive efficiency of units from a few input and output criteria. While DEA is effective for policymakers, BSC is more suitable for individual SME. The proposed method that uses structural equation modeling (SEM) approach to derive the relationship of criteria and criteria weights formulates regression-type models for a specific region as well as for specific SME. The SEM-based supply chain sustainability performance measurement model is beneficial to policymakers as they can determine means for improvement at a regional level. The proposed method could also facilitate managers/owners of individual SMEs with measures for improving their supply chain sustainability performance. The method has been applied to three varied geographical locations in the UK, France and India in order to demonstrate its effectiveness. © 2018, The Author(s).},
author_keywords={France and India;  Performance measurement;  SMEs;  Structural equation modeling (SEM);  Supply chain;  Sustainability;  The UK},
references={Abdul-Rashid, S.H., Sakundarini, N., Raja Ghazilla, R.A., Thurasamy, R., The impact of sustainable manufacturing practices on sustainability performance: Empirical evidence from Malaysia (2017) International Journal of Operations & Production Management, 37 (2), pp. 182-204; Ahn, H., How to individualise your balanced scorecard (2005) Measuring Business Excellence, 9 (1), pp. 5-12; Ammenberg, J., Hjelm, O., Tracing business and environmental effects of environmental management systems—A study of networking small and medium-sized enterprises using a joint environmental management system (2003) Business Strategy and the Environment, 12 (3), pp. 163-174; Arbuckle, J.L., (2014) Amos 23.0 user’s Guide, , Chicago, IBM SPSS; Barcus, A., Montibeller, G., Supporting the allocation of software development work in distributed teams with multi-criteria decision analysis (2008) Omega, 36 (3), pp. 464-475; Belu, C., Ranking corporations based on sustainable and socially responsible practices. A data envelopment analysis (DEA) approach (2009) Sustainable Development, 17 (4), pp. 257-268; Benn, S., Dunphy, D., Griffiths, A., Enabling change for corporate sustainability: An integrated perspective (2007) Australasian Journal of Environmental Management, 13 (3), pp. 156-165; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2014) Production Planning & Control, 25 (8), pp. 698-714; Bollen, K.A., (1989) Structural equations with latent variables, , Wiley, New York; Borga, F., Citterio, A., Noci, G., Pizzurno, E., Sustainability report in small enterprises: Case studies in Italian furniture companies (2009) Business Strategy and the Environment, 18 (3), pp. 162-176; Bryman, A., Cramer, D., (2005) Quantitative data analysis with SPSS12 and 13. A guide for social scientists, , Routledge, East Sussex; Castka, P., Balzarova, M.A., Bamber, C.J., How can SMEs effectively implement the CSR agenda? A UK case study perspective (2004) Corporate Social Responsibility and Environment Management, 11 (1), pp. 140-149; Charnes, A., Cooper, W.W., Rhodes, E.L., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2, pp. 429-444; Chay, T., Xu, Y., Tiwari, A., Chay, F., Towards lean transformation: The analysis of lean implementation frameworks (2015) Journal of Manufacturing Technology Management, 26 (7), pp. 1031-1052; Chen, S.-C., Lee, A.H.I., Performance measurement of SMEs’ employees by fuzzy multi-criteria decision theory (2010) IEEE Xplore; Chen, Y., Zhu, J., Measuring information technology’s indirect impact on firm performance (2004) Information Technology and Management, 5 (1-2), pp. 9-22; Chiu, Y.-H., Huang, C.-W., Ma, C.-M., Assessment of China transit and Economic efficiencies in a modified value-chains DEA model (2011) European Journal of Operational Research, 209 (2), pp. 95-103; Devins, D., Johnson, S., Sutherland, J., Employer characteristics and employee training outcomes in UK SMEs: A multivariate analysis (2004) Journal of Small Business and Enterprise Development, 11 (4), pp. 449-457; Easton, L., Murphy, D.J., Pearson, J.N., Purchasing performance evaluation: With data envelopment analysis (2002) European Journal of Purchasing & Supply Management, 8 (3), pp. 123-134; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal Management Review, 7 (1), pp. 25-27; Griethuijsen, R.A.L.F., Eijck, M.W., Haste, H., Brok, P.J., Skinner, N.C., Mansour, N., Global patterns in students’ views of science and interest in science (2014) Research in Science Education, 45 (4), pp. 581-603; Groves, C., Frater, L., Lee, R., Stokes, E., Is There room at the bottom for CSR? Corporate social responsibility and nanotechnology in the UK (2011) Journal of Business Ethics, 101 (4), pp. 525-552; Hair, J., Black, W., Babin, B., Anderson, R., Tatham, R., (2006) Multivariate data analysis, , 6, Pearson Prentice Hall, Uppersaddle River, NJ; Halkos, G.E., Salamouris, D.S., Efficiency measurement of the Greek commercial banks with the use of financial ratios: A data envelope analysis approach (2004) Management Accounting Research, 15 (2), pp. 201-224; Hart, S.L., Milstein, M.B., Creating sustainable value (2003) Academy of Management Executive, 17 (2), pp. 56-69; Heene, M., Hilbert, S., Draxler, C., Ziegler, M., Bühner, M., Masking misfit in confirmatory factor analysis by increasing unique variances: A cautionary note on the usefulness of cutoff values of fit indices (2011) Psychological Methods, 16 (3), pp. 319-336; Hoejmose, S., Brammer, S., Millington, A., An empirical examination of the relationship between business strategy and socially responsible supply chain management (2013) International Journal of Operations & Production Management, 33 (5), pp. 589-621; Hoque, Z., 20 years of studies on the balanced scorecard: Trends, accomplishments, gaps and opportunities for future research (2014) The British Accounting Review, 46 (1), pp. 33-59; Hu, L.T., Bentler, P.M., Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives (1999) Structural Equation Modeling, 6 (1), pp. 1-55; Hu, Q., Mason, R., Williams, S.J., Found, P., Lean implementation within SMEs: A literature review (2015) Journal of Manufacturing Technology Management, 26 (7), pp. 980-1012; Hubbard, G., Measuring organizational performance: Beyond the triple bottom line (2009) Business Strategy and the Environment, 18, pp. 177-191; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement systems (2001) International Journal of Operations & Production Management, 21 (8), pp. 1096-1115; Iacobucci, D., Structural equations modeling: Fit indices, sample size, and advanced topics (2010) Journal of Consumer Psychology, 20, pp. 90-98; Jamali, D., Zanhour, M., Keshishian, T., Peculiar strengths and relational attributes of SMEs in the context of CSR (2009) Journal of Business Ethics, 87 (3), pp. 355-377; Johnson, M.P., Schaltegger, S., Two decades of sustainability management tools for SMEs: How far have we come? (2016) Journal of Small Business Management, 54 (2), pp. 481-505; Jöreskog, K.G., A general method for analysis of covariance structures (1970) Biometrika, 57, pp. 239-251; Kamarudin, A.B., Aslan, A.S., Modelling sustainable performance of SME through green management implementation in the new economic transition (2017) Proceeding of Mechanical Engineering Research, 2017, pp. 219-221; Kaplan, R.S., Norton, D.P., The balanced scorecard—Measures that drive performance (1992) Harvard Business Review, 70 (1-2), pp. 71-79; Koh, S.C.L., Simpson, M., Change and uncertainty in SME manufacturing environments using ERP (2005) Journal of Manufacturing Technology Management, 16 (5-6), pp. 629-653; Lawrence, S.R., Collins, E., Pavlovich, K., Arunachalam, M., Sustainability practices of SMEs: The case of NZ (2006) Business Strategy and the Environment, 15 (4), pp. 242-257; Lewis, W.G., Pun, K.F., Lalla, T.R.M., Empirical investigation of the hard and soft criteria of TQM in ISO 9001 certified small and medium-sized enterprises (2006) The International Journal of Quality & Reliability Management, 23 (8), pp. 964-985; Libby, T., Salterio, S.E., Webb, A., The balanced scorecard: The effects of assurance and process accountability on managerial judgment (2004) Accounting Review, 79 (4), pp. 1075-1094; Marques, G., Gourc, D., Lauras, M., Multi-criteria performance analysis for decision making in project management (2010) International Journal of Project Management, 29, pp. 1057-1069; Marsh, H.W., Hau, K.-T., Wen, Z., In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings (2004) Structural Equation Modeling, 11 (3), pp. 320-341; Masurel, E., Why SMEs invest in environmental measures: Sustainability evidence from small and medium-sized printing firms (2007) Business Strategy and the Environment, 16 (3), pp. 190-201; Nejati, M., Quazi, A., Amran, A., Ahmad, N.H., Social responsibility and performance: Does strategic orientation matter for small businesses? (2016) Journal of Small Business Management; Nguyen, T.H., Waring, T.S., The adoption of customer relationship management (CRM) technology in SMEs: An empirical study (2013) Journal of Small Business and Enterprise Development, 20 (4), pp. 824-848; Nørreklit, H., The balance on the balanced scorecard—A critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Pala, M., Edum-Fotwe, F., Ruikar, K., Doughty, N., Peters, C., Contractor practices for managing extended supply chain tiers (2014) Supply Chain Management, 19 (1), pp. 31-45; Patyal, V.S., Koilakuntla, M., Infrastructure and core quality practices in Indian manufacturing organizations: Scale development and validation (2015) Journal of Advances in Management Research, 12 (2), pp. 141-175; Petts, J., The regulator-regulated relationship and environmental protection: Perceptions in small and medium sized enterprises (2000) Environment and Planning, 18 (3), pp. 191-192; Petts, J., Herd, A., O’hecocha, M., Environmental responsiveness, individuals and organizational learning: SME experience (1998) Journal of Environmental Planning and Management, 41 (6), pp. 711-731; Prabawani, P., Measuring SMEs’ sustainability: A literature review and agenda for research (2013) International Journal of Management and Sustainability, 2 (12), pp. 193-207; Quah, H.S., Udin, Z.M., Supply chain management from the perspective of value chain flexibility: An exploratory study (2011) Journal of Manufacturing Technology Management, 22 (4), pp. 506-526; Rao, P., Singh, A., La O’Castillo, O., Intal, P., Jr., Sajid, A., A metric for corporate environmental indicators … For small and medium enterprises in the Philippines (2009) Business Strategy and the Environment, 18 (1), pp. 14-31; Roxas, B., Ashill, N., Chadee, D., Effects of entrepreneurial and environmental sustainability orientations on firm performance: A study of small businesses in the Philippines (2016) Journal of Small Business Management; Saad, M., Kumar, V., Bradford, J., An investigation into the development of the absorptive capacity of manufacturing SMEs (2017) International Journal of Production Research, 55 (23), pp. 6916-6931; Santos, M., CSR in SMEs: Strategies, practices, motivations and obstacles (2011) Social Responsibility Journal, 7 (3), pp. 490-508; Saranga, H., Moser, R., Performance evaluation of purchasing and supply management using value chain DEA approach (2010) European Journal of Operational Research, 207 (1), pp. 197-205; Shewell, P., Migiro, S., Data envelopment analysis in performance measurement: A critical analysis of the literature (2016) Problems and Perspectives in Management, 14 (3), pp. 705-713; Shibin, K.T., Dubey, R., Gunasekaran, A., Hazen, B., Roubaud, D., Gupta, S., Foropon, C., Examining sustainable supply chain management of SMEs using resource based view and institutional theory (2017) Annals of Operations Research, pp. 1-26; Simpson, M., Taylor, N., Barker, K., Environmental responsibility in SMEs: Does it deliver competitive advantage? (2004) Business Strategy and the Environment, 13 (3), pp. 156-171; Soheilirad, S., Govindan, K., Mardani, A., Zavadskas, E.K., Nilashi, M., Zakuan, N., Application of data envelopment analysis models in supply chain management: A systematic review and meta-analysis (2017) Annals of Operations Research, pp. 1-55; Su-Yol, L., Drivers for the participation of small and medium-sized suppliers in green supply chain initiatives (2008) Supply Chain Management, 13 (3), pp. 185-198; (1999) SPSS Base 10.0 for Windows User’s Guide, , . SPSS Inc., Chicago, IL; Towers, N., Burnes, B., A composite framework of supply chain management and enterprise planning for small and medium-sized manufacturing enterprises (2008) Supply Chain Management, 13 (5), pp. 349-355; Tseng, Y.F., Yen-Chun, J.W., Wu, W.-H., Chun-Yu, C., Exploring corporate social responsibility education: The small and medium-sized enterprise viewpoint (2010) Management Decision, 48 (10), pp. 1514-1528; Wang, C.H., Gopal, R.D., Zionts, S., Use of data envelopment analysis in assessing information technology impact on firm performance (1997) Annals of Operations Research, 73, pp. 191-213; Whyman, P.B., Petrescu, A.I., Workplace flexibility practices in SMEs: Relationship with performance via redundancies, absenteeism, and financial turnover (2015) Journal of Small Business Management, 53 (4), p. 1097; Williamson, D., Lynch-Wood, G., Drivers of environmental behaviour in manufacturing SMEs and implications for CSR (2006) Journal of Business Ethics, 6 (3), pp. 317-330; Wolff, J.A., Pett, T.L., Small-firm performance: Modeling the role of product and process improvements (2006) Journal of Small Business Management, 44 (2), pp. 268-284; Wong, W.-P., Wong, K.-Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management & Data Systems, 107 (3), pp. 361-381; Wyld, J., Pugh, G., Tyrrall, D., Can powerful buyers “exploit” SME suppliers? (2012) Journal of Small Business and Enterprise Development, 19 (2), pp. 322-334; Zhai, Y.M., Sun, W.Q., Tsai, S.B., Wang, Z., Zhao, Y., Chen, Q., An empirical study on entrepreneurial orientation, absorptive capacity, and SMEs’ innovation performance: A sustainable perspective (2018) Sustainability, 10 (2), p. 314},
correspondence_address1={Malesios, C.; Aston UniversityUnited Kingdom; email: c.malesios@aston.ac.uk},
publisher={Springer},
issn={02545330},
language={English},
abbrev_source_title={Ann. Oper. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Abdelaziz2020557,
author={Abdelaziz, F.B. and Alaya, H. and Dey, P.K.},
title={A multi-objective particle swarm optimization algorithm for business sustainability analysis of small and medium sized enterprises},
journal={Annals of Operations Research},
year={2020},
volume={293},
number={2},
pages={557-586},
doi={10.1007/s10479-018-2974-0},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050658301&doi=10.1007%2fs10479-018-2974-0&partnerID=40&md5=929e3c0446b85851dc512ef1021d1a59},
affiliation={NEOMA Business School, Rouen Campus, Boulevard André Siegfried, Mont-Saint-Aignan, 76130, France; Logistics and Innovation Technology Research Center, IHE Paris, Paris, France; Tunis Business School, Tunis, Tunisia; Aston Business School, Aston University, Birminghan, United Kingdom},
abstract={Sustainability is the major issue of small and medium sized enterprises (SMEs) all across the globe. Although SMEs contribute to GDP of any country their negative contribution to environment is also significant. Prior studies on SMEs’ sustainability mainly classified into three categories—the correlation between environmental and social practices with economic performance, sustainable supply chain performance measurement, and empirical research on sustainability practices. There is no study that objectively derives the sustainable structure of SMEs through optimal combination of sustainability practices (inputs) and performance (outputs). Therefore, the main objective of this paper is to generate optimal structure of sustainable SMEs by combining neural network and particle swarm algorithm while considering Multi-Objective framework. The study uses data from 54 SMEs of Normandy in France and 30 SMEs of Midlands in the UK. The data was gathered through questionnaire survey. As we do not have the explicit expression of our objective functions, we train a neural network on our databases in order to enable the generation of value of the different objectives for any profile. We design and run a multi-objective version of particle swarm optimization (MPSO) to generate efficient companies’ structures. The weighted sum method is then used for different weights. The comparison of observed data and the results of the PSO analysis facilitates to derive improvement measures for each individual SME. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
author_keywords={Multi-objective programming;  Neural network;  Particle swarm optimization;  Sustainability practices and performance},
references={Abdelaziz, F.B., El-Baz, H., An optimization model based on neural network and particle swarm: An application case from the UAE (2010) Engineering Systems Management and Its Applications (ICESMA), 2010 Second International Conference on IEEE, pp. 1-6; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach (2014) Production Planning & Control Manuscript, 25 (8), pp. 698-714; Bourlakis, M., Maglaras, G., Aktasc, E., Gallear, D., Fotopoulos, C., Firm size and sustainable performance in food supply chains: Insights from Greek SMEs (2014) International Journal of Production Economics, 152, pp. 112-130; Charnes, A., Cooper, W.W., Deterministic equivalents for optimizing and satisficing under chance constraints (1963) Operations Research, 11 (1), pp. 18-39; Charnes, A., Cooper, W.W., Ferguson, R.O., Optimal estimation of executive compensation by linear programming (1955) Management Science, 1 (2), pp. 138-151; Dey, P.K., Cheffi, W., Nunes, B., Managing supply chain integration: Contemporary approaches and scope for further research (2013) Production Planning and Control, Guest Editorial, 24 (8-9), pp. 653-657; Eberhart, R.C., Kennedy, J., A new optimizer using particle swarm theory (1995) Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1, pp. 39-43; Gass, S., Saaty, T., The computational algorithm for the parametric objective function (1955) Naval Research Logistics Quarterly, 2 (1-2), pp. 39-45; Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N., Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process (2014) IJPE, 147, pp. 555-568; Haimes, Y.Y., Ladson, L.S., Wismer, D.A., Bicriterion formulation of problems of integrated system identification and system optimization (1971) IEEE Transactions on Systems Man and Cybernetics, 3, p. 296; Hu, Y.H., Hwang, J.N., (2001) Handbook of neural network signal processing, , (eds), CRC Press, Boca Raton; Huang, X., Tan, B.L., Ding, X., An exploratory survey of green supply chain management in Chinese manufacturing small and medium-sized enterprises: Pressures and drivers (2015) Journal of Manufacturing Technology Management, 26 (1), pp. 80-103; Jayaram, J., Dixit, M., Motwani, J., Supply chain management capability of small and medium sized family businesses in India, a multiple case study approach (2014) IJPE, 147, pp. 472-485; Jenkins, H., A business opportunity model of corporate social responsibility for small and medium sized enterprise (2009) Business Ethics: A European Review, 18 (1), pp. 21-36; Johnson, M.P., Sustainability management and small and medium-sized enterprises: Managers’ awareness and implementation of innovative tools (2015) Corporate Social Responsibility and Environmental Management, 22 (5), pp. 271-285; Kerr, I.R., Leadership strategies for sustainable SME operations (2006) Business Strategy and the Environment, 15 (1), pp. 30-39; (2005) KPMG International survey of corporate responsibility reporting 2005, , University of Amsterdam, Amsterdam; Looney, C.G., (1997) Pattern recognition using neural networks: theory and algorithms for engineers and scientists, , Oxford University Press, New York, NY; Malesios, C., Skouloudis, A., Dey, P.K., Abdelaziz, F.B., Kantartzis, A., Evangelinos, K., The impact of SME sustainability practices and performance on economic growth from a managerial perspective: Some modeling considerations and empirical analysis results (2009) Business Strategy and the Environment; Moore, S.B., Manring, S.L., Strategy development in small and medium sized enterprises for sustainability and increased value creation (2009) Journal of cleaner production, 17 (2), pp. 276-282; Norgard, M., Ravn, O., Poulsen, N.K., Hansen, L.K., (2000) Neural networks for modelling and control of dynamic systems: A practitioner’s handbook, , Advanced textbooks control and signal processing, Springer, Berlin; Petridis, K., Dey, P.K., Measuring incineration plants’ performance using combined data envelopment analysis, goal programming and mixed integer linear programming (2018) Annals of Operations Research, 267 (1-2), pp. 467-491; Seuring, S., Sarkis, J., Müller, M., Rao, P., Sustainability and supply chain management—An introduction to the special issue (2008) Journal of Cleaner Production, 16 (15), pp. 1545-1551; Trianni, A., Cagno, E., Farné, S., Barriers, drivers and decision-making process for industrial energy efficiency: A broad study among manufacturing small and medium-sized enterprises (2016) Applied Energy, 162, pp. 1537-1551; van Hoof, B., Thiell, M., Collaboration capacity for sustainable supply chain management: Small and mediumsized enterprises in Mexico (2014) Journal of Cleaner Production, 67, pp. 239-248; Walker, H., Preuss, L., Fostering sustainability through sourcing from small businesses: public sector perspectives (2008) Journal of Cleaner Production, 16 (15), pp. 1600-1609},
correspondence_address1={Abdelaziz, F.B.; NEOMA Business School, Rouen Campus, Boulevard André Siegfried, France; email: fouad.ben.abdelaziz@neoma-bs.fr},
publisher={Springer},
issn={02545330},
language={English},
abbrev_source_title={Ann. Oper. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Saeed2020496,
author={Saeed, M.A. and Kersten, W.},
title={Sustainability performance assessment framework: a cross-industry multiple case study},
journal={International Journal of Sustainable Development and World Ecology},
year={2020},
volume={27},
number={6},
pages={496-514},
doi={10.1080/13504509.2020.1764407},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086920463&doi=10.1080%2f13504509.2020.1764407&partnerID=40&md5=bbd37ea8ed5dda8a12103f1917501be5},
affiliation={Institute of Business Logistics and General Management, Hamburg University of Technology, Hamburg, Germany},
abstract={Increase in consumer awareness, and criticism from a multitude of stakeholders are often the cause of enormous pressure on organizations to take sustainability initiatives in order to minimize the ecological and social impacts of their businesses. Consequently, they are expected to track, measure, improve, and report financial as well as the non-financial performance of their operations. The main objective of this article is to understand the current state of sustainability-related performance assessment, in general, and examine the sustainability-related information disclosure by industrial corporations, in particular. The analysis of existing standards, guidelines, scientific literature, and cross-industry multiple case study identified a significant gap in the sustainability-related performance assessment between literature and practice. Also, it revealed a clear lack in the availability and use of standardized sustainability performance measurement systems. Furthermore, we identified that corporations publishing sustainability reports disclose higher quality information than corporations publishing their sustainability efforts only as a part of their annual reports. In total, we identified a coherent and consolidated list of 89 indicators for the sustainability performance evaluation of industrial corporations. Moreover, we developed a framework to assess the sustainability performance of organizations within a supply chain as well as the overall supply chain sustainability performance. It will assist industrial decision-makers to benchmark their organization’s sustainability performance and implement strategies to improve the overall sustainability of their businesses. © 2020 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={cross-industry multiple case study;  performance measurement;  secondary data analysis;  Sustainability performance indicators;  sustainable supply chain management},
keywords={assessment method;  business;  decision making;  guideline;  literature review;  performance assessment;  supply chain management;  sustainability},
references={Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) J Clean Prod, 86, pp. 360-377; Andalib Ardakani, D., Soltanmohammadi, A., Investigating and analysing the factors affecting the development of sustainable supply chain model in the industrial sectors (2019) Corporate Social Responsibility Environ Manage, 26, pp. 199-212; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management–what has been achieved? (2015) Supply Chain Manage, 20, pp. 664-680; Biermann, F., Kanie, N., Kim, R.E., Global governance by goal-setting: the novel approach of the UN sustainable development goals (2017) Curr Opin Environ Sustainability, 26-27, pp. 26-31; Bryman, A., Bell, E., (2011) Business research methods, , 3rd, Oxford: Oxford University Press, : 0199583404, ed; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: moving toward new theory (2008) Int J Phys Distrib Logist Manage, 38, pp. 360-387; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput Ind Eng, 62, pp. 801-818; Daub, C.-H., Assessing the quality of sustainability reporting: an alternative methodological approach (2007) J Clean Prod, 15, pp. 75-85; Durach, C.F., Kembro, J., Wieland, A., A new paradigm for systematic literature reviews in supply chain management (2017) J Supply Chain Manage, 53, pp. 67-85; Elo, S., Kääriäinen, M., Kanste, O., Pölkki, T., Utriainen, K., Kyngäs, H., Qualitative content analysis: a focus on trustworthiness (2014) SAGE Open, 4; (2016) Volkswagen light duty diesel vehicle violations for model years 2009–2016, , http://www.epa.gov/vw, USA: EPA, accessed 2016 Mar20; Fink, A., (2014) Conducting research literature reviews: from the internet to paper, , 4th, Thousand Oaks (California): SAGE, : 9781483301037, ed; (2016) The world’s biggest public companies: 2016 ranking, , USA: Forbes, accessed 2016 May26; Frey, B., (2020) Reporting on the SDGs: enhance and mainstream corporate reporting on the sustainable development goals: UN global compact, , http://www.unglobalcompact.org/take-action/action-platforms/sdg-reporting, accessed 2020 Apr20; Fukase, A., Toshiba accounting scandal draws record fine from regulators (2015) The Wall Street Journal, , http://www.wsj.com/articles/toshiba-accounting-scandal-draws-record-fine-from-regulators-1449472485, accessed 2016 Mar20; Gold, S., Seuring, S., Beske, P., The constructs of sustainable supply chain management–a content analysis based on published case studies (2010) Prog Ind Ecol, 7, p. 114; Gong, M., Simpson, A., Koh, L., Tan, K.H., Inside out: the interrelationships of sustainable performance metrics and its effect on business decision making: theory and practice (2018) Resourc Conserv Recycl, 128, pp. 155-166; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int J Prod Econ, 140, pp. 69-82; Hoskins, T., Reliving the Rana plaza factory collapse: a history of cities in 50 buildings, day 22 (2015) The Guardian, , http://www.theguardian.com/cities/2015/apr/23/rana-plaza-factory-collapse-history-cities-50-buildings,checked on 3/6/2019, accessed 2019 Jun3; Hsu, C.-C., Choon Tan, K., Hanim Mohamad Zailani, S., Jayaraman, V., Supply chain drivers that foster the development of green initiatives in an emerging economy (2013) Int J Oper Prod Manage, 33 (6), pp. 656-688; Hubbard, G., Measuring organizational performance: beyond the triple bottom line (2009) Bus Strategy Environ, 18, pp. 177-191; Jokić, M., Ball, R., (2006) Qualität und Quantität wissenschaftlicher Veröffentlichungen: bibliometrische Aspekte der Wissenschaftskommunikation, 15. , Jülich: Forschungszentrum Jülich (Schriften des Forschungszentrums Jülich. Reihe Bibliothek), : 1433-5557; Köksal, D., Strähle, J., Müller, M., Freise, M., Social sustainable supply chain management in the textile and apparel industry—a literature review (2017) Sustainability, 9, p. 100; Kolk, A., Trends in sustainability reporting by the fortune global 250 (2003) Bus Strategy Environ, 12, pp. 279-291; (2011) Sustainability reporting. What you should know, , http://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/iarcs-sustainability-reporting-what-you-should-know.pdf, KPMG, accessed 2016 Mar16; (2015) Currents of change. The KPMG survey of corporate responsibility 2015, , http://assets.kpmg.com/content/dam/kpmg/pdf/2016/02/kpmg-international-survey-of-corporate-responsibility-reporting-2015.pdf, KPMG, accessed 2016 Nov30; Levering, R., Vos, B., Organizational drivers and barriers to circular supply chain operations (2018) Operations management and sustainability: new research perspectives, pp. 43-66. , de Boer L., Houman Andersen P., (eds), Cham: Palgrave Macmillan,. In:, editors.,; p; Lozano, R., Huisingh, D., Inter-linking issues and dimensions in sustainability reporting (2011) J Clean Prod, 19, pp. 99-107; Lu, Y., Nakicenovic, N., Visbeck, M., Stevance, A.-S., Policy: five priorities for the UN sustainable development goals (2015) Nature, 520, pp. 432-433; Malhotra, N.K., (2010) Marketing research: an applied orientation, , 6th, Upper Saddle River (NJ): Pearson, : 9780136085430, ed; Martins, C.L., Pato, M.V., Supply chain sustainability: a tertiary literature review (2019) J Clean Prod, 225, pp. 995-1016; Nikolaou, I.E., Tsalis, T.A., Development of a sustainable balanced scorecard framework (2013) Ecol Indic, 34, pp. 76-86; Nisen, M., How Nike solved its sweatshop problem (2013) Business Insider, , http://www.businessinsider.de/how-nike-solved-its-sweatshop-problem-2013-5?r=US&IR=T, accessed 2019 June3; Portier, C., Frey, B., Midgley, L., Rees, C., (2018) Integrating the SDGs into corporate reporting: a practical guide, , https://d306pr3pise04h.cloudfront.net/docs/publications%2FPractical_Guide_SDG_Reporting.pdf, USA: GRI, UN Global Compact, Pwc, Shift; Qorri, A., Mujkić, Z., Kraslawski, A., A conceptual framework for measuring sustainability performance of supply chains (2018) J Clean Prod, 189, pp. 570-584; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: design and development guidelines (2013) Int J Prod Perform Manage, 62, pp. 805-826; Robson, C., (2011) Real world research: a resource for users of social research methods in applied settings / Colin Robson, , 3rd, Oxford: Wiley-Blackwell, : 1405182407, ed; Roca, L.C., Searcy, C., An analysis of indicators disclosed in corporate sustainability reports (2012) J Clean Prod, 20, pp. 103-118; Romero, S., Ruiz, S., Fernandez-Feijoo, B., Sustainability reporting and stakeholder engagement in Spain: different instruments, different quality (2019) Bus Strategy Environ, 28, pp. 221-232; Saeed, M.A., Kersten, W., Supply chain sustainability performance indicators - a content analysis based on published standards and guidelines (2017) Logist Res, 10; Saeed, M.A., Kersten, W., Drivers of sustainable supply chain management: identification and Classification (2019) Sustainability, 11, p. 1137; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decis Support Syst, 54, pp. 1513-1520; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J Clean Prod, 16, pp. 1699-1710; Shnayder, L., van Rijnsoever, F.J., Hekkert, M.P., Putting your money where your mouth is: why sustainability reporting based on the triple bottom line can be misleading (2015) Plos ONE, 10; Székely, F., Knirsch, M., Responsible leadership and corporate social responsibility: metrics for sustainable performance (2005) Eur Manag J, 23, pp. 628-647; Tajbakhsh, A., Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2015) J Clean Prod, 105, pp. 74-85; Tate, W.L., Ellram, L.M., Kirchoff, J.F.O.N., Corporate social responsibility reports: a thematic analysis related to supply chain management (2010) J Supply Chain Manage, 46, pp. 19-44; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) Int J Prod Res, 53, pp. 6473-6494; Teuteberg, F., Wittstruck, D., A systematic review of sustainable supply chain management (2010) Multikonferenz Wirtschaftsinformatik, 2010, p. 203; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) Br J Manage, 14, pp. 207-222; Global goals for people and planet (2015) UN Global Compact, , http://www.unglobalcompact.org/sdgs/about, accessed 2020 Apr20; Varsei, M., Soosay, C.A., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Manage, 19, pp. 242-257; Wolf, J., (2008) The nature of supply chain management research: insights from a content analysis of international supply chain management literature from 1990 to 2006, , 1st, Wiesbaden: Gabler (Gabler Edition Wissenschaft: Einkauf, Logistik und Supply Chain Management), : 9783834909985, ed; Yin, R.K., (2014) Case study research: design and methods, , 5th, London: SAGE Publications, : 9781483302003, ed},
correspondence_address1={Saeed, M.A.; Institute of Business Logistics and General Management, Germany; email: muhammad.saeed@tuhh.de},
publisher={Taylor and Francis Ltd.},
issn={13504509},
coden={IJSDB},
language={English},
abbrev_source_title={Int. J. Sustainable Dev. World Ecol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Roy2020,
author={Roy, S. and Das, M. and Ali, S.M. and Raihan, A.S. and Paul, S.K. and Kabir, G.},
title={Evaluating strategies for environmental sustainability in a supply chain of an emerging economy},
journal={Journal of Cleaner Production},
year={2020},
volume={262},
doi={10.1016/j.jclepro.2020.121389},
art_number={121389},
note={cited By 22},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082949925&doi=10.1016%2fj.jclepro.2020.121389&partnerID=40&md5=cefdb8b0c31f20d3330b3310a13ac272},
affiliation={Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh; UTS Business School, University of Technology Sydney, Sydney, Australia; Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK  S4S 0A2, Canada},
abstract={Due to an increased pressure to be environmentally sustainable, many manufacturing organizations, especially from developing countries like Bangladesh, are attempting to make necessary changes in practices and supply chains. However, those attempts need to be applied strategically with the objective to be both environmentally sustainable and economically viable. This paper offers a decision-making methodology by integrating a fuzzy cognitive map (FCM) and data envelopment analysis (DEA) for evaluating strategies for environmental sustainability based on their impact on the overall supply chain network of an organization. This paper first identifies 18 generic strategies for environmental sustainability and three supply chain performance measurement (PM) factors. Afterwards, the cause-effect relationships among these strategies and PM factors are utilized to capture the complicated relationships by FCM. The extended delta rule (EDR) learning algorithm was used in association with FCM to quantify the impact of those strategies on supply chain PM factors. Finally, DEA is used to prioritize strategies using these impact values. A real-life case using a fast-moving consumer goods (FMCG) manufacturer from Bangladesh is presented to justify the applicability of the proposed methodology. The results reflect the usefulness of this methodology for evaluating strategies for environmental sustainability in a supply chain (SC), specifically in the FMCG sector of an emerging economy. Thus, other manufacturing organizations from any industry can use this methodology to evaluate strategies for environmental sustainability. © 2020 Elsevier Ltd},
author_keywords={Bangladesh Fast-moving consumer good (FMCG) chain;  Data envelopment analysis;  Environmental sustainability;  Extended delta rule;  Fuzzy cognitive map;  Learning algorithm},
keywords={Cognitive systems;  Data envelopment analysis;  Decision making;  Developing countries;  Fuzzy rules;  Learning algorithms;  Manufacture;  Supply chains, Cause-effect relationships;  Decision making methodology;  Environmental sustainability;  Extended delta rule;  Fast moving consumer goods;  Fuzzy cognitive map;  Manufacturing organizations;  Supply chain performance measurements, Sustainable development},
references={Abbasi, M.N., Hassan, N.M., Sustainable logistic operations – study of leading MNC from FMCG sector of Pakistan (2013) Pakistan J. Soc. Sci., 33 (2); Ahmad, W.N.K.W., Rezaei, J., Sadaghiani, S., Tavasszy, L.A., Evaluation of the external forces affecting the sustainability of oil and gas supply chain using Best Worst Method (2017) J. Clean. Prod., 153, pp. 242-252; Ahmed, S., Ahmed, S., Shumon, M.R.H., Quader, M.A., Cho, H.M., Mahmud, M.I., Prioritizing strategies for sustainable end-of-life vehicle management using combinatorial multi-criteria decision making method (2016) Int. J. Fuzzy Syst., 18 (3), pp. 448-462; Alam, M., Murad, W., Hanifa, A., Ozturk, I., Relationships among carbon emissions, economic growth, energy consumption and population growth : testing Environmental Kuznets Curve hypothesis for Brazil, China, India and Indonesia (2016) Ecol. Indicat., 70, pp. 466-479; Angelakoglou, K., Gaidajis, G., A review of methods contributing to the assessment of the environmental sustainability of industrial systems (2015) J. Clean. Prod., 108, pp. 725-747; Annema, J.A., Mouter, N., Razaei, J., Cost-benefit analysis (CBA), or multi-criteria decision-making (MCDM) or both: politicians’ perspective in transport policy appraisal (2015) Transport. Res. Procedia, 21 (2), pp. 1603-1633; Arsić, S., Nikolić, D., Živković, Ž., Hybrid SWOT-ANP-FANP model for prioritization strategies of sustainable development of ecotourism in National Park Djerdap, Serbia (2017) For. Pol. Econ., 80, pp. 11-26; Awan, U., Kraslawski, A., Huiskonen, J., Governing interfirm relationships for social sustainability: the relationship between governance mechanisms, sustainable collaboration, and cultural intelligence (2018) Sustainability, 10 (12), p. 4473; Awan, U., Kraslawski, A., Huiskonen, J., Understanding influential factors on implementing social sustainability in Manufacturing Firms : an interpretive structural modelling (ISM) analysis (2018) Procedia Manuf., 17, pp. 1039-1048; Awan, U., Kraslawski, A., Huiskonen, J., Understanding the relationship between stakeholder pressure and sustainability performance in manufacturing firms in Pakistan (2017) Procedia Manuf., 11, pp. 768-777; Awasthi, A., Govindan, K., Gold, S., Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach (2018) Int. J. Prod. Econ., 195, pp. 106-117; Baker, B., Derfler-Rozin, R., Pitesa, M., Johnson, M., Stock market responses to unethical behavior in organizations: an organizational context model (2019) Organ. Sci., 30 (2), pp. 319-336; Basu, S., Roy, M., Pal, P., Corporate greening in a large developing economy: pollution prevention strategies (2018) Environ. Dev. Sustain., pp. 1-31; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3), pp. 275-292; Blome, C., Paulraj, A., Schuetz, K., Supply chain collaboration and sustainability: a profile deviation analysis (2014) Int. J. Oper. Prod. Manag., 34 (5), pp. 639-663; Brinch, M., Brinch, M., Understanding the value of big data in supply chain management and its business processes towards a conceptual framework (2018) Int. J. Oper. Prod. Manag., 38 (7), pp. 1589-1614; Butts, M.M., Michels, L.C., The sources of four commonly reported cutoff criteria: what did they really say? (2006) Organ. Res. Methods, 9 (2), pp. 202-220; Calabrese, A., Costa, R., Levialdi, N., Menichini, T., Integrating sustainability into strategic decision-making: a fuzzy AHP method for the selection of relevant sustainability issues (2018) Technol. Forecast. Soc. Change, 139, pp. 155-168; Caritte, V., Acha, S., Shah, N., Enhancing corporate environmental performance through reporting and roadmaps (2015) Bus. Strat. Environ., 24 (5), pp. 289-308; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Chege, S.M., Wang, D., The influence of technology innovation on SME performance through environmental sustainability practices in Kenya (2019) Technol. Soc., 60, p. 101210; Chen, C.C., Shih, H.S., Shyur, H.J., Wu, K.S., A business strategy selection of green supply chain management via an analytic network process (2012) Comput. Math. Appl., 64 (8), pp. 2544-2557; Chen, L., Yang, B., Shen, X., Xie, Z., Sun, F., Thermodynamic optimization opportunities for the recovery and utilization of residual energy and heat in China ’ s iron and steel industry : a case study (2015) Appl. Therm. Eng., 86, pp. 151-160; Chiou, T.Y., Chan, H.K., Lettice, F., Chung, S.H., Transportation research Part E: logistics and transportation review. The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in taiwan (2011), 47 (6), pp. 822-836; Chou, Y.C., Yen, H.Y., Dang, V.T., Sun, C.C., Assessing the human resource in science and technology for Asian countries: application of fuzzy AHP and fuzzy TOPSIS (2019) Symmetry, 11 (2), p. 251; Chowdhury, M.H., QFD Apparel: supply chain sustainability through complying buyers ’ requirements in apparel industry : a fuzzy QFD approach Supply chain sustainability through complying buyers ’ requirements in apparel industry : a fuzzy QFD approach (2012) The Proceedings of the 26th Annual Australian and New Zealand Academy of Management Conference (ANZAM), , ANZAM; Chowdhury, P., Paul, S.K., Applications of MCDM methods in research on corporate sustainability: A systematic literature review (2020) Management of Environmental Quality, 31 (2), pp. 385-405; Chowdhury, M.M.H., Quaddus, M.A., A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: the case of garment industry of Bangladesh (2015) Omega, 57, pp. 5-21; Comaniţă, E.D., Ghinea, C., Hlihor, R.M., Simion, I.M., Smaranda, C., Favier, L., Challenges and opportunities in green plastics: an assessment using the ELECTRE decision-aid method (2015) Environ. Eng. Manag. J., 14 (3), pp. 689-702; Costantini, V., Crespi, F., Marin, G., Paglialunga, E., Eco-innovation, sustainable supply chains and environmental performance in European industries (2017) J. Clean. Prod., 155, pp. 141-154; Da Fonseca, L.M.C.M., ISO 14001:2015: an improved tool for sustainability (2015) J. Ind. Eng. Manag., 8 (1), pp. 37-50; Dangelico, R.M., Pontrandolfo, P., Being “green and competitive”: the impact of environmental actions and collaborations on firm performance (2015) Bus. Strat. Environ., 24 (6), pp. 413-430; Deliya, M., Consumer behavior towards the new packaging of fmcg products (2012) Natl. Mon. Refereed J. Res. Commer. Manag., 1 (11), pp. 199-211; Dhull, S., Narwal, M.S., Prioritizing the drivers of green supply Chain management in Indian manufacturing industries using fuzzy TOPSIS method: government, industry, environment, and public perspectives (2018) Process Integrat. Optim. Sustain., 2 (1), pp. 47-60; Diabat, A., Govindan, K., An analysis of the drivers affecting the implementation of green supply chain management (2011) Resour. Conserv. Recycl., 55 (6), pp. 659-667; Dooley, K., Value chain systematicity: promoting organizational creativity and environmental sustainability in low velocity industries (2016) J. Clean. Prod., 140, pp. 1903-1913; Dotoli, M., Epicoco, N., Falagario, M., Sciancalepore, F., A cross-efficiency fuzzy data envelopment analysis technique for performance evaluation of decision making units under uncertainty (2015) Comput. Ind. Eng., 79, pp. 103-114; Esfahbodi, A., Zhang, Y., Watson, G., Sustainable supply chain management in emerging economies: trade-offs between environmental and cost performance (2016) Int. J. Prod. Econ., 181, pp. 350-366; Farjana, S.H., Huda, N., Parvez Mahmud, M.A., Environmental impact assessment of european non-ferro mining industries through life-cycle assessment (2018) IOP Conference Series: Earth and Environmental Science, 154. , IOP Publishing Issue 1; Fonseca, L.M., Domingues, J.P., Exploratory research of ISO 14001:2015 transition among Portuguese organizations (2018) Sustainability, 10 (3), p. 781; Fonseca, L.M., Domingues, J.P., Pereira, M.T., Martins, F.F., Zimon, D., Assessment of circular economy within Portuguese organizations (2018) Sustainability, 10 (7), p. 2521; Fullerton, D., Muehlegger, E., Who bears the economic burdens of environmental Regulations ? (2019) Rev. Environ. Econ. Pol., 13 (1), pp. 62-82; Goodland, R., The concept of environmental sustainability (1995) Annu. Rev. Ecol. Systemat., 26 (1), pp. 1-24; Govindan, K., Azevedo, S.G., Carvalho, H., Cruz-Machado, V., Impact of supply chain management practices on sustainability (2014) J. Clean. Prod., 85, pp. 212-225; Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N., Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process (2014) Int. J. Prod. Econ., 147, pp. 555-568; Goyal, P., Rahman, Z., Kazmi, A.A., Identification and prioritization of corporate sustainability practices using analytical hierarchy process (2015) J. Model. Manag., 10 (1), pp. 23-49; Henriques, A., Richardson, J., (2013) The Triple Bottom Line: Does it All Add up, , Routledge; Hoque, A., Clarke, A., Greening of industries in Bangladesh: pollution prevention practices (2013) J. Clean. Prod., 51, pp. 47-56; Hossain, U., Roy, I., Supply chain management for sustainable RMG growth in Bangladesh (2016) Int. J. Sci. Res., 5 (4), pp. 1242-1248; Hsu, C.C., Tan, K.C., Mohamad Zailani, S.H., Strategic orientations, sustainable supply chain initiatives, and reverse logistics: empirical evidence from an emerging market (2016) Int. J. Oper. Prod. Manag., 36 (1), pp. 86-110; Islam, M.S., Tseng, M.L., Karia, N., Lee, C.H., Assessing green supply chain practices in Bangladesh using fuzzy importance and performance approach (2018) Resour. Conserv. Recycl., 131 (May), pp. 134-145; Ji, G., Gunasekaran, A., Yang, G., Constructing sustainable supply chain under double environmental medium regulations (2014) Int. J. Prod. Econ., 147, pp. 211-219; Kabir, G., Sadiq, R., Tesfamariam, S., A review of multi-criteria decision-making methods for infrastructure management (2014) Struct. Infrastruct. Eng., 10 (9), pp. 1176-1210; Keeso, A., Big Data and Environmental Sustainability: a Conversation Starter (2014), Smith School of Enterprise and the Environment (Working Paper Series); Kiszová, Z., Mazurek, J., Modeling dependence and feedback in ANP with fuzzy cognitive maps (2012) Proceedings of 30th International Conference Mathematical Methods in Economics Modeling, December, pp. 558-563; Kolokotroni, M., Tassou, S.A., Lesh, B., Energy aspects and ventilation of food retail buildings (2015) Adv. Build. Energy Res., 9 (1), pp. 1-19; Kosko, B., Fuzzy cognitive maps (1986) Int. J. Man Mach. Stud., 24 (1), pp. 65-75; Lee, J., Kwon, H., The synergistic effect of environmental sustainability and corporate reputation on market value added (MVA) in manufacturing firms (2019) Int. J. Prod. Res., 57 (22), pp. 7123-7141; Leigh, M., Li, X., Industrial ecology, industrial symbiosis and supply chain environmental sustainability: a case study of a large UK distributor (2015) J. Clean. Prod., 106, pp. 632-643; Lieder, M., Rashid, A., Towards circular economy implementation : a comprehensive review in context of manufacturing industry (2016) J. Clean. Prod., 115, pp. 36-51; Luthra, S., Garg, D., Haleem, A., Identifying and ranking of strategies to implement green supply chain management in Indian manufacturing industry using analytical hierarchy process (2013) J. Ind. Eng. Manag., 6 (4), pp. 930-962; MacArthur, E., Towards the Circular Economy, Economic and Business Rationale for an Accelerated Transition (2013), Ellen MacArthur Foundation Cowes, UK; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Malesios, C., De, D., Moursellas, A., Dey, P., Evangelinos, K., Review of Statistical Modeling Approaches for Sustainability Analysis of Small and Medium Sized Enterprises (2019); Mangla, S., Kumar, P., Barua, M.K., An evaluation of attribute for improving the green supply chain performance via DEMATEL method (2014) Int. J. Mech. Eng. Robot. Res., 1 (1), pp. 30-35; Mangla, S.K., Kumar, P., Barua, M.K., Prioritizing the responses to manage risks in green supply chain: an Indian plastic manufacturer perspective (2015) Sustain. Prod. Consum., 1, pp. 67-86; Mangla, S.K., Govindan, K., Luthra, S., Prioritizing the barriers to achieve sustainable consumption and production trends in supply chains using fuzzy Analytical Hierarchy Process (2017) J. Clean. Prod., 151, pp. 509-525; Mariadoss, B.J., Tansuhaj, P.S., Mouri, N., Marketing capabilities and innovation-based strategies for environmental sustainability: an exploratory investigation of B2B firms (2011) Ind. Market. Manag., 40 (8), pp. 1305-1318; Markman, G.D., Krause, D., Theory building surrounding sustainable supply chain management: assessing what we know, exploring where to go (2016) J. Supply Chain Manag., 52 (2), pp. 3-10; Mårtensson, K., Westerberg, K., Corporate environmental strategies towards sustainable development (2016) Bus. Strat. Environ., 25 (1), pp. 1-9; Masi, D., Day, S., Godsell, J., Supply chain configurations in the circular economy: a systematic literature review (2017) Sustainability, 9 (9), p. 1602; Mathiyazhagan, K., Datta, U., Singla, A., Krishnamoorthi, S., Identification and prioritization of motivational factors for the green supply chain management adoption: case from Indian construction industries (2018) Opsearch, 55 (1), pp. 202-219; Mazzi, A., Toniolo, S., Mason, M., Aguiari, F., Scipioni, A., What are the benefits and difficulties in adopting an environmental management system? The opinion of Italian organizations (2016) J. Clean. Prod., 139, pp. 873-885; Mittal, V.K., Sangwan, K.S., Prioritizing barriers to green manufacturing: environmental, social and economic perspectives (2014) Procedia CIRP, 17, pp. 559-564; Moktadir, M.A., Rahman, T., Rahman, M.H., Ali, S.M., Paul, S.K., Drivers to sustainable manufacturing practices and circular economy: a perspective of leather industries in Bangladesh (2018) J. Clean. Prod., 174, pp. 1366-1380; Montabon, F., Pagell, M., Wu, Z., Making sustainability sustainable (2016) J. Supply Chain Manag., 52 (2), pp. 11-27; Morel, M., Kwakye, F., Green Marketing: Consumers Attitude towards Eco-Friendly Products and Purchase Intention in the Fast Moving Consumer Goods (FMCG) Sector (2012); Murmura, F., Liberatore, L., Bravi, L., Casolani, N., Evaluation of Italian companies’ perception about ISO 14001 and eco management and audit scheme III: motivations, benefits and barriers (2018) J. Clean. Prod., 203, pp. 125-142; Nagaraju, D.B., D, T.H., Consumers∖’ perception analysis-Market awareness towards ecofriendly FMCG products-A case study of Mysore district (2014) IOSR J. Bus. Manag., 16 (4), pp. 64-71; Naidoo, M., Gasparatos, A., Corporate environmental sustainability in the retail sector : drivers, strategies and performance measurement (2018) J. Clean. Prod., 203, pp. 125-142; Nallusamy, S., Ganesan, M., Balakannan, K., Shankar, C., Environmental sustainability evaluation for an automobile manufacturing industry using multi-grade fuzzy approach (2016) International Journal of Engineering Research in Africa, 19. , Trans Tech Publications Ltd; Nallusamy, S., Sri Lakshmana Kumar, D., Balakannan, K., Chakraborty, P.S., MCDM tools application for selection of suppliers in manufacturing industries using AHP, fuzzy logic and ANN (2016) International Journal of Engineering Research in Africa, 19. , Trans Tech Publications Ltd; Ngan, S.L., How, B.S., Teng, S.Y., Promentilla, M.A.B., Yatim, P., Er, A.C., Lam, H.L., Prioritization of sustainability indicators for promoting the circular economy: the case of developing countries (2019) Renew. Sustain. Energy Rev., 111, pp. 314-331; Olatunji, S.O., Olawumi, T.O., Aje, I.O., Rethinking partnering among quantity surveying firms in Nigeria (2017) J. Construct. Eng. Manag., 143 (11), pp. 1-12; Özesmi, U., Özesmi, S.L., Ecological models based on people ’ s knowledge : a multi-step fuzzy cognitive mapping approach (2004) Ecol. Model., 176 (1-2), pp. 43-64; Padmanabhan, S., Rajarathinam, M., Green packaging – reflection on consumer product buying towards the path of environmental sustainability (2016) Asian J. Res. Soc. Sci. Humanit., 6 (7), pp. 1718-1731; Papageorgiou, E.I., Active Hebbian learning algorithm to train fuzzy cognitive maps (2004) Int. J. Approx. Reason., 37 (3), pp. 219-249; Papageorgiou, E.I., Learning algorithms for fuzzy cognitive maps — a review study (2012) IEEE Trans. Syst. Man Cybern. C Appl. Rev., 42 (2), pp. 150-163; Papageorgiou, E.I., Stylios, C., Groumpos, P.P., Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links (2006) Int. J. Hum. Comput. Stud., 64 (8), pp. 727-743; Pasha, M., Hasanmahmood, A., Rahman, I., Hasnat, A., Assessment of ship breaking and recycling industries in Bangladesh - an effective step towards the achievement of environmental sustainability (2012) Proceedings of the International Conference on Agricultural, Environment and Biological Sciences, pp. 43-47; Pearce, D.W., Turner, R.K., Economics of Natural Resources and the Environment (1990), John Hopkins University Press Baltimore, ML, USA (Elsevier: New York, NY, USA); Pigosso, D.C., McAloone, T.C., Maturity-based approach for the development of environmentally sustainable product/service-systems (2016) CIRP J. Manuf. Sci. Technol., 15, pp. 33-41; Piyathanavong, V., Garza-reyes, J.A., Kumar, V., The adoption of operational environmental sustainability approaches in the Thai manufacturing sector (2019) J. Clean. Prod., 220, pp. 507-528; Prakash, C., Barua, M.K., Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment (2015) J. Manuf. Syst., 37, pp. 599-615; Radu, C., Caron, M.A., Arroyo, P., Integration of carbon and environmental strategies within corporate disclosures (2020) J. Clean. Prod., 244, p. 118681; Reichel, A., De Schoenmakere, M., Gillabel, J., Martin, J., Hoogeveen, Y., (2016) Circular Economy in Europe: Developing the Knowledge Base, 2. , European Environment Agency Report; Ren, J., Gao, S., Tan, S., Dong, L., Hydrogen economy in China : strengths – weaknesses – opportunities – threats analysis and strategies prioritization (2015) Renew. Sustain. Energy Rev., 41, pp. 1230-1243; Ren, J., Tan, S., Evan, M., Sovacool, B.K., Dong, L., Sustainability, shale gas, and energy transition in China : assessing barriers and prioritizing strategic measures (2015) Energy, 84, pp. 551-562; Rezaee, M.J., Yousefi, S., An intelligent decision making approach for identifying and analyzing airport risks (2018) J. Air Transport. Manag., 68, pp. 14-27; Rostamzadeh, R., Ghorabaee, M.K., Govindan, K., Esmaeili, A., Nobar, H.B.K., Evaluation of sustainable supply chain risk management using an integrated fuzzy TOPSIS- CRITIC approach (2018) J. Clean. Prod., 175, pp. 651-669; Røyne, F., Berlin, J., Ringström, E., Life cycle perspective in environmental strategy development on the industry cluster level: a case study of five chemical companies (2015) J. Clean. Prod., 86, pp. 125-131; Sarkis, J., Zhu, Q., Environmental sustainability and production: taking the road less travelled (2018) Int. J. Prod. Res., 56 (1-2), pp. 743-759; Sellitto, M.A., Hermann, F.F., Prioritization of green practices in GSCM: case study with companies of the peach industry (2016) Gestão Produção, 23, pp. 871-886; Sellitto, M.A., Assessment of the effectiveness of green practices in the management of two supply chains (2018) Bus. Process Manag. J., 24 (1), pp. 23-48; Serrai, W., Abdelli, A., Mokdad, L., Hammal, Y., Towards an efficient and a more accurate web service selection using MCDM methods (2017) J. Comput. Sci., 22, pp. 253-267; Seuring, S., Sarkis, J., Müller, M., Rao, P., Sustainability and supply chain management - an introduction to the special issue (2008) J. Clean. Prod., 16 (15), pp. 1545-1551; Shahbazi, S., Wiktorsson, M., Kurdve, M., Jönsson, C., Bjelkemyr, M., Material efficiency in manufacturing: Swedish evidence on potential, barriers and strategies (2016) J. Clean. Prod., 127, pp. 438-450; Sidhu, R., Arora, V., Benchmarking sustainability performance of suppliers using ISO 14001 and rough set QFD-based approach (2020) Handbook of Research on Interdisciplinary Approaches to Decision Making for Sustainable Supply Chains, pp. 55-72; Singh, P.K., Sarkar, P., A framework based on fuzzy AHP-TOPSIS for prioritizing solutions to overcome the barriers in the implementation of ecodesign practices in SMEs (2019) Int. J. Sustain. Dev. World Ecol., 26 (6), pp. 506-521; Somsuk, N., Laosirihongthong, T., Prioritization of applicable drivers for green supply chain management implementation toward sustainability in Thailand (2017) Int. J. Sustain. Dev. World Ecol., 24 (2), pp. 175-191; Somsuk, N., Prioritizing drivers of sustainable competitive advantages in green supply chain management based on fuzzy AHP (2014) J. Med. Bioeng., 3 (4); Sorrell, S., Reducing energy demand : a review of issues, challenges and approaches (2015) Renew. Sustain. Energy Rev., 47, pp. 74-82; Sueyoshi, T., Goto, M., Resource utilization for sustainability enhancement in Japanese industries (2018) Appl. Energy, 228, pp. 2308-2320; Suhi, S.A., Enayet, R., Haque, T., Ali, S.M., Moktadir, M.A., Paul, S.K., Environmental sustainability assessment in supply chain: an emerging economy context (2019) Environ. Impact Assess. Rev., 79, p. 106306; Thurner, T.W., Roud, V., Greening strategies in Russia's manufacturing - from compliance to opportunity (2016) J. Clean. Prod., 112, pp. 2851-2860; Tumpa, T.J., Ali, S.M., Rahman, M.H., Paul, S.K., Chowdhury, P., Rehman Khan, S.A., Barriers to green supply chain management: an emerging economy context (2019) J. Clean. Prod., 236, p. 117617; Walker, H., Seuring, S., Sarkis, J., Klassen, Fabbe-Costes, N., Roussat, C., Taylor, M., Taylor, A., Sustainable supply chains: a framework for environmental scanning practices (2014) Int. J. Oper. Prod. Manag., 34 (5), pp. 664-694; Walker, H., Seuring, S., Sarkis, J., Klassen, R., Huq, F.A., Stevenson, M., Zorzini, M., Social sustainability in developing country suppliers: an exploratory study in the ready made garments industry of Bangladesh (2014) Int. J. Oper. Prod. Manag., 34 (5), pp. 610-638; Wanninayake, W.M.C.B., Randiwela, P., Consumer attractiveness towards green products of FMCG sector: an empirical study (2018) Proceedings of the Oxford Business and Economics Conference, Oxford: UK; Wced, Report of the World commission on environment and development (1987) Our Common Future, 17, pp. 1-91; Whitehead, J., Prioritizing sustainability indicators: using materiality analysis to guide sustainability assessment and strategy (2017) Bus. Strat. Environ., 26 (3), pp. 399-412},
correspondence_address1={Paul, S.K.; UTS Business School, Australia; email: Sanjoy.Paul@uts.edu.au},
publisher={Elsevier Ltd},
issn={09596526},
coden={JCROE},
language={English},
abbrev_source_title={J. Clean. Prod.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yazdani20204325,
author={Yazdani, M. and Pamucar, D. and Chatterjee, P. and Chakraborty, S.},
title={Development of a decision support framework for sustainable freight transport system evaluation using rough numbers},
journal={International Journal of Production Research},
year={2020},
volume={58},
number={14},
pages={4325-4351},
doi={10.1080/00207543.2019.1651945},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85070915019&doi=10.1080%2f00207543.2019.1651945&partnerID=40&md5=bfcb5f06acddd91538f1f679d2562c10},
affiliation={Department of Management, Universidad Loyola Andalucia, Seville, Spain; Department of Logistics, Military Academy, University of Defence in Belgrade, Belgrade, Serbia; Department of Mechanical Engineering, MCKV Institute of Engineering, Howrah, India; Department of Production Engineering, Jadavpur University, Kolkata, India},
abstract={Among various operational decision-making tasks in a transportation system, sustainable performance evaluation has a very promising and direct influence on the community as well as environment. To prevent and reduce the negative impacts of a freight transportation system, a constant monitoring and performance measurement system has of paramount significance in the process of supply chain management. However, studies of complex transport evaluation systems are very scarce in the existing literature. This paper aims to resolve the problem of freight transport system’s performance measurement while developing a comprehensive framework with incorporation of sustainable elements and establishing a rough set-based decision-making approach. The applicability of the proposed framework is investigated to evaluate the performance of seven freight transportation companies in Spain. A decision support tool is designed by integrating rough number-based decision-making trial and evaluation laboratory (DEMATEL) and multi-attributive border approximation area comparison (MABAC) methods for their performance appraisal. Sensitivity analysis and comparison with other popular methods are also performed to validate the efficacy of the proposed approach. It is established that rough number-based methodologies have advantages over fuzzy or interval-based models. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={DEMATEL;  evaluation;  MABAC;  rough number;  sensitivity analysis;  sustainable freight transportation system},
keywords={Decision making;  Decision support systems;  Monitoring;  Petroleum reservoir evaluation;  Sensitivity analysis;  Supply chain management, DEMATEL;  evaluation;  MABAC;  Rough numbers;  sustainable freight transportation system, Freight transportation},
references={Andrés, L., Padilla, E., Energy Intensity in Road Freight Transport of Heavy Goods Vehicles in Spain (2015) Energy Policy, 85, pp. 309-321; Awasthi, A., Chauhan, S.S., Using AHP and Dempster-Shafer Theory for Evaluating Sustainable Transport Solutions (2011) Environmental Modelling & Software, 26 (6), pp. 787-796; Bai, C., Fahimnia, B., Sarkis, J., Sustainable Transport Fleet Appraisal Using a Hybrid Multi-objective Decision Making Approach (2017) Annals of Operations Research, 250 (2), pp. 309-340; Barabino, B., Deiana, E., Tilocca, P., Measuring Service Quality in Urban Bus Transport: A Modified SERVQUAL Approach (2012) International Journal of Quality and Service Sciences, 4 (3), pp. 238-252; Baran, J., Żak, J., Multiple Criteria Evaluation of Transportation Performance for Selected Agribusiness Companies (2014) Procedia–Social and Behavioral Sciences, 111, pp. 320-329; Barbosa, S.B., Ferreira, M.G.G., Nickel, E.M., Cruz, J.A., Forcellini, F.A., Garcia, J., de Andrade Guerra, J.B.S.O., Multi-criteria Analysis Model to Evaluate Transport Systems: An Application in Florianópolis, Brazil (2017) Transportation Research Part A: Policy and Practice, 96, pp. 1-13; Bauer, J., Bektaş, T., Crainic, T.G., Minimizing Greenhouse Gas Emissions in Intermodal Freight Transport: An Application to Rail Service Design (2010) Journal of the Operational Research Society, 61 (3), pp. 530-542; Bhattacharya, A., Kumar, S.A., Tiwari, M.K., Talluri, S., An Intermodal Freight Transport System for Optimal Supply Chain Logistics (2014) Transportation Research Part C: Emerging Technologies, 38, pp. 73-84; Bojanic, D., Kovač, M., Bojanic, M., Ristic, V., Multi-criteria Decision-making in a Defensive Operation of the Guided Anti-tank Missile Battery: An Example of the Hybrid Model Fuzzy AHP-MABAC (2018) Decision Making: Applications in Management and Engineering, 1 (1), pp. 51-66; Bonferroni, C., Sullemedie Multiple di Potenze (1950) Bollettino Unione MatematicaItaliana, 5 (3-4), pp. 267-270; Borén, S., Nurhadi, L., Ny, H., Robèrt, K.-H., Broman, G., Trygg, L., A Strategic Approach to Sustainable Transport System Development–Part 2: The Case of a Vision for Electric Vehicle Systems in Southeast Sweden (2017) Journal of Cleaner Production, 140 (1), pp. 62-71; Bozanic, D., Pamucar, D., Karovic, S., Use of the Fuzzy AHP-MABAC Hybrid Model in Ranking Potential Locations for Preparing Laying-up Positions (2016) Military Technical Courier, 64 (3), pp. 705-729; Bray, S., Caggiani, L., Dell’Orco, M., Ottomanelli, M., Features Selection Based on Fuzzy Entropy for Data Envelopment Analysis Applied to Transport Systems (2014) Transportation Research Procedia, 3, pp. 602-610; Celik, E., Bilisik, O.N., Erdogan, M., Gumus, A.T., Baracli, H., An Integrated Novel Interval Type-2 Fuzzy MCDM Method to Improve Customer Satisfaction in Public Transportation for Istanbul (2013) Transportation Research Part E: Logistics and Transportation Review, 58, pp. 28-51; Centobelli, P., Cerchione, R., Esposito, E., Environmental Sustainability in the Service Industry of Transportation and Logistics Service Providers: Systematic Literature Review and Research Directions (2017) Transportation Research Part D: Transport and Environment, 53, pp. 454-470; Chatterjee, P., Mondal, S., Boral, S., Banerjee, A., Chakraborty, S., A Novel Hybrid Method for Non-traditional Machining Process Selection Using Factor Relationship and Multi-attributive Border Approximation Method (2017) Facta Universitatis Series: Mechanical Engineering, 15 (3), pp. 439-456; Chatterjee, K., Pamucar, D., Zavadskas, E.K., Evaluating the Performance of Suppliers Based on Using the R’AMATEL-MAIRCA Method for Green Supply Chain Implementation in Electronics Industry (2018) Journal of Cleaner Production, 184, pp. 101-129; Cruz, L., Barata, E., Ferreira, J.P., Performance in Urban Public Transport Systems: A Critical Analysis of the Portuguese Case (2012) International Journal of Productivity and Performance Management, 61 (7), pp. 730-751; Dadsena, K.K., Sarmah, S.P., Naikan, V.N.A., Risk Evaluation and Mitigation of Sustainable Road Freight Transport Operation: A Case of Trucking Industry (2019) International Journal of Production Research, pp. 1-23. , https://doi.org/10.1080/00207543.2019.1578429; Demir, E., Hrušovský, M., Jammernegg, W., Van Woensel, T., Green Intermodal Freight Transportation: Bi-objective Modelling and Analysis (2019) International Journal of Production Research, pp. 1-19. , https://doi.org/10.1080/00207543.2019.1620363; Ellram, L.M., Murfield, M.L.U., Environmental Sustainability in Freight Transportation: A Systematic Literature Review and Agenda for Future Research (2017) Transportation Journal, 56 (3), pp. 263-298; Gigovic, L., Pamucar, D., Lukic, D., Markovic, S., Application of the GIS-Fuzzy DEMATEL MCDA Model for Ecotourism Development Site Evaluation: A Case Study of Dunavskiklju, Serbia (2016) Land Use Policy, 58, pp. 348-365; Golini, R., Guerlain, C., Lagorio, A., Pinto, R., An Assessment Framework to Support Collective Decision Making on Urban Freight Transport (2018) Transport, 33 (4), pp. 890-901; Hanaoka, S., Kunadhamraks, P., Multiple Criteria and Fuzzy Based Evaluation of Logistics Performance for Intermodal Transportation (2009) Journal of Advanced Transportation, 43 (2), pp. 123-153; Hawas, Y.E., Khan, M., Basu, N., Evaluating and Enhancing the Operational Performance of Public Bus Systems Using GIS-based Data Envelopment Analysis (2012) Journal of Public Transportation, 15 (2), pp. 19-44; Holmgren, J., The Efficiency of Public Transport Operations–An Evaluation Using Stochastic Frontier Analysis (2013) Research in Transportation Economics, 39 (1), pp. 50-57. , https://www.acte.es/, Accessed June 12, 2018; Jacyna-Gołda, I., Gołębiowski, P., Jachimowski, R., Kłodawski, M., Lewczuk, K., Szczepański, E., Pielecha, J., Traffic Distribution Into Transport Network for Defined Scenarios of Transport System Development in Aspect of Environmental Protection (2014) Journal of KONES, 21 (4), pp. 183-192; Jamshidi, A., Jamshidi, F., Ait-Kadi, D., Ramudhin, A., A Review of Priority Criteria and Decision-making Methods Applied in Selection of Sustainable City Logistics Initiatives and Collaboration Partners (2018) International Journal of Production Research, pp. 1-19. , https://doi.org/10.1080/00207543.2018.1540892; Johansson, E., Hiselius, L.W., Koglin, T., Wretstrand, A., Evaluation of Public Transport: Regional Policies and Planning Practices in Sweden (2017) Urban, Planning and Transport Research, 5 (1), pp. 59-77; Kaur, J., Sidhu, R., Awasthi, A., Chauhan, S., Goyal, S., A DEMATEL Based Approach for Investigating Barriers in Green Supply Chain Management in Canadian Manufacturing Firms (2018) International Journal of Production Research, 56 (1-2), pp. 312-332; Kazemi, N., Modak, N.M., Govindan, K., A Review of Reverse Logistics and Closed Loop Supply Chain Management Studies Published in IJPR: A Bibliometric and Content Analysis (2018) International Journal of Production Research, pp. 1-24. , https://doi.org/10.1080/00207543.2018.1471244; Kengpol, A., Tuammee, S., The Development of a Decision Support Framework for a Quantitative Risk Assessment in Multimodal Green Logistics: An Empirical Study (2016) International Journal of Production Research, 54 (4), pp. 1020-1038; Krstić, M., Tadić, S., Brnjac, N., Zečević, S., Intermodal Terminal Handling Equipment Selection Using a Fuzzy Multi-criteria Decision-making Model (2019) Promet–Traffic & Transportation, 31 (1), pp. 89-100; Kucukaltan, B., Irani, Z., Aktas, E., A Decision Support Model for Identification and Prioritization of Key Performance Indicators in the Logistics Industry (2016) Computers in Human Behavior, 65, pp. 346-358; Kumar, A., Anbanandam, R., Development of Social Sustainability Index for Freight Transportation System (2019) Journal of Cleaner Production, 210, pp. 77-92; Lin, C.-L., Hsieh, M.-S., Tzeng, G.-H., Evaluating Vehicle Telematics System by Using a Novel MCDM Techniques with Dependence and Feedback (2010) Expert Systems with Applications, 37 (10), pp. 6723-6736; Liu, Z., Ming, X., A Framework with Revised Rough-DEMATEL to Capture and Evaluate Requirements for Smart Industrial Product-service System of Systems (2019) International Journal of Production Research, pp. 1-19. , https://doi.org/10.1080/00207543.2019.1577566; Marchet, G., Melacini, M., Perotti, S., Environmental Sustainability in Logistics and Freight Transportation: A Literature Review and Research Agenda (2014) Journal of Manufacturing Technology Management, 25 (6), pp. 775-811; Marcucci, E., Gatta, V., Scaccia, L., Urban Freight, Parking and Pricing Policies: An Evaluation from a Transport Providers’ Perspective (2015) Transportation Research Part A, 74, pp. 239-249; Mardani, A., Zavadskas, E.K., Khalifah, Z., Jusoh, A., Nor, K.M., Multiple Criteria Decision-making Techniques in Transportation Systems: A Systematic Review of the State of the Art Literature (2016) Transport, 31 (3), pp. 359-385; Márquez, L., Cantillo, V., Evaluating Strategic Freight Transport Corridors Including External Costs (2013) Transportation Planning and Technology, 36 (6), pp. 529-546; Martí, L., Martín, J.C., Puertas, R., A DEA-logistics Performance Index (2017) Journal of Applied Economics, 20 (1), pp. 169-192; McGinnis, M.A., Spillan, J.E., Virzi, N., An Empirical Study Comparing Guatemalan and United States Logistics Strategies (2012) International Journal of Logistics Management, 23 (1), pp. 77-95; Nassereddine, M., Eskandari, H., An Integrated MCDM Approach to Evaluate Public Transportation Systems in Tehran (2017) Transportation Research Part A: Policy and Practice, 106, pp. 427-439; Nikolic, V., Milovancevic, M., Petkovic, D., Jocic, D., Savic, M., Parameters Forecasting of Laser Welding by the Artificial Intelligence Techniques (2018) Facta Universitatis, Series: Mechanical Engineering, 16 (2), pp. 193-201; Nunic, Z., Evaluation and Selection of the PVC Carpentry Manufacturer Using the FUCOM-MABAC Model (2018) Operational Research in Engineering Sciences: Theory and Applications, 1 (1), pp. 13-28; Omrani, H., Awasthi, A., Ion, L., Trigano, P., A Hybrid Approach for Evaluating Environmental Impacts for Urban Transportation Mode Sharing (2009) Journal of Decision Systems, 18 (2), pp. 185-201; Pamucar, D., Bozanic, D., Lukovac, V., Komazec, N., Normalized Weighted Geometric Bonferroni Mean Operator of Interval Rough Numbers–Application in Interval Rough DEMATEL-COPRAS (2018) Facta Universitatis Series: Mechanical Engineering, 16 (2), pp. 171-191; Pamucar, D., Cirovic, G., The Selection of Transport and Handling Resources in Logistics Centres Using Multi-attributive Border Approximation Area Comparison (MABAC) (2015) Expert Systems with Applications, 42 (6), pp. 3016-3028; Pamucar, D., Mihajlovic, M., Obradovic, R., Atanaskovic, P., Novel Approach to Group Multi-criteria Decision Making Based on Interval Rough Numbers: Hybrid DEMATEL-ANP-MAIRCA Model (2017) Expert Systems with Applications, 88, pp. 58-80; Pamucar, D., Petrovic, I., Cirovic, G., Modification of the Best-worst and MABAC Methods: A Novel Approach Based on Interval-valued Fuzzy-rough Numbers (2018) Expert Systems with Applications, 91, pp. 89-106; Pamucar, D., Stevic, Ž., Zavadskas, E.K., Integration of Interval Rough AHP and Interval Rough MABAC Methods for Evaluating University Web Pages (2018) Applied Soft Computing, 67, pp. 141-163; Pathak, D.K., Thakur, L.S., Rahman, S., Performance Evaluation Framework for Sustainable Freight Transportation Systems (2019) International Journal of Production Research, , https://doi.org/10.1080/00207543.2019.1602741; Peng, X., Yang, Y., Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making (2016) International Journal of Intelligent Systems, 31 (10), pp. 989-1020; Rai, H.B., van Lier, T., Meers, D., Macharis, C., Improving Urban Freight Transport Sustainability: Policy Assessment Framework and Case Study (2017) Research in Transportation Economics, 64, pp. 26-35; Rajak, S., Parthiban, P., Dhanalakshmi, R., Sustainable Transportation Systems Performance Evaluation Using Fuzzy Logic (2016) Ecological Indicators, 71, pp. 503-513; Roy, J., Pamucar, D., Adhikary, K., Kar, S., A Rough Strength Relational DEMATEL Model for Analysing the Key Success Factors of Hospital Service Quality (2018) Decision Making: Applications in Management and Engineering, 1 (1), pp. 121-142; Shiau, T.A., Evaluating Transport Infrastructure Decisions Under Uncertainty (2014) Transportation Planning and Technology, 37 (6), pp. 525-538; Song, W., Ming, X., Wu, Z., Zhu, B., A Rough TOPSIS Approach for Failure Mode and Effects Analysis in Uncertain Environments (2014) Quality and Reliability Engineering International, 30 (4), pp. 473-486; Sun, M., Liu, J., Normalized Geometric Bonferroni Operators of Hesitant Fuzzy Sets and Their Application in Multiple Attribute Decision Making (2013) Journal of Information & Computational Science, 10 (9), pp. 2815-2822; Tamura, M., Watanabe, K., Nachi, Y., Environmental Impact Evaluation for Transportation of Building Materials Into Tokyo District (2011) International Journal of Sustainable Building Technology and Urban Development, 2 (1), pp. 80-86; Tuzkaya, U.R., Evaluating the Environmental Effects of Transportation Modes Using an Integrated Methodology and an Application (2009) International Journal of Environmental Science & Technology, 6 (2), pp. 277-290; Tuzkaya, G., Gülsün, B., Önsel, Ş., A Methodology for the Strategic Design of Reverse Logistics Networks and Its Application in the Turkish White Goods Industry (2011) International Journal of Production Research, 49 (15), pp. 4543-4571; Tuzkaya, U.R., Önüt, S., A Fuzzy Analytic Network Process Based Approach to Transportation-Mode Selection Between Turkey and Germany: A Case Study (2008) Information Sciences, 178 (15), pp. 3133-3146; Vaidya, O.S., Evaluating the Performance of Public Urban Transportation Systems in India (2014) Journal of Public Transportation, 17 (4), pp. 174-191; Vannieuwenhuyse, B., Gelders, L., Pintelon, L., An Online Decision Support System for Transportation Mode Choice (2003) Logistics Information Management, 16 (2), pp. 125-133; Vasiljevic, M., Fazlollahtabar, H., Stevic, Z., Veskovic, S., A Rough Multicriteria Approach for Evaluation of the Supplier Criteria in Automotive Industry (2018) Decision Making: Applications in Management and Engineering, 1 (1), pp. 82-96; Wang, L., Framework for Evaluating Sustainability of Transport System in Megalopolis and its Application (2014) IERI Procedia, 9, pp. 110-116; Xue, Y.-X., You, J.-X., Lai, X.-D., Liu, H.-C., An Interval-valued Intuitionistic Fuzzy MABAC Approach for Material Selection with Incomplete Weight Information (2016) Applied Soft Computing, 38, pp. 703-713; Yang, C.-H., Lee, K.-C., Chen, H.-C., Incorporating Carbon Footprint with Activity-based Costing Constraints Into Sustainable Public Transport Infrastructure Project Decisions (2016) Journal of Cleaner Production, 133, pp. 1154-1166; Yayla, A.Y., Oztekin, A., Gumus, A.T., Gunasekaran, A., A Hybrid Data Analytic Methodology for 3PL Transportation Provider Evaluation Using Fuzzy Multi-criteria Decision Making (2015) International Journal of Production Research, 53 (20), pp. 6097-6113; Yazdani, M., Zarate, P., Coulibaly, A., Zavadskas, E.K., A Group Decision Making Support System in Logistics and Supply Chain Management (2017) Expert Systems with Applications, 88, pp. 376-392; Yu, S.-M., Wang, J., Wang, J.-Q., An Interval Type-2 Fuzzy Likelihood-based MABAC Approach and Its Application in Selecting Hotels on A Tourism Website (2017) International Journal of Fuzzy Systems, 19 (1), pp. 47-61; Żak, J., Multiple-criteria and Group Decision Making in the Fleet Selection Problem for a Public Transportation System (2017) Transportation Research Procedia, 27, pp. 43-52; Żak, J., Galińska, B., Design and Evaluation of Global Freight Transportation Solutions (Corridors). Analysis of a Real World Case Study (2018) Transportation Research Procedia, 30, pp. 350-362; Zhou, W., He, J.M., Intuitionistic Fuzzy Normalized Weighted Bonferroni Mean and its Application in Multicriteria Decision-making (2012) Journal of Applied Mathematics, 22. , https://doi.org/10.1155/2012/136254; Zhu, B., Xu, Z.S., Xia, M.M., Hesitant Fuzzy Geometric Bonferroni Means (2012) Information Sciences, 205, pp. 72-85},
correspondence_address1={Chatterjee, P.; Department of Mechanical Engineering, India; email: prasenjit2007@gmail.com},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Albishri2020390,
author={Albishri, D.Y. and Sundarakani, B. and Gomisek, B.},
title={An empirical study of relationships between goal alignment, centralised decision-making, commitment to networking and supply chain effectiveness using structural equation modelling},
journal={International Journal of Logistics Research and Applications},
year={2020},
volume={23},
number={4},
pages={390-415},
doi={10.1080/13675567.2019.1700219},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076373226&doi=10.1080%2f13675567.2019.1700219&partnerID=40&md5=6613c55f5b8e0ff225b67b7534a20033},
affiliation={Faculty of Business, University of Wollongong in Dubai, Dubai, United Arab Emirates},
abstract={The aim of this paper is to investigate the underlying factors of supply chain effectiveness of the networked organisations that are involved in the logistics industry in the UAE context. In particular, the study is to empirically test the relationships between goal alignment, commitment to networking and decision-making with four aspects of supply chain effectiveness. A questionnaire-based survey primarily inductive in nature was used to gather quantitative data from various supply chain organisations across industry verticals that are operating in the UAE. The data for this study were gathered and analysed employing survey responses from 154 representative organisations of various supply chain and logistics sector in the UAE. In this research, structural equation modelling has been applied to test these identified factors and their effect on supply chain effectiveness and its performance. The research discovered that the relationship between supply chain goal alignment, commitment to networking and decision-making were significantly and positively correlated with supply chain effectiveness. The findings provide insight into the field of supply chain effectiveness as part of supply chain performance. Several recommendations are offered for supply chain members to improve supply chain effectiveness while implementing dimensions of their strategic supply chain. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={structural equation modelling;  Supply chain effectiveness;  supply chain performance measurement},
references={Abdul-Jalbar, B., Gutierrez, J., Puerto, J., Sicilia, J., Policies for Inventory/Distribution Systems: The Effect of Centralization vs. Decentralization (2003) International Journal of Production Economics, 81-82 (1), pp. 281-293; Akdogan, A., Demirtas, O., Managerial Role in Strategic Supply Chain Management (2014) Procedia–Social and Behavioral Sciences, 150, pp. 1020-1029; Akyuz, G., Erkan, T., Supply Chain Performance Measurement: A Literature Review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; The United Arab Emirates at 40: A Balance Sheet (2011) Middle East Policy, 18 (4), pp. 44-45; Albaloushi, H., Skitmore, M., Supply Chain Management in the UAE Construction Industry (2008) International Journal of Construction Management, 8 (1), pp. 53-71; Amoako-Gyampah, K., Boakye, K.G., Adaku, E., Famiyeh, S., Supplier Relationship Management and Firm Performance in Developing Economies: A Moderated Mediation Analysis of Flexibility Capability and Ownership Structure (2019) International Journal of Production Economics, 208, pp. 160-170; Anderson, J., Gerbing, D., Structural Equation Modeling in Practice: A Review and Recommended Two-step Approach (1988) Psychological Bulletin, 103, pp. 411-423; Arshinder, K., Arun, K., Deshmukh, S., A Review on Supply Chain Coordination: Coordination Mechanisms, Managing Uncertainty and Research Directions (2011) Supply Chain Coordination under Uncertainty, pp. 39-82; Arzu Akyuz, G., Erman Erkan, T., Supply Chain Performance Measurement: A Literature Review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Ashai, Z., El Dahshan, M., Kubba, J., Talati, H., Youssefi, P., (2007) The Transport and Logistics Cluster in the United Arab Emirates, , http://www.isc.hbs.edu/resources/courses/moc-course-at-harvard/Documents/pdf/studentprojects/UAE_TransportLogisticsCluster_2007.pdf, Accessed June 1, 2017; Babbar, S., Addae, H., Gosen, J., Prasad, S., Organizational Factors Affecting Supply Chains in Developing Countries (2008) International Journal of Commerce and Management, 18 (3), pp. 234-251; Babbie, E., (2007) The Practice of Social Research, , 11th ed, Belmont, CA: Thomson Wadsworth, ed; Bagchi, P., Skjoett-Larsen, T., Integration of Information Technology and Organizations in a Supply Chain (2002) International Journal of Logistics Management, 14, pp. 89-108; Bagozzi, R., Yi, Y., Advanced Topics in Structural Equation Models (1994) Advanced Methods of Marketing Research, pp. 1-52. , Bagozzi R.P., (ed), Cambridge, MA: Blackwell, and,. edited by; Barney, J.B., Strategic Factor Markets: Expectations, Luck, and Business Strategy (1986) Management Science, 32 (10), pp. 1231-1241; Barney, J., Firm Resources and Sustained Competitive Advantage (1991) Journal of Management, 17 (1), pp. 99-120; Barney, J.B., Ketchen, D.J., Wright, M., The Future of Resource-based Theory (2011) Journal of Management, 37 (5), pp. 1299-1315; Beamon, B., Measuring Supply Chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bentler, P.M., (1995) EQS Structural Equation Program Manual, , Encino, CA: Multivariate Software Inc; Bessant, J., Caffyn, S., Gallagher, M., An Evolutionary Model of Continuous Improvement Behaviour (2001) Technovation, 21 (2), pp. 67-77; Bontis, N., Booker, L., Serenko, A., The Mediating Effect of Organizational Reputation on Customer Loyalty and Service Recommendation in the Banking Industry (2007) Management Decision, 45 (9), pp. 1426-1445; Bowersox, D.J., The Strategic Benefits of Logistics Alliances (1990) Harvard Business Review, 68 (4), pp. 36-45; Bowersox, D., Closs, D., Stank, T., (1999) 21st Century Logistics: Making Supply Chain Integration a Reality, , Oak Brook, IL: Council of Logistics Management; Boyer, K., Morgan, L., Empirical Elephants–Why Multiple Methods Are Essential to Quality Research in Operations and Supply Chain Management (2008) Journal of Operations Management, 26 (3), pp. 338-344; Cao, M., Vonderembse, M., Zhang, Q., Raguhunathan, T., Supply Chain Collaboration: Conceptualisation and Instrument Development (2010) International Journal of Production Research, 48 (22), pp. 6613-6635; Cao, M., Zhang, Q., Supply Chain Collaboration: Impact on Collaborative Advantage and Firm Performance (2011) Journal of Operations Management, 29 (3), pp. 163-180; Carraresi, L., Mamaqi, X., Albisu, L.M., Banterle, A., (2012) Proceedings in Food System Dynamics, , Strategic Capabilities and Performance: An Application of Resource-based View Italian Food SMEs. 186–209; Cassel, C., Hackl, P., Westlund, A., On Measurement of Intangible Assets: A Study into the Robustness of Partial Least Squares (2000) Total Quality Management, 11 (7), pp. 897-908; Chan, H.K., Chan, F.T.S., Effect of Information Sharing in Supply Chains with Flexibility (2009) International Journal of Production Research, 47 (1), pp. 213-232; Chandra, C., Grabis, J., Tumanyan, A., Problem Taxonomy: A Step Towards Effective Information Sharing in Supply Chain Management (2007) International Journal of Production Research, 45 (1), pp. 2507-2544; Chen, I.J., Paulraj, A., Towards a Theory of Supply Chain Management: The Constructs and Measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Chen, I.J., Paulraj, A., Understanding Supply Chain Management: Critical Research and a Theoretical Framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Chen, I.J., Paulraj, A., Lado, A.A., Strategic Purchasing, Supply Management and Firm Performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Chin, W.W., The Partial Least Squares Approach for Structural Equation Modeling (1998) Modern Methods for Business Research, pp. 295-336. , Marcoulides G.A., (ed), Mahwah, NJ: Lawrence Erlbaum Associates,. edited by; Chin, W.W., Newsted, P.R., Structural Equation Modelling Analysis with Small Samples Using Partial Least Squares (1999) Statistical Strategies for Small Sample Research, pp. 307-341. , Hoyle R.H., (ed), Thousand Oaks, CA: Sage, and,. edited by; Clark, T.H., Lee, H.G., Performance, Interdependence and Coordination in Business-to-business Electronic Commerce and Supply Chain Management (2000) Information Technology and Management, 1 (1-2), pp. 85-105; Cohen, M.A., Lee, H.L., Strategic Analysis of Integrated Production Distribution Systems: Models and Methods (1988) Operations Research, 36 (2), pp. 216-228; Cousins, P.D., The Alignment of Appropriate Firm and Supply Strategies for Competitive Advantage (2005) International Journal of Operations & Production Management, 25 (5), pp. 403-428; Cronbach, L., Coefficient Alpha and the Internal Structure of Tests (1951) Psychometrika, 16, pp. 297-334; Crook, T., Ketchen, D., Combs, J., Todd, S., Strategic Resources and Performance: A Meta-analysis (2008) Strategic Management Journal, 29, pp. 1141-1154; Cross, R., Liedtka, J., Weiss, L., A Practical Guide to Social Networks (2005) Harvard Business Review, 83 (3), pp. 124-132; Dash, S., Bruning, E., Guin, K., Antecedents of Long-term Buyer-seller Relationships: a Cross Cultural Integration (2007) Academy of Marketing Science Review, 11, pp. 1-28; Denham, M.C., Prediction Intervals in Partial Least Squares (1997) Journal of Chemometrics, 11, pp. 39-52; Deshpande, A., Supply Chain Management Dimensions, Supply Chain Performance and Organizational Performance: An Integrated Framework (2012) International Journal of Business and Management, 7 (8), pp. 2-19; de Souza Miguel, P.L., Brito, L.A.L., Supply Chain Management Measurement and Its Influence on Operational Performance (2011) Journal of Operations and Supply Chain Management, 4 (2), pp. 56-70; Dillman, D.A., (1978) Mail and Telephone Surveys: The Total Design Method, , New York: Wiley; Dion, P.A., Banting, P.M., Picard, S., Blenkhorn, D.L., JIT Implementation: A Growth Opportunity for Purchasing (1992) International Journal of Purchasing and Materials Management, 28 (4), pp. 32-38; Donlon, J.P., Maximizing Value in the Supply Chain (1996) Chief Executive, 117, pp. 54-63; Dubey, R., Gunasekaran, A., Papadopoulos, T., Green Supply Chain Management: Theoretical Framework and Further Research Directions (2017) Benchmarking: An International Journal, 24, pp. 184-218; Efron, B., Tibshirani, R., (1993) An Introduction to the Bootstrap, , New York, NY: Chapman and Hall; (2019), https://government.ae/en/about-the-uae/strategies-initiatives-and-awards/localgovernments-strategies-and-plans/dubai-industrial-strategy-2030; Fabrigar, L., Wegener, D., MacCallum, R., Strahan, E., Evaluating the Use of Exploratory Factor Analysis in Psychological Research (1999) Psychological Methods, 4, pp. 272-299; Fahy, J., Smithee, A., Strategic Marketing and the Resource Based View of the Firm (1999) Academy of Marketing Science Review, 10, pp. 1-20; Falk, R., Miller, N., (1992) A Primer for Soft Modeling, , Akron, OH: University Akron Press; Fantazy, K., Tipu, S., Kumar, V., Conceptualizing the Relative Openness of Supply Chain and its Impact on Organizational Performance (2016) Benchmarking: An International Journal, 23 (5), pp. 1264-1285; Fawcett, S., Jones, S., Fawcett, A., Supply Chain Trust: The Catalyst for Collaborative Innovation (2012) Business Horizons, 55 (2), pp. 163-178; Fornell, C., Bookstein, F., Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory (1982) Journal of Marketing Research, 19 (4), pp. 440-452; Fornell, C., Larcker, D., Evaluating Structural Equation Models with Unobservable Variables and Measurement Error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Fox, M., Barbuceanu, M., Teigen, R., Agent-oriented Supply-chain Management (2000) International Journal of Flexible Manufacturing Systems, 12 (2-3), pp. 165-188; Strategic Analysis of Logistics Market in UAE (2011) Industry Report, P452-18, pp. 67-85; UAE Logistics Market to be Worth $27B in 2015 (2017) The Supply Chain and Logistics, P452-18, 13 (8), pp. 67-85; Gefen, D., Rigdon, E.E., Straub, D., Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research (2011) MIS Quarterly, 35, pp. iii-xiv; Gefen, D., Straub, D., A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example (2005) Communications of the AIS, 16 (5), pp. 91-109; Germain, R., Claycomb, C., Dröge, C., Supply Chain Variability, Organizational Structure, and Performance: The Moderating Effect of Demand Unpredictability (2008) Journal of Operations Management, 26 (5), pp. 557-570; Green, K.W., McGaughey, R., Casey, M., Does Supply Chain Management Strategy Mediate the Association Between Market Orientation and Organizational Performance? (2006) Supply Chain Management Journal, 11 (5), pp. 407-414; Grossman, M., The Role of Trust and Collaboration in the Internet-enabled Supply Chain (2004) Journal of American Academy of Business, 5 (1-2), pp. 391-397; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hair, J., Anderson, R., Tatham, R., Black, W., (1998) Multivariate Data Analysis, , 5th ed, Upper Saddle River, NJ: Prentice Hall; Hair, J., Black, W., Babin, B., (2010) Multivariate Data Analysis: A Global Perspective, , Pearson Education; Hair, J., Hult, G., Ringle, C., Sarstedt, M., (2014) A Primer on Partial Least Squares Structural Equation Modeling, , Thousand Oaks: Sage; Hair, J., Sarstedt, M., Hopkins, L., Kuppelwieser, V.G., Partial Least Squares Structural Equation Modeling (PLS-SEM) an Emerging Tool in Business Research (2014) European Business Review, 26 (2), pp. 106-121; Hamel, G., Competition for Competence and Inter-partner Learning Within International Strategic Alliances (1991) Strategic Management Journal, 12 (4), pp. 83-103; Handfield, R., Bechtel, C., The Role of Trust and Relationship Structure in Improving Supply Chain Responsiveness (2002) Industrial Marketing Management, 31 (4), pp. 367-382; Handfield, R.B., Krause, D.R., Scannell, T.V., Monczka, R.M., Avoid the Pitfalls in Supplier Development (2000) Sloan Management Review, 41, pp. 37-49; Hu, L.T., Bentler, P.M., Kano, Y., Can Test Statistics in Covariance Structure Analysis Be Trusted? (1992) Psychological Bulletin, 112, pp. 351-362; Hui, B.S., Wold, H., Consistency and Consistency at Large in Partial Least Squares Estimates (1982) Systems Under Indirect Observation, Part II, pp. 119-130. , Joreskog K.G., Wold H., (eds), Amsterdam: Sage, and,. edited by; Hult, G.T.M., Ketchen, D.J., Arrfelt, M., Strategic Supply Chain Management: Improving Performance through a Culture of Competitiveness and Knowledge Development (2007) Strategic Management Journal, 28 (10), pp. 1035-1052; Kampstra, R.P., Ashayeri, J., Gattorna, J.L., Realities of Supply Chain Collaboration (2006) The International Journal of Logistics Management, 17 (3), pp. 312-330; Kaplan, R., Norton, D., Rugelsjoen, P., Managing Alliances with the Balanced Scorecard (2010) Harvard Business Review, 88, pp. 114-120; Kerlinger, F.N., (1992) Foundations of Behavioural Research, , New York: Holt, Rinehart, and Winston; Kerlinger, F.N., Lee, H.B., Bhanthumnavin, D., (2000) Foundations of Behavioral Research: The Most Sustainable Popular Textbook by Kerlinger & Lee (2000); Kim, S.W., Effects of Supply Chain Management Practices, Integration and Competition Capability on Performance (2006) Supply Chain Management: An International Journal, 11 (3), pp. 241-248; Kim, D., Lee, R., Systems Collaboration and Strategic Collaboration: Their Impacts on Supply Chain Responsiveness and Market Performance (2010) Decision Sciences, 41 (4), pp. 955-981; Kim, J.-O., Mueller, C.W., (1978) Factor Analysis: Statistical Methods and Practical Issues, , Beverly Hills, CA: Sage Publications; (2018) UAE Industrial Logistics Market Review, , https://content.knightfrank.com/research/794/documents/en/uae-industrial-logistics-market-review-h1-2018-5658.pdf; Krause, D.R., Handfield, R.B., Tyler, B.B., The Relationships Between Supplier Development, Commitment, Social Capital Accumulation and Performance Improvement (2007) Journal of operations management, 25 (2), pp. 528-545; Laihonen, H., Pekkola, S., Impacts of Using a Performance Measurement System in Supply Chain Management: A Case Study (2015) International Journal of Production Research, 54 (18), pp. 5607-5617; Lee, H.L., The Triple–a Supply Chain (2004) Harvard Business Review, 83, pp. 102-112; Leonczuk, D., Categories of Supply Chain Performance Indicators: An Overview of Approaches, Business (2016) Management and Education, 14 (1), pp. 103-115; Leończuk, D., Ryciuk, U., Szymczak, M., Nazarko, J., Measuring Performance of Adaptive Supply Chains (2019) SMART Supply Network, pp. 89-110. , Cham: Springer, and; Li, S., Nathan, B.R., Nahan, T.S., Rao, S.S., Development and Validation of a Measurement Instrument for Studying Supply Chain Management Practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Lin, C., Chow, W.S., Madu, C.N., Kuei, C.-H., Yu, P.P., A Structural Equation Model of Supply Chain Quality Management and Organizational Performance (2005) International Journal Production Economics, 96 (3), pp. 355-365; Macroulides, G.A., Saunders, C., Editor's Comments: PLS: A Silver Bullet? (2006) MIS Quarterly, 30 (2), pp. iii-iix; Mafini, C., Loury-Okoumba, W., Extending Green Supply Chain Management Activities to Manufacturing Small and Medium Enterprises in a Developing Economy (2018) South African Journal of Economic and Management Sciences, 21 (1), pp. 2-12; Masudin, I., Wastono, T., Zulfikarijah, F., The Effect of Managerial Intention and Initiative on Green Supply Chain Management Adoption in Indonesian Manufacturing Performance (2018) Cogent Business & Management, 5, pp. 1-19; Matthews, R.L., Social Capital, a Theory for Operations Management: A Systematic Review of the Evidence (2011) International Journal of Production Research, 50 (24), pp. 7081-7099; McKelvey, B., Van De Ven and Johnson's “Engaged Scholarship”: Nice Try, but … (2006) Academy of Management Review, 31 (4), pp. 822-829; Michalski, M., Montes-Botella, J.L., Narasimhan, R., The Impact of Asymmetry on Performance in Different Collaboration and Integration Environments in Supply Chain Management (2018) Supply Chain Management: An International Journal, 23 (1), pp. 33-49; Min, S., Roath, A., Daugherty, P.J., Genchev, S.E., Chen, H., Arndt, A.D., Supply Chain Collaboration: What’s Happening? (2005) International Journal of Logistics Management, 16 (2), pp. 237-256; Moberg, C., Culter, B., Cross, A., Speh, T., Identifying Antecedents of Information Exchange Within Supply Chains (2002) International Journal of Physical Distribution and Logistics Management, 32 (9), pp. 755-770; Moberg, C.R., Speh, T.W., Evaluating the Relationship Between Questionable Business Practices and the Strength of Supply Chain Relationships (2003) Journal of Business Logistics, 24 (2), pp. 1-19; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Newbert, S.L., Value, Rareness, Competitive Advantage, and Performance: A Conceptual-level Empirical Investigation of the Resource-based View of the Firm (2007) Strategic Management Journal, 29 (7), pp. 745-768; Newson, R., (2005) Link Function and Probit analysis. Data Analysis II, Statalist; Nunnally, J.E., (1978) Psychometric Theory, , 2nd ed, New York, NY: McGraw-Hill; Okongwu, U., Lauras, M., Dupont, L., Humez, V., A Decision Support System for Optimising the Order Fulfilment Process (2012) Production Planning & Control, 23 (8), pp. 581-598; Pallant, J., (2011) SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS for Windows, , 4th ed, McGraw Hill: Open University Press; Powell, T.C., Competitive Advantage: Logical and Philosophical Considerations (2001) Strategic Management Journal, 22 (9), pp. 875-888; Prahinski, C., Benton, W.C., Supplier Evaluations: Communication Strategies to Improve Supplier Performance (2004) Journal of Operations Management, 22 (1), pp. 39-62; Reinartz, W., Haenlein, M., Henseler, J., (2009) An Empirical Comparison of the Efficacy of Covariance-based and Variance-based SEM, , 1–51. INSEAD Working Paper Series. MKT(44; Ringle, C., Sarstedt, M., Straub, D., Editor's Comments: A Critical Look at the Use of PLS-SEM in MIS Quarterly (2012) MIS Quarterly, 36 (1), p. is7; Sabath, R.E., Autry, C.W., Automatic Replenishment Programs: The Impact of Organizational Structure (2001) Journal of Business Logistics, 22 (1), pp. 91-105; Sahay, B.S., Mohan, R., Supply Chain Management Practices in Indian Industry (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 582-606; Savino, M.M., Batbaatar, E., Investigating the Resources for Integrated Management Systems Within Resource-based and Contingency Perspective in Manufacturing firms (2015) Journal of Cleaner Production, 104, pp. 392-402; Shapiro, J.F., On the Connections among Activity-based Costing, Mathematical Programming Models for Analyzing Strategic Decisions, and the Resource-based View of the firm (1999) European Journal of Operations Research, 118 (2), pp. 295-314; Sharma, M., Song Jin, Y., Measuring Supply Chain Efficiency and Congestion (2010) Engineering Letters, 18 (4), pp. 384-390; Shepherd, C., Gunter, H., Measuring Supply Chain Performance: Current Research and Future Directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shin, N., Park, S.H., Park, S., Partnership-based Supply Chain Collaboration: Impact on Commitment, Innovation, and Firm Performance (2019) Sustainability, 11, p. 449; Shqairat, A., Sundarakani, B., An Empirical Study of oil and gas Value Chain Agility in the UAE (2018) Benchmarking: An International Journal, 25 (9), pp. 3541-3569; Singh, A., Teng, J.T.C., Enhancing Supply Chain Outcomes Through Information Technology and Trust (2016) Computers in Human Behavior, 54, pp. 290-300; Sobel, M., Weiss, G.H., Play-the-Winner Rule and Inverse Sampling for Selecting the Best of k ≥ 3 Binomial Populations (1972) The Annals of Mathematical Statistics, 43 (6), pp. 1808-1826; Soni, G., Kodali, R., A Critical Review of Empirical Research Methodology in Supply Chain Management (2012) Journal of Manufacturing Technology Management, 23 (6), pp. 753-779; Soosay, C.A., Hyland, P.W., Ferrer, M., Supply Chain Collaboration: Capabilities for Continuous Innovation (2008) Supply Chain Management: An International Journal, 13 (2), pp. 160-169; Storer, M., Hyland, P., Ferrer, M., Santa, R., Griffiths, A., Strategic Supply Chain Management Factors Influencing Agribusiness Innovation Utilization (2014) The International Journal of Logistics Management, 25 (3), pp. 487-521; Sudman, S., Bradburn, N., Schwarz, N., (1996) Thinking About Answers: The Application of Cognitive Processes to Survey Methodology, , San Francisco, CA: Jossey-Bass; Sundarakani, B., Transforming Dubai Logistics Corridor Into a Global Logistics Hub (2017) Asian Journal of Management Cases, 14 (2), pp. 135-156; Sundarakani, B., Abdul Razzak, H., Manikandan, S., Creating Competitive Advantage in the Global Catering Supply Chain: A Case Study of Flight Catering (2018) International Journal of Logistics: Research and Applications, 21 (5), pp. 481-501; Sundarakani, B., Sian-Lai, Y., Goh, M., de Souza, R., Studying the Sustainability of Third Party Logistics Growth Using System Dynamics (2019) Journal of Modelling in Management, 14 (4), pp. 872-895; Tan, K.C., Lyman, S.B., Wisner, J.D., Supply Chain Management: A Strategic Perspective (2002) International Journal of Operations and Production Management, 22 (6), pp. 614-631; Tenenhaus, M., Vinzi, V., Chatelin, Y., Lauro, C., PLS Path Modeling (2005) Computational Statistics & Data Analysis, 48 (1), pp. 159-205; Tracey, M., Lim, J., Vonderembse, M., The Impact of Supply Chain Management Capabilities on Organization Performance (2005) Supply Chain Management, 10 (9), pp. 179-191; Tsai, W., Ghoshal, S., Social Capital and Value Creation: The Role of Intrafirm Networks (1998) Academy of management Journal, 41 (4), pp. 464-476; Tyndall, G., Gopal, C., Partsch, W., Kamauff, J., (1998) Supercharging Supply Chains: New Ways to Increase Value Through Global Operational Excellence, , New York: John Wiley & Sons; Um, K.H., Kim, S.M., The Effects of Supply Chain Collaboration on Performance and Transaction Cost Advantage: The Moderation and Nonlinear Effects of Governance Mechanisms (2019) International Journal of Production Economics, , Forthcoming, 2019; Wernerfelt, B., A Resource Based View of the firm (1984) Strategic Management Journal, 5, pp. 171-180; Whitten, G.D., Green, K.W., Jr., Zelbst, P.J., Triple–a Supply Chain Performance (2012) International Journal of Operations & Production Management, 32 (1), pp. 28-48; Wilkens, U., Menzel, D., Pawlowsky, P., Inside the Black-box: Analysing the Generation of Core Competencies and Dynamic Capabilities by Exploring Collective Minds. An Organisational Learning Perspective (2004) Management Review, 15 (1), pp. 8-26; Wittingslow, G., Markham, S., Customer-driven Modelling of Satisfaction Behaviour (1999) Australasian Journal of Market Research, 7 (2), pp. 29-38; Wu, W., Chiang, C., Wu, Y., Tu, H., The Influencing Factors of Commitment and Business Integration on Supply Chain Management (2004) Industrial Management & Data Systems, 104 (3-4), pp. 322-333; Xu, L., Beamon, B.M., Supply Chain Coordination and Cooperation Mechanisms: An Attribute-based Approach (2006) Journal of Supply Chain Management, 42 (1), pp. 4-12; Yap, L.L., Tan, C.L., The Effect of Service Supply Chain Management Practices on the Public Healthcare Organizational Performance (2012) International Journal of Business and Social Science, 3 (16), pp. 216-224; Yim, B., Leem, B., The Effect of the Supply Chain Social Capital (2013) Industrial Management & Data Systems, 113 (3), pp. 324-349},
correspondence_address1={Sundarakani, B.; Faculty of Business, United Arab Emirates; email: balansundarakani@uowdubai.ac.ae},
publisher={Taylor and Francis Ltd.},
issn={13675567},
language={English},
abbrev_source_title={Int. J. Logist. Res. Applic.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bajec2020513,
author={Bajec, P. and Tuljak-Suban, D. and Bajor, I.},
title={A warehouse social and environmental performance metrics framework [Socialni in okoljski kriteriji za merjenje uspešnosti v skladišču]},
journal={Promet - Traffic - Traffico},
year={2020},
volume={32},
number={4},
pages={513-526},
doi={10.7307/ptt.v32i4.3390},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089394988&doi=10.7307%2fptt.v32i4.3390&partnerID=40&md5=f5870878931b3306c533ee33916c60a5},
affiliation={University of Ljubljana, Faculty of Maritime Studies and Transport, Pot pomorščakov 4, Portorož, 6320, Slovenia; University of Zagreb, Faculty of Transport and Traffic Sciences, Vukelićeva 4, Zagreb, 10000, Croatia},
abstract={To improve the supply chain performance in all three aspects of sustainability (social, economic, and environmental), a comprehensive sustainable performance measurement system that captures all the supply chain partners’ efforts and commitments is required. Warehouse, as the second largest logistics source of environmental pollution in the supply chain has been almost completely overlooked and ignored in the past studies. To fill this gap, a warehouse performance metrics framework for environmental and social performance measures was proposed using a novel Fuzzy Delphi and Best-worst methodological approach. The method is less time-consuming than the Analytic Hierarchy Process or Analytic Network Process, it does not address whether criteria are dependent or independent, requires fewer comparisons of criteria, but still produces reliable and credible results. The presented framework consists of 32 equally formulated environmental and social performance indicators, including formulas and measurement units. The 14 most important indicators are ranked according to the requirements of different stakeholders. © 2020, Faculty of Transport and Traffic Engineering. All rights reserved.},
author_keywords={Best-worst method;  Environmental performance;  Fuzzy Delphi;  Performance indicators;  Social performance;  Warehouse},
keywords={analytical hierarchy process;  logistics;  performance assessment;  social development;  stakeholder;  supply chain management;  sustainability},
references={Beske-Janssen, P, Johnson, MP, Schaltegger, S., 20 years of performance measurement in sustainable supply chain management–what has been achieved? Supply chain management (2015) An International Journal, 20 (6), pp. 664-680; Burritt, R, Schaltegger, S., Accounting towards sustainability in production and supply chains (2014) The British Accounting Review, 46 (4), pp. 327-343; Tajbakhsh, A, Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2015) Journal of Cleaner Production, 105, pp. 74-85; Hahn, T, Pinkse, J, Preuss, L, Figge, F., Tensions in corporate sustainability: Towards an integrative framework (2015) Journal of Business Ethics, 127 (2), pp. 297-316; Kusi-Sarpong, S, Gupta, H, Sarkis, J., A supply chain sustainability innovation framework and evaluation methodology (2019) International Journal of Production Research, 57 (7), pp. 1990-2008; Maas, K, Schaltegger, S, Crutzen, N., Integrating corporate sustainability assessment, management accounting, control, and reporting (2016) Journal of Cleaner Production, 136, pp. 237-248; Jakhar, SK., Performance evaluation and a flow allocation decision model for a sustainable supply chain of an apparel industry (2015) Journal of Cleaner Production, 87, pp. 391-413; Tseng, M-L, Lim, MK, Wong, W-P, Chen, Y-C, Zhan, Y., A framework for evaluating the performance of sustainable service supply chain management under uncertainty (2018) International Journal of Production Economics, 195, pp. 359-372; Chardine-Baumann, E, Botta-Genoulaz, V., A framework for sustainable performance assessment of supply chain management practices (2014) Computers & Industrial Engineering, 76, pp. 138-147; Erol, I, Sencer, S, Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70 (6), pp. 1088-1100; Schaltegger, S, Burritt, R, Varsei, M, Soosay, C, Fahimnia, B, Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Management: An International Journal, 19 (3), pp. 242-257; Baskaran, V, Nachiappan, S, Rahman, S., Indian textile suppliers' sustainability evaluation using the grey approach (2012) International Journal of Production Economics, 135 (2), pp. 647-658; Büyüközkan, G, Çifçi, G., A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers (2012) Expert Systems with Applications, 39 (3), pp. 3000-3011; Kannan, D, de Sousa Jabbour, ABL, Jabbour, CJC., Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company (2014) European Journal of Operational Research, 233 (2), pp. 432-447; Kumar, A, Jain, V, Kumar, S., A comprehensive environment friendly approach for supplier selection (2014) Omega, 42 (1), pp. 109-123; Shaw, K, Shankar, R, Yadav, SS, Thakur, LS., Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain (2012) Expert Systems with Applications, 39 (9), pp. 8182-8192; Zeydan, M, Çolpan, C, Çobanoğlu, C., A combined methodology for supplier selection and performance evaluation (2011) Expert Systems with Applications, 38 (3), pp. 2741-2751; Benjaafar, S, Li, Y, Daskin, M., Carbon footprint and the management of supply chains: Insights from simple models (2012) IEEE Transactions on Automation Science and Engineering, 10 (1), pp. 99-116; Carmona, R, Fehr, M, Hinz, J, Porchet, A., Market design for emission trading schemes (2010) Siam Review, 52 (3), pp. 403-452; Diabat, A, Govindan, K., An analysis of the drivers affecting the implementation of green supply chain management (2011) Resources, Conservation and Recycling, 55 (6), pp. 659-667; Gong, X, Zhou, SX., Optimal production planning with emissions trading (2013) Operations Research, 61 (4), pp. 908-924; Lee, K-H, Saen, RF., Measuring corporate sustainability management: A data envelopment analysis approach (2012) International Journal of Production Economics, 140 (1), pp. 219-226; Subramanian, R, Subramanyam, R., Key factors in the market for remanufactured products (2012) Manufacturing & Service Operations Management, 14 (2), pp. 315-326; Bank, R, Murphy, R., Warehousing Sustainability Standards Development (2013) Proceedings of the IFIP International Conference on Advances in Production Management Systems, pp. 294-301. , Prabhu Taisch M, Kiritsis D. (eds) 9-12 Sep State College, PA, USA. Springer; 2013; Tan, K-S, Daud Ahmed, M, Sundaram, D., Sustainable enterprise modelling and simulation in a warehousing context (2010) Business Process Management Journal, 16 (5), pp. 871-886; Rüdiger, D, Schön, A, Dobers, K., Managing Greenhouse Gas Emissions from Warehousing and Transshipment with Environmental Performance Indicators (2016) Transportation Research Procedia, 14, pp. 886-895; Żuchowski, W., Division of environmentally sustainable solutions in warehouse management and example methods of their evaluation (2015) LogForum, 11 (2); Fichtinger, J, Ries, JM, Grosse, EH, Baker, P., Assessing the environmental impact of integrated inventory and warehouse management (2015) International Journal of Production Economics, 170, pp. 717-729; Amjed, TW, Harrison, NJ, A Model for sustainable warehousing: From theory to best practices (2013) Proceedings of the International Decision Sciences Institute and Asia Pacific DSI Conference, , editors; Chen, X, Wang, X, Kumar, V, Kumar, N., Low carbon warehouse management under cap-and-trade policy (2016) Journal of Cleaner Production, 139, pp. 894-904; Bartolini, M, Bottani, E, Grosse, EH., Green warehousing: Systematic literature review and bibliometric analysis (2019) Journal of Cleaner Production, 226, pp. 242-258; Banaszewska, A, Cruijssen, F, Dullaert, W, Gerdessen, JC., A framework for measuring efficiency levels—The case of express depots (2012) International Journal of Production Economics, 139 (2), pp. 484-495; McKinnon, A., (2010) Environmental sustainability. Green logistics: Improving the environmental sustainability of logistics, , London; Ries, JM, Grosse, EH, Fichtinger, J., Environmental impact of warehousing: A scenario analysis for the US (2017) International Journal of Production Research, 55 (21), pp. 6485-6499; Staudt, F, Di Mascolo, M, Alpan, G, Rodriguez, CMT., Warehouse performance measurement: Classification and mathematical expressions of indicators (2014) ILS 2014 – 5th International Conference Information Systems, Logistics and Supply Chain, , Aug Breda, Netherlands; 2014; (2015) Environment Management System-Requirements with guidance to use, , ISO 14000:2015. Geneva, Switzerland: ISO; (2006) Environmental Management – Environmental communication – Guidelines and Examples, , ISO 14063:2006. Geneva, Switzerland: ISO; (2018) Materials, p. 10. , GRI 301: Amsterdam: Global Reporting Initiative; (2018) GRI 302: Energy, p. 15. , Amsterdam: Global Reporting Initiative (GRI); (2018) Water and effluents, p. 23. , GRI 303: Amsterdam: Global Reporting Initiative (GRI); (2018) Emissions, p. 21. , GRI 305: Amsterdam: Stichting Global Reporting Initiative (GRI); (2016) Training and education, p. 12. , GRI 404: Amsterdam: Global Reporting Initiative (GRI); (2018) Occupational health and safety, p. 31. , GRI 403: Amsterdam: Global Reporting Initiative (GRI); Sarkar, P, Joung, CB, Carrell, J, Feng, SC., Sustainable manufacturing indicator repository (2011) ASME Proceedings on 31st Computers and Information in Engineering Conference, , 29-31 Aug Washington, DC; 2011; Kibira, D, Brundage, MP, Feng, S, Morris, K., Procedure for selecting key performance indicators for sustainable manufacturing (2018) Journal of Manufacturing Science and Engineering, 140 (1), p. 011005; (2005) Environmental Key Performance Indicators: Reporting Guidelines for UK Business, p. 76. , Defra. London: Department for Environment, Food and Rural Affairs; (2016) Effluents and waste, p. 13. , GRI 306: Amsterdam: Global Reporting Initiative (GRI); (2016) Biodiversity, p. 13. , GRI 304: Amsterdam: Global Reporting Initiative (GRI); (2016) Labor/Management relations, p. 9. , GRI 402: Amsterdam: Global Reporting Initiative (GRI); (2016) Non-discrimination, p. 8. , GRI 406: Amsterdam: Global Reporting Initiative (GRI); (2016) Local communities, p. 13. , GRI 413: Amsterdam: Global Reporting Initiative (GRI); Ishikawa, A, Amagasa, M, Shiga, T, Tomizawa, G, Tatsuta, R, Mieno, H., The max-min Delphi method and fuzzy Delphi method via fuzzy integration (1993) Fuzzy Sets and Systems, 55 (3), pp. 241-253; Wang, M-L, Lin, Y-H., To construct a monitoring mechanism of production loss by using Fuzzy Delphi method and fuzzy regression technique – A case study of IC package testing company (2008) Expert Systems with Applications, 35 (3), pp. 1156-1165; Mlinarić, TJ, Đorđević, B, Krmac, E., Evaluation Framework for Key Performance Indicators of Railway ITS (2018) Promet – Traffic&Transportation, 30 (4), pp. 491-500; Habibi, A, Jahantigh, FF, Sarafrazi, A., Fuzzy Delphi technique for forecasting and screening items (2015) Asian Journal of Research in Business Economics and Management, 5 (2), pp. 130-143; Saffie, NAM, Rasmani, KA., Fuzzy delphi method: Issues and challenges (2016) 2016 International Conference on Logistics, Informatics and Service Sciences (LISS), , 24-27 July Sidney, Australia. IEEE; 2016; Abdi, K, Talebpour, M, Fullerton, J, Ranjkesh, MJ, Jabbari Nooghabi, H., Converting sports diplomacy to diplomatic outcomes: Introducing a sports diplomacy model (2018) International Area Studies Review, 21 (4), pp. 365-381; Hsu, Y-L, Lee, C-H, Kreng, VB., The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative technology selection (2010) Expert Systems with Applications, 37 (1), pp. 419-425; Tuljak-Suban, D, Bajec, P., The Influence of Defuzzification Methods to Decision Support Systems Based on Fuzzy AHP with Scattered Comparison Matrix: Application to 3PLP Selection as a Case Study (2018) International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 26, pp. 475-491. , (03); Saaty, TL., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; Herman, MW, Koczkodaj, WW., A Monte Carlo Study of Parwise Comparison (1996) Inf Process Lett, 57 (1), pp. 25-29; Rezaei, J., Best-worst multi-criteria decision-making method (2015) Omega, 53, pp. 49-57; Beemsterboer, DJC, Hendrix, EMT, Claassen, GDH., On solving the Best-Worst Method in multi-criteria decision-making (2018) IFAC-PapersOnLine, 51 (11), pp. 1660-1665; Rezaei, J., Best-worst multi-criteria decision-making method: Some properties and a linear model (2016) Omega, 64, pp. 126-130},
correspondence_address1={Bajec, P.; University of Ljubljana, Pot pomorščakov 4, Slovenia; email: patricija.bajec@fpp.uni-lj.si},
publisher={Faculty of Transport and Traffic Engineering},
issn={03535320},
coden={POMEE},
language={English},
abbrev_source_title={Promet Traffic Traffico},
document_type={Article},
source={Scopus},
}

@ARTICLE{Xie2020711,
author={Xie, Y. and Yin, Y. and Xue, W. and Shi, H. and Chong, D.},
title={Intelligent supply chain performance measurement in Industry 4.0},
journal={Systems Research and Behavioral Science},
year={2020},
volume={37},
number={4},
pages={711-718},
doi={10.1002/sres.2712},
note={cited By 29},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086360602&doi=10.1002%2fsres.2712&partnerID=40&md5=ed2fb9a3b5a887c7e81782669944fc52},
affiliation={School of Economics and Management, Xidian University, Xi'an, China; School of Foreign Studies, Xi'an University, Xi'an, China; Department of Computer Information Systems, California State Polytechnic University, Pomona, CA, United States; School of Management, California Lutheran University, Thousand Oaks, CA, United States},
abstract={The concept of Industry 4.0 has been widely recognized by the world since its introduction in 2011 Hannover Industrial Expo. Industry 4.0 strategy does not only revolutionize the manufacturing system and processes but also lead to the formation of the intelligent supply chain. Currently, Industry 4.0-based intelligent supply chain has attracted a lot of attention from academia; however, studies on performance measurement indicator of intelligent supply chain are still lacking. To fill this gap, this paper first introduces the impact of Industry 4.0 on supply chain management. After analysing key characteristics of intelligent supply chain, the paper proposes a performance measurement indicator framework consisting of seven indicators. This indicator framework enriches the theoretical knowledge of supply chain performance evaluation and provides an efficient way to improve the operational performance of intelligent supply chain management. © 2020 John Wiley & Sons, Ltd.},
author_keywords={Industry 4.0;  intelligent supply chain;  performance measurement indicator;  supply chain management},
keywords={Industry 4.0, Indicator frameworks;  Key characteristics;  Operational performance;  Performance measurement indicators;  Supply chain performance;  Supply chain performance measurements, Supply chain management},
references={Barreto, L., Amaral, A., Pereira, T., Industry 4.0 implications in logistics: An overview (2017) Procedia Manufacturing, 13, pp. 1245-1252. , https://doi.org/10.1016/j.promfg.2017.09.045; Bertoncel, T., Erenda, I., Bach, M.P., Roblek, V., Meško, M., A managerial early warning system at a smart factory: An intuitive decision-making perspective (2018) Systems Research and Behavioral Science, 35 (4), pp. 406-416. , https://doi.org/10.1002/sres.2542; Bicocchi, N., Cabri, G., Mandreoli, F., Mecella, M., Dynamic digital factories for agile supply chains: An architectural approach (2019) Journal of Industrial Information Integration, 15, pp. 111-121. , https://doi.org/10.1016/j.jii.2019.02.001; Cai, H., Xu, L., Xu, B., Xie, C., Qin, S., Jiang, L., IoT-based configurable information service platform for product lifecycle management (2014) IEEE Transactions on Industrial Informatics, 10 (2), pp. 1558-1567; Chen, H., Theoretical foundations for cyber-physical systems: A literature review (2017) Journal of Industrial Integration and Management, 2 (3). , https://doi.org/10.1142/S2424862217500130; Chen, H., Yao, M., Chong, D., Research on institutional innovation of China's green insurance investment (2019) Journal of Industrial Integration and Management, 4 (1). , https://doi.org/10.1142/S2424862219500039; Chen, Y., A survey on industrial information integration 2016-2019 (2020) Journal of Industrial Integration and Management, 5 (1), pp. 33-163. , https://doi.org/10.1142/S2424862219500167; De Souza, V., Borsato, M., Bloemhof, J., Designing eco-effective reverse logistics networks (2017) Journal of Industrial Integration and Management, 2 (1). , https://doi.org/10.1142/S2424862217500038; Donaldson, A., Walker, P., Information governance—A view from the NHS (2004) International Journal of Medical Informatics, 73 (3), pp. 281-284. , https://doi.org/10.1016/j.ijmedinf.2003.11.009; Ghadimi, P., Wang, C., Lim, M.K., Heavey, C., Intelligent sustainable supplier selection using multi-agent technology: Theory and application for Industry 4.0 supply chains (2019) Computers & Industrial Engineering, 127, pp. 588-600. , https://doi.org/10.1016/j.cie.2018.10.050; Gorkhali, A., Xu, L.D., Enterprise application integration in industrial integration: A literature review (2016) Journal of Industrial Integration and Management, 1 (4). , https://doi.org/10.1142/S2424862216500147; Hämäläinen, E., Inkinen, T., Industrial applications of big data in disruptive innovations supporting environmental reporting (2019) Journal of Industrial Information Integration, 16, p. 100105. , https://doi.org/10.1016/j.jii.2019.100105; Hecklau, F., Galeitzke, M., Flachs, S., Kohl, H., Holistic approach for human resource management in Industry 4.0 (2016) Procedia Cirp, 54 (1), pp. 1-6. , https://doi.org/10.1016/j.procir.2016.05.102; Ivanov, L.A., Bokova, E.S., Muminova, S.R., Katuhin, L.F., Nanotechnologies: A review of inventions and utility models. Part I (2020) Nanotechnologies in Construction, 12 (1), pp. 27-33. , https://doi.org/10.15828/2075-8545-2020-12-1-27-33; Ivanov, L.A., Razumeev, K.E., Bokova, E.S., Muminova, S.R., The inventions in nanotechnologies as practical solutions. Part V (2019) Nanotechnologies in Construction, 11 (6), pp. 719-729. , https://doi.org/10.15828/2075-8545-2019-11-6-719-729; Kiel, D., Müller, J.M., Arnold, C., Voigt, K.I., Sustainable industrial value creation: Benefits and challenges of Industry 4.0 (2017) International Journal of Innovation Management, 21 (8); Kooper, M.N., Maes, R., Lindgreen, E.R., On the governance of information: Introducing a new concept of governance to support the management of information (2011) International Journal of Information Management, 31 (3), pp. 195-200. , https://doi.org/10.1016/j.ijinfomgt.2010.05.009; Lee, J., Kao, H.A., Yang, S., Service innovation and smart analytics for Industry 4.0 and big data environment (2014) Procedia Cirp, 16 (1), pp. 3-8. , https://doi.org/10.1016/j.procir.2014.02.001; Li, L., Supply chain management: Concepts, techniques and practices (2007) Enhancing the value through collaboration, , https://doi.org/10.1142/6273, World scientific publishing company; Li, L., Effects of enterprise technology on supply chain collaboration: analysis of China-linked supply chain (2012) Enterprise Information Systems, 6 (1), pp. 55-77. , https://doi.org/10.1080/17517575.2011.639904; Li, L., The path to Made-in-China: How this was done and future prospects (2013) International Journal of Production Economics, 146 (1), pp. 4-13. , https://doi.org/10.1016/j.ijpe.2013.05.022; Li, L., (2014) Managing supply chain and logistics: Competitive strategy for a sustainable future, , https://doi.org/10.1142/9151, World Scientific Publishing Company; Li, L., China's manufacturing locus in 2025: With a comparison of “Made-in-China 2025” and “Industry 4.0” (2018) Technological Forecasting and Social Change, 135, pp. 66-74. , https://doi.org/10.1016/j.techfore.2017.05.028; Li, L., Wang, B., Cook, D.P., Enhancing green supply chain initiatives via empty container reuse (2014) Transportation Research Part E: Logistics and Transportation Review, 70, pp. 190-204. , https://doi.org/10.1016/j.tre.2014.06.018; Li, L., Wang, B., Wang, A., An emergency resource allocation model for maritime chemical spill accidents (2014) Journal of Management Analytics, 1 (2), pp. 146-155. , https://doi.org/10.1080/23270012.2014.943137; Li, L., Warfield, J.N., Perspectives on quality coordination and assurance in global supply chains (2011) International Journal of Production Research, 49 (1), pp. 1-4. , https://doi.org/10.1080/00207543.2010.508932; Liao, Y., Deschamps, F., Loures, E.D.F.R., Ramos, L.F.P., Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal (2017) International Journal of Production Research, 55 (12), pp. 3609-3629. , https://doi.org/10.1080/00207543.2017.1308576; Lin, Y., Zhang, W., An incentive model between a contractor and multiple subcontractors in a green supply chain based on robust optimization (2020) Journal of Management Analytics, online published., pp. 1-29. , https://doi.org/10.1080/23270012.2020.1747030; Lu, Y., Industrial integration: A literature review (2016) Journal of Industrial Integration and Management, 1 (2); Lu, Y., Artificial intelligence: A survey on evolution, models, applications and future trends (2019) Journal of Management Analytics, 6 (1), pp. 1-29. , https://doi.org/10.1080/23270012.2019.1570365; Mohanty, M., Shankar, R., A hierarchical analytical model for performance management of integrated logistics (2019) Journal of Management Analytics, 6 (2), pp. 173-208. , https://doi.org/10.1080/23270012.2019.1608326; Nabhani, F., Uhl, C., Kauf, F., Shokri, A., Supply chain process optimisation via the management of variance (2018) Journal of Management Analytics, 5 (2), pp. 136-153. , https://doi.org/10.1080/23270012.2018.1424571; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 107-118. , https://doi.org/10.1016/S0925-5273(98)00223-0; Nikander, J., Suitability of papiNet-standard for straw biomass logistics (2017) Journal of Industrial Information Integration, 6, pp. 11-21. , https://doi.org/10.1016/j.jii.2017.04.004; Ounnar, F., Pujo, P., Mekaouche, L., Giambiasi, N., Customer–supplier relationship management in an intelligent supply chain network (2007) Production Planning and Control, 18 (5), pp. 377-387. , https://doi.org/10.1080/09537280701403736; Peruzzini, M., Stjepandić, J., Editorial to the special issue information integration for innovative design and smart manufacturing (2017) Journal of Industrial Integration and Management, 2 (4); Peruzzini, M., Stjepandić, J., Editorial to the special issue “transdisciplinary analytics in supply chain management” (2018) Journal of Management Analytics, 5 (2), pp. 75-80. , https://doi.org/10.1080/23270012.2018.1443405; Schmidt, R., Möhring, M., Härting, R.C., Reichstein, C., Neumaier, P., Jozinović, P., Industry 4.0-potentials for creating smart products: Empirical research results (2015) International conference on business information systems, pp. 16-27. , https://doi.org/10.1007/978-3-319-19027-3_2, June)., Springer, Cham; Shi, X., Li, L.X., Yang, L., Li, Z., Choi, J.Y., Information flow in reverse logistics: An industrial information integration study (2012) Information Technology and Management, 13 (4), pp. 217-232. , https://doi.org/10.1007/s10799-012-0116-y; Teixeira, K.C., Borsato, M., Development of a model for the dynamic formation of supplier networks (2019) Journal of Industrial Information Integration, 15, pp. 161-173. , https://doi.org/10.1016/j.jii.2018.11.007; Walton, S.V., Handfield, R.B., Melnyk, S.A., The green supply chain: Integrating suppliers into environmental management processes (1998) International Journal of Purchasing and Materials Management, 34 (1), pp. 2-11. , https://doi.org/10.1111/j.1745-493X.1998.tb00042.x; Weyer, S., Schmitt, M., Ohmer, M., Gorecky, D., Towards Industry 4.0—Standardization as the crucial challenge for highly modular, multi-vendor production systems (2015) Ifac-Papersonline, 48 (3), pp. 579-584. , https://doi.org/10.1016/j.ifacol.2015.06.143; Witkowski, K., Internet of things, big data, Industry 4.0—Innovative solutions in logistics and supply chains management (2017) Procedia Engineering, 182, pp. 763-769. , https://doi.org/10.1016/j.proeng.2017.03.197; Xie, C., Cai, H., Xu, L., Jiang, L., Bu, F., Linked semantic model for information resource service toward cloud manufacturing (2017) IEEE Transactions on Industrial Informatics, 13 (6), pp. 3338-3349. , https://doi.org/10.1109/TII.2017.2723501; Xu, L., Enterprise systems: State-of-the-art and future trends (2011) IEEE Transactions on Industrial Informatics, 7 (4), pp. 630-640. , https://doi.org/10.1109/TII.2011.2167156; Xu, L., Information architecture for supply chain quality management (2011) International Journal of Production Research, 49 (1), pp. 183-198. , https://doi.org/10.1080/00207543.2010.508944; Xu, L., (2014) Enterprise integration and information architecture: A systems perspective on industrial information integration, , https://doi.org/10.1201/b17156, CRC Press; Xu, L., Inaugural issue editorial (2016) Journal of Industrial Information Integration, 1, pp. 1-2; Xu, L., He, W., Li, S., Internet of things in industries: A survey (2014) IEEE Transactions on Industrial Informatics, 10 (4), pp. 2233-2243. , https://doi.org/10.1109/TII.2014.2300753; Xu, L., Viriyasitavat, W., Application of blockchain in collaborative Internet-of-things services (2019) IEEE Transactions on Computational Social Systems, 6 (6), pp. 1295-1305. , https://doi.org/10.1109/TCSS.2019.2913165; Xu, L., Wang, C., Bi, Z., Yu, J., AutoAssem: An automated assembly planning system for complex products (2012) IEEE Transactions on Industrial Informatics, 8 (3), pp. 669-678. , https://doi.org/10.1109/TII.2012.2188901; Xu, L., Wang, C., Bi, Z., Yu, J., Object-oriented templates for automated assembly planning of complex products (2013) IEEE Transactions on Automation Science and Engineering, 11 (2), pp. 492-503; Xu, L.D., Xu, E.L., Li, L., Industry 4.0: State of the art and future trends (2018) International Journal of Production Research, 56 (8), pp. 2941-2962. , https://doi.org/10.1080/00207543.2018.1444806; Yang, J., Yu, K., The role of an integrated logistics and procurement service offered by a 3PL firm in supply chain (2019) Journal of Management Analytics, 6 (1), pp. 49-66. , https://doi.org/10.1080/23270012.2019.1568921; Yli-Ojanperä, M., Sierla, S., Papakonstantinou, N., Vyatkin, V., Adapting an agile manufacturing concept to the reference architecture model Industry 4.0: A survey and case study (2019) Journal of Industrial Information Integration, 15, pp. 147-160. , https://doi.org/10.1016/j.jii.2018.12.002; Zandieh, M., Aslani, B., A hybrid MCDM approach for order distribution in a multiple-supplier supply chain: A case study (2019) Journal of Industrial Information Integration, 16, p. 100104. , https://doi.org/10.1016/j.jii.2019.08.002; Zeng, L., Li, L., Duan, L., Business intelligence in enterprise computing environment (2012) Information Technology and Management, 13 (4), pp. 297-310. , https://doi.org/10.1007/s10799-012-0123-z; Zhang, C., Chen, Y., A review of research relevant to the emerging industry trends: Industry 4.0, IoT, block chain, and business analytics (2020) Journal of Industrial Integration and Management, 5 (1), pp. 165-180. , https://doi.org/10.1142/S2424862219500192; Zhang, R., Wang, K., Service supply chain research: A conceptual model based on business processes (2019) Journal of Industrial Integration and Management, 4 (3). , https://doi.org/10.1142/S2424862219500076; Zheng, X., Martin, P., Brohman, K., Xu, L., Cloudqual: A quality model for cloud services (2014) IEEE Transactions on Industrial Informatics, 10 (2), pp. 1527-1536. , https://doi.org/10.1109/TII.2014.2306329},
correspondence_address1={Yin, Y.; School of Economics and Management, China; email: 540783849@qq.com; Xue, W.; School of Foreign Studies, China; email: xuewei05@163.com},
publisher={John Wiley and Sons Ltd},
issn={10927026},
language={English},
abbrev_source_title={Syst. Res. Behav. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tian2020,
author={Tian, X. and Sarkis, J.},
title={Expanding green supply chain performance measurement through emergy accounting and analysis},
journal={International Journal of Production Economics},
year={2020},
volume={225},
doi={10.1016/j.ijpe.2019.107576},
art_number={107576},
note={cited By 17},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076558418&doi=10.1016%2fj.ijpe.2019.107576&partnerID=40&md5=c89865612d7441c4b260f5a3e8024179},
affiliation={School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, 200030, China; School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute for Sustainable Resources, Bartlett School of Environment, Energy and Resources, University College London, Central House, 14 Upper Woburn Place, London, WC1H 0NN, United Kingdom; Worcester Polytechnic Institute, Worcester, MA  01609-2280, United States; Humlog Institute, Hanken School of Economics, Helsinki, Finland},
abstract={Emergy accounting has existed for decades. Emergy evaluates the donor side contribution of nature at region or country, macro-level analyses. It has been rarely applied and considered for organizational or supply chain management. In this study we provide an introduction and background on how emergy accounting analysis can be adjusted and applied at the supply chain level. Supplier selection is the example supply chain application for which actual emergy measures are used. The purpose of this study is to introduce the concept as a valuable tool for investigation by operations and supply chain management scholars and practitioners. The application provides some initial insight. This work at the nexus of sustainable supply chains and performance measurement is an initial study with significant future opportunities. These opportunities include effectively internalizing environmental and resource externalities for more thoughtful business models and evaluations. Implications and future research directions are summarized for this important, yet understudied field. It contributes by expanding the supplier performance measurement field. © 2019 Elsevier B.V.},
author_keywords={Eco-design;  Emergy accounting;  Green supply chain management;  Supply chain management;  Sustainability},
keywords={Ecodesign;  Sustainable development, Emergy accountings;  Future research directions;  Green supply chain management;  Operations and supply chain managements;  Performance measurements;  Supplier performance measurement;  Supply chain applications;  Sustainable supply chains, Supply chain management},
references={Abel, T., Human transformities in a global hierarchy: emergy and scale in the production of people and culture (2010) Ecol. Model., 221, pp. 2112-2117; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: a literature review (2010) Int. J. Prod. Res., 48 (17), pp. 5137-5155; Amaral, L.P., Martins, N., Gouveia, J.B., A review of emergy theory, its application and latest developments (2016) Renew. Sustain. Energy Rev., 54, pp. 882-888; Asamoah, E.F., Zhang, L., Liang, S., Pang, M., Tang, S., Emergy perspectives on the environmental performance and sustainability of small-scale gold production systems in Ghana (2017) Sustainability, 9 (11), p. 2034; Ascione, M., Campanella, L., Cherubini, F., Ulgiati, S., Environmental driving forces of urban growth and development : an emergy-based assessment of the city of Rome, Italy (2009) Landsc. Urban Plan., 93, pp. 238-249; Bai, C., Sarkis, J., Green supplier development: analytical evaluation using rough set theory (2010) J. Clean. Prod., 18 (12), pp. 1200-1210; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management–what has been achieved? (2015) Supply Chain Manag.: Int. J., 20, pp. 664-680; Brandt-Williams, S.L., Handbook of Emergy Evaluation: a Compendium of Data for Emergy Computation Issued in a Series of Folios (2001), Folio# 4 Emergy of Florida Agriculture 32611-36450; Brown, M.T., Bardi, E., Handbook of emergy evaluation. A compendium of data for emergy computation issued in a series of folios (2001) Folio, 3; Brown, M.T., Cohen, M.J., Sweeney, S., Predicting national sustainability: the convergence of energetic, economic and environmental realities (2009) Ecol. Model., 220, pp. 3424-3438; Brown, M.T., Ulgiati, S., Emergy evaluation of the biosphere and natural capital (1999) Ambio, 28, pp. 486-493; Brown, M.T., Ulgiati, S., Emergy measures of carrying capacity to evaluate economic investments (2001) Popul. Environ., 22, pp. 471-501; Brown, M.T., Ulgiati, S., Emergy measures of carrying capacity to evaluate economic investments (2001) Popul. Environ., 22, pp. 471-501; Brown, M.T., Ulgiati, S., Energy quality, emergy, and transformity: H.T. Odum's contributions to quantifying and understanding systems (2004) Ecol. Model., 178, pp. 201-213; Brown, M.T., Ulgiati, S., Assessing the global environmental sources driving the geobiosphere: a revised emergy baseline (2016) Ecol. Model., 339, pp. 126-132; Cai, Z.F., Zhang, L.X., Zhang, B., Chen, Z.M., Emergy-based analysis of Beijing–Tianjin–Tangshan region in China (2009) Commun. Nonlinear Sci. Numer. Simul., 14, pp. 4319-4331; Chae, B., Developing key performance indicators for supply chain: an industry perspective (2009) Supply Chain Manag.: Int. J., 14, pp. 422-428; Chen, W., Liu, W., Geng, Y., Ohnishi, S., Sun, L., Han, W., Tian, X., Zhong, S., Life cycle based emergy analysis on China's cement production (2016) J. Clean. Prod., 131, pp. 272-279; Cheng, W., Appolloni, A., D'Amato, A., Zhu, Q., Green Public Procurement, missing concepts and future trends–A critical review (2018) J. Clean. Prod., 176, pp. 770-784; Christensen, J., Accounting errors and errors of accounting (2010) Account. Rev., 85 (6), pp. 1827-1838; Corcelli, F., Ripa, M., Ulgiati, S., Efficiency and sustainability indicators for papermaking from virgin pulp-An emergy-based case study (2018) Resour. Conserv. Recycl., 131, pp. 313-328; De Benedetto, L., Klemeš, J., The environmental bill of material and technology routing: an integrated LCA approach (2010) Clean Technol. Environ. Policy, 12 (2), pp. 191-196; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ., 142, pp. 247-258; Fan, Y., Fang, L., Qiao, Q., Yao, Y., Emergy analysis on industrial symbiosis of an industrial park-A case study of Hefei economic and technological development area (2017) J. Clean. Prod., 141, pp. 791-798; Franzese, P.P., Russo, G.F., Ulgiati, S., Geographical information system (GIS) and emergy synthesis evaluation of urban waste management (2008) Sustain. Energy Prod. Consum., pp. 339-352; Fu, X., Zhu, Q., Sarkis, J., Evaluating green supplier development programs at a telecommunications systems provider (2012) Int. J. Prod. Econ., 140 (1), pp. 357-367; Geng, Y., Sarkis, J., Ulgiati, S., Zhang, P., Measuring China's circular economy (2013) Science, 339, pp. 1526-1527; Geng, Y., Tian, X., Sarkis, J., Ulgiati, S., China-USA trade: indicators for equitable and environmentally balanced resource exchange (2017) Ecol. Econ., 132, pp. 245-254; Geng, Y., Zhang, P., Ulgiati, S., Sarkis, J., Emergy analysis of an industrial park: the case of Dalian, China (2010) Sci. Total Environ., 408 (22), pp. 5273-5283; Genovese, A., Lenny Koh, S., Bruno, G., Esposito, E., Greener supplier selection: state of the art and some empirical evidence (2013) Int. J. Prod. Res., 51, pp. 2868-2886; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140, pp. 69-82; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmark, 12, pp. 330-353; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Manag.: Int. J., 9, pp. 23-29; Jabbour, C.J.C., de Sousa Jabbour, A.B.L., Sarkis, J., Unlocking effective multi-tier supply chain management for sustainability through quantitative modeling: lessons learned and discoveries to be made (2018) Int. J. Prod. Econ.; Jaklič, T., Juvančič, L., Kavčič, S., Debeljak, M., Complementarity of socio-economic and emergy evaluation of agricultural production systems: the case of Slovenian dairy sector (2014) Ecol. Econ., 107, pp. 469-481; Jomo, K.S., Rudiger, A., Trade liberalization and economic development (2009) Science, 323, pp. 211-212; Kangas, P.C., Handbook of Emergy Evaluation (2002), Center for Environmental Policy; Ketchen, D.J., Jr., Hult, G.T.M., Bridging organization theory and supply chain management: the case of best value supply chains (2007) J. Oper. Manag., 25 (2), pp. 573-580; Kharrazi, A., Kraines, S., Lan, H., Yarime, M., Advancing quantification methods of sustainability: a critical examination emergy, exergy, ecological footprint, and ecological information-based approaches (2014) Ecol. Indicat., 37, pp. 81-89; Kim, H.M., Laskowski, M., Toward an ontology‐driven blockchain design for supply‐chain provenance (2018) Intell. Syst. Account. Financ. Manag., 25, pp. 18-27; Lee, I., Lee, K., The Internet of Things (IoT): applications, investments, and challenges for enterprises (2015) Bus. Horiz., 58, pp. 431-440; Lei, K.P., Chen, F.P., Wang, Z.S., The emergy synthesis and sustainability analysis of city's environment and economy (2006) Acta Ecol. Sin., 26, pp. 439-448; Li, D., Wang, R., Hybrid Emergy-LCA (HEML) based metabolic evaluation of urban residential areas: the case of Beijing, China (2009) Ecol. Complex., 6, pp. 484-493; Lin, H., Government–business partnership formation for environmental improvements (2014) Organ. Environ., 27 (4), pp. 383-398; Liu, J., Feng, Y., Zhu, Q., Sarkis, J., Green supply chain management and the circular economy: reviewing theory for advancement of both fields (2018) Int. J. Phys. Distrib. Logist. Manag., 48, pp. 794-817; Londoño, N.A.C., Velásquez, H.I., McIntyre, N., Comparing the environmental sustainability of two gold production methods using integrated Emergy and Life Cycle Assessment (2019) Ecol. Indicat., 107, p. 105600; Lou, B., Ulgiati, S., Identifying the environmental support and constraints to the Chinese economic growth-An application of the Emergy Accounting method (2013) Energy Policy, 55, pp. 217-233; Lu, H., Campbell, D., Chen, J., Qin, P., Ren, H., Conservation and economic viability of nature reserves: an emergy evaluation of the Yancheng Biosphere Reserve (2007) Biol. Conserv., 139, pp. 415-438; Lugaric, L., Krajcar, S., Transforming cities towards sustainable low-carbon energy systems using emergy synthesis for support in decision making (2016) Energy Policy, 98, pp. 471-482; Lv, C., Wu, Z., Emergy analysis of regional water ecological–economic system (2009) Ecol. Eng., 35, pp. 703-710; Markussen, M.V., Kulak, M., Smith, L.G., Nemecek, T., Østergård, H., Evaluating the sustainability of a small-scale low-input organic vegetable supply system in the United Kingdom (2014) Sustainability, 6, pp. 1913-1945; Mura, M., Longo, M., Micheli, P., Bolzani, D., The evolution of sustainability measurement research (2018) Int. J. Manag. Rev., 20, pp. 661-695; Mendoza Beltran, A., Prado, V., Font Vivanco, D., Henriksson, P.J., Guinée, J.B., Heijungs, R., Quantified uncertainties in comparative life cycle assessment: what can Be concluded? (2018) Environ. Sci. Technol., 52 (4), pp. 2152-2161; Mu, H., Feng, X., Chu, K.H., Improved emergy indices for the evaluation of industrial systems incorporating waste management (2011) Ecol. Eng., 37, pp. 335-342; Nachtmann, H., Needy, K.L., Methods for handling uncertainty in activity based costing systems (2003) Eng. Econ., 48 (3), pp. 259-282; Narasimhan, R., Talluri, S., Mendez, D., Supplier evaluation and rationalization via data envelopment analysis: an empirical examination (2001) J. Supply Chain Manag., 37 (2), pp. 28-37; Odum, H.T., Environmental Systems and Public Policy (1988), University of Florida Gainesville; Odum, H.T., Environmental Accounting: EMERGY and Environmental Decision Making (1996), Wiley New York; Odum, H.T., Odum, E.P., The energetic basis for valuation of ecosystem services (2000) Ecosystems, 3, pp. 21-23; Ohnishi, S., Dong, H., Geng, Y., Fujii, M., Fujita, T., A comprehensive evaluation on industrial & urban symbiosis by combining MFA, carbon footprint and emergy methods-Case of Kawasaki, Japan (2017) Ecol. Indicat., 73, pp. 513-524; Pan, J.M., Zhang, H., Wang, X.Q., Liu, G.Y., Update methods of the global national environmental accounting database (NEAD) (2017) J. Environ. Account. Manag., 5 (2), pp. 104-115; Pulselli, R.M., Integrating emergy evaluation and geographic information systems for monitoring resource use in the Abruzzo region (Italy) (2010) J. Environ. Manag., 91, pp. 2349-2357; Pulselli, R.M., Rustici, M., Marchettini, N., An integrated framework for regional studies: emergy based spatial analysis of the province of cagliari (2007) Environ. Monit. Assess., 133 (1-3), pp. 1-13; Raugei, M., Emergy indicators applied to human economic systems-A word of caution (2011) Ecol. Model., 222, pp. 3821-3822; Ren, J., Tan, S., Yang, L., Goodsite, M.E., Pang, C., Dong, L., Optimization of emergy sustainability index for biodiesel supply network design (2015) Energy Convers. Manag., 92, pp. 312-321; Ren, J.M., Zhang, L., Wang, R.S., Measuring the sustainability of policy scenarios: emergy-based strategic environmental assessment of the Chinese paper industry (2010) Ecol. Complex., 7, pp. 156-161; Reza, B., Sadiq, R., Hewage, K., Emergy-based life cycle assessment (Em-LCA) of multi-unit and single-family residential buildings in Canada (2014) Int. J. Sustain. Built Environ., 3, pp. 207-224; Rugani, B., Benetto, E., Improvements to emergy evaluations by using life cycle assessment (2012) Environ. Sci. Technol., 46 (9), pp. 4701-4712; Saberi, S., Kouhizadeh, M., Sarkis, J., Blockchain technology: a panacea or pariah for resources conservation and recycling? (2018) Resour. Conserv. Recycl., 130, pp. 80-81; Sarkis, J., Talluri, S., A model for strategic supplier selection (2002) J. Supply Chain Manag., 38, pp. 18-28; Schmitt, T.G., Resolving uncertainty in manufacturing systems (1984) J. Oper. Manag., 4 (4), pp. 331-345; Stohler, M., Rebs, T., Brandenburg, M., Toward the Integration of Sustainability Metrics into the Supply Chain Operations Reference (SCOR) Model. Social and Environmental Dimensions of Organizations and Supply Chains (2018), pp. 49-60; Seuring, S., Müller, M., Core issues in sustainable supply chain management–a Delphi study (2008) Bus. Strateg. Environ., 17 (8), pp. 455-466; Sweeney, S., Cohen, M.J., King, D.M., Brown, M.T., Creation of a global emergy database for standardized national emergy synthesis (2007) Proceedings of the 4th Biennial Emergy Research Conference, , M.T. Brown Center for Environmental Policy Gainesville, FL; Tate, W.L., Ellram, L.M., Sustainable Supply Chains and Social Networks: an Overview, Handbook on the Sustainable Supply Chain (2019), Edward Elgar Publishing; Tian, X., Geng, Y., Dai, H.C., Fujita, T., Wu, R., Liu, Z., Masui, T., Xie, Y., The effects of household consumption pattern on regional development: a case study of Shanghai (2016) Energy, 103, pp. 49-60; Tognetti, A., Grosse-Ruyken, P.T., Wagner, S.M., Green supply chain network optimization and the trade-off between environmental and economic objectives (2015) Int. J. Prod. Econ., 170, pp. 385-392; Tuni, A., Rentizelas, A., An innovative eco-intensity based method for assessing extended supply chain environmental sustainability (2018) Int. J. Prod. Econ.; Ulgiati, S., Brown, M.T., Labor and services as information carriers in emergy-LCA accounting (2014) J. Environ. Account.; Manag., 2, pp. 163-170; Vilbiss, C.D., Brown, M.T., Final Technical Report “Emergy Research Support for Supply Chains” (2015), US Environmental Protection Agency; Wagner, S.M., Krause, D.R., Supplier development: communication approaches, activities and goals (2009) Int. J. Prod. Res., 47 (12), pp. 3161-3177; Wang, L., Zhang, J., Ni, W., Emergy evaluation of eco-industrial park with power plant (2005) Ecol. Model., 189, pp. 233-240; Wu, Y., Cegielski, C.G., Hazen, B.T., Hall, D.J., Cloud computing in support of supply chain information system infrastructure: understanding when to go to the cloud (2013) J. Supply Chain Manag., 49, pp. 25-41; Yang, H., Li, Y., Shen, J., Hu, S., Evaluating waste treatment, recycle and reuse in industrial system: an application of the eMergy approach (2003) Ecol. Model., 160, pp. 13-21; Yang, J., Chen, B., Emergy analysis of a biogas-linked agricultural system in rural China-a case study in Gongcheng Yao Autonomous County (2014) Appl. Energy, 118, pp. 173-182; Zhang, B., Chen, G.Q., Yang, Q., Chen, Z.M., Chen, B., Li, Z., How to guide a sustainable industrial economy: emergy account for resources input of Chinese industry (2011) Procedia Environ. Sci., 5, pp. 51-59; Zhang, L., Chen, B., Yang, Z., Chen, G., Jiang, M., Liu, G., Comparison of typical mega cities in China using emergy synthesis (2009) Commun. Nonlinear Sci. Numer. Simul., 14, pp. 2827-2836; Zhang, X., Emergy analysis of agricultural eco-economic system in longdong loess pleteau (2004) Res. Agric. Mod., 25, pp. 367-370; Zhong, S., Geng, Y., Kong, H., Liu, B., Tian, X., Chen, W., Qian, Y., Ulgiati, S., Emergy-based sustainability evaluation of erhai lake basin in China (2018) J. Clean. Prod., 178, pp. 142-153},
correspondence_address1={Sarkis, J.; Worcester Polytechnic InstituteUnited States; email: jsarkis@wpi.edu},
publisher={Elsevier B.V.},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yadav2020292,
author={Yadav, S. and Garg, D. and Luthra, S.},
title={Development of IoT based data-driven agriculture supply chain performance measurement framework},
journal={Journal of Enterprise Information Management},
year={2020},
volume={34},
number={1},
pages={292-327},
doi={10.1108/JEIM-11-2019-0369},
note={cited By 31},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106350629&doi=10.1108%2fJEIM-11-2019-0369&partnerID=40&md5=d447e64743f7e72d5856e6f2324bb4c7},
affiliation={Department of Mechanical Engineering, National Institute of Technology, Kurukshetra, India; Department of Mechanical Engineering, Government Polytechnic, Jhajjar, India},
abstract={Purpose: Performance measurement (PM) of any supply chain is prerequisite for improving its competitiveness and sustainability. This paper develops a framework for supply chain performance measurement (SCPM) for agriculture supply chain (ASC) based on internet of things (IoT). Moreover, this article explains the role of IoT in data collection and communication (SC visibility) based on the supply chain operation reference (SCOR) model. Design/methodology/approach: This research identifies various key performance indicators (KPIs) and also their role in SCPM for improving its sustainability by using SCOR. Further, Shannon entropy is utilized for weighing the basic processes of SCPM and by using weights, fuzzy TOPSIS is applied for ranking of identified KPIs at metrics level 2 (deeper level). Findings: “Flexibility” and “Responsiveness” have been reported as two most important KPIs in IoT based SCPM framework for ASC towards achieving sustainability. Research limitations/implications: In this research, metrics are explained only at SCOR level 2. But, this research will guide the managers and practitioners of various organizations to set their benchmark for comparing their performance at different levels of business processes. Further, this paper has managerial implications to develop an effective system for PM of IoT based data-driven ASC. Originality/value: By using IoT based data driven system, this article fills the gap between SCPM by measuring different SC strategies in their performance measurable form of reliable, responsive and asset management etc. © 2020, Emerald Publishing Limited.},
author_keywords={Agriculture supply chain (ASC);  Fuzzy TOPSIS;  Internet of things (IoT);  Key performance indicators (KPIs);  Supply chain operation reference (SCOR) model;  Supply chain performance measurement (SCPM)},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: review and classification (2012) Journal of Organizational Management Studies, pp. 1-20. , http://ibimapublishing.com/articles/JOMS/2012/872753/872753.pdf; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean and agile supply chain: an ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Ahi, P., Searcy, C., Jaber, M.Y., Energy-related performance measures employed in sustainable supply chains: a bibliometric analysis (2016) Sustainable Production and Consumption, 7, pp. 1-15; Ahumada, O., Villalobos, J.R., Application of planning models in the agri-food supply chain: a review (2009) European Journal of Operational Research, 196 (1), pp. 1-20; Akkawuttiwanich, P., Yenradee, P., Fuzzy QFD approach for managing SCOR performance indicators (2018) Computers & Industrial Engineering, 122, pp. 189-201; Allaoui, H., Guo, Y.H., Choudhary, A., Bloemhof, J., Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach (2017) Journals of Computer & Operation Research, 89, pp. 369-384; Angerhofer, B., Angelides, M.C., A model and A performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; ArzuAkyuz, G., ErmanErkan, T., Supply chain performance measurement: a literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Awa, H.O., Ojiabo, O.U., Orokor, L.E., Integrated technology-organization-environment (TOE) taxonomies for technology adoption (2017) Journal of Enterprise Information Management, 30 (6), pp. 893-921; Ball, P., Albores, P., Macbryde, J., Requirements for modeling E-business processes (2004) Production Planning and Control: The Management of Operations, 15 (8), pp. 776-785; Barratt, M., Oke, A., Antecedents of supply chain visibility in retail supply chains: a resourcebasedtheory perspective (2007) Journal of Operations Management, 25 (6), pp. 1217-1233; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beitzen-Heineke, E.F., Balta-Ozkan, N., Reefke, H., The prospects of zero-packaging grocery stores to improve the social and environmental impacts of the food supply chain (2017) Journal of Cleaner Production, 140, pp. 1528-1541; Ben-Daya, M., Hassini, E., Bahroun, Z., Internet of things and supply chain management: a literature review (2017) International Journal of Production Research, 57 (15-16), pp. 1-24; Bevilacqua, M., Ciarapica, F.E., Giacchetta, G., A fuzzy-QFD approach to supplier selection (2006) Journal of Purchasing and Supply Management, 12 (1), pp. 14-27; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bojadziev, G., Bojadziev, M., (1995) Fuzzy Sets, Fuzzy Logic, Applications, Advances in Fuzzy Systems-Applications and Theory, , 5.World Scientific, Singapore; Bolstorff, P., Rosenbaum, R., (2012) Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , AMACOM, New York, NY; Bu´rca, S.-D., Fynes, B., Marshall, D., Strategic technology adoption: extending ERP across the supply chain (2005) Journal of Enterprise Information Management, 18 (4), pp. 427-440; Bulsara, H.P., Qureshi, M.N., Patel, H., Green supply chain performance measurement: an exploratory study (2016) International Journal of Logistic System and Management, 23, p. 476; Burg, J., (1967) Maximum Entropy Spectral Analysis. Paper Presented at 37th Meeting of the Society of Exploration Geophysicists, , Society of Exploration Geophysicists: Oklahoma City; Cassell, C., Nadin, S., Gray, M.O., The use and effectiveness of benchmarking in SMEs (2001) Benchmarking: An International Journal, 8 (3), pp. 212-222; Castro, N.R., Swart, J., Building a roundtable for a sustainable hazelnut supply chain (2017) Journal of Cleaner Production, 168, pp. 1398-1412; Chan, F.T.S., Qi, H.J., AnInnovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: a process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Charles, H., Godfray, J., Beddington, J.R., Crute, I.R., Haddad, L., Lawrence, D., Muir, J.F., Pretty, J., Food security: the challenge of feeding 9 billion people (2010) Science, 327, pp. 812-818; Che, Z.H., Clustering and selecting suppliers based on simulated annealing algorithms (2012) Computers and Mathematics with Applications, 63, pp. 228-238; Chen, C.T., Extensions of the TOPSIS for group decision-making under fuzzy environment (2000) Fuzzy Sets and Systems, 114, pp. 1-9; Cheng, J.C.P., Law, K.H., Bjornsson, H., Jones, A., Sriram, D., Modellingand monitoring ofconstructionsupplychains (2010) Advance Engineering Information, 24, pp. 435-455; Chiffoleau, Y., Millet-Amrani, S., Canard, A., From short food supply chains to sustainable agriculture in urban food systems: food democracy as a vector of transition (2016) Agriculture, 6 (4), p. 57; Chimphango, A.F., Gorgens, J.F., Postharvest technology for advancing sustainable bioenergy production for food processing (2015) British Food Journal, 117 (12), pp. 2850-2862; Chopra, S., Meindl, P., (2016) Supply Chain Management: Strategy, Planning and Operation, , 6th ed., Pearson, Prentice Hall, NJ; Choy, K.L., Lee, W.B., Lo, V., An enterprise collaborative management system: a case study of supplier relationship management (2004) Journal of Enterprise Information Management, 17 (3), pp. 191-207; Coltman, T.R., Tallon, P.P., Sharma, R., Queiroz, M., Strategic IT alignment: twenty-five years on (2015) Journal of Information Technology, 30, pp. 91-100; Dania, W.A.P., Xing, K., Amer, Y., Collaboration behavioural factors for sustainable agri-food supply chains: a systematic review (2018) Journal of Cleaner Production, 186, pp. 851-864; Deng, H., Yeh, C.H., Willis, R.J., Inter-company comparison using modified TOPSIS with objective weights (2000) Computers and Operations Research, 27, pp. 963-973; Doernberg, A., Zasada, I., Bruszewska, K., Skoczowski, B., Piorr, A., Potentials and limitations of regional organic food supply: a qualitative analysis of two food chain types in the Berlin metropolitan region (2016) Sustainability, 8 (11). , Article 1125; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things: toward more practical SCPM (2017) Industrial Management and Data Systems, 117 (2), pp. 267-286; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation model (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Faez, F., Ghodsypour, S.H., O'Brien, C., Vendor selection and order allocation using an integrated fuzzy case-based reasoning and mathematical programming model (2009) International Journal of Production Economics, 121, pp. 395-408; Fang, C., Liu, X., Pardalos, P., Pei, M.J., Optimization for a three-stage production in the internet of things: procurement, production and product recovery and acquisition (2016) TheInternational Journal of Advanced Manufacturing Technology, 83, pp. 689-710; Fawcett, S.E., Cooper, M.B., Logistics performance measurement and customer success (1998) Industrial Marketing Management, 27 (4), pp. 341-357; Fisher, M.L., (1997) What is the right supply chain for your product?, , https://hbr.org/1997/03/what-is-the-right-supply-chain-for-your-product, accessed, 12 January 2016; Folan, P., Browne, J., A review of performance measurement: towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Gammelgaard, B., Vesth, H., The SCOR model – a critical review (2004) Proceedings of Operations Management as a Change Agent Conferences, INSEAD, 1, pp. 233-241; Golan, A., Judge, G., Miller, D., (1996) Maximum Entropy Econometrics: Robust Estimation With Limited Data, , John, Wiley and Sons, New York; Govindan, K., Mangla, S.K., Luthra, S., Prioritising indicators in improving supply chain performance using fuzzy AHP: insights from the case example of four Indian manufacturing companies (2017) Production Planning and Control: The Management of Operations, 28 (6-8), pp. 552-573; Govindan, K., Sustainable consumption and production in the food supply chain: a conceptual framework (2018) International Journal of Production Economics, 195, pp. 419-431; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Project Organisation and Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Subramanian, N., Tiwari, M.K., Information technology governance in internet of things supply chain networks (2016) Industrial Management and Data Systems, 116 (7); Hadi-Vencheh, A., Niazi-Motlagh, M., An improved voting analytic hierarchy process–data envelopment analysis methodology for supplier selection (2011) International Journal of Computer Integrated Manufacturing, 24, pp. 189-197; Hall, D.C., Saygin, C., Impact of information sharing on supply chain performance (2012) The International Journal of Advanced Manufacturing Technology, 58 (1-4), pp. 397-409; Harland, C.M., A taxonomy of supply networks (2001) Journal of Supply Chain Management, 37 (4), pp. 20-27; Hausman, W.H., Supply chain performance metrics (2004) The Practice of Supply Chain Management: Where Theory and Application Converge, pp. 61-73. , Harrison, T.P., Lee, H.L. and Neale, J.J.,(Eds), Springer Science & Business Media, New York, NY; Heard, B.R., Taiebat, M., Xu, M., Miller, S.A., Sustainability implications of connected and autonomous vehicles for the food supply chain (2018) Resources, Conservation & Recycling, 128, pp. 22-24; Hervani, A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hsieh, H., Lu, S., Tzeng, G., Fuzzy MCDM approach for planning and design tenders selection in public office buildings (2004) International Journal of Project Management, 22, pp. 573-584; Hsu, C.W., Hu, A.H., Applying hazardous substance management to supplier selection using analytic network process (2009) Journal of Cleaner Production, 17 (2), pp. 255-264; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48 (2), pp. 377-394; Hwang, Y.D., Lin, Y.C., Lyu, J., Jr., The performance evaluation of SCOR sourcing process – the case study of taiwan's TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Irani, Z., Sharif, A.M., Sustainable food security futures: perspectives on food waste and information across the food supply chain (2016) Journal of Enterprises Information Management, 29 (2), pp. 171-178; Jelsma, I., Slingerland, M., Giller, K.E., Bijman, J., Collective action in a smallholder oil palm production system in Indonesia: the key to sustainable and inclusive smallholder palm oil (2017) Journal of Rural Studies, 54, pp. 198-210; Johnson, R., Fraser, E.D., Hawkins, R., Overcoming barriers to scaling up sustainable alternative food systems: a comparative case study of two ontario-based wholesale produce auctions (2016) Sustainability, 8 (4), p. 328; Kaipia, R., Dukovska-Popovska, I., Loikkanen, L., Creating sustainable fresh food supply chains through waste reduction (2013) International Journal of Physical Distribution and Logistics Management, 43 (3), pp. 262-276; Kamble, S.S., Gunasekaran, A., Gawankar, S.A., Sustainable Industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives (2018) Journal of Process Safety and Environmental Protection, 117, pp. 408-425; Kaplan, R.S., Norton, D.P., (2006) Alignment: Using the Balanced Scorecard to Create Corporate Synergies, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Norton, D.P., The balanced scorecard: measures that drive performance (2015) Harvard Business Review, 83 (7), p. 172; Kasi, V., Systemic assessment of SCOR for modelling supply chains (2005) Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 87; Kazancoglu, Y., Kazancoglu, I., Sagnak, M., Fuzzy DEMATEL-based green supply chain management performance: application in cement industry (2018) Industrial Management Data System, 118, pp. 412-431; KianiMavi, R., Green supplier selection: a fuzzy AHP and fuzzy ARAS approach (2015) International Journal of Services and Operations Management, 22 (2), pp. 165-118; Kilincci, O., Onal, S.A., Fuzzy AHP approach for supplier selection in a washing machine company (2011) Expert Systems with Applications, 38, pp. 9656-9664; Lapide, L., What about measuring supply chain performance (2000) Achieving Supply Chain Excellence through Technology, 2 (2), pp. 287-297; Lima Junior, F.R., Osiro, L., Carpinetti, L.C.R., A comparison between fuzzy AHP and fuzzy TOPSIS methods to supplier selection (2014) Application Software Computation, 21, pp. 194-209; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: a literature review (2017) Computers and Industrial Engineering, 113, pp. 333-346; Lin, R.H., An integrated FANP-MOLP for supplier evaluation and order allocation (2009) Applied Mathematical Modelling, 33, pp. 2730-2736; Liu, P., Huang, S.H., Mokasdar, A., Zhou, H., Hou, L., The impact of additive manufacturing in the aircraft spare parts supply chain: supply chain operation reference (SCOR) model based analysis (2014) Production Planning and Control: The Management of Operations, 25 (13-14), pp. 1169-1181; Lockamy, A., III, McCormack, K., Linking SCOR planning practices to supply chain performance: an exploratory study (2004) International Journal of Operations and Production Management, 24 (11-12), pp. 1192-1218; Luthra, S., Garg, D., Haleem, A., Green supplier selection towards sustainability in Indian automobile industry (2013) Proceedings of GLOGIFT Department of Management Studies, pp. 494-505. , IIT, Delhi; Luthra, S., Mangla, S.K., Chan, F.T., Venkatesh, V.G., Evaluating the drivers to information and communication technology for effective sustainability initiatives in supply chains (2018) International Journal of Information Technology and Decision Making, 17 (1), pp. 311-338; Luthra, S., Mangla, S.K., Garg, D., Kumar, A., Internet of things (IoT) in agriculture supply chain management: a developing country perspective (2018) Emerging Markets from a Multidisciplinary Perspective, pp. 209-220. , Springer, Cham; Mangla, S.K., Kumar, P., Barua, M.K., Risk analysis in green supply chain using fuzzy AHP approach: a case study (2015) Resources, Conservation and Recycling, 104, pp. 375-390; Marchand, M., Raymond, L., Researching performance measurement systems: an information systems perspective (2008) International Journal of Operations and Production Management, 28 (7), pp. 663-686; Marconi, M., Marilungo, E., Papetti, A., Germani, M., Traceability as a means to investigate supply chain sustainability: the real case of A leather shoe supply chain (2017) International Journal of Production Research, 55, pp. 6638-6652; Matthews, C.R., Linking the supply chain to TQM (2006) Quality Progress, 39 (11), pp. 29-36; Mendoza, A., Ventura, J.A., Analytical models for supplier selection and order quantity allocation (2012) Applied Mathematical Modelling, 36 (8), pp. 3826-3835; Millet, P.A., Schmitt, P., Botta-Genoulaz, V., The SCOR model for the alignment of business processes and information systems (2015) Enterprise Information Systems, 3 (4), pp. 393-407; Miranda-Ackerman, M.A., Azzaro-Pantel, C., Extending the scope of eco-labelling in the food industry to drive change beyond sustainable agriculture practices (2017) Journal of Environmental Management, 204, pp. 814-824; Mokhtarian, M.N., Hadi-Vencheh, A., A new fuzzy TOPSIS method based on left and right scores: an application for determining an industrial zone for dairy products factory (2012) Applied Soft Computing, 12, pp. 2496-2505; Musavi, M., Bozorgi-Amiri, A., A multi-objective sustainable hub location-scheduling problem for perishable food supply chain (2017) Journal of Computer and Industrial Engineering, 113, pp. 766-778; Myles, J.F., (2008) Discovering critical success factors for implementing an automated performance measurement system: a case study approach, , PhD Thesis, Edith Cowan University; Naik, G., Suresh, D.N., (2018) Challenges of creating sustainable agri-retail supply chains, , doi:, IIMB Management; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: a literature review on approaches, techniques, and criteria (2013) Journal of Operations and Supply Chain Management, 6 (2), pp. 94-113; Neely, A., Gregory, M., Platts, K., Performance measurement systems design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Network, G.F., (2012) World footprint: do we fit on the planet, , http://www.Footprintnetwork.org/en/index.php/GFN/page/worldfootprint; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain Score card (2005) European Journal of Information Systems, 14 (4), pp. 335-346; Perçin, S., Use of fuzzy AHP for evaluating the benefits of information-sharing decisions in a supply chain (2008) Journal of Enterprise Information Management, 21 (3), pp. 263-284; Perić, T., Babić, Z., Veža, I., Vendor selection and supply quantities determination in A bakery by AHP and fuzzy multi-criteria programming (2013) International Journal of Computer Integrated Manufacturing, 26, pp. 816-829; Potts, J., Lynch, M., Wilkings, A., Hupp, E.G., Cunningham, M., Voora, V., (2014) The Sate of Sustainability Initiatives Review 2014: Standards and the Green Economy, , International Institute for Sustainable Development (IISD) and the International Institute for Environment and Development (IIED), London; Ramaa, A., Rangaswamy, T., Subramanya, K., A review of literature on performance measurement of supply chain network (2009) Second International Conference on Emerging Trends in Engineering and Technology, , doi:, IEEE, December 16-18, accessed, 18 January 2016; Reefke, H., Sundaram, D., Key themes and research opportunities in sustainable supply chain management–identification and evaluation (2017) Omega, 66, pp. 195-211; Reyes, H.G., Giachetti, R., Usingexpertstodevelopasupplychainmaturity model inMexico (2010) Supply Chain Management: An International Journal, 15 (6), pp. 415-424; Rezaei, M., AkbarpourShirazi, M., Karimi, B., IoT-based framework for performance measurement: a real-time supply chain decision alignment (2017) Industrial Management and Data Systems, 117 (4), pp. 688-712; Robinson, C.J., Malhotra, M.K., Defining the concept of supply chain quality management and its relevance to academic and industrial practice (2005) International Journal of Production Economics, 96 (3), pp. 315-337; Roder, A., Tibken, B., A methodology for modeling inter-company supply chains and for evaluating A method of integrated product and process documentation (2006) European Journal of Operational Research, 169 (3), pp. 1010-1029; Rooeinfar, R., Azimi, P., Pourvaziri, H., Multi-echelon supply chain network modelling and optimization via simulation and meta-heuristic algorithms (2016) Scientia Iranica, 23 (1), pp. 330-347; Rosenfeld, R., (1994) Adaptive statistical language modeling: a maximum entropy approach, , PhD Thesis, School of Computer Science. Carnegie Mellon University; Schmitt, E., Keech, D., Maye, D., Barjolle, D., Kirwan, J., Comparing the sustainability of local and global food chains: a case study of cheese products in Switzerland and the UK (2016) Sustainability, 8 (5), p. 419; Schoeman, C., Sanchez, V.R., GreensupplychainoverviewandaSouth african casestudy (2009) Proceedings of the 28th Southern African Transport Conference (SATC).ISBN978-1-920017-39-2, PretoriaS, pp. 569-579. , Africa6-9 July; Schöggl, J.P., Fritz, M.M.C., Baumgartner, R.J., Toward supply chain-wide sustainability assessment: a conceptual framework and an aggregation method to assess supply chain performance (2016) Journal of Cleaner Production, 131, pp. 822-835; Scupola, A., SMEs' e-commerce adoption: perspectives from Denmark and Australia (2009) Journal of Enterprise Information Management, 22 (1-2), pp. 152-166; Sengül, U., Eren, M., Eslamian Shiraz, S., Gezder, V., Sengül, A.B., Fuzzy TOPSIS method for ranking renewable Energy supply systems in Turkey (2015) Renewable Energy, 7, pp. 617-625; Sgarbossa, F., Russo, I., A proactive model in sustainable food supply chain: insight from a case study (2017) International Journal of Production Economics, 183, pp. 596-606; Shannon, C.E., A mathematical theory of communication (2001) ACMSig Mobile Computing and Communications Review, 5 (1), pp. 3-55; Shapiro, J., (2001) Modeling the Supply Chain, , Wadsworth Group, Duxbury, MA; Sharma, V.K., Chandna, P., Bhardwaj, A., Green supply chain management related performance indicators in agro industry: a review (2017) Journal of Cleaner Production, 141 (1), pp. 1194-1208; Sharma, R., Kamble, S.S., Gunasekaran, A., Big GIS analytics framework for agriculture supply chains: a literature review identifying the current trends and future perspectives (2018) Computers and Electronics in Agriculture, 155, pp. 103-120; Shemshadi, A., Shirazi, H., Toreihi, M., Tarokh, M.J., A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting (2011) Expert Systems with Applications, 38 (10), pp. 12160-12167; Sianaki, O.A., Masoum, M.A., Potdar, V., A decision support algorithm for assessing the engagement of A demand response program in the industrial sector of the smart grid (2018) Computers and Industrial Engineering, 115, pp. 123-137; Sin Tan, K., Choy Chong, S., Lin, B., Cyril Eze, U., Internet-based ICT adoption among SMEs: demographic versus benefits, barriers, and adoption intention (2010) Journal of Enterprise Information Management, 23 (1), pp. 27-55; Singh, R.K., Sharma, M.K., Prioritizing the alternatives for flexibility in supply chains (2014) Production Planning and Control: The Management of Operations, 25 (2), pp. 176-192; Song, M., Wang, S., Can employment structure promote environment-biased technical progress? (2016) Technological Forecasting and Social Change, 112, pp. 285-292; Song, M., Wang, S., Wu, K., Environment-biased technological progress and industrial land use efficiency in China's new normal (2016) Annuals of Operations Research, 268, pp. 425-440; Song, M., Cen, L., Zheng, Z., Fisher, R., Liang, X., Wang, Y., How would big data support societal development and environmental sustainability? Insights and practices (2017) Journal of Cleaner Production, 142, pp. 489-500; Stadtler, H., Kilger, C., Meyr, H., (2015) Supply Chain Management and Advanced Planning, , Springer, Berlin; Stephens, S., Supply chain operations reference model version 5.0: a new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; Sun, S., Wang, X., Zhang, Y., Sustainable traceability in the food supply chain: the impact of consumer willingness to pay (2017) Sustainability, 9 (6), p. 999; Supply Chain Council, Introduction to GreenSCOR: introducing environmental considerations to the CSOR model (2008) Proceedings of the North America Conference and Exposition March, pp. 17-19; Supply Chain Council, (2010) Supply Chain Operations Reference Model SCOR Version 10.0, , The Supply Chain Council, SCOR: the supply chain reference 0-615-20259-4 (binder), New York; (2012) Supply Chain Operations Reference Model (SCOR), , Supply Chain Council, New York: Version 11.0; Swaminathan, J.M., Tayur, S.R., Models for supply chains in EBusiness (2003) Management Science, 49 (10), pp. 1387-1406; Tarafdar, M., Qrunfleh, S., Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility (2017) International Journal of Production Research, 55 (4), pp. 925-938; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains: a literature review and a research agenda (2013) International Journal of Productivity and Performance Management, 62, pp. 782-804; Teimoury, E., Fathian, M., Chambar, I., Automation of the supply chain performance measurement based on multi-agent system (2013) International Journal of Agile Systems and Management, 6 (1), pp. 25-42; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Thanki, S., Thakkar, J., A quantitative framework for lean and green assessment of supply chain performance (2018) International Journal of Productivity and Performance Management, 67, pp. 366-400; Thunberg, M., Persson, F., Using the SCOR model's performance measurements to improve construction logistics (2014) Production Planning and Control: The Management of Operations, 25 (13-14), pp. 1065-1078; Torabi, S.A., Hassini, E., Jeihoonian, M., Fulfilment source allocation, inventory trans-shipment, and customer order transfer in E-tailing (2015) Transportation Research Part E: Logistics and Transportation Review, 7 (9), pp. 128-144; Tseng, M., Lim, M., Wong, W.P., Sustainable supply chain management: a closed-loop network hierarchical approach (2015) Industrial Management and Data Systems, 115 (3), pp. 436-461; Van Huijstee, M.M., Francken, M., Leroy, P., Partnerships for sustainable development: a review of current literature (2007) Journal of Environmental Sciences, 4 (2), pp. 75-89; Vazquez-Martinez, G.A., Gonzalez-Compean, J.L., Sosa-Sosa, V.J., Morales-Sandoval, M., Perez, J.C., CloudChain: a novel distribution model for digital products based on supply chain principles (2018) International Journal of Information Management, 39, pp. 90-103; Verdouw, C.N., Wolfert, J., Beulens, A.J.M., Rialland, A., Virtualization of food supply chains with the internet of things (2016) Journal of Food Engineering, 176, pp. 128-136; Wang, T.C., Lee, H.D., Developing A fuzzy TOPSIS approach based on subjective weights and objective weights (2009) Expert Systems with Applications, 36, pp. 8980-8985; Wang, J., Yue, H., Food safety pre-warning system based on data mining for a sustainable food supply chain (2017) Food Control, 73, pp. 223-229; Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteria decision-making methodology (2007) International Journal of Production Economics, 91 (1), pp. 1-15; Wisner, J.T., Leong, K.C.G., (2012) Principles of Supply Chain Management: A Balanced Approach, , South-Western Cengage Learning, Mason; Wognum, P.N., Bremmers, H., Trienekens, J.H., van der Vorst, J.G., Bloemhof, J.M., Systems for sustainability and transparency of food supply chains-Current status and challenges (2011) Advance Engineering Information, 25 (1), pp. 65-76; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15, pp. 25-51; Xiao, R., Cai, Z., Zhang, X., Anoptimisationapproachtoriskdecision-making of closed-looplogisticsbasedonSCORmodel (2012) Optimisation, 61 (10), pp. 1221-1251; Zhang, Y., Wang, W., Liu, S., Xie, G., Real-time shop-floor production performance analysis method for the internet of manufacturing things (2014) Advances in Mechanical Engineering, 6, p. 270749; Zhong, D.R.Y., Tan, P.K., Bhaskaran, P.G., Data-driven food supply chain management and systems (2017) Industrial Management and Data Systems, 117 (9), pp. 1779-1781; Zhu, S., Song, J., Hazen, B.T., Lee, K., Cegielski, C., How supply chain analytics enables operational supply chain transparency: an organizational information processing theory perspective (2018) International Journal of Physical Distribution and Logistics Management, 48 (1), pp. 47-68; Zitnick, C.L., Kanade, T., Maximum entropy for collaborative filtering (2004) ACM Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, pp. 636-643; Dutta, S., Westenhoefer, W., (2008) Logisticsnewsgoinggreenisnotaseasyasitall First appears, , Green SCOR Supply Chain Council, Cardiff; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: a case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Stadtler, H., Supply chain management: an overview (2015) Supply Chain Management and Advanced Planning with Supply Chain Operations Reference Model, , Springer, Berlin, Heidelberg},
correspondence_address1={Luthra, S.; Department of Mechanical Engineering, India; email: sunilluthra1977@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={17410398},
language={English},
abbrev_source_title={J. Enterp. Inf. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang20201741,
author={Wang, W. and Huang, L. and Zhu, Y. and Jiang, L. and Sahu, A.K. and Sahu, A.K. and Sahu, N.K.},
title={Decision support system toward evaluation of resilient supplier: A novel fuzzy gain-loss computational approach},
journal={Kybernetes},
year={2020},
volume={49},
number={6},
pages={1741-1765},
doi={10.1108/K-05-2019-0345},
note={cited By 17},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076824602&doi=10.1108%2fK-05-2019-0345&partnerID=40&md5=f1af85c6f1c74c4411e70881d4334b81},
affiliation={Key Laboratory of Coastal Disaster and Defence (Hohai University), Ministry of Education, Nanjing, China; School of Public Administration, Hohai University, Nanjing, China; College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing, China; Department of Mechanical Engineering, Madanapalle Institute of Technology and Science, Madanapalle, India; Department of Industrial and Production Engineering, Guru Ghasidas University, Bilaspur, India; College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing, China},
abstract={Purpose: Supplier evaluation is a part of logistic management. In the present era, resilient supply chain performance (RSCP) assessment of the vendor enterprise is respected as a hot topic. The purpose of this paper is to enable the managers to map the performance in percentage system and also enabling managers for identifying the weak indices-metrics, which need to be improved up to ideal or standard level and strong indices-metrics. Design/methodology/approach: The authors found two research gaps via a literature survey. The first research gap revealed that the performance of a resilient supplier is computed solely in terms of a fuzzy mathematical scale. The articles are not yet published, which could measure the RSCP in percentage. The second research gap argued about the mitigation of the multi-level hierarchical resilient vendor/supplier evaluation framework for materializing RSCP and identifying weak and strong performing indices-metrics. To compensate the both research gaps, the authors developed a novel fuzzy gain-loss evolutionary computational approach to assess the performance of a firm in percentage. Next, a revised ranking technique coupled with trapezoidal fuzzy set based fuzzy performance importance index is implemented on the framework to seek weak and strong indices-metrics. The performance loss of each metric using the ideal solution concept considering the attitude of decision makers is also revealed. Findings: The authors found the RSC performance of supplier firm 74 per cent, whereas performance loss 26 per cent, while actual performance is compared with standard fuzzy performance index (SFPI). Performance loss 26 per cent can be compensated by improving the performance of weak indices-metrics. Originality/value: The novelty of the paper is that the authors used the ideal solution concept to compute the SFPI and compare it with actual FPI for evaluating the gain and loss of resilient supplier firm in percentage and identify weak and strong indices so that managers can improve the performance of weak indices. The work possesses the significant for all organizations, as research work enables the managers to map and improve the RSC performance of any vendor firm in future. The presented work considers the case of an automobile parts supplier industry to validate the developed approach. © 2019, Emerald Publishing Limited.},
author_keywords={Disasters;  Fuzzy set theory;  Performance measurement;  Resilient supply chain performance measurement (RSCPM)},
keywords={Automotive industry;  Computational methods;  Decision making;  Decision support systems;  Managers;  Supply chains, Computational approach;  Design/methodology/approach;  Evaluation framework;  Fuzzy performance index;  Logistic management;  Supplier Evaluations;  Supplier industry;  Supply chain performance, Information analysis},
references={Allahviranloo, T., Saneifard, R., Defuzzification method for ranking fuzzy numbers based on center of gravity (2012) Iranian Journal of Fuzzy Systems, 9 (6), pp. 57-67; Azadeh, A., Soufi, H.R., Farahani, M.H., Abdollahi, M., Green-resilient supplier selection: an integrated approach (2013) Institute of Industrial Engineering (IIE) Conference, College of Engineering, , University of Tehran; Balcik, B., Beamon, B.M., Krejci, C.C., Muramatsu, K.M., Ramirez, M., Coordination in humanitarian relief chains: practices challenges and opportunities (2010) International Journal of Production Economics, 126 (1), pp. 22-34; Banyai, A., Banyai, T., Illes, B., Optimization of consignment-store-based supply chain with black hole algorithm (2017) Complexity, 12; Banyai, T., Tamas, P., Illes, B., Stankeviciutė, Z.A., Optimization of municipal waste collection routing: impact of industry 4.0 technologies on environmental awareness and sustainability (2019) Int. J. Environ. Res. Public Health, 16 (4); Bányai, T., Illes, B., Banyai, A., An integrated first mile and last mile supply approach (2018) Complexity, 2018; Bidhandi, R.A., Valmohammadi, C., Effects of supply chain agility on profitability (2017) Business Process Management Journal, pp. 1064-1082; Buckley, J.J., Fuzzy hierarchical analysis (1985) Fuzzy Sets and Systems, 17 (3), pp. 233-247; Chan, F.T.S., Kumar, N., Global supplier development considering risk factors using fuzzy extended AHP-based approach (2007) Omega, 35 (4), pp. 417-431; Chern, C.C., Chen, Y.L., Kung, L.C., A heuristic relief transportation planning algorithm for emergency supply chain management (2010) International Journal of Computer Mathematics, 87 (7), pp. 1638-1664; Chopra, S., Sodhi, M.S., Avoiding supply chain breakdown (2004) MIT Sloan Management Review, 46 (1), pp. 53-62; Chou, S.Y., Dat, L.Q., Yu, V.F., A revised method for ranking fuzzy numbers using maximizing set and minimizing set (2011) Computers and Industrial Engineering, pp. 1342-1348; Christopher, M., Peck, H., Building the resilient supply chain (2004) The International Journal of Logistics Management, 15 (2), pp. 1-13; Datta, P.P., Christopher, M., Allen, P., Agent-based modeling of complex production/distribution systems to improve resilience (2007) International Journal of Logistics Research and Applications, pp. 187-203; Datta, S., Sahu, A.K., Mahapatra, S.S., Green supply chain performance benchmarking using integrated IVFN-TOPSIS methodology (2013) International Journal of Process Management and Benchmarking, 3 (4), pp. 511-551; Datta, S., Sahu, A.K., Sahu, S.K., Mahapatra, S.S., Supply chain flexibility assessment and decision-making: a fuzzy intelligent approach (2015) International Journal of Business Excellence, 8 (6), pp. 675-699; De Boer, L., Labro, E., Morlacchi, P., A review of methods supporting supplier selection (2001) European Journal of Purchasing and Supply Management, 7 (2), pp. 75-89; Dubois, D., Prade, H., Ranking fuzzy numbers in the setting of possibility theory (1983) Information Sciences, 30 (3), pp. 183-224; Dubois, D., Prade, H., Recent models of uncertainty and imprecision as a basis for decision theory: toward less normative frameworks (1985) Intelligent Decision Support in Process Environment, pp. 3-24. , Spring-Verlag, New York, NY; Fahimnia, B., Davarzani, H., Eshragh, A., Planning of complex supply chains: a performance comparison of three meta-heuristic algorithms (2018) Computers and Operations Research, 89, pp. 241-252; Fiksel, J., Sustainability and resilience: toward a systems approach (2006) Sustainability Science and Practice and Policy, 2 (2), pp. 14-21; Fuller, T., (2012) Floodwaters Are Gone, but Supply Chain Issues Linger, , The New York, NY Times, New York, NY; Giunipero, L.C., Eltantawy, R.A., Securing the upstream supply chain: a risk management approach (2004) International Journal of Physical Distribution and Logistics Management, pp. 698-713; Goh, M., Lim, J.Y.S., Meng, F., A stochastic model for risk management in global supply chain networks (2007) European Journal of Operational Research, 182 (1), pp. 164-173; Gunasekaran, C.P., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Guoping, C., Xinqiu, Z., Research on supply chain resilience evaluation (2008) Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management; Haldar, A., Ray, A., Banerjee, D., Ghosh, S., A hybrid, MCDM model for resilient supplier selection (2012) International Journal of Management Science and Engineering Management, 7 (4), pp. 284-292; Haldar, A., Ray, A., Banerjee, D., Ghosh, S., An integrated approach for supplier selection (2012) Procedia Engineering, 38, pp. 2087-2107; Haldar, A., Ray, A., Banerjee, D., Ghosh, S., Resilient supplier selection under a fuzzy environment (2014) International Journal of Management Science and Engineering Management, 9 (2), pp. 147-156; Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.M., Tuominena, M., Risk management processes in supplier networks (2004) International Journal of Production Economics, 90 (1), pp. 47-58; Huang, L., Huang, J., Wang, W., The sustainable development assessment of reservoir resettlement based on a BP neural network (2018) International Journal of Environmental Research and Public Health, 15 (1), p. 146; Jajja, M.S.S., Kannan, V.R., Brah, S.A., Hassan, S.H., Linkages between firm innovation strategy, suppliers, product innovation, and business performance: insights from resource dependence theory (2017) International Journal of Operations and Production Management, pp. 1054-1075; Joseph, M., Sunil, S., Targeting a just-in-case supply chain for the inevitable next disaster (2002) Supply Chain Management Review, 6 (5), pp. 18-23; Karatzas, A., Johnson, M., Bastl, M., Manufacturer-supplier relationships and service performance in service triads (2017) International Journal of Operations and Production Management, pp. 950-969; Kern, D., Moser, R., Hartmann, E., Moder, M., Supply risk management: model development and empirical analysis (2012) International Journal of Physical Distribution and Logistics Management, pp. 60-82; Kleindorfer, P., Saad, G., Managing disruption risks in supply chains (2005) Production and Operations Management, pp. 53-68; Klibi, W., Martel, A., Guitouni, A., The design of robust value-creating supply chain networks: a critical review (2010) European Journal of Operational Research, 203 (2), pp. 283-293; Kumar, R.P., Ravi, V., Bankruptcy prediction in banks and firms via statistical and intelligent techniques-a review (2007) European Journal of Operational Research, pp. 1-28; Mahmoudi, A., Soheil, S.-N., Makui, A., Vakili, M.R., An extension on PROMETHEE based on the typical hesitant fuzzy sets to solve multi-attribute decision-making problem (2016) Kybernetes, 45 (8), pp. 1213-1231; Matarazzo, B., Munda, G., New approaches for the comparison of L-R fuzzy numbers: a theoretical and operational analysis (2001) Fuzzy Sets and Systems, 118 (3), pp. 407-418; Mehraliana, G., Gatari, A.R., Morakabati, M., Vatanpour, H., Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies (2012) Iranian Journal of Pharmaceutical Research, 11 (1), pp. 209-219; Micheli, G.J.L., Cagno, E., Giulio, A.D., Reducing the total cost of supply through risk-efficiency-based supplier selection in the EPC industry (2009) Journal of Purchasing and Supply Management, 15 (3), pp. 166-177; Micheli, G.J.L., Cagno, E., Zorzini, M., Supply risk management vs supplier selection to manage the supply risk in the EPC supply chain (2008) Management Research News, 31 (11), pp. 846-866; Mishra, S., Sahu, A.K., Datta, S., Mahapatra, S.S., Application of fuzzy integrated MULTI-MOORA method towards supplier/partner selection in agile supply chain (2015) International Journal of Operational Research, 22 (4), pp. 466-514; Nabernegg, S., Friedl, B.B., Munoz, P., Tietz, M., Vogel, J., National policies for global emission reductions: effectiveness of carbon emission reductions in international supply chains (2019) Ecological Economics, 158, pp. 146-157; Nitin, K.S., Atul, K.S., Anoop, K.S., Green supply chain management assessment under chains of uncertain indices: an intellectual approach (2018) Journal of Modeling in Management, 13 (4). , https://doi.org/10.1108/JM2-07-2017-0068; Peck, H., Drivers of supply chain vulnerability: an integrated framework (2005) International Journal of Physical Distribution and Logistics Management, pp. 210-232; Pettit, T.J., Fiksel, J., Croxton, K.L., Ensuring supply chain resilience: development of a conceptual framework (2011) Journal of Business Logistics, 31 (1), pp. 1-21; Ponomarov, S.Y., Holcomb, M.C., Understanding the concept of supply chain resilience (2009) The International Journal of Logistics Management, 20 (1), pp. 124-143; Revilla, E., Saenz, M.J., The impact of risk management on the frequency of supply chain disruptions: a configurational approach (2017) International Journal of Operations and Production Management, pp. 557-576; Sahu, N.K., Datta, S., Mahapatra, S.S., Performance evaluation of green supplier using generalized trapezoidal fuzzy numbers set (2012) National Conference on Emerging Challenges for Sustainable Business; Sahu, A.K., Sahu, A.K., Sahu, N.K., Performance estimation of G-L-a supply chain of individual firm under imperfect data’ for book (2017) Theoretical and Practical Advancements for Fuzzy System Integration, pp. 245-277. , IGI Global Publisher; Sahu, A.K., Sahu, N.K., Sahu, A.K., (2017) Fuzziness-A Mathematical Tool,” for Book’ Theoretical and Practical Advancements for Fuzzy System Integration, pp. 1-30. , IGI Global Publisher; Sahu, A.K., Sahu, A.K., Sahu, N.K., Benchmarking of advanced manufacturing machines using fuzzy-TOPSIS’ for book (2017) Theoretical and Practical Advancements for Fuzzy System Integration, pp. 309-350. , IGI Global Publisher; Sahu, N.K., Sahu, A.K., Sahu, A.K., Fuzzy-AHP: a boon in 3PL decision making process’ for book (2017) Theoretical and Practical Advancements for Fuzzy System Integration, pp. 97-125. , IGI Global Publisher; Sahu, A.K., Sahu, N.K., Sahu, A.K., Knowledge based decision support system for appraisement of sustainable partner under fuzzy cum non-fuzzy information (2018) Kybernetes, 47 (6). , https://doi.org/10.1108/K-01-2017-0020; Sahu, N.K., Sahu, A.K., Sahu, A.K., Cluster approach integrating weighted geometric aggregation operator to appraise industrial robot: knowledge based decision support system (2018) Kybernetes, 47 (3). , https://doi.org/10.1108/K-11-2016-0332; Sawik, T., Selection of resilient supply portfolio under disruption risks (2013) Omega, 41 (2), pp. 259-269; Secundo, G., Magarielli, D., Esposito, E., Passiante, G., Supporting decision-making in service supplier selection using a hybrid fuzzy extended AHP approach: a case study (2017) Business Process Management Journal, 23 (1), pp. 196-222; Sheffi, Y., (2005) The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage, , MIT Press, Cambridge, MA; Sheffi, Y., Building a resilient organization (2006) The Bridge Washington National, 36 (1), pp. 30-36; Sheffi, Y., (2007) Building a Resilient Organization the Bridge: Linking Engineering and Society, pp. 32-38. , National Academy of Engineering, Springer; Shemshadi, A., Toreihi, M., Shirazi, H., Tarokh, M.J., Supplier selection based on supplier risk: an ANP and fuzzy TOPSIS approach (2011) Journal of Mathematics and Computer Science, 2 (1), pp. 111-121; Soto, G.M., ‘Study on the calculation of the annual percentage rate of charge for consumer credit Agreements’ (2009) European Commission Directorate General Health and Consumer Protection, , Universidad De Murcia, Murcia; Stoltz, P.G., (2004) Building Resilience for Uncertain Times, , Wiley, Hoboken, NJ; Turkulainen, V., Kauppi, K., Nermes, E., Institutional explanations: missing link in operations management? Insights on supplier integration (2017) International Journal of Operations and Production Management, pp. 1117-1140; Viswanadham, N., Samvedi, A., Supplier selection based on supply chain ecosystem, performance and risk criteria (2013) International Journal of Production Research, 51 (21); Wagner, S., Neshat, N., Assessing the vulnerability of supply chains using graph theory (2010) International Journal of Production Economics, 126 (1), pp. 121-129; Waters, D., (2007) Supply Chain Risk Management: Vulnerability and Resilience in Logistics, , Kogan Page, Philadelphia; Wang, W., Huang, L., Liang, X., On simulation-based reliability of complex emergency logistics network in post-accident rescue (2018) International Journal of Environmental Research and Public Health, 15 (1), p. 79; Wang, W., Chen, J., Liu, Q., Guo, Z., Green project planning with realistic multi-objective consideration in developing sustainable port (2018) Sustainability, p. 2385; Wu, T., Liu, X., An interval type-2 fuzzy ANP approach to evaluate enterprise technological innovation ability (2016) Kybernetes, 45 (9), pp. 1486-1500; Xu, X., Liu, J., Li, H., Analysis of subway station capacity with the use of queueing theory (2014) Transportation Research Part C: Emerging Technologies, 38, pp. 28-43; Yu, Q., Hou, F., An approach for green supplier selection in the automobile manufacturing industry (2016) Kybernetes, 45 (4), pp. 571-588; Tahriri, F., Osman, M.R., Ali, A., Mohd, R., A review of supplier selection methods in manufacturing industries (2008) Suranaree Journal of Science and Technology, pp. 201-208; Sahu, A.K., Sahu, A.K., Sahu, N.K., Appraise the economic values of logistic handling system under mixed information’ for book (2017) Theoretical and Practical Advancements for Fuzzy System Integration, pp. 278-308. , IGI Global Publisher},
correspondence_address1={Huang, L.; School of Public Administration, China; email: lily8214@126.com},
publisher={Emerald Group Holdings Ltd.},
issn={0368492X},
language={English},
abbrev_source_title={Kybernetes},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Fri2020,
author={Fri, M. and Douaioui, K. and Tetouani, S. and Mabrouki, C. and Semma, E.A.},
title={A DEA-ANN framework based in Improved Grey Wolf Algorithm to evaluate the performance of container terminal.},
journal={IOP Conference Series: Materials Science and Engineering},
year={2020},
volume={827},
number={1},
doi={10.1088/1757-899X/827/1/012040},
art_number={012040},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086719158&doi=10.1088%2f1757-899X%2f827%2f1%2f012040&partnerID=40&md5=a4871887eaac050221c0d753aa43a25d},
affiliation={LMII-Faculty of Sciences and Technology, Hassan 1st University, PO Box 577, Settat, Morocco; CELOG-ESITH, PO Box 7731 OULFA, Casablanca, Morocco},
abstract={Managing the performance of port container terminals is one of the major challenges in the supply chain management. In response to this challenge, we propose a new framework to assess managers in evaluating the global performance of operations in port container terminals. The new framework integrates the Data Envelopment Analysis (DEA) and Artificial Neural Network (ANN). The DEA is used to compute the efficient score of the system. In ANN, we use the improved grey wolf optimizer based on Levy's flights to improve learning. In order to prove the efficiency of our model, we apply the framework in 2 ports container terminal: Tangier Med Port and Casablanca Port. The result is compared to standard algorithms and outperforms the cited algorithm in order to avoid local minima. The new trainer improved grey wolf optimizer is also evaluated using four known classification datasets and on three approximation functions datasets. © 2020 Published under licence by IOP Publishing Ltd.},
author_keywords={Artificial Neural Network (ANN);  Data Envelopment Analysis (DEA);  Levy Flights;  Performance Measurement System (PMS);  Port Container Terminal},
references={Douaioui, K., Fri, M., Mabrouki, C., Semma, E.A., (2018) 2018 4th International Conference on Logistics Operations Management (GOL), pp. 1-6; Bentaleb, F., Mabrouki, C., Semma, A., Key Performance Indicators Evaluation and Performance Measurement in Dry Port-Seaport System: A Multi Criteria Approach (2015) Journal of ETA Maritime Science, 3 (2), pp. 97-116; Bichou, K., Gray, R., A logistics and supply chain management approach to port performance measurement (2004) Maritime Policy & Management, 31 (1), pp. 47-67; Chul-Hwan, H., (2018) Assessing the Impacts of Port Supply Chain Integration on Port Performance; Sea Ports, E., (2012) PPRISM Port PeRformance Indicators: Selection and Measurement Project Executive Report (PPRISM WP4 D4.2); Fri, M., Fedouaki, F., Douaioui, K., Mabrouki, C., Semma, E.A., Supply Chain Performance Evaluation Models, State-of-the-Art and Future Directions (2019) International Journal of Engineering and Advanced Technology, 9 (1), pp. 6336-6347; Fri, M., Douaioui, K., Lamii, N., Mabrouki, C., Semma, E.A., Ezziyyani, M., (2020) Advanced Intelligent Systems for Sustainable Development (AI2SD'2019), 1104, pp. 1-12; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Mirjalili, S., Mirjalili, S.M., Lewis, A., Grey Wolf Optimizer (2014) Advances in Engineering Software, 69, pp. 46-61; Mirjalili, S., Mirjalili, S.M., Lewis, A., Let a biogeography-based optimizer train your Multi-Layer Perceptron (2014) Information Sciences, 269, pp. 188-209},
correspondence_address1={Fri, M.; LMII-Faculty of Sciences and Technology, PO Box 577, Morocco; email: frimouhsene@gmail.com},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Abidi2020125,
author={Abidi, H. and de Leeuw, S. and Dullaert, W.},
title={Performance management practices in humanitarian organisations},
journal={Journal of Humanitarian Logistics and Supply Chain Management},
year={2020},
volume={10},
number={2},
pages={125-168},
doi={10.1108/JHLSCM-05-2019-0036},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083529034&doi=10.1108%2fJHLSCM-05-2019-0036&partnerID=40&md5=1486ad11cb329598410aa7611883bf2a},
affiliation={FOM-ILD, Essen, Germany; Nottingham Business School, Nottingham Trent University, Nottingham, United Kingdom; Department of Supply Chain Analytics, Vrije Universiteit Amsterdam, Amsterdam, Netherlands},
abstract={Purpose: We examine how design and implementation practices for supply chain performance management that have proven successful in commercial organisations apply to humanitarian organisations (HOs) to guide the process of designing and implementing performance management in humanitarian organisations. Design/methodology/approach: We identify from the literature ten successful practices regarding the design and implementation of supply chain performance management in commercial businesses. We apply these, using action research over a four-year period, at Médecins sans Frontières (MSF) Belgium and draw conclusions from this. Findings: We find that tools and techniques, such as workshops and technical sheets, are essential in designing and implementing supply chain performance measurement projects at HOs. Furthermore, making a link to an IT project is crucial when implementing performance measurement systems at HOs. Overall, our case study shows that performance management practices used in business can be applied and are relevant for humanitarian supply chains. Originality/value: Previous research has argued that there are few empirical studies in the domain of performance management at humanitarian organisations. To the best of our knowledge, this paper is the first to provide a longitudinal understanding of the design and implementation of supply chain performance measurement at HOs. © 2020, Hella Abidi, Sander de Leeuw and Wout Dullaert.},
author_keywords={Action research;  Empirical study;  Humanitarian supply chain;  Performance management practices;  Performance measurement and management},
references={Abidi, H., Scholten, K., Applicability of performance measurement systems to humanitarian supply chains (2015) Humanitarian Logistics and Sustainability, pp. 235-260. , Klumpp, M., de Leeuw, S., Regattieri, A. and de Souza, R. (Ed.), Springer, Cham; Abidi, H., de Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: a systematic literature review (2014) Supply Chain Management: International Journal, 19 (5-6), pp. 592-608; Abidi, H., Dullaert, W., De Leeuw, S., Lysko, D., Klumpp, M., Strategic partner evaluation criteria for logistics service provider networks (2019) International Journal of Logistics Management, 30 (2), pp. 438-466; Ahmed, W., Najmi, A., Khan, F., Aziz, H., Developing and analyzing framework to manage resources in humanitarian logistics (2019) Journal of Humanitarian Logistics and Supply Chain Management, 9 (2), pp. 270-291; Anjomshoae, A., Hassan, A., Wong, K., An integrated AHP-based scheme for performance measurement in humanitarian supply chains (2019) International Journal of Productivity and Performance Management, 68 (5), pp. 938-957; Anjomshoae, A., Hassan, A., Kunz, N., Wong, K., de Leeuw, S., Toward a dynamic balanced scorecard model for humanitarian relief organizations' performance management (2017) Journal of Humanitarian Logistics and Supply Chain Management, 7 (2), pp. 194-218; Atkinson, M., Developing and using a performance management framework: a case study (2012) Measuring Business Excellence, 16 (3), pp. 47-56; Baharmand, H., Comes, T., Lauras, M., Defining and measuring the network flexibility of humanitarian supply chains: insights from the 2015 Nepal earthquake (2017) Annals of Operations Research, 283 (1), pp. 961-1000; Balcik, B., Beamon, B.M., Facility location in humanitarian relief (2008) International Journal of Logistics, 11 (2), pp. 101-121; Balcik, B., Beamon, B.M., Krejci, C.C., Muramatsu, K.M., Ramirez, M., Coordination in humanitarian relief chains: practices, challenges and opportunities (2010) International Journal of Production Economics, 126 (1), pp. 22-34; Balcik, B., Bozkir, C.D.C., Kundakcioglu, O.E., A literature review on inventory management in humanitarian supply chains (2016) Surveys in Operations Research and Management Science, 21 (2), pp. 101-116; Bals, L., Tate, W.L., Sustainable supply chain design in social businesses: advancing the theory of supply chain (2018) Journal of Business Logistics, 39 (1), pp. 57-79; Battista, P., Verhun, D., Customer relationship management: the promise and the reality (2000) CMA Management, 74 (4), pp. 34-37; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B.M., Humanitarian relief chains: issues and challenges (2004) Proceedings of the 34th International Conference on Computers and Industrial Engineering, 34, pp. 77-82. , University of Washington, Seattle, WA; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Beamon, B.M., Kotleba, S.A., Inventory management support systems for emergency humanitarian relief operations in South Sudan (2006) The International Journal of Logistics Management, 17 (2), pp. 187-212; Behl, A., Dutta, P., Humanitarian supply chain management: a thematic literature review and future directions of research (2019) Annals of Operations Research, 283 (1), pp. 1001-1044; Bititci, U.S., Mendibil, K., Nudurupati, S., Turner, T., Garengo, P., The interplay between performance measurement, organizational culture and management styles (2004) Measuring Business Excellence, 8 (3), pp. 28-41; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Production Management, 20 (6), pp. 692-704; Bottani, E., Bertolini, M., Rizzi, A., Romagnoli, G., Monitoring on-shelf availability, out-of-stock and product freshness through RFID in the fresh food supply chain (2017) International Journal of RF Technologies, 8 (1-2), pp. 33-55; Bourne, M., Kennerley, M., Franco-Santos, M., Managing through measures: a study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), pp. 373-395; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: an action research (2011) International Journal of Production Economics, 133 (2), pp. 751-760; Cavalluzzo, K.S., Ittner, C.D., Implementing performance measurement innovations: evidence from government (2004) Accounting, Organizations and Society, 29 (3), pp. 243-267; Coghlan, D., Brannick, T., (2010) Doing Action Research in Your Own Organization, , 3rd ed., Sage, London; Coughlan, P., Coghlan, D., Action research for operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 220-240; Cozzolino, A., (2012) Humanitarian Logistics: Cross-sector Cooperation in Disaster Relief Management, , Springer Science & Business Media; da Mota Pedrosa, A., Customer integration during innovation development: an exploratory study in the logistics service industry (2012) Creativity and Innovation Management, 21 (3), pp. 263-276; De Leeuw, S., Van den Berg, J.P., Improving operational performance by influencing shopfloor behavior via performance management practices (2011) Journal of Operations Management, 29 (3), pp. 224-235; De Waal, A., Kourtit, K., Performance measurement and management in practice: advantages, disadvantages and reasons for use (2013) International Journal of Productivity and Performance Management, 62 (5), pp. 446-473; De Waal, A.A., Counet, H., Lessons learned from performance management systems implementations (2009) International Journal of Productivity and Performance Management, 58 (4), pp. 367-390; D'Haene, C., Verlinde, S., Macharis, C., Measuring while moving (humanitarian supply chain performance measurement – status of research and current practice) (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (2), pp. 146-161; Dubey, R., Luo, Z., Gunasekaran, A., Akter, S., Hazen, B.T., Douglas, M.A., Big data and predictive analytics in humanitarian supply chains: enabling visibility and coordination in the presence of swift trust (2018) International Journal of Logistics Management, 29 (2), pp. 485-512; Eltantawy, R., Paulraj, A., Giunipero, L., Naslund, D., Thute, A.T., Towards supply chain coordination and productivity in a three echelon supply chain: action research study (2015) International Journal of Operations and Production Management, 35 (6), pp. 895-924; Fagundes, L.D., Amorim, E.S., da Silva Lima, R., Action research in reverse logistics for end-of-life tire recycling (2017) Systemic Practice and Action Research, 30 (5), pp. 553-568; Ferreira, A., Otley, D., The design and use of performance management systems: an extended framework for analysis (2009) Management Accounting Research, 20 (4), pp. 263-282; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations and Production Management, 27 (8), pp. 784-801; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: a review of their consequences and a framework for research (2012) Management Accounting Research, 23 (2), pp. 79-119; Garengo, P., Biazzo, S., Bititci, U., Performance measurement systems in SMEs: a review and a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Gatignon, A., Van Wassenhove, L.N., Charles, A., The Yogyakarta earthquake: humanitarian relief through IFRC's decentralized supply chain (2010) International Journal of Production Economics, 126 (1), pp. 102-110; Gralla, E., Goentzel, J., Van de Walle, B., Understanding the information needs of field-based decision-makers in humanitarian response to sudden onset disasters (2015) ISCRAM 2015 Conference Proceedings – 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway, , Palen, L. Buscher, M., Comes, T. and Hughes, A., and,(Eds), University of Agder (UiA; Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S., Assimilation of big data and predictive analytics for supply chain and organizational performance: a resource-based view perspective (2017) Journal of Business Research, 70, pp. 308-317; Gutierrez, D.M., Scavarda, L.F., Fiorencio, L., Martins, R.A., Evolution of the performance measurement system in the Logistics Department of a broadcasting company: an action research (2015) International Journal of Production Economics, 160, pp. 1-12; Haavisto, I., Goentzel, J., Measuring humanitarian supply chain performance in a multi-goal context (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (3), pp. 300-324; Holguín-Veras, J., Pérez, N., Jaller, M., Van Wassenhove, L.N., Aros-Vera, F., On the appropriate objective function for post-disaster humanitarian logistics models (2013) Journal of Operations Management, 31 (5), pp. 262-280; Huang, G.Q., Zhang, X.Y., Liang, L., Towards integrated optimal configuration of platform products, manufacturing processes, and supply chains (2005) Journal of Operations Management, 23 (3-4), pp. 267-290; Johnson, H., Johansson, M., Andersson, M., Barriers to improving energy efficiency in short sea shipping: an action research case study (2014) Journal of Cleaner Production, 66, pp. 317-327; Kaplan, R.S., Norton, D.P., (2001) The Strategy-Focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment, , Harvard Business Press, Harvard; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Kovács, G., Spens, K.M., Humanitarian logistics and supply chain management: the start of a new journal (2011) Journal of Humanitarian Logistics and Supply Chain Management, 1 (1), pp. 5-14; Richey, R.G., Kovács, G., Spens, K., Identifying challenges in humanitarian logistics (2009) International Journal of Physical Distribution & Logistics Management, 39 (6), pp. 506-528; Kueng, P., Meier, A., Wettstein, T., Performance measurement systems must Be engineered (2001) Communications of the Association for Information Systems, 7 (3), pp. 2-26; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-456; Lu, Q., Goh, M., De Souza, R., A SCOR framework to measure logistics performance of humanitarian organizations (2016) Journal of Humanitarian Logistics and Supply Chain Management, 6 (2), pp. 222-239; Marchand, M., Raymond, L., Researching performance measurement systems: an information systems perspective (2008) International Journal of Operations and Production Management, 28 (7), pp. 663-686; Meekings, A., Unlocking the potential of performance measurement: a practical implementation guide (1995) Public Money and Management, 15 (4), pp. 5-12; Melnyk, S.A., Bititci, U.S., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Micheli, P., Mura, M., Agliati, M., Exploring the roles of performance measurement systems in strategy implementation: the case of a highly diversified group of firms (2011) International Journal of Operations and Production Management, 31 (10), pp. 1115-1139; Najmi, A., Makui, A., A conceptual model for measuring supply chain's performance (2012) Production Planning and Control, 23 (9), pp. 694-706; National Performance Management Advisory Commission, (2010) A performance management framework for state and local government: from measurement and reporting to management and improving, , http://www.gfoa.org/national-performance-management-advisory-commission-releases-framework-state-and-local-governments, accessed, 22 December 2017; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International journal of operations & production management, 25 (12), pp. 1228-1263; Neely, A.D., Adams, C., Kennerley, M., (2002) The performance prism: The scorecard for measuring and managing business success, , London: Prentice Hall Financial Times; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: a structured approach (1997) International Journal of Operations and Production Management, 17 (11), pp. 1131-1152; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Oloruntoba, R., Gray, R., Humanitarian aid_ an agile supply chain? (2006) Supply Chain Management: An International Journal, 11 (2), pp. 115-120; Oloruntoba, R., Gray, R., Customer service in emergency relief chains (2009) International Journal of Physical Distribution & Logistics Management, 39 (6), pp. 486-505; Pateman, H., Hughes, K., Cahoon, S., Humanizing humanitarian supply chains: a synthesis of key challenges (2013) The Asian Journal of Shipping and Logistics, 29 (1), pp. 81-102; Pettit, S.J., Beresford, A.K.C., Port development: from gateways to logistics hubs (2009) Maritime Policy & Management, 36 (3), pp. 253-267; Saaty, T.L., (1980) The Analytic Hierarchy Process, , McGraw Hill. International, New York; Saputra, T., Pots, O., de Smidt-Destombes, K.S., de Leeuw, S., The impact of Mean Time between Disasters on inventory pre-positioning strategy (2015) Disaster Prevention and Management, 24 (1), pp. 115-131; Scholten, K., Sharkey Scott, P., Fynes, B., (2009) Supply Chain Management Concepts and Humanitarian Aid Agencies–An Exploratory Study; Schön, A., Al-Saadi, S., Grubmueller, J., Schumann-Bölsche, D., Developing a camp performance indicator system and its application to Zaatari, Jordan (2018) Journal of Humanitarian Logistics and Supply Chain Management, 8 (3), pp. 346-373; Schreyer, M., (2008) Entwicklung und Implementierung von Performance Measurement Systemen, 56 (21), pp. 6809-6827. , Springer, Wiesbaden; Schulz, S., Heigh, F.I., Logistics performance management in action within a humanitarian organization (2009) Management Research News, 32, pp. 1038-1049; Singh, R.K., Gupta, A., Gunasekaran, A., Analysing the interaction of factors for resilient humanitarian supply chain (2018) Journal International Journal of Production Research, 56 (21), pp. 6809-6827; Sousa, C.A.A., Nijs, W.F., Hendriks, P.H.J., Secrets of the beehive: performance management in university research organizations (2010) Human Relations, 63 (9), pp. 1439-1460; Stricker, N., Echsler Minguillon, F., Lanza, G., Selecting key performance indicators for production with a linear programming approach (2017) International Journal of Production Research, 55 (19), pp. 1-13; (2007) SCOR 8.0 Overview Booklet, , http://www.apics.org/apics-for-business/frameworks/scor, Supply Chain Council Editions, 2000: accessed, 22 December 2017; Tatham, P.H., Hughes, K., Humanitarian logistics metrics: where we are and how we might improve (2011) Humanitarian Logistics: Meeting the Challenge of Preparing for and Responding to Disasters, pp. 65-84. , Tatham, M.C. and Tatham, P. (Ed.), Kogan Page, London; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Thompson, F., Perry, C., Generalising results of an action research project in one work place to other situations: principles and practice (2004) European Journal of Marketing, 38 (3-4), pp. 401-417; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) International Journal of Operations and Production Management, 31 (12), pp. 1287-1310; Ukko, J., Tenhunen, J., Rantanen, H., Performance measurement impacts on management and leadership: perspectives of management and employees (2007) International Journal of Production Economics, 110 (1-2), pp. 39-51; Vega, D., Case studies in humanitarian logistics research (2018) Journal of Humanitarian Logistics and Supply Chain Management, 8 (2), pp. 134-152; Wakchaure, S.S., Jha, K.N., Determination of bridge health index using analytical hierarchy process (2012) Construction Management and Economics, 30 (2), pp. 133-149; Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How big data can make big impact: findings from a systematic review and a longitudinal case study (2015) International Journal of Production Economics, 165, pp. 234-246; Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R., Childe, S.J., Big data analytics and firm performance: Effects of dynamic capabilities (2017) Journal of Business Research, 70, pp. 356-365; Wouters, M., Wilderom, C., Developing performance-measurement systems as enabling formalization: a longitudinal field study of a logistics department (2008) Accounting, Organizations and Society, 33 (4), pp. 488-516; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning and Control The Management of Operations, 20 (8), pp. 678-690; Brannick, T., Coghlan, D., (2010) Doing Action Research in Your Own Organization, , Sage, Thousand Oaks; Da Mota Pedrosa, A., Naslund, D., Jasmand, C., Logistics case study based research: towards higher quality (2012) International Journal of Physical Distribution and Logistics Management, 42 (3), pp. 275-295},
correspondence_address1={de Leeuw, S.; Nottingham Business School, United Kingdom; email: sander.deleeuw@wur.nl},
publisher={Emerald Group Holdings Ltd.},
issn={20426747},
language={English},
abbrev_source_title={J. Humanit. Logist. Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Marimin2020,
author={Marimin and Djatna, T. and MacHfud and Asrol, M. and Papilo, P. and Taufik, B. and Darmawan, M.A.},
title={Supply chain performance measurement and improvement of palm oil agroindustry: A case study at Riau and Jambi Province},
journal={IOP Conference Series: Earth and Environmental Science},
year={2020},
volume={443},
number={1},
doi={10.1088/1755-1315/443/1/012056},
art_number={012056},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083026513&doi=10.1088%2f1755-1315%2f443%2f1%2f012056&partnerID=40&md5=dd4c34f56212e331f3172cd0930cb3a9},
affiliation={Department of Agro-industrial Technology, Faculty of Agricultural Technology, IPB University, Bogor Agricultural University, Bogor, Indonesia},
abstract={Supply chain performance measurement and improvement is required to control the performance and define a strategy to improve agroindustry's competitiveness. The objectives of the research were to identify supply chain configuration and mechanism, and to analyze the supply chain performance and define the supply chain performance improvement for palm oil agroindustry. The research was conducted at Riau Province and Jambi Province as the central palm oil production in Indonesia. The Supply Chain Operation Reference (SCOR) and Analytical Hierarchy Process (AHP) were applied to analyze the supply chain performance and the performance improvement strategies were formulated descriptively with expert discussion. The results showed that the average performance of farmers, traders and refinery at Riau Province were 77.77%, 74.60% and 79.20%. The average performance of farmers, traders and refinery at Riau Province were 72.66%, 75.39% and 83.11%. This research had succeeded to define the strategies to improve palm oil supply chains at Riau Province and Jambi Province. The strategies were focused on good agricultural and good handling practice, specifying the palm oil prices refractions based on quality and implementing the information system to improve the supply chain performance. © Published under licence by IOP Publishing Ltd.},
keywords={Agricultural robots;  Agriculture;  Commerce;  Refining;  Supply chains, Analytical Hierarchy Process;  Improvement strategies;  Oil production;  Oil supply chain;  Supply chain configuration;  Supply chain operation references;  Supply chain performance;  Supply chain performance measurements, Palm oil},
references={(2018) Statistik Kelapa Sawit Indonesia 2017, , BPS Jakarta (ID) : Badan Pusat Statistik); Asrol, M., Marimin, Machfud, Supply chain performance measurement and improvement for sugarcane agro-industry (2017) Int. J. Supply Chain Manag, 6, pp. 8-21; Agami, N., Saleh, M., Rasmy, M., A hybrid dynamic framework for supply chain performance improvement (2011) IEEE Syst. J, 6, pp. 469-478; Asrol, M., Mitigasi risiko dan peningkatan nilai tambah pada rantai pasok agroindustri gula tebu (2018) Supply Chain Management: Strategy, Planning and Operation, , Chopra S and Meindl P 2013 (New York (US) : Pearson); Marimin, M., Maghfiroh, N., (2010) Aplikasi Teknik Pengambilan Keputusan Dalam Manajemen Rantai Pasok (Application for Decision Making Technique in Supply Chain Management), , (Bogor (ID) : IPB Pr); Purwani, T., Nurcholis, L., Supply chain performance improvement strategy through quality of synergy in the automative components industry (2016) J. Din. Manaj, 7, pp. 32-44; Qi, Y., Huo, B., Wang, Z., Yeung, H.Y.J., The impact of operations and supply chain strategies on integration and performance (2017) Int. J. Prod. Econ, 185, pp. 162-174; Fonseca, L.M., Lima, V.M., Impact of supplier management strategies on the organizational performance of iso 9001 certified organizations (2015) Qual. Innov. Prosper, 19, pp. 32-54; Bittencourt, F., Rabelo, R.J., A systematic approach for VE partners selection using SCOR model and the AHP method (2008) Collab. Method Their Breed. Environ, 186, pp. 99-108; Kasi, V., Systemic assessment of SCOR for modeling supply chains (2005) Proceedings of the 38th Annual Hawaii International Conference on System Sciences, pp. 1-10; Van Der Vorst, J., Performance measurement in agri-food supply chain networks, an overview (2006) Quantifying Agri-food Supply Chain, pp. 15-26; (2012) Supply Chain Operations Reference Model Revision 11. 0, , SCC New York (US) : Supply Chain Council); Palma-Mendoza, J.A., Analytical hierarchy process and SCOR model to support supply chain re-design (2014) Int. J. Inf. Manage, 34, pp. 634-638; Monczka, R.M., Handfield, R., Giunipero, L.C., Petterson, J.L., (2016) Purchasing and Supply Chain Management, , Sixth Edition; (2017) Statistik Perkebunan Indonesia Kelapa Sawit 2015-2017, , Dirjenbun (Jakarta (ID) ); Asrol, M., Marimin, M., Machfud, M., Yani, M., Soft system methodology framework for fair and balanced of risk and value-added distribution in sugarcane agroindustry supply chains (2018) Int. J. Supply Chain Manag, 7, pp. 476-493; Yolandika, C., Nurmalina, R., Suharno, S., Rantai pasok brokoli di kecamatan lembang kabupaten bandung barat dengan pendekatan food supply chain networks supply chain of broccoli in lembang, west bandung district with food supply chain networks approach (2016) Pertan. Terap, 16, pp. 155-162; Marimin, M., Safriyana, S., Evaluation of palm oil supply chain ' s performance, added value, and performance improvement : A case study at X Co (2018) IOP Conference Series: Earth and Environmental Science; Lee, J.S.H., Ghazoul, J., Obidzinski, K., Koh, L.P., Oil palm smallholder yields and incomes constrained by harvesting practices and type of smallholder management in Indonesia (2014) Agron. Sustain. Dev, 34, pp. 501-513; Alwarritzi, W., Nanseki, T., Chomei, Y., Analysis of the factors influencing the technical efficiency among oil palm smallholder farmers in Indonesia (2015) Procedia Environ. Sci, 28, pp. 630-638; Hwang, Y.D., Lin, Y.C., Lyu, J., The performance evaluation of scor sourcing process\-The case study of Taiwan's tft-lcd industry (2008) Int. J. Prod. Econ, 115, pp. 411-423; Jamehshooran, B.G., Shaharoun, A.M., Haron, H.N., Assessing supply chain performance through applying the scor model (2015) Int. J. Supply Chain Manag, 4, pp. 1-11},
editor={Nurhadi B.},
publisher={Institute of Physics Publishing},
issn={17551307},
language={English},
abbrev_source_title={IOP Conf. Ser. Earth Environ. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Gawankar20201574,
author={Gawankar, S.A. and Gunasekaran, A. and Kamble, S.},
title={A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context},
journal={International Journal of Production Research},
year={2020},
volume={58},
number={5},
pages={1574-1593},
doi={10.1080/00207543.2019.1668070},
note={cited By 93},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073980601&doi=10.1080%2f00207543.2019.1668070&partnerID=40&md5=e10493e7cc66f09456958239dc484d40},
affiliation={Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, India; School of Business and Public Administration, California State University, Bakersfield, CA, United States},
abstract={The use of digital technologies such as ‘internet of things’ and ‘big data analytics’ have transformed the traditional retail supply chains into data-driven retail supply chains referred to as ‘Retail 4.0.’ These big data-driven retail supply chains have the advantage of providing superior products and services and enhance the customers shopping experience. The retailing industry in India is highly competitive and eager to transform into the environment of retail 4.0. The literature on big data in the supply chain has mainly focused on the applications in manufacturing industries and therefore needs to be further investigated on how the big data-driven retail supply chains influence the supply chain performance. Therefore, this study investigates how the retailing 4.0 context in India is influencing the existing supply chain performance measures and what effect it has on the organisational performance. The findings of the study provide valuable insights for retail supply chain practitioners on planning BDA investments. Based on a survey of 380 respondents selected from retail organisations in India, this study uses governance structure as the moderating variable. Implications for managers and future research possibilities are presented. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={big data;  governance structure;  organisational performance;  performance measures;  retail 4.0;  supply chain management},
keywords={Big data;  Data Analytics;  Information management;  Investments;  Supply chain management;  Surveys, Digital technologies;  Governance structures;  Manufacturing industries;  Organisational performance;  Performance measure;  Products and services;  retail 4.0;  Supply chain performance, Sales},
references={Ahimbisibwe, A., The Influence of Contractual Governance Mechanisms, Buyer–Supplier Trust, and Supplier Opportunistic Behavior on Supplier Performance (2014) Journal of African Business, 15 (2), pp. 85-99; Albort-Morant, G., Leal-Millán, A., Cepeda-Carrión, G., The Antecedents of Green Innovation Performance: A Model of Learning and Capabilities (2016) Journal of Business Research, 69 (11), pp. 4912-4917; Anand, N., Grover, N., Measuring Retail Supply Chain Performance: Theoretical Model Using key Performance Indicators (KPIs) (2015) Benchmarking: An International Journal, 22 (1), pp. 135-166; Anbanandam, R., Banwet, D.K., Shankar, R., Evaluation of Supply Chain Collaboration: A Case of Apparel Retail Industry in India (2009) International Journal of Productivity and Performance Management, 60 (2), pp. 82-98; Arranz, N., Arroyabe, M.F., Fdez. de Arroyabe, J.C., Alliance-Building Process as Inhibiting Factor for SME International Alliances (2016) British Journal of Management, 27 (3), pp. 497-515; Asrol, M., Marimin, M., Machfud, M., Supply Chain Performance Measurement and Improvement for Sugarcane Agro-Industry (2017) International Journal of Supply Chain Management, 6 (3), pp. 8-21; Ataseven, C., Nair, A., Assessment of Supply Chain Integration and Performance Relationships: A Meta-analytic Investigation of the Literature (2017) International Journal of Production Economics, 185, pp. 252-265; Banerjee, M., Development of Omnichannel in India: Retail Landscape, Drivers and Challenges (2019) Exploring Omnichannel Retailing, pp. 115-137. , Piotrowicz W., Cuthbertson R., (eds), Cham: Springer,. edited by; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Beecham, R., (2016), https://www.intel.in/content/www/in/en/retail/solutions/documents/future-retail-through-iot-paper.html, The Future of Retail Through the Internet of Things; Bird, A., Dauriz, L., Edelman, D.C., Kullmann, M., The Digital Retail (R) Evolution. Retail Marketing and Branding: A Definitive Guide to Maximizing ROI (2012) Retail Marketing and Branding, pp. 179-196. , Piotrowicz W., Cuthbertson R., (eds), 2nd ed, and,., edited by; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance Measurement: Challenges for Tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Blome, C., Schoenherr, T., Eckstein, D., The Impact of Knowledge Transfer and Complexity on Supply Chain Flexibility: A Knowledge-based View (2014) International Journal of Production Economics, 147, pp. 307-316; Bocquet, R., Mothe, C., Can a Governance Structure Foster Cluster Ambidexterity through Knowledge Management? An Empirical Study of Two French SME Clusters (2015) Knowledge Management Research & Practice, 13 (3), pp. 329-343; Bressanelli, G., Perona, M., Saccani, N., Challenges in Supply Chain Redesign for the Circular Economy: A Literature Review and a Multiple Case Study (2018) International Journal of Production Research, pp. 1-28; Browne, M.W., MacCallum, R.C., Kim, C.-T., Andersen, B.L., Glaser, R., When fit Indices and Residuals are Incompatible (2002) Psychological Methods, 7, pp. 403-421; Cao, M., Zhang, Q., Supply Chain Collaboration: Impact on Collaborative Advantage and Firm Performance (2011) Journal of Operations Management, 29, pp. 163-180; Carmines, E.G., McIver, J.P., Analyzing Models with Unobserved Variables: Analysis of Covariance Structures (1981) Social Measurement: Current Issues, pp. 65-115. , Bohrnstedt G.W., Borgatta E.F., (eds), Beverly Hills: Sage Publications, Inc, and,. edited by; Chen, I.J., Paulraj, A., Towards a Theory of Supply Chain Management: The Constructs and Measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Cho, E., Making Reliability Reliable A Systematic Approach to Reliability Coefficients (2016) Organizational Research Methods, 19 (4), pp. 1-32; Chutia, L.J., Baruah, P., Change Initiatives in the Retail Sector of India: A Literature Study (2014) Global Journal of Commerce and Management Perspective, 3 (5), pp. 35-40; Cooper, R.B., Zmud, R.W., Information Technology Implementation Research: A Technological Diffusion Approach (1990) Management Science, 36 (2), pp. 123-139; Creswell, J., (1994) Research Design: Qualitative and Quantitative Approach, , London: Sage Publication; Deshpande, A., Supply Chain Management Dimensions, Supply Chain Performance and Organizational Performance: an Integrated Framework (2012) International Journal of Business and Management, 7 (8), pp. 1833-1819; Ekambaram, A., (2017), https://www.analyticsindiamag.com/data-analytics-the-next-big-disruptor-in-retail, Data Analytics: The Next Big Disruptor Retail. Accessed February 26, 2019; Elofson, G., Robinson, W.N., Collective Customer Collaboration Impacts on Supply-Chain Performance (2007) International Journal of Production Research, 45 (11), pp. 2567-2594; Fornell, C., Larcker, D.F., Evaluating Structural Equations Models with Unobservable Variables and Measurement Error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Fosso Wamba, S., Gunasekaran, A., Papadopoulos, T., Ngai, E., Big Data Analytics in Logistics and Supply Chain Management (2018) The International Journal of Logistics Management, 29 (2), pp. 478-484; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary Performance Measurement Systems: a Review of Their Consequences and a Framework for Research (2012) Management Accounting Research, 23, pp. 79-119; Frohlich, M.T., Westbrook, R., Arcs of Integration: An International Study of Supply Chain Strategies (2001) Journal of Operations Management, 19 (2), pp. 185-200; Gawankar, S., Kamble, S., Raut, R., Development, Measurement and Validation of Supply Chain Performance Measures’ (SCMP) Scale in Indian Retail Sector (2016) Benchmarking: An International Journal, 23 (1), pp. 25-60; Gawankar, S., Kamble, S., Raut, R., An Investigation of Relationship Between Supply Chain Management Practices (SCMP) on Supply Chain Performance Measurement (SCMPM) of Indian Retail Chain Using SEM (2017) Benchmarking: An International Journal, 24 (1), pp. 257-295; Gorane, S., Kant, R., Supply Chain Practices and Organizational Performance: An Empirical Investigation of Indian Manufacturing Organizations (2017) The International Journal of Logistics Management, 28 (1), pp. 75-101; Grafton, J., Lillis, M.A., Widener, K.S., The Role of Performance Measurement and Evaluation in Building Organizational Capabilities and Performance (2010) Accounting, Organizations and Society, 35, pp. 689-706; Grover, P., Kar, A.K., Big Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature (2017) Global Journal of Flexible Systems Management, 18 (3), pp. 203-229; Gunasekaran, A., Irani, Z., Choy, K.-L., Filippi, L., Papadopoulos, T., Performance Measures and Metrics in Outsourcing Decisions: A Review for Research and Applications (2015) International Journal of Production Economics, 161, pp. 153-166; Gupta, S., Modgil, S., Gunasekaran, A., Big Data in Lean six Sigma: A Review and Further Research Directions (2019) International Journal of Production Research, pp. 1-23; Gupta, G., Tan, K.T.L., Ee, Y.S., Phang, C.S.C., Resource-Based View of Information Systems: Sustainable and Transient Competitive Advantage Perspectives (2018) Australasian Journal of Information Systems, 22, pp. 1-10; Hair, J., Black, W., Babin, B.Y.A., Anderson, R., Tatham, R., (2014) Multivariate Data Analysis. A Global Perspective, , New Delhi: Pearson Prentice Hall; Hair, J.F., Jr., Hult, G.T.M., Ringle, C., Sarstedt, M., (2017) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), , 2nd ed., Los Angeles: Sage; Hedgebeth, D., Data-driven Decision Making for the Enterprise: An Overview of Business Intelligence Applications (2007) Vine, 37 (4), pp. 414-420; Henseler, J., Ringle, C.M., Sarstedt, M., A new Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling (2015) Journal of the Academy of Marketing Science, 43 (1), pp. 115-135; Hofmann, E., Big Data and Supply Chain Decisions: The Impact of Volume, Variety and Velocity Properties on the Bullwhip Effect (2017) International Journal of Production Research, 55 (17), pp. 5108-5126; Huang, X., Iun, J., Liu, A., Gong, Y., Does Participative Leadership Enhance Work Performance by Inducing Empowerment or Trust? The Differential Effects on Managerial and non-Managerial Subordinates (2010) Journal of Organizational Behavior, 31 (1), pp. 122-143; Ivanov, D., Dolgui, A., Sokolov, B., The Impact of Digital Technology and Industry 4.0 on the Ripple Effect and Supply Chain Risk Analytics (2019) International Journal of Production Research, 57 (3), pp. 829-846; Jeble, S., Dubey, R., Childe, S.J., Papadopoulos, T., Roubaud, D., Prakash, A., Impact of big Data and Predictive Analytics Capability on SC Sustainability (2018) The International Journal of Logistics Management, 29 (2), pp. 513-538; Kamalahmadi, M., Parast, M.M., A Review of the Literature on the Principles of Enterprise and Supply Chain Resilience: Major Findings and Directions for Future Research (2016) International Journal of Production Economics, 171, pp. 116-133; Kamble, S.S., Gunasekaran, A., Big Data-Driven Supply Chain Performance Measurement System: A Review and Framework for Implementation (2019) International Journal of Production Research, pp. 1-22; Kamble, S.S., Gunasekaran, A., Parekh, H., Joshi, S., Modelling the Internet of Things Adoption Barriers in Food Retail Supply Chains (2019) Journal of Retailing and Consumer Services, 48, pp. 154-168; Krishnan, R., (2017), https://retail.economictimes.indiatimes.com/re-tales/big-data-and-ai-to-reinvent-retail-logistics/2773, Big Data and AI to Reinvent Retail Logistics. Accessed February 26, 2019; Kuo, Y.H., Kusiak, A., From Data to big Data in Production Research: The Past and Future Trends (2019) International Journal of Production Research, 57, pp. 4828-4853; Lakhal, L., The Relationship Between ISO 9000 Certification, TQM Practices, and Organizational Performance (2014) Quality Management Journal, 21 (3), pp. 38-48; Lapide, L., Predictive Metrics (2010) The Journal of Business Forecasting, 29 (2), pp. 23-29; Lee, S.B., Lee, W.H., An Empirical Study on the Effect of Bundling Service on User Acceptance of IoT Services (2018) Journal of Theoretical and Applied Information Technology, 96 (6), pp. 1701-1710. , http://www.jatit.org/volumes/Vol96No6/22Vol96No6.pdf; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The Impact of Supply Chain Management Practices on Competitive Advantage and Organizational Performance (2006) Omega, 34, pp. 107-124; Macaulay, S., Non-contractual Relations in Business: A Preliminary Study (1963) The Law and Society Canon, 28 (1), pp. 155-167. , http://www.jstor.org/stable/2090458; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply Chain Performance Measurement Systems: A Systematic Review and Research Agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Martin, R.P., Patterson, J.W., On Measuring Company Performance Within a Supply Chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; McFarlane, D., Sheffi, Y., The Impact of Automatic Identification on Supply Chain Operations (2003) The International Journal of Logistics Management, 14 (1), pp. 1-17; Migdadi, Y.K.A.A., Elzzqaibeh, D.A.S.I., The Evaluation of Green Manufacturing Strategies Adopted by ISO 14001 Certificate Holders in Jordan (2018) International Journal of Productivity and Quality Management, 23 (1), pp. 90-109; Monczka, R., Trent, R., Handfield, R., (1998) Purchasing and Supply Chain Management, , Cincinnati, OH: South-Western College Publishing; Müller, O., Fay, M., vom Brocke, J., The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics (2018) Journal of Management Information Systems, 35 (2), pp. 488-509; Nguyen, P.V., Nguyen, L.T., Performance Measurement of Supply Chain Integrationn in Manufacturing Firms’ of Southeast Vietnam (2017) European Journal of Economic and Management, 6 (2), pp. 23-32; O’Toole, T., Donaldson, B., Relationship Governance Structures and Performance (2000) Journal of Marketing Management, 16 (4), pp. 327-341; Pantano, E., Timmermans, H., What is Smart for Retailing? (2014) Procedia Environmental Sciences, 22, pp. 101-107; Patil, K., Retail Adoption of Internet of Things: Applying TAM Model (2016) Computing, Analytics and Security Trends (CAST), International Conference on, pp. 404-409. , Pune: IEEE; Podsakoff, P.M., MacKenzie, S.B., Podsakoff, N.P., Sources of Method Bias in Social Science Research and Recommendations on how to Control it (2012) Annual Review of Psychology, 63, pp. 539-569; Prudon, P., Confirmatory Factor Analysis as a Tool in Research Using Questionnaires: a Critique (2015) Comprehensive Psychology, 4, pp. 1-3; Raman, S., Patwa, N., Niranjan, I., Ranjan, U., Moorthy, K., Mehta, A., Impact of big Data on Supply Chain Management (2018) International Journal of Logistics Research and Applications, 21 (6), pp. 579-596; Ramdas, K., Spekman, R.E., Chain or Chackles: Understanding What Drives Supply-Chain Performance (2000) Interfaces, 30 (4), pp. 3-21; Saunders, M., Lewis, P., Thornhill, A., (2003) Research Methods for Business Students, , Harlow: Pearson Education Limited; Schoenherr, T., Speier-Pero, C., Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential (2015) Journal of Business Logistics, 36 (1), pp. 120-132; Schreiber, J.B., Nora, A., Stage, F.K., Barlow, E.A., King, J., Reporting Structural Equation Modeling and Confirmatory Factor Analysis Results: A Review (2006) The Journal of Educational Research, 99 (6), pp. 323-338; Sharma, S., Sharma, T., Mandal, P., Perception and Switch Intention of Rural Customers Towards Organised Retail (2018) International Journal of Business Forecasting and Marketing Intelligence, 4 (1), pp. 13-29; Shibin, K.T., Gunasekaran, A., Papadopoulos, T., Dubey, R., Singh, M., Wamba, S.F., Enablers and Barriers of Flexible Green Supply Chain Management: A Total Interpretive Structural Modeling Approach (2016) Global Journal of Flexible Systems Management, 17 (2), pp. 171-188; Singh, V.P., Chandra, R., Digital Technology and Retail Format Innovations (2016) Shrinkhla Ek Shodhparak Vaicharik Patrika, 4 (1), pp. 1-5; Srivastava, R.K., Changing Retail Scene in India (2008) International Journal of Retail and Distribution Management, 36 (9), pp. 714-721; Stefanovic, N., Collaborative Predictive Business Intelligence Model for Spare Parts Inventory Replenishment (2015) Computer Science and Information Systems, 12 (3), pp. 911-930; Tan, K.H., Ji, G., Lim, C.P., Tseng, M.L., Using big Data to Make Better Decisions in the Digital Economy (2017) International Journal of Production Research, 55 (17), pp. 4998-5000; Tan, K.H., Zhan, Y.Z., Ji, G., Ye, F., Chang, C., Harvesting big Data to Enhance Supply Chain Innovation Capabilities: An Analytic Infrastructure Based on Deduction Graph (2015) International Journal of Production Economics, 165, pp. 223-233; Tang, C., Tomlin, B., The Power of Flexibility for Mitigating Supply Chain Risks (2008) International Journal of Production Economics, 116 (1), pp. 12-27; Tara, S., (2018), https://www.entrepreneur.com/article/312831, How Big Data Can Help Retail Sector Grow India. Accessed February 26, 2019; Teoman, S., Ulengin, F., The Impact of Management Leadership on Quality Performance Throughout a Supply Chain: An Empirical Study (2017) Total Quality Management and Business Excellence, 29 (11-12), pp. 1427-1451; Trkman, P., McCormack, K., de Oliveira, M.P.V., Ladeira, M.B., The Impact of Business Analytics on Supply Chain Performance (2010) Decision Support Systems, 49, pp. 318-327; Tseng, M.L., Tan, R.R., Chiu, A.S.F., Chien, C.F., Kuo, T.S., Circular Economy Meets Industry 4.0: Can Big Data Drive Industrial Symbiosis? (2018) Resources, Conservation and Recycling, 131, pp. 146-147; Tung, J., A Study of Product Innovation on Firm Performance (2012) The International Journal of Organizational Innovation, 4 (3), pp. 84-97; Um, J., Lyons, A., Lamb, H.K.S., Cheng, T.C.E., Dominguez-Pery, C., Product Variety Management and Supply Chain Performance: A Capability Perspective on Their Relationships and Competitiveness Implications (2017) International Journal of Production Economics, 187, pp. 15-26; Vanichchinchai, A., Igel, B., The Impact of Total Quality Management on Supply Chain Management and Firm’s Supply Performance (2011) International Journal of Production Research, 49 (11), pp. 3405-3424; Vanpoucke, E., Vereecke, A., Muylle, S., Leveraging the Impact of Supply Chain Integration Through Information Technology (2017) International Journal of Operations and Production Management, 37 (4), pp. 510-530; Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R., Childe, S.J., Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities (2017) Journal of Business Research, 70, pp. 356-365; Wang, Y., Kung, L., Wang, W.Y.C., Cegielski, C.G., An Integrated big Data Analytics-Enabled Transformation Model: Application to Health Care (2018) Information and Management, 55 (1), pp. 64-79; Whitten, G., Green, K.W., Jr., Zelbst, P.J., Triple-A Supply Chain Performance (2012) International Journal of Operations and Production Management, 32 (1), pp. 28-48; Wiengarten, F., Onofrei, G., Humphreys, P., Fynes, B., A Supply Chain View on Certification Standards: Does Supply Chain Certification Improve Performance Outcomes? (2018) ISO 9001, ISO 14001, and New Management Standards. Measuring Operations Performance, pp. 193-214. , Heras-Saizarbitoria I., (ed), Cham: Springer, and,. edited by; Williams, L.J., Hartman, N., Cavazotte, F., Method Variance and Marker Variables: A Review and Comprehensive CFA Marker Technique (2010) Organizational Research Methods, 13, pp. 477-514; Yoo, S.H., Seo, Y.W., Effect of Supply Chain Structure and Power Dynamics on R&D and Market Performances (2017) Journal of Business Economics and Management, 18 (3), pp. 487-504; Zaefarian, G., Forkmann, S., Mitręga, M., Henneberg, S.C., A Capability Perspective on Relationship Ending and Its Impact on Product Innovation Success and Firm Performance (2017) Long Range Planning, 50 (2), pp. 184-199; Zhang, Y., Ren, S., Liu, Y., Si, S., A Big Data Analytics Architecture for Cleaner Manufacturing and Maintenance Processes of Complex Products (2017) Journal of Cleaner Production, 142 (20), pp. 626-641; Zhao, R., Liu, Y., Zhang, N., Huang, T., An Optimization Model for Green Supply Chain Management by Using a Big Data Analytic Approach (2016) Journal of Cleaner Production, 142, pp. 1085-1097},
correspondence_address1={Gunasekaran, A.; School of Business and Public Administration, United States; email: agunasekaran@csub.edu},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dixit20201,
author={Dixit, A. and Routroy, S. and Dubey, S.K.},
title={Analysis of government-supported health-care supply chain enablers: a case study},
journal={Journal of Global Operations and Strategic Sourcing},
year={2020},
volume={13},
number={1},
pages={1-16},
doi={10.1108/JGOSS-02-2019-0011},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068385801&doi=10.1108%2fJGOSS-02-2019-0011&partnerID=40&md5=4c6fcd5c855dca5afe0dd139fd2f21c7},
affiliation={Department of Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India; Department of Pharmacy, Birla Institute of Technology and Science, Pilani, India},
abstract={Purpose: The purpose of this paper is to identify, analyze and classify (i.e. driving and dependence power) the government-supported health-care supply chain enablers (GHSCEs) in rural areas of India for enhancing availability and minimizing wastage of generic medicines. Design/methodology/approach: A methodology is proposed using interpretive structural modeling (ISM) – fuzzy matriced impacts croises multiplication appliqueeaun classement (Fuzzy MICMAC) analysis to analyze the GHSCEs on the basis of inputs collected from various stakeholders about their driving and dependence power. Findings: The performance measurement system, employee recognition and reward, technology adoption, training cell and inbuilt analytical tool for IT system were found to be the appropriate GHSCEs where efforts and resources should be put for enhancing availability and minimizing wastage. Research limitations/implications: The proposed approach provides a platform for the both researchers and academicians to understand the GHSCEs and their relationships. It also provides the direction to the government for optimally allocating the efforts and resources to enhance the current performance level of generic drug distribution. Originality/value: Although many issues related to health-care supply chain have been widely researched and reported, no literature has been found for analysis of GHSCEs to choose the appropriate set of GHSCEs for supply chain performance improvement in general and developing country like India in specific. © 2019, Emerald Publishing Limited.},
author_keywords={Business improvement;  Corporate social responsibility;  Fuzzy MICMAC;  Generic drug distribution;  Health-care supply chain;  Interpretive structural modeling},
references={Adebanjo, A., Laosirihongthong, T., Samaranayake, P., Prioritizing lean supply chain management initiatives in healthcare service operations: a fuzzy AHP approach (2016) Production Planning and Control, 27 (12), pp. 953-966; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Ajmera, P., Jain, V., Modeling the factors affecting the quality of life in diabetic patients in India using total interpretive structural modeling (2019) Benchmarking: An International Journal, 26 (3), pp. 951-970; Aronsson, H., Abrahamsson, M., Spens, K., Developing lean and agile health care supply chains (2011) Supply Chain Management: An International Journal, 16 (3), pp. 176-183; Azzi, A., Persona, A., Sgarbossa, F., Bonin, M., Drug inventory management and distribution: outsourcing logistics to third‐party providers (2013) Strategic Outsourcing: An International Journal, 6 (1), pp. 48-64; Bahadori, M., Teymourzadeh, E., Tajik, H., Ravangard, R., Raadabadi, M., Hosseini, S.M., Factors affecting strategic plan implementation using interpretive structural modeling (ISM) (2018) International Journal of Health Care Quality Assurance, 31 (5), pp. 406-414; Benyoucef, M., Kuziemsky, C., Afrasiabi Rad, A., Elsabbahi, A., Modeling healthcare processes as service orchestrations and choreographies (2011) Business Process Management Journal, 17 (4), pp. 568-597; Bhatti, S.K., Singh, R.P., Singh, I., Analytical model for performance measurement in healthcare sector of Punjab (2016) Journal of Modelling in Management, 10 (2), pp. 226-237; Boakye, K.G., Blankson, C., Prybutok, V.R., Qin, H., An assessment of national healthcare service delivery: a ghanaian illustration (2017) International Journal of Quality and Reliability Management, 34 (5), pp. 649-666; Cagliano, A.C., De Marco, A., Rafele, C., Arese, M., A decision‐making approach for investigating the potential effects of near sourcing on supply chain (2012) Strategic Outsourcing: An International Journal, 5 (2), pp. 100-120; Chandra, D., Kumar, D., Fuzzy MICMAC analysis for improving supply chain performance of basic vaccines in developing countries (2017) Expert Review of Vaccines, 17 (3), pp. 263-281; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Chavali, A., Anupindi, R., (2012) Healthcare delivery in rural India – ITC experience, pp. 1-8. , ACCESS Health International, Center for Emereging Market Solutions Indian School of Business, Hyderabad; Chen, D., Enhancing hospital supply chain performance: a relational view and empirical test (2013) Journal of Operations Management, 31 (6), pp. 391-408; Chin, K., Tummala, V.M.R., Jendy, P.F., Leung, X.T., A study on supply chainmanagement practices - the Hong Kong manufacturing perspective (2004) International Journal of Physical Distribution and Logistics Management, 34 (6), pp. 505-524; Damali, U., Miller, J.L., Fredendall, L.D., Moore, D., Dye, C.J., Co-creating value using customer training and education in a healthcare service design (2016) Journal of Operations Management, 47-48 (1), pp. 80-97. , Vols; Dev, N.K., Shankar, R., Arvind, K., A study of complications affecting surgery performance: an ISM-based roadmap to patient flow (2013) International Journal of Health Care Quality Assurance, 26 (2), pp. 93-108; Faisal, M., Al-Esmael, B.A., Modeling the enablers of organizational commitment (2014) Business Process Management Journal, 20 (1), pp. 25-46; Fisher, R.J., (1990) The Social Psychology of Intergroup and International Conflict Resolution, , Springer-Verlag, New York, NY; Gemechu, D., (2007) Conflict and conflict resolution among Waliso Oromo of Eastern Mecha: the case of Guma, , Addis Ababa University; George, B., Enabling team learning in healthcare (2016) Action Learning: Research and Practice, 13 (2), pp. 101-117; Goldstein, S.M., Ward, P.T., Leong, G.K., Butler, T.W., The effect of location, strategy, and operations technology on hospital performance (2002) Journal of Operations Management, 20 (1), pp. 63-75; Gorane, S.J., Kant, R., Modelling the SCM enablers: an integrated ISM-fuzzy MICMAC approach (2013) Asia Pacific Journal of Marketing and Logistics, 25 (2), pp. 263-286; Gupta, U., Ramesh, A., Analyzing the barriers of health care supply chain in India: the contribution and interaction of factors (2015) Procedia - Social and Behavioral Sciences, 189, pp. 217-228; Henneman, T., (2005) Daniels' scientific method, , Workforce Management; Hussain, H., Awasthi, A., Tiwari, M.K., Interpretive structural modeling-analytic network process integrated framework for evaluating sustainable supply chain management alternatives (2016) Applied Mathematical Modeling, 40 (5-6), pp. 3671-3687; Kamaruddin, N.K., Udin, Z.M., Supply chain technology adoption in Malaysian automotive suppliers (2009) Journal of Manufacturing Technology Management, 20 (3), pp. 385-403; Kanda, M.K., Iravo, M.A., Access factors affecting supply chain efficiency of medical supplies in public health centers in Kenya: a case study of public health centers in elgeyo marakwet count (2015) International Journal of Academic Research in Accounting, Finance and Management Sciences, 5 (2), pp. 32-41; Kanji, G.K., Sá, P.M.E., Sustaining healthcare excellence through performance measurement (2003) Total Quality Management and Business Excellence, 14 (3), pp. 269-289; Kim, S.M., (2004) An empirical investigation of the impact of electronic commerce on supply chain management: a study in the healthcare industry, pp. 1-145. , ETD collection for University of Nebraska; Kim, M., Chai, S., The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective (2017) International Journal of Production Economics, 187, pp. 42-52; Kirytopoulos, K., Leopoulos, V., Voulgaridou, D., Supplier selection in pharmaceutical industry (2008) Benchmarking: An International Journal, 15 (4), pp. 494-516; Kumar, A., Ozdamar, L., Peng Ng, C., Procurement performance measurement system in the health care industry (2005) International Journal of Health Care Quality Assurance, 18 (2), pp. 152-166; Kumar, A., Ozdamar, L., Zhanget, C.N., Supply chain redesign in the healthcare industry of Singapore (2008) Supply Chain Management: An International Journal, 13 (2), pp. 95-103; Kumar, D., India’s rural healthcare systems: structural modeling (2018) International Journal of Health Care Quality Assurance, 31 (7), pp. 757-774; Lauer, C., Excellence in supply chain management (2004) Modern Healthcare, 34 (50), pp. 29-32; Lega, F., Marsilio, M., Villa, S., An evaluation framework for measuring supply chain performance in the public healthcare sector: evidence from the Italian NHS (2013) Production Planning and Control, 24 (10-11), pp. 931-947; McKone-Sweet, K.E., Hamilton, P., Willis, S.B., The ailing healthcare supply chain: a prescription for change (2005) The Journal of Supply Chain Management, 41 (1), pp. 4-17; Magretta, J., Dell, M., The power of virtual integration: an interview with dell computer’s Michael dell (1998) Harvard Business Review, 76 (2), p. 72; Malik, M.M., Abdallah, S., Ala’raj, M., Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review (2016) Big Data Analytics in Operations and Supply Chain Management, 1, pp. 1-26; Mantzana, V., Themistocleous, M., Morabito, V., Healthcare information systems and older employees’ training (2010) Journal of Enterprise Information Management, 23 (6), pp. 680-693; Mas, N., Seinfeld, J., Is managed care restraining the adoption of technology by hospitals? (2008) Journal of Health Economics, 27, pp. 1026-1045; Medows, B., (2011) How today’s supply chains increasingly define strategy and deliver performance, supply and demand chain executive, , 16 February; Miah, S.J., Ahsan, K., Msimangira, K.A.B., An approach of purchasing decision support in healthcare supply chain management (2013) Operations and Supply Chain Management, 6 (2), pp. 43-53; Nishat, F.M., Banwet, D.K., Shankar, R., Supply chain risk mitigation: modeling the enablers (2006) Business Process Management Journal, 12 (4), pp. 535-552; Papalexi, M., Bamford, D., Dehe, B., A case study of Kanban implementation within the pharmaceutical supply chain (2016) International Journal of Logistics Research and Applications, 19 (4), pp. 239-255; Patel, A., Norris, P., Gauld, R., Rades, T., Drug quality in South Africa: perceptions of key players involved in medicines distribution (2009) International Journal of Health Care Quality Assurance, 22 (5), pp. 547-560; Patri, R., Suresh, M., Modeling the enablers of agile performance in healthcare organization: a TISM approach (2017) Global Journal of Flexible Systems Management, 18 (3), pp. 251-272; Pheng, T.K., Hamdani, Y., Investigation on service supply chain in private hospitals Malaysia (2014) Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, pp. 1569-1574. , January 7-9; Pitta, D.A., Laric, M.V., Value chains in health care (2004) Journal of Consumer Marketing, 21 (7), pp. 451-464; Potdar, P.K., Routroy, S., Behera, A., Addressing the agile manufacturing impediments using interpretive structural modeling (2017) Materials Today: Proceedings, 4, pp. 1744-1751; (2011), www.rmsc.health.rajasthan.gov.in/content/raj/medical/rajasthan-medical-services-corporation-ltd-/en/home, (accessed: 20 December 2017; Routroy, S., Kumar, C.V.S., Strategy for supplier development program implementation: a case study (2015) International Journal of Services and Operations Management, 2 (2), pp. 238-264; Sage, A.P., (1977) Interpretive Structural Modeling: Methodology for Large-Scale Systems, pp. 91-164. , McGraw-Hill, New York, NY; Shah, N., Pharmaceutical supply chains: Key issues and strategies for optimization (2004) Computers and Chemical Engineering, 28 (6-7), pp. 929-941; Sharma, S., Pai, S.S., Analysis of operating effectiveness of a cold chain model using Bayesian networks (2015) Business Process Management Journal, 21 (4), pp. 722-742; Singh, P.V., Tatambhotla, A., Kalvakuntla, R., Understanding public drug procurement in India: a comparative qualitative study of five Indian states (2013) BMJ Journal, 3 (2), pp. 1-12; Singh, R.K., Developing the framework for coordination in supply chain of SMEs (2011) Business Process Management Journal, 17 (4), pp. 619-638; Singh, R.K., Kumar, R., Kumar, P., Strategic issues in pharmaceutical supply chains: a review (2016) International Journal of Pharmaceutical and Healthcare Marketing, 10 (3), pp. 234-257; Srinivasan, V., Chandwani, R., HRM innovations in rapid growth contexts: the healthcare sector in India (2014) The International Journal of Human Resource Management, 25 (10), pp. 1505-1525; (2010) Annual health survey, , www.sihfwrajasthan.com/ppts/Full/Annual%20Health%20Survey, (accessed, Government of Rajasthan: 24 December 2017; Teuta, B., Internal supply chain and operational failure a cast of hospital service in Elbasan city, Albania (2015) International Journal of Economics, Commerce and Management, 3 (4), pp. 1-10; Thakur, V., Anbanandam, R., Healthcare waste management: an interpretive structural modeling approach (2016) International Journal of Health Care Quality Assurance, 29 (5), pp. 559-581; Thomas, C.C., Transforming productivity and performance in healthcare and other public services: how training and development could make a more strategic contribution (2010) Industrial and Commercial Training, 42 (5), pp. 251-259; Tucker, L.A., Heisler, W.S., Janisse, D.L., (2013) Organizational factors that contribute to operational failures in hospitals, pp. 14-23. , Harvard Business School, 4 September; Vaishnavi, V., Sureshand Dutta, M., A study on the influence of factors associated with organizational readiness for change in healthcare organizations using TISM (2019) Benchmarking: An International Journal, , https://doi.org/10.1108/BIJ-06-2018-0161; Ventrice, C., (2003) Make Their Day! Employee Recognition That Works, , Berrett-Koehler Publishers; Vishal, A.B., Ravi, K., An integrated ISM fuzzy MICMAC approach for modeling the supply chain knowledge flow enablers (2016) International Journal of Production Research, 54 (24), pp. 7374-7399; Wang, S.P., Lee, W., A joint economic lot-sizing model for the hospital’s supplier with capacitated warehouse constraint (2013) Journal of Industrial and Production Engineering, 30 (3), pp. 202-210; (2003) Guidelines for the Storage of Essential Medicines and Other Health Commodities, , U.S. Agency for International Development; (2013) Economist intelligence unit, , 14 August; Wyld, D.C., Genuine medicine? (2008) Competitiveness Review, 18 (3), pp. 206-216; Yadav, P., Health product supply chains in developing countries: Diagnosis of the root causes of underperformance and an agenda for reform (2015) Health Systems and Reform, 1 (2), pp. 142-154; Yongyi, S., Perspective on supply chain management in the healthcare industry (2013) 2nd International Conference on Science and Social Research, pp. 630-633. , The Atlantis Press; Zheng, J., Roehrich, J.K., Lewis, M.A., The dynamics of contractual and relational governance: evidence from long term public–private procurement arrangements (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 43-54},
correspondence_address1={Routroy, S.; Department of Mechanical Engineering, India; email: srikantaroutroy@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={23985364},
language={English},
abbrev_source_title={J. Global Operations Strategic Sourc.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Rabbi2020,
author={Rabbi, M. and Ali, S.M. and Kabir, G. and Mahtab, Z. and Paul, S.K.},
title={Green supply chain performance prediction using a Bayesian belief network},
journal={Sustainability (Switzerland)},
year={2020},
volume={12},
number={3},
doi={10.3390/su12031101},
art_number={1101},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081244651&doi=10.3390%2fsu12031101&partnerID=40&md5=782bb3d481040c38d9cda4d988ff5860},
affiliation={Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh; Industrial Systems Engineering, University of Regina, Regina, SK S4S 0A2, Canada; Department of Industrial and Production Engineering, Military Institute of Science and Technology, Dhaka, 1216, Bangladesh; UTS Business School, University of Technology Sydney, Sydney, NSW  2007, Australia},
abstract={Green supply chain management (GSCM) has emerged as an important issue to lessen the impact of supply chain activities on the natural environment, as well as reduce waste and achieve sustainable growth of a company. To understand the effectiveness ofGSCM, performance measurement of GSCM is a must. Monitoring and predicting green supply chain performance can result in improved decision-making capability for managers and decision-makers to achieve sustainable competitive advantage. This paper identifies and analyzes various green supply chain performance measures and indicators. A probabilistic model is proposed based on a Bayesian belief network (BBN) for predicting green supply chain performance. Eleven green supply chain performance indicators and two green supply chain performance measures are identified through an extensive literature review. Using a real-world case study of a manufacturing industry, the methodology of this model is illustrated. Sensitivity analysis is also performed to examine the relative sensitivity of green supply chain performance to each of the performance indicators. The outcome of this research is expected to help managers and practitioners of GSCM improve their decision-making capability, which ultimately results in improved overall organizational performance. © 2020 by the authors.},
author_keywords={Bayesian belief network;  Green supply chain;  Performance measurement;  Sustainability},
keywords={Bayesian analysis;  decision making;  manufacturing;  prediction;  supply chain management;  sustainability;  waste management},
references={Gong, R., Xue, J., Zhao, L., Zolotova, O., Ji, X., Xu, Y., A Bibliometric Analysis of Green Supply Chain Management Based on the Web of Science (WOS) Platform (2019) Sustainability, 11, p. 3459. , [CrossRef]; Kim, B., Oh, H., The impact of decision-making sharing between supplier and manufacturer on their collaboration performance (2005) Supply Chain Manag. Int. J, 10, pp. 223-236. , [CrossRef]; Jian, J., Guo, Y., Jiang, L., An, Y., Su, J., A multi-objective optimization model for green supply chain considering environmental benefits (2019) Sustainability, 11, p. 5911. , [CrossRef]; Hoole, R., Five ways to simplify your supply chain (2005) Supply Chain Manag. Int. J, 10, pp. 3-6. , [CrossRef]; Panchal, G., Jain, V., A review on select models for supply chain formation (2011) Int. J. Bus. Perform. Supply Chain Model, 3, p. 113. , [CrossRef]; Panicker, V.V., Sridharan, R., Modelling supply chain decision problem with fixed charge-A review (2011) Int. J. Bus. Perform. Supply Chain Model, 3, p. 195. , [CrossRef]; Xing, G., Xia, B., Guo, J., Sustainable cooperation in the green supply chain under financial constraints (2019) Sustainability, 11, p. 5977. , [CrossRef]; Sarkis, J., Manufacturing's role in corporate environmental sustainability-Concerns for the new millennium (2001) Int. J. Oper. Prod. Manag, 21, pp. 666-686. , [CrossRef]; Lewis, H., Gertsakis, J., Grant, T., Morelli, N., Sweatman, A., (2001) Design and Environment: A Global Guide to Designing Greener Goods;, , Greenleaf Pub: Austin, TX, USA; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach (2014) Prod. Plan. Control, 25, pp. 698-714. , [CrossRef]; Dvorsky, J., Popp, J., Virglerova, Z., Kovács, S., Oláh, J., Assessing the importance of market risk and its sources in the SME of the Visegrad Group and Serbia (2018) Adv. Decis. Sci, 22, pp. 1-22; Oláh, J., Virglerova, Z., Popp, J., Kliestikova, J., Kovács, S., The Assessment of Non-Financial Risk Sources of SMES in the V4 Countries and Serbia (2019) Sustainability, 11, p. 4806. , [CrossRef]; Oláh, J., Kovács, S., Virglerova, Z., Lakner, Z., Kovacova, M., Popp, J., Analysis and Comparison of Economic and Financial Risk Sources in SMEs of the Visegrad Group and Serbia (2019) Sustainability, 11, p. 1853. , [CrossRef]; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) Int. J. Manag. Rev, 9, pp. 53-80. , [CrossRef]; Parsaeifar, S., Bozorgi-Amiri, A., Naimi-Sadigh, A., Sangari, M.S., A game theoretical for coordination of pricing, recycling, and green product decisions in the supply chain (2019) J. Clean. Prod, 226, pp. 37-49. , [CrossRef]; Lima-Junior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on SCOR®metrics and multilayer perceptron neural networks (2019) Int. J. Prod. Econ, 212, pp. 19-38. , [CrossRef]; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Comput. Ind. Eng, 113, pp. 333-346. , [CrossRef]; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ, 183, pp. 299-315. , [CrossRef]; Mishra, D., Gunasekaran, A., Papadopoulos, T., Hazen, B., Green supply chain performance measures: A review and bibliometric analysis (2017) Sustain. Prod. Consum, 10, pp. 85-99. , [CrossRef]; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag, 21, pp. 71-87. , [CrossRef]; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12, pp. 330-353. , [CrossRef]; Lau, K.H., Benchmarking green logistics performance with a composite index (2011) Benchmarking, 18, pp. 873-896. , [CrossRef]; Testa, F., Iraldo, F., Shadows and lights of GSCM (green supply chain management): Determinants and effects of these practices based on a multi-national study (2010) J. Clean. Prod, 18, pp. 953-962. , [CrossRef]; Wilkerson, T., Can one green deliver another? (2005) Harv. Bus. Rev, pp. 3-4; Cousins, P.D., Lawson, B., Petersen, K.J., Fugate, B., Investigating green supply chain management practices and performance: The moderating roles of supply chain ecocentricity and traceability (2019) Int. J. Oper. Prod. Manag, 39, pp. 767-786. , [CrossRef]; Zhu, W., He, Y., Green product design in supply chains under competition (2017) Eur. J. Oper. Res, 258, pp. 165-180. , [CrossRef]; Shohan, S., Ali, S.M., Kabir, G., Ahmed, S.K., Suhi, S.A., Haque, T., Green supply chain management in the chemical industry: Structural framework of drivers (2019) Int. J. Sustain. Dev. World Ecol, 26, pp. 752-768. , [CrossRef]; Yu, W., Ramanathan, R., An empirical examination of stakeholder pressures, green operations practices and environmental performance (2015) Int. J. Prod. Res, 53, pp. 6390-6407. , [CrossRef]; Uddin, S., Ali, S.M., Kabir, G., Suhi, S.A., Enayet, R., Haque, T., An AHP-ELECTRE framework to evaluate barriers to green supply chain management in the leather industry (2019) Int. J. Sustain. Dev. World Ecol, 26, pp. 732-751. , [CrossRef]; Banerjee, M., Mishra, M., Retail supply chain management practices in India: A business intelligence perspective (2017) J. Retail. Consum. Serv, 34, pp. 248-259. , [CrossRef]; Swami, S., Shah, J., Channel coordination in green supply chain management (2013) J. Oper. Res. Soc, 64, pp. 336-351. , [CrossRef]; Azevedo, S.G., Carvalho, H., Cruz-Machado, V., A proposal of LARG Supply Chain Management Practices and a Performance Measurement System (2011) Int. J. e-Educ. e-Bus. e-Manag. e-Learn, 1, pp. 7-14. , [CrossRef]; Maleki, M., Machado, V.C., Supply chain performance monitoring using Bayesian network (2013) Int. J. Bus. Perform. Supply Chain Model, 5, p. 177. , [CrossRef]; Bai, C., Sarkis, J., Green supplier development: A review and analysis (2019) Handbook on the Sustainable Supply Chain;, , Edward Elgar Publishing: Chatham, UK; Vahdani, B., Mousavi, S.M., Tavakkoli-Moghaddam, R., Hashemi, H., A new enhanced support vector model based on general variable neighborhood search algorithm for supplier performance evaluation: A case study (2017) Int. J. Comput. Intell. Syst, 10, pp. 293-311. , [CrossRef]; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag, 19, pp. 275-292. , [CrossRef]; King, A.A., Lenox, M.J., Lean and Green? an Empirical Examination of the Relationship Between Lean Production and Environmental Performance (2009) Prod. Oper. Manag, 10, pp. 244-256. , [CrossRef]; Klassen, R.D., McLaughlin, C.P., The Impact of Environmental Management on Firm Performance (1996) Manag. Sci, 42, pp. 1199-1214. , [CrossRef]; Sarkis, J., Rasheed, A., Greening the manufacturing function (1995) Bus. Horiz, 38, pp. 17-27. , [CrossRef]; Rehman, M.A., Seth, D., Shrivastava, R.L., Impact of green manufacturing practices on organisational performance in Indian context: An empirical study (2016) J. Clean. Prod, 137, pp. 427-448. , [CrossRef]; Vazquez-Brust, D.A., Campos, L.M.S., Mapping lean manufacturing practices and green manufacturing practices in supply chains (2019) Handbook on the Sustainable Supply Chain;, p. 291. , Edward Elgar Publishing: Chatham, UK; Choudhary, K., Sangwan, K.S., Benchmarking Indian ceramic enterprises based on green supply chain management pressures, practices and performance (2018) Benchmarking Int. J, 25, pp. 3628-3653. , [CrossRef]; Chekima, B., Wafa, S.A., Igau, O.A., Chekima, S., Sondoh, S.L., Jr., Examining green consumerism motivational drivers: Does premium price and demographics matter to green purchasing? (2016) J. Clean. Prod, 112, pp. 3436-3450. , [CrossRef]; Jaiswal, D., Kant, R., Green purchasing behaviour: A conceptual framework and empirical investigation of Indian consumers (2018) J. Retail. Consum. Serv, 41, pp. 60-69. , [CrossRef]; Faruk, A.C., Lamming, R.C., Cousins, P.D., Bowen, F.E., Analyzing, mapping, and managing environmental impacts along supply chains (2002) J. Ind. Ecol, 5, pp. 13-36. , [CrossRef]; Green, K.W., Jr., Zelbst, P.J., Meacham, J., Bhadauria, V.S., Green supply chain management practices: Impact on performance (2012) Supply Chain Manag. Int. J, 17, pp. 290-305. , [CrossRef]; Tseng, M.-L., Islam, M.S., Karia, N., Fauzi, F.A., Afrin, S., A literature review on green supply chain management: Trends and future challenges (2019) Resour. Conserv. Recycl, 141, pp. 145-162. , [CrossRef]; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) J. Clean. Prod, 86, pp. 360-377. , [CrossRef]; Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E.K., Antucheviciene, J., Supplier evaluation and selection in fuzzy environments: A review of MADM approaches (2017) Econ. Res. Istraživanja, 30, pp. 1073-1118. , [CrossRef]; Maditati, D.R., Munim, Z.H., Schramm, H.-J., Kummer, S., A review of green supply chain management: From bibliometric analysis to a conceptual framework and future research directions (2018) Resour. Conserv. Recycl, 139, pp. 150-162. , [CrossRef]; Sen, D.K., Datta, S., Mahapatra, S.S., Sustainable supplier selection in intuitionistic fuzzy environment: A decision-making perspective (2018) Benchmarking Int. J, 25, pp. 545-574. , [CrossRef]; Kuo, R.J., Wang, Y.C., Tien, F.C., Integration of artificial neural network and MADA methods for green supplier selection (2010) J. Clean. Prod, 18, pp. 1161-1170. , [CrossRef]; Yazdani, M., Chatterjee, P., Zavadskas, E.K., Zolfani, S.H., Integrated QFD-MCDM framework for green supplier selection (2017) J. Clean. Prod, 142, pp. 3728-3740. , [CrossRef]; Tang, X., Wei, G., Gao, H., Models for multiple attribute decision making with interval-valued pythagorean fuzzy muirhead mean operators and their application to green suppliers selection (2019) Informatica, 30, pp. 153-186. , [CrossRef]; Jenssen, M.M., de Boer, L., Implementing life cycle assessment in green supplier selection: A systematic review and conceptual model (2019) J. Clean. Prod, 229, pp. 1198-1210. , [CrossRef]; Xu, X.-G., Shi, H., Cui, F.-B., Quan, M.-Y., Green Supplier Evaluation and Selection Using Interval 2-Tuple Linguistic Hybrid Aggregation Operators (2018) Informatica, 29, pp. 801-824. , [CrossRef]; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: A study in the application of the Analytical Hierarchy Process (2002) Eur. J. Oper. Res, 141, pp. 70-87. , [CrossRef]; Pineda-Henson, R., Culaba, A.B., Mendoza, G.A., Evaluating environmental performance of pulp and paper manufacturing using the analytic hierarchy process and life-cycle assessment (2002) J. Ind. Ecol, 6, pp. 15-28. , [CrossRef]; Lin, R.-J., Chen, R.-H., Nguyen, T.-H., Green supply chain management performance in automobile manufacturing industry under uncertainty (2011) Procedia Soc. Behav. Sci, 25, pp. 233-245. , [CrossRef]; Rostamzadeh, R., Govindan, K., Esmaeili, A., Sabaghi, M., Application of fuzzy VIKOR for evaluation of green supply chain management practices (2015) Ecol. Indic, 49, pp. 188-203. , [CrossRef]; Gunasekaran, A., Subramanian, N., Rahman, S., Green supply chain collaboration and incentives: Current trends and future directions (2015) Transp. Res. Part E: Logist. Transp. Rev, 74, pp. 1-10. , [CrossRef]; Li, Y., Research on the performance measurement of green supply chain management in China (2011) J. Sustain. Dev, 4, p. 101. , [CrossRef]; Vachon, S., Green supply chain practices and the selection of environmental technologies (2007) Int. J. Prod. Res, 45, pp. 4357-4379. , [CrossRef]; Laosirihongthong, T., Adebanjo, D., Choon Tan, K., Green supply chain management practices and performance (2013) Ind. Manag. Data Syst, 113, pp. 1088-1109. , [CrossRef]; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking Int. J, 17, pp. 320-339. , [CrossRef]; Seman, N.A.A., Zakuan, N., Jusoh, A., Arif, M.S.M., Saman, M.Z.M., Green supply chain management: A review and research direction (2012) Int. J. Manag. Value Supply Chains, 3, pp. 1-18. , [CrossRef]; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: Pressures, practices and performance (2005) Int. J. Oper. Prod. Manag, 25, pp. 449-468. , [CrossRef]; Noci, G., Designing 'green'vendor rating systems for the assessment of a supplier's environmental performance (1997) Eur. J. Purch. Supply Manag, 3, pp. 103-114. , [CrossRef]; Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A., A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences (2013) Resour. Conserv. Recycl, 74, pp. 170-179. , [CrossRef]; Cucchiella, F., D'Adamo, I., Issue on supply chain of renewable energy (2013) Energy Convers. Manag, 76, pp. 774-780. , [CrossRef]; Kurien, G.P., Qureshi, M.N., Performance measurement systems for green supply chains using modified balanced score card and analytical hierarchical process (2012) Sci. Res. Essays, 7, pp. 3149-3161; Cai, Z., Sun, S., Si, S., Yannou, B., Identifying product failure rate based on a conditional Bayesian network classifier (2011) Expert Syst. Appl, 38, pp. 5036-5043. , [CrossRef]; Abolbashari, M.H., Chang, E., Hussain, O.K., Saberi, M., Smart buyer: A Bayesian network modelling approach for measuring and improving procurement performance in organisations (2018) Knowl. Based Syst, 142, pp. 127-148. , [CrossRef]; Langseth, H., Portinale, L., Bayesian networks in reliability (2007) Reliab. Eng. Syst. Saf, 92, pp. 92-108. , [CrossRef]; Mahadevan, S., Zhang, R., Smith, N., Bayesian networks for system reliability reassessment (2001) Struct. Saf, 23, pp. 231-251. , [CrossRef]; Baesens, B., Verstraeten, G., van den Poel, D., Egmont-Petersen, M., van Kenhove, P., Vanthienen, J., Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers (2004) Eur. J. Oper. Res, 156, pp. 508-523. , [CrossRef]; Abad-Grau, M.M., Arias-Aranda, D., Operations strategy and flexibility: Modeling with Bayesian classifiers (2006) Ind. Manag. Data Syst, 106, pp. 460-484. , [CrossRef]; Gambelli, D., Bruschi, V., A Bayesian network to predict the probability of organic farms' exit from the sector: A case study from Marche, Italy (2010) Comput. Electron. Agric, 71, pp. 22-31. , [CrossRef]; Neapolitan, R.E., Learning Bayesian Networks (2003) Mol. Biol, 6, p. 674. , [CrossRef]; Gopnik, A., Tenenbaum, J.B., Bayesian networks, Bayesian learning and cognitive development (2007) Dev. Sci, 10, pp. 281-287. , [CrossRef]; (2014) Netica Version 4.16. Canada: Norsys Software Corp, , www.norsys.com/Accessed, (accessed on 15 September 2017); Kabir, G., Balek, N.B.C., Tesfamariam, S., Consequence-based framework for buried infrastructure systems: A Bayesian belief network model (2018) Reliab. Eng. Syst. Saf, 180, pp. 290-301. , [CrossRef]; Kabir, G., Cruz, A.M., Suda, H., Tesfamariam, S., Giraldo, F.M., Earthquake-related Natech risk assessment using a Bayesian belief network model (2019) Struct. Infrastruct. Eng, pp. 1-15. , [CrossRef]},
correspondence_address1={Kabir, G.; Industrial Systems Engineering, Canada; email: golam.kabir@uregina.ca},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kamble202065,
author={Kamble, S.S. and Gunasekaran, A.},
title={Big data-driven supply chain performance measurement system: a review and framework for implementation},
journal={International Journal of Production Research},
year={2020},
volume={58},
number={1},
pages={65-86},
doi={10.1080/00207543.2019.1630770},
note={cited By 99},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077156156&doi=10.1080%2f00207543.2019.1630770&partnerID=40&md5=4f81387b038867cad1f3a49321a8a29a},
affiliation={Operations and Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, India; School of Business and Public Administration, California State University, Bakersfield, Bakersfield, CA, United States},
abstract={Performance measures and metrics (PMM) is identified to be an essential aspect of managing diverse supply chains. The PMM improves the firm’s performance by providing open and transparent communication between the various stakeholders of an organisation. The literature suggests that big data analytics has a positive impact on the supply chain and firm performance. Presently, the literature lack studies that recognise the PMM relevant to big data-driven supply chain (BDDSC). The present study is based on a comprehensive review of 66 papers published with the primary objective to identify the various PMMs used to evaluate the BDDSC. The findings suggest that the PMMs applicable to BDDSC can be classified into two non-mutually exclusive categories. The first category represents 24 performance measures used to evaluate the performance of the big data analytics capability and the second category represents 130 measures used for assessing the performance of BDDSC processes. The study also reports the emergence of new performance measures based on increasing use of predictive and social analytics in BDDSC. Based on the results of the study a framework on BDDSC performance measurement system is proposed which will guide the managers to have a robust performance measurement system in their organisation. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={big data;  data analytics;  performance measures;  predictive analytics;  supply chain management},
keywords={Data Analytics;  Predictive analytics;  Supply chain management, Data driven;  Firm Performance;  Performance measure;  Performance measurement system;  Primary objective;  Robust performance;  Supply chain performance measurements, Big data},
references={Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J., How to Improve Firm Performance Using Big Data Analytics Capability and Business Strategy Alignment? (2016) International Journal of Production Economics, 182, pp. 113-131. , https://doi.org/10.1016/j.ijpe.2016.08.018; Ali, A.I., Ghoniem, A., Franke, A., Evaluating Capacity Management Tactics for a Legacy Manufacturing Plant (2014) Journal of the Operational Research Society, 65 (9), pp. 1361-1370. , https://doi.org/10.1057/jors.2013.82; Anwar, M., Khan, S.Z., Shah, S.Z.A., Big Data Capabilities and Firm’s Performance: A Mediating Role of Competitive Advantage (2018) Journal of Information and Knowledge Management, 17 (4). , https://doi.org/10.1142/S0219649218500454; Arya, V., Sharma, P., Singh, A., De Silva, P.T.M., An Exploratory Study on Supply Chain Analytics Applied to Spare Parts Supply Chain (2017) Benchmarking: An International Journal, 24 (6), pp. 1571-1580; Arzu Akyuz, G., Erman Erkan, T., SC Performance Measurement: A Literature Review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Bag, S., Big Data and Predictive Analysis is Key to Superior Supply Chain Performance: A South African Experience (2017) International Journal of Information Systems and Supply Chain Management, 10 (2), pp. 66-84. , https://doi.org/10.4018/IJISSCM.2017040104; Barbosa, M.W., Vicente, A., Ladeira, M.B., Oliveira, M.P., Managing SC Resources with Big Data Analytics: A Systematic Review (2017) International Journal of Logistics Research and Applications, 21 (3), pp. 177-200; Barney, J., Firm Resources and Sustained Competitive Advantage (1991) Journal of Management, 17 (1), pp. 99-120; Barratt, M., Barratt, R., Exploring Internal And External Supply Chain Linkages: Evidence from the Field (2011) Journal of Operations Management, 29 (5), pp. 514-528; Barratt, M., Oke, A., Antecedents of Supply Chain Visibility in Retail Supply Chains: A Resource-Based Theory Perspective (2007) Journal of Operations Management, 25 (6), pp. 217-1233; Bauer, K., (2005) Information Management Magazine, , http://www.informationmanagement.com/issues/20051101/1040476-1.html, Predictive Analytics: The Next Wave KPIs; Beamon, B.M., Measuring SC Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bock, S., Isik, F., A New Two-Dimensional Performance Measure in Purchase Order Sizing (2015) International Journal of Production Research, 53 (16), pp. 4951-4962. , https://doi.org/10.1080/00207543.2015.1005769; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, Implementing and Updating Performance Measurement Systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Brinch, M., Understanding the Value of big Data in SC Management and its Business Processes: Towards a Conceptual Framework (2018) International Journal of Operations & Production Management, , https://doi.org/10.1108/IJOPM-05-2017-0268; Chadegani, A.A., Salehi, H., Yunus, M., A Comparison Between Two Main Academic Literature Collections: Web of Science and Scopus Databases (2013) Asian Social Science, 9 (5). , https://doi.org/10.5539/ass.v9n5p18; Chae, B.K., Insights From Hashtag SC and Twitter Analytics: Considering Twitter and Twitter Data for SC Practice and Research (2015) International Journal of Production Economics, 165, pp. 247-259; Chae, B.K., Olson, D.L., Business Analytics for Supply Chain: A Dynamic-Capabilities Framework (2013) International Journal of Information Technology and Decision Making, 12 (1), pp. 9-26. , https://doi.org/10.1142/S0219622013500016; Chae, B.K., Olson, D., Sheu, C., The Impact of Supply Chain Analytics on Operational Performance: A Resource-Based View (2014) International Journal of Production Research, 52 (16), pp. 4695-4710. , https://doi.org/10.1080/00207543.2013.861616; Chae, B., Yang, C., Olson, D., Sheu, C., The Impact of Advanced Analytics and Data Accuracy on Operational Performance: A Contingent Resource Based Theory (RBT) Perspective (2014) Decision Support Systems, 59 (1), pp. 119-126. , https://doi.org/10.1016/j.dss.2013.10.012; Chan, F.T., Performance Measurement in a SC (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chavez, R., Yu, W., Jacobs, M.A., Feng, M., Data-driven Supply Chains, Manufacturing Capability and Customer Satisfaction (2017) Production Planning and Control, 28 (11-12), pp. 906-918. , https://doi.org/10.1080/09537287.2017.1336788; Chen, D.Q., Preston, D.S., Swink, M., How the Use of Big Data Analytics Affects Value Creation in SC Management (2015) Journal of Management Information Systems, 32 (4), pp. 4-39; de Oliveira, M.P.V., Ladeira, M.B., McCormack, K., (2009) The Statistical Analysis of SCM Process Maturity Levels and Practices, , In 26th German logistics congress, Berlin; de Oliveira, M.P.V., McCormack, K., Trkman, P., Business Analytics in SCs–The Contingent Effect of Business Process Maturity (2012) Expert Systems with Applications, 39 (5), pp. 5488-5498; De Toni, A., Tonchia, S., Performance Measurement Systems-Models, Characteristics and Measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-71; DeLone, W.H., McLean, E.R., Information Systems Success: The Quest for the Dependent Variable (1992) Information Systems Research, 3 (1), pp. 60-95; DePietro, R., Wiarda, E., Fleischer, M., The Context for Change: Organization (1990) Technology and Environment, 199, pp. 151-175. , Lexington, MA: Lexington Books, and; Fernando, Y., Chidambaram, R.R.M., Wahyuni-TD, I.S., The Impact of Big Data Analytics and Data Security Practices on Service Supply Chain Performance (2018) Benchmarking: An International Journal, 25 (9), pp. 4009-4034. , https://doi.org/10.1108/BIJ-07-2017-0194; Ghattas, J., Soffer, P., Peleg, M., Improving Business Process Decision Making Based on Past Experience (2014) Decision Support Systems, 59 (1), pp. 93-107. , https://doi.org/10.1016/j.dss.2013.10.009; Gopal, P.R.C., Thakkar, J., A Review on SC Performance Measures and Metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Gravili, G., Benvenuto, M., Avram, A., Viola, C., The Influence of the Digital Divide on Big Data Generation Within Supply Chain Management (2018) International Journal of Logistics Management, 29 (2), pp. 592-628. , https://doi.org/10.1108/IJLM-06-2017-0175; Gunasekaran, A., Kobu, B., Performance Measures and Metrics in Logistics and SC Management: A Review of Recent Literature (1995–2004) for Research and Applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S., Big Data and Predictive Analytics for Supply Chain and Organizational Performance (2017) Journal of Business Research, 70, pp. 308-317. , https://doi.org/10.1016/j.jbusres.2016.08.004; Gunasekaran, A., Patel, C., McGaughey, R.E., A Framework for Supply Chain Performance Measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a SC Environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Yusuf, Y.Y., Adeleye, E.O., Papadopoulos, T., Agile Manufacturing Practices: The Role of Big Data and Business Analytics with Multiple Case Studies (2018) International Journal of Production Research, 56 (1-2), pp. 385-397. , https://doi.org/10.1080/00207543.2017.1395488; Gupta, M., George, J.F., Toward the Development of a Big Data Analytics Capability (2016) Information and Management, 53 (8), pp. 1049-1064. , https://doi.org/10.1016/j.im.2016.07.004; Hazen, B.T., Skipper, J.B., Ezell, J.D., Boone, C.A., Big Data and Predictive Analytics for SC Sustainability: A Theory-Driven Research Agenda (2016) Computers & Industrial Engineering, 101, pp. 592-598; Hofmann, E., Big Data and SC Decisions: The Impact of Volume, Variety and Velocity Properties on the Bullwhip Effect (2017) International Journal of Production Research, 55 (17), pp. 5108-5126; Huang, Y., Handfield, R.B., Measuring the Benefits of ERP on Supply Management Maturity Model: A “big Data” Method (2015) International Journal of Operations and Production Management, 35 (1), pp. 2-25. , https://doi.org/10.1108/IJOPM-07-2013-0341; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted SC Configuration Based on SC Operations Reference (SCOR) Model (2005) Computers & Industrial Engineering, 48 (2), pp. 377-394; Huang, C.K., Wang, T., Huang, T.Y., Initial Evidence on the Impact of Big Data Implementation on Firm Performance (2018) Information Systems Frontiers, pp. 1-13. , https://doi.org/10.1007/s10796-018-9872-5; Hwang, Y.D., Lin, Y.C., Lyu Jr, J., The Performance Evaluation of SCOR Sourcing Process—The Case Study of Taiwan's TFT-LCD Industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Jain, R., Singh, A.R., Yadav, H.C., Mishra, P.K., Using Data Mining Synergies for Evaluating Criteria at Pre-Qualification Stage of Supplier Selection (2014) Journal of Intelligent Manufacturing, 25 (1), pp. 165-175; Jeble, S., Dubey, R., Childe, S.J., Papadopoulos, T., Roubaud, D., Prakash, A., Impact of Big Data and Predictive Analytics Capability on Supply Chain Sustainability (2018) International Journal of Logistics Management, 29 (2), pp. 513-538. , https://doi.org/10.1108/IJLM-05-2017-0134; Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., Childe, S.J., Modelling Quality Dynamics, Business Value and Firm Performance in a Big Data Analytics Environment (2017) International Journal of Production Research, 55 (17), pp. 5011-5026. , https://doi.org/10.1080/00207543.2016.1154209; Juuso, E.K., Lahdelma, S., Intelligent Performance Measures for Condition-Based Maintenance (2013) Journal of Quality in Maintenance Engineering, 19 (3), pp. 278-294. , https://doi.org/10.1108/JQME-05-2013-0026; Kaplan, R.S., Norton, D.P., In Search of Excellence–der Maßstab muß neu definiert werden (1992) Harvard Manager, 14 (4), pp. 37-46; Kasi, V., Systemic Assessment of SCOR for Modeling SCs (2005) System Sciences, 2005. HICSS'05. Proceedings of the 38th Annual Hawaii International Conference on, p. 87b. , https://doi.org/10.1109/%20HICSS.2005.574, IEEE; Kim, A., Oh, K., Jung, J., Kim, B., Imbalanced Classification of Manufacturing Quality Conditions Using Cost-Sensitive Decision Tree Ensembles (2018) International Journal of Computer Integrated Manufacturing, 31 (8), pp. 701-717. , https://doi.org/10.1080/0951192X.2017.1407447; Krishnamoorthi, S., Mathew, S.K., Business Analytics and Business Value: A Comparative Case Study (2018) Information & Management, 55 (5), pp. 643-666; Kumar, A., Shankar, R., Choudhary, A., Thakur, L.S., A Big Data MapReduce Framework for Fault Diagnosis in Cloud-Based Manufacturing (2016) International Journal of Production Research, 54 (23), pp. 7060-7073; Kumar, A., Shankar, R., Thakur, L.S., A Big Data Driven Sustainable Manufacturing Framework for Condition-Based Maintenance Prediction (2017) Journal of Computational Science, 27, pp. 428-439; Kwon, O., Lee, N., Shin, B., Data Quality Management, Data Usage Experience and Acquisition Intention of Big Data Analytics (2014) International Journal of Information Management, 34 (3), pp. 387-394. , https://doi.org/10.1016/j.ijinfomgt.2014.02.002; Lapide, L., Predictive Metrics (2010) The Journal of Business Forecasting, 29 (2), p. 23; LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N., Big Data, Analytics and the Path From Insights to Value (2011) MIT Sloan Management Review, 52 (2), p. 21; Lee, I., Big Data: Dimensions, Evolution, Impacts, and Challenges (2017) Business Horizons, 60 (3), pp. 293-303. , https://doi.org/10.1016/j.bushor.2017.01.004; Lockamy III, A., McCormack, K., Linking SCOR Planning Practices to SC Performance: An Exploratory Study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., (2011) Big Data: The Next Frontier for Innovation, Competition, and Productivity, , San Francisco, CA: McKinsey Global Institute; Matthias, O., Fouweather, I., Gregory, I., Vernon, A., Making Sense of Big Data–Can it Transform Operations Management? (2017) International Journal of Operations and Production Management, 37 (1), pp. 37-55. , https://doi.org/10.1108/IJOPM-02-2015-0084; Medori, D., Steeple, D., A Framework for Auditing and Enhancing Performance Measurement Systems (2000) International Journal of Operations & Production Management, 20 (5), pp. 520-533; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply Chain Performance Measures and Metrics: A Bibliometric Study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Müller, O., Fay, M., vom Brocke, J., The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics (2018) Journal of Management Information Systems, 35 (2), pp. 488-509. , https://doi.org/10.1080/07421222.2018.1451955; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Ntabe, E.N., LeBel, L., Munson, A.D., Santa-Eulalia, L.A., A Systematic Literature Review of the SC Operations Reference (SCOR) Model Application with Special Attention to Environmental Issues (2015) International Journal of Production Economics, 169, pp. 310-332; Pang, K.-W., Chan, H.-L., Data Mining-Based Algorithm for Storage Location Assignment in a Randomised Warehouse (2017) International Journal of Production Research, 55 (14), pp. 4035-4052; Popovič, A., Hackney, R., Tassabehji, R., Castelli, M., The Impact of Big Data Analytics on Firms’ High Value Business Performance (2018) Information Systems Frontiers, 20 (2), pp. 209-222. , https://doi.org/10.1007/s10796-016-9720-4; Raguseo, E., Vitari, C., Investments in Big Data Analytics and Firm Performance: An Empirical Investigation of Direct and Mediating Effects (2018) International Journal of Production Research, 56 (15), pp. 5206-5221. , https://doi.org/10.1080/00207543.2018.1427900; Ramanathan, R., Philpott, E., Duan, Y., Cao, G., Adoption of Business Analytics and Impact on Performance: A Qualitative Study in Retail (2017) Production Planning & Control, 28 (11-12), pp. 985-998; Richey, R.G., Morgan, T.R., Lindsey-Hall, K., Adams, F.G., A Global Exploration of Big Data in the Supply Chain: Global Exploration of Big Data (2016) International Journal of Physical Distribution and Logistics Management, 46 (8), pp. 710-739. , https://doi.org/10.1108/IJPDLM-05-2016-0134; (2008) Introduction to GreenSCOR: Introducing Environmental Considerations to the CSOR Model, , March, proceedings of the North America conference and exposition March (17–19; Schläfke, M., Silvi, R., Möller, K., A Framework for Business Analytics in Performance Management (2013) International Journal of Productivity and Performance Management, 62 (1), pp. 110-122. , https://doi.org/10.1108/17410401311285327; Schoenherr, T., Swink, M., The Roles of SC Intelligence and Adaptability in New Product Launch Success (2015) Decision Sciences, 46 (5), pp. 901-936; Shepherd, C., Günter, H., Measuring SC Performance: Current Research and Future Directions (2006) International Journal of Productivity and Performance Management, 55 (3), pp. 242-258; Song, P., Zheng, C., Zhang, C., Yu, X., Data Analytics and Firm Performance: An Empirical Study in an Online B2C Platform (2018) Information and Management, 55 (5), pp. 633-642. , https://doi.org/10.1016/j.im.2018.01.004; Srinivasan, R., Swink, M., An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective (2018) Production and Operations Management, 27 (10), pp. 1849-1867. , https://doi.org/10.1111/poms.12746; St-Pierre, J., Delisle, S., An Expert Diagnosis System for the Benchmarking of SMEs’ Performance (2006) Benchmarking, 13 (1-2), pp. 106-119. , https://doi.org/10.1108/14635770610644619; Stefanovic, N., Collaborative Predictive Business Intelligence Model for Spare Parts Inventory Replenishment (2015) Computer Science and Information Systems, 12 (3), pp. 911-930; Tan, K.H., Managerial Perspectives of Big Data Analytics Capability Towards Product Innovation (2018) Strategic Direction, 34 (8), pp. 33-35. , https://doi.org/10.1108/SD-06-2018-0134; Tan, K.H., Zhan, Y.Z., Ji, G., Ye, F., Chang, C., Harvesting Big Data to Enhance SC Innovation Capabilities: An Analytic Infrastructure Based on Deduction Graph (2015) International Journal of Production Economics, 165 (2015), pp. 223-233; Teece, D.J., Pisano, G., Shuen, A., Dynamic Capabilities and Strategic Management (1997) Strategic Management Journal, 18 (7), pp. 509-533; Tranfield, D., Denyer, D., Smart, P., Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review (2003) British Journal of Management, 14, pp. 207-222; Trieu, V., Getting Value From Business Intelligence Systems: A Review and Research Agenda (2017) Decision Support Systems, 93, pp. 111-124. , https://doi.org/10.1016/j.dss.2016.09.019; Trkman, P., McCormack, K., De Oliveira, M.P.V., Ladeira, M.B., The Impact of Business Analytics on Supply Chain Performance (2010) Decision Support Systems, 49 (3), pp. 318-327. , https://doi.org/10.1016/j.dss.2010.03.007; Viet, N.Q., Behdani, B., Bloemhof, J., The Value of Information in Supply Chain Decisions: A Review of the Literature and Research Agenda (2018) Computers & Industrial Engineering, 120, pp. 68-82; Waller, M.A., Fawcett, S.E., Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform SC Design and Management (2013) Journal of Business Logistics, 34 (2), pp. 77-84; Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘big Data’can Make big Impact: Findings From a Systematic Review and a Longitudinal Case Study (2015) International Journal of Production Economics, 165, pp. 234-246; Wamba, S.F., Akter, S., Kang, H., Bhattacharya, M., Upal, M., The Primer of Social Media Analytics (2016) Journal of Organizational and End User Computing, 28 (2), pp. 1-12. , https://doi.org/10.4018/JOEUC.2016040101; Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R., Childe, S.J., Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities (2017) Journal of Business Research, 70, pp. 356-365. , https://doi.org/10.1016/j.jbusres.2016.08.009; Wong, H., Potter, A., Naim, M., Evaluation of Postponement in the Soluble Coffee Supply Chain: A Case Study (2011) International Journal of Production Economics, 131 (1), pp. 355-364. , https://doi.org/10.1016/j.ijpe.2010.08.015; Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., Ibrahim, O., Influence of Big Data Adoption on Manufacturing Companies’ Performance: An Integrated DEMATEL-ANFIS Approach (2018) Technological Forecasting and Social Change, 137, pp. 199-210. , https://doi.org/10.1016/j.techfore.2018.07.043; Yu, C., Matta, A., A Statistical Framework of Data-Driven Bottleneck Identification in Manufacturing Systems (2016) International Journal of Production Research, 54 (21), pp. 6317-6332. , https://doi.org/10.1080/00207543.2015.1126681; Zhan, Y., Tan, K.H., An Analytic Infrastructure for Harvesting Big Data to Enhance Supply Chain Performance (2018) European Journal of Operational Research, , https://doi.org/10.1016/j.ejor.2018.09.018, and; Zhan, Y., Tan, K.H., Ji, G., Chung, L., Tseng, M., A Big Data Framework for Facilitating Product Innovation Processes (2017) Business Process Management Journal, 23 (3), pp. 518-536; Zhu, S., Song, J., Hazen, B.T., Lee, K., Cegielski, C., How SC Analytics Enables Operational SC Transparency: An Organizational Information Processing Theory Perspective (2018) International Journal of Physical Distribution & Logistics Management, 48 (1), pp. 47-68},
correspondence_address1={Gunasekaran, A.; School of Business and Public Administration, United States; email: agunasekaran@csub.edu},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Review},
source={Scopus},
}

@ARTICLE{Charkha202099,
author={Charkha, P.G. and Jaju, S.B.},
title={Decision Support System for Supply Chain Performance Measurement: Case of Textile Industry},
journal={Studies in Big Data},
year={2020},
volume={64},
pages={99-131},
doi={10.1007/978-3-030-25778-1_5},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132865144&doi=10.1007%2f978-3-030-25778-1_5&partnerID=40&md5=c8aba2680bdb07662e32a53493763ec7},
affiliation={Department of Mechanical Engineering, Nutan Maharashtra Institute of Engineering & Technology, Maharashtra, Pune, India; Department of Mechanical Engineering, G. H. Raisoni College of Engineering, Maharashtra, Nagpur, India},
abstract={The chapter aims to propose decision support framework for identification of key performance indicators for supply chain performance measurement in textile industry. To meet the objective, hierarchical model is developed including criteria, sub-criteria, alternatives. Proposed decision support system (DSS) is developed with objective as textile supply chain performance measurement with 4 stakeholder value creation perspectives (Financial, Customer, Internal business processes, Innovation and learning) suggested by balanced scorecard (BSC) as criteria, having 23 sub-criteria (key performance indicators) and 3 supply chain operations (Procurement-Production-Distribution) as alternatives. Structured Delphi method and Minitab 17.0 software for statistical analysis are used for the development of decision support framework. Whereas, for establishing pair wise comparison and analyzing the relative importance of these criteria, sub-criteria and alternative, analytical hierarchy process (AHP) is used. The paper demonstrates the application of decision support system in identifying key performance indicators and analyzing the importance of KPIs towards overall performance of supply chain in textile. The proposed system is put to test at four textile case industries. Case company wise implication as well general implications on textile supply chain performance is presented. Also, managerial implication and limitations are presented. Proposed study presented novel approach in developing DSS for textile supply chain performance measurement, which is very rare one. This DSS will be used for identifying key performance metrics/indicators and also analyzing their importance towards supply chain performance. Use of BSC perspective has given framework wholesome coverage of all stakeholders’ consideration. Also, involvement of supply chain cyclic operation added consideration of supply chain partners. © 2020, Springer Nature Switzerland AG.},
author_keywords={Analytical hierarchy process (AHP);  Decision support system (DSS);  Delphi method;  Key performance indicators (KPIs);  Performance measurement;  Supply chain management (SCM);  Supply chain performance measurement (SCPM)},
keywords={Artificial intelligence;  Benchmarking;  Decision making;  Hierarchical systems;  Supply chain management;  Textile industry;  Textiles, Analytical Hierarchy Process;  Decision support system;  Delphi method;  Key performance indicator;  Key performance indicators;  Performance measurements;  Supply chain management;  Supply chain performance measurement;  Supply chain performance measurements, Decision support systems},
references={Agrawal, S., Singh, R.K., Murtaza, Q., Prioritizing critical success factors for reverse logistics implementation using fuzzy-TOPSIS methodology (2016) J. Ind. Eng. Int., 12, pp. 15-27. , https://doi.org/10.1007/s40092-015-0124-8; Arianezhad, M.G., Makuie, M., Khayatmoghadam, S., Developing and solving two echelon inventory system for perishable items in a supply chain: Case study (Mashhad Behrouz Company) (2013) J. Ind. Eng. Int., 9; Babazadeh, R., Razmi, J., Ghodsi, R., Supply chain network design problem for a new market opportunity in an agile manufacturing system (2012) J. Ind. Eng. Int., 8; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng., 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain using the analytical hierarchy process (2007) Prod. Plan. Control Manag. Oper., 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Prod. Plan. Control Manag. Oper., 20 (8), pp. 678-690. , https://doi.org/10.10080/09537280903069897; Bhagwat, R., Chan, F.T.S., Sharma, M.K., Performance measurement model for supply chain management in SMEs (2008) Int. J. Glob. Small Bus., 2 (4), pp. 428-444; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Bititci, U., Cavalieri, S., Cieminski, G., Implementation of performance measurement system: Public and private sector (2005) Prod. Plan. Control Manag. Oper., 16 (2), pp. 99-100; Bourne, M., Neely, A., Implementing performance measurement system: A literature review (2003) Int. J. Bus. Perform. Manage., 5 (1), pp. 1-23; Browne, J., Devlin, J., Rolstadas, A., Andersen, B., Performance measurement: The ENAPS approach (1998) Int. J. Bus. Transform, 1 (2), pp. 73-84; Cao, N., Zhang, Z., Man To, K., Po Ng, K., How are supply chains coordinated? An empirical observation in textile-apparel businesses (2008) J. Fashion Mark. Manage., 12 (3), pp. 384-397; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol., 21, pp. 534-548. , https://doi.org/10.1007/s001700300063; Chan, Y.C.L., An analytic hierarchy framework for evaluating balanced scorecards of healthcare organizations (2006) Can. J. Adm. Sci., 23 (2), pp. 85-104; Chandra, C., Kumar, S., An application of system analysis methodology to manage logistic in textile supply chain (2000) Supply Chain Manage, 5 (5), pp. 234-245; Charkha, P.G., Jaju, S.B., Supply chain performance measurement system: An overview (2014) Int. J. Bus. Perform. Supply Chain Model., 6 (1), pp. 40-60; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) Int. J. Logist. Syst. Manage., 6 (1), pp. 216-230; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) Int. J. Logist. Manage., 8 (1), pp. 1-13; Dalalah, D., Al-Oqla, F., Hayajneh, M.: Application of analytical hierarchy process in multi criteria analysis of selection of cranes. Jordan J. Mech. Ind. Eng. 4(5), 567–578 (2010). ISSN: 1995-6665; Digalwar, A.K., Sangwan, K.S., An overview of existing performance measurement frameworks in the context of world class manufacturing performance measurement (2011) Int. J. Serv. Oper. Manage., 9 (1), pp. 60-82; Gopal, P.R.C., Thakkar, J., A review of supply chain performance measures and metrics: 2000–2011 (2012) Int. J. Prod. Perform. Manage., 61 (5), pp. 518-547; Gunasekaran, A., Kobu, P., Performance measures and metrics in logistics and supply chain management: Review of recent literature (1995–2004) for research and application. Int. J. Prod (2007) Res, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in supply chain environment (2001) Int. J. Prod. Oper. Manage., 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-348; Hall, D.C., Saygin, C., Impact of information sharing on supply chain performance (2011) Int. J. Adv. Manuf. Technol., 58 (1-4), pp. 397-409. , https://doi.org/10.1007/s00170-011-3389-0; Jadhav, J.R., Mantha, S.S., Rane, S.B., Analysis of interactions among the barriers to JIT production: Interpretive structural modeling approach (2015) J. Ind. Eng. Int., 11, pp. 331-352. , https://doi.org/10.1007/s40092-014-0092-4; Jadhav, J.R., Mantha, S.S., Rane, S.B., Roadmap for Lean implementation in Indian automotive component manufacturing industry: Comparative study of UNIDO model and ISM model (2015) J. Ind. Eng. Int., 11, pp. 179-198. , https://doi.org/10.1007/s40092-014-0074-6; Kumar, S., Luthra, S., Haleem, A., Customer involvement in greening the supply chain: An interpretive structural modeling methodology (2013) J. Ind. Eng. Int., 9; Lam, J.K.C., Textile and apparel supply chain management in Hong Kong (2006) Int. J. Cloth. Sci. Technol., 18 (4). , https://doi.org/10.1108/09556220610668-491; Mathur, A., Dangayach, G.S., Mittal, M.L., Sharma, M.K., Performance measurement in automated manufacturing (2011) Meas. Bus. Excel., 15 (1), pp. 77-91; Moghaddam, R.T., Forouzanfar, F., Ebrahimnejad, S., Incorporating location, routing, and inventory decisions in a bi-objective supply chain design problem with risk-pooling (2013) J. Ind. Eng. Int., 9; Moon, K.K.L., Yi, C.Y., Ngai, E.W.T., An instrument for measuring supply chain flexibility for the textile and clothing companies (2012) Eur. J. Oper. Res., 22 (2), pp. 191-203; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Bus. Process Manag. J., 10 (5), pp. 522-536; Saaty, T.L., How to make a decision: The analytic hierarchy process (1990) Eur. J. Oper. Res., 48, pp. 9-26; Saaty, T.L., Keams, K.P., (1985) Analytical Planning: The Origination of Systems, , Pergamon Press; Sanders, N.R.: Supply Chain Management: A Global Perspective. Wiley, New York (2011). ISBN 978-0-470-91395-6; Sardar, S., Lee, Y.H., Recent researches and future research directions in textile supply chain management (2012) UTCC Int. J. Bus. Econ., 4 (2); Shah, N., Process industry supply chains: Advances and challenges (2005) Comput. Chem. Eng., 29, pp. 1225-1235; Sharma, M.K., Bhagwat, R., Performance measurement system: Case studies from SMEs in India (2007) Int. J. Prod. Qual. Manage., 2 (4), pp. 475-509; Singh, R.K., Sharma, M.K., Prioritizing the alternatives for flexibility in supply chain (2014) Prod. Plan. Control Manag. Oper., 25 (2), pp. 176-192. , https://doi.org/10.1080/09537287.2013.782951; Singh, R.K., Sharma, M.K., Selecting competitive supply chain using fuzzy AHP and extent analysis (2015) J. Ind. Prod. Eng., 31 (8), pp. 524-538. , https://doi.org/10.1080/21681015.999723; Tabrizi, B.H., Razmi, J., A multi-period distribution network design model under demand uncertainty (2013) J. Ind. Eng. Int., 9; Taghizadeh, H., Hafez, E., The investigation of supply chain’s reliability measure: A case study (2012) J. Ind. Eng. Int., 8; Teng, S.G., Jaramillo, H., A model for evaluation and selection of suppliers in global textile and apparel supply chains (2005) Int. J. Phys. Distrib. Logist. Manag., 35 (7), pp. 503-523; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking Int. J., 16 (5), pp. 702-723; Turner, T.J., Bititci, U.S., Nudurupati, S., Implementation and impact of performance measures in two SMEs in Central Scotland (2005) Prod. Plan. Control Manag. Oper., 16 (2), pp. 135-151; Varma, S., Wadhwa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance: application of analytical hierarchy process to balanced scorecard (2008) Asia Pac. J. Mark. Logist., 20 (3), pp. 343-356; Vommi, V.B., Kakollu, S., A simple approach to multiple attribute decision making using loss functions (2016) J. Ind. Eng. Int., , https://doi.org/10.1007/s40092-016-0174-6; Wong, J.K., Li, H., Application of analytical hierarchy process (AHP) in multi-criteria analysis of the selection of intelligent building system (2007) Build. Environ., , https://doi.org/10.1016/j.buildenv.2006.11.019; Yadav, V., Sharma, M.K., An application of hybrid data envelopment analytical hierarchy process approach for supplier selection (2015) J. Enterp. Inf. Manage., (2), pp. 218-242; Bedi, J.S., Report on Assessing the Prospects for India’s Textile and Clothing Sector (2009) National Council of Applied Economic Research; Chandra, P., The textile and apparel industry in India (2006) Oxford Companion to Economies in India, 2006; Charkha, P.G., Jaju, S.B.: Performance measurement system for supply chain management: case of a textile industry in India. In: Supply Chain Management: Application for Manufacturing & Service Industries, pp. 141–168. Nova Science Publishers, Inc. (2016). ISBN 978-1-63484-987-6; Kaplan, R.S., Nortan, D.P., (1992) The Balanced Scorecard—Measures that Drive Performance, , Harvard Business School; Lapide, L., What about measuring supply chain performance? (2000) Supply Chain Manage. Rev., 4 (2), pp. 287-297; Swaminathan, J.M., Managing supply chain operation in India (2007) Building Supply Chain Excellence in Emerging Economies, pp. 137-154. , pp; Xia, L.X.X., Lim, R., Ma, B., AHP Based Supply Chain Performance Measurement System. 1-4244-0826-1/07. IEEE Xplore Digital (2007) Library},
correspondence_address1={Charkha, P.G.; Department of Mechanical Engineering, Maharashtra, India; email: pgcharkha@gmail.com},
publisher={Springer Science and Business Media Deutschland GmbH},
issn={21976503},
language={English},
abbrev_source_title={Stud. Big. Data.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{daCostaPinto2020647,
author={da Costa Pinto, E. and Scavarda, L.F. and de Oliveira, G.S.},
title={Performance Measurement System: A Case Study},
journal={Springer Proceedings in Business and Economics},
year={2020},
pages={647-657},
doi={10.1007/978-3-030-23816-2_63},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126263039&doi=10.1007%2f978-3-030-23816-2_63&partnerID=40&md5=e84f68e9ec95fe659eb8becdf2b879ab},
affiliation={Petrobras, Rio de Janeiro, Brazil; PUC-Rio, Rio de Janeiro, Brazil},
abstract={This paper analyzes the performance measurement system (PMS) from the perspective of the redesign phase. It presents the findings of a case study in the oil industry. It addresses a research–practice gap regarding the lack of studies in this phase when compared to the other ones, especially the design phase. © 2020, Springer Nature Switzerland AG.},
author_keywords={Business process;  Logistics;  Supply chain},
references={Beamon, B., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: An action research (2011) Int. J. Prod. Econ., 133, pp. 751-760; Coelho, A.L., Nascimento, S., Coelho, C., Bortoluzzi, S., Ensslin, L., Avaliação de desempenho organizacional: Uma investigação científica das principais ferramentas gerenciais (2008) Congresso Brasileiro De Custos, p. 15. , Curitiba. Anais…Paraná: ABC; Coelho, M.Q.: Indicadores de performance para projetos sociais: a perspectiva dos stakeholders. Alcance Biguaçu 11(3), 423–444. set/dez.2004 (2004); Costa Pinto, E., Scavarda, L.F., Simões, G., Redesenho de Sistema de Medição de Desempenho: Um framework e sua aplicação na cadeia de suprimento de óleo e gás (2018) SIMPEP – UNESP – São Paulo; de Chermatony, L., Dall’Olmo, R., Defining a brand: Beyond the literature (1998) J. Mark. Manage., 14, pp. 41-443; Dornhofer, M., Gunthner, W.A., A research and industry perspective on automotive logistics performance measurement (2017) Int. J. Logist. Manage., 28 (1), pp. 102-126; Fernandez, N.S., Scavarda, L.F., Leiras, A., Hamacher, S., Diseño de Sistemas de medición de desempeño de provedores: Experiências de um caso de estúdio (2012) Produção, 22 (1), pp. 43-57; Ghalayni, A.M., Noble, J.S., The changing basis of performance measurement (1996) Int. J. Oper. Prod. Manage., 16 (8), pp. 63-80; Goshu, Y.Y., Kitaw, D., Performance measurement and its recent challenge: A literature review (2017) Int. J. Bus. Perform. Manage., 18 (4), pp. 381-402; Gunasekaran, A., Kobu, B., Performance measures and a metrics in SCM: A review of recent literature (1995–2004) and applications (2007) Int. J. Prod. Res., 45, pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Gutierrez, D.M., Scavarda, L.F., Florencio, L., Martins, R.A., Evolution of the performance measurement system in the logistics department of a broadcasting company: An action research (2015) Int. J. Prod. Econ., 160 (1-12); Hourneaux, F., Carneiro-Da-cunha, J.A., Corrêa, H.L., Performance measurement and management systems: Different usages in Brazilian manufacturing companies (2017) Manag. Auditing J., 32 (2), pp. 148-166; Lapide, L., True measures of supply chain performance (2000) Supply Chain Management Review, Pp. 25–28, July/August; Martins, R.A., Costa Neto, P.L., Indicadores de desempenho para a gestão pela qualidade total: Uma proposta de sistematização (1998) Revista Produção, 5 (3), pp. 298-311; Melnyk, S.A., Bitici, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manage. Acc. Res., 25 (2), pp. 173-186; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) J. Oper. Manage., 22, pp. 209-217; Merchant, K.A., Measuring general managers performances (2006) Market, Accounting and Combination-Of-Measures Systems. Acc. Auditing Acc. J., 19 (6), pp. 893-917; Muscat, A.R., Fleury, A.C.C., (1993) Indicadores De Qualidade E Produtividade Na indústria Brasileira. Revista Indicadores Da Qualidade E Produtividade, pp. 83-107; Nascimento, S., Bortoluzzi, S.C., Dutra, A., Mapeamento dos indicadores de desempenho organizacional em pesquisas da área de administração, Ciências Contábeis e Turismo no período de 2000 a 2008 (2011) R. Adm., São Paulo, 46 (4), pp. 373-391; Neely, A., Kennerly, M., A framework of the factors affecting the evolution of performance measurement systems (2002) Int. J. Oper. Prod. Manage., 22 (11), pp. 1222-1245; Neely, A.D., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) Int. J. Prod. Econ., 46-47, pp. 423-431; Neves, L.W.A., Hamacher, S., Scavarda, L.F., Outsourcing from the perspectives of TCE and RBV: A multiple case study (2014) Produção (São Paulo. Impresso), 24, pp. 687-699; Pires, S., Gestão da cadeia de suprimentos e o modelo de consórcio modular. Revista de Administração/USP (1998) São Paulo 33(3); Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indicators from a study in the automotive industry (2004) Int. J. Prod. Econ., 89 (2), pp. 231-243; Shahin, A., Mahbod, M.A., Priorization of key performance indicators: Am integration of analytical hierarchy process and goal setting (2007) Int. J. Prod. Perform. Manage., 56 (3), pp. 226-240; Simon, R., Dávila, A., Kaplan, R., (2000) Performance Measurement and Control Systems for Implementing Strategy: Texts and Cases, , Prentice Halll, Upper Saddle River, N.J; Takashina, N.T., Flores, M.C.X., (1996) Indicadores Da Qualidade E Do Desempenho – Como Estabelecer E Medir Resultados. Qualitymark, , Rio de Janeiro; Wisner, J.D., Fawcett, S.E., Link firm strategy to operating decisions through performance measurements. Prod. Inventory Manage (1991) J. Third Quarter, pp. 5-11},
correspondence_address1={da Costa Pinto, E.; PetrobrasBrazil; email: edsonpinto@petrobras.com.br},
editor={Leiras A., Gonzalez-Calderon C.A., de Brito Junior I., Villa S., Yoshizaki H.T.},
publisher={Springer Science and Business Media B.V.},
issn={21987246},
isbn={9783030238155},
language={English},
abbrev_source_title={Springer Proc. Bus. Econ.},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Cordova2020479,
author={Cordova, M. and Coronado, F.},
title={Supply Chain Innovation and Sustainability Frontiers: A Balanced Scorecard Perspective},
journal={The Palgrave Handbook of Corporate Sustainability in the Digital Era},
year={2020},
pages={479-501},
doi={10.1007/978-3-030-42412-1_24},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115352344&doi=10.1007%2f978-3-030-42412-1_24&partnerID=40&md5=82a4343368f7c92620f0baa0ed78aa0f},
affiliation={Pontificia Universidad Católica del Perú, Lima, Peru; Pontificia Universidad Católica del Perú, Lima, Peru},
abstract={Global trade has been growing at a steady pace since the 1950s, which has brought increasing operational challenges to supply chains. Technological advances and the “digital revolution” bring new opportunities to improve key supply chain processes in many sectors, including transportation. However, while embracing innovation, firms have to ensure that they do not lose strategic focus and that their performance measurement systems continue to evolve. This study explores how disruptive innovations in supply chain operations may produce unexpected outcomes for firms’ strategy. We analyze four examples of technological innovation in transport under a Balanced Scorecard framework. Our reflections shed light on the potential trade-offs that organizations may face between different dimensions of performance to pursue sustainable development goals. © The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021, corrected publication 2021.},
references={Agrawal, P., Narain, R., Digital supply chain management: An overview (2018) IOP Conference Series: Materials Science and Engineering, 455 (1), p. 012074. , https://doi.org/10.1088/1757-899X/455/1/012074, Conference paper; Ballinger, B., Stringer, M., Schmeda-Lopez, D.R., Kefford, B., Parkinson, B., Greig, C., Smart, S., The vulnerability of electric vehicle deployment to critical mineral supply (2019) Applied Energy, 255, p. 113844. , https://doi.org/10.1016/j.apenergy.2019.113844; (2019) Cómo el GPS se volvió invaluable en sólo 3 décadas (y por qué el riesgo de una falla es tan estremecedor), , https://www.bbc.com/mundo/noticias-50398099, Retrieved from, Last call 04 Jan 2020; (2019) Inside Ocado’s burning robotic warehouse, , https://www.bbc.com/news/technology-47160448, Retrieved from, Last call 06 Jan 2020; Berry, A.J., Coad, A.F., Harris, E.P., Otley, D.T., Stringer, C., Emerging themes in management control: A review of recent literature (2009) The British Accounting Review, 41 (1), pp. 2-20. , https://doi.org/10.1016/j.bar.2008.09.001; (2018) Beverage plants boost productivity with automated guided vehicles, , https://www.bevindustry.com/articles/91357-beverage-plants-boost-productivity-with-automated-guided-vehicles-agvs, Retrieved from, Last call 03 Sept 2019; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain man-agement: Moving toward new theory (2008) International Journal of Physical Distribution and Logistics Management, 38 (5), pp. 360-387. , https://doi.org/10.1108/09600030810882816; Cedillo-Campos, M.G., Pérez-González, C.M., Piña-Barcena, J., Moreno-Quintero, E., Measurement of travel time reliability of road transportation using GPS data: A freight fluidity approach (2019) Transportation Research Part A: Policy and Practice, 130, pp. 240-288. , https://doi.org/10.1016/j.tra.2019.09.018; Choi, T.Y., Dooley, K.J., Rungtusanatham, M., Supply networks and com-plex adaptive systems: Control versus emergence (2001) Journal of Operations Management, 19 (3), pp. 351-366. , https://doi.org/10.1016/S0272-6963(00)00068-1; Chopra, S., Meindl, P., (2013) Administración de la Cadena de Suministro, Estrategia Planeación y Operación, , 5th ed, México: Pearson Educación; Coyle, J., Langley, J., Novack, R., Gibson, B., (2013) Administración de la Cadena de Suministro, Una Perspectiva Logística, , (9th ed.). México: Cengage Learning Editores; Dyllick, T., Hockerts, K., Beyond the business case for corporate sustainabil-ity (2002) Business Strategy and the Environment, 11 (2), pp. 130-141. , https://doi.org/10.1002/bse.323; (2019) Earth-I official company web site, , https://earthi.space, Retrieved from, Last call 04 Jan 2020; (2019) Savant service web site, , https://earthi.space/savant, Retrieved from, Last call 04 Jan 2020; Edvinsson, L., Malone, M., (1997) Intellectual capital: Realizing your company’s true value by finding its hidden brainpower, , New York: Harper Collins; Ferreira, A., Otley, D., The design and use of performance management sys-tems: An extended framework for analysis (2009) Management Accounting Research, 20 (4), pp. 263-282. , https://doi.org/10.1016/j.mar.2009.07.003; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard - Linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11 (5), pp. 269-284. , https://doi.org/10.1002/bse.339; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2001) Journal of Operations Management, 19 (2), pp. 185-200. , https://doi.org/10.1016/S0272-6963(00)00055-3; Frow, N., Marginson, D., Ogden, S., Encouraging strategic behavior while maintaining management control: Multifunctional project teams, budgets, and the negotiation of shared accountabilities in contemporary enterprises (2005) Management Accounting Research, 16 (3), pp. 269-292. , https://doi.org/10.1016/j.mar.2005.06.004; Gestión, (2018) Volvo fabrica camiones de basura eléctricos y compite con Tesla, , https://gestion.pe/economia/empresas/volvo-fabrica-camiones-basura-electricos-compite-tesla-252298-noticia, Retrieved from, Last call 26 Aug 2019; Gestión, (2019) Satélites espía: La nueva arma de los operadores para vigilar fundiciones de cobre, , https://gestion.pe/mundo/satelites-espia-la-nueva-arma-de-los-operadores-para-vigilar-fundiciones-de-cobre-noticia, Retrieved from, Last call 08 Oct 2019; Ghemawat, P., Distance still matters. The hard reality of global expansion (2001) Harvard Business Review, 79 (8), pp. 137-140; Gittleman, M., Monaco, K., Automation isn’t about to make truckers obso-lete (2019) Harvard Business Review, , https://hbr.org/2019/09/auto-mation-isnt-about-to-make-truckers-obsolete, Retrieved from, Last call 06 Jan 2020; Goksoy, A., Vayvay, O., Ergeneli, N., Gaining competitive advantage through innovation strategies: An application in warehouse management processes (2013) American Journal of Business and Management, 2 (4), pp. 304-321. , https://doi.org/10.11634/216796061706463; Gonzalez-Perez, M.A., Leonard, L., (2016) Climate change and the 2030 corporate agenda for sustainable development, , Bingley: Emerald Group Publishing Limited; Graves, S.C., Willems, S.P., Optimizing the supply chain configuration for new products (2005) Management Sciences, 51 (8), pp. 1165-1180. , https://doi.org/10.1287/mnsc.1050.0367; Guo, X., Lujan Jaramillo, Y.J., Bloemhof-Ruwaard, J., Claassen, G.D.H., On integrating crowdsourced delivery in last-mile logistics: A simulation study to quantify its feasibility (2019) Journal of Cleaner Production, 241, p. 118365. , https://doi.org/10.1016/j.jclepro.2019.118365; Hansen, E.G., Schaltegger, S., The sustainability balanced scorecard: A sys-tematic review of architectures (2016) Journal of Business Ethics, 133 (2), pp. 193-221. , https://doi.org/10.1007/s10551-014-2340-3; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353. , https://doi.org/10.1108/14635770510609015; Hsu, Y., Liu, C., Environmental performance evaluation and strategy man-agement using balanced scorecard (2010) Environmental Monitoring and Assessment, 170 (1-4), pp. 599-607. , https://doi.org/10.1007/s10661-009-1260-7; Innella, F., Arashpour, M., Bai, Y., Lean methodologies and techniques for modular construction: Chronological and critical review (2019) Journal of Construction Engineering and Management, 145 (12), p. 04019076. , https://doi.org/10.1061/(ASCE)CO.1943-7862.0001712; Islam, S., Simulation of truck arrival process at a seaport: Evaluating truck-sharing benefits for empty trips reduction (2018) International Journal of Logistics Research and Applications, 21 (1), pp. 94-112. , https://doi.org/10.1080/13675567.2017.1353067; Jabbour, A.B.L., Jabbour, C.J.C., Govindan, K., Kannan, D., Arantes, A.F., Mixed methodology to analyze the relationship between maturity of environmental management and the adoption of green supply chain management in Brazil (2014) Resources, Conservation and Recycling, 92, pp. 255-267. , https://doi.org/10.1016/j.resconrec.2014.02.004; Jabbour, A.B.L., Jabbour, C.J.C., Latan, H., Teixeira, A.A., de Oliveira, J.H.C., Quality management, environmental management maturity, green supply chain practices and green performance of Brazilian companies with ISO 14001 certification: Direct and indirect effects (2014) Transportation Research Part E: Logistics and Transportation Review, 67, pp. 39-51. , https://doi.org/10.1016/j.tre.2014.03.005; Kaplan, R., Norton, D., (1996) The balanced scorecard: Translating strategy into action, , Boston: Harvard Business School Press; Kasliwal, A., Furbush, N.J., Gawron, J.H., McBride, J.R., Wallington, T.J., De Kleine, R.D., Kim, H.C., Keoleian, G.A., Role of flying cars in sustain-able mobility (2019) Nature Communications, 10, p. 1555. , https://doi.org/10.1038/s41467-019-09426-0; Lane, B., Potter, S., The adoption of cleaner vehicles in the UK: Exploring the consumer attitude-action gap (2007) Journal of Cleaner Production, 15 (11-12), pp. 1085-1092. , https://doi.org/10.1016/j.jclepro.2006.05.026; Leonard, L., Gonzalez-Perez, M.A., (2013) Principles and strategies to bal-ance ethical, social and environmental concerns with corporate requirements, , https://doi.org/10.1108/S2051-5030(2013)0000012017, Bingley: Emerald Group Publishing Limited; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational perfor-mance (2006) Omega, 34 (2), pp. 107-124. , https://doi.org/10.1016/j.omega.2004.08.002; Liu, J., Yuan, C., Hafeez, M., Yuan, Q., The relationship between environ-ment and logistics performance: Evidence from Asian countries (2018) Journal of Cleaner Production, 204 (10), pp. 282-291. , https://doi.org/10.1016/j.jclepro.2018.08.310; (2020) DAF: Motored presentó el CF electric que recorre alrededor de 150, 000 km con propulsión eléctrica, , https://logistica360.pe/daf-motored-presento-el-cf-electric-que-recorre-alrededor-de-150-000-km-con-pro-pulsion-electrica, Retrieved from, Last call 15 Jan 2020; Londoño, A.A., Granada-Echeverri, M., Optimal placement of freight elec-tric vehicles charging stations and their impact on the power distribution network (2019) International Journal of Industrial Engineering Computations, 10 (4), pp. 535-556. , https://doi.org/10.5267/j.ijiec.2019.3.002; López-Avilés, A., Veldhuis, A.J., Leach, M., Yang, A., Sustainable energy opportunities in localized food production and transportation: A case study of bread in the UK (2019) Sustainable Production and Consumption, 20, pp. 98-116. , https://doi.org/10.1016/j.spc.2019.05.004; Lucianetti, L., The impact of strategy maps on balanced scorecard performance (2010) International Journal of Business Performance Management, 12 (1), pp. 21-36. , https://doi.org/10.1504/IJBPM.2010.036040; Marcucci, E., Gatta, V., Le Pira, M., Gamification design to foster stake-holder engagement and behavior change: An application to urban freight transport (2018) Transportation Research Part A: Policy and Practice, 118, pp. 119-132. , https://doi.org/10.1016/j.tra.2018.08.028; McCormick, K., Anderberg, S., Coenen, L., Neij, L., Advancing sustainable urban transformation (2013) Journal of Cleaner Production, 50, pp. 1-11. , https://doi.org/10.1016/j.jclepro.2013.01.003; Merchant, K.A., Otley, D.T., A review of the literature on control and accountability (2006) Handbooks of Management Accounting Research, 2, pp. 785-802. , https://doi.org/10.1016/S1751-3243(06)02013-X; (2018) Orica releases Bulkmaster 7 MMU, , https://www.miningmagazine.com/innovation/news/1332256/orica-releases-bulkmas-ter-7-mmu, Retrieved from, Last call 05 Jan 2020; Moreo, A., DeMicco, F.J., Xiong, L., Towards a model to measure the qual-ity of environmental sustainability: The hospitality environmental scorecard (2009) Journal of Quality Assurance in Hospitality and Tourism, 10 (1), pp. 44-58. , https://doi.org/10.1080/15280080802713728; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a pro-cess-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145. , https://doi.org/10.1108/01443570010343708; (2010) Globalisation, transport and the environment, , https://read.oecd-ilibrary.org/environment/globalisation-transport-and-the-environment_9789264072916-en#page1, Retrieved from, Last call 02 Sept 2019; (2017) ITF Transport Outlook 2017, , https://doi.org/10.1787/9789282108000-en, OECD Publishing, Paris. Retrieved from; Oke, A., Innovation types and innovation management practices in service com-panies (2007) International Journal of Operations and Production Management, 27 (6), pp. 564-587. , https://doi.org/10.1108/01443570710750268; (2018) Life as an MMU Operator - Orica Quarry Solutions, Stevenson Group, , https://www.youtube.com/watch?v=l3HQlYn6r9g, May 22, [video file]. Retrieved from; (2018) Delivering a step change in productivity and on bench safety with the next generation Bulkmaster™ 7 explosives delivery system, , https://www.orica.com/news%2D%2D-media/delivering-a-step-change-in-productivity-and-on-bench-safety-with-the-next-generation-bulkmaster-7-explo-sives-delivery-system#.XaMvz0YzY2w, Retrieved from, Last call 06 Oct 2019; Park, J.H., Kim, M., Lee, S., Lee, K.C., Development for autonomous load-ing and unloading for network-based unmanned forklift (2011) Journal of Institute of Control, Robotics and Systems, 17 (10), pp. 1051-1058. , https://doi.org/10.5302/J.ICROS.2011.17.10.1051; Patel, S., Swartz, C.L.E., Supply chain design with time-limited transporta-tion contracts (2019) Computers and Chemical Engineering, 131, p. 106579. , https://doi.org/10.1016/j.compchemeng.2019.106579; (2019) Automated forklifts elevate firms’ profit hopes, , https://phys.org/news/2019-07-automated-forklifts-elevate-firms-profit.html, Retrieved from, Last call 02 Sept 2019; Porter, M.E., Clusters and the new economics of competition (1998) Harvard Business Review, 76 (6), pp. 77-90; Porter, M.E., Van Der Linde, C., (2009) Verdes y competitivos: Acabar con la disyun-tiva. Ser Competitivo, , (4th ed.). Barcelona: Ediciones Deusto; Prideaux, B., The role of the transport system in destination development (2000) Tourism Management, 21 (1), pp. 53-63. , https://doi.org/10.1016/S0261-5177(99)00079-5; Reddy, J.P., Neelakanteswara, R., Krishnanand, L., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30, pp. 40-47. , https://doi.org/10.1016/j.promfg.2019.02.007; Render, B., Heizer, J., (2014) Principios de Administración de Operaciones, , (9th ed.). México: Pearson Education; (2018) A slew of electric truck plans may deliver the goods for China’s EV ambitions, , https://www.reuters.com/article/us-china-autos-trucks-electric-focus/a-slew-of-electric-truck-plans-may-deliver-the-goods-for-chi-nas-ev-ambitions-idUSKCN1NK35I, Retrieved from, Last call 05 Jan 020; (2019) Satélites espía: La nueva arma de los operadores para vigilar fundicio-nes de cobre, , https://lta.reuters.com/articulo/cobre-satelite-idL-TAKCN1WN1DM, Retrieved from, Last call 04 Jan 2020; (2019) Amazon to invest in French firm’s technology for self-driving froklifts, , https://www.reuters.com/article/us-balyo-amazon-com-autonomous/amazon-to-invest-in-french-firms-technology-for-self-driving-fork-lifts-idUSKCN1P40OZ, Retrieved from, Last call 06 Jan 2020; Schaltegger, S., Wagner, M., Integrative management of sustainability perfor-mance, measurement and reporting (2006) International Journal of Accounting, Auditing and Performance Evaluation, 3 (1), pp. 1-19. , https://doi.org/10.1504/IJAAPE.2006.010098; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710. , https://doi.org/10.1016/j.jclepro.2008.04.020; Simons, R., Control in an age of empowerment (1995) Harvard Business Review, 73 (2), pp. 80-86; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80. , https://doi.org/10.1111/j.1468-2370.2007.00202.x; Suresh, S., Vasantha, S., Influence of ICT in road transportation (2018) International Journal of Supply Chain Management, 7 (6), pp. 49-56; Sveiby, K.E., The intangible assets monitor (1997) Journal of Human Resource Costing and Accounting, 2 (1), pp. 73-97. , https://doi.org/10.1108/eb029036; Syu, J.L., Li, H.T., Chiang, J.S., Hsia, C.H., Wu, P.H., Hsieh, C.F., Li, S.A., A computer vision assisted system for autonomous forklift vehicles in real factory environment (2017) Multimedia Tools and Applications, 76 (18), pp. 18387-18407. , https://doi.org/10.1007/s11042-016-4123-6; Szymczak, M., Ryciuk, U., Leonczuk, D., Piotrowicz, W., Witkowski, K., Nazarko, J., Jakuszewicz, J., Key factors for information integration in the supply chain - measurement, technology and information characteristics (2018) Journal of Business Economics and Management, 19 (5), pp. 759-776. , https://doi.org/10.3846/jbem.2018.6359; Teece, D.J., Profiting from technological innovation: Implications for integra-tion, collaboration, licensing and public policy (1986) Research Policy, 15 (6), pp. 285-305. , https://doi.org/10.1016/0048-7333(86)90027-2; (2018) London, electric trucks are helping UPS make ‘eco-friendly’ deliveries, , https://www.nytimes.com/2018/09/21/busi-ness/energy-environment/electric-ups-trucks-in-london.html, Retrieved from, Last call 05 Jan 2020; (2001) Trade and transport efficiency, , https://unctad.org/en/Docs/rmt2001ch6_en.pdf, Retrieved from, Last call 28 Aug 2019; (2015) The sustainable development agenda, , https://www.un.org/sustainabledevelopment/development-agenda, Retrieved from, Last call 01 Oct 2019; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315. , https://doi.org/10.1016/j.ijpe.2006.11.030; (2019) Volvo Group y Samsung SDI forman una alianza estratégica para la electromovilidad, , https://www.volvogroup.pe/es-pe/news/2019/jul/news-3365888.html, Retrieved from, Last call 02 Sept 2019; (2018) Electrified transport - Crucial for sustainable urban develop-ment, , https://www.volvotrucks.com/en-en/news/volvo-trucks-magazine/2018/jul/electrified-transport.html, Retrieved from, Last call 05 Jan 2020; (2019) The Volvo FL electric: How does it work?, , https://www.volvotrucks.com/en-en/news/volvo-trucks-magazine/2019/nov/tech-in-focus-volvo-fl-electric.html, Retrieved from, Last call 05 Jan 2020; Wang, D.D., Assessing road transport sustainability by combining environmen-tal impacts and safety concerns (2019) Transportation Research Part D: Transport and Environment, 77, pp. 212-223. , https://doi.org/10.1016/j.trd.2019.10.022; Wang, J., Chi, H.L., Shou, W., Chong, H.Y., Wang, X., A coordinated approach for supply chain tracking in the liquefied natural gas industry (2018) Sustainability, 10 (12), p. 4822. , https://doi.org/10.3390/su10124822; Widener, S.K., An empirical analysis of the levers of control framework (2007) Accounting, Organizations and Society, 32 (7-8), pp. 757-788. , https://doi.org/10.1016/j.aos.2007.01.001; (2018) Connecting to compete 2018: Logistics performance index (LPI), , https://lpi.worldbank.org, Retrieved from, Last call 02 Sept 2019; (2019) Global competitiveness report 2019: How to end a lost decade of productivity growth, , http://www3.weforum.org/docs/WEF_TheGlobalCompetitivenessReport2019.pdf, Retrieved from, Last call 04 Oct 2019; (2018) Strong trade growth in 2018 rests on policy choices, , https://www.wto.org/english/news_e/pres18_e/pr820_e.htm, Retrieved from, Last call 08 Oct 2019; (2019) World trade report 2019: The future of services trade, , https://www.wto.org/english/res_e/booksp_e/00_wtr19_e.pdf, Retrieved from, Last call 07 Oct 2019; Wulf, C., Reuß, M., Grube, T., Zapp, P., Robinius, M., Hake, J.F., Stolten, D., Life cycle assessment of hydrogen transport and distribution options (2018) Journal of Cleaner Production, 199, pp. 431-443. , https://doi.org/10.1016/j.jclepro.2018.07.180; Zaid, A.A., Jaaron, A.A.M., Bon, A.T., The impact of green human resource management and green supply chain management practices on sustainable performance: An empirical study (2018) Journal of Cleaner Production, 204 (10), pp. 965-979. , https://doi.org/10.1016/j.jclepro.2018.09.062},
correspondence_address1={Cordova, M.; Pontificia Universidad Católica del PerúPeru; email: cordova.miguel@pucp.edu.pe},
publisher={Springer International Publishing},
isbn={9783030424121; 9783030424114},
language={English},
abbrev_source_title={The Palgrave Handb. of Corporate Sustainability in the Digital Era},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{El-Garaihy2020174,
author={El-Garaihy, W.H.},
title={Effectiveness of performance measurement framework on manufacturers supply chain – case of Saudi Arabia},
journal={Journal of Facilities Management},
year={2020},
volume={19},
number={2},
pages={174-194},
doi={10.1108/JFM-07-2020-0045},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100183331&doi=10.1108%2fJFM-07-2020-0045&partnerID=40&md5=e8bdd4bbedfa4e8b1810806eb8ff1d08},
affiliation={Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia},
abstract={Purpose: This study aims to provide a collaborative supply chain system using a combination of two already existed methods, i.e. levels of decision-making and supply chain operation reference (SCOR). Design/methodology/approach: A two-phase methodology was conducted based on field analysis and survey analysis. Field analysis included a critical analysis of literature related to supply chain performance measurement and identified related indicators. Findings: In a survey analysis, a questionnaire was developed to test the established indicators from managers and employees of manufacturing companies. It formulated a novel model for the performance measurement of the supply chain (SC) which included 4 factors (customer, efficiency, flexibility and product), with a total of 31 sub-components. Originality/value: The structure provides a tied horizontal (SCOR) and vertical (progressive decision) in relation to the features that characterize the SC performance. This system enables the decision-makers to make decisions at the strategic, tactical and operational levels. It reflects the performance measures reliability, authenticity and its application in various SC. It also improves communication with the regional party while ensuring a preventive schedule for maintenance. © 2020, Emerald Publishing Limited.},
author_keywords={Decision-making;  Performance measurement framework;  Saudi manufacturers;  Supply chain},
references={Abdallah, A.B., Al-Ghwayeen, W.S., Green supply chain management and business performance (2019) Business Process Management Journal, 26 (2); Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) Journal of Cleaner Production, 86, pp. 360-377; Ahire, S.L., Management science – total quality management interfaces: an integrative framework (1997) Interfaces, 27 (6), pp. 91-105; Anand, N., Grover, N., Measuring retail supply chain performance (2015) Benchmarking: An International Journal, 22 (1); Bagozzi, R.P., Performance and satisfaction in an industrial sales force: an examination of their antecedents and simultaneity (1980) Journal of Marketing, 44 (2), pp. 65-77; Bagozzi, R.P., Fornell, C., Theoretical concepts, measurements, and meaning (1982) The Second Generation of Multivariate Analysis, 2 (2), pp. 5-23; Bagozzi, R.P., Yi, Y., Phillips, L.W., Assessing construct validity in organizational research (1991) Administrative Science Quarterly, 36 (3), pp. 421-458; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Computers in Industry, 82, pp. 135-150; Beer, H.A., Micheli, P., Advancing performance measurement theory by focusing on subjects: Lessons from the measurement of social value (2018) International Journal of Management Reviews, 20 (3), pp. 755-771; Bentler, P.M., On the fit of models to covariances and methodology to the bulletin (1992) Psychological Bulletin, 112 (3), p. 400; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , https://doi.org/10.1016/j.cie.2007.04.001; Browne, J., Devlin, J., Rolstadas, A., Andersen, B., Performance measurement: the ENAPS approach (1997) International Journal of Business Transformation, 1, pp. 73-84; Campbell, D.T., Fiske, D.W., Convergent and discriminant validation by the multitrait-multimethod matrix (1959) Psychological Bulletin, 56 (2), p. 81; Cardoso, S.R., Barbosa-Póvoa, A.P., Relvas, S., Novais, A.Q., Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty (2015) Omega, 56, pp. 53-73; Chang, W., Ellinger, A.E., Kim, K.K., Franke, G.R., Supply chain integration and firm financial performance: a meta-analysis of positional advantage mediation and moderating factors (2016) European Management Journal, 34 (3), pp. 282-295; Chavez, R., Yu, W., Jacobs, M., Fynes, B., Wiengarten, F., Lecuna, A., Internal lean practices and performance: the role of technological turbulence (2015) International Journal of Production Economics, 160, pp. 157-171. , http://dx.doi.org/10.1016/j.ijpe.2014.10.005; Chopra, S., Sodhi, M., Reducing the risk of supply chain disruptions (2014) MIT Sloan Management Review, 55 (3), pp. 72-80; Christopher, M., (2016) Logistics and Supply Chain Management, , Pearson; Delgadillo, G.O., Castorena, O.H., Fuentes, M.D.L.Y., Zapata, F.J.O., The influence and management of the supply chain performance of manufacturing SMEs in Aguascalientes (2016) Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations, pp. 210-221. , M. and IGI Global; Ding, L., Velicer, W.F., Harlow, L.L., Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices (1995) Structural Equation Modeling: A Multidisciplinary Journal, 2 (2), pp. 119-143; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: a contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Frederico, G.F., Martins, R.A., Performance measurement systems for supply chain management: How to manage its maturity (2014) International Journal of Supply Chain Management, 3 (2), pp. 24-30; Gamage, A.S., Recruitment and selection practices in manufacturing SMEs in Japan: an analysis of the link with business performance (2014) Ruhuna Journal of Management and Finance, 1 (1), pp. 37-52; Gawankar, S., Kamble, S., Raut, R., Development, measurement, and validation of supply chain performance measurement (SCPM) scale in the indian retail sector (2016) Benchmarking: An International Journal, 23 (1), pp. 25-60; Gawankar, S.A., Kamble, S., Raut, R., An investigation of the relationship between supply chain management practices (SCMP) on supply chain performance measurement (SCPM) of indian retail chain using SEM (2017) Benchmarking: An International Journal, 24 (1), pp. 257-295; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2); Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L., (1998) Multivariate Data Analysis, 5 (3), pp. 207-219. , Upper Saddle River, NJ: Prentice hall; Hasibuan, S., Dzikrillah, N., Supply chain performance measurement and improvement for Indonesia chemical industry using SCOR and DMAIC method (2018) Journal of Engineering and Technology Management, 3 (3), pp. 146-155; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Ho, W., Zheng, T., Yildiz, H., Talluri, S., Supply chain risk management: a literature review (2015) International Journal of Production Research, 53 (16), pp. 5031-5069; Hong, J., Zhang, Y., Ding, M., Sustainable supply chain management practices, supply chain dynamic capabilities, and enterprise performance (2018) Journal of Cleaner Production, 172, pp. 3508-3519; Joreskog, K.G., Sorbom, D., (1989) LISREL 7: A Guide to the Program and Applications, , SPSS, Inc., Chicago; Jöreskog, K.G., Sörbom, D., (1993) LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language, , Scientific Software International; Katiyar, R., Meena, P.L., Barua, M.K., Tibrewala, R., Kumar, G., Impact of sustainability and manufacturing practices on supply chain performance: findings from an emerging economy (2018) International Journal of Production Economics, 197, pp. 303-316; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2); Laari, S., Töyli, J., Solakivi, T., Ojala, L., Firm performance and customer-driven green supply chain management (2016) Journal of Cleaner Production, 112, pp. 1960-1970; Leksono, E.B., Vanany, I., Using the DEMATEL approach to develop relationships of performance indicators on sustainable service only supply chain performance measurement (2018) IOP Conference Series: Materials Science and Engineering, 337 (1), p. 012023; Leończuk, D., Ryciuk, U., Szymczak, M., Nazarko, J., Measuring performance of adaptive supply chains (2019) SMART Supply Network, pp. 89-110. , Springer, Cham; Lyons, A.C., Mondragon, A.E.C., Piller, F., Poler, R., (2012) Customer-Driven Supply Chains: From Glass Pipelines to Open Innovation Networks, , Springer Science and Business Media; Marshall, D., McCarthy, L., Heavey, C., McGrath, P., Environmental and social supply chain management sustainability practices: construct development and measurement (2015) Production Planning and Control, 26 (8), pp. 673-690; Micheli, P., Mari, L., The theory and practice of performance measurement (2014) Management Accounting Research, 25 (2), pp. 147-156; Mira, M., Choong, Y., Thim, C., Mediating role of port supply chain integration between involvement of human resource practices and port performance in kingdom of Saudi Arabia (2019) Uncertain Supply Chain Management, 7 (3), pp. 507-516; Nunnally, J.C., (1994) Psychometric Theory 3E, , Tata McGraw-hill education; Pettit, T.J., Croxton, K.L., Fiksel, J., Ensuring supply chain resilience: development and implementation of an assessment tool (2013) Journal of Business Logistics, 34 (1), pp. 46-76; Saad, F., Gleason, D.M., Murray, R., Tchekmedyian, S., Venner, P., Lacombe, L., Chen, B., A randomized, placebo-controlled trial of zoledronic acid in patients with hormone-refractory metastatic prostate carcinoma (2002) Jnci Journal of the National Cancer Institute, 94 (19), pp. 1458-1468; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) International Journal of Production Research, 53 (16), pp. 4917-4926; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2010) Behavioral Operations in Planning and Scheduling, pp. 105-121. , Springer, Berlin, Heidelberg; Sillanpaa, I., Kess, P., Supply chain performance measurement framework for manufacturing industries–a theoretical approach (2011) In MIC 2011: Managing Sustainability? Proceedings of the 12th International Conference, Portorož, 23–26 November 2011 [Selected Papers], pp. 801-823. , University of Primorska, Faculty of Management Koper; Singhry, H.B., Abd Rahman, A., Imm, N.S., Effect of advanced manufacturing technology, concurrent engineering of product design, and supply chain performance of manufacturing companies (2016) The International Journal of Advanced Manufacturing Technology, 86 (1-4), pp. 663-669; Söderberg, L., Bengtsson, L., Kaulio, M., A model for outsourcing and governing of maintenance within the process industry (2017) Operations Management Research, 10 (1-2), pp. 20-32; Subramanian, N., Gunasekaran, A., Cleaner supply-chain management practices for twenty-first-century organizational competitiveness: practice-performance framework and research propositions (2015) International Journal of Production Economics, 164, pp. 216-233; Syed, M.S.A.H.A., (2012) Supply chain management: a model for implementation for SME's in Saudi Arabia, , Doctoral dissertation, Sheffield Hallam University; Tajbakhsh, A., Hassini, E., Performance measurement of sustainable supply chains: a review and research questions (2015) International Journal of Productivity and Performance Management, 64 (6), pp. 744-783. , https://doi-org.library.iau.edu.sa/10.1108/IJPPM-03-2013-0056; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) International Journal of Production Research, 53 (21), pp. 6473-6494; Tatoglu, E., Bayraktar, E., Golgeci, I., Koh, S.L., Demirbag, M., Zaim, S., How do supply chain management and information systems practices influence operational performance? Evidence from emerging country SMEs (2016) International Journal of Logistics Research and Applications, 19 (3), pp. 181-199; Tripathi, S., Gupta, M., A current review of supply chain performance measurement systems (2019) Advances in Industrial and Production Engineering, pp. 27-39. , Springer, Singapore; Tundys, B., Wiśniewski, T., The selected method and tools for performance measurement in the green supply chain – survey analysis in Poland (2018) Sustainability, 10 (2), p. 549; Varela, M., Viswanathan, M., Savings that hurt: production rationalization and its effect on prices (2020) Journal of Economics and Management Strategy, 29 (1), pp. 147-172; Wagner, S.M., Neshat, N., A comparison of supply chain vulnerability indices for different categories of firms (2012) International Journal of Production Research, 50 (11), pp. 2877-2891; Wagner, S.M., Grosse-Ruyken, P.T., Erhun, F., The link between supply chain fit and financial performance of the firm (2012) Journal of Operations Management, 30 (4), pp. 340-353; Wieland, A., Handfield, R.B., Durach, C.F., Mapping the landscape of future research themes in supply chain management (2016) Journal of Business Logistics, 37 (3), pp. 205-212; Xu, J., Jiang, X., Wu, Z., A sustainable performance assessment framework for plastic film supply chain management from a Chinese perspective (2016) Sustainability, 8 (10), p. 1042; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) International Journal of Operations and Production Management, 15 (4), pp. 1228-1263},
correspondence_address1={El-Garaihy, W.H.; Imam Abdulrahman Bin Faisal UniversitySaudi Arabia; email: whgaraihy@iau.edu.sa},
publisher={Emerald Group Holdings Ltd.},
issn={14725967},
language={English},
abbrev_source_title={J. Facil. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Abbas2020843,
author={Abbas, H. and Alawi, A.A. and Maktoumi, K.A.},
title={Prioritizing Service Supply-Chain Performance Measures Using Multi-Criteria Decision-Making Methodologies},
journal={Journal of Asian Finance, Economics and Business},
year={2020},
volume={7},
number={11},
pages={843-851},
doi={10.13106/jafeb.2020.vol7.no11.843},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097245623&doi=10.13106%2fjafeb.2020.vol7.no11.843&partnerID=40&md5=9c9cbb2b601eaacc4c8c4057cfbb2c66},
affiliation={Department of Business Administration, College of Applied Sciences, University of Technology & Applied Sciences, P.O. Box 347, PC 215, Sa’ada, Salalah, Oman; College of Applied Sciences, University of Technology & Applied Sciences, Salalah, Oman; Department of Human Resources, Majan Electricity Company, Sohar, Oman},
abstract={This study focuses on identifying and prioritizing the broader performance measures for the service supply chains by taking the case of Majan Electricity Company, Sohar, in the Sultanate of Oman. For an examination of the uniformity of ultimate objectives and the priorities therein, two strata of respondents with a total of fourteen respondents were approached for their valuable insights. Suitable structured instruments were personally administered to elicit the insightful and worthy responses. The two multi-criteria decision-making techniques, namely, the Fuzzy Analytical Hierarchy Process and the Best-Worst Method were used to reach a meaningful prioritization of the identified and refined broader performance measurement dimensions. The results show that there exists a minor gap between the two respondents’ groups in terms of their prioritizations. The major finding points to the difference in terms of the topmost priorities as revealed by the two set of respondents. For one group of respondents, the customer satisfaction matters the most, whereas for the other group, it is the overall profitability that matters the most. This gap against the utopian state assists in concluding that there is a requirement to reorient the employees so as to have a shared and common understanding of the organizational priorities. © Copyright: The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.},
author_keywords={Best-Worst Method;  Fuzzy Analytical Hierarchy Process;  Multi Criteria Decision Making;  Performance Measurement;  Service Supply Chains},
references={Abbas, H., Barriers to reverse logistics practices in pharmaceutical supply chains: an ISM approach (2018) International Journal of Business Excellence, 16 (1), pp. 47-60; Abbaspour, A., Supply chain analysis and improvement by using the SCOR model and Fuzzy AHP: A Case Study (2019) International Journal of Industrial Engineering and Management Science, 6 (2), pp. 51-73; Adivar, B., Hüseyinoğlu, I. Ö. Y., Christopher, M., A quantitative performance management framework for assessing omnichannel retail supply chains (2019) Journal of Retailing and Consumer Services, 48, pp. 257-269; Ayhan, M. B., A fuzzy AHP approach for supplier selection problem: A case study in a Gear motor company (2013) International Journal of Managing Value and Supply Chains, 4 (3), pp. 11-23; Beamon, B. M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Brewer, P. C., Speh, T. W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56; Butzer, S., Schötz, S., Petroschke, M., Steinhilper, R., Development of a performance measurement system for international reverse supply chains (2017) Procedia Cirp, 61, pp. 251-256; Cho, D. W., Lee, Y. H., Ahn, S. H., Hwang, M. K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Diouf, M., Kwak, C., Fuzzy AHP, DEA, and Managerial analysis for supplier selection and development; from the perspective of open innovation (2018) Sustainability, 10 (10), p. 3779; Duleba, S., Moslem, S., Sustainable urban transport development with stakeholder participation, an AHP-kendall model: A case study for mersin (2018) Sustainability, 10 (10), pp. 36-47; Ellram, L. M, Tate, W.L., Billington, C., Understanding and Managing the Services Supply Chain (2004) Journal of Supply Chain Management, 40 (4), pp. 17-32; Farajnejad, E., Lau, W. Y., A Fuzzy Based Early Warning System to Predict Banking Distress on Selected Asia-Pacific Countries (2017) Journal of Asian Finance, Economics and Business, 4 (1), pp. 39-49. , http://dx.doi.org/10.13106/jafeb.2017.vol4.no1.39; Ghorbanzadeh, O., Moslem, S., Blaschke, T., Duleba, S., Sustainable urban transport planning considering different stakeholder groups by an interval-AHP decision support model (2019) Sustainability, 11 (1), p. 9; Giannakis, M., Management of service supply chains with a service-oriented reference model: the case of management consulting (2011) Supply Chain Management: An International Journal, 16 (5), pp. 346-361; Hoang, L. K., Nguyen, K T., Fuzzy-AHP Application in Analyzing the Factors Affecting Quality of Rural Labor (2020) Journal of Asian Finance, Economics and Business, 7 (8), pp. 715-721. , https://doi.org/10.13106/jafeb.2020.vol7.no8.715; Jayaram, J., Xu, K., Determinants of quality and efficiency performance in service operations (2016) International Journal of Operations & Production Management, 36 (3), pp. 265-285; Kaplan, R. S., Norton, D. P., Transforming the balanced scorecard from performance measurement to strategic management: Part I (2001) Accounting Horizons, 15 (1), pp. 87-104; Kotler, P., Armstrong, G., (2010) Principles of marketing, , London, UK: Pearson education; Lee, H. J., Seong, M. H., A Study on the Effects of Business Service Quality on Satisfaction, Commitment, Performance, and Loyalty at a Private University (2020) Journal of Asian Finance, Economics and Business, 7 (9), pp. 439-453. , https://doi.org/10.13106/jafeb.2020.vol7.no9.439; Lin, A. J., Chang, H. Y., Business sustainability performance evaluation for Taiwanese banks—A hybrid multiple-criteria decision-making approach (2019) Sustainability, 11 (8), p. 2236; Ming, Z., Taeho, P., John, Y., Commonalities and differences between service and manufacturing supply chains: Combining operations management studies with supply chain management (2009) California Journal of Operations Management, 7 (1), pp. 136-143; Moslem, S., Ghorbanzadeh, O., Blaschke, T., Duleba, S., Analysing stakeholder consensus for a sustainable transport development decision by the fuzzy AHP and interval AHP (2019) Sustainability, 11 (12), p. 3271; Mou, W., Wong, W. K., McAleer, M., Financial credit risk evaluation based on core enterprise supply chains (2018) Sustainability, 10 (10), p. 3699; Nguyen, P. T., Nguyen, Q. L. H. T. T., Critical Factors Affecting Construction Price Index: An Integrated Fuzzy Logic and Analytical Hierarchy Process (2020) Journal of Asian Finance, Economics and Business, 7 (8), pp. 197-204. , https://doi.org/10.13106/jafeb.2020.vol7.no8.197; Nispeling, T., (2015) Multi-Criteria Supplier Selection in the Edible Oil Industry: The Case of a New Oils & Fats Plant in China, , Doctoral dissertation. Delft University of Technology, TU Delft; Nugraha, A. T., Hakimah, Y., Role of Relational Capabilities on the Supply Chain Performance of Indonesian Textile Sector with Moderating Effect of Technology Adoption (2019) International Journal of Supply Chain Management, 8 (5), pp. 509-522; Palang, D., Tippayawong, K.Y., Performance evaluation of tourism supply chain management: the case of Thailand (2019) Business Process Management Journal, 25 (6), pp. 1193-1207; Rezaei, J., Best-worst multi-criteria decision-making method (2015) Omega, 53, pp. 49-57; Saaty, T. L., Peniwati, K., Shang, J. S., The analytic hierarchy process and human resource allocation: Half the story (2007) Mathematical and Computer Modelling, 46 (7-8), pp. 1041-1053; Saleheen, F., Habib, M.M., Hanafi, Z., An Empirical Study on Supply Chain Management Performance Measurement through AHP (2018) International Journal of Supply Chain Management, 7 (6), pp. 349-356; Sambasivan, M., Mohamed, Z. A., Nandan, T., Performance measures and metrics for e‐supply chains (2009) Journal of Enterprise Information Management, 22 (3), pp. 346-360; Sampson, S. E., Customer-Supplier Duality and Bidirectional Supply Chains in Service Organizations (2000) International Journal of Service Industry, 11 (4), pp. 348-359; Santos, T. F. D., Leite, M. S. A., Performance measurement system in supply chain management: application in the service sector (2016) International Journal of Services and Operations Management, 23 (3), pp. 298-315; Sengupta, K., Heiser, D. R., Cook, L. S., Manufacturing and Service Supply Chain Performance: A Comparative Analysis (2006) Journal of Supply Chain Management, 42 (4), pp. 4-15; Shujaat, M., Naghavi, N., Mubarak, F., Impact of supplier relational capital on supply chain performance in Pakistani textile industry (2019) Asian Economics and Finance Review, 9 (3), pp. 318-328; Stevenson, W.J., (2002) Operations Management, , (7th ed). New York, NY: McGraw-Hill; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Tong, K. H., Nguyen, Q. L. H. T. T., Nguyen, T. T. M., Nguyen, P. T., Vu, N. B., Applying the Fuzzy Decision-Making Method for Program Evaluation and Management Policy of Vietnamese Higher Education (2020) Journal of Asian Finance, Economics and Business, 7 (9), pp. 719-726. , https://doi.org/10.13106/jafeb.2020.vol7.no9.719; Tripathi, S., Gupta, M. A, Current Review of Supply Chain Performance Measurement Systems (2019) Advances in Industrial and Production Engineering, pp. 27-39. , Singapore: Springer; Wang, Y., Wallace, S. W., Shen, B., Choi, T. M., Service supply chain management: A review of operational models (2015) European Journal of Operational Research, 247 (3), pp. 685-698; Wen, Y-F., Evaluation of Supply Chain Performance for Shipping Industry by Using AHP Method (2015) International Journal of Computer & Mathematical Sciences, 4 (10), pp. 117-129; Xu, J., Yu, L., Gupta, R., Evaluating the Performance of the Government Venture Capital Guiding Fund Using the Intuitionistic Fuzzy Analytic Hierarchy Process (2020) Sustainability, 12 (17), p. 6908; Zhang, R.Q., Chen, H.Q., A Review of Service Supply Chain and Future Prospects (2015) Journal of Service Science and Management, 8 (4), pp. 485-495},
correspondence_address1={Abbas, H.; Department of Business Administration, P.O. Box 347, PC 215, Sa’ada, Oman; email: haidar.sal@cas.edu.om},
publisher={Korea Distribution Science Association (KODISA)},
issn={22884637},
language={English},
abbrev_source_title={J. Asian Financ. Econ. Bus.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{NoAuthor2020,
title={Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management, IOEM 2020},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2020},
number={August},
page_count={26},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096550348&partnerID=40&md5=f3c7b4f94961166961f166ca67b8f067},
abstract={The proceedings contain 444 papers. The special focus in this conference is on Industrial Engineering and Operations Management. The topics include: Strategies, incentives and determinants of corporate social responsibility; adolescent fear anxiety and knowledge of covid-19; case study review of the effects of covid-19 on the supply chain of manufacturing companies in california; analysis of service quality and customer satisfaction in shoes raw material industry; the power of customer value on customer satisfaction at non-formal education industry; demographic factors that impact anxiety levels; a cross-industry study identifying enabling factors of supply chains that successfully responded to covid-19; artemis robot: Building artificially intelligent robots with child-like curiosity; environmental observation and potency of lindur fruit (Bruguiera gymnorrhiza) as alternative food substance; relationship between lecturer competencies and students’ learning achievement in social sciences education courses; the influence factors of the development of performance measurement systems in indonesia central government; a study on the effects of sleep on children’s reaction time; tradeoff curves for target costing of assembly processes; monitoring run-off toxicity using daphnia magna; analysis of stock market reactions to covid-19 pandemic; improving performance of epidemic healthcare management during covid-19 outbreak using lss dmaic approach: A case study for bangladesh; an assessment of the philippines’ condition on coronavirus disease (Covid-19) through dmaic approach; analyze national and global emergency responses to the covid-19 pandemic through the quality lens and data driven approach of the lean six sigma; mitigating the transmission of covid-19 in the philippines: A six sigma approach.},
publisher={IEOM Society},
issn={21698767},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Darom2020,
author={Darom, M. and Plant, E.},
title={The development of a performance measurement system for indirect procurement: a Delphi study},
journal={Measuring Business Excellence},
year={2020},
doi={10.1108/MBE-05-2019-0047},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094971217&doi=10.1108%2fMBE-05-2019-0047&partnerID=40&md5=fe9223ada20813ae7c2d46f3e6ec9e59},
affiliation={University of South Wales, Pontypridd, United Kingdom; The Business School, Edinburgh, United Kingdom; Napier University – Craiglockhart Campus, Edinburgh, United Kingdom},
abstract={Purpose: This study aims to address the current gap in knowledge of indirect procurement performance management. It attempts to argue the need for a specific and tailored performance management approach for the indirect procurement function that incorporates a balanced approach, beyond financial measures. Design/methodology/approach: The case study approach evaluated key performance indicators from a balanced scorecard (BSC) perspective in the development of a performance measurement system (PMS) for a Middle Eastern university’s indirect procurement division. It initially reviewed the literature to assess potential indicators for this context. It used vision and mission statement analysis alongside expert interviews to augment the literature. The candidate indicators were then evaluated and ranked by an expert panel through applying a four-round Delphi technique. Findings: Twenty-nine procurement-specific indicators are suggested in a BSC framework. The five highest-ranked indicators were not in the financial perspective unlike other BSC studies in the broader field of supply chain management (SCM). Practical implications: The study suggests a framework and indicators for a procurement PMS for practitioners to consider. It also highlights there is no one-size-fits-all and that organisations need to tailor PM to the organisation and divisional strategy and operational needs. This study aids the development of guidelines for executives and procurement management that wish to develop indicators and a PMS. Originality/value: This study contributes to knowledge by partly addressing the under-researched field of indirect procurement PM. The literature suggested that various roles in SCM require specific PM indicators. This study puts forward a BSC framework with 29 indicators specifically for indirect procurement. Fourteen of these indicators were derived from non-literature sources. This study enhances knowledge and contributes to the limited debate and evidence on indirect procurement PM and the broader PM literature. © 2020, Emerald Publishing Limited.},
author_keywords={Balanced scorecard;  Delphi study;  Key performance indicators;  Metrics;  Performance measurement;  Procurement},
references={Akkawuttiwanich, P., Yenradee, P., Evaluation of SCOR KPIs using a predictive MILP model under fuzzy parameters (2017) Int. J Sup. Chain. Mgt, 6 (1), p. 172; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Computers in Industry, 82, pp. 135-150; Barrows, E., Neely, A., (2011) Managing Performance in Turbulent Times: analytics and Insight, , John Wiley & Sons; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Belvedere, V., Grando, A., Legenvre, H., Testing the EFQM model as a framework to measure a company’s procurement performance (2018) Total Quality Management & Business Excellence, 29 (5-6), pp. 633-651; Bernardo, N., (2018) Agile Procurement: Volume I: Adding Value with Lean Processes, , Springer International Publishing: Imprint: Palgrave Macmillan, Cham; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Biazzo, S., Garengo, P., (2012) Performance Measurement with the Balanced Scorecard: A Practical Approach to Implementation within SMEs, , Springer Verlag; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach' (2010) Facilities, 28 (5-6), pp. 249-260; Bititci, U.S., Measuring your way to profit' (1994) Management Decision, 32 (6), pp. 16-24; Bititci, U.S., Bourne, M., Cross, J.A., Nudurupati, S.S., Sang, K., Editorial: towards a theoretical foundation for performance measurement and management (2018) International Journal of Management Reviews, 20 (3), pp. 653-660; Bourne, M., Melnyk, S., Bititci, U., Performance measurement and management: theory and practice (2018) International Journal of Operations & Production Management, 38 (11), pp. 2010-2021; Bourne, M., Franco-Santos, M., Micheli, P., Pavlov, A., Performance measurement and management: a system of systems perspective (2018) International Journal of Production Research, 56 (8), p. 2788; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20, pp. 754-771; Brancheau, J.C., Wetherbe, J.C., Key issues in information systems management (1987) MIS Quarterly, 11 (1), pp. 23-45; Brandon-Jones, A., E-procurement quality from an internal customer perspective: construct development, refinement, and replication using a mixed-methods approach (Report) (2017) International Journal of Operations & Production Management, 37 (12), pp. 1741-1772; Brandon-Jones, A., Silvestro, R., Measuring internal service quality: comparing the gap-based and perceptions-only approaches (2010) International Journal of Operations & Production Management, 30 (12), pp. 1291-1318; Caniato, F., Luzzini, D., Ronchi, S., Purchasing performance management systems: an empirical investigation (2014) Production Planning & Control, 25 (7), pp. 616-635; Carter, J.R., Smeltzer, L.R., Narasimhan, R., Human resource management within purchasing management: its relationship to total quality management success (2000) The Journal of Supply Chain Management, 36 (2), pp. 52-62; Cavinato, J.L., Purchasing performance: what makes the magic? (1987) Journal of Purchasing and Materials Management, 23 (3), pp. 10-16; Chae, B., Developing key performance indicators for supply chain: an industry perspective' (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chennell, A., Dransfield, S., Field, J., Fisher, N., Saunders, I., Shaw, D., OPM: a system for organisational performance measurement (2000) Performance Measurement-Past, Present and Future; Chia, A., Goh, M., Hum, S.-H., Performance measurement in supply chain entities: balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Cox, A., Chicksand, D., Ireland, P., Davies, T., Sourcing indirect spend: a survey of current internal and external strategies for non-revenue-generating goods and services (2005) The Journal of Supply Chain Management, 41 (2), pp. 39-51; Cunha Callado, A.A., Jack, L., Balanced scorecard metrics and specific supply chain roles (2015) International Journal of Productivity and Performance Management, 64 (2), pp. 288-300; Dalkey, N., Helmer, O., An experimental application of the Delphi method to the use of experts (1963) Management Science, 9 (3), pp. 458-467; Davis, T.S., Novack, R.A., Why metrics matter (2012) Supply Chain Management Review, 16 (4), pp. 10-17; de Oliveira, C.A., Carneiro, J., Esteves, F., Conceptualizing and measuring the ‘strategy execution’ construct (2018) Journal of Business Research, 105; Ellram, L.M., Tate, W.L., Billington, C., Understanding and managing the services supply chain (2004) The Journal of Supply Chain Management, 40 (4), pp. 17-32; Finn, D.W., Baker, J., Marshall, G.W., Anderson, R., Total quality management and internal customers: measuring internal service quality (1996) Journal of Marketing Theory & Practice, 4 (3), p. 36; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA, London; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: a review of their consequences and a framework for research (2012) Management Accounting Research, 23 (2), pp. 79-119; Gamini, G., Reliability of the internal service encounter (2011) International Journal of Quality & Reliability Management, 28 (9), pp. 1003-1018; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: a review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Gebauer, J., Segev, A., Emerging technologies to support indirect procurement: two case studies from the petroleum industry (2000) Information Technology and Management, 1 (1-2), pp. 107-128; Goodman, C.M., The Delphi technique: a critique (1987) Journal of Advanced Nursing, 12 (6), pp. 729-734; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Guersola, M., De Lima, E.P., Teresinha Arns Steiner, M., Supply chain performance measurement: a systematic literature review (2018) International Journal of Logistics Systems and Management, 31 (1), p. 22; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Hasson, F., Keeney, S., Enhancing rigour in the Delphi technique research (2011) Technological Forecasting and Social Change, 78 (9), pp. 1695-1704; Hasson, F., Keeney, S., McKenna, H., Research guidelines for the Delphi survey technique (2000) Journal of Advanced Nursing, 32 (4), pp. 1008-1015; Hofmann, E., (2014) Performance Measurement and Incentive Systems in Purchasing: More than Just Savings, , Springer Berlin Heidelberg: Imprint: Springer, Berlin, Heidelberg; Hübner-Bloder, G., Ammenwerth, E., Key performance indicators to benchmark hospital information systems – a Delphi study (2009) Methods of Information in Medicine, 48 (6), p. 508; Johnston, R., Internal service – barriers, flows and assessment (2008) International Journal of Service Industry Management, 19 (2), pp. 210-231; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kasiri, N., Sharda, R., Hardgrave, B., A balanced scorecard for item-level RFID in the retail sector: a Delphi study (2012) European Journal of Information Systems, 21 (3), pp. 255-267; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, 70, pp. 45-50; Keil, M., Tiwana, A., Bush, A., Reconciling user and project manager perceptions of IT project risk: a Delphi study1 (2002) Information Systems Journal, 12 (2), pp. 103-119; Kendall, M., Gibbons, J.D., (1990) Rank Correlation Methods Edward Arnold, pp. 29-50. , A division of Hodder & Stoughton, A Charles Griffin title, London; Komatina, N., Nestic, S., Aleksić, A., Analysis of the performance measurement models according to the requirements of the procurement business process (2019) International Journal of Industrial Engineering and Management, 10 (3); Kumar, A., Ozdamar, L., Ng, C.P., Procurement performance measurement system in the health care industry (2005) International Journal of Health Care Quality Assurance, 18 (2), pp. 152-166; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lapide, L., True measures of supply chain performance (2000) Supply Chain Management Review, 4 (3), pp. 25-28; Large, R.O., König, T., A gap model of purchasing's internal service quality: concept, case study and internal survey (2009) Journal of Purchasing and Supply Management, 15 (1), pp. 24-32; Lavrakas, P., (2008) Encyclopedia of Survey Research Methods, , Sage Publications; Loughlin, K.G., Moore, L.F., Using Delphi to achieve congruent objectives and activities in a pediatrics department (1979) Academic Medicine, 54 (2), pp. 101-106; Lynch, R.E., Cross, K.F., (1991) Measure up! Yardsticks for Continuous Improvement, , Blackwell, Cambridge; McKenna, H.P., The Delphi technique: a worthwhile research approach for nursing? (1994) Journal of Advanced Nursing, 19 (6), pp. 1221-1225; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., The impact of supplier performance measurement systems on supplier performance (2018) International Journal of Operations & Production Management, 38 (11), pp. 2040-2061; Mapes, J., New, C., Szwejczewski, M., Performance trade-offs in manufacturing plants (1997) International Journal of Operations & Production Management, 17 (10), pp. 1020-1033; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Micheli, P., Mura, M., Executing strategy through comprehensive performance measurement systems (2017) International Journal of Operations & Production Management, 37 (4), pp. 423-443; Mills, C., (2017) Performance Management: A Practical Guide, p. 274. , p., Author House UK; Mills, A.J., Durepos, G., Wiebe, E., (2010) Sage Encyclopedia of Case Study Research, , Sage, Thousand Oaks, CA; Minjoon, J., Shaohan, C., Examining the relationships between internal service quality and its dimensions, and internal customer satisfaction (2010) Total Quality Management & Business Excellence, 21 (2), pp. 205-223; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Nair, A., Jayaram, J., Das, A., Strategic purchasing participation, supplier selection, supplier evaluation and purchasing performance (2015) International Journal of Production Research, 53 (20), pp. 6263-6278; Neely, A., The evolution of performance measurement research: developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Nevo, D., Chan, Y.E., A Delphi study of knowledge management systems: scope and requirements (2007) Information & Management, 44 (6), pp. 583-597; Okoli, C., Pawlowski, S.D., The Delphi method as a research tool: an example, design considerations and applications (2004) Information & Management, 42 (1), pp. 15-29; Okwir, S., Nudurupati, S.S., Ginieis, M., Angelis, J., Performance measurement and management systems: a perspective from complexity theory (2018) International Journal of Management Reviews, 20 (3), pp. 731-754; Parasuraman, A., Zeithaml, V.A., Berry, L.L., SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; Parasuraman, A., Zeithaml, V.A., Berry, L.L., Reassessment of expectations as a comparison standard in measuring service quality: implications for further research (1994) Journal of Marketing, 58 (1), pp. 111-124; Pohl, M., Förstl, K., Achieving purchasing competence through purchasing performance measurement system design – a multiple-case study analysis (2011) Journal of Purchasing and Supply Management, 17 (4), pp. 231-245; Powell, C., The Delphi technique: myths and realities (2003) Journal of Advanced Nursing, 41 (4), pp. 376-382; Pun, K.F., White, A.S., A performance measurement paradigm for integrating strategy formulation: a review of systems and frameworks (2005) International Journal of Management Reviews, 7 (1), pp. 49-71; Ravelomanantsoa, M.S., Ducq, Y., Vallespir, B., A state of the art and comparison of approaches for performance measurement systems definition and design (2019) International Journal of Production Research, 57 (15-16), pp. 5026-5046; Rowe, G., Wright, G., The Delphi technique as a forecasting tool: issues and analysis (1999) International Journal of Forecasting, 15 (4), pp. 353-375; Ruel, E., Wagner, W.E., III, Gillespie, B.J., (2015) The Practice of Survey Research: Theory and Applications, , Sage Publications; Sangwa, N.R., Sangwan, K.S., Development of an integrated performance measurement framework for lean organizations (2018) Journal of Manufacturing Technology Management, 29 (1), pp. 41-84; Schmidt, R.C., Managing Delphi surveys using nonparametric statistical techniques* (1997) Decision Sciences, 28 (3), pp. 763-774; Shao, J., Moser, R., Henke, M., Multidimensional supply performance framework: a conceptual development and empirical analysis (2012) International Journal of Production Economics, 138 (1), pp. 26-34; Sharma, P., Kong, T.T.C., Kingshott, R.P.J., Internal service quality as a driver of employee satisfaction, commitment and performance: exploring the focal role of employee well-being (2016) Journal of Service Management, 27 (5), pp. 773-797; Smith, R., Mobley, R.K., Chapter 6 – Key Performance Indicators (2008) Rules of Thumb for Maintenance and Reliability Engineers, , Butterworth-Heinemann, Burlington; Taticchi, P., (2010) Business Performance Measurement and Management, , Springer; Taticchi, P., Balachandran, K.R., Forward performance measurement and management integrated frameworks (2008) International Journal of Accounting & Information Management, 16 (2), pp. 140-154; Towill, D.R., The seamless supply chain – the predator's strategic advantage (1997) International Journal of Technology Management, 13 (1), pp. 37-56; Tsai, Y.-C., Cheng, Y.-T., Analyzing key performance indicators (KPIs) for e-commerce and internet marketing of elderly products: a review (2012) Archives of Gerontology and Geriatrics, 55 (1), pp. 126-132; Verweire, K., Van den Berghe, L., (2004) Integrated Performance Management: New Hype or New Paradigm?, , SAGE Publications, London; Von der Gracht, H.A., Consensus measurement in Delphi studies: review and implications for future quality assurance (2012) Technological Forecasting and Social Change, 79 (8), pp. 1525-1536; Wagner, S.M., Kaufmann, L., Overcoming the main barriers in initiating and using purchasing-BSCs (2004) Journal of Purchasing and Supply Management, 10 (6), pp. 269-281; Williams, P.L., Webb, C., The Delphi technique: a methodological discussion (1994) Journal of Advanced Nursing, 19 (1), pp. 180-186; Yeung, J.F.Y., Chan, A.P.C., Chan, D.W.M., A computerized model for measuring and benchmarking the partnering performance of construction projects' (2009) Automation in Construction, 18 (8), pp. 1099-1113; Young, W.H., Hogben, D., An experimental study of the Delphi technique (1978) Education Research Perspective, 5, pp. 57-62; Young, J.A., Varble, D.L., Purchasing's performance as seen by its internal customers: a study in a service organization (1997) Journal of Supply Chain Management, 33 (3), pp. 36-41; Zhang, Y., Wildemuth, B.M., Unstructured interviews (2009) Applications of Social Research Methods to Questions in Information and Library Science, pp. 222-231; Zimmermann, F., Foerstl, K., A meta‐analysis of the ‘purchasing and supply management practice–performance link’ (2014) Journal of Supply Chain Management, 50 (3), pp. 37-54},
correspondence_address1={Darom, M.; University of South WalesUnited Kingdom; email: m_darom@hotmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={13683047},
language={English},
abbrev_source_title={Measuring Bus. Excellence},
document_type={Article},
source={Scopus},
}

@ARTICLE{Arabshahi2020343,
author={Arabshahi, H. and Fazlollahtabar, H.},
title={Supply chain innovation performance evaluation based on the cross-efficiency concept: Case study of Supply Chain Innovation Award's winners, runner-ups and finalists},
journal={International Journal of Logistics Systems and Management},
year={2020},
volume={36},
number={3},
pages={343-369},
doi={10.1504/IJLSM.2020.108692},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093816405&doi=10.1504%2fIJLSM.2020.108692&partnerID=40&md5=be323014e02ede0c1213647a076655a4},
affiliation={Department of Innovation and Technology Management, Mazandaran University of Science and Technology, Babol, Iran; Department of Industrial Engineering, School of Engineering, Damghan University, Damghan, Iran},
abstract={This paper suggests a DEA-based structure for efficiency evaluation of supply chain innovations considering innovation risk and performance improvement. In this model, risk paradigms of supply chain innovation and popular supply chain performance measures are considered as the input and output of CCR-output oriented model respectively. Defect of DEA in ranking the efficient units has been solved by cross-efficiency concept and a practical case study of Supply Chain Innovation Award's winners, runner-ups and finalists is implemented. The obtained results lead to develop the 'effective innovation' concept within the supply chain management field which provides an innovation with the lowest possible risk and the highest positive effect on supply chain performance measures. Copyright © 2020 Inderscience Enterprises Ltd.},
author_keywords={Data envelopment analysis;  DEA;  Efficiency evaluation;  Innovation risk;  SCI;  Supply chain innovation;  Supply Chain Innovation Award},
references={Andersen, P., Petersen, N., A procedure for ranking efficient units in data envelopment analysis (1993) Management Science, 39 (10), pp. 1261-1264; Arabshahi, H., Fazlollahtabar, H., Iterative dichotomiser decision tree for risk analysis in innovation management (2016) International Journal of Risk and Contingency Management, 5 (4), pp. 16-26; Arabshahi, H., Fazlollahtabar, H., A DEA-based framework for innovation risk management in production systems: Case study of innovative activities in industries (2017) International Journal of Environmental Science and Technology, 14 (10), pp. 2193-2204; Arif-Uz-Zaman, K., Nazmul Ahsan, A., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612; Arlbjørn, J., de Haas, H., Munksgaard, K., Exploring supply chain innovation (2011) Logistics Research, 3 (1), pp. 3-18; Autry, C., Griffis, S., Supply chain capital: The impact of structural and relational linkage on firm execution and innovation (2008) Journal of Business Logistics, 29 (1), pp. 157-173; Banker, R., Charnes, A., Cooper, W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Management Science, 30 (9), pp. 1078-1092; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bello, D., Lohtia, R., Sangtani, V., An institutional analysis of supply chain innovations in global marketing channels (2004) Industrial Marketing Management, 33 (1), pp. 57-64; Chan, F., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chapman, R., Soosay, C., Kandampully, J., Innovation in logistic services and the new business model (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 630-650; Charnes, A., Cooper, W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Chen, C., Yan, H., Network DEA model for supply chain performance evaluation (2011) European Journal of Operational Research, 213 (1), pp. 147-155; Chen, Y., Liang, L., Yang, F., A DEA game model approach to supply chain efficiency (2006) Annals of Operations Research, 145 (1), pp. 5-13; Chiesa, V., (2001) R&D Strategy and Organization 1st Ed., , Imperial College Press, London; Doyle, J., Green, R., Efficiency and cross-efficiency in DEA: Derivations, meanings and uses (1994) Journal of the Operational Research Society, 45 (5), pp. 567-578; Fallahpour, A., Olugu, E., Musa, S., Khezrimotlagh, D., Wong, K., An integrated model for green supplier selection under fuzzy environment: Application of data envelopment analysis and genetic programming approach (2015) Neural Computing and Applications, 27 (3), pp. 707-725; Flint, D., Larsson, E., Gammelgaard, B., Mentzer, J., Logistics innovation: A customer value-oriented social process (2005) Journal of Business Logistics, 26 (1), pp. 113-147; Grawe, S., Logistics innovation: A literature-based conceptual framework (2009) The International Journal of Logistics Management, 20 (3), pp. 360-377; Guersola, M., de Lima, E., Steiner, M., Supply chain performance measurement: A systematic literature review (2018) International Journal of Logistics Systems and Management, 31 (1), p. 109; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Guo, J., Liu, J., Qiu, L., Research on supply chain performance evaluation based on DEA/AHP model (2006) IEEE Asia-Pacific Conference on Services Computing (APSCC'06); Huang, Z., Zhuang, Y., Kong, C., Study on the affecting mechanism between relational risk perception and cooperation innovation project performance (2013) iBusiness, 5 (3), pp. 74-79; Isaksson, R., Johansson, P., Fischer, K., Detecting supply chain innovation potential for sustainable development (2010) Journal of Business Ethics, 97 (3), pp. 425-442; Janvier-James, A., A new introduction to supply chains and supply chain management: Definitions and theories perspective (2011) International Business Research, 5 (1), pp. 194-207; Krabbe, M., Leverage supply chain innovation (2007) Industrial Engineer, 39 (12), pp. 26-30; Li, D., O'Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 59 (1-3), pp. 147-157. , Nos; Liang, L., Yang, F., Cook, W., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; Luo, J., Hu, Z., Risk paradigm and risk evaluation of farmers cooperatives' technology innovation (2015) Economic Modelling, 44 (1), pp. 80-85; Mahdiloo, M., Saen, R., Lee, K., Technical, environmental and eco-efficiency measurement for supplier selection: An extension and application of data envelopment analysis (2015) International Journal of Production Economics, 168 (1), pp. 279-289; Marhavilas, P., Koulouriotis, D., Gemeni, V., Risk analysis and assessment methodologies in the work sites: On a review, classification and comparative study of the scientific literature of the period 2000-2009 (2011) Journal of Loss Prevention in the Process Industries, 24 (5), pp. 477-523; Mirhedayatian, S., Azadi, M., Farzipoor Saen, R., A novel network data envelopment analysis model for evaluating green supply chain management (2014) International Journal of Production Economics, 147 (2), pp. 544-554; Mu, J., Peng, G., MacLachlan, D., Effect of risk management strategy on NPD performance (2009) Technovation, 29 (3), pp. 170-180; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Palmer, T., Wiseman, R., Decoupling risk taking from income stream uncertainty: A holistic model of risk (1999) Strategic Management Journal, 20 (11), pp. 1037-1062; Peng Wong, W., Yew Wong, K., Supply chain performance measurement system using DEA modeling (2007) Industrial Management & Data Systems, 107 (3), pp. 361-381; Raut, R., An integrated Delphi-AHP-DEA-LPP multi criteria decision making approach for supplier selection and order quantity allocation system (2014) International Journal of Logistics Systems and Management, 18 (3), p. 366; Saki, P., Ebrahimnejad, S., An integrated approach for measuring the performance of suppliers in the pharmaceutical industry: A case study (2015) International Journal of Logistics Systems and Management, 22 (3), p. 267; Schumpeter, J., (1934) The Theory of Economic Development 1st Ed., , Harvard University Press, Cambridge, Mass; Shafiee, M., Hosseinzadeh Lotfi, F., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Applied Mathematical Modelling, 38 (21-22), pp. 5092-5112. , Nos; Soosay, C., Hyland, P., Ferrer, M., Supply chain collaboration: Capabilities for continuous innovation (2008) Supply Chain Management: An International Journal, 13 (2), pp. 160-169; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply-chain management (1997) Logistics Information Management, 10 (2), pp. 62-67; Wang, J., Lin, W., Huang, Y., A performance-oriented risk management framework for innovative R&D projects (2010) Technovation, 30 (11-12), pp. 601-611. , Nos; Wang, L., Fan, H., Study about risk evaluation for enterprise' technology innovation based-on grey hierarchy method (2009) International Conference on Management and Service Science; Wu, J., Wu, Z., Integrated risk management and product innovation in China: The moderating role of board of directors (2014) Technovation, 34 (8), pp. 466-476; Yang, F., Wu, D., Liang, L., Bi, G., Wu, D., Supply chain DEA: Production possibility set and performance evaluation model (2009) Annals of Operations Research, 185 (1), pp. 195-211},
correspondence_address1={Arabshahi, H.; Department of Innovation and Technology Management, Iran; email: h.arabshahi2012@yahoo.com},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Hanák2020155,
author={Hanák, T. and Nekardová, I.},
title={Selecting and evaluating suppliers in the Czech construction sector},
journal={Periodica Polytechnica Social and Management Sciences},
year={2020},
volume={28},
number={2},
pages={155-161},
doi={10.3311/PPSO.13328},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092719159&doi=10.3311%2fPPSO.13328&partnerID=40&md5=ad6eac2b4c2186f2ef22ebc4656931b6},
affiliation={Institute of Structural Economics and Management, Faculty of Civil Engineering, Brno University of Technology, Veveří 331/95, Brno, 602 00, Czech Republic},
abstract={Selecting a capable contractor is one of the crucial prerequisites for the successful completion of a construction project. This paper addresses the issue of selecting and measuring the performance of suppliers. Using a web-based questionnaire survey, quantitative data was collected from Czech construction companies that act as main contractors and subcontractors within the supply chain. The data was evaluated by applying absolute and relative frequency, the relative importance index and the chi-square test of independence. The findings revealed several managerial implications for purchasing professionals in both areas. The findings were of interest with regard to the significance of individual selection criteria in the selection of suppliers. Data was analysed on both the usage of performance measurement systems and the frequency of performance measurement. We concluded that somewhat large differences exist between main contractors and subcontractors in terms of the use of selection criteria and that performance measurement is not universally applied in the Czech construction sector. © 2020 Budapest University of Technology and Economics. All rights reserved.},
author_keywords={Construction;  Contractor;  Performance measurement;  Supply chain},
references={Araújo, M. C. B., Alencar, L. H., Mota, C. M. M., Contractor selection in construction industry: A multicriteria model (2016) 2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 519-523. , https://doi.org/10.1109/IEEM.2015.7385701, Singapore, Singapore; Ceric, A., Trust in Construction Projects: Literature Analysis Using Keywords (2015) Organization, Technology and Management in Construction: An International Journal, 7 (1), pp. 1179-1185. , https://doi.org/doi:10.5592/otmcj.2015.1.2; Commission Recommendation 2003/361/EC of 6 May 2003 concerning the definition of micro, small and medium-sized enterprises (notified under document number C(2003) 1422) (Text with EEA relevance), , https://eur-lex.europa.eu/legal-content/en/ALL/?uri=celex%3A32003H0361, [online] [Accessed: 15 August 2018]; https://www.czso.cz, Czech Statistical Office, official web site, [online] [Accessed 17 September 2018]; Čujan, Z., Málek, Z., Výrobní a obchodní logistika (2008), (Production and business logistics), Univerzita Tomáše Bati Zlíně, Zlín, Czech Republic. (in Czech); de Araújo, M. C. B., Alencar, L. H., Mota, C. M. M., Model for contractor performance evaluation in construction industry (2017) 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 002631-002635. , https://doi.org/doi:10.1109/SMC.2016.7844636, Budapest, Hungary; Dormohammadi, A., Mohammadfam, I., Zarei, E., Presentation of a practical framework for performance assessment of HSE in construction contractors (2017) Iran Occupational Health, 13 (6), pp. 10-22. , http://ioh.iums.ac.ir/browse.php?a_code=A-10-1588-1&slc_lang=en&sid=1, [online] [Accessed: 17 August 2018]; Elyamany, A., Abdelrahman, M., Cheng, F. C., Doetkott, C., Developing a Quality Measure for Evaluating the Contractor Performance in Asphalt Pavement Construction (2013) International Journal of Pavement Research and Technology, 6 (1), pp. 22-30. , https://doi.org/10.6135/ijprt.org.tw/2013.6(1).22; Hanák, T., Šelih, J., On-line reverse auctions in construction industry (2017) Građjevinar, 69 (9), pp. 821-830. , https://doi.org/10.14256/JCE.1352.2015; Hasnain, M., Thaheem, M. J., Ullah, F., Best Value Contractor Selection in Road Construction Projects: ANP-Based Decision Support System (2018) International Journal of Civil Engineering, 16 (6), pp. 695-714. , https://doi.org/10.1007/s40999-017-0199-2; Jaskowski, P., Biruk, S., Bucon, R., Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment (2010) Automation in Construction, 19 (2), pp. 120-126. , https://doi.org/10.1016/j.autcon.2009.12.014; Kovačić, N., Vukomanović, M., Radujković, M., Implementation of Design and Build model in Croatian Construction Industry (2014) International Scientific Conference People, Buildings and Environment 2014 (PBE2014), pp. 232-242. , https://www.fce.vutbr.cz/ekr/pbe/Proceedings/2014/022_14181.pdf, Kroměříž, Czech Republic, [online] [Accessed: 07 August 2018]; Kozik, R., Korytárová, J., Hanák, T., Radziszewska-Zielina, E., Exploration of Contractor Evaluation Process in the Management of Public Works Contracts (2015) Project Management as a Spectrum of Scientific Problems in Engineering and Management, pp. 55-66. , WSOWL, Wroclaw, Poland; Kraljic, P., Purchasing must become supply management (1983) Harvard Business Review, 1983, pp. 109-117; Krzemiński, M., The selection of construction sub-contractors using the fuzzy sets theory (2015) AIP Conference Proceedings, 1648 (1), p. 600005. , https://doi.org/10.1063/1.4912837, Articl; Larsen, J. K., Shen, G. Q., Lindhard, S. M., Brunoe, T. D., Factors Affecting Schedule Delay, Cost Overrun, and Quality Level in Public Construction Projects (2016) Journal of Management in Engineering, 32 (1), p. 04015032. , https://doi.org/10.1061/(ASCE)ME.1943-5479.0000391, Articl; Leśniak, A., Piskorz, G., Spišáková, M., Mačková, D., Causes of delays in construction works resulting from the provisions of the contract in Poland and Slovakia (2018) Scientific Review Engineering and Environmental Sciences, 27 (1), pp. 71-81. , https://doi.org/10.22630/PNIKS.2018.27.1.7; Markovits-Somogyi, R., Gecse, G., Bokor, Z., Basic efficiency measurement of Hungarian logistics centres using data envelopment analysis (2011) Periodica Polytechnica Social and Management Sciences, 19 (2), pp. 97-101. , https://doi.org/10.3311/pp.so.2011-2.06; Pesämaa, O., Larsson, J., Erik Eriksson, P., Role of Performance Feedback on Process Performance in Construction Projects: Client and Contractor Perspectives (2018) Journal of Management in Engineering, 34 (4), p. 04018023. , https://doi.org/10.1061/(ASCE)ME.1943-5479.0000619, Articl; Qian Li, Z., Chen Tan, H., Anumba, C., Choy Chia, F., Development of a web-based system for managing suppliers’ performance and knowledge sharing in construction project (2017) Built Environment Project and Asset Management, 7 (2), pp. 117-129. , https://doi.org/doi:10.1108/BEPAM-10-2015-0061; Rajgor, M., Paresh, C., Dhruv, P., Chirag, P., Dhrmesh, B., RII & IMPI: Effective Techniques for Finding Delay in Construction Project (2016) International Research Journal of Engineering and Technology (IRJET), 3 (1), pp. 1173-1177. , https://www.irjet.net/archives/V3/i1/IRJET-V3I1203.pdf, [online] [Accessed: 20 September 2018]; Safa, M., Shahi, A., Haas, C. T., Hipel, K. W., Supplier selection process in an integrated construction materials management model (2014) Automation in Construction, 48, pp. 64-73. , https://doi.org/doi:10.1016/j.autcon.2014.08.008; Samson, A., Voyer, B. G., Emergency purchasing situations: Implications for consumer decision-making (2014) Journal of Economic Psychology, 44, pp. 21-33. , https://doi.org/doi:10.1016/j.joep.2014.05.004; Singh, D., Tiong, R. L. K., Contractor Selection Criteria: InvestigationofOpinionsofSingaporeConstructionPractitioners (2006) Journal of Construction Engineering and Management, 132 (9), pp. 998-1008. , https://doi.org/10.1061/(ASCE)0733-9364(2006)132:9(998); Taylan, O., Kabli, M. R., Porcel, C., Herrera-Viedma, E., Contractor Selection for Construction Projects Using Consensus Tools and Big Data (2018) International journal of Fuzzy Systems, 20 (4), pp. 1267-1281. , https://doi.org/10.1007/s40815-017-0312-3; Topcu, Y. I., A decision model proposal for construction contractor selection in Turkey (2004) Building and Environment, 39 (4), pp. 469-481. , https://doi.org/10.1016/j.buildenv.2003.09.009; Ulubeyli, S., Manisali, E., Kazaz, A., Subcontractor Selection Practices in International Construction Projects (2010) Journal of Civil Engineering and Management, 16 (1), pp. 47-56. , https://doi.org/10.3846/jcem.2010.04; Yousef AlMaian, R., LaScola Needy, K., Walsh, K. D., Da Alves, T. C. L., Supplier Quality Management Inside and Outside the Construction Industry (2015) EMJ - Engineering Management Journal, 27 (1), pp. 11-22. , https://doi.org/10.1080/10429247.2015.11432032; Vörösmarty, G., Dobos, I., Supplier Evaluation with Environmental Aspects and Common DEA Weights (2019) Periodica Polytechnica Social and Management Sciences, 27 (1), pp. 17-25. , https://doi.org/10.3311/PPso.11814; Vukomanovic, M., Radujkovic, M., The Balanced Scorecard and EFQM Working Together in a Performance Management Framework in Construction Industry (2013) Journal of Civil Engineering and Management, 19 (5), pp. 683-695. , https://doi.org/10.3846/13923730.2013.799090; Zavadskas, E. K., Turskis, Z., Tamošaitiene, J., Contractor Selection of Construction in a Competitive Environment (2008) Journal of Business Economics and Management, 9 (3), pp. 181-187. , https://doi.org/10.3846/1611-1699.2008.9.181-187},
correspondence_address1={Hanák, T.; Institute of Structural Economics and Management, Veveří 331/95, Czech Republic; email: hanak.t@fce.vutbr.cz},
publisher={Budapest University of Technology and Economics},
issn={14163837},
language={English},
abbrev_source_title={Period Polytech Soc. Manage. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Erik20201923,
author={Erik, A. and Kuvvetli, Y.},
title={A new approach to supply chain performance assessment [Tedarik zinciri performans degerlendirmesi için yeni bir yaklasim]},
journal={Journal of the Faculty of Engineering and Architecture of Gazi University},
year={2020},
volume={35},
number={4},
pages={1923-1937},
doi={10.17341/gazimmfd.691906},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85091862367&doi=10.17341%2fgazimmfd.691906&partnerID=40&md5=7dfe21c0785aa736266ab9e729e7d00e},
affiliation={Department of Industrial Engineering, Cukurova University, Adana, 01330, Turkey},
abstract={Supply chains contain the management of all processes and information from raw materials to finished products. Therefore, it is getting important to deal with holistic approach and performance measurement for supply chains. SCOR model is one of the most common approaches that used for measuring the performance of supply chains. In this study, two different performance measurement systems that base on SCOR model is proposed. According to the first proposed approach, different clusters are obtained by clustering analysis in order to evaluate the performance of the enterprises with similar characteristics. The k-means approach is used to implement the clustering analysis. In the second approach, the fuzzy entropy based alpha level is applied for weighting attributes and performance evaluation is performed for the SCOR model accordingly. In the case study, dataset with 17 different attributes are obtained from 54 different enterprises and the results of the fundamental SCOR model and the two proposed approaches are investigated. © 2020 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.},
author_keywords={Case study;  Clustering analysis;  Fuzzy shannon's entropy;  Scor model;  Supply chain performance measurement},
keywords={Supply chains, Clustering analysis;  Finished products;  Holistic approach;  New approaches;  Performance measurement system;  Performance measurements;  Supply chain performance;  Weighting attributes, K-means clustering},
references={Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F, Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Iç, Y.T., Tekin, M., Pamukoglu, F.Z., Yildirim, S.E., Development of a financial Performance Benchmarking model for corporate firms (2015) Journal of the Faculty of Engineering and Architecture of Gazi University, 30 (1), pp. 71-85; Ghalayini, A.M., Noble, J.S., The Changing Basis of Performance Measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Cąlik, A., Bulanik Çok-Amaçli Dogrusal Programlama ve Aralik Tip-2 Bulanik AHP Yöntemi ile Yesil Tedarikçi Seçimi (2018) Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 39, pp. 96-109; Yildiz, O., Dagdeviren, M., Çetinyokus, T., A decision support system to evaluate employee performance and its application (2008) Journal of the Faculty of Engineering and Architecture of Gazi University, 23 (1), pp. 239-248; Bourne, M., Milss, J., Wilcox, M., Neely, A., Platts, K., Designing, Implementing and Updating Performance Measurement Systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Lebas, M.J., Performance Measurement and Performance Management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International journal of operations & production management, 24 (12), pp. 1192-1218; Kocaoglu, B., (2009) Tedarik Zinciri Performansi Ölçümü Için Stratejik ve Operasyonel Hedefleri Bütünlestiren SCOR Modeli Temelli Bir Yapi, , Doktora Tezi, Yildiz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Istanbul; Huang, S. H., Sheoran, S. K., Keskar, H., Computerassisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48 (2), pp. 377-394; Puffal, D. P., Kuhn, L. D., A Strategic Approach Of An Integrated Supply Chain Management Model And Scor Model Contributions (2018) Brazilian Journal of Management & Innovation, 6 (1), pp. 142-165; Ren, C., Dong, J., Ding, H., Wang, W., A SCOR-based framework for supply chain performance management (2006) 2006 IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 1130-1135. , Shangai-China, June; Ntabe, E. N., Lebel, L., Munson, A. D., Santa-Eulalia, L. A., A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues (2015) International Journal of Production Economics, 169, pp. 310-332; Akkawuttiwanich, P., ve Yenradee, P., Fuzzy QFD approach for managing SCOR performance indicators (2018) Computers & Industrial Engineering, 122, pp. 189-201; Riós, J. R., Duque, D. F. M., Gómez, J. C. O., Operational supply chain risk identification and prioritization from SCOR model (2019) Ingenieria y Universidad, 23 (1), pp. 1-20; Lima-Junior, F. R., ve Carpinetti, L. C. R., Combining SCOR model and fuzzy TOPSIS for supplier evaluation and management (2016) International Journal of Production Economics, 174, pp. 128-141; Edwards, M. G., (2018) An Investigation into Establishing the Validity of the Supply Chain Operations Reference (SCOR) Model within Aid and Development Initiatives, , Master Tezi, University of Wollongong Sydney Business School; Lima-Junior, F. R., ve Carpinetti, L. C. R., Predicting supply chain performance based on SCOR metrics and multilayer perceptron neural networks (2019) International Journal of Production Economics, 212, pp. 19-38; Dissanayake, C. K., ve Cross, J. A., Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM (2018) International Journal of Production Economics, 201, pp. 102-115; Huan, S. H., Sheoran, S. K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Sellitto, M. A., Pereira, G. M., Borchardt, M., da Silva, R. I., Viegas, C. V., A SCOR-based model for supply chain performance measurement: Application in the footwear industry (2015) International Journal of Production Research, 53 (16), pp. 4917-4926; Rezaei, M., Shirazi, M. A., Karimi, B., A multi-objective SCOR-based decision alignment for supply chain performance management (2018) Scientia Iranica. Transaction E, Industrial Engineering, 25 (5), pp. 2807-2823; Liu, Y., Xu, J., Xu, M., Green Construction Supply Chain Performance Evaluation Based on BSC-SCOR (2018) 15th International Conference on Service Systems and Service Management (ICSSSM), 1 (6). , Zhejiang-China, 21-22 Temmuz; Zhou, H., Benton, W. C., Schilling, D. A., Milligan, G. W., Supply chain integration and the SCOR model (2011) Journal of Business Logistics, 32 (4), pp. 332-344; Wang, H., Su, J., Cao, H., The study on manufacturing suppliers performance evaluation and choice model (2012) International Technology Management Conference, 184, p. 187. , Dallas, TX, USA, 25-27 Haziran; Sundarakani, B., Abdul Razzak, H., Manikandan, S., Creating a competitive advantage in the global flight catering supply chain: A case study using SCOR model (2018) International Journal of Logistics Research and Applications, 21 (5), pp. 481-501; Agar, F., (2010) Tedarik Zinciri Yönetiminde SCOR Modeli, Tedarik Süreci Performans Degerlendirmesi ve SCORcard Uygulamasi, , Yüksek Lisans Tezi, Istanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Istanbul; Li, L., Su, Q., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) International Journal of Production Research, 49 (1), pp. 33-57; Çelik, C., Kiral, G., Kümeleme yöntemiyle konut talebinin incelenmesi: Türkiye il gruplari üzerine bir uygulama (2018) Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 27 (1), pp. 123-138; Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S., Constrained k-means clustering with background knowledge (2001) Eighteenth International Conference on Machine Learning (ICML 2001), 577, p. 584. , Williams College, Williamstown, MA, USA, 28 Haziran-1 Temmuz; Hançer, E., Differential evolution based multiple kernel fuzzy clustering (2019) Journal of the Faculty of Engineering and Architecture of Gazi University, 34 (3), pp. 1281-1294; Isik, M., ve Cąmurcu, A. Y., K-means ve asiri küresel cmeans algoritmalari ile belge madenciligi (2010) Marmara Fen Bilimleri Dergisi, 22 (1), pp. 1-18; Lotfi, F. H., Fallahnejad, R., Imprecise Shannon's entropy and multi attribute decision making (2010) Entropy, 12 (1), pp. 53-62; Aytekin, A., Karamasa, Ç., Erbas, Ç., BIST'te Faaliyet Gösteren Sigorta Sirketlerinin Finansal Performanslarinin Bulanik Shannon Entropi Tabanli Bulanik TOPSIS Yöntemiyle Incelenmesi (2017) Alphanumeric Journal, 5 (1), pp. 71-84},
correspondence_address1={Erik, A.; Department of Industrial Engineering, Turkey; email: admerk01@gmail.com},
publisher={Gazi Universitesi},
issn={13001884},
language={Turkish},
abbrev_source_title={J. Fac. Eng. Archit. Gazi Uni.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Saputra20202328,
author={Saputra, I.W. and Yuniaristanto, Z.Z.Z. and Hisjam, M. and Zakaria, R.},
title={System dynamics simulation based on SCOR performance at table tennis table manufacturer in Indonesia},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2020},
volume={0},
number={March},
pages={2328-2338},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088990266&partnerID=40&md5=c0a2b4cec2b1ac06d2ab499fba24f01a},
affiliation={Department of Industrial Engineering, Universitas Sebelas Maret, Surakarta, 57126, Indonesia},
abstract={The growth in the industrial sector will make it easier for companies to offer their products and develop their markets more broadly, one of them are the sports industry. Every company is required to continue to make improvements in all aspects so they won't be left behind. This research is expected to be an input to maintain the supply chain management of the company at the world level. The results of supply chain performance measurements using the SCOR® 11.0 performance attribute are then performed using system Dynamics simulations which built using Powersim software. Results shown that their lowest score are the Cost attribute, so it must be their main focus on improvement. This simulation results are expected to be a basis for improvements on the supply chain management especially at Table Tennis Table Manufacturer in Indonesia. © IEOM Society International.},
author_keywords={Performance measurement;  SCOR;  Simulation;  System dynamics},
references={Ariani, A., Ulya, M., Jakfar, A., Weighted Analysis of Key Performance Indicator (KPI) with SCOR model using Analitical Hierarchy Process (AHP) for Mozarella Cheese Production at CV Brawijaya Dairy Industry, Junrejo Kota Batu (2017) Agrointek, 11 (1), pp. 27-36; Erkan, T., Ugur, B., Supply Chain Performance Measurement: A Case Study about Applicability of SCOR Model in A Manufacturing Industry firm (2011) International Journal of Business and Management Studies, 3 (1), pp. 381-390; Georgiadis, P., Vlachos, D., Iakovou, E., A System Dynamics Modeling Framework for the Strategic Supply Chain Management of Food Chains (2004) Journal of Food Engineering, 70 (3), pp. 351-364; Hadiyati, E., Analysis of Service Quality and its Influences on Customer Loyalty (Case study on PT. Pos Indonesia (Persero) Lawang Post Office (2010) Jurnal Manajemen Pemasaran Modern, 2 (2), pp. 81-90; Huan, S., Sheoran, S., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management, 9 (1), pp. 23-29; Ikasari, N., Sutopo, W., Supply Chain Performance Measurement using Hybrid SCOR Model and System Dynamics (2019) Advances in Intelligent System Research, 171; Forrester, J.W., Lessons from system Dynamics modeling (1987) System Dynamics Review, 3 (2); Kumar, D., Kumar, D., SD Modelling of Healthcare SC in Rural Parts of Uttarakhand, India (2005) Proceedings of the 2015 IEEE IEEM; Lima-Junior, F., Carpinetti, L., Combining SCOR® Model and Fuzzy TOPSIS for Supplier Evaluation and Management (2016) International Journal of Production Economics, 174, pp. 128-141; Liu, P., Huang, S., Mokasdar, A., Zhou, H., Hou, L., The Impact of Additive Manufacturing in the Aircraft Spare Parts Supply Chain: Supply Chain Operation Reference (SCOR) Model Based Analysis (2013) Production Planning and Control: The Management of Operations, 25 (13-14), pp. 1169-1181; Lockamy, A., McCormack, K., The Development of a Supply Chain Management Process Maturity Model Using the Concepts of Business Process Orientation (2004) Supply Chain Management, 9 (4), pp. 272-278; Sumari, S., Ibrahim, R., Zakaria, N., Hamid, A., Comparing Three Simulation Model Using Taxonomy: System Dynamics Simulation, Discrete Event Simulation and Agent Based Simulation (2013) International Journal of Management Excellence, 1 (3), p. 54; (2012) Supply Chain Operation Reference Model Revision 11.0, , Supply Chain Council, Inc. United States of America; Sutawijaya, A., Marlapa, E., Supply Chain Management: SCOR Analysis and Implementation at PT. Indoturbine (2016) MIX: Jurnal Ilmiah Manajemen, 6 (1), pp. 121-138; Treadwell, J., Clark, O., Bennett, E., Dynamics simulation of phosphorus flows through Montreal's food and waste systems (2018) Resources, Conservation and Recycling, 131, pp. 122-133; Trienekens, J.H., Hvolby, H.H., Performance Measurement and Improvement in Supply Chains (2000) Proceedings of the 3rd CINet Conference, pp. 399-408},
correspondence_address1={Saputra, I.W.; Department of Industrial Engineering, Indonesia; email: yuniaristanto@ft.uns.ac.id},
publisher={IEOM Society},
issn={21698767},
isbn={9781532359491; 9781532359514; 9781532359521},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kurien2020142,
author={Kurien, G. and Qureshi, M.N. and Selvam, J.J.D.},
title={Supply chain performance measurement practices of Indian industries},
journal={International Journal of Supply Chain Management},
year={2020},
volume={9},
number={3},
pages={142-154},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087769470&partnerID=40&md5=452c5d4b63b9b859af183210413577d1},
affiliation={Institute of Management, CHRIST University, Bangalore, India; Department of Industrial Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia},
abstract={In any industry, the supply chain performance plays a crucial role and it is vital in growth of the industry. Through this study, an attempt is made to find some insight to the supply chain performance measurement practices of Indian industries through an exploratory survey. The study reveals almost all the respondents (84%) felt that supply chain performance measurement system employed in their organisation has a clear purpose. Also, the study reveals that most supply chain performance measurement system provides high importance to quality measurements and includes both financial and non-financial indicators. The Multivariate analysis revealed three factors emerged from this study are 'Strategic Orientation' followed by 'Internal Focus' and 'Motivation and Control'. The study contributes to understanding the objectives of implementing supply chain performance measurement systems and metrics (measures) used in supply chain performance measurement systems. © ExcelingTech Pub.},
author_keywords={Indian supply chains;  Performance management of supply chains;  Supply chain performance;  Supply chain performance measurement},
references={Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 19982015 (2016) Comput. Ind, 82, pp. 135-150; Avittathur, B., Jayaram, J., Supply chain management in emerging economies (2016) DECISION, 43 (2), pp. 117-124. , Jun; Chandak, A., Chandak, S., Dalpati, A., Vaishno, S., Vishwavidyalaya, V., A Study of Impact of Supply Chain Strategy on Supply Chain Performance: An Empirical Investigation on Automobile Industry in India (2018) J. Supply Chain Manag. Syst, 7 (3), pp. 1-7; Saleheen, F., Mamun Habib, M., Hanafi, Z., Supply Chain Performance Measurement Model: A Literature Review (2018) Int. J Sup. Chain. Mgt, 7 (3), pp. 70-77; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ, 183, pp. 299-315. , November 2016; Jharkharia, S., Shankar, R., Supply chain management: Some sectoral dissimilarities in the Indian manufacturing industry (2006) Supply Chain Manag. An Int. J, 11 (4), pp. 345-352; Dangayach, G.S., Deshmukh, S.G., Evidence of manufacturing strategies in Indian industry: A survey (2003) Int. J. Prod. Econ, 83, pp. 279-298; Sahay, B.S., Gupta, J.N.D., Mohan, R., Managing supply chains for competitiveness: The Indian scenario (2006) Supply Chain Manag. An Int. J, 11 (1), pp. 15-24; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking An Int. J, 13 (1-2), pp. 36-53; Banerjee, M., Mishra, M., Retail supply chain management practices in India: A business intelligence perspective (2017) J. Retail. Consum. Serv, 34, pp. 248-259. , Jan; Mani, V., Gunasekaran, A., Papadopoulos, T., Hazen, B., Dubey, R., Supply chain social sustainability for developing nations: Evidence from india (2016) Resour. Conserv. Recycl, 111, pp. 42-52. , Aug; Sahay, B.S., Mohan, R., Supply chain management practices in Indian industry (2003) Int. J. Phys. Distrib. Logist. Manag, 33 (7), pp. 582-606; Sahay, B.S., Cavale, V., Mohan, R., The 'Indian' supply chain architecture (2003) Supply Chain Manag. An Int. J, 8 (2), pp. 93-106; Rahman, Z., Use of Internet in supply chain management: A study of Indian companies (2004) Ind. Manag. Data Syst, 104 (1), pp. 31-41; Kapoor, V., Ellinger, A.E., Transforming supply chain operations in response to economic reform: The case of a motorcycle manufacturer in India (2004) Supply Chain Manag. An Int. J, 9 (1), pp. 16-22; Kurien, G.P., Qureshi, M.N., Analysis and measurement of supply chain flexibility (2015) Int. J. Logist. Syst. Manag, 21 (1); Taticchi, P., Balachandran, K.R., Forward performance measurement and management integrated frameworks (2008) Int. J. Account. Inf. Manag, 16 (2), pp. 140-154; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Bus. Process Manag. J, 14 (4), pp. 512-529; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) Int. J. Business, Manag. Soc. Sci, 2 (4), pp. 19-34; Sankar Mishra, U., Kumar Sahoo, A., Evaluating Performance of Supply Chain Management System: A Conceptual Analysis in BSNL (2013) Int. J Sup. Chain. Mgt, 2 (2), pp. 84-91; Charkha, P.G., Jaju, S.B., Identification of performance measures for textile supply chain: Case of small & medium size enterprise (2015) Int. J. Supply Chain Manag, 4 (3), pp. 50-58; Mathur, B., Gupta, S., Meena, M.L., Dangayach, G.S., Impact of Supply Chain Practices on Organizational Performance with Moderating Effect of Supply Chain Performance in Indian Health Care Industry (2018) Int. J Sup. Chain. Mgt, 7 (4), pp. 30-41; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag, 21 (1-2), pp. 71-87. , Jan},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{NoAuthor2020,
title={4th International Conference on Research in Intelligent and Computing in Engineering, RICE 2019},
journal={Advances in Intelligent Systems and Computing},
year={2020},
volume={1125},
page_count={1204},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084225232&partnerID=40&md5=488f0d83157b6c75d3e6c007eb3a3fd7},
abstract={The proceedings contain 123 papers. The special focus in this conference is on Research in Intelligent and Computing in Engineering. The topics include: Concept of cancer treatment by heating methodology of microwave; dynamics of self-guided rocket control with the optimal angle coordinate system combined with measuring target parameters for frequency modulated continuous wave radar; an approach of utilizing binary bat algorithm for pattern nulling; An application of WSN in smart aquaculture farming; a newly developed approach for transmit beamforming in multicast transmission; post-quantum commutative deniable encryption algorithm; Indoor positioning using BLE iBeacon, smartphone sensors, and distance-based position correction algorithm; assessing the transient structure with respect to the voltage stability in large power system; cellular automata approach for optimizing radio coverage: A case study on archipelago surveillance; smart bicycle: IoT-based transportation service; LNA nonlinear distortion impacts in multichannel direct RF digitization receivers and linearization techniques; novel approach to detect and extract the contents in a picture or image; Modified biological model of meat in the frequency range from 50 Hz to 1 MHz; A research on clustering and identifying automated communication in the HTTP environment; Sequential all-digital background calibration for channel mismatches in time-interleaved ADC; Comparison BICM-ID to turbo code in wide band communication systems in the future; a design of a vestibular disorder evaluation system; about model separation techniques and control problems of wheeled mobile robots; Fuzzy supply chain performance measurement model based on SCOR 12.0; lightweight convolution neural network based on feature concatenate for facial expression recognition; light fidelity system.},
editor={Solanki V.K., Hoang M.K., Lu Z., Pattnaik P.K.},
publisher={Springer},
issn={21945357},
isbn={9789811527791},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Majumder20201129,
author={Majumder, D. and Bhattacharjee, R. and Dam, M.},
title={Fuzzy supply chain performance measurement model based on SCOR 12.0},
journal={Advances in Intelligent Systems and Computing},
year={2020},
volume={1125},
pages={1129-1139},
doi={10.1007/978-981-15-2780-7_116},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084176457&doi=10.1007%2f978-981-15-2780-7_116&partnerID=40&md5=3e38cd42b2053f5fa4c4eac2222e3912},
affiliation={Department of Mathematics, JIS College of Engineering, Kalyani, 741235, India; Mahishakati Netaji Subhash Chandra High School, Kahankia, 743245, India},
abstract={This paper presents a novel fuzzy performance measurement model for supply chain based on hierarchical structure of metrics proposed in Supply Council Operations Reference (SCOR®) model (version 12.0). Performance measurement of supply chains predominantly suffers from uncertainty and vagueness due to the involvement of subjective judgement of the decision-makers. In the proposed model, the fuzzy set theoretic approach has been used to address the inherent vagueness and Mamdani fuzzy inference reasoning is employed to capture the causal relationship among the metrics proposed in SCOR model. The novelty of this study is the employment of newly defined metrics and their hierarchical structure suggested in the latest version of SCOR® model, SCOR® 12.0. © Springer Nature Singapore Pte Ltd. 2020.},
author_keywords={Mamdani fuzzy inference system;  Performance measurement;  SCOR® model;  Supply chain},
keywords={Decision making;  Intelligent computing;  Supply chains;  Uncertainty analysis, Causal relationships;  Decision makers;  Hierarchical structures;  Mamdani fuzzy inferences;  Performance measurements;  Set-theoretic approach;  Subjective judgement;  Supply chain performance measurements, Fuzzy inference},
references={Beamon, B.M., Measuring supply chain performance (1999) Int J Oper Prod Manag, 19 (3), pp. 275-292; Chopra, S., Meindl, P., Supply chain management: Strategy, planning, and operation (2013) Pearson Education, , Harlow, Essex; Thomas, D., Griffin, P., Coordinated supply chain management (1996) Eur J Oper Res, 94 (1), pp. 1-15; Melo, M.T., Nickel, S., Saldanhadagama, F., Facility location and supply chain manage-ment—a review (2009) Eur J Oper Res, 196, pp. 401-412; Geissbauer, R., Roussel, J., Schrauf, S., Strom, M.A., (2013) Nextgeneration Supply Chains Efficient, Fast and Tailored, , https://www.pwc.com/gx/en/consultingservices/supplychain/global-supplychainsurvey/assets/globalsupplychainsurvey2013.pdf, Accessed 16 Jan 2018; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decis Support Syst, 54, pp. 1513-1520; (2017) Supply Chain Operations Reference Model, Version, 12, p. 0. , http://www.supplychain.org/resources/scor/12.0; Palmamendoza, J.A., Analytical hierarchy process and SCOR model to support supply chain redesign (2014) Int J Inform Manag, 34, pp. 634-638; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Silva, R.I., Viegas, C.V., A SCORbased model for supply chain performance measurement: Application in the footwear industry (2015) Int J Prod Res, 53 (16), pp. 4917-4926. , https://doi.org/10.1080/00207543.2015.1005251; Limajunior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Comput Ind Eng, 113, pp. 333-346; Limajunior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks (2019) Int J Prod Econ, , https://doi.org/10.1016/j.ijpe.2019.02.001; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int J Prod Econ, 134, pp. 177-187; Elbaz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Syst Appl, 38, pp. 6681-6688; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput Ind Eng, 62 (3), pp. 801-818. , https://doi.org/10.1016/j.cie.2011.11.014; Agami, N., Saleh, M., Rasmy, M., An innovative fuzzy logic based approach for supply chain performance management (2014) IEEE Syst J, 8, pp. 336-342; Jakhar, S.K., Barua, M.K., An integrated model of supply chain performance evaluation and decisionmaking using structural equation modelling and fuzzy AHP (2014) Prod Plan Control, 25, pp. 938-957; Neely, A., (2002) Business Performance Measurement, , MA, Cambridge University Press, Cambridge; Striteska, M., Spickova, M., Review and comparison of performance measurement systems (2012) J Org Manag Stud, 2012; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) Int J Prod Perf Manag, 55 (3-4), pp. 242-258; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int J Prod Econ, 87 (3), pp. 333-347; Zadeh, L.A., Fuzzy sets (1965) Inform Control, 8 (3), pp. 338-353; Mamdani, E.H., Applications of fuzzy algorithm for control a simple dynamic plant (1974) Proc IEEE, 121 (12), pp. 1585-1588; Zadeh, L.A., Making computers think like people (1984) IEEE Spectr, 8, pp. 26-32; (2019) Supply Chain Surveys, , www.apqc.org, Accessed 26 Feb 2019; Biswas, A., Majumder, D., Genetic algorithm based hybrid fuzzy system for assessing morningness (2014) Adv Fuzzy Syst, 2014, pp. 1-9; Debnath, J., Majumder, D., Biswas, A., Air quality assessment using weighted interval type2 fuzzy inference system (2018) Ecol Inform, 46, pp. 133-146},
correspondence_address1={Majumder, D.; Department of Mathematics, India; email: debamath@rediffmail.com},
editor={Solanki V.K., Hoang M.K., Lu Z., Pattnaik P.K.},
publisher={Springer},
issn={21945357},
isbn={9789811527791},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{KalpaniDissanayake20201543,
author={Kalpani Dissanayake, C. and Cross, J.A.},
title={A framework for integrating PLS-PM with supply chain performance hierarchy},
journal={Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016},
year={2020},
pages={1543-1548},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084056488&partnerID=40&md5=2038ff90432c4505cf66cac22462c856},
affiliation={Department of Industrial Engineering, Texas Tech University, USA, Box 43061, Lubbock, TX  79409-3061, United States},
abstract={Supply chain (SC) is a network, which comes into existence with the formation of every organization, and becomes automatically complex as the organization progresses. Performance measurement in supply chain is considered to be a key for the survival of organizations and it is not a secret that many academics and practitioners are attending to developing an effective performance evaluation mechanism amidst its complexity. By this article, which is written based on an on-going doctoral study, authors wish to illustrate the advantages of applying partial least squares- path modeling method (PLS-PM), a form of structural equation modeling technique (SEM), combined with the SC performance hierarchy concept, for measuring performance in supply chains. The method is tested with the help of a previously published case study and a hypothetical case study, to ensure the combination could produce results in a productive manner, which was confirmed after the tests. Few being the ability to produce an overall SC performance score (SCPS), and the ability to estimate the impact of different performance measurement areas on overall SC performance. Therefore, with further testing, the proposed method can eventually be implemented in industry as a managerial decision making tool. © 2016 Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016. All rights reserved.},
author_keywords={Partial least squares path modeling;  Supply chain performance measurement;  Supply chain performance measurement hierarchy;  Supply chain performance score},
keywords={Complex networks;  Decision making;  Engineering research;  Industrial research;  Supply chains;  Systems engineering, Doctoral studies;  Effective performance;  Managerial decision making;  Measuring performance;  Partial least square (PLS);  Performance measurements;  Structural equation modeling techniques;  Supply chain performance, Least squares approximations},
references={Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Kurien, G.P., Qureshi, M.N., Performance measurement systems for green supply chains using modified balanced score card and analytical hierarchical process (2012) Scientific Research and Essays, 7 (36), pp. 3149-3161; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Spitzer, D.R., (2007) Transforming Performance Measurement: Rethinking the Way We Measure and Drive Organizational Success, , AMACOM Div American Mgmt Assn; Chan, F.T.S., Qi, H.J., A fuzzy basis channel-spanning performance measurement method for supply chain management (2002) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 216 (8), pp. 1155-1167; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Kocaoğlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Sanchez, G., (2013) PLS Path Modeling with R, , Online, January; Shirouyehzad, H., Panjehfouladgaran, H., Dabestani, R., Badakhshian, M., (2011) Vendor Performance Measurement Using Fuzzy Logic Controller; Henseler, J., Ringle, C.M., Sinkovics, R.R., The use of partial least squares path modeling in international marketing (2009) Advances in International Marketing (AIM), 20, p. 300; Ringle, C.M., Wende, S., Becker, J.-M., (2015) SmartPLS 3, , http://www.smartpls.com, Boenningstedt: SmartPLS GmbH},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={Proc. Ind. Syst. Eng. Res. Conf., ISERC},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zainuddin2020490,
author={Zainuddin, N. and Saifudin, A.M. and Deraman, N. and Osman, A.A.},
title={The effect of halal traceability system on halal supply chain performance},
journal={International Journal of Supply Chain Management},
year={2020},
volume={9},
number={1},
pages={490-498},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080917162&partnerID=40&md5=e7c2edb047146115b991caddfa99e240},
affiliation={School of Technology Management and Logistics (STML), Universiti Utara Malaysia (UUM), Sintok, Malaysia; School of Business and Management (SBM), Universiti Utara Malaysia (UUM), Sintok, Malaysia},
abstract={Supply chain management represents a set of interdependent organisations that work closely together to manage the flow of goods or services and information along the value-added chain of products, in order to realise end customer value at the lowest possible costs. Halal Supply Chain performance measures can be developed based on existing measures of the conventional supply chain as long as the primary objective is preserving halal integrity rather than minimising cost or maximising profit. The objective of this paper is to see the effect of halal traceability system towards halal supply chain performance using SEM techniques. Halal Traceability system is a system that enables the identification of products' and product ingredients' halal status backward and forward, along the production chain. This article focus will be on the survey method only. The instrument development was based on a literature survey. This study focuses on the population of halal certified food and beverages (F&B) manufacturers as disclose in JAKIM Halal Portal. About 254 questionnaire sets answered by the intended respondents out of 500 being distributed and no data are detected as missing. All questions are completely answered by the respondents. This study has confirmed the effect of Halal Traceability system on Halal Supply Chain performance. Having demonstrated the significance of the hypothesised effect with a substantial total variance explained (i.e. above 75 percent) and sufficient predictive relevance, this study provides strong empirical evidence that a high level of Halal Supply Chain performance can be achieved through the effective implementation of Halal Traceability system. © 2020 ExcelingTech Pub, UK.},
author_keywords={Halal certification and labelling;  Halal industry;  Halal portal;  Halal supply chain performance},
references={Burgess, K., Singh, P.J., Koroglu, R., Supply chain management: A structured literature review and implications for future research (2006) International Journal of Operations & Production Management, 26, pp. 703-729; Folkerts, H., Koehorst, H., Challenges in international food supply chains: Vertical coordination in the European agribusiness and food industries (1998) British Food Journal, 100, pp. 385-388; Sundram, V.P.K., Ibrahim, A.R., Chandran Govindaraju, V., Supply chain management practices in the electronics industry in Malaysia: Consequences for supply chain performance (2011) Benchmarking: An International Journal, 18, p. 834855; Syed Marzuki, S.Z., Hall, C.M., Ballantine, P.W., Restaurant manager and halal certification in Malaysia (2012) Journal of Foodservice Business Research, 15, pp. 195-214; Sungkar, I., Hashim, D., The global halal food market and updates on global halal standards (2009) Presenting atThe World BankFirst EAP Regional Agribusiness Trade and Investment Conference "Agro-enterprise Without Borders Singapore, p. 3031; Wan Omar, W.M., Developing a model for Halal Food Supply Chain implementation (2017) Doctor of Philosophy, School of Business IT and Logistics, , RMIT University, Melbourne, Australia; Guidelines for Halal Assurance Management System of Malaysia halal certification (2011) Terms and definitions, , ed. Putrajaya, Malaysia: Jabatan Kemajuan Islam Malaysia (JAKIM); Jack, D., Pardoe, T., Ritchie, C., Scottish Quality Cereals and Coastal Grains-combinable crop assurance in action (1998) Supply Chain Management: An International Journal, 3, pp. 134-138; Wilson, T., Clarke, W., Food safety and traceability in the agricultural supply chain: Using the Internet to deliver traceability (1998) Supply Chain Management: An International Journal, 3, pp. 127-133; Timon, D., O'Reilly, S., An evaluation of traceability systems along the Irish beef chain (1998) Long-term prospects for the beef industry, INRA, Ivry-sur-Seine, pp. 219-225; Zailani, S., Arrifin, Z., Abd Wahid, N., Othman, R., Fernando, Y., Halal traceability and halal tracking systems in strengthening halal food supply chain for food industry in Malaysia (A review) (2010) Journal of food Technology, 8, pp. 74-81; Meuwissen, M.P., Velthuis, A.G., Hogeveen, H., Huirne, R.B., Traceability and certification in meat supply chains (2003) Journal of Agribusiness, 21, pp. 167-182; Ab Talib, M.S., Md Sawari, S.S., Abdul Hamid, A.B., Thoo, A.C., Emerging halal food market: An Institutional Theory of halal certificate implementation (2016) Management Research Review, 39, pp. 987-997; Mohd Bahrudin, S.S., Illyas, M.I., Desa, M.I., Tracking and tracing technology for halal product integrity over the supply chain (2011) Electrical Engineering and Informatics (ICEEI), 2011 International Conference on, pp. 1-7. , Bandung, Indonesia; Gellynck, X., Verbeke, W., Consumer perception of traceability in the meat chain (2001) Agrarwirtschaft, 50, pp. 368-373; Mohamad, M.A., Mansor, S., Ahmad, N., Wan Adnan, W.A., Muhammad Wali, I., The reliability of halal product transportation using GPS tracking system (2016) Journal of Theoretical & Applied Information Technology, 90; Anir, N.A., Nizam, M.N.M.H., Azmi, M., The users perceptions and opportunities in Malaysia in introducing RFID system for Halal food tracking (2008) WSEAS Transactions on information science and applications, 5, pp. 843-852; Melatu Samsi, S.Z., Tasnim, R., Ibrahim, O., Stakeholders' role for an efficient traceability system in halal industry supply chain (2011) Annual International Conference on Enterprise Resource Planning and Supply Chain Management (ERP-SCM 2011), pp. 14-15; Poniman, D., Purchase, S., Sneddon, J., Traceability systems in the Western Australia halal food supply chain (2015) Asia Pacific Journal of Marketing and Logistics, 27, pp. 324-348; The importance of traceability for public health and consumer protection (2001) Revue scientifique et technique (International Office of Epizootics), 20, pp. 363-371; Abd Rahman, A., Singhry, H.B., Hanafiah, M.H., Abdul, M., Influence of perceived benefits and traceability system on the readiness for Halal Assurance System implementation among food manufacturers (2017) Food Control, 73, pp. 1318-1326; Pettitt, R.G., Traceability in the food animal industry and supermarket chains (2001) Revue scientifique et technique (International Office of Epizootics), 20, pp. 584-597; Disney, W., Green, J., Forsythe, K., Wiemers, J., Weber, S., Benefit-cost analysis of animal identification for disease prevention and control (2001) Revue scientifique et technique (International Office of Epizootics), 20, pp. 385-405; Omar, E.N., Jaafar, H.S., Halal supply chain in the food industry-A conceptual model (2011) 2011 IEEE Symposium on Business, Engineering and Industrial Applications (ISBEIA), pp. 384-389. , Langkawi, Malaysia; Omar, E.N., Jaafar, H.S., Osman, M.R., Assessing halalan-toyyiban food supply chain in the poultry industry (2012) International Halal Conference 2012 (INHAC), , 000-000, Kuala Lumpur, Malaysia; Melatu Samsi, S.Z., Ibrahim, O., Tasnim, R., Review on knowledge management as a tool for effective traceability system in halal food industry supply chain (2012) Journal of Research And Innovation In Information Systems, 1 (1), pp. 78-85; Dabbene, F., Gay, P., Tortia, C., Traceability issues in food supply chain management: A review (2014) Biosystems engineering, 120, pp. 65-80; Agarwal, A., Shankar, R., Analyzing alternatives for improvement in supply chain performance (2002) Work study, 51, pp. 32-37; Tieman, M., van der Vorst, J.G., Che Ghazali, M., Principles in halal supply chain management (2012) Journal of Islamic Marketing, 3, pp. 217-243; Fugate, B.S., Mentzer, J.T., Stank, T.P., Logistics performance: Efficiency, effectiveness, and differentiation (2010) Journal of business logistics, 31, pp. 43-62; Tieman, M., The application of Halal in supply chain management: In-depth interviews (2011) Journal of Islamic Marketing, 2, pp. 186-195; Abdul Majid, Z., Kamarulzaman, N.H., Abdul Rashid, R., Developing halal food supply chain integrity model in logistics industry: A conceptual review (2014) Malaysia International Halal Research & Education Conferences (MIHREC 2014): Innovation for a Sustainable Halal Industry, pp. 114-122. , Serdang, Malaysia; Wong, Y.C., Wong, K.Y., Ali, A., A Study on Lean Manufacturing implementation in the Malaysian electrical and electronics industry (2009) European Journal of Scientific Research, 38, p. 521535; Jusoh, R., Ibrahim, D.N., Zainuddin, Y., The performance consequence of multiple performance measures usage: Evidence from the Malaysian manufacturers (2008) International Journal of Productivity and Performance Management, 57, pp. 119 and 136; Mohamed Ismail, R., Mediation impact of manufacturing strategies on external environmental factors and manufacturing performance (2014) Doctor of Philosophy, Othman Yeop Abdullah Graduate School of Business, , Universiti Utara Malaysia, Kedah, Malaysia; Sekaran, U., (2003) Research methods for business: A skill building approach, , New York: John Wiley & Sons; Gefen, D., Straub, D., Boudreau, M.-C., Structural equation modeling and regression: Guidelines for research practice (2000) Communications of the association for information systems, 4, p. 7; Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., (2014) A primer on partial least squares structural equation modeling (PLS-SEM), , 1st ed. Thousand Oaks, CA: SAGE Publications Inc; Ramayah, T., Cheah, J., Chuah, F., Ting, H., Memon, M.A., (2018) Partial Least Squares Structural Equation Modelling(PLS-SEM) using SmartPLS 3.0: An updated practical guide to statistical analysis, , 2nd ed. Kuala Lumpur, Malaysia: Pearson Malaysia Sdn. Bhd; Zhang, Z., Yuan, K.-H., (2018) Practical Statistical Power Analysis Using Webpower, , https://webpower.psychstat.org, (January 8th). and R (Eds); Cain, M.K., Zhang, Z., Yuan, K.-H., Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation (2017) Behavior research methods, 49, pp. 1716-1735; Ramayah, T., Yeap, J., Ahmad, N.H., Halim, H.A., Rahman, S.A., Testing a confirmatory model of facebook usage in smartpls using consistent PLS (2017) International Journal of Business and Innovation, 3, pp. 1-14; Kline, R.B., (2011) Principles and practice of structural equation modeling, , Guilford publications; Ngah, A.H., Barriers and enablers to the adoption of Halal Transportation and Halal Warehousing services among Halal Manufacturers in Malaysia (2016) Doctor of Philosophy (Technology Management), Faculty of Industrial Management, , Universiti Malaysia Pahang (UMP), Pahang, Malaysia; Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., (2017) A primer on partial least squares structural equation modeling (PLS-SEM), , 2nd ed. Thousand Oaks, CA: SAGE Publications Inc; Ringle, C.M., Wende, S., Becker, J.-M., (2015) SmartPLS 3, , http://www.smartpls.com, (Dec 1st); Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing research, 18, pp. 39-50. , Feb 1981; Byrne, B.M., (2016) Structural equation modeling with AMOS: Basic concepts, applications, and programming, , Abingdon, UK: Routledge; Henseler, J., Ringle, C.M., Sarstedt, M., A new criterion for assessing discriminant validity in variance-based structural equation modeling (2015) Journal of the Academy of Marketing Science, 43, pp. 115-135; Cohen, J., (1988) Statistical power analysis for the behavioral sciences, , Hillsdale, New Jersey: Lawrence Earlbaum Associates; Shafii, Z., Nor, W.S.K.W.M., Halal traceability framework for halal food production (2012) World Appl Sci J, 17, p. 15},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ukko2020,
author={Ukko, J. and Saunila, M.},
title={Understanding the practice of performance measurement in industrial collaboration: From design to implementation},
journal={Journal of Purchasing and Supply Management},
year={2020},
volume={26},
number={1},
doi={10.1016/j.pursup.2019.02.001},
art_number={100529},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078665974&doi=10.1016%2fj.pursup.2019.02.001&partnerID=40&md5=4e1e688e21f3ee2ba2f099ad0ef547cb},
affiliation={Department of Industrial Engineering and Management School of Engineering Science, LUT University, Saimaankatu 11, Lahti, FI-15140, Finland},
abstract={This study focuses on the design and implementation of a performance measurement system (PMS) for industrial collaboration. This article provides insights from two case studies of industrial collaboration. The research aims to study the crucial characteristics of PMS design and implementation in industrial collaboration, in which multiple tiers, extending beyond the buyer-supplier dyad, are considered. Prerequisites and challenges are crucial characteristics that should be examined in the design and implementation of a PMS. The results indicate three main characteristics affect the design and implementation of a PMS in an industrial collaboration: 1) the importance of collaboration, 2) the importance of coordination and integrated information systems within the system implementation, and 3) the importance of acknowledging the impact of varied customer needs. © 2019 Elsevier Ltd},
author_keywords={Buyer-supplier relationship;  Industrial collaboration;  Network;  Performance management;  Performance measurement;  Performance measurement system;  Supply chain},
references={Akyuz, A.G., Erkan, E.T., Supply chain performance measurement: a literature review (2010) Int. J. Prod. Res., 48 (17), pp. 5137-5155; Amir, A.M., Performance measurement system design in service operations (2014) Manag. Res. Rev., 37 (8), pp. 728-749; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decis. Support Syst., 42 (1), pp. 283-301; Aramyan, L.H., Oude Lansink, A.G., Van Der Vorst, J.G., Van Kooten, O., Performance measurement in agri-food supply chains: a case study (2007) Supply Chain Manag.: Int. J., 12 (4), pp. 304-315; Baines, T., Lightfoot, H.W., Servitization of the manufacturing firm: Exploring the operations practices and technologies that deliver advanced services (2014) Int. J. Oper. Prod. Manag., 34 (1), pp. 2-35; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3), pp. 275-292; Bierbusse, P., Siesfeld, T., Measures that matter (1997) J. Strateg. Perform. Meas., 1 (2), pp. 6-11; Bititci, U.S., Martinez, M., Albores, P., Parung, J., Creating and managing value in collaborative network (2004) Int. J. Phys. Distrib. Logist. Manag., 34 (3), pp. 251-268; Bititci, U.S., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: challenges for tomorrow (2012) Int. J. Manag. Rev., 14 (3), pp. 305-327; Bititci, U., Turner, T., Mackay, D., Kearney, D., Parung, J., Walters, D., Managing synergy in collaborative enterprises (2007) Prod. Plan. Control, 6 (18), pp. 454-465; Bourne, M., Researching performance measurement system implementation: the dynamics of success and failure (2005) Prod. Plan. Control, 16 (2), pp. 101-113; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20 (7), pp. 754-771; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives. Perceptions of participating managers (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1288-1310; Busi, M., Bititci, U.S., Collaborative performance management: present gaps and future research (2006) Int. J. Product. Perform. Manag., 55 (1), pp. 7-25; Camarinha-Matos, L.M., Afsarmanesh, H., Galeano, N., Molina, A., Collaborative networked organizations – concepts and practice in manufacturing enterprises (2009) Comput. Ind. Eng., 57 (1), pp. 46-60; Cavalieri, S., Pezzotta, G., Product–service systems engineering: state of the art and research challenges (2012) Comput. Ind., 63 (4), pp. 278-288; Chan, F.T., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol., 21 (7), pp. 534-548; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Bus. Process Manag. J., 14 (4), pp. 512-529; Chelariu, C., Kwame Asare, A., Brashear-Alejandro, T., “A rose, by any other name”…: relationship typology and performance measurement in supply chains (2014) J. Bus. Ind. Mark., 29 (4), pp. 332-343; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: a framework for contextual analysis (2011) Int. J. Product. Perform. Manag., 60 (6), pp. 583-602; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag., 21 (1-2), pp. 71-87; Henri, J., Management control systems and strategy: a resource-based perspective (2006) Account. Organ. Soc., 31 (6), pp. 529-558; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: a case study from the packaging industry (2009) Prod. Plan. Control, 20 (1), pp. 68-81; Huxham, C., (1996) Creating Collaborative Advantage, , Sage Publications London; Håkansson, H., Lind, J., Accounting and network coordination (2004) Account. Organ. Soc., 29 (1), pp. 51-72; Kald, M., Nilsson, F., Performance measurement at Nordic companies (2000) Eur. Manag. J., 18 (1), pp. 113-127; Kaplan, R.S., Norton, D.P., The balanced scorecard: measures that drive performance (1992) Harv. Bus. Rev., 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., The Balanced Scorecard: Translating Strategy into Action (1996), Harvard Business School Press Boston, MA; Kaplan, R.S., Norton, D.P., Rugelsjoen, B., Managing alliances with the balanced scorecard (2010) Harv. Bus. Rev., 88 (1), pp. 68-75; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1222-1245; Koufteros, X., Verghese, A.J., Lucianetti, L., The effect of performance measurement systems on firm performance: a cross-sectional and a longitudinal study (2014) J. Oper. Manag., 32 (6), pp. 313-336; Kulmala, H.I., Lönnqvist, A., Performance measurement of networks: towards a nonfinancial approach (2006) Int. J. Netw. Virtual Organ., 3 (3), pp. 299-316; Leseure, M., Shaw, N., Chapman, G., Performance measurement in organizational networks: an exploratory case study (2001) Int. J. Bus. Perform. Manag., 3 (1), pp. 30-46; Liang, Y.H., Performance measurement of interorganizational information systems in the supply chain (2015) Int. J. Prod. Res., 53 (18), pp. 5484-5499; Lovelock, C., Gummesson, E., Wither service marketing? In search of new paradigm and fresh perspectives (2004) J. Serv. Res., 47, pp. 9-20; Lu, X.H., Huang, L.H., Heng, M.S., Critical success factors of inter-organizational information systems—A case study of Cisco and Xiao Tong in China (2006) Inf. Manag., 43 (3), pp. 395-408; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Maestrini, V., Luzzini, D., Shani, A.B.R., Canterino, F., The action research cycle reloaded: conducting action research across buyer-supplier relationships (2016) J. Purch. Supply Manag., 22 (4), pp. 289-298; Maestrini, V., Martinez, V., Neely, A., Luzzini, D., Caniato, F., Maccarrone, P., The relationship regulator: a buyer-supplier collaborative performance measurement system (2018) Int. J. Oper. Prod. Manag.; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: a survey in the mines (2006) Manag. Account. Res., 17 (3), pp. 315-339; Meekings, A., Unlocking the potential of performance measurement: a practical implementation guide (1995) Public Money Manag., 15 (4), pp. 5-12; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res., 25 (2), pp. 173-186; Meredith, J., Building operations management theory through case and field research (1998) J. Oper. Manag., 16 (4), pp. 441-454; Neely, A., The performance measurement revolution: why now and what next? (1999) Int. J. Oper. Prod. Manag., 19 (2), pp. 205-228; Neely, A., Gregory, M., Platts, K., Performance measurement system design. A literature review and research agenda (1995) Int. J. Oper. Prod. Manag., 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: developing and testing a process-based approach (2000) Int. J. Oper. Prod. Manag., 20 (10), pp. 1119-1145; Niebecker, K., Eager, D., Kubitza, K., Improving cross-company project management performance with a collaborative project scorecard (2008) Int. J. Manag. Proj. Bus., 1 (3), pp. 368-386; Niebecker, K., Eager, D., Moulton, B., Collaborative and cross-company project management within the automotive industry using the Balanced Scorecard (2010) Int. J. Manag. Proj. Bus., 3 (2), pp. 328-337; Nollet, J., Beaulieu, M., Fabbe-Costes, N., The impact of performance measurement on purchasing group dynamics: the Canadian experience (2017) J. Purch. Supply Manag., 23 (1), pp. 17-27; Nudurupati, S.S., Bititci, U.S., Implementation and impact of IT-supported performance measurement systems (2005) Prod. Plan. Control, 16 (2), pp. 152-162; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Ind. Manag. Data Syst., 110 (9), pp. 1297-1318; Parung, J., Bititci, U.S., A conceptual metric for managing collaborative network (2006) J. Model. Manag., 1 (2), pp. 116-136; Parung, J., Bititci, U.S., A metric for collaborative networks (2008) Bus. Process Manag. J., 14 (5), pp. 654-674; Pekkola, S., Ukko, J., Designing a performance measurement system for collaborative network (2016) Int. J. Oper. Prod. Manag., 36 (11), pp. 1410-1434; Pettigrew, A., Whipp, R., Rosenfield, R., Competitiveness and the management of strategic change processes (1989) The Competitiveness of European Industry: Country Policies and Company Strategies, , A. Francis P.K.M. Tharakan Routledge London; Pisano, G.P., Verganti, R., Which kind of collaboration is right for you? (2008) Harv. Bus. Rev., 86 (12), pp. 78-86; Pohl, M., Förstl, K., Achieving purchasing competence through purchasing performance measurement system design—A multiple-case study analysis (2011) J. Purch. Supply Manag., 17 (4), pp. 231-245; Radnor, Z.J., Barnes, D., Historical analysis of performance measurement and management in operations management (2007) Int. J. Product. Perform. Manag., 56 (5), pp. 384-396; Ramsey, J., Wagner, B., Organisational supplying behaviour: understanding supplier needs, wants and preferences (2009) J. Purch. Supply Manag., 15, pp. 127-138; Schneiderman, A., Why balanced scorecards fail (1999) J. Strateg. Perform. Meas., pp. 6-11. , (Special Edition); Ukko, J., Pekkola, S., Customer-centered measurement of service operations: a B2B case study (2016) Oper. Manag. Res., 9 (1), pp. 11-21; Ukko, J., Pekkola, S., Saunila, M., Rantala, T., Performance measurement approach to show the value for the customer in an industrial service network (2015) Int. J. Bus. Perform. Manag., 16 (2-3), pp. 214-229; Ukko, J., Tenhunen, J., Rantanen, H., Performance measurement impacts on management and leadership – Perspectives of management and employees (2007) Int. J. Prod. Econ., 110 (1-2), pp. 39-51; Verdecho, M.J., Alfaro, J.J., Rodriguez-Rodriguez, R., Foundations for collaborative performance measurement (2009) Prod. Plan. Control, 20 (3), pp. 193-205; Yin, R.K., Case Study Research. Design and Methods (2003), 3rd ed Sage Publications},
correspondence_address1={Ukko, J.; Department of Industrial Engineering and Management School of Engineering Science, Saimaankatu 11, Finland; email: juhani.ukko@lut.fi},
publisher={Elsevier Ltd},
issn={14784092},
language={English},
abbrev_source_title={J. Purch. Supply Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Khan202043,
author={Khan, S.A. and Chaabane, A. and Dweiri, F.},
title={Supply chain performance measurement systems: A qualitative review and proposed conceptual framework},
journal={International Journal of Industrial and Systems Engineering},
year={2020},
volume={34},
number={1},
pages={43-64},
doi={10.1504/IJISE.2020.104315},
note={cited By 16},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077630138&doi=10.1504%2fIJISE.2020.104315&partnerID=40&md5=c7a4e0c55affaa26bc9d9cbda90b9334},
affiliation={Department of Automated Manufacturing Engineering, École de Technologie Supérieure, Montréal, Canada; Industrial Engineering and Engineering Management Department, University of Sharjah, Sharjah, United Arab Emirates},
abstract={Management of supply chain (SC) is becoming more challenging with every passing day due to high competition, globalisation, and digitalisation because of the recent adoption of internet of things (IoT) technologies in order to increase supply chain visibility. Due to this fact, importance of supply chain performance measurement systems (SCPMS) has been increased significantly. To cope with these challenges and remain competitive, organisations are keen to evaluate SC performance more precisely. Therefore, this paper adopts a qualitative review methodology to find out if existing SCPMS are in line with the current emerging technology trends of managing SC and measuring SC performance and if not, what will be the characteristics of future SCPMS. Results show particularly that existing SCPMSs are not adequate to cope with the complexity and the technology advancement observed in supply chain management as a smart way for measuring modern SC performance is needed. Finally, this study proposes a conceptual supply chain performance measurement (SCPM) framework to fill the identified research gaps. © 2020 Inderscience Enterprises Ltd.},
author_keywords={Integrated framework;  Knowledge base system;  Qualitative review;  SCM;  SCPMS;  Supply chain management;  Supply chain performance measurement systems},
keywords={Internet of things;  Knowledge based systems, Integrated frameworks;  Internet of Things (IOT);  Knowledge base system;  Management of supply chains;  SCPMS;  Supply chain performance measurements;  Supply chain visibility;  Technology advancement, Supply chain management},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: Review and classification (2012) The Journal of Organizational Management Studies, pp. 1-20; Akyuz, A., Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Almeida, A., Bastos, J., Francisco, R., Azevedo, A., Ávila, P., Sustainability assessment framework for proactive supply chain management (2016) International Journal of Industrial and Systems Engineering, 24 (2), pp. 198-222; Angelov, P., (2012) Autonomous Learning Systems: From Data Streams to Knowledge in Real-Time, , John Wiley and Sons; Asadi, N., Performance indicators in internal logistic systems (2012) International Conference on Innovation and Information Management, pp. 48-52; Ashrafuzzaman, M., Al-Maruf, A., Mahbubul, I.M., Malek, A.B.M.A., Mukaddes, A.M.M., Quality function deployment approach to measure supply chain performance: A case study on garments accessories industries (2016) International Journal of Industrial and Systems Engineering, 22 (1), pp. 96-120; Balasubramanian, P., Tewary, A.K., Design of supply chains: Unrealistic expectations on collaboration (2005) Sadhana-Academy Proceedings in Engineering Sciences, 30, pp. 463-473; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998-2015 (2016) Computers in Industry, pp. 135-150; Bauer, W., (2014) 2014 Industrie 4.0 - Volkswirtschaftliches Potenzial Für Deutschland, , BITKOM, Berlin; Beamon, B.M., Measuring supply chain performance (1999) Industrial Engineering, 19 (3), pp. 275-292; Beamon, B.M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations and Production Management, 17 (5), pp. 522-534; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), p. 110. , Nos; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14, pp. 512-529; Chen, C.-T., Lin, C.-T., Huang, S.-F., A fuzzy approach for supplier evaluation and selection in supply chain management (2006) International Journal of Production Economics, 102 (2), pp. 289-301; Christopher, M., (2005) Logistics and Supply Chain Management: Creating Value-Adding Networks, , FT Prentice Hall, London; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Davis, T., Effective supply chain management (1993) Sloan Management Review, 34 (4), pp. 35-46; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge - Measuring Operations for World-Class Competition, , Dow Jones-Irwin, Homewood, IL; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things (2017) Industrial Management and Data Systems, 117 (2), pp. 267-286; Dweiri, F., Khan, S.A., Development of a universal supply chain management performance index (2012) International Journal of Business Performance and Supply Chain Modelling, 4 (3-4), pp. 232-236. , Nos; Dweiri, F., Khan, S.A., Shamsuzzaman, M., The use of statistical process control techniques in the web application products (2015) International Journal of Industrial and Systems Engineering, 20 (1), pp. 69-83; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258. , Elsevier; Geisberger, E., Broy, M., (2012) AgendaCPS - Integrierte Forschungsagenda CyberPhysical Systems, , Springer, Berlin; Globerson, S., Issues in developing a performance criteria system for an organisation (1985) International Journal of Production Research, 23 (4), pp. 639-646; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management; Hwarng, H.B., Chong, C.S.P., Hie, N., Burgess, T.F., Modelling a complex supply chain: Understanding the effect of simplified assumptions (2005) International Journal of Production Research, 43 (13), pp. 2829-2872; Ishibuchi, H., Tsukamoto, N., Nojima, Y., Evolutionary many-objective optimization: A short review (2008) 2008 IEEE Congress on Evolutionary Computation, CEC, pp. 2419-2426; Kaplan, R.S., Bruns, W., (1987) Accounting and Management: A Field Study Perspective, , Harvard Business School Press, Boston, MA 02163; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Keegan, D., Eiler, R., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50; Khan, S.A., Importance of measuring supply chain management performance (2013) Industrial Engineering and Management, 2 (5), pp. 1-2; Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G., Van Brussel, H., Reconfigurable manufacturing systems (1999) CIRP Annals - Manufacturing Technology, 48 (2), pp. 527-540; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) Social Sciences, 2 (4), pp. 19-34; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19. , http://dx.doi.org/10.1108/09574090110806190; Lauras, M., Lamothe, J., Pingaud, H., A business process oriented method to design supply chain performance measurement systems (2011) International Journal of Business Performance Management, 12 (4), pp. 354-376; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73. , Vol3; Levy, D.L., International sourcing and supply chain stability (1995) Journal of International Business Studies, 26 (2), p. 343; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Computers and Industrial Engineering, 113, pp. 333-346; Lin, C.T., Chiu, H., Chu, P.Y., Agility index in the supply chain (2006) International Journal of Production Economics, 100 (2), pp. 285-299; Maestrini, V., Luzzini, D., Caniato, F., Ronchi, S., Measuring supply chain performance: A lifecycle framework and a case study (2018) International Journal of Operations & Production Management, 38 (4), pp. 934-956. , https://doi.org/10.1108/IJOPM-07-2015-0455, online; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, pp. 299-315; Manikandan, M., Chidambaranathan, S., Developing a two-dimensional framework to review the supply chain performance measurement literature (2017) International Journal of Engineering Trends and Technology, 43 (2), pp. 86-96; Marwah, A., Thakar, G., Gupta, R., Determinants of supply chain performance in the indian manufacturing organizations (2014) Proposed Conceptual Model, 5 (1), pp. 14-27; Mathiyalagan, P., Mannan, K., Parthiban, P., (2014) Performance Evaluation in Supply Chain Using Balanced Scorecard, 1 (1), pp. 4-10; Miranzadeh, A., Sajadi, S.M., Tavakoli, M.M., Simulation of a single product supply chain model with ARENA (2015) International Journal of Industrial and Systems Engineering, 19 (1), p. 18; Mishra, P., Sharma, R.K., Measuring business performance in a SCN using Six Sigma methodology - A case study (2017) International Journal of Industrial and Systems Engineering, 25 (1), pp. 76-109; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Neely, A., The evolution of performance measurement research (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Gregory, M., Platts, K., Erratum (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Oswald, M., Seek and ye shall not find necessarily: The Google Spain Decision, the Surveillant on the street and Privacy Vigilantism (2014) Digital Enlightenment Yearbook; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Putnik, G., Sluga, A., Elmaraghy, H., Teti, R., Koren, Y., Tolio, T., Hon, B., Scalability in manufacturing systems design and operation: State-of-the-art and future developments roadmap (2013) CIRP Annals - Manufacturing Technology, 62 (2), pp. 751-774; Ramaa, A., Rangaswamy, T., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Second International Conference on Emerging Trends in Engineering and Technology; Schweiger, C., (2011) Use and Deployment of Mobile Device Technology for Real-Time Transit Information, , National Academy of Sciences, Washington; Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , Nos; Sillanpää, I., Empirical study of measuring supply chain performance (2015) Benchmarking: An International Journal, 22 (2), pp. 290-308; Smith, M.F., Lancioni, R.A., Oliva, T.A., The effects of management inertia on the supply chain performance of produce-to-stock firms (2005) Industrial Marketing Management, 34 (6), pp. 614-628; Stern, J.M., Stewart, G.B., Chew, D.H., The EVA® financial management system (1995) Journal of Applied Corporate Finance, 8 (2), pp. 32-46; Tangen, S., Improving the performance of a performance measure (2005) Measuring Business Excellence, 9 (2), pp. 4-11; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Meas. Bus. Excell., 14 (1), pp. 4-18; Tavana, M., Kaviani, M.A., Di Caprio, D., Rahpeyma, B., A two-stage data envelopment analysis model for measuring performance in three-level supply chains (2016) Measurement: Journal of the International Measurement Confederation, 78, pp. 322-333. , Elsevier Ltd; Wang, C., Heng, M., Chau, P., (2007) Supply Chain Management - Issues in the New Era of Collaboration and Competition, , Idea Group Publishing, London; Wong, W.P., Wong, K.Y., Peng Wong, W., Yew Wong, K., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381},
correspondence_address1={Khan, S.A.; Department of Automated Manufacturing Engineering, Canada; email: sharfuddin-ahmed.khan.1@etsmtl.ca},
publisher={Inderscience Publishers},
issn={17485037},
language={English},
abbrev_source_title={Int. J. Ind. Syst. Eng.},
document_type={Review},
source={Scopus},
}

@CONFERENCE{Kusrini2019,
author={Kusrini, E. and Rifai, M.A.B. and Miranda, S.},
title={Performance measurement using supply chain operation reference (SCOR) model: A case study in a small-medium enterprise (SME) in Indonesia},
journal={IOP Conference Series: Materials Science and Engineering},
year={2019},
volume={697},
number={1},
doi={10.1088/1757-899X/697/1/012014},
art_number={012014},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078204795&doi=10.1088%2f1757-899X%2f697%2f1%2f012014&partnerID=40&md5=9c8ae230f87aac63d388046acd44d282},
affiliation={Industrial Engineering Department, Faculty of Industrial Technology, Universitas Islam Indonesia, Yogyakarta, Indonesia},
abstract={Nowadays, measuring supply chain performance is a topic that attracts many researchers. Performance measurement can be used as a reference to improve performance in order to compete in the market. This study aims to measure the supply chain performance in a small and medium enterprise (SME) producing sports clothes in Yogyakarta. This research utilizes the performance attributes from the Supply Chain Operation Reference Model (SCOR). The business process is identified as the baseline to determine performance metrics on each process (plan, source, make, deliver, return and enable) and performance attributes, i.e. reliability, responsiveness, agility, cost, and asset management efficiency. According to the experts within the company, there are only 27 of 40 performance metrics obtained valid. The overall performance score is at a good level with a value of 77.89. Among the metrics, it is found 9 metrics in marginal and average level while the remaining metrics gained the value of more than 70. This supply chain performance analysis can support the company decision making in order to improve its performance at an excellent level. © Published under licence by IOP Publishing Ltd.},
author_keywords={Performance metric;  SCOR;  SME;  Supply chain performance measurement},
keywords={Decision making;  Ergonomics, Performance measurements;  Performance metrices;  SCOR;  Small and medium enterprise;  Small medium enterprise;  Supply chain operation references;  Supply chain performance;  Supply chain performance measurements, Supply chains},
references={Levi, S.D., Kaminsky, P., Levi, E.S., (2008) Designing and Managing the Supply Chain: Concepts Strategies and Case Studies; Kulkarni, S., Sharma, A., (2004) Supply Chain Management; Angelova, B., Zekiri, J., (2011) J. of Academic Research in Business and Social Sciences, pp. 2222-6990; Durukan, T., Bozaci, I., (2011) J. of Business and Social Science, 23; Liputra, D.T., Santoso Susanto, N.A., (2018) Jurnal Rekayasa Sistem Industri, 7 (2), pp. 119-125; Agami, N., Saleh, M., Rasmy, M., (2012) J. of Organizational Man. Studies, 20; Gunasekaran, A., Patel, A., McGaughey, R.E., (2004) J. Production Economic, 87 (3), pp. 333-347; Liebetruth, T., (2017) Procedia Engineering, 192, pp. 539-544; Golparvar, M., Seifbarghy, M., (2009) J. of Industrial Engineering, pp. 59-69; Huan, S.M., Sheoran, S.K., Wang, G., (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Hasibuan, A., Arfah, M., Parinduri, L., Hernawati, T., Suliawati, Harahap, B., Purwadi, A., (2018) J. of Physics: Conference Series; Information on www.supplychain.org; Delipinar, G.E., Kocaoglu, B., (2016) Procedia - Social and Behavioral Sciences, 229, pp. 398-406; Kazemkhanlou, H., Ahadi, H.R., (2014) Proc. Int. Conference on Industrial Engineering and Operations Management, p. 274; Wibowo, M., Sholeha, (2015) Procedia Engineering, 125, pp. 25-31; Wigati, D.T., Khoirano, A.D., Alsana, S., Utama, D.R., (2017) J. Industrial Services, pp. 46-52; Meliantika, Tanjung, W.N., Nurhasanah, N., (2014) Seminar National on Innovation and Application of Technology in Industry, p. 2085; Pourjavad, E., Shahin, A., (2018) Intern. J. of Fuzzy Systems, 20 (3), pp. 901-912; (2017) Supply Chain Operation Reference Model SCOR, Version 12.0, , APICS; Hausman, W.H., The Practice of Supply Chain Management: Where Theory and Application Converge (ISOR), pp. 61-67; Joshi, A., Kale, S., Chandel, S., Pal, D., (2015) British Journal of Applied Science & Technology, 7 (4), pp. 396-403; Aramyan, L.H., Lansink, A.G., Van Der Vorst, J., Kooten, O.F., (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Waaly, A.N., Ridwan, A.Y., Akbar, M.D., (2018) MATEC Web of Conferences, 204, pp. 01008-01015; Kusrini, E., Subagyo, Masruroh, N.A., (2015) Management Science and Applications, pp. 551-562; Trinekens, J.H., Hvolby, H.H., (2000) Proc. of the 3rd CINET Conference (Aalborg), pp. 399-409},
publisher={IOP Publishing Ltd},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Kusrini2019,
author={Kusrini, E. and Helia, V.N. and Maharani, M.P.},
title={Supply Chain Performance Measurement Using Supply Chain Operation Reference (SCOR) in Sugar Company in Indonesia},
journal={IOP Conference Series: Materials Science and Engineering},
year={2019},
volume={697},
number={1},
doi={10.1088/1757-899X/697/1/012010},
art_number={012010},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078197728&doi=10.1088%2f1757-899X%2f697%2f1%2f012010&partnerID=40&md5=c2d4e2775888a7314ff14bed3a2fe812},
affiliation={Industrial Engineering Department, Faculty of Industrial Technology, Universitas Islam Indonesia, Yogyakarta, Indonesia},
abstract={This study aims to measure supply chain performance by using SCOR (Supply Chain Operation Reference), a process-based model. It focuses on five business processes namely Plan, Source, Make, Deliver, and Return. There are 45 KPIs grouped into 5 main KPI attributes: Reliability, Responsiveness, Agility, Cost, and Asset Management. In order to get a more precise performance value, then weighted by using Analytical Hierarchy Process (AHP) method. The final value is obtained from the multiplication of the value of performance by the weight of importance. This study was conducted by taking a case study at one of the sugar company in Indonesia. The results of this study indicate that the supply chain performance of the sugar company is in a good category with the final grade of 70.94 from the 0-100 scale. Value of each process consecutively as follows: Plan 18.47, Source 16.12, Make 10.73, Deliver 10.79, and Return 7.1. Some recommendations are given to the company for evaluation and refinement focused on low business process values. © Published under licence by IOP Publishing Ltd.},
keywords={Ergonomics, Analytical Hierarchy Process;  Business Process;  Indonesia;  Performance value;  Process-based modeling;  Supply chain operation references;  Supply chain performance;  Supply chain performance measurements, Supply chains},
references={Delipinar, G.E., Kocaoglu, B., Using SCOR Model to Gain Competitive Advantage: A Literature Review (2016) Procedia - Social and Behavioral Sciences, 229, pp. 398-406; David, R., Josep, J., Study on performance measurement systems-Measures and Metrics (2014) Int. J of Scientific and Research Publications, 4, p. 1; Ioannou, G., Streamlining the supply chain of the Hellenic sugar industry (2005) J. of Food Eng., 70 (3), pp. 323-332; Fts, C., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply-chain management' (1997) Logistics Information Management, 10 (2), pp. 62-67; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of Supply Chain Operations Reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Lee, J.S., Shiu, T., Yi-Siang, Sivakumar, P., The applications of SCOR in manufacturing: Two cases in Taiwan (2012) Procedia Engineering, 38, pp. 2548-2563; Prakash, S., Sandeep Gunjan, S., Rathore, A., (2013) Supply Chain Operations Reference (SCOR) Model: An Overview and A Structured Literature Review of Its Application; Persson, F., Jonas, B., Orjan, G., (2010) New Challenges, New Approaches, pp. 211-218; Palma-Mendoza, J.A., Neailey, K., Roy, R., Business process re-design methodology to support supply chain integration (2014) Int. J. of Inf. Mgt, 34 (2), pp. 167-176; Saaty, T.L., Vargas, I.G., (2012) Models, Methods, Concepts, & Applications of the Analytic Hierarchy Process, 175; Bukhori, I.B., Widodo, K.H., Ismoyowati, D., Evaluation of Poultry Supply Chain Performance in XYZ Slaughtering House Yogyakarta Using SCOR and AHP Method (2015) Agriculture and Agricultural Science Procedia, 3, pp. 221-225; Butdee, S., Phuangsalee, P., Uncertain risk assessment modelling for bus body manufacturing supply chain using AHP and fuzzy AHP (2019) Procedia Manufacturing, 30, pp. 663-670; Ntabe, E.N., Lebel, L., Munson, A.D., Santa-Eulalia, L.A., A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues (2015) Int. J. of Production Economics, 169, pp. 310-332; (2012) Supply Chain Operations Reference Model Revision 11.0., , SCC; Zuniga, R., Marcus, S., Klaus-Dieter, T., (2013) Dynamics in Logistics, pp. 211-220; Mollenkopf, D., Russo, I., Frankel, R., The returns management process in supply chain strategy (2007) Int. J. of Physic. Dist. & Log. Mgt, 37 (7), pp. 568-592},
correspondence_address1={Helia, V.N.; Industrial Engineering Department, Indonesia; email: vembri@uii.ac.id},
publisher={IOP Publishing Ltd},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Kusrini2019,
author={Kusrini, E. and Caneca, V.I. and Helia, V.N. and Miranda, S.},
title={Supply Chain Performance Measurement Usng Supply Chain Operation Reference (SCOR) 12.0 Model : A Case Study in A A Leather SME in Indonesia},
journal={IOP Conference Series: Materials Science and Engineering},
year={2019},
volume={697},
number={1},
doi={10.1088/1757-899X/697/1/012023},
art_number={012023},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078089727&doi=10.1088%2f1757-899X%2f697%2f1%2f012023&partnerID=40&md5=3cf2ba5c4ade852cf40f8a2711ecece2},
affiliation={Industrial Engineering Department, Faculty of Industrial Technology, Versitas Islam Indonesia, Yogyakarta, Indonesia},
abstract={The leather industry is one of the basic ingredients in fashion and textile/apparel that has good development potential. This can be used to take advantage of the opportunity for the leather industry which is still importing 60-70%. Supply chain measurement is needed to determine production capability and moreover to benchmark for its company, government, and academics. Measurement of supply chain performance is carried out using the newest-SCOR 12.0 method with a performance and processes approach. The study was conducted on XYZ SME and limited to bag product which is one of the largest leather industries in the Bantul area, Special Region of Yogyakarta. The value of supply chain performance is 54.29, which based on performance indicators. This value included in the average category. It can be inferred as the benchmark for the leather industry around areas to improve supply chain performance. © Published under licence by IOP Publishing Ltd.},
keywords={Ergonomics;  Leather;  Supply chains;  Textile industry, Development potential;  Indonesia;  Leather industries;  Performance indicators;  Production capabilities;  Supply chain operation references;  Supply chain performance;  Supply chain performance measurements, Benchmarking},
references={Ma, M., Analisis Supply Chain Management pada PT. Monier di Sidoarjo (2014) Balance Economics, Business, Mgt, and Acc. J., 10, p. 19; (2017) Quick Reference Guide SCOR (Supply Chain Operations Reference) 12.0, , APICS; Risqiyah, I.A., Santoso, I., Risiko Rantai Pasok Agroindustri Salak Menggunakan Fuzzy FMEA (2015) Jurnal Manajemen & Agribisnis, 14; Hendayani, R., Pangestika, S., Analisis Kinerja Suplier pada Usaha Kecil Menengah (UKM) Jamur Tiram di Lampung Timur (2015) Jurnal Studi Manajemen Dan Bisnis, 2; Chin, T.A., Aba, H., Rasli, A., Baharun, R., Adoption of Supply Chain Management in SMEs (2012) Social and Behavioral Sciences, 65, pp. 614-619; Prakash, S., Soni, G., Sandeep, Rathore, A., (2013) Int. Conf. on Smart Technologies for Mech. Eng. (STME-2013), p. 55; Vistasusiyanti, Kindangen, P., Palandeng, I.D., Analisis Manajemen Rantai Pasokan Spring Bed pada PT. Massindo Sinar Pratama Kota Manado (2017) Jurnal EMBA, 5, pp. 893-900; Zhou, H., Benton, W.C., Jr., Schilling, D.A., Milligan, G.W., Supply Chain Integration and the SCOR Model J. of Business Logistics, 32, pp. 332-344; Lima-Junior, F.R., Carpinetti, L.C., Predicting Supply Chain Performance based on SCOR metrics and multilayer perceptron neural networks (2019) Int. J. of Production Economics, 212, pp. 19-38; Wahyuniardi, R., Syarwani, M., Anggani, R., Pengukuran Kinerja Supply Chain dengan pendekatan Supply Chain Operation References (SCOR) (2017) Jurnal Ilmiah Teknik Industri, 16 (2), pp. 123-132; Hvolby, H.H., Trienekens, J., Supply chain planning opportunities for small and medium sized companies (2002) Computers in Industry, 49 (1), pp. 3-8; Dissanayake, C.K., Cross, J.A., Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM (2018) Int. J. of Production Economics, 201, pp. 102-115; Fisher, M.L., What Is the Right Supply Chain for Your Product? (1997) Harvard Business Review, pp. 105-116; Harris, G.A., Componation, P.J., Farrington, P.A., An exploration of Fisher's framework for the alignment of supply chain strategy with product characteristics (2010) Eng. Mgt. J., 22; Chan, A.T.L., Ewt, N., Kkl, M., The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry (2017) European J. of Op. Research, 259 (2), pp. 486-499; Čiarniene, R., Vienažindiene, M., Agility and responsiveness managing fashion supply chan (2014) Procedia - Social and Behavioral Sciences, 150, pp. 1012-1019; Kim, M., Chai, S., The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective (2017) Int. J. of Production Economics, 187, pp. 42-52; Ebrahim, Z., Ahmad, N.A., Ma, M., (2014) Int. Symp. on Research in Innovation and Sustainability 2014 (ISoRIS'14); Fancelloa, G., Schintua, A., Serraa, P., An experimental analysis of Mediterranean supply chains through the use of cost KPIs (2018) Transportation Research Procedia, 30, pp. 137-146; Delipinar, G.E., Kocaoglu, B., Using SCOR model to gain competitive advantage: A literature review (2016) Procedia - Social and Behavioral Sciences, 229, pp. 398-406},
correspondence_address1={Helia, V.N.; Industrial Engineering Department, Indonesia; email: vembri@uii.ac.id},
publisher={IOP Publishing Ltd},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Taptajani2019,
author={Taptajani, D.S. and Ikhwana, A. and Chandrahadinata, D. and Hakim, B.L.},
title={Implications on the total business approach to the press red brick supply chain},
journal={Journal of Physics: Conference Series},
year={2019},
volume={1402},
number={2},
doi={10.1088/1742-6596/1402/2/022052},
art_number={022052},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078249439&doi=10.1088%2f1742-6596%2f1402%2f2%2f022052&partnerID=40&md5=0a3f4b3ca50727b679ce00f44fa814f4},
affiliation={Department of Industrial Engineering, Sekolah Tinggi Teknologi Garut, Jalan Mayor Syamsu 1, Garut, 44151, Indonesia},
abstract={Many strategies can be done by the company when it has an impact/implication, including the main actors in the red brick supply chain network press consisting of five business actors namely suppliers, manufacturers, distributors, retailers, and consumers. Continuous performance measurement is the key to all types of business success, so the purpose of this paper is to review the impact of the entire supply chain on the total effort undertaken, where performance measurement systems in the context of supply chains generate potential avenues for future research especially for new business people to be able to see what things need to be considered when starting a red brick press business. The author has found and classified the supply chain performance measurement system as one method of approach and systematic decision-making techniques. This study reveals that the SCOR Model approach as an indicator of red brick supply chain performance measurement and Analyst Hierarchy Process (AHP) as a decision-making tool can make a real indicator in the implementation of the current red brick business along with the impact on the market so that it is expected to get a solution to declare the effect of the red brick supply chain. © Published under licence by IOP Publishing Ltd.},
keywords={Behavioral research;  Decision making;  Presses (machine tools);  Supply chains, Business success;  Decision making tool;  Hierarchy process;  Performance measurement system;  Performance measurements;  SCOR models;  Supply chain network;  Supply chain performance measurements, Brick},
references={Treiblmaier, H., The impact of the blockchain on the supply chain: A theory-based research framework and a call for action (2018) Supply Chain Manag., 23 (6), pp. 545-559; Shee, H., Miah, S.J., Fairfield, L., Pujawan, N., The impact of cloud-enabled process integration on supply chain performance and firm sustainability: The moderating role of top management (2018) Supply Chain Manag., 23 (6), pp. 500-517; Yang, Y., Jia, F., Xu, Z., Towards an integrated conceptual model of supply chain learning: An extended resource-based view (2019) Supply Chain Manag., 24 (2), pp. 189-214; Rajaguru, R., Matanda, M.J., Role of compatibility and supply chain process integration in facilitating supply chain capabilities and organizational performance (2019) Supply Chain Manag., 24 (2), pp. 301-330; Reddy, K.J.M., Rao, A.N., A review on supply chain performance measurement systems (2019) Procedia Manuf., 30, pp. 40-47; Wang, Q., Huo, B., The effect of intellectual capital on supply chain integration and competitive performance (2018) Acad. Manag. Proc., 2018 (1); Liboni, L.B., Cezarino, L.O., Jabbour, C.J.C., Oliveira, B.G., Stefanelli, N.O., Smart industry and the pathways to HRM 4.0: Implications for SCM (2019) Supply Chain Manag., 24 (1), pp. 124-146; Gligor, D., Bozkurt, S., Russo, I., Omar, A., A look into the past and future: Theories within supply chain management, marketing and management (2019) Supply Chain Manag., 24 (1), pp. 170-186; Butdee, S., Phuangsalee, P., Uncertain risk assessment modeling for bus body manufacturing supply chain using AHP and fuzzy AHP (2019) Procedia Manuf., 30, pp. 663-670; Lima-Junior, F.R., Carpinetti, L.C.R., Predicting supply chain performance based on SCOR ® metrics and multilayer perceptron neural networks (2019) Int. J. Prod. Econ., 212, pp. 19-38; (2011) Supply Chain Operations Reference Model, 5. , Supply Chain Council (SCOR); Delipinar, G.E., Kocaoglu, B., Using SCOR Model to gain competitive advantage: A literature review (2016) Procedia - Soc. Behav. Sci., 229, pp. 398-406; Kumar, R., Padhi, S.S., Sarkar, A., Supplier selection of an Indian heavy locomotive manufacturer: An integrated approach using Taguchi loss function, TOPSIS, and AHP (2019) Iimb Manag. Rev., 31 (1), pp. 78-90; Kähkönen, A.K., Lintukangas, K., Hallikas, J., Tramarico, C.L., Salomon, V.A.P., Marins, F.A.S., Multi-criteria assessment of the benefits of a supply chain management training considering green issues (2017) J. Clean. Prod., 23, pp. 249-256; Ye, Y., Huo, B., Zhang, M., Wang, B., Zhao, X., The impact of modular designs on new product development outcomes: The moderating effect of supply chain involvement (2018) Supply Chain Manag., 23 (5), pp. 444-458; Strojny, J., Implementation of the AHP and benchmarking in strategic analysis of polish regions (2015) Procedia - Soc. Behav. Sci., 213, pp. 229-235; Joshi, R., Banwet, D.K., Shankar, R., A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain (2011) Expert Syst. Appl., 38 (8), pp. 10170-10182; Huan, S.H., Sheoran, S.K., Wan, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Manag., 9 (1), pp. 23-29},
correspondence_address1={Taptajani, D.S.; Department of Industrial Engineering, Jalan Mayor Syamsu 1, Indonesia; email: deditaptajani@sttgarut.ac.id},
editor={Abdullah A.G., Nandiyanto A.B.D., Widiaty I., Danuwijaya A.A., Abdullah C.U.},
publisher={Institute of Physics Publishing},
issn={17426588},
language={English},
abbrev_source_title={J. Phys. Conf. Ser.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Oliveira20191611,
author={Oliveira, R. and Cubo, C. and Estrada, R. and Fernandes, A.C. and Afonso, P. and Carvalho, M.S. and Sampaio, P. and Roque, J. and Rebelo, M.},
title={A Composite Indicator for Supply Chain Performance Measurement: A Case Study in a Manufacturing Company},
journal={IEEE International Conference on Industrial Engineering and Engineering Management},
year={2019},
pages={1611-1615},
doi={10.1109/IEEM44572.2019.8978598},
art_number={8978598},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079648722&doi=10.1109%2fIEEM44572.2019.8978598&partnerID=40&md5=72869aa24951b72397329034834efec8},
affiliation={ALGORITMI Research Centre, University of Minho, Department of Production Systems, Braga, Portugal; Bosch Car Multimedia, Braga, Portugal},
abstract={This paper proposes a methodology to develop and implement a Composite Indicator (CI) to measure the performance of Supply Chain processes. It reflects the aggregation of individual measures, related to the same process, with a weighted average, in order to assess the global performance in terms of both efficiency and effectiveness. Through a case study in a manufacturing company, a concept validation was performed by implementing the methodology in the Return process of the Supply Chain. The results showed that the combination between a Composite Indicator and a Business Intelligence tool provides a better understanding of the overall performance of a given process, facilitating also the identification of root causes. This paper aims to contribute to the supply chain performance management research field, proposing a methodology to implement a Composite Indicator, which is a topic insufficiently explored in the existent literature. © 2019 IEEE.},
author_keywords={Composite Indicator;  Efficiency and Effectiveness;  Performance Measurement;  Quality Management;  Return process;  Supply Chain Management},
keywords={Efficiency;  Manufacture;  Quality management, Composite indicators;  Concept validations;  Global performance;  Manufacturing companies;  Performance measurements;  Supply chain performance;  Supply chain performance measurements;  Supply chain process, Supply chain management},
references={Fernandes, A.C., Sampaio, P., Sameiro, M., Truong, H.Q., Supply chain management and quality management integration: A conceptual model proposal (2017) Int. J. Qual. Reliab. Manag, 34 (1), pp. 53-67; Fish, L.A., Supply chain quality management (2011) Supply Chain Management-Pathways for Research and Practice, InTech; Foster, S.T., Jr., Towards an understanding of supply chain quality management (2008) J. Oper. Manag, 26 (4), pp. 461-467; Farris, J.A., Van Aken, E.M., Letens, G., Organizational performance measurement (2013) Handbook of Measurement in Science and Engineering, pp. 911-944. , Hoboken, NJ, USA: John Wiley & Sons, Inc; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ, 183, pp. 299-315. , Jan; Badawy, M., El-Aziz, A.A.A., Idress, A.M., Hefny, H., Hossam, S., A survey on exploring key performance indicators (2016) Futur. Comput. Informatics J, 1 (1-2), pp. 47-52. , Dec; Reisinger, H., Cravens, K.S., Tell, N., Prioritizing performance measures within the balanced scorecard framework (2003) MIR: Management International Review, 43, pp. 429-437. , Springer; Becker, W., Saisana, M., Paruolo, P., Vandecasteele, I., Weights and importance in composite indicators: Closing the gap (2017) Ecol. Indic, 80, pp. 12-22. , Sep; Fusco, E., Enhancing non-compensatory composite indicators: A directional proposal (2015) Eur. J. Oper. Res, 242 (2), pp. 620-630. , Apr; Mazziotta, M., Pareto, A., (2013) Methods for Constructing Composite Indices: One for All or All for One. METHODS for CONSTRUCTING COMPOSITE INDICES: ONE for ALL or ALL for ONE. 1; Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., Hoffman, A., Giovannini, E., Handbook on constructing composite indicators: Methodology and user guide (2005) OECD Stat. Work. Pap. OECD Publ; Saraiva, P., Sampaio, P., Cubo, C., Reis, M., Macroquality measurement: World state of quality and european quality scoreboard approaches and results (2018) Total Qual. Manag. Bus. Excell, pp. 1-17. , Apr; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod, 16 (15), pp. 1699-1710; (2017) SCOR 12. 0 Quick Reference Guide, , apics.org/docs/default-source/scc-nonresearch/apicsscc_scor_quick_reference_guide.pdf, APICS; Albores, P., Love, D.M., Weaver, M., Stone, J., (2006) An Evaluation of SCOR Modelling Techniques and Tools; Georgise, F.B., Thoben, K.-D., Seifert, M., Adapting the scor model to suit the different scenarios: A literature review & research agenda (2012) Int. J. Bus. Manag, 7 (6); Lepori, E., Damand, D., Barth, B., Benefits and limitations of the scor model in warehousing (2013) IFAC Proc, 46 (9), pp. 424-429. , Jan; Jacobs, R., Smith, P., Goddard, M., (2004) Measuring Performance: An Examination of Composite Performance Indicators CENTRE for HEALTH ECONOMICS, , York; Kristensen, K., Martensen, A., Gronholdt, L., Customer satisfaction measurement at post Denmark: Results of application of the european customer satisfaction index methodology (2000) Total Qual. Manag, 11 (7), pp. 1007-1015. , Sep; Cherchye, L., Creating composite indicators with dea and robustness analysis: The case of the technology achievement index (2008) J. Oper. Res. Soc, 59 (2), pp. 239-251; Sen, A., Anand, S., (1994) Human Development Index: Methodology and Measurement, , Human Development Report Office, New York; Azevedo, S.G., Carvalho, H., Ferreira, L.M., Matias, J.C.O., A proposed framework to assess upstream supply chain sustainability (2017) Environ. Dev. Sustain, 19 (6), pp. 2253-2273. , Dec; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., Development of composite sustainability performance index for steel industry (2007) Ecol. Indic, 7 (3), pp. 565-588. , Jul; Zhou, L., Tokos, H., Krajnc, D., Yang, Y., Sustainability performance evaluation in industry by composite sustainability index (2012) Clean Technol. Environ. Policy, 14 (5), pp. 789-803. , Oct; Garcia-Herrero, I., Measuring the vulnerability of an energy intensive sector to the eu ets under a life cycle approach: The case of the chlor-alkali industry (2017) Sustainability, 9 (5), p. 837. , May; Koh, S.C.L., Morris, J., Ebrahimi, S.M., Obayi, R., Integrated resource efficiency: Measurement and management (2016) Int. J. Oper. Prod. Manag, 36 (11), pp. 1576-1600. , Nov; Liang, H., Zhang, S., Su, Y., Evaluating the efficiency of industrialization process in prefabricated residential buildings using a fuzzy multicriteria decision-making method (2017) Math. Probl. Eng, 2017, pp. 1-12. , Sep; Bilgen, H.I., Varoglu, A., Methodology research of competitiveness and sample application for Turkey's defense industry (2016) Compet. Rev, 26 (5), pp. 537-558. , Oct; Zhou, P., Ang, B.W., Zhou, D.Q., Weighting and aggregation in composite indicator construction: A multiplicative optimization approach (2010) Soc. Indic. Res, 96 (1), pp. 169-181. , Mar; Booysen, F., An overview and evaluation of composite indices of development (2002) Soc. Indic. Res, 59 (2), pp. 115-151; Hudrliková, L., Composite indicators as a useful tool for international comparison: The Europe 2020 example (2013) Prague Econ. Pap, (4), pp. 459-473; Murray, D.G., (2013) Tableau Your Data Fast and Easy Visual Analysis with Tableau Software®, , and InterWorks team. Indianapolis: John Wiley & Sons, Inc},
correspondence_address1={Oliveira, R.; ALGORITMI Research Centre, Portugal; email: id6911@alunos.uminho.pt},
publisher={IEEE Computer Society},
issn={21573611},
isbn={9781728138046},
language={English},
abbrev_source_title={IEEE Int. Conf. Ind. Eng. Eng. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Tripathi2019516,
author={Tripathi, S. and Rangarajan, K. and Talukder, B.},
title={Segmental differences in pharmaceutical industry and its impact on supply chain performance},
journal={International Journal of Pharmaceutical and Healthcare Marketing},
year={2019},
volume={13},
number={4},
pages={516-540},
doi={10.1108/IJPHM-12-2018-0063},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073222396&doi=10.1108%2fIJPHM-12-2018-0063&partnerID=40&md5=3a59913cc110fee3eca880db36ba6884},
affiliation={Indian Institute of Foreign Trade – Kolkata Campus, Kolkata, India},
abstract={Purpose: Pharmaceutical industry involves highly specialized business processes where strong research and development focus along with market differentiation and localization are the deciders of success. This has led to evolution of segments and complexities in supply chain. This paper aims to focus on segmental differences in supply chain performance of Indian Pharmaceutical firms. Design/methodology/approach: This paper measures supply chain performance of select segmental players of the pharmaceutical industry using financial metrics and supply chain operations reference (SCOR) key performance indicators through a five-year timeline. The best performance results are compared across the segments to identify unique performance features, if any. The sample results are validated through hypothesis testing methodology. Findings: This paper has evidenced that the innovators segment is performing better in cash-to-cash cycle time and supply chain working capital productivity, whereas generics segment is doing better in distribution cost efficiency and total cost to serve aspects. Research limitations/implications: The paper is based on historical financial data of firms and measures the firm focused supply chain performance. The results may not be generalized in a global context but serve as a motivator for other researchers to take similar studies. The paper may further be analyzed with primary data of the firms to understand the segmental difference in customer focus supply chain performance measures. Practical implications: This paper has brought out important segmental supply chain performance features of the Indian pharmaceutical firms and identified segment-specific problems by integrating SCOR KPIs and financial metrics. Originality/value: This paper has integrated both SCOR KPIs and financial metrics to provide unique insights on segmental differences in the performance behavior of pharmaceutical supply chain. © 2019, Emerald Publishing Limited.},
author_keywords={Inventory management;  Logistics;  Supply chain management;  Supply chain performance measure;  Supply chains},
references={(2014) Supply chain operations reference model: overview of SCOR version 11; (2016) India’s Pharma Supply Chain: Does the Industry Have What It Takes to Win?, , AT Kearney, Chicago; Azghandi, R., Griffin, J., Jalali, M.S., Minimization of drug shortages in pharmaceutical supply chains: a simulation based analysis of drug recall patterns and inventory plolicies (2018) Complexity, 2018, pp. 1-11; Batuhan, K., Bahadır, J., Tanyas, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2011) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Benita, M.B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Berk, P., Gilbert, M., Herlant, M., Walter, G., (2013) Rethinking the Pharma Supply Chain: New Models for a New Era, , the Boston Consulting Group (BCG), Boston; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; (2017) What’s Next for Pharma Innovation in China, , McKinsey and Company, New York, NY; Black, K., (2013) Applied Business Statistics: Making Better Business Decisions, , John Wiley and Sons, Hoboken, NJ; Bolineni, P., (2016) The Indian Pharmaceutical Industry's Supply Chain Management Strategies, , https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=3497&context…, Walden University Scholar Works; Cai, J., Liu, X.Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chandrasekaran, N., Kumar, S.M., (2003) Pharmaceutical supply chain challenges and best practices, , www.ciilogistics.com, (accessed, CII –Institute of Logistics: December 2003; Chithambaranathan, P., Subramanian, N., Palaniappan, P.K., An innovative framework for performance analysis of members of supply chains (2015) Benchmarking: An International Journal, 22 (2), pp. 309-334; Chow, S.C., Liu, J.P., Statistical assessment of biosimilar products (2010) Journal of Biopharmaceutical Statistics, 20 (1), pp. 10-30; Das, P., Das, S., Competitiveness and its impact on research and development in Indian pharmaceutical industry (2015) DECISION, 42 (3), pp. 325-334; Das, C., Jharkharia, S., Effects of low carbon supply chain practices on environmental sustainability: an empirical study on Indian manufacturing firms (2019) South Asian Journal of Business Studies, 8 (1), pp. 2-25; Doering, T., Nallan, S.C., Forecasting and performance: conceptualizing forecasting management competence as a higher‐order construct (2016) Journal of Supply Chain Management, 52 (4), pp. 77-91; Ellinger, A.E., Natarajarathinam, M., Adams, F.G., Gray, J.B., Hofman, D., O’Marah, K., Supply chain management competency and firm financial success (2011) Journal of Business Logistics, 32 (3), pp. 214-226; Enyinda, C., Mbah, C., Ogbuehi, A., An empirical analysis of risk mitigation in the pharmaceutical industry supply chain: a developing–country perspective (2010) Thunderbird International Business Review, 52 (1), pp. 45-54; Esper, T.L., Defee, C.C., Mentzer, J.T., A framework of supply chain orientation (2010) The International Journal of Logistics Management, 21 (2), pp. 161-179; Felix, T.S., Chan, H.J., Qi, H.K.C., Henry, C.W.L., Ralph, W.L.I., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: a contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Grabowski, H., The evolution of the pharmaceutical industry over the past 50 years: a personal reflection (2011) International Journal of the Economics of Business, 18 (2), pp. 161-176; Greer, B.M., Theuri, P., Linking supply chain management superiority to multifaceted firm financial performance (2012) Journal of Supply Chain Management, 48 (3), pp. 97-106; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework of supply chain management performance (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hoffman, E., Competitive dynamics of generic drug manufacturing industry (2017) Business Economics, 52 (1), pp. 68-75; (2016) ITA pharmaceuticals top markets report, , www.trade.gov/topmarkets, DOC. USA; Jain, A., Mamani, H., Moinzadeh, K., Impact of retailers with knowledge of supplier's inventory on supply chain performance (2017) Production and Operations Management, 26 (3), pp. 542-556; Johnson, L., Pharmaceutical labeling improves efficiencies in a highly complex supply chain (2017) Material Handling and Logistics, , www.industryweek.com/industryweek-wire/marterial-handling-logistics; Kaplan, W.A., Ritz, L.S., Vitello, M., Wirtz, V.J., Policies to promote use of generic medicines in low and Middle income countries: a review of published literature, 2000-2010 (2012) Health Policy, 106 (3). , http://dx.doi.org/10.1016/j.healthpol.2012.04.015; Kazi, A., Ahsan, A.M.M., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612; Lambert, D.M., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lamouri, S., LucParis, J., Djelloul, S., A framework for analyzing supply chain performance evaluation models (2013) International Journal of Production Economics, 14 (2), pp. 247-258; Leończuk, D., Categories of supply chain performance indicators: an overview of approaches (2016) Business, Management and Education, 14 (1), pp. 103-115; Liao, Y., Hong, P., Rao, S.S., Supply management, supply flexibility and performance outcomes: an empirical investigation of manufacturing firms (2010) Journal of Supply Chain Management, 46 (3), pp. 6-22; Ling, L., Qin, S., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) International Journal of Production Research, 49 (1), pp. 33-57; MagazziniPammolli, L., Riccaboni, M., (2004) Dynamic competition in pharmaceuticals: patent expiry, generic penetration, and industry structure, , Munich Personal RePEc Archive, MPRA Paper 15968; Mehralian, G., Zarenezhad, F., Ghatari, A., Developing a model for an agile supply chain in pharmaceutical industry (2015) International Journal of Pharmaceutical and Healthcare Marketing, 9 (1), pp. 74-91; Mondal, S.S., Pingali, V., Competition and intellectual property policies in the Indian pharmaceutical sector (2017) Vikalpa: The Journal for Decision Makers, 42 (2), pp. 61-79; Narayana, S., Pati, R., Vrat, P., Research on management issues in the pharmaceutical industry: a literature (2012) International Journal of Pharmaceutical and Healthcare Marketing, 6 (4), pp. 351-375; Naylor, J.B., Naim, M.M., Berry, D., Legality: integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1), pp. 107-118; Petersen, K.J., Ragatz, G.L., Monczka, R.M., An examination of collaborative planning effectiveness and supply chain performance (2005) The Journal of Supply Chain Management, 41 (2), pp. 14-25; (2017), Pharmaxil.com/docs/Pharma_Fact_Book_2017.pdf; Remko, I., Hoek, V., Measuring the unmeasurable‐measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Schmitz, M., Dealing with pharmaceutical supply chain complexities (2016) Pharmaceutical Technology Outsourcing Resources, 2016 (2), pp. 28-56; Salazar, F., Strengths and weaknesses of SCOR model: supply chain biodiesel castor (2012) 62nd IIE Annual Conference and Expo, 3, pp. 2139-2148. , Orlando, FL; Seifbarghy, M., Reza, M., Sheikh, S.M., Analyzing the supply chain using SCOR model in a steel producing company (2010) The 40th International Conference on Computers and Industrial Engineering, 1, pp. 361-366. , IEEE, Awaji; Shah, N., Pharmaceutical supply chains: key issues and strategies for optimization (2004) Computers and Chemical Engineering, 28 (6-7), pp. 929-941; Shah, J., Singh, N., Benchmarking internal supply chain performance: development of a framework (2001) The Journal of Supply Chain Management, 37 (1), pp. 37-42; Shi, X., Liao, Z., The mediating effects of inter firm business process integration and joint teamwork on firm performance in supply chains (2013) Asia Pacific Journal of Management, 30 (4), pp. 1243-1264; Singal, G.L., Nanda, A., Kotwani, A., A comparative evaluation of price and quality of some branded versus branded- generic medicines of the same manufacturer in India (2011) Indian Journal of Pharmacology, 43 (2), pp. 131-136; Singh, R., Kumar, R., Kumar, P., Strategic issues in pharmaceutical supply chains: a review (2016) International Journal of Pharmaceutical and Healthcare Marketing, 10 (3), pp. 234-235; Smith, K.L., Smith, D., Performance measures in supply chains (2008) Australian Accounting Review, 15 (35), pp. 39-51; Talavera, M.G.V., Supply chain management practices and challenges: case studies of four supply chains (2015) Philippine Management Review, 22, pp. 53-74; Tripathi, S., Rangarajan, K., Talukder, B., Benchmarking supply chain performance – a case study in Indian petroleum sector (2018) Journal of Supply Chain Management System, 7 (2), pp. 8-25; Tucker, J., Yakatan, S., Yakatan, S., Biogenerics 2007: how far have we come? (2008) Journal of Commercial Biotechnology, 14 (1), pp. 56-64; Veleva, V., Berkelay, W.C., Jr., Benchmarking green chemistry adoption by ‘big pharma’ and generics manufacturers (2017) Benchmarking: An International Journal, 24 (5), pp. 1414-1436; Xie, Y., Breen, L., Greening community pharmaceutical supply chain in UK: a cross boundary approach (2012) Supply Chain Management: An International Journal, 7 (1), pp. 40-53; Yildirim, O., Gottwald, M., Schuler, P., Michel, M.C., Opportunities and challenges for drug development: public-private partnerships, adaptive designs and big data (2016) Frontiers in Pharmacology, 7, pp. 1-18; Yousefi, N., Alibabaei, A., Information flow in the pharmaceutical supply chain (2015) Iranian Journal of Pharmaceutical Research, 14 (4), pp. 1299-1303. , No; Zhao, S., Tan, H., Papanastassiou, M., Harzing, A., The internationalization of innovation towards the South: a historical case study of a global pharmaceutical corporation in China (1993-2017) (2019) Asia Pacific Journal of Management, , https://doi.org/10.1007/s10490-018-9620-x; (2019), CMIE/Prowess IQ/prowessiq.exe},
correspondence_address1={Tripathi, S.; Indian Institute of Foreign Trade – Kolkata CampusIndia; email: saswati@iift.edu},
publisher={Emerald Group Holdings Ltd.},
issn={17506123},
language={English},
abbrev_source_title={Int. J. Pharm. Healthc. Mark.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chiarini20191091,
author={Chiarini, A. and Brunetti, F.},
title={What really matters for a successful implementation of Lean production? A multiple linear regression model based on European manufacturing companies},
journal={Production Planning and Control},
year={2019},
volume={30},
number={13},
pages={1091-1101},
doi={10.1080/09537287.2019.1589010},
note={cited By 36},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064837809&doi=10.1080%2f09537287.2019.1589010&partnerID=40&md5=16fa8c3db4ded0070384fa2ae0c46e9a},
affiliation={Business Administration Department, University of Ferrara, Ferrara, Italy; Business Administration Department, University of Verona, Verona, Italy},
abstract={The main purpose of this paper is to provide a framework of factors for succeeding in Lean implementation, in particular in the manufacturing sector. From a literature review, 11 potential factors for a successful Lean implementation were found and tested as hypotheses to develop a multiple linear regression model. The factors were presented as questions in an on-line questionnaire completed by 108 Lean managers and production managers of European manufacturing; the managers could also contribute qualitative notes on the questions to improve our analysis of the data. Findings showed that a successful Lean implementation is connected with the setting of Lean strategies, training people, developing team building and effort, as well as putting in place a performance measurement system and measuring financial and economic results. The first three factors correlated with a catch-all leadership factor. However, other findings showed that empowerment and autonomy of workers were not relevant for Lean implementation. The managers also commented on the financial sustainability of the program, the relationship with ISO 9001 certification and the supply chain involvement. The results of this research, even if subjected to some limitations, contradicted some of the findings of other research and also introduced some novel and unexpected findings. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={critical success factors;  Lean manufacturing;  quantitative models;  regression analysis},
keywords={Factor analysis;  Lean production;  Linear regression;  Managers;  Personnel training;  Regression analysis;  Supply chains, Critical success factor;  European manufacturing;  Financial sustainability;  ISO 9001 certification;  Multiple linear regression models;  Online questionnaire;  Performance measurement system;  Quantitative models, Manufacture},
references={Abdulmalek, F.A., Rajgopal, J., Analyzing the Benefits of Lean Manufacturing and Value Stream Mapping via Simulation: A Process Sector Case Study (2007) International Journal of Production Economics, 107 (1), pp. 223-236; Achanga, P., Shehab, E., Roy, R., Nelder, G., Critical Success Factors for Lean Implementation within SMEs (2006) Journal of Manufacturing Technology Management, 17 (4), pp. 460-471; Allen, J.H., Making Lean Manufacturing Work for You (2000) Journal of Manufacturing Engineering, 2 (2), pp. 1-16; Almeida Marodin, G., Saurin, T.A., Managing Barriers to Lean Production Implementation: Context Matters (2015) International Journal of Production Research, 53 (13), pp. 3947-3962; Azadegan, A., Patel, P.C., Zangoueinezhad, A., Linderman, K., The Effect of Environmental Complexity and Environmental Dynamism on Lean Practices (2013) Journal of Operations Management, 31 (4), pp. 193-212; Bamber, L., Dale, B.G., Lean Production: A Study of Application in a Traditional Manufacturing Environment (2010) Production Planning & Control, 11 (3), pp. 291-298; Bateman, N., Philp, L., Warrender, H., Visual Management and Shop Floor Teams–Development, Implementation and Use (2016) International Journal of Production Research, 54 (24), pp. 7345-7358; Bevilacqua, M., Ciarapica, F.E., Paciarotti, C., Implementing Lean Information Management: The Case Study of an Automotive Company (2015) Production Planning & Control, 26 (10), pp. 753-768; Bhasin, S., Burcher, P., Lean Viewed as a Philosophy (2006) Journal of Manufacturing Technology Management, 17 (1), pp. 56-72; Bicheno, J., (1999) The New Lean Toolbox, , London: Picsie; Boscari, S., Danese, P., Romano, P., Implementation of Lean Production in Multinational Corporations: A Case Study of the Transfer Process from Headquarters to Subsidiaries (2016) International Journal of Production Economics, 176, pp. 53-68; Bruce, M., Daly, L., Towers, N., Lean or Agile: A Solution for Supply Chain Management in the Textiles and Clothing Industry? (2004) International Journal of Operations & Production Management, 24 (2), pp. 151-170; Bryman, A., Integrating Quantitative and Qualitative Research: How Is It Done? (2006) Qualitative Research, 6 (1), pp. 97-113; Chauhan, G., Singh, T.P., Measuring Parameters of Lean Manufacturing Realization (2012) Measuring Business Excellence, 16 (3), pp. 57-71; Cherrafi, A., Elfezazi, S., Govindan, K., Garza-Reyes, J.A., Benhida, K., Mokhlis, A., A Framework for the Integration of Green and Lean Six Sigma for Superior Sustainability Performance (2017) International Journal of Production Research, 55 (15), pp. 4481-4515; Chiarini, A., Integrating Lean Thinking into ISO 9001: A First Guideline (2011) International Journal of Lean Six Sigma, 2 (2), pp. 96-117; Chiarini, A., Douglas, A., The Impact of Logistics Solutions on Customer Satisfaction: An Exploratory Qualitative Study of Manufacturing Companies (2015) Sinergie, 33 (97), pp. 255-270; Chiarini, A., Vagnoni, E., World-Class Manufacturing by Fiat. Comparison with Toyota Production System from a Strategic Management, Management Accounting, Operations Management and Performance Measurement Dimension (2015) International Journal of Production Research, 53 (2), pp. 590-606; Deming, W.E., (1993) The New Economics for Industry, Education, Government, , Cambridge, MA: MIT Center for Advanced Engineering Study; Draper, N.R., Smith, H., (1981) Applied Regression Analysis, , 2nd ed, New York, NY: John Wiley and Sons; Emiliani, B., Stec, D.J., Grasso, L., Stodder, J., (2003) Better Thinking, Better Results: Using the Power of Lean as a Total Business Solution., , Kensington, CT: Center for Lean Business Management; Emiliani, M.L., Origins of Lean Management in America: The Role of Connecticut Businesses (2006) Journal of Management History, 12 (2), pp. 167-184; Erthal, A., Marques, L., National Culture and Organisational Culture in Lean Organisations: A Systematic Review (2018) Production Planning & Control, 29 (8), pp. 668-687; Eroglu, C., Hofer, C., Lean, Leaner, Too Lean? The Inventory-Performance Link Revisited (2011) Journal of Operations Management, 29 (4), pp. 356-369; Fielding, J., Gilbert, N., (2000) Understanding Social Statistics, , London, UK: Sage; Fullerton, R.R., Kennedy, F.A., Widener, S.K., Lean Manufacturing and Firm Performance: The Incremental Contribution of Lean Management Accounting Practices (2014) Journal of Operations Management, 32 (7-8), pp. 414-428; Garrahan, P., Stewart, P., (1992) The Nissan Enigma: Flexibility at Work in a Local Economy., , London, UK: Mansell; Hall, R., Lean and the Toyota Production System (2004) Target, 20 (3), pp. 22-27. , http://www.ame.org/sites/default/files/target_articles/04-20-3-Lean_and_TPS.pdf; Hines, P., Holweg, M., Rich, N., Learning to Evolve: A Review of Contemporary Lean Thinking (2004) International Journal of Operations & Production Management, 24 (10), pp. 994-1011; Hofer, C., Eroglu, C., Hofer, A.R., The Effect of Lean Production on Financial Performance: The Mediating Role of Inventory Leanness (2012) International Journal of Production Economics, 138 (2), pp. 242-253; Holweg, M., The Genealogy of Lean Production (2007) Journal of Operations Management, 25 (2), pp. 420-437; Imai, M., (1986) Kaizen: The Key to Japanese Competitiveness Success., , New York, NY: Random House Business Division; Jørgensen, F., Matthiesen, R., Nielsen, J., Johansen, J., Lean Maturity, Lean Sustainability (2007) Advances in Production Management Systems, IFIP — The International Federation for Information Processing, , Olhager J., Persson F., (eds), Boston, MA: Springer, and,. edited by; Karthi, S., Devadasan, S.R., Murugesh, R., Integration of Lean Six-Sigma with ISO 9001: 2008 Standard (2011) International Journal of Lean Six Sigma, 2 (4), pp. 309-331; Kennedy, F.A., Widener, S.K., A Control Framework: Insights from Evidence on Lean Accounting (2008) Management Accounting Research, 19 (4), pp. 301-323; Krafcik, J.F., The Triumph of the Lean Production System (1988) Sloan Management Review (Fall), 30 (1), pp. 41-51; Kutner, M.H., Nachtsheim, C., Neter, J., (2004) Applied Linear Regression Models, , New York, NY: McGraw-Hill/Irwin; Lewisbeck, M.S., Bryman, A., Liao, T.F., (2004) Research Methods. The Sage Encyclopedia of Social Science Research Methods, , London, UK: SAGE; Liker, J.K., (2004) The Toyota Way., , New York, NY: McGraw Hill; Liu, C.C., Niu, Z.W., Chang, P.C., Zhang, B., Assessment Approach to Stage of Lean Transformation Cycle Based on Fuzzy Nearness Degree and TOPSIS (2017) International Journal of Production Research, 55 (23), pp. 7223-7235; Martinez-Jurado, P.J., Moyano-Fuentes, J., Key Determinants of Lean Production Adoption: evidence from the Aerospace Sector (2014) Production Planning & Control, 25 (4), pp. 332-345; Martínez Sánchez, A., Pérez Pérez, M., Lean Indicators and Manufacturing Strategies (2001) International Journal of Operations & Production Management, 21 (11), pp. 1433-1452; Maskell, B.H., Kennedy, F.A., Why Do We Need Lean Accounting and How Does It Work? (2007) Journal of Corporate Accounting & Finance, 18 (3), pp. 59-73; Miles, J., Shevlin, M., (2007) Applying Regression & Correlation. A Guide for Students and Researchers., , London, UK: Sage, and, 2nd ed; Monden, Y., (2011) Toyota Production System: An Integrated Approach to Just-in-Time., , New York, NY: CRC Press; Monden, Y., Lee, J., How a Japanese Auto Maker Reduces Costs (1993) Management Accounting, 75 (2), pp. 22-26; Nakane, J., Hall, R.W., Ohno's Method (2002) Target, 1 (18), pp. 6-15. , http://www.ame.org/sites/default/files/target_articles/02-18-1-Ohnos_Method.pdf; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the Lean and Agile Manufacturing Paradigms in the Total Supply Chain (1999) International Journal of Production Economics, 62 (1), pp. 107-118; Negrão, L.L.L., Godinho Filho, M., Marodin, G., Lean Practices and Their Effect on Performance: A Literature Review (2017) Production Planning & Control, 28 (1), pp. 33-56; Netland, T.H., Schloetzer, J.D., Ferdows, K., Implementing Corporate Lean Programs: The Effect of Management Control Practices (2015) Journal of Operations Management, 36 (1), pp. 90-102; Netland, T.H., Critical Success Factors for Implementing Lean Production: The Effect of Contingencies (2016) International Journal of Production Research, 54 (8), pp. 2433-2448; Ohno, T., How the Toyota Production System Was Created (1982) Japanese Economy, 10 (4), pp. 83-101; Ohno, T., (1988) Toyota Production System: Beyond Large-Scale Production., , New York, NY: Productivity Press; Oliver, N., Delbridge, R., Lowe, J., Lean Production Practices: International Comparisons in the Auto Components Industry (1996) British Journal of Management, 7 (1), pp. 29-44; Panwar, A., Nepal, B.P., Jain, R., Rathore, A.P.S., On the Adoption of Lean Manufacturing Principles in Process Industries (2015) Production Planning & Control, 26 (7), pp. 564-587; Pakdil, F., Leonard, K.M., Implementing and Sustaining Lean Processes: The Dilemma of Societal Culture Effects (2017) International Journal of Production Research, 55 (3), pp. 700-717; Parry, G.C., Turner, C.E., Application of Lean Visual Process Management Tools (2006) Production Planning & Control, 17 (1), pp. 77-86; Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P., Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies (2003) Journal of Applied Psychology, 88 (5), pp. 879-903; Samuel, D., Found, P., Williams, S.J., How Did the Publication of the Book the Machine That Changed the World Change Management Thinking? Exploring 25 Years of Lean Literature (2015) International Journal of Operations & Production Management, 35 (10), pp. 1386-1407; Scherrer-Rathje, M., Boyle, T.A., Deflorin, P., Lean, Take Two! Reflections from the Second Attempt at Lean Implementation (2009) Business Horizons, 52 (1), pp. 79-88; Secchi, R., Camuffo, A., Rolling out Lean Production Systems: A Knowledge-Based Perspective (2016) International Journal of Operations & Production Management, 36 (1), pp. 61-85; Seth, D., Gupta, V., Application of Value Stream Mapping for Lean Operations and Cycle Time Reduction: An Indian Case Study (2005) Production Planning & Control, 16 (1), pp. 44-59; Slomp, J., Bokhorst, J.A., Germs, R., A Lean Production Control System for High-Variety/Low-Volume Environments: A Case Study Implementation (2009) Production Planning & Control, 20 (7), pp. 586-595; Soares, D.J.P., Bastos, J., Rodrigues, D.R.G., Pereira, J.P.G.T., Baptista, A.J.C., Lean Management Methods in Product Development-a Case Study Based on Human respect with Productivity Focus (2015) International Journal of Lean Enterprise Research, 1 (4), pp. 393-411; Tezel, A., Koskela, L., Aziz, Z., Lean Thinking in the Highways Construction Sector: Motivation, Implementation and Barriers (2018) Production Planning & Control, 29 (3), pp. 247-269; Wakamatsu, Y., (2007) Toyota Shiki Kaizen No Susume Kata: Saikyo No Gemba wo Tsukuri Ageru., , Tokyo, Japan: PHP Business; Wan, H.D., Chen, F.F., A Leanness Measure of Manufacturing Systems for Quantifying Impacts of Lean Initiatives (2008) International Journal of Production Research, 46 (23), pp. 6567-6584; Wee, H.M., Wu, S., Lean Supply Chain and Its Effect on Product Cost and Quality: A Case Study on Ford Motor Company (2009) Supply Chain Management: An International Journal, 14 (5), pp. 335-341; Wiengarten, F., Gimenez, C., Fynes, B., Ferdows, K., Exploring the Importance of Cultural Collectivism on the Efficacy of Lean Practices: Taking an Organisational and National Perspective (2015) International Journal of Operations & Production Management, 35 (3), pp. 370-391; Winship, C., Mare, R.D., Regression Models with Ordinal Variables (1984) American Sociological Review, 49 (4), pp. 512-525; Williams, K., Haslam, C., Williams, J., Cultler, T., Adcroft, A., Johal, S., Against Lean Production (1992) Economy and Society, 21 (3), pp. 321-354; Womack, J.P., Jones, D.T., Roos, D., (1990) The Machine That Changed the World., , New York, NY: Rawson Associates; Womack, J.P., Jones, D.T., (1996) Lean Thinking: Banish Waste and Create Wealth in Your Organisation., , New York, NY: Simon and Shuster; Yang, M.G.M., Hong, P., Modi, S.B., Impact of Lean Manufacturing and Environmental Management on Business Performance: An Empirical Study of Manufacturing Firms (2011) International Journal of Production Economics, 129 (2), pp. 251-261},
correspondence_address1={Chiarini, A.; Business Administration Department, Via Cantarane, 24, Italy; email: andrea.chiarini@univr.it},
publisher={Taylor and Francis Ltd.},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Girjatovics2019,
author={Girjatovics, A. and Shekar, S.S. and Kuznecova, O. and Pecerska, J.},
title={Simulation and SCOR: Performance metrics integration to supply chain performance measurement},
journal={60th International Scientific Conference on Information Technology and Management Science of Riga Technical University, ITMS 2019 - Proceedings},
year={2019},
doi={10.1109/ITMS47855.2019.8940657},
art_number={8940657},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077956740&doi=10.1109%2fITMS47855.2019.8940657&partnerID=40&md5=60deebf4ddd770956a5513b0d6353eb1},
affiliation={Supply Chain Development and Optimisation team, RIMI Baltic, Riga, Latvia; Logistics and Supply Chain Management, India; Department of Modelling and Simulation, Riga Technical University, Riga, Latvia},
abstract={Supply Chain Operations Reference (SCOR) model introduced by Supply Chain Council and now owned by APICS is a well-known tool among companies, whose focus is led on supply chain performance improvements. It elaborates four components such as library of standards SC processes, performance metrics, practices and skills required for the particular process. In spite of plenty of articles and references to SCOR, integration of SCOR framework to companies process and performance assessment is challenging due to a variety of managerial elements to be changed or implemented into ongoing business processes, including changes in information system data structure, reporting dashboards changes and daily routines adjustments. With the adequate respect to SCOR model from practitioners and consultancies, there is still a concern about practical value of the metrics against keeping business continuity during SCOR metrics implementation into company's business processes. The paper discusses the progress of implementation of the SCOR model in the enterprise, indicates the respective challenges, as well as highlights the advantages of quantitative simulation analysis of business processes in the enterprise based on the system of metrics of the SCOR model. © 2019 IEEE.},
author_keywords={Performance metrics;  SCOR;  Simulation},
keywords={Management;  Operations research, Performance assessment;  Performance metrics;  Quantitative simulation;  SCOR;  Simulation;  Supply chain operations reference models;  Supply chain performance;  Supply chain performance measurements, Supply chains},
references={Girjatovics, A., Pesoa, L.M., Kuznecova, O., Establishing Supply Chain process framework based on SCOR model: Case study (2018) Information Technology and Management Science, 21; Zhou, H., Benton, W.C., Jr., Schilling, D.A., Milligan, G.W., Supply chain integration and the SCOR model (2011) Journal of Business Logistics, 32 (4), pp. 332J-344J; Maxwell, C., (1892) A Treatise on Electricity and Magnetism, 3rd Ed, 2, pp. 68-73. , Oxford: Clarendon; (2012) Supply Chain Operations Reference Model, , Supply Chain Council, Inc Revisison V11.0; Prakash, S., Sandeep, G.S., Rathore, A.P.S., Supply chain operations reference (SCOR) model: An overview and a structured literature review of its application (2013) International Conference on Smart Technologies for Mechanical Engineering, , STME-2013; Bansal, S., Promise and problems of simulation technology in SCM domain (2002) Winter Simulation Conference, , San Diego, CA, USA, USA; Barnett, M., Miller, C., Analysis of the virtual enterprise using distributed supply chain modeling and simulation: An application of e-SCOR (2000) 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165), , Orlando, FL, USA, USA; Persson, F., Bartoll, C., Ganovic, A., Lidberg, M., Nilsson, M., Wibaeus, J., Winge, F., Supply chain dynamics in the SCOR model - A simulation modeling approach (2012) Proceedings of the 2012 Winter Simulation Conference (WSC), , Berlin, Germany; Dong, J., Ding, H., Ren, C., Wang, W., IBM SmartScor - A SCOR based supply chain transformation platform through simulation and optimization techniques (2006) Proceedings of the 2006 Winter Simulation Conference, , Monterey, CA, USA; Sitova, I., Pecerskaka, J.J., A Concept of simulation based SC performance analysis using SCOR matrices (2019) RTU Scientific Journal, pp. 85-90},
editor={Grabis J., Romanovs A., Kulesova G.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781728157092},
language={English},
abbrev_source_title={Int. Sci. Conf. Inf. Technol. Manag. Sci. Riga Tech. Univ., ITMS - Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Fri20196336,
author={Fri, M. and Fedouaki, F. and Douaioui, K. and Mabrouki, C. and Semma, E.A.},
title={Supply chain performance evaluation models, state-of-the-art and future directions},
journal={International Journal of Engineering and Advanced Technology},
year={2019},
volume={9},
number={1},
pages={6336-6347},
doi={10.35940/ijeat.A2049.109119},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074650346&doi=10.35940%2fijeat.A2049.109119&partnerID=40&md5=ad12165dbbbd49f8ee7da86f7af47074},
affiliation={School of Textile and Clothing Industries, Morocco; Department of Industrial Engineering, Higher National School of Arts and Crafts, University Hassan 2nd of Casablanca, Morocco; Laboratory of Engineering, Industrial Management and Innovation, Hassan 1st University Settat, Morocco; Hassan 1st University Settat, Morocco},
abstract={Continuous improvement in supply chain performance creates value not only for businesses, but also for customers and suppliers who interact in different levels. Given the criticality of supply chain management, companies use different maturity models to measure their performance. Currently, there are many performance evaluations models and the choice of the appropriate model for the company depends on several criteria. This work presents a review of all supply chain performance evaluation models and highlights the characteristics and constraints of the implementation of each supply chain performance evaluation models. This work will provide a good guideline to managers to choose the appropriate model for their organization. © BEIESP.},
author_keywords={Benchmarking;  Global supply chain;  Performance measurement system;  Supply chain performance evaluation models},
references={Kueng, P., Meier, A., Wettstein, T., Performance Measurement Systems Must Be Engineered (2001) CAIS, 7; Mabrouki, C., Bentaleb, F., Mousrij, A., A decision support methodology for risk management within a port terminal (2014) Safety Science, 63, pp. 124-132; Bentaleb, F., Mabrouki, C., Semma, A., A Multi-Criteria Approach for Risk Assessment of Dry Port-Seaport System (2015) Supply Chain Forum: An International Journal, 16 (4), pp. 32-49; Douaioui, K., Fri, M., Mabrouki, C., Semma, E.A., (2018) Smart Port: Design and Perspectives, pp. 1-6; Douaioui, K., Fri, M., Mabroukki, C., Semma, E.A., The interaction between industry 4.0 and smart logistics: Concepts and perspectives (2018) International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), IEEE, Tangier, pp. 128-132. , 2018; Lauras, M., Méthodes de diagnostic et d’évaluation de performance pour la gestion de chaînes logistiques (2004) L’Institut National Polytechnique De Toulouse; Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D.M., Rogers, D.S., The supply chain management processes (2001) The International Journal of Logistics Management, 12 (2), pp. 13-36; Stock, J.R., Strategic Logistics Management, , https://books.google.co.ma/books/about/Strategic_Logistics_Management.html?id=UCU3QAAACAAJ&redir_esc=y; Bolumole, Y.A., Knemeyer, A.M., Lambert, D.M., The Customer Service Management Process (2003) The International Journal of Logistics Management, 14 (2), pp. 15-31; Roxton, K.L., Lambert, D.M., García‐Dastugue, S.J., Rogers, D.S., The Demand Management Process (2002) The International Journal of Logistics Management, 13 (2), pp. 51-66; Croxton, K.L., The order fulfillment process (2003) The International Journal of Logistics Management, 14 (1), pp. 19-32; Rogers, D.S., Lambert, D.M., Knemeyer, A.M., The product development and commercialization process (2004) The International Journal of Logistics Management, 15 (1), pp. 43-56; Rogers, D.S., Lambert, D.M., Croxton, K.L., García‐Dastugue, S.J., The Returns Management Process (2002) The International Journal of Logistics Management, 13 (2), pp. 1-18; Zimmermann, R., (2006) Agent-Based Supply Network Event Management, , Springer Science & Business Media; Rüegg-Stürm, J., Introduction (2005) The New St. Gallen Management Model, pp. 1-6. , Palgrave Macmillan UK, London; Spur, G., Mertins, K., Jochem, R., Normung, D.I., (1996) Integrated Enterprise Modelling, , Beuth; Scheer, A.-W., (1994) Wirtschaftsinformatik. 4, , Aufl., Berlin, Heidelberg; Becker, J., Beverungen, D.F., Knackstedt, R., The challenge of conceptual modeling for product–service systems: Status-quo and perspectives for reference models and modeling languages (2010) Inf Syst E-Bus Manage, 8 (1), pp. 33-66; Nebl, T., Produktionswirtschaft: Production Management (2002) Oldenbourg; Cross, K., Lynch, R., Accounting for competitive performance (1989) Journal of Cost Management, 3 (1), pp. 20-28; Bitton, M., (1990) ECOGRAI: Méthode De Conception Et d’implantation De systèmes De Mesure De Performances Pour Organisations Industrielles, , PhD Thesis, Bordeaux 1; Kaanit, A., Mouss, N., Mouss, L., Un SIP Pour Le Pilotage Dun système De Production, Laboratoire d’Automatique Et De Productique (LAP)”, Département génie Industriel, , Université Hadj lakhdar–Batna–Algérie; Letouzey, A., (2001) Ordonnancement Interactif basé Sur Des Indicateurs: Applications à La Gestion De Commandes Incertaines Et à l’affectation Des opérateurs”, , PhD Thesis, Institut National Polytechnique de Toulouse; Giarini, O., Stahel, W.R., The hidden wealth (1986) Science & Public Policy, 13, pp. 83-102; Merlin-Brogniart, C., Nature et dynamique de l’innovation des nouveaux modèles de croissance: Le cas de l’écologie industrielle et de l’économie de la fonctionnalité (2017) Innovations, (3), pp. 65-95; Bourg, D., Buclet, N., L’économie de fonctionnalité. Changer la consommation dans le sens du développement durable (2005) Futuribles, (313), pp. 27-37; Stahel, W.R., (2010) The Performance Economy, , 2nd edition, Palgrave Macmillan, Houndsmille, Basingstoke; New York, NY; Gallaud, D., Laperche, B., Économie Circulaire et Développement Durable: Écologie Industrielle et Circuits Courts, ISTE (2016) Group; Kaplan, R.S., Anderson, S.R., (2004) «Time-Driven Activity-Based Costing*/Harvard Business Review; Kaplan, R.S., Anderson, S.R., The innovation of time-driven activity-based costing (2007) Journal of Cost Management, 21 (2), pp. 5-15; Armstrong, P., The costs of activity-based management (2002) Accounting, Organizations and Society, 27 (1-2), pp. 99-120; Hansen, S.C., Otley, D.T., van der Stede, W.A., Practice Developments in Budgeting: An Overview and Research Perspective (2003) SSRN Electronic Journal; Drury, C., Taylesdrury, M., (2000) Cost System Design and Profitabillity Analysis in UK Companies, , Elsevier, London; Stouthuysen, K., Swiggers, M., Reheul, A.-M., Roodhooft, F., Time-driven activity-based costing for a library acquisition process: A case study in a Belgian University (2010) Library Collections, Acquisitions, and Technical Services, 34 (2-3), pp. 83-91; Hoque, Z., 20 years of studies on the balanced scorecard: Trends, accomplishments, gaps and opportunities for future research (2014) The British Accounting Review, 46 (1), pp. 33-59; Burt, D.N., Dobler, D.W., Starling, S.L., (2003) World Class Supply Management: The Key to Supply Chain Management, , Irwin/McGraw-Hill; Theodore Farris, M., Hutchison, P.D., Cash-to-cash: The new supply chain management metric (2002) International Journal of Physical Distribution & Logistics Management, 32 (4), pp. 288-298; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply Chain Management: Implementation Issues and Research Opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-20; Plossl, G.W., (1985) Production and Inventory Control: Principles and Techniques, , 2nd Edn., Pearson, Englewood Cliffs, NJ; Sherman, R., (1994) ECR Vision to Reality: Creating Innovative Strategies to Astonish customers”, pp. 16-19. , Council of Logistics Management (Ed.), Proceedings, Annual Conference; Sherman, R., ECR–another acronym or a real opportunity (1994) Transportation and Distribution, June, 8, p. 60; Tietz, B., Effiziente Kundenpolitik als Problem der Informationspolitik” (1995) Handelsforschung 1995/96, pp. 175-186. , Springer; Chow, G., Heaver, T.D., Henriksson, L.E., (1995) STRATEGY, STRUCTURE AND PERFORMANCE: A FRAMEWORK FOR LOGISTICS RESEARCH, LOGISTICS AND TRANSPORTATION REVIEW, 31(4); Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48 (2), pp. 377-394; Pimor, Y., (1998) Logistique; Gruat La Forme-Chretien, F.-A., (2007) Référentiel d’évaluation De La Performance d’une chaîne Logistique: Application à Une Entreprise De l’ameublement”, , PhD Thesis, Lyon, INSA; la Forme, F.-A.G., Genoulaz, V.B., Campagne, J.-P., A framework to analyse collaborative performance (2007) Computers in Industry, 58 (7), pp. 687-697; Gilmour, P., A strategic audit framework to improve supply chain performance (1999) Jnl of Bus & Indus Marketing, 14 (5-6), pp. 355-366; Okar, C., Beidouri, Z., Mssassi, S., Barrijal, S., A maturity model for SCPMS project: An empirical investigation in large sized Moroccan companies (2011) International Journal of Computer Science Issues (IJCSI), 8 (2), p. 203; Bowersox, D.J., Closs, D.J., Stank, T.P., (1999) 21St Century Logistics: Making Supply Chain Integration Reality, , Council of Logistics Management, Oak Brook, Ill; Kazemkhanlou, H., Ahadi, H.R., Study of performance measurement practices in supply chain management” (2014) Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management, pp. 273-285; Nenni, M.E., Giustiniano, L., Increasing integration across the supply chain through an approach to match performance and risk (2013) American Journal of Applied Sciences, 10 (9), pp. 1009-1017; Estampe, D., (2014) Supply Chain Performance and Evaluation Models: Estampe/Supply Chain Performance and Evaluation Models, , John Wiley & Sons, Inc., Hoboken, NJ, USA; Rüegg-Stürm, J., (2001) What is a Firm? The St Gallen Model of The Firm (Smf)-Introduction to The Basic Categories of Modern Management”; Daub, C.-H., Ergenzinger, R., Enabling sustainable management through a new multi-disciplinary concept of customer satisfaction (2005) European Journal of Marketing, 39 (9-10), pp. 998-1012; Luczak, H., Kees, A., (1998) Das Aachener PPS-Modell”; Klein, R., Herrmann, K., Scheer, A.-W., Spath, D., (2004) Computer Aided Service Engineering, , Springer Berlin Heidelberg, Berlin, Heidelberg; Kueng, P., Krahn, A.J., (1999) Building a Process Performance Measurement System: Some Early experiences”; Gibbs, D., Deutz, P., Proctor, A., Industrial ecology and eco-industrial development: A potential paradigm for local and regional development? (2005) Regional Studies, 39 (2), pp. 171-183; Duret, B., Premiers retours d’expériences en écologie industrielle: études de cas en Europe et en Amérique du Nord (2007) Cahiers De La Chaire d’écologie Industrielle, Juillet, , université de technologie de Troyes; Gibaly, M.M.E., Diab, A.A.A., A model to integrate of the ABC and the BSC in the Egyptian companies: Aligning strategic efficiency and performance improvement (field study) (2012) Journal of American Science, 8 (6), pp. 543-554; Xiuli, C.N.Z., Study on SCOR Based Logistics Activity Costing System [J] (2006) Chinese Journal of Management, 4; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Akkucuk, U., (2015) Handbook of Research on Waste Management Techniques for Sustainability, , IGI Global; Jalaliyoon, N., Zarei, A., Hemati, M., Consideration of BSC and EFQM as a Combination Framework (2011) International Conference on Economics, Business and Management, pp. 161-165; Lohse, M., Ranch, J., LINKING CPFR TO SCOR IMATION’S EXPERIENCE (2001) SUPPLY CHAIN MANAGEMENT REVIEW, 5 (4), pp. 56-62. , JULY/AUG. 2001, ILL; Vidovic, D.I., Vuksic, V.B., Dynamic business process modelling using ARIS (2003) Proceedings of the 25Th International Conference on Information Technology Interfaces, 2003. ITI 2003., IEEE, pp. 607-612; Tatsiopoulos, I.P., Panayiotou, N., The integration of activity based costing and enterprise modeling for reengineering purposes (2000) International Journal of Production Economics, 66 (1), pp. 33-44; Abdelkabir, C., Abdellah, A.O., Bouayad, B., (2012) Application of ECOGRAI/BSC Method for Controlling Logistic Performance: Case of a Moroccan Clothing Company; Reif, R., Kronz, A., Miksch, K., Monitoring with Key Performance Indicators at E. ON Bayern AG Sales Service (2006) In Corporate Performance Management, Springer, pp. 99-124},
publisher={Blue Eyes Intelligence Engineering and Sciences Publication},
issn={22498958},
language={English},
abbrev_source_title={Int. J. Eng. Adv. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sufiyan201940,
author={Sufiyan, M. and Haleem, A. and Khan, S. and Khan, M.I.},
title={Evaluating food supply chain performance using hybrid fuzzy MCDM technique},
journal={Sustainable Production and Consumption},
year={2019},
volume={20},
pages={40-57},
doi={10.1016/j.spc.2019.03.004},
note={cited By 76},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066236133&doi=10.1016%2fj.spc.2019.03.004&partnerID=40&md5=d2e27df17446d83ed1f082bff32d898e},
affiliation={Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia IslamiaNew Delhi  110025, India},
abstract={A Performance Measurement System of any Food Supply Chain (FSC)consists of a complex array of performance criteria and their indicators which seems interdependent. There is a need to evaluate the performance of FSC with the help of important criteria and associated key indicators. In this study, we have identified the six important performance criteria and their key indicators using the integrated approach of literature review & expert opinions. Further, we have employed the approach of fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL)to corroborate the interrelationships among the identified performance criteria and their associated indicators. Study undertakes prioritisation of performance measures or criteria of FSC with the help of an upcoming concept of hybrid Multi-Criteria Decision Making (MCDM)technique. The study further utilises the performance indicators of FSC which are comprehensive and reflects the significant characteristics. Results identify three highly essential performance criteria (i.e., ”service to the customer” ”quality” and ”supply chain efficiency”)with top five important key indicators as ”customer satisfaction”, ”customer complaint”, ”on time delivery”, ”reverse logistics” and ”process quality”. Finding suggests towards proper coordination & collaboration among the partners within the framework of an FSC. The study considers, “Information sharing” as an essential factor for improving the coordination and collaboration in the supply chain. A significant limitation of this research is the subjective inputs of experts who belong to demography. © 2019 Institution of Chemical Engineers},
author_keywords={ANP;  DANP;  FSC;  Fuzzy DEMATEL;  Performance criteria},
references={Aramyan, L.H., Oude Lansink, A.G.J.M., van der Vorst, J.G.A.J., van Kooten, O., Performance measurement in agri-FSCs: a case study (2007) Supply Chain Manage. Int. J., 12 (4), pp. 304-315; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; Björklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of44 supply chains. supply chain manag (2012) An Int. J., 17, pp. 29-39; Bloemhof, J.M., van der Vorst, J.G.A.J., Marko, B., Hamid, A., Sustainability assessment of food chain logistics (2015) Int. J. Logist. Res. Appl.; Bourlakis, M., Maglaras, D., Fotopoulos, C., Examining sustainability performance in the supply chain: The case of the greek dairy sector (2013) Ind. Marke. Manage.; Bowersox, D.J., Closs, D.J., Logistical Management: The Integrated Supply Chain Process (1996), McGraw-Hill New York, NY; Bunte, F., Mulder, M., Tongeren, F., (1998), Van., De Vlieger, K. Meting van de ‘performance’ van agrarischeproductiekolommen, Research Report (163)Dutch Agricultural Economics Institute, The Hague; Chen, C.-A., Ming-Huang, L., Ya-Hui, Y., Branding Taiwan for tourism using ‘decision making trial and evaluation laboratory’ and ‘analytic network process’ methods (2012) Serv. Ind. J., 32 (1355), pp. 8-1373; Chiu, H.S., Gwo-HshiungTzeng, Shu-Kung, H., Cloud e-learning service strategies for improving e-learning innovation performance in a fuzzy environment by using a new hybrid fuzzy multiple attribute decision-making model (2016) Interact. Learn. Environ., 24 (1812), pp. 8-1835; Christopher, M., The agile supply chain: competing in volatile markets (2000) Ind. Mark. Manage., 29 (1), pp. 37-44; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) Int. J. Product. Perform. Manage., 60, pp. 583-602; Dong, G., DeFrancia, K., Chen, M., B, F., Zhang, C.H., Assessing Sustainability: Frameworks and Indices, White Paper (3) (2015), Columbia University Academic Commons New York, NY; Elena, M., GwenolaYannou, L.B., Yannou, B., Gaëlle, P., A template for sustainable food value chains (2017) Int. Food Agribusiness Manage. Rev., 20 (4); Elkington, J., Towards the sustainable corporation: Win-win-win business strategies for sustainable development (1994) Calif. Manage. Rev., 36 (2), pp. 90-100; Faranak, F., Ali, S.N.N., A model for measuring the performance of the meat supply chain (2013) British Food J., 115 (8), pp. 1090-1111; Folke, C., Resilience: the emergence of a perspective for social-ecological systems analyses (2006) Global Environ. Change, 16 (1), pp. 253-267; Gandhi, S., A combined approach using AHP and DEMATEL for evaluating success factors in implementation of green supply chain management in Indian manufacturing industries (2016) Int. J. Logist. Res. Appl., pp. 1-25; Garg, D., Luthra, S., Haleem, A., An evaluation of drivers in implementing sustainable manufacturing in India: Using DEMATEL approach (2014) Int. J. Soc. Educ. Econ. Manage. Eng., 8, pp. 3517-3522; Gellynck, X., AdriennMolnár, LusineAramyan, Supply chain performance measurement: the case of the traditional food sector in the EU (2008) J. Chain Network Sci., 8; Gülçin, B., Sezin, G., An integrated DEMATEL-ANP approach for renewable energy resources selection in Turkey (2016) Int. J. Production Economics, 182, pp. 435-448; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manage., pp. 71-87. , 21 Nos 1/2; Haleem, A., Khan, S., Khan, M., Traceability implementation in food supply chain: a grey-DEMATEL approach (2019) Information Processing In Agriculture; Hamid, A., YuhanGuo, A., Jacqueline, B., Sustainable agro-FSC design using two-stage hybrid multi-objective decision-making approach (2016) Comput. Oper. Res.; Hanne, K., Petri, H., SCOR Based fsc's sustainable performance evaluation model (2012) Adv. Mater. Res., 488-489, pp. 1039-1045; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains26 with a focus on metrics (2012) Int. J. Prod. Econ., 140, pp. 69-82; Hieber, R., Supply Chain Management – a Collaborative Performance Measurement Approach (2002), vdfHochschulverlag, Zu rich; (2018), Hui-Wen, Vivian Tang Modeling critical leadership competencies for junior high school principals: A hybrid MCDM model combining DEMATEL and ANP, Kybernetes; Jacques, T., Ruud, V.U., Jeremy, D., Onno, O., Assessment of innovation and performance in the fruit chain-the innovation-performance matrix (2008) British Food J., 110 (1), pp. 98-127; Jaffee, S., Siegel, P., Andrews, C., Rapid Agricultural Supply Chain Risk Assessment: A Conceptual Framework (Agriculture and Rural Development Discussion Paper 47) (2010), Washington D.C. US: The World Bank; Karkkainen, M., Increasing efficiency in the supply chain for short shelf life goods using RFID tagging (2003) Int. J. Retail Distrib. Manage., 31 (10), pp. 529-536; Khalili-Damghani, K., Taghavifard, M., Olfat, L., Feizi, K., (2012), Measuring agility performance in fresh FSCs: an ordinal two-stage data envelopment analysis. Int. J. Business Performance and Supply Chain Modelling, 4, Nos. 3/4; Khan, S., Haleem, A., Khan, M., Abidi, M., Al-Ahmari, A., Implementing traceability systems in specific supply chain management (SCM)through critical success factors (CSFs) (2018) Sustainability, 10 (2), p. 204; Lee, H.L., Billington, C., Managing supply chain inventory: pitfalls and opportunities (1999) Sloan Manage. Rev., 33, pp. 65-73; (2018), Lisa, Rolita, BayuSurarso,., RahmatGernowo,., E3S Web of Conferences 31, 12006.The Decision Making Trial and Evaluation Laboratory (DEMATAL)and Analytic Network Process (ANP)for Safety Management System Evaluation Performance; Manning, L., Mei, J., Soon building strategic resilience in the FSC (2016) British Food J., 118 (6), pp. 1477-1493; Manning, L., Soon, J.M., Development of sustainability indicator scoring (SIS)for the FSC (2016) British Food J., 118 (9), pp. 2097-2125; Pedro, J.G.P., Liou, J.J.H., Hsu, C.-C., Chuang, Y.-C., An integrated MCDM model for improving airline operational and financial performance (2018) J. Air Transp. Manage., 68, pp. 103-117; Persson, F., Olhager, J., Performance simulation of supply chain designs (2002) Int. J. Prod. Econ., 77 (3), pp. 231-245; Peyman, B.K., Bahram, S., Mostafa, A., Investigating readiness in the Iranian steel industry through six sigma combined with fuzzy Delphi and fuzzy DANP (2018) Decis. Sci. Lett., 7 (2018), pp. 465-480; Schonsleben, P., Integral Logistics Management: Planning and Control of Comprehensive Supply Chains (2004), St Lucie Press Boca Raton, FL; Seçme, N.Y., Bayrakdaroğlu, A., Kahraman, C., Fuzzy Performance evaluation in turkish banking sector using analytic hierarchy process and TOPSIS (2009) Expert Syst. Appl., 36 (9), pp. 11699-11709; Seyed-Hosseini, S.M., Safaei, N., Asgharpour, M.J., Reprioritization of failures in a system failure mode and effects analysis by decision making trial and evaluation laboratory technique (2006) Reliability Engineering & System Safety, 91 (8), pp. 872-881; Sharma, V., Dixit, A.R., Dixit, A.R., Qadri, M.A., Empirical assessment of the causal relationships among lean criteria using DEMATEL method (2016) Benchmarking: An International Journal, 23 (7), pp. 1834-1859; Sharp, J.M., Irani, Z., Desai, S., Working towards agile manufacturing in the UK industry (1999) Int. J. Prod. Econ., 62 (1-2), pp. 155-169; Shieh, J.I., Wu, H.H., Huang, K.K., A DEMATEL method in identifying key success factors of hospital service quality (2010) Knowl.-Based Syst., 23 (3), pp. 277-282; Shih-Hsin, C., Wei-Ting, L., Analyzing determinants for promoting emerging technology through intermediaries by using a DANP-based MCDA framework (2018) Technol. Forecast. Soc. Change, 131, pp. 94-110; Shu-kung, H., Ming-Tsang, L., Gwo-HshiungTzeng, Improving mobile commerce adoption using a new hybrid Fuzzy MADM model (2015) Int. J. Fuzzy Syst., 4081, pp. 5-015; Spekman, R.E., Kamauff, J.W., Myhr, N., An empirical investigation into supply chain management: A perspective on partnerships (1998) Supply Chain Manage., 3, pp. 53-67; Sufiyan, M., Haleem, A., Khan, S., Khan, M., Analysing attributes of food supply chain management: a comparative study (2019) Lecture Notes In Mechanical Engineering, pp. 515-523; Tajbakhsh, A., Hassini, E., Performance measurement of sustainable supply chains: a review and35 research questions (2015) Int. J. Product. Perform. Manage., 64, pp. 744-783; Tang, H., Chang, K., Yin, M., Sheu, R., Critical factors for implementing a programme for international MICE professionals: a hybrid MCDM model combining DEMATEL and ANP (2015) Current Issues In Tourism, 20 (14), pp. 1527-1550; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision support tools and performance measurement and sustainable supply chain management (2015) Int. J. Prod.39 Res., 53, pp. 6473-6494; Tilman, D., Cassman, R., Naylor, K.G., Polasky, S., Agricultural sustainability and intensive production practices (2002) Nature, 418, pp. 671-677; Touboulic, A., Walker, H., Theories in sustainable supply chain management: a structured literature review (2015) International Journal Of Physical Distribution & Logistics Management, 45 (1-2), pp. 16-42; (2012), pp. 16-18. , Tseng, Hao-Lin, Su, Chiu-Hung, Gwo-HshiungTzeng,., Improving cloud computing service in fuzzy environment - Combining fuzzy DANP and fuzzy VIKOR with a new hybrid FMCDM model. Proceedings of 2012 International Conference on Fuzzy Theory and Its ApplicationsNational Chung Hsing University, Taichung, Taiwan, Nov; Tsolakis, N.K., Keramydas, C.A., Toka, A.K., Aidonis, D.A., Iakovou, E.T., Agrifood supply chain management: A comprehensive hierarchical decision-making framework and a critical taxonomy (2013) Biosyst. Eng., 120, pp. 47-64; Vipul, J., LyesBenyoucef, Deshmukh, S.G., A new approach for evaluating agility in supply chains using Fuzzy association rules. mining (2007) Eng. Appl. Artif. Intell., 21 (2008), pp. 367-385; van der Vorst, J.G.A.J., Seth-Oscar, T., Durk Jouke van der, Z., Simulation modelling for food supply chain redesign; integrated decision making on product quality sustainability and logistics (2009) Int. J. Prod. Res., 47 (6611), pp. 23-6631; Yang, Y.P.O., Shieh, H.M., Leu, J.D., Tzeng, G.H., A novel hybrid MCDM model combined with DEMATEL and ANP with applications (2008) International Journal of Operations Research, 5 (3), pp. 160-168; Ying-Chyi, C., Chia-Han, Y., Ching-Hua, L., Van Thac, D., Pei-An, Y., Building criteria for evaluating green project management: An integrated approach of DEMATEL and ANP (2017) Sustainability, 9 (740)},
correspondence_address1={Haleem, A.; Department of Mechanical Engineering, India; email: ahaleem@jmi.ac.in},
publisher={Elsevier B.V.},
issn={23525509},
language={English},
abbrev_source_title={Sustain. Prod. Consum.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Fauzi2019,
author={Fauzi, A.R. and Ridwan, A.Y. and Juliani, W.},
title={Supply Chain Performance Measurement System Development for Shoes SME using Subcontract Production Strategy Based on Integrated SCOR-BSC Model},
journal={IOP Conference Series: Materials Science and Engineering},
year={2019},
volume={598},
number={1},
doi={10.1088/1757-899X/598/1/012126},
art_number={012126},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073236108&doi=10.1088%2f1757-899X%2f598%2f1%2f012126&partnerID=40&md5=f5827a80e50902369d45492b9da2ad84},
affiliation={School of Industrial Engineering, Telkom University, Bandung, Indonesia},
abstract={Textile and apparel companies are a company that is widely available in Indonesia. Small and Medium Enterprise is a business that is growing rapidly and has many competitors in Indonesia, one of which is engaged in the textile sector. SME proves as one of the businesses that can survive when the economic crisis hit the world in 1997-1998 and had an impact on the Indonesian economy [1]. Even so, SME has several problems, one of which is to develop its business. Performance measurement system required to develop its business. With a performance measurement system, SME can monitor business performance that can help companies in making future decisions to improve the performance of the company. This paper focuses on SME engaged in the shoe industry and using subcontract as its production strategy. This paper also using integrated SCOR-BSC model as the baseline to determine the KPI needed by the Shoe SME. The result of this research is integrated SCOR-BSC KPI which can be used as the basis on performance measurement system making. © Published under licence by IOP Publishing Ltd.},
keywords={Benchmarking;  Risk management;  Supply chains;  Systems engineering;  Textiles, Apparel companies;  Business performance;  Economic crisis;  Performance measurement system;  Production strategy;  Small and medium enterprise;  Supply chain performance measurements;  Textile sector, Shoe manufacture},
references={Suci, R.Y., (2017) Jurnal Ilmiah Cano Ekonomos, 6, pp. 51-58; Kurniasari, D.N., (2015) Jurnal NeO-Bis, pp. 98-109; Bolstorff, P., Rosenbaum, R., (2012) Supply Chain Excellence A Handbook for Dramatic Improvement Using the SCOR Model, , New York: AMACOM; Alina, D.C., Fernando, M.D., (2014) Vision de Futuro, 18, pp. 57-58; Thakkar, J., Kanda, A., Deshmukh, S., (2009) Benchmarking: An International Journal, 16, pp. 702-723; Mulyadi, (2005) UPP AMP YKPN; Waaly, N.A., Ridwan, Y.A., Akbar, D.M., (2018) International Mechanical and Industrial Engineering Conference (IMIEC), , Malang; Novar, M.F., Ridwan, Y.A., Santosa, B., (2018) 12th International Conference on Telecommunication Systems, Services, and Applications, , Yogyakarta; Kuswandi, Y.R., Ridwan, Y.A., El Hadi, M.R., (2018) Proceedings of the 12th International Conference on Telecommunication Systems, Services, and Applications, , Yogyakarta; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategies into Action Boston, , Harvard Business School Press; Jannah, B., Ridwan, Y.A., Haidi, R., (2018) Jurnal Rekayasa Sistem Dan Industri; Ismadhia, A., Ridwan, Y.A., Haidi, R., (2018) Jurnal Rekayasa Sistem Dan Industri; Widianingrung, L.D., Kabul, I., (2014) ComTech, pp. 1031-1040; Mulyana, J., Tjiang, L., Sianto, E.M., Gunawan, I., (2016) International Conference on Industrial Engineering and Operations Management, , Kuala Lumpur},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Rizkya2019,
author={Rizkya, I. and Syahputri, K. and Sari, R.M. and Siregar, I. and Utaminingrum, J.},
title={SCOR: Business Process Analysis and Supply Chain Performance in Building Materials Industry},
journal={IOP Conference Series: Materials Science and Engineering},
year={2019},
volume={598},
number={1},
doi={10.1088/1757-899X/598/1/012070},
art_number={012070},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073214264&doi=10.1088%2f1757-899X%2f598%2f1%2f012070&partnerID=40&md5=6f30b8918d813d28f0d684c54841ba4c},
affiliation={Departement of Industrial Enginnering, Faculty of Engineering, Universitas Sumatera Utara, Indonesia},
abstract={Companies engaged in manufacturing building materials such as nails have never measured supply chain performance. The Supply Chain Operations Reference (SCOR) is a supply chain method to evaluate the supply chain performance proposed by the Supply Chain Council (SCC). SCOR aims to analyze and evaluate the performance of the company's supply chain. SCOR is divided into business processes using SCOR Thread Diagrams that describe business processes in a simple and systematic way to help analyze the company's business processes and calculate supply chain performance measurements. The purpose of this study is to provide improvements to business processes and find out the current position of supply chain performance. The results obtained show that there are four performance metrics that are not achieved, namely delivery performance, perfect order fulfillment, order fulfillment lead time, and production flexibility. So, companies must make improvements to performance metrics that have not been achieved so that the company's supply chain performance will be even better. © Published under licence by IOP Publishing Ltd.},
keywords={Building materials;  Construction industry;  Risk management;  Systems engineering, Business process analysis;  Delivery performance;  Order fulfillment;  Performance metrics;  Production flexibility;  Supply chain operations references;  Supply chain performance;  Supply chain performance measurements, Supply chains},
references={Bolstorff, P., Rosenbaum, R., (2003) Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , New York: AMACOM; Thilakarathna, R.H., Dharmawardana, M.N., Rupasinghe, T., (2015) Proc. of 12th Int. Conf. on Business Management Colombo Sri Lanka; Lin, F., Huang, S., Lin, S., (2002) IEEE Transactions on Engineering Management, 49, pp. 258-268; Fox, M., (1993) The Integrated Supply Chain Management System, , University of Toronto: Internal Report; Trkman, P., Budler, M., Groznik, A., (2015) Supply Chain Management: An International Journal, 20, pp. 587-602; Kamal, M.M., Irani, Z., (2014) Supply Chain Management: An International Journal, 19, pp. 523-557; Yan, J., Li, K., Qiu, D., (2007) Americas Conference on Information Systems, p. 226; Zhou, H., Benton, W.C., Schilling, D.A., Milligan, G.W., (2011) J Bus Logist, 32, pp. 332-344; Richey, R., Roath, A., Whipple, J., Fawcett, S., (2010) J Bus Logist, 31, pp. 237-256; Sellitto, M.A., Pereira, G.M., Borchardt, M., Silva, R.I., Viegas, C.V., (2015) International of Production Research; Erkan, T.E., Bac, U., (2011) Int J Bus Managet Stud, 3, pp. 381-390; Jamehshooran, B.G., Shaharoun, A.M., Haron, H.N., (2015) Int J Sup Chain Mgt, 4, pp. 1-11; Pujawan, I.N., (2005) Supply Chain Management, , Surabaya: Guna Widya; (2010) Supply-Chain Operations Reference-model Overview Version 9. 0, , Supply-Chain Council},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Rizkya2019,
author={Rizkya, I. and Hidayati, J. and Syahputri, K. and Sari, R.M. and Siregar, I. and Siregar, K. and Utaminingrum, J.},
title={Measurement of Supply Chain Performance in Manufacturing},
journal={Journal of Physics: Conference Series},
year={2019},
volume={1230},
number={1},
doi={10.1088/1742-6596/1230/1/012056},
art_number={012056},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073438423&doi=10.1088%2f1742-6596%2f1230%2f1%2f012056&partnerID=40&md5=a702dec3af9cd66b7570375a8f62719a},
affiliation={Departement of Industrial Enginnering, Faculty of Engineering, Universitas Sumatera Utara, Indonesia},
abstract={Supply chain management has been a major component of a company's strategy to compete each other through the increasing of organizational productivity and profitability. This paper is useful to provide an application of supply chain performance assessment in the manufacturing industry as an effort to increase the company profitability. Performance assessment metrics of supply chain provides information in the position of the company achievement. That condition capable to provide good management by an overview of the company achievements to meet customer demand. There is no measurement of supply chain performance in good condition of the company. Company problem found and finally caused by the company unable to meet demand leads the loss of customer trust. The Performance of Activity (POA) model is capable to describe the real conditions of the company's supply chain performance. Measure the supply chain performance using POA model. POA model have 7 dimensions of assessment, namely cost, time, capacity, capability, productivity, utilization, and results. By using the performance of activity, there are two dimensions achieved, namely the availability of the company (90.8%) and machine utilization (75,1%). which is a dimension is not achieved well, namely the reliability (86.7%). © 2019 Published under licence by IOP Publishing Ltd.},
keywords={Productivity;  Profitability;  Supply chain management, Company's strategy;  Customer demands;  Customer trust;  Machine utilization;  Manufacturing industries;  Performance assessment;  Supply chain performance;  Two-dimension, Manufacture},
references={Chopra, S., Meindl, P., (2007) Supply Chain Management Strategy, Planning and Operations, 3rd Edition, Upper Saddle River, , (New Jersey: Pearson Education. Inc); Li, X., (2014) Discrete Dynamics in Nature and Society, 2014. , 2014; Lamming, R., Hampson, J.R., (1996) The British Journal of Management, 7 (S1), pp. S45-S62; Saunders, M.J., (1997) Strategic Purchasing and Supply Chain Management, , (London, UK: Pitman); Saunders, M.J., (1997) Proceedings of the 6th International IPSERA Conference, p. T1/6. , (Italy,) Naples 1-6 9; Hertz, H.S., (2007) Malcolm Baldrige Criteria for Performance Excellence, , (Jakarta: IQAF); Blanchard, B.S., (2003) Logistics Engineering and Management Sixth Edition, Hal 146-147. Upper Saddle River, , (New Jersey: Pearson Education. Inc); Bolstorff, P., Rosenbaum, R., (2003) Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , (New York: AMACOM); Morell, J.A., Metrics and Models for Evaluation of supply Chain Integration (2001) Supply Chain Yearbook, pp. 408-426; Lapide, L., (2000) What about Measuring Supply Chain Performance, , (AMR Research); Beamon, B.M., (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Gunasekaran, A., Patel, C., Tirtiroglu, E., (2001) Management Decision, 41, pp. 635-642; Chan, T., Sand Qi, H.J., (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Rumbiati, (2015) Jurnal Ilmu Manajemen, 5, pp. 75-97; Stefanovic, N., (2014) The Scientific World Journal, 2014. , 2014; Coyle, J.J., Bardi, E.J., Langley, C.J., (2003) The Management of Business Logistics: A Supply Chain Perspective, , 7 (Canada: Sheperd Inc)},
correspondence_address1={Rizkya, I.; Departement of Industrial Enginnering, Indonesia; email: indahrizkya@usu.ac.id},
publisher={Institute of Physics Publishing},
issn={17426588},
language={English},
abbrev_source_title={J. Phys. Conf. Ser.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Bohtan20191024,
author={Bohtan, A. and Mathiyazhagan, K. and Vrat, P.},
title={Modeling the public distribution system: a PO-P approach},
journal={OPSEARCH},
year={2019},
volume={56},
number={3},
pages={1024-1066},
doi={10.1007/s12597-019-00384-1},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066500061&doi=10.1007%2fs12597-019-00384-1&partnerID=40&md5=0f9b7f4197842001fdc0d852d387e9dd},
affiliation={The Northcap University, Sector 23A, Gurugram, India; Department of Mechanical Engineering, Amity School of Engineering and Technology, Amity University, Sector 125, Noida, Uttar Pradesh  201313, India},
abstract={Public distribution system (PDS) is the lifeline of food security in India which involves provisioning of foodgrains. To manage this supply chain more efficiently, an effective performance management system is a pre-requisite. This paper utilises the performance objectives-productivity approach to formalise a performance measurement methodology for the PDS supply chain of foodgrains at state level. Here, the actual values of the performance measures are compared with the objectivated values to arrive at the productivity index which indicates the degree of performance of PDS and its various components. In this model the optimal values of the “objectivated output” can be obtained either by solving a goal programming model or through benchmarking in the multi attribute utility theory framework. As fast movement of foodgrains is vital to reduce the degree of perishability due to losses during transit and storage, this multi-dimensional performance measurement system also rates the losses of foodgrains during various stages of freight transportation with a view to minimise the same. The primary aim in applying this method is to arrive at a benchmarking technique to assess if such supply chain is working effectively and efficiently which is akin to auditing the system. By using the given approach some standardisation could be brought into arrive at the authentic performance level of the PDS. © 2019, Operational Research Society of India.},
author_keywords={Performance measurement system;  Performance objectives-productivity;  Public distribution system;  Supply chain management},
references={Tarozzi, A., The Indian public distribution system as provider of food security: evidence from child nutrition in Andhra Pradesh (2005) Eur. Econ. Rev., 49, pp. 1305-1330; (2011) Mid Term Appraisal of 11Th Five-Year Plan 2007–12, , Planning Commission; (2011) Report of the Expert Committee on National Food Security Bill; (2011) Public Distribution System in India: A Brief Overview, , Preliminary Document, Version 0.0; Bohtan, A., Vrat, P., Vij, A.K., Supply chain of the Indian public distribution system: a new paradigm (2017) J. Adv. Manag. Res., 14 (1), pp. 110-123; Rajkumar, P., Service quality measurement of the public distribution system in the food grains supply chain (2013) Int. J. Bus. Perform. Supply Chain Model., 5 (2), pp. 131-147; Bhagwat, R.O., Raut, D.N., The performance evaluation of public distribution system using exploratory factor analysis (2016) J. Harm. Res. Eng., 4 (3), pp. 76-83; Vrat, P., Sardana, G.D., Concept of productivity: a plea for reappraisal (1984) Udyog Pragati, 8 (1), pp. 16-24; Sardana, G.D., Vrat, P., An MBO based mathematical model for measuring plant productivity (1983) Proceedings, All India Seminar on Productivity Maximisation in Industries, Institution of Engineers (India), , Calcutta; Sardana, G.D., Vrat, P., Performance objective-productivity (PO-P): a conceptual framework and mathematical model for productivity management (1983) Productivity, 24 (3), pp. 299-307; Sardana, G.D., Vrat, P., Models of productivity measurement- survey and critical appraisal (1984) Reading Material, APO Seminar on Productivity Measurement and Analysis, , National Productivity Council, New Delhi; Sardana, G.D., Vrat, P., Models of productivity measurement-survey and critical appraisal (1984) Productivity, 25 (3), pp. 271-289; Neely, A., Gregory, M., Platts, K., Performance measurement system design—a literature review and research agenda (1995) Int. J. Oper. Prod. Manag., 15 (4), pp. 80-116; Tangen, S., An overview of frequently used performance measures (2003) Work Study, 52 (7), pp. 347-354; Osborne, D., Gaebler, E., (1992) Reinventing Government—How the Entrepreneurial Spirit is Transforming the Public Sector, , Addison Wesley, Reading, MA; Propper, C., Wilson, D., (2017) The Use and Usefulness of Performance Measures in the Public Sector, , http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.148.448&rep=rep1&type=pdf, CMPO Working Paper Series No. 03/073, Accessed 21 Dec; Osbourne, S., Bovaird, T., Martin, S., Tricker, M., Waterson, P., Performance management and accountability in complex public programmes (1995) Financ. Account. Manag., 11, pp. 19-37; Sharifi, S., Bovaird, T., The financial management initiative in the UK public sector—the symbolic role of performance reporting (1995) Int. J. Public Adm., 18, pp. 467-490; Pidd, M., (2012) Measuring the Performance of Public Services-Principle and Practices, , Cambridge University Press, Cambridge; Kendrik, J.W., Creamer, D., (1965) Measuring Company Productivity. Handbook with Case Studies, , National Conference Board, New York; Ramsay, M.R., (1973) Overall Productivity Measurement, , Paper presented at World Productivity Conference. Indian Institution of Industrial Engineering, Bombay; Mali, P., (1978) Improving Total Productivity-MBO Strategies for Business, Government and Not-For-Profit Organisations, , Wiley, New York; Johnson, R.A., Kast, F.E., Rosenzweig, J.E., (1963) The Theory and Management of System, , Mc Graw Hill Kogakusha Ltd, Tokyo; Scharf, A.D., Productivity and problem solving—don’t collect data first (1984) Proceedings of Annual Conference. Institute of Industrial Engineers, , Chicago; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1222-1245; Ambekar, S., Kapoor, R., Mehta, P., Structural mapping of public distribution system using multi-agent systems (2015) Bus. Process Manag. J., 21 (5), pp. 1066-1090; Swaminathan, M., Consumer food subsidies in India: proposals for reform (2000) J. Peasant Stud., 27 (3), pp. 92-114; Ramaswami, B., Efficiency and equity of food market interventions (2002) Econ. Polit. Wkly., 37 (123), pp. 1129-1135; Mane, P., Targeting the poor or poor targeting: a case for strengthening the public distribution system of India (2006) J. Asian Afr. Stud., 41 (4), pp. 299-317; Kattumuri, R., Food Security and the Targeted Public Distribution System in India, , http://www.lse.ac.uk/asiaResearchCentre/_files/ARCWP38-Kattumuri.pdf, Asia Research Centre Working Paper No. 38, Accessed 21 Dec 2017; Nagavarapuy, S., Sekhriz, S., Who is Targeted by India’s Targeted Public Distribution System? Working Paper, , http://www.stanford.edu/group/SITE/archive/SITE_2011/2011_segment_2/2011_segment_2_papers/nagavarapu.pdf, Stanford University, Accessed 22 Dec 2017; Nagavarapuy, S., Sekhriz, S., (2017) Informal Monitoring Mechanisms in Public Service Delivery: Evidence from the Public Distribution System in India, , http://people.virginia.edu/~ss5mj/UP_TPDS.pdf.Accessed, Working paper. Virginia University, 23 Dec; Murthy, R.V., Ramanayya, T.V., (2017) Procurement Policy for Food Corporation of India Modifications and Implications, , https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2147608, IIM Bangalore Research Paper No. 250, Accessed 23 Dec; Jha, S., Ramaswami, B., (2011) The Percolation of Public Expenditure: Food Subsidies and the Poor in India and the Philippines, , Paper presented at NCAER-NBER: India Policy Forum, New Delhi, India; Khera, R., India’s public distribution system: utilisation and impact (2011) J. Dev. Stud., 47 (7), pp. 1038-1060; Basu, K., India’s foodgrain policy: an economic theory perspective (2011) Econ. Polit. Wkly., 46 (5), pp. 37-46; Thaller, C., Moraitakis, N., Rogers, H., Sigge, D., Clausen, U., Pfohl, H.C., Hartmann, E., Hellingrath, B., (2012) Analysis of the Logistics Research in India—White Paper, , http://web.iitd.ac.in/~ravi1/4a_White%20Paper%20Logistics%20Research%20in%20India%20%282%29.pdf, Accessed 23 Dec 2017; Shah, J., (2009) Supply Chain Management-Text and Cases, , Pearson Education Ltd, London; Sink, D.S., Tuttle, T.C., (1989) Planning and measurement of your organization of the future, , Industrial Engineering and Management Press, Norcross; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Manag. Account., 71, pp. 45-50; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) Int. J. Oper. Prod. Manag., 23 (2), pp. 213-229; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge—Measuring Operations for World-Class Competition, , Dow Jones-Irwin, Homewood; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) Int. J. Oper. Prod. Manag., 16 (8), pp. 63-80; De Toni, A., Tonicha, S., Manufacturing flexibility—a literature review (1997) Int. J. Prod. Res., 36 (6), pp. 1587-1617; Kaplan, R.S., Norton, D.P., The balanced scorecard—measures that drive performance (1992) Harv. Bus. Rev., 70 (1), pp. 71-79; Atkinson, A., Waterhouse, J., Wells, R., A stakeholder approach to strategic performance measurement (1997) Sloan Manag. Rev., 38 (3), p. 5; Lindner, C., Supply Chain Performance measurement—a Research of Occurring Problems and Challenges, , http://www.diva-portal.org/smash/get/diva2:221082/FULLTEXT01.pdf, Jönköping International Business School, Jönköping University, Accessed 22 Dec 2017; European Foundation for Quality Management (EFQM)., , http://www.efqm.org/sites/default/files/overview_efqm_2013_v1.pdf, Accessed 10 Dec 2017; Ahmed, A.H., Virtual integrated performance measurement (2002) Int. J. Qual. Reliab. Manag., 19 (4), pp. 414-441; Supply Chain Operation Research (SCOR), , https://www.scor.com/en, . Accessed 13 Dec 2017; Vrat, P., (2014) Materials Management—An Integrated Systems Approach, , Springer, New Delhi; Allard, R., Keal, J.E., Mount, W., Samuel, P.J., Smith, G.R., Swanack, A.R., (1971) Productivity Measurement—A Symposium for the Seventies, , Institute of Personal Management, London; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: a development guide (1997) Int. J. Oper. Prod. Manag., 17 (5), pp. 522-534; Vrat, P., Sardana, G.D., Sahay, B.S., (2009) Productivity Measurement for Business Excellence, , Narosa Publishing House, New Delhi; Aramyan, L.H., Lansink, A.G.O., Van Der Vorst, J.G., Van Kooten, O., Performance measurement in agri-food supply chains—a case study (2007) Supply Chain Manag. Int. J., 12 (4), pp. 304-315; Jha, S., Ramaswami, B., (2010) How Can Food Subsidies Work Better? Answers from India and the Philippines, , ADB Economics Working Paper Series. 221; Ahluwalia, D., Public distribution of food in India (1993) Food Policy, 8 (1), pp. 33-54; Ramaswami, B., Balakrishnan, P., Food prices and the efficiency of public intervention: the case of the public distribution system in India (2002) Food Policy, 27 (5), pp. 419-436; Jha, R., Bhattacharyya, S., Gaiha, R., Social safety nets and nutrient deprivation—an analysis of the National Rural Employment Guarantee Program and the Public Distribution System in India (2011) J. Asian Econ., 22 (2), pp. 189-201; Coates, J., Build it back better: deconstructing food security for improved measurement and action (2013) Glob. Food Secur., 2 (3), pp. 188-194; Cummings, R., Jr., Rashid, S., Gulati, A., Grain price stabilisation experiences in Asia: What have we learned? (2006) Food Policy, 31 (4), pp. 302-312; Parfitt, J., Barthel, M., Macnaughton, S., Food waste within food supply chains: quantification and potential for change to 2050 (2010) Philos. Trans. R. Soc. Biol. Sci., 365, pp. 3065-3081; Cavinato, J.L., Total cost value model for supply chain competitiveness (1992) J. Bus. Logis., 13 (2), pp. 285-291; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logist. Inf. Manag., 8 (2), pp. 38-44; Naim, M.M., The book that changed the world (1997) Manuf. Eng., 76, pp. 13-16; Benjamin, R., Wigand, R., Electronic markets and virtual value chains on the information superhighway (1997) Sloan Manag. Rev., 36 (2), pp. 62-72; Novich, N., Distribution strategy: Are you thinking small enough? (1990) Sloan Manag. Rev., 32 (1), pp. 71-77; Thomas, D.J., Griffin, P.M., Coordinated supply chain management (1996) Eur. J. Oper. Res., 94 (3), pp. 1-15; Lee, H.L., Billington, C., Managing supply chain inventory: pitfalls and opportunities (1992) Sloan Manag. Rev., 33, pp. 65-73; Van Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) Int. J. Oper. Prod. Manag., 21 (1-2), pp. 126-147; Slack, N., Chambers, S., Johnston, R., (2001) Operations Management, , 3, Pearson Education, London; (2017), https://pdsportal.nic.in/main.aspx, Accessed 24 Dec; Commission for Agricultural Costs & Practises, , http://cacp.dacnet.nic.in/, Accessed 23 Dec 2017; Directorate of Economics and Statistics, , http://eands.dacnet.nic.in/, Accessed 24 Dec 2017; Charnes, A., Cooper, W.W., Goal programming and multiple objective optimizations (1977) Eur. J. Oper. Res., 1, pp. 39-71; Kumar, S., Report of the high-level committee on reorienting the role and restructuring of Food Corporation of India. Government of India (2015) Planning Commission Report; Treasury, H.M., (2001) Choosing the Right FABRIC—A Framework for Performance Information, , https://www.nao.org.uk/wp-content/uploads/2013/02/fabric.pdf, London, TSO, Accessed 13 Sept 2018; El Turabi, A., Hallsworth, M., Ling, T., Grant, J., A novel performance monitoring framework for health research systems-experiences of the National Institute for Health Research in England (2011) Health Res. Policy Syst., 9, p. 13; (2017) Performance Management Framework: Measuring, Monitoring and Reporting Performance—Reference Guide, , https://www.forgov.qld.gov.au/sites/default/files/measuring-monitoring-reporting-performance.pdf, Accessed 13 Sep 18},
correspondence_address1={Mathiyazhagan, K.; Department of Mechanical Engineering, Sector 125, India; email: madii1984@yahoo.com},
publisher={Springer},
issn={00303887},
language={English},
abbrev_source_title={Opsearch},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zainuddin20191075,
author={Zainuddin, N. and Saifudin, A.M. and Deraman, N. and Mahidin, N.},
title={Effect of Halal certification and labelling process on Halal supply chain performance},
journal={International Journal of Supply Chain Management},
year={2019},
volume={8},
number={4},
pages={1075-1081},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071565735&partnerID=40&md5=2425999654c901809e5fbcaa31afe7ab},
affiliation={School of Technology Management and Logistics (STML), Universiti Utara Malaysia (UUM), Sintok, Malaysia; School of Business and Management (SBM), Universiti Utara Malaysia (UUM), Sintok, Malaysia},
abstract={Halal Industry has been growing at a rapid pace due to the growth of the Muslim population worldwide as well as industry internationalisation due to tourism and migration. Supply chain management represents a set of interdependent organisations that work closely together to manage the flow of goods or services and information along the value-added chain of products, in order to realise end customer value at the lowest possible costs. Halal Supply Chain performance measures can be developed based on existing measures of the conventional supply chain as long as the primary objective is preserving halal integrity rather than minimising cost or maximising profit. The objective of this paper is to see the effect of halal certification and labelling process towards halal supply chain performance using SEM techniques. Halal Certification term as a process of certifying products or services as pronounced by the Shariah Law. Halal labelling can be elaborated as marking, tagging or stamping a particular product or service with halal logo or symbol to indicate its halal status. This article focus will be on the survey method only. The instrument development was based on a literature survey. This study focuses on the population of halal certified food and beverages (F & B) manufacturers as disclose in JAKIM Halal Portal. About 254 questionnaire sets answered by the intended respondents out of 500 being distributed and no data are detected as missing. All questions are completely answered by the respondents. This study has confirmed the effect of Halal Certification and Labelling Process, on Halal Supply Chain performance. Having demonstrated the significance of the hypothesised effect with a substantial total variance explained (i.e. above 75 percent) and sufficient predictive relevance, this study provides strong empirical evidence that a high level of Halal Supply Chain performance can be achieved through the effective implementation of Halal Certification and Labelling Process. © ExcelingTech Pub, UK.},
author_keywords={Halal certification and labelling;  Halal industry;  Halal Portal;  Halal supply chain performance},
references={Squire, B., "Supply chain management: a structured literature review and implications for future research." (2006) International Journal of Operations & Production Management; Folkerts, H., Koehorst, H., "Challenges in international food supply chains: vertical co-ordination in the European agribusiness and food industries." (1998) British Food Journal, 100 (8), pp. 385-388; Sundram, V.P.K., Ibrahim, A.R., Govindaraju, V.G.R., "Supply chain management practices in the electronics industry in Malaysia: Consequences for supply chain performance." (2011) Benchmarking: An International Journal, 18 (6), pp. 834-855; Zannierah Syed Marzuki Sharifah, H.C.M., Ballantine, P.W., "Restaurant manager and halal certification in Malaysia." (2012) Journal of Foodservice Business Research, 15 (2), pp. 195-214; Sungkar, I.R.F.A.N., Hashim, D.A.R.H.I.M., "The global halal food market and updates on global halal standards." (2009) Presenting at The World BankFirst EAP Regional Agribusiness Trade and Investment Conference "Agro-enterprise Without Borders", , Holiday Inn Atrium, Singapore; Wan Omar, W., "Developing a model for halal food supply chain implementation." (2017); Beamon, B.M., "Measuring supply chain performance." (1999) International journal of operations & production management, 19 (3), pp. 275-292; Agarwal, A., Shankar, R., "Analyzing alternatives for improvement in supply chain performance." (2002) Work study, 51 (1), pp. 32-37; Tieman, M., Jack G.A.J van der Vorst, G.M.C., "Principles in halal supply chain management." (2012) Journal of Islamic Marketing, 3 (3), pp. 217-243; Bahrudin, S.S.M., Illyas, M.I., Desa, M.I., "Tracking and tracing technology for halal product integrity over the supply chain." (2011) Proceedings of the 2011 International Conference on Electrical Engineering and Informatics, , IEEE; Noordin, N., Nor Laila Md Noor, S.Z., "Strategic approach to halal certification system: An ecosystem perspective." (2014) Procedia-Social and Behavioral Sciences, 121, pp. 79-95; Zulfakar, M., "Australia's halal meat supply chain (AHMSC) operations: supply chain structure, influencing factors and issues." (2015); Kotler, P., Keller, K.L., "A framework for marketing management" (2016), (Global Edition) (6th ed.). England, UK: Pearson Education Limited; Noordin, N., "Value chain of Halal certification system: A case of the Malaysia Halal industry." (2009) European and Mediterranean conference on information systems, (2008); Omar, E.N., Jaafar, H.S., Osman, M.R., "Assessing Halalan-Toyyiban food supply chain in the poultry industry." (2012) International Halal Conference 2012 (INHAC); Malaysia, S., "Halal food-production, preparation, handling and storage-general guidelines (second revision)." (2009), Department of Standards, Selangor Darul Ehsan, Malaysia, MS1500; (2018) Halal food-Production, preparation, handling and storage-General guidelines (third revision) Terms and definitions, , Selangor, Malaysia: Department of Standards Malaysia (Standards Malaysia); (2008) Halal certification, , http://www.hdcglobal.com/publisher/gwm_halal_certification, Government-Why Malaysia?; Yusuf, A.H., Abdul Shukor, S., Ahmad Bustamam, U., "Halal certification vs business growth of food industry in Malaysia." (2016) Journal of Economics, Business and Management, 4 (3), pp. 247-251; Meuwissen, M.P., Velthuis, A.G., Hogeveen, H., Huirne, R.B., "Traceability and certification in meat supply chains" (2003) Journal of Agribusiness, 21 (2), pp. 167-182; Khan, M.I., Haleem, A., "Understanding Halal and Halal Certification & Accreditation System: A Brief Review." (2016) Saudi Journal of Business and Management Studies, 1 (1), pp. 32-42; Ab Talib Mohamed, S., Mohd Johan, M.R., Issues in halal packaging: a conceptual paper (2012) International Business and Management, 5 (2), pp. 94-98; Rezai, G., Mohamed, Z., Shamsudin, M.N., "Non-Muslim consumers' understanding of Halal principles in Malaysia." (2012) Journal of Islamic Marketing, 3 (1), pp. 35-46; Shafie, S., Othman, M.N., "Halal certification: An International Marketing Issues and Challenges." (2006) Proceeding at the International IFSAM VIIIth World Congress; Lodhi, A., "Understanding Halal Food Supply Chain (second)." (2010), HFRC UK Ltd, London; Omar, E.N., Jaafar, H.S., "Halal supply chain in the food industry-A conceptual model', paper presented to Business, Engineering and Industrial Applications (ISBEIA)." (2011) 2011 IEEE Symposium on; Talib, Z., Zailani, S., Zainuddin, Y., "Conceptualizations on the dimensions for halal orientation for food manufacturers: a study in the context of Malaysia." (2010) Pakistan Journal of Social Sciences, 7 (2), pp. 56-61; Halim, M., Mohd Mahyeddin Mohd, S., "The possibility of uniformity on halal standards in organization of Islamic countries (OIC) country." (2012) World Applied Sciences Journal, 17 (17), pp. 6-10; Ahmed, S., Hassan, M., "Survey and case investigations on application of quality management tools and techniques in SMIs." (2003) International Journal of Quality & Reliability Management, 20 (7), pp. 795-826; Jusoh, R., Ibrahim, D.N., Zainuddin, Y., "The performance consequence of multiple performance measures usage: Evidence from the Malaysian manufacturers." (2008) International Journal of Productivity and Performance Management, 57 (2), pp. 119 and -136; Wong, Y.C., Wong, K.Y., Ali, A., "A Study on Lean Manufacturing implementation in the Malaysian electrical and electronics industry" (2009) European Journal of Scientific Research, 38 (4), pp. 521-535; Sekaran, U., Bougie, R., "Research methods for business: A skill building approach" (2016), John Wiley & Sons; Gefen, D., Straub, D., Boudreau, M.-C., "Structural equation modeling and regression: Guidelines for research practice." (2000) Communications of the association for information systems, 4 (1), p. 7; Hair Jr Joseph, F., "A primer on partial least squares structural equation modeling (PLS-SEM)" (2016), Sage publications; Hair, J.F., "Research methods for business." (2007), Education+ Training; Ab Talib Mohamed, S., Abu Bakar Abdul Hamid, C.T.A., Can halal certification influence logistics performance? (2016) Journal of Islamic Marketing, 7 (4), pp. 461-475; Ab Talib, M.S., "Motivations and benefits of halal food safety certification." (2017) Journal of Islamic Marketing, 8 (4), pp. 605-624; Ezanee, M.E., Mohd Nasrun Mohd, N., Mahidin, N., Mohd Affendi Ahmad, P., "Improving 3RD Party Halal Local Service Providers in Halal Logistics: The Driving Factors from Malaysian F&B Manufacturing Perspectives" (2019) International Journal of Supply Chain Management (IJSCM), 8 (1), pp. 644-652},
correspondence_address1={Zainuddin, N.; School of Technology Management and Logistics (STML), Malaysia; email: nizamuddin@uum.edu.my},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Matondang2019,
author={Matondang, N. and Sitompul, F.R.},
title={Measurement and Proposed Improved Supply Chain Performance approach with PDCA frame work},
journal={IOP Conference Series: Materials Science and Engineering},
year={2019},
volume={505},
number={1},
doi={10.1088/1757-899X/505/1/012128},
art_number={012128},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069039426&doi=10.1088%2f1757-899X%2f505%2f1%2f012128&partnerID=40&md5=ca9432d6b9e275399af4852a1735494a},
affiliation={Industrial Engineering Department, Faculty of Engineering, University of Sumatera Utara, St. Almamater Kampus USU, Medan, 20155, Indonesia},
abstract={Supply Chain Management covers all the activities since material came from the supplier, then the material was processed into semi-finished products or finished products until the product is distributed to consumers. To know the performance of the company's Supply Chain, measurement is needed. From these measurements will be obtained result, so good or not good performance of the company's Supply Chain can be seen and can find the weaknesses of existing systems to be repaired. Given a good supply chain performance, the performance the company will increasingly focus and provide benefits, whether for the company, suppliers, and consumers. The company in this research is a company engaged in manufacturing Crude Palm Oil. The delayed raw material of fresh fruit bunches from suppliers resulted in fluctuating production numbers which need to be identified Supply Chain in the manufacturing process to maximize CPO production. By the result of supply chain performance measurement by using Supply Chain Operation Reference (SCOR) method there are 55 KPI that is 10 KPI for perspective plan, 5 KPI for perspective source, 15 KPI for perspective, deliver, 15 KPI for perspective make, and 10 KPI for perspective return. The result of scoring by using OMAX (Objective Matrix) method obtained by total index value equal to 6,1. Based on the traffic light system, the index is in the yellow category indicating that the performance of company supply chain as a whole has not achieved the expected performance even though the result is close to the target set. From KPI performance measurement result with OMAX calculation and traffic light system, it can be seen that KPI included in green category is 24 KPI, yellow category as many as 25 KPI, and red category as many as 6 KPI. © Published under licence by IOP Publishing Ltd.},
keywords={Industrial research;  Manufacture;  Palm oil;  Supply chain management;  Traffic signs, Finished products;  Manufacturing process;  Performance measurements;  Production number;  Semi-finished products;  Supply chain operation references;  Supply chain performance;  Supply chain performance measurements, Benchmarking},
references={Nazaruddin, M., (2017) IOP Conference Series: Materials Science and Engineering, , (Medan, Indonesia) Productivity Improvement With Green Approach To Palm Oil Factory Productivity; Michael, H., (2003) Essential of Supply Chain Management, , (John); Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operation and Production Management, 19; Crimi, T.A., Ralph, G.K., (2002) How to Achieve Cost Savings from Supply Chain Management; Techniques That Work; Nyoman, P.I., (2005) Supply Chain Management, , 1 (Surabaya: Guna Widya); Nitin, S.J., (2001) Decision Making with Depence and Feedback: The Analytic Network Process, , (Pittburgh, PA: RWS Publication); Rakhman, M., (2006) Pengukuruan Kinerja Supply Chain Dengan Model SCOR (Studi Kasus: PT. Atak Otomotif Indonesia Waru, , (Surabaya: Jurusan TI-UPN); (2010) Supply Chain Operations Reference Model SCOR Version 10.0, , http://www.supply-chain.org, Supply Chain Council (Washington DC); Lorie, S.T., (1993) Proses Hirarki Analitik Untuk Pengambilan Keputusan Dalam Situasi Yang Kompleks: Pengambilan Keputusan Bagi Para Pemimpin, , (Jakarta: PT. Pustaka Binaman Pressindo); Suryadi, K., Ali, R.M., (1998) Sistem Pendukung Keputusan, , (Bandung: PT. Remaja Rosdakarya); Riggs James, L., (1986) Production System: Planning, Analysis, and Control, , (Singapore: John Wiley amp; Sons); Metta Mega, R., (2015) Perancangan Model Pengukuran Kinerja Green Supply Chain Management Berdasarkan Green SCOR Dengan Pendekatan PDCA Pada Perusahaan Baja Hilir., , (Jurusan Teknik Industri Universitas Sultan Ageng Tirtayasa); (2017) Evaluasi Kinerja Rantai Pasok Sari Apel Untuk Meningkatkan Kinerja Perusahaan, , Ihwan (Malang: Jurusan Teknik Industri Universitas Brawijaya) dkk; Nurus, S., (2014) Performance Measurement Supply Chain Based on Core Process of Supply Chain Operation Reference (SCOR), , (Malang: Jurusan Teknik Industri Universitas Brawijaya); Mousamik, (2015) A Strategic Approach to Develop Green Supply Chains, 26, p. 670. , (Wilayah Persekutuan, Malaysia: department of Mechanical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur)},
editor={Ali A.Y.},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Putri2019,
author={Putri, Y.D. and Huda, L.N. and Sinulingga, S.},
title={The concept of supply chain management performance measurement with the supply chain operation reference model (Journal review)},
journal={IOP Conference Series: Materials Science and Engineering},
year={2019},
volume={505},
number={1},
doi={10.1088/1757-899X/505/1/012011},
art_number={012011},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069036357&doi=10.1088%2f1757-899X%2f505%2f1%2f012011&partnerID=40&md5=51779c1ee87994d9b8fd43dc3ef84661},
affiliation={Industrial Engineering Department, University of Sumatera Utara, Jalan Almamater USU Campus, Medan, 20155, Indonesia},
abstract={In the era of globalization requires companies to start revolutionizing supply chain performance measurement system for their businesses. Supply chain management is one of the important issues that are a concern for companies that want to continue to increase consumer satisfaction, increase payment utility, and save expenses. For this reason, a supply chain performance measurement system is needed. The purpose of this paper is to build a supply chain performance measurement framework using the supply chain operation reference (SCOR) model as a tool for supply chain performance measurement. One of the causes of various failure of improvement programs in the supply chain is the inability to obtain an overview of the current condition of the company, including its cultural aspects. The use of the SCOR model in building the concept of supply chain performance measurement makes companies able to evaluate supply chain performance holistically in monitoring and knowing where an organization is relative to competitors. This paper focuses on the application of the SCOR model for supply chain performance measurement in manufacturing companies. Some tools are described in this paper as a proposal to build a performance measurement framework in optimizing value in the supply chain. © Published under licence by IOP Publishing Ltd.},
keywords={Manufacture, Consumer satisfactions;  Cultural aspects;  Manufacturing companies;  Performance measurement frameworks;  Performance measurements;  Supply chain operation references;  Supply chain performance;  Supply chain performance measurements, Supply chain management},
references={Mutakin, A., Pengukuran Kinerja Manajemen Rantai Pasok Dengan Pendekatan Model Supply Chain Operation Reference Model 9.0 (2010) Jurnal Manajemen Dan Organisasi, 2 (3), p. 89; Elly Wuryaningtyas, D., Pengukuran Performansi Perusahaan Dengan Menggunakan Metode Supply Chain Operation Reference (SCOR) (2017) Seminar Dan Konferensi Nasional IDEC, p. 142; Nicolae, L.I., Tanasescu, D., Popa, V., Customer Expectations Management in Store Management (2012) Supply Chain Management Journal, 3; Pujawan, I., Nyoman, (2005) Supply Chain Management, , (Surabaya: Guna widya); Handfield, R., Nichols, E.L., Jr., (2002) Supply Chain Redesign: Transforming Supply Chain into Integrated Value System, , Handfield R. and Nichols E. L. Jr (New jersey: Financial Times-Prentice Hall); Rizki, D.A., Sitorus, P.M., Analisis Proses Supply Chain Management Dengan SCOR model di PT. Bukit asam, TBK (2012) Jurnal Teknologi, 3; Juliana, R., (2008) Evaluasi Kinerja Supply Chain Management Dengan Pendekatan Scor Model, , (Depok: program Magister Manajemen universitas indonesia) Tesis pada; Jhon, P., Panduan Penerapan Transformasi Rantai Suplai Dengan Model SCOR 15 Tahun Aplikasi Praktis Lintas Industri (2014) PPM Manajemen, , 979-442-394-7 cetakan ke 1; Anatan, L., Peran Implementasi Manajemen Rantai Pasokan dalam Perekonomian Era Global: Studi pada Perusahaan Manufaktur di Indonesia (2012) Zenith, pp. 122-131; Chiristian, P., An empirical supply chain measurement model for a national egg producer based on the supply chain operations reference model (SCOR) (2013) Journal of Transport and Supply Chain Management, 7 (1); Chain Council, S., (2008) Supply Chain Operations Reference Model Version 8.0. Working Paper; Indrajit, R.E., Djokopranoto, (2003) Konsep Manajemen Rantai Pasok: Strategi Mengelola Manajemen Rantai Pasok Bagi Perusahaan Modern di Indonesia, , (Jakarta: PT Gramedia Widiasarana Indonesia); Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply Chain Management: Implementation Issues and Research Opportunities (1998) International Journal of Logistic Management, 9; Misra, H., Dina, E., Analisis Pengukuran Kinerja Aliran Supply chain di PT. Asia Forestama Raya dengan Metode SCOR (2016) Prosiding Seminar Nasional XI; Saaty, T.L., (1993) Decision Making for Leaders: The Analytical Hierarchy Process for Decision in Complex World, , (Pittsburgh: RWS Publication); Ayag, Z., Ozdemir, R.G., A Fuzzy AHP Approach to Evaluating Machine Tool Alternative (2006) J Intell Manuf. Springer Science + Business Media Inc, 17, pp. 179-190. , J Intell Manuf. Springer Science + Business Media Inc; Aramyan, L.H., Ondersteijin, C.J.M., Van Kooten, O., Oude Lansink, A.G.J.M., Performance Indicators in Agri-food Production Chains. In: Quantifying the agrifood supply chain (2006) Agribusiness: An International Journal, 22; Hwang, D.Y., Lin, Y.C., Lyu, J., The Performance Evaluation of Scor Sourcing Process-The Case Study of Taiwan's Tft-Lcd Industry (2008) International Journal of Production Economics, 115 (2), p. 411; Pratiwi, A., Haryadi, S., Konsep Perbaikan Kinerja Supply Chain Management dengan Pendekatan SCOR Model (2013) Journal of Logistics and Supply Chain Management, 3, p. 157},
editor={Ali A.Y.},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Theagarajan2019629,
author={Theagarajan, S.S. and Manohar, H.L.},
title={Agility enabling practices to augment the supply chain performance of the leather footwear industry: Developing a fuzzy QFD based ASCM model},
journal={Applied Mathematics and Information Sciences},
year={2019},
volume={13},
number={4},
pages={629-642},
doi={10.18576/amis/130415},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085268382&doi=10.18576%2famis%2f130415&partnerID=40&md5=27c7dad6ae4ce67c597591239e9d099e},
affiliation={Project Planning and Business Development Department, CSIR-Central Leather Research Institute (CLRI), Chennai, India; Department of Management Studies, College of Engineering (CEG), Anna University, Chennai, India},
abstract={The purpose of the research study is to understand the impact of the agility enabling practices onto the Supply Chain Performance Measures (SCPMs) in Indian leather footwear industry by means of fuzzy QFD tool. The agility-enhancing SCPMs specific to Indian leather footwear industry were identified and prioritized through Delphi study. The supply chain practices were then ascertained for the scope to achieve agility in the supply chain through factor analysis. The technical correlation between the SC practices and their relationship with agility-enhancing SCPMs was determined using Quality Function Deployment (QFD) tool. Then the supply chain practices were prioritized using fuzzy logic in QFD and a fuzzy QFD based ASCMmodel has been developed. Through the present study, about twelve Agility Enabling Supply Chain Practices (AESCPs) are ascertained as relevant and applicable to the Indian leather footwear industry. AESCPs having high crisp values have to be essentially implemented in leather footwear industry to augment its supply chain performance. © 2019 NSP.},
author_keywords={Agility;  Footwear;  Fuzzy QFD;  Leather;  Supply chain performance;  Supply chain practices},
references={Carter, C.R., Rogers, D.S., Choi, T.Y., Toward the Theory of the Supply Chain (2015) Journal of Supply Chain Management, 51, pp. 89-97; Carter, C.R., Kosmol, T., Kaufmann, L., Toward a supply chain practice view (2017) Journal of Supply Chain Management, 53, pp. 114-122; Luo, W., Shi, Y., Venkatesh, V.G., Exploring the factors of achieving supply chain excellence: a NewZealand perspective (2018) Production Planning and Control, 29, pp. 655-667; Sharifi, H., Zhang, Z., A methodology for achieving agility in manufacturing organizations: An introduction (1999) International Journal of Production Economics, 62, pp. 7-22; Collin, J., Lorenzin, D., Plan for supply chain agility at Nokia: lessons from the mobile infrastructure industry (2006) International Journal of Physical Distribution and Logistics Management, 36, pp. 418-430; Mirghafoori, S.H., Andalib, D., Keshavarz, P., Developing green performance through supply chain agility in manufacturing industry: A case study approach (2017) Corporate Social Responsibility and Environmental Management, 24, pp. 368-381; Cecere, L., (2012) Sales and Operations Planning Improves Supply Chain Agility, , Supply Chain Insight; Agarwal, A., Shankar, R., Tiwari, M., Modeling agility of supply chain (2007) IndustrialMarketing Management, 36, pp. 443-457; Blome, C., Schoenherr, T., Rexhausen, D., Antecedents and enablers of supply chain agility and its effect on performance: a dynamic capabilities perspective (2013) International Journal of Production Research, 51, pp. 1295-1318; Gligor, D.M., Esmark, C.L., Holcomb, M.C., Performance outcomes of supply chain agility: when should you be agile? (2015) Journal of Operations Management, 33, pp. 71-82; Fayezi, S., Zutshi, A., O'Loughlin, A., Understanding and development of supply chain agility and flexibility: A structured literature review (2017) International Journal of Management Reviews, 19, pp. 379-407; Vinodh, S., Prasanna, M., Evaluation of agility in supply chains using multi-grade fuzzy approach (2011) International Journal of Production Research, 49, pp. 5263-5276; Vinodh, S., Devadasan, S.R., Vimal, K.E.K., Kumar, D., Design of agile supply chain assessment model and its case study in an Indian automotive components manufacturing organization (2013) Journal of Manufacturing Systems, 32, pp. 620-631; Singh Patel, B., Samuel, C., Sharma, S.K., Evaluation of agility in supply chains: a case study of an Indian manufacturing organization (2017) Journal of Manufacturing Technology Management, 28, pp. 212-231; (2017) Report, Export Performance of Leather and Leather Products during April March 2016-17 vis, , http://leatherindia.org/category/exports/, vis April March 2015-16; Korzeniewicz, M., Global commodity networks and the leather footwear industry: emerging forms of economic organization in a postmodern world (1992) Sociological Perspectives, 35, pp. 313-327; Abrunhosa, A., Sa, P.M.E., Are TQM principles supporting innovation in the Portuguese footwear industry? (2008) Technovation, 28, pp. 208-221; Lizbeth, N.A., The Impact of Operating in Multiple Value Chains for Upgrading: The Case of the Brazilian Furniture and Footwear Industries (2011) World Development, 39, pp. 1386-1397; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Incio Silva, R., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) International Journal of Production Research, 53, pp. 4917-4926; Bottani, E., A fuzzy QFD approach to achieve agility (2009) International Journal of Production Economics, 119, pp. 380-391; Baramichai, M., ZimmersJr, E.W., Marangos, C.A., Agile supply chain transformation matrix: an integrated tool for creating an agile enterprise (2007) Supply Chain Management: An International Journal, 12, pp. 334-348; Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8, pp. 338-353; Bottani, E., Rizzi, A., Strategic management of logistics service: A fuzzy-QFD approach (2006) International Journal of Production Economics, 103, pp. 585-599; Zarei, M., Mehrjerdi, Y.Z., Supply Chain Liability Using an Integrated AHP-Fuzzy-QFD Approach (2015) International Journal of Industrial Engineering and Production Research, 26, pp. 147-162; Lima-Junior, F.R., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Computers and Industrial Engineering, 113, pp. 333-346; Christopher, M., Towill, D., An integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution and Logistics Management, 31, pp. 235-246; Theagarajan, S.S., Manohar, H.L., Lean Management Practices to improve supply chain performance of Leather Footwear Industry (2015) Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management Dubai, , ieeexplore.ieee.org/document/7093717/, United Arab Emirates (UAE), March 3 5; Ghatari, A.R., Mehralian, G., Zarenezhad, F., Rasekh, H.R., Developing a Model for Agile Supply: An Empirical Study from Iranian Pharmaceutical Supply Chain (2013) Iranian Journal of Pharmaceutical Research, 2, pp. 193-205; Swafford, P.M., Ghosh, S., Murthy, N., Achieving supply chain agility through IT integration and flexibility (2008) International Journal of Production Economics, 116, pp. 288-297; Sangari, M.S., Razmi, J., Business intelligence competence (2015) agile capabilities, and agile performance in supply chain: An empirical study, International Journal of Logistics Management, 26, pp. 356-380; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23, pp. 618-641; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega-The International Journal of Management Science, 34, pp. 107-124; Qrunfleh, S., Tarafdar, M., Supply chain information systems strategy: Impacts on supply chain performance and firm performance (2014) International Journal of Production Economics, 147, pp. 340-350; Yusuf, Y.Y., Sarhadi, M., Gunasekaran, A., Agile manufacturing: the drivers (1999) concepts and attributes, International Journal of Production Economics, 62, pp. 33-43; Sangari, M.S., Razmi, J., Zolfaghari, S., Developing a practical evaluation framework for identifying critical factors to achieve supply chain agility (2015) Measurement, 62, pp. 205-214; Costello, A.B., Osborne, J.W., Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis (2005) Practical Assessment Research and Evaluation, 10, pp. 173-178; Soni, G., Kodali, R., Evaluating reliability and validity of lean (2012) agile and leagile supply chain constructs in Indian manufacturing industry, Production Planning and Control, 23, pp. 864-884; Nunnally, J.C., (1978) Psychometric Theory, , McGraw-Hill, New York, NY; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R., (2010) Multivariate data analysis; Joseph, I.N., Rajendran, C., Kamalanbhan, T.J., An instrument for measuring total quality management implementation in manufacturing-based business units in India (1999) International Journal of Production Research, 37, pp. 2201-2215; Saraph, J.V., Benson, P.G., Schroeder, R.G., An instrument for measuring the critical factors of quality management (1989) Decision sciences, 20, pp. 810-829; Field, A., (2005) Discovering statistics using SPSS, , 2nd edn. Sage, London; Malhotra, M.K., Grover, V., An assessment of survey research in POM: from constructs to theory (1998) Journal of Operations Management, 16, pp. 407-425; Hinton, P.R., Brownlow, C., McMurray, I., (2004) SPSS explained, , 2nd edn. Routledge; Clark, L.A., Watson, D., Constructing validity: Basic issues in objective scale development (1995) Psychological Assessment, 7, pp. 309-319; Armstrong, J.S., Overton, T.S., Estimating non-response bias in mail surveys (1977) Journal of Marketing Research, 4, pp. 396-402; Mitchell, T.R., An evaluation of the validity of correlational research conducted in organizations (1985) Academy of Management Review, 10, pp. 192-205; Podsakoff, P.M., MacKenzie, S.B., Lee, J., Podsakoff, N.P., Common method biases in behavioral research: a critical review of the literature and recommended remedies (2003) Journal of Applied Psychology, 88, pp. 879-903; Zimmermann, H.J., (1991) Fuzzy Set Theory and its Applications, , Second ed. Kluwer Academic Publishers, Boston; Chamodrakas, I., Alexopoulou, N., Martakos, D., Customer evaluation for order acceptance using a novel class of fuzzy methods based on TOPSIS (2009) Expert Systems with Applications, 36, pp. 7409-7415; Chien, C.J., Tsai, H.H., Using fuzzy numbers to evaluate perceived service and quality (2000) Fuzzy Sets and Systems, 116, pp. 289-300; Ngai, E., Chau, D., Chan, T., Information technology (2011) operational, and management competencies for supply chain agility: findings from case studies, The Journal of Strategic Information Systems, 20, pp. 232-249; Gligor, D.M., Holcomb, M.C., Stank, T.P., A multidisciplinary approach to supply chain agility: Conceptualization and scale development (2013) Journal of Business Logistics, 34, pp. 94-108; Haq, A.N., Boddu, V., Analysis of enablers for the implementation of leagile supply chain management using an integrated fuzzy QFD approach (2017) Journal of Intelligent Manufacturing, 28, pp. 1-12; Braunscheidel, M.J., Suresh, N.C., Cultivating Supply Chain Agility: Managerial Actions Derived from Established Antecedents (2018) In: Supply Chain Risk Management, pp. 289-309. , Springer, Singapore; Brusset, X., Does Supply Chain Visibility Enhance Agility? (2016) International Journal of Production Economics, 171, pp. 46-59; Namrata, S., Sahay, B.S., Ravi, S., Sarma, P.R.S., Supply chain agility: review (2017) classification and synthesis, International Journal of Logistics Research and Applications, 20, pp. 532-559; Tarafdar, M., Qrunfleh, S., Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility (2017) International Journal of Production Research, 55, pp. 925-938; Kim, M., Chai, S., The impact of supplier innovativeness (2017) information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective, International Journal of Production Economics, 187, pp. 42-52; Kannan, V.R., Tan, K.C., Just in time (2005) total quality management, and supply chain management: understanding their linkages and impact on business performance, Omega-The International Journal of Management Science, 33, pp. 153-162; Pantouvakis, A., Karakasnaki, M., The role of agility and organisational learning culture in forming long lasting relations in shipping (2018) International Journal of Shipping and Transport Logistics, 10, pp. 160-180; Achabal, D., McIntyre, S.H., Smith, S.A., Kalyanam, K., A decision support system for vendor managed inventory (2000) Journal of Retailing, 76, pp. 430-454; Chopra, S., Meindl, P., Kalra, D.V., Supply chain management strategy (2007) planning and operation, , 3rd ed. Dorling Kindersley India Pvt. Ltd., New Delhi; Sathish, T., Periyasamy, P., An extensive review of reverse logistics and its benefits in supply chain management (2018) International Journal of Mechanical and Production Engineering Research and Development, 8, pp. 165-178},
correspondence_address1={Manohar, H.L.; Department of Management Studies, India; email: mhansa@ymail.com},
publisher={Natural Sciences Publishing},
issn={19350090},
language={English},
abbrev_source_title={Appl. Mat. Inf. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Leksono2019,
author={Leksono, E.B. and Suparno, S. and Vanany, I.},
title={Integration of a balanced scorecard, DEMATEL, and ANP for measuring the performance of a sustainable healthcare supply chain},
journal={Sustainability (Switzerland)},
year={2019},
volume={11},
number={13},
doi={10.3390/su11133626},
art_number={3626},
note={cited By 33},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068693487&doi=10.3390%2fsu11133626&partnerID=40&md5=446cc7c10d8b61415842de81c1166933},
affiliation={Department of Industrial Engineering, Universitas Muhammadiyah Gresik, Gresik, 61121, Indonesia; Department of Industrial Engineering, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, 60111, Indonesia},
abstract={The main purpose of this study is to develop a sustainable healthcare supply chain performance measurement (SHSCPM) model, which simultaneously considers intangible characteristics and sustainability aspects to ensure customer and/or stakeholder satisfaction. This model combines a balanced scorecard (BSC) with a decision-making trial and evaluation laboratory (DEMATEL) and analytical network process (ANP). After the arrangement and classification of perspectives and indicators from a literature review, the strategy map of the BSC is designed with DEMATEL. Furthermore, this study used a survey with in-depth interviews of seven expert respondents to make a pair-wise comparison between perspectives and indicators in order to determine the weights of indicators, perspectives, and sustainability aspects on ANP. The study finds the following. First, the innovation and learning perspective that reflects intangible assets has the most influence on the others but is not important, while the customer perspective has the most importance for SHSC performance. Second, the economic aspect has the greatest weight, followed by social and environmental aspects. Finally, indicators of the financial and customer perspectives as drivers of SHSC performance consist of profit, quality of service, revenue, customer satisfaction, and stakeholder satisfaction. Further, indicators of the economic aspect of sustainability have the most effect on SHSC performance, followed by social and environmental aspects. Furthermore, human resources, as an intangible asset and key factor in social aspects, are main factor in improving SHSC performance. © 2019 by the authors.},
author_keywords={Intangible characteristics;  Performance measurement;  Performance of perspectives and indicators;  Sustainability aspects;  Sustainable healthcare supply chain},
keywords={decision making;  economic conditions;  health care;  integrated approach;  measurement method;  stakeholder;  supply chain management;  sustainability},
references={Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput. Ind. Eng, 62, pp. 801-818; Baltacioglu, T., Ada, E., Kaplan, M.D., And, O.Y., Kaplan, Y.C., A new framework for service supply chains (2007) Serv. Ind. J, 27, pp. 105-124; Giannakis, M., Conceptualizing and managing service supply chains (2011) Serv. Ind. J, 31, pp. 1809-1823; Drzymalski, J., Supply Chain Frameworks for the Service Industry: A Review of the Literature (2012) Eur. Int. J. Sci. Technol, 1, pp. 31-42; Yap, L.L., Tan, C.L., The effect of service supply chain management practices on the public healthcare organizational performance (2012) Int. J. Bus. Soc. Sci, 3, pp. 216-224; Kumar, A., Ozdamar, L., Zhang, C.N., Supply chain redesign in the healthcare industry of Singapore (2008) Supply Chain Manag. Int. J, 13, pp. 95-103; Pertumbuhan Ekonomi Indonesia Triwulan II-2014 (2014) Statistics News of Indonesia, pp. 1-9; (2015) East Java in Figure 2015, , Statistics of Jawa Timur Province: Surabaya, Indonesia; Leksono, E.B., Suparno, S., Vanany, I., Development of Performance Indicators Relationships on Sustainable Healthcare Supply Chain Performance Measurement Using Balanced Scorecard and DEMATEL (2018) Int. J. Adv. Sci. Eng. Inf. Technol, 8, pp. 115-122; Xu, X., Gursoy, D., A conceptual framework of sustainable hospitality supply chain management (2015) J. Hosp. Mark. Manag, 24, pp. 229-259; Ortas, E., Moneva J.M.;álvarez, I., Sustainable supply chain and company performance: Aglobal examination (2014) Supply Chain Manag. Int. J, 19, pp. 332-350; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ, 140, pp. 69-82; Hussain, M., Khan, M., Al-aomar, R., A framework for supply chain sustainability in service industry with Confirmatory Factor Analysis (2016) Renew. Sustain. Energy Rev, 55, pp. 1301-1312; Beske-janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management-What has been achieved? (2015) Supply Chain Manag. Int. J, 20, pp. 664-680; Chithambaranathan, P., Subramanian, N., Gunasekaran, A., Service supply chain environmental performance evaluation using grey based hybrid MCDM approach (2015) Intern. J. Prod. Econ, 166, pp. 163-176; Unger, S., Landis, A., Assessing the environmental, human health, and economic impacts of reprocessed medical devices in a Phoenix hospital's supply chain (2016) J. Clean. Prod, 112, pp. 1995-2003; Hussain, M., Ajmal, M.M., Gunasekaran, A., Khan, M., Exploration of Social Sustainability in Healthcare Supply Chain (2018) J. Clean. Prod, 203, pp. 977-989; Zigan, K., Macfarlane, F., Desombre, T., Intangible resources as performance drivers in European hospitals (2008) Int. J. Prod. Perform. Manag, 57, pp. 57-71; Smith, B.K., Nachtmann, H., Pohl, E.A., Quality measurement in the healthcare supply chain (2011) Qual. Manag. J, 18, pp. 50-60; Priyan, S., Uthayakumar, R., Optimal inventory management strategies for pharmaceutical company and hospital supply chain in a fuzzy-stochastic environment (2014) Oper. Res. Health Care, 3, pp. 177-190; Swinehart, K., Smith, A.E., Internal supply chain performance measurement: A health care continuous improvement implementation (2005) Int. J. Health Care Qual. Assur, 18, pp. 533-542; Chen, D.Q., Preston, D.S., Xia, W., Enhancing hospital supply chain performance: A relational view and empirical test (2013) J. Oper. Manag, 31, pp. 391-408; Lega, F., Marsilio, M., Villa, S., An evaluation framework for measuring supply chain performance in the public healthcare sector: Evidence from the Italian NHS (2013) Prod. Plan. Control, 24, pp. 931-947; Kaplan, R.S., Norton, D.P., Linking the Balanced Scorecard to strategy (1996) Calif. Manag. Rev, 39, pp. 53-79; Kaplan, R.S., Norton, D.P., Rugelsjoen, B., Managing alliances with the Balanced Scorecard (2010) Harv. Bus. Rev, pp. 114-121; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: Design and development guidelines (2013) Int. J. Prod. Perform. Manag, 62, pp. 805-826; Voelpel, S.C., Leibold, M., Eckhoff, R.A., The tyranny of the Balanced Scorecard in the innovation economy (2006) J. Intellect. Cap, 7, pp. 421-428; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Prod. Plan. Control, 20, pp. 678-690; Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resour. Conserv. Recycl, 55, pp. 593-603; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach (2014) Prod. Plan. Control, 25, pp. 698-714; Haghighi, S.M., Torabi, S.A., Ghasemi, R., An integrated approach for performance evaluation in sustainable supply chain networks (with a case study) (2016) J. Clean. Prod, 137, pp. 579-597; Gölcük, I., Baykasoglu, A., An analysis of DEMATEL approaches for criteria interaction handling within ANP (2016) Expert Syst. Appl, 46, pp. 346-366; Chen, F., Hsu, T., Tzeng, G., A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP (2011) Int. J. Hosp. Manag, 30, pp. 908-932; Hsu, C., Kuo, T., Chen, S., Hu, A.H., Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management (2013) J. Clean. Prod, 56, pp. 164-172; Su, C., Horng, D., Tseng, M., Chiu, A.S.F., Wu, K., Chen, H., Improving sustainable supply chain management using a novel hierarchical grey-DEMATEL approach (2016) J. Clean. Prod, 134, pp. 469-481; Saaty, T.L., Fundamentals of the analytic network process (1999) Proceedings of the ISAHP 1999, pp. 12-14. , Kobe, Japan, 12-14 August; Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Manag. Int. J, 19, pp. 242-257},
correspondence_address1={Leksono, E.B.; Department of Industrial Engineering, Indonesia; email: eko_budileksono@umg.ac.id},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Khan2019377,
author={Khan, S.A. and Chaabane, A. and Dweiri, F.},
title={A knowledge-based system for overall supply chain performance evaluation: a multi-criteria decision making approach},
journal={Supply Chain Management},
year={2019},
volume={24},
number={3},
pages={377-396},
doi={10.1108/SCM-06-2017-0197},
note={cited By 30},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061184280&doi=10.1108%2fSCM-06-2017-0197&partnerID=40&md5=1028edc68c1b437597676d16277da6e1},
affiliation={Department of Automated Manufacturing Engineering, Ecole de Technologie Superieure, Montreal, QC, Canada; Ecole de technologie superieure, Montreal, QC, Canada; Department of Industrial Engineering and Engineering Management, University of Sharjah, Sharjah, United Arab Emirates},
abstract={Purpose: Existing supply chain (SC) performance models are not able to cope with the potential of intensive SC digitalisation and establish a relationship between decisions and decision criteria. The purpose of this paper is to develop an integrated knowledge-based system (KBS) that creates a link between decisions and decision criteria (attributes) and evaluates the overall SC performance. Design/methodology/approach: The proposed KBS is grounded on the fuzzy analytic hierarchy process (fuzzy AHP), which establishes a relationship between short-term and long-term decisions and SC performance criteria (short-term and long-term) for accurate and integrated Overall SC performance evaluation. Findings: The proposed KBS evaluates the overall SC performance, establishes a relationship between decisions (long-term and short-term) and decision criteria of SC functions and provides decision makers with a view of the impact of their short-term or long-term decisions on overall SC performance. The proposed system was implemented in a case company where the authors were able to develop a SC performance monitoring dashboard for the company’s top managers and operational managers. Practical implications: The proposed KBS assists organisations and decision makers in evaluating their overall SC performance and helps in identifying underperforming SC functions and their associated criteria. It may also be considered as a tool for benchmarking SC performance against competitors. It can efficiently point to improvement directions and help decision makers improve overall SC performance. Originality/value: The proposed KBS provides a holistic and integrated approach, establishes a relationship between decisions and decision criteria and evaluates overall SC performance, which is one of the main limitations in existing supply chain performance measurement systems. © 2019, Emerald Publishing Limited.},
author_keywords={Decision making;  Fuzzy AHP;  Integration},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: review and classification (2012) The Journal of Organizational Management Studies, pp. 1-20. , www.ibimapublishing.com/journals/JOMS/joms.html; Agarwal, G., Vijayvargy, L., Green supplier assessment in environmentally responsive supply chains through analytical network process (2012) Lecture Notes in Engineering and Computer Science, pp. 1218-1223. , Springer, Heidelberg; Agarwal, P., Sahai, M., Mishra, V., Bag, M., Singh, V., A review of multi-criteria decision making techniques for supplier evaluation and selection (2011) International Journal of Industrial Engineering Computations, 2 (4), pp. 801-810; Ashrafzadeh, M., Rafiei, F.M., Isfahani, N.M., Zare, Z., Application of fuzzy TOPSIS method for the selection of warehouse location: a case study (2012) Interdisciplinary Journal of Contemporary Research in Business, 3, pp. 655-671; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 115-128; Azzone, G., Noci, G., Identifying effective PMSs for the deployment of green manufacturing strategies (1998) International Journal of Operations & Production Management, 18 (4), pp. 308-335; Beamon, B.M., (1999) Measuring supply chain performance, , Industrial Engineering; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18, pp. 666-680. , www.tandfonline.com/doi/abs/10.1080/09537280701614407; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning & Control, 20 (8), pp. 678-690; Bhagwat, R., Chan, F.T.S., Sharma, M.K., Performance measurement model for supply chain management in SMEs (2008) International Journal of Globalisation and Small Business, 2 (4), pp. 428-445; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Charkha, P.G., Jaju, S.B., Supply chain performance measurement system: an overview (2014) International Journal of Business Performance and Supply Chain Modelling, 6 (1), pp. 40-60; Chopra, S., Meindl, P., (2016) Supply Chain Management: Strategy, Planning, and Operation, , 6th ed, Pearson Prentice Hall, Upper Saddle River, NJ; Dargi, A., Anjomshoae, A., Galankashi, M.R., Memari, A., Tap, M.B.M., Supplier selection: a fuzzy-ANP approach (2014) Procedia Computer Science, 31, pp. 691-700; Diabat, A., Govindan, K., An analysis of the drivers affecting the implementation of green supply chain management (2011) Resources, Conservation and Recycling, 55 (6), pp. 659-667; Ding, J.F., Applying an integrated fuzzy MCDM method to select hub location for global shipping carrier-based logistics service providers (2013) WSEAS Transactions on Information Science and Applications, 10 (2), pp. 47-57; Dobrota, M., Macura, D., Šelmi, M., Multi criteria decision making for distribution center location selection – Serbia case study (2015) 2nd Logistics International Conference, pp. 32-37; Drzymalski, J., Odrey, N.G., Wilson, G.R., Aggregating performance measures of a multi-echelon supply chain using the analytical network and analytical hierarchy process (2010) International Journal of Services, Economics and Management, 2 (3-4), pp. 286-306; Dweiri, F., Fuzzy development of crisp activity relationship charts for facilities layout (1999) Computers and Industrial Engineering, 36 (1), pp. 1-16; Dweiri, F.T., Kablan, M.M., Using fuzzy decision making for the evaluation of the project management internal efficiency (2006) Decision Support Systems, 42 (2), pp. 712-726; Dweiri, F., Khan, S.A., Jain, V., Production planning forecasting method selection in a supply chain: a case study ‘production planning forecasting method selection in a supply chain: a case study (2015) International Journal of Applied Management Science, 7 (1), pp. 38-58; Dweiri, F., Kumar, S., Khan, S.A., Jain, V., Designing an integrated AHP based decision support system for supplier selection in automotive industry (2016) Expert Systems with Applications, 62, pp. 273-283; Fawcett, S.E., Osterhaus, P., Magnan, G.M., Brau, J.C., McCarter, M.W., Information sharing and supply chain performance: the role of connectivity and willingness (2007) Supply Chain Management: An International Journal, 12 (5), pp. 358-368. , https://doi.org/10.1108/13598540710776935; Forslund, H., ERP systems’ capabilities for supply chain performance management (2010) Industrial Management & Data Systems, 110 (3), pp. 351-367. , www.emeraldinsight.com/doi/10.1108/02635571011030024; Gaudenzi, B., Borghesi, A., Managing risks in the supply chain using the AHP method (2006) The International Journal of Logistics Management, 17 (1), pp. 114-136; Govindan, K., Diabat, A., Madan Shankar, K., Analyzing the drivers of green manufacturing with fuzzy approach (2015) Journal of Cleaner Production, 96, pp. 182-193. , http://dx.doi.org/10.1016/j.jclepro.2014.02.054; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; GunasekaranPatel, A., Tirtiroglu, E., (2001) Performance measures and metrics in a supply chain environment; Hu, A.H., Hsu, C.-W., Critical factors for implementing green supply chain management practice: an empirical study of electrical and electronics industries in Taiwan (2010) Management Research Review, 33 (6), pp. 586-608; Jain, V., Khan, S.A., Application of AHP in reverse logistics service provider selection: a case study (2017) International Journal of Business Innovation and Research, 12 (1), pp. 94-119; Jung, H., A fuzzy AHP-GP approach for integrated production-planning considering manufacturing partners (2011) Expert Systems with Applications, 38 (5), pp. 5833-5840; Kanda, A., Deshmukh, S.G., Coordination in supply chains: an evaluation using fuzzy logic (2007) Production Planning & Control, 18 (5), pp. 420-435; Kandel, A., (1992) Fuzzy Expert Systems, , CRC Press; Kaplan, R.S., Norton, D.P., The balanced scorecard–measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Khan, S.A., (2018) A knowledge base system for overall supply chain performance evaluation: a multi-criteria decision-making approach, , Doctoral dissertation, École de technologie supérieure; Khan, S.A., Dweiri, F., Chaabane, A., Fuzzy-AHP approach for warehouse performance measurement (2016) IEEE International Conference on Industrial Engineering and Engineering Management, pp. 871-875. , December; Khan, S.A., Dweiri, F., Jain, V., Integrating analytical hierarchy process and quality function deployment in automotive supplier selection (2016) International Journal of Business Excellence, 9 (2), pp. 156-177; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) Social Sciences, 2 (4), pp. 19-34; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lee-Kwang, H., Lee, J.H., A method for ranking fuzzy numbers and its application to decision-making (1999) IEEE Transactions on Fuzzy Systems, 7 (6), pp. 677-685; Mondragon, A.E.C., Lalwani, C., Mondragon, C.E.C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Management: An International Journal, 16 (1), pp. 43-56. , www.emeraldinsight.com/10.1108/13598541111103494; Nomesh, B., Bhandari, J., Saxena, P., Quantification of agility of a supply chain using fuzzy logic (2012) International Journal of Management, IT and Engineering, 2 (3), pp. 141-159; Ocampo, L., Clark, E., Tanudtanud, K.V., A sustainable manufacturing strategy from different strategic responses under uncertainty (2015) Journal of Industrial Engineering, 2015, pp. 1-11. , www.hindawi.com/journals/jie/2015/210568/ref/; Ohdar, R., Ray, P.K., Performance measurement and evaluation of suppliers in supply chain: an evolutionary fuzzy-based approach (2004) Journal of Manufacturing Technology Management, 15 (8), pp. 723-734; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations & Production Management, 25 (9), pp. 898-916. , www.emeraldinsight.com/10.1108/01443570510613956; Rezaei, M., Shirazi, M.A., Karimi, B., IoT-based framework for performance measurement a real-time supply chain decision alignment (2017) Industrial Management & Data Systems, 117 (4), pp. 688-712. , https://doi.org/10.1108/IMDS-08-2016-0331; Saaty, T.L., (1980) The analytic hierarchy process, pp. 1-11. , Education; Saaty, T.L., (1980) The analytic hierarchy process; Sahay, B.S., Ranjan, J., Real time business intelligence in supply chain analytics (2008) Information Management and Computer Security, 16 (1), pp. 28-48. , https://doi.org/10.1108/09685220810862733; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2011) Behavioral Operations in Planning and Scheduling, pp. 105-121. , Springer, Berlin; Simchi-LeviKaminsky, D., Simchi-Levi, E., (2008) Designing and managing the supply chain: concepts, strategies, and case studies; Sirigiri, P., Gangadhar, P.V., Kajal, K., Evaluation of teacher’s performance using fuzzy logic techniques (2012) International Journal of Computer Trends and Technology, 3 (2), pp. 200-210; (2012) Supply chain operations reference model, pp. 1-976. , Supply Chain Operations Management; TadicZecevic, S.R., Krstic, M.D., (2014) Ranking of logistics system scenarios for central business district, pp. 159-167. , Promet-Traffic & Transportation; Unahabhokha, C., Platts, K., Hua Tan, K., Predictive performance measurement system: a fuzzy expert system approach (2007) Benchmarking: An International Journal, 14 (1), pp. 77-91; Varma, S., Wadhwa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance: application of analytical hierarchy process to balanced scorecard (2008) Asia Pacific Journal of Marketing and Logistics, 20 (3), pp. 343-356; Yang, F., Wu, D., Liang, L., Bi, G., Wu, D.D., Supply chain DEA: production possibility set and performance evaluation model (2011) Annals of Operations Research, 185 (1), pp. 195-211; Yang, H., Anumba, C.J., Kamara, J.M., Carrillo, P., A fuzzy-based analytic approach to collaborative decision making for construction teams (2001) Logistics Information Management, 14 (5-6), pp. 344-354; Zadeh, L.A., Fuzzy logic (1988) Computer, 21 (4), pp. 83-93. , http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=53},
correspondence_address1={Khan, S.A.; Department of Automated Manufacturing Engineering, Canada; email: skhan@sharjah.ac.ae},
publisher={Emerald Group Holdings Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Widyarto2019137,
author={Widyarto, W.O. and Shofa, M.J. and Djamal, N.},
title={Key performance indicators on supply chain performance measurement in an electronic commerce: A literature review},
journal={International Journal of Engineering and Advanced Technology},
year={2019},
volume={8},
number={5},
pages={137-141},
doi={10.35940/ijeat.E1019.0585C19},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073743011&doi=10.35940%2fijeat.E1019.0585C19&partnerID=40&md5=4f56fd9c9a77a696e09a206274be6cf8},
affiliation={Industrial Engineering Department, Universitas Serang Raya, Serang, Banten, Indonesia},
abstract={Based on the information and communication technology revolution are characterized by Internet usage in various fields, the business environment also does changing by applying the concept of Electronic Commerce (e-commerce) on its process. Like business activities in general, e-commerce also requires the involvement of various parties, which are referred to Supply Chain Management (SCM). The challenge is while the Supply Chain (SC) performance measurement, that generally conducted in the company with non e-commerce, applied on ecommerce business process. Otherwise, only a few literature studies discuss partially dimension of performance measurement in e-commerce. Therefore, it is important to conduct a literature study about key performance indicators (KPIs) for measuring SC performance in e-commerce dimensions, and it would be explained in this paper. The contribution of this paper proposed the KPIs’ that could be used for measuring SC performance in an e-commerce business process in order to be a new approach on the development of SC performance measurement models. © BEIESP.},
author_keywords={E-commerce;  KPIs;  Literature review;  SC performance measurement},
references={Chan, B., Al-Hawamdeh, S., The Development Of ECommerce In Singapore: The Impact Of Government Initiatives (2002) Business Process Management Journal, 8 (3), pp. 278-288; DeLone, W.H., McLean, E.R., Measuring e-Commerce Success: Applying the DeLone & McLean Information Systems Success Model (2004) International Journal of Electronic Commerce, 9 (1), pp. 31-47; Gibbs, J.L., Kraemer, K.L., A Cross-Country Investigation Of The Determinants Of Scope Of ECommerce Use: An Institutional Approach (2004) Electronic Markets, 14 (2), pp. 124-137; Joong-Kun Cho, J., Ozment, J., Sink, H., Logistics Capability, Logistics Outsourcing And Firm Performance In An E-Commerce Market (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 336-359; Karmawan, I.G.M., Dampak Peningkatan Kepuasan Pelanggan dalam Proses Bisnis E-Commerce pada Perusahaan Amazon. com (2014) ComTech: Computer, Mathematics and Engineering Applications, 5 (2), pp. 748-762; Pujawan, I.N., Mahendrawathi, E.R., (2010) Supply Chain Management Edisi Kedua, , Surabaya: Guna Widya; Agrawal, N.M., Performance Measurement With Changing Business Environment (2014) Proceedings of IRF International Conference, pp. 40-50; De Sousa, T.B., Camparotti, C.E.S., Esposto, K.F., Guerrini, F.M., Alignment of Balanced Scorecard Perspectives With Supply Chain Management Objectives: A Literature Review (2014) Independent Journal of Management & Production, 5 (4), pp. 1050-1070; Kusrini, E., Subagyo, N.A., Masruroh, N.A., A New Approach To Design Supply Chain Key Performance Indicator For Actors And Regulator: A Case Study In Innovative Product In Indonesia (2016) International Journal of Business Performance Management, 17 (1), pp. 1-29; Ridwan, A., Murni, S., Pengukuran Kinerja Supply Chain Dengan Pendekatan Lean Six Sigma Supply Chain Management (Studi kasus di PT ALX Logistics) (2017) Journal Industrial Servicess, 3 (1), pp. 59-67; Kazemkhanlou, H., Ahadi, H., Study of Performance Measurement Practices in Supply Chain Management (2014) International Conference on Industrial Engineering and Operations Management, pp. 273-285. , no. Jan 7-9; Agami, N., Saleh, M., Rasmy, M., Supply Chain Performance Measurement Approaches: Review and Classification (2012) The Journal of Organizational Management Studies, 2012, pp. 1-20; Bigliardi, B., Bottani, E., Performance Measurement In The Food Supply Chain: A Balanced Scorecard Approach (2010) Facilities, 28 (5-6), pp. 249-260; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review Of Supply Chain Performance Measurement Systems: 1998-2015 (2016) Computers in Industry, 82, pp. 135-150; Gunasekaran, A., Kobu, B., Performance Measures And Metrics In Logistics And Supply Chain Management: A Review Of Recent Literature (1995-2004) For Research And Applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Bendavid, Y., Lefebvre, É., Lefebvre, L.A., Fosso-Wamba, S., Key Performance Indicators For The Evaluation Of RFID-Enabled B-to-B E-Commerce Applications: The Case Of A Five-Layer Supply Chain (2009) Information Systems and e-Business Management, 7 (1), pp. 1-20; Widyarto, W.O., Shofa, M.J., Djamal, N., Identification Of Performance Indicators Base On Logistic Capability For Supply Chain Performance Measurement In E-Commerce (2019) Asia Proceedings of Social Sciences, 4 (2), pp. 53-56; Pujawan, I.N., Mahendrawathi, E.R., (2017) Supply Chain Management (Edisi Ketiga)., , Yogyakarta: Andi; Shivashankar, D.K.K.R.G.S., Kadadevarmath, R.S., (2014) Impact on Manufacturing Process Through Effective Supply Chain Performance Measurement Approaches, 1 (2), pp. 118-123; Kusrini, E., Subagyo, Masruroh, N.A., Good Criteria For Supply Chain Performance Measurement (2014) International Journal of Engineering Business Management, 6 (1), pp. 1-7; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving Supply Chain Performance Management: A Systematic Approach To Analyzing Iterative KPI Accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Khare, A., Saxsena, A., Teewari, P., Supply Chain Performance Measures For Gaining Competitive Advantage: A Review (2012) Journal of Management and Strategy, 3 (2), pp. 25-32; Shepherd, C., Günter, H., Measuring Supply Chain Performance: Current Research And Future Directions (2006) International Journal of Productivity and Performance Management, 22 (1), pp. 242-258; Chan, F.T., Performance Measurement in a Supply Chain (2003) The international journal of advanced manufacturing technology, 21 (7), pp. 534-548; Gunasekaran, A., Patel, C., Tirtiroglu, E., (2001) Performance Measures And Metrics In A Supply Chain Environment, 21 (1-2); Kodong, F.R., Juwairiah, Simanjuntak, O.S., (2015) Manajemen Rantai Pasokan Pada E-Commerce Industri Makanan Ringan KWT An-Naba Yogyakarta, pp. 139-146. , Paper presented at the Seminar Nasional Informatika (SEMNASIF); Huang, J., Jiang, X., Tang, Q., An E-Commerce Performance Assessment Model: Its Development And An Initial Test On E-Commerce Applications In The Retail Sector Of China (2009) Information and Management, 46 (2), pp. 100-108; Javalgi, R., Ramsey, R., Strategic Issues Of ECommerce As An Alternative Global Distribution System (2001) International Marketing Review, 18 (4), pp. 376-391; Mansor, N., Abidin, A.F.A., The Application of ECommerce Among Malaysian Small Medium Enterprises (2010) European Journal of Scientific Research, 41 (4), pp. 591-605; Salwani, M.I., Marthandan, G., Norzaidi, M.D., Chong, S.C., E-Commerce Usage And Business Performance In The Malaysian Tourism Sector: Empirical Analysis (2009) Information Management and Computer Security, 17 (2), pp. 166-185; Forza, C., Survey Research In Operations Management: A Process-Based Perspective (2002) International Journal of Operations and Production Management, 22 (2), pp. 152-194; Hyvönen, J., Strategy, Performance Measurement Techniques And Information Technology Of The Firm And Their Links To Organizational Performance (2007) Management Accounting Research, 18 (3), pp. 343-366; Ariani, D., Dwiyanto, B.M., Analisis Pengaruh Supply Chain Management Terhadap Kinerja Perusahaan (Studi Kasus Pada Industri Kecil dan Mengenah Makanan Olahan Khas Padang SUmatera Barat) (2013) Dipenegoro Journal Of Management, 2 (3), pp. 1-10; Bremser, W.G., Chung, Q.B., A Framework For Performance Measurement In The E-Business Environment (2005) Electronic Commerce Research and Applications, 4 (4), pp. 395-412; Jauhari, J., Upaya Pengembangan Usaha Kecil Dan Menengah (UKM) Dengan Memanfaatkan ECommerce (2014) Jurnal Sistem Informasi, 2 (1), pp. 159-168; Looi, H.C., E-Commerce Adoption in Brunei Darussalam: A Quantitative Analysis of Factors Influencing Its Adoption (2018) Communications of the Association For Information Systems, 15; Ordanini, A., Rubera, G., How Does The Application Of An IT Service Innovation Affect Firm Performance? A Theoretical Framework And Empirical Analysis On Ecommerce (2010) Information and Management, 47 (1), pp. 60-67; Rai, A., Patnayakuni, R., Seth, N., Firm Performance Impacts Of Digitally Enabled Supply Chain Integration Capabilities (2006) Managerial MIS Quarterly, 30 (2), pp. 225-246; Gibbs, J., Kraemer, K.L., Dedrick, J., Environment And Policy Factors Shaping Global E-Commerce Diffusion: A Cross-Country Comparison (2003) The Information Society, 19 (1), pp. 5-18; Sezen, B., Relative Effects Of Design, Integration And Information Sharing On Supply Chain Performance (2008) Supply Chain Management: An International Journal, 13 (3), pp. 233-240; Wade, M.R., Nevo, S., Development And Validation Of A Perceptual Instrument To Measure E-Commerce Performance (2005) International Journal of Electronic Commerce, 10 (2), pp. 123-146; Ramanathan, R., The Moderating Roles Of Risk And Efficiency On The Relationship Between Logistics Performance And Customer Loyalty In E-Commerce (2010) Transportation Research Part E: Logistics and Transportation Review, 46 (6), pp. 950-962; Yaqoub, A.M., Pengaruh Mediasi Kepercayaan Pada Hubungan Natara Kolaborasi Supply Chain Dan Kinerja Operasi (2012) Jurnal Manajemen Dan Kewirausahaan, 14 (2), pp. 138-146; Lee, C.W., Kwon, I.W.G., Severance, D., Relationship Between Supply Chain Performance And Degree Of Linkage Among Supplier, Internal Integration, And Customer (2007) Supply Chain Management: An International Journal, 12 (6), pp. 444-452; Evans, R., E-commerce, Competitiveness and Local and Regional Governance in Greater Manchester and Merseyside: A Preliminary Assessment (2002) Urban Studies, 39 (5-6), pp. 947-975; Zhu, K., Kraemer, K.L., E-commerce Metrics For Net-Enhanced Organizations: Assessing The Value Of ECommerce To Firm Performance In The Manufacturing Sector (2002) Information Systems Research, 13 (3), pp. 275-295; Jeffcoate, J., Best Practice In SME Adoption Of ECommerce (2002) Benchmarking: An International Journal, 9 (2), pp. 122-132; Kannan, V.R., Tan, K.C., Just In Time, Total Quality Management, And Supply Chain Management: Understanding Their Linkages And Impact On Business Performance (2005) Omega, 33 (2), pp. 153-162; Mandasari, N., Widiartanto, (2016) Pengaruh Information Sharing, Knowledge Sharing Dan Relation Terhadap Performance Perusahaan (Studi Kasus Pada Usaha Kecil Menengah Mebel Di Kecamatan Tahunan Kabupaten Jepara), , Prosiding Seminar Nasional ASBIS Politeknik Negeri Banjarmasin; Stank, T.P., Keller, S.B., Daugherty, P.J., Supply Chain Collaboration And Logistical Service Performance (2001) Journal of Business Logistics, 22 (1), pp. 29-48; Risnandar, Wulandari, P.T.W.K., Integrasi Teknologi Informasi Dan Supply Chain Management (Studi Kasus: PT. X, West Java) (2010) Seminar dan Call For Paper Munas Aptikom, pp. 267-273; Ghamdi, R., Nguyen, A., Jones, V., A Study Of Influential Factors In The Adoption And Diffusion Of B2C E-Commerce (2013) International Journal of Advanced Computer Science and Applications, 4 (1), pp. 89-94; Scupola, A., SMEs’ E-Commerce Adoption: Perspectives From Denmark and Australia (2009) Journal of Enterprise Information Management, 22 (1-2), pp. 152-166; Palvia, P., The Role Of Trust In E-Commerce Relational Exchange: A Unified Model (2009) Information and Management, 46, pp. 213-220},
publisher={Blue Eyes Intelligence Engineering and Sciences Publication},
issn={22498958},
language={English},
abbrev_source_title={Int. J. Eng. Adv. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dobrzykowski201926,
author={Dobrzykowski, D.},
title={Understanding the Downstream Healthcare Supply Chain: Unpacking Regulatory and Industry Characteristics},
journal={Journal of Supply Chain Management},
year={2019},
volume={55},
number={2},
pages={26-46},
doi={10.1111/jscm.12195},
note={cited By 30},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064093706&doi=10.1111%2fjscm.12195&partnerID=40&md5=7e79823677ce477d98433864c6478037},
affiliation={Bowling Green State University, United States},
abstract={Hospital leaders face unprecedented pressure to improve traditional supply chain performance measures such as cost, quality, and customer experience. As such, healthcare executives are increasingly turning to the operations and SCM field to provide thought leadership and establish best practices around coordinating information, material, and financial flows in healthcare delivery. Unfortunately, our learnings from traditional manufacturing, and to some extent other service supply chains, do not always easily port to the downstream healthcare delivery supply chain due to regulatory issues and distinctive characteristics present in this network. Grounded in Institutional Theory, this study conceptualizes the downstream healthcare delivery supply chain, highlights important regulatory pressures that influence this supply chain, and examines the effects of government regulation and the unique characteristics of this network on coordination. In doing so, this conceptual study brings to bear important contextual considerations and motivates novel research areas where the SCM field can make substantial contributions. © 2019 Wiley Periodicals, Inc.},
author_keywords={healthcare operations and supply chain management;  legal and regulatory issues;  public policy;  service supply chains},
references={Abdulsalam, Y., Gopalakrishnan, M., Maltz, A., Schneller, E., The impact of physician-hospital integration on hospital supply management (2018) Journal of Operations Management, 57, pp. 11-22. , https://doi.org/10.1016/j.jom.2018.01.001; Agarwal, R., Gao, G., DesRoches, C., Jha, A., The digital transformation of healthcare: Current status and the road ahead (2010) Information Systems Research, 21, pp. 796-809. , https://doi.org/10.1287/isre.1100.0327; (2018) Trend Watch Chartbook 2018: Trends affecting hospitals and health systems, , http://www.aha.org/research/reports/tw/chartbook/index.shtml, Retrieved at, on January 2, 2018; (2015) US vs. UK: Allied Healthcare at Home and Abroad, , https://www.aimseducation.edu/blog/us-vs-uk-allied-healthcare/, (accessed February 21, 2019); Andritsos, D.A., Tang, C.S., Linking process quality and resource usage: an empirical analysis (2014) Production & Operations Management, 12, pp. 2163-2177. , https://doi.org/10.1111/poms.12249; Bhakoo, V., Choi, T., The iron cage exposed: Institutional pressures and heterogeneity across the healthcare supply chain (2013) Journal of Operations Management, 31, pp. 432-449. , https://doi.org/10.1016/j.jom.2013.07.016; Bhargava, H.K., Mishra, A.N., Electronic medical records and physician productivity: evidence from panel data analysis (2014) Management Science, 60, pp. 2543-2562. , https://doi.org/10.1287/mnsc.2014.1934; Bishop, T.F., Shortell, S.M., Ramsay, P.P., Copeland, K.R., Casalino, L.P., Trends in hospital-ownership of physician practices and the effect on processes to improve quality (2016) American Journal of Managed Care, 22, pp. 172-176; Bitner, M.J., Faranda, W.T., Hubbert, A.R., Zeithaml, V.A., Customer contributions and roles in service delivery (1997) International Journal of Service Industry Management, 8, pp. 193-205. , https://doi.org/10.1108/09564239710185398; Blome, C., Schoenherr, T., Kaesser, M., Ambidextrous governance in supply chains: The impact on innovation and cost performance (2013) Journal of Supply Chain Management, 49, pp. 59-80. , https://doi.org/10.1111/jscm.12033; Bohmer, R.M., Kindred, N., (2009) Newton-Wellesey Hospital, , Harvard Business School Case, 9-609-088, 1-19; Botelho, G., Wilson, J., (2014) Thomas Eric Duncan: First Ebola death in U.S., , http://www.cnn.com/2014/10/08/health/thomas-eric-duncan-ebola/index.html, (accessed date February 14, 2019); Boyer, K., Pronovost, P., What medicine can teach operations: What operations can teach medicine (2010) Journal of Operations Management, 28, pp. 367-371. , https://doi.org/10.1016/j.jom.2010.08.002; Bradley, R.B., Esper, T.L., In, J., Lee, K.B., Bichescu, B.C., Byrd, T.A., The joint use of RFID and EDI: Implications for hospital performance (2018) Production & Operations Management, 27, pp. 2071-2090. , https://doi.org/10.1111/poms.12955; Budryk, Z., (2016) Doc employment doesn't boost hospital outcomes–and may hurt morale, , https://www.fiercehealthcare.com/healthcare/doc-employment-doesn-t-boost-hospital-outcomes, FierceHealthcare, (September 20, 2016) (Retrieved on September 21, 2016 at; Burke, L.A., Weathington, B.L., Health insurance and job lock: Proposed consequences (2008) Employee Benefit Plan Review, 63, pp. 23-25; Butler, T.W., Leong, G.K., Everett, L.N., The operations management role in hospital strategic planning (1996) Journal of Operations Management, 14, pp. 137-156. , https://doi.org/10.1016/0272-6963(95)00041-0; (2018) Health Care Costs 101: A Continuing Economic Threat, , California Healthcare Almanac, May, 1–51; Carter, C.R., Rogers, D.S., Choi, T.Y., Toward the theory of the supply chain (2015) Journal of Supply Chain Management, 51, pp. 89-97. , https://doi.org/10.1111/jscm.12073; (2016) The Quality Payment Program Overview Fact Sheet, , https://www.nyspsych.org/assets/docs/quality_payment_program_overview_fact_sheet.pdf, Quality Payment Program,, 1-12. (Retrieved on January 4, 2018; (2017) Hospital Value-Based Purchasing, , Medicare Learning Network,, September, ICN 907664, 1-14; Chakraborty, S., Dobrzykowski, D.D., Supply chain practices for complexity in healthcare: A service-dominant logic view (2013) IUP Journal of Supply Chain Management, 10, pp. 53-75; Chandrasekaran, A., Senot, C., Boyer, K.K., Process management impact on clinical and experiential quality: Managing tensions between safe and patient-centered healthcare (2012) Manufacturing & Service Operations Management, 14, pp. 548-566. , https://doi.org/10.1287/msom.1110.0374; Chase, R.B., Tansik, D.A., The customer contact model for organization design (1983) Management Science, 29, pp. 1037-1050. , https://doi.org/10.1287/mnsc.29.9.1037; Choi, T.Y., Dooley, K.J., Rungtusanatham, M., Supply networks and complex adaptive systems: Control versus emergence (2001) Journal of Operations Management, 19, pp. 351-366. , https://doi.org/10.1016/S0272-6963(00)00068-1; Chung, M., (2017) Healthcare reform: Learning from other major healthcare systems, , https://pphr.princeton.edu/2017/12/02/unhealthy-health-care-a-cursory-overview-of-major-health-care-systems/, Princeton Public Health Review, December 2, 2017., (accessed February 27, 2019); (2016) CAHME Update, , AUPHA Leaders Update, March 15, 2016; (2017) CAHME Update, , AUPHA Leaders Update, June, 2017; Devaraj, S., Kohli, R., Performance impacts of information technology: Is actual usage the missing link? (2003) Management Science, 49, pp. 273-289. , https://doi.org/10.1287/mnsc.49.3.273.12736; DiMaggio, P., Powell, W., The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (1983) American Sociological Review, 48, pp. 147-160. , https://doi.org/10.2307/2095101; Ding, D.X., The effect of experience, ownership, and focus on productive efficiency: A longitudinal study of U.S. hospitals (2014) Journal of Operations Management, 32, pp. 1-14. , https://doi.org/10.1016/j.jom.2013.10.002; Ding, D.X., The impact of service design and process management on clinical quality: An exploration of synergetic effects (2015) Journal of Operations Management, 36, pp. 103-114. , https://doi.org/10.1016/j.jom.2015.03.006; Dobrzykowski, D.D., Callaway, S.K., Vonderembse, M.A., Examining pathways from innovation orientation to patient satisfaction: A relational view of healthcare delivery (2015) Decision Sciences, 46, pp. 863-899. , https://doi.org/10.1111/deci.12161; Dobrzykowski, D.D., Leuschner, R., Roh, J.J., Hong, P.C., Examining absorptive capacity in supply chains: Linking responsive strategy and firm performance (2015) Journal of Supply Chain Management, 51, pp. 3-28. , https://doi.org/10.1111/jscm.12085; Dobrzykowski, D.D., McFadden, K.L., Vonderembse, M.A., Examining the pathways to safety and financial performance in hospitals: A study of lean in professional service operations (2016) Journal of Operations Management, 42-43, pp. 39-51. , https://doi.org/10.1016/j.jom.2016.03.001; Dobrzykowski, D., Saboorideilami, V., Hong, P., Kim, S.C., A structured analysis of operations and supply chain management research in healthcare (1982–2011) (2014) International Journal of Production Economics, 147, pp. 514-530. , https://doi.org/10.1016/j.ijpe.2013.04.055; Dobrzykowski, D., Tarafdar, M., Linking electronic medical records use to physicians’ performance: A contextual analysis (2017) Decision Sciences Journal, 48, pp. 7-37; Dobrzykowski, D., Tarafdar, M., Understanding information exchange healthcare operations: Evidence from hospitals and patients (2015) Journal of Operations Management, 36, pp. 201-214. , https://doi.org/10.1016/j.jom.2014.12.003; (2019) Annual Report 2018, , Luxembourg, Europe, Publications Office of the European Union; (2009) Rules and Regulations, 74, pp. 56123-56131. , Department Of Health And Human Services Office of the Secretary 45 CFR Part 160 RIN 0991–AB55 HIPAA Administrative Simplification Enforcement; Ford, E., Scanlon, D., Promise and problems with supply chain management approaches to healthcare purchasing (2007) Healthcare Management Review, 32, pp. 192-202. , https://doi.org/10.1097/01.HMR.0000281623.35987.cf; Fredendall, L., Craig, J., Fowler, P., Damali, U., Barriers to swift, even flow in the internal supply chain of perioperative surgical service department: A case study (2009) Decision Sciences, 40, pp. 327-350. , https://doi.org/10.1111/j.1540-5915.2009.00232.x; Gan, X., Sethi, S.P., Yan, H., Channel coordination with a risk-neutral supplier and a downside-risk-averse retailer (2005) Production and Operations Management, 14, pp. 80-89; Gereffi, G., Lee, J., Why the world suddenly cares about global supply chains (2012) Journal of Supply Chain Management, 48, pp. 24-32. , https://doi.org/10.1111/j.1745-493X.2012.03271.x; Gnanlet, A., Gilland, W., Sequential and simultaneous decision making for optimizing health care resource flexibilities (2009) Decision Sciences Journal, 40, pp. 295-326. , https://doi.org/10.1111/j.1540-5915.2009.00231.x; Goh, J.M., Gao, G., Agarwal, R., Evolving work routines: Adaptive routinization of information technology in healthcare (2011) Information Systems Research, 22, pp. 565-585. , https://doi.org/10.1287/isre.1110.0365; Goodwyn, W., (2014) Was CDC too quick to blame Dallas nurses in care of Ebola patient?, , http://www.npr.org/2014/10/24/358574357/was-cdc-tooquick-to-blame-dallas-nurses-in-care-of-ebola-patient, National Public Radio., (accessed date February 14, 2019); (2013) Health (Amendment) Act 2013, , http://www.irishstatutebook.ie/eli/2013/act/31/enacted/en/print.html, (accessed March 10, 2019); Gray, J.V., Skowronski, K., Esenduran, G., Rungtusanatham, M.J., The reshoring phenomenon: What supply chain academics ought to know and should do (2013) Journal of Supply Chain Management, 49, pp. 27-33. , https://doi.org/10.1111/jscm.12012; Hahn, C., Hardy, S., Factors affecting acceptance of a hospital group purchasing program (1972) Journal of Purchasing, 8, pp. 54-68. , https://doi.org/10.1111/j.1745-493X.1972.tb00230.x; (2018) Pay for doctors: Find out about the different doctor pay grades in 2018, , https://www.healthcareers.nhs.uk/explore-roles/doctors/pay-doctors, (accessed on February 21, 2019); (2009), http://dayton.bizjournals.com/dayton/stories/2009/04/20/daily19.html, Dayton Business Journal, (accessed March 11, 2019); Herzlinger, R.E., Healthcare reform and its implications for the U.S. economy (2010) Business Horizons, 53, pp. 105-117. , https://doi.org/10.1016/j.bushor.2009.10.002; Herzlinger, R.E., Huckman, R.S., Lesser, J., Mayo clinic: The 2020 initiative (2016) Harvard Business School, , Case #9-615-027, 1–29; Hirschinger, M., Spickermann, A., Hartmann, E., Von Der Gracht, H., Darkow, I.-L., The future of logistics in emerging markets—fuzzy clustering scenarios grounded in institutional and factor-market rivalry theory (2015) Journal of Supply Chain Management, 51, pp. 73-93. , https://doi.org/10.1111/jscm.12074; Huang, X., Gattiker, T.F., Schroeder, R.G., Do competitive priorities drive adoption of electronic commerce applications? Testing the contingency and institutional views (2010) Journal of Supply Chain Management, 46, pp. 57-69. , https://doi.org/10.1111/j.1745-493X.2010.03198.x; Hwang, J., Christensen, C., Disruptive innovation in health care delivery: A framework for business-model innovation (2008) Health Affairs, 27, pp. 1329-1335. , https://doi.org/10.1377/hlthaff.27.5.1329; Ilie, V., Van Slyke, C., Parikh, M., Courtney, J., Paper versus electronic medical records: The effects of access on physicians (2009) Decision Sciences, 40, pp. 213-242. , https://doi.org/10.1111/j.1540-5915.2009.00227.x; (2001) Crossing the quality chasm: A new health system for the 21st century, , Washington, DC, National Academy Press; (2018) Public hospital bed charges, , https://help.irishlifehealth.ie/hc/en-us/articles/360000711769-Public-hospital-bed-charges, (accessed March 10, 2019); Jha, A.K., Meaningful use of electronic health records: The road ahead (2010) JAMA, 304, pp. 1709-1710. , https://doi.org/10.1001/jama.2010.1497; (2016) FAQ Integral Funding, , https://www.knov.nl/werk-en-organisatie/tekstpagina/128-2/integrale-bekostiging/hoofdstuk/1109/faq-integrale-bekostiging/, (accessed March 10, 2019); Laker, L.F., Froehle, C.M., Windeler, J.B., Lindsell, C.J., Quality and efficiency of the clinical decision-making process: Information overload and emphasis framing (2018) Production Operations Management, 27, pp. 2213-2225. , https://doi.org/10.1111/poms.12777; Lambert, D.M., Garcia-Dastugue, S.J., Cross-functional business processes for the implementation of service-dominant logic (2006) Service-dominant logic: What it is, what it is not, what it might be. The service-dominant logic of marketing: Dialog, debate, and directions, pp. 43-56. , S. L. Vargo, &, R. F. Lusch, (Eds.),, Armonk, New York, M.E. Sharpe; Li, L., Benton, W.C., Hospital technology and nurse staffing management decisions (2006) Journal of Operations Management, 24, pp. 676-691. , https://doi.org/10.1016/j.jom.2005.06.001; Li, X., Wang, Q., Coordination mechanisms of supply chain systems (2007) European Journal of Operational Research, 179, pp. 1-16. , https://doi.org/10.1016/j.ejor.2006.06.023; Machuca, J., Gonzalez-Zamora, M., Aguilar-Escobar, V., Service operations management (2007) Journal of Operations Management, 25, pp. 585-603. , https://doi.org/10.1016/j.jom.2006.04.005; Marley, K., Collier, D., Goldstein, S., The role of clinical and process quality in achieving patient satisfaction in hospital (2004) Decision Sciences Journal, 35, pp. 349-369. , https://doi.org/10.1111/j.0011-7315.2004.02570.x; Maull, R., Geraldi, J., Johnston, R., Service supply chains: A customer perspective (2012) Journal of Supply Chain Management, 48, pp. 72-86. , https://doi.org/10.1111/j.1745-493X.2012.03284.x; McDermott, C., Stock, G., Hospital operations and length of stay performance (2007) International Journal of Operations and Production Management, 27, pp. 1020-1042. , https://doi.org/10.1108/01443570710775847; Meijboom, B., Schmidt-Bakx, S., Westert, G., Supply Chain Management practices for improving patient-oriented care (2011) Supply Chain Management: An International Journal, 16, pp. 166-175. , https://doi.org/10.1108/13598541111127155; Miller, A.R., Tucker, C., Privacy protection and technology diffusion: The case of electronic medical records (2009) Management Science, 55, pp. 1077-1093. , https://doi.org/10.1287/mnsc.1090.1014; (2017) Poor salaries and overwork led to shortage of doctors in China, , https://today.mims.com/poor-salaries-and-overwork-led-to-shortage-of-doctors-in-china, (accessed on February 21, 2019); Nair, A., Nicolae, M., Narasimhan, R., Examining the impact of clinical quality and clinical flexibility on cardiology unit performance—Does experiential quality act as a specialized complementary asset? (2013) Journal of Operations Management, 31, pp. 505-522. , https://doi.org/10.1016/j.jom.2013.09.001; Nembhard, I.M., Alexander, J.T.H., Ramanujam, R., Why does the quality of health care continue to lag? Insights from management research (2009) Academy of Management Perspectives, 23, pp. 24-42. , https://doi.org/10.5465/AMP.2009.37008001; (2008) Final Report, , January 24, 2008; (2019) The NHS Long Term Plan, , https://www.longtermplan.nhs.uk/wp-content/uploads/2019/01/nhs-long-term-plan.pdf, (accessed on March 8, 2019); (2019) Funding and efficiency, , https://www.england.nhs.uk/five-year-forward-view/next-steps-on-the-nhs-five-year-forward-view/funding-and-efficiency/, (accessed on March 7, 2019); Ni, J., Huang, X., Discovery-to-recall in the automotive industry: A problem-solving perspective on investigation of quality failures (2017) Journal of Supply Chain Management, 54, pp. 71-95; Nyaga, G.N., Young, G.J., Zepeda, E.D., An analysis of the effects of intra- and interorganizational arrangements on hospital supply chain efficiency (2015) Journal of Business logistics, 36, pp. 340-354. , https://doi.org/10.1111/jbl.12109; (1999) Gainsharing Arrangements and CMPs for Hospital Payments to Physicians to Reduce or Limit Services to Beneficiaries, , https://oig.hhs.gov/fraud/docs/alertsandbulletins/gainsh.htm, Special Advisory Bulletin. July. (Retrieved on January 5, 2019 at; (2016) Non-Federal Acute Care Hospital Electronic Health Record Adoption, , ’ Health IT Quick-Stat #47. dashboard.healthit.gov/quickstats/pages/FIG-Hospital-EHR-Adoption.php. (accessed January 2, 2019); (2017) Health resources, , https://data.oecd.org/healthres/health-spending.htm, (accessed February 21, 2019); Prahalad, C., Ramaswamy, V., Co-creation experiences: The next practice in value creation (2004) Journal of Interactive Marketing, 18, pp. 5-14. , https://doi.org/10.1002/dir.20015; Pritchard, J.I., (2012) Muddy Waters: Making Sense of the healthcare supply chain in the era of reform, , Medical Distribution Solutions, Inc., Lawrenceville, GA; Queenan, C.C., Angst, C.M., Devaraj, S., Doctors’ orders—If they're electronic, do they improve patient satisfaction? A complements/substitutes perspective (2011) Journal of Operations Management, 29, pp. 639-649. , https://doi.org/10.1016/j.jom.2011.03.001; Roth, A., Gray, J., Shockley, J., Weng, R., (2015) The Use of Secondary Source Data for Measuring Performance in Operations Management Research, , https://ssrn.com/abstract=2271202, (January 5, 2015). SSRN; Sahin, F., Robinson, P.E., Flow coordination and information sharing in supply chains: Review, implications, and directions for future research (2002) Decision Sciences Journal, 33, pp. 505-535. , https://doi.org/10.1111/j.1540-5915.2002.tb01654.x; Sahni, N.R., Huckman, R.S., Chigurupati, A., Cutler, D.M., The IT transformation healthcare needs (2017) Harvard Business Review, Nov–Dec, pp. 129-138; Saldanha, J.P., Mello, J.E., Knemeyer, A.M., Vijayaraghavan, T.A.S., Implementing supply chain technologies in emerging markets: An institutional theory perspective (2015) Journal of Supply Chain Management, 51, pp. 5-26. , https://doi.org/10.1111/jscm.12065; Salzarulo, P.A., Mahar, S., Modi, S., Beyond patient classification: Using individual patient characteristics in appointment scheduling (2015) Production Operations Management, 25, pp. 1056-1072; Sampson, S.E., Spring, M., Service supply chains: Introducing the special topic forum (2012) Journal of Supply Chain Management, 48, pp. 30-50. , https://doi.org/10.1111/j.1745-493X.2012.03282.x; Schmoltzi, C., Wallenburg, C.M., Operational governance in horizontal cooperations of logistics service providers: Performance effects and the moderating role of cooperation complexity (2012) Journal of Supply Chain Management, 48, pp. 53-74. , https://doi.org/10.1111/j.1745-493X.2011.03262.x; Schneller, E.S., Smeltzer, L.R., (2006) Strategic management of the health care supply chain, , San Francisco, CA, Jossey-Bass; Senot, C., Chandrasekaran, A., Ward, P.T., Tucker, A.L., Moffat-Bruce, S.D., The impact of combining conformance and experiential quality on hospitals’ readmissions and cost performance (2016) Management Science, 26, pp. 829-848. , https://doi.org/10.1287/mnsc.2014.2141; Shah, R., Goldstein, S., Unger, B., Henry, T., Explaining anomolous high performance in a health care supply chain (2008) Decision Sciences Journal, 39, pp. 759-789. , https://doi.org/10.1111/j.1540-5915.2008.00211.x; Sharma, L., Chandrasekaran, A., Boyer, K.K., McDermott, C.M., The impact of Health Information Technology bundles on Hospital performance: An econometric study (2016) Journal of Operations Management, 41, pp. 25-41. , https://doi.org/10.1016/j.jom.2015.10.001; Simon, H.A., The business school: A problem in organizational design (1967) Journal of Management Studies, 4, pp. 1-16. , https://doi.org/10.1111/j.1467-6486.1967.tb00569.x; Sinha, K., Kohnke, E., Health care supply chain design: Toward linking the development and delivery of care globally (2009) Decision Sciences, 40, pp. 197-212. , https://doi.org/10.1111/j.1540-5915.2009.00229.x; Smeltzer, L.R., Ramanathan, V., Supply chain processes that lead to a competitive advantage for a manufacturer compared to a health care provider (2002) Proceedings – Annual Meeting of the Decision Sciences Institute, pp. 2561-2566; Smith, A.L., Bradley, R.V., Bichescu, B.C., Tremblay, M.C., IT governance characteristics, electronic medical records sophistication, and financial performance in U.S. Hospitals: An empirical investigation (2013) Decision Sciences Journal, 44, pp. 483-516. , https://doi.org/10.1111/deci.12019; Tachizawa, E.M., Wong, C.Y., The performance of green supply chain management governance mechanisms: A supply network and complexity perspective (2015) Journal of Supply Chain Management, 51, pp. 18-32. , https://doi.org/10.1111/jscm.12072; (1998) Common concerns: International issues in health care system reform, pp. 1-26. , New York, NY, The Commonwealth Fund; (2014) 30 Most Technologically Advanced Hospitals in the World, , https://www.topmastersinhealthcare.com/30-most-technologically-advanced-hospitals-in-the-world/, (accessed on March 7, 2019); Toussaint, J., (2012) Potent medicine, , Appleton, WI, ThedaCare Center for Healthcare Value; Tucker, A.L., The impact of operational failures on hospital nurses and their patients (2004) Journal of Operations Management, 22, pp. 1-169; Tucker, A.L., The impact of workaround difficulty on frontline employees’ response to operational failures: A laboratory experiment on medication administration (2016) Management Science, 62, pp. 1124-1144. , https://doi.org/10.1287/mnsc.2015.2170; (2018) Product Identifiers Under the Drug Supply Chain Security Act Questions and Answers Guidance for Industry, pp. 1-17; Van de Van, A.H., Delbecq, A.L., Koenig, R., Determinants of coordination modes within organizations (1976) American Sociological Review, 41, pp. 322-338; Varadkar, L., Harris, S., (2018) Sláintecare implementation strategy, pp. 1-88. , https://www.gov.ie/en/campaigns/slaintecare-implementation-strategy/, Rialtas na hEireann, Government of Ireland,, (accessed on March 7, 2019); Vargo, S., Lusch, F., Service-dominant logic: What it is, what it is not, what it might be (2006) The Service-Dominant Logic of Marketing: Dialog, Debate, and Directions, pp. 43-56. , R. F. Lusch, &, S. L. Vargo, (Eds.),, Armonk, New York, M.E. Sharpe; Vargo, S., Lusch, F., Service-dominant logic: Continuing the evolution (2008) Journal of the Academy of Marketing Science, 36, pp. 1-10. , https://doi.org/10.1007/s11747-007-0069-6; Vonderembse, M., Dobrzykowski, D., (2017) A healthcare solution: A patient-centered, resource management perspective, , Boca Raton, FL, CRC Press Taylor & Francis; Wani, D., Malhotra, M., Does the meaningful use of electronic health records improve patient outcomes? (2018) Journal of Operations Management, 60, pp. 1-18. , https://doi.org/10.1016/j.jom.2018.06.003; Wilson, E.V., Dobrzykowski, D.D., Cazier, J.A., The influence of media trust and internet trust on privacy-risking uses of e-health (2008) International Journal of Information Security and Privacy, 2, pp. 84-97. , https://doi.org/10.4018/IJISP; Young, G.J., Nyaga, G.N., Zepeda, D., Hospital employment of physicians and supply chain performance: An empirical investigation (2016) Health Care Management Review, 41, pp. 244-255. , https://doi.org/10.1097/HMR.0000000000000074},
correspondence_address1={Dobrzykowski, D.; Bowling Green State UniversityUnited States},
publisher={Blackwell Publishing Ltd},
issn={15232409},
language={English},
abbrev_source_title={J. Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{BeelaertsvanBlokland2019411,
author={Beelaerts van Blokland, W. and van de Koppel, S. and Lodewijks, G. and Breen, W.},
title={Method for performance measurement of car companies from a stability-value leverage perspective: The balancing act between investment in R&D, supply chain configuration and value creation},
journal={International Journal of Lean Six Sigma},
year={2019},
volume={10},
number={1},
pages={411-434},
doi={10.1108/IJLSS-03-2017-0024},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053029791&doi=10.1108%2fIJLSS-03-2017-0024&partnerID=40&md5=febcc0a426b24c4b2aa570c24f0acba7},
affiliation={Department of Transport Engineering and Logistics, Delft University of Technology, Delft, Netherlands; School of Aviation, Faculty of Science, University of New South Wales, Sydney, Australia; Faculty of Economics and Business, Rijksuniversiteit Groningen, Groningen, Netherlands},
abstract={Purpose: Today, most of the car manufacturers world-wide have embraced the principles of lean manufacturing on strategic and operational level. On strategic level car companies like Toyota (Womack et al., 1990) shifted 63 per cent of the value of the car towards the first, second and third tier suppliers for the co-production and co-development of cars as an effect of lean implementation. However, lean implementation was also followed by for instance Ford and GM in the USA, the latter company faced a sudden disruption in 2009 due to the break-out of the financial crisis in 2008, while Ford survived. Could this be foreseen? The exclusive use of (classic) financial performance indicators may give a false image of a company’s current and future performance. There is a need for a model to identify “the stars and the laggards’ regarding car companies by taking into account non-financial and intangible dimensions as advocated by Neely et al. (2003) regarding the third generation of business performance measurement systems. The purpose of this paper is therefor to propose a method to measure and benchmark car company performance which includes the non-financial R&D dimension as well as supply chain, value creating and employee dimensions. These dimensions are present in the value leverage model (van Blokland et al., 2012a, 2012b) which can serve as a basis for this method. The aim is to contribute to the third generation business performance measurement systems by further development of the value leverage model towards a maturity model for benchmarking car company performance. The proposed method can provide a big picture and give insight regarding company performance and direction of the performance. Design/methodology/approach: Value leverage can be measured by a correlation analysis regarding three dimensions, namely, supply chain, R&D and value creation, all relative to the employee or capita which results in the average value leverage (AVL) factor. This AVL factor can be used to compose a combined relative and absolute ranking. The score regarding the AVL results in a relative ranking expressing the level of stability regarding the car companies value chain and system. For the absolute ranking the car companies receive per variable parameter a score according to their absolute performance relative to the other car companies. The relative and absolute ranking are presented on the vertical and horizontal axes forming a matrix. The matrix is the basis for the stability-value leverage maturity model for measuring and benchmarking company performance. With the proposed method, the following main research question can be answered: “How can company performance be measured and benchmarked from a stability-value leverage perspective?”. Findings: With the proposed method, stability-value leverage performance can be measured. The relative ranking on the vertical axis and the absolute ranking form together a matrix which is presented by a scatterplot. A matrix with four maturity levels emerged from the analysis by introducing the average score of all the car companies together in the data set crossing the matrix vertical and horizontal. The four levels are as follows: Level I, low stability – low value leverage; Level II, low stability – high value leverage; Level III, high stability – low value leverage; and Level IV, high stability – high value leverage. Stability-value leverage performance of car companies can be measured over time which makes it possible to observe to which direction the car company migrates for instance from Level I to Level III, before and after the financial crises in 2008. The car companies BMW, Daimler, Audi, Ford and Honda are the best performing companies in stability-value leverage over the period 2000-2014, as they are situated at Level IV. With the findings, the main research question can be answered. The value leverage indicators can be used for measuring and benchmarking company performance regarding four maturity levels of stability and value leverage. The direction of performance can be observed as well. Research/limitations/implications: This research is limited to the car industry. Further research is devised to test the indicators for instance on the truck manufacturing industry. Further research towards new variables is part of the ongoing research. Practical/implications: With the value leverage maturity model, it is possible to inform stakeholders about stability, value leverage and value creation capability of car companies. Weak performing companies can be identified in an early stage with this method to anticipate for instance on possible discontinuation of a car company effecting in merger an acquisition processes. Social/implications: With the method stakeholders such as employees, users of cars and investors can be informed about how and why car companies perform in an unstable or stable manner. Originality/value: This research towards ranking and classification of car companies aligns with theories regarding lean manufacturing and maturity models, as these models are used to compare companies on their level of perfection or excellence. © 2018, Emerald Publishing Limited.},
author_keywords={Car industry;  Lean;  Leverage;  Maturity model},
references={Aune, T.B., Gressetvold, E., (2007) Innovating through Suppliers: A Network Approach, , International Marketing and Purchasing Group, Håkansson; Arnold, U., New dimensions of outsourcing: a combination of transaction cost economics and the core competencies concept (2000) European Journal of Purchasing and Supply Management, 6 (1), pp. 23-29; Becker, J., Knackstedt, R., Pöppelbuß, J., Developing maturity models for IT management – a procedure model and its application (2009) Business & Information Systems Engineering (Engineering), 1 (3), pp. 213-222; Bhasin, S., Measuring the leanness of an organisation (2011) International Journal of Lean Six Sigma, 2 (1), pp. 55-74; Chesbrough, H., (2003) Open Innovation: The New Imperative for Creating and Profiting from Technology, , Harvard Business School Press, Cambridge, MA; Choi, T.Y., Krause, D.R., The supply base and its complexity: implications for transaction costs, risks, responsiveness, and innovation (2005) Journal of Operations Management, 24 (5), pp. 637-652; Eadie, R., Perera, S., Heaney, G., Key process area mapping in the production of an e-capability maturity model for UK construction organisations (2011) Journal of Financial Management of Property and Construction, 16 (3), pp. 197-210; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Field, A., (2009) Discovering Statistics Using SPSS, , Sage Publications, London; Gottschalk, P., Maturity levels for interoperability in digital government (2009) Government Information Quarterly, 26 (1), pp. 75-81; Hagberg-Andersson, A., Virtanen, H., Kock, S., Dyadic cooperation and relationship effects: an empirical study (2007) International Marketing and Purchasing Group Conference, pp. 33-46; Hamel, G., Prahalad, C.K., (1994) Competing for the Future, , Harvard Business School Press, Boston, MA; Harter, D.E., Krishnan, M., Slaughter, S., Effects of process maturity on quality, cycle time, and effort in software product development (2000) Management Science, 46 (4), pp. 451-466; Kaplan, R.S., Norton, D.P., The balanced scorecard: measures that drive performance (1992) Harvard Business Review, 1, pp. 71-90; Kazanjian, R.K., Drazin, R., An empirical test of a stage of growth progression model (1989) Management Science, 35 (12), pp. 1489-1503; Lapide, L., MIT’s SC2020 project: the essence of excellence (2006) Supply Chain Management Review, 20 (6); Lefebvre, E.L., Lefebvre, L., (1998) Global Strategic Benchmarking, Critical Capabilities and Performance of Aerospace Subcontractors, , Elsevier Science, New York; Lockamy, A., McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278; Lovell, B.L.L., The new metrics of corporate value leverage: profit per employee (2007) The McKinsey Quarterly, 1, pp. 57-65; Lu, J.C., Yang, T., Wang, C.Y., A lean pull system design analysed by value stream mapping and multiple criteria decision-making method under demand uncertainty (2011) International Journal of Computer Integrated Manufacturing, 24 (3); McCormack, K., Johnson, W.C., (2001) Business Process Orientation: Gaining the e-Business Competitive Advantage, , St. Lucie Press, Florida; Murman, E., Allen, T., Bozdogan, K., Cutcher-Gershenfeld, J., McManus, H., Nightingale, D., Widnall, S., (2002) Lean Enterprise Value – Insights from MIT’s Lean Aerospace Initiative, , Houndmills, Basingstoke, Hampshire, New York Palgrave; Neely, A., Marr, B., Roos, G., Pike, S., Gupta, O., Towards the third generation of performance measurement (2003) Controlling, , (März/April)., Vahlen, Heft; Pam, N.D., (2010) The Impact of Innovation and the Role of Intellectual Property Rights on US Productivity, Competitiveness, Jobs, Wages and Exports, , NDP Consulting, Washington; Paulk, M.C., Curtis, B., Chrissis, M.B., Weber, C.V., Capability maturity model, version 1.1 (1993) IEEE Software, 10 (4), pp. 18-27; Petrick, I.J., Tipping the balance of power: the case of large scale systems integrators and their supply chains (2007) International Journal of Foresight and Innovation Policy, 3 (3), pp. 240-255; Röglinger, M., Pöppelbuß, J., Becker, J., Maturity models in business process management (2012) Business Process Management Journal, 18 (2), pp. 328-346; Rosemann, M., de Bruin, T., Towards a Business process management maturity model (2005) ECIS 2005 Proceedings of the Thirteenth European Conference on Information Systems, pp. 26-28; Simatupang, T.M., Sridharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmarking: An International Journal, 11 (1), pp. 9-30; Singh, B., Garg, S.K., Sharma, S.K., Grewal, C., Lean implementation and its benefits to production industry (2010) International Journal of Lean Six Sigma, 1 (2); Srinivasaraghavan, J., Allada, V., Application of mahalanobis distance as a lean assessment metric (2006) International Journal of Advanced Manufacturing Technology, 29 (11-12); Tarhan, A., Turetken, O., Reijers, H.A., Business process maturity models: a systematic literature review (2016) Information and Software Technology, 75, pp. 122-134; Trkman, P., Stemberger, M.I., Jaklic, J., Groznik, A., Process approach to supply chain integration (2007) Supply Chain Management: An International Journal, 12 (2), pp. 116-128; Tseng, F.M., Chiu, Y.J., Chen, J.S., Measuring business performance in the high-tech manufacturing industry: a case study of Taiwan’s large-sized TFT-LCD panel companies (2009) The International Journal of Management Science, 1, pp. 686-697; van Blokland, W.B., Fiksinski, M.A., Amoa, S.O.B., Maaskant, L., Measuring value-leverage in aerospace supply chains (2012) International Journal of Operations, Production and Management (IJOPM), 32 (8); van Blokland, W.B., Santema, S., UGent, A.H., de Jong, T., Elferink, N., Does value leverage pay off? A model for measuring value leverage capabilities in automotive large scale system integrators (2012) A Focused Issue on Competence Perspectives on New Industry Dynamics. Research in Competence Based Management, 6, pp. 209-235. , Emerald Publishing Limited, Bingley; Verwaal, E., Commandeur, H.R., Verbeke, W., Value creation and value claiming in strategic outsourcing decisions: a resource-contingency perspective (2006) Journal of Management, 35 (2); Wendler, R., The maturity of maturity model research: a systematic mapping study (2012) Information and Software Technology, 54 (12), pp. 1317-1339; Womack, J.P., Jones, D.T., Roos, D., (1990) The Machine That Changed the World, , Simon & Schuster, New York; Womack, J.P., Jones, D.T., (2003) Lean Thinking: Banish Waste and Create Wealth in Your Corporation, , Simon & Schuster, New York; Wong, Y.C., Wong, K.Y., Ali, A., A study on lean manufacturing implementation in the Malaysian electrical and electronics industry (2009) European Journal of Scientific Research, 38 (4), pp. 521-535},
correspondence_address1={Beelaerts van Blokland, W.; Department of Transport Engineering and Logistics, Netherlands; email: w.w.a.beelaertsvanblokland@tudelft.nl},
publisher={Emerald Group Holdings Ltd.},
issn={20404166},
language={English},
abbrev_source_title={Int. J. Lean Six Sigma},
document_type={Article},
source={Scopus},
}

@ARTICLE{Elgazzar2019296,
author={Elgazzar, S. and Tipi, N. and Jones, G.},
title={Key characteristics for designing a supply chain performance measurement system},
journal={International Journal of Productivity and Performance Management},
year={2019},
volume={68},
number={2},
pages={296-318},
doi={10.1108/IJPPM-04-2018-0147},
note={cited By 28},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059659561&doi=10.1108%2fIJPPM-04-2018-0147&partnerID=40&md5=c2b5204ae4a87b9cc22e6317ca4775f1},
affiliation={College of International Transport and Logistics, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt; Huddersfield Business School, University of Huddersfield, Huddersfield, United Kingdom},
abstract={Purpose: The purpose of this paper is to conduct a review of the literature that gives insight into design elements for constructing a supply chain performance measurement (SCPM) system. Design/methodology/approach: A systematic review of published research on SCPM systems and frameworks over the last two decades was conducted with the purpose of categorising key functions of SCPM systems by providing an insight into the design, functionality, implementation and practical implications of SCPM systems. Findings: The review revealed a set of functions governing the SCPM system that have not been fully explored in previous research: the process focus, prioritisation, integration and causality functions of a SCPM system. A relationship between two or more functions can be created to include more functionality based on the needs of the company to create a comprehensive performance measurement system. Research limitations/implications: The paper concludes with a conceptual framework to guide future research in the area of designing a SCPM system and define the main aspects that should be considered when developing a SCPM system. Practical implications: The paper brings a new dimension to the SCPM research by identifying the main functions of SCPM systems that could benefit practitioners to set up a SCPM system relevant to its intended function. The paper presents multiple potential stages of merging different functions in one SCPM system. Based on the company’s needs and context, the functionality of the SCPM system can be designed at four levels creating ten possible scenarios when designing a company’s SCPM system. Originality/value: The paper integrated the literature and findings of 269 research papers of the last two decades, upon which a conceptual framework was developed as a guide for constructing an effective SCPM system. © 2019, Emerald Publishing Limited.},
author_keywords={Supply chain performance;  Supply chain performance measurement system;  Systematic review},
references={Acquaye, A., Ibn-Mohammed, T., Genovese, A., Afrifa, G.A., Yamoah, F.A., Oppon, E., A quantitative model for environmentally sustainable supply chain performance measurement (2018) European Journal of Operational Research, 269 (1), pp. 188-205; Agami, N., Saleh, M., Rasmy, M., A hybrid dynamic framework for supply chain performance improvement (2012) IEEE Systems Journal, 6 (3), pp. 469-478; Agrell, P.J., Hatami-Marbini, A., Frontier-based performance analysis models for supply chain management: state of the art and research directions (2013) Computers & Industrial Engineering, 66 (3), pp. 567-583; Akyüz, G.A., Erkan, T.E., Supply chain performance measurement: a literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Aramyan, L.H., Meuwissen, M.P.M., Oude Lansink, A.G.J.M., van der Vorst, J.G.A.J., van Kooten, O., van der Lans, I.A., The perceived impact of quality assurance systems on tomato supply chain performance (2009) Total Quality Management & Business Excellence, 20 (6), pp. 633-653; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 115-128; Azevedo, S., Carvalho, H., Cruz-Machado, V., A proposal of LARG supply chain management practices and a performance measurement system (2011) International Journal of e-Education, e-Business, e-Management and e-Learning, 1 (1), pp. 7-14; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighbourhood rough sets (2012) International Journal of Production Research, 50 (9), pp. 2484-2500; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Barut, M., Faisst, W., Kanet, J.J., Measuring supply chain coupling: an information system perspective (2002) European Journal of Purchasing and Supply Management, 8 (3), pp. 161-171; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Birhanu, D., Lanka, K., Neelakanteswara, R.A., Supply chain strategies of manufacturers in Ethiopia (2018) International Journal of Productivity and Performance Management, 67 (1), pp. 318-340; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Bowersox, D.J., David, J.C., Theodore, P.S., (1999) 21st Century Logistics: Making Supply Chain Integration a Reality, , Council of Logistics Management, Chicago, IL; Bullingery, H.J., Kuehnery, M., Hoofy, A.V., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Cai, J., Liu, X., Liu, J., Xiao, Z., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support System, 46 (2), pp. 512-521; Camerinelli, E., (2009) Measuring the Value of the Supply Chain, , Ashgate Publishing Group; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: a process based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Nayak, A., Raj, R., Chong, A.Y.L., Manoj, T., An innovative supply chain performance measurement system incorporating research and development (R&D) and marketing policy (2014) Computers & Industrial Engineering, 69 (1), pp. 64-70; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Bus Process Manage Journal, 14 (4), pp. 512-529; Chithambaranathan, P., Subramanian, N., Palaniappan, P.L.K., An innovative framework for performance analysis of members of supply chains (2015) Benchmarking, 22 (2), pp. 309-334; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Christopher, M., (1992) Logistics and Supply Chain Management – Strategies for Reducing Costs and Improving Services, , Pitman Publishing, London; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: more than a new name for logistics (1997) International Journal of Logistics Management, 8 (1), pp. 1-14; Deshpande, A., Supply chain management dimensions, supply chain performance and organizational performance: an integrated framework (2012) International Journal of Business and Management, 7 (8), pp. 2-19; Dobson, P., Myles, J., Jackson, P., Making the case for critical realism: examining the implementation of automated performance management systems (2007) Information Resources Management Journal, 20 (2), pp. 138-152; Dörnhöfer, M., Günthner, W.A., A research and industry perspective on automotive logistics performance measurement (2017) The International Journal of Logistics Management, 28 (1), pp. 102-126; Dubey, R., Ali, S.S., Exploring antecedents of extended supply chain performance measures (2015) Benchmarking: An International Journal, 22 (5), pp. 752-772; Dubey, R., Gunasekaran, A., Chakrabarty, A., World-class sustainable manufacturing: framework and a performance measurement system (2015) International Journal of Production Research, 53 (17), pp. 5207-5223; Dubey, R., Gunasekaran, A., Samar Ali, S., Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: a framework for green supply chain (2015) International Journal of Production Economics, 160, pp. 120-132; El-Baz, A.M., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38 (6), pp. 6681-6688; Elgazzar, S., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain operations' performance to the company's financial strategy: a case study of an Egyptian natural bottled water company (2011) Proceedings of the 16th International Symposium on Logistics (ISL 2011), pp. 173-181. , Berlin: 10-13 July; Elgazzar, S., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes’ performance to a company’s financial strategic objectives (2012) European Journal of Operational Research, 223 (1), pp. 276-289; Estampe, D., Lamouri, S., Paris, J., Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Fabbe-Costes, N., Jahre, M., Supply chain integration and performance: a review of the evidence (2008) The International Journal of Logistics Management, 19 (2), pp. 130-154; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) International Journal of Production Economics, 134 (1), pp. 177-187; Gardner, D., (2004) Supply Chain Vector Methods for Linking the Execution of Global Business Models with Financial Performance, , J. Ross Publishing; Gimenez, C., Vaart, T.V.D., Donk, D.P.V., Supply chain integration and performance: the moderating effect of supply complexity (2012) International Journal of Operations & Production Management, 32 (5), pp. 583-610; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Govindan, K., Khodaverdi, R., Vafadarnikjoo, A., Intuitionistic fuzzy based DEMATEL method for developing green practices and performances in a green supply chain (2015) Expert Systems with Applications, 42 (20), pp. 7207-7220; Gruat La Forme, F.-A., Genoulaz, V.B., Campagne, J.-P., A framework to analyse collaborative performance (2007) Computers in Industry, 58 (7), pp. 687-697; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hassini, E., Surti, C., Searcy, C.A., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Helgason, S., International benchmarking experiences from OECD countries (1997) International Benchmarking Conference, the Danish Ministry of Finance, , www.oecd.org/dataoecd/11/55/1902957.pdf, Copenhagen: 20-21 February: (accessed, December 2010; Hellström, A., Eriksson, H., Are you viewing, mapping or organizing your processes? (2008) The TQM Magazine, 20 (2), pp. 166-174; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Huang, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management, 9 (1), pp. 23-29; Hull, B., The role of elasticity in supply chain performance (2005) International Journal of Production Economics, 98 (3), pp. 301-314; Hwang, Y.D., Lin, Y.C., Lyu, J., Jr., The performance evaluation of SCOR sourcing process – the case study of Taiwan’s TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Ip, W.H., Chan, S.L., Lam, C.Y., Modeling supply chain performance and stability (2011) Industrial Management & Data Systems, 111 (8), pp. 1332-1354; Jaipuria, S., Mahapatra, S.S., Performance improvement of manufacturing supply chain using back-up supply strategy (2015) Benchmarking: An International Journal, 22 (3), pp. 446-464; Jede, A., Teuteberg, F., Towards cloud-based supply chain processes: designing a reference model and elements of a research agenda (2016) International Journal of Logistics Management, 27 (2), pp. 438-462; Kahn, K.B., Mentzer, J.T., Logistics and interdepartmental integration (1996) International Journal of Physical Distribution & Logistics Management, 26 (8), pp. 6-14; Khalili-Damghani, K., Taghavi-Fard, M., Abtahi, A.R., A fuzzy two-stage DEA approach for performance measurement: real case of agility performance in dairy supply chains (2012) International Journal of Applied Decision Sciences, 5 (4), pp. 293-317; Kocaŏglu, B., Gülsün, B., Tanyas, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Kremers, L., (2010) The link between supply chain and finance, , Supply Chain Asia, 22-25; Lauras, M., Lamothe, J., Pingaud, H., A business process oriented method to design supply chain performance measurement systems (2011) International Journal of Business Performance Management, 12 (4), pp. 354-376; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: an introduction (2007) Journal of Operations Management, 25 (6), pp. 1075-1082; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., Measuring supply chain performance: a lifecycle framework and a case study (2018) International Journal of Operations & Production Management, 38 (4), pp. 934-956; (2001) Supply Chain Management, , Mentzer, J.T. (Ed.) (, Sage Publications, Thousand Oaks, CA; Mishra, D., Gunasekaran, A., Papadopoulos, T., Dubey, R., Supply chain performance measures and metrics: a bibliometric study (2018) Benchmarking: An International Journal, 25 (3), pp. 932-967; Moreira, M., Tjahjono, B., Applying performance measures to support decision-making in supply chain operations: a case of beverage industry (2016) International Journal of Production Research, 54 (8), pp. 2345-2365; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Morgan, C., Dewhurst, A., Using SPC to measure a national supermarket chain’s suppliers’ performance (2007) International Journal of Operations and Production Management, 27 (8), pp. 874-900; Neely, A., Gregory, M., Platts, K., Performance measurement system design, a literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Olugu, E.U., Wong, K.Y., An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry (2012) Expert Systems with Applications, 39 (1), pp. 375-384; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Perera, P.S.T., Perera, H.S.C., Wijesinghe, T.M., Environmental performance evaluation in supply chain (2013) The Journal of Business Perspective, 17 (1), pp. 53-61; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: an exploratory study (2015) International Journal of Productivity and Performance Management, 64 (8), pp. 1068-1091; Power, D., Supply chain management integration and implementation: a literature review (2005) Supply Chain Management: An International Journal, 10 (4), pp. 252-263; Presutti, W.D., Jr., Mawhinney, J.R., The supply chain finance link (2007) Supply Chain Management Review, 11 (6), pp. 32-38; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Second International Conference on Emerging Trends in Engineering and Technology, pp. 802-807. , http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5394476, (accessed, 17 September 2011; Ramanathan, U., Performance of supply chain collaboration – a simulation study (2014) Expert Systems with Applications, 41 (1), pp. 210-220; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28 (5), pp. 581-598; Sahu, A.K., Datta, S., Mahapatra, S.S., Green supply chain performance appraisement and benchmarking using fuzzy grey relation method (2015) International Journal of Business Information Systems, 20 (3), pp. 157-194; Sambasivan, M., Mohamed, Z.A., Nandan, T., Performance measures and metrics for e-supply chains (2009) Journal of Enterprise Information Management, 22 (3), pp. 346-360; Sambasivan, M., Nandan, T., Mohamed, Z.A., Consolidation of performance measures in a supply chain environment (2009) Journal of Enterprise Information Management, 22 (6), pp. 660-689; Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) International Journal of Productivity and Performance Management, 64 (6), pp. 784-810; Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68; Simatupang, T.M., Sridharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmarking: An International Journal, 11 (1), pp. 9-30; Simatupang, T.M., Sridharan, R., Benchmarking supply chain collaboration: an empirical study (2004) Benchmarking: An International Journal, 1000 (5), pp. 485-503; Spina, G., Caniato, F., Luzzini, D., Ronchi, S., Assessing the use of external grand theories in purchasing and supply management research (2015) Journal of Purchasing & Supply Management, 22 (1), pp. 18-30; Tajbakhsh, A., Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2015) Journal of Cleaner Production, 105, pp. 74-85. , Special Issue; Tangen, S., Improving the performance of a performance measure (2005) Measuring Business Excellence, 9 (2), pp. 4-11; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 782-804; Tavana, M., Mirzagoltabar, H., Mirhedayatian, S.M., Farzipoor Saen, R., Azadi, M., A new network epsilon-based DEA model for supply chain performance evaluation (2013) Computers & Industrial Engineering, 66 (2), pp. 501-513; Tejas, F., Srikanth, K., Supply chain metrics that measure up (2007) Supply & Demand Chain Executive, 8, pp. 50-53; Thanki, S., Thakkar, J., A quantitative framework for lean and green assessment of supply chain performance (2018) International Journal of Productivity and Performance Management, 67 (1), pp. 366-400; Theeranuphattana, A., Tang, J.C.S., A conceptual model of performance measurement for supply chains (2008) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Tipi, N.S., Crompton, R., Mayer, R., The way in which supply chains are measured – a critical view (2008) Proceedings of the 15th International Conference on Retailing and Services Science, , Zagreb: 14-17 July; Toyli, J., Häkkinen, L., Ojala, L., Naula, T., Logistics and financial performance, an analysis of 424 Finnish small and medium-sized enterprises (2008) International Journal of Physical Distribution & Logistics Management, 38 (1), pp. 57-80; Tseng, M., Lim, M., Wong, W.P., Sustainable supply chain management: a closed-loop network hierarchical approach (2015) Industrial Management and Data Systems, 115 (3), pp. 436-461; Tyagi, M., Kumar, P., Kumar, D., Assessment of critical enablers for flexible supply chain performance measurement system using fuzzy DEMATEL approach (2015) Global Journal of Flexible Systems Management, 16 (2), pp. 115-132; Tyagi, M., Kumar, P., Kumar, D., Modelling and analysis of barriers for supply chain performance measurement system (2017) International Journal of Operational Research, 28 (3), pp. 392-414; Wagner, S.M., Grosse-Ruyken, P.T., Erhun, F., The link between supply chain fit and financial performance of the firm (2012) Journal of Operations Management, 30 (4), pp. 340-353; Webster, J., Watson, R.T., Analyzing the past to prepare for the future: writing a literature review (2002) MIS Quarterly, 26 (2); Willis, K., Anderson, J., (2010) Foundations for an effective performance measurement system for drug law enforcement: companion document to NDLERF monograph series 34, developing the capacity and skills for national implementation of a drug law enforcement performance measurement framework, , www.ndlerf.gov.au/pub/DLE_Perf_Mgt_Foundations.pdf, The National Drug Law Enforcement Research Fund (NDLERF): (accessed, 19 November 2011; Wisner, P., Linking supply chain performance to a firm’s financial performance (2011) Supply Chain Management Review, , www.scmr.com/article/linking_supply_chain_performance_to_a_firms_financial_performance, (accessed, 15 June 2011; Woei, L.S., (2008) Supply chain performance and financial success of selected companies on bursa Malaysia, , master’s dissertation, University of Malaya, Kuala Lumpur; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51; Zuriekat, M., Salameh, R., Alrawashdeh, S., Participation in performance measurement systems and level of satisfaction (2011) International Journal of Business and Social Science, 2 (8), pp. 159-169},
correspondence_address1={Elgazzar, S.; College of International Transport and Logistics, Egypt; email: sara.elgazzar02@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Review},
source={Scopus},
}

@CONFERENCE{Gegitsidis201927,
author={Gegitsidis, F. and Delias, P.},
title={A Performance Measurement System for Staff of the Logistics Section: A Case Study for an Oil & Gas Company},
journal={Springer Proceedings in Business and Economics},
year={2019},
pages={27-45},
doi={10.1007/978-3-030-12169-3_3},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126194131&doi=10.1007%2f978-3-030-12169-3_3&partnerID=40&md5=4938a3b6a7f3db1c46468449e8633acd},
affiliation={Department of Petroleum & Natural Gas Technology, Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece; Department of Accounting and Finance, Eastern Macedonia and Thrace Institute of Technology, Kavala, Greece},
abstract={Companies recognize the central role performance measurement plays in their success and are therefore becoming increasingly enthusiastic about their performance measurement efforts. Performance measurement indicators not only support the daily operation of the organization, but they are valuable in formulating any emerging problems as well. Logistics encompasses a complex set of activities that require an equally complex collection of metrics to adequately measure performance. However, many performance measurement systems have neither kept up with the changing role and scope of logistics nor have they been systematically examined or evaluated. Performance measurement systems should be evaluated at both the individual metric and system-wide levels in order to maintain relevance and effectiveness. This study aims to present the empirical findings and lessons learned from a field research on the development of the performance measurement system (PMS) for the logistics department of an Oil & Gas company. The implementation focuses on the procedure rather than on the structure of PMS offering a conceptual procedural framework with information and insights on how to design, implement, use, and assess a PMS, addressing an important gap identified in the literature. This research focuses on the evaluation of staff of the supply chain. Specifically, we propose a set of recommended criteria that can be applied to evaluate staff of the relevant department and demonstrate the use of these criteria through the evaluation of drivers’ performance. The primary motivation for evaluating performance at that level is that measurement systems guide management decisions. A well-crafted system of metrics will lead toward better decision-making by managers. The entire research effort lasted almost 6 months, involving multiple interactions between a researcher from academia and a practitioner from the company. Ultimately, the main findings are as follows: (1) how we can evaluate a drive; (2) the indicators and their parameters that contribute in revealing our assumptions about “good” and “bad” driver; (3) developing a prototype PMS; and (4) the use and review of the new PMS has led to improvements in people’s behavior, development of organizational capabilities, and improved performance results. © 2019, Springer Nature Switzerland AG.},
author_keywords={Decision support system;  Multicriteria analysis;  Performance measurement;  Staff of logistics},
references={Akhtar, M., Mittal, R.K., Implementation issues and their impact on strategic performance management system effectiveness – an empirical study of Indian oil industry (2015) Measuring Business Excellence, 19 (2), pp. 71-82; Bichou, K., Gray, R., A logistics and supply chain management approach to port performance measurement (2004) Maritime Policy & Management, 31 (1), pp. 47-67; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal Manager Review, 14 (3), pp. 305-327; Burns, N., Grove, S., The practice of nursing research: Conduct, critique (2005) And Utilization, , 5th ed.). Philadelphia, PA: WB Saunders; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) The International Journal of Logistics Management, 6 (2), pp. 61-74; Christodoulakis, N., (2015) Analytical Methods and Multicriteria Decision Support Systems under Uncertainty: The Talos System, , Piraeus; Corbin, J., Strauss, A., (2008) Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, , 3rd ed.). Thousand Oaks, CA: Sage; Creswell, J., (2007) Qualitative Inquiry and Research Design: Choosing among Five Approaches: International Student Edition, , Thousand Oaks, CA: Sage; Delias, P., Matsatsinis, N., A genetic approach for strategic resource allocation planning (2009) Computational Management Science, 6 (3), pp. 269-280; Dyer, J., MAUT—multiattribute utility theory (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 265-292. , (pp., New York: Springer; Eccles, R., (1991) The Performance Measurement Manifesto, 69, pp. 131-137. , JanuaryFebruary, Harvard Business; Elg, M., Kollberg, B., Conditions for reporting performance measurement (2012) Journal of Total Quality Management & Business Excellence, 23 (1), pp. 63-77; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers & Industrial Engineering, 56 (7), pp. 663-680; Gunasekaran, A., Patel, C., Gaughey, R.E.M., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hoek, R.I.V., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 15-29; Jacquet-Lagrèze, E., Siskos, Y., Preference disaggregation: 20 years of MCDA experience (2001) European Journal of Operational Research, 130 (2), pp. 233-245; Johnson, R., Examining the validity structure of qualitative research (1997) Education, 118 (2), pp. 282-292; Matsatsinis, N., Delias, P., (2003) Agentallocator: An Agent-Based Multi-Criteria Decision Support System for Task Allocation. in Holonic and Multi-Agent Systems for Manufacturing, pp. 1082-1083. , (pp., Berlin: Springer; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Neely, A., Al Najjar, M., Management learning not management control: The true role of performance measurement (2006) California Management Review, 48 (3), pp. 101-114; Platts, K.W., Characteristics of methodologies for manufacturing strategy formulation (1994) Computer Integrated Manufacturing Systems, 7 (2), pp. 93-99; Platts, K.W., Gregory, M.J., Manufacturing audit in the process of strategy formulation (1990) International Journal of Production and Operations Management, 10 (9), pp. 5-26; (2017) Industry Overview. Calgary: Petroleum Service Association of Canada; Punch, K., (1998) Introduction to Social Research, , London: Sage; Siskos, Y., Yannacopoulos, D., UTASTAR: An ordinal regression method for building additive value functions (1985) Investigação Operacional, 5 (1), pp. 39-53; Siskos, J., Spyridakos, A., Yannacopoulos, D., MINORA: A multicriteria decision aiding system for discrete alternatives (1993) Journal of Information Science and Technology, 2 (2), pp. 136-149; Siskos, Y., Grigoroudis, E., Matsatsinis, N.F., (2005) UTA Methods. in Multiple Criteria Decision Analysis: State of the Art Surveys, pp. 297-334. , (pp., New York: Springer; Taylor, A., Taylor, M., Antecedents of effective performance measurement system implementation: An empirical study of UK manufacturing firms (2013) International Journal of Production Research, 51 (18), pp. 5485-5498; Thurmond, V., The point of triangulation (2001) Journal of Nursing Scholarship, 33 (3), pp. 254-256},
correspondence_address1={Gegitsidis, F.; Department of Petroleum & Natural Gas Technology, Greece},
editor={Sykianakis N., Polychronidou P., Karasavvoglou A.},
publisher={Springer Science and Business Media B.V.},
issn={21987246},
isbn={9783030121686},
language={English},
abbrev_source_title={Springer Proc. Bus. Econ.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dixit2019562,
author={Dixit, A. and Routroy, S. and Kumar Dubey, S.},
title={A strategy to improve resource utilization: Case study of generic drug distribution in Rajasthan},
journal={Materials Today: Proceedings},
year={2019},
volume={28},
pages={562-567},
doi={10.1016/j.matpr.2019.12.219},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090120164&doi=10.1016%2fj.matpr.2019.12.219&partnerID=40&md5=10869f2226bd2b44533d6544f303b402},
affiliation={Department of Mechanical Engineering, Birla Institute of Technology and Science, Pilani Rajasthan, India; Department of Pharmacy, Birla Institute of Technology and Science, Pilani Rajasthan, India},
abstract={The establishment of standard, universal and quality healthcare service is essential for developing countries such as India to enhance the quality of life. Various healthcare schemes such as Jan Aushadhi Scheme, Mukhyamantri Nishulk Dava Yojna etc are operating in India which provides free healthcare services including free distribution of medicines. The medicines provided in these schemes are mostly generic drugs and the supply chain has already been established for it. The variety of generic drugs varies in quantity across different storage points and consumption points, and also having different self-lives, costs and logistics requirements. It was observed that the performance of these supply chain needs to be improved in various dimensions. Therefore, generic drug distribution in Rajasthan is studied and analyzed in the current study. The different aspects of generic drug distribution considered in the study are process view of the existing supply chain, sourcing, ordering and procurement, logistics and warehousing, inventory management and information system. The use of various techniques such as value stream mapping, inbuilt analytical tool for ordering, 5S practice and performance measurement system is explored to develop a strategy for improved resource utilization. © 2019 Elsevier Ltd.},
author_keywords={Drug warehouse;  Generic drug distribution;  Healthcare;  Resource utilization;  Strategic fit},
references={(1978) International Conference on Primary Health Care, Kazakhstan, 6-12 September; World Health Statistics 2014-World Health Organization; (2015) Report of the Core-Committee for Revision of National List of Essential Medicines; Sharma, S., Chaudhury, R.R., Improving availability and accessibility of medicines: A tool for increasing healthcare coverage (2015) Arch. Med., 7, pp. 1-9; Prinja, S., Bahuguna, P., Tripathy, J.P., Kumar, R., Availability of medicines in public sector health facilities of two North Indian States (2015) Bmc Pharmacol. Toxicol., 16, pp. 1-11; http://rmsc.health.rajasthan.gov.in/content/raj/medical/rajasthan-medical-services-corporation-ltd-/en/about-us/background.html, Data is available on; Khorramshahgol, R., Al-Husain, R., A multi-objective approach to design strategic supply chains and develop responsiveness-efficiency frontiers (2018) Int. J. Logist. Manage., 29, pp. 365-386; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning, and Operations, , fifth ed. Prentice-Hall Upper Saddle River, NJ; Tamayo-Torres, I., Gutiérrez-Gutiérrez, L.J., Llorens-Montes, F.J., Martínez-López, F.J., Organizational learning and innovation as sources of strategic fit (2016) Ind. Manage. Data Syst., 116, pp. 1445-1467; Johnson, M., Critical success factors for B2B e-markets: A strategic fit perspective (2013) Market. Intell. Plann., 31, pp. 337-366; Roh, J., Hong, P., Min, H., Implementation of a responsive supply chain strategy in global complexity: The case of manufacturing firms (2014) Int. J. Prod. Econ., 147, pp. 198-210; Tsai, C., Tien, C., Does organizational strategic fit in supply chain relations affect the propensity for strategic change? (2011) Chin. Manage. Stud., 5, pp. 164-180; Chopra, S., Meindl, P., Kalra, D.V., (2016) Supply Chain Management. Strategy, Planning and Operation, pp. 32-34. , Pearson Education. Delhi; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Rev., 75, pp. 105-117; Routroy, S., Evaluation of supply chain strategies: A case study (2009) Int. J. of Bus. Perform. Supply Chain Model., 1, pp. 290-306; Al-Aomar, R.A., Applying 5S lean technology: An infrastructure for continuous process improvement (2011) Int. J. Indus. Manuf. Eng., 5, pp. 1-8; Gupta, S., Jain, S.K., An application of 5S concept to organize the workplace at a scientific instruments manufacturing company (2015) Int. J. of Lean Six Sigma., 6, pp. 73-88},
editor={Kumar V.S., Karthikeyan R., Upadhyaya P.},
publisher={Elsevier Ltd},
issn={22147853},
language={English},
abbrev_source_title={Mater. Today Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Mail201979,
author={Mail, A. and Chairany, N. and Fole, A.},
title={Evaluation of supply chain performance through integration of hierarchical based measurement system and traffic light system: A case study approach to iron sheet factory},
journal={International Journal of Supply Chain Management},
year={2019},
volume={8},
number={5},
pages={79-85},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082197860&partnerID=40&md5=90d3bd2cdea17a955f6bb895f7fc27a0},
affiliation={Faculty of Industrial Technology, Department of Industrial Technology, Universitas Muslim Indonesia, Makassar, Indonesia},
abstract={Some of literature shows that the SCM model is effective enough in evaluating and measuring whether the enterprise is went well, efficient and effective. Nevertheless, the available literature is still quite limited in offering perspective in integrating various methods in evaluating and the measurement of SC performance easily and simply. The SCOR model as one of approach in measuring the SC performance, operationally still need to be integrated with another method. This is intended to reveal the clarity of the target and the result of measurement in more effectively, comprehensively, and accountably. In this study, it would be conduct a measurement of SC performance of iron sheet factory enterprise within using the SCOR model through the hierarchical integration based measurement system with traffic light system. The results of this study indicate that the integration of several methods such as Hierarchical Based Measurement System and Traffic Light System can provide a solution for management to evaluate and measure company performance more effectively, comprehensively, accountably and integrated. © Exceling Tech Pub, UK.},
author_keywords={Analytical hierarchy process;  Comprehensive integrated;  SCOR Model;  Supply chain performance measurement;  Traffic light system},
references={Pujawan, I., Mahendrawati, E., Supply Chain Management, , 3rd ed. 2017: Andi Publisher; Bidgoli, H., (2010) The handbook of technology management, supply chain management, marketing and advertising, and global management, 2. , John Wiley & Sons; Mandal, S., (2012) Supply chain performance: review of empirical literature, p. 3. , Romanian Review of Social Sciences; Tarasewicz, R., Integrated Approach to Supply Chain Performance Measurement -Results of the Study on Polish Market (2016) Transportation Research Procedia, 14, pp. 1433-1442; Wisner, J.D.T., Keah-Choon and Leong, G Keong, Principles of supply chain management: A balanced approach 2014: Cengage Learning; Beamon, B.M., Balcik, B., (2008) Performance measurement in humanitarian relief chains, 21 (1), pp. 4-25; Kurien, G.P.Q., Muhammad, N., Study of performance measurement practices in supply chain management. International Journal of Business (2011) Management and Social Sciences, 2 (4), pp. 19-34; Bromberg, D., Performance Measurement (2009) Public Performance & Management Review, 33 (2), pp. 214-221; Reddy, K.J.M., Rao, N., A review on supply chain performance measurement systems (2019) Procedia Manufacturing, 30, pp. 40-47; Dissanayake, C.K., Cross, J.A., Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM (2018) International Journal of Production Economics, 201, pp. 102-115; Saleheen, F.H., Mamun Hanafi, Z., Supply chain performance measurement model: A literature review (2018) International Journal of Supply Chain Management, 7 (3), pp. 70-78; Cetinkaya, B.C., Richard and Ewer, Graham and Klaas-Wissing, Thorsten and Piotrowicz, Wojciech and Tyssen, Christoph Sustainable supply chain management: practical ideas for moving towards best practice, , 2011: Springer Science & Business Media; Xiao, R., Cai, Z., Zhang, X., An optimization approach to cycle quality network chain based on improved SCOR model (2009) Progress in Natural Science, 19 (7), pp. 881-890; Delipinar, G.E., Kocaoglu, B., Using SCOR Model to Gain Competitive Advantage: A Literature Review (2016) Procedia -Social and Behavioral Sciences, 229, pp. 398-406; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A Review of Literature on Performance Measurement of Supply Chain Network 2009 Second International Conference on Emerging Trends in Engineering & Technology. 2009; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010), 14 (1), pp. 4-18; Shafiee, M., Hosseinzadeh Lotfi, F., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Applied Mathematical Modelling, 38 (21), pp. 5092-5112},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tunyaplin2019292,
author={Tunyaplin, S. and Chanpuypetch, W.},
title={Development of a performance measurement system for a home furniture delivery and assembly logistics provider in Thailand},
journal={International Journal of Business Process Integration and Management},
year={2019},
volume={9},
number={4},
pages={292-306},
doi={10.1504/IJBPIM.2019.105686},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081599452&doi=10.1504%2fIJBPIM.2019.105686&partnerID=40&md5=ab5ad692e00520f0e7ee3ce2161d291d},
affiliation={Suwanpaisarn Transportation 2010, Co., Ltd., 35/17 Chao Khun Tahan Rd.,, Lat Krabang, Bangkok, 10520, Thailand; College of Maritime Studies and Management, Chiang Mai University, 119/76 Moo 1, Ekkachai Rd.,, Tha Chin, Muang, Samut Sakhon, 74000, Thailand},
abstract={The global furniture retail brand, IKEA, has an extraordinary characteristic. Their customers can tailor the product’s components for assembly. A last-mile delivery provider must be capable of providing both home delivery and assembly services. Their operational performance is often less efficient. Therefore, this article aims to develop a performance measurement system (PMS) for last-mile home furniture delivery and assembly services. The PMS was developed based on the case study of a provider that has been outsourced by the IKEA branch in Thailand. All related performance metrics were derived through the SCOR model. The key metrics were identified relying on the real operational data of the case and then prioritised using the analytic hierarchy process. The developed PMS was applied to measure the last-mile service provider’s performance of the IKEA branch in Thailand. It benefits a provider for monitoring and controlling the quality of service to gain customer satisfaction. Copyright © 2019 Inderscience Enterprises Ltd.},
author_keywords={Analytic hierarchy process;  Furniture supply chain;  HoFurDAS;  Home furniture delivery and assembly services;  Last-mile delivery;  Performance measurement system;  PMM;  SCOR;  Supply chain operations reference;  Thailand},
references={Ayers, J.B., (2006) Handbook of Supply Chain Management, , 2nd ed., Taylor and Francis, Boca Raton, FL; Burja, V., Mărginean, R., The study of factors that may influence the performance by the dupont analysis in the furniture industry (2014) Procedia Economics and Finance, 16, pp. 213-223; Erkan, T.E., Bac, U., Supply chain performance measurement: A case study about applicability of SCOR model in a manufacturing industry firm (2011) International Journal of Business and Management Studies, 3 (1), pp. 381-390; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Govindu, R., Chinnam, R., MASCF: A generic process-centered methodological framework for analysis and design of multi-agent supply chain systems (2007) Computers & Industrial Engineering, 53 (4), pp. 584-609; Georgise, F.B., Thoben, K.-D., Seifert, M., Adapting the SCOR model to suit the different scenarios: A literature review & research agenda (2012) International Journal of Business and Management, 7 (6), pp. 2-17; Guersola, M., Lima, E.P.D., Steiner, M.T.A., Supply chain performance measurement: A systematic literature review (2018) International Journal of Logistics Systems and Management, 31 (1), pp. 109-131; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hellström, D., Nilsson, F., Logistics-driven packaging innovation: A case study at IKEA (2011) International Journal of Retail and Distribution Management, 39 (9), pp. 638-657; Hemamala, K., Banerji, S., Sahay, M., A systems approach to mapping logistics systems performance of small and medium manufacturing enterprises (2017) International Journal of Logistics Systems and Management, 27 (2), pp. 164-186; Keong, C.K., Understanding the features of performance measurement system: A literature review (2013) Measuring Business Excellence, 17 (4), pp. 102-121; Lepori, E., Damand, D., Barth, B., Benefits and limitations of the SCOR model in warehousing (2013) IFAC Proceedings Volumes, 46 (9), pp. 424-429; Lockamy, A., III, McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Lu, Q., Goh, M., de Souza, R., A SCOR framework to measure logistics performance of humanitarian organizations (2016) Journal of Humanitarian Logistics and Supply Chain Management, 6 (2), pp. 222-239; Marchet, G., Melacini, M., Perotti, S., Sassi, C., Types of logistics outsourcing and related impact on the 3PL buying process: Empirical evidence (2018) International Journal of Logistics Systems and Management, 30 (2), pp. 139-161; Micheli, P., Mari, L., The theory and practice of performance measurement (2014) Management Accounting Research, 25 (2), pp. 147-156; Moharamkhani, A., Bozorgi-Amiri, A., Mina, H., Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS (2017) International Journal of Logistics Systems and Management, 27 (1), pp. 115-132; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Oyemomi, O., Liu, S., Neaga, I., Alkhuraiji, A., How knowledge sharing and business process contribute to organizational performance: Using the fsQCA approach (2016) Journal of Business Research, 69 (11), pp. 5222-5227; Pretorius, C., Ruthven, G.A., von Leipzig, K., An empirical supply chain measurement model for a national egg producer based on the supply chain operations reference model (2013) Journal of Transport and Supply Chain Management, 7 (1), pp. 1-13; Reisinger, H., Cravens, K.S., Tell, N., Prioritizing performance measures within the balanced scorecard framework (2003) Management International Review, 43 (4), pp. 429-437; Rodrigue, J.-P., Comtois, C., Slack, B., The ‘last mile’ in freight distribution (2009) The Geography of Transport Systems, p. 212. , 2nd ed.,. Routledge, London; Rompho, N., Boon-Itt, S., Measuring the success of a performance measurement system in Thai firms (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 548-562; Rotaru, K., Wilkin, C., Ceglowski, A., Analysis of SCOR’s approach to supply chain risk management (2014) International Journal of Operations and Production Management, 34 (10), pp. 1246-1268; Saaty, T.L., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; Sipahi, S., Timor, M., The analytic hierarchy process and analytic network process: An overview of applications (2010) Management Decision, 48 (5), pp. 775-808; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply-chain management (1997) Logistics Information Management, 10 (2), pp. 62-67; Ucal, I., Öztayşi, B., ANP in performance measurement and its application in a manufacturing system (2009) Proceedings of the International Symposium on the Analytic Hierarchy Process, , 29 July–1 August, Pittsburgh, USA; Vahlne, J.-E., Jonsson, A., Ambidexterity as a dynamic capability in the globalization of the multinational business enterprise (MBE): Case studies of AB Volvo and IKEA (2016) International Business Review, 26 (1), pp. 57-70; Wei, W., Low, J.F., Schiffauerova, A., Nobody wants to buy sour milk: Supply chain performance measure matters (2018) International Journal of Logistics Systems and Management, 29 (1), pp. 62-81},
correspondence_address1={Chanpuypetch, W.; College of Maritime Studies and Management, 119/76 Moo 1, Ekkachai Rd.,, Thailand; email: wirachchaya.c@cmu.ac.th},
publisher={Inderscience Publishers},
issn={17418763},
language={English},
abbrev_source_title={Int. J. Bus. Process Integr. Manage.},
document_type={Article},
source={Scopus},
}

@BOOK{Grant2019357,
author={Grant, D.B. and Shaw, S.},
title={Environmental or sustainable supply chain performance measurement standards and certifications},
journal={Handbook on the Sustainable Supply Chain},
year={2019},
pages={357-376},
doi={10.4337/9781786434272.00024},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081353758&doi=10.4337%2f9781786434272.00024&partnerID=40&md5=d13f6f9367d1bdca53d28149412fd787},
affiliation={Hanken School of Economics, Finland; University of Hull, United Kingdom},
abstract={Organizations are looking to adopt environmental or sustainable performance management standards and certifications for their supply chains to satisfy stakeholder concerns and to respond to increasing awareness and interest in sustainability. However, there are several challenges regarding which standards and/or certifications organizations should adopt. This chapter discusses these issues; examines 19popular standard and certification schemes; considers three key functional supply chain activities as regards available schemes, buildings and facilities, production and operations and transportation; and provides suggestions for further research into this topic’s continuing challenges. © Joseph Sarkis 2019. All rights reserved.},
references={Abukhader, S., Jönson, G., Logistics and the environment: is it an established subject? (2004) International Journal of Logistics: Research and Applications, 7 (2), pp. 137-149; Baker, P., Canessa, M., Warehouse design: a structured approach (2009) European Journal of Operational Research, 193 (2), pp. 425-436; Barnard, S., Elliott, M., The 10-tenets of adaptive management and sustainability: an holistic framework for understanding and managing the socio-ecological system (2015) Environmental Science and Policy, 51, pp. 181-191; Barney, J.B., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Beske-Janssen, P., Johnson, P.M., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management – what has been achieved? (2015) Supply Chain Management: An International Journal, 20 (6), pp. 664-680; (2017) British Retail Consortium Global Standards, , http://www.brcglobalstandards.com/., accessed 17 January 2017 at; (2017) BREAAM, , http://www.breeam.com/., accessed 17 January 2017 at; Brundtland, G.B., (1987) Our Common Future: World Commission on Environment and Development, , Oxford: Oxford University Press; (2016) What we do?, , http://www.bsci-intl.org/content/what-we-do-0., accessed 15 December 2016 at; (2016) Carbon Trust, , https://www.carbontrust.com/resources/guides/carbon-footprinting-and-reporting/crc-carbon-reduction-commitment., accessed 17 January 2017 at; Cervelini, F.M., Souza, M.T.S., A contribution of the cleaner production program to the ISO 14001 management system: a case study in the metal–mechanic sector (2009) Journal of Operations and Supply Chain Management, 2 (1), pp. 61-76; (2008) Implementing the Climate Change Act 2008, , http://www.legislation.gov.uk/ukpga/2008/27/pdfs/ukpga_20080027_en.pdf., accessed 16 December 2016 at; (2017) Carbon Reduction Commitment, , http://www.carbonreductioncommitment.co.uk/., accessed 17 January 2017 at; Davenport, C., With Trump in charge, climate change references purged from website (2017) New York Times, , https://www.nytimes.com/2017/01/20/us/politics/trump-white-house-website.html?_r=0., accessed 21 January 2017 at; (2006) Department for Environment, Food and Rural Affairs, , http://www.defra.gov.uk., accessed 4 April 2009 at; Earnhart, D., Leonard, J.M., Determinants of environmental audit frequency: the role of organization organizational structure (2013) Journal of Environmental Management, 128, pp. 497-513; Elkington, J., Towards the sustainable corporation: win-win-win business strategies for sustainable development (1994) California Management Review, 36 (2), pp. 90-100; (2017) European Commission Eco-Management and Audit Scheme, , http://ec.europa.eu/environment/emas/index_en.htm., accessed 17 January 2017; (2015) Environmental Protection Agency (EPA) LEED case study, , https://www.epa.gov/sites/production/files/2016-04/documents/leed-case-study_final-reduced.pdf., accessed 6 January 2017 at; (2016) About Electronic Product Environmental Assessment Tool (EPEAT), , http://www.epeat.net/about-epeat/., accessed 16 December 2016 at; (2016) About the Ethical Trading Initiative (ETI), , http://www.ethicaltrade.org/., accessed 16 December 2016 at; (2016) Fairtrade gold and precious metals, , http://www.fairtrade.org.uk/en/media-centre/news/november-2013/new-fairtrade-gold-and-precious-metals., accessed 15 December 2016 at; Faruk, A.C., Lamming, R.C., Cousins, P.D., Bowen, F.E., Analysing, mapping and managing environmental impacts along supply chains (2001) Journal of Industrial Ecology, 5 (2), pp. 13-36; Ferreira, L.M.D.F., Silva, C., Integrating sustainability metrics in the supply chain performance measurement system (2016) Multiple Helix Ecosystems for Sustainable Competitiveness, pp. 113-132. , in Marta Peris-Ortiz, João Ferreira, Luis Farinha and Nuno O. Fernandes (eds), Berlin: Springer; Goldenberg, S., Vidal, J., Taylor, L., Vaughan, A., Harvey, F., (2015) Paris climate deal: nearly 200 nations signal the end of the fossil fuel era, , https://www.theguardian.com/environment/2015/dec/12/paris-climate-deal-200-nations-sign-finish-fossil-fuel-era?CMP=Share_iOSApp_Other., accessed 26 January 2016 at; Grant, D.B., (2012) Logistics Management, , Harlow: Pearson Education; Grant, D.B., Elliott, M., (2016) A proposed framework for managing environmental causes and consequences of ocean traffic and ports, , paper presented at the 7th European Decision Sciences Institute Conference, Hanken School of Economics, Aalto University and the Finnish National Defence University, Helsinki, 26 May; Grant, D.B., Trautrims, A., Wong, C.Y., (2017) Sustainable Logistics and Supply Chain Management, , 2nd edn, London: Kogan Page; (2016) Global Reporting Initiative, , https://www.globalreporting.org/Pages/default.aspx., accessed 16 December 2016 at; Hagelaar, G.J.L.F., van der Vorst, J.G.A.J., Environmental supply chain management, using lifecycle assessment to structure supply chains (2002) International Food and Agribusiness Management Review, 4, pp. 399-412; Hart, S.L., A natural-resource based view of the organization (1995) Academy of Management Review, 20 (4), pp. 986-1014; Hart, S.L., Dowell, G., A natural resource based view of a firm: fifteen years after (2011) Journal of Management, 37 (5), pp. 1464-1479; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hillary, R., Environmental management systems and the smaller enterprise (2004) Journal of Cleaner Production, 12, pp. 561-569; (2017) International Air Transport Association, , http://www.iata.org/about/Pages/index.aspx., accessed 17 January 2017 at; (2013) Institute of Business Ethics (IBE) Ethical Indices, , http://www.ibe.org.uk/userfiles/ethicalindices.pdf., accessed 17 January 2017 at; (2016) Who are we?, , http://i-dg.co.uk/., accessed 15 December 2016 at; (2017) International Maritime Organization, , http://www.imo.org/en/Pages/Default.aspx., accessed 17 January 2017 at; (2017) Intergovernmental Panel on Climate Change, , http://www.ipcc.ch/index.htm., accessed 17 January 2017 at; Iraldo, F., Testa, F., Frey, M., Is an environmental management system able to influence environmental and competitive performance? The case of the eco-management and audit scheme (EMAS) in the European Union (2009) Journal of Cleaner Production, 17, pp. 1444-1452; (2017) International Organization for Standardization (ISO) 9000 – Quality management, , http://www.iso.org/iso/home/standards/management–standards/iso_9000.htm., accessed 17 January 2017 at; (2017) International Organization for Standardization (ISO) 14000 – Environmental management, , http://www.iso.org/iso/iso14000., accessed 17 January 2017 at; (2017) International Organization for Standardization (ISO) 26000 – Social responsibility, , http://www.iso.org/iso/home/standards/iso26000.htm., accessed 17 January 2017 at; Khan, Z., Cleaner production: an economical option for ISO certification in developing countries (2008) Journal of Cleaner Production, 16, pp. 22-27; (2017) Better buildings are our legacy, , http://www.usgbc.org/leed., accessed 4 January 2017 at; Lyon, T.P., Maxwell, J.W., Greenwash: corporate environmental disclosure under threat of audit (2011) Journal of Economics and Management Strategy, 20 (1), pp. 3-41; (2016) MSC Fisheries Standard, , https://www.msc.org/about-us/standards/fisheries-standard., accessed 16 December 2016 at; McKinnon, A.C., Benchmarking road freight transport: review of a government-sponsored programme (2009) Benchmarking: An International Journal, 16 (5), pp. 640-656; McKinnon, A.C., (2016) Low carbon logistics in a climate changed world, , paper presented at the Colloquium on Green Logistics Management: Balancing Environmental and Shareholder Priorities, IRISS-CNR Naples, 28 September; Metta, H., (2011) A Multi-Stage Decision Support Model for Coordinated Sustainable Product and Supply Chain Design, , Lexington, KY: College of Engineering, University of Kentucky; Morana, J., Gonzalez-Feliu, J., A sustainable urban logistics dashboard from the perspective of a group of operational managers (2015) Management Research Review, 38 (10), pp. 1068-1085; Mori Junior, R., Franks, D.M., Ali, S.H., (2015) Designing sustainability certification for impact: analysis of the design characteristics of 15 sustainability standards in the mining industry, , Brisbane: Centre for Social Responsibility in Mining, University of Queensland; (2016) UK’s largest solar panel is on a warehouse roof in Castle Donington, , http://www.nottinghampost.com/marks-spencer-complete-installation-uk-s-largest/story-26106643-detail/story.html., accessed 16 December 2016 at; Penrose, E., (1959) The Theory of the Growth of the Firm, , Oxford: Oxford University Press; Rawls, J., (1976) A Theory of Justice, , Oxford: Oxford University Press; (2016) Sustainable Rail Programme, , http://www.rssb.co.uk/improving-industry-performance/sustainable-development/sustainable-rail-programme., accessed 17 December 2016 at; Sarkis, J., Zhu, Q., Lai, K., An organizational theoretic review of green supply chain management literature (2011) International Journal of Production Economics, 130, pp. 1-15; (2016) Empowering responsible supply chains, , http://www.sedexglobal.com/., accessed 16 December 2016 at; Shaw, S.L., (2013) Developing and testing green performance measures for the supply chain, , https://hydra.hull.ac.uk/resources/hull:8108., PhD thesis, accessed 17 January 2017 at; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339; Shaw, S., Grant, D.B., Shenin, N., Chaisurayakarn, S., (2016) Using environmental reporting tools in the supply chain: perspectives from UK, Finland and Thailand, , paper presented at the 21st Logistics Research Network (LRN) Conference, University of Hull, UK, 8 September; Suchman, M.C., Managing legitimacy: strategic and institutional approaches (1995) Academy of Management Review, 20 (3), pp. 571-610; Swanson, D., Goel, L., Francisco, K., Stock, J.R., Applying theories from other disciplines to logistics and supply chain management: a systematic literature review (2017) Transportation Journal, 56 (3), pp. 299-356; To, W.M., Tang, M.N.F., The adoption of ISO 14001 environmental management systems in Macao SAR, China: trend, motivations, and perceived benefits (2014) Management of Environmental Quality: An International Journal, 25 (2), pp. 244-256; (2014) Greening the building supply chain, , http://www.unep.org/sbci/pdfs/greening_the_supply_chain_report.pdf., accessed 5 January 2017 at; (2016) What is life cycle assessment (LCA)?, , http://www.unep.org/resourceefficiency/Consumption/StandardsandLabels/MeasuringSustainability/LifeCycleAssessment/tabid/101348/Default.aspx., accessed 16 December 2016 at; (2017) Resource efficient and cleaner production, , http://www.unep.fr/scp/cp/., accessed 5 January 2017 at; Walker, H., Jones, N., Sustainable supply chain management across the UK private sector (2012) Supply Chain Management: An International Journal, 17 (1), pp. 15-28; Zhou, J., Sustainable transportation in the US: a review of proposals, policies, and programs since 2000 (2012) Frontiers of Architectural Research, 1, pp. 150-165},
publisher={Edward Elgar Publishing Ltd.},
isbn={9781786434272; 9781786434265},
language={English},
abbrev_source_title={Handb. on the Sustain. Supply Chain},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Alsharif2019230,
author={Alsharif, A.A. and Hassan, W.A. and Almaktoom, A.T.},
title={Identifying enablers of supply chain alignments},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2019},
number={November},
pages={230-239},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080949340&partnerID=40&md5=edff71bc3d1cc9f352243ea345342eb3},
affiliation={Department of Operations and Information Management, Effat University, PO Box 34689, Jeddah, 21478, Saudi Arabia},
abstract={Literatures on supply chain alignments identifies six main enablers of supply chain management who need to be managed efficiently and effectively to have an aligned supply chain. The six enablers are organizational structure, internal relational behavior, customer relational behavior, top management support system, information sharing and business performance measurement system. The purpose of this paper is to identify the enablers of supply chain alignment and study the relationship between enablers and supply chain alignments. The current research introduced a conceptual model, which has been tested and verified in bakery industries in Jeddah-Saudi Arabia. The data was collected using a questionnaire developed based on the previous literatures. Statistical techniques such as reliability and regression analysis were used to verify the validity of the proposed conceptual model, and to test the suggested hypotheses. According to the results of the study, the organizational structure and top management support are positively related to customer alignment, as well as customer alignment is positively related to shareholder alignment. The findings suggest that the successful implementation of supply chain alignment will contribute to improving supply chain management in an efficient and effective way. © IEOM Society International.},
author_keywords={Alignments;  Customer;  Enablers;  Shareholder;  Supply chain},
references={Almaktoom, A.T., Supply Chain Agility Quantification and Analysis (2019) International Journal of Operations and Quantitative Management, 25 (3), pp. 23-40; Almaktoom, A.T., Krishnan, K.K., Wang, P., Alsobhi, S., Assurance of system service level robustness in complex supply chain networks (2014) The International Journal of Advanced Manufacturing Technology, 74 (1-4), pp. 445-460; Anson, M., White, T., McGrew, W., Aligning the Interests of Agents and Owners: An Empirical Examination of Executive Compensation (2004) Ivey Business Journal, , https://iveybusinessjournal.com/publication/aligning-the-interests-of-agents-and-owners-an-empirical-examination-of-executive-compensation/, (June). Retrieved March 9, 2019, from; Ashenbaum, B., Maltz, A., Ellram, L., Barratt, M.A., Organizational Alignment and Supply Chain Governance Structure: Introduction and Construct Validation (2009) The International Journal of Logistics Management, 20 (2), pp. 169-186; Barratt, M., Oliveira, A., Exploring the Experiences of Collaborative Planning Initiatives (2001) International Journal of Physical Distribution & Logistics Management, 31 (4), pp. 266-289; Blessing, L.T., (2014) Drm, a design research methodology, , heidelberg: springer; Brown, S., Squire, B., Blackmon, K., The Contribution of Manufacturing Strategy Involvement and Alignment to World Class Manufacturing Performance (2007) International Journal of Operations & Production Management, 27 (3), pp. 282-302; Creswell, J., (2007) Qualitative inquiry and research design: Choosing among five approaches, , 2nd edition. Thousand Oaks, CA: sage publications; (2017) Performance Management in Supply Chain and Operations: Steering value chain activities towards exceptional performance, , London: Deloitte Touche Tohmatsu Limited; Dobre, O.-I., Employee Motivation and Organizational Performance (2013) Review of Applied Socio-Economic Research, 5 (1), pp. 53-60; Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., Childe, S.J., Supply Chain Agility, Adaptability and Alignment: Empirical Evidence from the Indian auto Components Industry (2018) International Journal of Operations & Production Management, 38 (1), pp. 129-148; Eggert, A., Ulaga, W., Schultz, F., Value Creating In the Relationship Life-cycle: A Quasi-Longitudinal Analysis (2006) Industrial Marketing Management, 35 (11), pp. 20-27; Flynn, B.B., Huo, B., Zhao, X., The Impact of Supply Chain Integration on Performance: A Contingency and Configuration Approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Godsell, J., Harrison, A., Emberson, C., Storey, J., Customer Responsive Supply Chain Strategy: An Unnatural (2006) International Journal of Logistics: Research and Apllications, 9 (1); Kumar, S.R., Pugazhendhi, S., Information Sharing in Supply Chains: An Overview (2012) Procedia Engineering, 38 (1), pp. 2147-2154; Krishnan, K.K., Almaktoom, A.T., Udayakumar, P., Optimisation of stochastic assembly line for balancing under high variability (2016) International Journal of Industrial and Systems Engineering, 22 (4), pp. 440-465; Lambert, D.M., García-Dastugue, S.J., Croxton, K.L., An Evaluation of Process-oriented Supply Chain Management Frameworks (2005) Journal of Business Logistics, 26 (1), pp. 25-51; Lee, H.L., The Triple-A Supply Chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Lin, C., Kuei, C.-H., Cha, K.-W., Identifying Critical Enablers and Pathways to high Performance Supply Chain Quality Management (2013) International Journal of Operations & Production Management, 33 (3), pp. 347-370; Lotfi, Z., Mukhtar, M., Sahran, S., Zadeh, A.T., Information Sharing in Supply Chain Management (2013) Procedie Technology, 11 (1), pp. 298-304; Menor, L.J., Roth, A.V., New Service Development Competence in Retail Banking: Construct Development and Measurement Validation (2007) Journal of Operations Management, 25 (1), pp. 825-846; Mokadem, M.E., ISO 9000 Moderation Role over Supply Chain Alignment in Manufacturing Context (2016) Journal of Manufacturing Technology Management, 27 (3), pp. 338-636; Morgan, C., Structure, speed and salience:Performance measurement in the supply chain (2004) Business process management Journal, p. 522; Sandberg, E., Abrahamsson, M., The Role of Top Management in Supply Chain Management Practices (2010) International Journal of Retail & Distribution Management, 38 (1), pp. 57-69; Singh, P.J., Power, D., The Nature and Effectiveness of Collaboration between Firms, their Customers and Suppliers: A Supply Chain Perspective (2009) Supply Chain Management:An International Journal, 14 (3), pp. 189-200; Skipworth, H., Godsell, J., Wong, C.Y., Saghiri, S., Julien, D., Supply Chain Alignment for Improved Business Performance: An Empirical Study (2015) Supply Chain Management: An International Journal, 20 (5), pp. 511-533; Stefanovic, N., Proactive Supply Chain Performance Management with Predictive Analytics (2014) The Scientific World Journal, 3 (2), pp. 1-17; Storey, J., Emberson, C., Reade, D., The Barriers to Customer Responsive Supply Chain Management (2005) International Journal of Operations & Production Managemen, 25 (3), pp. 242-260; Tracey, M., Lim, J.S., Vonderembse, M.A., The Impact of Supply-chain Management Capabilities on Business Performance (2005) Supply Chain Management, 10 (3), pp. 179-191; Wong, C., Skipworth, H., Godsell, J., Saghiri, S., Julien, D., Towards a Theory of Supply Chain Alignment Enablers: A Systematic Literature Review (2012) An International Journal of Supply Chain Management, 17 (4), pp. 419-437},
publisher={IEOM Society},
issn={21698767},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Firman2019616,
author={Firman and Murboyono, R. and Anra, Y.},
title={Supply chain performance measurement and productivity in educational career assistance: Career development of educational personnel in Jambi University},
journal={International Journal of Supply Chain Management},
year={2019},
volume={8},
number={6},
pages={616-628},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078287496&partnerID=40&md5=9c107bcf35a3a3379444220bbf274f4d},
affiliation={FKIP Universitas Jambi, Indonesia},
abstract={Human resource management for the supply chains is recognized increasingly important for its role to fulfill the demands of work or position as a result of advances in science and technology and increasingly intense competition among similar organizations or institutions. Each employee is required to be able to work effectively and efficiently in completing work, and is able to show the quality and quantity of good work in order to develop the competitiveness of the organization. With regard to the issue, this study was carried out by using a quantitative associative approach with survey methods and applied path analysis and regression technique to test the directly and indirectly effect on each research variable. Furthermore, this study involved educational staff of Jambi University as the respondents of the study in human resource management. Based on the research approach, it was expected to produce a career development model to support the performance in giving excellent service to the community in Jambi University. The result of the study noted and emphasized that there was the effect of working climate on organizational culture as 11.3, working motivation on organizational culture as 12.4, working climate on career development as 41.7, working motivation for career development as 15.4, organizational culture on career development as 12.6, working climate on career development through organizational culture as 43.8, and working motivation on career development through organizational culture as 20.8. The result indicated that to increase working climate, working motivation and supply chai management should be increased, too. In other words, to improve career development, it can be achieved by improving the variables of those three variables; working climate, working motivation, and organizational culture. © ExcelingTech Pub, UK.},
author_keywords={Career development;  Human resource management;  Organizational culture;  Supply chain;  Working climate;  Working motivation},
references={Aima, H., Adam, R., Ali, H., Model of Employee Performance: Competence Analysis and Motivation (Case Study at PT Bank Bukopin, Tbk Center) (2017) Quest Journals-Journal of Research in Business and Management, 4 (11), pp. 49-59; Anthanasou, J.A.R., Esbroeck, V., (2008) International handbook of career guidance, , Springer Science, Business B. V; Bambuwela, P.M., Chamaru, A.A., Effects of glass ceiling on women career development in private sector organizations: A case of Sri lanka (2013) Journal of Competitiveness, 5 (2); Ahmad, M., Rajapaksha, A.U., Lim, J.E., Zhang, M., Bolan, N., Mohan, D., Vithanage, M., Ok, Y.S., Biochar as a sorbent for contaminant management in soil and water: A review (2014) Chemosphere, 1 (99), pp. 19-33. , Mar; Colquitt, (2009) Organizational behavior, improving performance and commitment in the workplace, , New York: McGraw-Hill Companies; Denison, D., (1990) Corporate culture and organisational effectiveness, , New York: John Wiley & Sons; Gayani, (2008) The impact of culture on career development of women in construction, , University of Salford: Research Institute for the Built and Human Environment; Gibson, (2006) Organization: Behavior, structure, process, , Texas: Business Publication, Inc; Gerrig, R.J., (2008) Psychology and life, , Boston: Pearson/Allyn and Bacon; Herberg, U., Clausen, T., (2010) Security issues in the optimized link state routing protocol version, 2. , (OLSRv2). arXiv preprint arXiv:1005.4505. May 25; Gottfredson, L.S., Applying Gottfredson's theory of circumscription and compromise in career guidance and counseling (2005) Career development and counseling: Putting theory and research to work, pp. 71-100. , In S. D. Brown & R. T. Lent (Eds.) Hoboken, NJ: Wiley; Hastho, J.N., Meilan, S., (2007) Manajemen Sumber Daya Manusia, , Ardana Media, Yogyakarta; Hofstede, G., (2001) Culture's Consequences: Comparing values, behaviors, institutions, and organizations across nations, , (2nd ed.). Thousand Oaks, CA: SAGE Publications; Wahid, A., Gelani, S., Ashraf, M., Foolad, M.R., (2007) Heat tolerance in plants: An overview. Environmental and experimental botany, 1-61 (3), pp. 199-223. , Dec; Ivancevich, J., (2007) Human resource management, , New York: McGraw-Hill; Johnson, J.J., McIntye, C.L., Organizational culture and climate correlates of job satisfaction (1998) Psychological Reports, 82, pp. 843-850; Hessel, V., Cortese, B., de Croon, M.H., Novel process windows-Concept, proposition and evaluation methodology, and intensified superheated processing (2011) Chemical Engineering Science, 1-66 (7), pp. 1426-1448. , Apr; Brophy, J., (2013) Motivating students to learn, , Routledge; May 13; Matandare, M.A., Botswana Unemployment Rate Trends by Gender: Relative Analysis with Upper Middle Income Southern African Countries (2000-2016) (2018) Dutch Journal of Finance and Management, 2 (2). , 04; Modrcin, M.J., Rutland, A.C., Youth in transition: A summary of service components (1989) Psychosocial Rehabilitation Journal, 12 (4), pp. 3-15; O'Reilly, C.A., III, Chatman, J., Caldwell, D.F., People and organizational culture: A profile comparison approach to assessing person-organization fit (1991) Academy of Management Journal, 34, pp. 487-516; Osibanjo, Career development as a determinat of organizational growth: Modelling the relationship between those constructs in the Nigerian Banking Industry (2014) American International Journal of Social Science, 3 (7); Paijan, P., Ali, H., Pengaruh Gaya Kepemimpinan Transformasional, Pelatihan Terhadap Motivasi Kerja. Serta Implikasi Kinerja Tenaga Kependidikan Di Universitas Mercu Buana Jakarta (2017) Jurnal Ekonomi, 21 (3); Parry, W.K., Thomson, Sarah, B.P., Leader career development: Who should take responsibility? (2003) Asia Pasific Journal of Human Resources, 41 (3); Villalón, J.C., Agustin, G.C., Gilabert, T.S.F., Puello, J.D.J.J., A review of software project testing (2016) Journal of Information Systems Engineering & Management, 1 (2), pp. 141-148. , https://doi.org/10.20897/lectito.201619; Prayetno, S., Ali, H., Analysis of Advocates Organizational Commitment and Advocates Work Motivation to Advocates Performance and its Impact on Performance Advocates Office (2017) International Journal of Economic Research; Rasool, Impact of organizational culture on employee's career salience: An empirical study of banking sector in Islamabad, Pakistan (2012) International Journal of Business and Social Science, 3 (7); Riduwan, Kuncoro, E.A., (2008) Cara Menggunakan dan memaknai analisis jalur (Path Analysis), , Bandung: Alfabeta; Robbins, S.P., Mary, C., (2010) Manajemen, , Jakarta: Erlangga; Chay Atri, V., Jalili, S., Amini Sabegh, Z.A.D., Studying the Impact of Invisible Employees on Productivity (2019) UCT Journal of Management and Accounting Studies, 7 (1), pp. 5-10; Chahine, I.C., Exposing the Conscious Self: Lived Problem Solving Experience in a Socio-Cultural Context (2018) International Electronic Journal of Mathematics Education, 13 (3), pp. 221-231. , https://doi.org/10.12973/iejme/3880; Rahmad, N.A., As'Ari, M.A., Ghazali, N.F., Shahar, N., Sufri, N.A.J., A Survey of Video Based Action Recognition in Sports (2018) Indonesian Journal of Electrical Engineering and Computer Science, 11 (3), pp. 987-993; Riyanto, S., Sutrisno, A., Ali, H., The Impact of Working Motivation and Working Environment on Employees Performance in Indonesia Stock Exchange (2017) International Review of Management and Marketing, 7 (3), pp. 342-348. , http://dergipark.org.tr/irmm/issue/32110/356036; Zikai, T., An Overview of Economical Corruption in USA and Analysis of its Future (2018) Journal of Humanities Insights, 2 (1), pp. 43-50; Siagian, S.P., (2012) Manajemen sumber daya manusia, , Jakarta: Bumi Aksara; Steers, R.M., Efektivitas kerja: Kaidah perilaku (1985) Diterjemahkan oleh Magdalena, , J. Jakarta: Erlangga; Ceisil, U., E-Awareness of University Student through Smart Phones and Developing Social Networks (2018) Journal of Humanities Insights, 2 (3), pp. 139-145; Sukmadinata, N.S., (2011) Landasan psikologi proses pendidikan, , Bandung: PT. Remaja Rosdakarya; Sunyoto, D., Manajemen sumber daya manusia (2013) Yogyakarta: CAPS (Center for Academic Publishing Service); Wickramaratne, W.P.R., Role of development culture and senior management support in career development (2013) International Journal of Arts and Commerce, 2 (6)},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kodrat20191885,
author={Kodrat, K.F. and Sinulingga, S. and Napitupulu, H. and Hadiguna, R.A.},
title={Supply chain performance measurement model of passion fruit agro-industry for sustainable micro, small, and medium enterprises with system dynamics in North Sumatra Province},
journal={International Journal on Advanced Science, Engineering and Information Technology},
year={2019},
volume={9},
number={6},
pages={1885-1891},
doi={10.18517/ijaseit.9.6.9076},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077575600&doi=10.18517%2fijaseit.9.6.9076&partnerID=40&md5=2d702ee63e4479a4454f70b2a4a37dd2},
affiliation={Industrial Engineering Department, Universitas Sumatera Utara, Medan, Indonesia; Industrial Engineering Department, Universitas Andalas, Padang, Indonesia},
abstract={Supply chain management covers integrated activities of planning, coordinating, and controlling of all business processes and practices in the supply chain to meet the demands of consumers at the lowest possible cost. Sustainable supply chain can be achieved by taking into account three pillars of sustainable development, namely, economy, social, and environment. Passion fruit agro-industry supply chain actors consist of farmers, collectors, agro-industries, and retailers. This study aims at identifying factors affecting the passion fruit agro-industry supply chain performance for sustainable micro, small, and medium enterprises and designing a supply chain performance measurement model of MSME passion fruit agro-industry with system dynamics. This research used a survey method through observations, in-depth interviews, and expert opinions. Data were processed using a combination of the hard system (system dynamics analysis) and soft system (expert opinions).The feedback structure along the passion fruit syrup agro-industry system was modeled in a causal loop diagram using system dynamics through version 10 of Powersim Studio software. It was then translated into a stock-flow diagram as the implementation of the simulation model. The model was verified using a computerized statistical analysis (Absolute Means Error) technique. To formulate recommendations and development strategy scenarios for sustainable MSME passion fruit agro-industry supply chain, a system approach method was used and expected to produce an effective and operational decision in accordance with the objectives previously set. The results obtained through expert opinions and Analytic Hierarchy Process (AHP) which have the highest weight with various conditions for each variable. Seven key variables were proven to influence the performance of passion fruit agro-industry supply chain, which were also used to design a sustainable MSME supply chain development scenario. The behavior of the sustainable passion fruit agro-industry supply chain model took the form of positive growth and the error rate of 7.43. Developing a sustainable MSMEPFASCPM can be carried out with a moderate strategy by developing: 1) pattern of good relationships among the actors, 2) gradual increase in the availability of raw materials, 3) environmental support capability, 4) gradual increase of profits, 5) moderate skills, 6) employment opportunities, and 7) land availability. © 2019 Insight Society.},
author_keywords={Agro-industry;  Supply chain;  Sustainable;  System dynamics},
references={Heizer, J., Reinder, B., (2014) Operations Management. Sustainability and Supply Chain Management, , Eleventh ed. Pearson, Boston; Chopra, S., Meindl, P., (2007) Supply chain management: strategy, planning, and operation, , [third edition]. New Jersey: Prentice Hall; Murthy, P.S., Naidu, M.M., Sustainable management of coffee industry by-products and value addition [A review] (2012) Resources, Conservation and Recycling, 66, pp. 45-58; Aprillya, M.R., Suryani, E., Dzulkarnain, A., System dynamics simulation model to increase paddy production for food security (2019) Journal of Information Systems Engineering and Business Intelligence, 5 (1). , http://ejournal.unair.ac.id/index.php/JISEBI; Brodowska, A.J., Nowak, A., Smigielski, K., Ozone in the food industry: Principles of ozone treatment, mechanisms of action, and applications: An overview (2018) Critical Reviews in Food Science and Nutrition, 58 (13); Bala, B.K., Bhuiyan, M.G.K., Alam, M.M., Arshad, F.M., Sidique, S.F., Alias, E.F., Modelling of supply chain of rice in Bangladesh (2017) International Journal of Systems Science: Operations & Logistics, 4 (2), pp. 181-197; Chapman, A., Darby, S., Evaluating sustainable adaptation strategies for vulnerable mega-deltas using system dynamics modelling: Rice agriculture in the Mekong Delta's An Giang Province, Vietnam (2016) Science of the Total Environment, pp. 326-338; Allena-Ozolina, S., Bazbauers, G., System Dynamics Model of Research, Innovation and Education System for Efficient Use of Bio-Resources (2017) Energy Procedia, 128 (2017), pp. 350-357; Wang, F.X., Lai, X., Shi, N., A multi-objective optimization for green supply chain network design (2011) Decision Support Systems, 51, pp. 262-269; Wu, Z., Pagell, M., Balancing priorities: Decision-making in sustainable supply chain management (2011) Journal of Operations Management, 29, pp. 577-590; Chaabane, A., Ramudhin, A., Paquet, M., Design of sustainable supply chains under the emission trading scheme (2012) Int. J. Production Economics, 135, pp. 37-49; Fleury, A.M., Davies, B., Sustainable supply chains-minerals and sustainable development, going beyond the mine (2012) Resources Policy, 37, pp. 175-178; Shiddekh, M.A.I., dan Erma, S., Model Sistem Dinamik Spasial Untuk Mengurangi Tingkat Kepadatan Ruas Jalan Utama Kota Surabaya Dengan Metode Smart Mobility (2018) Jurnal Teknik ITS, 7 (1), pp. 2337-3520. , (2301-928X Print); Barisa, A., Rosa, M., A System Dynamics Model for CO2 Emission Mitigation Policy Design in Road Transport Sector (2018) Energy Procedia, 147 (2018), pp. 419-427; Grigoryev, I., (2015) AnyLogic 7 in Three Days: A Quick Course in Simulation, , Modeling -2nd Edition. Ilya Grigoryev; Zailani, S., Jeyaraman, K., Vengadasan, G., Premkumar, R., Sustainable supply chain management (SSCM) in Malaysia: A Survey (2012) Int. J. Production Economics, , Article-inpress; Vachon, S., Mao, Z., Linking supply chain strength to sustainable development: A Country-Level Analysis (2008) Journal of Cleaner Production, 16, pp. 1552-1560; Zarei, M., Fakhrzad, M.B., Paghaleh, M.J., Food supply chain leanness using a developed QFD model (2011) Journal of Food Engineering, 102, pp. 25-33; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70, pp. 1088-1100; Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable supply management: an empirical study (2011) Int. J. Production Economics, , Article inpress; Cordero, P., Carbon footprint estimation for a sustainable improvement of supply chains: State of the art (2013) Journal of Industrial Engineering and Management, 6 (3), pp. 805-813; Gebresenbet, G., Bosona, T., Logistics and Supply Chains in Agriculture and Food (2012) Pathways to Supply Chain Excellence, , A. Groznik (ed.). In Tech Publisher; Munasinghe, M., (2010) Sustainomics framework and practical application, , MIND Press.Srilanka: Munasinghe Institute for Development},
publisher={Insight Society},
issn={20885334},
language={English},
abbrev_source_title={Int. J. Adv. Sci. Eng. Inf. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lemghari2019567,
author={Lemghari, R. and Sarsri, D. and Okar, C. and Es-Satty, A.},
title={Supply chain performance measurement in the automotive sector: A structured content analysis},
journal={Uncertain Supply Chain Management},
year={2019},
volume={7},
number={4},
pages={567-588},
doi={10.5267/j.uscm.2019.6.002},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068010127&doi=10.5267%2fj.uscm.2019.6.002&partnerID=40&md5=6e72c3fae9f7759b3f9356277dc2dfba},
affiliation={National School of Applied Sciences of Tangier, Abdelmalek Essaâdi University, Tangier, Morocco; National School of Applied Sciences of Berrechid, Hassan the 1st University, Settat, Morocco},
abstract={This study, both descriptive and exploratory, aims to examine the scientific literature on the supply chain performance measurement (SCPM) in the automotive sector over the last 12 years. The analysis featured 30 articles corresponding to the research topic of the Scopus database. The most pertinent articles, authors, keywords, countries, research laboratories and journals are presented using the analysis of bibliographies on the subject from 2007 to 2018 in the Industrial Engineering domain. The conclusion is performed by the systematic analysis that the automotive sector is well controlled on the subject of performance. The selected studies show that lack of understanding of the performance measurement system (PMS) and lack of management dedication are the main obstacles. Finally, various guidelines are provided to optimize the supply chain in the automotive sector. © 2019 by the authors; licensee Growing Science, Canada.},
author_keywords={Automotive sector;  Performance measurement;  Supply chain},
references={Abraham, M.R., Importance of a theoretical framework for research (2008) ACS Symposium Series, 976, pp. 47-66; Afonso, M.H., Souza, J.D., Ensslin, S.R., Ensslin, L., Como construir conhecimento sobre o tema de pesquisa? Aplicação do processo Proknow-C na busca de literatura sobre avaliação do desenvolvimento sustentável (2011) Revista De Gestão Social E Ambiental, 5 (2), pp. 47-62; Aracıoğlu, B., Zalluhoğlu, A.E., Candemir, C., Measuring and evaluating performance within the strategic management perspective: A study on performance measurement of a seafood company (2013) Procedia-Social and Behavioral Sciences, 99, pp. 1026-1034; Beamon, B.M., Supply chain design and analysis:: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Behrouzi, F., Wong, K.Y., An investigation and identification of lean supply chain performance measures in the automotive SMEs (2011) Scientific Research and Essays, 6 (24), pp. 5239-5252; Bolstorff, P., Rosenbaum, R., (2011) Supply Chain Excellence: a Handbook for Dramatic Improvement Using the SCOR Model, , Third Edition. Third Edit. AMACOM; New York; NY; Brandão, H.P., Guimarães, T.D.A., Gestão de competências e gestão de desempenho: Tecnologias distintas ou instrumentos de um mesmo construto? (2001) Revista De Administração De Empresas, 41 (1), pp. 8-15; Cagnin, F., Oliveira, M.C., Simon, A.T., Helleno, A.L., Vendramini, M.P., Proposal of a method for selecting suppliers considering risk management: An application at the automotive industry (2016) International Journal of Quality & Reliability Management, 33 (4), pp. 488-498; Cao, H., Dickson, M.A., Cobb, K., Carper, M., Scudder, C., Wong, C., Applying a sustainability performance measurement tool in designing and developing automotive employee uniforms (2015) International Journal of Fashion Design, Technology and Education, 8 (2), pp. 78-86; Carr, J.E., Hasan, M., An empirical study of performance measurement systems in New Zealand manufacturing companies (2009) Australian Journal of Mechanical Engineering, 7 (1), pp. 45-52; Chahid, M.T., El Alami, J., Soulhi, A., El Alami, N., Performance Measurement Model for Moroccan Automotive Suppliers Using PMQ and AHP (2014) Modern Applied Science, 8 (6), p. 137; Charan, P., Supply chain performance issues in an automobile company: A SAP‐LAP analysis (2012) Measuring Business Excellence, 16 (1), pp. 67-86; Charan, P., Shankar, R., Baisya, R.K., Modelling the barriers of supply chain performance measurement system implementation in the Indian automobile supply chain (2009) International Journal of Logistics Systems and Management, 5 (6), p. 614; Creswell, J.W., (2007) Projeto De Pesquisa: Métodos Qualitativo, Quantitativo E Misto, traduçao Luciana De Oliveira Da Rocha; Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D.M., Rogers, D.S., The supply chain management processes (2001) The International Journal of Logistics Management, 12 (2), pp. 13-36; Dörnhöfer, M., Günthner, W.A., A research and industry perspective on automotive logistics performance measurement (2017) The International Journal of Logistics Management, 28 (1), pp. 102-126; Dörnhöfer, M., Schröder, F., Günthner, W.A., Logistics performance measurement system for the automotive industry (2016) Logistics Research, 9 (1), p. 11; Drohomeretski, E., da Costa, S.E.G., de Lima, E.P., de Oliveira Neves, T.R., The Application of sustainable practices and performance measures in the automotive industry: A systematic literature review (2015) Engineering Management Journal, 27 (1), pp. 32-44; Eccles, R., The performance measurennent manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; van Eck, N., Waltman, L., Software survey: VOSviewer, a computer program for bibliometric mapping (2009) Scientometrics, 84 (2), pp. 523-538; El-Khalil, R., Simulation analysis for managing and improving productivity: A case study of an automotive company (2015) Journal of Manufacturing Technology Management, 26 (1), pp. 36-56; Ensslin, L., Ensslin, S.R., Uma análise bibliométrica da literatura sobre estratégia e avaliação de desempenho (2012) Gestão & Produção, 19 (1), pp. 59-78; Ensslin, S.R., Ensslin, L., de Oliveira Lacerda, R.T., de Souza, V.H.A., Disclosure of the state of the art of performance evaluation applied to project management (2014) American Journal of Industrial and Business Management, 4 (11), p. 677; Eskafi, S., Roghanian, E., Jafari-Eskandari, M., Designing a performance measurement system for supply chain using balanced scorecard, path analysis, cooperative game theory and evolutionary game theory: A Case Study (2015) International Journal of Industrial Engineering Computations, 6 (2), pp. 157-172; Eswaramurthi, K.G., Mohanram, P.V., Improvement of manufacturing performance measurement system and evaluation of overall resource effectiveness (2013) American Journal of Applied Sciences, 10 (2), p. 131; Habidin, N.F., Zubir, A.F.M., Fuzi, N.M., Latip, N.A.M., Azman, M.N.A., Sustainable Performance Measures for Malaysian Automotive Industry (2015) World Applied Sciences Journal, 33 (6), pp. 1017-1024; Fuchs, C., Beck, D., Lienland, B., Kellner, F., The role of IT in automotive supplier supply chains (2018) Journal of Enterprise Information Management, 31 (1), pp. 64-88; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement of the after-sales service network—Evidence from the automotive industry (2007) Computers in Industry, 58 (7), pp. 698-708; George, A.L., Bennett, A., (2005) Case Studies and Theory Development in the Social Sciences, , mit Press; Gholampour, G., Rahman, A., Abdul, B.I.N., The evaluation of operational performance of supply chain at automotive industry (2015) Journal of Theoretical & Applied Information Technology, 73 (3); Goldenstein, M., Casotti, B.P., Panorama do setor automotivo: As mudanças estruturais da indústria e as perspectivas para o Brasil (2008) BNDES Setorial, 28, pp. 147-188; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Hadiguna, R.A., Jaafar, H.S., Mohamad, S., Performance measurement for sustainable supply chain in automotive industry: A conceptual framework (2011) International Journal of Value Chain Management, 5 (3-4), pp. 232-250; Han, W., Huang, Y., Macbeth, D., Performance measurement of cross-culture supply chain partnership: A case study in the Chinese automotive industry (2018) International Journal of Production Research, 56 (7), pp. 2437-2451; Horner, M., Performance measurement (2006) Measurement, 1 (4), pp. 43-54; Humphrey, J., Memedovic, O., The global automotive industry value chain: What prospects for upgrading by developing countries (2003) SSRN; Jasiński, D., Meredith, J., Kirwan, K., A comprehensive framework for automotive sustainability assessment (2016) Journal of Cleaner Production, 135, pp. 1034-1044; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance the balanced scorecard—Measures that drive performance (1992) Harvard Business Review, pp. 71-79; Kennedy, J.M., Vargus, B., Challenges in survey research and their implications for philanthropic studies research (2001) Nonprofit and Voluntary Sector Quarterly, 30 (3), pp. 483-494; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2), pp. 213-229; Kumar, D., Garg, C.P., Evaluating sustainable supply chain indicators using fuzzy AHP: Case of Indian automotive industry (2017) Benchmarking: An International Journal, 24 (6), pp. 1742-1766; Bardin, L., (2016) Análise De Conteúdo, p. 70. , https://madmunifacs.files.wordpress.com/2016/08/anc3a1lise-de-contec3bado-laurence-bardin.pdf, São Paulo: Edições 70, São Paulo: Edições; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Leeuw, S.D., Beekman, L., Supply chain-oriented performance measurement for automotive spare parts (2008) International Journal of Automotive Technology and Management, 8 (1), p. 56; Lemghari, R., Okar, C., Sarsri, D., Benefits and limitations of the SCOR® model in Automotive Industries (2018) MATEC Web of Conferences, 200. , EDP Sciences; Lemghari, R., Okar, C., Sarsri, D., Supply Chain Performance Measurement: A Case Study about Applicability of SCOR® Model in Automotive Industry Firm (2018) MATEC Web of Conferences, 200, p. 16. , EDP Sciences, 2018; Marodin, G.A., Frank, A.G., Tortorella, G.L., Saurin, T.A., Contextual factors and lean production implementation in the Brazilian automotive supply chain (2016) Supply Chain Management: An International Journal, 21 (4), pp. 417-432; Marwah, A.K., Thakar, G., Gupta, R.C., Implications of performance measurement approaches on supply chain performance (2012) National Conference on Emerging Challenges for Sustainable Business, pp. 1731-1760; Mingers, J., Leydesdorff, L., A review of theory and practice in scientometrics (2015) European Journal of Operational Research, 246 (1), pp. 1-19; Mon, D.L., Cheng, C.H., Lin, J.C., Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight (1994) Fuzzy Sets and Systems, 62 (2), pp. 127-134; Moyano-Fuentes, J., Sacristán-Díaz, M., Jose Martinez-Jurado, P., Cooperation in the supply chain and lean production adoption: Evidence from the Spanish automotive industry (2012) International Journal of Operations & Production Management, 32 (9), pp. 1075-1096; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neto, M.S., Pires, S.R.I., Performance measurement in supply chains: A study in the automotive industry (2012) Gestao E Producao, 19 (4), pp. 733-746; Oliveira, A.S., de Barros, M.D., de Carvalho Pereira, F., Gomes, C.F.S., da Costa, H.G., Prospective scenarios: A literature review on the Scopus database (2018) Futures, 100, pp. 20-33; Olugu, E.U., Wong, K.Y., An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry (2012) Expert Systems with Applications, 39 (1), pp. 375-384; Ong, T.S., Teh, B.H., Factors influencing the design and use of performance measurement systems in the Malaysian electrical and electronics industry (2009) International Journal of Economics and Management, 2 (2), pp. 437-457; Osati, M., Manouchehr, M., Performance measurement of electricity suppliers using PROMETHEE and balance scorecard (2016) Management Science Letters, 6 (6), pp. 387-394; Petrillo, A., de Felice, F., Zomparelli, F., Performance measurement for world-class manufacturing: A model for the Italian automotive industry (2017) Total Quality Management & Business Excellence, pp. 1-28; Phongpetra, V., Johri, L.M., Impact of business strategies of automobile manufacturers in Thailand (2011) International Journal of Emerging Markets, 6 (1), pp. 17-37; Ptak, C.A., Schragenheim, E., (2003) ERP: Tools, Techniques, and Applications for Integrating the Supply Chain, , Crc Press; Ab Rahman, M.N., Khamis, N.K., Zain, R.M., Deros, B.M., Mahmood, W.H.W., Implementation of 5S practices in the manufacturing companies: A case study (2010) American Journal of Applied Sciences, 7 (8), pp. 1182-1189; Saleh, A., Mendeley (2012) Journal of the Canadian Health Libraries Association, 33 (1), pp. 29-30; Salleh, N.A.M., Kasolang, S., Jaafar, H.A., Review study of developing an integrated TQM with LM framework model in Malaysian automotive industry (2012) The TQM Journal, 24 (5), pp. 399-417; Zahoor Sarwar, S., Ishaque, A., Ehsan, N., Saeed Pirzada, D., Moeen Nasir, Z., Identifying productivity blemishes in Pakistan automotive industry: A case study (2012) International Journal of Productivity and Performance Management, 61 (2), pp. 173-193; Schulze, A., Paul Macduffie, J., Täube, F.A., Introduction: Knowledge generation and innovation diffusion in the global automotive industry—change and stability during turbulent times (2015) Industrial and Corporate Change, 24 (3), pp. 603-611; Sillanpää, I., Supply chain performance measurement in the manufacturing industry (2010) A Single Case Study Research to Develop a Supply Chain Performance Measurement Framework, , University of Oulu. Acta Univ. Oul; Sturgeon, T., van Biesebroeck, J., Gereffi, G., Value chains, networks and clusters: Reframing the global automotive industry (2008) Journal of Economic Geography, 8 (3), pp. 297-321; Thun, J.H., Hoenig, D., An empirical analysis of supply chain risk management in the German automotive industry (2011) International Journal of Production Economics, 131 (1), pp. 242-249; Vanalle, R.M., Santos, L.B., Green supply chain management in Brazilian automotive sector (2014) Management of Environmental Quality: An International Journal, 25 (5), pp. 523-541; Vaz, C., Rauen, T., Lezana, Á., Sustainability and innovation in the automotive sector: A structured content analysis (2017) Sustainability, 9 (6), p. 880; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60, pp. 53-60},
correspondence_address1={Lemghari, R.; National School of Applied Sciences of Tangier, Morocco; email: lemghari.radouane@gmail.com},
publisher={Growing Science},
issn={22916822},
language={English},
abbrev_source_title={Uncertain Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{NoAuthor2019,
title={14th Global Congress on Manufacturing and Management, GCMM 2018},
journal={Procedia Manufacturing},
year={2019},
volume={30},
page_count={662},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067312577&partnerID=40&md5=9287bffa733d3ab35a5b4e0143e6def6},
abstract={The proceedings contain 91 papers. The topics discussed include: a review on supply chain performance measurement systems; an analysis of heat distribution in the production of SiC process; evaluation of particle beam lithography for fabrication of metallic nano-structures; facilitating industry translation of custom 3D printed bone prostheses and scaffolds through quality by design; simulation on high speed train with driving algorithm and rules for operation image control; uncertain risk assessment modeling for bus body manufacturing supply chain using AHP and fuzzy AHP; A comparison of traditional manufacturing vs additive manufacturing, the best method for the job; and a review of dynamic job shop scheduling techniques.},
editor={Zhu Y., Gibson I., Yarlagadda P.K.D.V., Xavior A.M.},
publisher={Elsevier B.V.},
issn={23519789},
language={English},
abbrev_source_title={Procedia Manuf.},
document_type={Conference Review},
source={Scopus},
}

@CONFERENCE{JaganMohanReddy201940,
author={Jagan Mohan Reddy, K. and Neelakanteswara Rao, A. and Krishnanand, L.},
title={A review on supply chain performance measurement systems},
journal={Procedia Manufacturing},
year={2019},
volume={30},
pages={40-47},
doi={10.1016/j.promfg.2019.02.007},
note={cited By 42},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067289658&doi=10.1016%2fj.promfg.2019.02.007&partnerID=40&md5=c4eb11651292dd858889e7802f0ad42c},
affiliation={Department of Mechanical Engineering, National Institute of Technology Warangal, Warangal, 506004, India; Department of Mechanical Engineering, National Institute of Technology Warangal, Warangal, 506004, India},
abstract={In today's fierce global environment, continuous performance measurement is the key mantra for any kind of business successes. The performance measurement system is a framework to measure the efficiency of the supply chain. The purpose of this paper is to review the dearth of research into performance measurement systems in the context of the supply chain by reviewing the contemporary literature for the last two decades and evoking the potential avenues for future research. For this purpose, the authors have considered and classified the supply chain performance measurement systems as approaches and techniques and followed a systematic literature review procedure. Finally, this paper discloses that simulation techniques are more suitable than other performance techniques and approaches for the supply chain performance measurement in a volatile environment. The study also provides a strong basis for future researchers and academicians in applying the performance measurement systems in the context of the dynamic supply chain. © 2019 The Authors. Published by Elsevier Ltd.},
author_keywords={Supply chain modeling;  Supply chain performance measurement;  Supply chain simulation, Approaches;  Techniques},
references={Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20, pp. 1119-1145; Neely, A., Gregory, M., Platts, K., Performance measurement system design - A literature review and research agenda (1995) Int. J. Oper. Prod. Manag., 15, pp. 80-116; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol., 21, pp. 534-548; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) Int. J. Product. Perform. Manag., 55, pp. 242-258; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) Int. J. Prod. Res., 45, pp. 2819-2840; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) Int. J. Prod. Res., 48, pp. 5137-5155; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) Int. J. Product. Perform. Manag., 60, pp. 583-602; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000‐2011 (2012) International Journal of Productivity and Performance Management, 61, pp. 518-547; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) J. Oper. Supply Chain Manag., 6, pp. 94-113; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Computers in Industry82, pp. 135-150; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Ross-Smith, K.K., Yearworth, M., Dynamics of operational procurement: Systems modelling for performance tracking and auditing (2011) 29th Int. Conf. Syst. Dyn. Soc., pp. 1-22; Bullinger, H.-J., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) Int. J. Prod. Res., 40, pp. 3533-3543; Gunasekaran, A., Patel, C., Mcgaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87, pp. 333-347; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking An Int. J., 16, pp. 702-723; Wanous, M.A., Mohammed, A proposed value model for prioritising supply chain performance measures (2009) Int. J. Business Performance and Supply Chain Modelling, 1, pp. 115-128; Mishra, P., Sharma, R.K., Benchmarking SCM performance and empirical analysis: A case from paint industry (2014) Logist. Res., 7; Bagloee, S.A., Shnaiderman, M., Tavana, M., Ceder, A., A logic-based model for facility placement planning in supply chain management (2015) Int. J. Logist. Syst. Manag., 20, p. 122; Govindan, K., Mangla, S.K., Luthra, S., The Management of Operations Prioritising indicators in improving supply chain performance using fuzzy AHP: Insights from the case example of four Indian manufacturing companies (2017) Prod. Plan. Control., 7287, pp. 1-22; Qazi, A., Dickson, A., Quigley, J., Gaudenzi, B., Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks (2018) Int. J. Prod. Econ., 196, pp. 24-42; Venkatesh, V.G., Zhang, A., Deakins, E., Luthra, S., Mangla, S., A fuzzy AHP-TOPSIS approach to supply partner selection in continuous aid humanitarian supply chains (2018) Ann. Oper. Res.; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) Eur. J. Oper. Res., 144, pp. 306-320; Kaplan, R.S., Norton, D.P., (1992) The Balanced Scorecard - Measures That Drive Performance the Balanced Scorecard — Measures, , Harward Bus. Reivew; Anand, M., Sahay, B.S., Saha, S., Balanced scorecard in Indian companies (2005) Vikalpa, 30, pp. 11-25; Davis, E.W., Spekman, R.E., (2004) The Extended Enterprise: Gaining Competitive Advantage through Collaborative Supply Chains, , Pearson education; Chai, J., Liu, J.N.K., Ngai, E.W.T., Application of decision-making techniques in supplier selection: A systematic review of literature (2013) Expert Syst. Appl., 40, pp. 3872-3885; Trivedi, A., Rajesh, K., A framework for performance measurement in supply chain using balanced score card method: A case study (2013) Int. J. Recent Trends Mech. Eng., 1, pp. 20-23; Khanaposhtani, G.F., Jafari, S.S., Ariana, F., Formulating the supply chain strategy of automotive industry in Iran using balanced Scorecard, System Dynamics, and Game Theory (2017) Mark. Brand. Res., 4, pp. 135-147; Xia, D., Yu, Q., Gao, Q., Cheng, G., Sustainable technology selection decision-making model for enterprise in supply chain: Based on a modi fi ed strategic balanced scorecard (2017) J. Clean. Prod., 141, pp. 1337-1348; Rasolofo-Distler, F., Distler, F., Using the balanced scorecard to manage service supply chain uncertainty: Case studies in French real estate services (2018) Knowl. Process Manag., 25, pp. 129-142; Thanki, S., Thakkar, J., A quantitative framework for lean and green assessment of supply chain performance (2018) Int. J. Product. Perform. Manag., 67, pp. 366-400; Hoque, Z., James, W., Linking balanced scorecard measures to size and market factors: Impact on organizational performance (2000) J. Manag. Account. Res., 12, pp. 1-17; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus., 21, pp. 75-94; Li, L.L.T., (2010) An Integrated Framework for Supply Chain Performance Measurement Using Six-Sigma Metrics, pp. 387-406; Wong, W.P., Peng Wong, W., Yew Wong, K., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15, pp. 25-51; Ghatari, A.R., Mehralian, G., Zarenezhad, F., Rasekh, H., Developing a model for agile supply: An empirical study from Iranian pharmaceutical supply chain, Iran (2013) J. Pharm. Res., 12, pp. 189-201; Essajide, D., Rachidi, L., Planning and modelling of Pharmaceuticals Wholesale-Distributors supply Chain using SCOR model: A Moroccan case study I (2017) Int. J. Manag. Sci. Bus. Res., 6, pp. 35-51; Zuniga, J., Icarte, R., Griffiths, G., Lopez, J., Quezada, J., Modeling of critical products supply chain required to affected people on earthquakes and tsunamis through use of SCOR model (2018) Dynamics Logist. Proc. 6th Int. Conf. LDIC., pp. 55-58; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21, pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87, pp. 333-347; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Prod. Plan. Control., 18, pp. 666-680; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng., 53, pp. 43-62; Pramod, V.R., Banwet, D.K., Performance measurement of SHER service supply chain: A balanced score card - ANP approach (2011) Int. J. Bus. Excell., 4, p. 321; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (2013) Prod. Plan. Control., 24, pp. 702-720; Dollinger, S.J., Malmquist, D., Pearcy, D.H., Giunipero, L.C., Swafford, P.M., Ghosh, S., Murthy, N.N., Understanding supply chain management: Critical research and a theoretical framework (2009) International Journal of Production Economics, 1, pp. 231-241; Charkha, P.G., Jaju, S.B., Supply chain performance measurement system: An overview (2014) Int. J. Bus. Perform. Supply Chain Model., 6, pp. 40-60; Li, G., Yang, H., Sun, L., Sohal, A.S., The impact of IT implementation on supply chain integration and performance (2009) Int. J. Prod. Econ., 120, pp. 125-138; Kumar, R., Surendra, K., Information technology barriers in Indian sugar supply chain: An AHP and fuzzy AHP approach (2018) Benchmarking: An International Journal, 25, pp. 1978-1991; Charnes, E.R.A., Cooper, W.W., (1978) Measuring the Efficiency of Decision Making Units, 2, pp. 429-444; Peng Wong, W., Yew Wong, K., Supply chain performance measurement system using DEA modeling (2007) Ind. Manag. Data Syst., 107, pp. 361-381; Shafiee, M., Shams-E-Alam, N., Supply chain performance evaluation with rough data envelopment analysis (2011) Bus. Econ. Res., 1, pp. 57-61; Gallear, D., Ghobadian, A., Li, Y., Oregan, N., Childerhouse, P., Naim, M., An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams (2014) Prod. Plan. Control., 25, pp. 1182-1197; Boudaghi, E., Farzipoor Saen, R., Developing a novel model of data envelopment analysis–discriminant analysis for predicting group membership of suppliers in sustainable supply chain (2017) Comput. Oper. Res., pp. 1-12; Dobos, I., Vörösmarty, G., Magyar, B., Körútja, T., Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA) (2018) Int. J. Prod. Econ., pp. 1-2; Omrani, H., Keshavarz, M., Ghaderi, S.F., Evaluation of supply chain of a shipping company in Iran by a fuzzy relational network data envelopment analysis model (2018) Sci. Iran., 25, pp. 868-890; Özbayrak, M., Papadopoulou, T.C., Akgun, M., Systems dynamics modelling of a manufacturing supply chain system (2007) Simul. Model. Pract. Theory., 15, pp. 1338-1355; Sunil, C., Meindl, P., Supply chain management (2007) Strategy, Planning & Operation, Das Summa Summarum Des Management, pp. 265-275; Chiu, M.C., Okudan, G., An investigation on the impact of product modularity level on supply chain performance metrics: An industrial case study (2014) J. Intell. Manuf., 25, pp. 129-145; Fan, C., Fan, S.S.K.S., Wang, C.S., Tsai, W.P., Modeling computer recycling in Taiwan using system dynamics (2018) Resour. Conserv. Recycl., 128, pp. 167-175; Langroodi, R.R.P., Amiri, M., A system dynamics modeling approach for a multi-level, multi-product, multi-region supply chain under demand uncertainty (2016) Expert Syst. Appl., 51, pp. 231-244; Li, S., He, Y., Chen, L., Dynamic strategies for supply disruptions in production-inventory systems (2016) Int. J. Prod. Econ., pp. 1-14; Long, Q., Knowledge-Based Systems Data-driven decision making for supply chain networks with agent-based computational experiment (2018) Knowledge-Based Syst, 141, pp. 55-66},
correspondence_address1={Krishnanand, L.; Department of Mechanical Engineering, India; email: lanka@nitw.ac.in},
editor={Zhu Y., Gibson I., Yarlagadda P.K.D.V., Xavior A.M.},
publisher={Elsevier B.V.},
issn={23519789},
language={English},
abbrev_source_title={Procedia Manuf.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Nattassha201957,
author={Nattassha, R. and Handayati, Y. and Yudoko, G. and Simatupang, T.M. and Adhiutama, A. and Mulyono, N.B.},
title={Performance measurement system for the cold fish supply chain: The case of National Fish Logistics System in Indonesia},
journal={International Journal of Agricultural Resources, Governance and Ecology},
year={2019},
volume={15},
number={1},
pages={57-76},
doi={10.1504/IJARGE.2019.099805},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066031027&doi=10.1504%2fIJARGE.2019.099805&partnerID=40&md5=734b1c523cd0f7d4822bb71598d0d8db},
affiliation={School of Business and Management, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung, Indonesia},
abstract={While fisheries represent one of the most important economic sectors in Indonesia, the industry still experiences problems related to imbalances in product distribution. The volume of fish products might be abundant in one area, but scarce in another, a problem which has prompted the Ministry of Maritime Affairs and Fisheries to develop a logistics system for fisheries, the National Fish Logistics System (Sistem Logistik Ikan Nasional or SLIN). To monitor the implementation of the SLIN, a tool is required to assess its performance. This research aims to develop such a performance measurement tool. Through a combination of analysing previous research and conducting focus group discussions on the implementation goals of the SLIN, seven performance indicators have been devised together with their respective weightings from the perspective of SLIN stakeholders. These performance indicators can be used to measure SLIN performance in different geographical areas, in addition to identifying the areas and aspects that should be prioritised. Copyright © 2019 Inderscience Enterprises Ltd.},
author_keywords={Cold chain;  Fish supply chain;  National Fish Logistics System;  Performance measurement system},
references={Amnifu, D., Campaign highlights importance of fish consumption (2017) The Jakarta Post, , https://www.thejakartapost.com/news/2017/04/29/campaign-highlights-importanceof-fish-consumption.html, online accessed 3 December 2017; Azhar, M., Suhartoyo, S., Suharso, P., Herawati, V.E., Trihastuti, N., Prospect on implementation of National Fish Logistics System: Case in Indonesia (2018) E3S (Environment, Energy and Earth Sciences) Web of Conferences, the 2nd Scientific Communication in Fisheries and Marine Sciences (SCiFiMaS 2018), 47; (2017) Unsafe' Frozen Products Being Recalled, , http://www.bbc.com/news/uk-scotland-38618700, online accessed 2 November 2017; Begum, H., Siwar, C., Alam, A.S.A.F., Choy, E.A., Ishak, S., Alam, L., Enhancing sustainability amongst oil palm smallholders in Malaysia (2018) International Journal of Agricultural Resources, Governance and Ecology, 14 (1), pp. 62-79; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; (2018) Trends in Marine Resources and Fisheries Management in Indonesia: A 2018 Review, , California Environmental Associates California Environmental Associates, California; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Donnelly, K.A.-M., Olsen, P., Catch to landing traceability and the effects of implementation: A case study from the Norwegian white fish sector (2012) Food Control, 27 (1), pp. 228-233; Donnelly, K.A.-M., Thakur, M., Sakai, J., Following the mackerel - Cost and benefits of improved information exchange in food supply chains (2013) Food Control, 33 (1), pp. 25-31; (2016) The State of World Fisheries and Aquaculture 2016: Contributing to Food Security and Nutrition for All, , FAO Food and Agriculture Organization, Rome; Galvão, J.A., Margeirsson, S., Garate, C., Viðarsson, J.R., Oetterer, M., Traceability system in cod fishing (2010) Food Control, 21 (10), pp. 1360-1366; Gorospe, K.D., Michaels, W., Pomeroy, R., Elvidge, C., Lynch, P., Wongbusarakum, S., Brainard, R.E., The mobilization of science and technology fisheries innovations towards an ecosystem approach to fisheries management in the Coral Triangle and Southeast Asia (2016) Marine Policy, 74, pp. 143-152; Grunow, M., Piramuthu, S., RFID in highly perishable food supply chains - Remaining shelf life to supplant expiry date? (2013) International Journal of Production Economics, 146 (2), pp. 717-727; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Henson, S., Brouder, A.M., Mitullah, W., Food safety requirements and food exports from developing countries: The case of fish exports from Kenya to the European Union (2000) American Journal of Agricultural Economics, 82 (5), pp. 1159-1169; Hoque, M.S., Jacxsens, L., De Meulenaer, B., Alam, A.K.M.N., Quantitative risk assessment for formalin treatment in fish preservation: Food safety concern in local market of Bangladesh (2016) Procedia Food Science, 6, pp. 151-158; Hsu, Y.C., Chen, A.P., Wang, C.H., A RFID-enabled traceability system for the supply chain of live fish (2008) IEEE International Conference Automation and Logistics, ICAL 2008, pp. 81-86; (2017) Yummy Market Brand Fish Products Recalled Due to Potential Presence of Dangerous Bacteria - Updated Food Recall Warning - Canadian Food Inspection Agency, , http://www.inspection.gc.ca/about-the-cfia/newsroom/food-recall-warnings/completelisting/2017-10-19/eng/1508459830872/1508459834472, online accessed 2 November 2017; Joshi, R., Banwet, D.K., Shankar, R., A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain (2011) Expert Systems with Applications, 38 (8), pp. 10170-10182; Karlsen, K.M., Donnelly, K.A.-M., Olsen, P., Granularity and its importance for traceability in a farmed salmon supply chain (2011) Journal of Food Engineering, 102 (1), pp. 1-8; Karlsen, K.M., Sørensen, C.F., Forås, F., Olsen, P., Critical criteria when implementing electronic chain traceability in a fish supply chain (2011) Food Control, 22 (8), pp. 1339-1347; Kashav, S., Cerchione, R., Singh, R., Centobelli, P., Shabani, A., Food cold chain management: From a structured literature review to a conceptual framework and research agenda (2018) International Journal of Logistics Management, 29 (3), pp. 792-821; Mercier, S., Villeneuve, S., Mondor, M., Uysal, I., Time-temperature management along the food cold chain: A review of recent developments (2017) Comprehensive Reviews in Food Science and Food Safety, 16 (4), pp. 647-667; Micheni, A., Mburu, D., Kanampiu, F., Mugai, N., Kihanda, F., Glyphosate-based herbicides on weeds management and maize performance under conservation agriculture practices in eastern Kenya (2014) International Journal of Agricultural Resources, Governance and Ecology, 10 (3), pp. 257-268; (2014) Ministerial Decree of Maritime Affairs and Fisheries No. 5 Year 2014 Regarding National Fish Logistics System (SLIN), , http://jdih.kkp.go.id/peraturan/5-permen-kp-2014.pdf, Ministry of Maritime Affairs and Fisheries Jakarta, Indonesia online accessed 2 November 2017; Parreño-Marchante, A., Alvarez-Melcon, A., Trebar, M., Filippin, P., Advanced traceability system in aquaculture supply chain (2014) Journal of Food Engineering, 122 (1), pp. 99-109; Randrup, M., Storøy, J., Lievonen, S., Margeirsson, S., Árnason, S.V., Ólavsstovu, D.Í., Møller, S.F., Frederiksen, M.T., Simulated recalls of fish products in five Nordic countries (2008) Food Control, 19 (11), pp. 1064-1069; Lack of cold storage hampers fish processing industry (2016) The Jakarta Post, , https://www.thejakartapost.com/news/2016/01/02/lack-cold-storage-hampers-fishprocessing-industry.html, The Jakarta Post online accessed 3 December 2017; Thorpe, A., Bennett, E., Market-driven international fish supply chains: The case of Nile perch from Africa's Lake Victoria (2004) International Food and Agribusiness Management Review, 7 (4), pp. 40-57; (2012) Coastal and Marine Ecosystems - Marine Jurisdictions: Coastline Length, , https://web.archive.org/web/20120419075053/http://earthtrends.wri.org/text/coastal-marine/variable-61.html, online accessed 3 December 2017},
correspondence_address1={Nattassha, R.; School of Business and Management, Jl. Ganesha 10, Indonesia; email: ruth_nattassha@sbm-itb.ac.id},
publisher={Inderscience Publishers},
issn={14624605},
coden={IJARD},
language={English},
abbrev_source_title={Int. J. Agric. Resour. Gov. Ecol.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Tripathi201927,
author={Tripathi, S. and Gupta, M.},
title={A current review of supply chain performance measurement systems},
journal={Lecture Notes in Mechanical Engineering},
year={2019},
pages={27-39},
doi={10.1007/978-981-13-6412-9_4},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065426564&doi=10.1007%2f978-981-13-6412-9_4&partnerID=40&md5=0bf07025caaecb4d823bcd280385261a},
affiliation={Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Allahabad, India},
abstract={A supply chain is the network of entities (individual or organisation) who work together to fulfil customer demands efficiently with maximum profits without compromising quality of product or service. Traditionally, supply chains were assessed based on financial measures but increasing competition and changing scenarios. Supply chains today are assessed based on various non-financial parameters such as customer satisfaction, sustainability and resilience. Performance measurement quantifies the extent up to which a supply chain is achieving its perceived objectives based on performance indicators. With diverse sectors of industry and varying goals of different supply chains, the performance indicators vary as well. The paper intends to provide a critical review on supply chain performance measurement literature by compiling key performance indicators for various sectors to understand the diversity and complication of performance measurement systems. © Springer Nature Singapore Pte Ltd. 2019.},
author_keywords={KPI;  Performance measurement;  Supply chain;  Supply chain performance},
keywords={Benchmarking;  Customer satisfaction, 'current;  Customer demands;  KPI;  Maximum profits;  Performance indicators;  Performance measurement system;  Performance measurements;  Quality of product;  Supply chain performance;  Supply chain performance measurements, Supply chains},
references={Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London; Mentzer, J.T., Keebler, J.S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) J Bus Logist, 22, pp. 1-25; Kaplan, R.S., Norton, D.P., The balanced scorecard—measures that drive performance (1992) Harv Bus Rev, pp. 71-79; Keegan, D.P., Robert, G., Charles, R., Are your performance measures obsolete? (1989) Manag Account Res, 12, p. 45; Lynch, R.L., Cross, K.F., (1991) Measure Up! the Essential Guide to Measuring Business Performance, , Mandarin, London; Brignall, T.J., Fitzgerald, L., Johnston, R., Silvestro, R., Performance measurement in service businesses (1991) Manag Account, 69, p. 34; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part II (2001) Am Account Assoc, 15, pp. 147-160; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., (2000) Performance Measurement System Design: Developing and Testing a Process-Based Approach; Neely, A., Gregory, M., Platts, K., Neely, A., Gregory, M., Platts, K., (2002) A Literature Review and Research Agenda; Neely, A., (2004) The Performance Measurement Revolution: Why Now and What Next ?; Gupta, M., Narain, R., Investigation into barriers to adoption of e-procurement and measures of performance (2012) Int J Procure Manag, 5, pp. 567-607; Balfaqih, H., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998-2015 (2016) Comput Ind, 82, pp. 135-150; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) Int J Product Perform Manag, 61, pp. 518-547. , https://doi.org/10.1108/17410401211232957; Maestrini, V., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2016) Intern J Prod Econ, , https://doi.org/10.1016/j.ijpe.2016.11.005; Lima-Junior, R.F., Carpinetti, L.C.R., Quantitative models for supply chain performance evaluation: A literature review (2017) Comput Ind Eng, , https://doi.org/10.1016/j.cie.2017.09.022; Bulsara, H.P., Qureshi, M.N., Patel, H., Supply chain performance measurement—an exploratory study (2014) Int J Logist Syst Manag, 18, pp. 231-249; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) Int J Prod Res, 48, pp. 5137-5155. , https://doi.org/10.1080/00207540903089536; Haene, C.D., Verlinde, S., Macharis, C., Measuring while moving (Humanitarian supply chain performance measurement—status of research and current practice) (2015) J Humanit Logist Supply Chain Manag, 5, pp. 146-161. , https://doi.org/10.1108/JHLSCM-04-2013-0016; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management—what has been achieved? (2015) Supply Chain Manag an Int J, 20, pp. 664-680. , https://doi.org/10.1108/SCM-06-2015-0216; Abidi, H., de Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: A systematic literature review (2014) Supply Chain Manag an Int J, 19, pp. 593-608. , https://doi.org/10.1108/SCM-09-2013-0349; Estampe, D., Lamouri, S., Paris, J., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Intern J Prod Econ, 142, pp. 247-258. , https://doi.org/10.1016/j.ijpe.2010.11.024; Katiyar, R., Barua, M.K., Modelling the barriers of supply chain performance measurement in the Indian automotive industries (2014) Int J Intercult Inf Manag, 4, pp. 51-66; Ramaa, A., Subramanya, K.N., Rangaswamy, T.M., Performance measurement system of supply chain—an empirical study (2013) Int J Bus Perform Supply Chain Model, 5, pp. 343-360; Sillanpää, I., Empirical study of measuring supply chain performance (2015) Benchmark Int J, 22, pp. 290-308. , https://doi.org/10.1108/BIJ-01-2013-0009; Katiyar, B.M., Meena, P., Modelling the measures of supply chain performance in the Indian automotive industry (2015) Benchmark Int J, 22, pp. 665-696. , https://doi.org/10.1108/BIJ-09-2014-0091; Bulsara, H.P., Qureshi, M.N., Green supply chain performance measurement: An exploratory study (2016) Int J Logist Syst Manag, 23, pp. 476-498; Das, D., Development and validation of a scale for measuring sustainable supply chain management practices and performance (2017) J Clean Prod, , https://doi.org/10.1016/j.jclepro.2017.07.006; Mishra, D., Gunasekaran, A., Papadopoulos, T., Hazen, B., (2017) Green Supply Chain Performance Measures: A Review and Bibliometric Analysis. Sustain Prod Consum, , https://doi.org/10.1016/j.spc.2017.01.003; Gawankar, S., Kamble, S., Raut, R., Development, measurement and validation of supply chain performance measurement (SCPM) scale in Indian retail sector (2016) Benchmark Int J, 23, pp. 25-60. , https://doi.org/10.1108/BIJ-06-2013-0068; Jothimani, D., Samarah, S., Supply chain performance measurement for third party logistics (2014) Benchmark Int J, 21, pp. 944-963. , https://doi.org/10.1108/BIJ-09-2012-0064; Chithambaranathan, P., Subramanian, N., Subramanian, P., An innovative framework for performance analysis of members of supply chains (2015) Benchmark Int J, 22, pp. 309-334. , https://doi.org/10.1108/bij-11-2012-0081; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) Int J Logist Syst Manag, 18, pp. 216-230; Dhone, N.C., Kamble, S.S., Development and validation of an integrated supply chain operational performance model for Indian automobile industry (2016) Int J Procure Manag, 9; Golghate, C.D., Pawar, M.S., Measurement and analysis of the plastic films green supply chain performance (2013) Int J Intell Enterp, 2; Sahu, A., Datta, S., Mahapatra, S., Use of IVFNs and MULTIMOORA method for supply chain performance measurement, benchmarking and decision-making: An empirical study (2014) Int J Bus Excell, 7, pp. 237-280; Joshi, R., Banwet, D.K., Shankar, R., Gandhi, J., Performance improvement of cold chain in an emerging economy (2012) Prod Plan Control Manag Oper, 23, pp. 817-836. , https://doi.org/10.1080/09537287.2011.642187; Tewari, S., Misra, M., Developing supply chain evaluation framework through performance assessment approach (2013) Int J Bus Perform Supply Chain Model, 5, pp. 28-45; Tarasewicz, R., Integrated approach to supply chain performance measurement—results of the study on Polish market (2016) Transp Res Procedia, 14, pp. 1433-1442. , https://doi.org/10.1016/j.trpro.2016.05.216; Butzer, S., Schötz, S., Petroschke, M., Steinhilper, R., Development of a performance measurement system for international reverse supply chains (2017) Procedia CIRP, 61, pp. 251-256. , https://doi.org/10.1016/j.procir.2016.11.264; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Ind Manag Data Syst, 110, pp. 1297-1318. , https://doi.org/10.1108/02635571011087400; Kirwan, J., Maye, D., Brunori, G., Acknowledging complexity in food supply chains when assessing their performance and sustainability (2017) J Rural Stud, 52, pp. 21-32. , https://doi.org/10.1016/j.jrurstud.2017.03.008; Mara, U.T., Alam, S., Performance measurement for sustainable supply chain in automotive industry: A conceptual framework (2011) Int J Value Chain Manag, 5, pp. 232-250; Santiteerakul, S., Sekhari, A., Bouras, A., Sopadang, A., Sustainability performance measurement framework for supply chain management (2015) Int J Prod Dev, 20, pp. 221-238; Fritz, M.M.C., Baumgartner, R.J., Sch, J., Toward supply chain-wide sustainability assessment: A conceptual framework and an aggregation method to assess supply chain performance (2016) J Clean Prod, pp. 1-14. , https://doi.org/10.1016/j.jclepro.2016.04.035; Uysal, F., An integrated model for sustainable performance measurement in supply chain (2012) Procedia Soc Behav Sci, 62, pp. 689-694. , https://doi.org/10.1016/j.sbspro.2012.09.117; Bai, C., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Manag an Int J, 17, pp. 78-92. , https://doi.org/10.1108/13598541211212221; Taticchi, P., Tonelli, F., Pasqualino, R., Taticchi, P., Performance measurement of sustainable supply chains: A literature review and a research agenda (2013) Int J Product Perform Manag Perform, 62, pp. 782-804. , https://doi.org/10.1108/IJPPM-03-2013-0037; Tajbakhsh, T., Hassini, H., Performance measurement of sustainable supply chains: A review and research questions (2015) Int J Product Perform Manag, 64, pp. 744-783. , https://doi.org/10.1108/IJPPM-03-2013-0056; Liebetruth, T., Sustainability in performance measurement and management systems for supply chains (2017) Procedia Eng, 192, pp. 539-544. , https://doi.org/10.1016/j.proeng.2017.06.093; Yakovleva, N., Sarkis, J., Sloan, T., Sustainable benchmarking of supply chains: The case of the food industry (2012) Int J Prod Res, 50, pp. 1297-1317. , https://doi.org/10.1080/00207543.2011.571926; Carvalho, H., Azevedo, S.G., Supply chain performance management: Lean and green paradigms (2010) Int J Bus Perform Supply Chain Model, 2, pp. 304-333; Mondragon, A.E.C., Lalwani, C., Mondragon, C.E.C., Mondragon, A.E.C., Lalwani, C., Measures for auditing performance and integration in closed-loop supply chains (2014) Supply Chain Manag an Int J, 16, pp. 43-56. , https://doi.org/10.1108/13598541111103494; Kafa, N., Hani, Y., Mhamedi, A.E.L., Sustainability performance measurement for green supply chain management (2013) Proceedings of the Volumes IFAC, , https://doi.org/10.3182/20130911-3-br-3021.00050; Kumar, P., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (2013) Prod Plan Control, 24, pp. 702-720; Ferreira, L.M.D.F., Silva, C., Azevedo, S.G., Silva, C., An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM) (2016) Benchmark Int J, 23, pp. 1398-1422. , https://doi.org/10.1108/BIJ-08-2013-0087; Zaman, K., Ahsan, A., Lean supply chain performance measurement (2014) Int J Product Perform Manag, 63, pp. 588-612. , https://doi.org/10.1108/IJPPM-05-2013-0092; Damghani, K., Fard, M., Abtahi, A., A fuzzy two-stage DEA approach for performance measurement: Real case of agility performance in dairy supply chains (2012) Int J Appl Decis Sci, 5, pp. 293-317; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput Ind Eng, 62, pp. 801-818. , https://doi.org/10.1016/j.cie.2011.11.014; Yang, J., On the construction and implementation methods for performance measurement of reverse supply chain (2010) 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010), pp. 899-903; Pungchompoo, S., Sopadang, A., A supply chain performance measurement improved with integrated methods FDSM, MOO and DEA part I: A conceptual of performance measurement framework in Thailand: Frozen shrimp chains (2010) Proceedings of the 2010 IEEE IEEM, pp. 235-239; Santos, T.F., Leite, M.S., Performance measurement system in supply chain management: Application in the service sector (2016) Int J Serv Oper Manag, 23, pp. 298-315; Pungchompoo, S., Sopadang, A., Confirmation and evaluation of performance measurement model for the Thai frozen shrimp chain (2015) Bus Process Manag J, 21, pp. 837-856. , https://doi.org/10.1108/BPMJ-06-2014-0053; Wibowo, M.A., Nur, M., The analysis of supply chain performance measurement at construction project (2015) Procedia Eng, 125, pp. 25-31. , https://doi.org/10.1016/j.proeng.2015.11.005; Chiadamrong, N., Sophonsaritsook, P., Relationships between supply chain capabilities, competitive advantage and business performance: An exploratory study of the food industry in Thailand (2015) Int J Logist Syst Manag, 20, pp. 447-479; Kusrini, E., Subagyo Masruroh, N., A new approach to design supply chain key performance indicator for actors and regulator: A case study in innovative product in Indonesia (2016) Int J Bus Perform Manag, 17, pp. 1-29; Azevedo, S., Carvalho, H., Machado, V.C., Using interpretive structural modelling to identify and rank performance measures (2013) An Application in the Automotive. Balt J Manag, 8, pp. 208-230. , https://doi.org/10.1108/17465261311310027; Bai, C., Sarkis, J., Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Manag an Int J, 19, pp. 275-291. , https://doi.org/10.1108/SCM-12-2013-0441; Wang, C., Performance evaluation of heterogeneous bank supply chain systems from the perspective of measurement and decomposition (2017) Comput Ind Eng, , https://doi.org/10.1016/j.cie.2017.05.028; Jauhar, S.K., Pant, M., Nagar, A.K., Sustainable educational supply chain performance measurement through DEA and differential evolution: A case on Indian HEI (2016) J Comput Sci, , https://doi.org/10.1016/j.jocs.2016.10.007; Rezaei, M., Shirazi, M., Karimi, B., IoT-based framework for performance measurement (2017) Ind Manag Data Syst, 117, pp. 688-712. , https://doi.org/10.1108/IMDS-08-2016-0331; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things: Toward more practical SCPMS (2017) Ind Manag Data Syst, 117, pp. 267-286. , https://doi.org/10.1108/imds-03-2016-0096; Teimoury, E., Fathain, M., Chambar, I., Automation of the supply chain performance measurement based on multi-agent system (2013) Int J Agil Syst Manag, 6, pp. 25-42; Liu, F.F., Liu, Y., A methodology to assess the supply chain performance based on virtual-gap measures (2017) Comput Ind Eng, , https://doi.org/10.1016/j.cie.2017.06.010; Laihonen, H., Pekkola, S., Impacts of using a performance measurement system in supply chain management: A case study (2016) Int J Prod Res, , https://doi.org/10.1080/00207543.2016.1181810; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: An exploratory study (2015) Int J Product Perform Manag, 64, pp. 1068-1091. , https://doi.org/10.1108/IJPPM-04-2014-0064; Supeekit, T., Somboonwiwat, T., Kritchanchai, D., DEMATEL-modified ANP to evaluate internal hospital supply chain performance (2016) Comput Ind Eng, , https://doi.org/10.1016/j.cie.2016.07.019; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains (2011) Int J Product Perform Manag, 60, pp. 583-602. , https://doi.org/10.1108/17410401111150760; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28, pp. 249-260. , https://doi.org/10.1108/02632771011031493; Banomyong, R., Supatn, N., Banomyong, R., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Manag an Int J, 16, pp. 20-31. , https://doi.org/10.1108/13598541111103476; Lauras, M., A business process oriented method to design supply chain performance measurement systems (2011) Jacques Lamothe and Hervé Pingaud. Int J Bus Perform Manag, 12, pp. 354-376; Stewart, G., Supply-chain operations reference model (SCOR): The first framework for integrated supply-chain (1997) Logist Inf Manag Supply-Chain, 10, pp. 62-67; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int J Oper Prod Manag, 21, pp. 71-87; Panjehfouladgaran, H., Yusuf, R., Fuzzy performance measurement for supply chain management in Malaysian rubber glove manufacturer (2016) Int J Logist Syst Manag, 24, pp. 178-199; Pettersson, A.I., Anders, S., Performance measurements in supply chains within Swedish industry (2011) Int J Logist Syst Manag, 9, pp. 69-88; André, A., Callado, C., Jack, L., Balanced scorecard metrics and specific supply chain roles (2015) Int J Product Perform Manag, 64, pp. 288-300. , https://doi.org/10.1108/IJPPM-05-2014-0071; Haavisto, I., Goentzel, J., Measuring humanitarian supply chain performance in a multi-goal context (2015) J Humanit Logist Supply Chain Manag, 5, pp. 300-324. , https://doi.org/10.1108/JHLSCM-07-2015-0028; Whitten, G.D., Jr, K.W.G., Zelbst, P.J., Whitten, G.D., Triple-A supply chain performance (2012) Int J Oper Prod Manag, 32, pp. 28-48. , https://doi.org/10.1108/01443571211195727; Chan, F.T.S., Nayak, A., Raj, R., Chong, A.Y., Manoj, T., An innovative supply chain performance measurement system incorporating research and development (R & D) and marketing policy (2014) Comput Ind Eng, 69, pp. 64-70. , https://doi.org/10.1016/j.cie.2013.12.015; Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) Int J Product Perform Manag, 64, pp. 784-810. , https://doi.org/10.1108/IJPPM-11-2013-0185; Ramanathan, U., Performance of supply chain collaboration—a simulation study (2014) Expert Syst Appl, 41, pp. 210-220. , https://doi.org/10.1016/j.eswa.2013.07.022},
correspondence_address1={Tripathi, S.; Department of Mechanical Engineering, India; email: shubs0508@gmail.com},
publisher={Pleiades journals},
issn={21954356},
language={English},
abbrev_source_title={Lect. Notes Mech. Eng.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{NoAuthor2019,
title={24th International Joint Conference on Industrial Engineering and Operations Management, IJCIEOM 2018},
journal={Springer Proceedings in Mathematics and Statistics},
year={2019},
volume={281},
page_count={568},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065313383&partnerID=40&md5=3c20c765da6f195c84674cf3c83d2d2e},
abstract={The proceedings contain 49 papers. The special focus in this conference is on Industrial Engineering and Operations Management. The topics include: Quality control in the context of industry 4.0; aligning e-service attributes for hedonic and utilitarian consumption: An exploratory study in the context of consumer electronics; extracting relevant quality dimensions from online customer reviews in accommodation services; internet of things (IoT): Technological indicators from patent analysis; Insertion of risk management in quality management systems with the advent of ISO 9001:2015: Descriptive and content analyzes; occupational stress and job satisfaction among brazilian managers: The case of commercial area; exploring complexity in sustainable biomass supply chain management; supply chain governance: A study in wineries from Brazil and Spain; productivity increase in a cellular battery line using lean kaizen and tools; lean healthcare project leader: A framework based on functions and competencies; flexibility practices in disaster response—a process approach based evaluation; a literature review of mathematical programming applications in the fresh agri-food supply chain; a bibliographic review on social technologies; the importance of education in enhancing the innovation capacity in serbia; descriptive evaluation in the public transport service—a study about of the satisfaction of customers; new product development in the context of industry 4.0: Insights from the automotive components industry; risk analysis of a scientific conference organization: A Brazilian case; preface; Performance measurement system to continuously improve a Brazilian industrial engineering program: A process to ABET accreditation; QR code based signage to support automated driving systems on rural area roads.},
editor={Melao N., Reis J., Pinelas S.},
publisher={Springer New York LLC},
issn={21941009},
isbn={9783030149727},
language={English},
abbrev_source_title={Springer Proc. Math. Stat.},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Al-Mehannah2019309,
author={Al-Mehannah, H.G. and Jasim, F.A. and Al-Mousawi, H.A.H.},
title={Social and economic impacts of supply chain performance measurement and productivity with particular reference to Iraq},
journal={International Journal of Supply Chain Management},
year={2019},
volume={8},
number={2},
pages={309-316},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064969056&partnerID=40&md5=ab72ec9564ac97ec5012bd802f6f0633},
affiliation={College of Management and Economy, University of Kufa, Iraq; Ministry of Labor and Social Issues, Russian Federation},
abstract={A lot of countries suffer from the large size of the informal economy as some estimates suggest that the volume of the informal economy in the economies of developing countries, represents a range from a high percentage of (30% -70%) of the gross domestic product of these countries. It means that a large proportion of economic activities in developing countries is operated with a level less than optimal level of operation and with high cost, which limits the benefit from the resources and energies available to these facilities that operate within this economy. In the 1980s, we witnessed the deployment of a number of new production strategies, such as timely production, lean manufacturing, Kanban systems and comprehensive quality management. These new production strategies were used by a number of companies to improve the management system, increase production and improve process quality, and reduce waste production and inventory costs, in order to improve competition in various markets. Recently, while these strategies focus more on improving supply chain management to achieve their goals, henceforth a number of developed supply chain management strategies can be analyzed with the help of a computer. Choice-based decision support tools play a crucial role in collaborating and managing decision support markets from a set of multiple component supply chains. This study concludes that the reason for the spread and growth of informal economy is related to economic, social and legal factors, which include the lack of employment opportunities that have resulted from decrease in the number of projects in public and private sectors and their limitations as well as some obstacles, the most important of which, is the expansion of financial and administrative corruption. In addition to that, the high volume of the informal economy has positive effects and other negative ones that exceed the positive effects on economy, and the most important of these effects are tax evasion, lack of social protection for workers, and deformation of data and statistics. Therefore, this study has recommended that state should give more attention to this economy by collecting and analyzing data and information on the labor market, preparing studies that show its economic feasibility, enhancing and expanding the extension of social protection, health, and occupational safety, improving governance climate in the country, strengthening the ruling of law and the legislations of labor law, improving the business environment for micro and small enterprises, establishing industrial and commercial complexes, facilitating the extraction of activity licenses, investing in education, and developing vocational training centers. © ExcelingTech Pub, UK.},
author_keywords={Business environment;  Economic Impacts;  Occupational safety;  Productivity;  Supply Chain Performance},
references={Ahmed, M., (2011) Inclusion of Informal Sector under Formal Umbrella, , http://blog-montada.imf.org, paper published online . 16 Nov; Bu Dalal, A., Informal Sector in Algerian Labour Market: Analytical Evaluative Study for the period (2001-2010) (2014) Arab Economic Research Journal, (65); Bu Ra'da, H., (2014) Role of Informal Economy in Algeria: Study on Parallel Payment Market, published master thesis, , Wahran University, College of Economic Sciences, Algeria; Thabit, F., International Experiences in Developing the Informal Sector (2011) a prelude for the seminar "Towards Incorporation of Hawkers in Formal Economy System", , Qairo; Hamooda, R., (2012) Strategies of Managing Informal Economy in Light of Planning for Sustainable Development: Comparative Study Between Algeria and Egypt, , published master thesis, Farhat Abbas Staif University, College of Economic and Commercial Sciences, Algeria; Zhang, Y., Huang, J.H., Cost-based pricing model with value-added tax and corporate income tax for a supply chain network Appl. Math. Modelling, 38, pp. 168-180; Mahmood, M., Muhala, R., (2011) Informal Economy in Syria, Causes and Economic and Social Impacts Tishreen University Journal of Scientific Research and Studies, Economic and Legal Sciences Series, 33 (4); Arman, N., (2015) Informal Economy: Concept, Characteristics, Integration, , Palestine; Christopher, M., Mena, C., Khan, O., Yurt, O., "Approaches to managing global sourcing risk" (2011) Supply Chain Management: An International Journal, 16 (2), pp. 67-81; Kara, M., (2010) "Problems of Informal Economy in Algeria with Presentation of and Comparison to Experiences of Mexico, Tunisia, and Senegal", , Published Ph.D. Dissertation to obtain a Ph.D. degree in Economic Sciences. Algeria, Mantori Qustantiniya University, College of Economic Sciences and Management; Tesfay, Y.Y., Sakita, B.M., Mawrides, E.K., Developing a New Market Strategy from Supply Chain Management Perspective: A Case of Jotun in Brazil (2014) Int. J Sup. Chain . Mgt, 3 (2), pp. 94-110; Simchi-Levi, D., Kaminsky, P., Simshi-Levi, E., (2000) Design and Managing the Supply Chain, p. 12. , USA, McGraw-Hill},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Azhdari201937,
author={Azhdari, B.},
title={A Bayesian Inference Analysis of Supply Chain Enablers, Supply Chain Management Practices, and Performance},
journal={Communications in Computer and Information Science},
year={2019},
volume={966},
pages={37-53},
doi={10.1007/978-3-030-16035-7_3},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064035036&doi=10.1007%2f978-3-030-16035-7_3&partnerID=40&md5=5d7b0684f62921a7d0092238a7a78e54},
affiliation={Department of Management, Islamic Azad University, Khark Branch, Shohada St., Khark Island, Boushehr, Iran},
abstract={In this study, a Causal Bayesian network (CBN) model of the causal relationships between supply chain enablers, supply chain management practices and supply chain performances is empirically developed and analyzed. Study data collected from a sample of 199 manufacturing firms producing the most influential products in Iran’s economy. Resultant CBN model revealed important causalities between study variables of interest. Afterwards, using Dirichlet estimator of TETRAD 6-4-0 software, conditional probability estimation with Bayesian networks, also known as Bayesian inference was developed. The outcomes of this study in general, support the idea that SC enablers, especially IT technologies, don’t have direct impact on SC performance. Also forward Bayesian inference provided deeper understanding of causal relationships in supply chain context, such as what antecedents must be available to reach better level at each critical supply chain performance measures. Also it is found out that in any tier of supply chain concepts; there may be some important intra-relations which worth of further studies. © 2019, Springer Nature Switzerland AG.},
author_keywords={Bayesian inference;  Causal Bayesian network;  Supply chain management;  Supply chain performance},
keywords={Bayesian networks;  Inference engines;  Operations research, Bayesian inference;  Bayesian inference analysis;  Causal Bayesian network;  Causal relationships;  Conditional probability estimation;  Manufacturing firms;  Supply chain management practices;  Supply chain performance, Supply chain management},
references={Hsu, C.C., Tan, K.C., Kannan, V.R., Keong Leong, G., Supply chain management practices as a mediator of the relationship between operations capability and firm performance (2009) Int. J. Prod. Res., 47 (3), pp. 835-855; Halley, A., Beaulieu, M., Mastery of operational competencies in the context of supply chain management (2009) Supply Chain Manage. Int. J., 14 (1), pp. 49-63; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega, 34, pp. 107-124; Azhdari, B., Integrating fuzzy cognitive mapping and bayesian network learning for supply chain causal modeling (2018) The 7Th International Conference on Operations Research and Enterprise Systems, , Funchal; Narasimhan, R., Jayanth, J., Causal linkages in supply chain management: An exploratory study of North American manufacturing firms (1998) Decis. Sci., 29 (3), pp. 579-605; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2001) J. Oper. Manage., 19, pp. 185-200; Tan, K.C., Lyman, S.B., Winser, J.D., Supply chain management: A strategic perspective (2002) J. Oper. Prod. Manage., 22 (6), pp. 614-631; Li, S., Lin, B., Accessing information sharing and information quality in supply chain management (2006) Decis. Support Syst., 42, pp. 1641-1656; Shin, H., Collier, D.A., Wilsom, D.D., Supply management orientation and supplier/buyer performance (2000) J. Oper. Manage., 18, pp. 317-333; Lockamy, A., III., Mcormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) Int. J. Oper. Prod. Manage., 24 (12), pp. 1192-1218; Lee, C.W., Kwon, I.-W.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer (2007) Supply Chain Manage. Int. J., 12 (6), pp. 444-452; Sezen, B., Relative effects of design, integration and information sharing on supply chain performance (2008) Supply Chain Manage. Int. J., 13 (3), pp. 233-240; Ibrahim, S.E., Ogunyemi, O., The effect of linkages and information sharing on supply chain and export performance: An empirical study of Egyptian textile manufacturers (2012) J. Manufact. Technol. Manage., 23 (4), pp. 441-463; Li, G., Yang, H., Sun, L., Sohal, A.S., The impact of IT implementation on supply chain integration and performance (2009) Int. J. Prod. Econ., 120, pp. 125-138; Zelbst, P.J., Green, K.W., Jr., Swer, V.E., Baker, G., RFID utilization and information sharing: The impact on supply chain performance (2010) J. Bus. Ind. Mark., 25 (8), pp. 582-589; González-Benito, J., Information technology investment and operational performance in purchasing: The mediating role of supply chain management practices and strategic integration of purchasing (2007) Ind. Manage. Data Syst., 107 (2), pp. 201-228; Sanders, N.R., An empirical study of the impact of e-business technologies on organizational collaboration and performance (2007) J. Oper. Manage., 25, pp. 1332-1347; Zhou, H., Benton, W.C., Jr., Supply chain practice and information sharing (2007) J. Oper. Manage., 25, pp. 1348-1365; Li, W., Humphreys, P.K., Yeung, A.C., Edwin Cheng, T.C., The impact of specific supplier development efforts on buyer competitive advantage: An empirical model (2007) Int. J. Prod. Econ., 106, pp. 230-247; Johnson, P.F., Klassen, R.D., Leenders, M.R., Awaysheh, A., Utilizing e-business technologies in supply chains: The impact of firm characteristics and teams (2007) J. Oper. Manage., 25, pp. 1255-1274; Devaraj, S., Krajewski, L., Wei, J.C., Impact of eBusiness technologies on operational performance: The role of production information integration in the supply chain (2007) J. Oper. Manage., 25, pp. 1199-1216; Bayraktar, E., Demirbag, M., Koh, S.L., Tatoglu, E., Zaim, H., A causal analysis of the impact of information systems and supply chain management practices on operational performance: Evidence from manufacturing SMEs in Turkey (2009) Int. J. Prod. Econ., 122, pp. 133-149; Davis-Sramek, B., Germain, R., Karthik, I., Supply chain technology: The role of environment in predicting performance (2010) J. Acad. Mark. Sci., 38, pp. 42-55; Sundram, V.P.K., Ibrahim, A.R., Govindaraju, V.C., Supply chain management practices in the electronics industry in Malaysia: Consequences for supply chain performance (2011) Benchmarking: Int. J., 18 (6), pp. 834-855; Hamister, J.W., Supply chain management practices in small retailers (2012) Int. J. Retail Distrib. Manage., 40 (6), pp. 427-450; Ding, J., Krämer, B., Bai, Y., Chen, H., Backward inference in bayesian networks for distributed systems management (2005) J. Netw. Syst. Manage., 13 (4), pp. 409-427; Antai, I., Supply chain vs supply chain competition: A niche-based approach (2011) Manage. Res. Rev., 34 (10), pp. 1107-1124; Markis, S., Zoupas, P., Chryssolouris, G., Supply chain control logic for enabling adaptability under uncertainty (2011) Int. J. Prod. Res., 49 (1), pp. 121-137; Garvey, M.D., Carnovale, S., Yeniyurt, S., An analytical framework for supply network risk propagation: A bayesian network approach (2015) Eur. J. Oper. Res., 243 (2), pp. 618-627; Li, G., Yang, H., Sun, L., Sohal, A.S., The impact of IT implementation on supply chain integration and performance (2009) Int. J. Prod. Econ., 120 (1), pp. 128-138; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) J. Oper. Manage., 23, pp. 618-641; Bagozzi, R.P., Measurement and meaning in information systems and oraganizational research: Methodological and philosophical foundations (2011) MIS Q, 35 (2), pp. 261-292; King, G., Honaker, J., Joseph, A., Scheve, K., Analyzing incomplete political science data: An alternative algorithm for multiple imputation (2001) Am. Polit. Sci. Rev., 95 (1), pp. 49-69; O’leary-Kelly, S.W., Vokurka, R.J., The empirical assessment of construct validity (1998) J. Oper. Manage., 16, pp. 387-405; Bentler, P.M., Chou, C.-P., Practical issues in structural modeling (1987) Sociol. Methods Res., 16 (1), pp. 78-117; Hu, L.-T., Bentler, P.M., Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives (1999) Struct. Eqn. Model. Multi. J., 6 (1), pp. 1-55; Schumacker, R.E., Lomax, R.G., (2004), 2nd edn. Lawrence Erlbaum Associates, Mahwah; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) J. Market. Res., 18 (1), pp. 39-50; Mackenzie, S.B., Podsakoff, P.M., Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques (2011) MIS Q, 35 (2), pp. 293-334; Shiu, E., Pervan, S.J., Bove, L.L., Beatty, S.E., Reflections on discriminant validity: Reexamining the Bove et al. (2009) findings (2011) J. Bus. Res., 64, pp. 497-500; Bagozzi, R.P., Phillips, L., Representing and testing organizational theories: A holistic construal (1982) Adm. Sci. Q., 27, pp. 459-489; Heckerman, D., Bayasian networks for data mining (1997) Data Min. Knowl. Disc., 1, pp. 79-119; Mcoll-Kennedy, J.R., Anderson, R.D., Subordinate-manager gender combination and perceived leadership style influence on emotions, self-esteem and organizational commitment (2005) J. Bus. Res., 58, pp. 115-125; (2018), http://www.phil.cmu.edu/tetrad, Accessed 03 May; Anderson, R.D., Vastag, G., Causal modeling alternatives in operations research: Overview and application (2004) Eur. J. Oper. Res., 156, pp. 92-109; Attaran, M., RFID: An enabler of supply chain operations (2007) Supply Chain Manage. Int. J., 12 (4), pp. 249-257; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Manage. Int. J., 16 (1), pp. 20-31},
correspondence_address1={Azhdari, B.; Department of Management, Shohada St., Iran; email: bajdari@ut.ac.ir},
editor={Parlier G.H., Liberatore F., Demange M.},
publisher={Springer Verlag},
issn={18650929},
isbn={9783030160340},
language={English},
abbrev_source_title={Commun. Comput. Info. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Saputri2019,
author={Saputri, V.H.L. and Sutopo, W. and Hisjam, M. and Ma'aram, A.},
title={Sustainable agri-food supply chain performance measurement model for GMO and Non-GMO using data envelopment analysis method},
journal={Applied Sciences (Switzerland)},
year={2019},
volume={9},
number={6},
doi={10.3390/app9061199},
art_number={1199},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063746400&doi=10.3390%2fapp9061199&partnerID=40&md5=5a63bc13afb10f304d39d4daba3a6aa5},
affiliation={Master Program of Industrial Engineering, Universitas Sebelas Maret, Surakarta, 57126, Indonesia; Department of Industrial Engineering, Faculty of Engineering, Universitas Sebelas Maret, Surakarta, 57126, Indonesia; School of Mechanical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM, Johor Bahru, 81310, Malaysia},
abstract={The increase in food demand in Indonesia is one of the consequences of the imbalance between population growth and declining food products. One of alternative technologies that can be used in plant breeding programs to increase agricultural production, in order to meet food demands, is genetically modified organism (GMO) technology. This technology presents a lot of pros and cons among the public-related impacts that will be accepted by consumers. The purpose of this study was to determine the level of sustainability between GMO and non-GMO foods. The performance measurement model for GMO and non-GMO foods was considered according to the seven issues of sustainability that represented environmental, social, and economic aspects. The assessment method was conducted by using Adjusted Profit (AP) with Total Price Recovery (TPR) indicators and Total Factor Productivity (TFP) by utilizing the Data Envelopment Analysis (DEA) Method. Assessments made on each supply chain component included agriculture, processing, and transport to wholesalers/retailers. This study used numerical examples of rice production in Indonesia. The research results found that the performance of non-GMO rice chain is better than GMO rice. It indicates that non-GMO rice is more sustainable. The results show that the proposed model can be applied to measure the sustainability of GMO and Non-GMO agri-food supply chain performance. © 2019 by the authors.},
author_keywords={Adjusted profit;  Agri-food supply chain management;  DEA;  Genetically modified organism;  Performance measurement;  Sustainability},
references={Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply Chain Management: Implementation Issues and Research Opportunities (1998) Int. J. Logist. Manag, 9, pp. 1-20; Iakovou, E., Vlachos, D., Achillas, C., Anastasiadis, F., A Methodological Framework for the Design of Green Supply Chains for the Agrifood Sect (2012) In Proceedings of the 2nd International Conference on Supply Chains (2nd Olympus ICSC), , www.teicm.gr/logistics/images/logisticsdocs/.5/5_1_ICSC_12_IAKOVOU.pdf, Katerini, Greece, 5-6 October (accessed on 5 November 2017); Yakovleva, N., Sarkis, J., Sloan, T., Sustainable Benchmarking of Supply Chains: The Case of the Food Industry (2011) Int. J. Prod. Res, 50, pp. 1297-1317; (2013), United Nation: New York, NY, USA; (2003) World Agriculture: Towards 2015/2030, , Earthscan Publication: London, UK; Martin, M.A., Baumgardt, B.R., The Origin of Biotechnology ans Its Potential for Agriculture (1991) Agricultural Biotechnology: Issues and Choices, , Purdue Research Foundation: West Lafayette, IN, USA; Juma, C., (2015) The New Harvest: Agricultural Innovation in Africa, , Oxford University Press: Oxford, UK; James, C., (2003) Preview: Global Status of Commercially Transgenic Crops: 2003, , (ISAAA Briefs No. 30-2003); The International Service for the Acquisition of Agri-biotech Applications: Ithaca, NY, USA; Karmana, I.W., Adopsi Tanaman Transgenik dan Beberapa Aspek Perkembangannya (2009) Ganec Swara, 3, pp. 12-21; Santosa, D.A., Analisis Resiko Lingkungan Tanaman Transgenik (2000) J. Ilmu Tanah Dan Lingkung, 3, pp. 32-36; (2015) Agricultural Biotechnology Annual, , Global Agricultural Information Network: Bangkok, Thailand; Sun, Y., Yu, X., Tan, Z., Xu, X., Yan, Q., Efficiency Evaluation of Operation Analysis Systems Based on Dynamic Data Envelope Analysis Models from a Big Data Perspective (2017) Appl. Sci, 7, p. 624; Fu, H., Qu, Y., Pan, Y., Efficiency of Cassava Production in China: Empirical Analysis of Field Surveys from Six Provinces (2018) Appl. Sci, 8, p. 1356; Ma, F., Wang, W., Sun, Q., Liu, F., Li, X., Integrated Transport Efficiency and Its Spatial Convergence in China's Provinces: A Super-SBM DEA Model Considering Undesirable Outputs (2018) Appl. Sci, 8, p. 1698; Gaitan-Cremaschi, D., Kamali, F.P., van Evert, F.K., Meuwissen, M.P.M., Oude Lansink, A.G.J.M., Benchmarking the sustainability performance of the Brazilian non-GM and GM soybean meal chains: An indicator-based approach (2015) Food Policy, 55, pp. 22-32; Gaitan-Cremaschi, D., Meuwissen, M.P.M., Oude Lasink, A.G.J.M., Jansen, D.M., Van Evert, F.K., Bosch, R., Van de Pol, M.A., Novel Index Approach for Measuring Biomass Sustainability (2014) Issue Paper for the Organization for Economic Co-Operation and Development (OECD) Workshop on "Sustainable Biomass Drives the Next Bio-Economy", , OECD: Paris, France; Barnes, A.P., Publicly-funded UK agricultural R&D and social total factor productivity (2002) Agric. Econ, 27, pp. 65-74; Barnes, A.P., Mcvittie, A., (2006) Measuring the Sustainability of the UK Food Chain, , Working Papers 46003; Scotland's Rural College, Land Economy & Environment Research Group: Scotland, UK; Glending, M.J., Dailey, A.G., Williams, A.G., Van Evert, F.K., Goulding, K.W.T., Whitmore, A.P., Is it possible to increase the sustainability of arable and ruminant agriculture by reducing input? (2009) Agric. Syst, 99, pp. 117-125; Central Bureau of Statistics, , https://www.bps.go.id/, (accessed on 1 November 2017); Viswanathan, P.K., Lalitha, N., GM technology and sustainable agriculture future: Empirical evidences from Bt cotton farmers in Maharashtra and Gujarat in India (2010) J. Dev. Agric. Econ, 2, pp. 7-17; Azadi, H., Ho, P., Genetically modified and organic crops in developing countries: A review of options for food security (2010) Biotechnol. Adv, 28, pp. 160-168; Carpenter, J.E., Impact of GM crops on biodiversity (2011) Gm Crop, 1, pp. 7-23; Francis, C.A., Wezel, A., Agroecology and Agricultural Change (2015) Int. Encycl. Soc. Behav. Sci, 1, pp. 484-487},
correspondence_address1={Saputri, V.H.L.; Master Program of Industrial Engineering, Indonesia; email: virdahersy@student.uns.ac.id},
publisher={MDPI AG},
issn={20763417},
language={English},
abbrev_source_title={Appl. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Talib2019535,
author={Talib, A.H. and Ahmed, H.A.A. and Al-msary, A.J.K.},
title={Evaluating the steps of building the organizational structure by supply chain performance measurement and productivity: A case of the general directorate of vocational education},
journal={International Journal of Supply Chain Management},
year={2019},
volume={8},
number={1},
pages={535-544},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063264020&partnerID=40&md5=1dc1ce46dd346db977a24d6d39a49fe6},
affiliation={General Directorate of Vocational Education (GDVE), Ministry of Education, Iraq; Faculty of Administration and Economics, University of Babylon, Iraq; AL-Furat AL-Awsat Technical University, Technical Collage, Al-Mussaib, Iraq},
abstract={Proper supply chain performance plays a crucial role in the success of the organization. Therefore, it is essential to use an appropriate supply chain performance assessment system to continuously improve it. The purpose of this research is to analyze supply chain performance according to the operational reference model of supply chain. Organizational structure is one of the fundamental issues and important themes for organizational business as technological developments, increased competition and continuous change are the rudiments of the contemporary business environment. Several questions from the expression of the problem of research provoked the necessity of the study. To identify the reality of the organizational structure in the Directorate General of Vocational Education and the steps needed to be followed are the objectives of this study. The population of the study is selected from a group of departmental managers and various administrative levels of the divisions in the organization are surveyed. The main tool for data collection is questionnaire while only 31 responses are realized from the distribution. A set of statistical methods were used to analyse answers in order to reach final results of the study. The study concluded that effective communication plays an important role between the various administrative levels for the purpose of achieving the objective of the organization. Similarly, the study expresses the importance of preparing detailed lists and clear tasks of the work of the Directorate. The task can be performed efficiently due to possession of human resources with high efficiency and appropriate expertise by the directorate. Lastly, a number of recommendations based on the results of the study are presented to the organizations. The study equally outlined the purpose to involve employees at various administrative levels in the executive levels of the goal setting process and the importance of establishing clear organizational relationship between employees at different administrative levels (Unit, Division and Department). © ExcelingTech Pub, UK.},
author_keywords={Directorate general for vocational education;  Organizational structure;  Productivity;  Supply chain performance},
references={Hussein, A., (2010), article published in the Arab Forum for Human Resources Management; Merzouk, I.I., (2006) Building an Educated Organization in the Framework of Integrating Knowledge Management Processes and Core Capacities, , PhD thesis, Baghdad University, Faculty of Management and Economics, Department of Public Administration, Iraq, Baghdad; Wali, A.M., (2012), http://kenanaonline.com/users/EBENALRAFDEN/downloads/48178; Radwan, M.A.F., Edition, F., (2012) Total Quality Management, , Arab Group for Training and Publishing, Egypt, Cairo; Daft, R.L., (2010) "Understanding the Theory and Design of Organizations", , 10th edition, South-Western, Cengage Learning, Printed in China by China Translation & Printing Services Limited; Mintzberg, H., (2009) Tracking strategies: Toward a general theory of strategy formation, , New York, NY: Oxford University Press; Greenberg, J., (2011) Behavior in Organizations, , (10th ed). Upper Saddle River, NJ: Prentice Hall; Maduenyi, S., Oluremi Oke, A., Fadeyi, O., (2015) Impact of Organizational Structure on Organizational Performance, , International Conterence on African Development Issues, Social and Economic Models for Development Track; Desler, G., (2010) Human Resources Management, , Fourth Edition, translated by: Abdel Muttal, Mohamed Sayed Ahmed, Dar Al-Marikh Publishing, Saudi Arabia, Riyadh; Al-Salem, S.M., (2008) Organization Theory, Structure and Design, , Third Edition, Dar Wael Publishing and Distribution, Amman-Jordan; Al-Shamma, K.M.H., (1991) Principles of Management with a Focus on Business Management, , Al Muthanna Publishing, Distribution and Printing Office, Amman, Jordan; Al-Shamma, K.K., (2007) Theory of Organization, , Amman, Dar Al-Masirah for Publishing, Distribution and Printing; Tarawneh, H.A., (2012) Organization Theory, , First Edition, Dar Al-Hamdul Al-Nashr Al-Ansharwa, Amman-Jordan; Zaidullina, C.N., Demyanova, O.V., (2017) Enhancement of the Choice of Innovation Strategy of Industrial Enterprise, p. 297. , Astra Saviness; Blind, T., Salman, M., (2010) "Organizational Behavior in Business Organizations"; Al-Anzi, A.S., Edition, F., Organization Theory and Organizational Behavior (2016) Memory for Publishing and Distribution, , Iraq, Baghdad},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wu2018,
author={Wu, Z. and Zhai, S. and Hong, J. and Zhang, Y. and Shi, K.},
title={Building sustainable supply chains for organizations based on QFD: A case study},
journal={International Journal of Environmental Research and Public Health},
year={2018},
volume={15},
number={12},
doi={10.3390/ijerph15122834},
art_number={2834},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058755224&doi=10.3390%2fijerph15122834&partnerID=40&md5=8a4bc146341aaaa4e21228df798b639d},
affiliation={Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai, 201306, China; International Business School, Shanghai University of International Business and Economics, Shanghai, 201620, China; School of Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai, 201620, China},
abstract={Environmental protection has been increasingly emphasized by stakeholders, including social organizations, the government, and the public. As a result, building a sustainable supply chain has now become a part of social corporate responsibility as well as a challenge for firms, including small and medium-sized enterprises (SMEs). Taking a chemical enterprise (called enterprise C, in this paper) as an example, this paper uses quality function deployment (QFD) techniques and sets up a house of quality (HOQ) to investigate how a SME can achieve sustainable supply chain management. Specifically, in this paper we build a performance measurement system focusing on economic performance, environmental performance, and social performance. These three types of performance measure, in turn, include fifty-nine secondary indicators. Furthermore, an analytic hierarchy process (AHP) has been adopted to calculate the weight of each indicator. Benchmarking has also been used, to determine how much the enterprise should improve on each indicator. Based on the HOQ model, we conclude that avoiding waste, recycling resources, and sustainable exploitation are most important internal abilities of enterprise C. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.},
author_keywords={House of quality;  Quality function deployment;  Sustainable supply chain},
keywords={article;  benchmarking;  human;  organization;  performance measurement system;  recycling;  economics;  environmental protection;  organization;  procedures;  quality control;  standards;  sustainable growth, Conservation of Natural Resources;  Organizations;  Quality Control;  Sustainable Growth},
references={Hsu, C.H., Chang, A.Y., Luo, W., Identifying key performance factors for sustainability development of SMEs–integrating QFD and fuzzy MADM methods (2017) J. Clean. Prod., 161, pp. 629-645; Johnson, M.P., Schaltegger, S., Two decades of sustainability management tools for SMEs: How far have we come? (2016) J. Small Bus. Manag., 54, pp. 481-505; Pedersen, E.R., The many and the few: Rounding up the SMEs that manage CSR in the supply chain (2009) Supply Chain Manag. Int. J., 14, pp. 109-116; Loucks, E.S., Martens, M.L., Cho, C.H., Engaging small-and medium-sized businesses in sustainability (2010) Sustain. Account. Manag. Policy J., 1, pp. 178-200; Thanki, S., Govindan, K., Thakkar, J., An investigation on lean-green implementation practices in Indian SMEs using analytical hierarchy process (AHP) approach (2016) J. Clean. Prod., 135, pp. 284-298; Burke, S., Gaughran, W.F., Developing a framework for sustainability management in engineering SMEs (2007) Robot. Comput. Integr. Manuf., 23, pp. 696-703; Hertwich, E.G., Peters, G.P., Carbon footprint of nations: A global, trade-linked analysis (2009) Environ. Sci. Technol., 43, pp. 6414-6420; Bai, C., Sarkis, J., Green supplier development: Analytical evaluation using rough set theory (2010) J. Clean. Prod., 18, pp. 1200-1210; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organizational resources: A literature review (2010) Corp. Soc. Responsib. Environ. Manag., 17, pp. 230-245; Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Shibin, K.T., Wamba, S.F., Sustainable supply chain management: Framework and further research directions (2017) J. Clean. Prod., 142, pp. 1119-1130; McCormack, K., Bronzoladeira, M., Paulo Valadares de Oliveira, M., Supply chain maturity and performance in Brazil. Supply Chain Manag (2008) Int. J., 13, pp. 272-282; Ketchen, D.J., Jr., Hult, G.T.M., Building theory about supply chain management: Some tools from the organizational sciences (2011) J. Supply Chain Manag., 47, pp. 12-18; Sarkis, J., A strategic decision framework for green supply chain management (2003) J. Clean. Prod., 11, pp. 397-409; Walker, H., Di Sisto, L., McBain, D., Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors (2008) J. Purch. Supply Manag., 14, pp. 69-85; Ninlawan, C., Seksan, P., Tossapol, K., Pilada, W., The implementation of green supply chain management practices in electronics industry (2010) Proc. Int. Multiconf. Eng. Comput. Sci., 3, pp. 17-19; Sarkis, J., Zhu, Q., Lai, K., An organizational theoretic review of green supply chain management literature (2011) Int. J. Prod. Econ., 130, pp. 1-15; Acquaye, A., Feng, K., Oppon, E., Salhi, S., Ibn-Mohammed, T., Genovese, A., Hubacek, K., Measuring the environmental sustainability performance of global supply chains: A multi-regional input-output analysis for carbon, sulphur oxide and water footprints (2017) J. Environ. Manag., 187, pp. 571-585; Chabowski, B.R., Mena, J.A., Gonzalez-Padron, T.L., The structure of sustainability research in marketing, 1958–2008: A basis for future research opportunities (2011) J. Acad. Mark. Sci., 39, pp. 55-70; Closs, D.J., Speier, C., Meacham, N., Sustainability to support end-to-end value chains: The role of supply chain management (2011) J. Acad. Mark. Sci., 39, pp. 101-116; Scott, A., Corporate social responsibility greening the supply chain (2011) Chem. Week, 173, pp. 21-24; Feng, Y., Zhu, Q., Lai, K.H., Corporate social responsibility for supply chain management: A literature review and bibliometric analysis (2017) J. Clean. Prod., 158, pp. 296-307; Kumar, D., Rahman, Z., Analyzing enablers of sustainable supply chain: ISM and fuzzy AHP approach (2017) J. Model. Manag., 12, pp. 498-524; Mollenkopf, D., Stolze, H., Tate, W.L., Ueltschy, M., Green, lean, and global supply chains (2010) Int. J. Phys. Distrib. Logist. Manag., 40, pp. 14-41; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: Drivers and practices (2006) J. Clean. Prod., 14, pp. 472-486; Testa, F., Iraldo, F., Shadows and lights of GSCM (Green Supply Chain Management): Determinants and effects of these practices based on a multi-national study (2010) J. Clean. Prod., 18, pp. 953-962; Ching, H.Y., Moreira, M.A., Management systems and good practices related to the sustainable supply chain management (2014) J. Manag. Sustain, 4, p. 34; Carter, C.R., Jennings, M.M., Logistics social responsibility: An integrative framework (2002) J. Bus. Logist., 23, pp. 145-180; Zhu, Q., Sarkis, J., Lai, K., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) J. Environ. Manag., 85, pp. 179-189; Matos, S., Hall, J., Integrating sustainable development in the supply chain: The case of life cycle assessment in oil and gas and agricultural biotechnology (2007) J. Oper. Manag., 25, pp. 1083-1102; Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable supply management: An empirical study (2012) Int. J. Prod. Econ., 140, pp. 168-182; Giménez, C., Lourenço, H.R., E-SCM: Internet’s impact on supply chain processes (2008) Int. J. Logist. Manag., 19, pp. 309-343; Wittstruck, D., Teuteberg, F., Understanding the success factors of sustainable supply chain management: Empirical evidence from the electrics and electronics industry (2012) Corp. Soc. Responsib. Environ. Manag., 19, pp. 141-158; Silvestre, B.S., A hard nut to crack! Implementing supply chain sustainability in an emerging economy (2015) J. Clean. Prod., 96, pp. 171-181; Hong, J., Zhang, Y., Ding, M., Sustainable supply chain management practices, supply chain dynamic capabilities, and enterprise performance (2018) J. Clean. Prod., 172, pp. 3508-3519; Silvestre, B.S., Sustainable supply chain management in emerging economies: Environmental turbulence, institutional voids and sustainability trajectories (2015) Int. J. Prod. Econ., 167, pp. 156-169; Esfahbodi, A., Zhang, Y., Watson, G., Sustainable supply chain management in emerging economies: Trade-offs between environmental and cost performance (2016) Int. J. Prod. Econ., 181, pp. 350-366; Galal, N.M., Moneim, A.F.A., Developing sustainable supply chains in developing countries (2016) Procedia Cirp, 48, pp. 419-424; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) Int. J. Prod. Econ., 111, pp. 299-315; Murphy, P.R., Poist, R.F., Braunschweig, C.D., Management of Environmental Issues in Logistics: Current Status and Future Potential (1994) Transp. J., 34, pp. 48-56; Drumwright, M.E., Socially Responsible Organizational Buying: Environmental Concern as a Noneconomic Buying Criterion (1994) J. Mark., 58, pp. 1-19; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag., 21, pp. 71-87; Lynch, R.L., Cross, K.F., (1995) Measure Up!: Yardsticks for Continuous Improvement, , Blackwell: Cambridge, UK; Jayaram, J., Xu, K., Nicolae, M., The direct and contingency effects of supplier coordination and customer coordination on quality and flexibility performance (2011) Int. J. Prod. Res., 49, pp. 59-85; Krajnc, D., Glaviè, P., A model for integrated assessment of sustainable development (2005) Resour. Conserv. Recycl., 43, pp. 189-208; Azapagic, A., Perdan, S., Indicators of sustainable development for industry: A general framework (2000) Process Saf. Environ. Prot., 78, pp. 243-261; Darnall, N., Jolley, G.J., Handfield, R., Environmental management systems and green supply chain management: Complements for sustainability? (2008) Bus. Strategy Environ., 17, pp. 30-45; de Benedetto, L., Klemeš, J., The Environmental Performance Strategy Map: An integrated LCA approach to support the strategic decision-making process (2009) J. Clean. Prod., 17, pp. 900-906; Barbiroli, G., Raggi, A., A method for evaluating the overall technical and economic performance of environmental innovations in production cycles (2003) J. Clean. Prod., 11, pp. 365-374; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) J. Oper. Manag., 22, pp. 265-289; Michelsen, O., Fet, A.M., Dahlsrud, A., Eco-efficiency in extended supply chains: A case study of furniture production (2006) J. Environ. Manag., 79, pp. 290-297; Hutchins, M.J., Sutherland, J.W., An exploration of measures of social sustainability and their application to supply chain decisions (2008) J. Clean. Prod., 16, pp. 1688-1698; O’Connor, M., Spangenberg, J.H., A methodology for CSR reporting: Assuring a representative diversity of indicators across stakeholders, scales, sites and performance issues (2008) J. Clean. Prod., 16, pp. 1399-1415; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) Int. J. Prod. Econ., 101, pp. 99-108; Veleva, V., Ellenbecker, M., Indicators of sustainable production: Framework and methodology (2001) J. Clean. Prod., 9, pp. 519-549; Castka, P., Balzarova, M.A., ISO 26000 and supply chains—On the diffusion of the social responsibility standard (2008) Int. J. Prod. Econ., 111, pp. 274-286; Wood, L.C., Wang, C., Abdul-Rahman, H., Jamal Abdul-Nasir, N.S., Green hospital design: Integrating quality function deployment and end-user demands (2016) J. Clean. Prod., 112, pp. 903-913},
correspondence_address1={Zhai, S.; School of Business Administration, China; email: zhaisj@lixin.edu.cn},
publisher={MDPI AG},
issn={16617827},
pubmed_id={30545110},
language={English},
abbrev_source_title={Int. J. Environ. Res. Public Health},
document_type={Article},
source={Scopus},
}

@ARTICLE{Saleheen2018349,
author={Saleheen, F. and Habib, M.M. and Hanafi, Z.},
title={An empirical study on supply chain management performance measurement through AHP},
journal={International Journal of Supply Chain Management},
year={2018},
volume={7},
number={6},
pages={349-356},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060878663&partnerID=40&md5=3c757f77c2d663aa4902640c176a5c38},
affiliation={School of Quantitative Sciences, Universiti Utara Malaysia (UUM), Kedah, Malaysia; BRAC Business School (BBS), BRAC University, Bangladesh},
abstract={Supply chain management (SCM) performance measurement has gained noteworthy considerations from researchers and practitioners in the manufacturing paradigms for improving organizational effectiveness that can be applied to pronounce and review the historical performance, as well as to set performance targets for the future. Precise diagnosis and analysis of SCM performance is imperative to attain and enhance organizational productivity and profitability through focusing strategic, tactical and operational planning as well as control. Enterprises scrutinize the best fit to evaluate the existing approaches of performance measurement framework including financial and operational. The researcher investigates a multi criteria decision analysis (MCDA) approach to demonstrate how analytic hierarchy process (AHP) is applied to select the best performance measurement framework and illustrates a decision model to structure the problem related to select the supply chain performance measurement system in a hierarchical form with substitutes to the decision maker. The study also determines that there are nine methods where supply chain performance could be evaluated. However, it is yet to establish and validate a comprehensive approach which can establish both qualitative and quantitative factors along with different decision making levels and rank the decision preferences which constitutes a gap between the theory and their potential application. Therefore, future contributions to the subject is indispensable through new innovation of a comprehensive supply chain performance measurement model. © ExcelingTech Pub, UK.},
author_keywords={Analytic hierarchy process (AHP);  Multi-criteria decision analysis (MCDA);  Supply chain performance measurement system (SCPMS)},
references={Gunasekaran, A., Patel, C., Mc Gaughey, R.E., 'A Framework for Supply Chain Performance Measurement,' (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Dangayach, G.S., Deshmukh, S.G., 'Evidence of manufacturing strategies in Indian industry: a survey' (2003) International Journal of Production Economics, 83 (3), pp. 279-298; Srinivasan, M., Mukherjee, D., Gaur, A.S., 'Buyer-supplier partnership quality and supply chain performance: Moderating role of risks, and environmental uncertainty' (2011) European Management Journal, 29, pp. 260-271; Brown, J., 'How the Global Recession Has Altered the Raw Materials Industry', p. 2018. , https://www.pcbsolutions.com/pcb-market-monitor/how-theglobal-recession-has-altered-the-rawmaterials-industry/; Nedaa Agami, M.S., Mohamed, R., 'Operations Research Department, Faculty of Computers and Information, Cairo University, Cairo, Egypt IBIMA Publishing.' (2012) Journal of Organizational Management Studies, 2012, p. 20. , http://www.ibimapublishing.com/journals/JOMS/joms.html; Simatupang, T.M., Sridharan, R., "Benchmarking supply chain collaboration": An empirical study (2004) Benchmarking: An International Journal, 11 (5), pp. 484-503; Simatupang, T.M., Sridharan, R., "A benchmarking scheme for supply chain Collaboration. Benchmarking" (2004) An International Journal, 11 (1), pp. 9-30; Gunasekaran, A., Patel, C., McCaughey, R.E., 'A framework for supply chain performance measurement' (2004) International Journal of Production Economics", 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., McCaughey, R.E., 'A framework for supply chain performance measurement' (2004) International Journal of Production Economics", 87 (3), pp. 333-347; Yeh, D.Y., Cheng, C.H., Chi, M.L., 'A modified two-tuple FLC model for evaluating the performance of SCM: By the Six Sigma DMAIC process, Applied Soft Computing' (2007) (JOURNAL), 7, pp. 1027-1034; Gunasekaran, A., Patel, C., McCaughey, R.E., 'A framework for supply chain performance measurement' (2004) International Journal of Production Economics", 87 (3), pp. 333-347; Folan, P., Browne, J., 'A review of performance measurement: Towards Performance Management Computers in Industry' (2005), 56, pp. 663-680; Thakkar, J., Kanda, A., Deshmukh, S.G., "Supply chain performance measurement framework for small and medium scale enterprises" (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Copacino, W.C., (1997) 'Supply Chain Management: The Basics and Beyond', , St. Lucie Press, Boca Raton, Florida; Lambert, D.M., Cooper, M.C., 'Issues in Supply Chain Management' (2000) Industrial Marketing Management, 29, pp. 65-83; Ballou, R.H., Gilbert, S.M., Mukherjee, A., 'New Managerial Challenges from Supply Chain Opportunities' (2000) Industrial Marketing Management, 29, pp. 7-18; Kaplan, R.S., Norton, D.P., 'Using the balanced scorecard as a strategic management system' (1996) Harvard Business Review, 74 (1), pp. 75-85 and 315; Brewer, P.C., Speh, T.W., 'Using the balanced SCOR card to measure e supply chain performance' (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Huang, S.H., Sheoran, S.K., Keslar, H., 'Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model' (2005) (journal) Computers & Industrial Engineering, 48, pp. 377-394; Beamon, B.M., Balcik, B., 'Performance measurement in humanitarian relief chains' (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25. , 2008; Otto, A., Kotzab, H., 'Does supply chain management really pay? Six perspective to measure the performance of managing a supply chain' (2002) European Journal of Operational research, 144 (2), pp. 306-320; Gunasekaran, A., Patel, C., Tirtiroglu, E., 'Performance measurement and metrics in a supply chain environment' (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., 'A conceptual model of performance measurement for supply chains' (2003) Management Decision, 45 (7), pp. 635-642; Yeh, D.Y., Cheng, C.H., Chi, M.L., 'A modified two-tuple FLC model for evaluating the performance of SCM: By the Six Sigma DMAIC process, Applied Soft Computing' (2007) (JOURNAL), 7, pp. 1027-1034; Kurien, G.P., Qureshi, M.N., 'Study of Performance Practices to Supply Chain Management and Social Sciences' (2011) International Journal of Business management, 2 (4), pp. 19-24; Robb, D.J., Xie, B., Arthanari, T., 'supply chain and operations practice and performance in Chinese furniture manufacturing' (2008) International Journal of Production Economics, 112, pp. 683-699; Tan, K.C., Lyman, S.B., Wisner, J.D., "Supply chain management: a strategic perspective" (2002) International Journal of Operations & Production Management, 22 (6), pp. 614-631; Hafeez, K., Zhang, Y., Malak, N., 'Determining key capabilities of a firm using analytic hierarchy process' (2001) International Journal of Production Economics, 76, pp. 39-51; Saleheen, F., Habib, M., Hanafi, Z., 'Supply Chain Performance Measurement Model: A Literature Review:' (2018) International Journal on Supply Chain Management, , August; Saleheen, F., Habib, M., Pathik, B.B., Hanafi, Z., 'Demand and Supply Planning in Retail Operations' (2014) International Journal of Business and Economics Research. Special Issue: Supply Chain Management: Its Theory and Applications (Scopus), 2014, 3, pp. 51-56. , 6-1; Saleheen, F., Miraz, M.H., Habib, M., Hanafi, Z., 'Challenges of Warehouse Operations: A Case Study in Retail Supermarket' (2014) International Journal of Supply Chain Management (IJSCM) (Scopus), 3 (4), pp. 63-67; Saleheen, F., Miraz, M.H., Habib, M., Hanafi, Z., "Performance measurement framework of supply chain management: A multi criteria decision analysis (MCDA), AHP approach" (2017) International Conference on Business and Management (ICBM), , Online-Version in Dhaka, Bangladesh, 21-22 September; Rajat, K.B., "Selection of Supply Chain Performance Measurement System Using AHP Approach" (2007) POMS 18th Annual Conference, , Dallas, Texas, U.S.A, 4-7 May},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ramezankhani2018531,
author={Ramezankhani, M.J. and Torabi, S.A. and Vahidi, F.},
title={Supply chain performance measurement and evaluation: A mixed sustainability and resilience approach},
journal={Computers and Industrial Engineering},
year={2018},
volume={126},
pages={531-548},
doi={10.1016/j.cie.2018.09.054},
note={cited By 77},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054670106&doi=10.1016%2fj.cie.2018.09.054&partnerID=40&md5=5fef8fbb8996d636a975e05593ffea53},
affiliation={School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran},
abstract={In recent years, governments and social bodies have imposed mandates to tackle environmental and social issues. Furthermore, responding effectively to supply chain disruptions prevented many businesses from bankruptcy and complete shutdown. Therefore, adopting sustainability and resilience concepts at an operational level have recently become an inevitable necessity for business supply chains in order to survive in the tough rapid-changing competitive market environment. This paper proposes a novel dynamic network data envelopment analysis framework as a comprehensive performance management system to dynamically assess a supply chain's performance from both sustainability and resilience viewpoints over the course of time. The proposed model is also associated with a hybrid method using Quality Function Deployment (QFD) together with Decision Making Trial and Evaluation Laboratory (DEMATEL) to systematically select the best sustainability and resilience factors, which are then used in the data envelopment analysis model. The proposed framework is applied to an automotive manufacturing sector to demonstrate its capabilities and effectiveness. © 2018},
author_keywords={DEMATEL;  Dynamic network data envelopment analysis;  QFD;  Supply chain performance management;  Supply chain resilience;  Sustainability},
keywords={Data envelopment analysis;  Decision making;  Information management;  Quality control;  Quality function deployment;  Supply chains;  Sustainable development, Data envelopment analysis models;  Decision making trial and evaluation laboratories;  DEMATEL;  Dynamic network;  Quality function deployments (QFD);  Supply chain performance;  Supply chain performance measurements;  Supply chain resiliences, Supply chain management},
references={Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) Journal of Cleaner Production, 86, pp. 360-377; Amindoust, A., Ahmed, S., Saghafinia, A., Bahreininejad, A., Sustainable supplier selection: A ranking model based on fuzzy inference system (2012) Applied Soft Computing, 12 (6), pp. 1668-1677; Azadi, M., Jafarian, M., Farzipoor Saen, R., Mirhedayatian, S.M., A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context (2015) Computers & Operations Research, 54, pp. 274-285; Azadnia, A.H., Saman, M.Z.M., Wong, K.Y., Sustainable supplier selection and order lot-sizing: An integrated multi-objective decision-making process (2015) International Journal of Production Research, 53 (2), pp. 383-408; Azevedo, S.G., Govindan, K., Carvalho, H., Cruz-Machado, V., GResilient index to assess the greenness and resilience of the automotive supply chain (2011) Discussion Papers of Business and Economics, p. 8; Bairamzadeh, S., Pishvaee, M.S., Saidi-Mehrabad, M., Multiobjective robust possibilistic programming approach to sustainable bioethanol supply chain design under multiple uncertainties (2015) Industrial & Engineering Chemistry Research, 55 (1), pp. 237-256; Boukherroub, T., Ruiz, A., Guinet, A., Fondrevelle, J., An integrated approach for sustainable supply chain planning (2015) Computers & Operations Research, 54, pp. 180-194; Büyüközkan, G., Berkol, Ç., Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment (2011) Expert Systems with Applications, 38 (11), pp. 13731-13748; Cardoso, S.R., Paula Barbosa-Póvoa, A., Relvas, S., Novais, A.Q., Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty (2015) Omega, 56, pp. 53-73; Carvalho, H., Barroso, A.P., Machado, V.H., Azevedo, S., Cruz-Machado, V., Supply chain redesign for resilience using simulation (2012) Computers & Industrial Engineering, 62 (1), pp. 329-341; Cha, S.-C., Juo, P.-W., Liu, L.-T., Chen, W.-N., Riskpatrol: A risk management system considering the integration risk management with business continuity processes (2008) IEEE international conference on intelligence and security informatics, 2008. ISI 2008, pp. 110-115. , IEEE; Chaabane, A., Ramudhin, A., Paquet, M., Designing supply chains with sustainability considerations (2011) Production Planning & Control, 22 (8), pp. 727-741; Chang, D.-S., Kuo, L.R., Chen, Y., Industrial changes in corporate sustainability performance – An empirical overview using data envelopment analysis (2013) Journal of Cleaner Production, 56, pp. 147-155; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Christopher, M., Peck, H., Building the resilient supply chain (2004) The International Journal of Logistics Management, 15 (2), pp. 1-14; Cook, W.D., Liang, L., Zhu, J., Measuring performance of two-stage network structures by DEA: A review and future perspective (2010) Omega, 38 (6), pp. 423-430; Cook, W.D., Tone, K., Zhu, J., Data envelopment analysis: Prior to choosing a model (2014) Omega (United Kingdom), 44, pp. 1-4; Cook, W.D., Zhu, J., Rank order data in DEA: A general framework (2006) European Journal of Operational Research, 174 (2), pp. 1021-1038; Dai, J., Blackhurst, J., A four-phase AHP–QFD approach for supplier assessment: A sustainability perspective (2012) International Journal of Production Research, 50 (19), pp. 5474-5490; Faramarzi, G.R., Khodakarami, M., Shabani, A., Farzipoor Saen, R., Azad, F., New network data envelopment analysis approaches: An application in measuring sustainable operation of combined cycle power plants (2015) Journal of Cleaner Production, 108, pp. 232-246; Färe, R., Grosskopf, S., Intertemporal production frontiers: With dynamic DEA (2012), Springer Science & Business Media; Färe, R., Grosskopf, S., Whittaker, G., Network dea (2007) Modeling data irregularities and structural complexities in data envelopment analysis, pp. 209-240. , Springer; Ghadimi, P., Heavey, C., Sustainable supplier selection in medical device industry: Toward sustainable manufacturing (2014) Procedia CIRP, 15, pp. 165-170; Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2013) Journal of Cleaner Production, 47, pp. 345-354; Haldar, A., Ray, A., Banerjee, D., Ghosh, S., A hybrid MCDM model for resilient supplier selection (2012) International Journal of Management Science and Engineering Management, 7 (4), pp. 284-292; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hsu, C.-W., Hu, A.H., Chiou, C.-Y., Chen, T.-C., Using the FDM and ANP to construct a sustainability balanced scorecard for the semiconductor industry (2011) Expert Systems with Applications, 38 (10), pp. 12891-12899; Jayaram, J., Dixit, M., Motwani, J., Supply chain management capability of small and medium sized family businesses in India: A multiple case study approach (2014) International Journal of Production Economics, 147, pp. 472-485; Jiang, W., Sha, E.H.-M., Zhuge, Q., Wu, L., Efficient assignment algorithms to minimize operation cost for supply chain networks in agile manufacturing (2017) Computers & Industrial Engineering, 108, pp. 225-239; Kannan, D., Jabbour, A.B.L.D.S., Jabbour, C.J.C., Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company (2014) European Journal of Operational Research, 233 (2), pp. 432-447; Kannan, G., Pokharel, S., Sasi Kumar, P., A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider (2009) Resources, Conservation and Recycling, 54 (1), pp. 28-36; Kao, C., Network data envelopment analysis: A review (2014) European Journal of Operational Research, 239 (1), pp. 1-16; Kim, Y., Choi, T.Y., Yan, T., Dooley, K., Structural investigation of supply networks: A social network analysis approach (2011) Journal of Operations Management, 29 (3), pp. 194-211; Kordrostami, S., Amirteimoori, A., Un-desirable factors in multi-component performance measurement (2005) Applied Mathematics and Computation, 171 (2), pp. 721-729; Lee, B.L., Worthington, A.C., A network DEA quantity and quality-orientated production model: An application to Australian university research services (2016) Omega (United Kingdom), 60, pp. 26-33; Lozano, S., Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector (2016) Omega (United Kingdom), 60, pp. 73-84; Lozano, S., Gutiérrez, E., Moreno, P., Network DEA approach to airports performance assessment considering undesirable outputs (2013) Applied Mathematical Modelling, 37 (4), pp. 1665-1676; Min, H., Kim, I., Green supply chain research: Past, present, and future (2012) Logistics Research, 4 (1), pp. 39-47; Mirhedayatian, S.M., Azadi, M., Farzipoor Saen, R., A novel network data envelopment analysis model for evaluating green supply chain management (2014) International Journal of Production Economics, 147, pp. 544-554; Mitra, K., Gudi, R.D., Patwardhan, S.C., Sardar, G., Towards resilient supply chains: Uncertainty analysis using fuzzy mathematical programming (2009) Chemical Engineering Research and Design, 87 (7), pp. 967-981; Motevali Haghighi, S., Torabi, S.A., Ghasemi, R., An integrated approach for performance evaluation in sustainable supply chain networks (with a case study) (2016) Journal of Cleaner Production, 137, pp. 579-597; Nakamichi, K., Hanaoka, S., Kawahara, Y., Estimation of cost and CO2 emissions with a sustainable cross-border supply chain in the automobile industry: A case study of Thailand and neighboring countries (2016) Transportation Research Part D: Transport and Environment, 43, pp. 158-168; Nouri, J., Hosseinzadeh Lotfi, F., Borgheipour, H., Atabi, F., Sadeghzadeh, S.M., Moghaddas, Z., An analysis of the implementation of energy efficiency measures in the vegetable oil industry of Iran: A data envelopment analysis approach (2013) Journal of Cleaner Production, 52, pp. 84-93; Peng, P., Snyder, L.V., Lim, A., Liu, Z., Reliable logistics networks design with facility disruptions (2011) Transportation Research Part B: Methodological, 45 (8), pp. 1190-1211; Ponomarov, S.Y., Holcomb, M.C., Understanding the concept of supply chain resilience (2009) The International Journal of Logistics Management, 20 (1), pp. 124-143; Sawik, T., Selection of supply portfolio under disruption risks (2011) Omega, 39 (2), pp. 194-208; Schmitt, A.J., Singh, M., A quantitative analysis of disruption risk in a multi-echelon supply chain (2012) International Journal of Production Economics, 139 (1), pp. 22-32; Seiford, L.M., Zhu, J., Modeling undesirable factors in efficiency evaluation (2002) European Journal of Operational Research, 142 (1), pp. 16-20; Song, M., Wang, S., Liu, W., A two-stage DEA approach for environmental efficiency measurement (2014) Environmental Monitoring and Assessment, 186 (5), pp. 3041-3051; Tajbakhsh, A., Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2015) Journal of Cleaner Production, 105, pp. 74-85; Tang, C.S., Perspectives in supply chain risk management (2006) International Journal of Production Economics, 103 (2), pp. 451-488; Tone, K., A slacks-based measure of efficiency in data envelopment analysis (2001) European Journal of Operational Research, 130 (3), pp. 498-509; Tone, K., Tsutsui, M., Network DEA: A slacks-based measure approach (2009) European Journal of Operational Research, 197 (1), pp. 243-252; Tone, K., Tsutsui, M., Dynamic DEA: A slacks-based measure approach (2010) OMEGA The International Journal of Management Science, 38 (3-4), pp. 145-156; Tone, K., Tsutsui, M., Dynamic DEA with network structure: A slacks-based measure approach (2014) Omega (United Kingdom), 42 (1), pp. 124-131; Tseng, M.-L., Lin, R.-J., Lin, Y.-H., Chen, R.-H., Tan, K., Close-loop or open hierarchical structures in green supply chain management under uncertainty (2014) Expert Systems with Applications, 41 (7), pp. 3250-3260; Türkay, M., Saraçoğlu, Ö., Arslan, M.C., Sustainability in supply chain management: Aggregate planning from sustainability perspective (2016) PLOS ONE, 11 (1), p. e0147502. , https://doi.org/10.1371%2Fjournal.pone.0147502; Vahidi, F., Torabi, S.A., Ramezankhani, M.J., Sustainable supplier selection and order allocation under operational and disruption risks (2018) Journal of Cleaner Production, 174, pp. 1351-1365; Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Management: An International Journal, 19 (3), pp. 242-257; Wilhelm, M., Blome, C., Wieck, E., Xiao, C.Y., Implementing sustainability in multi-tier supply chains: Strategies and contingencies in managing sub-suppliers (2016) International Journal of Production Economics, 182, pp. 196-212; Wu, D., Olson, D.L., A comparison of stochastic dominance and stochastic DEA for vendor evaluation (2008) International Journal of Production Research, 46 (8), pp. 2313-2327; You, Y.Q., Jie, T., A study of the operation efficiency and cost performance indices of power-supply companies in China based on a dynamic network slacks-based measure model (2016) Omega (United Kingdom), 60, pp. 85-97; Yu, M.M., Chen, L.H., Hsiao, B., Dynamic performance assessment of bus transit with the multi-activity network structure (2016) Omega (United Kingdom), 60, pp. 15-25; Zha, Y., Liang, N., Wu, M., Bian, Y., Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach (2016) Omega (United Kingdom), 60, pp. 60-72; Zhu, J., Cook, W.D., Modeling data irregularities and structural complexities in data envelopment analysis (2007), Springer Science & Business Media},
correspondence_address1={Torabi, S.A.; School of Industrial Engineering, Iran; email: satorabi@ut.ac.ir},
publisher={Elsevier Ltd},
issn={03608352},
coden={CINDD},
language={English},
abbrev_source_title={Comput Ind Eng},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Marimin2018,
author={Marimin and Safriyana},
title={Evaluation of palm oil supply chain's performance, added value, and performance improvement: A case study at X Co.},
journal={IOP Conference Series: Earth and Environmental Science},
year={2018},
volume={196},
number={1},
doi={10.1088/1755-1315/196/1/012001},
art_number={012001},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058041558&doi=10.1088%2f1755-1315%2f196%2f1%2f012001&partnerID=40&md5=ca5f8ddb9a62ecd1ae42fcc3e073600a},
affiliation={Department of Agroindustrial Technology, Faculty of Agricultural Technology, Bogor Agricultural University, Bogor, Indonesia},
abstract={In palm oil supply chain, when the productivity and export market are being increased, usually the negative impacts to sustainability issues (economic, social, and environmental aspects) will arise. It also has direct impacts to supply chain itself, so it required to recognize the supply chain performance position. The objectives of this research were to evaluate performance, added value, and develop sustainability improvement strategies to increase performance of palm oil supply chain at X Co. Palm oil supply chain's performance measurement was obtained by SCOR (Supply Chain Operations Reference) approach. It showed the performance level of plantation was above average (91.24%) and manufacture was at average (86.19%). Added value ratio was measured by using modified Hayami method. It showedthat the added valued of plantation and manufacture were 21.86% and 37.55% respectively. ANP (Analytical Network Process) method with BOCR (benefit, opportunity, cost, and risk) analysis was used in selecting sustainability improvement strategies to increase performance of palm oil supply chain, the weighting results showed that production plan's improvement was the highest. © Published under licence by IOP Publishing Ltd.},
author_keywords={Added value;  ANP;  palm oil;  performance evaluation;  SCOR},
keywords={Agriculture;  Cost benefit analysis;  Energy resources;  Manufacture;  Production control;  Risk analysis;  Risk assessment;  Supply chains;  Sustainable development, Added values;  Analytical network process;  Performance evaluations;  Performance improvements;  Performance measurements;  SCOR;  Supply chain operations references;  Supply chain performance, Palm oil},
references={(2016) [VSII] Van der Schaar Indonesia Investments, , http://www.indonesia-investments.com/id/bisnis/komoditas/minyak-sawit/item166?04-12-2016, Minyak kelapa sawit; Laudon, K.C., Laudon, J.P., (2007) Sistem Informasi Manajemen Mengelola Perusahaan Digital, Edisi 10, , (Jakarta: Salemba Empat); Marimin, Maghfiroh, N., (2010) Aplikasi Teknik Pengambilan Keputusan Dalam Manajemen Rantai Pasok, , (Bogor: IPB Press); Hadiguna, R.A., Model penilaian risiko berbasis kinerja untuk rantai pasok kelapa sawit berkelanjutan di Indonesia (2012) J. Tek. Ind., 14, pp. 13-24; Ulfah, M., (2016) Rancang Bangun Model Manajemen Risiko Rantai Pasok Gula Rafinasi, , (Bogor: Institut Pertanian Bogor); Syarif, H., Marimin, Sukardi, Modifikasi metode hayami untuk perhitungan nilai tambah pada rantai pasok industri kelapa sawit (2012) J. Tek. Ind. Per., 22, pp. 22-31; Syarif, H., Marimin, Agent based modelling for investment and operational risk considerations in palm oil supply chain (2014) Int. J. of Sup. Chain Mgt., 3, pp. 34-40; Septiani, W., Marimin, Herdiyeni, Y., Haditjaroko, L., Risk depedency chain model of dairy agro-industry supply chain using fuzzy logic approach (2016) Sup. Chain Forum: An Int. J., 17 (4), pp. 218-230; Camargo, M.E., Zanandrea, G., Pacheco, M.T.M., Malafia, G.C., Da Motta, M.E.V., Supply chain management operations reference (SCOR): Study bibliometric (2013) Int. J. of Ops. and Log. Mgt., 2, pp. 1-13; Palma-Mendoza, J.A., Neailey, K., Roy, R., Business process re-design methodology to support supply chain integration (2014) Int. J. of Info. Mgt., 34 (2), pp. 167-176; Saaty, T.L., (2005) Theory and Applications of the Analityc Network Process, , (Pittsburgh: Ellsworth Avennue); Coltrain, D., Barton, D., Boland, M., (2000) Value Added: Opportunities and Strategies, , (Kansas: Arthur Capper Cooperative Center, Kansas State University); Li, W., Yuanyuan, Z., (2005) A Game Analysis on Profit Distribution of Two Echelon Supply Chain with Principal and Subordinate, , (Jiangsu: Jiangsu University of Science amp; Technology); Hayami, Y., Kawagoe, T., Morooka, Y., Siregar, M., (1987) Agricultural Marketing and Processing in Upland Java A Perspective from A Sunda Village, , (Bogor: The CPGRT Centre); Sugiyanto, M., (2004) Rancang Bangun Sistem Penunjang Keputusan Agroindustri Jambu Mete (Studi Kasus di Kabupaten Wonogiri), , (Bogor: Institut Pertanian Bogor); Marimin, Feifi, D., Martini, S., Suharjito, Hidayat, S., Added value and performance analyses of edamame soybean supply chain: A case study (2010) Ops. and Sup. Chain Mgt., 3, pp. 148-163; Chandra, C., Grabis, J., (2007) Supply Chain Configuration: Concepts, Solutions, and Applications, , (Michigan: Springer); Chapman, P., Christoper, M., Jutnerr, U., Peck, H., Wilding, R., Identifying and managing supply-chain vulnerability (2002) J. Inst. of Log. and Trans., 4, pp. 59-64; Georgise, F.B., Thoben, K.D., Seifert, M., Adapting the SCOR model to suit the different scenarios: A literature review&research agenda (2012) Int. J. of Bus. and Mgt., 7, pp. 1-17; (2006) Evaluasi Kebijakan Perkebunan Kelapa Sawit, , [KPPU] Komisi Pengawas Persaingan Usaha (Jakarta: Komisi Pengawas Persaingan Usaha Republik Indonesia); Lee, Y., Wu, W., Tzeng, G., An effective decision-making method using a combined QFD and ANP approach (2008) WSEAS Transactions on Bus. and Econ., 5, pp. 541-551; Musango, J.K., Brent, A.C., A conceptual framework for energy technology sustainability assessment (2011) Energy Sustainibility Development J., 15 (1), pp. 84-91; Kurka, T., Blackwood, D., Participatory selection of sustainability criteria and indicatorsfor bioenergy developments (2013) Renewable Sustainibility Energy J., 24, pp. 92-102; (2017) Ketika Parlemen Eropa Keluarkan Resolusi Soal Sawit, , http://www.mongabay.co.id/2017/05/18/mongabay-berita-ketika-parlemen-eropa-keluarkan-resolusi-soal-sawit/18-05-2017, [MI] Mongabay Indonesia},
editor={Muntasib E.K.S.H.},
publisher={Institute of Physics Publishing},
issn={17551307},
language={English},
abbrev_source_title={IOP Conf. Ser. Earth Environ. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Moazzam20181258,
author={Moazzam, M. and Akhtar, P. and Garnevska, E. and Marr, N.E.},
title={Measuring agri-food supply chain performance and risk through a new analytical framework: a case study of New Zealand dairy},
journal={Production Planning and Control},
year={2018},
volume={29},
number={15},
pages={1258-1274},
doi={10.1080/09537287.2018.1522847},
note={cited By 43},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060817161&doi=10.1080%2f09537287.2018.1522847&partnerID=40&md5=079643850d85ee4de5e96e1eaa416665},
affiliation={Logistics and Supply Chain Management (SCM), National University of Sciences and Technology Business School, Islamabad, Pakistan; Logistics and Supply Chain Management, School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand; Management Systems and Logistics, Faculty of Business, Law and Politics, University of Hull, Hull, United Kingdom; IESEG School of Management, Paris, France; Management Science, Abasyn University, Peshawar, Pakistan; Agribusiness, Institute of Agriculture and Environment, Massey University, Palmerston North, New Zealand},
abstract={Many researchers and practitioners have long recognized the significance of measuring performance. Although general guidelines for measuring business performance are widely available, no appropriate measurement frameworks have been developed for measuring agri-food supply chain performance. Particularly, food quality and risk-related indicators have not been well integrated into existing performance measurement systems. Our research, therefore, addresses this knowledge gap by first providing an in-depth review of extant performance measurement systems and frameworks. It then develops an analytical framework by extending the Supply Chain Operations Reference (SCOR) model which has been extensively implemented across non-food industries. The analytical framework is further validated by utilizing a case study of 50 farmers and 10 dairy companies, operating in the New Zealand dairy industry. Our pilot testing and subsequent findings show that the individual metrics interlocked with the analytical framework are in-line with the key industrial practices adapted by the New Zealand dairy industry. In addition, the framework is flexible and scalable to evaluate and benchmark other agri-food supply chains–ranging from fresh products such as fruits and vegetables to processed foods such as canned fruits. The findings further show that the detailed information required for measuring the level-3 SCOR metrics is not easily available in the industry, as researchers need to access specific company records that may be confidential. Consequently, this study provides how agri-food supply chain managers can employ our new analytical framework in-conjunction with the SCOR model for a deeper understanding of the complicated performance measurement indicators applied in their agri-food production systems and relevant supply chains. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={case study of New Zealand dairy;  literature review;  measuring agri-food supply chain performance and risk;  Performance measurement frameworks;  SCOR model},
keywords={Dairy products;  Food supply;  Fruits;  Risk assessment;  Supply chains, Agri-food supply chains;  Literature reviews;  New zealand;  Performance measurement frameworks;  SCOR models, Canning},
references={Ahi, P., Searcy, C., An Analysis of Metrics Used to Measure Performance in Green and Sustainable Supply Chains (2015) Journal of Cleaner Production, 86, pp. 360-377; Akhtar, P., Tse, M., Khan, Z., Rao-Nicholson, R., Data-Driven and Adaptive Leadership Contributing to Sustainability of Global Agri-Food Supply Chains Connected with Emerging Markets (2015) International Journal of Production Economics, 181, pp. 392-401; Akhtar, P., Khan, Z., Frynas, J.G., Tse, Y.K., Rao‐Nicholson, R., Essential Micro‐Foundations for Contemporary Business Operations: Top Management Tangible Competencies, Relationship‐Based Business Networks and Environmental Sustainability (2017) British Journal of Management, 29, pp. 43-62; Aramyan, L., Lansink, A., Van der Vorst, J., Van Kooten, O., Performance Measurement in Agri-Food Supply Chains: A Case Study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Aramyan, L., Ondersteijn, C., Kooten, O., Lansink, A., “Performance Indicators in Agri-Food Production Chains (2006) Quantifying the Agri-Food Supply Chain, pp. 49-66. , Ondersteijn C.J., Wijnands J.H., Huirne R.B., Kooten O., (eds), Dordrecht, The Netherlands: Springer, and,. edited by; Aramyan, L.H., Meuwissen, M.P.M., Oude Lansink, A.G.J.M., van der Vorst, J.G.A.J., van Kooten, O., Van der Lans, I.A., The Perceived Impact of Quality Assurance Systems on Tomato Supply Chain Performance (2009) Total Quality Management, 20 (6), pp. 633-653; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance Measurement of Supply Chain Management Using the Analytical Hierarchy Process (2007) Production Planning and Control, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., Performance Measurement of Supply Chain Management: A Balanced Scorecard Approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bigliardi, B., Bottani, E., Performance Measurement in the Food Supply Chain: A Balanced Scorecard Approach (2010) Facilities, 28 (5-6), pp. 249-260; Bourlakis, M., Maglaras, G., Gallear, D., Fotopoulos, C., Examining Sustainability Performance in the Supply Chain: The Case of the Greek Dairy Sector (2014) Industrial Marketing Management, 43 (1), pp. 56-66; Brewer, P.C., Speh, T.W., Using the Balanced Scorecard to Measure Supply Chain Performance (2000) Journal of Business Logistics, 21 (1), pp. 75-94; Bullinger, H.J., Kühner, M., Van Hoof, A., Analysing Supply Chain Performance Using a Balanced Measurement Method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Chan, F.T.S., Performance Measurement in a Supply Chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., An Innovative Performance Measurement Method for Supply Chain Management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Christopher, M., Supply Chain Management: The past, the Present and the Future (1995) Manufacturing Engineer, 74 (5), pp. 213-217; (2013), http://www.comcom.govt.nz, Information PaperThe Setting of the Farm Gate Milk Price New Zealand; (2014) Dairy Review: iFAB 2013, , New Zealand: Ministry of Business, Innovation & Employment; De Toni, A., Tonchia, S., Performance Measurement Systems: Models, Characteristics and Measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-71; Fattahi, F., Nookabadi, A.S., Kadivar, M., A model for measuring the performance of the meat supply chain (2013) British Food Journal, 115 (8), pp. 1090-1111; Gaiardelli, P., Saccani, N., Songini, L., Performance Measurement of the After-Sales Service Network-Evidence from the Automotive Industry (2007) Computers in Industry, 58 (7), pp. 698-708; Gerbens-Leenes, P.W., Moll, H.C., Schoot Uiterkamp, A.J.M., Design and Development of a Measuring Method for Environmental Sustainability in Food Production Systems (2003) Ecological Economics, 46 (2), pp. 231-248; Gilmour, P., A Strategic Audit Framework to Improve Supply Chain Performance (1999) Journal of Business & Industrial Marketing, 14 (5-6), pp. 355-366; Govindan, K., Mangla, S.K., Luthra, S., Prioritising Indicators in Improving Supply Chain Performance Using Fuzzy AHP: Insights from the Case Example of Four Indian Manufacturing Companies (2017) Production Planning & Control, 28 (6-8), pp. 552-573; Gunasekaran, A., Patel, C., McGaughey, R.E., A Framework for Supply Chain Performance Measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hadiguna, R.A., Tjahjono, B., A Framework for Managing Sustainable Palm Oil Supply Chain Operations: A Case of Indonesia (2017) Production Planning & Control, 28 (13), pp. 1093-1106; Hemme, T., (2015) IFCN Dairy Report 2015, International Farm Comparison Network. Keil, , Germany: IFCN Dairy Research Center; Hofmann, E., Quantifying and Setting off Network Performance (2006) International Journal of Networking and Virtual Organisations, 3 (3), pp. 317-339; Holmberg, S., A Systems Perspective on Supply Chain Measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-Assisted Supply Chain Configuration Based on Supply Chain Operations Reference (SCOR) Model (2005) Computers & Industrial Engineering, 48 (2), pp. 377-394; Hwang, Y.D., Lin, Y.C., Lyu, J., The Performance Evaluation of SCOR Sourcing Process: The Case Study of Taiwan's TFT-LCD Industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Irfan, D., Xu, X., Deng, S., He, Z., A SCOR Reference Model of the Supply Chain Management System in an Enterprise (2008) The International Arab Journal of Informantion Technology, 5 (3), pp. 288-295; Jamehshooran, B.G., Shaharoun, M., Haron, H.N., Assessing Supply Chain Performance through Applying the SCOR Model (2015) International Journal of Supply Chain Management, 4 (1), pp. 1-11; Joshi, R., Banwet, D.K., Shankar, R., Gandhi, J., Performance Improvement of Cold Chain in an Emerging Economy (2012) Production Planning & Control, 23 (10-11), pp. 817-836; Kaplan, R.S., Norton, D.P., The Balanced Scorecard–Measures That Drive Performance (1992) Harvard Business Review, 43 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston, MA: Harvard Business Press; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lapide, L., What about Measuring Supply Chain Performance? (2000) Achieving Supply Chain Excellence through Technology, 2, pp. 287-297; Leat, P., Revoredo-Giha, C., Risk and Resilience in Agri-Food Supply Chains: The Case of the ASDA PorkLink Supply Chain in Scotland (2013) Supply Chain Management: An International Journal, 18 (2), pp. 219-231; Li, D., O'Brien, C., Integrated Decision Modelling of Supply Chain Efficiency (1999) International Journal of Production Economics, 59 (1-3), pp. 147-157; Li, L., Su, Q., Chen, X., Ensuring Supply Chain Quality Performance through Applying the SCOR Model (2011) International Journal of Production Research, 49 (1), pp. 33-57; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and Validation of a Measurement Instrument for Studying Supply Chain Management Practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Liu, C.H., The Effect of a Quality Management System on Supply Chain Performance: An Empirical Study in Taiwan (2009) International Journal of Management, 26 (2), pp. 285-294; Luning, P.A., Marcelis, W.J., Wim, M.F.J., (2002) Food Quality Management: A Techno-Managerial Approach, , Wageningen: Wageningen Pers; Manzini, R., Accorsi, R., The New Conceptual Framework for Food Supply Chain Assessment (2013) Journal of Food Engineering, 115 (2), pp. 251-263; Marucheck, A., Greis, N., Mena, C., Cai, L., Product Safety and Security in the Global Supply Chain: Issues, Challenges and Research Opportunities (2011) Journal of Operations Management, 29 (7-8), pp. 707-720; McCormack, K., Wilkerson, T., Marrow, D., Davey, M., Shah, M., Yee, D., (2008), Managing Risk Your Organization with the SCOR Methodology. The Supply Chain Council Risk Research Team; Millet, P.A., Schmitt, P., Botta-Genoulaz, V., The SCOR Model for the Alignment of Business Processes and Information Systems (2009) Enterprise Information Systems, 3 (4), pp. 393-407; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Otto, A., Kotzab, H., Does Supply Chain Management Really Pay? Six Perspectives to Measure the Performance of Managing a Supply Chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Papakiriakopoulos, D., Pramatari, K., Collaborative Performance Measurement in Supply Chain (2010) Industrial Management & Data Systems, 110 (9), pp. 1297-1318; Persson, F.C., Bartoll, A., Ganovic, M., Lidberg, M., Nilsson, J., Wibaeuswinge, F., Supply Chain Dynamics in the SCOR Model—A Simulation Modeling Approach (2012) Simulation Conference (WSC), Proceedings of the 2012 Winter; (2016) 10Th Conference of the Performance Measurement Association 26–29 June 2016 Edinburgh, Scotland, , http://www.pmaconference.co.uk/special-issues.html; Pohlen, T.L., A Framework for Evaluating Supply Chain Performance (2003) Journal of Transportation Management, 14 (2), pp. 1-21; Prakash, S., Prakash, S., Soni, G., Soni, G., Rathore, A.P.S., Rathore, A.P.S., Singh, S., Singh, S., Risk Analysis and Mitigation for Perishable Food Supply Chain: A Case of Dairy Industry (2017) Benchmarking: An International Journal, 24 (1), pp. 2-23; Ramaa, A.T.M., Rangaswamysubramanya, K.N., A Review of Literature on Performance Measurement of Supply Chain Network (2009) Second International Conference on Emerging Trends in Engineering and Technology ICETET-09; Rangone, A., An Analytical Hierarchy Process Framework for Comparing the Overall Performance of Manufacturing Departments (1996) International Journal of Operations & Production Management, 16 (8), pp. 104-119; Reiner, G., Hofmann, P., Efficiency Analysis of Supply Chain Processes (2006) International Journal of Production Research, 44 (23), pp. 5065-5087; Rota, C.N., Reynoldszanasi, C., Collaboration and sustainable relationships: Their contribution to the life cycle analysis in agri-food supply chains (2012) Proceedings in Food System Dynamics, pp. 574-583; Simatupang, T.M., Sridharan, R., A Benchmarking Scheme for Supply Chain Collaboration (2004) Benchmarking: An International Journal, 11 (1), pp. 9-30; (2015) New Zealand in Profile, , http://www.stats.govt.nz/browse_for_stats/snapshots-of-nz/nz-in-profile-2015.aspx; Stewart, G., Supply Chain Operations Reference (SCOR) Model: The First Cross-Industry Framework for Integrated Supply Chain Management (1997) Logistics Information Management, 10 (2), pp. 62-67; (2010) Supply Chain Operations Reference (SCOR) Model, Version 10.0, , Pittsburgh: Supply Chain Council, Inc; Tanquintic-Misa, E., (2014), New Zealands Fonterra Loses Danone Supply Contract, Slapped with Court Action; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply Chain Performance Measurement Framework for Small and Medium Scale Enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Tummala, R., Schoenherr, T., Assessing and Managing Risks Using the Supply Chain Risk Management Process (SCRMP) (2011) Supply Chain Management: An International Journal, 16 (6), pp. 474-483; (2015), Dairy: World Markets and Trade; (2010), https://gain.fas.usda.gov/Recent%20GAIN%20Publications/Dairy%20and%20Products%20Annual_Wellington_New%20Zealand_10-19-2010.pdf, New Zealand Dairy and Products Annual Milk Supply, Dairy Production and Exports Report; Van Aken, E.M., Coleman, G.D., Building Better Measurement (2002) Industrial Management, 44 (4), pp. 28-33; Van der Spiegel, M., (2004) Measuring Effectiveness of Food Quality Management, , Wageningen, The Netherlands: Wageningen Universiteit; Van der Spiegel, M., Luning, P., Ziggers, G., Jongen, W., Evaluation of Performance Measurement Instruments on Their Use for Food Quality Systems (2005) Critical Reviews in Food Science and Nutrition, 44 (7-8), pp. 501-512; Van der Vorst, J., (2000), Effective Food Supply Chains: Generating, Modelling and Evaluating Supply Chain Scenarios. Doctoral thesis, Wageningen University; Van der Vorst, J., Performance Measurement in Agri-Food Supply-Chain Networks: An Overview (2006) Quantifying the Agri-food supply chain, pp. 15-26. , Ondersteijn C.J., Wijnands J.H., Huirne R.B., Van Kooten O., (eds), Dordrecht, The Netherlands: Springer,. edited by; Van der Vorst, J., Beulens, A.J.M., van Beek, P., Modelling and Simulating Multi-Echelon Food Systems (2000) European Journal of Operational Research, 122 (2), pp. 354-366; van Der Vorst, J.L., Peetersbloemhof, J.M., Sustainability Assessment Framework for Food Supply Chain Logistics: Empirical Findings from Dutch Food Industry (2013) Proceedings in Food System Dynamics, pp. 480-491; Vanteddu, G., Chinnam, R.B., Yang, K., A Performance Comparison Tool for Supply Chain Management (2006) International Journal of Logistics Systems and Management, 2 (4), pp. 342-356; Varma, S., Deshmukh, S.G., Evaluating Petroleum Supply Chain Performance: Overcoming Shortcomings of Balanced Scorecard (2009) Global Journal of Flexible Systems Management, 10 (4), p. 11; Wagner, S.M., Bode, C., An Empirical Examination of Supply Chain Performance along Several Dimensions of Risk (2008) Journal of Business Logistics, 29 (1), pp. 307-325; Wang, X., Tiwari, P., Chen, X., Communicating Supply Chain Risks and Mitigation Strategies: A Comprehensive Framework (2017) Production Planning & Control, 28, pp. 1023-1036; Wang, X., Tiwari, P., Chen, X., Communicating Supply Chain Risks and Mitigation Strategies: A Comprehensive Framework (2017) Production Planning & Control, 28 (13), pp. 1023-1036; Widyaningrum, D., Masruroh, N.A., Development of the Sea Fishery Supply Chain Performance Measurement System: A Case Study (2012) International Journal of Supply Chain Management, 1 (3), pp. 20-32; Wiengarten, F., Longoni, A., A Nuanced View on Supply Chain Integration: A Coordinative and Collaborative Approach to Operational and Sustainability Performance Improvement (2015) Supply Chain Management: An International Journal, 20 (2), pp. 139-150; Yakovleva, N., Measuring the Sustainability of the Food Supply Chain: A Case Study of the UK (2007) Journal of Environmental Policy & Planning, 9 (1), pp. 75-100; Yao, K., Liu, C., An Integrated Approach for Measuring Supply Chain Performance (2006) Journal of Modern Accounting and Auditing, 2 (10), p. 17; Zubair, M., Mufti, N., Identification and Assessment of Supply Chain Risks Associated with Dairy Products Sector (2015) Journal of Basic & Applied Sciences, 11, pp. 167-175},
correspondence_address1={Akhtar, P.; Management Systems and Logistics, United Kingdom; email: p.akhtar@ieseg.fr},
publisher={Taylor and Francis Ltd.},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{Hald2018256,
author={Hald, K.S. and Mouritsen, J.},
title={The evolution of performance measurement systems in a supply chain: A longitudinal case study on the role of interorganisational factors},
journal={International Journal of Production Economics},
year={2018},
volume={205},
pages={256-271},
doi={10.1016/j.ijpe.2018.09.021},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054887600&doi=10.1016%2fj.ijpe.2018.09.021&partnerID=40&md5=7fb5a20f64f46156c21654ef56d857d3},
affiliation={Copenhagen Business School, Department of Operations Management, Solbjerg Plads 3, Frederiksberg, 2000, Denmark},
abstract={Supply chain performance measurement systems are important because they are the central managerial mechanisms for achieving efficient and effective supply chain management. Therefore, it is relevant to add to the many conceptual contributions on the subject to explore how such systems unfold and develop in practice. The objective of the present study is to investigate how forces located outside focal firm boundaries influence the evolution of performance measurement systems in supply chains. An evolutionary and dialectic approach is applied, acknowledging that change may be the result of collective action and of opposing influences and forces. Using a longitudinal case study approach, the emergence, proliferation and reconfiguration of three varied yet interrelated performance measurement systems designed to manage a supply chain in the hearing aid industry are explored. The case shows how the evolution of performance measurement systems in supply chains may be informed by multiple influences, some of which are external to the firm. Specifically, ‘interconnectivity of performance measures’ ‘availability and ownership of performance information’ and ‘performance representations’ are all found to be important factors influencing the evolution of the observed performance measurement systems. The framework of the proposed factors here builds on and extends previous research, which has not explicitly incorporated the potential influences of external entities and the supply chain context. Thus, the findings expand our knowledge on how performance measurement systems develop over time in supply chains. With this new knowledge, managers should be better equipped to develop robust and enduring performance measurement systems in supply chains. © 2018 Elsevier B.V.},
author_keywords={Case study;  Evolution;  Performance measurement system;  Supply chain},
keywords={Audition;  Graphical user interfaces;  Hearing aids;  Managers;  Supply chains, Collective action;  Evolution;  Inter-organisational;  Interconnectivity;  Longitudinal case study;  Performance measure;  Performance measurement system;  Supply chain performance measurements, Supply chain management},
references={Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: a literature review (2010) Int. J. Prod. Res., 48 (17), pp. 5137-5155; Andon, P., Baxter, J., Chua, W.-F., Accounting change as relational drifting: a field study of experiments with performance measurement (2007) Manag. Account. Res., 18, pp. 273-308; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decis. Support Syst., 42 (1), pp. 283-301; Anthony, R.N., Govindarajan, V., Dearden, J., (2007) Management Control Systems, 12. , McGraw-Hill New York, NY; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Prod. Plann. Contr., 20 (8), pp. 678-690; Barratt, M., Choi, T.Y., Li, M., Qualitative case studies in operations management: trends, research outcomes, and future research implications (2011) J. Oper. Manag., 29 (4), pp. 329-342; Barber, E., How to measure the “value” in value chains (2008) Int. J. Phys. Distrib. Logist. Manag., 38 (9), pp. 685-698; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3), pp. 275-292; Bititci, U.S., Carrie, A., McDevitt, L., Integrated performance measurement systems: a development guide (1997) Int. J. Oper. Prod. Manag., 17 (5), pp. 522-534; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20 (6), pp. 692-704; Bititci, U.S., Mendibil, K., Martinez, V., Aibores, P., Measuring and managing performance in extended enterprises (2005) Int. J. Oper. Prod. Manag., 25 (4), pp. 333-353; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organisational culture (2006) Int. J. Oper. Prod. Manag., 26 (12), pp. 1325-1350; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20 (7), pp. 754-771; Bourne, M., Implementation Issues, Hand Book of Performance Measurement (2001), GEE Publishing Ltd London; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: perceptions of participating managers (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1288-1310; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: an action research (2011) Int. J. Prod. Econ., 133 (2), pp. 751-760; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logist., 21 (1), pp. 75-93; Bryman, A., Social Research Methods (2015), Oxford university press; Burney, L.L., Matherly, M., Examining performance measurement from an integrated perspective (2007) J. Inf. Syst., 21 (2), pp. 49-68; Burt, R.S., Structural Holes (1992), Harvard University Press Cambridge, Mass; Busco, C., Quattrone, P., Riccaboni, A., Management Accounting: issues in interpreting its nature and change (2007) Manag. Account. Res., 18 (2), pp. 125-149; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decis. Support Syst., 46, pp. 512-521; Carlsson-Wall, M., Kraus, K., Messner, M., Performance measurement systems and the enactment of different institutional logics: insights from a football organization (2016) Manag. Account. Res., 32, pp. 45-61; Chan, F.T., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Manag.: Int. J., 8 (3), pp. 209-223; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships (2008) Int. J. Oper. Prod. Manag., 28 (3), pp. 238-258; Dweekat, A.J., Hwang, G., Park, J., A supply chain performance measurement approach using the internet of things (2017) Ind. Manag. Data Syst., 117 (2), pp. 267-286; Dyer, J.H., Singh, H., The relational view: cooperative strategy and sources of interorganizational competitive advantage (1998) Acad. Manag. Rev., 23 (4), pp. 660-679; Eisenhardt, K.M., Making fast strategic decisions in high-velocity environments (1989) Acad. Manag. J., 32 (3), pp. 543-576; Eisenhardt, K.M., Graebner, M.E., Theory building from cases: opportunities and challenges (2007) Acad. Manag. J., 50 (1), pp. 25-32; Esposito, E., Passaro, R., The evolution of supply chain relationships: an interpretative framework based on the Italian inter-industry experience (2009) J. Purch. Supply Manag., 15 (2), pp. 114-126; Estampe, D., Lamouri, S., Paris, J., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ., 142 (2), pp. 247-258; Ferreira, L.F., Silva, C., Azevedo, S.G., An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM) (2016) Benchmark Int. J., 23 (6), pp. 1398-1422; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Prod. Plann. Contr., 16 (6), pp. 531-544; Frohlich, M.T., Westbrook, R., Arcs of integration: an international study of supply chain strategies (2001) J. Oper. Manag., 19 (2), pp. 185-200; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int. J. Prod. Econ., 134 (1), pp. 177-187; Ganji Jamehshooran, B., Shaharoun, M., Norehan Haron, H., Assessing supply chain performance through applying the SCOR model (2015) Int. J. Supply Chain Manag., 4, pp. 1-11; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Manag.: Int. J., 12 (6), pp. 400-411; Glaser, B.G., Strauss, A.L., The Discovery of Grounded Theory: Strategies for Qualitative Research (1967), Wiedenfeld and Nicholson London; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Gunasekaran, A., Kobu, B., Performance measures and metrics in SCM: a review of recent literature (1995–2004) and applications (2007) Int. J. Prod. Res., 45, pp. 2819-2840; Gulledge, T., Chavusholu, T., Automating the construction of supply chain key performance indicators (2008) Ind. Manag. Data Syst., 108 (6), pp. 750-774; Gutierrez, D.M., Scavarda, L.F., Fiorencio, L., Martins, R.A., Evolution of the performance measurement system in the Logistics Department of a broadcasting company: an action research (2015) Int. J. Prod. Econ., 160, pp. 1-12; Hald, K.S., Ellegaard, C., Supplier evaluation processes: the shaping and reshaping of supplier performance (2011) Int. J. Oper. Prod. Manag., 31 (8), pp. 888-910; Hanson, J.D., Melnyk, S.A., Calantone, R.A., Defining and measuring alignment in performance management (2011) Int. J. Oper. Prod. Manag., 31 (10), pp. 1089-1114; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: a case study from the packaging industry (2009) Prod. Plann. Contr., 20 (1), pp. 68-81; Holmberg, S., A systems perspective on supply chain measurements (2000) Int. J. Phys. Distrib. Logist. Manag., 30 (10), pp. 847-868; Jääskeläinen, A., Thitz, O., Prerequisites for performance measurement supporting purchaser–supplier collaboration (2018) Benchmark Int. J., 25 (1), pp. 120-137; Jazayeri, M., Scapens, R., The Business Values Scorecard within BAE Systems: the evolution of a performance measurement system (2008) Br. Account. Rev., 40 (1), pp. 48-70; Johansson, T., Siverbo, S., Why is research on management accounting change not explicitly evolutionary? Taking the next step in the conceptualisation of management accounting change (2009) Manag. Account. Res., 20 (2), pp. 146-162; Kaplan, R.S., Norton, D.P., Having trouble with your strategy? Then map it (2000) Harv. Bus. Rev., 78 (5), pp. 167-176; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) Int. J. Oper. Prod. Manag., 23 (2), pp. 213-229; Laihonen, H., Pekkola, S., Impacts of using a performance measurement system in supply chain management: a case study (2016) Int. J. Prod. Res., 54 (18), pp. 5607-5617; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: an empirical analysis (2014) J. Purch. Supply Manag., 20 (2), pp. 113-129; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Maestrini, V., Luzzini, D., Caniato, F., Maccarrone, P., Ronchi, S., Measuring supply chain performance: a lifecycle framework and a case study (2018) Int. J. Oper. Prod. Manag., 38 (4), pp. 934-956; Malina, M., Selto, F., Choice and change of measures in performance measurement models (2004) Manag. Account. Res., 15 (4), pp. 441-469; McCutcheon, D.M., Meredith, J.R., Conducting case study research in operations management (1993) J. Oper. Manag., 11 (3), pp. 239-256; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: dealing with the metrics maze (2004) J. Oper. Manag., 22 (3), pp. 209-218; Munir, R., Baird, K., Perera, S., Performance measurement system change in an emerging economy bank (2013) Account., Audit. Account. J., 26 (2), pp. 196-233; Mentzer, J.T., DeWitt, W., Keebler, J.S., Soonhoong, M., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) J. Bus. Logist., 22 (2), pp. 1-25; Miles, M., Huberman, A.M., Qualitative Data Analysis: An Expanded Sourcebook (1994), Sage, Thousand Oaks, CA; Mondragon, A.E.C., Lalwani, C., Mondragon, C.E.C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Manag.: Int. J., 16 (1), pp. 43-56; Morgan, C., Dewhurst, A., Using SPC to measure a national supermarket chain's suppliers’ performance (2007) Int. J. Oper. Prod. Manag., 27 (8), pp. 874-900; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Performance measurement system design: developing and testing process a process-based approach (2000) Int. J. Oper. Prod. Manag., 20 (9-10), pp. 1119-1145; Neely, A., Adams, C., Kennerley, M., The Performance Prism: the Scorecard for Measuring and Managing Business Success (2002), Financial Times/Prentice Hall London; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) Int. J. Oper. Prod. Manag., 25 (12), pp. 1228-1263; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour. Conserv. Recycl., 55 (6), pp. 567-579; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) Eur. J. Inf. Syst., 14 (4), pp. 335-346; Pekkola, S., Ukko, J., Designing a performance measurement system for collaborative network (2016) Int. J. Oper. Prod. Manag., 36 (11), pp. 1410-1434; Pettigrew, A.M., Longitudinal field research on change: theory and practice (1990) Organ. Sci., 1 (3), pp. 267-292; Pfeffer, J.S., Salancik, G., The External Control of Organizations: a Resource Dependence Perspective. New York (1978); Prahinski, C., Benton, W.C., Supplier evaluations: communication strategies to improve supplier performance (2004) J. Oper. Manag., 22, pp. 39-62; Prahinski, C., Fan, Y., Supplier evaluations: the role of communication quality (2007) J. Supply Chain Manag., 43 (3), pp. 16-28; Quattrone, P., Hopper, T., What does organizational change mean? Speculations on a taken for granted category (2001) Manag. Account. Res., 12, pp. 403-435; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: indications from a study in the automotive industry (2004) Int. J. Prod. Econ., 89 (2), pp. 231-243; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) Int. J. Prod. Res., 53 (16), pp. 4917-4926; Shafiee, M., Lotfi, F.H., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Appl. Math. Model., 38 (21), pp. 5092-5112; Sharif, A.M., Irani, Z., Lloyd, D., Information technology and performance management for build-to-order supply chains (2007) Int. J. Oper. Prod. Manag., 27 (11), pp. 1235-1253; Sillanpää, I., Empirical study of measuring supply chain performance (2015) Benchmark Int. J., 22 (2), pp. 290-308; Smith, K.G., Carroll, S.J., Ashford, S.J., Intra-and interorganizational cooperation: toward a research agenda (1995) Acad. Manag. J., 38 (1), pp. 7-23; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmark Int. J., 16 (5), pp. 702-723; Thompson, D., The Concise Oxford English Dictionary (1995), ninth ed. Clarendon Press Oxford; Ulaga, W., Capturing value creation in business relationships: a customer perspective (2003) Ind. Market. Manag., 32 (8), pp. 677-693; Van de Ven, A.H., Poole, M.S., Explaining development and change in organizations (1995) Acad. Manag. Rev., 20 (3), pp. 510-540; Van Hoek, R.I., Measuring the unmeasurable – measuring and improving performance in the supply chain (1998) Supply Chain Manag., 4, pp. 187-192; Verona, G., Ravasi, D., Unbundling dynamic capabilities: an exploratory study of continuous product innovation (2003) Ind. Corp. Change, 12 (3), pp. 577-606; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) Int. J. Oper. Prod. Manag., 22 (2), pp. 195-219; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: an interdisciplinary review (1999) Int. J. Prod. Econ., 60-61 (3), pp. 53-60; Wickramatillake, C.D., Koh, S.C.L., Gunasekaran, A., Arunachalam, S., Measuring performance within the supply chain of a large scale project (2007) Supply Chain Manag.: Int. J., 12 (1), pp. 52-59; Wouters, M., Sportel, M., The role of existing measures in developing and implementing performance measurement systems (2005) Int. J. Oper. Prod. Manag., 25 (11), pp. 1062-1082; Wouters, M., Wilderom, C., Developing performance-measurement systems as enabling formalization: a longitudinal field study of a logistics department (2008) Account., Org. Soc., 33 (4-5), pp. 488-516; Yin, R.K., Case Study Research: Design and Methods (2003), Sage Publications London},
correspondence_address1={Hald, K.S.; Copenhagen Business School, Solbjerg Plads 3, Denmark; email: ksh.om@cbs.dk},
publisher={Elsevier B.V.},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Article},
source={Scopus},
}

@ARTICLE{Rehman2018522,
author={Rehman, S.T. and Khan, S.A. and Kusi-Sarpong, S. and Hassan, S.M.},
title={Supply chain performance measurement and improvement system: A MCDA-DMAIC methodology},
journal={Journal of Modelling in Management},
year={2018},
volume={13},
number={3},
pages={522-549},
doi={10.1108/JM2-02-2018-0012},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053451365&doi=10.1108%2fJM2-02-2018-0012&partnerID=40&md5=53e7cdaf1e8a4d98413c5f7d55c0bd8e},
affiliation={NED University of Engineering and Technology, Karachi, Pakistan; University of Sharjah College of Engineering, Sharjah, United Arab Emirates; Eco-Engineering and Management Consult Limited, Accra, Ghana},
abstract={Purpose: The purpose of this study is to adopt a supply chain performance measurement (SCPM) framework as proposed by (Dweiri and Khan 2012) to model a novel SCPM index (SCPMI) system to measure and improve supply chain performance (SCP). Design/methodology/approach: The adopted SCPM framework developed by Dweiri and Khan (2012) is used to model a generic SCPMI framework aided by analytic hierarchy process (AHP) method and inputs from industrial experts. To exemplify the applicability and efficiency of the generic SCPMI system, an automobile assembling company from an emerging economy was used. This SCMPI system is used to measure, improve and measure post-improvement SCP guided by DMAIC (define, measure, analyze, improve and control) methodology. Findings: The study’s initial measurement results showed an average SCP of the case company over a four-month period as 82 per cent. DMAIC methodology was used to identify inherent problems and proposed improvements. The post-improvement SCP measurement saw an improvement from an average of 82 to 83.82 per cent over the four-month period. Practical implications: The proposed generic SCPMI framework aided by AHP-DMAIC has been successfully implemented in a case company. After implementation, managers and decision-makers saw an improvement in their SCP. The proposed SCPMI system and results can be useful for benchmarking by manufacturing organizations for continuous SCP improvement. Originality/value: An original SCPMI framework proposed is general in nature and can be applied in any organization. © 2018, Emerald Publishing Limited.},
author_keywords={Decision analysis;  Decision support systems;  Decision-making;  Performance management;  Supply chain management},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: review and classification (2012) The Journal of Organizational Management Studies, 2012, pp. 1-20; Antony, J., Kumar, M., Madu, C.N., Six sigma in small‐ and medium‐sized UK manufacturing enterprises (2005) International Journal of Quality & Reliability Management, 22 (8), pp. 860-874; Bahri, M., St-Pierre, J., Sakka, O., Economic value added: a useful tool for SME performance management (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 603-621; Bai, C., Kusi-Sarpong, S., Sarkis, J., An implementation path for green information technology systems in the Ghanaian mining industry (2017) Journal of Cleaner Production, 164, pp. 1105-1123. , https://doi.org/10.1016/j.jclepro.2017.05.151; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292. , https://doi.org/10.1108/01443579910249714; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62. , https://doi.org/10.1016/j.cie.2007.04.001; Chakrabortty, R.K., Kumar, T., Biswas, I.A., Reducing process variability by using dmaic model: a case study in Bangladesh (2013) International Journal for Quality Research, 7 (1), pp. 127-140; Chang, S.L., Wang, R.C., Wang, S.Y., Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle (2006) International Journal of Production Economics, 100 (2), pp. 348-359; Chopra, S., Meindl, P., (2016) Supply Chain Management: Strategy, Planning, and Operation, , 6th edn., Pearson Prentice Hall, Upper Saddle River, New Jersey; Closs, D.J., Speier, C., Meacham, N., Sustainability to support end-to-end value chains: the role of supply chain management (2010) Journal of Academy of Marketing Science, 39, pp. 101-116; Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D.M., Rogers, D.S., The supply chain management processes (2001) The International Journal of Logistics Management, 12 (2), pp. 13-36; Dey, P.K., Analytic hierarchy process helps evaluate project in Indian oil pipelines industry (2004) International Journal of Operations & Production Management, 24 (6), pp. 588-604; Dey, P.K., Integrated project evaluation and selection using multiple-attribute decision-making technique (2006) International Journal of Production Economics, 103 (1), pp. 90-103; Dey, P.K., Hariharan, S., Clegg, B.T., Measuring the operational performance of intensive care units using the analytic hierarchy process approach (2006) International Journal of Operations & Production Management, 26 (8), pp. 849-865; Dweiri, F., Khan, S.A., Development of a universal supply chain management performance index (2012) International Journal of Business Performance and Supply Chain Modelling, 4 (3-4), pp. 232-236; Dweiri, F., Khan, S.A., Jain, V., Production planning forecasting method selection in a supply chain: a case study (2015) International Journal of Applied Management Science, 7 (1), pp. 38-58; Dweiri, F., Kumar, S., Khan, S.A., Jain, V., Designing an integrated AHP based decision support system for supplier selection in automotive industry (2016) Expert Systems with Applications, 62, pp. 273-283; Eldin, S., Hamza, A., Design process improvement through the DMAIC six sigma approach: a case study from the Middle east (2008) International Journal of Six Sigma and Competitive Advantage, 4 (1), pp. 35-47; Eljelly, A.M., Liquidity – profitability tradeoff: an empirical investigation in an emerging market (2004) International Journal of Commerce and Management, 14 (2), pp. 48-61. , https://doi.org/10.1108/10569210480000179; Fırat, S.Ü.O., Akan, M.Ö.A., Ersoy, E., Gök, S., Ünal, U., A six sigma DMAIC process for supplier performance evaluation using AHP and Kano’s model (2017) International Journal of Business Analytics, 4 (2), pp. 37-61; Gaudenzi, B., Borghesi, A., Managing risks in the supply chain using the AHP method (2006) The International Journal of Logistics Management, 17 (1), pp. 114-136; Gejdoš, P., Continuous quality improvement by statistical process control (2015) Procedia Economics and Finance, 34 (15), pp. 565-572; Gijo, E.V., Scaria, J., Antony, J., Application of six sigma methodology to reduce defects of a grinding process (2011) Quality and Reliability Engineering International, 27 (8), pp. 1221-1234; Goldstein, R., Ju, N., Leland, H., An ebit-based model of dynamic capital structure (2001) The Journal of Business, 74 (4), pp. 483-512; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1); Handfield, R.B., Nichols, E.L., Jr., (1999) Introduction to Supply Chain Management, , Prentice Hall, Inc, Upper Saddle River, New Jersey; Huang, X., Tauchen, G., The relative contribution of jumps to total price variance (2005) Journal of Financial Econometrics, 3 (4), pp. 456-499; Jagdev, H., Brennan, A., Browne, J., (2004) Strategic Decision Making in Modern Manufacturing, , Kluwer Academic Publishers, Massachusetts, MA; Jain, V., Khan, S.A., Application of AHP in reverse logistics service provider selection: a case study (2017) International Journal of Business Innovation and Research, 12 (1), pp. 94-119; Jirasukprasert, P., Arturo Garza-Reyes, J., Kumar, V., K. Lim, M., A six sigma and DMAIC application for the reduction of defects in a rubber gloves manufacturing process (2014) International Journal of Lean Six Sigma, 5 (1), pp. 2-21; Kaplan, R.S., Norton, D.P., The balanced scorecard–measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Khan, S.A., Importance of measuring supply chain management performance (2013) Industrial Engineering & Management, 2 (5), pp. 1-2; Khan, S.A., Dweiri, F., Chaabane, A., Fuzzy-AHP approach for warehouse performance measurement (2016) IEEE International Conference on Industrial Engineering and Engineering Management., pp. 871-875; Khan, S.A., Dweiri, F., Jain, V., Integrating analytical hierarchy process and quality function deployment in automotive supplier selection (2016) International Journal of Business Excellence, 9 (2), pp. 156-177; Kusi-Sarpong, S., Sarkis, J., Wang, X., Green supply chain practices and performance in ghana’s mining industry: a comparative evaluation based on DEMATEL and AHP (2016) International Journal of Business Performance and Supply Chain Modelling, 8 (4), p. 320; Kusi-Sarpong, S., Sarkis, J., Wang, X., Assessing green supply chain practices in the Ghanaian mining industry: a framework and evaluation (2016) International Journal of Production Economics, 181, pp. 325-341; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19. , http://dx.doi.org/10.1108/09574090110806190; Leończuk, D., Categories of supply chain performance indicators: an overview of approaches (2016) Business, Management and Education, 14 (1), pp. 103-115; MacCormack, J., Hollis, A., Zareipour, H., Rosehart, W., The large-scale integration of wind generation: impacts on price, reliability and dispatchable conventional suppliers (2010) Energy Policy, 38 (7), pp. 3837-3846; Madhani, P.M., Demand chain management: integrating marketing and supply chain management for enhancing customer value proposition (2013) Changing Paradigms in Services Marketing, pp. 261-275. , Himalaya Publishing House, Mumbai; Naude, M.J., Badenhorst-weiss, J.A., The effect of problems on supply chain wide efficiency (2011) Journal of Transport and Supply Chain Management, 5 (1), pp. 278-298; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Oren, S.S., Capacity payments and supply adequacy in competitive electricity markets (2000) Symposium of Specialists in Electric Operational and Expansion Planning, pp. 1-8; Ouyang, L.Y., Chuang, B.R., A periodic review inventory-control system with variable lead time (2001) International Journal of Information and Management Sciences, 12, pp. 1-13; Poston, R., Grabski, S., Financial impacts of enterprise resource planning implementations (2001) International Journal of Accounting Information Systems, 2 (4), pp. 271-294; Pyzdek, T., Keller, P., Building the responsive six sigma organization (2010) The SIx Sigma Handbook: A Complete Guide for Green Belts, Black Belts and Managers at All Levels, pp. 3-36. , McGraw-Hill, New York, NY; Qureshi, M.I., Janjua, S.Y., Zaman, K., Lodhi, M.S., Tariq, Y.B., Internationalization of higher education institutions: implementation of DMAIC cycle (2014) Scientometrics, 98 (3), pp. 2295-2310; Rezaei, A.R., Çelik, T., Baalousha, Y., Performance measurement in a quality management system (2011) Scientia Iranica, 18 (3), pp. 742-752; Rimantho, D., Rahman, T.A., Cahyadi, B., Tina Hernawati, S., Application of six sigma and AHP in analysis of variable lead time calibration process instrumentation (2017) AIP Conference Proceedings; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Saaty, T., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98. , www.colorado.edu/geography/leyk/geog_5113/readings/saaty_2008.pdf; Sharahi, S., Abedian, M., Supply chain and logistics in national, international and governmental environment (2009) Supply Chain and Logistics in National, International and Governmental Environement, pp. 21-42. , Springer Dordrecht Heidelberg, London, New York, NY; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2011) Behavioral Operations in Planning and Scheduling, , https://doi.org/10.1007/978-3-642-13382-4_6, Springer, Berlin, Heidelberg; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2008) Designing and Managing the Supply Chain: concepts, Strategies, and Case Studies, , McGraw-Hill, Irwin, Boston; Supply chain operations reference model (2012) Supply Chain Operations Management, pp. 1-976. , https://doi.org/10.1108/09576059710815716; Thakkar, J., Deshmukh, S.G., Kanda, A., Implementing six sigma in service sector using AHP and alderfer’s motivational model - a case of educational services (2006) International Journal of Six Sigma and Competitive Advantage, 2 (4); Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteria decision-making methodology (2004) International Journal of Production Economics, 91 (1), pp. 1-15; Yeh, D.Y., Cheng, C.H., Chi, M.L., A modified two-tuple FLC model for evaluating the performance of SCM: by the six sigma DMAIC process (2007) Applied Soft Computing, 7 (3), pp. 1027-1034; Adams, C.W., Gupta, P., Wilson, C.E., (2003) Six Sigma Deployment, pp. 79-104. , Butterworth Heinemann, Amsterdam; Montgomery, D.C., Woodall, W.H., An overview of six sigma (2008) International Statistical Review, 76 (3), pp. 329-346; Omachonu, V.K., Ross, J.E., Swift, J.A., (2004) Principles of total quality, , http://books.google.com/books?id=Qie6Cc_IUpsC&pgis=1%5Cnhttps://imchekedu.files.wordpress.com/2013/09/total-quality-management.pdf},
correspondence_address1={Kusi-Sarpong, S.; Eco-Engineering and Management Consult LimitedGhana; email: simonov2002@yahoo.com},
publisher={Emerald Group Holdings Ltd.},
issn={17465664},
language={English},
abbrev_source_title={J. Model. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Dweekat2018,
author={Dweekat, A. and Al-Aomar, R.},
title={An IoT-Enabled Framework for Dynamic Supply Chain Performance Management},
journal={2018 IEEE Technology and Engineering Management Conference, TEMSCON 2018},
year={2018},
doi={10.1109/TEMSCON.2018.8488417},
art_number={8488417},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056516399&doi=10.1109%2fTEMSCON.2018.8488417&partnerID=40&md5=a440af966a7b24bdaa31638a5006f2dc},
affiliation={Industrial Engineering, Seoul National University, South Korea; Engineering Management College of Engineering, Abu Dhabi University, Abu Dhabi, United Arab Emirates},
abstract={This paper presents a framework of smart real-time performance monitoring and controlling of next-generation supply chains. It is based on enabling a set of Internet-of- Things (IoT) functions across the supply chain for dynamic and intelligent information processing and performance management. This is realized through the utilization of IoT technology in conjunction with workflow modeling to address the dynamic aspects of supply chain in a real-time environment. The proposed mechanism is referred to as Dynamic Supply Chain Performance Management (DSCPM) which is a computerized event-driven system that runs in real-time, monitors a set of selected supply chain performance measures, and effectively enables real-time decision-making. DSCPM functionality is based on the concepts of Complex Event Processing (CEP) to manage the massive event-instances across the supply chain and to convert them into beneficial information. Adopting DSCPM is expected to facilitate realtime corrective actions that reduce wastes and costs and maximize value across the supply chain. © 2018 IEEE.},
author_keywords={Internet of Things;  Performance management;  SCOR model;  Supply chain management},
keywords={Decision making;  Internet of things, Complex event processing (CEP);  Intelligent information processing;  Internet of Things (IOT);  Performance management;  Real time decision-making;  Real time performance;  SCOR models;  Supply chain performance, Supply chain management},
references={Stadtler, C.H., Kilger, H., Meyr, H., (2015) Supply Chain Management and Advanced Planning, , Springer, Berlin; Arzu, A.G., Erman, E.T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement system: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Stephens, S., Supply chain operations reference model version 5. 0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; Hopp, W.J., Spearman, M.L., (2001) Factory Physics, , 3rd edition, Waveland Press, Long Grove; Wang, L., Some applications of RFID in manufacturing engineering (2012) International Journal of Information Engineering, 2 (4), pp. 152-157; Xu, D.L., He, W., Li, S., Internet of things in industries: A survey (2014) IEEE Transactions on Industrial Informatics, 10 (4), pp. 2233-2243; Tajima, M., Strategic value of RFID in supply chain management (2007) Journal of Purchasing and Supply Management, 13 (4), pp. 261-273; Moisescu, M., Sacala, I., Towards the development of interoperable sensing systems for the future enterprise (2016) Journal of Intelligent Manufacturing, 27 (1), pp. 33-54; Bi, L.Z., Xu, D., Wang, C., Internet of things for enterprise systems of modern manufacturing (2014) IEEE Transactions on Industrial Informatics, 10 (2), pp. 1537-1546; Leung, J., Cheung, W., Chu, S.C., Aligning RFID applications with supply chain strategies (2014) Information & Management, 51 (2), pp. 260-269; Dweekat, G., Hwang, P.J., A supply chain performance measurement approach using the internet of things: Toward more practical SCPMS (2017) Industrial Management & Data Systems, 117 (2), pp. 267-286; Dweekat, A.J., (2018) Internet of Things-Enabled Dynamic Performance Measurement for Real-Time Supply Chain Management-Toward Smarter Supply Chain, , Doctoral dissertatiohn},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781538620472},
language={English},
abbrev_source_title={IEEE Technol. Eng. Manag. Conf., TEMSCON},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Maestrini2018298,
author={Maestrini, V. and Maccarrone, P. and Caniato, F. and Luzzini, D.},
title={Supplier performance measurement systems: Communication and reaction modes},
journal={Industrial Marketing Management},
year={2018},
volume={74},
pages={298-308},
doi={10.1016/j.indmarman.2018.07.002},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050208232&doi=10.1016%2fj.indmarman.2018.07.002&partnerID=40&md5=052d32770d53b02a35b362f9e585b3c1},
affiliation={School of Management, Politecnico di Milano Via Lambruschini 4/b, Milano, 20156, Italy; Eada Business School Barcelona, Carrer d'Aragó 204, Barcelona, 08011, Spain},
abstract={The paper examines the dynamics arising from the adoption of supplier performance measurement systems along the supply chain, specifically considering: (1) the way the buyer company communicates the performance information to the supplier and (2) the way the supplier reacts to the performance reported. The empirical investigation consists of multiple case studies, including nine big multinational companies within three tiers of the automotive supply chain: Elaborating on the theoretical nuance of Signalling Theory, four different communication modes from the buyer side (measuring actor – the Signaller) and three different reactions from the supplier side (measured actor – the Receiver) have been identified, each emerging under different circumstances. The relationship among the communication and reaction modes along the supply chain is critically discussed. © 2018 Elsevier Inc.},
author_keywords={Buyer-supplier relationship;  Case studies;  Supplier performance measurement system;  Supply chain},
references={Baglieri, E., Secchi, R., Croom, S., Exploring the impact of a supplier portal on the buyer-supplier relationship. The case of Ferrari auto (2007) Industrial Marketing Management, 35, pp. 1010-1017; Barratt, M., Barratt, R., Exploring internal and external supply chain linkages: Evidence from the field (2011) Journal of Operations Management, 29 (5), pp. 514-528; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bird, R.B., Smith, E.A., Signaling theory, strategic interaction, and symbolic capital (2005) Current Anthropology, 46, pp. 221-248; Bourlakis, M., Maglaras, G., Gallear, D., Fotopoulos, C., (2014), pp. 56-66. , “Examining sustainability performance in the supply chain: The case of the Greek dairy sector” Industrial Marketing Management, Vo. 43; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20, pp. 754-771; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5, pp. 1-24; Bullinger, H., Kühner, M., Analyzing supply chain performance using a balanced measurement method (2010) International Journal of Production Research, 40 (15), pp. 37-41; Carr, A.S., Pearson, J.N., Strategically managed buyer – Supplier relationships and performance outcomes (1999) Journal of Operations Management, 17, pp. 497-519; Certo, S.T., Influencing initial public offering investors with prestige: Signaling with board structures (2003) Academy of Management Review, 28, pp. 432-446; Choi, T.Y., Hartley, J.L., An exploration of supplier selection practices across the supply chain (1996) Journal of Operations Management, 14 (4), pp. 333-343; Choi, T.Y., Hong, Y., (2002), pp. 469-493. , “Unveiling the structure of supply networks: Case studies in Honda, Acura, and DaimlerChrysler” Journal of Operations Management, Vo. 20 No. 5; Connelly, B.L., Certo, S.T., Ireland, R.D., Reutzel, C.R., Signaling theory: A review and assessment (2011) Journal of Management, 37 (1), pp. 39-67; Corsaro, D., Snehota, I., (2011), pp. 1042-1054. , “Alignment and misalignment in business relationship” Industrial Marketing Management, Vo. 40; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships: The mediating role of socialization mechanisms (2008) International Journal of Operations and Production Management, 28 (3), pp. 238-258; De Boer, L., Labro, E., Morlacchi, P., A review of methods supporting supplier selection (2001) Journal of Purchasing and Supply Management, 7 (2), pp. 75-89; De Boer, L., Van der Wegen, L., Practice and promise of formal supplier selection: A study of four empirical cases (2003) Journal of Purchasing and Supply Management, 9 (3), pp. 109-118; Eisenhardt, K.M., Graebner, M.E., Theory building from cases: Opportunities and challenges (2007) Academy of Management Journal, 50, pp. 25-32; Eisenhardt, K.M.K., Building theories from case study research (1989) The Academy of Management Review, 14 (4), pp. 532-550; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: A review of their consequences and a framework for research (2012) Management Accounting Research, 23 (2), pp. 79-119; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hald, K.S., Ellegaard, C., Supplier evaluation processes: The shaping and reshaping of supplier performance (2011) International Journal of Operations and Production Management, 31 (8), pp. 888-910; Henri, J.F., Management control systems and strategy: A resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558; Jain, M., Khalil, S., Johnston, W.J., Cheng, J., The performance implications of power-trust relationship: The moderating role of commitment in the supplier-retailer relationship (2014) Industrial Marketing Management, 43, pp. 312-321; Kannan, V.R., Tan, K.C., Supplier selection and assessment: Their impact on business performance (2002) The Journal of Supply Chain Management, 38 (3), pp. 11-21; Koufteros, X., Verghese, A.J., Lucianetti, L., The effect of performance measurement systems on firm performance: A cross-sectional and a longitudinal study (2014) Journal of Operations Management, 32 (6), pp. 313-336; Lester, R.H., Certo, S.T., Dalton, C.M., Dalton, D.R., Cannella, A.A., Initial public offering investor valuations: An examination of top management team prestige and environmental uncertainty (2006) Journal of Small Business Management, 44, pp. 1-26; Lockström, M., Lei, L., Antecedents to supplier integration in China: A partial least squares analysis (2013) International Journal of Production Economics, 141 (1), pp. 295-306; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: An empirical analysis (2014) Journal of Purchasing and Supply Management, 20 (2), pp. 113-129; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: A survey in the mines (2006) Management Accounting Research, 17 (3), pp. 315-339; Maltz, E., Is all communication created equal? An investigation into the effects of communication mode on perceived information quality (2000) Journal of Product Innovation Management, 17 (2), pp. 110-127; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Mohr, J.J., Fisher, R.J., Nevin, J.R., Collaborative communication in interfirm relationships: Moderating effects on integration and control (1996) Journal of Marketing, 60 (3), pp. 103-115; Narasimhan, R., Talluri, S., Multiproduct, multicriteria model for supplier selection with product life-cycle considerations (2006) Decision Science, 37 (4), pp. 577-603; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; O'Reilly, C.A., III, Variations in decision makers’ use of information sources: The impact of quality and accessibility of information (1982) Academy of Management Journal, 25 (4), pp. 756-771; Paparoidamis, N., Katsikeas, C., Chumpitaz, R., The role of supplier performance in building customer trust and loyalty: A cross-country examination (2017) Industrial Marketing Management, , (Article in press); Paulraj, A., Lado, A.A., Chen, I.J., Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer–supplier relationships (2008) Journal of Operations Management, 26 (1), pp. 45-64; Ploetner, O., Ehret, M., From relationships to partnership – New forms of cooperation between buyer and seller (2006) Industrial Marketing Management, 35, pp. 4-9; Prahinski, C., Benton, W., Supplier evaluations: Communication strategies to improve supplier performance (2004) Journal of Operations Management, 22 (1), pp. 39-62; Prahinski, C., Fan, Y., Supplier evaluations: The role of communication quality (2007) Journal of Supply Chain Management, 43 (3), pp. 16-28; Purdy, L., Astad, U., Safayeni, F., Perceived effectiveness of automotive supplier evaluation process (1994) International Journal of Operations and Production Management, 14 (6), pp. 91-103; Purdy, L., Safayeni, F., Strategies for supplier evaluation: A framework for potential advantages and limitations (2000) IEEE Transactions on Engineering Management, 47 (4), pp. 435-443; Ramanathan, U., Gunasekaran, A., Subramanian, N., Supply chain collaboration performance metrics: A conceptual framework (2011) Benchmarking: An International Journal, 18 (6), pp. 856-872; Sarkis, J., Talluri, S., A model for strategic supplier selection (2002) Journal of Supply Chain Management, 38 (1), pp. 18-28; Sharif, A.M., Irani, Z., Lloyd, D., Information technology and performance management for build-to-order supply chains (2007) International Journal of Operations and Production Management, 27 (11), pp. 1235-1253; Simpson, P.M., Siguaw, J.A., White, S.C., Measuring the performance of suppliers: An analysis of evaluation processes (2002) Journal of Supply Chain Management, 38 (1), pp. 29-41; Spence, M., Job market signaling (1973) Quarterly Journal of Economics, 87, pp. 355-374; Spence, M., Signaling in retrospect and the informational structure of markets (2002) American Economic Review, 92, pp. 434-459; Suazo, M.M., Martinez, P.G., Sandoval, R., Creating psychological and legal contracts through human resource practices: A signaling theory perspective (2009) Human Resource Management Review, 19, pp. 154-166; Trent, R.J., Creating the ideal supplier scorecard (2010) Supply Chain Management Review, 14 (2); Van der Valk, W., van Iwaarden, J., Monitoring in service triads consisting of buyers, subcontractors and end customers (2011) Journal of Purchasing and Supply Management, 17 (3), pp. 198-206; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 195-219; Wong, C.Y., Boon-Itt, S., Wong, C.W., The contingency effects of environmental uncertainty on the relationship between supply chain integration and operational performance (2011) Journal of Operations Management, 29 (6), pp. 604-615; Wu, Z., Choi, T.Y., Supplier-supplier relationships in the buyer–supplier triad: Building theories from eight case studies (2005) Journal of Operations Management, 24 (1), pp. 27-52; Wuyts, S., Geyskens, I., The formation of buyer-supplier relationships: Detailed contract drafting and close partner selection (2005) Journal of Marketing, 69 (4), pp. 103-117; Wynstra, F., Weggeman, M., van Weele, A., (2003), pp. 69-83. , “Exploring purchasing integration in product development” Industrial Marketing Management, Vo. 32; Yin, R., Case study research: Design and methods (1994), 2nd ed. Sage Publishing Beverly Hills, CA; Zhang, Y., Wiersema, M.F., Stock market reaction to CEO certification: The signaling role of CEO back- ground (2009) Strategic Management Journal, 30, pp. 693-710},
correspondence_address1={Maestrini, V.; School of Management, Politecnico di Milano Via Lambruschini 4/b, Italy; email: vieri.maestrini@polimi.it},
publisher={Elsevier Inc.},
issn={00198501},
coden={IMMAD},
language={English},
abbrev_source_title={Ind. Mark. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Izadikhah2018241,
author={Izadikhah, M. and Saen, R.F. and Roostaee, R.},
title={How to assess sustainability of suppliers in the presence of volume discount and negative data in data envelopment analysis?},
journal={Annals of Operations Research},
year={2018},
volume={269},
number={1-2},
pages={241-267},
doi={10.1007/s10479-018-2790-6},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042224712&doi=10.1007%2fs10479-018-2790-6&partnerID=40&md5=68aee8b62790a628a3b3296b79c75c97},
affiliation={Department of Mathematics, College of Science, Arak Branch, Islamic Azad University, Arāk, Iran; Department of Industrial Management, Faculty of Management and Accounting, Karaj Branch, Islamic Azad University, Karaj, P. O. Box: 31485-313, Iran; Young Researchers and Elite Club, Arak Branch, Islamic Azad University, Arāk, Iran},
abstract={Supplier selection is an important decision-making problem in supply chain management. Selecting appropriate suppliers based on sustainability criteria including economic, environmental, and social criteria can help companies move toward sustainable development. Data envelopment analysis (DEA) is applied to recognize the most sustainable supplier. However, conventional DEA models can be applied only to performance measurement systems characterized by positive input–output data. However, in real world, data can be negative. We propose a new DEA model for evaluating sustainability of suppliers in presence of negative data and volume discounts. Also, we present a super-efficiency model for ranking suppliers. In addition, we prove some main properties of our proposed method. Finally, we present a case study to demonstrate applicability of proposed model. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.},
author_keywords={Data envelopment analysis (DEA);  Negative data;  Super-efficiency;  Supplier selection;  Sustainable supply chain management;  Volume discounts},
references={Abdollahi, M., Arvan, M., Razmi, J., An integrated approach for supplier portfolio selection: Lean or agile? (2015) Expert Systems with Applications, 42 (1), pp. 679-690; Ahmady, N., Azadi, M., Sadeghi, S.A.H., Saen, R.F., A novel fuzzy data envelopment analysis model with double frontiers for supplier selection (2013) International Journal of Logistics Research and Applications, 16 (2), pp. 87-98; Aissaoui, N., Haouari, M., Hassini, E., Supplier selection and order lot sizing modeling: A review (2007) Computers & Operations Research, 34 (12), pp. 3516-3540; Allahyar, M., Rostamy-Malkhalifeh, M., Negative data in data envelopment analysis: Efficiency analysis and estimating returns to scale (2015) Computers & Industrial Engineering, 82, pp. 78-81; Amindoust, A., Ahmed, S., Saghafinia, A., Bahreininejad, A., Sustainable supplier selection: A ranking model based on fuzzy inference system (2012) Applied Soft Computing, 12 (6), pp. 1668-1677; Amirteimoori, A., Jahanshahloo, G., Kordrostami, S., Ranking of decision making units in data envelopment analysis: A distance-based approach (2005) Applied Mathematics and Computation, 171 (1), pp. 122-135; Andersen, P., Petersen, N.C., A procedure for ranking efficient units in data envelopment analysis (1993) Management Science, 39 (10), pp. 1261-1264; Awasthi, A., Chauhan, S.S., Goyal, S.K., A fuzzy multicriteria approach for evaluating environmental performance of suppliers (2010) International Journal of Production Economics, 126 (2), pp. 370-378; Azadeh, A., Alem, S.M., A flexible deterministic, stochastic and fuzzy data envelopment analysis approach for supply chain risk and vendor selection problem: Simulation analysis (2010) Expert Systems with Applications, 37 (12), pp. 7438-7448; Azadi, M., Farzipoor Saen, R., Developing a chance-constrained free replicability hull model for supplier selection (2012) International Journal of Logistics Systems and Management, 12 (4), pp. 375-394; Azadi, M., Farzipoor Saen, R., Tavana, M., Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data (2012) International Journal of Industrial and Systems Engineering, 10 (2), pp. 167-196; Azadi, M., Jafarian, M., Saen, R.F., Mirhedayatian, S.M., A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context (2015) Computers & Operations Research, 54, pp. 274-285; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Balf, F.R., Rezai, H.Z., Jahanshahloo, G.R., Lotfi, F.H., Ranking efficient DMUs using the Tchebycheff norm (2012) Applied Mathematical Modelling, 36 (1), pp. 46-56; Ballew, P.D., Schnorbus, R.H., The impact of the auto industry on the economy (1994) Chicago Fed Letter, pp. 79-82; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Management Science, 30 (9), pp. 1078-1092; Baskaran, V., Nachiappan, S., Rahman, S., Indian textile suppliers’ sustainability evaluation using the grey approach (2012) International Journal of Production Economics, 135 (2), pp. 647-658; Bender, P., Brown, R., Isaac, H., Shapiro, J., Improving purchasing productivity at IBM with a normative decision support system (1985) Interfaces, 15 (3), pp. 106-115; Benton, W., Quantity discount decision under conditions of multiple items, multiple suppliers and resource limitation (1991) International Journal of Production Research, 29 (10), pp. 1953-1961; Beske, P., Land, A., Seuring, S., Sustainable supply chain management practices and dynamic capabilities in the food industry: A critical analysis of the literature (2014) International Journal of Production Economics, 152, pp. 131-143; Bowersox, D.J.C., (1996) Logistical management: The integrated supply chain process, , McGraw-Hill, New York; Büyüközkan, G., Çifçi, G., A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information (2011) Computers in Industry, 62 (2), pp. 164-174; Çelebi, D., Bayraktar, D., An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information (2008) Expert Systems with Applications, 35 (4), pp. 1698-1710; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Chen, Y., Ranking efficient units in DEA (2004) Omega, 32 (3), pp. 213-219; Chen, Y.-J., Structured methodology for supplier selection and evaluation in a supply chain (2011) Information Sciences, 181 (9), pp. 1651-1670; Cheng, G., Zervopoulos, P., Qian, Z., A variant of radial measure capable of dealing with negative inputs and outputs in data envelopment analysis (2013) European Journal of Operational Research, 225 (1), pp. 100-105; Clift, R., Metrics for supply chain sustainability (2003) Clean Technologies and Environmental Policy, 5 (3-4), pp. 240-247; Cooper, W.W., Seiford, L.M., Tone, K., (2002) Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software, , Kluwer Academic Publishers, Dordrecht; Dao, V., Langella, I., Carbo, J., From green to sustainability: Information Technology and an integrated sustainability framework (2011) The Journal of Strategic Information Systems, 20 (1), pp. 63-79; de Boer, L., Labro, E., Morlacchi, P., A review of methods supporting supplier selection (2001) European Journal of Purchasing & Supply Management, 7 (2), pp. 75-89; Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic multiplicative DEA model for measuring the efficiency and ranking of DMUs under VRS technology (2014) Indian Journal of Science and Technology, 7 (11), pp. 1765-1773; Dibachi, H., Behzadi, M.H., Izadikhah, M., Stochastic modified MAJ model for measuring the efficiency and ranking of DMUs (2015) Indian Journal of Science and Technology, 8 (8), pp. 549-555; Dobos, I., Vörösmarty, G., Green supplier selection and evaluation using DEA-type composite indicators (2014) International Journal of Production Economics, 157, pp. 273-278; Ehsanbakhsh, H., Izadikhah, M., Applying BSC-DEA model to performance evaluation of industrial cooperatives: An application of fuzzy inference system (2015) Applied Research Journal, 1 (1), pp. 9-26; Emrouznejad, A., Anouze, A.L., Thanassoulis, E., A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA (2010) European Journal of Operational Research, 200 (1), pp. 297-304; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70 (6), pp. 1088-1100; Falagario, M., Sciancalepore, F., Costantino, N., Pietroforte, R., Using a DEA-cross efficiency approach in public procurement tenders (2012) European Journal of Operational Research, 218 (2), pp. 523-529; Floridi, M., Pagni, S., Falorni, S., Luzzati, T., An exercise in composite indicators construction: Assessing the sustainability of Italian regions (2011) Ecological Economics, 70 (8), pp. 1440-1447; Gauthier, C., Measuring corporate social and environmental performance: The extended life-cycle assessment (2005) Journal of Business Ethics, 59 (1-2), pp. 199-206; Giannakis, M., Papadopoulos, T., Supply chain sustainability: A risk management approach (2016) International Journal of Production Economics, 171, pp. 455-470; Gimenez, C., Tachizawa, E.M., Extending sustainability to suppliers: a systematic literature review (2012) Supply Chain Management: An International Journal, 17 (5), pp. 531-543; Gold, S., Awasthi, A., Sustainable global supplier selection extended towards sustainability risks from (1 + n)th tier suppliers using fuzzy AHP based approach (2015) IFAC-PapersOnLine, 48 (3), pp. 966-971; Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2013) Journal of Cleaner Production, 47, pp. 345-354; Handfield, R.B., Nichols, E.L., (1999) Introduction to supply chain management, , Prentice Hall, Upper Saddle River; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hsu, C.-W., Hu, A.H., Applying hazardous substance management to supplier selection using analytic network process (2009) Journal of Cleaner Production, 17 (2), pp. 255-264; Humphreys, P.K., Wong, Y.K., Chan, F.T.S., Integrating environmental criteria into the supplier selection process (2003) Journal of Materials Processing Technology, 138 (1-3), pp. 349-356; Hutchins, M.J., Sutherland, J.W., An exploration of measures of social sustainability and their application to supply chain decisions (2008) Journal of Cleaner Production, 16 (15), pp. 1688-1698; Izadikhah, M., Saen, R.F., A new data envelopment analysis method for ranking decision making units: An application in industrial parks (2015) Expert Systems; Izadikhah, M., Saen, R.F., Evaluating sustainability of supply chains by two-stage range directional measure in the presence of negative data (2016) Transportation Research Part D: Transport and Environment, 49, pp. 110-126; Izadikhah, M., Saen, R.F., A new preference voting method for sustainable location planning using geographic information system and data envelopment analysis (2016) Journal of Cleaner Production, 137, pp. 1347-1367; Izadikhah, M., Saen, R.F., Ahmadi, K., How to assess sustainability of suppliers in the presence of dual-role factor and volume discounts? A data envelopment analysis approach (2017) Asia-Pacific Journal of Operational Research, 34 (3), pp. 1-25; Izadikhah, M., Saen, R.F., Ahmadi, K., How to assess sustainability of suppliers in volume discount context? A new data envelopment analysis approach (2017) Transportation Research Part D: Transport and Environment, 51, pp. 102-121; Jahanshahloo, G.R., Afzalinejad, M., A ranking method based on a full-inefficient frontier (2006) Applied Mathematical Modelling, 30 (3), pp. 248-260; Jahanshahloo, G.R., Junior, H.V., Lotfi, F.H., Akbarian, D., A new DEA ranking system based on changing the reference set (2007) European Journal of Operational Research, 181 (1), pp. 331-337; Jahanshahloo, G.R., Lotfi, F.H., Khanmohammadi, M., Kazemimanesh, M., Rezaie, V., Ranking of units by positive ideal DMU with common weights (2010) Expert Systems with Applications, 37 (12), pp. 7483-7488; Jahanshahloo, G.R., Memariani, A., Lotfi, F.H., Rezai, H.Z., A note on some of DEA models and finding efficiency and complete ranking using common set of weights (2005) Applied Mathematics and Computation, 166 (2), pp. 265-281; Jahanshahloo, G.R., Piri, M., Data envelopment analysis (DEA) with integer and negative inputs and outputs (2013) Journal of Data Envelopment Analysis and Decision Science, 2013, pp. 1-15; Jahanshahloo, G.R., Pourkarimi, L., Zarepisheh, M., Modified MAJ model for ranking decision making units in data envelopment analysis (2006) Applied Mathematics and Computation, 174 (2), pp. 1054-1059; Jahanshahloo, G.R., Sanei, M., Lotfi, F.H., Shoja, N., Using the gradient line for ranking DMUs in DEA (2004) Applied Mathematics and Computation, 151 (1), pp. 209-219; Jain, V., Kumar, A., Kumar, S., Chandra, C., Weight restrictions in data envelopment analysis: A comprehensive genetic algorithm based approach for incorporating value judgments (2015) Expert Systems with Applications, 42 (3), pp. 1503-1512; Jakhar, S.K., Performance evaluation and a flow allocation decision model for a sustainable supply chain of an apparel industry (2015) Journal of Cleaner Production, 87, pp. 391-413; Karsak, E.E., Dursun, M., An integrated supplier selection methodology incorporating QFD and DEA with imprecise data (2014) Expert Systems with Applications, 41 (16), pp. 6995-7004; Katsikeas, C.S., Paparoidamis, N.G., Katsikea, E., Supply source selection criteria: the impact of supplier performance ondistributor performance (2004) Industrial Marketing Management, 33 (8), pp. 755-764; Keskin, G.A., İlhan, S., Özkan, C., The fuzzy ART algorithm: A categorization method for supplier evaluation and selection (2010) Expert Systems with Applications, 37 (2), pp. 1235-1240; Khodakarami, M., Shabani, A., Saen, R.F., Azadi, M., Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management (2015) Measurement, 70, pp. 62-74; Kiron, D., Kruschwitz, N., Haanaes, K., Velken, I.V.S., Sustainability nears a tipping point (2012) MIT Sloan Management Review, 53 (2), pp. 69-74; Kleinsorge, I.K., Schary, P.B., Tanner, R.D., Data envelopment analysis for monitoring customer supplier relationships (1992) Journal of Accounting and Public Policy, 11 (4), pp. 357-372; Kordrostami, S., Noveiri, M.J.S., Evaluating the efficiency of decision making units in the presence of flexible and negative data (2012) Indian Journal of Science and Technology, 5 (12), pp. 78-84; Kumar, A., Jain, V., Kumar, S., A comprehensive environment friendly approach for supplier selection (2014) Omega, 42 (1), pp. 109-123; Kuo, R.J., Wang, Y.C., Tien, F.C., Integration of artificial neural network and MADA methods for green supplier selection (2010) Journal of Cleaner Production, 18 (12), pp. 1161-1170; Lee, A.H.I., Kang, H.-Y., Hsu, C.-F., Hung, H.-C., A green supplier selection model for high-tech industry (2009) Expert Systems with Applications, 36 (4), pp. 7917-7927; Lin, C., Madu, C.N., Kuei, C.-H., Tsai, H.-L., Wang, K.-N., Developing an assessment framework for managing sustainability programs: A analytic network process approach (2015) Expert Systems with Applications, 42 (5), pp. 2488-2501; Liu, F.-H.F., Peng, H.H., Ranking of units on the DEA frontier with common weights (2008) Computers & Operations Research, 35 (5), pp. 1624-1637; Lotfi, F.H., Noora, A.A., Jahanshahloo, G.R., Gerami, J., Mozaffari, M.R., A slacks-base measure of super-efficiency for DEA with negative data (2010) Australian Journal of Basic and Applied Sciences, 4 (12), pp. 6197-6210; Lu, Y.H., Chen, P.C., Hsiao, T.Y., Operational efficiency in credit departments of farmers’ associations with consideration of non-performing loans and negative values: Application of dynamic network DEA (2014) The Macrotheme Review, 3 (1), pp. 150-164; Mafakheri, F., Breton, M., Ghoniem, A., Supplier selection-order allocation: A two-stage multiple criteria dynamic programming approach (2011) International Journal of Production Economics, 132 (1), pp. 52-57; Mahdiloo, M., Saen, R.F., Lee, K.-H., Technical, environmental and eco-efficiency measurement for supplier selection: An extension and application of data envelopment analysis (2015) International Journal of Production Economics, 168, pp. 279-289; Martins, A.A., Mata, T.M., Costa, C.A.V., Sikdar, S.K., Framework for sustainability metrics (2007) Industrial and Engineering Chemistry Research, 46 (10), pp. 2962-2973; Matin, R.K., Amin, G.R., Emrouznejad, A., A modified semi-oriented radial measure for target setting with negative data (2014) Measurement, 54, pp. 152-158; Matin, R.K., Azizi, R., A two-phase approach for setting targets in DEA with negative data (2011) Applied Mathematical Modelling, 35 (12), pp. 5794-5803; Mehrabian, S., Alirezaee, M.R., Jahanshahloo, G.R., A complete efficiency ranking of decision making units in data envelopment analysis (1999) Computational Optimization and Applications, 14, pp. 261-266; Mirhedayatian, S.M., Azadi, M., Saen, R.F., A novel network data envelopment analysis model for evaluating green supply chain management (2014) International Journal of Production Economics, 147, pp. 544-554; Mirhedayatian, S.M., Vahdat, S.E., Jelodar, M.J., Saen, R.F., Welding process selection for repairing nodular cast iron engine block by integrated fuzzy data envelopment analysis and TOPSIS approaches (2013) Materials and Design, 43, pp. 272-282; Oliveira, R.C., Lourenço, J.C., A multicriteria model for assigning new orders to service suppliers (2002) European Journal of Operational Research, 139 (2), pp. 390-399; Orji, I.J., Wei, S., An innovative integration of fuzzy-logic and systems dynamics in sustainable supplier selection: A case on manufacturing industry (2015) Computers & Industrial Engineering, 88, pp. 1-12; Pal, O., Gupta, A.K., Garg, R.K., Supplier selection criteria and methods in supply chains: A review (2013) International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 7 (10), pp. 2667-2773; Portela, M.C.A.S., Thanassoulis, E., Malmquist-type indices in the presence of negative data: An application to bank branches (2010) Journal of Banking & Finance, 34 (7), pp. 1472-1483; Portela, M.C.A.S., Thanassoulis, E., Simpson, G., Negative data in DEA: A directional distance approach applied to bank branches (2004) The Journal of the Operational Research Society, 55 (10), pp. 1111-1121; Punniyamoorthy, M., Mathiyalagan, P., Parthiban, P., A strategic model using structural equation modeling and fuzzy logic in supplier selection (2011) Expert Systems with Applications, 38 (1), pp. 458-474; Saen, R.F., Using super-efficiency analysis for ranking suppliers in the presence of volume discount offers (2008) International Journal of Physical Distribution & Logistics Management, 38 (8), pp. 637-651; Saen, R.F., Suppliers selection in volume discount environments in the presence of both cardinal and ordinal data (2009) International Journal of Information Systems and Supply Chain Management, 2 (1), pp. 69-80; Saen, R.F., A decision model for ranking suppliers in the presence of cardinal and ordinal data, weight restrictions, and nondiscretionary factors (2009) Annals of Operations Research, 172 (1), pp. 177-192; Saen, R.F., A new algorithm for ranking suppliers in volume discount environments (2010) Asia Pacific Management Review, 15 (3), pp. 341-358; Saen, R.F., Zohrehbandian, M., A data envelopment analysis approach to supplier selection in volume discount environments (2008) International Journal of Procurement Management, 1 (4), pp. 472-488; Sarkis, J., Dhavale, D.G., Supplier selection for sustainable operations: A triple-bottom-line approach using a Bayesian framework (2015) International Journal of Production Economics, 166, pp. 177-191; Scheel, H., Undesirable outputs in efficiency valuations (2001) European Journal of Operational Research, 132 (2), pp. 400-410; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Sharp, J.A., Meng, W., Liu, W., A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs (2007) The Journal of the Operational Research Society, 58 (12), pp. 1672-1677; Shaverdi, M., Heshmati, M.R., Eskandaripour, E., Tabar, A.A.A., Developing sustainable SCM evaluation model using fuzzy AHP in publishing industry (2013) Procedia Computer Science, 17, pp. 340-349; Sridhar, K., Jones, G., The three fundamental criticisms of the triple bottom line approach: An empirical study to link sustainability reports in companies based in the Asia-Pacific region and TBL shortcomings (2013) Asian Journal of Business Ethics, 2 (1), pp. 91-111; Tahriri, F., Osman, R., Ali, A., Yusuff, R.M., Esfandiary, A., AHP approach for supplier evaluation and selection in a steel manufacturing company (2008) Journal of Industrial Engineering and Management, 1 (2), pp. 54-76; Talluri, S., Narasimhan, R., Nair, A., Vendor performance with supply risk: A chance-constrained DEA approach (2006) International Journal of Production Economics, 100 (2), pp. 212-222; Tao, L., Chen, Y., Liu, X., Wang, X., An integrated multiple criteria decision making model applying axiomatic fuzzy set theory (2012) Applied Mathematical Modelling, 36 (10), pp. 5046-5058; Tohidi, G., Khodadadi, M., Allocation models for DMUs with negative data (2013) Journal of Industrial Engineering International, 9 (16), pp. 1-6; Toloo, M., Nalchigar, S., A new DEA method for supplier selection in presence of both cardinal and ordinal data (2011) Expert Systems with Applications, 38 (12), pp. 14726-14731; Torgersen, A.M., Forsund, F.R., Kittelsen, S.A.C., Slack-adjusted efficiency measures and ranking of efficient units (1996) Journal of Productivity Analysis, 7 (4), pp. 379-398; Trapp, A.C., Sarkis, J., Identifying robust portfolios of suppliers: A sustainability selection and development perspective (2016) Journal of Cleaner Production, 112, pp. 2088-2100; Tseng, M.L., Chiu, A.S.F., Evaluating firm’s green supply chain management in linguistic preferences (2013) Journal of Cleaner Production, 40, pp. 22-31; Tsoulfas, G.T., Pappis, C.P., Environmental principles applicable to supply chains design and operation (2006) Journal of Cleaner Production, 14 (18), pp. 1593-1602; Turner, I., An independent system for the evaluation of contract tenders (1988) Operational Research Society, 39 (6), pp. 551-561; Wang, M., Li, Y., Supplier evaluation based on Nash bargaining game model (2014) Expert Systems with Applications, 41 (9), pp. 4181-4185; Wang, Y.-M., Luo, Y., Liang, L., Ranking decision making units by imposing a minimum weight restriction in the data envelopment analysis (2009) Journal of Computational and Applied Mathematics, 223 (1), pp. 469-484; Weber, C.A., A data envelopment analysis approach to measuring vendor performance (1996) Supply Chain Management: An International Journal, 1 (1), pp. 28-39; Wu, D., Supplier selection: A hybrid model using DEA, decision tree and neural network (2009) Expert Systems with Applications, 36 (5), pp. 9105-9112; Wu, D.D., A systematic stochastic efficiency analysis model and application to international supplier performance evaluation (2010) Expert Systems with Applications, 37 (9), pp. 6257-6264; Wu, D., Olson, D.L., Supply chain risk, simulation, and vendor selection (2008) International Journal of Production Economics, 114 (2), pp. 646-655; Wu, J., Yang, F., Liang, L., A modified complete ranking of DMUs using restrictions in DEA models (2010) Applied Mathematics and Computation, 217 (2), pp. 745-751; Xia, W., Wu, Z., Supplier selection with multiple criteria in volume discount environments (2007) Omega, 35, pp. 494-504; Yeh, W.-C., Chuang, M.-C., Using multi-objective genetic algorithm for partner selection in green supply chain problems (2011) Expert Systems with Applications, 38 (4), pp. 4244-4253; Yu, J.-R., Tsai, C.-C., A decision framework for supplier rating and purchase allocation: A case in the semiconductor industry (2008) Computers & Industrial Engineering, 55 (3), pp. 634-646; Zeydan, M., Çolpan, C., Çobanoğlu, C., A combined methodology for supplier selection and performance evaluation (2011) Expert Systems with Applications, 38 (3), pp. 2741-2751; Zhou, X., Pedrycz, W., Kuang, Y., Zhang, Z., Type-2 fuzzy multi-objective DEA model: An application to sustainable supplier evaluation (2016) Applied Soft Computing, 46, pp. 424-440; Zhu, Q., Dou, Y., Sarkis, J., A portfolio-based analysis for green supplier management using the analytical network process (2010) Supply Chain Management: An International Journal, 15, pp. 306-319},
correspondence_address1={Saen, R.F.; Department of Industrial Management, Iran; email: farzipour@yahoo.com},
publisher={Springer New York LLC},
issn={02545330},
language={English},
abbrev_source_title={Ann. Oper. Res.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Kuhi2018256,
author={Kuhi, K. and Kaare, K. and Koppel, O.},
title={Ensuring performance measurement integrity in logistics using blockchain},
journal={Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018},
year={2018},
pages={256-261},
doi={10.1109/SOLI.2018.8476737},
art_number={8476737},
note={cited By 25},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055657473&doi=10.1109%2fSOLI.2018.8476737&partnerID=40&md5=45cea432bd364c845e9f9fb7d751796a},
affiliation={Research Centre of Logistics and Transportation, Tallinn University of Technology, Tallinn, 19086, Estonia},
abstract={Information and communication technology has been a positive dynamic force behind improving performance. Simultaneously enabling digital business ecosystems to become an increasingly popular concept for modelling and building distributed systems in heterogeneous, decentralized and open environments. These business solutions have created an opportunity for automated business relations and transactions as well as implementing blockchain based performance measurement systems (PMS), allowing PMS to be more advanced giving validation and verification of received data hence the indicators. This paper evaluates feasibility of blockchain based PMS for logistics industry. Today the performance of supply chain stakeholders including transport logistics is lacking transparency and restraining innovative digital improvements towards a connected, smart and efficient ecosystem. Current public blockchains are not feasible for given use case due to limited throughput, and transaction cost. In this paper we have evaluated different technological alternatives and found several new developments that make blockchain technology suitable for ensuring logistics sector performance measurement integrity. Validated indicators can add trustworthiness and visibility to management and process improvement decisions, as well as be subject to smart contracts and monetary transactions. © 2018 IEEE.},
author_keywords={blockchain technology;  performance measurement;  supply chain;  valid performance indicators},
keywords={Ecosystems;  Network security;  Supply chain management;  Supply chains, Digital business ecosystems;  Improving performance;  Information and Communication Technologies;  Performance indicators;  Performance measurement system;  Performance measurements;  Technological alternatives;  Validation and verification, Blockchain},
references={Brynjolfsson, E., McAfee, A., (2014) The Second Machine Age: Work, Progress, and Prosperity in A Time of Brilliant Technologies, , New York: W. W. Norton and Company; Wang, K., Logistics 4.0 solution-new challenges and opportunities (2016) Proceedings of the 6th International Workshop of Advanced Manufacturing and Automation. Atlantis Press; Coyle, J.J., Langley, C.J., Gibson, B., Novack, R.A., Bardi, E.J., Supply chain management: A logistics perspective (2009) Mason: South-Western Cengage Learning; Oswald, M., Li, Q., McNeil, S., Trimbath, S., Measuring infrastructure performance: Development of a national infrastructure index (2011) Public Works Management & Policy, 16 (4), pp. 373-394; (1995) How to Measure Performance. A Handbook of Techniques and Tools, , Washington: U.S. Department of Energy; Kivinen, P., Lukka, A., (2004) Value Added Logistical Support Service: Logistics Cost Structure and Performance in the New Concept, , Research report, Part 3. Lappeenranta: Lappeenranta University of Technology; https://e-estonia.com, [Accessed: 28-June-2018]; Xu, X., Weber, I., Staples, M., Zhu, L., Bosch, J., Bass, L., Pautasso, C., Rimba, P., A taxonomy of blockchain-based systems for architecture design (2017) ICSA'17: IEEE International Conference on Software Architecture; Bharadwaj, A., El Sawy, O.A., Pavlou, P.A., Venkatraman, N., Digital business strategy: Toward a next generation of insights (2013) MIS Quarterly, pp. 471-482. , June; Glaser, F., Pervasive decentralisation of digital infrastructures: A framework for blockchain enabled system and use case analysis (2017) Proceedings of the 50th Hawaii International Conference on System Sciences; Morabito, V., (2017) Business Innovation Through Blockchain. The B Perspective, , Cham: Springer International Publishing; Valenta, M., Sandner, P., Comparison of ethereum, hyperledger and corda (2017) FSBC Working Paper; https://www-935.ibm.com/services/us/gbs/thoughtleadership/blockchain/fastforwardii, [Accessed: 27-June-2018]; Kõrbe Kaare, K., Koppel, O., Performance indicators to support evaluation of road investments (2012) Discussions on Estonian Economic Policy, (2), pp. 88-107; Kuhi, K., Kõrbe Kaare, K., Koppel, O., A concept for performance measurement and evaluation in network industries (2015) Proc. Estonian Academy of Sciences, 64, pp. 536-542; Kuhi, K., Kõrbe Kaare, K., Koppel, O., Using probabilistic models for missing data prediction in network industries performance measurement systems (2014) Procedia Engineering, (100), pp. 1348-1353; https://www.kinno.fi/en/smartlog, [Accessed: 31-May-2018]; (2014) North Sea-Baltic Core Network Corridor Study, , Final Report. PROXIMARE Consortium},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781538645222},
language={English},
abbrev_source_title={Proc. IEEE Int. Conf. Serv. Oper. Logist., Inf., SOLI},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Methavitakul2018191,
author={Methavitakul, B. and Santiteerakul, S.},
title={Analysis of key dimension and sub-dimension for Supply Chian of Industry to fourth Industry Performance Measurement},
journal={Proceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018},
year={2018},
pages={191-195},
doi={10.1109/SOLI.2018.8476765},
art_number={8476765},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85055639659&doi=10.1109%2fSOLI.2018.8476765&partnerID=40&md5=1266fc837fbfa066d12c3ed56096071e},
affiliation={Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand},
abstract={In 2013 Germany, a world leader's Industrializes nation published its »Industry 4.0» strategic plan. This strategic plan motivated many countries in the world to develop their own industry. Thailand is one developing country that invest in this strategy. As we know Thailand food industry is the most important and need to be change. The food industry can answer human behavior and have variety. With changing of human behavior, Enterprise should invest and upgrading and improving in production. In industry 4.0 perspective, it is possible that the global industry should focus on and be ready to adapt to the industry 4.0. This paper will present performance measurement by assessing readiness and Maturity Model in part of selecting dimension and sub-dimension. © 2018 IEEE.},
author_keywords={Industry 4.0;  Logistics and Supply Chain Performance measurement;  Maturity model;  Readiness assessment},
keywords={Behavioral research;  Developing countries;  Strategic planning;  Supply chains, Food industries;  Global industry;  Human behaviors;  Key dimensions;  Maturity model;  Performance measurements;  Readiness assessment;  Supply chain performance measurements, Industry 4.0},
references={Santos, K., Loures, E., Piechnicki, F., Canciglieri, O., Opportunities assessment of product development process in (2017) 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, , Modena, Italy; Li, L., China's manufacturing locus in 2025: With a comparison of (2017) Made in China 2025 and Industry 4.0 Technological Forecasting and Social Change; Santos, M.Y., Sá, J.O.E., Andrade, C., Lima, F.V., Costa, E., Costa, C., Martinho, B., Galvão, J., A big data system supporting Bosch Braga Industry 4.0 strategy (2017) International Journal of Information Management, pp. 750-760; Schumachera, A., Erolb, S., Sihna, W., A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises (2016) Changeable Agile, Recomfiguration and Virtual Production, pp. 161-166; Industry 4.0 Readiness Online Self-Check for Businesses, , https://www.industrie40-readiness.de/lang=en, IMPULS; An Industry 4.0 readiness assessment tool International Institute for Product and Service Innovation, pp. 1-19. , University of Warwick; Granite State Quality Council, Education Criteria for Performance Excellence, Granite State Quality Council, pp. 1-4; Amrina, E., Vilsi, A.L., Key performance indicators for sustainable manufacrturing evaluation in cement industry (2015) 12th Global Conference on Sustainable Manufacturing; Simota, J., Tupa, J., Steiner, F., Aspects of risk management implementation for Industry 4.0 (2017) 27th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM2017, , Modena, Italy; Tonelli, F., Demartini, M., Loleo, A., Testa, C., A novel methodology for manufacturing firms value modeling and mapping to improve operational performance in the industry 4.0 era (2016) 49th CIRP Conference in Manufacturing Systems, Stuttgart, Germany; Saaty, L.T., Shang, S.J., An innovetive orders-of-magnitude approach to AHP-based multi-criteria decision making: Prioritizing intangible human acts (2011) European Journal of Operational Research, 214 (3), pp. 703-715. , 1 Novenber; Blagojevic, B., Srdjevic, B., Srdjevic, Z., Zoranovic, T., Heuristic aggregation of individual judgments in AHP group decision making using simulated annealing algorithm (2016) Information Sciences, pp. 260-273; Kursunoglua, S., Ichlasb, Z.T., Kayac, M., Leaching method selection for Caldag lateritic nickel ore by the analytic hierarchy process AHP (2017) Hydrometallurgy, pp. 179-184; Liebrechta, C., Jacob, A., Kuhnlea, A., Lanza, G., Multi-criteria evaluation of manufacturing systems 4.0 under uncertainty (2017) The 50th CIRP Conference on Manufacturing Systems; Reh, F.J., Understanding Pareto's Principle-The 80-20 Rule, , https://www.Thebalance.com/pareto-sprinciple-the-80-20-rule-2275148, 8 August 2017. [Accessed 10 December 2017]; Schuh, G., Anderl, R., Gausemeier, J., Hompel, M.T., Wahlster, W., Industrie 4.0 Maturity index (2017) Acatech STUDY, pp. 1-60; Antonsson, M., INDUSTRY 4.0 where are swedish manufacturers in the transition towards industry 4.0 (2017) CHALMERS UNIVERSITY of TECHNOLOGY, pp. 11-44; Galvão, J., Martinho, B., Costa, C., Costa, E., Lima, F.V., Andrade, C., Sá, J.O.E., Santos, M.Y., A Big Data system supporting bosch braga industry 4.0 strategy (2017) International Journal of Information Management, pp. 750-760},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781538645222},
language={English},
abbrev_source_title={Proc. IEEE Int. Conf. Serv. Oper. Logist., Inf., SOLI},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lemghari2018,
author={Lemghari, R. and Okar, C. and Sarsri, D.},
title={Supply chain performance measurement: A case study about applicability of scor® model in automotive industry firm},
journal={MATEC Web of Conferences},
year={2018},
volume={200},
doi={10.1051/matecconf/201820000016},
art_number={00016},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054304235&doi=10.1051%2fmatecconf%2f201820000016&partnerID=40&md5=174050dc13cbb0556ec45d0b875ab8db},
affiliation={Innovative Technologies Laboratory, National School of Applied Sciences, Tangier, 90000, Morocco; Higher School of Technology of Berrechid, Hassan the University, Settat, 26000, Morocco},
abstract={The evaluation of a supply chain is a major priority of companies; it is a task that remains difficult due to the complexity of these systems [1]. This evaluation involves a selection of performance measurement indicators, which are appropriate to the management of this chain. It is then necessary to have a structured approach and adequate methodological tools [2]. Indeed, we propose in this paper a practical method that will model in the first place a Moroccan automotive supply chain, according to the SCOR® model (Supply Chain Operations Reference), proposed by the Supply Chain Council. This method will also identify at each level the appropriate indicators for the performance evaluation depending on the strategic vision. In this context our research problem is made, it is interested in the contribution of the business modelling to improve logistics performance. To the best knowledge of the authors, this is the first work that proposes a case study believed to be easy to understand, practical and suitable for the automotive sector. In short, this study is a real application leap to resolve the problematic unanswered of practical SCOR® model using an industrial application in the Moroccan automotive sector. © The Authors, published by EDP Sciences, 2018.},
references={Mentzer, J.T., Keebler, J.S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) J. Bus. Logist., 22 (2), pp. 1-25; Min, H., Zhou, G., Supply chain modeling: Past, present and future (2002) Comput. Ind. Eng., 43 (12), pp. 231-249; Logistics performance index (lpi) report (2016) The Gap Persists, , http://lpi.worldbank.org/international/global/2016, Washington, March 20. Accessed Mai 21, 2018; Sako, M., Helper, S., The information requirements of trust in supplier relations: Evidence from Japan, Europe and the United States (1998) Trust and Economic Learning, pp. 23-47; Grosche, P., Mayrhofer, U., Schmid, S., La configuration et la coordination internationales de la chaîne de valeur dans l'industrie automobile allemande (2015) Financ. Contrôle Strat., pp. 1-27; Pierreval, H., Bruniaux, R., Caux, C., A continuous simulation approach for supply chains in the automotive industry (2007) Simul. Model. Pract. Theory, 15 (2), pp. 185-198; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Garcia Reyes, H., Giachetti, R., Using experts to develop a supply chain maturity model in Mexico (2010) Supply Chain Manag. An Int. J., 15 (6), pp. 415-424; McCormack, K., Ladeira, M.B., De Oliveira, M.P.V., Supply chain maturity and performance in Brazil (2008) Supply Chain Manag. An Int. J., 13 (4), pp. 272-282; Bolstorff, P., Rosenbaum, R., (2011) Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , Third Edition; Rotaru, K., Wilkin, C., Ceglowski, A., Analysis of scor's approach to supply chain risk management (2014) Meas. Bus. Excell., 21 (2), pp. 191-206; Supply chain council (2012) Supply Chain Operations Reference Model (SCOR); Lima-Junior, F.R., Carpinetti, L.C.R., Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management (2016) Int. J. Prod. Econ., 174, pp. 128-141; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int. J. Prod. Econ., 134 (1), pp. 177-187; Apics supply chain council (2017) SCOR Supply Chain Operations Reference Model Ver. 12.0 Quick Reference Guide; Lebas, M.J., Performance measurement and performance management (1995) Int. J. Prod. Econ., 41 (13), pp. 23-35; Neto, M.S., Pires, S.R.I., Performance measurement in supply chains: A study in the automotive industry (2012) New Advances in Vehicular Technology and Automotive Engineering, 19 (4), pp. 733-746; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process based approach (2000) Int. J. Oper. Prod. Manag., 20 (10), pp. 1119-1145; Dörnhöfer, M., Schröder, F., Günthner, W.A., Logistics performance measurement system for the automotive industry (2016) Logist. Res., 9 (1), pp. 1-26; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement of the after-sales service network-Evidence from the automotive industry (2007) Comput. Ind., 58 (7), pp. 698-708; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking An Int. J., 13 (1-2), pp. 36-53; Sánchez, M.A., Pérez Pérez, M., Supply chain flexibility and firm performance: A conceptual model and empirical study in the automotive industry (2005) Int. J. Oper. Prod. Manag., 25 (7), pp. 681-700; Han, W., Huang, Y., Macbeth, D., Performance measurement of cross-culture supply chain partnership: A case study in the Chinese automotive industry (2017) Int. J. Prod. Res., 56, pp. 2437-2451; Velda, A.M.E., Dhiba, Y., International colloquium on logistics and supply chain management: Competitiveness and innovation in automobile and aeronautics industries (2017) LOGISTIQUA 2017, pp. 106-111; Eisenhardt, K.M., Building theories from case study research (1989) Academy of Management Review, 14 (4), pp. 532-550; Yin, R., Case study research: Design and methods (1994) Applied Social Research Methods Series, , 1st edition; Yin, R., Case study research: Design and methods (2009) Applied Social Research Methods Series, , 4th edition; McCutcheon, D.M., Meredith, J.R., Conducting case study research in operations management (1993) J. Oper. Manag., 11 (3), pp. 239-256},
correspondence_address1={Lemghari, R.; Innovative Technologies Laboratory, Morocco; email: lemghari.radouane@gmail.com},
editor={El Majd B.A., El Ghazi H.},
publisher={EDP Sciences},
issn={2261236X},
language={English},
abbrev_source_title={MATEC Web Conf.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Acquaye2018188,
author={Acquaye, A. and Ibn-Mohammed, T. and Genovese, A. and Afrifa, G.A. and Yamoah, F.A. and Oppon, E.},
title={A quantitative model for environmentally sustainable supply chain performance measurement},
journal={European Journal of Operational Research},
year={2018},
volume={269},
number={1},
pages={188-205},
doi={10.1016/j.ejor.2017.10.057},
note={cited By 71},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034017956&doi=10.1016%2fj.ejor.2017.10.057&partnerID=40&md5=91631d32b3d877cbbab85e4359de19f3},
affiliation={Kent Business School, University of Kent, Canterbury, CT2 7PE, United Kingdom; Centre for Energy, Environment and Sustainability, University of Sheffield, Sheffield, S10 1FL, United Kingdom; Advanced Resource Efficiency Centre, University of Sheffield, Sheffield, S10 1FL, United Kingdom; Logistics and Supply Chain Management Research Centre, Management School, University of Sheffield, Sheffield, United Kingdom; The Business School, Canterbury Christ Church University, Canterbury, CT1 1NX, United Kingdom; Brunel Business School, Brunel University London, Uxbridge, London  UB8 3PH, United Kingdom},
abstract={The development of robust mechanisms for supply chain performance measurement have been identified as an integral step needed for the transition towards sustainable supply chain systems and a greener global economy. However, measuring the environmental performance of supply chains is a challenging task, due to several factors, such as the lack of standardised methodologies and the inherent multi-criteria nature of the problem. By leveraging the capability of a Multi-Regional Input–Output framework to handle the complex and global nature of supply chains, the current work presents a robust environmental sustainable performance measurement model underpinned by industrial lifecycle thinking. As a result, some theoretical insights are provided and an empirical application of the model to the Metal Products industry of the BRICS (Brazil, Russia, India, China, and South Africa) nations undertaken in an attempt to address some of the methodological and applied measurement challenges. In particular, this allowed the modelling of carbon emissions trends within, and between the BRICS nations and with the Rest-of-the-World over a 20-year period (1992–2011) as well as providing an opportunity to hypothesis on their future carbon emissions performances. Specific analyses of the Metal Product industry showed that demand represents the main driver for the increasing carbon footprint. However, the overall decline in reported carbon footprint was due to improvements in emissions intensity and efficiency gains induced by technology. The study further assesses the effects of imports and economic growth on carbon footprint and discusses the implications of the study to sustainability transition processes in the BRICS nations. © 2017 The Authors},
author_keywords={BRICS;  Industry lifecycle thinking;  Operational research in environment and climate change;  Supply chain;  Sustainable performance measurement},
keywords={Chains;  Climate change;  Economics;  Environmental impact;  Environmental management;  Life cycle;  Supply chains;  Sustainable development, BRICS;  Environmental performance;  Industry lifecycle;  Operational research;  Supply chain performance measurements;  Sustainability transition;  Sustainable performance;  Sustainable supply chains, Carbon footprint},
references={Abbasi, M., Nilsson, F., Themes and challenges in making supply chains environmentally sustainable (2012) Supply Chain Management: An International Journal, 17 (5), pp. 517-530; Acquaye, A.A., Duffy, A.P., Input–output analysis of Irish construction sector greenhouse gas emissions (2010) Building and Environment, 45 (3), pp. 784-791; Acquaye, A., Feng, K., Oppon, E., Salhi, S., Ibn-Mohammed, T., Genovese, A., Measuring the environmental sustainability performance of global supply chains: A multi-regional input-output analysis for carbon, sulphur oxide and water footprints (2017) Journal of Environmental Management, 187, pp. 571-585; Acquaye, A., Genovese, A., Barrett, J., Koh, L., Benchmarking carbon emissions performance in supply chains (2014) Supply Chain Management: An International Journal, 19 (3), pp. 306-321; Afful-Dadzie, A., Afful-Dadzie, E., Turkson, C., A TOPSIS extension framework for re-conceptualizing sustainability measurement (2016) Kybernetes, 45 (1), pp. 70-86; Afionis, S., Sakai, M., Scott, K., Barrett, J., Gouldson, A., Consumption‐based carbon accounting: Does it have a future? (2017) Wiley Interdisciplinary Reviews: Climate Change, 8 (1), pp. 1-19; Ahi, P., Searcy, C., Assessing sustainability in the supply chain: A triple bottom line approach (2015) Applied Mathematical Modelling, 39 (10), pp. 2882-2896; Akashi, O., Hanaoka, T., Matsuoka, Y., Kainuma, M., “A projection for global CO2 emissions from the industrial sector through 2030 based on activity level and technology changes (2011) Energy, 36, pp. 1855-1867; Alcott, B., Population matters in ecological economics (2012) Ecological Economics, 80, pp. 109-120; Allwood, J.M., Ashby, M.F., Gutowski, T.G., Worrell, E., Material efficiency: A white paper (2011) Resources, Conservation and Recycling, 55 (3), pp. 362-381; Allwood, J.M., Cullen, J.M., Milford, R.L., “Options for achieving a 50% cut in industrial carbon emissions by 2050 (2010) Environmental Science and Technology, 44 (6), pp. 1888-1894; Arens, M., Worrell, E., Schleich, J., Energy intensity development of the German iron and steel industry between 1991 and 2007 (2012) Energy, 45 (1), pp. 786-797; Azapagic, A., Perdan, S., Indicators of sustainable development for industry: A general framework (2000) Process Safety and Environmental Protection, 78 (4), pp. 243-261; Barrett, J., Peters, G., Wiedmann, T., Scott, K., Lenzen, M., Roelich, K., Le Quéré, C., Consumption-based GHG emission accounting: A UK case study (2013) Climate Policy, 13 (4), pp. 451-470; Bazan, E., Jaber, M.Y., Zanoni, S., Supply chain models with greenhouse gases emissions, energy usage and different coordination decisions (2015) Applied Mathematical Modelling, 39 (17), pp. 5131-5151; Beske, P., Seuring, S., Putting sustainability into supply chain management (2014) Supply Chain Management: An International Journal, 19 (3), pp. 322-331; Bhagwati, J., Panagariya, A., The theory of preferential trade agreements: Historical evolution and current trends (1996) The American Economic Review, 86 (2), pp. 82-87; Bosetti, V., Carraro, C., Tavoni, M., Climate change mitigation strategies in fast-growing countries: The benefits of early action (2009) Energy Economics, 31, pp. S144-S151; Sixth BRICS Summit – Fortaleza Declaration (2014), http://brics6.itamaraty.gov.br/media2/press-releases/214-sixth-brics-summit-fortaleza-declaration, (Accessed 15 April 2015); Cerri, C.C., Maia, S.M.F., Galdos, M.V., Cerri, C.E.P., Feigl, B.J., Bernoux, M., Brazilian greenhouse gas emissions: The importance of agriculture and livestock (2009) Scientia Agricola, 66 (6), pp. 831-843; Chakraborty, D., Mukhopadhyay, K., “The methodology (2014) Water pollution and abatement policy in India, 10, pp. 47-51. , Springer Netherlands; Court, C.D., Munday, M., Roberts, A., Turner, K., Can hazardous waste supply chain ‘hotspots’ be identified using an input–output framework (2015) European Journal of Operational Research, 241 (1), pp. 177-187; Daly, H.E., Farley, J., Ecological economics: Principles and applications (2011), Island Press Washington; Devika, K., Jafarian, A., Nourbakhsh, V., Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques (2014) European Journal of Operational Research, 235 (3), pp. 594-615; Dey, A., Laguardia, P., Srinivasan, M., Building sustainability in logistics operations: A research agenda (2011) Management Research Review, 34 (11), pp. 1237-1259; Ding, H., Liu, Q., Zheng, L., Assessing the economic performance of an environmental sustainable supply chain in reducing environmental externalities (2016) European Journal of Operational Research, 255 (2), pp. 463-480; http://www.climateaction.org.za/cop17-cmp7/sa-government-position-on-climate-change, South African Government's position on climate change. (2013). (Accessed 8th April 2015); Ebiefung, A., Kostreva, M., The generalized Leontief input–output model and its application to the choice of new technology (1993) Annals of Operations Research, 44 (2), pp. 161-172; Epstein, D., “New development? The BRICS Bank and the International System (2014) Harvard International Review, 36 (2), p. 12; Ewing, B.R., Hawkins, T.R., Wiedmann, T.O., Galli, A., Ercin, A.E., Weinzettel, J., Integrating ecological and water footprint accounting in a multi-regional input–output framework (2012) Ecological Indicators, 23, pp. 1-8; Fawcett, S.E., Magnan, G.M., The rhetoric and reality of supply chain integration (2002) International Journal of Physical Distribution & Logistics Management, 32 (5), pp. 339-361; Fischedick, M., Roy, J., Abdel-Aziz, A., Acquaye, A., Allwood, J.M., Ceron, J.-P., Industry (2014) Climate change 2014: Mitigation of climate change. contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change, , Cambridge University Press Cambridge, United Kingdom and New York, NY, USA; Fischer, C., Newell, R.G., Environmental and technology policies for climate mitigation (2008) Journal of Environmental Economics and Management, 55 (2), pp. 142-162; Font, X., Harris, C., Rethinking standards from green to sustainable (2004) Annals of Tourism Research, 31 (4), pp. 986-1007; Fourcade, M., The material and symbolic construction of the BRICs: Reflections inspired by the RIPE Special Issue (2013) Review of International Political Economy, 20 (2), pp. 256-267; Freeman, S.L., Niefer, M.J., Roop, J.M., Measuring industrial energy intensity: Practical issues and problems (1997) Energy Policy, 25 (7), pp. 703-714; Ganesh, M., Raghunathan, S., Rajendran, C., The value of information sharing in a multi-product, multi-level supply chain: Impact of product substitution, demand correlation, and partial information sharing (2014) Decision Support Systems, 58, pp. 79-94; Genovese, A., Morris, J., Piccolo, C., Koh, S.L., Assessing redundancies in environmental performance measures for supply chains (2017) Journal of Cleaner Production, , 10.1016/j.jclepro.2017.05.186; Gereffi, G., Humphrey, J., Sturgeon, T., The governance of global value chains (2005) Review of International Political Economy, 12 (1), pp. 78-104; Gonzalez, E.D.R.S., Sarkis, J., Huisingh, D., Huatuco, L.H., Maculan, N., Montoya-Torres, J.R., Making real progress toward more sustainable societies using decision support models and tools: Introduction to the special volume (2015) Journal of Cleaner Production, 105, pp. 1-13; (2011), http://www.theguardian.com/sustainable-business/brics-economies-emerging-markets-global-values, BRICS’ emergence raises the environmental stakes. Guardian., (Accessed 20th March 2015); Guinée, J.B., Handbook on life cycle assessment operational guide to the ISO standards (2002) The International Journal of Life Cycle Assessment, 7 (5), pp. 311-313; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gutowski, T.G., Sahni, S., Allwood, J.M., Ashby, M.F., Worrell, E., “The energy required to produce materials: Constraints on energy-intensity improvements, parameters of demand (2013) Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 371; Hart, S.L., A natural-resource-based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1014; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Heltberg, R., Siegel, P.B., Jorgensen, S.L., Addressing human vulnerability to climate change: Toward a ‘no-regrets’ approach (2009) Global Environmental Change, 19 (1), pp. 89-99; Holweg, M., Disney, S., Holmström, J., Småros, J., Supply chain collaboration: Making sense of the strategy continuum (2005) European Management Journal, 23 (2), pp. 170-181; Horvath, L., Collaboration: The key to value creation in supply chain management (2001) Supply Chain Management: An International Journal, 6 (5), pp. 205-207; Hu, G., Bidanda, B., Modeling sustainable product lifecycle decision support systems (2009) International Journal of Production Economics, 122 (1), pp. 366-375; Huang, Y.A., Weber, C.L., Matthews, H.S., Categorization of scope 3 emissions for streamlined enterprise carbon footprinting (2009) Environmental Science & Technology, 43 (22), pp. 8509-8515; Hubacek, K., Feng, K., Chen, B., Changing lifestyles towards a low carbon economy: An IPAT analysis for China (2011) Energies, 5 (1), pp. 22-31; Ibn-Mohammed, T., Greenough, R., Taylor, S., Ozawa-Meida, L., Acquaye, A., Integrating economic considerations with operational and embodied emissions into a decision support system for the optimal ranking of building retrofit options (2014) Building and Environment, 72, pp. 82-101; Ibn-Mohammed, T., Koh, S.C.L., Reaney, I.M., Acquaye, A., Schileo, G., Mustapha, K.B., Perovskite solar cells: An integrated hybrid lifecycle assessment and review in comparison with other photovoltaic technologies (2017) Renewable and Sustainable Energy Reviews, 80, pp. 1321-1344; Section 7.6.3: Costing methodologies: Top–down and bottom–up models (2001), http://www.ipcc.ch/ipccreports/tar/wg3/index.php?idp=310, (Accessed 15 March 2015); ISO 14041: Environmental management – Lifecycle assessment – Goal and scope definition and Inventory analysis (1998), ISO. Geneva; Summary for policymakers (2014) Climate change 2014, mitigation of climate change. contribution of working group III to the fifth assessment report of the intergovernmental panel on climate change, , Cambridge University Press Cambridge, United Kingdom and New York, NY, USA; Jaehn, F., Sustainable operations (2016) European Journal of Operational Research, 253 (2), pp. 243-264; Jakob, M., Steckel, J.C., Edenhofer, O., Consumption-versus production-based emission policies (2014) Annual Review of Resource Economics, 6 (1), pp. 297-318; Kagawa, S., Suh, S., Hubacek, K., Wiedmann, T., Nansai, K., Minx, J., CO2 emission clusters within global supply chain networks: Implications for climate change mitigation (2015) Global Environmental Change; Khanna, P., New BRICS bank a building block of alternative world order (2014) New Perspectives Quarterly, 31 (4), pp. 46-48; Kucukvar, M., Egilmez, G., Tatari, O., “Sustainability assessment of US final consumption and investments: Triple-bottom-line input–output analysis (2014) Journal of Cleaner Production, 81, pp. 234-243; Koh, S.L., Genovese, A., Acquaye, A.A., Barratt, P., Rana, N., Kuylenstierna, J., Decarbonising product supply chains: design and development of an integrated evidence-based decision support systemthe supply chain environmental analysis tool (SCEnAT) (2013) International Journal of Production Research, 51 (7), pp. 2092-2109; Koh, S.C.L., Ibn-Mohammed, T., Acquaye, A., Feng, K., Reaney, I.M., Hubacek, K., Fujii, H., Khatab, K., Drivers of US toxicological footprints trajectory 1998–2013 (2016) Scientific Reports, 6, p. 39514; Lai, K.-H., Wong, C.W.Y., Green logistics management and performance: Some empirical evidence from Chinese manufacturing exporters (2012) Omega, 40 (3), pp. 267-282; Larsen, H.N., Hertwich, E.G., The case for consumption-based accounting of greenhouse gas emissions to promote local climate action (2009) Environmental Science & Policy, 12 (7), pp. 791-798; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Leal-Arcas, R., The BRICS and climate change (2013) International affairs forum, 4, pp. 22-26. , Taylor & Francis; Lehtinen, J., Ahola, T., Is performance measurement suitable for an extended enterprise? (2010) International Journal of Operations & Production Management, 30 (2), pp. 181-204; Lenzen, M., Moran, D., Kanemoto, K., Geschke, A., Building Eora: A global multi-region input–output database at high country and sector resolution (2013) Economic Systems Research, 25 (1), pp. 20-49; Leontief, W., Quantitative input and output relations in the economic systems of the United States (1936) The Review of Economic Statistics, 18 (3), pp. 105-125; Mahlberg, B., Luptacik, M., Eco-efficiency and eco-productivity change over time in a multisectoral economic system (2014) European Journal of Operational Research, 234 (3), pp. 885-897; Majeau-Bettez, G., Strømman, A.H., Hertwich, E.G., Evaluation of process-and input–output-based life cycle inventory data with regard to truncation and aggregation issues (2011) Environmental Science & Technology, 45 (23), pp. 10170-10177; Marchi, V.D., Maria, E.D., Micelli, S., “Environmental strategies, upgrading and competitive advantage in global value chains (2013) Business Strategy and the Environment, 22 (1), pp. 62-72; Mathews, J.A., Tan, H., “Circular Economy: Lessons from China (2016) Nature, 531, pp. 440-442; McDonough, W., Braungart, M., Cradle to cradle: Remaking the way we make things (2002), North Point Press New York, NY; Miller, R.E., Blair, P.D., Input-output analysis: Foundations and extensions (2009), Cambridge University Press Cambridge; Min, S., Roath, A.S., Daugherty, P.J., Genchev, S.E., Chen, H., Arndt, A.D., Supply chain collaboration: What's happening? (2005) The International Journal of Logistics Management, 16 (2), pp. 237-256; Müller, E., Hilty, L.M., Widmer, R., Schluep, M., Faulstich, M., (2014), 48 (4), pp. 2102-2113. , “Modeling metal stocks and flows: A review of dynamic material flow analysis methods. Environmental Science & Technology”; Napp, T., Gambhir, A., Hills, T., Florin, N., Fennell, P., “A review of the technologies, economics and policy instruments for decarbonising energy-intensive manufacturing industries (2014) Renewable and Sustainable Energy Reviews, 30, pp. 616-640; Nayyar, D., “BRICS, developing countries and global governance (2016) Third World Quarterly, 37 (4), pp. 575-591; Neilson, J., Pritchard, B., Yeung, H.W.C., “Global value chains and global production networks in the changing international political economy: An introduction (2014) Review of International Political Economy, 21 (1), pp. 1-8; O'Mahony, M., Timmer, M.P., Output, input and productivity measures at the industry level: The EU KLEMS database (2009) The Economic Journal, 119 (538), pp. F374-F403; Paroussos, L., Fragkos, P., Capros, P., Fragkiadakis, K., Assessment of carbon leakage through the industry channel: The EU perspective (2015) Technological Forecasting and Social Change, 90, pp. 204-219; Peters, G.P., Carbon footprints and embodied carbon at multiple scales (2010) Current Opinion in Environmental Sustainability, 2 (4), pp. 245-250; Porter, M., Competitive advantage: Creating and sustaining superior performance (1985), Free Press New York, NY; Prell, C., Feng, K., Sun, L., Geores, M., Hubacek, K., The economic gains and environmental losses of US consumption: A world-systems and input–output approach (2014) Social Forces, 93 (1), pp. 405-428; Roy, R., Sustainable product-service systems (2000) Futures, 32 (3-4), pp. 289-299; Schaltegger, S., Lüdeke-Freund, F., Hansen, E.G., “Business cases for sustainability: The role of business model innovation for corporate sustainability (2012) International Journal of Innovation and Sustainable Development, 6 (2), pp. 95-119; Serrenho, A.C., Mourão, Z.S., Norman, J., Cullen, J.M., Allwood, J.M., “The influence of UK emissions reduction targets on the emissions of the global steel industry (2016) Resources, Conservation and Recycling, 107, pp. 174-184; Shaw, N., BRIC by BRIC: How our emerging markets are tackling carbon emissions? (2013), http://www.sustainablebusinesstoolkit.com/bric-countries-carbon-emissions/, (Accessed 20th March 2015); Sundarakani, B., De Souza, R., Goh, M., Wagner, S.M., Manikandan, S., Modeling carbon footprints across the supply chain (2010) International Journal of Production Economics, 128 (1), pp. 43-50; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) International Journal of Production Research, 53 (21), pp. 6473-6494; Takahashi, K., Nansai, K., Tohno, S., Nishizawa, M., Kurokawa, J.-I., Ohara, T., Production-based emissions, consumption-based emissions and consumption-based health impacts of PM2.5 carbonaceous aerosols in Asia (2014) Atmospheric Environment, 97, pp. 406-415; Timmer, M.P., Erumban, A.A., Los, B., Stehrer, R., de Vries, G.J., “Slicing up global value chains (2014) The Journal of Economic Perspectives, 28 (2), pp. 99-118; Tukker, A., Dietzenbacher, E., Global multiregional input–output frameworks: An introduction and outlook (2013) Economic Systems Research, 25 (1), pp. 1-19; Global guidance principles for life cycle assessment databases: A basis for greener processes and products (2011), http://www.lifecycleinitiative.org/wp-content/uploads/2012/12/2011%20-%20Global%20Guidance%20Principles.pdf, United Nations Environment ProgrammeMilan Italy Accessed: 20 January 2015, Available at; Country analysis: Brazil (2013), http://www.eia.gov/beta/international/country.cfm?iso=BRA, (Accessed 10 March 2015); Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Management: An International Journal, 19 (3), pp. 242-257; Ward, H., Burger, M., Chang, Y.J., Fürstmann, P., Neugebauer, S., Radebach, A., Assessing carbon dioxide emission reduction potentials of improved manufacturing processes using multiregional input output frameworks (2017) Journal of Cleaner Production, 163, pp. 154-165; Xi confident in BRICS future (2014), http://chinawatch.washingtonpost.com/2014/07/xi_confident_in_brics_future/, (Accessed: 20th March 2015); West, G.R., Jackson, R.W., Simulating impacts on regional economies: A modelling alternative (2015) Hospitality travel and tourism: Concepts, methodologies, tools and applications, , I.R.M. (Association IGI Global Pennsylvania; World bank open data (2015), http://data.worldbank.org/, Washington, USA (Accessed 2nd May 2015); World bank open data: Russian federation (2016), http://data.worldbank.org/country/russian-federation?view=chart, Washington, USA (accessed: 1 March 2017); Wu, L., Liu, S., Liu, D., Fang, Z., Xu, H., Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model (2015) Energy, 79, pp. 489-495; Wu, X., Zhang, Z., Input–output analysis of the Chinese construction sector (2005) Construction Management and Economics, 23 (9), pp. 905-912; Xu, Y., Dietzenbacher, E., A structural decomposition analysis of the emissions embodied in trade (2014) Ecological Economics, 101, pp. 10-20; Yang, Q., Song, B., Eco-design for product lifecycle sustainability (2006) Proceedings of the IEEE international conference on industrial informatics, Singapore, pp. 548-553; Yuan, Z., Bi, J., Moriguichi, Y., The circular economy: A new development strategy in China (2006) Journal of Industrial Ecology, 10 (1‐2), pp. 4-8; Zhu, J., (2014) Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets, 213. , Springer; Zhu, Q., Sarkis, J., Lai, K.-H., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111 (2), pp. 261-273},
correspondence_address1={Acquaye, A.; Kent Business School, United Kingdom; email: a.a.acquaye@kent.ac.uk},
publisher={Elsevier B.V.},
issn={03772217},
coden={EJORD},
language={English},
abbrev_source_title={Eur J Oper Res},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Regragui201851,
author={Regragui, H. and Sefiani, N. and Azzouzi, H.},
title={Improving performance through measurement: The application of BSC and AHP in healthcare organization},
journal={2018 International Colloquium on Logistics and Supply Chain Management, LOGISTIQUA 2018},
year={2018},
pages={51-56},
doi={10.1109/LOGISTIQUA.2018.8428262},
art_number={8428262},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052491157&doi=10.1109%2fLOGISTIQUA.2018.8428262&partnerID=40&md5=21b73841c79022bd67757975bb77345a},
affiliation={Equipe de Recherche, Ingénierie, Innovation et Management des Systèmes Industriels, Université Abdelmalek Saadi, Tanger, Morocco},
abstract={This paper introduces a conceptual framework for evaluating performance in healthcare organizations. The challenge of evaluating performance resides in the determination of the appropriate model with the appropriate indicators that meet the characteristics of the framework. In this end, our study is based on the development of structured approach for evaluating performance for hospitals in Morocco. The scope of this study is developed through three main phases. First, related literature of performance measurement systems was conducted, to identify the appropriate model to our context. Second, after analyzing different PMS, we have found that BSC presents the convenient one to hospitals. Based on the literature review and panel of experts, we determined the criteria, sub-criteria and key performance indicators (KPIs). Third, the relative weights of the chosen evaluators were calculated by Analytical Hierarchy Process (AHP) technique. The analysis results highlight the critical aspects of evaluation criteria as well as the gaps to enhance hospital performance for achieving desired target. This model can be a useful tool for evaluating and comparing the performance of hospitals. © 2018 IEEE.},
author_keywords={Analytic Hierarchy Process (AHP);  Balanced Scorecard (BSC);  Healthcare organization;  Performance evaluation},
keywords={Analytic hierarchy process;  Benchmarking;  Health care;  Hierarchical systems;  Hospitals;  Petroleum reservoir evaluation, Analytic hierarchy process (ahp);  Analytical Hierarchy Process;  Balanced scorecards;  Conceptual frameworks;  Healthcare organizations;  Key performance indicators;  Performance evaluations;  Performance measurement system, Supply chain management},
references={Kocakulah, M.C., Austill, A.D., Balanced Scorecard Application in the Health Care Industry : A Case Study (2007) Journal of Health Care Finance, 34 (1), pp. 72-99; Koumpouros, Y., Balanced scorecard: Application in the General Panarcadian Hospital of Tripolis, Greece (2013) International Journal of Health Care Quality Assurance, 26 (4), pp. 286-307; Brailsford, S., Vissers, J., OR in healthcare: A European perspective (2011) European Journal of Operational Research, 212 (2), pp. 223-234; Taticchi, P., Tonelli, F., Cagnozzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Bahadori, M., Sadeghifar, J., Hamouzadeh, P., Hakimzadeh, S.M., Nejati, M., Combining multiple indicators to assess hospital performance in Iran using the Pabon Lasso Model (2011) The Australasian Medical Journal, 4 (4), p. 175; Kanji, G.K., Performance Measurement System (2002) Total Quality Management, 13 (5), pp. 715-728; Goshtasebi, A., Vahdaninia, M., Gorgipour, R., Samanpour, A., Maftoon, F., Farzadi, F., Assessing hospital performance by the Pabon Lasso Model (2009) Iranian Journal of Public Health, 38 (2), pp. 119-124; Kairu, E.W., Wafula, M.O., Okaka, O., Odera, O., Kayode, E., Effects of Balanced Scorecard on Performance of Firms in the Service Sector (2013) European Journal of Business and Management, 5 (9), pp. 81-89; El-Jardali, F., Saleh, S., Ataya, N., Jamal, D., Design, implementation and scaling up of the balanced scorecard for hospitals in Lebanon: Policy coherence and application lessons for low and middle income countries (2011) Health Policy, 103 (2-3), pp. 305-314; Chiu, W.T., Yang, C.M., Lin, H.W., Chu, T.B., Development and implementation of a nationwide health care quality indicator system in Taiwan (2007) International Journal for Quality in Health Care : Journal of the International Society for Quality in Health Care / ISQua, 19 (1), pp. 21-28; Aboueljinane, L., Sahin, E., Jemai, Z., A review on simulation models applied to emergency medical service operations (2013) Computers & Industrial Engineering, 66 (4), pp. 734-750; Kadri, F., Chaabane, S., Harrou, F., Tahon, C., Time series modelling and forecasting of emergency department overcrowding (2014) Journal of Medical Systems; Brailsford, S., Vissers, J., OR in healthcare: A European perspective (2011) European Journal of Operational Research, 212, pp. 223-234; Okwo, I.M., Marire, I.M., Performance Measurement in Business Organizations : An Empirical Analysis of the Financial Performance of Some Breweries in Nigeria (2012) Journal of Finance and Accounting, 3 (11), pp. 48-58; Aguinis, H., (2011) Performance Management, 2011, p. 45; Kaplan, R.S., Norton, D.P., (2005) The Balanced Scorecard: Measures That Drive Performance, , Harvard Business School Publishing; De Toni, A., Tonchia, S., Performance measurement systemsmodels, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-71; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Erik, E.J., Arjan J, M., Performance management models and purchasing: Relevance still lost (2005) The 14th IPSERA Conference, pp. 687-697. , Archamps, France, March 20-23; John, D., Integration: Is it the key to effective implementation of the EFQM Excellence Model? (2008) International Journal of Quality & Reliability Management, 25 (4), pp. 383-399; Cross, K.F., Lynch, R.L., The "sMART" way to define and sustain success (1988) Global Business and Organizational Excellence, 8 (1), pp. 23-33; Norlina, H., (2006) Development A Balanced Scorecard Model for Evaluation of Project Management and Performance, , University of Birmingham; Fitzgerald, L., Brignall, T.J., Johnston, R., Silvestro, R., (1991) Product Costing in Service Organizations, Management Accounting Research, 2 (4), pp. 227-248; Kaplan, R.S., Norton, D.P., Transforming the Balanced Scorecard from Performance Measurement to Strategic Management: Part II (2001) Accounting Horizons, 15 (2), pp. 147-216; Pink, G.H., McKillop, I., Schraa, E.G., Preyra, C., Montgomery, C., Baker, G.R., Creating a Balanced Scorecard for a hospital system (2001) Journal of Health Care Finance, 27 (3), pp. 1-20; MacStravic, S., A really balanced scorecard (1999) Health Forum Journal, 42 (3), pp. 64-67; Leung, L.C., Lam, K.C., Cao, D., Implementing the balanced scorecard using the analytic hierarchy process & the analytic network process (2006) Journal of the Operational Research Society, 57 (6), pp. 682-691},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781538678374},
language={English},
abbrev_source_title={Int. Colloq. Logist. Supply Chain Manag., LOGISTIQUA},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Chorfi2018191,
author={Chorfi, Z. and Benabbou, L. and Berrado, A.},
title={An integrated performance measurement framework for enhancing public health care supply chains},
journal={Supply Chain Forum},
year={2018},
volume={19},
number={3},
pages={191-203},
doi={10.1080/16258312.2018.1465796},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046028623&doi=10.1080%2f16258312.2018.1465796&partnerID=40&md5=175caa76af1acd4be99b346b579c9e93},
affiliation={Department of Industrial Engineering, Ecole Mohammadia d’Ingénieurs, Mohammed V University of Rabat, Rabat, Morocco},
abstract={Performance improvement is a key step in quality management standards and awards such as ISO 9001, MBQA and EFQM. It is also indispensable in process improvement programs relying on PDCA or DMAIC. From a Supply Chain perspective, performance management is necessary to enhance its quality by improving both effectiveness and efficiency. Generally, the scope of performance management involves activities, such as defining, planning, measuring, monitoring and improving a system’s performance. This paper introduces a framework based on the Balanced scorecard (BSC) and the (SCOR) model to engineer an integrated performance measurement system for managing public healthcare supply chains. This generic performance measurement system which is meant to monitor the supply chain’s performance against its strategic objectives, is then customized to include the most relevant indicators according to the Decision-Maker’s preferences. As an illustration, the suggested framework is applied to develop a performance measurement system for the pharmaceutical products supply chain for the Moroccan public sector. © 2018, © 2018 KEDGE Business School.},
author_keywords={Balanced Scorecard (BSC);  Performance improvement;  Performance measurement system;  Public healthcare supply chain;  SCOR model},
references={Akyuz, G.A., Erkan, T.E., Supply Chain Performance Measurement: A Literature Review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of Supply Chain Performance Measurement Systems: 1998-2015 (2016) Computers in Industry, 82, pp. 135-150; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance Measurement of Supply Chain Management: A Balanced Scorecard Approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Bhagwat, R., Sharma, M.K., Performance Measurement of Supply Chain Management Using the Analytical Hierarchy Process (2007) Production Planning & Control: the Management of Operations; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning and Operation, pp. 1-528. , 5th, Hall, and ed. PEARSON, Prentice,. Upper Saddle River, N.J; Chorfi, Z., Benabbou, L., Berrado, A., Selection of Key Performance Indicators for Supply Chain Monitoring Using MCDA (2015) 10th International Conference on Intelligent Systems: Theories and Applications (SITA2015), , IEEE. ENSIAS, Rabat Morocco; Cuthbertson, R., Piotrowicz, W., Performance Measurement Systems in Supply Chains: A Framework for Contextual Analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking Supply Chain Processes’ Performance to a Company’s Financial Strategic Objectives (2012) European Journal of Operational Research, 223, pp. 276-289; Estampe, D., Lamouri, S., Paris, J.L., Djelloul, S.B., (2013) A Framework for Analysing Supply Chain Performance Evaluation Models, , International Journal of Production Economics 142; 247–258; Frazelle, E., (2001) Supply Chain Strategy, , McGraw Hill Professional,. Logistics Management Library,. 1–357; Gopal, P.R.C., Thakkar, J., A Review on Supply Chain Performance Measures and Metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Grigoroudis, E., Orfanoudaki, E., Zopounidis, C., Strategic Performance Measurement in A Healthcare Organisation: A Multiple Criteria Approach Based on Balanced Scorecard (2011) Omega, 40, pp. 104-119; Guitoni, A., Martel, J.M., Tentative Guidelines to Help Choosing an Appropriate MCDA Method (1998) European Journal of Operational Research, 109, pp. 501-521; (2011) Performance Management and Measurement, , United States; (2001) Crossing the Quality Chasm: A New Health System for the 21st Century, , Washington, DC: National Academy Press; Kaplan, R.S., Strategic Performance Measurement and Management in Non-Profit Organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 353-370; Kaplan, R.S., Norton, D.P., The Balanced Scorecard - Measures that Drive Performance (1992) Harvard Business Review, pp. 71-79. , January-February, and; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating the Strategy into Action, , Harvard Business school press, Boston, and, 1–322; Kumar, A., Ozdamar, L., Ng, C.P., Procurement Performance Measurement System in the Health Care Industry (2005) International Journal of Quality Health Care Assurance, 18 (2), pp. 152-166; Lakri, S., Dallery, Y., Jemai, Z., Measurement and Management of Supply Chain Performance: Practices in Today’s Large Companies (2015) Supply Chain Forum: An International Journal, 16 (4), pp. 16-30; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply Chain Performance Measurement Systems: A Systematic Review and Research Agenda (2016) International Journal of Production Economics, 183, pp. 299-315; Neely, A., (2002) Business Performance Measurement: Theory and Practice, pp. 3-12. , Cambridge University Press. Cambridge, United kingdom; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Poluha, R.G., (2007) Application of the SCOR Model in Supply Chain Management, Business Logistics Management, Library of Congress, pp. 70-87. , Cambria Press, New york;; Robinson, C.J., Malhotra, M.K., Defining the Concept of Supply Chain Quality Management and Its Relevant to Academic and Industrial Practice (2005) International Journal of Production Economics, 96, pp. 315-337; Slagmulder, R., Van Wassenhove, L.N., Hewlett-Packard: Performance Measurement in the Supply Chain (2004) Supply Chain Forum: An International Journal, 5, pp. 2,66-72; (2007), pp. 1-46. , Monitoring and Evaluation indicators for assessing logistics systems performance., U.S. Agency for International Development. ; (2010) Measuring Supply Chain Performance: Guide to Key Performance Indicators for Public Health Manager, , U.S. Agency for International Development; (2014) Economic Evaluation: Guide to Approaches for Public Health Supply Chains, pp. 1-64. ,  U.S. Agency for International Development; Veen-Dirks, V.P.M.G., Management Control Systems in a Supply Chain Context (2006) Department of Operations, Planning, Accounting and Control Eindhoven University of Technology.; (2015) Global Reference List of 100 Core Health Indicators, , World Health organization,. Switzerland; Zizlavsky, O., The Balanced Scorecard: Innovative Performance Measurement and Management Control System (2014) Journal of Technology Management & Innovation, 9 (3), pp. 210-222},
correspondence_address1={Chorfi, Z.; Ecole Mohammadia d’Ingénieurs, Avenue Ibn Sina, B.P. 765, Morocco; email: zoubidachorfi@research.emi.ac.ma},
publisher={Taylor and Francis Ltd.},
issn={16258312},
language={English},
abbrev_source_title={Supply Chain Forum},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Novar2018,
author={Novar, M.F. and Yanuar Ridwan, A. and Santosa, B.},
title={SCOR and ahp based monitoring dashboard to measure rice sourcing performance at Indonesian bureau of logistics},
journal={Proceeding of 2018 12th International Conference on Telecommunication Systems, Services, and Applications, TSSA 2018},
year={2018},
doi={10.1109/TSSA.2018.8708814},
art_number={8708814},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066892616&doi=10.1109%2fTSSA.2018.8708814&partnerID=40&md5=d93ee626e768a34d6a9837203a520233},
affiliation={School of Industrial and System Engineering, Telkom University, Bandung, Indonesia},
abstract={Rice is one of nine main commodities in Indonesia and it has been the main food for Indonesian people for over decades. Indonesian Bureau of Logistics (Bulog) is a company under Indonesian Government which is responsible to manage the distribution of the Rice Commodity. Sourcing process of the Rice Commodity is the most crucial process in Bulog. In spite of the importance of the process, the sourcing process was not maintained using any system at all. For such an important organization, which manages important product, the sourcing process of rice commodity at Bulog should be measured and monitored. Supply chain processes, such as sourcing product, have to be measured and monitored in order to assure that the processes are well executed and to give aid for improvements. Hence, in this paper, Supply Chain Measurement System is designed based on the Supply Chain Operations Reference model and Analytical Hierarchy Process. Thereafter, a monitoring system that is capable to monitor each of the metrics in the system is designed in a web-based application. This research leads to seventeen metrics in total which are distributed into four performance attributes on the designed Supply Chain Measurement System. The most important performance attribute in rice sourcing process was Reliability with 0.593 in AHP weighting. The designed monitoring system was able to provide a compact summary and a brief insight into the condition of the monitored process to the executives of the organization. © 2018 IEEE.},
author_keywords={AHP;  Monitoring System;  Performance Measurement System;  SCOR;  Supply Chain},
keywords={Hierarchical systems;  Supply chains;  Telecommunication services, Analytical Hierarchy Process;  Measurement system;  Monitoring system;  Performance measurement system;  SCOR;  Supply chain operations reference models;  Supply chain process;  Web-based applications, Monitoring},
references={Waaliy, A.N., Ridwan, A.Y., Akbar, M.D., Supply chain operation reference (SCOR) dan analytical hierarchy process (AHP) untuk mendukung green procurement pada industri penyamakan kulit (2018) J. Ind. Serv, 4 (1); Winanda, S.M., Ridwan, A.Y., El Hadi, R.M., Perancangan model pengukuran kinerja green procurement berdasarkan model SCOR pada industri penyamakan kulit (2018) J. Rekayasa Sist. Ind, 5 (2); Ismadhia, A., Ridwan, A., Hadi, R., A SCOR-based model for green sales and distribution performance measurement in the leather tanning industry (2018) J. Rekayasa Sist. Ind, 5 (1); Kevin Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Manag. An Int. J, 14 (6), pp. 422-428; Waaliy, A.N., Ridwan, A.Y., Akbar, M.D., Development of sustainable procurement monitoring system performance based on Supply Chain Operation Reference (SCOR) and Analytical Hierarchy Process (AHP) on leather tanning industry (2018) MATEC Web Conf, p. 201401008; Aramyan, L.H., Lansink, A.G.J.M.O., Van Der Vorst, J.A.J.G., Van Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Manag, 12 (4), pp. 304-315; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ, 183, pp. 299-315; Delipinar, G.E., Kocaoglu, B., Using scor model to gain competitive advantage: A literature review (2016) Procedia-Soc. Behav. Sci, 229, pp. 398-406; (2012) Supply Chain Operations Reference Model Rev. 11 0, , Supply Chain Council; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol, 21 (7), pp. 534-548; Kocaoglu, B., Gölsön, B., Tanyas, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) J. Intell. Manuf, 24 (1), pp. 113-132; Hartawan, P.G.W., Ridwan, A.Y., Witjaksono, W., Perancangan sistem pengadaan (procurement) berbasis OpenERP dengan menggunakan metode soft system methodology (studi kasus: Rumah Sakit Umum Daerah Al Ihsan) (2015) E-Proceeding of Engineering, 2 (2); Ridwan, A.Y., Designing a multidimensional data warehouse for procurement processes analysis using business dimensional lifecycle method (2015) Proceeding of the 8th International Seminar on Industrial Engineering and Management (ISIEM; Saaty, T.L., Decision making with the analytic hierarchy process (2008) Int. J. Serv. Sci, 1 (1), p. 83; Sellitto, M.A., Pereira, G.M., Borchardt, M., Da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: Application in the footwear industry (2015) Int. J. Prod. Res, 53 (16), pp. 4917-4926; Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Manag, 19 (3), pp. 275-291},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781538669402},
language={English},
abbrev_source_title={Proceeding Int. Conf. Telecommun. Syst., Serv., Appl., TSSA},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dissanayake2018102,
author={Dissanayake, C.K. and Cross, J.A.},
title={Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM},
journal={International Journal of Production Economics},
year={2018},
volume={201},
pages={102-115},
doi={10.1016/j.ijpe.2018.04.027},
note={cited By 39},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048162696&doi=10.1016%2fj.ijpe.2018.04.027&partnerID=40&md5=cd6026274b2389921ac5bc941ecdfd6f},
affiliation={Department of Industrial, Manufacturing, & Systems Engineering, Texas Tech University, Box 43061, Lubbock, TX  79409-3061, United States},
abstract={This research aims to introduce a systematic mechanism for developing an organization specific supply chain performance measurement (SCPM) model and for generating a single regression relationship between the overall supply chain performance (SCP) and each of the SCPM areas using an organization's numerical data. The research uses a case study conducted in a small-scale asphalt manufacturing plant located in West Texas, USA with a sample of 218 data records collected over a period of one year. In this article, the systematic mechanism is demonstrated using the data from the case study organization. It provides guidance for developing a hierarchical SCPM model for the organization using the Supply Chain Operations Reference (SCOR) model, and then to obtain the final regression equation for the overall SCP using the Structural Equations Modeling (SEM) technique. The results obtained from the SCPM mechanism are then compared with the managerial input on the SCP that was analyzed using the Analytical Hierarchical Process (AHP). The results of the comparison are used to validate the SCPM results. A major finding of this research is that the regression coefficients provide a clue for the level of sensitivity those measures have on the overall SCP under given organizational SC capacity and operating levels. The SCPM areas with higher regression coefficients reflect higher sensitivity, thus even a slight variation in the measures contributing to those areas will be contributing to a considerable impact on the overall SCP measures. The findings lead to the implication that managers should pay close attention to controlling the stability of the SC operational processes which are responsible for generating those SCP measures. The goal should be to maintain the desired output levels with the least amount of fluctuations. The regression results therefore provide insight to the managers for identifying the individual SCP measures to be managed more intensely than others based on their relative impacts on the overall SCP. The results are applicable as long as the organization does not change its organizational SC capacity and operating levels, thus can be used for better managing the SC in future. This research fulfills multiple research gaps highlighted in literature, mainly: the absence of a systematic approach to generate an organization specific SCPM model; and the absence of a SCPM system capable of generating a single cause–and-effect relationship between the overall SCP and the numerous hierarchical performance measures reflecting their relative impact. Therefore, this study can be viewed as an attempt to increase the level of awareness in the SC field. The article also proposes several future research directions to test the repeatability and validity of the proposed SCPM mechanism. © 2018 Elsevier B.V.},
author_keywords={Analytical hierarchical process (AHP);  Regression relationship;  Structural equations modeling (SEM);  Supply chain operations reference model (SCOR);  Supply chain performance measurement model},
keywords={Hierarchical systems;  Industrial research;  Managers;  Regression analysis;  Sensitivity analysis, Analytical hierarchical process;  Regression relationship;  Structural equations modeling (SEM);  Supply chain operations reference models;  Supply chain performance measurements, Supply chains},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: review and classification (2012) J. Organ. Manag. Stud., 2012, p. 1; Bagozzi, R.P., Heatherton, T.F., A general approach to representing multifaceted personality constructs: application to state self-esteem (1994) Struct. Equ. Model.: A Multidiscipl. J., 1 (1), pp. 35-67; Beamon, B.M., Supply chain design and analysis: models and methods (1998) Int. J. Prod. Econ., 55 (3), pp. 281-294; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: a literature review (2003) Int. J. Bus. Perform. Manag., 5 (1), pp. 1-24; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decis. Support Syst., 46 (2), pp. 512-521; Canada, J.R., Sullivan, W.G., Kulonda, D.J., White, J.A., Capital Investment Analysis for Engineering and Management (2005), third ed. Prentice Hall; Chan, F.T., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Manag.: Int. J., 8 (3), pp. 209-223; Chan, F.T., Qi, H.J., Chan, H., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) Manag. Decis., 41 (7), pp. 635-642; Dissanayake, C.K., Fuzzy logic applications in supply chain performance measurement (2015) Proceedings of the International Annual Conference of the American Society for Engineering Management, p. 1. , American Society for Engineering Management (ASEM); Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analyzing SCP evaluation models (2013) Int. J. Prod. Econ., 142 (2), pp. 247-258; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Hoaglin, D.C., Iglewicz, B., Fine-tuning some resistant rules for outlier labeling (1987) J. Am. Stat. Assoc., 82 (400), pp. 1147-1149; Hooper, D., Coughlan, J., Mullen, M., Structural Equation Modelling: Guidelines for Determining Model Fit. Articles (2008), p. 2; Kocaoğlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) J. Intell. Manuf., pp. 1-20; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) Inter. J. Bus. Manag. Soc. Sci., 2 (4), pp. 19-34; Lambert, D.M., Supply Chain Management: Processes, Partnerships, Performance (2008), Supply Chain Manag. Inst; Lambert, D., Pohlen, T., SC metrics (2001) Int. J. Logist. Manag., 12 (1), pp. 1-19; Li, L., Su, Q., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) Int. J. Prod. Res., 49 (1), pp. 33-57; Rönkkö, M., McIntosh, C.N., Antonakis, J., Edwards, J.R., Partial least squares path modeling: time for some serious second thoughts (2016) J. Oper. Manag., 47, pp. 9-27; Saaty, T.L., The Analytic Hierarchy Process (1980), McGraw-Hill Book Company; Savin, N.E., White, K.J., The Durbin-Watson test for serial correlation with extreme sample sizes or many regressors (1977) Econ. J. the Econ. Soc., pp. 1989-1996; Schumacker, R.E., Lomax, R.G., A Beginner's Guide to Structural Equation Modeling (2004), Psychology Press; Sezen, B., Relative effects of design, integration and information sharing on supply chain performance (2008) Supply Chain Manag.: Int. J., 13 (3), pp. 233-240; Sposito, V.A., Hand, M.L., Skarpness, B., On the efficiency of using the sample kurtosis in selecting optimal lpestimators (1983) Commun. Stat. Simulat. Comput., 12 (3), pp. 265-272; Stewart, G., Supply-chain operations reference model (SCOR): the first cross-industry framework for integrated supply-chain management (1997) Logist. Inf. Manag., 10 (2), pp. 62-67; Sundaram, A., A painstaking exploration on the influence of perceived benefits towards training on training and development in indian it/ITES industry (2016) J. Internet Bank. Commer., 21 (2), p. 1; Supply-chain council, Supply Chain Operations Reference Model (SCOR) Overview- Version 10.0 (2011); Theeranuphattana, A., Tang, J.C., A conceptual model of performance measurement for supply chains: alternative considerations (2007) J. Manuf. Technol. Manag., 19 (1), pp. 125-148; Ullman, J.B., Structural equation modeling: reviewing the basics and moving forward (2006) J. Pers. Assess., 87 (1), pp. 35-50; Weber, C.A., Current, J.R., Desai, A., Non-cooperative negotiation strategies for vendor selection (1998) Eur. J. Oper. Res., 108 (1), pp. 208-223; Wolf, E.J., Harrington, K.M., Clark, S.L., Miller, M.W., Sample size requirements for structural equation models: an evaluation of power, bias, and solution propriety (2013) Educ. Psychol. Meas., 73 (6), pp. 913-934; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Ind. Manag. Data Syst., 107 (3), pp. 361-381; Wu, T., Blackhurst, J., Supplier evaluation and selection: an augmented DEA approach (2009) Int. J. Prod. Res., 47 (16), pp. 4593-4608},
correspondence_address1={Dissanayake, C.K.; Department of Industrial, Box 43061, United States; email: kalpani.dissanayake@ttu.edu},
publisher={Elsevier B.V.},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Article},
source={Scopus},
}

@ARTICLE{Vallurupalli201872,
author={Vallurupalli, V. and Bose, I.},
title={Business intelligence for performance measurement: A case based analysis},
journal={Decision Support Systems},
year={2018},
volume={111},
pages={72-85},
doi={10.1016/j.dss.2018.05.002},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047624616&doi=10.1016%2fj.dss.2018.05.002&partnerID=40&md5=8e48ebf9c65beab6db182b3a445bf21d},
affiliation={Indian Institute of Management Calcutta, India},
abstract={The adoption of IT-based performance measurement systems (PMS) has increased in recent times. The proliferation of business intelligence (BI) has significantly impacted performance measurement in organizations. In this paper a novel process-based framework is proposed to enable end-to-end analysis of technology driven PMS implementation in an organization. The framework has been used to study PMS implementation in a large manufacturing firm in India. The analysis of the case provides key lessons about successful planning, execution and adoption of a BI based PMS as well as identification of critical success factors (CSF) in the implementation of PMS, that would be of interest to organizations planning to implement a similar system. © 2018 Elsevier B.V.},
author_keywords={Business intelligence;  Case study;  Critical success factors;  Framework;  Key performance indicators;  Manufacturing firm;  Performance measurement systems},
keywords={Benchmarking;  Competitive intelligence;  Manufacture;  Supply chain management, Critical success factor;  Framework;  Key performance indicators;  Manufacturing firms;  Performance measurement system, Information analysis},
references={Neely, A., Al Najjar, M., Management learning not management control: the true role of performance management? (2006) California Management Review, 48, pp. 101-114; Grosswiele, L., Röglinger, M., Friedl, B., A decision framework for the consolidation of performance measurement systems (2013) Decision Support Systems, 54 (2), pp. 1016-1029; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Halachmi, A., Performance measurement, accountability, and improved performance (2002) Public Performance & Management Review, 25, pp. 370-374; Radnor, Z.J., Barnes, D., Historical analysis of performance measurement and management in operations management (2007) International Journal of Productivity and Performance Management, 56 (5-6), pp. 384-396; Watson, H.J., Frolick, M.N., Determining information requirements for an EIS (1993) MIS Quarterly, 17 (3), pp. 255-269; Frolick, M.N., Ariyachandra, T.R., Business performance management: one truth (2006) Information Systems Management, 23 (1), pp. 41-48; ITEuropa, Marketing Push Expected in Business Intelligence Market (2014), http://www.iteuropa.com/?q=marketing-push-expected-business-intelligence-market, Retrieved from; Marchand, M., Raymond, L., Researching performance measurement systems: an information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Kwon, T.H., Zmud, R.W., Unifying the fragmented models of information systems implementation (1987) Critical Issues in Information Systems Research, pp. 227-251. , John Wiley & Sons, Inc; Lewin, K., Group decision and social change (1952) Readings in Social Psychology, 40, pp. 463-469; Cooper, R.B., Zmud, R.W., Information technology implementation research: a technological diffusion approach (1990) Management Science, 36, pp. 123-139; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20, pp. 754-771; Bourne, M., Researching performance measurement system implementation: the dynamics of success and failure (2005) Production Planning and Control, 16, pp. 101-113; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: a literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Yin, R., Case Study Research: Design and Methods (2014), Sage Publications; Lapointe, L., Rivard, S., A multilevel model of resistance to information technology implementation (2005) MIS Quarterly, 29 (3), pp. 461-491; Paré, G., Investigating information systems with positivist case study research (2004) Communications of the Association for Information Systems, 13 (Article 18), pp. 233-264; Trochim, W.M.K., Outcome pattern matching and program theory (1989) Evaluation and Program Planning, 12 (4), pp. 355-366; Yeoh, W., Koronios, A., Critical success factors for business intelligence systems (2010) The Journal of Computer Information Systems, 50 (3), pp. 23-32; Seah, M., Hsieh, M.H., Weng, P.D., A case analysis of Savecom: the role of indigenous leadership in implementing a business intelligence system (2010) International Journal of Information Management, 30 (4), pp. 368-373; Watson, H.J., Goodhue, D.L., Wixom, B.H., The benefits of data warehousing: why some organizations realize exceptional payoffs (2002) Information and Management, 39 (6), pp. 491-502; Yeoh, W., Popovič, A., Extending the understanding of critical success factors for implementing business intelligence systems (2016) Journal of the Association for Information Science and Technology, 67, pp. 134-147; Abrahams, A.S., Jiao, J., Wang, G.A., Fan, W., Vehicle defect discovery from social media (2012) Decision Support Systems, 54 (1), pp. 87-97; Bhargava, H.K., Power, D.J., Sun, D., Progress in web-based decision support technologies (2007) Decision Support Systems, 43 (4), pp. 1083-1095; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Cooper, R., Kaplan, R.S., Measure costs right: make the right decision (1988) Harvard Business Review, 66, pp. 96-103; Simons, R., Performance Measurement and Control Systems for Implementing Strategy (2013), Pearson New International Edition. Pearson Higher Education; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Flapper, S.D.P., Fortuin, L., Stoop, P.P.M., Towards consistent performance management systems (1996) International Journal of Operations & Production Management, 16 (7), pp. 27-37; Bititci, U.S., Turner, U., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Kanji, G.K., E Sá, P.M., Kanji's business scorecard (2002) Total Quality Management, 13 (1), pp. 13-27; Sureshchandar, G.S., Leisten, R., Holistic scorecard: strategic performance measurement and management in the software industry (2005) Measuring Business Excellence, 9 (2), pp. 12-29; Barnabè, F., A “system dynamics-based balanced scorecard” to support strategic decision making: insights from a case study (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 446-473; Bititci, U.S., Carrie, A.S., Mcdevitt, L., Integrated performance measurement systems: a development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Kueng, P., Meier, A., Wettstein, T., Performance measurement systems must be engineered (2001) Communications of the Association for Information Systems, 7 (Article 3), pp. 1-26; Bose, R., Understanding management data systems for enterprise performance management (2006) Industrial Management & Data Systems, 106 (1), pp. 43-59; Creamer, G., Freund, Y., Learning a board balanced scorecard to improve corporate performance (2010) Decision Support Systems, 49, pp. 365-385; Rabl, T., Gómez-Villamor, S., Solving big data challenges for enterprise application performance management (2012) Proceedings of the VLDB Endowment, 5, pp. 1724-1735; Sidorova, Y., Arnaboldi, M., Radaelli, J., Social media and performance measurement systems: towards a new model? (2016) International Journal of Productivity and Performance Management, 65, pp. 139-161; Vera-Baquero, A., Colomo-Palacios, R., Molloy, O., Real-time business activity monitoring and analysis of process performance on big-data domains (2016) Telematics and Informatics, 33 (3), pp. 793-807; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; De Waal, A.A., Counet, H., Lessons learned from performance management systems implementations (2009) International Journal of Productivity and Performance Management, 58, pp. 367-390; Jääskeläinen, A., Sillanpää, V., Overcoming challenges in the implementation of performance measurement (2013) International Journal of Public Sector Management, 26, pp. 440-454; De Waal, A.A., Behavioral factors important for the successful implementation and use of performance management systems (2003) Management Decision, 41 (8), pp. 688-697; Dutta, D., Bose, I., Managing a big data project: the case of ramco cements limited (2015) International Journal of Production Economics, 165, pp. 293-306},
correspondence_address1={Bose, I.; Indian Institute of Management CalcuttaIndia; email: indranil_bose@yahoo.com},
publisher={Elsevier B.V.},
issn={01679236},
coden={DSSYD},
language={English},
abbrev_source_title={Decis Support Syst},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Deshmukh2018,
author={Deshmukh, A.J. and Vasudevan, H.},
title={A Combined approach for supplier selection using AHP and Fuzzy AHP in Indian Gear Manufacturing MSMEs},
journal={IOP Conference Series: Materials Science and Engineering},
year={2018},
volume={376},
number={1},
doi={10.1088/1757-899X/376/1/012122},
art_number={012122},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049908493&doi=10.1088%2f1757-899X%2f376%2f1%2f012122&partnerID=40&md5=d8860d0fdba21cd091586d74478ff0f1},
affiliation={SVKMs NMIMS MPSTME, Mumbai, Vile Parle (W), India; D. J. Sanghvi College of Engineering, Mumbai, Vile Parle (W), India},
abstract={Industries these days across the world are required to be a part of single economy and work-culture. As a result, manufacturing industries are moving towards adding higher value activities and therefore even the requirements for suppliers in many cases undergo significant changes. By ensuring the best supplier, industries save material costs and increase their competitive advantage. Gear manufacturing companies are main suppliers for India's leading manufacturing firms, as they outsource manufacturing of gears to Micro, Small and Medium Enterprises (MSMEs). Extant literature indicate that the Gear manufacturing companies follow traditional supply chain process and partially follows green supply chain as well as sustainability aspects. It is therefore important to understand the role, Gear manufacturing industries play in their supplier selection process. This study has explored the traditional and green manufacturing factors considered during the supplier selection in the Indian MSME Gear manufacturing sector, using tools such as AHP and FAHP. AHP is one of the best tools for deciding among the complex criteria structure at different levels. FAHP, being synthetic extension of classical AHP, considers the fuzziness of the decision makers. Results of this study indicate that, from amongst all 8 main traditional and green supply chain performance measures, cost is found to be the most important criteria for supplier selection even today in Indian industries. But, looking at the sub-factors, it is clear that the industries not only focus on purchase cost, but they also think of the disposal cost of the component, meaning they consider few aspects of green criteria as well in their decisions. © Published under licence by IOP Publishing Ltd.},
keywords={Competition;  Costs;  Decision making;  Hierarchical systems;  Supply chains, Competitive advantage;  Green manufacturing;  Green supply chain;  Manufacturing firms;  Manufacturing industries;  Small and medium enterprise;  Supplier selection process;  Supply chain process, Gear manufacture},
references={Deshmukh, A., Vasudevan, H., Emerging supplier selection criterion in the context of traditional and green supply chain management (2016) International Journal of Managing Value and Supply Chain (IJMVSC), 5 (1), pp. 19-33; Deshmukh, A., Vasudevan, H., Analysis of Supplier Selection Criteria in Traditional as well as Green Supply Chain Management in Indian MSMEs (2016) International Journal of Business Quantitative Economics and Applied Management Research, 3, pp. 73-85; Deshmukh, A., Chaudhari, A., (2011) Technology System and Management, 145, pp. 283-291. , (Springer) A Review for Supplier Selection Criteria and Methods; Singh, A.R., Mishra, P.K., Rajeev, J., Prioritization of Supplier Selection Criteria: A Fuzzy-AHP Approach (2013) MIT International Journal of Mechanical Engineering, 3, pp. 34-42; Alyank, G., Armaneri, O., An Integrated Supplier Selection and Order Allocation Approch in battery company (2009) Makine MühendisleriOdasi, 19, pp. 2-19; Alessio, I., Ashraf, L., Review of the main development in Analytic Hierarcy Process (2011) Expert System with Applications, 30, pp. 14336-14345; Askin, O., Guzin, O., Comparison of AHP and Fuzzy AHP for the Multi-criteria Decision Making Processes with Linguistic Evaluations (2007) Istanbul Ticaret University Journal of Institute of Science, 11, pp. 65-85; Chamodrakas, I., Batis, D., Martakos, D., Supplier selection in electronic marketplaces using saisficing and fuzzy AHP (2010) Expert System with Applications, 37 (1), pp. 490-498; Chan, F.T.S., Kumar, N., Global supplier development considering risk factors using fuzzy extended AHP-based approach (2007) International Journal of Management Science, Omega, 35 (4), pp. 417-431; Hui-Feng, C., Tzong-Ru, L., Ching-Kuei, K., The key factors for selecting electronics manufacturing service suppliers-An example of company in Taiwan (2015) Management and Production Engineering Review, 6 (4), pp. 4-14; Jafar, R., Roland, O., Multi-criteria supplier segmentation using a fuzzy preference relations based AHP (2013) European Journal of Operational Research, 225 (1), pp. 75-84; Lee, A.H.I., Kang, H.Y., Hsu, C.F., Hung, H.C., A green supplier selection model for high-tech industry (2009) Expert Systems with Applications, 36 (4), pp. 7917-8798. , 791-798; Small and Medium Enterprises Annual Report, , Ministry of Micro 2015-16; Muralidharan, C., Anantharaman, N., Deshmukh, S.G., A multi-criteria group decision-making model for supplier rating (2002) Journal of Supply Chain Management, 38 (4), pp. 22-33; Saaty, T.L., (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, , (New York: McGraw Hill); Tas, A., A fuzzy AHP approach for selecting a global supplier in pharmaceutical industry (2012) African Journal of Business Management, 6, pp. 5073-5084; Hsu, Y.L., Lee, C.H., Kreng, V.B., The application of Fuzzy Delphi Method and Fuzzy AHP in lubricant regenerative selection (2010) Expert System with Applications, 37 (1), pp. 419-425; Zadeh, L., Fuzzy sets (1965) Information and Control, 8 (3), pp. 338-353},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Rodrigues2018748,
author={Rodrigues, A.P. and Fernandes, M.L. and Rodrigues, M.F.F. and Bortoluzzi, S.C. and Gouvea da Costa, S.E. and Pinheiro de Lima, E.},
title={Developing criteria for performance assessment in municipal solid waste management},
journal={Journal of Cleaner Production},
year={2018},
volume={186},
pages={748-757},
doi={10.1016/j.jclepro.2018.03.067},
note={cited By 56},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046032723&doi=10.1016%2fj.jclepro.2018.03.067&partnerID=40&md5=16c8f8296e0345c15b56f9cbdd873f6c},
affiliation={Federal University of Technology - Parana, Pato Branco, Brazil; Pontifical Catholic University of Parana, Curitiba, Brazil},
abstract={Sustainable development is being approached as new economic paradigm that establishes a general context for organizations and institutions develop their strategies and processes. This is particularly true for public administration, where a change process is being taking place towards the development of sustainable and smart cities. Complex operations networks and closed-loop supply chains define structures for managing the flow of materials, energy and information. This paper addresses the problem of managing solid waste in small cities, and based on an in depth case study that is used to illustrate the application of a performance measurement assessment exercise. The development of the assessment model is based on an adaptation of the methodology for Multi-criteria Decision Aid – Constructivist (MCDA-C), that allows the comparison of the proposed strategic objectives for solid waste management and the available performance information. Experts play an important role in aligning objectives with the performance measurement system, as the performance indicators could be objectively defined, and integrated to the assessment model, which produces at the end a global score that could be used for benchmarking, particularly for tracking the evolution over the years. The model is applied in the context of a small city that formally addresses sustainability in its institutional strategic plan. The model is tailor-made for the application case, and it is verified that the sustainability index for assessing the solid waste management obtained a score of 30.5 points, considering as reference the anchorage of intervals with performance “Neutral” 0 (zero) points and “Good” 100 (one hundred) points. It is identified objective opportunities for improvement in areas associated to governance and operational efficiency. It could be concluded that the performance assessment of the municipal solid waste management becomes an important element to ensure compliance, and to realize the sustainability strategy plan. Besides that, it provides information to control and an integrated management of the key performance dimensions, mainly due to the identification of factors that connect measures to diagnosis and improvement processes. Based on a case study, a performance assessment could take place and shows its value to the practice of municipal solid waste management. © 2018 Elsevier Ltd},
author_keywords={Constructivist;  Methodology for multi-criteria decision aid;  Performance assessment;  Solid waste management;  Sustainable operations},
keywords={Benchmarking;  Decision making;  Decision support systems;  Municipal solid waste;  Planning;  Public administration;  Solid wastes;  Supply chain management;  Supply chains;  Sustainable development, Constructivist;  Multi-criteria decision aids;  Operational efficiencies;  Performance assessment;  Performance measurement system;  Performance measurements;  Sustainability strategies;  Sustainable operations, Waste management},
references={Ahen, F., Zettinig, P., What is the biggest question in CSR research? (2015) Foresight, 17 (3), pp. 274-290; AK, H., Braida, W., Sustainable municipal solid waste management decision making: development and implementation of a single score sustainability index (2015) Manag. Environ. Qual. An Int. J., 26 (6), pp. 909-928; Bana, E., Costa, C., The use of multi-criteria decision analysis to support the search for less conflicting policy options in a multi-actor context: case study (2001) J. Multi-Criteria Decis. Analysis, 10 (2), pp. 111-125; Bana, E., Costa, C.A., Ensslin, L., Corrêa, E.C., Vansnick, J.C., Decision Support Systems in action: integrated application in a multicriteria decision aid process (1999) Eur. J. Operational Res., 113 (2), pp. 315-335; Bana, E., Costa, C., Angulo-Meza, L., Oliveira, M.D., O método Macbeth e aplicação no Brasil (2013) Engevista, 15, pp. 3-27; Bernardo, M., Silva, R.S., Using action research to implement selective waste collection program in a Brazilian city (2017) Syst. Pract. Action Res., 30 (6), pp. 593-608; Bhander, G.S., Christensen, T.H., Hauschild, M.Z., EASEWASTE-life cycle modeling capabilities for waste management technologies (2010) Int. J. Life Cycle Assess., 15 (4), pp. 403-416; Boland, T., Fowler, A., A systems perspective of performance management in public sector organisations (2000) Int. J. Public Sect. Manag., 13 (5), pp. 417-446; Brasil, Lei 12.305-PNRS (2012), 2a Edição Brasilia; Campos, H.K.T., Recycling in Brazil: challenges and prospects (2014) Resour. Conservation Recycl., 85, pp. 130-138; Carpes, M.M.M., Ensslin, L., Ensslin, S.R., Avaliação do desempenho das práticas de responsabilidade social na gestão organizacional por meio da metodologia MCDA Construtivista: uma abordagem aos modelos já existentes (2006) Rev. Alcance, 13 (1), pp. 91-112; Chen, Y.-C., Lo, S.-L., Evaluation of greenhouse gas emissions for several municipal solid waste management strategies (2016) J. Clean. Prod., 113, pp. 606-612; Chen, X., Pang, J., Zhang, Z., LI, H., Sustainability assessment of solid waste management in China: a decoupling and decomposition analysis (2014) Sustainability, 6 (12), pp. 9268-9281; Corbett, L.M., Sustainable operations management: a typological approach (2009) J. Industrial Eng. Manag., 2 (1), pp. 10-30; Del Borghi, A., Gallo, M., Del Borghi, M., A survey of life cycle approaches in waste management (2009) Int. J. Life Cycle Assess.; Den Boer, J., Den Boer, E., Jager, J., LCA-IWM: a decision support tool for sustainability assessment of waste management systems (2007) Waste Manag., 27 (8), pp. 1032-1045; Deslatte, A., Feiock, R.C., Wassel, K., Urban pressures and innovations: sustainability commitment in the face of fragmentation and inequality (2017) Rev. Policy Res., 34 (5), pp. 700-724; Dutra, A., Molin, L.H.D., Estudo de caso: Proposta de modelo multicritério de avaliação de desempenho: um estudo de caso na secretaria municipal de indústria e comércio de Tubarão/SC (2013) Revista Interdisciplinar Científica Aplicada, 7 (1), pp. 15-47. , TRI I; Dutra, A., Ripoll-Feliu, V.M., Fillol, A.G., Ensslin, S.R., Ensslin, L., The construction of knowledge from the scientific literature about the theme seaport performance evaluation (2015) Int. J. Prod. Perform. Manag., 64 (2), pp. 243-269; Ensslin, L., Montibeller, G.N., Noronha, S.M., Apoio à Decisão: metodologias para Estruturação de Problemas e Avaliação Multicritério de Alternativas (2001) Ed. Insul.; Ensslin, S.R., Carvalho, F.N.D.E., Gallon, A.V., Ensslin, L., Uma metodologia multicritério (MCDA-C) para apoiar o gerenciamento do capital intelectual organizacional (2008) RAM - Rev. Adm. Mackenzie, 9 (7), pp. 136-163; Ensslin, L., Giffhorn, E., Ensslin, S.R., Petri, S.M., Vianna, W.B., Avaliação do desempenho de empresas terceirizadas com o uso da metodologia multicritério de apoio à decisão-construtivista (2010) Pesqui. Oper., 30 (1), pp. 125-152; Ensslin, L., Ensslin, S.R., Pinto, H.D.M., Processo de investigação e análise bibliométrica: avaliação da qualidade dos serviços bancários (2013) Rev. Adm. Contemp., 17 (3), pp. 325-349; Ensslin, L., Rolim, S., Rocha, S., Modelo multicritério de apoio á decisão construtivista no processo de avaliação de fornecedores (2013) Produção, 2 (Pac 2), pp. 402-421; Ensslin, L., Rolim, S., Dutra, A., Nunes, N.A., Reis, C., BPM governance: a literature analysis of performance evaluation (2017) Bus. Process Manag. J., 23 (Iss 1), pp. 71-86; Fanning, A.L., Contrasting values in the sustainability debate: limitations of economic valuations and their role in decision-making (2016) Int. J. Sustain. Dev., 19 (1), pp. 185-200; Federico, G., Rizzo, G., Traverso, M., In itinere strategic environmental assessment of an integrated provincial waste system (2009) Waste Manag. Res.  J. Int. Solid Wastes Public Clean. Assoc. ISWA, 27 (4), pp. 390-398; Ferreira, M.A., Jabbour, C.J.C., Jabbour, A.B.L.S., Maturity levels of material cycles and waste management in a context of green supply chain management: an innovative framework and its application to Brazilian cases (2017) J. Material Cycles Waste Manag., 19 (1), pp. 516-525; Ferrer, G., Sustainability : what does it mean for the operations Manager ? (2008) J. Operations Supply Chain Manag., 1 (2), pp. 1-16; Gheewala, S.H., LCA of waste management systems-research opportunities (2009) Int. J. Life Cycle Assess., 14 (7), pp. 589-590; Gnan, L., Hinnan, A., Monteduro, F., Scarozza, D., Corporate governance and management practices: stakeholder involvement, quality and sustainability tools adoption (2013) J. Manag. Gov., 17 (4), pp. 907-937; Guimarães, B., Simões, P., Marques, R.C., Does performance evaluation help public managers? A Balanced Scorecard approach in urban waste services (2010) J. Environ. Manag., 91 (12), pp. 2632-2638; Gui-Zhen, H.E., Multi-indicator assessment of water environment in government environmental auditing (2007) J. Environ. Sci., 19, pp. 494-501; Gunasekaran, A., Spalanzani, A., Sustainability of manufacturing and services: investigations for research and applications (2012) Int. J. Prod. Econ., 140 (1), pp. 35-47; Hazlett, S.-A., Mcadam, R., Walker, T., The role of operations management in public sector policy and practice alignment: a local government analysis (2012) Prod. Plan. {&} Control, 7287 (June 2015), pp. 1-14; Homsy, G.C., Warner, M.E., Cities and sustainability: polycentric action and multilevel governance (2015) Urban Affairs Review, 51 (1), pp. 46-73; Kleindorfer, P., Singhal, K., Van Wassenhove, L., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Lacerda, R.T.D.O., Ensslin, L., Ensslin, S.R., Uma análise bibliométrica da literatura sobre estratégia e avaliação de desempenho (2012) Gestão & Produção, 19 (1), pp. 59-78; Lacerda, R.T.O., Ensslin, L., Ensslin, S., Research opportunities in strategic management field: a performance measurement approach (2014) International Journal of Business Performance Management, 15 (2), pp. 158-174; Larran, M., Herrera, J., Calzado, Y., Andrades, J., A proposal for measuring sustainability in universities: a case study of Spain (2014) International Journal of Sustainability in Higher Education, (4), pp. 1-2; Li, H., Nitivattananon, V., Li, P., Developing a sustainability assessment model to analyze China's municipal solid waste management enhancement strategy (2015) Sustainability, 7 (2), pp. 1116-1141; Longaray, A.A., Ensslin, L., Ensslin, S.R., Da Rosa, I.O., Assessment of a Brazilian public hospital's performance for management purposes: a soft operations research case in action (2015) Operations Research for Health Care, 5, pp. 28-48; Longaray, A.A., Ensslin, L., Ensslin, S., Dutra, A., Munhoz, P.R., Da, S., Modelo multicritério de apoio à decisão construtivista para avaliação de desempenho do trade marketing : um caso ilustrado no setor farmacêutico mcda-c (2016) Produção Online, 16, pp. 49-76; Lozano, R., Incorporation and institutionalization of SD into universities: breaking through barriers to change (2006) Journal of Cleaner Production, 14 (9-11), pp. 787-796; Lundberg, K., Balfors, B., Folkeson, L., Framework for environmental performance measurement in a Swedish public sector organization (2009) Journal of Cleaner Production, 17 (11), pp. 1017-1024; Machado, C.G., Pinheiro De Lima, E., Gouvea da Costa, S.E., Angelis, J.J., Mattioda, R.A., Framing maturity based on sustainable operations management principles (2017) International Journal of Production Economics, 190 (1), pp. 3-21; Marttunen, M., Lienert, J., Belton, V., Structuring problems for Multi-Criteria Decision Analysis in practice: a literature review of method combinations (2017) European Journal of Operational Research, 263 (1), pp. 1-17; Mendes, P., Santos, A.C., Perna, F., Ribau Teixeira, M., The balanced scorecard as an integrated model applied to the Portuguese public service: a case study in the waste sector (2012) Journal of Cleaner Production, 24, pp. 20-29; Mendes, P., Santos, A.C., Nunes, L.M., Teixeira, M.R., Evaluating municipal solid waste management performance in regions with strong seasonal variability (2013) Ecological Indicators, 30, pp. 170-177; Milutinović, B., Stefanović, G., Kyoseva, V., Yordanova, D., Dombalov, Y., Sustainability assessment and comparison of waste management systems: the Cities of Sofia and Niš case studies (2016) Waste Management & Research, 34 (9), pp. 896-904; Molin, L.H.D., Dutra, A., Proposta de modelo multicritério de avaliação de desempenho : um estudo de caso na secretaria municipal de indústria e comércio de tubarão/sc (2013) Revista Interdisciplinar Científica Aplicada; Moraes, L., de Garcia, R., Ensslin, L., da Conceição, M.J., de Carvalho, S.M., The multicriteria analysis for construction of benchmarkers to support the Clinical Engineering in the Healthcare Technology Management (2010) European Journal of Operational Research, 200 (2), pp. 607-615; Nouri, D., Sabour, M.R., Ghanbarzadehlak, M., Industrial solid waste management through the application of multi-criteria decision-making analysis: a case study of Shamsabad industrial complexes (2018) Journal of Material Cycles and Waste Management, 20 (1), pp. 43-58; Peltola, T., Aarikka-Stenroos, L., Viana, E., Mäkinen, S., Value capture in business ecosystems for municipal solid waste management: comparison between two local environments (2016) Journal of Cleaner Production, 137 (1), pp. 1270-1279; Ramos, T.B., Alves, I., Subtil, R., Joanaz de Melo, J., Environmental performance policy indicators for the public sector: the case of the defence sector (2007) Journal of Environmental Management, 82 (4), pp. 410-432; Riddell, W., Bhatia, K.K., Parisi, M., Foote, J., J.I., III, Assessing carbon dioxide emissions from energy use at a university (2009) International Journal of Sustainability in Higher Education, 10, pp. 266-278; Rodrigues, A.P., Fernandes, M.L., Bortoluzzi, S.C., Pinheiro de, L.I.M.A., E. Gouvea da Costa, S.E., Avaliação de desempenho das operações sustentáveis na gestão municipal de resíduos : análise das ferramentas e indicadores propostos da literatura (2017) XXXVII Encontro nacional de engenharia de producao, , Anais; Silva da Rosa, F., Rolim Ensslin, S., Ensslin, L., Joao Lunkes, R., Environmental disclosure management: a constructivist case (2012) Management Decision, 50 (6), pp. 1117-1136; Ryan-Fogarty, Y., O'Regan, B., Moles, R., Greening healthcare: systematic implementation of environmental programmes in a university teaching hospital (2015) Journal of Cleaner Production, 126, pp. 248-259; Santos, S.M., Silva, M.M., Melo, R.M., Gavazza, S., Florencio, L., Kato, M.T., Multi-criteria analysis for municipal solid waste management in a Brazilian metropolitan area (2017) Environmental Monitoring and Assessment, 189 (1), pp. 561-574; Shekdar, A.V., Sustainable solid waste management: an integrated approach for Asian countries (2009) Waste Management, 29 (4), pp. 1438-1448; Simpson, M., Gupt, Y., The economics of the informal sector in solid waste management (2010) Collaborative Working Group Series, 5; Sukholthaman, P., Chanvarasuth, P., Sharp, A., Analysis of waste generation variables and people's attitudes towards waste management system: a case of Bangkok, Thailand (2017) Journal of Material Cycles and Waste Management, 19 (2), pp. 645-656; Usapein, P., Chavalparit, O., Evaluating the performance of 3R options to reduce landfill wastes using the 3R indicator (3RI): case study of polyethylene factories in Thailand (2015) Journal of Material Cycles and Waste Management, 17 (2), pp. 303-311; Veleva, V., Ellenbecker, M., Indicators of sustainable production: framework and methodology (2001) Journal of Cleaner Production, 9 (6), pp. 519-549; Vicente, P., Reis, E., Segmenting households according to recycling attitudes in a Portuguese urban area (2007) Resources, Conservation and Recycling, 52 (1), pp. 1-12; Wilson, D.C., Rodic, L., Cowing, M.J., Velis, C.A., Whiteman, A.D., Scheinberg, A., Vilches, R., Oelz, B., ‘Wasteaware’ benchmark indicators for integrated sustainable waste management in cities (2015) Waste Management, 35 (1), pp. 329-342; Zaman, A.U., Measuring waste management performance using the ‘Zero Waste Index’: the case of Adelaide, Australia (2014) Journal of Cleaner Production, 66 (1), pp. 407-419},
correspondence_address1={Rodrigues, A.P.; Federal University of Technology - ParanaBrazil; email: alexsandro_pinto@yahoo.com.br},
publisher={Elsevier Ltd},
issn={09596526},
coden={JCROE},
language={English},
abbrev_source_title={J. Clean. Prod.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Santos201818,
author={Santos, S.P. and Belton, V. and Howick, S. and Pilkington, M.},
title={Measuring organisational performance using a mix of OR methods},
journal={Technological Forecasting and Social Change},
year={2018},
volume={131},
pages={18-30},
doi={10.1016/j.techfore.2017.07.028},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028505161&doi=10.1016%2fj.techfore.2017.07.028&partnerID=40&md5=de635d98203da538820baa0259b76220},
affiliation={Faculty of Economics and Center for Advanced Studies in Management and Economics (CEFAGE), University of Algarve, Portugal; University of Strathclyde, Department of Management Science, United Kingdom; Ex-employee of HM Revenue & Customs, United Kingdom},
abstract={Performance measurement has become an increasingly important issue in recent years. In spite of the remarkable progress that has been achieved in this area of research, many performance measurement initiatives fall short of their potential in supporting decision-making. This paper argues that adopting a multi-method approach to assessing performance has the potential to result in more comprehensive and effective performance measurement systems. To support this assertion, the paper discusses the development of a performance measurement system for a Business Tax Department, which combined the use of several operational research (OR) techniques including qualitative system dynamics, data envelopment analysis and multiple criteria decision analysis. The use of these OR techniques was influential in developing and implementing the performance measurement system and has the potential to be transferred to other contexts. © 2017 Elsevier Inc.},
author_keywords={Data envelopment analysis;  Multi-method modelling;  Multiple criteria decision analysis;  Performance measurement;  Qualitative system dynamics},
keywords={Data envelopment analysis;  Decision theory;  Operations research;  Supply chain management;  System theory, Effective performance;  Multi methods;  Multi-method approach;  Multiple criteria decision analysis;  Organisational performance;  Performance measurement system;  Performance measurements;  System Dynamics, Decision making, business;  data envelopment analysis;  decision making;  multicriteria analysis;  performance assessment;  qualitative analysis;  research method;  tax system},
references={Ackermann, F., Belton, V., Shafti, S., Wisniewski, M., Building a balanced scorecard the OR way (2005) Paper Presented at IFORS 2005, , USA Hawaii; Ackermann, F., Eden, C., Williams, T., Modeling for litigation: mixing qualitative and quantitative approaches (1997) Interfaces, 27 (2), pp. 48-65; Allen, R., Athanassopoulos, A., Dyson, R.G., Thanassoulis, E., Weight restrictions and value judgements in data envelopment analysis: evolution, development and future directions (1997) Ann. Oper. Res., 73, pp. 13-34; Almeida, P.N., Dias, L.C., Value-based DEA models: application-driven developments (2012) J. Oper. Res. Soc., 63, pp. 16-27; Amado, C.A., Santos, S.P., Marques, P., Integrating the data envelopment analysis and the balanced scorecard approaches for enhanced performance assessment (2012) Omega, 40, pp. 390-403; André, F.J., Herrero, I., Riesgo, L., A modified DEA model to estimate the importance of objectives with an application to agricultural economics (2010) Omega, 38, pp. 371-382; Athanassopoulos, A.D., Decision support for target-based resource allocation of public services in multiunit and multilevel systems (1998) Manag. Sci., 44 (2), pp. 173-187; Athanassopoulos, A.D., Podinovski, V.V., Dominance and potential optimality multiple criteria models decision analysis with imprecise information (1997) J. Oper. Res. Soc., 48, pp. 142-150; Bagherikahvarin, M., De Smet, Y., A ranking method based on DEA and PROMETHEE II (2016) Measurement, 89, pp. 333-342; Baležentis, A., Baležentis, T., Assessing the efficiency of Lithuanian transport sector by applying the methods of MULTIMOORA and Data Envelopment Analysis (2011) Transport, 26 (3), pp. 263-270; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Manag. Sci., 30 (9), pp. 1078-1092; Belton, V., Integrating data envelopment analysis with multiple criteria decision analysis (1992) Proc. IXth Internat. Conf. Multiple Criteria Decision Making, pp. 71-79. , A. Goicoechea L. Duckstein S. Zionts Springer Berlin; Belton, V., Vickers, S., Demystifying DEA - a visual interactive approach based on multiple criteria analysis (1993) J. Oper. Res. Soc., 44 (9), pp. 883-896; Bennett, P.G., On linking approaches to decision aiding: issues and prospects (1985) J. Oper. Res. Soc., 36 (7), pp. 659-669; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failutre of performance measurement initiatives - perceptions of participating managers (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1288-1310; Bouyssou, D., Using DEA as a tool for MCDM: some remarks (1999) J. Oper. Res. Soc., 50, pp. 974-978; Casu, B., Shaw, D., Thanassoulis, E., Using a group support system to aid input–output identification in DEA (2005) J. Oper. Res. Soc., 56 (12), pp. 1363-1372; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Chen, T.Y., Chen, L.H., DEA performance evaluation based on BSC indicators incorporated: the case of semiconductors industry (2007) Int. J. Product. Perform. Manag., 56 (4), pp. 335-357; Chitnis, A., Vaidya, O.S., Efficiency ranking method using DEA and TOPSIS (ERM-DT): case of an Indian bank (2016) Benchmark. Int. J., 23 (1), pp. 165-182; Cooper, W.W., Li, S., Seiford, L.M., Tone, K., Thrall, R.M., Zhu, J., Sensitivity and stability analysis in DEA: some recent developments (2001) J. Prod. Anal., 15 (3), pp. 217-246; Cooper, W., Ruiz, J., Sirvent, I., Choosing weights from alternative optimal solutions of dual multiplier models in DEA (2007) Eur. J. Oper. Res., 180 (1), pp. 443-458; Cooper, W.W., Seiford, L., Zhu, J., Handbook on Data Envelopment Analysis (2004), Kluwer Academic Publishers Massachusetts; Coyle, R.G., System Dynamics Modelling: A Practical Approach (1996), Chapman & Hall London; Doyle, R.H., Green, J.R., DEA and MCDM (1993) Omega, 6, pp. 713-715; Dyson, R.G., Strategy, performance and operational research (2000) J. Oper. Res. Soc., 51 (1), pp. 5-11; Eden, C., Cognitive mapping and problem structuring for system dynamics model building (1994) Syst. Dyn. Rev., 10 (2-3), pp. 257-276; Eden, C., Ackermann, F., Making Strategy: The Journey of Strategic Management (1998), pp. 303-320. , Sage London; Emrouznejad, A., Anouze, A.L., Data envelopment analysis with classification and regression tree – a case of banking efficiency (2010) Expert. Syst., 27 (4), pp. 231-246; Ertuğrul, I., Karakaşoğlu, N., Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods (2009) Expert Syst. Appl., 36, pp. 702-715; Ferreira, F.A.F., Santos, S.P., Rodrigues, P.M.M., Adding value to bank branch performance evaluation using cognitive maps and MCDA: a case study (2011) J. Oper. Res. Soc., 62, pp. 1320-1333; Ferreira, F.A.F., Santos, S.P., Rodrigues, P.M.M., Spahr, R.W., Evaluating retail banking service quality and convenience with MCDA techniques: a case study at the bank branch level (2014) J. Bus. Econ. Manag., 15 (1), pp. 1-21; Ferreira, F.A.F., Santos, S.P., Rodrigues, P.M.M., Spahr, R.W., How to create indices for bank branch financial performance measurement using MCDA techniques: an illustrative example (2014) J. Bus. Econ. Manag., 15 (4), pp. 708-728; Ferreira, F.A.F., Spahr, R.W., Santos, S.P., Rodrigues, P.M.M., A multiple criteria framework to evaluate bank branch potential attractiveness (2012) Int. J. Strateg. Prop. Manag., 16 (3), pp. 254-276; Francis, G., Holloway, J., Beyond comparisons—the role for the operational researcher in benchmarking (2002) J. Oper. Res. Soc., 53 (3), pp. 283-291; García-Valderrama, T., Muleno-Mendigirri, E., Revuelta-Bordoy, D., Relating the perspectives of the balanced scorecard for R&D by means of DEA (2009) Eur. J. Oper. Res., 196, pp. 1177-1189; Giokas, D.I., Pentzaropoulos, G.C., Efficiency ranking of the OECD member states in the area of telecommunications: a composite AHP/DEA study (2008) Telecommun. Policy, 32, pp. 672-685; Gouveia, M.C., Dias, L.C., Antunes, C.H., Additive DEA based on MCDA with imprecise information (2008) J. Oper. Res. Soc., 59 (1), pp. 54-63; Greasley, A., Using DEA and simulation in guiding operating units to improved performance (2005) J. Oper. Res. Soc., 56, pp. 727-731; Halme, M., Joro, T., Korhonen, P., Salo, S., Wallenius, J., A value efficiency approach to incorporating preference information in data envelopment analysis (1999) Manag. Sci., 45, pp. 103-115; Henao, F., Franco, L.A., Unpacking multimethodology: impacts of a community development intervention (2016) Eur. J. Oper. Res., 253 (3), pp. 681-696; Howick, S., Using system dynamics to analyse disruption and delay in complex projects for litigation: can the modeling purposes be met? (2003) J. Oper. Res. Soc., 54, pp. 222-229; Howick, S., Ackermann, F., Mixing OR methods in practice: past, present and future directions (2011) Eur. J. Oper. Res., 215, pp. 503-511; Howick, S., Eden, C., Ackermann, F., Williams, T.M., Building confidence in models for multiple audiences: the modelling cascade (2008) Eur. J. Oper. Res., 186 (3), pp. 1068-1083; Howland, M., Rowse, J., Measuring bank branch efficiency using data envelopment analysis: managerial and implementation issues (2006) Infor, 44 (1), pp. 49-63; Jackson, M.C., Towards coherent pluralism in management science (1999) J. Oper. Res. Soc., 50 (1), pp. 12-23; Joro, T., Korhonen, P., Wallenius, J., Structural comparison of data envelopment analysis and multiple objective linear programming (1998) Manag. Sci., 44, pp. 962-970; Joshi, R., Banwet, D.K., Shankar, R., A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain (2011) Expert Syst. Appl., 38, pp. 10170-10182; Jyoti, B., D.K., Deshmukh, S.G., Evaluating performance of national R&D organizations using integrated DEA-AHP technique (2008) Int. J. Product. Perform. Manag., 57 (5), pp. 370-388; Kaplan, R.S., Norton, D.P., The balanced scorecard measures that drive performance (1992) Harv. Bus. Rev., pp. 71-79. , (January-February); Korhonen, P., Syrjänen, M., Resource allocation based on efficiency analysis (2004) Manag. Sci., 50, pp. 1134-1144; Lee, S., Using data envelopment analysis and decision trees for efficiency analysis and recommendation of B2C controls (2010) Decis. Support. Syst., 49, pp. 486-497; Mavrotas, G., Trifillis, P., Multicriteria decision analysis with minimum information: combining DEA with MAVT (2006) Comput. Oper. Res., 33, pp. 2083-2098; McCunn, P., The balanced scorecard: the eleventh commandment (1998) Manag. Account., pp. 34-36. , (December); McMullen, P.R., Frazier, G.V., Using simulation and data envelopment analysis to compare assembly line balancing solutions (1998) J. Prod. Anal., 11, pp. 149-168; Meekings, A., Unlocking the potential of performance measurement: a guide to practical implementation (1995) Public Money Manag., pp. 1-8. , (October–December); Mingers, J., Variety is the spice of life: combining soft and hard OR/MS methods (2000) Int. Trans. Oper. Res., 7, pp. 673-691; Mingers, J., A classification of the philosophical assumptions of management science methods (2003) J. Oper. Res. Soc., 54, pp. 559-570; Mingers, J., Brocklesby, J., Multimethodology: towards a framework for mixing methodologies (1997) Omega, 25 (5), pp. 489-509; Mingers, J., Liu, W., Meng, W., Using SSM to structure the identification of inputs and outputs in DEA (2009) J. Oper. Res. Soc., 60, pp. 168-179; Mingers, J., Rosenhead, J., Problem structuring methods in action (2004) Eur. J. Oper. Res., 152 (3), pp. 530-554; Morgan, J.S., Howick, S., Belton, V., A toolkit of designs for mixing discrete event simulation and system dynamics (2017) Eur. J. Oper. Res., 257, pp. 907-918; Munro, I., Mingers, J., The use of multimethodology in practice – results of a survey of practitioners (2002) J. Oper. Res. Soc., 53, pp. 369-378; Munro, I., Mingers, J., Viewpoint: the practice of UK OR practitioners (2004) J. Oper. Res. Soc., 55, pp. 90-93; Neely, A., Bourne, M., Why measurement initiatives fail (2000) Meas. Bus. Excell., 4, pp. 3-6; Pidd, M., Mixing other methods with simulation is no big deal (2012) Proceedings of the 44th Winter Simulation Conference, , C. Laroque J. Himmelspach R. Pasupathy O. Rose A.M. Uhrmacher Germany Berlin; Podinovski, V., Weight restrictions and radial measures of efficiency (2002) Warwick Business School Research Paper, 352, p. 2002; Pollack, J., Multimethodology in series and parallel: strategic planning using hard and soft OR (2009) J. Oper. Res. Soc., 60 (2), pp. 156-167; Richardson, G.P., Problems with causal-loop diagrams (1986) Syst. Dyn. Rev., 2 (2), pp. 158-170; Richardson, G.P., Problems in causal-loop diagrams revisited (1997) Syst. Dyn. Rev., 13 (3), pp. 247-252; Samoilenko, S., Osei-Bryson, K., Increasing the discriminatory power of DEA in the presence of the sample heterogeneity with cluster analysis and decision trees (2008) Expert Syst. Appl., 34, pp. 1568-1581; Santos, S.P., Belton, V., Howick, S., Adding value to performance measurement by using systems dynamics and multicriteria analysis (2002) Int. J. Oper. Prod. Manag., 22, pp. 1246-1272; Santos, S.P., Belton, V., Howick, S., Enhanced performance measuring using OR: a case study (2008) J. Oper. Res. Soc., 59, pp. 762-775; Sarkis, J., A comparative analysis of DEA as a discrete alternative multiple criteria decision tool (2000) Eur. J. Oper. Res., 123, pp. 543-557; Scheel, H., EMS: Efficiency Measurement System User's Manual Version 1.3 (2000), Universitat Dortmund Dortmund, Germany; Schneiderman, A.M., Why balanced scorecards fail (1999) J. Strateg. Perform. Measurement, pp. 6-11. , (January); Schultz, M., Hatch, M.J., Living with multiple paradigms: the case of paradigm interplay in organisational culture studies (1996) Acad. Manag. Rev., 21 (2), pp. 529-557; Seçme, N.Y., Bayrakdaroğlu, A., Kahraman, C., Fuzzy performance evaluation in Turkish banking sector using analytic hierarchy process and TOPSIS (2009) Expert Syst. Appl., 36, pp. 11699-11709; Seol, H., Choi, J., Park, G., Park, Y., A framework for benchmarking service process using data envelopment analysis and decision tree (2007) Expert Syst. Appl., 32, pp. 432-440; Seol, H., Lee, H., Kim, S., Park, Y., The impact of information technology on organizational efficiency in public services: a DEA-based DT approach (2008) J. Oper. Res. Soc., 59 (2), pp. 231-238; Sharma, M.J., Yu, S.J., Benchmark optimization and attribute identification for improvement of container terminals (2010) Eur. J. Oper. Res., 201, pp. 568-580; Smith, P.C., Goddard, M., Performance management and operational research: a marriage made in heaven? (2002) J. Oper. Res. Soc., 53 (3), pp. 247-255; Sterman, J.D., Business Dynamics Systems Thinking and Modeling for a Complex World (2000), McGraw-Hill London; Stewart, T.J., Relationships between data envelopment analysis and multiple criteria decision analysis (1996) J. Oper. Res. Soc., 47, pp. 654-665; Sun, C., A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods (2010) Expert Syst. Appl., 37, pp. 7745-7754; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) Int. J. Prod. Econ., 64, pp. 231-241; Tsang, A.H.C., Jardine, A.K.S., Kolodny, H., Measuring maintenance performance: a holistic approach (1999) Int. J. Oper. Prod. Manag., 19 (7), pp. 691-715; Tseng, F.M., Chiu, Y.J., Chen, J.S., Measuring business performance in the high-tech manufacturing industry: a case study of Taiwan's large-sized TFT-LCD panel companies (2009) Omega, 37 (3), pp. 686-697; Van Camp, J., Braet, J., Taxonomizing performance measurement systems’ failures (2016) Int. J. Product. Perform. Manag., 65 (5), pp. 672-693; Wallenius, J., Dyer, J.S., Fishburn, P.C., Steuer, R.E., Zionts, S., Deb, K., Multiple criteria decision making, multiattribute utility theory: recent accomplishments and what lies ahead (2008) Manag. Sci., 54 (7), pp. 1336-1349; Wang, J.-C., Corporate performance efficiency investigated by data envelopment analysis and balanced scorecard (2006) J. Am. Acad. Bus., 9 (2), pp. 312-318; Wu, D., Detecting information technology impact on firm performance using DEA and decision tree (2006) Int. J. Inf. Technol. Manag., 5, pp. 162-174; Wu, D., Yang, Z., Liang, L., Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank (2006) Expert Syst. Appl., 31 (1), pp. 108-115; Yang, J.B., Wong, B.Y., Xu, D.L., Stewart, T.J., Integrating DEA-oriented performance assessment and target setting using interactive MOLP methods (2009) Eur. J. Oper. Res., 195, pp. 205-222; Yurdakul, M., Íç, Y.T., Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches (2005) Int. J. Prod. Res., 43 (21), pp. 4609-4641},
correspondence_address1={Santos, S.P.; University of Algarve, Campus de Gambelas, Portugal; email: ssantos@ualg.pt},
publisher={Elsevier Inc.},
issn={00401625},
language={English},
abbrev_source_title={Technol. Forecast. Soc. Change},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cha20181614,
author={Cha, H.S. and Kim, K.H.},
title={Measuring Project Performance in Consideration of Optimal Best Management Practices for Building Construction in South Korea},
journal={KSCE Journal of Civil Engineering},
year={2018},
volume={22},
number={5},
pages={1614-1625},
doi={10.1007/s12205-017-0156-2},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027042896&doi=10.1007%2fs12205-017-0156-2&partnerID=40&md5=6b6bd7fd9f8deb836cc1255aa2480b7e},
affiliation={Dept. of Architectural Engineering, Ajou University, Suwon, 16499, South Korea},
abstract={A variety of Performance Measurement Systems (PMS) are aimed to provide owners and contractors with sound decision-making by identifying the Best Management Practices (BMP). Most of them, however, are not effectively linked with project characteristics (i.e., project type, owner requirement, and/or project circumstances). Furthermore, a continuous feedback system is necessary in order to pin-point “how-to” solutions for the subject project. Compared to the conventional PMS, this study is focused on; (1) identifying, if any, quantifiable relationships among project characteristics, project performance, and BMP; (2) developing a systematic framework for predicting the performance level of the subject project; and (3) validating the feedback algorithm by detecting the most suitable management practices for a given construction project. Although the proposed system is limited to Korean building construction industry, the main contribution of this research is twofold. First, this study has conducted the first-ever empirical analysis by quantifying the project performance and selecting the optimal BMP in consideration of project characteristics. Second, the continuous feedback approach in the field of PMS is proven by effectively diagnosing the vulnerability of the project in terms of the various types of project performance. © 2018, Korean Society of Civil Engineers.},
author_keywords={Best Management Practice (BMP);  Performance Measurement System (PMS);  performance prediction;  Project characteristics;  quantitative analysis},
keywords={Chemical analysis;  Construction;  Construction industry;  Decision making;  Supply chain management, Best management practices;  Building construction;  Building construction industry;  Construction projects;  Management practices;  Performance measurement system;  Performance prediction;  Project characteristics, Project management},
references={Ahadzie, D.K., Proverbs, D.G., Olomolaiye, P.O., A model for predicting the performance of project managers at the construction phase of mass house building projects (2008) ASCE J. Constr. Eng. Manage., 134 (8), pp. 618-629; Alarcon, L.F., Ashley, D.B., Modeling project performance for decision making (1996) ASCE J. Constr.Eng. Manage., 122 (3), pp. 265-273; Armentrout, D.R., Engineering productivity management and performance measurement (1986) ASCE J. Manage. Engr., 2 (3), pp. 141-147; Bassioni, H.A., Price, A.D.F., Hassan, T.M., Performance measurement in construction (2004) ASCE J. Manage. Engr., 20 (2), pp. 42-50; Cha, H.S., O’Connor, J.T., Characteristics for leveraging value management processes on capital facility projects (2006) ASCE Journal of Mgmt. Engr., 22 (3), pp. 135-147; Cha, H.S., Shin, G.Y., Predicting project cost performance level by assessing risk factors of building construction (2011) Journal of Asian Architecture and Building Engineering, 10 (2), pp. 437-444; Cha, H.S., Kim, C.K., Quantitative approach for project performance measurement on building construction in South Korea (2011) KSCE Journal of Civil Engineering, 15 (8), pp. 1319-1328; Chan, T.K., Measuring performance of the Malaysian construction industry (2009) Constr. Manage. Econ., 27 (12), pp. 1231-1244; Cho, Y.S., Cha, H.S., A Study of project characteristics and project performance level of difficulty (2010) KICEM Journal of Construction Engineering & Management, 11 (6), pp. 78-88; Choi, J., Yun, S., Oliveira, D.P., Developing a cost normalization framework for phase-based performance assessment of construction projects (2016) Canadian Journal of Civil Engineering, 43 (12), pp. 1075-1086; Han, S., Kim, D., Kim, H., Jang, W., A web-based integrated system for international project risk management (2008) Automation in Construction, 17, pp. 342-356; Haponava, T., Al-Jibouri, S., Proposed system for measuring project performance using process-based key performance indicators (2012) ASCE J. Manage. Engr., 28 (2), pp. 140-149; Hwang, B., Thomas, S., Degezelle, D., Caldas, C., Development of a benchmarking framework for pharmaceutical capital projects (2008) Constr. Manage. Econ., 26 (2), pp. 177-195; Kagioglou, M., Cooper, R., Aouad, G., Performance management in construction: A conceptual framework (2001) J. Constr. Manage. Econ., 19 (1), pp. 85-95; Kaplan, R.S., The evolution of management accounting (1984) Account. Res., 59 (3), pp. 390-418; Ko, Y., Cha, H., Development of project performance predicting system considering construction project characteristics (2011) KICEM Journal of Construction Engineering & Management, 12 (1), pp. 62-72; Ko, Y.J., Cha, H., Shin, D., Kim, K., Selecting optimum management practices according to project characteristics for improving project performance (2012) KICEM Journal of Construction Engineering & Management, 13 (1), pp. 3-13; Lee, S., Thomas, S., Tucker, R., Web-Based Benchmarking System for the Construction Industry (2005) ASCE J. of Construction & Management, pp. 790-798; Lema, N.M., Price, A.D.F., Benchmarking: Performance improvement toward competitive advantage (1995) ASCE J. Manage. Eng., 11 (1), pp. 28-37; Leu, S., Lin, Y., Project performance evaluation based on statistical process control techniques (2008) ASCE J. Constr. Eng. Manage., 134 (10), pp. 813-819; Olumide, A., Walewski, J., Anderson, S., Assessing CII best practices applicability (2012) Construction Research Congress, pp. 2439-2448; Ozorhon, B., Arditi, D., Dikmen, I., Birgonul, M., Toward a multidimensional performance measure for international joint ventures in construction (2011) ASCE J. of Construction Engineering & Management, pp. 403-411; Ramirez, R.R., Alarcon, L.F., Knight, P., Benchmarking system for evaluating management practices in the construction industry (2004) ASCE J. Manage. Engr., 20 (3), pp. 110-117; Sheskin, D., (2003) Handbook of parametric and nonparametric statistical procedures, , CRC Press, Boca Raton, Florida; Yeung, J.F.Y., Chan, A.P.C., Chan, D.W.M., Chiang, Y.H., Yang, H., Developing a benchmarking model for construction projects in Hong Kong (2013) ASCE J. Constr. Eng. Manage., 139 (6), pp. 705-716; Yu, I., Kim, K., Jung, Y., Chin, S., Comparable performance measurement system for construction companies (2007) ASCE J. Manage. Engr., 23 (3), pp. 131-139; Yun, S., Choi, J., Oliveira, D., Mulva, S., Development of performance metrics for phase-based capital project benchmarking (2015) International Journal of Project Management, 34 (3), pp. 389-402},
correspondence_address1={Cha, H.S.; Dept. of Architectural Engineering, South Korea; email: hscha@ajou.ac.kr},
publisher={Springer Verlag},
issn={12267988},
language={English},
abbrev_source_title={KSCE J. Civ. Eng.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Leksono2018,
author={Leksono, E.B. and Suparno and Vanany, I.},
title={Using DEMATEL approach to develop relationships of performance indicators on sustainable service only supply chain performance measurement},
journal={IOP Conference Series: Materials Science and Engineering},
year={2018},
volume={337},
number={1},
doi={10.1088/1757-899X/337/1/012023},
art_number={012023},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046286858&doi=10.1088%2f1757-899X%2f337%2f1%2f012023&partnerID=40&md5=71a6a1f4c9cc0f71ea9a1d4374f12aac},
affiliation={Department of Industrial Engineering, Universitas Muhammadiyah Gresik, Gresik, 61121, Indonesia; Department of Industrial Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS, Sukolilo-Surabaya, 60111, Indonesia},
abstract={Service only supply chain (SOSC) concept is service supply chain (SSC) implementation on pure services. The globalization and stakeholder pressure makes operation of SSC should give the attention to the environment effect, community, economic and intangibility assets. SOSC performance measurement (SOSCPM) may be developed for measuring of performance for sustainability aspects and intangibility assets to meet customer satisfaction. This article discusses sustainable SOSCPM based on balanced scorecard (BSC), include sustainability aspects, intangibility and relations between perspectives and indicators. From literature review, it is found 34 performance indicators that must be confirm to expert and SC actors by survey. From survey validation using weighted average and level of consensus, it is found 29 valid indicators for processed by DEMATEL. From DEMATEL, it is found 26 indicators can be used on sustainable SOSCPM. Furthermore, innovation and growth perspective most influence to other, and customer perspective most important. Intangibility indicators incorporated on innovation and growth perspective very related with human resources. Finally, relations between perspectives and indicator used to design of BSC strategy maps. © Published under licence by IOP Publishing Ltd.},
keywords={Benchmarking;  Customer satisfaction;  Supply chains;  Surveys;  Systems engineering, Balanced scorecards;  Customer perspectives;  Performance indicators;  Performance measurements;  Service supply chains;  Stakeholder pressures;  Supply chain performance measurements;  Sustainable services, Sustainable development},
references={Baltacioglu, T., Ada, E., Kaplan, M.D., Yurt, O., Cem Kaplan, Y., A new framework for service supply chains (2007) Serv. Ind. J, 27 (2), pp. 105-124; Giannakis, M., Conceptualizing and managing service supply chains (2011) Serv. Ind. J, 31 (11), pp. 1809-1823; Drzymalski, J., (2012) Supply Chain Frameworks for the Service Industry : A Review of the Literature, 1, pp. 31-42. , December; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput. Ind. Eng., 62 (3), pp. 801-818; Wang, Y., Wallace, S.W., Shen, B., Choi, T., Service supply chain management : A review of operational models (2015) Eur. J. Oper. Res., 247 (3), pp. 685-698; Pertumbuhan Ekonomi Indonesia Triwulan II-2014 in (2014) Berita Resmi Statistik 16/02/Th., pp. 1-9. , Bps; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Linton, J., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) J. Oper. Manag., 25 (6), pp. 1075-1082; Xu, X., Gursoy, D., A conceptual framework of sustainable hospitality supply chain management (2014) J. Hosp. Mark. Manag., pp. 1-31; Marr, B., Adams, C., The balanced scorecard and intangible assets : Similar ideas, unaligned concepts (2006) Meas. Bus. Excell., 8, pp. 18-27; Zigan, K., Zeglat, D., Intangible resources in performance measurement systems of the hotel industry (2010) Facilities, 28 (13-14), pp. 597-610; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res., 25 (2), pp. 173-186; Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Manag. An Int. J., 19 (3), pp. 242-257; Tajbakhsh, A., Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2014) J. Clean. Prod., , Aug; Zhang, X., Song, H., Huang, G.Q., Tourism supply chain management: A new research agenda (2009) Tour. Manag., 30 (3), pp. 345-358; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management - What has been achieved ? (2015) Supply Chain Manag. An Int. J., 20 (6), pp. 664-680; Kaplan, R.S., Norton, D.P., Linking the Balanced Scorecard to strategy (1996) Calif. Manage. Rev., 39 (1), pp. 53-79; Kaplan, R.S., Norton, D.P., Response to S. Voelpel et al., 'The tyranny of the Balanced Scorecard in the innovation economy,' (2006) Journal of Intellectual Capital, 7 (1), pp. 43-60. , 2006; J. Intellect. Cap., 7, pp. 421-428; Kaplan, R.S., Norton, D.P., Rugelsjoen, B., Managing alliances with the Balanced Scorecard (2010) Harv. Bus. Rev., pp. 114-121. , February 2010; Leksono, E.B., Suparno, Vanany, I., Development of conceptual framework for sustainable supply chain performance measurement in service industries in (2016) 8th Widyatama Int. Seminar on Sustainability, pp. 142-148; Gölcük, I., Baykasoglu, A., An analysis of DEMATEL approaches for criteria interaction handling within ANP (2016) Expert Syst. Appl., 46, pp. 346-366; Chen, F., Hsu, T., Tzeng, G., A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP (2011) Int. J. Hosp. Manag., 30 (4), pp. 908-932; Wei, Y., Hu, Q., Xu, C., Ordering, pricing and allocation in a service supply chain (2013) Int. J. Prod. Econ., 144 (2), pp. 590-598; Wu, I.L., Chuang, C.H., Hsu, C.H., Information sharing and collaborative behaviors in enabling supply chain performance: A social exchange perspective (2014) Int. J. Prod. Econ., 148, pp. 122-132; Sigala, M., A supply chain management approach for investigating the role of tour operators on sustainable tourism: The case of TUI (2008) J. Clean. Prod., 16 (15), pp. 1589-1599; Büyüközkan, G., Cifci, G., An integrated QFD framework with multiple formatted and incomplete preferences : A sustainable supply chain application (2013) Appl. Soft Comput., 13 (9), pp. 3931-3941; Hussain, M., Khan, M., Al-Aomar, R., A framework for supply chain sustainability in service industry with Confirmatory Factor Analysis (2016) Renew. Sustain. Energy Rev., 55, pp. 1301-1312; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2009) Prod. Plan. Control, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management : A balanced scorecard approach (2007) Comput. Ind. Eng., 53 (1), pp. 43-62; Matos, S., Hall, J., Integrating sustainable development in the supply chain: The case of life cycle assessment in oil and gas and agricultural biotechnology (2007) J. Oper. Manag., 25 (6), pp. 1083-1102; Schwartz, K., Tapper, R., Font, X., A sustainable supply chain management framework for tour operators (2008) J. Sustain. Tour, 16 (3), pp. 298-314; Wicks, A.M., St Clair, L., Kidney, C.S., Competing values in healthcare : Balancing the (Un) Balanced Scorecard (2007) J. Healthc. Manag., 52 (5), pp. 309-324; Keating, B., Quazi, A., Kriz, A., Coltman, T., In pursuit of a sustainable supply chain: Insights from Westpac Banking Corporation (2008) Supply Chain Manag. An Int. J., 13 (3), pp. 175-179; Smith, B.K., Nachtmann, H., Pohl, E.A., Quality measurement in the healthcare supply chain (2011) Qual. Manag. J., 18 (4), pp. 50-60; De Vries, J., Huijsman, R., Supply chain management in health services: An overview (2011) Supply Chain Manag. An Int. J., 16 (3), pp. 159-165; Chithambaranathan, P., Subramanian, N., Gunasekaran, A., Service supply chain environmental performance evaluation using grey based hybrid MCDM approach (2015) Int. J. Prod. Econ., pp. 1-14; Fu, N., Flood, P.C., Bosak, J., Morris, T., O'Regan, P., Exploring the performance effect of HPWS on professional service supply chain management (2013) Supply Chain Manag. An Int. J., 18 (3), pp. 292-307; Adriana, B., Environmental supply chain management in tourism: The case of large tour operators (2009) J. Clean. Prod., 17 (16), pp. 1385-1392; Gopalakrishnan, K., Yusuf, Y.Y., Musa, A., Abubakar, T., Ambursa, H.M., Sustainable supply chain management: A case study of British Aerospace (BAe) Systems (2012) Int. J. Prod. Econ., 140 (1), pp. 193-203; Brindley, C., Oxborrow, L., Aligning the sustainable supply chain to green marketing needs: A case study (2014) Ind. Mark. Manag., 43 (1), pp. 45-55; Font, X., Tapper, R., Schwartz, K., Kornilaki, M., Sustainable supply chain management in tourism (2008) Bus. Strateg. Environ., 17 (4), pp. 260-271; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes performance to a company's financial strategic objectives (2012) Eur. J. Oper. Res., 223 (1), pp. 276-289; Lega, F., Marsilio, M., Villa, S., An evaluation framework for measuring supply chain performance in the public healthcare sector : Evidence from the Italian NHS (2013) Prod. Plan. Control, 24 (10-11), pp. 931-1011. , 931-947; Chen, D.Q., Preston, D.S., Xia, W., Enhancing hospital supply chain performance: A relational view and empirical test (2013) J. Oper. Manag., 31 (6), pp. 391-408; Narayana, S.A., Kumar, R., Vrat, P., Managerial research on the pharmaceutical supply chain - A critical review and some insights for future directions (2014) J. Purch. Supply Manag., 20 (1), pp. 18-40; Lee, H.K., Fernando, Y., The antecedents and outcomes of the medical tourism supply chain (2015) Tour. Manag., 46, pp. 148-157; Parker, J., Delay, D., The future of the healthcare supply chain (2008) Healthc. Financ. Manag, 62, pp. 66-69; Wu, T., Wu, Y.C., Chen, Y.J., Goh, M., Aligning supply chain strategy with corporate environmental strategy: A contingency approach (2014) International Journal of Production Economics, pp. 220-229; Hohenstein, N.O., Feisel, E., Hartmann, E., Human resource management issues in supply chain management research (2014) Int. J. Phys. Distrib. Logist. Manag., 44 (6), pp. 434-463; Feil, A.A., De Quevedo, D.M., Schreiber, D., Selection and identification of the indicators for quickly measuring sustainability in micro and small furniture industries (2015) Sustain. Prod. Consum, 3, pp. 34-44},
correspondence_address1={Leksono, E.B.; Department of Industrial Engineering, Indonesia; email: eko_budileksono@umg.ac.id},
editor={Kurniati N., Dewi R.S., Karningsih P.D., Arvitrida N.I., Hartanto D., Dewi D.S.},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Han20182437,
author={Han, W. and Huang, Y. and Macbeth, D.},
title={Performance measurement of cross-culture supply chain partnership: a case study in the Chinese automotive industry},
journal={International Journal of Production Research},
year={2018},
volume={56},
number={7},
pages={2437-2451},
doi={10.1080/00207543.2017.1377357},
note={cited By 21},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029898912&doi=10.1080%2f00207543.2017.1377357&partnerID=40&md5=9fb45407f41c1df5e318c2384375b601},
affiliation={Nottingham Business School, Nottingham Trent University, Nottingham, United Kingdom; Cardiff Business School, Cardiff University, Cardiff, United Kingdom; Southampton Business School, University of Southampton, Southampton, United Kingdom},
abstract={This study explores a performance measurement system for a dynamic supply chain partnership in a cross-cultural context. An initial framework is constructed by reviewing the existing literature, followed by an in-depth case study in the Chinese automotive industry, where the framework is refined to address the multi-cultural setting. A performance measurement, system which includes the relationship strategy and operational measurement criteria for a supply chain partnership, has been developed. The relationship strategy contains elements of strategy orientation, management style, interdependence, mutual organisational characteristics and common goals. The operational measurement criteria consist of commitment, trust, communication behaviour, information sharing, participation decision, quality, production performance, delivery, cost, supplier strength, attitude, compromise and loyalty. The last three operational measurement criteria are found to be particularly relevant to the cross-cultural feature. While existing studies tend to focus on either specific measures or individual organisations, this paper for the first time proposes a comprehensive framework to measure the performance of supply chain partnerships. The cross-cultural perspective provides a further unique view on how a performance measurement system can be responsive to the dynamics in practice. © 2017 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={automotive industry;  case study;  cross-cultural collaboration;  performance measures;  supply chain partnership},
keywords={Mergers and acquisitions;  Supply chain management;  Supply chains, Cross-cultural collaboration;  Cultural settings;  Dynamic supply chains;  Information sharing;  Performance measure;  Performance measurement system;  Performance measurements;  Production performance, Automotive industry},
references={Andaleeb, S.S., An Experimental Investigation of Satisfaction a Commitment in Marketing Channel: To Role of Trust and Dependence (1996) Journal of Retailing, 72 (1), pp. 77-93; Anderson, J.C., Narus, J.A., A Model of Distributor Firm and Manufacturer Firm Working Partnerships (1990) Journal of Marketing, 54 (1), pp. 42-58; Angeles, R., Nath, R., Partner Congruence in Eletronic Data Interchange (EDI) Enabled Relationship (2001) Journal of Business Logistics, 22 (2), pp. 109-127; Angerhofer, B.J., Angelides, M.C., A Model and a Performance Measurement System for Collaborative Supply Chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Austin, J.E., Seitanidi, M.M., Collaborative Value Creation: A Review of Partnering Between Nonprofits and Businesses. Part 2: Partnership Processes and Outcomes (2012) Nonprofit and Voluntary Sector Quarterly, 18, pp. 1-40; Baker, M.J., (2003) Business and Management Research: How to Complete Your Research Project Successfully, , Edinburgh: Western Publishers Ltd; Benton, W.C., Maloni, M., The Influence of Power Driven Buyer/Seller Relationship on Supply Chain Satisfaction (2005) Journal of Operations Management, 23 (1), pp. 1-22; Bititci, U., Mendibil, K., Martinez, V., Albores, P., Measuring and Managing Performance in Extended Enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance Measurement: Challenges for Tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Bititci, U.S., Modelling of Performance Measurement Systems in Manufacturing Enterprises (1995) International Journal of Production Economics, 42 (2), pp. 137-147; Brouthers, K.D., Brouthers, L.E., Wilkinson, T.J., Strategic Alliance: Choose Your Partner (1995) Long Rang Planning, 28 (3), pp. 18-25; Cai, S., Goh, M., Souza, R., Li, G., Knowledge Sharing in Collaborative Supply Chains: Twin Effects of Trust and Power (2013) International Journal of Production Research, 51 (7), pp. 2060-2076; Cao, M., Zhang, Q., Supply Chain Collaboration: Impact on Collaborative Advantage and Firm Performance (2011) Journal of Operations Management, 29, pp. 163-180; Cao, M., Vonderembse, M.A., Zhang, Q., Ragu-Nathan, T.S., Supply Chain Collaboration: Conceptualisation and Instrument Development (2010) International Journal of Production Research, 48 (22), pp. 6613-6635; Carr, A.S., Pearson, J.N., Strategically Managed Buyer–Supplier Relationships and Performance Outcomes (1999) Journal of Operations Management, 17 (5), pp. 497-519; Chan, F., Qi, H., An Innovative Performance Measurement Method for Supply Chain Management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chen, I.J., Paulraj, A., Lado, A.A., Strategic Purchasing, Supply Management, and Firm Performance (2004) Journal of Operations Management, 22, pp. 505-523; Child, J., Faulkner, D., Tallman, S., (2005) Cooperative Strategy, , New York: Oxford University Press, and; Choi, T.Y., Hartley, J.L., An Exploration of Supplier Selection Practices Across the Supply Chain (1996) Journal of Operations Management, 14 (4), pp. 333-343; Cousins, D.L.B., Squire, B., Performance Measurement in Strategic Buyer–Supplier Relationships: The Mediating Role of Socialization Mechanisms (2008) International Journal of Operations & Production Management, 28 (3), pp. 238-258; Covin, J.G., Slevin, D.P., The Influence of Organization Structure on the Utility of an Entrepreneurial Top Management Style (1988) Journal of Management Studies, 25 (3), pp. 217-234; Cox, A., Managing With Power: Strategies for Improving Value Appropriation From Supply Chain Relationships (2001) The Journal of Supply Chain Management, 37 (1), pp. 42-47; Craighead, C.W., Hult, G.T.M., Ketchen, D.J., The Effects of Innovation–Cost Strategy, Knowledge, and Action in the Supply Chain on Firm Performance (2009) Journal of Operations Management, 27 (5), pp. 405-421; Crane, T.G., Felder, J.P., Thompson, P.J., Thompson, M.G., Sanders, S.R., Partnering Measures (1999) Journal of Management in Engineering, 15 (2), pp. 37-42; Datta, D.K., Organizational Fit and Acquisition Performance: Effects of Post-Acquisition Integration (1991) Strategic Management Journal, 12 (4), pp. 81-297; Demirbag, M., Weir, D.T.H., Mirza, H., Trust, Inter-Partner Conflicts, Cultural Distance, Commitment and Joint Venture Performance: An Empirical Analysis of International Joint Ventures in Turkey (2003) Journal of Transnational Management Development, 8 (1-2), pp. 111-140; Douma, M.U., Bilderbeek, J., Idenburg, P.J., Looise, J.K., Strategic Alliances: Managing the Dynamics of Fit (2000) Long Range Planning, 33 (4), pp. 579-598; Eisenhardt, K.M., Building Theories From Case Study Research (1989) Academy of Management Review, 14, pp. 532-550; Eisenhardt, K.M., Schoonhoven, C.B., Resource-Based View of Strategic Alliance Formation: Strategic and Social Effects in Entrepreneurial Firms (1996) Organization Science, 7 (2), pp. 136-150; Ellram, L.M., Supply‐Chain Management: The Industrial Organisation Perspective (1991) International Journal of Physical Distribution & Logistics Management, 21 (1), pp. 13-22; Elofson, G., Robinson, W., Collective Customer Collaboration Impacts on Supply-Chain Performance (2007) International Journal of Production Research, 45 (11), pp. 660-679; Frazier, G.L., Spekman, R.E., O’neal, C.R., Just-In-Time Exchange Relationships in Industrial Markets (1988) The Journal of Marketing, 52 (4), pp. 52-67; Fredendall, L.D., Letmathe, P., Uebe-Emden, N., Supply Chain Management Practices and Intellectual Property Protection in China: Perceptions of Mittelstand Managers (2016) International Journal of Operations & Production Management, 36 (2), pp. 135-163; Griffith, D.A., Myers, M.B., Harvey, M.G., An Investigation of National Culture’s Influence on Relationship and Knowledge Resources in Interorganizational Relationships Between Japan and the United States (2006) Journal of International Marketing, 14 (3), pp. 1-32; Gulati, R., Social Structure and Alliance Formation Patterns: A Longitudinal Analysis (1995) Administrative Science Quarterly, 40 (4), pp. 619-652; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hagen, R., Globalization, University Transformation and Economic Regeneration: A UK Case Study of Public/Private Sector Partnership (2002) International Journal of Public Sector Management, 15 (3), pp. 204-218; Hamel, G., Prahalad, C.K., (1994) Competing for the Future, , New York: Free Press, and; Hewett, K., Money, R.B., Sharma, S., National Culture and Industrial Buyer–Seller Relationships in the United States and Latin America (2006) Journal of the Academy of Marketing Science, 34 (3), pp. 386-402; Hofstede, G., Culture and Organizations (1980) International Studies of Management & Organization, 10 (4), pp. 15-41; Hofstede, G., Hofstede, G.J., Minkov, M., (1991) Cultures and Organizations: SOFTWARE of the Mind, , London: McGraw-Hill, and; Holweg, M., Luo, J., Oliver, N., The Past, Present and Future of China’s Automotive Industry: A Value Chain Perspective (2008) International Journal of Technological Learning, Innovation and Development, 2 (1-2), pp. 76-118; Hugh, H.J., Faulkner, P.C., Public–Private Partnerships in Sustainable Development (1995) Review of European Community & International Environmental Law, 4 (2), pp. 133-136; Hult, G.T.M., Ketchen, D.J., Arrfelt, M., Strategic Supply Chain Management: Improving Performance Through A Culture of Competitiveness and Knowledge Development (2007) Strategic Management Journal, 28 (10), pp. 1035-1052; Jia, F., Lamming, R., Cultural Adaptation in Chinese-Western Supply Chain Partnerships–Dyadic Learning in an International Context (2013) International Journal of Operations & Production Management, 33 (5), pp. 528-561; Johnston, R., Staughton, R., Establishing and Developing Strategic Relationships–The Role for Operations Managers (2009) International Journal of Operations & Production Management, 29 (6), pp. 564-590; Kale, P., Singh, H., Perlmutter, H., Learning and Protection of Proprietary Assets in Strategic Alliances: Building Relational Capital (2000) Strategic Management Journal, 21 (3), pp. 217-237; Kelly, M.J., Schaan, J., Joncas, H., Managing Alliance Relationships: Key Challenges in the Early Stages of Collaboration (2002) R & D Management, 32 (1), pp. 11-22; Kogut, B., Singh, H., The Effect of National Culture on the Choice of Entry Mode (1988) Journal of International Business Studies, 19 (3), p. 432; Koufteros, X., Verghese, A.J., Lucianetti, L., The Effect Of Performance Measurement Systems on Firm Performance: A Cross-Sectional and a Longitudinal Study (2014) Journal of Operations Management, 32 (6), pp. 313-336; Kwon, I.W.G., Suh, T., Factors Affecting the Level of Trust and Commitment in Supply Chain Relationships (2004) The Journal of Supply Chain Management, 40 (1), pp. 4-14; Lambert, D.M., Shmeterman, M.A., Supplier Relationship Management as a Macro Business Process (2012) Supply Chain Management: An International Journal, 17 (3), pp. 337-352; Lambert, D.M., Emmelhainz, M.A., Gardner, J.T., Developing and Implementing Supply Chain Partnerships (1996) The International Journal of Logistics Management, 7 (2), pp. 1-18; Landeros, R., Reck, R., Plank, R.E., Maintaining Buyer–Supplier Partnerships (1995) Journal of Supply Chain Management, 31 (2), pp. 2-12; Lavie, D., Haunschild, P.R., Khanna, P., Organizational Differences, Relational Mechanisms, and Alliance Performance (2012) Strategic Management Journal, 33 (13), pp. 1453-1479; Lee, H.L., Whang, S., E-Business and Supply Chain Integration (2004) The Practice of Supply Chain Management, 62, pp. 123-138; Leong, G.K., Snyder, D.L., Ward, P.T., Research in the Process and Content of Manufacturing Strategy (1990) Omega, 18 (2), pp. 109-122; Liu, Y., Huang, Y., Luo, Y., Zhao, Y., How Does Justice Matter in Achieving Buyer–Supplier Relationship Performance? (2012) Journal of Operations Management, 30, pp. 355-367; Macbeth, D.K., Ferguson, N., (1994) Partnership Sourcing: An Integrated Supply Chain Approach, , London: Pitman, and; Mahlendorf, M.D., Rehring, J., Schäffer, U., Wyszomirski, E., Influencing Foreign Subsidiary Decisions Through Headquarter Performance Measurement Systems (2012) Management Decision, 50 (4), pp. 688-717; Maloni, M.J., Benton, W.C., Supply Chain Partnerships: Opportunities for Operations Research (1997) European Journal of Operational Research, 101 (3), pp. 419-429; Markoczy, L., National Culture and Strategic Change in Belief Formation (2000) Journal of International Business Studies, 31 (3), pp. 427-442; Maylor, H., Blackmon, K., (2005) Researching Business and Management, , Hampshire: Palgrave MacMillan, and; Mcevily, B., Perrone, V., Zaheer, A., Trust as an Organizing Principle (2003) Organization Science, 14 (1), pp. 91-103; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Anderen, B., Is Performance Measurement and Management Fit for the Future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Mendleson, N., Polonsky, M.J., Using Strategic Alliances to Develop Credible Green Marketing (1995) Journal of Consumer Marketing, 12 (2), p. 4; Mikler, J.J., Harrison, N.E., Varieties of Capitalism and Technological Innovation for Climate Change Mitigation (2012) New Political Economy, 17 (2), pp. 179-208; Miles, M.B., Huberman, A.M., (1994) Qualitative Data Analysis: A Sourcebook, , Beverly Hills: Sage, and; Mohr, J., Spekman, R., Characteristics of Partnership Success: Partnership Attributes, Communication Behavior, and Conflict Resolution Techniques (1994) Strategic Management Journal, 15 (2), pp. 135-152; Morgan, R.M., Hunt, S.D., The Commitment-Trust Theory of Relationship Marketing (1994) Journal of Marketing, 58 (3), pp. 20-38; Narayanan, S., Narasimhan, R., Schoenherr, T., Assessing the Contingent Effects of Collaboration on Agility Performance in Buyer–Supplier Relationships (2015) Journal of Operations Management, 33, pp. 140-154; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nyaga, G.N., Whipple, J.M., Lynch, D.F., Examining Supply Chain Relationships: Do Buyer and Supplier Perspectives on Collaborative Relationships Differ? (2010) Journal of Operations Management, 28 (2), pp. 101-114; O’Reilly, C.A., Chatman, J., Organizational Commitment and Psychological Attachment: The Effects of Compliance, Identification, and Internalization on Prosocial Behavior (1986) Journal of Applied Psychology, 71 (3), pp. 492-499; Ohmae, K., The Global Logic of Strategic Alliances (1989) Harvard Business Review, 67 (2), pp. 143-154; Pansiri, J., The Influence of Managers’ Characteristics and Perceptions in Strategic Alliance Practice (2005) Management Decision, 43 (9), pp. 1097-1113; Paulraj, A., Lado, A., Chen, I., Inter-Organizational Communication as a Relational Competency: Antecedents and Performance Outcomes in Collaborative Buyer–Supplier Relationships (2008) Journal of Operations Management, 26 (January), pp. 45-64; Pidduck, A.B., Issues in Supplier Partner Selection (2006) Journal of Enterprise Information Management, 19 (3), pp. 262-276; Rezaei, J., A Two-Way Approach to Supply Chain Partner Selection (2015) International Journal of Production Research, 53 (16), pp. 4888-4902; Ribbink, D., Grimm, C.M., The Impact of Cultural Differences on Buyer–Supplier Negotiations: An Experimental Study (2014) Journal of Operations Management, 32 (3), pp. 114-126; Richards, M., Yang, Y., Determinants of Foreign Ownership in International R & D Joint Ventures: Transaction Costs and National Culture (2007) Journal of International Management, 13 (2), pp. 110-130; Sahay, B.S., Understanding Trust in Supply Chain Relationships (2003) Industrial Management & Data Systems, 103 (8), pp. 553-563; Schein, E., (1985) Organizational Culture and Leadership, , New York: Harcourt Brace Jovanovich; Spekman, R.E., Sawhney, K., (1990) Toward a Conceptual Understanding of the Antecedents of Strategic Alliances, , Cambridge, MA:, and; Tse, D.K., Lee, K.H., Vertinsky, I., Wehrung, D.A., Does Culture Matter? A Cross-Cultural Study of Executives” Choice, Decisiveness, and Risk Adjustment in International Marketing (1988) Journal of Marketing, 52 (October), pp. 81-95; Vanpoucke, E., Vereecke, A., Boyer, K.K., Triggers and Patterns of Integration Initiatives in Successful Buyer–Supplier Relationships (2014) Journal of Operations Management, 32 (1-2), pp. 15-33; Veloso, F., Kumar, R., The Automotive Supply Chain: Global Trends and Asian Perspectives (2002) ERD Working Paper, , 3; Voss, C., Tsikriktsis, N., Frohlich, M., Case Research in Operations Management (2002) International Journal of Operations & Production Management, 22, pp. 195-219; Walter, A., Müller, T.A., Helfert, G., Ritter, T., Functions of Industrial Supplier Relationships and Their Impact on Relationship Quality (2003) Industrial Marketing Management, 32 (2), pp. 159-169; Weber, Y., Shenkar, O., Raveh, A., National and Corporate Cultural Fit in Mergers/Acquisitions: An Exploratory Study (1996) Management Science, 42 (8), pp. 1215-1227; Yeung, A.C.L., Strategic Supply Management, Quality Initiatives, and Organizational Performance (2008) Journal of Operations Management, 26 (4), pp. 490-502; Yin, R.K., (2003) Case Study Research: Design and Methods, , 3rd ed, London: Sage; Zaheer, A., Venkatraman, N., Relational Governance as an Interorganizational Strategy: An Empirical Test of the Role of Trust in Economic Exchange (1995) Strategic Management Journal, 16 (5), pp. 373-392; Zhang, L.H., Goffin, K., ‘Managing the Transition’–Supplier Management in Interational Joint Ventures in China (2001) International Journal of Physical Distribution & Logistics Management, 31 (2), pp. 74-95; Zhao, X., Flynn, B.B., Roth, A.V., Decision Sciences Research in China: A Critical Review and Research Agenda?Foundations and Overview (2006) Decision Sciences, 37 (4), pp. 451-496; Zhu, Q., Sarkis, J., Lai, K.H., Green Supply Chain Management: Pressures, Practices And Performance Within the Chinese Automobile Industry (2007) Journal of Cleaner Production, 15, pp. 1041-1052},
correspondence_address1={Huang, Y.; Cardiff Business School, United Kingdom; email: HuangY66@cardiff.ac.uk},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Maestrini2018934,
author={Maestrini, V. and Luzzini, D. and Caniato, F. and Maccarrone, P. and Ronchi, S.},
title={Measuring supply chain performance: a lifecycle framework and a case study},
journal={International Journal of Operations and Production Management},
year={2018},
volume={38},
number={4},
pages={934-956},
doi={10.1108/IJOPM-07-2015-0455},
note={cited By 24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043482986&doi=10.1108%2fIJOPM-07-2015-0455&partnerID=40&md5=9c60bb8177a3bba0c653741f09bc8793},
affiliation={Department of Management, Economics and Industrial Engineering, School of Management of Politecnico di Milano, Politecnico di Milano, Milan, Italy; Department of Marketing, Operations and Supply, Escuela de Alta Direccion y Administracion, Barcelona, Spain; Department of Information System and Supply Chain Management, Audencia Business School, Nantes, France},
abstract={Purpose: The purpose of this paper is to provide a conceptual framework of the supply chain performance measurement system (SCPMS) lifecycle (highlighting key activities of the design, implementation, use and review phases) and to investigate how the different actors involved in the SCPMS perceive the system and can act to allow for an effective adoption. Design/methodology/approach: The SCPMS lifecycle framework is developed grounding on performance measurement and supply chain (SC) management literature. To answer the two theory-building research questions, an in-depth case study involving seven firms across three tiers of the mass retail SC has been conducted. Findings: The empirical evidence highlighted potential SCPMS benefits (including operational performance improvement, higher control, lower information overloading and higher SC integration) and criticalities/barriers to an effective adoption (lack of industry standards, lack of trust regarding data reliability, SCPMS as a tool of power, lack of interest in the system and the performance metrics). Several elements characterizing the SCPMS lifecycle could increase its effective adoption: engaging SC partners in the design phase; relying on a rigorous primary data collection and performance measures calculation; and actively exploiting the tool through a systematic discussion on performance and establishing incentive/disincentives plans. Originality/value: The paper contributes to the conceptualization of SCPMSs and to clarify how to ensure an effective SCPMS adoption: apart from relationship-specific attributes, SCPMS lifecycle phases are suggested to have a key role. © 2018, Emerald Publishing Limited.},
author_keywords={Case study;  Lifecycle;  Performance measurement system;  Supply chain},
references={Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bhakoo, V., Choi, T., The iron cage exposed: institutional pressures and heterogeneity across the healthcare supply chain (2013) Journal of Operations Management, 31 (6), pp. 432-449; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organizational culture (2006) International Journal of Operations and Production Management, 26 (12), pp. 1325-1350; Botta-Genoulaz, V., Campagne, J.-P., Llerena, D., Pellegrin, C., (2010) Supply Chain Performance: Collaboration, Alignment and Coordination, , ISTE Ltd and John Wiley & Sons, Inc., London and Hoboken, NY; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: perception of participating managers (2002) International Journal of Operations and Production Management, 22 (11), pp. 1288-1310; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: an action research (2011) International Journal of Production Economics, 133 (2), pp. 751-760; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure SC performance (2000) Journal of Business Logistics, 211 (1), pp. 75-93; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: a process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: the constructs and measurements (2004) Journal of Operations Management, 23 (2), pp. 618-641; Chenhall, R., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: an explorative case study (2005) Accounting, Organization and Society, 30 (5), pp. 392-422; Cheung, M.S., Myers, M., Mentzer, J.T., Does relationship learning lead to relationship value? A cross-national supply chain investigation (2010) Journal of Operations Management, 28 (6), pp. 472-487; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships: the mediating role of socialization mechanisms (2008) International Journal of Operations and Production Management, 28 (3), pp. 238-258; Eisenhardt, K.M., Building theories from case study research (1989) Academy of Management Review, 14 (4), pp. 532-550; Eisenhardt, K.M., Graebner, M.E., Theory building from cases: opportunities and challenges (2007) Academy of Management Journal, 50 (1), pp. 25-32; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: a review of their consequences and a framework for research (2012) Management Accounting Research, 23 (2), pp. 79-119; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gutierrez, D.M., Scavarda, L.F., Fiorencio, L., Martins, R.A., Evolution of the performance measurement system in the logistics department of a broadcasting company: an action research (2015) International Journal of Production Economics, 160, pp. 1-12; Hald, K.S., Ellegaard, C., Supplier evaluation processes: the shaping and reshaping of supplier performance (2011) International Journal of Operations and Production Management, 31 (8), pp. 888-910; Henri, J.F., Management control systems and strategy: a resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558; Kannan, V.R., Tan, K.C., Supplier selection and assessment: their impact on business performance (2002) Journal of Supply Chain Management, 38 (3), pp. 11-21; Kocaoğlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2011) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Koh, S.C.L., Morris, J., Ebrahimi, S.M., Obayi, R., Integrated resource efficiency: measurement and management (2016) International Journal of Operations & Production Management, 36 (11), pp. 1576-1600; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system design: a case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: an empirical analysis (2014) Journal of Purchasing and Supply Management, 20 (2), pp. 113-129; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: a systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: a survey in the mines (2006) Management Accounting Research, 17 (3), pp. 315-339; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-217; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Micheli, P., Manzoni, J.F., Strategic performance measurement: benefits, criticalities and paradoxes (2010) Long Range Planning, 43 (4), pp. 465-476; Micheli, P., Mura, M., Executing strategy through comprehensive performance measurement systems (2017) International Journal of Operations & Production Management, 37 (4), pp. 423-443; Mondragon, A.E.C., Lalwani, C., Mondragon, C.E.C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Management: An International Journal, 16 (1), pp. 43-56; Moreira, M., Tjahjono, B., Applying performance measures to support decision-making in supply chain operations: a case of beverage industry (2016) International Journal of Production Research, 54 (8), pp. 2345-2365; Neely, A., The evolution of performance measurement research: developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Nyaga, G.N., Whipple, G.M., Lynch, D.F., Do buyer and supplier perspectives on collaborative relationships differ? (2010) Journal of Operations Management, 28 (2), pp. 101-114; Prahinski, C., Benton, W., Supplier evaluations: communication strategies to improve supplier performance (2004) Journal of Operations Management, 22 (1), pp. 39-62; Van Hoek, R.I., Measuring the unmeasurable – measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Voss, C., Tsikriktsis, N., Frolich, M., Case research in operations management (2002) International Journal of Operations & Production Management, 22 (2), pp. 195-219; Yin, R.K., (2003) Case Study Research: Design and Methods, , 3rd ed., Sage Publications, Thousand Oaks, CA; Zhao, X., Huo, B., Selen, W., Yeung, J.H.Y., The impact of internal integration and relationship commitment on external integration (2011) Journal of Operations Management, 29 (1), pp. 17-32; Giunipero, L.C., Hooker, R.E., Yoon, T.O.M.E., Brudvig, S., A decade of SCM literature: past, present and future implications (2008) Journal of Supply Chain Management, 44 (4), pp. 66-86},
correspondence_address1={Maestrini, V.; Department of Management, Italy; email: vieri.maestrini@polimi.it},
publisher={Emerald Group Holdings Ltd.},
issn={01443577},
language={English},
abbrev_source_title={Int. J. Oper. Prod. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ahmad20183,
author={Ahmad, K. and Zabri, S.M.},
title={The deployment of performance measurement system under the supply chain management environment: The case of Malaysian manufacturing companies},
journal={Management and Production Engineering Review},
year={2018},
volume={9},
number={1},
pages={3-12},
doi={10.24425/119395},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046810540&doi=10.24425%2f119395&partnerID=40&md5=09c472b9d51d359f7c76495d7fd4f48f},
affiliation={Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, 86400, Malaysia},
abstract={Performance measurement system in supply chain management (SCM) has been receiving increasing attention by business organizations as a way to evaluate efficiency in supply chain activities. Assessing the performance of supply chain uncovers the gap between planning and actual performance as to trace the potential problems thus ascertain necessary areas for improvement. This research aims to investigate the application of performance measurement system in SCM as well as exploring its relationship with organization’s performance among Malaysian manufacturing firms. By utilizing the questionnaire method, respondents involved were requested to indicate the extent to which they use a number of 24 selected performance measures that are related to SCM. The results show that the majority of the observed manufacturing firms utilize specific performance measurement tools in evaluating the supply chain performance. The current performance measurement techniques, the Balanced Score Card is adopted by around a quarter of the total responding firms followed by Supply Chain Operations References Model – SCOR, which attracts total users of only a fifth of the total respondents. In particular, performance measures under customer service category recorded the highest number of usage followed by cost-based performance measures and operations management. The results of this investigation also unveil few major points that are important to be highlighted. Firstly, the obtained outcomes of this study bring to light the significant relationships between the utilization of supply chain performance measures under customer service, operations management and organizational performance. In addition, this study discovered a significant correlation between the size of the organization and the extent of use of supply chain performance measures and how these two variables positively correlated. Lastly, the findings also suggested that the performance measures for SCM has been playing a crucial role in enhancing the performance of the organizations and is increasingly operated as the firms grow in size. Based on the brief highlighted points listed above, it is not an exaggeration to say that this research contributes new information to the body of knowledge in performance measurement system in SCM and its associations with organizational performance. © 2018 Polska Akademia Nauk. All rights reserved.},
author_keywords={Malaysia;  Manufacturing sector;  Performance;  Performance measures;  Supply chain management},
references={Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 553 (4), pp. 242-258; Aramyan, L., Lansink, A., Vorst, J., Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Lambert, D.M., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3-4), pp. 275-292; Bullinger, H.J., Kuhner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operation & Production Management, 21 (1-2), pp. 71-87; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L.A., Conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management – An International Journal, 8 (3), pp. 209-223; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-19; Grant, B., Lambert, M., Stock, R., Ellram, M., (2006) Fundamentals of Logistics Management, , McGraw-Hill, Maidenhead; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 873, pp. 333-347; Huang, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference SCOR model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (11-12), pp. 1192-1218; Li, S., Subba Rao, S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain practices (2005) Journal of Operations Management, 23, pp. 618-641; Hervani, A.A., Helms, M.M., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains, a framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Angerhofer, B.J., Angelides, M.C.T., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42, pp. 283-301; Saad, M., Patel, B., An investigation of supply chain performance measurement in the indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Johnson, M., Templar, S., The relationships between supply chain and firm performance: The development and testing of a unified proxy (2011) International Journal of Physical Distribution & Logistics Management, 41 (2), pp. 88-103; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Shi, M., Yu, W., Supply chain management and financial performance: Literature review and future directions (2013) International Journal of Operations & Production Management, 33 (10), pp. 1283-1317; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Ellram, L.M., Liu, B., The financial impact of supply management (2002) Supply Chain Management Review, 6 (6), pp. 30-37; Bryant, L., Jones, D.A., Widener, S.K., Managing value creation within the firm: An examination of multiple performance measures (2004) Journal of Management Accounting Research, 161, pp. 107-131; Baier, C., Hartmann, E., Moser, R., Strategic alignment and purchasing efficacy: An exploration analysis of their impact on financial performance (2008) Journal of Supply Chain Management, 44 (4), pp. 36-52; Fullerton, R.R., Wempe, W.F., Lean manufacturing non-financial performance, and financial performance (2009) International Journal of Operations & Production Management, 29 (3), pp. 214-240; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: A contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Ahmad, K., The adoption of management accounting practices in Malaysian small and medium-sized enterprises (2013) Asian Social Science, 10 (2), pp. 236-249; Ahmad, K., Mohamed Zabri, S., The effect of non-financial performance measurement system on firm performance (2016) International Journal of Economics and Financial, 6 (S6), pp. 50-54; Frezatti, F., The “economic paradigm” in management accounting return on equity and the use of various management accounting artifacts in a brazilian context (2007) Managerial Auditing Journal, 22 (5), pp. 514-532; Ismail, T.H., Performance evaluation measures in the private sector: Egyptian practice (2007) Managerial Auditing Journal, 22 (5), pp. 503-513; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437},
correspondence_address1={Ahmad, K.; Faculty of Technology Management and Business, Malaysia; email: kamilah@uthm.edu.my},
publisher={Polish Academy of Sciences},
issn={20808208},
language={English},
abbrev_source_title={Manag. Prod. Eng. Review},
document_type={Article},
source={Scopus},
}

@ARTICLE{Traverso2018597,
author={Traverso, M. and Bell, L. and Saling, P. and Fontes, J.},
title={Towards social life cycle assessment: a quantitative product social impact assessment},
journal={International Journal of Life Cycle Assessment},
year={2018},
volume={23},
number={3},
pages={597-606},
doi={10.1007/s11367-016-1168-8},
note={cited By 33},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986243952&doi=10.1007%2fs11367-016-1168-8&partnerID=40&md5=8cd17d9fc4d0fec0c40744cc44a83598},
affiliation={BMW Group, Knorrstraße 147, Munich, 80788, Germany; The Goodyear Tire & Rubber Company, Akron, OH, United States; BASF SE - We create chemistry, Ludwigshafen am Rhein, Germany; PRé Sustainability, Amersfoort, Netherlands},
abstract={Purpose: The main goal of this paper is to present the feasibility of the quantitative method presented in the Product Social Impact Assessment (PSIA) handbook throughout a case study. The case study was developed to assess the social impacts of a tire throughout its entire life cycle. We carried out this case study in the context of the Roundtable for the Product Social Metrics project in which 13 companies develop two methodologies, a qualitative and a quantitative one, for assessing the social impact of product life cycle. Methods: The quantitative methodology implemented for assessing the social impact of a Run On Flat tire mounted in a BMW 3 series consists of 26 indicators split in three groups. Each group represents a stakeholder group. Primary data of the quantitative indicators were collected along the product life cycle of the Run On Flat by involving the companies, which owned the main steps of the product life cycle. Throughout this case study, an ideal/worst-case scenario was defined for the distance-to-target approach to compare the social performances of more products when they are available. Results and discussion: The implementation of the PSIA quantitative method to a Run On Flat illustrated the necessity to have a referencing step in order to interpret the results. This is particularly important when the results are used to support decision-making process in which no experts are involved. It frequently happens in a big company where the management level has to take often decisions on different topics. Reference values were defined using ideal or worst-case-target scenarios (Fontes et al. 2014). For those topics where it was possible, an ideal/ethical scenario was defined, e.g., 0 h of child labor per product. In other cases, we defined a worst-case scenario, e.g., 0 training hours per product. It was then possible to interpret the results using a distance-to-target approach. A matrix was developed in the case study for identifying in which step of the product life cycle data is not available; that means we need more transparency in the supply chain. Conclusions: Each value of the matrix can be compared to the ideal/worst scenario to compare the step to each other and to identify along the product life cycle which step and the relative supplier that needs further measures to improve the product performance. Furthermore, a quantitative value for each indicator related to the product life cycle is calculated and compared with the ideal/worst scenario. The case study on Run On Flat represents the first implementation of the quantitative method of PSIA. © 2016, Springer-Verlag Berlin Heidelberg.},
author_keywords={Product social impact assessment, Reference scenario, Social life cycle assessment, Stakeholder groups},
keywords={rubber;  steel, car driving;  decision making;  feasibility study;  flat tire;  human;  life cycle;  life cycle assessment;  motor vehicle tire;  performance measurement system;  priority journal;  product development;  product life cycle;  product social impact assessment;  program sustainability;  quantitative analysis;  Review;  rubber industry;  social aspect;  social life;  sociometric status;  stakeholder engagement;  wellbeing;  working time},
references={Benoît, C., Norris, G.A., Valdivia, S., Ciroth, A., Moberg, A., Bos, U., Prakash, S., Beck, T., The guidelines for social life cycle assessment of products: just in time! (2010) Int J Life Cycle Assess, 15, pp. 156-163; Benoît-Norris, C., Traverso, M., Valdivia, S., Vickery-Niederman, G., Franze, J., Azuero, L., Ciroth, A., Aulisio, D., (2013) The Methodological Sheets for Social Life Cycle Assessment—a practical support to implement S-LCA of Product. Oral Presentation and proceedings of SETAC Europe 19th LCA Case Study Symposium in Rome, Italy, 11–13 November 2013; Benoît-Norris, G., Vickery-Niederman, G., Valdivia, S., Franze, J., Traverso, M., Ciroth, A., Mazijn, B., Introducing the UNEP/SETAC methodological sheets for subcategories of social LCA (2011) Int J Life Cycle Assess, 16 (7), pp. 682-690; Benoit-Norris, C., Cavan, D.A., Norris, G., Identifying social impacts in product supply chains: overview and application of the social hotspot database (2012) Sustainability, 4 (12), pp. 1946-1965; Ciroth, A., Franze, J., (2011) LCA of an Ecolabeled notebook—consideration of social and environmental impacts along the entire life cycle, ISBN 978–1–4466-0087-0, Berlin, life cycle, http://www.greendelta.com/uploads/media/LCA_laptop_final.pdf, viewed March 2013; Dreyer, L., Hauschild, M., Schierbeck, J., A framework for social life cycle impact assessment (2006) Int J Life Cycle Assess, 11 (2), pp. 88-97; Dreyer, L.C., Hauschild, M.Z., Schierbeck, J., Characterisation of social impacts in LCA (2010) Int J Life Cycle Assess, 15 (3), pp. 247-259; (2006) ISO 14040, , ISO, Genova; Finkbeiner, M., Schau, E., Lehmann, A., Traverso, M., Towards life cycle sustainability assessment (2010) Sustainability, 2 (10), pp. 3309-3322; Fontes, J., Bolhuis, A., Bogaers, K., Saling, P., van Gelder, R., Traverso, M., Das Gupta, J., Challis, D., (2014) Handbook of Product Social Impact Assessment, , http://product-social-impact-assessment.com; (2006) Handbook of Product Social Impact Assessment Version 3.0, , Http://product-social-impact-assessment.com/, Fontes et al (2016). visited on 22.01.2016; (2016) Goodyear Website, , https://www.goodyear.com/en-US/tire-warranty/tread-life, visited on 22.01.2016; Hunkeler, D., Societal LCA methodology and case study (2006) Int J Life Cycle Assess, 11 (6), pp. 371-382; Jørgensen, A., Lai, L.C.H., Hauschild, M.Z., Assessing the validity of impact pathways for child labour and well-being in social life cycle assessment (2010) Int J Life Cycle Assess, 15 (1), pp. 5-16; Klöpffer, W., Life-Cycle based methods for sustainable product development (2003) Int J Life Cycle Assess, 8 (3), pp. 157-159; Kloepffer, W., Life cycle sustainability assessment of products (2008) Int J Life Cycle Assess, 13 (2), pp. 89-95; Lehmann, A., Zschieschang, E., Traverso, M., Finkbeiner, M., Schebek, L., Social aspects for sustainability assessment of technologies—challenges for social life cycle assessment (SLCA) (2013) Int J Life Cycle Assess, 18 (8), pp. 1581-1592; Martínez-Blanco, J., Lehmann, A., Muñoz, P., Antón, A., Traverso, M., Rieradevall, J., Finkbeiner, M., Application challenges for the social Life Cycle Assessment of fertilizers within life cycle sustainability assessment (2014) J Clean Prod, 69 (15), pp. 34-48; Neugebauer, S., Traverso, M., Scheumann, R., Chang, J., Wolf, K., Finkbeienr, M., Impact pathways to address social well-being and social justice in SLCA—fair wage and level of education (2014) Sustainability, 6 (8), pp. 4457-4839; O’Brien, M., Doig, A., Clift, R., Social and environmental life cycle assessment (SELCA) (1996) Int. J Life Cycle Assess, 1 (4), pp. 231-237; Ramirez, P., Petti, L., Haberland, N.T., Ugaya, C., Subcategory assessment method for social life cycle assessment. Part 1: methodological framework (2014) Int J Life Cycle Assess, 19 (8), pp. 1515-1523; Saling, P., Maisch, R., Silvani, M., König, N., Assessing the environmental-hazard potential for life cycle assessment, eco-efficiency and SEEbalance® (2005) Int J Life Cycle Assess, 10 (5), pp. 364-371; Traverso, M., Asdrubali, F., Francia, A., Finkbeiner, M., Towards life cycle sustainability assessment: an implementation to photovoltaic modules (2012) Int J Life Cycle Assess, 17 (8), pp. 1068-1079. , COI: 1:CAS:528:DC%2BC38XhtFCnu73L; Traverso, M., Finkbeiner, M., Jørgensen, A., Schneider, L., Life cycle sustainability dashboard (2012) J Ind Ecol, 16, pp. 680-688; Traverso, M., Wagner, V., Trouvay, B., Kluge, J., Geckeler, F., Brattig, S., (2013) A comprehensive approach of sustainability assessment of product in the automobile sector: challenges and benefits. Proceeding of Life Cycle Management conference 2013, Invited as presenter; Ugaya, C., Brones, F., Corrêa, S., (2011) S-LCA: preliminary results of Natura’s Cocoa soap bar. Proceedings of Life Cycle Mangement conference 2011, Berlin; (2009) Guidelines for Social Life Cycle Assessment of Products, , United Nations Environment Program, Paris SETAC Life Cycle Initiative United Nations Environment Programme; (2013) The Methodological Sheets for Subcategories in Social Life Cycle Assessment (S-LCA), , United Nations Environment Program, Paris SETAC Life Cycle Initiative United Nations Environment Programme},
correspondence_address1={Traverso, M.; BMW Group, Knorrstraße 147, Germany; email: marziatraverso@gmail.com},
publisher={Springer Verlag},
issn={09483349},
coden={IJLCF},
language={English},
abbrev_source_title={Int. J. Life Cycle Assess.},
document_type={Review},
source={Scopus},
}

@CONFERENCE{Ramakrishnan201862,
author={Ramakrishnan, P. and Ma, Y.},
title={Adaptive supply chain systems – IoT based conceptual framework},
journal={ACM International Conference Proceeding Series},
year={2018},
pages={62-68},
doi={10.1145/3194188.3194205},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047542278&doi=10.1145%2f3194188.3194205&partnerID=40&md5=c9e687e189c11bd088be0fd7442a6d70},
affiliation={Department of Mechanical Engineering, University Of Alberta, Edmonton, AB, Canada},
abstract={Internet of Things (IoT) has evolved in the recent days, connecting almost everything, starting from electronic devices to people, products, and machines together. Data collected from these connections serve as the basis for solving any real-world problems. In a supply chain network, several data are collected across different players and at different levels. The collected data are processed using different techniques to transform data into meaningful information. This gathered information is internal to the organization, helps in improving the internal or immediate supply chain. The information collected can be stored centralized where all the supply chain partners can access and share required information, leading to performance improvement of the entire [1] supply chain. A research framework is proposed which aims to achieve performance improvement along the entire supply chain. The framework starts by explaining how data collection can be done smartly using IoT. Next, how big data analytical tools can be used to transform data into information is discussed. Then, a mathematical model is proposed to measure the performance of internal and immediate supply chain. Then, how information sharing across the different supply chain partners can be achieved using state-of-the-art technologies is explained. Mathematical model proposed is expanded to measure the performance of the external and the entire supply chain. A case study was done to prove the proposed framework. Lastly how this research paves way for future research is discussed. © 2018 Association for Computing Machinery.},
author_keywords={Big data;  Internet of things;  Supply chain management;  Supply chain performance measurement},
keywords={Big data;  Chains;  Electronic commerce;  Information dissemination;  Supply chain management, Conceptual frameworks;  Internet of Things (IOT);  Performance improvements;  State-of-the-art technology;  Supply chain network;  Supply chain partners;  Supply chain performance measurements;  Supply chain systems, Internet of things},
references={Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M., Internet of things for smart cities (2014) IEEE Internet of Things Journal, 1 (1), pp. 22-32; Rose, K., Eldridge, S., Chapin, L., The internet of things: An overview (2015) The Internet Society (ISOC), pp. 1-50; Boualouache, A.E., Nouali, O., Moussaoui, S., Derder, A., A BLE-based data collection system for IoT (2015) The New Technologies of Information and Communication (NTIC), 2015 First International Conference on, , Paper; Hromic, H., Le Phuoc, D., Serrano, M., Antonić, A., Žarko, I.P., Hayes, C., Decker, S., Real time analysis of sensor data for the internet of things by means of clustering and event processing (2015) The Communications (ICC), 2015 IEEE International Conference on, , Paper; Hemant, G., Subhas, M., Xiang, G., Nagender, S., WSN-and IOT-based smart homes and their extension to smart building (2015) Sensors, 15 (5), pp. 10350-10379; Henschen, D., (2014) 16 Top Big Data Analytics Platforms, , https://www.informationweek.com/bigdata/big-data-analytics/16-top-big-data-analytics-platforms/d/d-id/1113609; Soldatos, J., Kefalakis, N., Hauswirth, M., Serrano, M., Calbimonte, J.-P., Riahi, M., Žarko, I.P., Openiot: Open source internet-of-things in the cloud (2015) Interoperability and Open-Source Solutions for The Internet of Things, pp. 13-25. , Springer; Liu, C., Yang, C., Zhang, X., Chen, J., External integrity verification for outsourced big data in cloud and IoT: A big picture (2015) Future Generation Computer Systems, 49, pp. 58-67; Dijkman, R.M., Sprenkels, B., Peeters, T., Janssen, A., Business models for the internet of things (2015) International Journal of Information Management, 35 (6), pp. 672-678; Professional, L., (2016) Apple Experimenting with Ultra Fast Light-Based Li-Fi Wireless Data for Future iPhones, , https://www.ledprofessional.com/technology/electronics/apple-experimenting-with-ultra-fast-light-based-li-fi-wireless-data-for-future-iphones; Whitmore, A., Agarwal, A., Da Xu, L., The internet of things—A survey of topics and trends (2015) Information Systems Frontiers, 17 (2), pp. 261-274; Cheng, B., Longo, S., Cirillo, F., Bauer, M., Kovacs, E., Building a big data platform for smart cities: Experience and lessons from santander (2015) The Big Data (BigData Congress), 2015 IEEE International Congress on, , Paper; Riggins, F.J., Wamba, S.F., Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics (2015) The System Sciences (HICSS), 2015 48th Hawaii International Conference on, , Paper; Sun, Y., Song, H., Jara, A.J., Bie, R., Internet of things and big data analytics for smart and connected communities (2016) IEEE Access, 4, pp. 766-773; Mineraud, J., Mazhelis, O., Su, X., Tarkoma, S., A gap analysis of internet-of-things platforms (2016) Computer Communications, 89, pp. 5-16; Kumarage, H., Khalil, I., Alabdulatif, A., Tari, Z., Yi, X., Secure data analytics for cloud-integrated internet of things applications (2016) IEEE Cloud Computing, 3 (2), pp. 46-56; Alam, F., Mehmood, R., Katib, I., Albeshri, A., Analysis of eight data mining algorithms for smarter internet of things (IoT) (2016) Procedia Computer Science, 98, pp. 437-442; Amatriain, X., Pujol, J.M., Data mining methods for recommender systems (2015) Recommender Systems Handbook, pp. 227-262. , Springer; Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M., Internet of things: A survey on enabling technologies, protocols, and applications (2015) IEEE Communications Surveys & Tutorials, 17 (4), pp. 2347-2376; Hipp, R., (2015) SQL for The IoT; Meola, A., (2016) How IoT Logistics Will Revolutionize Supply Chain Management, , http://www.businessinsider.com/internet-of-things-logisticssupply-chain-management-2016-10?IR=T; Business Insider, U., (2016) Verizon'S Telogis and John Deere Have Partnered on Connected Construction Equipment, , http://uk.businessinsider.com/verizons-telogis-partners-with-john-deere-to-analyze-tractor-data-2016-11?IR=T; Amine, C., Leyton, F., (2017) Internet of Things (IoT): To Revolutionise Supply Chain Management, , http://www.leyton.com/blog/?p=1286-internet-of-things-iot-to-revolutionizse-supply-chain-management; Shrouf, F., Miragliotta, G., Energy management based on internet of things: Practices and framework for adoption in production management (2015) Journal of Cleaner Production, 100, pp. 235-246; Qu, T., Lei, S., Wang, Z., Nie, D., Chen, X., Huang, G.Q., IoT-based real-time production logistics synchronization system under smart cloud manufacturing (2016) The International Journal of Advanced Manufacturing Technology, 84 (1-4), pp. 147-164; Atanasov, I., Nikolov, A., Pencheva, E., Dimova, R., Ivanov, M., An approach to data annotation for internet of things (2015) International Journal of Information Technology and Web Engineering (IJITWE), 10 (4), pp. 1-19; Babiceanu, R.F., Seker, R., Manufacturing operations, internet of things, and big data: Towards predictive manufacturing systems (2015) Service Orientation in Holonic and Multi-Agent Manufacturing, pp. 157-164. , Springer; Qin, Y., Sheng, Q.Z., Curry, E., Matching over linked data streams in the internet of things (2015) IEEE Internet Computing, 19 (3), pp. 21-27; Fang, S., Xu, L., Zhu, Y., Liu, Y., Liu, Z., Pei, H., Zhang, H., An integrated information system for snowmelt flood early-warning based on internet of things (2015) Information Systems Frontiers, 17 (2), pp. 321-335; Weinberg, B.D., Milne, G.R., Andonova, Y.G., Hajjat, F.M., Internet of things: Convenience vs. Privacy and secrecy (2015) Business Horizons, 58 (6), pp. 615-624; Hossain, M.S., Muhammad, G., Cloud-assisted industrial internet of things (iiot)–enabled framework for health monitoring (2016) Computer Networks, 101, pp. 192-202; Gaur, A., Scotney, B., Parr, G., McClean, S., Smart city architecture and its applications based on IoT (2015) Procedia Computer Science, 52, pp. 1089-1094; Gnimpieba, Z.D.R., Nait-Sidi-Moh, A., Durand, D., Fortin, J., Using internet of things technologies for a collaborative supply chain: Application to tracking of pallets and containers (2015) Procedia Computer Science, 56, pp. 550-557; Barricelli, B.R., Valtolina, S., Designing for end-user development in the internet of things (2015) The International Symposium on End User Development, , Paper; Pasquier, T.F.-M., Singh, J., Bacon, J., Information flow control for strong protection with flexible sharing in paas (2015) The Cloud Engineering (IC2E), 2015 IEEE International Conference on, , Paper; Kaivooja, J., Virtanen, P., Jalonen, H., Stenvall, J., The effects of the internet of things and big data to organizations and their knowledge management practices (2015) The International Conference on Knowledge Management in Organizations, , Paper; Ratasuk, R., Vejlgaard, B., Mangalvedhe, N., Ghosh, A., NB-IoT system for M2M communication (2016) The Wireless Communications and Networking Conference (WCNC), 2016 IEEE, , Paper; He, D., Zeadally, S., An analysis of RFID authentication schemes for internet of things in healthcare environment using elliptic curve cryptography (2015) IEEE Internet of Things Journal, 2 (1), pp. 72-83},
publisher={Association for Computing Machinery},
isbn={9781450363686},
language={English},
abbrev_source_title={ACM Int. Conf. Proc. Ser.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Equbal20181401,
author={Equbal, M.A. and Equbal, A. and Kumari, A. and Ohdar, R.},
title={A comprehensive model for supply chain performance measurement: Application in the coal beneficiation plant of steel manufacturing company},
journal={IEEE International Conference on Industrial Engineering and Engineering Management},
year={2018},
volume={2017-December},
pages={1401-1406},
doi={10.1109/IEEM.2017.8290123},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045285773&doi=10.1109%2fIEEM.2017.8290123&partnerID=40&md5=a31cdba558d1f9285b02c9c089fc6b9b},
affiliation={Department of Mechanical Engineering, Cambridge Institute of Technology, Ranchi, India; Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Ranchi, India; Department of Computer Application, Marwari College, Ranchi, India; Department of Forge Technology, National Institute of Foundry and Forge Technology, Ranchi, India},
abstract={Companies are looking for a single solution or a set of matrices that they can apply to measure their supply chain performance but literature reveals that such a single solution does not exist. An important component in supply chain design and analysis is the establishment of an appropriate performance measurement and evaluation system. An effective supply chain performance measurement system must align with a company's own supply chain processes. In this paper, a comprehensive supply chain performance measurement and evaluation (CSCPME) methodology based on the study of several established performance measurement models is developed. The critical performance measures reflect the five specific criteria, viz. effectiveness, efficiency, quality, productivity, and profitability. The proposed CSCPME is applied to a real case study for one of its performance measure (effectiveness) evaluation. The developed CSCPME methodology may provide a framework for companies to build their in-house supply chain performance measurement systems. © 2017 IEEE.},
author_keywords={CSCPME;  Effectiveness;  Efficiency;  Productivity;  Profitability;  Quality;  Supply Chain Performance Measurement},
keywords={Coal industry;  Efficiency;  Image quality;  Productivity;  Profitability;  Supply chain management, CSCPME;  Effectiveness;  Performance measurement and evaluation;  Performance measurements;  Steel manufacturing;  Supply chain performance;  Supply chain performance measurements;  Supply chain process, Supply chains},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Huang, S.H., Sheoran, S.K., Keskar, H., Computerassisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48, pp. 377-394; Aramyan, L.H., Lansink, A.O., Vorst, J., Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Lin, Y., Wang, Y., Yu, C., Investigating the drivers of the innovation in channel integration and supply chain performance: A strategy orientated perspective (2010) International Journal of Production Economics, 127 (2), pp. 320-332; Chen, C., Yan, H., Network DEA model for supply chain performance evaluation (2011) European Journal of Operational Research, 213 (1), pp. 147-155; Halman, J.I.M., Voordijk, J.T., Balanced framework for measuring performance of supply chains in house building (2012) Journal of Construction Engineering and Management, 138 (12), pp. 1444-1450; Thunberg, M., Persson, F., Using the SCOR model's performance measurements to improve construction logistics (2013) Production Planning & Control, 25 (13-14), pp. 1065-1078; Frederico, G.F., Martins, R.A., Performance measurement systems for supply chain management: How to manage its maturity (2014) International Journal of Supply Chain Management, 3 (2), pp. 24-30; Charkha, P.G., Jaju, S.B., Identification of performance measures for textile supply chain: Case of small & medium size enterprise (2015) International Journal of Supply Chain Management, 4 (3), pp. 50-58; Ray, P.K., Sahu, S., Productivity Measurement through Multi-criteria Decision Making (1990) Engineering Cost and Decision Making, 20, pp. 151-163},
publisher={IEEE Computer Society},
issn={21573611},
isbn={9781538609484},
language={English},
abbrev_source_title={IEEE Int. Conf. Ind. Eng. Eng. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Katiyar201848,
author={Katiyar, R. and Barua, M.K. and Meena, P.L.},
title={Analysing the Interactions Among the Barriers of Supply Chain Performance Measurement: An ISM with Fuzzy MICMAC Approach},
journal={Global Business Review},
year={2018},
volume={19},
number={1},
pages={48-68},
doi={10.1177/0972150917713283},
note={cited By 31},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035123390&doi=10.1177%2f0972150917713283&partnerID=40&md5=55438eabb31028aa77347fa63ba66efc},
affiliation={School of Management, IMS Unison University, Dehradun, Uttarakhand, India; Department of Management Studies, Indian Institute of Technology, Roorkee, Uttarakhand, India; School of Management, New York Institute of Technology, Wisser Library, Old Westbury, NY, United States},
abstract={The purpose of this article is to explore key barriers in measuring supply chain performance (SCP). In today’s competitive business environment, companies are facing various issues in measuring SCP because of various constraints and these constraints act as a barrier. This article first explores the key barriers that affect SCP measurement (SCPM) and then investigates the mutual relationships among these barriers. Interpretive Structural Modeling (ISM) with fuzzy Matrice d’ Impacts Croises Multiplication Appliqué an Classement (MICMAC) approach is employed to investigate the interrelationships among key barriers. The results show that the lack of awareness related to SCPM is one of the critical barriers. Further, various strategies are suggested to eradicate the critical barriers in measuring SCP for the managers. © 2018, © 2018 International Management Institute, New Delhi.},
author_keywords={barriers;  fuzzy MICMAC;  interpretive structural modeling;  Supply chain performance measurement},
references={Andrews-Speed, P., (2004) Energy policy and regulation in the People’s Republic of China, , The Hague/London/New York, NY, Kluwer Law International; Aragon-Correa, J.A., Strategic proactivity and firm approach to the natural environment (1998) The Academy of Management Journal, 41 (5), pp. 556-567; Balon, V., Sharma, A.K., Barua, M.K., Assessment of barriers in green supply chain management using ISM: A case study of the automobile industry in India (2016) Global Business Review, 17 (1), pp. 116-135; Bansal, P., Roth, K., Why companies go green: A model of corporate ecological responsiveness (2000) Academy of Management Journal, 43 (4), pp. 717-736; Bender, P.S., Debunking five supply chain myths (2000) Supply Chain Management Review, 4 (1), pp. 52-58; Berry, M.A., Rondinelli, D.A., Proactive corporate environmental management: A new industrial revolution (1998) Academy of Management Executive, 12 (2), pp. 38-50; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Burange, L.G., Yamini, S., (2008) Competitiveness of firms in Indian automobile industry, , Mumbai, India, Department of Economics, University of Mumbai; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Charan, P., Shankar, R., Baisya, R.K., Modelling the barriers of supply chain performance measurement system implementation in the Indian automobile supply chain (2009) International Journal of Logistics Systems and Management, 5 (6), pp. 614-630; Chatterjee, A.K., Mateen, A., Chakraborty, A., On the equivalence of some supply chain coordination models (2015) Opsearch, 52 (2), pp. 392-400; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: The constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Dubey, R., Singh, T., Gupta, O.K., Impact of agility, adaptability and alignment on humanitarian logistics performance: Mediating effect of leadership (2015) Global Business Review, 16 (5), pp. 812-831; Duperrin, J.C., Godet, M., (1973) Methode de hierarhisation des elements d’un systeme economique du [Methods for the Hierarchization of the Elements of a System, Economic report of the CEA], pp. 45-51. , Paris, CEA, (; Faisal, M.N., Banwet, D.K., Shankar, R., Supply chain risk mitigation: Modeling the enablers (2006) Business Process Management Journal, 12 (4), pp. 535-552; Farris, D.R., Sage, A.P., On the use of interpretive structural modeling for worth assessment (1975) Computers & Electrical Engineering, 2 (2-3), pp. 149-174; Foster, T.A., Muller, E.J., Third parties: Your passport to profits (1990) Distribution, 89 (10), pp. 31-32; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) International Journal of Production Economics, 134 (1), pp. 177-187; Gant, R.M., Prospering in dynamically-competitive environments: Organizational capability as knowledge integration (1996) Organizational Science, 7 (4), pp. 375-387; Gehani, R., Significance of cross-cultural trust in streamlining supply-chains for global enterprises (2000) Global Business Review, 1 (2), pp. 173-192; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hamel, G., Prahalad, C.K., Strategic intent (1989) Harvard Business Review, 67 (3), pp. 63-76; Hillary, R., (2000) Small and medium sized enterprises and the environment: Business imperatives, pp. 11-22. , Sheffield, Greenleaf Publishing, (; Hosseini, A., Identification of green management of system’s factors: A conceptualized model (2007) International Journal of Management Science and Engineering Management, 2 (3), pp. 221-228; Jharkharia, S., Shankar, R., Supply chain management: Some sectoral dissimilarities in the Indian manufacturing industry (2006) Supply Chain Management: An International Journal, 11 (4), pp. 345-352; Joshi, D., Nepal, B., Rathore, A.P.S., Sharma, D., On supply chain competitiveness of Indian automotive component manufacturing industry (2013) International Journal of Production Economics, 143 (1), pp. 151-161; Kilpatrick, J., Factor, R., Logistics in Canada survey: Tracking year 2000 supply chain issues and trends (2000) Materials Management & Distribution, 45 (1), pp. 16-20; Kumar, G., Banerjee, R.N., Supply chain collaboration index: An instrument to measure the depth of collaboration (2014) Benchmarking: An International Journal, 21 (2), pp. 184-204; Kuo, T.C., Ma, H.Y., Huang, S.H., Hu, A.H., Huang, C.S., Barrier analysis for product service system using interpretive structural model (2009) The International Journal of Advanced Manufacturing Technology, 49 (1-4), pp. 407-417; Lin, C.-Y., Adoption of green supply in Taiwan logistic industry (2007) Journal of International Management Studies, 2 (2), pp. 90-98. , www.jimsjournal.org/10%2520Chieh-Yu%20Lin.pdf, Retrieved from; Lin, C.-Y., Ho, Y.-H., An empirical study on logistics service providers’ intention to adopt green innovations (2008) Journal of Technology & Innovation, 3 (1), pp. 17-26; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Luthra, S., Kumar, V., Kumar, S., Haleem, A., Barriers to implement green supply chain management in automobile industry using interpretive structural modeling technique: An Indian perspective (2011) Journal of Industrial Engineering and Management, 4 (2), pp. 231-257; Mathiyazhagan, K., Govindan, K., NoorulHaq, A., Geng, Y., An ISM approach for the barrier analysis in implementing green supply chain management (2013) Journal of Cleaner Production, 47, pp. 283-297; Meena, P.L., Sarmah, S.P., Development of a supplier satisfaction index model (2012) Industrial Management & Data Systems, 112 (8), pp. 1236-1254; Min, H., Galle, W.P., Green purchasing practices of US firms (2001) International Journal of Operations & Production Management, 21 (9), pp. 1222-1238; (2006) Automotive mission plan 2006–2016, , New Delhi, Government of India; Moberg, C.R., Speh, T.W., Freese, T., SCM: Making the vision a reality (2003) Supply Chain Management Review, 7 (5), pp. 34-39. , September, October; Monczka, R.M., Morgan, J.P., What’s wrong with supply chain management? (1997) Purchasing, 122 (1), pp. 69-72; Mudgal, R.K., Shankar, R., Talib, P., Raj, T., Modelling the barriers of green supply chain practices: An Indian perspective (2010) International Journal of Logistics Systems and Management, 7 (1), pp. 81-107; Orsato, R., Competitive environmental strategies: When does it pay to be green? (2006) California Management Review, 48 (2), pp. 127-143; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14 (4), pp. 335-346; Perron, G.M., (2005) Barriers to environmental performance improvements in Canadian SMEs, , Caa, Dalhousie University; Porter, M.E., Linde, C., Green and competitive: Ending the stalemet (1995) Harvard Business Review, 73 (5), pp. 120-134. , September, October, & vaer; Qureshi, M.N., Kumar, D., Kumar, P., An integrated model to identify and classify the key criteria and their role in the assesment of 3PL services (2008) Asia Pacific Journal of Marketing and Logistics, 20 (2), pp. 1355-5855; Ramdas, K., Spekman, R.E., Chain or shackles: Understanding what drives supply-chain performance (2000) Interfaces, 30 (4), pp. 3-21; Ravi, V., Shankar, R., Analysis of interactions among the barriers of reverse logistics (2005) Technological Forecasting and Social Change, 72 (8), pp. 1011-1029; Ren, C., Chai, Y., Liu, Y., Active performance management in supply chains (2004) IEEE International Conference on Systems, Man and Cybernetics, 7, pp. 6036-6041; Rogers, D.S., Tibben-Lembke, R.S., (1998) Going backwards: Reverse logistics trends and practices, , Pittsburgh, PA, Reverse Logistics Executive Council; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Sage, A.P., (1977) Interpretive structural modeling: Methodology for large-scale systems, pp. 91-164. , New York, NY, McGraw-Hill, (; Sarkar, A., Mohapatra, P.K.J., Evaluation of supplier capability and performance: A method for supply base reduction (2006) Journal of Purchasing and Supply Management, 12 (3), pp. 148-163; Saxena, J.P., Sushil, P., Scenario building: A critical study of energy conservation in the Indian cement industry (1992) Technological Forecasting and Social Change, 41 (2), pp. 121-146. , Vrat; Scupola, A., The adoption of Internet commerce by SMEs in the South of Italy: An environmental, technological and organizational perspective (2003) Journal of Global Information Technology Management, 6 (1), pp. 52-71; Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68; Sharma, H.D., Gupta, A.D., The objectives of waste management in India: A futures inquiry (1995) Technological Forecasting and Social Change, 48 (3), pp. 285-309. , Sushil; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shore, B., Venkatachalam, A.R., Evaluating the information sharing capabilities of supply chain partners: A fuzzy logic model (2003) International Journal of Physical Distribution & Logistics Management, 33 (9), pp. 804-824; Singh, R.K., Garg, S.K., Deshmukh, S.G., Strategy development for competitiveness: A study on Indian auto component sector (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 285-304; Thompson, D., (2002) Tools for environmental management: A practical introduction and guide, p. 452. , Gabriola Island, BC, New Society Publishers, (p; Van den Bosch, F.A.J., Van Riel, C.B.M., Buffering and bridging as environmental strategies of firms (1998) Business Strategies and the Environment, 7 (1), pp. 24-31; Walker, H., Sisto, L.D., McBrain, D., Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 69-85; Wang, G., Wang, Y., Zhao, T., Analysis of interactions among the barriers to energy saving in China (2008) Energy Policy, 36 (1), pp. 1879-1889; Warfield, J.N., Developing interconnection matrices in structural modelling (1974) IEEE Transactions on Systems, Man and Cybernetics, 4 (1), pp. 81-87; Welch, J., Wietfeldt, P., How to leverage your systems investment (2005) Supply Chain Management Review, 9 (8), pp. 24-30. , November; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modelling (2007) Industrial Management & Data Systems, 107 (3), pp. 361-381; Yeh, D.Y., Cheng, C.H., Chi, M.L., A modified two-tuple FLC model for evaluating the performance of SCM: By the six sigma DMAIC process (2007) Applied Soft Computing, 7 (3), pp. 027-1034; Zhihong, W., (2004) Economic development and energy issues in China, pp. 13-14. , February, 13, 14, the First KEIO-UNU-JFIR Panel Meeting, The 21st Century Center of Excellence Program, Economic Development and Human Security, How to Improve Governance at the Inter-Governmental, Governmental and Private Sector Levels in Japan and Asia Presented at; Zhu, Q., Sarkis, J., The moderating effects of institutional pressures on emergent green supply chain practices and performance (2007) International Journal of Production Research, 45 (18-19), pp. 4333-4355},
correspondence_address1={Meena, P.L.; School of Management, Room 343, United States; email: plmeena1@gmail.com},
publisher={Sage Publications India Pvt. Ltd},
issn={09721509},
language={English},
abbrev_source_title={Global Bus. Rev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Szymczak2018759,
author={Szymczak, M. and Ryciuk, U. and Leończuk, D. and Piotrowicz, W. and Witkowski, K. and Nazarko, J. and Jakuszewicz, J.},
title={Key factors for information integration in the supply Chain – measurement, technology and information characteristics},
journal={Journal of Business Economics and Management},
year={2018},
volume={19},
number={5},
pages={759-776},
doi={10.3846/jbem.2018.6359},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062280736&doi=10.3846%2fjbem.2018.6359&partnerID=40&md5=c0a6cd6ad2923ed12ad4349c4cd6ad8b},
affiliation={Department of International Logistics, Faculty of International Business and Economics, Poznan University of Economics and Business, Poznan, Poland; International Chinese and Central-Eastern Europe Institute of Logistics and Service Science, Faculty of Engineering Management, Bialystok University of Technology, Bialystok, Poland; Department of Marketing, Supply Chain Management and Social Responsibility, Hanken School of Economics, Helsinki, Finland; Department of Logistics, Faculty of Economics and Management, University of Zielona Gora, Zielona Gora, Poland},
abstract={This paper aims to identify key factors related to information management and integration in the supply chain. The initial set of factors was identified in the literature, namely, information quality and content, performance metrics, and information technologies. The construct was then tested empirically using the sample of 200 Polish companies. Computer-Assisted Telephone Interviews (CATI) were based on a structured questionnaire, then, data exploratory factor analysis (EFA) was performed. The conducted EFA determined four main factors for information management and integration in the supply chain that should be considered, namely, information characteristics, ICT used in information management, the scope of information and shared performance metrics. Results confirmed the importance of some of the factors related to information management in the supply chain, however having a different set of variables than indicated in the literature. Additionally, research introduced new factors, namely, information characteristics and the scope of shared information, instead of information quality and information content. Moreover, findings suggested that the key role in supply chain information integration was related to established technologies, focused on transport, customer, and ERP management, while emerging technologies, such as cloud computing and data mining were not of the top importance among the surveyed companies. © 2018 The Author(s).},
author_keywords={Exploratory Factor Analysis (EFA);  Information integration;  Information management;  Information sharing technologies;  Supply Chain Performance Management (SCPM);  Supply chain performance measurement},
references={Anatan, L., Factors influencing Supply Chain competitive advantage and performance (2014) International Journal of Business and Information, 9 (3), pp. 311-334; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Computers in Industry, 82, pp. 135-150. , https://doi.org/10.1016/j.compind.2016.07.002; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25. , https://doi.org/10.1108/09513550810846087; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56; Campo, S., Rubio, N., Yagüe, M.J., Information technology use and firm’s perceived performance in Supply Chain management (2010) Journal of Business-To-Business Marketing, 17 (4), pp. 336-364. , https://doi.org/10.1080/10517120903574649; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818. , https://doi.org/10.1016/j.cie.2011.11.014; Chodakowska, E., Nazarko, J., Network DEA Models for Evaluating Couriers and Messengers (2017) Procedia Engineering, 182, pp. 106-111. , https://doi.org/10.1016/j.proeng.2017.03.130; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602. , https://doi.org/10.1108/17410401111150760; Fawcett, S.E., Ellram, L.M., Ogden, J.A., (2007) Supply Chain Management. from Vision to Implementation, , Upper Saddle River, NJ: Pearson Education; Fawcett, S.E., Osterhaus, P., Magnan, G.M., Brau, J.C., McCarter, M.W., Information sharing and supply chain performance: The role of connectivity and willingness (2007) Supply Chain Management: An International Journal, 12 (5), pp. 358-368. , https://doi.org/10.1016/j.sbspro.2015.01.1225; Fiala, P., Information sharing in supply chains (2005) Omega, 33 (5), pp. 419-423. , https://doi.org/10.1016/j.omega.2004.07.006; Field, A., (2009) Discovering Statistics Using SPSS, , London: SAGE Publications; Fynes, B., Voss, C., de Búrca, S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354. , https://doi.org/10.1016/j.ijpe.2004.05.008; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21, pp. 71-87. , https://doi.org/10.1108/01443570110358468; Hilbert, M., Big Data for development: A review of promises and challenges (2016) Development Policy Review, 34 (1), pp. 135-174. , https://doi.org/10.1111/dpr.12142; Jharkharia, S., Shankar, R., IT enablement of supply chains: Modelling the enablers (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 700-712. , https://doi.org/10.1108/17410400410569116; Jung, S., Exploratory factor analysis with small sample sizes: A comparison of three approaches (2013) Behavioural Processes, 97, pp. 90-95. , https://doi.org/10.1016/j.beproc.2012.11.016; Laney, D., (2001) 3D Data Management: Controlling Data Volume, Velocity and Variety, Application Delivery Strategies, , https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-DataVolume-Velocity-and-Variety.pdf, META Group Inc; Lee, H.L., Whang, S., Information sharing in a supply chain (2000) International Journal of Manufacturing Technology and Management, 1 (1), pp. 79-93. , https://doi.org/10.1504/IJMTM.2000.001329; Li, S., Lin, B., Accessing Information sharing and information quality in supply chain management (2006) Decision Support Systems, 42, pp. 1641-1656. , https://doi.org/10.1016/j.dss.2006.02.011; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) The International Journal of Management Science, 34 (2), pp. 107-124. , https://doi.org/10.1016/j.omega.2004.08.002; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641. , https://doi.org/10.1016/j.jom.2005.01.002; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operations Research, 156 (2), pp. 267-286. , https://doi.org/10.1016/S0377-2217(02)00918-9; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315. , https://doi.org/10.1016/j.ijpe.2016.11.005; McAdam, R., Hazlett, S.A., Galbraith, B., The role of performance measurement models in multi level alignment: An exploratory case analysis in the utilities sector (2014) International Journal of Operations & Production Management, 34 (9), pp. 1153-1183. , https://doi.org/10.1108/IJOPM-09-2011-0313; Mentzer, J.T., Dewitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25. , https://doi.org/10.1002/j.2158-1592.2001.tb00001.x; Moberg, C.R., Cutler, B.D., Gross, A., Speh, T.W., Identifying antecedents of information exchange within supply chains (2002) International Journal of Physical Distribution & Logistics Management, 32 (9), pp. 755-770. , https://doi.org/10.1108/09600030210452431; Monczka, R.M., Petersen, K.J., Handfield, R.B., Ragatz, G.L., Success factors in strategic supplier alliances: The buying company perspective (1998) Decision Sciences, 29, pp. 553-577. , https://doi.org/10.1111/j.1540-5915.1998.tb01354.x; Montoya-Torres, J.R., Ortiz-Vargas, D.A., Collaboration and information sharing in dyadic supply chains: A literature review over the period 2000–2012 (2014) Estudios Gerenciales, 30, pp. 343-354. , https://doi.org/10.1016/j.estger.2014.05.006; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536. , https://doi.org/10.1108/14637150410559207; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116. , https://doi.org/10.1108/01443579510083622; Nunnally, J.C., Bernstein, I.H., (1994) Psychometric Theory, , New York: McGraw-Hill; Piotrowicz, W., Cuthbertson, R., Performance measurement and metrics in supply chains: An exploratory study (2015) International Journal of Productivity and Performance Management, 64 (8), pp. 1068-1091. , https://doi.org/10.1108/IJPPM-04-2014-0064; Piotrowicz, W., Irani, Z., Analysing B2B electronic procurement benefits: Information systems perspective (2010) Journal of Enterprise Information Management, 23 (4), pp. 559-579. , https://doi.org/10.1108/17410391011061816; Ramanathan, U., Gunasekaran, A., Subramanian, N., Supply chain collaboration performance metrics: A conceptual framework (2011) Benchmarking: An International Journal, 18 (6), pp. 856-872. , https://doi.org/10.1108/14635771111180734; Rossoni, L., Engelbert, R., Bellegard, N.L., Normal science and its tools: Reviewing the effects of factor analysis in management (2016) Revista De Administração (RAUSP), 51 (2), pp. 198-211. , https://doi.org/10.5700/rausp1234; Ruhi, U., Turel, O., Driving visibility, velocity and versatility: The role of mobile technologies in supply chain management (2005) Journal of Internet Commerce, 4 (3), pp. 95-117. , https://doi.org/10.1300/J179v04n03_06; Sahin, F., Robinson, E.P., Flow coordination and information sharing in supply chains: Review, implications, and directions for future research (2007) Decision Sciences, 33 (4), pp. 505-536. , https://doi.org/10.1111/j.1540-5915.2002.tb01654.x; Sezen, B., Relative effects of design, integration and information sharing on supply chain performance (2008) Supply Chain Management: An International Journal, 13 (3), pp. 233-240. , https://doi.org/10.1108/13598540810871271; Shapiro, J.F., Challenges of strategic supply chain planning and modelling (2004) Computers & Chemical Engineering, 28 (6-7), pp. 855-861. , https://doi.org/10.1016/j.compchemeng.2003.09.013; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., Designing & Managing the Supply Chain (2003) Concepts, Strategies & Case Studies, , 2nd ed.). New York: McGraw-Hill/Irwin; Szuster, M., Szymczak, M., Innovation, knowledge and information management in supply chains (2016) Economics and Management, 8 (1), pp. 26-36. , https://doi.org/10.1515/emj-2016-0003; Tabachnick, B.G., Fidell, L.S., (2013) Using Multivariate Statistics, , Boston: Pearson; Tan, G.W., (1999) The Impact of Demand Information Sharing on Supply Chain Network (Dissertation), , University of Illinois at Urbana-Champaign; Whipple, J.M., Frankel, R., Daugherty, P.J., Information support for alliances: Performance implications (2002) Journal of Business Logistics, 23, pp. 67-82. , https://doi.org/10.1002/j.2158-1592.2002.tb00026.x; Wu, I.-L., Chuang, C.-H., Hsu, C.-H., Information sparing and collaborative behaviors in enabling supply chain performance: A social exchange perspective (2014) International Journal of Production Economics, 148 (C), pp. 122-132. , https://doi.org/10.1016/j.ijpe.2013.09.016; Yu, Z., Yan, H., Edwin Cheng, T.C., Benefits of information sharing with supply chain partnerships (2001) Industrial Management & Data Systems, 101 (3), pp. 114-121. , https://doi.org/10.1108/02635570110386625; Zhou, H., Benton, W.C., Jr., Supply chain practice and information sharing (2007) Journal of Operations Management, 25 (6), pp. 1348-1365. , https://doi.org/10.1016/j.jom.2007.01.009},
correspondence_address1={Nazarko, J.; International Chinese and Central-Eastern Europe Institute of Logistics and Service Science, Poland; email: j.nazarko@pb.edu.pl},
publisher={Vilnius Gediminas Technical University},
issn={16111699},
language={English},
abbrev_source_title={J. Bus. Econ. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kruse201830,
author={Kruse, C.J. and Mitchell, K.N. and DiJoseph, P.K. and Kang, D.H. and Schrank, D.L. and Eisele, W.L.},
title={Developing and implementing a port fluidity performance measurement methodology using automatic identification system data},
journal={Transportation Research Record},
year={2018},
volume={2672},
number={11},
pages={30-40},
doi={10.1177/0361198118796353},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060908531&doi=10.1177%2f0361198118796353&partnerID=40&md5=cffbc4ae3f1747647461960831e4fd2f},
affiliation={Center for Ports & Waterways, Texas A&M Transportation Institute, The Texas A&M University System, College Station, TX, United States; US Army Engineer Research and Development Center Coastal and Hydraulics Laboratory, Vicksburg, MS, United States; Texas A&M Transportation Institute, The Texas A&M University System, College Station, TX, United States},
abstract={The U.S. Army Corps of Engineers (USACE) is responsible for the maintenance of federally authorized navigation channels and associated infrastructure. As such, USACE requires objective performance measures for determining the level of service being provided by the hundreds of maintained navigation projects nationwide. To this end, the U.S. Army Engineer Research and Development Center partnered with Texas A&M Transportation Institute to develop a freight fluidity assessment framework for coastal ports. The goal was to use archival automatic identification system (AIS) data to develop and demonstrate how ports can be objectively compared in relation to fluidity, or the turnaround time reliability of oceangoing vessels. The framework allows USACE to evaluate maintained navigation project conditions alongside port system performance indices, thereby providing insight into questions of required maintained channel dimensions. The freight fluidity concept focuses on supply chain performance measures such as travel time reliability and end-to-end shipping costs. Although there are numerous research efforts underway to implement freight fluidity, this is the first known application to U.S. ports. This paper covers AIS data inputs, quality control, and performance measures development, and also provides a demonstration application of the methodology at the Port of Mobile, Alabama, highlighting travel time and travel time reliability operating statistics for the overall port area. This work provides foundational knowledge to practitioners and port stakeholders looking to improve supply chain performance and is also valuable for researchers interested in the development and application of multimodal freight fluidity performance measures. © National Academy of Sciences: Transportation Research Board 2018.},
keywords={Automation;  Navigation;  Quality control;  Reliability;  Supply chains;  Travel time, Automatic identification system;  Demonstration application;  Development and applications;  Navigation projects;  Objective performance measures;  Performance measurements;  Supply chain performance;  Travel time reliability, Fluidity},
references={Transport Canada, , https://stats.tc.gc.ca/Fluidity/Login.aspx, Accessed February 8, 2017; Eisele, W.L., Tardif, L.P., Villa, J.C., Schrank, D.L., Lomax, T.J., Evaluating Global Freight Corridor Performance for Canada. (2011) Journal of Transportation of the Institute of Transportation Engineers, 1, pp. 39-57; (2014) Transportation Research Circular Number E-C187: Developing Freight Fluidity Performance Measures: Supply Chain Perspective on Freight System Performance., , Transportation Research Board of the National Academies, Washington, D.C; Eisele, W.L., Villa, J., (2015) Transportation Research Circular E-C207: Freight Fluidity Scale of Analysis., , Transportation Research Board of the National Academies, Washington, D.C; (2019) Civil Works Direct Program Development Policy Guidance., , http://www.publications.usace.army.mil/Portals/76/Publications/EngineerCirculars/EC_11-2-214.pdf?ver=2017-07-26-102815-160, Fiscal Year; Navigation Mission, , http://www.usace.army.mil/Missions/Civil-Works/Navigation/; Navigation Program, Services and Projects, , http://www.usace.army.mil/Portals/2/docs/civilworks/5yr_devplan/fy11_5yrplan.pdf; (2009) Technical Characteristics for an Automatic Identification System using Time-division Multiple Access in the VHF Maritime Mobile Band., , ITU-R M.1371.4. Geneva, Switzerland; Scully, B., Mitchell, K.N., (2015) Archival Automatic Identification System (AIS) Data for Navigational Project Performance Evaluation., , ERDC/CHL CHETN-IX-40. US Army Corps of Engineers. August; Scully, B., Mitchell, K.N., Underkeel Clearance Reliability Model for Dredged Navigation Channels. (2017) Transportation Research Record: Journal of the Transportation Research Board, 2611, pp. 41-49; Farhadi, N., Parr, S., Mitchell, K.N., Wolshon, B., Use of Nationwide Automatic Identification System Data to Quantify Resiliency of Marine Transportation Systems (2016) Transportation Research Record: Journal of the Transportation Research Board, 2549, pp. 9-18; Mitchell, K.N., Scully, B., Waterway Performance Monitoring via Automatic Identification System (AIS) Data. (2014) Transportation Research Record: Journal of the Transportation Research Board, 2426, pp. 20-26; Scully, B., (2016) Management of Coastal Navigation Channels Based on Vessel Underkeel Clearance in Transit. PhD dissertation., , Mississippi State University; (2014) Nationwide Automatic Identification System, , https://www.navcen.uscg.gov/?pageName=NAISmain, Accessed July 30, 2014; Navigation Enews., 20. , http://chl.erdc.usace.army.mil/media/1/2/4/6/Nav_enews_Oct_2011.pdf; Calder, B., Schwehr, K., Traffic Analysis for Calibration of Risk Assessment Methods (2009) U.S. Hydrographic Conference, , 11-14 May 2009, International Hydrographic Organization, Norfolk, VA; (2015) Waterborne Commerce of the United States, Calendar Year 2014, Part 2 2 Waterways and Harbors Gulf Coast, Mississippi River System and Antilles, , http://www.navigationdatacenter.us/wcsc/webpub14/webpubpart-2.htm, Army Corps of Engineers Institute for Water Resources, Accessed February 8, 2017; (2014) American Association of Port Authorities, , http://www.aapa-ports.org/unifying/content.aspx?ItemNumber=21048, Accessed February 8, 2017; American Association of Port Authorities, , http://www.aapa-ports.org/unifying/content.aspx?ItemNumber=21048, Accessed February 8, 2017; (2014) 2014 Vessel Calls in U.S. Ports, Selected Terminals and Lightering Areas, , https://www.marad.dot.gov/resources/data-statistics/, Accessed February 8, 2017; Kruse, J., (2017) DRAFT Technical Memorandum -Port of Mobile System Performance Analysis, , (unpublished). Texas A&M Transportation Institute, College Station, TX, November; (2017) Travel Time Reliability: Making It There on Time, All the Time, , https://ops.fhwa.dot.gov/publications/tt_reliability/TTR_Report.htm, Accessed February 8, 2017; Port Performance Freight Statistics Program., , https://www.rita.dot.gov/bts/port_performance/freight_statistics_program, Accessed February 8, 2017},
correspondence_address1={Kruse, C.J.; Center for Ports & Waterways, United States; email: j-kruse@tti.tamu.edu},
publisher={SAGE Publications Ltd},
issn={03611981},
coden={TRRED},
language={English},
abbrev_source_title={Transp Res Rec},
document_type={Article},
source={Scopus},
}

@ARTICLE{AlKaabi2018807,
author={Al Kaabi, B.R. and Jowmer, B.},
title={The use of sustainable balanced scorecard for strategic planning and resource efficiency improvement based on supply chain performance measurement and productivity},
journal={International Journal of Supply Chain Management},
year={2018},
volume={7},
number={5},
pages={807-818},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060895027&partnerID=40&md5=aa12006a96b91dc980187eb5dfb6f533},
affiliation={Department of Accounting, Faculty of Management and Economics, University of Mustansiriya, Iraq; Faculty of Management and Economics, University of Mustansiriya, Iraq},
abstract={Performance assessment of supply chain management can be helpful in managing. One of the new approaches in this area is the use of a balanced scorecard that addresses the supply chain management strategy as a set of essential goals. Balanced Scorecard has no control role and its criteria do not apply to describing past performance. Rather, these criteria are a tool for explaining the organization's strategy and, by integrating and coordinating activities at its various levels, enables the achievement of organizational goals. In this paper, the supply chain literature and performance evaluation of supply chain management have been investigated first. The aim of this study is to establish a sustainable balanced scorecard for the selected population with the inclusion of resources, objectives, indicators, measures, issues and dimensions of the sustainability of Iraqi universities using the philosophy of strategic planning following a scientific and modern manner. Through the diagnosis of strengths and weaknesses following by a proposed treatment, the card is designed to improve the efficiency of the resources at the Faculty of Management and Economics in University of Mustansiriya. The study concludes that what is needed to ensure the assessment of the performance and integration of the dimensions of sustainability no longer in accordance with the modern methods and techniques. Also, the most important of the planning process of the College of Management and Economics is to identify the strength and weakness of their positions and by comparing the actual reality of the quality standards of higher education organizations and improve the efficiency of their resources. © ExcelingTech Pub, UK.},
author_keywords={Balanced scorecard;  Performance assessment;  Strategic planning and resource efficiency;  Supply chain management},
references={Abdrabou, O.A., Elgeuoshy, M., "STRATEGIC PLANNING IN EGYPTIAN HIGHER EDUCATION FROM SOWT TO BALANCED SCORECARD: THE CASE OF ITIL" (2015) Arab Academy for Science, Technology and Maritime Transport, 9 (5). , Egypt, 2016; Ahmed, S.A., (2011) "Application of Resource Consumption Accounting (RCA) in an Educational Institute", , http://www.m5zn.com/newuploads/2013/03/05/pdf/m5zn_59f97bf6a9fd28c.pdf, Institute of Business?. Management, Karachi; Bagdoniene, D., Daunoriene, A., Asta, I., Ausra, S., (2011) "Integration Of Sustainable Development Principles Into The Balanced Scorecard", 5 (3), pp. 447-460. , (11); Bieker, T., "Managing corporate sustainability with the Balanced Scorecard: Developing a Balanced Scorecard for Integrity Management" (2002) oikos PhD summer academy "Sustainability, Corporations and Institutional Arrangements; Blocher, E.J., Stout, D.E., Cokins, G., (2010) "Cost Management", , 5 th. ed., Mc Graw Hill; Crosson, S.V., Needles, B.E., (2011) "Managerial Accounting", , 9th. ed., South-Western Cengage Learning; Divan, S.M., (2012) "Changing "The Way We Do Things" Presenting A Strategic Organizational Culture Framework", , California State University, Sacramento, Master of Public Policy & Administration; Epstein, M., Roy, M.J., "Sustainability in Action: Identifying and Measuring the Key Performance Drivers" (2001) LRP long range planning, (34), pp. 585-604. , www.Irpjournal.com; Farid, D., Nejati, M., Headar, M., "Balanced Scorecards Application in universities higher education institutes: implementation guide in an Iranian context" (2008) Series, (2), pp. 31-45. , Yazd University Iran; Kaplan, R.S., "Conceptual Foundations of the Balanced Scorecard" (2009) Harvard Business School, 3. , Harvard University; Kinney, M.R., Raiborn, C.A., (2011) "cost accounting", , 8th. ed. South-Western Cengage Learning; Moller, A., Stefan, S., (2002) "The Sustainability Balanced Scorecard as a Framework for Eco-efficiency Analysis", 9 (4). , http://mitpress.mit.edu/jie, University of Lueneburg, Germany; Nayeri, M.D., Mashhadi, M.M., Mohajeri, K., "Universities Strategic Evaluation Using Balanced Scorecard" (2008) International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 2 (1). , 2008; Sidiropoulos, M., Mouzakitis, Y., Adamides, E., Goutsos, S., "Applying Sustainable Indictors to corporate strategy: The Eco-balanced Scorecard" (2004) Environmental Research, Engineering and Management},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Widyaningrum2018,
author={Widyaningrum, D.},
title={Key performance indicators (KPIs) on Vannamei shrimp supply chain performance (a preliminary research)},
journal={IOP Conference Series: Materials Science and Engineering},
year={2018},
volume={403},
number={1},
doi={10.1088/1757-899X/403/1/012046},
art_number={012046},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054906787&doi=10.1088%2f1757-899X%2f403%2f1%2f012046&partnerID=40&md5=2593740e52dd9dc2e6bd1cd71c908b30},
affiliation={Department of Industrial Engineering, Muhammadiyah Gresik University, Gresik, Indonesia},
abstract={Higher competition makes the company should be able to fulfill consumer needs quickly, precisely and low cost. It can achieve by designing good supply chain. To know supply chain's success, we must measure its performance with proper KPIs (Key Performance Indicators). But, to get proper KPIs is not easy because of each supply chain has unique characteristics, one of them is vannamei shrimp supply chain. Vannamei shrimp is perishable products, seasonal in production, and highly dependence on nature. This preliminary work will propose a set of proper KPIs by considering its uniqueness to measuring its performance. This work done by mapping vannamei shrimp supply chain network first, then determines the KPIs from six literatures. The stakeholders mostly consist of small and medium enterprises (SMEs). We get 34 KPIs from this study, which are 31 KPIs can be use in general supply chain and three special KPIs which only use for vannamei shrimp supply chain. Furthermore, this research will be done to complete the KPIs by considering stakeholders suggestions, weighting the KPIs by AHP and develop vannamei shrimp supply chain performance measurement system (PMS). The PMS will be used to measure vannamei shrimp supply chain and compared with another PMS. © Published under licence by IOP Publishing Ltd.},
keywords={Benchmarking;  Shellfish;  Supply chain management;  Supply chains, Consumer needs;  Key performance indicators;  Low costs;  Perishable product;  Small and medium enterprise;  Supply chain network;  Supply chain performance;  Supply chain performance measurements, Costs},
references={(2015) The World Estimated Farmed Shrimp Production in 2013 and 2014, , shrimpnews.com/FreeReportsFolder/NewsReportFolder/WorldProductionIn2013And2014.html, Shrimpnews [Online] [Accessed 11 June 2017]; (2016) Statistik Indonesia 2016, , Badan Pusat Statistik [Online] [Accessed 11 June 2017]; (2015) Ministry of Marine Affairs and Fisheries Republic of Indonesia, , http://www.djpb.kkp.go.id, Ministry of Marine Affairs and Fisheries Republic of Indonesia [Online] [Accessed 11 June 2017]; Widyaningrum, D., Masruroh, N.A., Development of the Sea Fishery Supply Chain Performance Measurement System: A Case Study (2012) International Journal of Supply Chain Management (IJSCM), 1, pp. 20-32; Tarasewicz, R., Integrated approach to supply chain performance measurement - Results of the study on Polish market (2016) Transportation Research Procedia, 14, pp. 1433-1442; Wibowo, M.A., Sholeh, M.N., The Analysis of Supply Chain Performance Measurement at Construction Project (2015) Procedia Engineering, 125, pp. 25-31; Butzera, S., Schötzb, S., Petroschkea, M., Steinhilper, R., Development of a Performance Measurement System for International Reverse Supply Chains (2017) Procedia CIRP, 61, pp. 251-256; Chopra, S., Meindl, P., (2007) Supply Chain Management Strategy, Planning and Operation, 3, pp. 38-59. , (Upper Saddle River: NJ); Aramyan, L.H., Lansink, A.G.J.M.O., Vorst, J.G.A.J., Kooten, O., Performance Measurement in Agri-food Supply Chain: A Case Study (2007) Supply Chain Manangement: An International Journal, 12, pp. 304-315; Jamil, C.M., Mohamed, R., Performance Measurement System (PMS) in Small Medium Enterprises (SMEs): A Practical Modified Framework (2011) World Journal of Social Sciences, 1, pp. 200-212; Fauske, H., Kollberg, M., Dreyer, H.C., Bolseth, S., Criteria for Supply Chain Performance Measurement Systems (2006) 14th International Annual EurOMA Conference, , (Ankara, Turkey); Cai, J., Liu, X., Xiao, Z., Liu, J., Improving Supply Chain Performance Management: A Systematic Approach to Analyzing Iterative KPI Accomplishment (2009) Decision Support Systems, 46, pp. 512-521; The Evolution of Performance Measurement Research: Developments in the Last Decade and a Research Agenda for the Next (2005) International Journal of Operations & Production Management, 25, pp. 1264-1277. , Neely; Uysal, F., An integrated model for sustainable performance measurement in supply chain (2012) Procedia - Social and Behavioral Sciences, 62, pp. 689-694; Najmi, Makui, A., Providing Hierarchical Approach for Measuring Supply Chain Performance Using AHP and DEMATEL Methodologies (2010) International Journal of Industrial Engineering Computations, 1, pp. 199-212; Tzong-Ru, L., Yi-Siang, S., Sivakumar, P., The Application SCOR in Manufacturing: Two Cases in Taiwan (2012) Procedia Engineering, 38, pp. 2548-2563; Kurien, G.P., Qureshi, M.N., Study of Performance Measurement Practices in Supply Chain Management (2011) International Journal of Business, Management and Social Sciences, 2, pp. 19-34; Delipinar, G.E., Kocaoglu, B., Using SCOR model to gain competitive advantage: A Literature Review (2016) Procedia - Social and Behavioral Sciences, 229, pp. 398-406; Bukhori, B., Widodo, K.H., Ismoyowati, D., Ev2aluation of Poultry Supply Chain Performance in XYZ Slaughtering House Yogyakarta using SCOR and AHP Method Agriculture and Agricultural Science Procedia, 3, pp. 221-225},
correspondence_address1={Widyaningrum, D.; Department of Industrial Engineering, Indonesia; email: dzakiyah@umg.ac.id},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Vukovic20183,
author={Vukovic, V. and Tumbas, P. and Rakovic, L. and Maric, M. and Pavlicevic, V.},
title={A performance measurement system for software testing process},
journal={Lecture Notes in Business Information Processing},
year={2018},
volume={333},
pages={3-20},
doi={10.1007/978-3-030-00060-8_1},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053914392&doi=10.1007%2f978-3-030-00060-8_1&partnerID=40&md5=949f2d93d248f291f7295f5a062b5765},
affiliation={Faculty of Economics in Subotica, University of Novi Sad, Segedinski put 9-11, Subotica, 24000, Serbia},
abstract={Proper management of development processes in software engineering can be achieved by means of continuous planning, measurement, monitoring and assessment of process performance indicators. The software testing process is also characterised by numerous, well-known performance indicators, based on which it is possible to plan and measure its performance. A question imposes itself, however, to which extent these performance indicators are really applicable in practice (measurable), sensitive and relevant to the software testing process. Theoretical and empirical research was conducted in order to obtain an answer to this research question. Experts in the software testing domain were surveyed, so that they would evaluate individual performance indicators of the software testing process, previously identified in papers published in referent scientific journals and conference proceedings. The result of conducted research in this paper is a developed performance measurement system for software testing process, with the primary objective to harmonise the existing differences between theory and practice in performance management of this process. © Springer Nature Switzerland AG 2018.},
author_keywords={Performance indicators;  Performance measurement system;  Software testing},
keywords={Benchmarking;  Information systems;  Information use;  Supply chain management, Continuous planning;  Development process;  Individual performance;  Monitoring and assessment;  Performance indicators;  Performance management;  Performance measurement system;  Scientific journals, Software testing},
references={Stainer, L., Performance management and corporate social responsibility: The strategic connection (2006) Strateg. Change, 15 (5), pp. 253-264; Balaban, N., Ristic, Z., Upravljanje performansom (2013) M&I Systems, Co., Novi Sad; Rummler, G., Brache, A., (1995) Improving Performance, How to Manage the White Space on the Organization Chart, , Jossey-Bass, San Francisco; Matkovic, P., Tumbas, P., A comparative overview of the evolution of software development models (2010) Int. J. Ind. Eng. Manag. (IJIEM), 1 (4), pp. 162-172; Naik, K., Tripathy, P., (2008) Software Testing and Quality Assurance: Theory and Practice, , Wiley, New Jersey; Shimeall, S.C., (2002), http://www.stickyminds.com/sitewide.asp?ObjectId=3650&Function=e, Managing the Testing Process, Accessed 03 Mar 2016 from StickyMinds.com: Software Testing & QA Online Community; Hevner, A.R., March, S.T., Park, J., Ram, S., Design science in information systems research (2004) MIS Q, 28 (1), pp. 75-105; Sommerville, I., Ransom, J., An empirical study of industrial requirements engineering process assessment and improvement (2005) ACM Trans. Softw. Eng. Methodol., 14 (1), pp. 85-117; (2010) Tmmi Foundation: Test Maturity Model Integration (Tmmi) V3.1. Tmmi Foundation, , Dublin; Koomen, T., van der Aalst, L., Broekman, B., Vroon, M., (2006) Tmap Next. UTN, , Publishers, Hertogenbosch; (2016), https://strongqa.com/uploads/document/doc/41/testing-strategy.doc, Accessed 04 Mar 2016 sa Strong QA; (2005) Rational Team: Rational Unified Process, V7.0. IBM Rational Software Corporation, , Armonk; Kitchenham, B.A., Procedures for Performing Systematic Reviews (2004) Keele University, Eversleigh; Kitchenham, B.A., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., Linkman, S., Systematic literature reviews in software engineering-a systematic literature review (2009) Inf. Softw. Technol., 51, pp. 7-15; Dyba, T., Dingsoyr, T., Empirical studies of agile software development: A systematic review (2008) Inf. Softw. Technol., 50, pp. 833-859; Kan, S.H., Parrish, J., Manlove, D., In-process metrics for software testing (2001) IBM Syst. J., 40 (1), pp. 220-241; Barnes, D.J., Hopkins, T.R., Applying software testing metrics to lapack (2006) PARA 2004. LNCS, 3732, pp. 228-236. , https://doi.org/10.1007/11558958_27, Dongarra, J., Madsen, K., Waśniewski, J. (eds.), Springer, Heidelberg; Nirpal, P.B., Kale, K., A brief overview of software testing metrics (2011) Int. J. Comput. Sci. Eng. IJCSE, 3 (1), pp. 204-2012; Singh, Y., Kaur, A., Suri, B., An empirical study of product metrics (2008) Innovative Techniques in Instruction Technology, E-Learning, E-Assessment, and Education, pp. 64-72. , https://doi.org/10.1007/978-1-4020-8739-4_12, Iskander, M. (ed.), Springer, London; Afzal, W., Torkar, R., Incorporating metrics in an organizational test strategy (2008) International Conference on Software Testing Verification and Validation Workshop, pp. 304-315. , IEEE Computer Society, Washington; Baroudi, R., (2010), KPI Mega Library. Scotts Valley; Kanij, T., Merkel, R.G., Grundy, J., Performance assessment metrics for software testers (2012) 2012 5Th International Workshop on Co-Operative and Human Aspects of Software Engineering (CHASE), Proceedings, pp. 63-65. , Sharp, H., Dittrich, Y., de Souza, C.R.B., Cataldo, M., Hoda, R. (eds., IEEE, Institute of Electrical and Electronics Engineers, Piscataway; Lazic, L., Mastorakis, N., Cost effective software test metrics – Part 2 (2007) Proceedings of the 4Th IASME/WSEAS International Conference on Engineering Education, pp. 144-153; Lazic, L., Mastorakis, N., Cost effective software test metrics – Part 1 (2007) Proceedings of the 4Th IASME/WSEAS International Conference on Engineering Education, pp. 137-143; Eldo, K.J., Maheswari, D., Survey on software measurement systems based on software metrics (2015) Int. J. Appl. Eng. Res., 10 (19), pp. 40090-40095; Landis, J.R., Koch, G.G., The measurement of observer agreement for categorical data (1977) Biometrics, 33, pp. 159-174},
correspondence_address1={Pavlicevic, V.; Faculty of Economics in Subotica, Segedinski put 9-11, Serbia; email: pavlicevic@ef.uns.ac.rs},
editor={Wrycza S., Maslankowski J.},
publisher={Springer Verlag},
issn={18651348},
isbn={9783030000592},
language={English},
abbrev_source_title={Lect. Notes Bus. Inf. Process.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Choe201830,
author={Choe, J.-M.},
title={Electronic commerce, MCSs cha nge, a nd the improvement of supply-chain performance},
journal={Global Business and Finance Review},
year={2018},
volume={23},
number={2},
pages={30-48},
doi={10.17549/gbfr.2018.23.2.30},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053888347&doi=10.17549%2fgbfr.2018.23.2.30&partnerID=40&md5=06f68a3fc153dcd8039f0ab0a2500b37},
affiliation={School of Business, Kyungpook National University, Daegu, South Korea},
abstract={This paper aims to demonstrate the positive impact of the fit between the usage levels of business-to-business (B2B) electronic commerce (EC) and forms of management control systems (MCSs) on the supply-chain performance of a firm. This study analyses data taken from 114 manufacturing firms that are listed on the Korean stock market. We adopt the complementarity perspective to suggest the fit among research variables. To demonstrate the impact of the fit on the supply-chain performance of a firm, this study employs a cluster analysis that implies a systems approach of fit. This study also utilizes a subgroup analysis to show the moderating effects of top management support, resource capabilities, and suppliers’ capabilities on the fit. The empirical results show that under high adoption degrees of EC, if both the use levels of non-financial performance measurement system (NPMS) are high and organic structures are employed, the supply-chain performance of a firm is more enhanced. However, the results also indicate that when EC is highly adopted, low usage levels of NPMS and mechanistic structures can reduce supply-chain performance. In the examination of the impact of supply-chain performance on a firm’s overall performance, positive effects are demonstrated. From the results of a subgroup analysis, the facilitating roles of organizational resource capabilities, top management support, and suppliers’ pressure and capabilities for the development of appropriate forms of MCSs under high EC adoption levels are partially confirmed. The designs of MCSs additionally include such components as reward systems and communication networks. In this study, only the core design variables of MCSs are considered. © 2018 People and Global Business Association.},
author_keywords={Electronic commerce;  Management control systems;  Non-financial performance measurements;  Organic structures;  Supply-chain performance},
references={Artz, M., Homburg, C., Rajab, T., Performance measurement system design and functional strategic decision influence: The role of performance measure properties (2012) Accounting, Organizations and Society, 37, pp. 445-460; Barua, A., Konana, P., Whinston, B., Yin, F., Driving e-business excellence (2001) Sloan Management Review, 38, pp. 36-44; Broadbent, J., Laughlin, R., Performance management systems: A conceptual model (2009) Management Accounting Research, 20, pp. 283-295; Byrd, A., Davidson, W., Examining possible antecedents of IT impact on the supply chain and its effect on firm performance (2003) Information & Management, 41, pp. 243-255; Chang, H., Wong, H., Adoption of e-procurement and participation of e-marketplace on firm performance: Trust as a moderator (2010) Information & Management, 47, pp. 262-270; Cheng, J., Fu, Y., Inter-organizational relationships and knowledge sharing through the relationship and institutional orientations in supply chains (2013) International Journal of Information Management, 33, pp. 473-484; Chenhall, H., Management control systems design within its organizational context: Findings from contingency-based research and directions for the future (2003) Accounting, Organizations and Society, 28, pp. 127-168; Chenhall, H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting, Organizations and Society, 30, pp. 395-422; Choe, J.M., The strategic alignment of management accounting information systems, and organizational performance (2017) Global Business and Finance Review, 22, pp. 50-64; Chu, C., Smithson, S., E-business and organizational change: A structurational approach (2007) Information Systems Journal, 17, pp. 369-389; Dedrick, J., Xu, X., Zhu, X., How does information technology shape supply-chain structure?: Evidence on the number of suppliers (2008) Journal of Management Information Systems, 25 (2), pp. 41-72; Ekawati, W., Yasa, N.K., Service marketing mix strategy and service loyalty towards hotel’s success (2018) Global Business and Finance Review, 23, pp. 63-74; Flamholtz, G., Das, K., Tsui, S., Toward an integrative framework of organizational control (1985) Accounting, Organizations and Society, 10 (1), pp. 35-50; Ferreira, A., Otley, D., The design and use of performance management systems: An extended framework for analysis (2009) Management Accounting Research, 20, pp. 263-282; Haas, M., Kleingeld, A., Multilevel design of performance measurement systems: Enhancing strategic dialogue through the organization (1999) Management Accounting Research, 10, pp. 233-261; Hall, M., Do comprehensive performance measurement systems help or hinder managers’ mental model development? (2011) Management Accounting Research, 22, pp. 68-83; Hartono, E., Li, X., Na, K., Simpson, T., The role of the quality of shared information in inter-organizational systems use (2010) International Journal of Information Management, 30, pp. 399-407; Henri, J., Management control systems and strategy: A resource-based perspective (2006) Accounting, Organizations and Society, 31, pp. 529-558; Hoque, Z., James, W., Linking balanced scorecard measures to size and market factors: Impact on organizational performance (2000) Journal of Management Accounting Research, 12, pp. 1-17; Huo, B., Zhang, C., Zhao, X., The effect of IT and relationship commitment on supply chain coordination: A contingency and configuration approach (2015) Information & Management, 52 (6), pp. 728-740; Iyer, N.S., Germain, R., Claycomb, C., B2B e-commerce supply chain integration and performance: A contingency fit perspective on the role of environment (2009) Information & Management, 46, pp. 313-322; Ittner, D., Larcker, F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting, Organizations and Society, 28, pp. 715-741; Jean, B., Sinkovics, R., Kim, D., The impact of technological, organizational and environmental characteristics on electronic collaboration and relationships performance in international customer-supplier relationships (2014) Information & Management, 51, pp. 854-864; Klein, R., Rai, A., Inter-firm strategic information flows in logistics supply chain relationships (2009) MIS Quarterly, 33 (4), pp. 735-762; Kurnia, S., Johnston, R.B., The need for a processual view of inter-organizational systems adoption (2000) Journal of Strategic Information Systems, 9, pp. 295-319; Kurnia, S., Karnali, R., Rahim, M., A qualitative study of business-to-business electronic commerce adoption within the Indonesian grocery industry: A multi-theory perspective (2015) Information & Management, 52 (4), pp. 518-536; Lin, H., Inter-organizational and organizational determinants of planning effectiveness for internet-based inter-organizational systems (2006) Information & Management, 43, pp. 423-433; Liu, C., Sia, C., Wei, K., Adopting organizational virtualization in B2B firms: An empirical study in Singapore (2008) Information & Management, 45, pp. 429-437; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: A survey in the mines (2006) Management Accounting Research, 17, pp. 315-339; Milgrom, P., Roberts, J., Complementarities and fit strategy, structure, and organizational change in manufacturing (1995) Journal of Accounting and Economics, 19, pp. 79-208; Overby, E., Mitra, S., Physical and electronic wholesale markets: An empirical analysis of product sorting and market function (2014) Journal of Management Information Systems, 31 (2), pp. 11-46; Palma-Mendoza, A., Neailey, K., Roy, R., Business process re-design methodology to support supply chain integration (2014) International Journal of Information Management, 34, pp. 167-176; Paulrja, A., Lado, A., Chen, J., Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships (2008) Journal of Operations Management, 26, pp. 45-64; Qu, G., Pinsonneault, A., Tomiuk, D., Wang, S., Liu, Y., The impact of social trust on open and closed B2B e-commerce: A Europe-based study (2015) Information & Management, 52, pp. 151-159; Quaddus, M., Hofmeyer, G., An investigation into the factors influencing the adoption of B2B trading exchanges in small businesses (2006) European Journal of Information Systems, 16, pp. 202-215; Saraf, N., Langdon, S., Gosain, S., IS application capabilities and relational value in inter-firm partnerships (2007) Information Systems Research, 18 (3), pp. 320-339; Seddon, P.B., Implications for strategic IS research of the resource-based theory of the firm: A reflection (2014) Journal of Strategic Information Systems, 23 (4), pp. 257-269; Sharma, S., Durand, R.M., Gur-Arie, O., Identification and analysis of moderator variables (1981) Journal of Marketing Research, 18, pp. 291-300; Shi, X., Liao, Z., Inter-firm dependence, inter-firm trust, and operational performance: The mediating effect of e-business integration (2015) Information & Management, 52, pp. 943-950; Sim, K.L., Killough, N., The performance effects of complementarities between manufacturing practices and management accounting systems (1998) Journal of Management Accounting Research, 10, pp. 325-346; Soliman, S., Janz, D., An exploratory study to identify the critical factors affecting the decision to establish internet-based inter-organizational information systems (2004) Information & Management, 41, pp. 697-706; Son, J., Benbasat, I., Organizational buyers' adoption and use of B2B electronic marketplaces: Efficiency-and legitimacy-oriented perspective (2007) Journal of Management Information Systems, 24 (2), pp. 55-99; van de Ven, F., Drazin, D., The concept of fit in contingency theory (1985) Research in Organizational Behavior, 12, pp. 333-365; Wang, T.G., Tai, C.F., Wei, H., A virtual integration theory of improved supply-chain performance (2006) Journal of Management Information Systems, 23 (2), pp. 41-64; Wang, T.G., Tai, C.F., Grover, V., Examining the relational benefits of improved inter-firm information processing capability in buyer-supplier dyads (2013) MIS Quarterly, 37 (1), pp. 149-173; Wong, W.Y., Lai, K., Cheng, T., Value of information integration to supply chain management: Roles of internal and external contingencies (2012) Journal of Management Information Systems, 28 (3), pp. 161-199; Yoo, J., OEM participation in SME suppliers’ new product development efforts (2016) Global Business and Finance Review, 21, pp. 88-101; Youn, S.H., Yang, M., Kim, J.H., Hong, P., Supply-chain information capabilities and performance outcomes: An empirical study of Korean steel suppliers (2014) International Journal of Information Management, 34 (3), pp. 369-380; Zhao, K., Xia, M., Forming interoperability through inter-organizational systems standards (2014) Journal of Management Information Systems, 30 (4), pp. 269-298; Zeng, D., Ouyang, H., Zhou, N., Hu, J., The role of embeddedness in achieving IT-enabled organizational transformation: A case of Xi’an Aero-Engine PLC (XAEP) (2015) International Journal of Information Management, 35, pp. 632-635; Zhu, K., Kraemer, L., Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry (2005) Information Systems Research, 16 (1), pp. 61-84; Zhu, Z., Zhao, J., Tang, X., Zhang, Y., Leveraging e-business process for business value: A layered structure perspective (2015) Information & Management, 52 (6), pp. 679-691},
correspondence_address1={Choe, J.-M.; School of Business, Sankyuk-dong 1370, South Korea; email: choejj@knu.ac.kr},
publisher={People and Global Business Association},
issn={10886931},
language={English},
abbrev_source_title={Glob. Bus. Finance Rev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jooste201892,
author={Jooste, J.L. and Botha, L.J.},
title={Improvements towards the identification and quantification of relationships between key performance indicators},
journal={South African Journal of Industrial Engineering},
year={2018},
volume={29},
number={2},
pages={92-101},
doi={10.7166/29-2-1872},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053287496&doi=10.7166%2f29-2-1872&partnerID=40&md5=e5598a1f1573c06e21c8a4a0650f02f8},
affiliation={Department of Industrial Engineering, Stellenbosch University, South Africa},
abstract={The quantitative relationships at the performance measurement system (QRPMS) is a methodology for identifying and quantifying relationships between key performance indicators. These relationships present additional information for decision-making purposes. QRPMS employs the Guttman-Kaiser criterion (K1) to complete a critical step during the methodology. This study presents evidence that the K1 criterion has limitations that compromise the reliability of QRPMS’s results. An improved QRPMS version is developed, and the results of the existing and improved QRPMS are compared in a mining industry case study. It is shown that the improved QRPMS delivers more accurate and reliable results. © 2018, South African Institute of Industrial Engineering. All rights reserved.},
keywords={Decision making;  Supply chain management, Critical steps;  Industry case studies;  Key performance indicators;  Performance measurement system;  Reliable results, Benchmarking},
references={Shrinivasan, Y.B., Dasgupta, G.B., Desai, N., Nallacherry, J., A method for assessing influence relationships among KPIs of service systems (2012) ICSOC 2012. LNCS, 7636, pp. 191-205. , Liu, C., Ludwig, H., Toumani, F. & Yu, Q., Heidelberg: Springer; Dasgupta, G.B., Shrinivasan, Y., Nayak, T.K., Nallacherry, J., Basu, S., Pautasso, C., Zhang, L., Optimal strategy for proactive service delivery management using inter-KPI influence relationships service-oriented computing (2013) Proceedings of the 11Th International Conference, ICSOC 2013, pp. 131-145. , & Fu, X., Berlin Heidelberg: Springer; Rodríguez, R.R., Saiz, J.J.A., Bas, A.O., Relationships among key performance indicators within the performance measurement system context: Literature review (2006) Performance Measurement and Management Conference, pp. 689-701. , London, July 2006; Rodriguez, R.R., Saiz, J.J.A., Bas, A.O., Quantitative relationships between key performance indicators for supporting decision-making processes (2009) Computers in Industry, 60 (2), pp. 104-113; Kang, N., Zhao, C., Li, J., Horst, J.A., Analysis of key operation performance data in manufacturing systems (2015) IEEE International Conference on Big Data, 2015, pp. 2767-2770. , Santa Clara, CA; Guo, X., Yu, P., Li, W., Qiu, X., Clustering-based KPI data association analysis method in cellular networks (2016) 2016 IEEE/IFIP Network Operations and Management Symposium, pp. 1101-1104. , Istanbul, 2016; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64 (1), pp. 231-241; Youngblood, A.D., Collins, T.R., Addressing balanced scorecard trade-off issues between performance metrics using multi-attribute utility theory (2003) Engineering Management Journal, 15 (1), pp. 11-18; Bauer, K., KPI reduction the correlation way (2005) Information Management, 15 (2), p. 64; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Tabachnick, B.G., Fidell, L.S., (2001) Using Multivariate Statistics, , 4th edition, Boston: Allyn &amp; Bacon; Yeomans, K.A., Golder, P.A., The Guttman-Kaiser criterion as a predictor of the number of common factors (1982) The Statistician, 31, pp. 221-229; Lance, C.E., Vandenberg, R.J., (2009) Statistical and Methodological Myths and Urban Legends: Doctrine, Verity and Fable in the Organizational and Social Sciences, , New York: Taylor & Francis; Zwick, W.R., Velicer, W.F., Comparison of five rules for determining the number of components to retain (1986) Psychological Bulletin, 99 (3), p. 432; Velicer, W.F., Eaton, C.A., Fava, J.L., Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components (2000) Problems and Solutions in Human Assessment, 2000, pp. 41-71. , Goffin, R.D & Helmes, E, Boston: Springer; Cortina, J.M., Big things have small beginnings: An assortment of “minor” methodological misunderstandings (2002) Journal of Management, 28 (3), pp. 339-362; Zwick, W.R., Velicer, W.F., Factors influencing four rules for determining the number of components to retain (1982) Multivariate Behavioral Research, 17 (2), pp. 253-269; Zhu, M., Ghodsi, A., Automatic dimensionality selection from the scree plot via the use of profile likelihood (2006) Computational Statistics & Data Analysis, 51 (2), pp. 918-930; Patel, B., Chaussalet, T., Millard, P., Balancing the NHS balanced scorecard! (2008) European Journal of Operational Research, 185 (3), pp. 905-914; Cadima, J., Jolliffe, I.T., Loading and correlations in the interpretation of principle components (1995) Journal of Applied Statistics, 22 (2), pp. 203-214; Dunteman, G.H., (1989) Principal Components Analysis, , Newbury Park, California: Sage; Chin, W.W., Partial least squares is to LISREL as principal components analysis is to common factor analysis (1995) Technology Studies, 2 (2), pp. 315-319},
correspondence_address1={Jooste, J.L.; Department of Industrial Engineering, South Africa; email: wyhan@sun.ac.za},
publisher={South African Institute of Industrial Engineering},
issn={1012277X},
language={English},
abbrev_source_title={South Afr. J. Ind. Eng.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Guersola2018109,
author={Guersola, M. and De Lima, E.P. and Steiner, M.T.A.},
title={Supply chain performance measurement: a systematic literature review},
journal={International Journal of Logistics Systems and Management},
year={2018},
volume={31},
number={1},
pages={109-131},
doi={10.1504/IJLSM.2018.094193},
note={cited By 26},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052628082&doi=10.1504%2fIJLSM.2018.094193&partnerID=40&md5=54906ed58869c76e4d925d8fc61739b0},
affiliation={Polytechnic School, Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Rua Imaculada Conceição, Curitiba, 1155, Brazil},
abstract={Through a structured overview of the literature, this paper provides an update and maps the current state of research involving the supply chain performance measurement and management. The results can be used as guidance for future studies. The main research limitations are related to the selected scientific databases, document types and search terms, which can exclude papers that could be pertinent. The findings show that the field of research is still very immature. The reviewed publications are from journals from various areas and researchers from several countries, which shows the breadth of interest in the topic and its importance. Many papers conducted studies to identify the issues involved in supply chain performance measurement and management, and to propose frameworks to solve them, but few studies have been made about effective applications of these proposals. Content analysis covers the most addressed approaches used for the performance measurement and management. Copyright © 2018 Inderscience Enterprises Ltd.},
author_keywords={AHP;  Analytic hierarchy process;  Balanced scorecard;  BSC;  Data envelopment analysis;  DEA;  Fuzzy logic;  Performance management;  Performance measurement;  SCOR;  Supply chain;  Supply chain management;  Supply chain operations reference;  Systematic literature review},
references={Abidi, H., De Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: A systematic literature review (2014) Supply Chain Management: An International Journal, 19 (5-6), pp. 592-608; Agami, N., Saleh, M., Rasmy, M., A hybrid dynamic framework for supply chain (2012) IEEE Systems Journal, 6 (3), pp. 469-478; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Alexander, A., Walker, H., Naim, M., Decision theory in sustainable supply chain management: A literature review (2014) Supply Chain Management: An International Journal, 19 (5-6), pp. 504-522; Allen, R., Weights restrictions and value judgements in data envelopment analysis: Evolution, development and future directions (1997) Annals of Operations Research, 73 (13), pp. 13-34; Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), pp. 217-223; Aramyan, L.H., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Arif-Uz-Zaman, K., Ahsan, A.M.M.N., Lean supply chain performance measurement (2014) International Journal of Productivity and Performance Management, 63 (5), pp. 588-612. , http://www.scopus.com/inward/record.url?eid=2-s2.0-84902469167&partnerID=tZOtx3y1, accessed 2 December 2014; Ashby, A., Leat, M., Hudson-Smith, M., Making connections: A review of supply chain management and sustainability literature (2012) Supply Chain Management: An International Journal, 17 (5), pp. 497-516; Azadeh, A., Asadzadeh, S.M., Mehrangohar, M., Fathi, E., Integration of genetic algorithm, analytic hierarchy process and computer simulation for optimisation of operator allocation in manufacturing systems with weighted variables (2014) Int. J. Logistics Systems and Management, 17 (3), pp. 318-339; Bai, C., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Balfaqih, H., Review of supply chain performance measurement systems: 1998–2015 (2016) Computers in Industry, 82, pp. 135-150; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Behrouzi, F., Wong, K.Y., An integrated stochastic-fuzzy modeling approach for supply chain leanness evaluation (2013) International Journal of Advanced Manufacturing Technology, 68 (5-8), pp. 1677-1696; Berrah, L., Clivillé, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Computers in Industry, 58 (7), pp. 709-719. , http://www.scopus.com/inward/record.url?eid=2-s2.0-34447538190&partnerID=tZOtx3y1, accessed 3 December 2014; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62. , http://www.scopus.com/inward/record.url?eid=2-s2.0-34447249471&partnerID=tZOtx3y1, accessed 11 July 2014; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260. , http://www.scopus.com/inward/record.url?eid=2-s2.0-77952362262&partnerID=tZOtx3y1, Amaratunga, D. (Ed): Nos. online accessed 8 December 2014; Bititci, U., Performance measurement: Challenges for tomorrow* (2012) International Journal of Management Reviews, 14 (3), pp. 305-327. , http://www.doi.wiley.com/10.1111/j.1468-2370.2011.00318.x, accessed 15 July 2014; Cai, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521. , http://www.scopus.com/inward/record.url?eid=2-s2.0-57749086941&partnerID=tZOtx3y1, accessed 7 December 2014; Cedillo-Campos, M., Sánchez-Ramírez, C., Dynamic self-assessment of supply chains performance: An emerging market approach (2013) Journal of Applied Research and Technology, 11 (3), pp. 338-347. , http://www.scopus.com/inward/record.url?eid=2-s2.084881185719&partnerID=tZOtx3y1; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428. , Kevin; Chan, F.T.S., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548. , http://www.scopus.com/inward/record.url?eid=2-s2.0-0038046206&partnerID=tZOtx3y1, accessed 8 December 2014; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620. , http://www.scopus.com/inward/record.url?eid=2-s2.070350238227&partnerID=tZOtx3y1, Min, H. (Ed): online accessed 8 December 2014; Cho, D.W., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818. , http://www.scopus.com/inward/record.url?eid=2-s2.0-84858080790&partnerID=tZOtx3y1, accessed 28 July 2014; Colicchia, C., Strozzi, F., Supply chain risk management: A new methodology for a systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 403-418; Cucchiella, F., Implementation of a real option in a sustainable supply chain: An empirical study of alkaline battery recycling (2014) International Journal of Systems Science, 45 (6), pp. 1268-1282; Delbufalo, E., Outcomes of inter-organizational trust in supply chain relationships: A systematic literature review and a meta-analysis of the empirical evidence (2012) Supply Chain Management: An International Journal, 17 (4), pp. 377-402; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38 (6), pp. 6681-6688. , http://www.scopus.com/inward/record.url?eid=2-s2.0-79951576782&partnerID=tZOtx3y1, accessed 3 December 2014; Estampe, D., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Fayezi, S., O’Loughlin, A., Zutshi, A., Agency theory and supply chain management: A structured literature review (2012) Supply Chain Management: An International Journal, 17 (5), pp. 556-570; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Ganga, G.M.D., Carpinetti, L.C.R., Politano, P.R., Gestão do desempenho em cadeias de suprimentos usando lógica fuzzy (2011) Gestão & Produção, 18 (4), pp. 755-774; Gligor, D.M., Holcomb, M.C., Understanding the role of logistics capabilities in achieving supply chain agility: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 438-453; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353. , http://www.scopus.com/inward/record.url?eid=2-s2.0-22944491753&partnerID=tZOtx3y1, accessed 15 October 2014; Hipel, K.W., Kilgour, D.M., Bashar, M.A., Fuzzy preferences in multiple participant decision making (2011) Scientia Iranica, 18 (3), pp. 627-638; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers and Industrial Engineering, 48 (2), pp. 377-394; Irfan, D., Xiaofei, X., Chun, D.S., A SCOR reference model of the supply chain management system in an enterprise (2008) The International Arab Journal of Information Technology, 5 (3), pp. 288-295; Jalali Naini, S.G., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 593-603; Kamal, M.M., Irani, Z., Analysing supply chain integration through a systematic literature review: A normative perspective (2014) Supply Chain Management: An International Journal, 19 (5-6), pp. 523-557; Kaplan, R.S., Norton, D.P., The balanced scorecard – Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Khodabakhshi, M., Tavana, M., Abootaleb, F.B., Optimistic and pessimistic performance and congestion analysis in fuzzy data envelopment analysis (2016) Int. J. Logistics Systems and Management, 24 (1), pp. 1-25; Khodakarami, M., Developing distinctive two-stage data envelopment analysis models: An application in evaluating the sustainability of supply chain management (2015) Measurement, 70, pp. 62-74; Kinra, A., Kotzab, H., A macro-institutional perspective on supply chain environmental complexity (2008) International Journal of Production Economics, 115 (2), pp. 283-295; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of The Operational Research Society, 54 (5), pp. 507-514; Lai, K., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-456; Lee, H., Whang, S., Decentralized multi-echelon supply chains: Incentives and information (1999) Management Science, 45 (5), pp. 633-640; Leuschner, R., Rogers, D.S., Charvet, F.F., A meta-analysis of supply chain integration and firm performance (2013) Journal of Supply Chain Management, 49 (2), pp. 34-57; Liang, L., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49. , http://www.scopus.com/inward/record.url?eid=2-s2.0-33747822841&partnerID=tZOtx3y1, accessed 8 December 2014; Lockamy, A., III, McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Luthra, S., Using AHP to evaluate barriers in adopting sustainable consumption and production initiatives in a supply chain (2016) Intern. Journal of Production Economics, 181, pp. 342-349; Maestrini, V., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Production Economics, 183, pp. 299-315; Mangla, S.K., Kumar, P., Barua, M.K., Risk analysis in green supply chain using fuzzy AHP approach: A case study (2015) Resources, Conservation and Recycling, 104, pp. 375-390; Melnyk, S.A., Is performance measurement and management fit for the future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Miemczyk, J., Johnsen, T.E., Macquet, M., Sustainable purchasing and supply management: A structured literature review of definitions and measures at the dyad, chain and network levels (2012) Supply Chain Management: An International Journal, 17 (5), pp. 478-496; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) Journal of Operations and Supply Chain Management, 6 (2), pp. 94-113; Nederhof, A.J., Bibliometric monitoring of research performance in the social sciences and the humanities: A review (2006) Scientometrics, 66 (1), pp. 81-100; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design – A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Nudurupati, S.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Ohdar, R., Ray, P.K., Performance measurement and evaluation of suppliers in supply chain: An evolutionary fuzzy-based approach (2004) Journal of Manufacturing Technology Management, 15 (8), pp. 723-734. , http://www.scopus.com/inward/record.url?eid=2-s2.0-10444243337&partnerID=tZOtx3y1, accessed 26 November 2014; Olugu, E.U., Wong, K.Y., An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry (2012) Expert Systems with Applications, 39 (1), pp. 375-384. , http://www.scopus.com/inward/record.url?eid=2-s2.081855169991&partnerID=tZOtx3y1, accessed 28 November 2014; Persson, F., Araldi, M., The development of a dynamic supply chain analysis tool-integration of SCOR and discrete event simulation (2009) International Journal of Production Economics, 121 (2), pp. 574-583; Pourghasemi, H.R., Pradhan, B., Gokceoglu, C., Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran (2012) Natural Hazards, 63 (965), pp. 965-996; Power, D., Supply chain management integration and implementation: A literature review (2005) Supply Chain Management: An International Journal, 10 (4), pp. 252-263; Putthinoi, S., Household reverse logistics analysis using the SCOR model to improve home safety of the elderly in the municipality (2015) Int. J. Logistics Systems and Management, 21 (3), pp. 348-364; Ramanathan, U., Performance of supply chain collaboration – A simulation study (2014) Expert Systems with Applications, 41 (1), pp. 210-220; Ramli, N.A., Munisamy, S., Modeling undesirable factors in efficiency measurement using data envelopment analysis: A review (2013) Journal of Sustainability Science and Management, 8 (1), pp. 126-135; Ray, S.C., (2004) Data Envelopment Analysis: Theory and Techniques for Economics and Operations Research, , Cambridge University Press; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: Design and development guidelines (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 805-826. , http://www.scopus.com/inward/record.url?eid=2-s2.0-84885065896&partnerID=tZOtx3y1, Huaccho Huatuco, L.D. and Mo, J.R. (Eds): online accessed 8 December 2014; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28 (5), pp. 581-598; Santa-Eulalia, L.A., Integrated methodological frameworks for modeling agent– Based advanced supply chain planning systems: A systematic literature review (2013) Journal of Industrial Engineering and Management, 4 (4), pp. 624-668; Schaltegger, S., Burritt, R., Measuring and managing sustainability performance of supply chains, review and sustainability supply chain management framework (2014) Supply Chain Management: An International Journal, 19 (3), pp. 232-241; Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68. , http://www.scopus.com/inward/record.url?eid=2-s2.0-34548163843&partnerID=tZOtx3y1, accessed 8 December 2014; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , http://www.scopus.com/inward/record.url?eid=2-s2.033645668377&partnerID=tZOtx3y1, accessed 11 October 2014; Siham, L., Designing supply chain performance measurement and management systems: A systemic perspective (2015) 4th IEEE International Conference on Advanced Logistics and Transport (ICAl T), pp. 211-216; Tajbakhsh, A., Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2015) Journal of Cleaner Production, 105, pp. 74-85; Tan, K.C., Lyman, S.B., Wisner, J.D., Supply chain management: A strategic perspective (2002) International Journal of Operations & Production Management, 22 (5-6), pp. 614-631; Taticchi, P., A review of decision-support tools and performance measurement and sustainable supply chain management (2014) International Journal of Production Research, 52 (21), pp. 6473-6494; Thamsatitdej, P., Green supply chain management, triple bottom line, and organizational theory – Prioritization with fuzzy AHP method (2015) 2015 IEEE International Conference, pp. 1588-1592; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14 (3), pp. 207-222; Wickramatillake, C.D., Measuring performance within the supply chain of a large scale project (2007) Supply Chain Management: An International Journal, 12 (1), pp. 52-129; Wong, C., Towards a theory of supply chain alignment enablers: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 419-437; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management & Data Systems, 107 (3), pp. 361-381; Xian, S., Qiu, D., Zhang, S., A fuzzy principal component analysis approach to hierarchical evaluation model for balanced supply chain scorecard grading (2013) Journal of Optimization Theory and Applications, 159 (2), pp. 518-535. , http://www.scopus.com/inward/record.url?eid=2-s2.0-84886095489&partnerID=tZOtx3y1, accessed 8 December 2014},
correspondence_address1={Guersola, M.; Polytechnic School, Rua Imaculada Conceição, Brazil; email: mariana.guersola@pucpr.br},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Striteska201836,
author={Striteska, M. and Jelinkova, L.},
title={Critical issues of comprehensive performance measurement and management process},
journal={Communications in Computer and Information Science},
year={2018},
volume={877},
pages={36-47},
doi={10.1007/978-3-319-95204-8_4},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051950868&doi=10.1007%2f978-3-319-95204-8_4&partnerID=40&md5=fa7d52a80da9b91d376933e2cee59d14},
affiliation={Institute of Business Economics and Management, Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic; Department of Management Theories, Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia},
abstract={An effectively functioning performance management process has become the key to success in today’s tough competitive environment. It is necessary to have comprehensive knowledge of new approaches to performance measurement in order to create and implement such a process. The main aim of this paper is to contribute toward building on such knowledge by designing a performance measurement and management process diagram. To fulfill this goal, the features of a performance measurement system have been determined based on research in the literature; additionally, key elements that are necessary for a comprehensive process of performance management have been defined using case study analysis. The diagram also identifies critical elements of contemporary performance management process related to the provision of balanced performance indicators and their subsequent incorporation in strategic planning. The findings show that competitive-related performance aspects such as innovation, core competencies, and employee engagement are not sufficiently integrated in performance measurement. © 2018, Springer Nature Switzerland AG.},
author_keywords={Indicators;  Management;  Measurement;  Performance;  Process},
keywords={Indicators (instruments);  Management;  Measurement;  Processing;  Strategic planning;  Supply chain management, Competitive environment;  Comprehensive performance;  Performance;  Performance indicators;  Performance management;  Performance measurement and managements;  Performance measurement system;  Performance measurements, Knowledge management},
references={Allhoff, D., Allhoff, W., (2008) Rétorika a Komunikace, pp. 108-172. , 14th edn, Grada, Prague, 978-80-247-2283-2; Ambler, T., Kokkimaki, F., Puntoni, S., Assessing marketing performance measurement in manufacturing companies (2004) Br. Acount. Rev., 37 (3), pp. 261-297; Bengtsson, M., How to plan and perform a qualitative study using content analysis (2016) Nursingplus Open, 2, pp. 8-14; Bititci, U.S., Carrie, A.S., Turner, T., (2006) Integrated Performance Measurement Systems: Structure and Dynamics. Business Performance Measurement. Theory and Practice, , Cambridge University Presess, Cambridge; Bititci, U.S., Turner, T., Integrated performance measurement systems: Structure and dynamics (2002) Business Performance Measurement: Theory and Practice, , Cambridge University Press, Cambridge, 052-180-342X; Daly, K., Corporate performance metrics to top board agendas (2011) Finan. Exec., 27 (1), pp. 50-53; Frigo, M.L., Krumwiede, K.R., Balanced scorecards: A rising trend in strategic performance measurement (1999) J. Strateg. Perform. Meas., 3 (1), pp. 42-44; Gomes, C.F., Yasin, M.M., Lisboa, J.V., Performance measurement practices in manufacturing firms revisited (2011) Int. J. Oper. Prod. Manag., 31 (1), pp. 5-30. , https://doi.org/10.1108/01443571111098726; Hoque, Z., James, W., Linking balanced scorecard measures to size and market factors: Impact on organizational performance (2000) J. Manag. Account. Res., 12, pp. 23-45; Chow, C.W., van der Stede, W.A., The use and usefulness of nonfinancial performance measures (2006) Manag. Account. Q, 7 (3), p. 8; Ittner, C.D., Larcker, D.F., Assessing empirical research in managerial accounting: A value-based management perspective (2001) J. Account. Econ., 32, pp. 349-410; Ittner, C.D., Larcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial service firms (2003) Account. Organ. Soc., 28 (7), pp. 715-741. , ISSN 0361-3682; Kagioglou, M., Cooper, R., Aouad, G., Performance management in construction: A conceptual framework (2001) Constr. Manag. Econ., 19, pp. 85-95; Kennerly, M., Neely, A., Measuring performance in a changing business environment (2003) Int. J. Oper. Prod. Manag., 23 (2), pp. 213-229. , https://doi.org/10.1108/0144370310458465; Kennerly, M., Neely, A., A framework of the factors affecting the evolution of performance measurement system (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1222-1245; Krippendorff, K., (2012) Content Analysis: An Introduction to Its Methodology, , Sage, Thousand Oaks; Lingle, J., Schiemann, W., From balanced scorecard to strategic gauges: Is measurement worth it? (1996) Manag. Rev., 85, pp. 56-61; Manoochehri, G., (1999) The Road to Manufacturing Excellence: Using Performance Measures to Become World-Class, Industrial Management, pp. 7-13. , March–April; Neely, A., The evolution of performance measurement research: Development in the last decade and a research agenda for the next (2005) Int. J. Oper. Prod. Manag., 17 (11), pp. 1131-1152; Nenadal, J., (2004) Modely měření a zlepšování Spokojenosti zákazníků: Od Teorie K Praxi, , Národní informační středisko pro podporu jakosti, Prague, 80-02-01686-6; Pedersen, E.R.G., Sudzina, F., Which firms use measures? Internal and external factors shaping the adoption of performance measurement systems in Danish firms (2012) Int. J. Oper. Prod. Manag., 32 (1), pp. 4-27; Robinson, H.S., Carrillo, P.M., Anumba, C.J., Al-Ghassani, A.M., Review and implementation of performance management models in construction engineering organizations (2005) Constr. Innov., 5, pp. 203-217. , ISSN 1471-4175; Said, A.A., Hassabelnaby, H.R., Wier, B., An empirical investigation of the performance consequences of nonfinancial measures (2003) J. Manag. Account. Res., 15, pp. 193-223; Scharlau, C., (2010) Trénink úspěšné Komunikace: Jak uspět vkaždém Rozhovoru V práci I osobním životě, pp. 33-57. , Grada, Prague, 978-80-247-3301-2; Stivers, B.P., How nonfinacial performance measures are used (1998) Manag. Account., 79 (8), p. 44. , ProQuest; Striteska, M., Jelinkova, L., Trends in performance management: Comparison and summary of surveys (2015) Finance and Performance of Firms in Science, Education and Practice, 23–24 April, pp. 1443-1479. , ISBN 978-80-7454-482-8; Striteska, M., Jelinkova, L., The characteristics of effective performance measurement system: Case study analysis (2015) Proceedings of Energy, Environment, Development and Economics. Conferences of Inase.Org in Zakynthos, pp. 290-295. , Greece, 16–20 July, 2227-4359, ISBN 978-1-61804-324-5; Tellis, W.M., Application of a case study methodology (1997) Qual. Rep., 3 (3), pp. 1-19; Tsang, A.H.C., Jardine, A.K.S., Kolodny, H., Measuring maintenance performance: a holistic approach (1999) Int. J. Oper. Prod. Manag., 19 (7), pp. 691-692. , https://doi.org/10.1108/01443579910271674, ISSN 0144-3577; Venanzi, D., (2012) Financial Performance Measures and Value Creation: The State of The Art, , https://doi.org/10.1007/978-88-470-2451-9_2, SpringerBriefs in Business; Wagner, J., (2009) Merení výkonnosti: Jak Merit, Vyhodnocovat a využívat Informace O podnikové výkonnosti, pp. 36-39. , Grada Publishing, Praha; Yadav, N., Sushil, V., Sagar, M., Performance measurement and management frameworks: Research trends of the last two decades (2013) Bus. Process Manag. J., 19 (6), pp. 947-970. , https://doi.org/10.1108/bpmj-01-2013-0003},
correspondence_address1={Jelinkova, L.; Department of Management Theories, Slovakia; email: lucie.jelinkova21@gmail.com},
editor={Hadzima B., Uden L., Ting I.},
publisher={Springer Verlag},
issn={18650929},
isbn={9783319952031},
language={English},
abbrev_source_title={Commun. Comput. Info. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Konneh20182101,
author={Konneh, K.V. and Helmi, S.A. and Ma'aram, A. and Hisjam, M.},
title={System dynamics approach to supply chain performance measurement in small and medium enterprise},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2018},
volume={2018-March},
pages={2101-2110},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051482116&partnerID=40&md5=3712b1c09192b4e7e4ccf3640bc33edb},
affiliation={Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Johor Bahru, Johor, 81310, Malaysia; Center for Engineering Education, Universiti Teknologi Malaysia, Johor Bahru, Johor, 81310, Malaysia; Department of Industrial Engineering, Faculty of Engineering, Sebelas Maret University, Surakarta, Indonesia},
abstract={The concept of supply chain management has today become a tool that both manufacturing and service organizations have been using to help win greater market share. Small-medium enterprises have found it difficult to measure their performance due to the complex external environment. In this paper, analysis of a single case study of an SME through survey and interview is done in order to measure the performance of its responsive supply chain. A system dynamics modeling approach is used to model the variables that have greater influence on the performance system through the development of a causal loop and stock and flow diagrams. Comparisons of the experimental results shows that the domestic purchase of the raw material and quality efficiency will help to reduce the future lead-time and import requirements which will help the company to achieve product availability which is the main competitive strategy of a responsive supply chain. © IEOM Society International.},
author_keywords={Performance measurement;  SME;  Supply chain;  System dynamics},
references={Barnes, M., Dickinson, T., Coulton, L., Dransfield, S., Field, J., Fisher, N., Shaw, D., A new approach to performance measurement for small to medium enterprises (1998) In conference proceeding Performance Measurement-Theory and Practice, 2, pp. 86-92; Beamon, B.M., 'Measuring supply chain management' (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Christopher, M., Towill, D., An Integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution and Logistics, 31 (4), pp. 235-246; Chopra, Meindl, (2010) Supply chain management: Strategy, planning, and operation, , (4th ed.). Upper Saddle River, New Jersey; http://www.smecorp.gov.my/images/Publication/Annual-report/BI/annual-report-2013.pdf; Forrester, J.W., (1961) Industrial Dynamics, , MIT Press, Cambridge, Mass; Gunasekaran, A., Patel, C., McCaughey, E.R., 'Framework for supply chain performance management (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Handfield, R.B., Bechtel, 'Role of trust and Relationship Structure in Improving Supply Chain Responsiveness' (2002) Industrial Marketing Management, 31, pp. 367-382; Hvolby, H.H., Thorstensen, A., 'Performance measurement in small and medium-sized enterprises' (2000) Third conference on 'simulating manufacturing excellence in SME', , Coventry UK, April; Jungman, H., Okkonen, J., Rasila, T., Seppä, M., Use of performance measurement in V2C activity (2004) Benchmarking: An International Journal, 11 (2), pp. 175-189; Kaplan, R.S., Norton, D.P., 'The BSC-measures that drive performance (1992) Harvard Business Review January-February, pp. 71-79; Kurien, G.P., Qureshi, M.N., 'Study of performance measurement practices in supply chain management (2011) International Journal of Business Management and Social Sciences, 2 (4), pp. 19-34; http://www.koko.gov.my/lkm/index.cfm; Mason-Jones, R., Naylor, B., Towill, D.R., 'Engineering the leagile supply chain.' (2000) International Journal of Agile Management Systems, 2 (1), pp. 54-61; http://www.moa.gov.my/en/fungsi; Moullin, M., 'Defining performance measurement, ' (2003) Perspective on Performance, 2 (2), p. 3; Neely, A., Gregory, M., Platts, K., 'Performance measurement design, a literature review and research agenda' (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Adams, C., Crowe, P., 'The performance prism in practice' (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Cocca, P., Alberti, M., 'A framework to assess performance measurement systems in SMEs.' (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 186-200; Prater, E., Biehl, M., Smith, M.A., 'International supply chain agility-Tradeoffs between flexibility and uncertainty' (2001) International Journal of Operations & Production Management, 21 (5-6), pp. 823-839; Chalmeta, R., Palomero, S., Matilla, M., 'Methodology to develop a performance measurement system in small and medium-sized enterprises' (2012) International Journal of Computer Integrated Manufacturing, 25 (8), pp. 716-740; Roth, A., De Meyer, A., Ammo, A., 'International manufacturing strategies: A comparative analysis' (1989) Managing International Manufacturing, , K. Ferdows (Ed.). North-Holland: Elsevier Science Publishers; Towill, D.R., 'Industrial dynamics modeling of supply chain' (1996) Logistics Information Management, 9 (4), pp. 43-56; Ward, P.T., Bickford, D.J., Leong, G.K., 'Configurations of manufacturing strategy, business strategy, environment, and structure' (1996) Journal of Management, 22 (4), pp. 597-626; Yu, Z., Yan, H., Edwin Cheng, T.C., Benefits of information sharing with supply chain partnerships (2001) Industrial management & Data systems, 101 (3), pp. 114-121},
correspondence_address1={Helmi, S.A.; Faculty of Mechanical Engineering, Malaysia; email: helmi@utm.my},
publisher={IEOM Society},
issn={21698767},
isbn={9781532359446},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Staudt2018607,
author={Staudt, F.H. and Alpan, G. and Di Mascolo, M. and Rodriguez, C.M.T.},
title={A scale definition for an integrated performance indicator},
journal={ILS 2018 - Information Systems, Logistics and Supply Chain, Proceedings},
year={2018},
pages={607-616},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050993936&partnerID=40&md5=486bae32f0b2ad4a9726f891d2c7fe33},
affiliation={Federal University of Santa Catarina, Joinville, Brazil; University Grenoble Alpes, Grenoble, France; Federal University of Santa Catarina, Florianópolis, Brazil},
abstract={Performance measurement system provides information to compare the current results with the present objectives. As the processes have become more complex, more indicators are necessary to assess the performance of individual processes, making harder the managers evaluation of the global performance of the system. One possible solution to help managers in evaluating indicators interrelationships is to aggregate indicators in an integrated measure. However, besides the definition of such an integrated indicator, it is also necessary to define a scale for it to permit interpretation. Thus, this paper demonstrates a methodology to build a scale for an integrated performance indicator using an optimization algorithm to define the scale boundaries. The methodology is applied in a warehouse scenario for illustration. © ILS 2018 - Information Systems, Logistics and Supply Chain, Proceedings. All rights reserved.},
author_keywords={Integrated indicator;  Optimization;  Performance evaluation;  Scale},
keywords={Information systems;  Information use;  Managers;  Optimization;  Supply chains, Evaluating indicators;  Global performance;  Optimization algorithms;  Performance evaluations;  Performance indicators;  Performance measurement system;  Scale, Benchmarking},
references={Kucukaltan, B., Irani, Z., Aktas, E., A decision support model for identification and prioritization of key performance indicators in the logistics industry (2016) Comput. Human Behav., 65, pp. 346-358; Gunasekaran, A., Ngai, E.W.T., The future of operations management: An outlook and analysis (2012) Int. J. Prod. Econ., 135, pp. 687-701; Wu, Y.-C.J., Dong, T.-P., Chang, C.-L., Liao, Y.-C., A collaborative learning lesson from using effective information technology combinations (2015) Comput. Human Behav., 51, pp. 986-993; Gunasekaran, A., Irani, Z., Choy, K.-L., Filippi, L., Papadopoulos, T., Performance measures and metrics in outsourcing decisions: A review for research and applications (2015) Int. J. Prod. Econ., 161, pp. 153-166; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) J. Oper. Manag., 22, pp. 209-217; Maté, A., Trujillo, J., Mylopoulos, J., Specification and derivation of key performance indicators for business analytics: A semantic approach (2017) Data Knowl. Eng., 108, pp. 30-49; Rodriguez, R.R., Saiz, J.J.A., Bas, A.O., Quantitative relationships between key performance indicators for supporting decision-making processes (2009) Comput. Ind., 60, pp. 104-113; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) Eur. J. Oper. Res., 156, pp. 267-286; Franceschini, F., Galetto, M., Maisano, D., Mastrogiacomo, L., Properties of performance indicators in operations management: A reference framework (2008) Int. J. Product. Perform. Manag., 57, pp. 137-155; Lauras, M., Marques, G., Gourc, D., Towards a multi-dimensional project performance measurement system (2010) Decis. Support Syst., 48, pp. 342-353; Franceschini, F., Galetto, M., Maisano, D., Viticchi, L., The condition of uniqueness in manufacturing process representation by performance/quality indicators (2006) Qual. Reliab. Eng. Int., 22, pp. 567-580; Sohn, S., Han, H., Jeon, H., Development of an air force warehouse logistics index to continuously improve logistics capabilities (2007) Eur. J. Oper. Res., 183, pp. 148-161; Johnson, A., McGinnis, L., Performance measurement in the warehousing industry (2011) IIE Trans, 43, pp. 220-230; Luo, S., Liu, L., Shu-Quan, L., Comprehensive evaluation of logistics performance for agricultural products distribution center (2010) 2010 2nd International Conference on E-Business and Information System Security, pp. 1-4. , IEEE; Jung, H.W., Investigating measurement scales and aggregation methods in spice assessment method (2013) Inf. Softw. Technol., 55, pp. 1450-1461; Staudt, F.H., Di Mascolo, M., Alpan, G., Rodriguez, C.M.T., Warehouse performance measurement: Classification and mathematical expressions of indicators (2014) ILS 2014 - 5th Int. Conf. Inf. Syst. Logistics Supply Chain, 22; Manly, B.F.J., (2004) Multivariate Statistical Methods: A Primer, pp. 75-90. , Chapman & Hall/ CRC; Staudt, F.H., (2015) Global Warehouse Management: A Methodology to Determine An Integrated Performance Measurement, , Université Grenoble Alpes; Gentle, J.E., (2007) Matrix Algebra - Theory, Computations, and Applications in Statistics, , Springer; Powell, M.J.D., Mathematical programming essays in honor of george b. dantzig. part II - on the quadratic programming algorithm of goldfarb and idnani (1985) Mathematical Programming Studies, pp. 44-61. , R. W. Cottle, Ed. Heidelberg: Springer Berlin Heidelberg; (2012) Cades Solutions - User'S Manual, , http://vesta-system.cades-solutions.com/, Vesta-Systems, version 2.9.” Vesta-Systems, Grenoble},
publisher={INSA Lyon},
language={English},
abbrev_source_title={ILS - Inf. Syst., Logist. Supply Chain, Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Saleheen201870,
author={Saleheen, F. and Habib, M.M. and Hanafi, Z.},
title={Supply chain performance measurement model: A literature review},
journal={International Journal of Supply Chain Management},
year={2018},
volume={7},
number={3},
pages={70-78},
note={cited By 20},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049190189&partnerID=40&md5=4b3496abfd54c3638393b21e09bd2d75},
affiliation={School of Quantitative Science, Universiti Utara Malaysia (UUM), Kedah, Sintok, 06010, Malaysia},
abstract={Supply chain management is being envisaged as an extended enterprise connecting business in different places and facilitating allies to propel competitive advantage in the era of globalization. Substantial research has been undertaken along with literatures on supply chain performance management from cost and non-cost standpoint, strategic, functional or emphasis on operational aspects; perspectives from commercial as well as financial arenas. In order to gratify customer orders rapidly and efficiently than competitors, supply chain needs to warrant continuous upgradation of its processes and competitive strategies and to apprehend how supply chain contests? it is indispensable to realize the overall performance of the supply chain. However, still many companies miscarry to acquire effective performance measurement tools and techniques to attain integrated supply chain management (SCM). The rationale of this paper is to evaluate the literature on performance measurement for supply chain to apprehend current practices, recognize gaps and advocate future research itineraries. The paper also offers a synopsis and appraisal of the performance measurement used through different supply chain models. © ExcelingTech Pub, UK.},
author_keywords={Activity-Based Costing;  Balanced Score Card (BSC);  Hybrid of SCOR and BSC Approaches;  Supply Chain Operations Reference Model (SCOR);  Supply chain performance measurement system (SCPMS)},
references={Kurien, G.P., Qureshi, M.N., 'Study of performance measurement practices in supply chain management' (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Saleheen, F., Habib, M.M., Hanafi, Z., 'Performance measurement framework of supply chain management: A multi criteria decision analysis (MCDA), AHP approach' (2017) International Conference on Business and Management (ICBM), , Online-Version in Dhaka, Bangladesh, 21-22 September; Beamon, B.M., Balcik, B., 'Performance measurement in humanitarian relief chains' (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25. , 2008; Bromberg, D., 'Performance measurement: A system with a purpose or a purposeless system?' (2009) Public Performance and Management Review, 33 (2), pp. 214-221; Kaplan, R.S., Norton, D.P., 'Transforming the balanced scorecard from performance measurement to strategic management: Part 1' (2001) Accounting Horizons, 15 (1), pp. 87-104; Kaplan, R.S., Norton, D.P., 'Transforming the balanced scorecard from performance measurement to strategic management: Part 1' (2001) Accounting Horizons, 15 (1), pp. 87-104; Abu-Suleiman, A., Boardman, B., Priest, J.W., 'A Framework for an Integrated Supply Chain Performance Management System,' (2004) Industrial Engineering Research Conference, , Houston, TX; Lohman, C., Fortuin, L., Wounters, M., 'Designing a Performance Measurement System a Case Study,' (2004) European Journal of Operational Research, 156 (2), pp. 267-286. , 2004; Huang, S.H., Sheoran, S.K., Wang, G., 'A Review and Analysis of Supply Chain Operations Reference (SCOR) Model,' (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Lockamy, A., III, McCormack, K., 'Linking SCOR Planning Practices to Supply Chain Performance: An Exploratory Study,' (2004) International Journal of Operations and Production Management, 24 (11-12), pp. 1192-1218; Ramaa, A., Rangaswamy, T., Subramanya, K., 'A Review of Literature on Performance Measurement of Supply Chain Network,' (2009) 2nd International Conference on Emerging Trends in Engineering and Technology, pp. 802-807; Lambert, D.M., Pohlen, T.L., 'Supply Chain Metrics,' (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Otto, A., Kotzab, H., 'Does Supply Chain Management Really Pay? Six Perspectives to Measure the Performance of Managing a Supply Chain,' (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Lapide, L., 'What about Measuring Supply Chain Performance?' (2000) AMR Research, ASCET-White Paper, 2 (15), pp. 287-297; Gunasekaran, A., Patel, C., McGaughey, R.E., 'A Framework for Supply Chain Performance Measurement,' (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Christopher, M., (2005) 'Creating Value-Adding Networks', , FT Prentice Hall, London; Kurien, G.P., Qureshi, M.N., 'Study of Performance Measurement Practices in Supply Chain Management,' (2011) International Journal of Business Management and Social Sciences, 2 (4), pp. 19-34; Zulkiffli, S., Perera, N., 'A literature analysis on business performance for SMES-subjective or objective measures?' (2011) 2011 SIBR Conference on Interdisciplinary Business and Economics Research, pp. 1-9. , Bangkok, Thailand: Society of Interdisciplinary Business Research (SIBR); Neely, A., 'The evolution of performance measurement research: Developments in the last decade and a research agenda for the next' (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Medori, D., Steeple, D., 'A Framework for Auditing and Enhancing Performance Measurement Systems,' (2000) International Journal of Operations and Production Management', 20 (10), pp. 1119-1145; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking, 16 (5), pp. 702-723; Chan, F.T.S., Qi, H.J., 'An Innovative Performance Measurement Method for Supply Chain Management,' (2003) Supply Chain Management: An International Journal, 8 (3-4), pp. 209-223; Chan, F.T.S., 'Performance Measurement in a Supply Chain,' (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Charan, P., Shankar, R., Baisya, R., 'Selection of Supply Chain Performance Measurement System Using AHP Approach,' (2007) POMS 18th Annual Conference Dallas, , Texas, May 4; Sharma, M.K., Bhagwat, R., 'An Integrated BSC-AHP Approach for Supply Chain Management Evaluation,' (2007) Journal of Measuring Business Excellence, 11 (3), pp. 57-68; Wong, W.P., Wong, K.Y., 'Supply Chain Performance Measurement System Using DEA Modeling,' (2007) Journal of Industrial Management and Data Systems Information, 107 (3), pp. 361-381; Neely, A., Adams, C., Kennerley, M., (2002) 'The Performance Prism: The Scorecard for Measuring and Managing Business Success', , Financial Times-Prentice Hall: London; Lockamy, A., McCormack, K., 'Linking SCOR planning practices to Supply chain performance: an exploratory study' (2004) International Journal of Operations & Production Management, 24 (11-12), pp. 1192-1218; Huang, S.H., Sheoran, S.K., Wang, G., 'A Review and Analysis of Supply Chain Operations Reference (SCOR) Model,' (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Agami, N., Saleh, M., Rasmy, M.H., ' Supply Chain Performance Measurement Approaches: Review and Classification' (2012) Journal of Organizational Management Studies, 2012 (2012), p. 20; Taticchi, P., Tonelli, F., Cagnazzo, L., 'Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Basu, R., Little, C., Millard, C., 'Case study: A fresh approach of the Balanced Scorecard in the Heathrow Terminal 5 project.' (2009) Measuring Business Excellence, 13 (4), pp. 22-33; Rajesh, R., 'Generic balanced scorecard framework for third party logistics service provider.' (2012) International Journal of Production Economics, 140 (1), pp. 269-282; Barber, E., 'How to measure the 'value' in value chains' (2008) International Journal of Physical Distribution & Logistics Management, 38 (9), pp. 685-698; Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R., 'Development of a balanced scorecard: An integrated approach of Interpretive Structural Modeling and Analytic Network Process' (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 25-59; Reefke, H., Trocchi, M., 'Balanced scorecard for sustainable supply chains: design and development guidelines.' (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 805-826; Cagnazzo, L., Taticchi, P., Brun, A., 'The role of performance measurement systems to support quality improvement initiatives at supply chain level' (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Agami, N., Saleh, M.R., 'M. A Hybrid Dynamic Framework for Supply Chain Performance Improvement (2012) IEEE Systems Journal, 6 (3), pp. 469-478},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Maharaja201839,
author={Maharaja, R. and Devadasan, S.R. and Sakthivel, M.},
title={Supply chain performance measurement: A future research agenda for acquiring competitiveness through the implementation of leagile manufacturing paradigm},
journal={International Journal of Services and Operations Management},
year={2018},
volume={30},
number={1},
pages={39-50},
doi={10.1504/IJSOM.2018.091439},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048392806&doi=10.1504%2fIJSOM.2018.091439&partnerID=40&md5=3feb31c10481e942cdfedfb4868053cd},
affiliation={Lakshmi Machine Works Ltd, Periyanaickenpalayam, Coimbatore, Tamilnadu, India; Production Engineering Department, PSG College of Technology, Coimbatore, Tamilnadu, 641004, India; Regional Campus, Anna University, Somaiyampalayam, Coimbatore, Tamilnadu, India},
abstract={In this paper, a literature survey conducted to study the state of the art research and pinpoint the future agenda of research on leagile manufacturing is reported. The information and knowledge derived by conducting this literature survey have indicated that the researchers have been enunciating the determination of a juncture which is called the decoupling point to determine the proportion of lean and agile manufacturing implementation in the concerned traditional manufacturing companies. These researchers have also found that supply chain management plays a crucial role in ensuring the successful implementation of leagile manufacturing paradigm. The ultimate results of conducting this literature survey revealed that efforts are yet to be made for measuring the process of achieving leagility through supply chains. At this moment, researchers are required to contribute a model for measuring the performance of leagile supply chains. In this context, this paper ends with suggesting the steps to be followed for developing leagile supply chain measurement model. Such model will be useful for the engineers and managers for strengthening the weak spots to successfully implement leagile manufacturing paradigm in traditional manufacturing companies and acquire competitive strength. Copyright © 2018 Inderscience Enterprises Ltd.},
author_keywords={Decoupling point;  Leagile manufacturing paradigm;  Leagile supply chain;  Supply chain management;  Supply chain performance measurement},
references={Abdollahi, M., Arvan, M., Razmi, J., An integrated approach for supplier portfolio selection: Lean or agile? (2015) Expert Systems with Applications, 42 (1), pp. 679-690; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagility supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Banomyong, R., Veerakachen, V., Supatn, N., Implementing leagility in reverse logistics channels (2008) International Journal of Logistics: Research and Applications, 11 (1), pp. 31-47; Bortolotti, T., Boscari, S., Danese, P., Successful lean implementation: Organizational culture and soft lean practices (2015) International Journal of Production Economics, 160, pp. 182-201; Buyukozkan, G., Kayakutlu, G., Karakadilar, I.S., Assessment of lean manufacturing effect on business performance using Bayesian belief networks (2015) Expert Systems with Applications, 42 (19), pp. 6539-6551; Chan, F.T.S., Kumar, V., Performance optimization of a leagility inspired supply chain model: A CFGTSA algorithm based approach (2009) International Journal of Production Research, 47 (3), pp. 777-799; Chan, F.T.S., Kumar, V., Tiwari, M.K., The relevance of outsourcing and leagility strategies in performance optimization of an integrated process planning and scheduling model (2009) International Journal of Production Research, 47 (1), pp. 119-142; Christopher, M., Towill, D.R., The supply chain strategy conundrum: To be lean or agile or to be lean and agile? (2002) International Journal of Logistics Management, 5 (3), pp. 299-309; Ebina, T., Matsushima, N., Shimizu, D., Product differentiation and entry timing in a continuous time spatial competition model (2015) European Journal of Operation Research, 247 (3), pp. 904-913; Garg, R., Kumar, V., Manufacturing system: Flexibility perspective (2013) International Journal of Engineering Trends and Technology, 4 (6), pp. 2305-2312; Garn, W., Aitken, J., Agile factorial production for a single manufacturing line with multiple products (2015) European Journal of Operational Research, 245 (3), pp. 754-766; Huang, Y.Y., Li, S.J., How to achieve leagility: A case study of a personal computer original equipment manufacturer in Taiwan (2010) Journal of Manufacturing Systems, 29 (2-3), pp. 63-70; Jones, R.M., Naylor, B., Towill, D.R., Lean, agile or leagility? Matching your supply chain to the marketplace (2000) International Journal of Production Research, 38 (17), pp. 4061-4070; Khanchanapong, T., Prajogo, D., Sohal, A.S., Cooper, B.K., Yeung, A.C.L., Cheng, T.C.E., The unique and complementary effects of manufacturing technologies and lean practices on manufacturing operational performance (2014) International Journal of Production Economics, 153, pp. 191-203; Krishnamurthy, R., Yauch, C.A., Leagile manufacturing: A proposed corporate infrastructure (2007) International Journal of Operations and Production Management, 27 (6), pp. 588-604; Kupiainen, E., Mantyla, M.V., Itkonen, J., Using metrics in agile and lean software development - A systematic literature review of industrial studies (2015) International Journal of Information and Software Technology, 62, pp. 143-163; Mason-Jones, R., Naylor, B., Towill, D.R., Engineering the leagile supply chain (2000) International Journal of Agile Management Systems, 2 (1), pp. 54-61; Mellat-Parast, M., A longitudinal assessment of the linkages among the Baldrige criteria using independent reviewers' scores (2015) International Journal of Production Economics, 164, pp. 24-34; Naim, M., Gosling, J., On leanness, agility and leagile supply chains (2010) International Journal of Production Economics, 131 (2), pp. 342-354; Naylor, J.B., Naim, M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (2), pp. 107-118; Nieuwenhuis, P., Katsifou, E., More sustainable automotive production through understanding decoupling points in leagile manufacturing (2015) Journal of Cleaner Production, 95, pp. 232-241; Purvis, L., Gosling, J., Naim, M., The development of a lean, agile and leagile supply network taxonomy based on different types of flexibility (2014) International Journal of Production Economics, 151, pp. 100-111; Soni, G., Kodali, R., Evaluating reliability and validity of lean, agile and leagile supply chain constructs Indian manufacturing industry (2012) Production Planning & Control, 23 (10-11), pp. 864-884; Stratton, R., Warburton, R.D.H., The strategic integration of agile and lean supply (2003) International Journal of Production Economics, 85 (2), pp. 183-198; Suarez, E., Calvo-Mora, A., Roldán, J.L., The role of strategic planning in excellence management systems (2016) European Journal of Operational Research, 248 (2), pp. 532-542; Valmohammadi, C., Roshanzamir, S., The guidelines of improvement: Relations among organizational culture, TQM and performance (2015) International Journal of Production Economics, 164, pp. 167-178; Vinodh, S., Aravindraj, S., Evaluation of leagility in supply chains using fuzzy logic approach (2012) International Journal of Production Research, 51 (4), pp. 1186-1195; Wu, C., Strategic and operational decisions under sales competition and collection competition for end-of-use products in remanufacturing (2015) International Journal of Production Economics, 169, pp. 11-20; Zhang, Z., Sharifi, H., A methodology for achieving agility in manufacturing organizations (2000) International Journal of Operations and Production Management, 20 (4), pp. 496-513},
correspondence_address1={Maharaja, R.; Lakshmi Machine Works LtdIndia; email: maharajah-r@yahoo.co.in},
publisher={Inderscience Publishers},
issn={17442370},
language={English},
abbrev_source_title={Int. J. Serv. Oper. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jeble2018513,
author={Jeble, S. and Dubey, R. and Childe, S.J. and Papadopoulos, T. and Roubaud, D. and Prakash, A.},
title={Impact of big data and predictive analytics capability on supply chain sustainability},
journal={International Journal of Logistics Management},
year={2018},
volume={29},
number={2},
pages={513-538},
doi={10.1108/IJLM-05-2017-0134},
note={cited By 137},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047940350&doi=10.1108%2fIJLM-05-2017-0134&partnerID=40&md5=dd9e94a8b349237fbeeda9f4113873e6},
affiliation={Symbiosis Centre for Research and Innovation, Symbiosis International University, Pune, India; Montpellier Business School, Montpellier, France; Plymouth Business School, Plymouth University, Plymouth, United Kingdom; Kent Business School, University of Kent, Kent, United Kingdom; Montpellier Research in Management, Montpellier Business School, Montpellier, France; School of General Management, National Institute of Construction Management and Research, Pune, India},
abstract={Purpose: The purpose of this paper is to develop a theoretical model to explain the impact of big data and predictive analytics (BDPA) on sustainable business development goal of the organization. Design/methodology/approach: The authors have developed the theoretical model using resource-based view logic and contingency theory. The model was further tested using partial least squares-structural equation modeling (PLS-SEM) following Peng and Lai (2012) arguments. The authors gathered 205 responses using survey-based instrument for PLS-SEM. Findings: The statistical results suggest that out of four research hypotheses, the authors found support for three hypotheses (H1-H3) and the authors did not find support for H4. Although the authors did not find support for H4 (moderating role of supply base complexity (SBC)), however, in future the relationship between BDPA, SBC and sustainable supply chain performance measures remain interesting research questions for further studies. Originality/value: This study makes some original contribution to the operations and supply chain management literature. The authors provide theory-driven and empirically proven results which extend previous studies which have focused on single performance measures (i.e. economic or environmental). Hence, by studying the impact of BDPA on three performance measures the authors have attempted to answer some of the unresolved questions. The authors also offer numerous guidance to the practitioners and policy makers, based on empirical results. © 2018, Emerald Publishing Limited.},
author_keywords={Big data and predictive analytics (BDPA);  Contingency theory (CT);  India;  Partial least squares (PLS);  Resource-based view (RBV);  Structural equation modeling;  Supply base complexity (SBC);  Supply chain management (SCM);  Sustainability},
references={Abbott, D., (2014) Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst, , John Wiley & Sons, NJ; Agarwal, R., Dhar, V., Editorial – big data, data science, and analytics: the opportunity and challenge for IS research (2014) Information Systems Research, 25 (3), pp. 443-448; Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R., Childe, S.J., How to improve firm performance using big data analytics capability and business strategy alignment? (2016) International Journal of Production Economics, 182, pp. 113-131; Amit, R., Schoemaker, P.J., Strategic assets and organizational rent (1993) Strategic Management Journal, 14 (1), pp. 33-46; An, Q., Wen, Y., Xiong, B., Yang, M., Chen, X., Allocation of carbon dioxide emission permits with the minimum cost for Chinese provinces in big data environment (2017) Journal of Cleaner Production, 142, pp. 886-893; Aragón-Correa, J.A., Sharma, S., A contingent resource-based view of proactive corporate environmental strategy (2003) Academy of Management Review, 28 (1), pp. 71-88; Ashby, M.F., (2012) Materials and the Environment: Eco-Informed Material Choice, , Elsevier, Oxford; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Barney, J.B., Organizational culture: can it be a source of sustained competitive advantage? (1986) Academy of Management Review, 11 (3), pp. 656-665; Barney, J.B., Looking inside for competitive advantage (1995) The Academy of Management Executive, 9 (4), pp. 49-61; Barney, J., Wright, M., Ketchen, D.J., Jr., The resource-based view of the firm: ten years after 1991 (2001) Journal of Management, 27 (6), pp. 625-641; Barratt, M., Oke, A., Antecedents of supply chain visibility in retail supply chains: a resource-based theory perspective (2007) Journal of Operations Management, 25 (6), pp. 1217-1233; Becker, W.C., Consequences of different kinds of parental discipline (1964) Review of Child Development Research in Review of Child Development Research, pp. 169-208. , Hoffman, M.L. and Hoffman, L.W.(Eds), Russell Sage Foundation, New York, NY; Bharadwaj, A.S., A resource-based perspective on information technology capability and firm performance: an empirical investigation (2000) MIS Quarterly, 24 (1), pp. 169-196; Bhatt, G.D., Grover, V., Types of information technology capabilities and their role in competitive advantage: an empirical study (2005) Journal of Management Information Systems, 22 (2), pp. 253-277; Bin, S., Zhiquan, Y., Jonathan, L.S.C., Jiewei, D.K., Kurle, D., Cerdas, F., Herrmann, C., A big data analytics approach to develop industrial symbioses in large cities (2015) Procedia CIRP, 29, pp. 450-455; Bose, R., Advanced analytics: opportunities and challenges (2009) Industrial Management & Data Systems, 109 (2), pp. 155-172; Bozarth, C.C., Warsing, D.P., Flynn, B.B., Flynn, E.J., The impact of supply chain complexity on manufacturing plant performance (2009) Journal of Operations Management, 27 (1), pp. 78-93; Braganza, A., Brooks, L., Nepelski, D., Ali, M., Moro, R., Resource management in big data initiatives: processes and dynamic capabilities (2017) Journal of Business Research, 70, pp. 328-337; Brandon‐Jones, E., Squire, B., Autry, C.W., Petersen, K.J., A contingent resource‐based perspective of supply chain resilience and robustness (2014) Journal of Supply Chain Management, 50 (3), pp. 55-73; Brundtland, G.H., Khalid, M., (1987) Our Common Future, , Oxford University Press, Oxford, New York, NY; Brush, T.H., Artz, K.W., Toward a contingent resource-based theory: the impact of information asymmetry on the value of capabilities in veterinary medicine (1999) Strategic Management Journal, 20 (3), pp. 223-250; Bughin, J., Chui, M., Manyika, J., Clouds, big data, and smart assets: Ten tech-enabled business trends to watch (2010) McKinsey Quarterly, 56 (1), pp. 75-86; Burns, T.E., Stalker, G.M., (1961) The management of innovation”, , Tavistock, London; Caridi, M., Crippa, L., Perego, A., Sianesi, A., Tumino, A., Do virtuality and complexity affect supply chain visibility? (2010) International Journal of Production Economics, 127 (2), pp. 372-383; Carmeli, A., Tishler, A., Resources, capabilities, and the performance of industrial firms: a multivariate analysis (2004) Managerial and Decision Economics, 25 (6-7), pp. 299-315; Chae, H.C., Koh, C.E., Prybutok, V.R., Information technology capability and firm performance: contradictory findings and their possible causes (2014) MIS Quarterly, 38 (1), pp. 305-326; Chandra Shukla, A., Deshmukh, S.G., Kanda, A., Environmentally responsive supply chains: learnings from the Indian auto sector (2009) Journal of Advances in Management Research, 6 (2), pp. 154-171; Chen, H., Chiang, R.H., Storey, V.C., Business intelligence and analytics: from big data to big impact (2012) MIS Quarterly, 36 (4), pp. 1165-1188; Chin, W.W., Commentary: issues and opinion on structural equation modeling (1998) MIS Quarterly, 22 (1), pp. Vii-Xvi; Chin, W.W., How to write up and report PLS analyses (2010) Handbook of Partial Least Squares, Handbooks of Computational Statistics, pp. 655-690. , Esposito Vinzi, V.(Eds), Springer, Berlin, Heidelberg; Choi, T.Y., Krause, D.R., The supply base and its complexity: implications for transaction costs, risks, responsiveness, and innovation (2006) Journal of Operations Management, 24 (5), pp. 637-652; Cohen, J., (1988) Statistical Power Analysis for the Behavioral Sciences, , 2nd ed., Lawrence Earlbaum Associates, Hillsdale, NJ; Cohen, J., A power primer (1992) Psychological Bulletin, 112 (1), pp. 155-159; Davenport, T.H., How strategists use ‘big data’ to support internal business decisions, discovery and production (2014) Strategy & Leadership, 42 (4), pp. 45-50; Davenport, T.H., Dyché, J., Big data in big companies (2013) International Institute for Analytics, p. 3. , www.sas.com/content/dam/SAS/en_us/doc/research1/big-data-big-companies-executive-summary-106462.pdf, p.: (accessed, May 17, 2017; Davenport, T.H., Patil, D.J., Data scientist (2012) Harvard Business Review, 90 (5), pp. 70-76; De Gennaro, M., Paffumi, E., Martini, G., Big data for supporting Low-Carbon road Transport Policies in Europe: applications, challenges and opportunities (2016) Big Data Research, 6, pp. 11-25; De Geus, A.P., Planning as learning (1988) Harvard Business Review, 66 (2), pp. 70-74; Dubey, R., Gunasekaran, A., Education and training for successful career in big data and business analytics (2015) Industrial and Commercial Training, 47 (4), pp. 174-181; Dubey, R., Gunasekaran, A., Childe, S.J., Wamba, S.F., Papadopoulos, T., The impact of big data on world-class sustainable manufacturing (2016) The International Journal of Advanced Manufacturing Technology, 84 (1-4), pp. 631-645; Dubey, R., Altay, N., Gunasekaran, A., Blome, C., Papadopoulos, T., Childe, S.J., Supply chain agility, adaptability and alignment: empirical evidence from the Indian Auto Components Industry (2018) International Journal of Operations & Production Management, 38 (1), pp. 129-148; Duncan, R.B., Characteristics of organizational environments and perceived environmental uncertainty (1972) Administrative Science Quarterly, 17 (3), pp. 313-327; Elkington, J., Towards the sustainable corporation: win-win-win business strategies for sustainable development (1994) California Management Review, 36 (2), pp. 90-100; Fawcett, S.E., Waller, M.A., Miller, J.W., Schwieterman, M.A., Hazen, B.T., Overstreet, R.E., A trail guide to publishing success: tips on writing influential conceptual, qualitative, and survey research (2014) Journal of Business Logistics, 35 (1), pp. 1-16; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (3), pp. 39-50; Foster, R., Kaplan, S., (2001) Creative Destruction, , McKinsey & Company Inc; Garratt, B., Learning is the core of organisational survival: action learning is the key integrating process (1987) Journal of Management Development, 6 (2), pp. 38-44; Goldberg, R.A., Yagan, J.D., (2007) McDonald’s Corporation: Managing a Sustainable Supply Chain, p. 16. , p., Harvard Business School, Boston, MA; Grant, R.M., The resource-based theory of competitive advantage: implications for strategy formulation (1991) California Management Review, 33 (3), pp. 114-135; Grant, R.M., Toward a knowledge‐based theory of the firm (1996) Strategic Management Journal, 17 (S2), pp. 109-122; Guide, V.D.R., Ketokivi, M., Notes from the editors: redefining some methodological criteria for the journal (2015) Journal of Operations Management, (37), pp. v-viii; Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S., Big data and predictive analytics for supply chain and organizational performance (2017) Journal of Business Research, 70, pp. 308-317; Gupta, M., George, J.F., Toward the development of a big data analytics capability (2016) Information & Management, 53 (8), pp. 1049-1064; Hair, J.F., Jr., Hult, G.T.M., Ringle, C., Sarstedt, M., (2016) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), , Sage Publications, CA; Hair, J.F., Sarstedt, M., Ringle, C.M., Mena, J.A., An assessment of the use of partial least squares structural equation modeling in marketing research (2012) Journal of the Academy of Marketing Science, 40 (3), pp. 414-433; Hazen, B.T., Overstreet, R.E., Boone, C.A., Suggested reporting guidelines for structural equation modeling in supply chain management research (2015) The International Journal of Logistics Management, 26 (3), pp. 627-641; Huang, S., Chaovalitwongse, W.A., Computational optimization and statistical methods for big data analytics: applications in neuroimaging (2015) The Operations Research Revolution, , https://doi.org/10.1287/educ; Iyer, A., Seshadri, S., Vasher, R., (2009) Toyota Supply Chain Management: A Strategic Approach to Toyota’s Renowned System, , McGraw Hill Professional, New York, NY; Ji-fan Ren, S., Fosso Wamba, S., Akter, S., Dubey, R., Childe, S.J., Modelling quality dynamics, business value and firm performance in a big data analytics environment (2017) International Journal of Production Research, 55 (17), pp. 5011-5026; Keeso, A., (2014) Big data and environmental sustainability: a conversation starter, , Working Paper 14-04, Smith School working paper series; Ketokivi, M.A., Schroeder, R.G., Perceptual measures of performance: fact or fiction? (2004) Journal of Operations Management, 22 (3), pp. 247-264; Kitchin, R., Big Data, new epistemologies and paradigm shifts (2014) Big Data & Society, 1 (1); Koo, D., Piratla, K., Matthews, C.J., Towards sustainable water supply: schematic development of big data collection using internet of things (IoT) (2015) Procedia Engineering, 118, pp. 489-497; Koseleva, N., Ropaite, G., Big data in building energy efficiency: understanding of big data and main challenges (2017) Procedia Engineering, 172, pp. 544-549; Kushwaha, G.S., Sharma, N.K., Green initiatives: a step towards sustainable development and firm’s performance in the automobile industry (2016) Journal of Cleaner Production, 121, pp. 116-129; Laney, D., (2001) 3d Data Management: Controlling Data Volume, Velocity and Variety, , META; Laroche, M., Bergeron, J., Barbaro-Forleo, G., Targeting consumers who are willing to pay more for environmentally friendly products (2001) Journal of Consumer Marketing, 18 (6), pp. 503-520; LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N., Big data, analytics and the path from insights to value (2011) MIT Sloan Management Review, 52 (2), pp. 21-32; Lawrence, P.R., Lorsch, J.W., Differentiation and integration in complex organizations (1967) Administrative Science Quarterly, 12 (1), pp. 1-47; Lindsey, A., King, E., McCausland, T., Jones, K., Dunleavy, E., What we know and don’t: eradicating employment discrimination 50 years after the Civil Rights Act (2013) Industrial and Organizational Psychology, 6 (4), pp. 391-413; Ling-Yee, L., Marketing resources and performance of exhibitor firms in trade shows: a contingent resource perspective (2007) Industrial Marketing Management, 36 (3), pp. 360-370; Lokers, R., Knapen, R., Janssen, S., van Randen, Y., Jansen, J., Analysis of Big Data technologies for use in agro-environmental science (2016) Environmental Modelling & Software, 84, pp. 494-504; Luthra, S., Garg, D., Haleem, A., The impacts of critical success factors for implementing green supply chain management towards sustainability: an empirical investigation of Indian automobile industry (2016) Journal of Cleaner Production, 121, pp. 142-158; McAfee, A., Brynjolfsson, E., Davenport, T.H., Patil, D.J., Barton, D., Big data. The management revolution (2012) Harvard Business Review, 90 (10), pp. 61-67; Mani, V., Agrawal, R., Sharma, V., Supplier selection using social sustainability: AHP based approach in India (2014) International Strategic Management Review, 2 (2), pp. 98-112; Mani, V., Agrawal, R., Sharma, V., Supply chain social sustainability: a comparative case analysis in Indian manufacturing industries (2015) Procedia-Social and Behavioral Sciences, 189, pp. 234-251; Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., (2011) Big data: the next frontier for innovation, competition, and productivity”, , www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_frontier_for_innovation, McKinsey Global Institute: (accessed, August 16, 2017; Mata, F.J., Fuerst, W.L., Barney, J.B., Information technology and sustained competitive advantage: a resource-based analysis (1995) MIS Quarterly, 19 (4), pp. 487-505; Mateos-Aparicio, G., Partial least squares (PLS) methods: origins, evolution, and application to social sciences (2011) Communications in Statistics-Theory and Methods, 40 (13), pp. 2305-2317; Miller, D., Friesen, P.H., Strategy‐making and environment: the third link (1983) Strategic Management Journal, 4 (3), pp. 221-235; Pan, Y., Tian, Y., Liu, X., Gu, D., Hua, G., Urban big data and the development of city intelligence (2016) Engineering, 2 (2), pp. 171-178; Pedler, M., Burgoyne, J., Boydell, T., (1991) The Learning Company: A Strategy for Sustainable Growth, , McGraw-Hill, Maidenhead; Peng, D.X., Lai, F., Using partial least squares in operations management research: a practical guideline and summary of past research (2012) Journal of Operations Management, 30 (6), pp. 467-480; Plambeck, E., Lee, H.L., Yatsko, P., Improving environmental performance in your Chinese supply chain (2012) MIT Sloan Management Review, 53 (2), pp. 43-51; Podsakoff, P.M., Organ, D.W., Self-reports in organizational research: Problems and prospects (1986) Journal of Management, 12 (4), pp. 531-544; Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., Podsakoff, N.P., Common method biases in behavioral research: a critical review of the literature and recommended remedies (2003) Journal of Applied Psychology, 88 (5), pp. 879-903; Porter, M.E., Linde, C.V., Toward a new conception of the environment-competitiveness relationship (1995) Journal of Economic Perspectives, 9 (4), pp. 97-118; Reinartz, W., Haenlein, M., Henseler, J., An empirical comparison of the efficacy of covariance-based and variance-based SEM (2009) International Journal of research in Marketing, 26 (4), pp. 332-344; Revans, R.W., What is action learning? (1982) Journal of Management Development, 1 (3), pp. 64-75; Ringle, C.M., Sarstedt, M., Straub, D.W., Editor’s comments: a critical look at the use of PLS-SEM in MIS quarterly (2012) MIS Quarterly, 36 (1), pp. iii-xiv; Ross, J.W., Beath, C.M., Goodhue, D.L., Develop long-term competitiveness through IT assets (1996) Sloan Management Review, 38 (1), pp. 31-45; Smart, C., Vertinsky, I., Strategy and the environment: a study of corporate responses to crises (1984) Strategic Management Journal, 5 (3), pp. 199-213; Song, M., Cen, L., Zheng, Z., Fisher, R., Liang, X., Wang, Y., Huisingh, D., How would big data support societal development and environmental sustainability? Insights and practices (2017) Journal of Cleaner Production, 142, pp. 489-500; Stata, R., Organizational learning: the key to management innovation (1989) Sloan Management Review, 30 (3), pp. 63-74; Svensson, G., Wagner, B., Implementing and managing economic, social and environmental efforts of business sustainability: propositions for measurement and structural models (2015) Management of Environmental Quality: An International Journal, 26 (2), pp. 195-213; Swanson, L.A., Zhang, D.D., Perspectives on corporate responsibility and sustainable development (2012) Management of Environmental Quality: An International Journal, 23 (6), pp. 630-639; Teece, D.J., Intangible assets and a theory of heterogeneous firms (2015) Intangibles, Market Failure and Innovation Performance, , Bounfour, A. and Miyagawa, T.(Eds), Springer, Cham; Thun, J.H., Hoenig, D., An empirical analysis of supply chain risk management in the German automotive industry (2011) International Journal of Production Economics, 131 (1), pp. 242-249; Tomer, J.F., (1987) Organizational Capital: The Path to Higher Productivity and Well-Being, , Praeger Publishers, New York, NY; Trudel, R., Cotte, J., Does it pay to be good? (2009) MIT Sloan Management Review, 50 (2), p. 61; Vachon, S., Klassen, R.D., An exploratory investigation of the effects of supply chain complexity on delivery performance (2002) IEEE Transactions on Engineering Management, 49 (3), pp. 218-230; Virakul, B., Global challenges, sustainable development, and their implications for organizational performance (2015) European Business Review, 27 (4), pp. 430-446; Waller, M.A., Fawcett, S.E., Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management (2013) Journal of Business Logistics, 34 (2), pp. 77-84; Wamba, S.F., Akter, S., Edwards, A., Chopin, G., Gnanzou, D., How ‘big data’ can make big impact: findings from a systematic review and a longitudinal case study (2015) International Journal of Production Economics, 165, pp. 234-246; Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J.F., Dubey, R., Childe, S.J., Big data analytics and firm performance: effects of dynamic capabilities (2017) Journal of Business Research, 70, pp. 356-365; Williamson, O.E., (1975) Markets and Hierarchies: Analysis and Antitrust Implications - A Study in the Economics of Internal Organizations, , Free Press and Macmillan, New York, NY; Wilson, J.P., The triple bottom line: undertaking an economic, social, and environmental retail sustainability strategy (2015) International Journal of Retail & Distribution Management, 43 (4-5), pp. 432-447; Winston, A., Resilience in a hotter world (2014) Harvard Business Review, 92 (4), pp. 56-64; Wixom, B.H., Watson, H.J., An empirical investigation of the factors affecting data warehousing success (2001) MIS Quarterly, 25 (1), pp. 17-41; Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J., Big data in smart farming–a review (2017) Agricultural Systems, 153, pp. 69-80; Wu, Z., Pagell, M., Balancing priorities: decision-making in sustainable supply chain management (2011) Journal of Operations Management, 29 (6), pp. 577-590; Xie, H., He, Y., Xie, X., Exploring the factors influencing ecological land change for China’s Beijing–Tianjin–Hebei region using big data (2017) Journal of Cleaner Production, 142, pp. 677-687; Zadek, S., The path to corporate responsibility (2004) Harvard Business Review, 82 (12); Zhao, R., Liu, Y., Zhang, N., Huang, T., An optimization model for green supply chain management by using a big data analytic approach (2017) Journal of Cleaner Production, 142, pp. 1085-1097; Adomavicius, G., Tuzhilin, A., Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions (2005) IEEE Transactions on Knowledge and Data Engineering, 17 (6), pp. 734-749; Boyd, D., Crawford, K., Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon (2012) Information, Communication & Society, 15 (5), pp. 662-679; Butner, K., The smarter supply chain of the future (2010) Strategy & Leadership, 38 (1), pp. 22-31; Dutta, D., Bose, I., Managing a big data project: the case of ramco cements limited (2015) International Journal of Production Economics, 165, pp. 293-306; Fan, J., Han, F., Liu, H., Challenges of big data analysis (2014) National Science Review, 1 (2), pp. 293-314; Harford, T., Big data: a big mistake? (2014) Significance, 11 (5), pp. 14-19; Hazen, B.T., Boone, C.A., Ezell, J.D., Jones-Farmer, L.A., Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications (2014) International Journal of Production Economics, 154, pp. 72-80; Kim, Y., Jeong, S.R., Dwivedi, R., Zhang, J., Competitive intelligence in social media Twitter: iPhone 6 vs. Galaxy S5 (2016) Online Information Review, 40 (1); Kristof, K., Baedeker, C., Kolberg, S., Liedtke, C., (2008) Policy instruments for resource efficiency, , Refereed Sessions I-II Monday March 10, p. 201; Liu, X., Singh, P.V., Srinivasan, K., A structured analysis of unstructured big data by leveraging cloud computing (2016) Marketing Science, 35 (3), pp. 363-388; McGahan, A., Unlocking the big promise of big data (2013) Totman Management, 6 (1), pp. 53-57; Mayer-Schönberger, V., Cukier, K., (2013) Big Data: A Revolution that will Transform How We Live, Work, and Think, , Houghton Mifflin Harcourt, London; Minelli, M., Chambers, M., Dhiraj, A., (2012) Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses, , John Wiley & Sons, NJ; Nair, P.R., Supply chain analytics (2012) CSI Communications, 33 (9), pp. 11-12; Porter, M.E., (1985) Competitive Advantage: Creating and Sustaining Superior Performance, , FreePress, New York, NY; Provost, F., Fawcett, T., Data science and its relationship to big data and data-driven decision making (2013) Big Data, 1 (1), pp. 51-59; Song, M.L., Fisher, R., Wang, J.L., Cui, L.B., Environmental performance evaluation with big data: theories and methods (2016) Annals of Operations Research, pp. 1-14. , (in press); Van Der Aalst, W., Process mining: overview and opportunities (2012) ACM Transactions on Management Information Systems, 3 (2), pp. 1-17; Zhong, R.Y., Huang, G.Q., Lan, S., Dai, Q.Y., Chen, X., Zhang, T., A big data approach for logistics trajectory discovery from RFID-enabled production data (2015) International Journal of Production Economics, 165, pp. 260-272},
correspondence_address1={Dubey, R.; Montpellier Business SchoolFrance; email: rameshwardubey@gmail.com},
publisher={Emerald Group Publishing Ltd.},
issn={09574093},
language={English},
abbrev_source_title={Int. J. Logist. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Mishra2018932,
author={Mishra, D. and Gunasekaran, A. and Papadopoulos, T. and Dubey, R.},
title={Supply chain performance measures and metrics: a bibliometric study},
journal={Benchmarking},
year={2018},
volume={25},
number={3},
pages={932-967},
doi={10.1108/BIJ-08-2017-0224},
note={cited By 77},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046071908&doi=10.1108%2fBIJ-08-2017-0224&partnerID=40&md5=41190155780ea976d1624469530dec4a},
affiliation={Indian Institute of Technology Kanpur, Kanpur, India; School of Business and Public Administration, California State University, Bakersfield, CA, United States; Kent Business School, University of Kent, Canterbury, United Kingdom; Montpellier Business School, Montpellier, France},
abstract={Purpose: The purpose of this paper is to review the existing literature on supply chain performance measures and metrics (PMMs). It provides a critical evaluation of 234 articles published in past 24 years. Design/methodology/approach: The paper examines the studies published from 1991 to 2014 by adopting the bibliometric technique of citation and co-citation analysis. Findings: The analysis of the results indicate that the number of articles on supply chain PMMs is increasing at its fastest pace in the past few years. Furthermore, the study identifies some of the most influential articles on performance measurement and metrics. Finally, it concludes that there has been a transition from traditional to more sophisticated performance measurement system. Research limitations/implications: This study focuses only on supply chain performance measurement and metrics and excludes research on performance management and control. Thus, researchers may explore and extend this area of research. Originality/value: To the knowledge of the authors, this is the first study to review the literature on supply chain PMMs by using citation and co-citation analysis. The study includes 234 articles over the time of 24 years (1991-2014). © 2018, Emerald Publishing Limited.},
author_keywords={Citation and co-citation analysis;  Performance measurement;  Performance measures and metrics;  Supply chain measurement systems},
references={Aramyan, L.H., Lansink, A.G.J.M.O., Van Der Vorst, J.G.A.J., Kooten, O.V., Performance measurement in agri-food supply chains: a case study (2007) Supply Chain Management, 12 (4), pp. 304-315; Azzone, G., Noci, G., Identifying effective PMSs for the deployment of “green” manufacturing strategies (1998) International Journal of Operations and Production Management, 18 (4), pp. 308-335; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Benjaafar, S., Ramakrishnan, R., Modelling, measurement and evaluation of sequencing flexibility in manufacturing systems (1996) International Journal of Production Research, 34 (5), pp. 1195-1230; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning and Control, 18 (8), pp. 666-680; Bititci, U., Garengo, P., Döfler, V., Mendibil, K., Performance measurement: questions for tomorrow (2009) Advanced Production Management Systems; Bititci, U.S., Modelling of performance measurement systems in manufacturing enterprises (1995) International Journal of Production Economics, 42 (2), pp. 137-147; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: a development guide (1997) International Journal of Operations and Production Management, 17 (5), pp. 522-534; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Production Management, 20 (6), pp. 692-704; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: perceptions of participating managers (2002) International Journal of Operations and Production Management, 22 (11), pp. 1288-1310; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management, 14 (6), pp. 422-428; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223; Chen, C., Ibekwe-SanJuan, F., Hou, J., The structure and dynamics of co-citation clusters: a multiple-perspective co-citation analysis (2010) Journal of the American Society for Information Science, 61 (7), pp. 1386-1409; Chen, I.J., Chung, C.-H., An examination of flexibility measurements and performance of flexible manufacturing systems (1996) International Journal of Production Research, 34 (2), pp. 379-394; Chen, L., Olhanger, J., Tang, O., Manufacturing facility location and sustainability: a literature review and research agenda (2014) International Journal of Production Economics, 149, pp. 154-163; Chen, P., Redner, S., Community structure of the physical review citation network (2010) Journal of Informetrics., 4 (3), pp. 278-290; Chia, A., Goh, M., Hum, S.-H., Performance measurement in supply chain entities: balanced scorecard perspective (2009) Benchmarking, 16 (5), pp. 605-620; Cocca, P., Alberti, M., A framework to assess performance measurement systems in SMEs (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 186-200; Colicchia, C., Strozzi, F., Supply chain risk management: a new methodology for a systematic literature review (2012) Supply Chain Management: An International Journal, 17 (1), pp. 403-418; Cottrill, C.A., Rogers, E.M., Mills, T., Co-citation analysis of the scientific literature of innovation research traditions: Diffusion of innovations and technology transfer (1989) Knowledge, 11 (2), pp. 181-208; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships: the mediating role of socialization mechanisms (2008) International Journal of Operations and Production Management, 28 (3), pp. 238-258; Crawford, K.M., Cox, J.F., Designing performance measurement systems for just-in-time operations (1990) The International Journal of Production Research, 28 (11), pp. 2025-2036; Culnan, M., The intellectual development of management information systems (1986) Management Science, 32 (2), pp. 156-172; De Toni, A., Tonchia, S., Performance measurement systems models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 46-70; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge – Measuring Operations for World-Class Competition, , Dow Jones-Irwin, Homewood, IL; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Eksoz, C., Mansouri, A., Bourlakis, M., Collaborative forecasting in the food supply chain: a conceptual framework (2014) International Journal of Production Economics, 158, pp. 120-135; Esposito, E., Evangelista, P., Investigating virtual enterprise models: literature review and empirical findings (2014) International Journal of Production Economics, 148 (C), pp. 145-157; Fahimnia, B., Sarkis, J., Davarzani, H., Green supply chain management: a review and bibliometric analysis (2015) International Journal of Production Economics, 162, pp. 101-114; Fan, D., Lo, C.K.Y., Ching, V., Kan, C.W., Occupational health and safety issues in operations management: a systematic and citation network analysis review (2014) International Journal of Production Economics, 158, pp. 334-344; Fitzgerald, L., Johnson, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA, London; Flapper, S.D.P., Fortuin, L., Stoop, P.P.M., Towards consistent performance management systems (1996) International Journal of Operations and Production Management, 16 (7), pp. 27-37; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production Planning and Control, 16 (6), pp. 531-544; Forslund, H., Jonsson, P., The impact of forecast information quality on supply chain performance (2007) International Journal of Operations and Production Management, 27 (1), pp. 90-107; Fry, T.D., Cox, J.F., Manufacturing performance: local versus global measures (1989) Production and Inventory Management Journal, 30 (2), p. 52; Fynes, B., Voss, C., Burca, D.S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354; Garengo, P., Bititci, U., Towards a contingency approach to performance measurement: an empirical study in Scottish SMEs (2007) International Journal of Operations and Production Management, 27 (8), pp. 802-825; Garfield, E., Citation analysis as a tool in journal evaluation (1972) Science, 178 (4060), pp. 471-479; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations and Production Management, 16 (8), pp. 63-80; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management, 12 (6), pp. 400-411; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., James Williams, H., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Irani, Z., Choy, K.-L., Filippi, L., Papadopoulos, T., Performance measures and metrics in outsourcing decisions: a review for research and applications (2015) International Journal of Production Economics, 161, pp. 153-166; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353; Hudson, M., Lean, J., Smart, P.A., Improving control through effective performance measurement in SMEs (2001) Production Planning and Control, 12 (8), pp. 804-813; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement systems (2001) International Journal of Operations and Production Management, 21 (8), pp. 1096-1115; Ittner, C.D., Larcker, D.F., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, 81 (11), pp. 88-95+139; Johnson, T., Kaplan, R.S., (1987) The Relevance Lost: The Rise and Fall of Management Accounting, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Measuring manufacturing performance: a new challenge for managerial accounting research (1983) The Accounting Review, 58 (4), pp. 686-705; Kaplan, R.S., (1990) Measures for Manufacturing Excellence, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California Management Review, 39 (1), pp. 53-79; Kaplan, R.S., Norton, D., (1997) Translating Strategy into Action, The Balanced Score Card, , Harvard Business School Press, Boston, MA; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Strategic Finance, 70 (12), p. 45; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2), pp. 213-229; Kim, G., Park, C.S., Yoon, K.P., Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement (1997) International Journal of Production Economics, 50 (1), pp. 23-33; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Lee, C.W., Kwon, I.-W.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer (2007) Supply Chain Management, 12 (6), pp. 444-452; Leydesdorff, L., Vaughan, L., Co-occurrence matrices and their applications in information science: extending ACA to the web environment (2006) Journal of the American Society for Information Science & Technology, 57 (12), pp. 1616-1628; Lin, C.-Y., Ho, Y.-H., RFID technology adoption and supply chain performance: an empirical study in China’s logistics industry (2009) Supply Chain Management: An International Journal, 14 (5), pp. 369-378; Ljungberg, Õ., Measurement of overall equipment effectiveness as a basis for TPM activities (1998) International Journal of Operations and Production Management, 18 (5), pp. 495-507; Lockamy, A., III, McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218; Lynch, R.L., Cross, K.F., (1991) Measure Up, , Blackwell, Cambridge, MA; MacRoberts, M.H., MacRoberts, B.R., Problems of citation analysis: a critical review (1989) Journal of the American Society for Information Science, 40 (5), pp. 342-349; MacRoberts, M.H., MacRoberts, B.R., Problems of citation analysis: a study of uncited and seldom-sited influences (2010) Journal of the American Society for Information Science and Technology, 61 (1), pp. 1-12; McCormack, K., Ladeira, M.B., Oliviera, M.P., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Martin, P.R., Patterson, J.W., On measuring company performance within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Moxham, C., Boaden, R., The impact of performance measurement in the voluntary sector: Identification of contextual and processual factors (2007) International Journal of Operations and Production Management, 27 (8), pp. 826-845; Muchiri, P., Pintelon, L., Performance measurement using overall equipment effectiveness (OEE): Literature review and practical application discussion (2008) International Journal of Production Research, 46 (13), pp. 3517-3535; Muchiri, P., Pintelon, L., Gelders, L., Martin, H., Development of maintenance function performance measurement framework and indicators (2011) International Journal of Production Economics, 131 (1), pp. 295-302; Nagarur, N., Some performance measures of flexible manufacturing systems (1992) International Journal of Production Research, 30 (4), pp. 799-809; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-12; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , Prentice, London; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431. , Vols; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: a structured approach (1997) International Journal of Operations and Production Management, 17 (11), pp. 1131-1152; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Neely, A.D., (1994) Performance measurement system design – third phase, , Performance Measurement System Design Workbook, April; Neely, A.D., (1998) Measuring Business Performance, , Profile Books; Nerur, S.P., Rasheed, A.A., Natarajan, V., The intellectual structure of the strategic management field: an author co-citation analysis (2008) Strategic Management Journal, 29 (3), pp. 319-336; Parker, C., Performance measurement (2000) Work Study, 49 (2), pp. 63-66; Persson, O., Danell, R., Schneider, J.W., How to use Bibexcel for various types of bibliometric studies (2009) Celebrating Scholarly Communication Studies: A Festschrift for Olle Persson at his 60th Birthday, 5, pp. 9-24. , ström, F., Danell, R., Larsen, B. and Wiborg Schneider, J., and(Eds), International Society for Scientometrics and Informetics, Leuven; Pilkington, A., Fitzgerald, R., Operations management themes, concepts and relationships: a forward retrospective of IJOPM (2006) International Journal of Operations and Production Management, 26 (11), pp. 1255-1275; Pilkington, A., Liston-Heyes, C., Is production and operations management a discipline? A citation/co- citation study (1999) International Journal of Operations and Production Management, 19 (1), pp. 7-20; Pilkington, A., Meredith, J., The evolution of the intellectual structure of operations management – 1980-2006: a citation/co-citation analysis (2009) Journal of Operations Management, 27 (3), pp. 185-202; Ramos-Rodriguez, A.R., Ruiz-Navarro, J., Changes in the intellectual structure of strategic management research: a bibliometric study of the Strategic Management Journal, 1980-2000 (2004) Strategic Management Journal, 25 (10), pp. 981-1004; Rangone, A., An analytical hierarchy process framework for comparing the overall performance of manufacturing departments (1996) International Journal of Operations and Production Management, 16 (8), pp. 104-119; Ritchie, B., Brindley, C., Supply chain risk management and performance: a guiding framework for future development (2007) International Journal of Operations and Production Management, 27 (3), pp. 303-322; Sharplin, A., Mabry, R., The relative importance of journals used in management research: an alternative ranking (1985) Human Relations, 38 (2), pp. 139-149; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Small, H., Co-citation in the scientific literature: a new measure of the relationship between two documents (1973) Journal of the Association for Information Science and Technology, 24 (4), pp. 265-269; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative Models for Performance Measurement System (2000) International Journal of Production Economics, 64 (1), pp. 231-241; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Taylor, A., Taylor, M., Operations management research: contemporary themes, trends and potential future directions (2009) International Journal of Operations and Production Management, 29 (12), pp. 1316-1340; Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R., Development of a balanced scorecard: an integrated approach of interpretive structural modeling (ISM) and analytic network process (ANP) (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 25-59; (1995) World Class Logistics, , Council of Logistics Management, Oak Brook, IL; Van Hoek, R.I., ‘Measuring the immeasurable’ - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Visich, J.K., Li, S., Khumawala, B.M., Reyes, P.M., (2009) International Journal of Operations and Production Management, 29 (12), pp. 1290-1315. , Empirical evidence of RFID impacts on supply chain performance; Vokurka, R.J., The relative importance of journals used in operations management research: a citation analysis (1996) Journal of Operations Management, 14 (4), pp. 345-355; Waggoner, D.B., Neely, A.D., Kennerley, M.P., Forces that shape organisational performance measurement systems: an interdisciplinary review (1999) International Journal of Production Economics, 60, pp. 53-60; White, G.P., A survey and taxonomy of strategy-related performance measures for manufacturing (1996) International Journal of Operations and Production Management, 16 (3), pp. 42-61; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking, 15 (1), pp. 25-51; Wouters, M., A developmental approach to performance measures – results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78; Wisner, J.D., Fawcett, S.E., Linking firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, 32 (3), p. 5; Alfaro, J., Ortiz, A., Poler, R., Performance measurement system for business processes (2007) Production Planning and Control, 18 (8), pp. 641-654; Allesina, S., Azzi, A., Battini, D., Regattieri, A., Performance measurement in supply chains: New network analysis and entropic indexes (2010) International Journal of Production Research, 48 (8), pp. 2297-2321; Andersen, B., Jordan, P., Setting up a performance benchmarking network (1998) Production Planning and Control, 9 (1), pp. 13-19; Anderson, K., McAdam, R., A critique of benchmarking and performance measurement: lead or lag? (2004) Benchmarking, 11 (5), pp. 465-483; Anvari, F., Edwards, R., Performance measurement based on a total quality approach (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 512-528; Azzone, G., Rangone, A., Measuring manufacturing competence: a fuzzy approach (1996) International Journal of Production Research, 34 (9), pp. 2517-2532; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighborhood rough sets (2012) International Journal of Production Research, 50 (9), pp. 2484-2500; Baker, R.P., Maropoulos, P.G., Manufacturing capability measurement for cellular manufacturing systems (1998) International Journal of Production Research, 39 (9), pp. 2511-2527; Barbosa, D.H., Musetti, M.A., The use of performance measurement system in logistics change process: proposal of a guide (2011) International Journal of Productivity and Performance Management, 60 (4), pp. 339-359; Beatham, S., Anumba, C., Thorpe, T., Hedges, I., KPIs: a critical appraisal of their use in construction (2004) Benchmarking, 11 (1), pp. 93-117; Behery, M., Jabeen, F., Parakandi, M., Adopting a contemporary performance management system: a fast-growth small-to-medium enterprise (FGSME) in the UAE (2014) International Journal of Productivity and Performance Management, 63 (1), pp. 22-43; Berrah, L., Mauris, G., Vernadat, F., Information aggregation in industrial performance measurement: Rationales, issues and definitions (2004) International Journal of Production Research, 42 (20), pp. 4271-4293; Berrah, L., Mauris, G., Vernadat, F., Industrial performance measurement: an approach based on the aggregation of unipolar or bipolar expressions (2006) International Journal of Production Research, 44 (18-19), pp. 4145-4158; Bititci, U., Cavalieri, S., Von Cieminski, G., Implementation of performance measurement systems: private and public sectors (2005) Production Planning and Control, 16 (2), pp. 99-100; Bititci, U.S., Nudurupati, S.S., Turner, T.J., Creighton, S., Web enabled performance measurement systems: Management implications (2002) International Journal of Operations and Production Management, 22 (11), pp. 1273-1287; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organizational culture (2006) International Journal of Operations and Production Management, 26 (12), pp. 1325-1350; Björklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management, 17 (1), pp. 29-39; Bond, T.C., The role of performance measurement in continuous improvement (1999) International Journal of Operations and Production Management, 19 (12), pp. 1318-1334; Bourne, M., Researching performance measurement system implementation: the dynamics of success and failure (2005) Production Planning and Control, 16 (2), pp. 101-113; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: an action research (2011) International Journal of Production Economics, 133 (2), pp. 751-760; Breitbarth, T., Mitchell, R., Lawson, R., Service performance measurement in a New Zealand local government organization (2010) Business Horizons, 53 (4), pp. 397-403; Bukchin, J., A comparative study of performance measures for throughput of a mixed model assembly line in a JIT environment (1998) International Journal of Production Research, 36 (10), pp. 2669-2685; Bullinger, H.-J., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Busby, J.S., Williamson, A., The appropriate use of performance measurement in non-production activity: the case of engineering design (2000) International Journal of Operations and Production Management, 20 (3), pp. 336-358; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Camarinha-Matos, L.M., Abreu, A., Performance indicators for collaborative networks based on collaboration benefits (2007) Production Planning and Control, 18 (7), pp. 592-609; Caridi, M., Crippa, L., Perego, A., Sianesi, A., Tumino, A., Measuring visibility to improve supply chain performance: a quantitative approach (2010) Benchmarking, 17 (4), pp. 593-615; Carmona, S., Grönlund, A., Measures vs actions: the balanced scorecard in Swedish law enforcement (2003) International Journal of Operations and Production Management, 23 (11-12), pp. 1475-1496; Cedergren, S., Wall, A., Norström, C., Evaluation of performance in a product development context (2010) Business Horizons, 53 (4), pp. 359-369; Chen, C.-C., Yeh, T.-M., Yang, C.-C., Performance measurement for new product development: a model based on total costs (2006) International Journal of Production Research, 44 (21), pp. 4631-4648; Chen, K.S., Huang, M.L., Performance measurement for a manufacturing system based on quality, cost and time (2006) International Journal of Production Research, 44 (11), pp. 101-123; Chen, T.-Y., Chen, C.-B., Peng, S.-Y., Firm operation performance analysis using data envelopment analysis and balanced scorecard: a case study of a credit cooperative bank (2008) International Journal of Productivity and Performance Management, 57 (7), pp. 523-539; Chew, E.P., Huang, H.C., Horiana, Performance measures for returnable inventory: a case study (2002) Production Planning and Control, 13 (5), pp. 462-469; Choong, K.K., Has this large number of performance measurement publications contributed to its better understanding? A systematic review for research and applications (2014) International Journal of Production Research, 52 (14), pp. 4174-4197; Choong, K.K., The Fundamentals of Performance measurement systems: a systematic approach to theory and a research agenda (2014) International Journal of Productivity and Performance Management, 63 (7), pp. 879-922; Choy, K.L., Chow, H.K.H., Lee, W.B., Chan, F.T.S., Development of performance measurement system in managing supplier relationship for maintenance logistics providers (2007) Benchmarking, 14 (3), pp. 352-368; Danese, P., Romano, P., Relationship between downstream integration, performance measurement systems and supply network efficiency (2012) International Journal of Production Research, 50 (7), pp. 2002-2013; Daniels, R.C., Burns, N.D., Behavioural consequences of performance measures in cellular manufacturing (1997) International Journal of Operations and Production Management, 17 (11), pp. 1066-1080; Daniels, R.C., Burns, N.D., A framework for proactive performance measurement system introduction (1997) International Journal of Operations and Production Management, 17 (1), pp. 100-116; Davies, A.J., Kochhar, A.K., Manufacturing best practice and performance studies: a critique (2002) International Journal of Operations and Production Management, 22 (3), pp. 289-305; De Ron, A.J., Measure of manufacturing performance in advanced manufacturing systems (1995) International Journal of Production Economics, 41 (1-3), pp. 147-160; De Toni, A., Tonchia, S., Lean organization, management by process and performance measurement (1996) International Journal of Operations and Production Management, 16 (2), pp. 221-236; De Toni, A., Nassimbeni, G., Tonchia, S., An instrument for quality performance measurement (1995) International Journal of Production Economics, 38 (2-3), pp. 119-207; De Toni, A.F., Fornasier, A., Montagner, M., Nonino, F., A performance measurement system for facility management: the case study of a medical service authority (2007) International Journal of Productivity and Performance Management, 56 (5-6), pp. 417-435; Dev, N.K., Shankar, R., Dey, P.K., Reconfiguration of supply chain network: an ISM-based roadmap to performance (2014) Benchmarking, 21 (3), pp. 386-411; Díaz, M.S., Gil, M.J.A., MacHuca, J.A.D., Performance measurement systems, competitive priorities, and advanced manufacturing technology: some evidence from the aeronautical sector (2005) International Journal of Operations and Production Management, 25 (8), pp. 781-799; Duffy, J.A.M., Fitzsimmons, J.A., Jain, N., Identifying and studying ‘best-performing’ services: an application of DEA to long-term care (2006) Benchmarking, 13 (3), pp. 232-251; Elg, M., Broryd, K.P., Kollberg, B., Performance measurement to drive improvements in healthcare practice (2013) International Journal of Operations and Production Management, 33 (11), pp. 1623-1651; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analyzing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Farris, J.A., van Aken, E.M., Letens, G., Chearksul, P., Coleman, G., Improving the performance review process: a structured approach and case application (2011) International Journal of Operations and Production Management, 31 (4), pp. 376-404; Fernandes, B.H.R., Mills, J.F., Fleury, M.T.L., Resources that drive performance: an empirical investigation (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 340-354; Fogarty, D.W., Work in process: performance measures (1992) International Journal of Production Economics, 26 (1-3), pp. 169-172; Ganguly, K., Integration of analytic hierarchy process and Dempster-Shafer theory for supplier performance measurement considering risk (2014) International Journal of Productivity and Performance Management, 63 (1), pp. 85-102; Garcia, F.A., Marchetta, M.G., Camargo, M., Morel, L., Forradellas, R.Q., A framework for measuring logistics performance in the wine industry (2012) International Journal of Production Economics, 135 (1), pp. 284-298; Garengo, P., Bernardi, G., Organizational capability in SMEs: Performance measurement as a key system in supporting company development (2007) International Journal of Productivity and Performance Management, 56 (5-6), pp. 518-532; Gelders, L., Mannaerts, P., Maes, J., Manufacturing strategy, performance indicators and improvement programmes (1994) International Journal of Production Research, 32 (4), pp. 797-805; Gomes, C.F., Yasin, M.M., Lisboa, J.V., An examination of manufacturing organizations’ performance evaluation analysis, implications and a framework for future research (2004) International Journal of Operations and Production Management, 24 (5-6), pp. 488-513; Gomes, C.F., Yasin, M.M., Lisboa, J.V., Performance measurement practices in manufacturing firms revisited (2011) International Journal of Operations and Production Management, 31 (1), pp. 5-30; Gong, L., Sun, B., Efficiency measurement of production operations under uncertainty (1995) International Journal of Production Economics, 39 (1-2), pp. 55-66; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Gregory, M.J., Integrated performance measurement: a review of current practice and emerging trends (1993) International Journal of Production Economics, 30-31 (C), pp. 281-296. , Vols; Greiling, D., Performance measurement in the public sector: the German experience (2005) International Journal of Productivity and Performance Management, 54 (7), pp. 551-567; Greiling, D., Performance measurement: a remedy for increasing the efficiency of public services? (2006) International Journal of Productivity and Performance Management, 55 (6), pp. 448-465; Greiling, D., Balanced scorecard implementation in German non-profit organizations (2010) International Journal of Productivity and Performance Management, 59 (6), pp. 534-554; Gupta, M., Ko, H.-J., Min, H., TOC-based performance measures and five focusing steps in a job-shop manufacturing environment (2002) International Journal of Production Research, 40 (4), pp. 907-930; Gurd, B., Gao, T., Lives in the balance: an analysis of the balanced scorecard (BSC) in healthcare organizations (2008) International Journal of Productivity and Performance Management, 57 (1), pp. 6-21; Haapasalo, H., Ingalsuo, K., Lenkkeri, T., Linking strategy into operational management: A survey of BSC implementation in Finnish energy sector (2006) Benchmarking, 13 (6), pp. 701-717; Hanson, J.D., Melnyk, S.A., Calantone, R.A., Defining and measuring alignment in performance management (2011) International Journal of Operations and Production Management, 31 (10), pp. 1089-1114; Herremans, I.M., Ryans, J.K., Jr., The case for better measurement and reporting of marketing performance (1995) Business Horizons, 38 (5), pp. 51-60; Ho, C.-J., Measuring system performance of an ERP-based supply chain (2007) International Journal of Production Research, 45 (6), pp. 1255-1277; Ho, C.-T., Wu, Y.-S., Benchmarking performance indicators for banks (2006) Benchmarking, 13 (1-2), pp. 147-159; Inman, R.R., Gonsalvez, D.J.A., Measuring and analysing supply chain schedule stability: a case study in the automotive industry (1997) Production Planning and Control, 8 (2), pp. 194-204; JääSkeläInen, A., Laihonen, H., LöNnqvist, A., Distinctive features of service performance measurement (2014) International Journal of Operations and Production Management, 34 (12), pp. 1466-1486; Jonsson, P., Lesshammar, M., Evaluation and improvement of manufacturing performance measurement systems - the role of OEE (1999) International Journal of Operations and Production Management, 19 (1), pp. 55-78; Joo, S.-J., Stoeberl, P.A., Fitzer, K., Measuring and benchmarking the performance of coffee stores for retail operations (2009) Benchmarking, 16 (6), pp. 741-753; Jothimani, D., Sarmah, S.P., Supply chain performance measurement for third party logistics (2014) Benchmarking, 21 (6), pp. 944-963; Jusoh, R., Ibrahim, D.N., Zainuddin, Y., The performance consequence of multiple performance measures usage: evidence from the Malaysian manufacturers (2008) International Journal of Productivity and Performance Management, 57 (2), pp. 119-136; Kaplan, R.S., Norton, D.P., The balanced scorecard: measures that drive performance (2005) Harvard Business Review, 83 (7-8), pp. 172-180+194; Kayakutlu, G., Buyukozkan, G., Assessing performance factors for a 3PL in a value chain (2011) International Journal of Production Economics, 131 (2), pp. 441-452; Keebler, J.S., Plank, R.E., Logistics performance measurement in the supply chain: a benchmark (2009) Benchmarking, 16 (6), pp. 785-798; Knox Lovell, C.A., Measuring the macroeconomic performance of the Taiwanese economy (1995) International Journal of Production Economics, 39 (1-2), pp. 165-178; Koh, S.C.L., Gunasekaran, A., Saad, S.M., A business model for uncertainty management (2005) Benchmarking, 12 (4), pp. 383-400; Kolay, M.K., Sahu, K.C., Performance measurement as a surrogate value of organizational human resource (1995) International Journal of Operations and Production Management, 15 (5), pp. 40-59; Kollberg, B., Elg, M., The practice of the balanced scorecard in health care services (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 427-455; Kulatunga, U., Amaratunga, D., Haigh, R., Performance measurement in the construction research and development (2007) International Journal of Productivity and Performance Management, 56 (8), pp. 673-688; Kutucuoglu, K.Y., Hamali, J., Irani, Z., Sharp, J.M., A framework for managing maintenance using performance measurement systems (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 173-194; Kuwaiti, M.E., Performance measurement process: definition and ownership (2004) International Journal of Operations and Production Management, 24 (1-2), pp. 55-78; Kuwaiti, M.E., Kay, J.M., The role of performance measurement in business process re-engineering (2000) International Journal of Operations and Production Management, 20 (12), pp. 1411-1426; Lai, I.K.W., Benchmarking performance measures for extended enterprise in China (2010) Benchmarking, 17 (5), pp. 692-704; Länsiluoto, A., Järvenpää, M., Greening the balanced scorecard (2010) Business Horizons, 53 (4), pp. 385-395; Lazzarotti, V., Manzini, R., Mari, L., A model for R&D performance measurement (2011) International Journal of Production Economics, 134 (1), pp. 212-223; Leachman, C., Pegels, C.C., Shin, S.K., Manufacturing performance: evaluation and determinants (2005) International Journal of Operations and Production Management, 25 (9), pp. 851-874; Lehtinen, J., Ahola, T., Is performance measurement suitable for an extended enterprise? (2010) International Journal of Operations and Production Management, 30 (2), pp. 181-204; Liu, J., Love, P.E.D., Smith, J., Regan, M., Sutrisna, M., Public-private partnerships: a review of theory and practice of performance measurement (2014) International Journal of Productivity and Performance Management, 63 (4), pp. 499-512; Lockamy, A., III, Quality-focused performance measurement systems: a normative model (1998) International Journal of Operations and Production Management, 18 (8), pp. 740-766; Lockamy, A., III, Spencer, M.S., Performance measurement in a theory of constraints environment (1998) International Journal of Production Research, 36 (8), pp. 2045-2060; MacBryde, J., Paton, S., Grant, N., Bayliss, M., Performance measurement driving change: a case from the defence sector (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 462-482; McAdam, R., Bailie, B., Business performance measures and alignment impact on strategy: the role of business improvement models (2002) International Journal of Operations and Production Management, 22 (9-10), pp. 972-996; McAdam, R., Hazlett, S.-A., Galbraith, B., The role of performance measurement models in multi level alignment: an exploratory case analysis in the utilities sector (2014) International Journal of Operations and Production Management, 34 (9), pp. 1153-1183; Marri, H.B., Gunasekaran, A., Grieve, R.J., Performance measurements in the implementation of CIM in small and medium enterprises: an empirical analysis (2000) International Journal of Production Research, 38 (17), pp. 4403-4411; Meekings, A., Effective review meetings: the counter-intuitive key to successful performance measurement (2005) International Journal of Productivity and Performance Management, 54 (3), pp. 212-220; Mendibil, K., MacBryde, J., Designing effective team-based performance measurement systems: an integrated approach (2005) Production Planning and Control, 16 (2), pp. 208-225; Mendibil, K., MacBryde, J., Factors that affect the design and implementation of team-based performance measurement systems (2006) International Journal of Productivity and Performance Management, 55 (2), pp. 118-142; Mettänen, P., Design and implementation of a performance measurement system for a research organization (2005) Production Planning and Control, 16 (2), pp. 178-188; Mhamdia, A.B.H.S., Performance measurement practices in software ecosystem (2013) International Journal of Productivity and Performance Management, 62 (5), pp. 514-533; Micheli, P., Kennerley, M., Performance measurement frameworks in public and non-profit sectors (2005) Production Planning and Control, 16 (2), pp. 125-134; Micheli, P., Mura, M., Agliati, M., Exploring the roles of performance measurement systems in strategy implementation: the case of a highly diversified group of firms (2011) International Journal of Operations and Production Management, 31 (10), pp. 1115-1139; Moffett, S., Anderson-Gillespie, K., McAdam, R., Benchmarking and performance measurement: A statistical analysis (2008) Benchmarking, 15 (4), pp. 368-381; Monge, C.A.M., Rao, S.S., Gonzalez, M.E., Sohal, A.S., Performance measurement of AMT: a cross-regional study (2006) Benchmarking, 13 (1-2), pp. 135-146; Moxham, C., Performance measurement: examining the applicability of the existing body of knowledge to nonprofit organisations (2009) International Journal of Operations and Production Management, 29 (7), pp. 740-763; Moxham, C., Understanding third sector performance measurement system design: a literature review (2014) International Journal of Productivity and Performance Management, 63 (6), pp. 704-726; Najmi, M., Kehoe, D.F., The role of performance measurement systems in promoting quality development beyond ISO 9000 (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 159-172; New, C.C., Szwejczewski, M., Performance measurement and the focused factory: empirical evidence (1995) International Journal of Operations and Production Management, 15 (4), pp. 63-79; Noori, H., Gillden, D., A performance measuring matrix for capturing the impact of AMT (1995) International Journal of Production Research, 33 (7), pp. 2037-2048; Nudurupati, S.S., Bititci, U.S., Implementation and impact of IT-supported performance measurement systems (2005) Production Planning and Control, 16 (2), pp. 152-162; O’Donnell, F.J., Duffy, A.H.B., Modelling design development performance (2002) International Journal of Operations and Production Management, 22 (11), pp. 1198-1221; Palmer, E., Parker, D., Understanding performance measurement systems using physical science uncertainty principles (2001) International Journal of Operations and Production Management, 21 (7), pp. 981-999; Parkan, C., Measuring the operational performance of a public transit company (2002) International Journal of Operations and Production Management, 22 (5-6), pp. 693-720; Parkan, C., Wu, M.L., On the equivalence of operational performance measurement and multiple attribute decision making (1997) International Journal of Production Research, 35 (11), pp. 2963-2988; Perona, M., Miragliotta, G., Complexity management and supply chain performance assessment. a field study and a conceptual framework (2004) International Journal of Production Economics, 90 (1), pp. 103-115; Power, D., Determinants of business-to-business e-commerce implementation and performance: A structural model (2005) Supply Chain Management, 10 (2), pp. 96-113; Pradhan, S.K., Routroy, S., Analyzing the performance of supplier development: a case study (2014) International Journal of Productivity and Performance Management, 63 (2), pp. 209-233; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: design and development guidelines (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 805-826; Reiner, G., Hofmann, P., Efficiency analysis of supply chain processes (2006) International Journal of Production Research, 44 (23), pp. 5065-5087; Rolstadås, A., Enterprise performance measurement (1998) International Journal of Operations and Production Management, 18 (9-10), pp. 989-999; Rompho, N., Boon-itt, S., Measuring the success of a performance measurement system in Thai firms (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 548-562; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking, 13 (1-2), pp. 36-53; Saccani, N., Songini, L., Gaiardelli, P., The role and performance measurement of after-sales in the durable consumer goods industries: an empirical study (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 259-283; Sandström, J., Toivanen, J., The problem of managing product development engineers: can the balanced scorecard be an answer? (2002) International Journal of Production Economics, 78 (1), pp. 79-90; Santos, S.P., Belton, V., Howick, S., Adding value to performance measurement by using system dynamics and multicriteria analysis (2002) International Journal of Operations and Production Management, 22 (11), pp. 1246-1272; Sarkis, J., Quantitative models for performance measurement systems–alternate considerations (2003) International Journal of Production Economics, 86 (1), pp. 81-90; Sarrico, C.S., Rosa, M.J., Measuring and comparing the performance of Portuguese secondary schools: a confrontation between metric and practice benchmarking (2009) International Journal of Productivity and Performance Management, 58 (8), pp. 767-786; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89 (2), pp. 231-243; Sellitto, M.A., Pereira, G.M., Borchardt, M., Da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2014) International Journal of Production Research, 53 (16), pp. 4917-4926; Seth, N., Deshmukh, S.G., Vrat, P., A framework for measurement of quality of service in supply chains (2006) Supply Chain Management, 11 (1), pp. 82-94; Sezen, B., Changes in performance under various lengths of review periods in a periodic review inventory control system with lost sales: a simulation study (2006) International Journal of Physical Distribution and Logistics Management, 36 (5), pp. 360-373; Shi, M., Yu, W., Supply chain management and financial performance: literature review and future directions (2013) International Journal of Operations and Production Management, 33 (10), pp. 1283-1317; Silvestro, R., Performance topology mapping: understanding the drivers of performance (2014) International Journal of Production Economics, 156, pp. 269-282; Slater, S.F., Olson, E.M., Reddy, V.K., Strategy-based performance measurement (1997) Business Horizons, 40 (4), pp. 37-44; Smoot, P.J., Ittner, C., Larcker, D., Coming up short on non-financial performance measurement [2] (multiple letters) (2004) Harvard Business Review, 82 (2), pp. 116-117; Soderberg, M., Kalagnanam, S., Sheehan, N.T., Vaidyanathan, G., When is a balanced scorecard a balanced scorecard? (2011) International Journal of Productivity and Performance Management, 60 (7), pp. 688-708; Sousa, G.W.L., Carpinetti, L.C.R., Groesbeck, R.L., Van Aken, E., Conceptual design of performance measurement and management systems using a structured engineering approach (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 385-399; Stainer, A., Logistics–a productivity and performance perspective (1997) Supply Chain Management, 2 (2), pp. 53-62; Stevanovic, V., Feek, C., Kay, R., Using routine data for benchmarking and performance measurement of public hospitals in New Zealand (2005) Benchmarking, 12 (6), pp. 498-507; Tapinos, E., Dyson, R.G., Meadows, M., The impact of the performance measurement systems in setting the ‘direction’ in the university of Warwick (2005) Production Planning and Control, 16 (2), pp. 189-198; Tapinos, E., Dyson, R.G., Meadows, M., The impact of performance measurement in strategic planning (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 370-384; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains: a literature review and a research agenda (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 782-804; Taylor, A., Taylor, M., Antecedents of effective performance measurement system implementation: an empirical study of UK manufacturing firms (2013) International Journal of Production Research, 51 (18), pp. 5485-5498; Taylor, A., Taylor, M., Factors influencing effective implementation of performance measurement systems in small and medium-sized enterprises and large firms: a perspective from contingency theory (2014) International Journal of Production Research, 52 (3), pp. 847-866; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking, 16 (5), pp. 702-723; Tracey, M., Lim, J.-S., Vonderembse, M.A., The impact of supply-chain management capabilities on business performance (2005) Supply Chain Management, 10 (3), pp. 179-191; Tucker, M., Pitt, M., Customer performance measurement in facilities management: a strategic approach (2009) International Journal of Productivity and Performance Management, 58 (5), pp. 407-422; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) International Journal of Operations and Production Management, 31 (12), pp. 1287-1310; Unahabhokha, C., Platts, K., Tan, K.H., Predictive performance measurement system: a fuzzy expert system approach (2007) Benchmarking, 14 (1), pp. 77-91; Upadhaya, B., Munir, R., Blount, Y., Association between performance measurement systems and organisational effectiveness (2014) International Journal of Operations and Production Management, 34 (7), pp. 853-875; Upton, D., Just-in-time and performance measurement systems (1998) International Journal of Operations and Production Management, 18 (11), pp. 1101-1110; Valmohammadi, C., Servati, A., Performance measurement system implementation using Balanced Scorecard and statistical methods (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 493-511; Van Aken, E.M., Letens, G., Coleman, G.D., Farris, J., Van Goubergen, D., Assessing maturity and effectiveness of enterprise performance measurement systems (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 400-418; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 15-29; Wahlers, J.L., Cox, J.F., III, Competitive factors and performance measurement: applying the theory of constraints to meet customer needs (1994) International Journal of Production Economics, 37 (2-3), pp. 229-240; Wei-Shong, L.P., Kuo-Chung, M.A., The internal performance measures of bank lending: a value-added approach (2006) Benchmarking, 13 (3), pp. 272-289; White, D.S., Gunasekaran, A., Roy, M.H., Performance measures and metrics for the creative economy (2014) Benchmarking, 21 (1), pp. 46-61; Wickramatillake, C.D., Koh, S.C.L., Gunasekaran, A., Arunachalam, S., Measuring performance within the supply chain of a large scale project (2007) Supply Chain Management, 12 (1), pp. 52-59; Wouters, M., Sportel, M., The role of existing measures in developing and implementing performance measurement systems (2005) International Journal of Operations and Production Management, 25 (11), pp. 1062-1082; Yang, C.-C., Lin, W.-T., Pai, F.-Y., Yeh, T.-M., The use of fuzzy measures in a performance-evaluation model for ERP implementation among Taiwanese semiconductor manufacturers (2007) International Journal of Production Research, 45 (20), pp. 4735-4752; Yilmaz, Y., Bititci, U., Performance measurement in the value chain: manufacturing v. tourism (2006) International Journal of Productivity and Performance Management, 55 (5), pp. 371-389; Yurdakul, M., Measuring long-term performance of a manufacturing firm using the Analytic Network Process (ANP) approach (2003) International Journal of Production Research, 41 (11), pp. 2501-2529; Yurdakul, M., Iç, Y., Development of a performance measurement model for manufacturing companies using the AHP and TOPSIS approaches (2005) International Journal of Production Research, 43 (21), pp. 4609-4641; Zeydan, M., Çolpan, C., A new decision support system for performance measurement using combined fuzzy TOPSIS/DEA approach (2009) International Journal of Production Research, 47 (15), pp. 4327-4349; Zhang, X., van Donk, D.P., van der Vaart, T., Does ICT influence supply chain management and performance?: a review of survey-based research (2011) International Journal of Operations and Production Management, 31 (11), pp. 1215-1247; Zigan, K., Macfarlane, F., Desombre, T., Intangible resources as performance drivers in European hospitals (2008) International Journal of Productivity and Performance Management, 57 (1), pp. 57-71},
correspondence_address1={Dubey, R.; Montpellier Business SchoolFrance; email: r.dubey@montpellier-bs.com},
publisher={Emerald Group Publishing Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Leksono2018115,
author={Leksono, E.B. and Suparno and Vanany, I.},
title={Development of performance indicators relationships on sustainable healthcare supply chain performance measurement using balanced scorecard and DEMATEL},
journal={International Journal on Advanced Science, Engineering and Information Technology},
year={2018},
volume={8},
number={1},
pages={115-122},
doi={10.18517/ijaseit.8.1.3852},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042719538&doi=10.18517%2fijaseit.8.1.3852&partnerID=40&md5=f2faad4ff86f2521ec7683085715885a},
affiliation={Department of Industrial Engineering, University of Muhammadiyah Gresik, Jl. Sumatera 101 GKB, Gresik, 61121, Indonesia; Department of Industrial Engineering, Institut Teknologi Sepuluh Nopember, Kampus ITS Sukolilo, Surabaya, 60111, Indonesia},
abstract={Sustainable healthcare supply chain performance measurement (SHSCPM) concept is still less developed. Globalization and pressure from stakeholder demand the operation of the supply chain to give attention to the environment effect, community, economic and intangibility assets. SHSCPM is feasibly developed for measuring the performance of simultaneous sustainability aspects and intangibility assets to meet customer satisfaction. This article discusses SHSCPM based on the balanced scorecard (BSC) with attention to the sustainability aspects, intangibility assets and relationships between the performance of perspectives and indicators. The perspectives and indicators of performance were identified by literature and the confirmed and validated by the survey to 7 expert respondents. We found 5 perspectives and 39 indicators from literature which were then confirmed to expert through a survey with an in-depth interview. From a survey that validated with a weighted average (WA) and level of consensus (LC), we found 31 valid indicators. Finally, 29 indicators from DEMATEL process were selected to be used on SHSCPM. The DEMATEL process found 2 indicators aren't important and influence for other, namely inventory cost and regulations and laws. Besides, the four results on this study: intangibility indicators incorporated on innovation and growth were most affect to other indicators which the intangibility indicators were related with human resource, indicators on customer perspective were most important compared to other indicators, indicators on economic aspect were most important compared indicators on environmental and social aspects, and indicators on social aspect were not affected by other indicators. After that, human resource and customer were main factors for SHSCPM. Finally, relationships between perspectives and indicators used to design of BSC strategy map. © Advanced Science Engineering Information Technology. 2018.},
author_keywords={Balanced scorecard;  DEMATEL;  Level of consensus;  SHSCPM;  Weighted average},
references={Baltacioglu, T., Ada, E., Kaplan, M.D., Yurt And, O., Cem Kaplan, Y., A New Framework for Service Supply Chains (2007) Serv. Ind. J, 27 (2), pp. 105-124; Giannakis, M., Conceptualizing and managing service supply chains (2011) Serv. Ind. J, 31 (11), pp. 1809-1823; Drzymalski, J., (2012) "Supply Chain Frameworks for the Service Industry: A Review of the Literature, ", 1, pp. 31-42. , Dec; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput. Ind. Eng, 62 (3), pp. 801-818; "Pertumbuhan Ekonomi Indonesia 2013, " (2014) Berita Resmi Statistik, pp. 1-9. , 16/02/Th. XVII, 201; (2015), Jawa Timur Dalam Angka; Yap, L.L., Tan, C.L., "The Effect of Service Supply Chain Management Practices on the Public Healthcare Organizational Performance (2012), 3 (16), pp. 216-224; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ, 140 (1), pp. 69-82; Xu, X., Gursoy, D., "A Conceptual Framework of Sustainable Hospitality Supply Chain Management, " (2014) J. Hosp. Mark. Manag, pp. 1-31; Chithambaranathan, P., Subramanian, N., Gunasekaran, A., Int. J. Production Economics Service supply chain environmental performance evaluation using grey based hybrid MCDM approach (2015) Intern. J. Prod. Econ, (2006), pp. 1-14; Unger, S., Landis, A., 'Assessing the environmental, human health, and economic impacts of reprocessed medical devices in a Phoenix hospital's supply chain, ' (2016) J. Clean. Prod, 112, pp. 1995-2003; Ortas, E., Moneva, J.M., Álvarez, I., Sustainable supply chain and company performance: A global examination (2014) Supply Chain Manag. An Int. J, 19 (3), pp. 332-350; Marr, B., Adams, C., The balanced scorecard and intangible assets: similar ideas, unaligned concepts (2006) Meas. Bus. Excell, 8 (3), pp. 18-27; Zigan, K., Macfarlane, F., Desombre, T., (2008) Intangible resources as performance drivers in European hospitals, 57 (1), pp. 57-71; Zigan, K., Zeglat, D., Intangible resources in performance measurement systems of the hotel industry (2010) Facilities, 28 (13-14), pp. 597-610; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res, 25 (2), pp. 173-186; Smith, B.K., Nachtmann, H., Pohl, E.A., Quality Measurement in the Healthcare Supply Chain (2011) Qual. Manag. J, 18 (4), pp. 50-60; Priyan, S., Uthayakumar, R., Optimal inventory management strategies for pharmaceutical company and hospital supply chain in a fuzzy-stochastic environment (2014) Oper. Res. Heal. Care, 3 (4), pp. 177-190. , Dec; Swinehart, K., Smith, A.E., Internal supply chain performance measurement: a health care continuous improvement implementation (2005) Int. J. Health Care Qual. Assur. Inc. Leadersh. Health Serv, 18 (6-7), pp. 533-542. , Jan; Chen, D.Q., Preston, D.S., Xia, W., Enhancing hospital supply chain performance: A relational view and empirical test (2013) J. Oper. Manag, 31 (6), pp. 391-408. , Sep; Lega, F., Marsilio, M., Villa, S., An evaluation framework for measuring supply chain performance in the public healthcare sector: evidence from the Italian NHS (2013) Prod. Plan. Control, 24 (10-11), pp. 931-947; Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Manag. An Int. J, 19 (3), pp. 242-257; Beske-janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management-what has been achieved ? (2015) Supply Chain Manag. An Int. J, 20 (6), pp. 664-680; Kaplan, R.S., Norton, D.P., Voelpel, S., 'The tyranny of the Balanced Scorecard in the innovation economy, ', Journal of Intellectual Capital, Vol. 7 No. 1, 2006, pp. 43-60 (2006) J. Intellect. Cap, 7 (3), pp. 421-428. , Response to; Voelker, K.E., Rakich, J.S., French, G.R., The balanced scorecard in healthcare organizations: A performance measurement and strategic planning methodology (2001) Hosp. Top, 79 (3), pp. 13-24; Wicks, A.M., Clair, L.S., Charles, K.S., Competing Values in Healthcare: Balancing the (Un) Balanced Scorecard (2007) J. Healthc. Manag, 52 (5), pp. 309-324; Leksono, E.B., Suparno Vanany, I., Development of Conceptual Framework for Sustainable Supply Chain Performance Measurement in Service Industries In 8th Widyatama International Seminar on Sustainability 2016, p. 142. , paper 27; Gölcük, I., Baykasoglu, A., 'An analysis of DEMATEL approaches for criteria interaction handling within ANP, ' (2016) Expert Syst. Appl, 46, pp. 346-366; Chen, F., Hsu, T., Tzeng, G., A balanced scorecard approach to establish a performance evaluation and relationship model for hot spring hotels based on a hybrid MCDM model combining DEMATEL and ANP (2011) Int. J. Hosp. Manag, 30 (4), pp. 908-932; Purbey, S., Mukherjee, K., Bhar, C., Performance measurement system for healthcare processes (2007) Int. J. Product. Perform. Manag, 56 (3), pp. 241-251; Samuel, C., Gonapa, K., Chaudhary, P.K., Mishra, A., Supply chain dynamics in healthcare services (2010) Int. J. Health Care Qual. Assur, 23 (7), pp. 631-642; Zahraee, S.M., Rohani, J.M., Firouzi, A., Shahpanah, A., 'Efficiency improvement of blood supply chain system using Taguchi method and dynamic simulation, ' (2015) Procedia Manuf, 2, pp. 1-5; Dey, P.K., Hariharan, S., Clegg, B.T., Measuring the operational performance of intensive care units using the analytic hierarchy process approach (2006) Int. J. Oper. Prod. Manag, 26 (8), pp. 849-865; Cifalino, A., Baraldi, S., Training programs and performance measurement Evidence from healthcare organisations (2007) J. Hum. Resour. Costing Account, 13 (4), pp. 294-315; Coulson-Thomas, C., Transforming productivity and performance in healthcare and other public services: how training and development could make a more strategic contribution (2010) Ind. Commer. Train, 42 (5), pp. 251-259; Does, R.J.M.M., De Mast, J., Schoonhoven, M., 'Discussion of ' An Emerging Science of Improvement in Health Care, " (2015) Qual. Eng, 27, pp. 35-40; Debata, B.R., Patnaik, B., Mahapatra, S.S., Sree, K., Interrelations of service quality and service loyalty dimensions in medical tourism (2015) Benchmarking An Int. J, 22 (1), pp. 18-55; Kumar, A., Ozdamar, L., Peng Ng, C., Procurement performance measurement system in the health care industry (2005) Int. J. Health Care Qual. Assur, 18 (2), pp. 152-166; Kumar, A., Ozdamar, L., Zhang, C.N., Supply chain redesign in the healthcare industry of Singapore (2008) Supply Chain Manag. An Int. J, 13 (2), pp. 95-103; Dobrzykowski, D., Saboori Deilami, V., Hong, P., Kim, S.-C., A structured analysis of operations and supply chain management research in healthcare (1982-2011) (2014) Int. J. Prod. Econ, 147 (2014), pp. 514-530; Chong, Y.-L.A., Zhou, L., 'Demand chain management: Relationships between external antecedents, web-based integration and service innovation performance, ' (2014) Int. J. Prod. Econ, 154, pp. 48-58; Chen, L., Wang, Y., A conceptual framework for Taiwan ' s hospital clinical performance indicators (2015) J. Formos. Med. Assoc, 114 (5), pp. 381-383; Kwon, I.G., Kim, S., Martin, D.G., Healthcare supply chain management; strategic areas for quality and financial improvement (2016) Technol. Forecast. Soc. Chang; Falasca, M., Kros, J.F., Success factors and performance outcomes of healthcare industrial vending systems: An empirical analysis (2016) Technol. Forecast. Soc. Chang; Gomes, C.F., Yasin, M.M., Assessing operational effectiveness in healthcare organizations: a systematic approach (2010) Int. J. Health Care Qual. Assur, 23 (2), pp. 127-140; de Vries, J., Huijsman, R., Supply chain management in health services: an overview (2011) Supply Chain Manag. An Int. J, 16 (3), pp. 159-165; Al-Saa'da, R.J., Abu Taleb, Y.K., Al Abdallat, M.E., Al-Mahasneh, R.A.A., Awni Nimer, N., Al-Weshah, G.A., Supply Chain Management and Its Effect on Health Care Service Quality: Quantitative Evidence from Jordanian Private Hospitals (2013) J. Manag. Strateg, 4 (2), pp. 42-52; Katsaliaki, K., Mustafee, N., Kumar, S., A game-based approach towards facilitating decision making for perishable products: An example of blood supply chain (2014) Expert Syst. Appl, 41 (9), pp. 4043-4059; Chakraborty, S., Bhattacharya, S., Dobrzykowski, D.D., Impact of Supply Chain Collaboration on Value Co-creation and Firm Performance: A Healthcare Service Sector Perspective (2014) Procedia Econ. Financ, 11 (14), pp. 676-694; Narayana, S.A., Kumar, R., Vrat, P., Journal of Purchasing & Supply Management Managerial research on the pharmaceutical supply chain-A critical review and some insights for future directions (2014) J. Purch. Supply Manag, 20 (1), pp. 18-40; Aronsson, H., Abrahamsson, M., Spens, K., Developing lean and agile health care supply chains (2011) Supply Chain Manag. An Int. J, 16 (3), pp. 176-183; Parker, J., DeLay, D., The future of the healthcare supply chain (2008) Healthc. Financ. Manag, 62 (4), pp. 66-69; Lee, H.K., Fernando, Y., 'The antecedents and outcomes of the medical tourism supply chain, ' (2015) Tour. Manag, 46, pp. 148-157; Feil, A.A., De Quevedo, D.M., Schreiber, D., 'Selection and identification of the indicators for quickly measuring sustainability in micro and small furniture industries, ' (2015) Sustain. Prod. Consum, 3, pp. 34-44},
correspondence_address1={Leksono, E.B.; Department of Industrial Engineering, Jl. Sumatera 101 GKB, Indonesia; email: eko_budileksono@umg.ac.id},
publisher={Insight Society},
issn={20885334},
language={English},
abbrev_source_title={Int. J. Adv. Sci. Eng. Inf. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wei201862,
author={Wei, W. and Low, J.F. and Schiffauerova, A.},
title={Nobody wants to buy sour milk: Supply chain performance measure matters},
journal={International Journal of Logistics Systems and Management},
year={2018},
volume={29},
number={1},
pages={62-81},
doi={10.1504/IJLSM.2018.088584},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85038599182&doi=10.1504%2fIJLSM.2018.088584&partnerID=40&md5=0edb2607055bb1045595c849516472b4},
affiliation={Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada; Department of Engineering Systems and Management, Masdar Institute of Science and Technology, Abu Dhabi, United Arab Emirates},
abstract={In the increasingly competitive manufacturing landscape, the key to thriving is in managing the supply chain. Supply chain performance measurement systems, which gauge the performance of a supply chain on different metrics, are indispensable tools for supply chain management. By introducing a weighted and industry-independent performance measure and incorporating it in a questionnaire distributed to manufacturing companies, we were able to compare supply chain performance between different categories of manufacturers. We identified management as the biggest impediment towards implementation of supply chain performance measures. The results also show that supply chains in light industry are flexible but members of each supply chain interact poorly. Heavy industry supply chains suffer from inflexibility but have good interactions between members within each supply chain. Despite international supply chains having many advantages over domestic supply chains, our survey respondents indicated that global supply chains perform better than local supply chains. And, supply chains which have members who are actively cooperating, whether in the form of a pact, partnership, or alliance, perform better than supply chains where such cooperation does not exist. Finally, our study found that the performance of supply chains with performance measurement systems in place trumps the performance of supply chains without them. Copyright © 2018 Inderscience Enterprises Ltd.},
author_keywords={Comparative analysis;  SCM;  Supply chain;  Supply chain management;  Supply chain performance measurement system},
references={Basnet, C., Corner, J., Wisner, J., Tan, K.-C., Benchmarking supply chain management practice in New Zealand (2003) Supply Chain Management: An International Journal, 8 (1), pp. 57-64; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Chen, V.C., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218; Benton, W., Maloni, M., The influence of power driven buyer/seller relationships on supply chain satisfaction (2005) Journal of Operations Management, 23 (1), pp. 1-22; Besterfield, D., Besterfield-Michna, C., Besterfield, G., Besterfield-Sacre, M., (1995) Total Quality Management, Prentice Hall International Editions, , Prentice Hall, London; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach (2014) Production Planning & Control, 25 (8), pp. 698-714; Bigliardi, B., Dormio, A.I., Galati, F., Balanced scorecard for the public administration: Issues from a case study (2011) International Journal of Business, Management and Social Sciences, 2 (5), pp. 1-16; Bititci, U., Cavalieri, S., Cieminski, G., Implementation of performance measurement systems: Private and public sectors (2005) Production Planning and Control, 16 (2), pp. 99-100; Blome, C., Schoenherr, T., Eckstein, D., The impact of knowledge transfer and complexity on supply chain flexibility: A knowledge-based view (2014) International Journal of Production Economics, 147, pp. 307-316; Bosnjak, M., Tuten, T.L., Prepaid and promised incentives in web surveys an experiment (2003) Social Science Computer Review, 21 (2), pp. 208-217; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Bowersox, D.J., Closs, D.J., Cooper, M.B., (2002) Supply Chain Logistics Management, 2. , McGraw-Hill New York, NY; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), p. 75; Bulsara, H.P., Qureshi, M.N., Patel, H., Supply chain performance measurement – An exploratory study (2014) International Journal of Logistics Systems and Management, 18 (2), pp. 231-249; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) International Journal of Logistics Systems and Management, 18 (2), pp. 216-230; Choi, T.Y., Rungtusanatham, M., Comparison of quality management practices: Across the supply chain and industries (1999) Journal of Supply Chain Management, 35 (4), pp. 20-27; Chopra, S., Sodhi, M., Managing risk to avoid supply-chain breakdown (2004) MIT Sloan Management Review, 46 (1), pp. 53-61. , Fall 2004; Danese, P., Romano, P., Formentini, M., The impact of supply chain integration on responsiveness: The moderating effect of using an international supplier network (2013) Transportation Research Part E: Logistics and Transportation Review, 49 (1), pp. 125-140; Dellana, S.A., Kros, J.F., An exploration of quality management practices, perceptions and program maturity in the supply chain (2014) International Journal of Operations & Production Management, 34 (6), pp. 786-806; Dixon, J.R., (1990) The New Performance Challenge: Measuring Operations for World-Class Competition, , Irwin Professional Pub, Homewood, IL; Ellinger, A.E., Improving marketing/logistics cross-functional collaboration in the supply chain (2000) Industrial Marketing Management, 29 (1), pp. 85-96; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N., Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process (2014) International Journal of Production Economics, 147, pp. 555-568; Green, K.W., Jr., Zelbst, P.J., Meacham, J., Bhadauria, V.S., Green supply chain management practices: Impact on performance (2012) Supply Chain Management: An International Journal, 17 (3), pp. 290-305; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Jack, E.P., Raturi, A., Sources of volume flexibility and their impact on performance (2002) Journal of Operations Management, 20 (5), pp. 519-548; Kaplan, R.S., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, 70 (12), pp. 134-147; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Kurien, G.P., Qureshi, M.N., Analysis and measurement of supply chain flexibility (2015) International Journal of Logistics Systems and Management, 21 (1), pp. 70-91; Manuj, I., Mentzer, J.T., Global supply chain risk management strategies (2008) International Journal of Physical Distribution & Logistics Management, 38 (3), pp. 192-223; Meixell, M.J., Gargeya, V.B., Global supply chain design: A literature review and critique (2005) Transportation Research Part E: Logistics and Transportation Review, 41 (6), pp. 531-550; Mohr, J., Spekman, R., Characteristics of partnership success: Partnership attributes, communication behavior, and conflict resolution techniques (1994) Strategic Management Journal, 15 (2), pp. 135-152; Panjehfouladgaran, H., Yusuff, R., Fuzzy performance measurement for supply chain management in Malaysian rubber glove manufacturer (2016) International Journal of Logistics Systems and Management, 24 (2), pp. 178-199; Perotti, S., Micheli, G.J., Cagno, E., Motivations and barriers to the adoption of green supply chain practices among 3PLs (2015) International Journal of Logistics Systems and Management, 20 (2), pp. 179-198; Rexhausen, D., Pibernik, R., Kaiser, G., Customer-facing supply chain practices – The impact of demand and distribution management on supply chain success (2012) Journal of Operations Management, 30 (4), pp. 269-281; Satariano, A., Burrows, P., Apple’s supply-chain secret? Hoard lasers’ (2011) Technology, 4, p. 50; Saxenian, A., Regional networks and the resurgence of silicon valley (1990) California Management Review, 33 (1), pp. 89-112; Saxenian, A., The origins and dynamics of production networks in silicon valley (1991) Research Policy, 20 (5), pp. 423-437; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Simchi-Levi, D., (2005) Designing and Managing The Supply Chain, , McGraw-Hill College, New York; Thomas, J., Why your supply chain doesn’t work (1999) Logistics Management and Distribution Report, 38 (6), pp. 42-44; Vidal, C.J., Goetschalckx, M., Strategic production-distribution models: A critical review with emphasis on global supply chain models (1997) European Journal of Operational Research, 98 (1), pp. 1-18; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60, pp. 53-60; Webster, M., Supply system structure, management and performance: A conceptual model (2002) International Journal of Management Reviews, 4 (4), pp. 353-369; Yusuf, Y., Hawkins, A., Musa, A., El-Berishy, N., Schulze, M., Abubakar, T., Ethical supply chains: Analysis, practices and performance measures (2014) International Journal of Logistics Systems and Management, 17 (4), pp. 472-497},
correspondence_address1={Low, J.F.; Department of Engineering Systems and Management, United Arab Emirates; email: jlow@masdar.ac.ae},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gong2018155,
author={Gong, M. and Simpson, A. and Koh, L. and Tan, K.H.},
title={Inside out: The interrelationships of sustainable performance metrics and its effect on business decision making: Theory and practice},
journal={Resources, Conservation and Recycling},
year={2018},
volume={128},
pages={155-166},
doi={10.1016/j.resconrec.2016.11.001},
note={cited By 48},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034080412&doi=10.1016%2fj.resconrec.2016.11.001&partnerID=40&md5=65372e1a94cb44cd6638b61a7b7d624a},
affiliation={The University of Sheffield, Centre for Energy, Environment and Sustainability, Logistics and Supply Chain Management Research Centre, Management School, Conduit Road, Sheffield, S10 1FL, United Kingdom; Nottingham University Business School, Jubilee Campus, Wollaton Road, Nottingham, NG8 1BB, United Kingdom},
abstract={There has been an increasing interest in the use of decision-making models to achieve sustainability goal in recent decades. However, a systematic review of performance metrics, which are an important element of decision-making models to evaluate the outcomes regarding firm's economic, environmental and social performance, is lacking. This study provides critical reflections on the current state of literature and industry development regarding sustainable performance metrics and offers concrete suggestions to guide future research. This study contributes to existing studies by (1) exploring the interrelationship between sustainable triple-bottom performance in the decision making process; (2) integrating corporate governance mechanism into decision making process for sustainable consideration; and (3) conducting a comparison between academic theory and industry practice regarding the performance metrics proposed and employed. © 2016 Elsevier B.V.},
author_keywords={Business decision making;  Corporate governance mechanism;  Performance metrics;  Sustainable supply chain management},
keywords={Decision theory;  Industrial management;  Supply chain management;  Sustainable development, Business decisions;  Corporate governance;  Decision making models;  Decision making process;  Industry development;  Performance metrics;  Sustainable performance;  Sustainable supply chains, Decision making, business;  corporate strategy;  decision making;  governance approach;  performance assessment;  supply chain management;  sustainability;  theoretical study, academic achievement;  Article;  commercial phenomena;  decision theory;  ecology;  economic aspect;  environmental decision making;  environmental factor;  industrial production;  performance measurement system;  social aspect;  sustainable development;  systematic review},
references={Abdul, Z., Ibrahim, S., Executive and management attitudes towards corporate social responsibility in Malaysia (2002) Corporate Gov.: Int. J. Bus. Soc., 2 (4), pp. 10-16. , http://dx.doi.org/10.1108/14720700210447641; Adhitya, A., Halim, I., Srinivasan, R., Decision support for green supply chain operations by integrating dynamic simulation and LCA indicators: diaper case study (2011) Environ. Sci. Technol., 45 (23), pp. 10178-10185. , http://dx.doi.org/10.1021/es201763q; Bai, C.G., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Manage.— Int. J., 19 (3), pp. 275-291. , http://dx.doi.org/10.1108/Scm-12-2013-0441; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Manage.: Int. J., 17 (1), pp. 78-92. , http://dx.doi.org/10.1108/13598541211212221; Bear, S., Rahman, N., Post, C., The impact of board diversity and gender composition on corporate social responsibility and firm reputation (2010) J. Bus. Ethics, 97 (2), pp. 207-221. , http://dx.doi.org/10.1007/s10551-010-0505-2; Campbell, J.L., Why would corporations behave in socially responsible ways? An institutional theory of corporate social responsibility (2007) Acad. Manage. Rev., 32 (3), pp. 946-967. , http://dx.doi.org/10.5465/AMR.2007.25275684; Carroll, A.B., A three-dimensional conceptual model of corporate performance (1979) Acad. Manage. Rev., 4 (4), pp. 497-505. , http://dx.doi.org/10.5465/AMR.1979.4498296; Chaabane, A., Ramudhin, A., Paquet, M., Design of sustainable supply chains under the emission trading scheme (2012) Int. J. Prod. Econ., 135 (1), pp. 37-49. , http://dx.doi.org/10.1016/j.ijpe.2010.10.025; Chen, R.H., Lin, Y.H., Tseng, M.L., Multicriteria analysis of sustainable development indicators in the construction minerals industry in China (2015) Resour. Policy, 46, pp. 123-133. , http://dx.doi.org/10.1016/j.resourpol.2014.10.012; Chin, M.K., Hambrick, D.C., Treviño, L.K., Political ideologies of CEOs the influence of executives’ values on corporate social responsibility (2013) Adm. Sci. Q., 58 (2), pp. 197-232. , http://dx.doi.org/10.1177/0001839213486984; Choi, J., Wang, H., Stakeholder relations and the persistence of corporate financial performance (2009) Strateg. Manage. J., 30 (8), pp. 895-907. , http://dx.doi.org/10.1002/smj.759; Choudhary, A., Sarkar, S., Settur, S., Tiwari, M.K., A carbon market sensitive optimization model for integrated forward-reverse logistics (2015) Int. J. Prod. Econ., 164, pp. 433-444. , http://dx.doi.org/10.1016/j.ijpe.2014.08.015; Cucchiella, F., D'Adamo, I., Issue on supply chain of renewable energy (2013) Energy Convers. Manage., 76, pp. 774-780. , http://dx.doi.org/10.1016/j.enconman.2013.07.081; Daily, C.M., Dalton, D.R., Cannella, A.A., Corporate governance: decades of dialogue and data (2003) Acad. Manage. Rev., 28 (3), pp. 371-382. , http://dx.doi.org/10.5465/AMR.2003.10196703; David, R.J., Han, S.K., A systematic assessment of the empirical support for transaction cost economics (2004) Strateg. Manage. J., 25 (1), pp. 39-58. , http://dx.doi.org/10.1002/smj.359; Dekkers, R., Chang, C.M., Kreutzfeldt, J., The interface between product design and engineering and manufacturing: a review of the literature and empirical evidence (2013) Int. J. Prod. Econ., 144, pp. 316-333. , http://dx.doi.org/10.1016/j.ijpe.2013.02.020; Denyer, D., Neely, A., Introduction to special issue: innovation and productivity performance in the UK (2004) Int. J. Manage. Rev., 5, pp. 131-135. , http://dx.doi.org/10.1111/j.1460-8545.2004.00100.x; Diamond, J.M., Guns, Germs, and Steel: A Short History of Everybody for the Last 13,000 Years (1998), Random House; Eccles, R.G., Ioannou, I., Serafeim, G., The impact of corporate sustainability on organizational processes and performance (2014) Manage. Sci., 60 (11), pp. 2835-2857. , http://dx.doi.org/10.1287/mnsc.2014.1984; Elghali, L., Clift, R., Sinclair, P., Panoutsou, C., Bauen, A., Developing a sustainability framework for the assessment of bioenergy systems (2007) Energy Policy, 35 (12), pp. 6075-6083. , http://dx.doi.org/10.1016/j.enpol.2007.08.036; Elkington, J., (1997), Cannibals with forks. The triple bottom line of 21 st century; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecol. Econ., 70 (6), pp. 1088-1100. , http://dx.doi.org/10.1016/j.ecolecon.2011.01.001; Faruk, A.C., Lamming, R.C., Cousins, P.D., Bowen, F.E., Analyzing, mapping, and managing environmental impacts along supply chains (2001) J. Ind. Ecol., 5 (2), pp. 13-36. , http://dx.doi.org/10.1162/10881980152830114; Fichtinger, J., Ries, J.M., Grosse, E.H., Baker, P., Assessing the environmental impact of integrated inventory and warehouse management (2015) Int. J. Prod. Econ., 170, pp. 717-729. , http://dx.doi.org/10.1016/j.ijpe.2015.06.025; Fogliatto, F.S., Da Silveira, G.J.C., Borenstein, D., The mass customization decade: an updated review of the literature (2012) Int. J. Prod. Econ., 138, pp. 14-25. , http://dx.doi.org/10.1016/j.ijpe.2012.03.002; Frooman, J., Socially irresponsible and illegal behavior and shareholder wealth a meta-analysis of event studies (1997) Bus. Soc., 36 (3), pp. 221-249. , http://dx.doi.org/10.1177/000765039703600302; Gadema, Z., Oglethorpe, D., The use and usefulness of carbon labelling food: a policy perspective from a survey of UK supermarket shoppers (2011) Food Policy, 36, pp. 815-822. , http://dx.doi.org/10.1016/j.foodpol.2011.08.001; Gillan, S.L., Recent developments in corporate governance: an overview (2006) J. Corp. Fin., 12, pp. 381-402. , http://dx.doi.org/10.1016/j.jcorpfin.2005.11.002; Gimenez, C., Tachizawa, E.M., Extending sustainability to suppliers: a systematic literature review (2012) Supply Chain Manage.—Int. J., 17, pp. 531-543. , http://dx.doi.org/10.1108/13598541211258591; Godos-Díez, J.L., Fernández-Gago, R., Martínez-Campillo, A., How important are CEOs to CSR practices? An analysis of the mediating effect of the perceived role of ethics and social responsibility (2011) J. Bus. Ethics, 98 (4), pp. 531-548. , http://dx.doi.org/10.1007/s10551-010-0609-8; Gosling, J., Naim, M.M., Engineer-to-order supply chain management: a literature review and research agenda (2009) Int. J. Prod. Econ., 122 (2), pp. 741-754. , http://dx.doi.org/10.1016/j.ijpe.2009.07.002; Hacklin, F., Wallnöfer, M., The business model in the practice of strategic decision making: insights from a case study (2012) Manage. Decis., 50, pp. 166-188. , http://dx.doi.org/10.1108/00251741211203515; Harms, D., Hansen, E.G., Schaltegger, S., Strategies in sustainable supply chain management: an empirical investigation of large german companies (2013) Corp. Soc. Responsib. Environ. Manage., 20, pp. 205-218. , http://dx.doi.org/10.1002/csr.1293; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140, pp. 69-82. , http://dx.doi.org/10.1016/j.ijpe.2012.01.042; Heckman, J.J., Sample selection bias as a specification error (1979) Econometrica, 47 (1). , http://dx.doi.org/10.3386/w0172; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: Int. J., 12, pp. 330-353. , http://dx.doi.org/10.1108/14635770510609015; Hillman, A.J., Dalziel, T., Boards of directors and firm performance: integrating agency and resource dependence perspectives (2003) Acad. Manage. Rev., 28 (3), pp. 383-396. , http://dx.doi.org/10.5465/AMR.2003.10196729; Hubbard, G., Measuring organizational performance: beyond the triple bottom line (2009) Bus. Strategy Environ., 18 (3), pp. 177-191. , http://dx.doi.org/10.1002/bse.564; Huq, F.A., Stevenson, M., Zorzini, M., Social sustainability in developing country suppliers An exploratory study in the ready made garments industry of Bangladesh (2014) Int. J. Oper. Prod. Manage., 34, pp. 610-638. , http://dx.doi.org/10.1108/Ijopm-10-2012-0467; Hutchins, M.J., Sutherland, J.W., An exploration of measures of social sustainability and their application to supply chain decisions (2008) J. Clean. Prod., 16, pp. 1688-1698. , http://dx.doi.org/10.1016/j.jclepro.2008.06.001; Jensen, M.C., Value maximization, stakeholder theory, and the corporate objective function (2002) Bus. Ethics Q., 2, pp. 235-256. , http://dx.doi.org/10.2307/3857812; Johnson, R.A., Greening, D.W., The effects of corporate governance and institutional ownership types on corporate social performance (1999) Acad. Manage. J., 42 (5), pp. 564-576. , http://dx.doi.org/10.2307/256977; Jones, T.M., Instrumental stakeholder theory: a synthesis of ethics and economics (1995) Acad. Manage. Rev., 20 (2), pp. 404-437. , http://dx.doi.org/10.5465/AMR.1995.9507312924; Joyner, B.E., Payne, D., Evolution and implementation: a study of values, business ethics and corporate social responsibility (2002) J. Bus. Ethics, 41 (4), pp. 297-311. , http://dx.doi.org/10.1023/A:1021237420663; Keupp, M.M., Palmie, M., Gassmann, O., The strategic management of innovation: a systematic review and paths for future research (2012) Int. J. Manage. Rev., 14, pp. 367-390. , http://dx.doi.org/10.1111/j.1468-2370.2011.00321.x; Koh, S.C.L., Genovese, A., Acquaye, A.A., Barratt, P., Rana, N., Kuylenstierna, J., Gibbs, D., Decarbonising product supply chains: design and development of an integrated evidence-based decision support system—the supply chain environmental analysis tool (SCEnAT) (2013) Int. J. Prod. Res., 51, pp. 2092-2109. , http://dx.doi.org/10.1080/00207543.2012.705042; Koh, S.C.L., Morris, J., Ebrahimi, S.M., Obayi, R., Integrated resource efficiency: measurement and management (2016) Int. J. Oper. Prod. Manage., 36 (11), pp. 1576-1600; Kumar, S., DeGroot, R.A., Choe, D., Rx for smart hospital purchasing decisions: the impact of package design within US hospital supply chain (2008) Int. J. Phys. Distrib. Logist. Manage., 38, pp. 601-615. , http://dx.doi.org/10.1108/09600030810915134; Lake, A., Acquaye, A., Genovese, A., Kumar, N., Koh, S.C.L., An application of hybrid life cycle assessment as a decision support framework for green supply chains (2015) Int. J. Prod. Res., 53, pp. 6495-6521. , http://dx.doi.org/10.1080/00207543.2014.951092; Leseure, M.J., Bauer, J., Birdi, K., Neely, A., Denyer, D., Adoption of promising practices: a systematic review of the evidence (2005) Int. J. Manage. Rev., 5, pp. 169-190. , http://dx.doi.org/10.1111/j.1460-8545.2004.00102.x; Li, Y.J., Zhao, X.K., Shi, D., Li, X., Governance of sustainable supply chains in the fast fashion industry (2014) Eur. Manage. J., 32, pp. 823-836. , http://dx.doi.org/10.1016/j.emj.2014.03.001; Liu, S.F., Kasturiratne, D., Moizer, J., A hub-and-spoke model for multi-dimensional integration of green marketing and sustainable supply chain management (2012) Ind. Mark. Manage., 41, pp. 581-588. , http://dx.doi.org/10.1016/j.indmarman.2012.04.005; Matos, S., Silvestre, B.S., Managing stakeholder relations when developing sustainable business models: the case of the Brazilian energy sector (2013) J. Clean. Prod., 45, pp. 61-73. , http://dx.doi.org/10.1016/j.jclepro.2012.04.023; Meho, L.I., Yang, K., Impact of data sources on citation counts and rankings of LIS faculty: web of Science versus Scopus and Google Scholar (2007) J. Am. Soc. Inf. Sci. Technol., 58 (13), pp. 2105-2125. , http://dx.doi.org/10.1002/asi.20677; Metta, H., Badurdeen, F., Integrating sustainable product and supply chain design: modeling issues and challenges (2013) IEEE Trans. Eng. Manage., 60, pp. 438-446. , http://dx.doi.org/10.1109/Tem.2012.2206392; Moed, H.F., (2010), The Source-Normalized Impact per Paper (SNIP) is a valid and sophisticated indicator of journal citation impact. arXiv preprint arXiv:1005.4906; Morali, O., Searcy, C., A review of sustainable supply chain management practices in Canada (2013) J. Bus. Ethics, 117, pp. 635-658. , http://dx.doi.org/10.1007/s10551-012-1539-4; Muduli, K., Govindan, K., Barve, A., Geng, Y., Barriers to green supply chain management in Indian mining industries: a graph theoretic approach (2013) J. Clean. Prod., 47, pp. 335-344. , http://dx.doi.org/10.1016/j.jdepro.2012.10.030; Nagurney, A., Nagurney, L.S., Li, D., Securing the sustainability of global medical nuclear supply chains through economic cost recovery, risk management, and optimization (2015) Int. J. Sustain. Transp., 9, pp. 405-418. , http://dx.doi.org/10.1080/15568318.2013.779127; Newbert, S.L., Empirical research on the resource-based view of the firm: an assessment and suggestions for future research (2007) Strateg. Manage. J., 28 (2), pp. 121-146. , http://dx.doi.org/10.1002/smj.573; Pimentel, B.S., Gonzalez, E.S., Barbosa, G.N.O., Decision-support models for sustainable mining networks: fundamentals and challenges (2016) J. Clean. Prod., 112, pp. 2145-2157. , http://dx.doi.org/10.1016/j.jclepro.2015.09.023; Pittaway, L., Robertson, M., Munir, K., Denyer, D., Neely, A., Networking and innovation: a systematic review of the evidence (2004) Int. J. Manage. Rev., 5, pp. 137-168. , http://dx.doi.org/10.1111/j.1460-8545.2004.00101.x; Rostamzadeh, R., Govindan, K., Esmaeili, A., Sabaghi, M., Application of fuzzy VIKOR for evaluation of green supply chain management practices (2015) Ecol. Indic., 49, pp. 188-203. , http://dx.doi.org/10.1016/j.ecolind.2014.09.045; Sanders, W.G., Carpenter, M.A., Internationalization and firm governance: the roles of CEO compensation, top team composition, and board structure (1998) Acad. Manage. J., 41 (2), pp. 158-178. , http://dx.doi.org/10.2307/257100; Sarkis, J., Dhavale, D.G., Supplier selection for sustainable operations: a triple-bottom-line approach using a Bayesian framework (2015) Int. J. Prod. Econ., 166, pp. 177-191. , http://dx.doi.org/10.1016/j.ijpe.2014.11.007; Schuler, D.A., Cording, M., A corporate social performance–corporate financial performance behavioral model for consumers (2006) Acad. Manage. Rev., 31 (3), pp. 540-558. , http://dx.doi.org/10.5465/AMR.2006.21318916; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod., 16, pp. 1699-1710. , http://dx.doi.org/10.1016/j.jclepro.2008.04.020; Shen, L.X., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A., A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences (2013) Resour. Conserv. Recycl., 74, pp. 170-179. , http://dx.doi.org/10.1016/j.resconrec.2012.09.006; Subramoniam, R., Huisingh, D., Chinnam, R.B., Remanufacturing for the automotive aftermarket-strategic factors: literature review and future research needs (2009) J. Clean. Prod., 17, pp. 1163-1174. , http://dx.doi.org/10.1016/j.jclepro.2009.03.004; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2015) Int. J. Prod. Res., 53, pp. 6473-6494. , http://dx.doi.org/10.1080/00207543.2014.939239; Thorpe, R., Holt, R., Macpherson, A., Pittaway, L., Using knowledge within small and medium-sized firms: a systematic review of the evidence (2005) Int. J. Manage. Rev., 7 (4), pp. 257-281. , http://dx.doi.org/10.1111/j.1468-2370.2005.00116.x; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-Informed management knowledge by means of systematic review (2003) Br. J. Manage., 14, pp. 207-222. , http://dx.doi.org/10.1111/1467-8551.00375; Tseng, S.C., Hung, S.W., A strategic decision-making model considering the social costs of carbon dioxide emissions for sustainable supply chain management (2014) J. Environ. Manage., 133, pp. 315-322. , http://dx.doi.org/10.1016/j.jenvman.2013.11.023; United Nations. Department of Economic, Indicators of Sustainable Development: Guidelines and Methodologies (2007), United Nations Publications; Vachon, S., Mao, Z.M., Linking supply chain strength to sustainable development: a country-level analysis (2008) J. Clean. Prod., 16, pp. 1552-1560. , http://dx.doi.org/10.1016/j.jclepro.2008.04.012; Vahabzadeh, A.H., Asiaei, A., Zailani, S., Green decision-making model in reverse logistics using FUZZY-VIKOR method (2015) Resour. Conserv. Recycl., 103, pp. 125-138. , http://dx.doi.org/10.1016/j.resconrec.2015.05.023; van Hoek, R., Johnson, M., Sustainability and energy efficiency: research implications from an academic roundtable and two case examples (2010) Int. J. Phys. Distrib. Logist. Manage., 40, pp. 148-158. , http://dx.doi.org/10.1108/09600031011018064; Verghese, K.L., Horne, R., Carre, A., PIQET: The design and development of an online streamlined LCA tool for sustainable packaging design decision support (2010) Int. J. Life Cycle Assess., 15, pp. 608-620. , http://dx.doi.org/10.1007/s11367-010-0193-2; Waddock, S.A., Graves, S.B., The corporate social performance-financial performance link (1997) Strateg. Manage. J., 18 (4), pp. 303-319. , http://www.jstor.org/stable/3088143; Wakefield, J., Apple, Samsung and Sony face child labour claims (2016) BBC News, , http://www.bbc.co.uk/news/technology-35311456, [Online] (Accessed 06 April 2016); Wang, H.F., Hsu, H.W., Resolution of an uncertain closed-loop logistics model: an application to fuzzy linear programs with risk analysis (2010) J. Environ. Manage., 91, pp. 2148-2162. , http://dx.doi.org/10.1016/j.jenvman.2010.05.009; Wu, Z., Pagell, M., Balancing priorities: decision-making in sustainable supply chain management (2011) J. Oper. Manage., 29, pp. 577-590. , http://dx.doi.org/10.1016/j.jom.2010.10.001; Zhang, Z.H., Awasthi, A., Modelling customer and technical requirements for sustainable supply chain planning (2014) Int. J. Prod. Res., 52, pp. 5131-5154. , http://dx.doi.org/10.1080/00207543.2014.899717; Zingales, L., Survival of the fittest or the fattest? Exit and financing in the trucking industry (1998) J. Fin., 3, pp. 905-938. , http://dx.doi.org/10.1111/0022-1082.00039},
correspondence_address1={Gong, M.; The University of Sheffield, Conduit Road, United Kingdom; email: mgong2@sheffield.ac.uk},
publisher={Elsevier B.V.},
issn={09213449},
coden={RCREE},
language={English},
abbrev_source_title={Resour. Conserv. Recycl.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Harwati2017,
author={Harwati and Permana, Y.},
title={Islamic value to the modification of BSC model (a case study in evaluating company performance)},
journal={IOP Conference Series: Materials Science and Engineering},
year={2017},
volume={277},
number={1},
doi={10.1088/1757-899X/277/1/012004},
art_number={012004},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039711364&doi=10.1088%2f1757-899X%2f277%2f1%2f012004&partnerID=40&md5=08321b038220af187a73cba33139c115},
affiliation={Departement of Industrial Engineering, Universitas Islam Indonesia, Jl. Kaliurang Km 14.4, Yogyakarta, Indonesia},
abstract={Halal is the key word for the Muslim community in consuming goods or services. Providing halal products is the main focus for companies with the largest consumers from the Muslim community. Halal is not only from the ingredients but also from the process in its production. The purpose of this study is to design a model of performance measurement for a company in order to provide halal products. Balance Score Card (BSC) is used as a framework. BSC as a performance measurement system has advantages not only focus on financial aspect but also non finance. Islamic values are incorporated into four perspective of BSC. They are: Selection of sharia financial institutions in financial perspective, payment method by consumer in consumer perspective, number of product with halal certificate and menu clarify in internal business process perspective, then accuracy of payroll time in learning and growth perspective. The modified BSC model obtains 17 Key Performance Indicators (KPI). The model is applied in a culinary industry (restaurant). Structured questionnaires and scheduled interviews are used to collect the important data. The result show that these industry fail to meet 8 from total 17 KPI so performance achievement is only 70.81%, and only reach 22.58% in Islamic value aspect. This is recorded by the company as an input to improve their performance in order to compete in halal food business. © Published under licence by IOP Publishing Ltd.},
author_keywords={BSC;  Culinary;  Halal;  Islamic values;  Modified},
keywords={Benchmarking;  Compensation (personnel);  Finance;  Planning;  Product design;  Supply chain management;  Surveys;  Sustainable development;  Wages, Culinary;  Financial institution;  Halal;  Islamic values;  Key performance indicators;  Model of performance;  Modified;  Performance measurement system, Industrial management},
references={Abbas, A., Soroosh, N., Mona, J., Information Technology Project Evaluation: An Integrated Data Envelopment Analysis and Balanced Scorecard Approach (2010) Expert Systems with Applications, 37 (8), pp. 5931-5938; Alireza, N., Peter, A., Pantea, F., Influence of National Culture and Balanced Organizational Culture on the Hotel Industry's Performance (2017) International Journal of Hospitality Management, 63, pp. 22-32; Alexandre Veronese, B., Jorge, C., Silva Jorge Ferreira, D., Herbert, K., Multidimensional assessment of organizational performan Integrating BSC and AHP (2012) Journal of Business Research, 65 (12), pp. 1790-1799; Domagoja Buljan, B., Senka Borovac, Z., Personal Finances and Hierarchy of Human Need (2011) Economy Transdisciplinarity Cognition; Bacau, 14, pp. 337-348; Bonne, Verbeke, Religious values informing halal meat production and the control and delivery of halal credence quality (2008) Agriculture and Human Values, 25 (1), pp. 35-47; Hung-Yi, W., Yi-Kuei, L., Chi-Hsiang, C., Performance Evaluation of Extension Education Centers in Universities Based on the Balanced Scorecard (2011) Evaluation and Program Planning, 34 (1), pp. 37-50; Grigoroudis, E., Orfanoudaki, E., Zopounidis, C., Strategic Performance Measurement in A Healthcare Organisation: A Multiple Criteria Approach Based on Balanced Scorecard (2012) Omega, 40 (1), pp. 104-119; Eddy, C., Van Veen-Dirk Paula, M.G., Financial versus non-financial information: The impact of information organization and presentation in Balanced Scorecard (2010) Accounting, Organizations and Society, 35 (6), pp. 565-578; Ford, J.B., LaTour, M.S., Honeycutt, E.D., An examination of the cross-cultural female response to offensive sex role portrayals in advertising (1997) Journal of International Marketing Review, 14 (6), pp. 409-423. , Electronic version; Hamid, T., Aida, J., Aslan Azimi, A., Using Balanced Scorecard in Educational Organizations (2010) Procedia Social and Behavioral Sciences, 2 (2), pp. 5544-5548; Hava, (2009) New Organization to Sort Out Rotten Apples in European Halal Food Market. Sunday's Zaman; Hao-Chen, H., Mei-Chi, L., Lee-Hsuan, L., Developing Strategic Measurement and Improvement for the Biopharmaceutical Firm Using the BSC Hierarchy (2011) Expert Systems with Applications, 38 (5), pp. 4875-4881; Hsueh-Fen, C., Ying-Hui, H., Ray-E, C., Application of the Balanced Scorecard to An Academic Medical Center in Taiwan: The Effect of Warning Systems on Improvement of Hospital Performance (2012) Journal of the Chinese Medical Association, 75 (10), pp. 530-535; Hung-Yi, W., Gwo-Hshiung, T., Yi-Hsuan, C., A Fuzzy MCDM Approach for Evaluating Banking Performance Based on Balanced Scorecard (2009) Expert Systems with Applications, 36 (6), pp. 10135-10147; Jaroslava, K., Michaela, D., Lenka, K., Innovative Approaches to the Modification of BSC Model (2014) Procedia - Social and Behavioral Sciences, 143, pp. 168-173; Jin-Su, K., Chun-Fang, C., Kitipop, H., Stephen, D., Corporate Social Responsibility and Sustainability Balanced Scorecard: The Case Study of Family-Owned Hotels (2015) International Journal of Hospitality Management, 48, pp. 124-134; Kaplan, R., Norton, D., (1996) The Balanced Scorecard: Translating Strategy into Action, , (Boston: Harvard Business Press); Mohsen, V., Maryam, D., Mehdi, A., A Novel Hybrid MCDM Model for Performance Evaluation of Research and Technology Organizations Based on BSC Approach (2016) Evaluation and Program Planning, 58, pp. 125-140; Masoud Rahiminezhad, G., Syed Ahmad, H., Pooria, H., Supplier Selection in Automobile Industry: A Mixed Balanced Scorecard-Fuzzy AHP Approach (2016) Alexandria Engineering Journal, 55 (1), pp. 93-100; Sangjae, L., Sung Bum, P., Gyoo Gun, L., Using Balanced Scorecards for the Evaluation of ''Software-As-A-Service (2013) Information & Management, 50 (7), pp. 553-561},
editor={Maulidya R.},
publisher={Institute of Physics Publishing},
issn={17578981},
language={English},
abbrev_source_title={IOP Conf. Ser. Mater. Sci. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Catapan20171,
author={Catapan, A.M. and Yamakawa, E.K. and Siebert, L.C. and Aoki, A.R. and Lima, E.P.D.},
title={Residential customer satisfaction performance assessment model for electricity Service for a Power Utility in Brazil},
journal={PICMET 2017 - Portland International Conference on Management of Engineering and Technology: Technology Management for the Interconnected World, Proceedings},
year={2017},
volume={2017-January},
pages={1-7},
doi={10.23919/PICMET.2017.8125464},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85043475168&doi=10.23919%2fPICMET.2017.8125464&partnerID=40&md5=9249801867abf9d8f07d7d1352a15947},
affiliation={Parana State Power Utility COPEL, Department of Customer Studies, Curitiba, Brazil; Institutos Lactec, Department of Electricity and Materials, Curitiba, Brazil; Pontifical Catholic University of Parana, Department of Systems and Production Engineering, Curitiba, Brazil},
abstract={Measuring organizational performance is a necessary condition for improving management systems and the achievement of companieś strategic objectives. To evaluate customer satisfaction has been a challenge for companies since high levels of satisfaction correlate with high levels of performance. Particularly in the provision of public services such as water, sanitation, gas and energy, it can assess the level of consumer satisfaction with power utilities. The design of the performance measurement system model and the respective formulation of its indicators is, therefore, a critical element in the design of the service operations management system. Performance evaluation in utilitieś services is strongly connected to customer satisfaction. This work presents a literature review of customer satisfaction and performance evaluation and a set of performance measures to form the customer satisfaction for electric power supply services. The main result is a framework regarding the performance indicators to be monitored for electricity services customer satisfaction, the frequency of measurement, the frequency of review, who is responsible for the measurement and the source of data. © 2017 IEEE.},
keywords={Electric power systems;  Power quality;  Sales;  Supply chain management, Consumer satisfactions;  Electric power supply;  Organizational performance;  Performance assessment models;  Performance evaluations;  Performance indicators;  Performance measurement system;  Service operations management, Customer satisfaction},
references={Nudurupati, S., Tebboune, S., Hardman, J., Contemporary performance measurement and management (PMM) in digital economies (2016) Production Planning & Control, 27 (3), pp. 226-235; Melnyk, S.A., Bittici, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future (2014) Management Accounting Research, 25 (2), pp. 173-186; Simmons, R., Performance measurement & control systems for implementing strategy: Text & cases (2000) Upper Saddle River; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-217; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Neely, A., Gregory, M., Platts, K., Performance measurement system design-A literature review and research agenda (2005) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Lebas, M.J., Performance measurement and performance management (1995) International Journal Production Economics, 41 (23-25), pp. 23-35; Lima De, E.P., Gouvea Da Costa, S.E., Angelis, J.J., Munik, J., Performance measurement systems: A consensual analysis of their roles (2013) International Journal of Production Economics, 146 (2), pp. 524-542; Neely, A., Bourne, M., Mill, J., Platts, K., Richards, H., (2002), Getting the measure of your business Cambridge: Cambridge Press; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Verbeeten, F.H.M., Boons, A.N.A.M., Strategic priorities, performance measures and performance: An empirical analysis in Dutch firms (2009) European Management Journal, 27 (2), pp. 113-128; Neelly, A.D., Al Najjar, M., Management learning not management control: The true role of performance measurement (2006) California Management Review, 48 (3), pp. 100-114; Yeung, A.C.L., Lai, K.H., Yee, R.W.Y., Organizational learning, innovativeness, organizational performance: A qualitative investigation (2007) International Journal of Production Research, 45 (11), pp. 2459-2477; Sun, K.A., Kim, D.Y., Does customer satisfaction increase firm performance An application of American customer satisfaction index (ACSI) (2013) International Journal of Hospitality Management, 35 (1), pp. 68-77; Steven, A.B., Dong, Y., Dresner, M., Linkages between customer service, customer satisfaction and performance in the airline industry: Investigation of nonlinearities and moderating effects (2012) Transportation Research Part E: Logistics and Transportation Review, 48 (4), pp. 743-754; Hartmann, P., Apaolaza Ibañez, V., Managing customer loyalty in liberalized residential energy markets: The impact of energy branding (2007) Energy Policy, 35 (4), pp. 2661-2672; Çelen, A., Yalçin, N., Performance assessment of Turkish electricity distribution utilities: An application of combined FAHP/TOPSIS/DEA methodology to incorporate quality of service (2012) Utilities Policy, 23 (1), pp. 59-71; Dassler, T., Parker, D., Saal, D.S., Methods and trends of performance benchmarking in UK utility regulation (2006) Utilities Policy, 14 (3), pp. 166-174; Kelly, J.M., The missing manifesto: What economists should be saying about electric utility restructuring in the United States (2003) The Electricity Journal, 16 (1), pp. 13-24; Neely, A.D., Richards, H., Mills, J.F., Platts, K.W., Bourne, M.C.S., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Parasuraman, A., Zeithaml, V.A., Berry, L.L., SERVQUAL: A multipleitem scale for measuring consumer perception of service quality (1989) Journal of Retailing., 64 (1), pp. 12-40; Bearden, W.O., Durand, R.M., Barry, J., Mason, J.B., Tell, E., Dimensions of consumer satisfaction/dissatisfaction with services: The case of electric utilities (1978) Journal of the Academy of Marketing Science, 6 (4), pp. 278-290; Fiorio, C.V., Florio, M., Would you say that the price you pay for electricity is fair Consumers' satisfaction and utility reforms in the EU15 (2011) Energy Economics, 33 (2), pp. 178-187; Marchetti, R., Prado, P.H.M., Avaliacąõ da satisfacąõ do consumidor utilizando o método de equações estruturais: Um modelo aplicado ao setor elétrico brasileiro. (Evaluation of consumer satisfaction using the structural equations method: A model applied to the Brazilian electric sector) (2004) Revista de Administracąõ Contemporânea, 8 (4), pp. 9-32; Pyon, C.U., Lee, M.J., Park, S.C., Decision support system for service quality management using customer knowledge in public service organization (2009) Expert Systems with Applications, 36 (4), pp. 8227-8238; Rodriguez, P.G., Vasquez Burguete, J.L., Vaughan, R., Edwards, J., Quality dimensions in the public sector: Municipal services and citizen's perception (2009) International Review On Public and Nonprofit Marketing, 6 (1), pp. 75-90; Navartnam, K.K., Harris, B., Customer service in an Australian quality award winning public sector service industry (1994) International Journal of Public Sector Management, 7 (2), pp. 42-49; Medjoudj, R., Aissani, D., Hainm, K.D., Power customer satisfaction and profitability analysis using multi-criteria decision making methods (2013) International Journal of Electrical Power & Energy Systems, 45 (1), pp. 331-339; Royo, M.P., Tricás, J., Tomás, X., Improving quality in the Spanish electrical sector: A QFD application (2005) Total Quality Management & Business Excellence, 16 (4), pp. 555-569; Sullivan, M.J., Suddeth, B.N., Vardell, T., Vojdani, A., Interruption costs, customer satisfaction and expectations for service reliability (1996) IEEE Transactions On Power Systems, 11 (2), pp. 989-995; Medjoudj, R., Laifa, A., Aissani, D., Decision making on power customer satisfaction and enterprise profitability analysis using the analytic Hierarchy process (2012) International Journal of Production Research, 50 (17), pp. 4793-4805; Chowdhury, A.A., Koval, D.O., Two views of utility performance indices (2009) IEEE Industry Applications Magazine, 15 (5), pp. 14-20; Silvestre, B., Hall, J., Matos, S., Figueira, L.A., Privatization of electricity distribution in the northeast of Brazil: The good, the bad, the ugly or the naive (2010) Energy Policy, 38 (11), pp. 7001-7013; Yam, R.C.M., Tam, A.Y.K., Tang, E.P.Y., Mok, C.K., TQM: A change management model for market orientation (2005) Total Quality Management & Business Excellence, 16 (4), pp. 439-461; Bai, C., Lai, F., Chen, Y., Hutchinson, J., Conceptualising the perceived service quality of public utility services: A multi-level, multi-dimensional model (2008) Total Quality Management & Business Excellence, 19 (10), pp. 1055-1070},
editor={Anderson T.R., Niwa K., Kocaoglu D.F., Daim T.U., Kozanoglu D.C., Perman G., Steenhuis H.-J.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781890843366},
language={English},
abbrev_source_title={PICMET - Portland Int. Conf. Manag. Eng. Technol.: Technol. Manag. Interconnected World, Proc.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Leksono2017,
author={Leksono, E.B. and Suparno and Vanany, I.},
title={The sustainability and performance measurement on supply chain in services industry: A literature review},
journal={AIP Conference Proceedings},
year={2017},
volume={1902},
doi={10.1063/1.5010676},
art_number={020059},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034447844&doi=10.1063%2f1.5010676&partnerID=40&md5=779f25baeb7be16f4cb6a8f88e916355},
affiliation={Department of Industrial Engineering, Universitas Muhammadiyah Gresik and Industrial Engineering Doctorate Stud., Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia; Department of Industrial Engineerin, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia},
abstract={The services industry growth has been significant relation with economic growth. A new paradigm is needed for services sector development. The supply chain and performance measurement able to sustain of services industry growth. The supply chain implementation in the services industry called service supply chain (SSC). The globalization and stakeholder pressure makes operation of SSC should more attention to sustainability issue which consists of economic, social and environment simultaneously on SSC. Furthermore, services industry can develop by implementation of the sustainable SSC and its performance measurement. The sustainable SSC implementation can minimize of negative operation effect to environment and social, and maximize of profit. Sustainable service supply chain performance measurements (SSSCPM) are still less explored. The purpose of this paper is to review the literature in the field SSC, SSSC, SSC performance measurement (SSCPM) and SSSCPM for identification of the SSSCPM frameworks and indicators. Beside, the result of review able to look opportunities for develop a new framework for SSSCPM at the operational level, tactical and strategic, multiplayer and close loop, the effectiveness of the integration and development of modeling and simulation for evaluation in the future. © 2017 Author(s).},
references={Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput. Ind. Eng., 62 (3), pp. 801-818. , Apr; Drzymalski, J., (2012) Supply Chain Frameworks for the Service Industry: A Review of the Literature, 1, pp. 31-42. , December; Giannakis, M., Conceptualizing and managing service supply chains (2011) Serv. Ind. J., 31 (11), pp. 1809-1823; Comm, C.L., Mathaisel, D.F.X., Sustaining higher education using Wal-Mart's best supply chain management practices (2008) Int. J. Sustain. High. Educ., 9 (2), pp. 183-189; Blanchard, C., Comm, C.L., Mathaisel, D.F.X., Adding value to service providers: Benchmarking Wal-Mart (2008) Benchmarking An Int. J., 15 (2), pp. 166-177; Chardine-Baumann, E., Botta-Genoulaz, V., A Framework for Sustainable Performance Assessment of Supply Chain Management Practices (2014) Comput. Ind. Eng., 76, pp. 138-147. , Aug; Kottak, C.P., An Anthropological Take on Sustainable Development: A Comparative Study of Change (2004) Hum. Organ., 63 (4), pp. 50-510; Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Manag. An Int. J., 19 (3), pp. 242-257; Tajbakhsh, A., Hassini, E., A data envelopment analysis approach to evaluate sustainability in supply chain networks (2014) J. Clean. Prod., , Aug; Beamon, B.M., Supply Chain Design and Analysis: Models and Methods (1998) Int. J. Prod. Econ., 55 (3), pp. 281-294; Ellram, L.M., Tate, W.L., Understanding and Managing the Services Supply Chain (2004) J. Supply Chain Manag., 40 (4), pp. 17-32; Waart, D., (2004) 5 Steps to Service Supply Chain Excellence; Baltacioglu, T., Ada, E., Kaplan, M.D., Yurt, O., Kaplan, Y.C., A new framework for service supply chains (2007) Serv. Ind. J., 27 (2), pp. 105-124; Decelle, A.-F.C., Young, B.I., Das, B.P., Case, K., Rahimifard, S., Anumba, C.J., Bouchlaghem, D.M., A review of approaches to supply chain communications: From manufacturing to construction (2007) ITcon, 12, pp. 73-102. , May 2005; Beamon, B.M., Sustainability and the Future of Supply Chain Management (2008) Oper. Supply Chain Manag., 1 (1), pp. 4-18; Habib, M., (2010) Supply Chain Management: Theory and Its Future Perspectives, 1 (1), pp. 79-87; Smith, B.K., Nachtmann, H., Pohl, E.A., Quality measurement in the healthcare supply chain (2011) Qual. Manag. J., 18 (4), pp. 50-60; De Vries, J., Huijsman, R., Supply chain management in health services: An overview (2011) Supply Chain Manag. An Int. J., 16 (3), pp. 159-165. , May; Sakhuja, S., Jain, V., Service supply chain: An integrated conceptual framework (2012) CIE42 Proceedings, pp. 2161-2169. , July; Lee, H.K., Fernando, Y., The antecedents and outcomes of the medical tourism supply chain (2015) Tour. Manag., 46, pp. 148-157. , Feb; Seth, N., Deshmukh, S.G., Vrat, P., A conceptual model for quality of service in the supply chain (2006) Int. J. Phys. Distrib. Logist. Manag., 36 (7), pp. 547-575; Linton, J., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) J. Oper. Manag., 25 (6), pp. 1075-1082. , Nov; Badurdeen, F., Metta, H., Gupta, S., (2009) Taxonomy of Research Directions for Sustainable Supply Chain Management, pp. 1256-1262; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) Int. J. Phys. Distrib. Logist. Manag., 41 (1), pp. 46-62; Ashby, A., Leat, M., Hudson-Smith, M., Making connections: A review of supply chain management and sustainability literature (2012) Supply Chain Manag. An Int. J., 17 (5), pp. 497-516; Boons, F., Baumann, H., Hall, J., Conceptualizing sustainable development and global supply chains (2012) Ecol. Econ., 83, pp. 134-143. , v; Hussain, M., Khan, M., Al-Aomar, R., A framework for supply chain sustainability in service industry with Confirmatory Factor Analysis (2016) Renew. Sustain. Energy Rev., 55, pp. 1301-1312; Morali, O., Searcy, C., A Review of Sustainable Supply Chain Management Practices in Canada (2013) J. Bus. Ethics, 117 (3), pp. 635-658. , v; Beske, P., Seuring, S., Putting sustainability into supply chain management (2014) Supply Chain Manag. An Int. J., 19 (3), pp. 322-331; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., (2014) Quantitative Models for Sustainable Supply Chain Management: Developments and Directions, 233, pp. 299-312; Lockamy, I., Archie, McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) Int. J. Oper. Prod. Manag., 24, pp. 11-12. , 1192 - 1218; Yilmaz, Y., Bititci, U., Performance measurement in the value chain: Manufacturing v. Tourism (2006) Int. J. Product. Perform. Manag., 55 (5), pp. 371-389; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) Int. J. Product. Perform. Manag., 55 (3-4), pp. 242-258; Sengupta, K., Heiser, D.R., Cook, L.S., Manufacturing and Service Supply Chain Performance: A Comparative Analysis (2006) J. Supply Chain Manag., 42 (4), pp. 4-15; Olugu, E.U., Wong, K.Y., Supply Chain Performance Evaluation: Trends and Challenges (2009) Am. J. Eng. Appl. Sci., 2 (1), pp. 202-211; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) Int. J. Prod. Res., 48 (17), pp. 5137-5155. , Se; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ., 142 (2), pp. 247-258. , Apr; Elrod, C., Murray, S., Bande, S., (2013) A Review of Performance Metrics for Supply Chain Management, 25 (3); Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking An Int. J., 17 (3), pp. 320-339; Uysal, F., An Integrated Model for Sustainable Performance Measurement in Supply Chain (2012) Procedia - Soc. Behav. Sci., 62 (1995), pp. 689-694. , Oct; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Manag. An Int. J., 17 (1), pp. 78-92; Avinash, S., Prakash, K., A framework for performance measurement system of supply chain management (2011) Int. J. Adv. Eng. Technol., 2 (4), pp. 182-190. , D and M, ",", vol; Wang, Y., Wallace, S.W., Shen, B., Choi, T., Service supply chain management: A review of operational models 1:? (2015) Eur. J. Oper. Res., 247 (3), pp. 685-698; Fisk, R.P., Brown, S.W., Bitner, M.J., Tracking the evolution of the services marketing literature (1993) Track. Evol. Serv. Mark. Lit., 69 (1), pp. 61-103; Anderson, E.G., Jr., Moririce, J., Douglas, A simulation game for teaching services-oriented supply chain management: Does information sharing help managers with service capacity decision? (2000) Prod. Oper. Manag., 9 (1), pp. 40-55; Sauber, M.H., McSurely, H.B., Tummala, V.M.R., Developing supply chain management program: A competency model (2008) Qual. Assur. Educ., 16 (4), pp. 375-391; Krishna, M.M., Subbiah, K.V., (2012) A Research Frame Work on Role of Information in Educational Supply Chain, 17 (5), pp. 617-621; Krishna, M., Venkata, S., (2012) Supply Chain Management: The Educational Organization Perspective, 3 (1), pp. 277-281. , M and; Varma, S., Managing Faculty Demand in Technical Education: A Supply Chain Model for IITs (2012) South Asian J. Manag., 19 (4), pp. 124-135; Keating, B., Quazi, A., Kriz, A., Coltman, T., In pursuit of a sustainable supply chain: Insights from Westpac Banking Corporation (2008) Supply Chain Manag. An Int. J., 13 (3), pp. 175-179; Akkermans, H.A., Van Wassenhove, L.N., Searching for the grey swans: The next 50 years of production research (2013) Int. J. Prod. Res., 51 (23-24), pp. 6746-6755. , Nov; Schmidberger, S., Bals, L., Hartmann, E., Jahns, C., Ground handling services at European hub airports: Development of a performance measurement system for benchmarking (2009) Int. J. Prod. Econ., 117 (1), pp. 104-116. , Jan; Bichou, K., Gray, R., A logistics and supply chain management approach to port performance measurement (2004) Marit. Policy Manag., 31 (1), pp. 47-67; Singh, R., Sandhu, H.S., Metri, B.A., Kaur, R., Relating organised retail supply chain (2010) J. Bus. Perspect., 14 (3), pp. 173-190; Rynja, G.M., Moy, D.C., Laboratory service evaluation: Laboratory product model and the supply chain (2006) Benchmarking An Int. J., 13 (3), pp. 324-336; Giannakis, M., Management of service supply chains with a service-oriented reference model: The case of management consulting (2011) Supply Chain Manag. An Int. J., 16 (5), pp. 346-361; Fu, N., Flood, P.C., Bosak, J., Morris, T., O'Regan, P., Exploring the performance effect of HPWS on professional service supply chain management (2013) Supply Chain Manag. An Int. J., 18 (3), pp. 292-307; Ross, A., Jayaraman, V., Robinson, P., Optimizing 3PL service delivery using a cost-to-serve and action research framework (2007) Int. J. Prod. Res., 45 (1), pp. 83-101. , Jan; Liu, W., Xie, D., Xu, X., Quality supervision and coordination of logistic service supply chain under multi-period conditions (2013) Int. J. Prod. Econ., 142 (2), pp. 353-361. , Apr; Richey, R.G., Jr, Hilton, C.B., Harvey, M.G., Beielspacher, L.S., Tokman, M., Moeller, M., Aligning operant resources for global performance: An assessment of supply chain human resource management (2011) J. Manag. Organ., 17 (3), pp. 364-382; Tate, W.L., Ellram, L.M., Service supply management structure in offshore outsourcing (2012) J. Supply Chain Manag., 48 (4), pp. 8-29; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integr. Manuf. Syst., 14 (3), pp. 179-190; Font, X., Tapper, R., Schwartz, K., Kornilaki, M., Sustainable supply chain management in tourism (2008) Bus. Strateg. Environ., 17, pp. 260-271; Schwartz, K., Tapper, R., Font, X., A sustainable supply chain management framework for tour operators (2008) J. Sustain. Tour., 16 (3), pp. 298-314; Zhang, X., Song, H., Huang, G.Q., Tourism supply chain management: A new research agenda (2009) Tour. Manag., 30 (3), pp. 345-358. , Jun; Veronneau, S., (2008) Three Essays on Cruise ShiSupply Chain Management; Kress, N., Wisner, J., (2012) A Supply Chain Model for Library Quality and Service Improvement, 5 (2), pp. 40-53; Samuel, C., Gonapa, K., Chaudhary, P.K., Mishra, A., Supply chain dynamics in healthcare services (2010) Int. J. Health Care Qual. Assur., 23 (7), pp. 631-642. , Jan; Yap, L.L., Tan, C.L., (2012) The Effect of Service Supply Chain Management Practices on the Public Healthcare Organizational Performance, 3 (16), pp. 216-224; Chen, D.Q., Preston, D.S., Xia, W., Enhancing hospital supply chain performance: A relational view and empirical test (2013) J. Oper. Manag., 31 (6), pp. 391-408. , Se; Al-Saa'Da, R.J., Taleb, Y.K.A., Al Abdallat, M.E., Al-Mahasneh, R.A.A., Nimer, N.A., Al-Weshah, G.A., Supply Chain Management and Its Effect on Health Care Service Quality: Quantitative Evidence from Jordanian Private Hospitals (2013) J. Manag. Strateg., 4 (2), pp. 42-52. , May; Dobrzykowski, D., Deilami, V.S., Hong, P., Kim, S.-C., A structured analysis of operations and supply chain management research in healthcare (1982-2011 (2014) Int. J. Prod. Econ., 147 (2014), pp. 514-530. , Jan; Katsaliaki, K., Mustafee, N., Kumar, S., A game-based approach towards facilitating decision making for perishable products: An example of blood supply chain (2014) Expert Syst. Appl., 41 (9), pp. 4043-4059. , Jul; Shi, X., Liao, Z., Managing supply chain relationships in the hospitality services: An empirical study of hotels and restaurants (2013) Int. J. Hosp. Manag., 35, pp. 112-121. , Dec; Cheng, J.C.P., Law, K.H., Bjornsson, H., Jones, A., Sriram, R., A service oriented framework for construction supply chain integration (2010) Autom. Constr., 19 (2), pp. 245-260. , Mar; Gou, J., Shen, G., Chai, R., (2013) Model of Service-oriented Catering Supply Chain Performance Evaluation, 6 (1), pp. 215-226; Brindley, C., Oxborrow, L., Aligning the sustainable supply chain to green marketing needs: A case study (2014) Ind. Mark. Manag., 43 (1), pp. 45-55. , Jan; Bitran, G.R., Gurumurthi, S., Sam, S.L., (2007) The Need for Third-Party Coordination in Supply Chain Governance the Need for Third-Party Coordination in Supply Chain Governance, , 48309; Arlbjørn, J.S., Freytag, P.V., De Haas, H., Service supply chain management: A survey of lean application in the municipal sector (2011) Int. J. Phys. Distrib. Logist. Manag., 41 (3), pp. 277-295; Vermeulen, W.J.V., Kok, M.T.J., Government interventions in sustainable supply chain governance: Experience in Dutch front-running cases (2012) Ecol. Econ., 83, pp. 183-196. , v; López, L., Zúñiga, R., Dynamics of judicial service supply chains (2014) J. Bus. Res., 67 (7), pp. 1447-1454. , Jul; Hall, J., Matos, S., Incorporating impoverished communities in sustainable supply chains (2010) Int. J. Phys. Distrib. Logist. Manag., 40 (1), pp. 124-147; Mann, H., Kumar, U., Kumar, V., Jit, I., Mann, S., Drivers of Sustainable Supply Chain Management (2010) IUP J. Oper. Manag., 9 (4), pp. 52-64; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82. , v; Matos, S., Hall, J., Integrating sustainable development in the supply chain: The case of life cycle assessment in oil and gas and agricultural biotechnology (2007) J. Oper. Manag., 25 (6), pp. 1083-1102. , v; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decis. Support Syst., 54 (4), pp. 1513-1520. , Mar; Matos, S., Silvestre, B.S., Managing stakeholder relations when developing sustainable business models: The case of the Brazilian energy sector (2013) J. Clean. Prod., 45, pp. 61-73. , Apr; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347. , Feb; Gulledge, T., Chavusholu, T., Automating the construction of supply chain key performance indicators (2008) Ind. Manag. Data Syst., 108 (6), pp. 750-774; Lin, L.-C., Li, T.-S., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Softw. Qual. J., 18 (3), pp. 387-406. , May; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng., 53, pp. 43-62; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995 - 2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Muratoglu, K., (2008) Performance Measurement of Supply Chain Via Balanced Scorecard: The Case of A Brewing Grou, 10, pp. 330-338; Tracht, K., Niestegge, A., Schuh, P., Demand Planning based on Performance Measurement Systems in Closed Loop Supply Chains (2013) Procedia CIRP, 12, pp. 324-329; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2009) Prod. Plan. Control, 18 (8), pp. 666-680; Vaidya, O., Hudnurkar, M., Multicriteria supply chain performance evaluation (2013) Int. J. Product. Perform. Manag., 62 (3), pp. 293-316. , Mar; Boon-Itt, S., Pongpanarat, C., Measuring Service Supply Chain Management Processes: The Application of the Q-Sort Technique (2011) Int. J. Innov. Manag. Technol., 2 (3), pp. 217-221; Nazemi, S., Management, P., Akbarnejhad, K., Koloukhi, A.S., Branch, T., (2013) Determining and Prioritizing Supply Chain Performance Evaluation Measures Using Lean Thinking Criteria, (2004), pp. 114-126; Baghalian, A., Rezapour, S., Farahani, R.Z., Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case (2013) Eur. J. Oper. Res., 227 (1), pp. 199-215. , May; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking An Int. J., 15 (1), pp. 25-51; Tavana, M., Mirzagoltabar, H., Mirhedayatian, S.M., Saen, R.F., Azadi, M., A new network epsilon-based DEA model for supply chain performance evaluation (2013) Comput. Ind. Eng., 66 (2), pp. 501-513. , Oct; Ip, W.H., Chan, S.L., Lam, C.Y., Modeling supply chain performance and stability (2011) Ind. Manag. Data Syst., 111 (8), pp. 1332-1354; Pohlen, T.L., Coleman, B.J., Evaluating Internal Operations and Supply Chain Performance Using EVA and ABC (2005) S.A.M. Adv. Manag. J., 70 (2), pp. 45-58; Ordoobadi, S.M., Application of AHP and Taguchi loss functions in supply chain (2010) Ind. Manag. Data Syst., 110 (8), pp. 1251-1269; Low, C., Chen, Y.H., A model measurement system for collaborative supply chain partners (2013) J. Manuf. Syst., 32 (1), pp. 180-188. , Jan; Shafiee, M., Lotfi, F.H., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Appl. Math. Model., , Mar; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Manag. Decis., 41 (7), pp. 635-642; Alhyari, S., Al-Ali, A.M., Nuseir, M., (2014) Assessing supply chain partnershiperformance in services organization: Conceptual model, 7 (7), pp. 1-16; Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable supply management: An empirical study (2012) Int. J. Prod. Econ., 140 (1), pp. 168-182. , v},
correspondence_address1={Leksono, E.B.; Department of Industrial Engineering, Indonesia; email: ekobudileksono@yahoo.com},
editor={Jauhari W.A., Kurniawan D., Sutopo W., Nor F.M.},
publisher={American Institute of Physics Inc.},
issn={0094243X},
isbn={9780735415904},
language={English},
abbrev_source_title={AIP Conf. Proc.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Sarjono2017186,
author={Sarjono, H. and Suprapto, A.T. and Megasari, L.},
title={Supply chain performance measurement using SCOR model in the distribution company in Indonesia},
journal={2017 3rd International Conference on Information Management, ICIM 2017},
year={2017},
pages={186-189},
doi={10.1109/INFOMAN.2017.7950372},
art_number={7950372},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85024404529&doi=10.1109%2fINFOMAN.2017.7950372&partnerID=40&md5=1eaa3fcbe7e93ac300cabcdf5b0d3bdb},
affiliation={School of Business Management, Bina Nusantara University, Jakarta, Indonesia},
abstract={The purpose of this study is to measure the supply chain performance of the company with the approach of Supply Chain Operations Reference (SCOR) Model and reveal the difference in the application of SCOR Model in the distributor company with retail and manufacturing company. This research was conducted at HOLIP Corp. This study use qualitative and quantitative data with descriptive type of research. Methods of research data analysis using the SCOR Model. The research result is the company's supply chain performance is already good and the SCOR model is more maximal if used in the retail and manufacturing company than distributor company. © 2017 IEEE.},
author_keywords={Measurement;  SCOR model;  Supply chain performance},
keywords={Information management;  Manufacture;  Measurements;  Supply chains, Distribution companies;  Manufacturing companies;  Methods of researches;  Quantitative data;  SCOR models;  Supply chain operations reference models;  Supply chain performance;  Supply chain performance measurements, Industrial research},
references={Erkan, T.E., Bac, U., Supply chain performance measurement: A case study about applicability of SCOR Model in a manufacturing industry firm (2011) Journal of Business and Management Studies, 3 (1), pp. 381-390; (2010) Supply Chain Operations Reference Model, , SCC. Cypress: Supply Chain Council, Inc; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning, and Operation, , Essex: Pearson Education Limited; Schroeder, R.G., (2007) Operations Management: Contemporary Concepts and Cases, , New York: McGraw-Hill; Heizer, J., Render, B., (2014) Operations Management: Sustainability and Supply Chain Management, , Essex: Pearson Education Limited; Georgise, F.B., Thoben, D.K., Adapting the SCOR Model to suit the different scenarios: A literature review & research agenda (2012) International Journal of Business and Management, 7 (6), pp. 2-17; Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: Review and classification (2012) Journal of Organizational Management Studies, 2012, 2012, pp. 1-20; Paul, J., (2014) Panduan Penerapan Transformasi Rantai Suplai Dengan Model SCOR®, , Jakarta: PPM; Sekaran, U., Bougie, R., (2013) Research Methods for Business: A Skill-building Approach, , West Sussex: John Wiley & Sons Ltd; Bolstorff, P., Rosenbaum, R., (2007) A Handbook for Dramatic Improvement Using the SCOR Model, , New York: AMACOM},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781509063048},
language={English},
abbrev_source_title={Int. Conf. Inf. Manag., ICIM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pungchompoo2017131,
author={Pungchompoo, S. and Dunyakul, Y.},
title={Effects of collaborative factors on supply chain performance measurement in Thai frozen shrimp supply chain},
journal={2017 4th International Conference on Industrial Engineering and Applications, ICIEA 2017},
year={2017},
pages={131-135},
doi={10.1109/IEA.2017.7939193},
art_number={7939193},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021391051&doi=10.1109%2fIEA.2017.7939193&partnerID=40&md5=5d55f51ba2e18b788dbb48e371bde9a7},
affiliation={Industrial Engineering Department, Engineering Faculty, Rajamangala University of Technology Srivijaya, Songkhla, Thailand},
abstract={This research aims to investigate factor affecting and causal relationship of the supply chain collaborative and supply chain performance measurement model for the Thai frozen shrimp chain by using integrated method between a structural equation modeling (SEM) and an analytical network process (ANP), which the method confirmed and proved the construction of the framework and identified causal relationship of supply chain collaboration to the important key performance indicators of the chain from 220 supply chain members' point of views. The result shows a completed SEM model that it represents a path between criteria in each latent variable and also showed factor loading score between SCC and SCPM. The parameter estimation used maximum likelihood estimation and model fitting to evaluate the degree. From the SEM analysis, the CMIN/df is 0.982 and χ2 = 74. The root mean square error of approximation (RMSEA) was 0.000 at 95% CI, which is less than 0.080. This indicates a good fit and reaches an acceptable level. With a comparative fit index (CFI) of 0.999 and a goodness of fit index (GFI) of 0.958, it represents the overall goodness of fit of the GFI model, which is higher than 0.90. Therefore, the model can be acceptably conceptualized as a causal relationship construct model significantly. © 2017 IEEE.},
author_keywords={performance measurement;  structural equation modeling (SEM);  supply chain collaboration},
keywords={Benchmarking;  Maximum likelihood;  Maximum likelihood estimation;  Mean square error;  Shellfish, Analytical network processes (ANP);  Goodness-of-fit indices;  Key performance indicators;  Performance measurements;  Root mean square errors;  Structural equation modeling;  Supply chain collaboration;  Supply chain performance measurements, Supply chains},
references={Liao, S.H., Kuo, F.I., The study of relationships between the collaboration for supply chain, supply chain capacities and firm performance : A case of the Taiwan's TFT-LCD industry (2014) International Journal of Production Economics, 156, pp. 295-304; Simatupang, T.M., Sridharan, R., (2005) International Journal of Physical Distribution & Logistics Management, 35 (1), pp. 44-62; Ramanathan, U., Bentley, Y., Pang, G., The role of collaboration in the UK green supply chains : An exploratory study of the perspectives of suppliers, logistics and retailers (2014) Journal of Cleaner Production, 70, pp. 231-241; Fu Jeng, D.J., Generating a casual model of supply chain collaboration using the fuzzy DEMATEL technique (2015) Computer & Industrial Engineering, 87, pp. 281-295; Cao, M., Zhang, Q., Supply chain collaboration : Impact on collaborative and firm performance (2011) Journal of Operation Management, 29, pp. 163-180; Ramanathan, U., Performance of supply chain collaboration-A simulation study (2014) Expert System with Application, 41, pp. 210-220; Lin, C., Chow, W.S., Madu, C.N., Kuei, C.H., Yu, P.P., A structural equation model of supply chain quality management and organizational performance (2005) International Journal of Production Economics, 96, pp. 355-365; Lee, H.L., Padamnabhan, V., Whang, S., The bullwhip effect in supply (1977) Sloan Management Review, 38 (3), pp. 93-102; Mathopoulos, A., Vlachopoulou, M., Manos, B., A conceptual framework for supply chain collaboration: Empirical evidence for agri-food industry (2007) An International Journal of Supply Chain Management, 12 (3), pp. 177-186; Gao, S., Zhang, Z., Cao, C., Calculating weight methods in complete matrices and incomplete matrices (2010) Journal of Software, 5 (3), pp. 304-311; Motoya-Torres, J.R., Ortiz-Vargas, D.A., Collaboration and information sharing in dyadic supply chain: A literature review over the period 2000-2012 (2014) Estudios Greeciales, 30, pp. 342-354; Kaplan, R.S., Norton, D.P., The balance scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-90; Bhagwat, R., Shama, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18 (8), pp. 666-680; Li, S., Rao, S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for study supply chain management practices (2005) Journal of Operations Management, 23, pp. 618-641; Zhu, Q., Sarkis, J., Lai, K.H., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal OfProduction Economics, 111 (2), pp. 261-273; Kim, S.W., An investigation on the direct and indirect effect of supply chain integration on firm performance (2009) The International Journal of Production, 119, pp. 328-346; Punniyamoorthy, M., Mathiyalagan, P., Parthiban, P., A strategic model using structural equation modeling and fuzzy logic in supplier selection (2011) Expert Systems with Applications, 38 (1), pp. 458-474; Tseng, Y., Lee, T., Comparing appropriate decision support of human resource practices on organizational performance with DEA/AHP model (2009) Expert Systems with Applications, 36 (3), pp. 6548-6558; Agricultural statistics (2012) Ministry of Agriculture and Cooperatives, , Office of Agriculture Economic; Pungchompoo, S., Sopadang, A., Confirmation and evaluation of performance measurement model for the Thai frozen shrimp (2015) Business Process Management Journal, 21 (4), pp. 837-856; Chan, F.T.S., Performance measurement in supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Beamon, B.M., Supply chain design and analysis: Model and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Zhang, X., Aramyan, L.H., A concept framework for supply chain governance an application to agri-food chains in China (2009) China Agricultural Economic Review, 1 (2); Chan, F.T.S., Qi, H.J., An innovative performance measurement method for Supply chain management (2003) Supply Chain Management : An International Journal, 8 (3), pp. 209-223; Akintoye, A., McIntosh, G., Fitzgerald, E., A survey of supply chain collaboration and management in the UK construction industry (2000) European Journal of Purchasing & Supply Management, 6, pp. 159-168},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781509067749},
language={English},
abbrev_source_title={Int. Conf. Ind. Eng. Appl., ICIEA},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Hwang20172590,
author={Hwang, G. and Lee, J. and Park, J. and Chang, T.-W.},
title={Developing performance measurement system for Internet of Things and smart factory environment},
journal={International Journal of Production Research},
year={2017},
volume={55},
number={9},
pages={2590-2602},
doi={10.1080/00207543.2016.1245883},
note={cited By 131},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991475088&doi=10.1080%2f00207543.2016.1245883&partnerID=40&md5=cad8ad5e7214b1fbd52991ce50f4d966},
affiliation={Department of Industrial Engineering, Seoul National University, Seoul, South Korea; Department of Industrial and Management Engineering, Kyonggi University, Suwon, South Korea},
abstract={To cope with large fluctuations in the demand of a commodity, it is necessary for the manufacturing system to have rapid reactive ability. This requirement may be secured by performance measurement. Although manufacturing companies have used information systems to manage performance, there has been the difficulty of capturing real-time data to depict real situations. The recent development and application of the Internet of Things (IoT) has enabled the resolution of this problem. In demonstration of the functionality of IoT, we developed an IoT-based performance model consistent with the ISA-95 and ISO-22400 standards, which define manufacturing processes and performance indicator formulas. The development comprised three steps: (1) Selection of the Key Performance Indicators of the Overall Equipment Effectiveness (OEE), and the development of an IoT-based production performance model, (2) Implementation of the IoT-based architecture and performance measurement process using Business Process Modelling and (3) Validation of the proposed model through virtual factory simulation. We investigated the effect of the IoT-workability on the OEE, based on the final results of the simulation, both for the planned and actual productions. The simulation results showed that the proposed model represented the timestamp data acquired by IoT and captured the entire production process, thus enabling the determination of real-time performance indicators. © 2016 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={Internet of Things;  manufacturing information systems;  OEE;  performance measures;  process modelling},
keywords={Benchmarking;  Information management;  Information systems;  Internet;  Manufacture;  Process engineering;  Real time systems;  Supply chain management;  Systems engineering;  Waste disposal, Architecture and performance;  Development and applications;  Key performance indicators;  Manufacturing information systems;  Overall equipment effectiveness;  Performance measure;  Performance measurement system;  Process modelling, Internet of things},
references={Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and Improving Performance Measurement Systems: An Action Research (2011) International Journal of Production Economics, 133 (2), pp. 751-760; Dai, Q., Zhong, R., Huang, G.Q., Ting, Q., Zhang, T., Luo, T.Y., Radio Frequency Identification-enabled real-time Manufacturing Execution System: A Case Study in an Automotive Part Manufacturer (2011) Review of. International Journal of Computer Integrated Manufacturing, 25 (1), pp. 51-65; Desbiens, E., Chaabane, A., (2014) Visual Notation Approach for MES Integration, , Paper presented at the IIE Annual Conference, Proceedings; Fang, J., Huang, G.Q., Li, Z., Event-driven Multi-agent Ubiquitous Manufacturing Execution Platform for Shop Floor Work-in-progress Management (2013) International Journal of Production Research, 51 (4), pp. 1168-1185; Gupta, P., Vardhan, S., Optimizing OEE, Productivity and Production Cost for Improving Sales Volume in an Automobile Industry through TPM: A Case Study (2016) International Journal of Production Research, 54 (10), pp. 2976-2988; Hao, Y., Karbowski, R., Shamsuzzoha, A., Helo, P., Designing of Cloud-based Virtual Factory Information System (2013) Advances in Sustainable and Competitive Manufacturing Systems, pp. 415-426. , Azevedo A., (ed), Springer International; Kádár, B., Terkaj, W., Sacco, M., Semantic Virtual Factory Supporting Interoperable Modelling and Evaluation of Production Systems (2013) CIRP Annals - Manufacturing Technology, 62 (1), pp. 443-446; Kern, D., Moser, R., Hartmann, E., Moder, M., Supply Risk Management: Model Development and Empirical Analysis (2012) International Journal of Physical Distribution & Logistics Management, 42 (1), pp. 60-82; Mejjaouli, S., Babiceanu, R., Holonic Condition Monitoring and Fault-recovery System for Sustainable Manufacturing Enterprises (2014) Service Orientation in Holonic and Multi-agent Manufacturing and Robotics, pp. 31-46. , Borangiu T., Trentesaux D., Thomas A., (eds), Springer International; Meyer, S., Ruppen, A., Magerkurth, C., Internet of Things-Aware Process Modeling: Integrating IoT Devices as Business Process Resources (2013) Advanced Information Systems Engineering, pp. 84-98. , Salinesi C., Norrie M.C., Pastor Ó., (eds), Berlin: Springer; Muchiri, P., Pintelon, L., Performance Measurement Using Overall Equipment Effectiveness (OEE): Literature Review and Practical Application Discussion (2008) International Journal of Production Research, 46 (13), pp. 3517-3535; Muchiri, P.N., Pintelon, L., Martin, H., De Meyer, A.-M., Empirical Analysis of Maintenance Performance Measurement in Belgian Industries (2009) Review of International Journal of Production Research, 48 (20), pp. 5905-5924; Muthiah, K.M.N., Huang, S.H., Overall Throughput Effectiveness (OTE) Metric for Factory-level Performance Monitoring and Bottleneck Detection (2007) International Journal of Production Research, 45 (20), pp. 4753-4769; Ou-Yang, C., Lin, Y.D., BPMN-based Business Process Model Feasibility Analysis: A Petri Net Approach (2008) International Journal of Production Research, 46 (14), pp. 3763-3781; Prades, L., Romero, F., Estruch, A., García-Dominguez, A., Serrano, J., Defining a Methodology to Design and Implement Business Process Models in BPMN according to the Standard ANSI/ISA-95 in a Manufacturing Enterprise (2013) Procedia Engineering, 63, pp. 115-122; Qu, T., Thürer, M., Wang, J., Wang, Z., Fu, H., Li, C., Huang, G.Q., System Dynamics Analysis for an Internet-of-things-enabled Production Logistics System (2016) International Journal of Production Research; Saenz de Ugarte, B., Artiba, A., Pellerin, R., Manufacturing Execution System–A Literature Review (2009) Production Planning & Control, 20 (6), pp. 525-539; Tsarouhas, P.H., Evaluation of Overall Equipment Effectiveness in the Beverage Industry: A Case Study (2013) International Journal of Production Research, 51 (2), pp. 515-523; Unver, H.O., An ISA-95-Based Manufacturing Intelligence System in Support of Lean Initiatives (2013) The International Journal of Advanced Manufacturing Technology, 65 (5-8), pp. 853-866; Yoon, J.-S., Shin, S.-J., Suh, S.-H., A Conceptual Framework for the Ubiquitous Factory (2012) International Journal of Production Research, 50 (8), pp. 2174-2189; Zhong, R.Y., Dai, Q.Y., Qu, T., Hu, G.J., Huang, G.Q., RFID-enabled Real-time Manufacturing Execution System for Mass-customization Production (2013) Robotics and Computer-Integrated Manufacturing, 29 (2), pp. 283-292},
correspondence_address1={Chang, T.-W.; Department of Industrial and Management Engineering, South Korea; email: keenbee@kgu.ac.kr},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Boonsothonsatit20176,
author={Boonsothonsatit, G. and Supeekit, T.},
title={CAusal Linkages to systematically evaluate supply chain performance (CALLS) for SMEs in Thailand},
journal={2017 6th International Conference on Industrial Technology and Management, ICITM 2017},
year={2017},
pages={6-10},
doi={10.1109/ICITM.2017.7917886},
art_number={7917886},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020101240&doi=10.1109%2fICITM.2017.7917886&partnerID=40&md5=37fab34144843fbee168559b6344a204},
affiliation={Institute of Field Robotics (FIBO), King Mongkut's University of Technology Thonburi, Bangkok, Thailand; Department of Industrial Engineering, Faculty of Engineering, Mahidol University, Nakhonpathom, Thailand},
abstract={The business environment has become fiercely competitive due to higher customer expectations. The fierce competition has forced companies to enhance their competitiveness which leads to a need of supply chain performance measurement. Its existing tool appropriate and used broadly for SMEs in Thailand is the supply chain assessment tool (SCPAT) of Banomyong and Supatn. The SCPAT is capable of quantitatively measuring and evaluating individual supply chain measures, but not considering their causal linkages as holistic system thinking. This paper aims to develop a CAusaL Linkage-based Systematic tool (CALLS) as an extension of the SCPAT. The developed CALLS were successfully validated in an industrial case. It provided suggestions based on sensitivity analyses to the causal supply chain measures. © 2017 IEEE.},
author_keywords={causal linkages;  small to medium-sized enterprises;  supply chain performance;  Thailand},
keywords={Sensitivity analysis, Assessment tool;  Business environments;  Customer expectation;  Small to medium-sized enterprise;  Supply chain performance;  Supply chain performance measurements;  System thinkings;  Thailand, Supply chains},
references={Grant, B., Lambert, M., Stock, R., Ellram, M., (2006) Fundamentals of Logistics Management, , McGraw-Hill, Maidenhead; Min, S., Mentzer, J., Developing and and measuring supply chain management concepts (2004) Journal of Business Logistics., 25 (1), pp. 63-92; Arvis, J.F., Saslavsky, D., Ojala, L., Shepherd, B., Busch, C., Raj, A.O., Connecting to complete: Trade logistics in the global economy (2014) The International Bank for Reconstruction and Development, , The World Bank, Washington DC; Banomyong, R., Supatn, N.O., Develping a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; (2004) CSCMP, Supply Chain Management Process Standards, Council of Supply Chain Management Professionals, , Oak Brook, IL; Sinha, R.K., Babu, A.S., Quality of customer service in supply chain system: A diagnostic study (1998) International Journal of Quality & Reliability Management, 15 (8-9), pp. 844-859; Nairn, M.M., Childerhouse, P., Disney, S., Towill, D., A supply chain diagnostic methodology: Determining the vector of change (2002) Computers and Industrial Engineering, 43 (112), pp. 135-157; Foggin, J., Mentzer, J., Monroe, C., A supply chain diagnostic tool (2004) International Journal of Physical Distribution & Logistics Management, 34 (10), pp. 827-855; Charan, P., Shankar, R., Baisya, R., Analysis of interactions among the variables of supply chain performance measurement 10 system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Chan, F., Chan, H., Lau, H., Lp, R., An AHP approach in benchmarking logistics performance of the postal industry (2006) Benchmarking: An International Journal, 13 (6), pp. 636-661; Vu, R., Tzeng, G.H., A soft computing method for multi-criteria decision making with dependence and feedback (2006) Applied Mathematics and Computation, 180, pp. 63-75},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781509053292},
language={English},
abbrev_source_title={Int. Conf. Indus. Technol. Manag., ICITM},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Balfaqih20174721,
author={Balfaqih, H. and Nopiah, Z.M. and Al-Nory, M.T. and Saibani, N.},
title={Sustainable framework for water desalination supply chain: Delphi-AHP technique},
journal={Advanced Science Letters},
year={2017},
volume={23},
number={5},
pages={4721-4724},
doi={10.1166/asl.2017.8844},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023771017&doi=10.1166%2fasl.2017.8844&partnerID=40&md5=41e3e047598cf0ece9d6ca85aeced4ac},
affiliation={National University of Malaysia, Malaysia; Massachusetts Institute of Technology, United States; Effat University, Saudi Arabia},
abstract={The interest in desalination has been growing steadily for the recent decades due to the global increased demand for fresh water. However, there are few studies considered evaluating the desalination supply chain as a whole. Thus, the purpose of this study is to develop a framework that is designed to evaluate the sustainability of desalination supply chain. Different desalination and supply chain performance measures have been reviewed and distributed into economic, social, operation, and environmental perspective. The performance measures and metrics have been further refined by a panel of experts using Delphi-AHP technique. A weight factor has been assigned to each perspective and metric according to its significant in desalination supply chain. The proposed framework could serve as a reference for the researchers in desalination industry. It is believed that both academics and practitioners would benefit from the framework developed. © 2017 American Scientific Publishers All rights reserved.},
author_keywords={AHP;  Delphi;  Desalination;  Performance measurement;  Supply chain;  Sustainability},
references={Alegre, H., Baptista, J.M., Cabrera, E., Cubillo, F., Duarte, P., Hirner, W., Merkel, W., Parena, R., (2013) Water Intelligence Online, p. 12. , 9781780405292; Al-Nory, M.T., Brodsky, A., Bozkaya, B., Graves, S.C., (2014) Desalination, 347, p. 144; Al-Nory, M.T., Graves, S.C., (2013) IDA Journal of Desalination and Water Reuse, 2 (64), p. 5; Coday, B.D., (2015) Desalination, 369, p. 188; Ghaffour, (2009) Desalination Water Treat, 5, p. 48; Ghaffour, N., Missimer, T.M., Amy, G.L., (2013) Desalination, 309, p. 197; Gopal, P.R.C., Thakkar, J., (2016) Production Planning and Control, 27, p. 49; Raluy, G., Serra, L., Uche, J., (2006) Energy, 31, p. 2361; Muga, H.E., Mihelcic, J.R., (2008) J. Environ. Manag, 88, p. 437; IDA desalination yearbook, Global Data Report Desal Data 2013-2014; Sadhwani, J.J., Veza, J.M., Carmelo, S., (2005) Desalination, 185, p. 1; Mezher, T., Fath, H., Abbas, Z., Khaled, A., (2011) Desalination, 266, p. 263; (2010) Lipovetsky, International Journal of the Analytic Hierarchy Process, p. 2. , S; Mezher, T., Fath, H., Abbas, Z., Khaled, A., (2011) Desalination, 266, p. 263; Shahabi, M.P., Anda, M., Ho, G., (2014) Desalin. Water Treat, p. 1; Poustie, M.S., Deletic, A., Brown, R.R., Wong, T., De Haan, F.J., Skinner, R., (2014) Urban Water J, p. 1; Einav, R., Lokiec, F., (2003) Desalination, 156, p. 79; Raluy, R.G., Serra, L., Uche, J., Valero, A., (2004) Desalination, 167, p. 445; Saaty, T.L., (2000) Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, 6. , Rws Publications; Lattemann, S., El Habr, H., (2009) Desalin. Water Treat, 3, p. 217; Hopner, T., (1999) Desalination, 124, p. 1; Wibowo, M.A., Sholeh, M.N., (2015) Procedia Engineering, 125, p. 25; Scientific assessment of ozone depletion (1998) Global Ozone Research and Monitoring Project, p. 1999. , World Meteorological Organization, Geneva; (2003) Guidelines for Safe Recreational Water Environments: Coastal and Fresh Waters, 1. , World Health Organization; Saif, Y., Almansoori, A., (2014) Desalination, 351, p. 184; Zhou, J., Chang, V.W., Fane, A.G., (2014) Water Res, 61, p. 210},
correspondence_address1={Balfaqih, H.; National University of MalaysiaMalaysia},
publisher={American Scientific Publishers},
issn={19366612},
language={English},
abbrev_source_title={Adv. Sci. Lett.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chaurey201752,
author={Chaurey, M.},
title={Emerging technology in SCM},
journal={Journal of Maritime Research},
year={2017},
volume={14},
number={1},
pages={52-57},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116430958&partnerID=40&md5=729bf89c119ec72699c5e36fc63e7a5d},
affiliation={ICFAI Business School, Hyderabad, India},
abstract={With the recent celebration of “International Women Day” and festive of color. Today we will focus on Emerging technology in the Supply Chain. Facility location decisions play a critical role in the strategic design of supply chain networks. In this paper, new technology and models in the context of supply chain management is given. We identify basic features that such models and technology must capture to support decision-making involved in strategic supply chain planning. In particular, the integration of location decisions with other decisions relevant to the design of a supply chain network is discussed. Furthermore, aspects related to the structure of the optimization of cost in supply chain are also addressed. Significant contributions to the current state-of-the-art are surveyed taking into account numerous factors. Supply chain performance measures and optimization techniques are also reviewed. Applications of facility location models to supply chain network design ranging across various industries are presented. New Techs presently use by the industry in the market for saving their time and cost. At last a brief statement given by ASSOCHAM supporting the Logistics sector in India. © SEECMAR j All rights reserved.},
author_keywords={Internet of Things (IoTs);  Supply Chain Management (SCM)},
references={Sankar, R., (2017) Five Ways to Optimize Supply Chain Management, , http://www.industryweek.com-/planning-amp-forecasting/five-ways-optimize-supply-chain-managementTheWorldBankGroup, IndustryWeek, (2017) Global Rankings, (2015). http://lpi.-worldbank.org/international/global; Open Learning Campus, , https://olc.-worldbank.org/, TheWorld Bank Group; (2016) India can save $50 bn in logistics costs, raise growth of exports, says study, , http://www.firstpost.com/business/logistics-india-exports-assocham-new-investments-2758804.html, IANS. FirstPost.com; Lambert Sebastian, D.M., An evaluation of processoriented supply chain management frameworks (2011) Journal of Business Logistics, , https://onlinelibrary.wiley.com/doi/10.10-02/j.2158-1592.2005.tb00193.x},
correspondence_address1={Chaurey, M.; ICFAI Business SchoolIndia},
publisher={Universidad de Cantabria},
issn={16974840},
language={English},
abbrev_source_title={J. Marit. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gilbert20172203,
author={Gilbert, S.S. and Thakare, N. and Ramanujapuram, A. and Akkihal, A.},
title={Assessing stability and performance of a digitally enabled supply chain: Retrospective of a pilot in Uttar Pradesh, India},
journal={Vaccine},
year={2017},
volume={35},
number={17},
pages={2203-2208},
doi={10.1016/j.vaccine.2016.11.101},
note={cited By 21},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016453112&doi=10.1016%2fj.vaccine.2016.11.101&partnerID=40&md5=69fd296bd72595c77c7cd9aec8a78e82},
affiliation={The Gates Foundation, United States; Logistimo, India},
abstract={Background Immunization supply chains in low resource settings do not always reach children with necessary vaccines. Digital information systems can enable real time visibility of inventory and improve vaccine availability. In 2014, a digital, mobile/web-based information system was implemented in two districts of Uttar Pradesh, India. This retrospective investigates improvements and stabilization of supply chain performance following introduction of the digital information system. Methods All data were collected via the digital information system between March 2014 and September 2015. Data included metadata and transaction logs providing information about users, facilities, and vaccines. Metrics evaluated include adoption (system access, timeliness and completeness), data quality (error rates), and performance (stock availability on immunization session days, replenishment response duration, rate of zero stock events). Stability was defined as the phase in which quality and performance metrics achieved equilibrium rates with minimal volatility. The analysis compared performance across different facilities and vaccines. Results Adoption appeared sufficiently high from the onset to commence stability measures of data quality and supply chain performance. Data quality stabilized from month 3 onwards, and supply chain performance stabilized from month 13 onwards. For data quality, error rates reduced by two thirds post stabilization. Although vaccine availability remained high throughout the pilot, the three lowest-performing facilities improved from 91.05% pre-stability to 98.70% post-stability (p < 0.01; t-test). Average replenishment duration (as a corrective response to stock-out events) decreased 52.3% from 4.93 days to 2.35 days (p < 0.01; t-test). Diphtheria-tetanus-pertussis vaccine was significantly less likely to be stocked out than any other material. Conclusion The results suggest that given sufficient adoption, stability is sequentially achieved, beginning with data quality, and then performance. Identifying when a pilot stabilizes can enable more predictable, reliable cost estimates, and outcome forecasts in the scale-up phase. © 2017 The Authors},
author_keywords={Immunization;  India;  Information systems;  mHealth;  Rural;  Supply chains},
keywords={diphtheria pertussis tetanus vaccine;  vaccine, Article;  error;  human;  medical information system;  performance measurement system;  priority journal;  retrospective study;  Uttar Pradesh;  drug storage;  electronics;  India;  information system;  preventive health service;  procedures;  supply and distribution, Drug Storage;  Electronics;  Humans;  Immunization Programs;  India;  Information Systems;  Retrospective Studies;  Vaccines},
references={http://www.who.int/immunization/programmes_systems/supply_chain/en/, Immunization supply chain and logistics. <> [accessed 05.04.16]; (2015), http://www.who.int/entity/immunization/programmes_systems/supply_chain/EVM-Global-Data-Analysis-2009-2014.pdf?ua=1, WHO and UNICEF. Effective Vaccine Management Global Data Analysis: 2009-2014;. <> [accessed 05.04.16]; Shieshia, M., Strengthening community health supply chain performance through an integrated approach: using mHealth technology and multilevel teams in Malawi (2014) J Global Health, 4 (2); Njuguna, H.N., http://dx.doi.org/10.1186/s12911-014-0107-5, A comparison of smartphones to paper-based questionnaires for routine influenza sentinel surveillance, Kenya, 2011–2012. BMC Medical Informatics and Decision-making 2014. 14 (107); Agarwal, S., Perry, H.B., Long, L.A., Labrique, A.B., Evidence on feasibility and effective use of mHealth strategies by frontline health workers in developing countries: systematic review (2015) Trop Med Int Health, 20 (8), pp. 1003-1014; Medhi, I., Jain, M., Tewari, A., Bhavsar, M., Matheke-Fischer, M., Cutrell, E., http://research.microsoft.com/pubs/170446/Medhi-NordiCHI2012-CommCareCasestudy.pdf, Combating rural child malnutrition through inexpensive mobile phones. Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design. ACM; 2012. <> [accessed 05.04.16]; (2009), https://www.w3.org/2008/10/MW4D_WS/papers/dtree.pdf, Bogan, et al. Improving Standards of Care with Mobile Applications in Tanzania. Presented at the W3C Workshop on the Role of Mobile Technologies in Fostering Social and Economic Development in Africa;. <> [accessed 04.04.16]; Githinji, S., Reducing stock-outs of life saving malaria commodities using mobile phone text-messaging: SMS for life study in Kenya (2013) PLOS One; Chayachati, K.H., A pilot study of an mHealth application for healthcare workers: poor uptake despite high reported acceptability at a rural South African community-based MDR-TB treatment program (2013) PLOS One, 8 (5); Ramanujapuram, A., Akkihal, A., Improved Performance of Rural Supply Chains Using Mobile Phones. ACM DEV-5 2014. doi: 2674377.2674382; https://gsmaintelligence.com/research/?file=4113a57d43a9e93968e7ed00123ba4b2&download, The Mobile Economy: India 2015. GSMA 2015. <> [accessed 03.03.16]; Manda, T.D., Herstad, J., (2009), http://africommconference.org/2009/Presentations/Day2/Implementing%20Mobile%20Phone%20Solutions%20for%20Health%20in%20Resource%20Constrained%20Areas.pdf, Implementing Mobile Phone Solutions for Health in Resource Constrained Areas: Understanding the Opportunities and Challenges. AFRICOMM;. <> [accessed 04.04.16]; http://www.censusindia.gov.in/vital_statistics/AHSBulletins/AHS_Factsheets_2012-13/FACTSHEET-UTTAR_PRADESH.pdf, Office of the Registrar General & Census Commissioner, Vital Statistics Division. Annual Health Survey 2012-2013 Fact Sheet: Uttar Pradesh. New Delhi: India Ministry of Home Affairs, Government of India. 2013. Accessed 31st March 2016 at <>; (2011), http://www.census2011.co.in/census/state/uttar+pradesh.html, Census Organization of India. Uttar Pradesh Population Census Data 2011. New Delhi: India Ministry of Home Affairs, Government of India;. <> [accessed 05.04.16]; Dutta, S., Bhattacharya, M., Gangal, M., Polpakara, D., (2013), http://unicef.in/Uploads/Publications/Resources/pub_doc106.pdf, National Effective Vaccine Management Assessment: India;. New Delhi: India National Rural Health Mission, National Institute for Health and Family Welfare & UNICEF. <> [accessed 30.03.16]; Manya, A., www.IST-Africa.org/Conference2012, National Roll out of District Health Information Software (DHIS 2) in Kenya, 2011 – Central Server and Cloud based Infrastructure. IST-Africa Conference; 2012. <> [accessed 05.04.16]; Ngabo, F., Designing and implementing an innovative SMS-based alert system (Rapid-SMS-MCH) to monitor pregnancy and reduce maternal and child deaths in Rwanda (2012) Pan Afr Med J, 13 (31); Choucri, N., http://web.mit.edu/smadnick/www/wp/2006-02.pdf, Understanding & Modeling State Stability: Exploiting System Dynamics. Working Paper CISL# 2006–02. Cambridge: MIT. <> [accessed 19.04.16]; http://www.villagereach.org/wp-content/uploads/2016/03/All-Provinces_PerformanceReport_2015_FINAL.pdf, DLS Performance Report - Cabo Delgado, Gaza, Maputo and Niassa 2015; 2015. <> [accessed 11.04.16]; http://www.villagereach.org/wp-content/uploads/2014/12/All-Provinces-DLS-Performance-Report-Jan-Dec-2014-EN-FINAL.pdf, DLS Performance Report - Cabo Delgado, Gaza, Maputo and Niassa 2014; 2014. <> [accessed 11.04.16]; Muthusemy, N., eVIN pilot mid-year assessment; 2015. Immunization Technical Support Unit, Indian Ministry of Health and Family Welfare. Available from the authoring agency upon request; Agarwal, S., http://www.bmj.com/content/352/bmj.i1174, Guidelines for reporting of health interventions using mobile phones: mobile health (mhealth) evidence reporting and assessment (mERA) checklist; 2016. BMJ. <> [accessed 04.10.16]},
correspondence_address1={Gilbert, S.S.; The Gates FoundationUnited States; email: sskyegilbert@gmail.com},
publisher={Elsevier Ltd},
issn={0264410X},
coden={VACCD},
pubmed_id={28364932},
language={English},
abbrev_source_title={Vaccine},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kasonde20172134,
author={Kasonde, M. and Steele, P.},
title={The people factor: An analysis of the human resources landscape for immunization supply chain management},
journal={Vaccine},
year={2017},
volume={35},
number={17},
pages={2134-2140},
doi={10.1016/j.vaccine.2017.01.084},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016450282&doi=10.1016%2fj.vaccine.2017.01.084&partnerID=40&md5=9358047c294bcc9174f370eb8f569f6d},
affiliation={UNICEF Supply Division, Copenhagen, Denmark; Pamela Steele Associates, Oxford, United Kingdom},
abstract={Human resources is the backbone of any system and the key enabler for all other functions to effectively perform. This is no different with the Immunization Supply Chain, more so in todays’ complex operating environment with the increasing strain caused by new vaccines and expanding immunization programmes (Source: WHO, UNICEF). In order to drive the change that is required for sustainability and continuous improvement, every immunization supply chain needs an effective leader. A dedicated and competent immunization supply chain leader with adequate numbers of skilled, accountable, motivated and empowered personnel at all levels of the health system to overcome existing and emerging immunization supply chain (ISC) challenges. Without an effective supply chain leader supported by capable and motivated staff, none of the interventions designed to strengthen the supply chain can be effective or sustainable (Source: Gavi Alliance SC Strategy 2014). This landscape analysis was preceded by an HR Evidence Review (March 2014) and has served to inform global partner strategies and country activities, as well as highlight where most support is required. The study also aimed to define the status quo in order to create some form of baseline against which to measure the impact of interventions related to HR going forward. The analysis was comprised of a comprehensive desk review, a survey of 40 respondents from 32 countries and consultations with ISC practitioners in several forums. The findings highlight key areas that should inform the pillars of a HR capacity development plan. At the same time, it revealed that there are some positive examples of where countries are actively addressing some of the issues identified and putting in place mechanisms and structures to optimize the SC function. © 2017},
author_keywords={Capacity development;  Human resources;  Incentives;  Leadership;  Performance;  Professionalisation;  Strategy;  Training;  Workforce},
keywords={Article;  human;  immunization;  leadership;  organization;  performance measurement system;  personnel management;  policy;  priority journal;  professionalism;  resource management;  health care manpower;  organization and management;  preventive health service;  questionnaire;  supply and distribution, vaccine, Health Manpower;  Humans;  Immunization Programs;  Organization and Administration;  Surveys and Questionnaires;  Vaccines},
references={Pamela, S., (2015), Gavi alliance immunization supply chain strategy: assessment of the human resources landscape for immunization supply chain management; October; Bornbusch, A., A business approach to transforming public health supply systems (2014) Reprod Health Supply Coalit; Brown, A.N., Developing medicines supply competency in pacific island countries: a needs-based approach to education (2012) Pharm Educ, 12 (1), pp. 49-52; Estampe, D., A framework for analysing supply chain performance evaluation models (2013) Int J Prod Econ, 142 (2), pp. 247-258; (2016), HR for SCM online discussion. Shining the spotlight on people; June; Gómez-Cedeño, M., Impact of human resources on supply chain management and performance (2015) Ind Manage Data Syst, 115 (1), pp. 129-157; Hohenstein, N.-O., Feisel, E., Hartmann, E., Human resource management issues in supply chain management research (2004) Int J Phys Distribut Logistics Manage, 44 (6), pp. 434-463; McAfee, R.B., Glassman, M., Honeycutt, E.D., Jr., The effects of culture and human resource management policies on supply chain management strategy (2002) J Bus Logistics, 23 (1), pp. 1-18; Joseph, M., Nadia, O., (2011), Supply chain evolution: introduction to a framework for supply chain strengthening of developing country public health programs. Arlington (VA): USAID|DELIVER PROJECT, Task Order 4;; Menon, S.T., Human resource practices, supply chain performance, and wellbeing (2012) Int J Manpower, 33 (7), pp. 769-785; (2015), People that Deliver. PtD human resources for supply chain management IAPHL online discussion analysis; January; Wendy, P., (2015), Data for Management: It's not just another report. Seattle (Wash): VillageReach;; Shub, A.N., Stonebraker, P.W., The human impact on supply chains: evaluating the importance of “soft” areas on integration and performance (2009) Supply Chain Manage: Int J, 14 (1), pp. 31-40; Steele, P., GAVI supply chain strategy people and practices evidence review (2014) J Pharmaceut Policy Pract, 7, p. 6; (2014), Systems for Improved Access to Pharmaceuticals and Services (SIAPS) Program. Promising practices: human resources. Arlington (VA): Management Sciences for Health;; (2013), USAID|DELIVER PROJECT, Task order 4. Human resource capacity development in public health supply chain management: assessment guide and tool. Arlington (VA): USAID|DELIVER PROJECT, Task Order 4;; VillageReach, Comparison of costs incurred in dedicated and diffused vaccine logistics systems: cost effectiveness of vaccine logistics in Cabo Delgado and Niassa Provinces, Mozambique (2009), VillageReach Seattle, Wash; VillageReach, Other duties as required: efficient use of human resources (2014), VillageReach Seattle, Wash; Wilding, R., Supply chain strategy in the board room (2010), Cranfield University School of Management Cranfield; Zaffran, M., The imperative for stronger vaccine supply and logistics systems (2013) Vaccine, 31, pp. B73-B80},
correspondence_address1={Kasonde, M.; UNICEF Supply DivisionDenmark; email: mkasonde@unicef.org},
publisher={Elsevier Ltd},
issn={0264410X},
coden={VACCD},
pubmed_id={28364921},
language={English},
abbrev_source_title={Vaccine},
document_type={Article},
source={Scopus},
}

@ARTICLE{Keathley-Herring201763,
author={Keathley-Herring, H.},
title={An Approach to Quantify the Factors That Affect Performance Measurement System Implementation},
journal={EMJ - Engineering Management Journal},
year={2017},
volume={29},
number={2},
pages={63-73},
doi={10.1080/10429247.2017.1298181},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019773280&doi=10.1080%2f10429247.2017.1298181&partnerID=40&md5=8d4499d756923b2e112b42e4f7714f9e},
affiliation={University of Central Florida, United States},
abstract={Although there have been many important academic advancements in the area of performance measurement systems, many practitioners and researchers still report encountering significant challenges during practical applications. This has led to the development of research to identify factors that affect the success of performance measurement systems. Previous studies attempt to identify and evaluate these factors, but there is little evidence of the inter-relationships and overall effects of various factors on the successful implementation of performance measurement systems. This article describes an exploratory study that applies an existing approach, originally proposed to design performance measurement systems, to investigate the effectiveness of the approach in quantifying the systemic effects of success factors on the implementation of a performance measurement system. The results of this study suggest that applying this approach provides quantitative assessments of the systemic effects of the success factors, which can be used to design more robust implementation strategies. © 2017 Taylor & Francis.},
author_keywords={Analytic Network Process;  Cognitive Mapping;  Implementation, Performance Measurement Systems;  Performance Measurement Systems;  Success Factors},
keywords={Cognitive systems, Analytic network process;  Cognitive mapping;  Exploratory studies;  Implementation strategies;  Inter-relationships;  Performance measurement system;  Quantitative assessments;  Success factors, Supply chain management},
references={Alonso, J.A., Lamata, M.T., Consistency in the analytic hierarchy process: A new approach (2006) International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 14 (4), pp. 445-459; Bento, A., Bento, R., (2006) Factors affecting the outcomes of performance management systems, , Paper presented at the, AMCIS 2006 Proceedings; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17, pp. 522-534; Bititci, U.S., Mendibil, K., Nudurupatitrevor, S., Turner, T., Garengo, P., The interplay between performance measurement, organizational culture and management styles (2004) Measuring Business Excellence, 8 (3), pp. 28-41; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Bourne, M., Neely, A., Mills, J., Platts, K., Why some performance measurement initiatives fail: Lessons from the change management literature (2003) International Journal of Business Performance Management, 5 (2-3), pp. 245-269; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; De Waal, A., Kourtit, K., Performance measurement and management in practice (2013) International Journal of Productivity and Performance Management, 62 (5), pp. 446-473; De Waal, A.A., Counet, H., Lessons learned from performance management systems implementations (2009) International Journal of Productivity and Performance Management, 58 (4), pp. 367-390; Durlak, J.A., DuPre, E.P., Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation (2008) American Journal of Community Psychology, 41 (3-4), pp. 327-350; Hacker, M., Doolen, T., Alignment at the top: A case study investigating this critical factor in project implementation (2007) Engineering Management Journal, 19 (1), pp. 38-42; Keathley, H., Van Aken, E., (2013) Systematic literature review on the factors that affect performance measurement system implementation, , Paper presented at the; Keathley-Herring, H., Van Aken, E., Gonzalez-Aleu, F., Deschamps, F., Letens, G., Orlandini, P.C., Assessing the maturity of a research area: Bibliometric review and proposed framework (2016) Scientometrics, 109 (2), pp. 927-951; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Kotter, J.P., (1996) Leading change, 35. , Boston, MA: Harvard Business Review; Lewis, B.J., Shea, G.P., Kvan, T., CADD implementation: Variables affecting success (1991) Engineering Management Journal, 3 (1), pp. 11-18; Mendibil, K., MacBryde, J., Factors that affect the design and implementation of team-based performance measurement systems (2006) International Journal of Productivity and Performance Management, 55, pp. 118-142; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Bourne, M., Why measurment initiatives fail (2000) Measuring Business Excellence, 4 (4), pp. 3-7; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Saaty, T.L., (1996) Decision making with dependence and feedback: The analytic network process, 4922. , Pittsburgh, PA: RWS Publications; Saaty, T.L., (1999) Fundamentals of the analytic network process, pp. 12-14. , Paper presented at the, 5th International Symposium on the Analytic Hierarchy Process; Saaty, T.L., (2006) The analytic hierarchy process, , http://www.mendeley.com/research/the-analytic-hierarchy-process/, Pittsburgh, PA: RWS Publications, Retrieved from; Saaty, T.L., Fundamentals of the analytic network process—Dependence and feedback in decision-making with a single network (2004) Journal of Systems Science and Systems Engineering, 13(2), 129–157; Sarkis, J., Quantitative models for performance measurement systems—Alternate considerations (2003) International Journal of Production Economics, 86 (1), pp. 81-90; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64 (1-3), pp. 231-241; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Tippett, D.D., Hamilton, B.J., Key management factors in implementing advanced technologies: A manufacturers survey (1992) Engineering Management Journal, 4 (1), pp. 23-30; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14, pp. 207-222; Westbrook, J.D., Utley, D.R., TQM—The effect of culture on implementation (1995) Engineering Management Journal, 7 (2), pp. 31-34},
correspondence_address1={Keathley-Herring, H.; Department of Industrial Engineering and Management Systems, 12800 Pegasus Dr., P.O. Box 162993, United States; email: Heather.Keathley@ucf.edu},
publisher={Taylor and Francis Ltd.},
issn={10429247},
coden={EMJOE},
language={English},
abbrev_source_title={EMJ Eng Manage J},
document_type={Article},
source={Scopus},
}

@ARTICLE{Equbal20171,
author={Equbal, M.A. and Ohdar, R.},
title={A comprehensive supply chain performance measurement and evaluation (Cspme) methodology},
journal={International Journal of Mechanical and Production Engineering Research and Development},
year={2017},
volume={7},
number={2},
pages={1-18},
art_number={IJMPERDAPR20171},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019702821&partnerID=40&md5=e09fee5c314e5f0e4889d6eca9734ffd},
affiliation={Department of Mechanical Engineering, Cambridge institute of Technology, TatisilwaiJharkhand, India; Department of Forge Technology, National Institute of Foundry and Forge Technology, HatiaJharkhand, India},
abstract={Supply chain management (SCM) has gained a tremendous amount of attention by both practitioners and academics since the last two decades. Supply chain performance measurement (SCPM) plays a vital role for set-up the concrete strategy and a driving force for improvement in organizational productivity. An important component in supply chain design and analysis is the establishment of an appropriate performance measurement and evaluation system. The purpose of such performance measurement system is to monitor and control the supply chain performance. Also, the selection of factors or different variables affecting performance of any organization is an important or vital step in developing a performance measurement system. In this paper, a comprehensive supply chain performance measurement and evaluation (CSCPME) methodology has been developed. The critical performance measures reflect the five specific criteria, viz. effectiveness, efficiency, quality, productivity, and profitability. The developed CSCPME methodology may be applicable for the performance measurement and evaluation of almost all the entities of a supply chain. © TJPRC Pvt. Ltd.},
author_keywords={Effectiveness;  Efficiency;  Productivity & profitability;  Quality;  SCM;  SCPM},
references={Agami, N., Saleh, M., Rasmy, M., A hybrid dynamic framework for supply chain performance improvement (2012) IEEE Systems Journal, 6 (3), pp. 469-478; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance Measurement of Supply Chain Management: A Balanced Scorecard Approach. Journal of (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems (1997) International Journal of Operations and Production Management, Bradford., 17 (5), pp. 522-534; Capacino, W.C., (1997) Supply Chain Management: The Basic and Beyond, , St Lucie, FL: The St Lucie Press; Caplice, C., Sheffi, Y., A Review and Evaluation of Logistics Performance Measurement Systems (1995) The International Journal of Logistics Management, 6 (1); Chan, F.T.S., Qi, H.J., An Innovative Performance Measurement Method for Supply Chain Management (2003) Supply Chain Management: An International Journal, 8 (3-4); Charan, P., Shankar, R., Baisya, R., (2007) Selection of Supply Chain Performance Measurement System Using AHP Approach.Poms 18Th Ann; Chen, I.J., Paulraj, A., Understanding Supply Chain Management:Critical Research and a Theoretical Framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Christopher, M., (2005) Logistics and Supply Chain Management: Creating Value-Adding Networks, , London: FT Prentice Hall; Equbal, A., Ohdar, R., Supply Chain Performance Measurement: A Review and Reflection (2015) Applied Mechanics and Materials., 813-814, pp. 1208-1216; Ghalayini, A.M., Noble, J.S., The Changing Basis of Performance Measurement (1996) International Journal of Operations and Production Management, 16 (8), pp. 63-80; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Hausman, W.H., Supply Chain Performance Metrics (2003) The Practice of Supply Chain Management: Where Theory and Application Converge, pp. 61-67. , T. P. Harrison, H. Lee and J. J. Neale, ed., New York: Springer Science and Business; Huang, S.H., Sheoran, S.K., Wang, G., A Review and Analysis of Supply Chain Operations Reference (SCOR) Model (2004) Supply Chain Management: An International Journal, 9 (1); Kaplan, R.S., Bruns, W., (1987) Accounting and Management: A Field Study Perspective: Harvard Business School Press; Kaplan, R.S., Norton, D.P., The Balanced Scorecard - Measures that Drive Performance (1992) Harvard Business Review, 70 (January-February), pp. 71-79; Kurien, G.P., Qureshi, M.N., Study of Performance Measurement Practices in Supply Chain Management. International Journal of Business Management and (2011) Social Sciences, 2 (4), pp. 19-34; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Lapide, L., What about Measuring Supply Chain Performance? (2000) AMR Research, ASCET - White Paper., 2 (15), pp. 287-297; Liang, L., Feng, F., Cook, W.D., Zhu, J., DEA Models for Supply Chain Efficiency Evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; Lochamy, A., Smith, W., Target costing for supply chain management: Criteria and selection (2000) Industrial Management and Data Systems, 100 (5), pp. 210-218; Lockamy, A., McCormack, K., Linking SCOR Planning Practices to Supply Chain Performance: An Exploratory Study (2004) International Journal of Operations and Production Management, 24 (11-12), pp. 1192-1218; Medori, D., Steeple, D., A Framework for Auditing and Enhancing Performance Measurement Systems (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Adams, C., Crowe, P., The Performance Prism in Practice. Journal of (2001) Measuring Business Excellence, 5 (2); Otto, A., Kotzab, H., Does Supply Chain Management Really Pay? Six Perspectives to Measure the Performance of Managing a Supply Chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Ramaa, A., Rangaswamy, T., Subramanya, K., A Review of Literature on Performance Measurement of Supply Chain Network (2009) 2nd International Conference on Emerging Trends in Engineering and Technology, pp. 802-807; Sellitto, M.A., A SCOR-based model for supply chain performance measurement: Application in the footwear industry (2015) International Journal of Production Research, 53 (16), pp. 4917-4926; Shafiee, M., Hosseinzadeh, L.F., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Applied Mathematical Modelling, 38 (21-22), pp. 5092-5112; Stephens, S., (2001) Supply Chain Operations Reference Model Version 5.0: A New Tool to Improve Supply Chain Efficiency; Stern, J.M., Stewart, G.B., Chew, D.H., The EVA® Financial Management System (1995) Journal of Applied Corporate Finance, 8 (2), pp. 32-46; Stewart, G., Supply chain benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Tan, K.C., A framework of supply chain management literature (2001) European Journal of Purchasing and Supply Management, 7 (1), pp. 39-48; Tangen, T., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Thomas, D.J., Griffin, P.M., Coordinated supply chain Management (1996) European Journal of Operations Research, 94, pp. 1-15; Thunberg, M., Persson, F., Using the SCOR model’s performance measurements to improve construction logistics (2013) Production Planning & Control., 25 (13-14), pp. 1065-1078; Wong, W.P., Wong, K.Y., Supply Chain Performance Measurement System Using DEA Modeling (2007) Journal of Industrial Management and Data Systems Information., 107 (3), pp. 361-381},
publisher={Transstellar Journal Publications and Research Consultancy Private Limited (TJPRC)},
issn={22496890},
language={English},
abbrev_source_title={Int. J. Mech. Prod. Eng. Res. Dev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Holimchayachotikul20171895,
author={Holimchayachotikul, P. and Leksakul, K.},
title={Predictive performance measurement system for retail industry using neuro-fuzzy system based on swarm intelligence},
journal={Soft Computing},
year={2017},
volume={21},
number={7},
pages={1895-1912},
doi={10.1007/s00500-016-2082-5},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016079917&doi=10.1007%2fs00500-016-2082-5&partnerID=40&md5=c848a7830ecb7f3809bd01095dd69c46},
affiliation={Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand; Excellence Center in Logistics and Supply Chain Management, Chiang Mai University, Chiang Mai, 50200, Thailand},
abstract={Between 2011 and 2013, convenience store retail business grew dramatically in Thailand. As a result, most companies have increasingly been choosing the application of performance measurement systems. This significantly results in poor performance measurement regarding future business lagging measure. To solve this problem, this research presents a hybrid predictive performance measurement system (PPMS) using the neuro-fuzzy approach based on particle swarm optimization (ANFIS-PSO). It is constructed from many leading aspects of convenience store performance measures and projects the competitive level of future business lagging measure. To do so, monthly store performance measures were first congregated from the case study value chains. Second, data cleaning and preparations by headquarter accounting verification were carried out before the proposed model construction. Third, these results were used as the learning dataset to derive a predictive performance measurement system based on ANFIS-PSO. The fuzzy value of each leading input was optimized by parallel processing PSO, before feeding to the neuro-fuzzy system. Finally, the model provides a future performance for the next month’s sales and expense to managers who focused on managing a store using desirability function (Di). It boosted the sales growth in 2012 by ten percentages using single PPMS. Additionally, the composite PPMS was also boosted by the same growth rate for the store in the blind test (July 2013–February 2014). From the experimental results, it can be concluded that ANFIS-PSO delivers high-accuracy modeling, delivering much smaller error and computational time compared to artificial neural network model and supports vector regression but its component searching time differs significantly because of the complexity of each model. © 2016, Springer-Verlag Berlin Heidelberg.},
author_keywords={Neuro-fuzzy;  Predictive performance measurement system;  Retailing value chain;  Swarm intelligence},
keywords={Artificial intelligence;  Chains;  Deep neural networks;  Fuzzy neural networks;  Fuzzy systems;  Neural networks;  Particle swarm optimization (PSO);  Sales;  Supply chain management;  Swarm intelligence, Artificial neural network modeling;  Desirability function;  Neuro-Fuzzy;  Neuro-fuzzy approach;  Parallel processing;  Performance measurement system;  Predictive performance;  Value chains, Fuzzy inference},
references={Almejalli, K., Dahal, K., Hossain, M.A., Real time identification of road traffic control measures. Advances in computational intelligence in transport, logistics, and supply chain management (2008) vol 144, , Advances in computational intelligence in transport, logistics, and supply chain management; Asiltürk, I., An intelligent system approach for surface roughness and vibrations prediction in cylindrical grinding (2012) Int J Comput Integr Manuf, 25, pp. 750-759; Bonabeau, E., Meyer, C., Swarm intelligence. A whole new way to think about business (2001) Harv Bus Rev, 106-114 (165), p. 79; Castellano, G., Castiello, C., Fanelli, A.M., Mencar, C., Knowledge discovery by a neuro-fuzzy modeling framework (2005) Fuzzy Sets Syst, 149, pp. 187-207; Cheng, J.H., Chen, S.S., Chuang, Y.W., An application of fuzzy delphi and Fuzzy AHP for multi-criteria evaluation model of fourth party logistics (2008) WSEAS Trans Syst, 7, pp. 466-478; Costa, H.R.N., La Neve, A., Study on application of a neuro-fuzzy models in air conditioning systems (2015) Soft Comput, 19, pp. 929-937; Deng, W., Chen, R., He, B., Liu, Y.Q., Yin, L.F., Guo, J.H., A novel two-stage hybrid swarm intelligence optimization algorithm and application (2012) Soft Comput, 16 (10), pp. 1707-1722; (2012) Department of Industrial Promotion, , http://www.dip.go.th/; Du, T.C.T., Wolfe, P.M., Implementation of fuzzy logic systems and neural networks in industry (1997) Comput Ind, 32, pp. 261-272; Eberhart, R., Kennedy, J., New optimizer using particle swarm theory. In: Proceedings of the international symposium on micro machine and human science (1995) In: Proceedings of the international symposium on micro machine and human science; Efendigil, T., Önüt, S., Kahraman, C., A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: a comparative analysis (2009) Expert Syst Appl, 36, pp. 6697-6707; Evans, J.R., Retailing in perspective: the past is a prologue to the future (2011) Int Rev Retail Distrib Consum Res, 21, pp. 1-31; Firat, M., Turan, M.E., Yurdusev, M.A., Comparative analysis of fuzzy inference systems for water consumption time series prediction (2009) J Hydrol, 374, pp. 235-241; Fishburn, P.C., Method for estimating addtive utilities (1997) Manag Sci, 13-17, pp. 435-453; (2015) MATLAB, 7, p. 9; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int J Prod Econ, 134, pp. 177-187; Garcia Infante, J.C., Medel Juarez, J.J., Sanchez Garcia, J.C., Evolutive neural fuzzy filtering: an approach (2010) WSEAS Trans Syst Control, 5, pp. 164-173; Gnana Sheela, K., Deepa, S.N., Performance analysis of modeling framework for prediction in wind farms employing artificial neural networks (2014) Soft Comput, 18, pp. 607-615; Gumus, A.T., Guneri, A.F., Keles, S., Supply chain network design using an integrated neuro-fuzzy and MILP approach: a comparative design study (2009) Expert Syst Appl, 36, pp. 12570-12577; Gumus, A.T., Guneri, A.F., A multi-echelon inventory management framework for stochastic and fuzzy supply chains (2009) Expert Syst Appl, 36, pp. 5565-5575; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) Int J Prod Res, 45, pp. 2819-2840; Gunasekaran, A., Ngai, E.W.T., Information systems in supply chain integration and management (2004) Eur J Oper Res, 159, pp. 269-295; Harding, J.A., Popplewell, K., Knowledge reuse and sharing through data mining manufacturing data (2006) In: 2006 IIE annual conference and exhibition; He, Z., Zhu, P., Park, S., A robust desirability function method for multi-response surface optimization considering model uncertainty (2012) Eur J Oper Res, 221, pp. 241-247; Holimchayachotikul, P., Value creation through collaborative supply chain: holistic performance enhancement road map (2014) Prod Plan Control, 25, pp. 912-922; Huang, X., Ho, D., Ren, J., Capretz, L.F., A soft computing framework for software effort estimation (2006) Soft Comput, 10, pp. 170-177; Jang, J.S.R., ANFIS: adaptive network based fuzzy inference system (1993) IEEE Trans Syst Man Cybern, 23 (3), pp. 665-684; Jilani, T.A., Burney, S.M.A., New method of learning and knowledge management in type-I fuzzy neural networks (2007) Adv Soft Comput, 42, pp. 730-740; Krzysztof, S., Neuro-fuzzy system with weighted attributes (2014) Soft Comput, 18, pp. 285-297; Leksakul, K., Forecast of off-season longan supply using fuzzy support vector regression and fuzzy artificial neural network (2015) Comput Electron Agric, 118, pp. 259-269; Liao, S.H., Training neural networks via simplified hybrid algorithm mixing Nelder-Mead and particle swarm optimization methods (2014) Soft Comput, 19, pp. 679-689; Linh, T.H., Osowski, S., Stodolski, M., On-line heart beat recognition using hermite polynomials and neuro-fuzzy network (2003) IEEE Trans Instrum Meas, 52, pp. 1224-1231; Liu, Z.Q., Self-spawning neuro-fuzzy system for rule extraction (2009) Soft Comput, 13, pp. 1013-1025; Lopez-Cruz, I.L., Hernandez-Larragoiti, L., Neuro-fuzzy models for air temperature and humidity of arched and venlo type greenhouses in central Maxico. Modelos neuro-difusos para temperatura y humedad del aire en invernaderos tipo cenital y capilla en el centro de maxico (2010) Agrociencia, 44, pp. 791-805; Marinakis, Y., Marinaki, M., Particle swarm optimization with expanding neighborhood topology for the permutation flowshop scheduling problem (2013) Soft Comput, 17, pp. 1159-1173; Mariscal, G., Marban, O., Fernandez, C., A survey of data mining and knowledge discovery process models and methodologies (2010) Knowl Eng Rev, 25, pp. 137-166; Marx-Gómez, J., Rautenstrauch, C., Nurnberger, A., Kruse, R., Neuro-fuzzy approach to forecast returns of scrapped products to recycling and remanufacturing (2002) Knowl Based Syst, 15, pp. 119-128; Mehrabad, M.S., Targeting performance measures based on performance prediction (2011) Int J Product Perform Manag, 61, pp. 46-68; Mohandes, M., Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS) (2011) Appl Energy, 88, pp. 4024-4032; Paladini, E.P., A fuzzy approach to compare human performance in industrial plants and service-providing companies (2009) WSEAS Trans Bus Econ, 6, pp. 557-569; Pessin, G., Swarm intelligence and the quest to solve a garbage and recycling collection problem (2013) Soft Comput, 17, pp. 2311-2325; Shen, K.Y., Tzeng, G.H., A decision rule-based soft computing model for supporting financial performance improvement of the banking industry (2015) Soft Comput, 19, pp. 859-874; Sheu, J.B., A hybrid neuro-fuzzy analytical approach to mode choice of global logistics management (2008) Eur J Oper Res, 189, pp. 971-986; Sun, C., A two-layer surrogate-assisted particle swarm optimization algorithm (2014) Soft Comput, 19, pp. 1461-1475; Svalina, I., An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: the case of close price indices (2013) Expert Syst Appl, 40, pp. 6055-6063; Tirian, G.O., Pinca, C.B., Cristea, D., Topor, M., Applications of fuzzy logic in continuous casting (2010) WSEAS Trans Syst Control, 5, pp. 133-142; Unahabhokha, C., Predictive performance measurement system: a fuzzy expert system approach (2007) Benchmarking, 14, pp. 77-91; Vukadinovic, K., Teodorovic, D., Pavkovic, G., An application of neurofuzzy modeling: the vehicle assignment problem (1999) Eur J Oper Res, 114, pp. 474-488; Wang, Y., Cai, Z.X., A dynamic hybrid framework for constrained evolutionary optimization (2012) IEEE Trans Syst Man Cybern Part B Cybern, 42 (1), pp. 203-217; Wong, B.K., Lai, V.S., A survey of the application of fuzzy set theory in production and operations management: 1998–2009 (2010) Int J Prod Econ, 129, pp. 157-168; Yao, X., Xu, G., Cui, Y., Fan, S., Wei, J., Application of the swarm intelligence in the organization of agricultural products logistics (2009) Proceedings of 2009 4th international conference on computer science and education, ICCSE, 2009, pp. 77-80; Zheng, Y.J., Ling, H.F., Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach (2013) Soft Comput, 17, pp. 1301-1314},
correspondence_address1={Leksakul, K.; Excellence Center in Logistics and Supply Chain Management, Thailand; email: komgrit@eng.cmu.ac.th},
publisher={Springer Verlag},
issn={14327643},
language={English},
abbrev_source_title={Soft Comput.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Mishra201785,
author={Mishra, D. and Gunasekaran, A. and Papadopoulos, T. and Hazen, B.},
title={Green supply chain performance measures: A review and bibliometric analysis},
journal={Sustainable Production and Consumption},
year={2017},
volume={10},
pages={85-99},
doi={10.1016/j.spc.2017.01.003},
note={cited By 105},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013675516&doi=10.1016%2fj.spc.2017.01.003&partnerID=40&md5=8935d87932bb8aae27e000850a228f26},
affiliation={Department of Industrial and Management Engineering, Kanpur, 208016, India; Charlton College of Business, University of Massachusetts Dartmouth, North Dartmouth, MA, United States; Kent Business School, University of Kent, Sail & Colour Loft, The Historic Dockyard, Chatham, ME4 4TE, United Kingdom; University of Tennessee, Department of Marketing and Supply Chain Management, Knoxville, TN, United States},
abstract={The concept of green supply chain management is evolving rapidly and gaining popularity in the research community. This research reviews the literature on green supply chain performance measures for the purpose of providing thorough insight into the field. Using bibliometric and network analysis, the research critically evaluates 653 articles published over the past 22 years and identifies some of the top contributing authors, organizations and key research topics related to the field. In addition, the most influential works based on citations and PageRank are also obtained and compared. At last, major research areas and potential future directions are identified by conducting network analysis. © 2017},
author_keywords={Bibliometric analysis;  Green supply chain management;  Network analysis;  Performance measures},
references={Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) J. Cleaner Prod., 52, pp. 329-341; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) J. Cleaner Prod., 86, pp. 360-377; Ahi, P., Searcy, C., Jaber, M.Y., Energy-related performance measures employed in sustainable supply chains: A bibliometric analysis (2016) Sustain. Consumpt. Prod., 7, pp. 1-15; Awasthi, A., Chauhan, S., Goyal, S.K., A fuzzy multi-criteria approach for evaluating environmental performance of suppliers (2010) Int. J. Prod. Econ., 126 (2), pp. 370-378; Azevedo, S.G., Carvalho, H., Machado, V.C., The influence of green practices on supply chain performance: A case study approach (2011) Transp. Res. E, 47, pp. 850-871; Bai, C., Sarkis, J., Green supplier development: analytical evaluation using rough set theory (2010) J. Cleaner Prod., 18, pp. 1200-1210; Barratt, M., Choi, T.Y., Li, M., Qualitative case studies in operations management: Trends, research outcomes, and future research implications (2011) J. Oper. Manage., 29 (4), pp. 329-342; Bastian, M., Heymann, S., Jacomy, M., Gephi: an open source software for exploring and manipulating networks (2009) Proceedings of the Third International AAAI Conference on Weblogs and Social Media, , AAAI Publications; Batagelj, V., Mrvar, A., Pajek: Program for Analysis and Visualization of Large Networks–Reference Manual (2011), University of Ljubljana Slovenia; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; Beamon, B.M., Designing the green supply chain (1999) Logist. Inform. Manag., 12 (4), pp. 332-342; Beamon, B.M., Environmental and sustainability ethics in supply chain management (2005) Sci. Eng. Ethics, 11, pp. 221-234; Bjorklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Manag. Int. J., 17 (1), pp. 29-39; Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E., Fast unfolding of communities in large networks (2008) J. Stat. Mech. Theory Exp., p. P10008; Bond, T.C., The role of performance measurement in continuous improvement (1999) Int. J. Oper. Prod. Manage., 19 (12), pp. 1318-1334; Boyer, K.K., Swink, M.K., Empirical elephants–why multiple methods are essential to quality research in operations and supply chain management (2008) J. Oper. Manag., 26 (3), pp. 337-348; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for sustainable supply chain management: developments and directions (2014) Eur. J. Oper. Res., 233, pp. 299-312; Brin, S., Page, L., The anatomy of a large-scale hypertextual Web search engine (1998) Comput. Netw. ISDN Syst., 30, pp. 107-117; Buyukozkan, G., Cifci, G., A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information (2011) Comput. Ind., 62 (2), pp. 164-174; Caniato, F., Caridi, M., Crippa, L., Moretto, A., Environmental sustainability in fashion supply chains: an exploratory case bases research (2012) Int. J. Prod. Econ., 135 (2), pp. 659-670; Carter, C.R., Ellram, L.M., Reverse logistics: a review of the literature and framework for future investigation (1988) J. Bus. Logist., 19 (1), pp. 85-102; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: moving toward new theory (2008) Int. J. Phys. Distrib. Logist. Manage., 38 (5), pp. 360-387; Chen, C., Ibekwe- SanJuan, F., Hou, J., The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis (2010) J. Amer. Soc. Inform. Sci., 61 (7), pp. 1386-1409; Chen, P., Xie, H., Maslov, S., Redner, S., Finding scientific gems with Google's PageRank algorithm (2007) J. Informetrics, 1 (1), pp. 8-15; Chicksand, D., Watson, G., Walker, H., Radnor, Z., Johnston, R., Theoretical perspectives in purchasing and supply chain management: an analysis of the literature (2012) Supply Chain Manag., 17, pp. 454-472; Childe, S.J., Case studies in operations management (2011) Prod. Plann. Control, 22 (2). , 107–107; Ciliberti, F., Pontrandolfo, P., Scozzi, B., Logistics social responsibility: standard adoption and practices in Italian companies (2008) Int. J. Prod. Econ., 113, pp. 88-106; Clauset, A., Newman, M.E.J., Moore, C., Finding community structure in very large networks (2004) Phys. Rev. E, 70, pp. 1-6; Carter, C.R., Easton, P.L., Sustainable supply chain management: evolution and future directions (2011) Int. J. Phys. Distrib. Logist. Manage., 41 (1), pp. 46-62; Cronin, B., Ding, Y., Popular and/or prestigious? Measures of scholarly esteem (2011) Inf. Process. Manag., 47, pp. 80-96; Culnan, M., The intellectual development of management information systems (1986) Manage. Sci., 32 (2), pp. 156-172; Ding, Y., Yan, E., Frazho, A., Caverlee, J., PageRank for ranking authors in co-citation networks (2009) J. Am. Soc. Inf. Sci. Technol., 60, pp. 2229-2243; Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S.J., Green supply chain management enablers: Mixed methods research (2015) Sustain. Prod. Consumption, 4, pp. 72-88; Eisenhardt, K., Building theories from case study research (1989) Acad. Manag. Rev., 14 (4), pp. 532-550; Epstein, M.J., Wisner, P.S., Good neighbors: implementing social and environmental strategies with BSC, Balanced Scorecard Report, Reprint Number B0105C 3 3 (2001), Harvard Business School Publishing Cambridge, MA; Fahimnia, B., Sarkis, J., Davarzani, H., Green supply chain management: A review and bibliometric analysis (2015) Int. J. Prod. Econ., 162, pp. 101-114; Fahimnia, B., Tang, C.S., Davarzani, H., Sarkis, J., Quantitative models for managing supply chain risks: a review (2015) European J. Oper. Res., 247 (1), pp. 1-15; Faruk, A.C., Lamming, R.C., Cousins, P.D., Bowen, F.E., Analyzing, mapping, and managing environmental impacts along supply chains (2002) J. Ind. Ecol., 5 (2), pp. 13-36; Fleischmann, M., Van Wassenhove, L.N., van Nunen, J.A.E.E., van der Laan, E.A., Dekker, R., Bloemhof-Ruwaard, J.M., Quantitative models for reverse logistics: a review (2007) European J. Oper. Res., 103 (1), pp. 1-17; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Comput. Ind., 56, pp. 663-680; Fynes, B., Voss, C., de Búrca, S., The impact of supply chain relationship quality on quality performance (2005) Int. J. Prod. Econ., 96 (3), pp. 339-354; Garfield, E., Citation Analysis as a Tool in Journal Evaluation (1972) Science, 178 (4060), pp. 471-479; Gephi, (2013) Gephi–Makes Graphs Handy; Gold, S., Seuring, S., Beske, P., The constructs of sustainable supply chain management e a content analysis based on published case studies (2010) Prog. Ind. Ecol., 7 (2), pp. 114-137; Govindan, K., Rajendran, S., Sarkis, J., Murugesan, P., Multi criteria decision making approaches for green supplier evaluation and selection: a literature review (2015) J. Cleaner Prod., 98, pp. 66-83; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) J. Supply Chain Manag., 30 (4), pp. 13-18; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manage., 21 (1-2), pp. 71-87; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: a study in the application of the analytical hierarchy process (2002) European J. Oper. Res., 141, pp. 70-87; Hansmann, K.W., Claudia, K., Environmental management policies (2001) Green Manufacturing and Operations: from Design to Delivery and Back, pp. 192-204. , J. Sarkis Greenleaf Publishing Sheffield; Harrington, H.J., Business Process Improvement (1991), McGraw Hill New York, NY; Hart, S.L., Ahuja, G., Does it pay to be green? An empirical examination of the relationship between emission reduction and firm performance (1996) Bus. Strategy Environ., 5 (1), pp. 30-37; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmark.: Int. J., 12 (4), pp. 330-353; Hicks, S., Morals make the money (2007) Australian CPA, 70 (4), pp. 72-73; Hjørland, B., Citation analysis: a social and dynamic approach to knowledge organization (2013) Inf. Process. Manag., 49, pp. 1313-1325; Ho, J.C., Shalishali, M.K., Tseng, T., Ang, D.S., Opportunities in green supply chain management (2009) Coast. Bus. J., 8 (1), pp. 18-31; Holt, D., Ghobadian, A., An empirical study of green supply chain management practices amongst UK manufacturers (2009) J. Manuf. Technol. Manag., 20, pp. 933-956; Hsu, C.W., Hu, A.H., Green supply chain management in the electronic industry (2008) Int. J. Environ. Sci. Technol., 5, pp. 205-216; Humphreys, P.K., Wong, Y.K., Chan, F.T.S., Integrating environmental criteria into the supplier selection process (2003) J. Mater. Process. Technol., 138 (1-3), pp. 349-356; Hutchison, J., Integrating environmental criteria into purchasing decision: value added? (1998) Green Purchasing: Opportunities and Innovations, pp. 164-178. , T. Russel Greenleaf Publishing Sheffield; Inderfurth, K., Product recovery behavior in a closed loop supply chain (2004) Supply Chain Management and Reverse Logistics, , H. Dyckhoff R. Lackes J. Reese Springer-Verlag Berlin; Igarashi, M., de Boer, L., Fet, A.M., What is required for greener supplier selection? A literature review and conceptual model development (2013) J. Purch. Supply Manag., 19, pp. 247-263; Ismail, S., Nason, E., Marjanovic, S., Grant, J., Bibliometrics as a tool for supporting prospective R&D decision-making in the health sciences (2009); Jabbour, A.B.L.S., Jabbour, C.J.C., Latan, H., Teixeira, A.A., Oliveira, J.H.C., Quality management, environmental management maturity, green supply chain practices and green performance of Brazilian companies with ISO 14001 certification: direct and indirect effects (2014) Transp. Res. Part E Logist. Transp. Rev., 67, p. 39e51; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) Int. J. Prod. Econ., 101, pp. 99-108; Kaplan, R.S., Measures for Manufacturing Excellence (1990), Harvard Business School Press Boston; Kaplan, R.S., Norton, D.P., The balanced scorecard–measures that drive performance (1992) Harv. Bus. Rev., 70 (1), pp. 71-80; Klassen, R.D., McLaughlin, C.P., The impact of environmental management on firm performance (1996) Manage. Sci., 42 (8), pp. 1199-1214; Kleindorfer, P.R., Singhal, K., Van Wassenhove, L.N., Sustainable Operations Management (2005) Prod. Oper. Manage., 14 (4), pp. 482-492; Kuo, R.J., Wang, Y.C., Tien, F.C., Integration of artificial neural network and MADA methods for green supplier selection (2010) J. Cleaner Prod., 18, pp. 1161-1170; Lai, K., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transp. Res. E, 38, pp. 439-456; Lau, K.H., Benchmarking green logistics performance with a composite index (2011) Benchmarking, 18 (6), pp. 873-896; Lee, A.H.I., Kang, H.Y., Hsu, C.F., Hung, H.C., A green supplier selection model for high-tech industry (2009) Expert Syst. Appl., 36, pp. 7917-7927; Lewis, H., Gretsakis, J., Design + Environment: A Global Guide to Designing Greener Goods (2001), Greenleaf Publishing Sheffield; Leydesdorff, L., Bibliometrics/citation networks (2011) Encyclopedia of Social Networks, , G.A. Barnett SAGE Publications, Inc. Thousand Oaks, CA; Leydesdorff, L., Vaughan, L., Co-occurrence matrices and their applications in information science: extending ACA to the web environment (2006) J. Amer. Soc. Inform. Sci. Technol., 57 (12), pp. 1616-1628; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: an introduction (2007) J. Oper. Manage., 25 (1), pp. 1075-1082; Lu, L.Y.Y., Wu, C.H., Kuo, T.C., Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis (2007) Int. J. Prod. Res., 45, pp. 4317-4331; MacRoberts, M.H., MacRoberts, B.R., Problems of citation analysis: A critical review (1989) J. Am. Soc. Inf. Sci., 40 (5), pp. 342-349; MacRoberts, M.H., MacRoberts, B.R., Problems of citation analysis: A study of uncited and seldom-sited influences (2010) J. Amer. Soc. Inform. Sci. Technol., 61 (1), pp. 1-12; Matos, S., Hall, J., Integrating sustainable development in the supply chain: The case of life cycle assessment in oil and gas and agricultural biotechnology (2007) J. Oper. Manage., 25 (6), pp. 1083-1102; Mezher, T., Ajam, M., Integrating quality, environmental and supply chain management systems into the learning organization (2006) Greening the Supply Chain, , J. Sarkis Springer-Verlag London; Mishra, D., Gunasekaran, A., Papadopoulos, T., Childe, S.J., (2016) Ann. Oper. Res.; Mishra, D., Gunasekaran, A., Childe, S.J., Papadopoulos, T., Dubey, R., Wamba, F.S., Vision, applications and future challenges of Internet of Things (2016) Ind. Manag. Data Syst., 116 (7), pp. 1331-1355; Min, H., Galle, W.P., Green purchasing practices of US firms (2001) Int. J. Oper. Prod. Manage., 21 (9), pp. 1222-1238; Nagel, M., Environmental quality in the supply chain of an original equipment manufacturer (2004) Greening the Supply Chain, , J. Sarkis P. Rao Greenleaf Publishing Sheffield (Chapter 16); Neely, A.D., Gregory, M., Platts, K., Performance measurement system design–a literature review and research agenda (1995) Int. J. Oper. Prod. Manage., 15 (4), pp. 80-116; Noci, G., Designing ‘green’ vendor rating systems for the assessment of a supplier's environmental performance (1997) Eur. J. Purch. Supply Manag., 3 (2), pp. 103-114; Olugu, E.U., Wong, K.Y., Supply chain performance evaluation: trends and challenges (2009) Amer. J. Eng. Appl. Sci., 2 (1), pp. 202-211; Pagell, M., Wu, Z., Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars (2009) J. Supply Chain Manag., 45, pp. 37-56; Paloviita, A., Stakeholder perceptions of alternative food entrepreneurs (2009) World Rev. Entrepreneur. Manag. Sustain. Dev., 5, pp. 395-406; Persson, O., Danell, R., Schneider, J.W., How to use Bibexcel for various types of bibliometric analysis (2009) Celebrating Scholarly Communication Studies, , F. Åstrom R. Danell B. Larsen J.W. Schneider; Pilkington, A., Liston-Heyes, C., Is production and operations management a discipline? A citation/co- citation study (1999) Int. J. Oper. Prod. Manage., 19 (1), pp. 7-20; Pilkington, A., Meredith, J., The evolution of the intellectual structure of operations management—1980–2006: a citation/co-citation analysis (2009) J. Oper. Manage., 27 (3), pp. 185-202; Pineda-Henson, P., Culaba, A.B., Mendoza, G.A., Evaluating environmental performance of pulp and paper manufacturing using the analytic hierarchy process and life cycle assessment (2002) J. Ind. Ecol., 6 (1), pp. 15-28; Preuss, L., In dirty chains: purchasing and greener manufacturing (2001) J. Bus. Ethics, 34, pp. 345-359; Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D., Defining and identifying communities in networks (2004) Proc. Natl. Acad. Sci., 101, pp. 2658-2663; Ramos-Rodríguez, A.R., Ruiz-Navarro, J., Changes in the intellectual structure of strategic management research: A bibliometric study of the Strategic Management Journal, 1980–2000 (2004) Strateg. Manag. J., 25 (10), pp. 981-1004; Rao, P., Greening the supply chain: a new initiative in South East Asia (2002) Int. J. Oper. Prod. Manage., 22 (6), pp. 632-655; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) Int. J. Oper. Prod. Manage., 25 (9), pp. 898-916; Rowley, J., Slack, F., Conducting a literature review (2004) Manag. Res. News, 27, pp. 31-39; Saaty, T.L., The Analytical Hierarchy Process (1980), McGraw-Hill New York, NY; Sarkis, J., Manufacturing strategy and environmental consciousness (1995) Technovation, 15 (2), pp. 79-97; Sarkis, J., Evaluating environmentally conscious business practices (1998) European J. Oper. Res., 107 (1), pp. 159-174; Sarkis, J., A strategic decision making framework for green supply chain management (2003) J. Cleaner Prod., 11 (4), pp. 397-409; Sarkis, J., Manufacturing's role in corporate environmental sustainability: concerns for the new millennium (2001) Int. J. Oper. Prod. Manage., 21 (5-6), pp. 666-685; Sarkis, J., Talluri, S., Eco efficiency measurement using data envelopment analysis: research and practitioner issues (2004) J. Environ. Assess. Policy Manag., 6 (1), pp. 91-123; Sarkis, J., Zhu, Q., Lai, K.H., An organizational theoretic review of green supply chain management literature (2011) Int. J. Prod. Econ., 130, pp. 1-15; Saunders, M., Lewis, P., Thornhill, A., Research Methods for Business Students (2009), Pearson Harlow; Seuring, S.A., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Manag.: Int. J., 13 (2), pp. 128-137; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decis. Support Syst., 54 (4), pp. 1513-1520; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Cleaner Prod., 16, pp. 1699-1710; Sharplin, A., Mabry, R., The relative importance of journals used in management research: an alternative ranking (1985) Human Relat., 38 (2), pp. 139-149; Shultz, C.J., II, Holbrook, M.B., Marketing and tragedy of the commons: a synthesis, commentary and analysis for action (1999) J. Public Policy Mark., 18 (2), pp. 218-229; Small, H., Co-citation in the scientific literature: A new measure of the relationship between two documents (1973) J. Am. Soc. Inf. Sci., 24, pp. 265-269; Spengler, T., Stolting, W., Ploog, M., Recovery planning in closed loop supply chain: an activity analysis based approach (2004) Supply Chain Management and Reverse Logistics, , H. Dyckhoff R. Lackes J. Reese Springer-Verlag Berlin; Srivastava, S.K., Green supply-chain management: a state-of-the-art literature review (2007) Int. J. Manag. Rev., 9, pp. 53-80; Steven, M., Network in reversed logistics (2004) Supply Chain Management and Reverse Logistics, , H. Dyckhoff R. Lackes J. Reese Springer-Verlag Berlin; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logist. Inform. Manag., 8 (2), pp. 38-44; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains: a literature review and a research agenda (2013) Int. J. Prod. Perform. Manag., 62, pp. 782-804; Testa, F., Iraldo, F., Shadows and lights of GSCM (green supply chain management): determinants and effects of these practices based on a multinational study (2010) J. Cleaner Prod., 18 (10-11), pp. 953-962; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) Br. J. Manag., 14, pp. 207-222; Vachon, S., Green supply chain practices and the selection of environmental technologies (2007) Int. J. Prod. Res., 45, pp. 4357-4379; Vachon, S., Klassen, R.D., Extending green practices across the supply chain: the impact of upstream and downstream integration (2006) Int. J. Oper. Prod. Manage., 26 (7), pp. 795-821; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain (2008) Int. J. Prod. Econ., 111, pp. 299-315; Vachon, S., Mao, Z., Linking supply chain strength to sustainable development: a country-level analysis (2008) J. Cleaner Prod., 16, pp. 1552-1560; Van Eck, N.J., Waltman, L., (2013) Manual for VOSviewer Version 1.5.4, , Universiteit Leiden and Erasmus Universiteit Rotterdam; Van Hoek, R.I., From reversed logistics to green supply chains (1999) Supply Chain Manag.: Int. J., 4 (3), pp. 129-134; Van Hock, R.I., Erasmus, From reversed logistics to green supply chains (2000) Logist. Solut., 2, pp. 28-33; Vokurka, R.J., The relative importance of journals used in Operations Management Research: A citation analysis (1996) J. Oper. Manage., 14 (4), pp. 345-355; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) Int. J. Oper. Prod. Manage., 22 (2), pp. 195-219; Walker, H., Di Sisto, L., McBain, D., Drivers and barriers to environmental supply chain management practices: lessons from the public and private sectors (2008) J. Purch. Supply Manag., 14, pp. 69-85; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmark.: Int. J., 15 (1), pp. 25-51; Yong-Hak, J., Web of Science (2013) Thomson Reuters, , http://wokinfo.com/media/pdf/WoSFS_08_7050.pdf; Zadeh, L.A., Fuzzy sets (1965) Inf. Control, 8 (3), pp. 338-353; Zhu, Q., Dou, Y., Sarkis, J., A portfolio-based analysis for green supplier management using the analytical network process (2010) Supply Chain Manag.: Int. J., 15, pp. 306-319; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) J. Oper. Manage., 22 (3), pp. 265-289; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: drivers and practices (2006) J. Cleaner Prod., 14, pp. 472-486; Zhu, Q., Sarkis, J., The moderating effects of institutional pressures on emergent green supply chain practices and performance (2007) Int. J. Prod. Res., 45, pp. 4333-4355; Zhu, Q., Sarkis, J., Geng, Y., Green supply-chain management practices in China: Drivers, practices and performance (2005) Int. J. Oper. Prod. Manage., 25 (5), pp. 449-468; Zhu, Q., Sarkis, J., Lai, K.H., Green supply chain management implications for “closing the loop” (2008) Transp. Res. Part E: Logist. Transp. Rev., 44 (1), pp. 1-18; Zingales, F., O'Rourke, A., Orssatto, R.J., Environment and socio-related balanced scorecard: exploration of critical issues, working paper 2002/47/CMER Center for the Management of Environmental Resources, INSEAD, Fontainebleau, France (2002); Zsidisin, G.A., Siferd, S.P., Environmental purchasing: a framework for theory development (2001) Eur. J. Purch. Supply Manag., 7 (1), pp. 61-73},
correspondence_address1={Hazen, B.; University of Tennessee, United States; email: benjamin.hazen@live.com},
publisher={Elsevier B.V.},
issn={23525509},
language={English},
abbrev_source_title={Sustain. Prod. Consum.},
document_type={Article},
source={Scopus},
}

@BOOK{Nuthall2017248,
author={Nuthall, L.},
title={Supply chain performance measures and systems},
journal={Gower Handbook of Supply Chain Management: Fifth Edition},
year={2017},
pages={248-266},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072981127&partnerID=40&md5=bb4d075a0a19d12575bfe7a63a157876},
affiliation={Continuous Innovation Culture, London, United Kingdom},
references={Ballantine, J., Brignall, S., 'Interactions and trade-offs in multi-dimensional performance management' (1996), research paper No. 247, October 1996, Warwick Business School Research Bureau: Working Paper; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , 2nd edn., London: Prentice Hall; Cross, K.F., Lynch, R.L., (1995) Measure Up!: Yardsticks for Continuous Improvement, , 2nd edn., Oxford: Blackwell Publishers; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1992) New Performance Challenge: Measuring Operations for World-Class Competition, , Dow Jones-Irwin/Apics Series in Production Management, Illinois; Judson, A.S., (1991) Changing Behavior in Organizations: Minimizing Resistance to Change, , Cambridge, MA: Blackwell Publishers; Kaplan, R.S., Norton, D.P., (1996) The balanced scorecard: translating strategy into action, , Boston: Harvard Business School Press; Nanni, A.J., Vollmann, T.E., (1992) New Performance Challenge: Measuring Operations for World-Class Competition, , Dow Jones-Irwin/ Apics Series in Production Management, Illinois; Shank, J.K., (1989) Strategic Cost Analysis: The Evolution from Managerial to Strategic Accounting, , New York: McGraw Hill; Tyndall, G.R., (1998) Supercharging supply chains: new ways to increase value through global operational excellence, , New York: Wiley and Sons},
correspondence_address1={Nuthall, L.; Continuous Innovation CultureUnited Kingdom},
publisher={Taylor and Francis},
isbn={9781315253855; 9780566085116},
language={English},
abbrev_source_title={Gower Handb. of Supply Chain Manag.: Fifth Ed.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Jauhar2017138,
author={Jauhar, S.K. and Pant, M. and Nagar, A.K.},
title={Sustainable educational supply chain performance measurement through DEA and differential evolution: A case on Indian HEI},
journal={Journal of Computational Science},
year={2017},
volume={19},
pages={138-152},
doi={10.1016/j.jocs.2016.10.007},
note={cited By 28},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041065570&doi=10.1016%2fj.jocs.2016.10.007&partnerID=40&md5=6a7e78d02e7c31b993375f4638316323},
affiliation={Indian Institute of Technology Roorkee, Roorkee, 247667, India; Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom},
abstract={Data envelopment analysis or DEA methodology is employed for assessing the relative efficiency of different homogeneous units. Through DEA one can analyze the areas which need more attention and can suggest measures for improving the performance of different sectors. Through this article, the authors have tried to analyze the relative efficiency of IITR (The Indian Institute of Technology Roorkee), a higher educational institute (HEI) in India. The efficiency of nineteen academic departments of IIT Roorkee is measured with respect to teaching and research. The novlty of the paper is twofold (1) the authiors have considered the environmental aspects (sustainability criteria) while measuring efficiency (2) Differential Evolution (DE) algorithm is employed in accordance with DEA on the fractional model generated for calculating efficiency. © 2016 Elsevier B.V.},
author_keywords={Data envelop analysis;  Differential evolution;  Educational supply chain management;  Higher educational institute;  Performance measurement},
keywords={Data envelopment analysis;  Efficiency;  Evolutionary algorithms;  Optimization;  Supply chain management, Calculating efficiencies;  Data envelop analysis;  Differential Evolution;  Differential evolution algorithms;  Educational Institutes;  Indian institute of technologies;  Performance measurements;  Supply chain performance measurements, Sustainable development},
references={Thor, L.M., Introducing the human side of total quality management into educational institutions (1994) Community Coll. J. Res. Pract., 18 (4), pp. 359-368; O'Brien, E.M., Deans, K.R., Educational supply chain: a tool for strategic planning in tertiary education? (1996) Market. Intell. Plann., 14 (2), pp. 33-40; Lau, A.K., Educational supply chain management: a case study (2007) On Horiz., 15 (1), pp. 15-27; Habib, M., An exploratory analysis of educational management for the universities? (2011) Int. J. Eng. Bus. Manage., 3 (3), pp. 16-24; Habib, M.M., Pathik, B.B., An investigation of education and research management for tertiary academic institutions (2012) Int. J. Eng. Bus. Manage., p. 4; Gopalakrishnan, G., How to apply academic supply chain management: the case of an international university (2015) Manage. J. Contemp. Manage. Issues, 20 (1), pp. 207-221; Bessent, A.M., Bessent, E.W., Charnes, A., Cooper, W.W., Thorogood, N.C., Evaluation of educational program proposals by means of DEA (1983) Educ. Adm. Q., 19 (2), pp. 82-107; Nunamaker, T.R., Using data envelopment analysis to measure the efficiency of non-profit organizations: a critical evaluation (1985) Managerial Decis. Econ., 6 (1), pp. 50-58; Ahn, T., Arnold, V., Charnes, A.B.R.A.H.A.M., Cooper, W.W., DEA and ratio efficiency analyses for public institutions of higher learning in Texas (1989) Res. Govern. Nonprofit Account., 5 (2), pp. 165-185; Breu, T.M., Raab, R.L., Efficiency and perceived quality of the nation's top 25 National Universities and National Liberal Arts Colleges: an application of data envelopment analysis to higher education (1994) Socioecon. Plann. Sci., 28 (1), pp. 33-45; Bougnol, M.L., Dulá, J.H., Validating DEA as a ranking tool: an application of DEA to assess performance in higher education (2006) Ann. Oper. Res., 145 (1), pp. 339-365; Tomkins, C., Green, R., An experiment in the use of data envelopment analysis for evaluating the efficiency of UK university departments of accounting (1988) Financ. Account. Manage., 4 (2), pp. 145-165; Beasley, J.E., Comparing university departments (1990) Omega Int. J. Manage. Sci., 18 (2), pp. 171-183; Beasley, J.E., Determining teaching and research efficiencies (1995) J. Oper. Res. Soc., pp. 441-452; Johnes, J., Performance assessment in higher education in Britain (1996) Eur. J. Oper. Res., 89 (1), pp. 18-33; Izadi, H., Johnes, G., Oskrochi, R., Crouchley, R., Stochastic frontier estimation of a CES cost function: the case of higher education in Britain (2002) Econ. Educ. Rev., 21 (1), pp. 63-71; Johnes, J., Data envelopment analysis and its application to the measurement of efficiency in higher education (2006) Econ. Educ. Rev., 25 (3), pp. 273-288; Johnes, G., Tone, K., The efficiency of Higher Education Institutions in England revisited: comparing alternative measures (2016) Tert. Educ. Manage.; Avkiran, N.K., Investigating technical and scale efficiencies of Australian universities through data envelopment analysis (2001) Socioecon. Plann. Sci., 35 (1), pp. 57-80; Abbott, M., Doucouliagos, C., The efficiency of Australian universities: a data envelopment analysis? (2003) Econ. Educ. Rev., 22 (1), pp. 89-97; Chu Ng, Y., Li, S.K., Measuring the research performance of Chinese higher education institutions: an application of data envelopment analysis (2000) Educ. Econ., 8 (2), pp. 139-156; Johnes, J., Li, Y.U., Measuring the research performance of Chinese higher education institutions using data envelopment analysis (2008) China Econ. Rev., 19 (4), pp. 679-696; Zhang, L., Bao, W., Sun, L., Resources and research production in higher education: a longitudinal analysis of Chinese Universities, 2000–2010 (2016) Res. Higher Educ., pp. 1-23; Zhang, L., Luo, Y., Evaluation of input output efficiency in higher education based on data envelope analysis (2016) Int. J. Database Theory Appl., 9 (5), pp. 221-230; Tyagi, P., Prasad Yadav, S., Singh, S.P., Relative performance of academic departments using DEA with sensitivity analysis (2009) Eval. Program Plann., 32 (2), pp. 168-177; Sahney, S., Thakkar, J., A comparative assessment of the performance of select higher education institutes in India (2016) Qual. Assur. Educ., 24 (2), pp. 278-302; Fandel, G., On the performance of universities in North Rhine-Westphalia, Germany: government's redistribution of funds judged using DEA efficiency measures (2007) Eur. J. Oper. Res., 176 (1), pp. 521-533; Arcelus, F.J., Coleman, D.F., An efficiency review of university departments? (1997) J. Syst. Sci., 28 (7), pp. 721-729; Kao, C., Hung, H.T., Efficiency analysis of university departments: an empirical study? (2008) Omega, 36 (4), pp. 653-664; Veiderpass, A., McKelvey, M., Evaluating the performance of higher education institutions in Europe: a nonparametric efficiency analysis of 944 institutions? (2016) Appl. Econ., 48 (16), pp. 1504-1514; Agasisti, T., Haelermans, C., Comparing efficiency of public universities among European countries: different incentives lead to different performances (2016) Higher Educ. Q., 70 (1), pp. 81-104; Eva, M., Sagarra, M., Agasisti, T., Assessing organizations’ efficiency adopting complementary perspectives: an empirical analysis through data envelopment analysis and multidimensional scaling, with an application to higher education (2016) Handbook of Operations Analytics Using Data Envelopment Analysis, pp. 145-166. , Springer US; Aleskerov, F.T., Belousova, V.Y.E., Petrushchenko, V.V., Measuring higher education institutions’ efficiency: data envelopment analysis and stochastic frontier approach (2015) Problemy Upravleniya, 5, pp. 2-19; Selim, S., Bursalıoğlu, S.A., Efficiency of higher education in Turkey: a bootstrapped two-stage DEA approach 1 (2015) Int. J. Stat. Appl., 5 (2), pp. 56-67; Tran, C.D.T., Villano, R.A., An empirical analysis of the performance of Vietnamese higher education institutions (2016) J. Further Higher Educ., pp. 1-15; Jablonsky, J., Efficiency analysis in multi-period systems: an application to performance evaluation in Czech higher education (2016) Cent. Eur. J. Oper. Res., 24 (2), pp. 283-296; Agasisti, T., Ricca, L., Comparing the efficiency of Italian public and private universities 2007–2011: an empirical analysis (2016) Ital. Econ. J., 2 (1), pp. 57-89; Barra, C., Zotti, R., Measuring efficiency in higher education: an empirical study using a bootstrapped data envelopment analysis (2016) Int. Adv. Econ. Res., 22 (1), pp. 11-33; Andersson, C., Antelius, J., Månsson, J., Sund, K., Technical efficiency and productivity for higher education institutions in Sweden (2016) Scand. J. Educ. Res., pp. 1-19; Sagarra, M., Mar-Molinero, C., Agasisti, T., Exploring the efficiency of mexican universities: integrating data envelopment analysis and multidimensional scaling (2016) Omega; Munoz, D.A., Assessing the research efficiency of higher education institutions in Chile: a data envelopment analysis approach (2016) Int. J. Educ. Manage., 30 (6), pp. 809-825; Kyratzi, P., Tsamadias, C., Giokas, D., Measuring the efficiency and productivity change of Greek universities over the time period 2005–2009 (2015) Int. J. Educ. Econ. Dev., 6 (2), pp. 111-129; Duygun, M., Prior, D., Shaban, M., Tortosa-Ausina, E., Disentangling the European airlines efficiency puzzle: a network data envelopment analysis approach (2016) Omega, 60, pp. 2-14; Liu, D., Measuring aeronautical service efficiency and commercial service efficiency of East Asia airport companies: an application of Network Data Envelopment Analysis (2016) J. Air Transport Manage., 52, pp. 11-22; Paradi, J.C., Zhu, H., A survey on bank branch efficiency and performance research with data envelopment analysis (2013) Omega, 41 (1), pp. 61-79; Kaffash, S., Marra, M., Data envelopment analysis in financial services: a citations network analysis of banks, insurance companies and money market funds (2016) Ann. Oper. Res., pp. 1-38; Manasakis, C., Apostolakis, A., Datseris, G., Using data envelopment analysis to measure hotel efficiency in Crete (2013) Int. J. Contemp. Hosp. Manage., 25 (4), pp. 510-535; Bhanot, N., Singh, H., Benchmarking the performance indicators of Indian Railway container business using data envelopment analysis (2014) Benchmarking Int. J., 21 (1), pp. 101-120; Masson, S., Jain, R., Ganesh, N.M., George, S.A., Operational efficiency and service delivery performance: a comparative analysis of Indian telecom service providers? (2016) Benchmarking Int. J., 23 (4), pp. 893-915; Jauhar, S.K., Pant, M., Dutt, R., Performance measurement of an Indian higher education institute: a sustainable educational supply chain management perspective (2016) Int. J. Syst. Assur. Eng. Manage., 1-14; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Manage. Sci., 30 (9), pp. 1078-1092; Srinivas, T., Data envelopment analysis: models and extensions (2000) Prod. Oper. Manage. Decis. Line, pp. 8-11; Ramanathan, R., (2003) A Tool for Performance Measurement, , Sage Publication Ltd. New Delhi; Despotis, D.K., Stamati, L.V., Smirlis, Y.G., Data envelopment analysis with nonlinear virtual inputs and outputs (2010) Eur. J. Oper. Res., 202 (2), pp. 604-613; Jauhar, S., Pant, M., Deep, A., Differential evolution for supplier selection problem: a DEA based approach (2014) Proceedings of the Third International Conference on Soft Computing for Problem Solving, pp. 343-353. , Springer India; Kumar Jauhar, S., Pant, S., Nagar, M.C., Differential evolution for sustainable supplier selection in pulp and paper industry: a DEA based approach (2015) Comput. Methods Mater. Sci. Methods Mater. Sci.; Jauhar, S.K., Pant, M., Deep, A., An approach to solve multi-criteria supplier selection while considering environmental aspects using differential evolution (2013) International Conference on Swarm, Evolutionary, and Memetic Computing, December2016, Springer International Publishing, pp. 199-208; Price, K., Storn, R., Differential evolution: a simple evolution strategy for fast optimization (1997) Dr Dobb's J., 22 (4), pp. 18-24; Sinuany-Stern, Z., Mehrez, A., Barboy, A., Academic departments efficiency via DEA? (1994) Comput. Oper. Res., 21 (5), pp. 543-556; Johnes, J., Johnes, G., Research funding and performance in UK university departments of economics: a frontier analysis (1995) Econ. Educ. Rev., 14 (3), pp. 301-314; Ozawa-Meida, L., Brockway, P., Letten, K., Davies, J., Fleming, P., Measuring carbon performance in a UK University through a consumption-based carbon footprint: De Montfort University case study (2013) J. Clean. Prod., 56, pp. 185-198; Coelli, T., A guide to DEAP version 2.1: a data envelopment analysis (computer) program (1996) Centre for Efficiency and Productivity Analysis, , University of New England Australia; Ray, T., Kang, T., Chye, S.K., An evolutionary algorithm for constrained optimization (2000) Proceeding of the Genetic and Evolutionary Computation Conference (GECCO 2000), pp. 771-777. , D. Whitley D. Goldberg E. Cantu-Paz L. Spector I. Parmee H.G. Beyer; Kuah, C.T., Wong, K.Y., Efficiency assessment of universities through data envelopment analysis (2011) Procedia Comput. Sci., 3, pp. 499-506; Jauhar, S.K., Pant, M., Genetic algorithms, a nature-inspired tool: review of applications in supply chain management (2015) Proceedings of Fourth International Conference on Soft Computing for Problem Solving, Springer India, pp. 71-86; Jauhar, S.K., Pant, M., Genetic algorithms in supply chain management: a critical analysis of the literature (2015) Sādhanā; Thangaraj, R., Pant, M., Abraham, A., Bouvry, P., Particle swarm optimization: hybridization perspectives and experimental illustrations (2011) Appl. Math. Comput., 217 (12), pp. 5208-5226; Sharma, T.K., Pant, M., Enhancing the food locations in an artificial bee colony algorithm? (2013) Soft Comput., 17 (10), pp. 1939-1965; Jauha, S.K., Pant, M., Recent trends in supply chain management: a soft computing approach (2013) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), Springer India, pp. 465-478; Jauhar, S.K., Pant, M., Using differential evolution to develop a carbon-integrated model for performance evaluation and selection of sustainable suppliers in indian automobile supply chain (2016) Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Springer Singapore, pp. 515-528},
correspondence_address1={Jauhar, S.K.; Indian Institute of Technology RoorkeeIndia; email: jauhar.sunil@gmail.com},
publisher={Elsevier B.V.},
issn={18777503},
language={English},
abbrev_source_title={J. Comput. Sci.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Wasitarini2017,
author={Wasitarini, D.E. and Sembiring, J.},
title={Performance measurement framework of E-Library using modified quantitative models for performance measurement system (QMPMS) method for ICT infrastructure},
journal={2016 International Conference on Information Technology Systems and Innovation, ICITSI 2016 - Proceedings},
year={2017},
doi={10.1109/ICITSI.2016.7858192},
art_number={7858192},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015816721&doi=10.1109%2fICITSI.2016.7858192&partnerID=40&md5=28859f831ed2d12b7426226ca65f59ee},
affiliation={School of Electrical Engineering and Informatics, Institute of Technology Bandung, Bandung, Indonesia},
abstract={Performance represents the completion of activities, programs, or policies to achieve the objectives, visions, and missions of organizations. The National Library of Indonesia is a government organization which provides public services. To improve its functions, the National Library established Electronic Library (E-Library) toward Information and Communication Technology (ICT) infrastructure aspect. The plan has been included in the Roadmap Grand Design E-Library 2015-2016. Owing to improve the performance constantly, the measurement and criteria are needed to quantify performance not only user satisfaction perspective. To implement it, the authors used modification method, Quantitative Models for Performance Measurement System (QMPMS), by designing performance measurement method from the result of triangulation from several performance measurement frameworks used by previous authors which was performance prism, Objective Matrix (OMAX) and feedback 360°. The statistical approach used is Structural Equation Modeling approach, with the help of SPSS AMOS version 18 in processing the data. The result of the research, new performance measurement framework design and the model modification design was successfully identified by 76 Key Performance Indicators (KPIs). © 2016 IEEE.},
author_keywords={E-Library;  feedback 360°;  ICT infrastructure;  OMAX;  performance prism;  QMPMS;  SEM},
keywords={Benchmarking;  Data handling;  Prisms;  Scanning electron microscopy;  Supply chain management, ICT infrastructures;  Information and Communication Technologies;  Key performance indicators;  OMAX;  Performance measurement frameworks;  Performance measurement system;  QMPMS;  Structural equation modeling, Digital libraries},
references={Neely, A., Adams, C., Crowe, P., (2001) Performance Prism Practice; Reeves, T.C., Buhr, S., Barker, L., Evaluating digital libraries (2005) Journal of IEEE; Fraticelli, C., Performance measurement for digital library services (2012) Reasearchgate; Xu, Y., Gan, L., Yan, D., Study on influence factors model of technology acceptance in digital library based on user cognition and tam (2010) Journal of IEEE; Yu, B., Al-Zahir, B.Z., Elwi, Al-Yousef, Design of performance evaluation system for the informatization of chemical enterprise based on performance prism (2011) Journal of IEEE; Hamidah, N.H., Deoranto, P., Astuti, R., Productivity analysis using objective matrix (omax) method: Case study on the production departement of sari roti pt nippon indosari corpindo, tbk pasuruan (2013) ITS Journal; Kerangka Konsep Variabel Dan Hipotesis, , https://agenta08.wordpress.com/2009/01/24/kerangka-konsep-variabeldan-hipotesis/, [Online]. [Accessed: 04-Apr-2016]; Hosseininezhad, F., Nadali, A., Balalpour, M., A fuzzy expert system for performance evaluation of hrm with 360 degree feedback approach (2011) Case Study: An Iranian IT Company; Bititci, U.S., Suwignjo, P., Dan Carrie, A.S., Quantitative models for performance measurement systems (2000) International Journal of Production Economics, 64; Bititci, U.S., Suwignjo, P., Carrie, A.S., Dan Turner, T.J., Performance measurement systems: Auditing and prioritisation of performance measures (2000) International Journal of Production Economics; Bititci, U.S., Suwignjo, P., Dan Carrie, A.S., Strategy management through quantitative modelling of performance measurement systems (2001) International Journal of Production Economics, 69; Ghozali, I., (2005) Model Persamaan Struktural Konsep Dan Aplikasi, , Third Edition, Book publisher of University of Diponegoro, Semarang; Ghozali, I., (2008) Structural Equation Modeling, Metode Alternatif Dengan Partial Least Square, , Book publisher of University of Diponegoro, Semarang; Glavan, L.M., Understanding process performance measurement systems (2011) Business Systems Research, 2 (2); (2014) Document and Information-Library Performance Indicators, , ISO 11620:2014; (2013) Laporan Tim Pengawas Implementasi Grand Desain Perpustakaan Digital Nasional Ta 2013, , National Library of Indonesia, Jakarta: National Library of Indonesia; (2014) Laporan Pengembangan Perpustakaan Digital Nasional Indonesia (E-Library) Tahun 2014, , National Library of Indonesia,Jakarta: National Library of Indonesia; (2007) National Library of Indonesia, Law No. 43 about Library},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781509024490},
language={English},
abbrev_source_title={Int. Conf. Inf. Technol. Syst. Innov., ICITSI - Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Asrol20178,
author={Asrol, M. and Marimin and Machfud},
title={Supply chain performance measurement and improvement for sugarcane agro-industry},
journal={International Journal of Supply Chain Management},
year={2017},
volume={6},
number={3},
pages={8-21},
note={cited By 19},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030530879&partnerID=40&md5=cc1f0bc31859244e524bb142aae9273e},
affiliation={Department of Agro-industrial Technology, Faculty of Agricultural Technology, Bogor Agricultural University, PO Box 220, Bogor, 16002, Indonesia},
abstract={The supply chain performance is an indicator of success company in conducting the business process. The measurement of supply chain performance is required to take control and recognize the company's performance position. The objectives of this research were to identify business process value-added within the supply chain, to measure the supply chain performance, and to formulate strategies for improving the supply chain performance. The value-added measurement was conducted by Hayami method [15]. The supply chain performance was measured by Supply Chain Operation Reference (SCOR) combine with fuzzy Analytic Hierarchy Process (AHP). Strategies for supply chain performance improvement were formulated by SWOT analyses combine with fuzzy-AHP. The value-added analysis result showed that the sugarcane factory was the highest value-added ratio in the supply chain. The supply chain performance measurement result showed that the sugarcane factory has poor performance while plantation was in very low performance. Evaluation of supply chain's internal and external factors indicated a strong condition and would deal with possibly great threats. This study suggested to conduct follow up research in the sugar production system in order to maintain improvement on qualities, yields, and performance which eventually lead to supply chain performance improvement. © ExcelingTech Pub, UK.},
author_keywords={Fuzzy AHP;  SCOR;  Sugarcane agro-industry;  Supply-chain performance;  SWOT},
references={Al-Ahmari, A.M., Ur-Rehman, A., Ali, S., Decision support system for the selection of advance manufacturing technologies (2016) Journal of Engg. Research, 4, pp. 130-150; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement system: 1998-2015 (2016) Computers in Industry, 82, pp. 135-150; Bittencourt, F., Rabelo, R.J., A systematic approach for VE partner's selection using SCOR model and the AHP method (2008) Collaborative Networks and Their Breeding Environments, pp. 99-108; Bozbura, B., Prioritization of Organizational Capital Measurement Indicators Using Fuzzy AHP (2007) International Journal of Approximate Reasoning, 44, pp. 124-147; Bunte, F., (2008) Pricing and Performance in Agrifood Supply Chain, 1 st Edition, , Wageningen University and Research Center, Netherland; Chang, W., Application of a quantification SWOT analytical method (2006) Mathematical and Computer Modelling, 43, pp. 158-169; Kong, F., Applying fuzzy analytic hierarchy process to evaluate success factors of e commers (2005) International Journal of Information and System Science, 1, pp. 406-412; Chiadamrongm, N., Kawtummachai, R., A methodology to support decision making on sugar distribution for export channel: a case study of Thai sugar industry (2008) Computer and Electronics in Agriculture, 64, pp. 248-261; Chopra, S., (2013) Meindl, Supply Chain Management: Strategy, Planning and Operation, , Pearson Prentice Hall, New Jersey, United States; Coltrain, D., Barton, D., Boland, M., (2000) Value added: Opportunities and Strategies, , Arthur Capper Cooperative Center, Department of Agricultural Economics. Kansas State University. Kansas City, United States; David, F.R., (2011) Strategic Management Concept and Cases 13th Edition, , Pearson Prentice Hall, New Jersey, United States; (2012) Agribisnis Gula Indonesia, , Laporan Intern, Dewan Gula Indonesia, Jakarta, Indonesia; Haile, M., Krupka, J., Fuzzy Evaluation of SWOT Analysis (2016) Int. J Sup. Chain. Mgt, 5 (2), pp. 172-179; Vayvay, O., Ozcan, Y., Cruz-Cunha, M.M., ERP consultant selection problem using AHP and ANP: a case study in Turkey (2012) Journal of Business Management and Economics, 3, pp. 106-117; Hidayat, S., Marimin, Agent Based Modeling for Investment and Operational Risk Considerations in Palm Oil Supply Chain (2014) Int. J Sup. Chain. Mgt, 3 (1), pp. 34-40; Higgins, A., Thorburn, P., Archer, A., Jakku, E., Opportunities for value chain research in sugar industries (2007) Agricultural System, 94, pp. 611-621; (2016) Sistem Informasi Pangan Strategis, , https://aplikasi2.pertanian.go.id/sipastra/, 20-12; Kajanusa, K., Kurttila, J., The use of value focused thinking and the A'WOT hybrid method in tourism management (2004) Tourism Management, 25, pp. 499-506; Kangas, J., Pesonen, Kurttila, Kajanusa (2001) A'WOT: integrating the AHP with SWOT analysis, pp. 189-198. , 6th ISAHP; 2001 Agu 2-4; Berne, Swiss; Kasi, V., Systematic assesment of SCOR modelling supply chains (2005) Proceedings of the 38th Hawai International Conference on System Sciences, , Georgia State University, Georgia; Li, W., Yuanyuan, Z., (2005) A Game Analysis on Profit Distribution of Two-echelon Supply Chain with Principal and Subordinate, , Jiangsu University and Technology, Jiangsu, China; Marimin, D., Machfud, M.A., Putra, M.P., Wiguna, B., Value chain analysis for green productivity improvement in the natural rubber supply chain: a case study (2014) Journal of Cleaner Production, 85, pp. 201-211; Marimin, D., Suharjito, T., Nugeraha, D.D., Bahar, E., A framework of intelligent decision support system for agro-industrial and agribusiness supply chain management (2010) AFITA 2010 International Conference, the Quality Information for Competitive Agricultural Based Production System and Commerce; Marimin, F.D., Martini, S., Astuti, R., Suharjito, H.S., Added value and performance analysis of Edamame soybean supply chain: a case study (2010) Operation and Supply Chain Management, 3, pp. 148-163; Neves, M.F., Trombil, V.G., Consoli, M.A., Measurement on sugar cane in Brazil (2010) International Food and Agribusiness Management Review, 13, pp. 1-18; Palma-Mendoza, J.A., Analytical hierarchy process and SCOR model to support supply chain re design (2014) International Journal of Information Management, 34, pp. 634-638; Palma-Mendoza, J.A., Neailey, K., Roy, R., Business process re-design methodology to support supply chain integration (2014) International Journal of Information Management, 34, pp. 167-176; Pesonen, M., Kurttila, M., Kangas, J., Kajanus, H.P., Assessing the priorities using a'wot among resource management strategies at the finish forest and park service (2000) Forest Science, 47, pp. 534-541; Qi, Y., Huo, B., Wang, Z., Yeung, H.Y., The impact of operation and supply chain strategies on integration performance (2016) Intern. Journal of Production Economics, 185, pp. 162-174; Rajnoha, R., Sujova, A., Dobrovic, J., Management and economics of business processes added value (2012) Procedia-Social and Behavioral Sciences, 62, pp. 1292-1296; Rana, S.M., Osman, A., Bahari, A.B., Solaiman, M., Determinants of Supply Chain Performance: A Strategic Point of View (2015) Int. J Sup. Chain. Mgt, 4 (3), pp. 94-102; Ravil, K., A fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge management adoption in supply chain to overcome its barriers (2014) Expert System with Application, 41 (2014), pp. 679-693; Rusono, N., Suanri, A., Candradijaya, A., Muharam, A., Martino, I., Tejaningsih, U.P., Susilowati, S.H., Maulana, M., (2013) Studi Pendahuluan Rencana Pembangunan Jangka Menengah Nasional (RPJMN) Bidang Pangan dan Pertanian 2015-2019, , Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional, Jakarta, Indonesia; Yao, D.Q., Yue, X., Liu, J., Vertical cost information sharing in a supply chain with value adding retailers (2008) Omega, 36, pp. 838-851; Frumklin, P., Keating, E.K., Diversification reconsidered: the risks and rewards of revenue concentration (2011) Journal of Social Entrepreneurship, 2, pp. 151-164; Saaty, T.L., Vargas, L.G., (2001) Models, Methods, Concepts and Applications of the Analytic Hierarchy Proces, , Kluwer Academic Publishers, Boston, United States; Sagbas, A., Mazmanoglu, A., Use of multicriteria decision analysis to assess alternative wind power plants (2014) Journal of Engg. Research, 2, pp. 148-162; Sevkli, M., Oztekin, A., Uysal, O., Torlak, G., Turkyilmaz, A., Delen, D., Development of a fuzzy ANP based SWOT analysis for the airline industry in Turkey (2012) Expert System with Application, 39, pp. 14-24; Shahba, S., Arjmandi, R., Monavari, M., Ghodusi, J., Application of multi-attribute decision making methods in SWOT analysis of mine waste management (case study:Sirjan's Golgohar iron mine, Iran) (2016) Resource Policy, 51, pp. 67-76; Sivaramane, N., Reddy, G.P., Ganesh, K.B., Ravichandran, S., Adaptation strategies for climate smart agriculture (2014) Agricultural Economics Research Review, 27; Statistics Indonesia-BPS (Badan Pusat Statistik) (2016) Indonesian Sugarcane Statistics, , BPS-Statistics Indonesia. Jakarta, Indonesia; Tesfay, Y.Y., Sakita, B.M., Mawrides, E.K., Developing a New Market Strategy from Supply Chain Management Perspective: A Case of Jotun in Brazil (2014) Int. J Sup. Chain. Mgt, 3 (2), pp. 94-110; Torfi, R., Selection of Project Managers in Construction Firms Using Analytic Hierarchy Process (AHP) and Fuzzy Topsis: A Case Study (2011) Journal of Construction in Developing Countries, 16, pp. 69-89; Vorst, J.G.A.J., Performance Measurement in Agrifood Supply Chain Networks: an overview (2006) Quantifying the Agri-food Supply Chain, pp. 13-24. , Logistic and Operation Research Group, Wageningen, Netherland; Wickramasinghe, T., Application of combined SWOT and Analytic Hierarchy Process (AHP) for Tourism revival strategic marketing planning: a case of Sri Lanka tourism (2010) Journal of the Eastern Asia Society for Transportation Studies, 8, pp. 954-969; Yuksel, I., Dagdeviren, M., Using the Analytical Network Process (ANP) in a SWOT analysis- a case study for a textile firm (2007) Information Science, 177, pp. 3364-3382; Zhang, Y., Huang, J.H., Cost-based pricing model with value-added tax and corporate income tax for a supply chain network (2013) Appl. Math. Modelling, 38, pp. 168-180; Chan, T.S., Chan, H.K., The future trend system wide modelling in supply chain studies (2005) International Journal of Advance Manufacturing Technology, 25, pp. 820-832; Christopher, M., Mena, C., Khan, O., Yurt, O., Approaches to managing global sourcing risk (2011) Supply Chain Management:An International Journal, 16 (2), pp. 67-81; Kamahlaradi, M., Parast, M.M., (2015) A review of the literature on the principles of enterprise supply chain resilience: major findings and directions for future research; Oliveira, J.B., Lima, R.S., Montevechi, J.A.B., Perfective and relationship in supply chain simulation: a systematic literature review (2016) Simulation Modelling and Practice Theory, 62, pp. 166-191; Dargi, A., Anjhomsoae, A., Galankashi, M.R., Memari, A., Masine, Supplier selection: a fuzzy-ANP approach (2014) Procidia Computer Science, 31 (2014), pp. 691-700; (2010) Gula Kristal-Bagian 3: Putih (SNI 3140.3-2010), , BSN, Indonesia},
correspondence_address1={Asrol, M.; Department of Agro-industrial Technology, PO Box 220, Indonesia; email: muhammadasrolnaa@gmail.com},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@BOOK{Grover2017677,
author={Grover, N.},
title={Performance measurement: Measuring retail supply chain performance},
journal={Decision Management: Concepts, Methodologies, Tools, and Applications},
year={2017},
volume={2-4},
pages={677-707},
doi={10.4018/978-1-5225-1837-2.ch031},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021374493&doi=10.4018%2f978-1-5225-1837-2.ch031&partnerID=40&md5=a976154244ee75431ca57590bff22581},
affiliation={University of Petroleum and Energy Studies, India},
abstract={The purpose of this chapter is to study what supply chain managers of a retail supply chain measure. In other words aim here to identify the key indicators for measuring retail supply chain performance. A qualitative approach is adopted. Published literature from refereed journals on supply chain performance measurement has been considered. Besides the need for organizations to adopt a holistic approach, firms remain focused on traditional financial measures (gross profit margin, Interest coverage, and debt and equity ratios). The chapter identifies key indicators for performance measurement and classifies them into four major categories: transport optimization, information technology optimization, inventory optimization and resource optimization. These key indicators are arranged precisely for retail industry. From a supply chain perspective, the non financial measures such as on-time delivery, training of employees, warehouse layout, etc. are also important aspects of measuring supply chain performance. Further research can be carried out to validate the relevance and applicability of identified indicators. The study can be further conducted to measure the interrelationships between the KPIs and their impact on financial performance of the firm. In this chapter the author attempts to identify the performance indicators specifically for retail supply chain. The identified measures are further categorized based on its operations. © 2017, IGI Global. All rights reserved.},
references={Adams, N.D., Brown, T.W., Firth, R.V., (1996) Warehouse and Distribution Automation Handbook, , New York: McGraw-Hill; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Aviv, Y., On the benefits of collaborative forecasting partnerships between retailers and manufacturers (2007) Management Science, 53 (5), pp. 777-794; Ballou, R.H., Gilbert, S.M., Mukherjee, A., New managerial challenges from supply chain opportunities (2000) Industrial Marketing Management, 29 (1), pp. 7-18; Barratt, M., Oke, A., Antecedents of supply chain visibility in retail supply chains: A resoure-based theory perspective (2007) Journal of Operations Management, 25 (6), pp. 1217-1233; Barua, A., Konana, P., Whinston, A.B., Yin, F., An empirical investigation of net-enabled business value (2004) Management Information Systems Quarterly, 28 (4), pp. 585-620; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bechtel, C., Jayaram, J., Supply chain management: a strategic perspective (1997) The International Journal of Logistics Management, pp. 15-34; Bourland, K.E., Powell, S.G., Pyke, D.K., Exploiting timely demand information to reduce inventories (1996) European Journal of Operational Research, 92 (2), pp. 239-253; Bower, J.L., Hout, T.M., Fast cycle capability for competitive power (1988) Harvard Business Review, pp. 110-118; Bowersox, D., Rodrigues, A., Calantone, R., Estimation of Global and National Logistics Expenditures: 2002 Data Update (2005) Journal of Business Logistics, 26 (2), pp. 1-16; Bowersox, D.J., Closs, D.J., Stank, T.P., Keller, S.B., How supply chain competency leads to business success (2000) Supply Chain Management Review, 4 (4), pp. 70-78; Cachon, G., Exact evaluation of batch-ordering policies in two-echelon supply chains with periodic review (2001) Operations Research, 49 (1), pp. 79-98; Cachon, G.P., Fisher, M., Supply chain inventory management and the value of shared information (2001) Management Science, 46 (8), pp. 1032-1048; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, pp. 612-621. , 2009; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Cannon, J.P., Perreault, W.D., Jr., Buyer-seller relationships in business markets (1999) JMR, Journal of Marketing Research, 36 (4), pp. 439-460; Chae, B.K., Developing key performance indicators for supply chain: An industry perspective (2009) Supply chain Management. International Journal (Toronto, Ont.), 14 (6), pp. 422-428; Chan, F.T., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chang, T., Fu, H., Lee, W., Lin, Y., Hsueh, H., A study of an augmented CPFR model for the 3C retail industry (2007) Supply Chain Management: An International Journal, 12 (3), pp. 200-209; Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D., Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information (2000) Management Science, 46 (3), pp. 436-443; Chen, I.J., Paulraj, A., Lado, A., Strategic purchasing, supply management and firm performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Christopher, M., (1992) Logistics and Supply Chain Management: Strategies for Reducing Costs and Improving Services, , London: Financial Times/Pitman; Closs, D.J., Goldsby, T.J., Clinton, S.R., Information technology influences on world class logistics capability (1997) International Journal of Physical Distribution & Logistics Management, 27 (1), pp. 4-17; Corsten, D., Gruen, T., Seeking on-shelf availability-an examination of the extent, the causes and the efforts to address retail out-of-stocks (2003) International Journal of Retail & Distribution Management, 31 (12), pp. 605-716; Coyle, J.J., Bardi, E.J., Langley, C.J., (2003) The Management of Business Logistics: A Supply Chain Perspective, , (7th ed.). Mason, OH: South-Western Thomson Learning; Davenport, T.H., (2000) Mission critical - realizing the promise of enterprise systems, , Boston, MA: Harvard Business School Press; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge-Measuring Operations for World-class Competition, , Homewood, IL: Dow Jones-Irwin; Dobler, D.W., Burt, D.N., (1996) Purchasing and Supply Management, , New York, NY: The McGraw-Hill Companies; Douglas, J., Building performance and its relevance to facilities management (1996) Facilities, 14 (3-4), pp. 23-32; Drucker, P.F., The economy's dark continent (1962) Fortune, pp. 265-270. , April; (2004) Differentiation for performance excellence in logistics 2004, , Hamburg, Germany: Deutscher Verkehrs-Verlag GmbH; Ellinger, A.E.D.P., The Relationship between Marketing/Logistics Interdepartmental Integration and Performance in U.S. Manufacturing Firms: An Empirical Study (2000) Journal of Business Logistics, 21 (2), pp. 1-22; Ellram, L.M., La Londe, B.J., Weber, M.M., Retail Logistics (1989) International Journal of Physical Distribution & Materials Management, 19 (12), pp. 29-39; Doing (2011) Business India; Ferguson, M., Ketzenberg, M., Sharing Information to Improve Retail Product Freshness of Perishables (2006) Production and Operations Management, 15 (1), pp. 57-73; Fiala, P., Information sharing in supply chains (2005) Omega, 33 (5), pp. 419-423; Fisher, M.L., What is the Right Supply Chain for your product (1997) Harvard Business Review; Flynn, B.B., Huo, B., Zhao, X., The impact of supplychain integration on performance: A contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Forrester, J.W., (1961) Industrial Dynamics, , Cambridge, MA: MIT Press; Forslund, H., Jonsson, P., The impact of forecast information quality on supply chain performance (2007) International Journal of Operations & Production Management, 27 (1), pp. 90-107; Frazelle, E.H., (2002) Supply Chain Strategy: The Logistics of Supply Chain Management, , New York: McGraw-Hill; Froehle, C., Roth, A., A Resource-Process Framework of New Service Development (2007) Production and Operations Management, 16 (2), pp. 169-188; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2001) Journal of Operations Management, 19 (2), pp. 185-200; Fynes, B., Voss, C., Búrca, S., The impact of supply chain relationship dynamics on manufacturing performance (2005) International Journal of Operations & Production Management, 25 (1), pp. 6-19; Fynes, B., Voss, C., de Búrca, S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354; Gavirneni, S., Kapuscinski, R., Tayur, S., Value of Information in Capacitated Supply Chains (1999) Management Science, 45 (1), pp. 16-24; Gelders, L., Mannaert, P., Maes, J., Manufacturing strategy performance indicators and improvement programs (1994) International Journal of Production Research, 32 (4), pp. 797-805; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Gopal, P.R., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Green, K.W., Jr., Whitten, D., In, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply Chain Management: An International Journal, 13 (4), pp. 317-327; Green, K.W., Jr., Whitten, D., Inman, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply chain management. International Journal (Toronto, Ont.), 13 (4), pp. 317-327; Gumbus, A., Introducing the balanced scorecard: Creating metrics to measure performance (2005) Journal of Management Education, 29 (4), pp. 617-630; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekarana, A., Patelb, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 84 (3), pp. 333-347; Gunasekran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Guruswamy, M., Sharma, K., Jos, M.M., (2007) FDI in Retail - IIIImplications of Wal Mart's Backdoor Entry, , Centre for Policy Alternatives; Gustin, C.M., Daugherty, P.J., Stank, T.P., The effects of information availability on logistics integration (1995) Journal of Business Logistics, 16 (1), pp. 1-21; Harrington, L., Untapped savings abound (1996) Industry Week, pp. 53-58. , July15; Hayes, R.H., Abernathy, W.J., Managing our way to economic decline (1980) Harvard Business Review, 62, pp. 95-101; Hill, C.A., Scudder, G.D., The use of electronic data interchange for supply chain coordination in the food industry (2002) Journal of Operations Management, 20 (4), pp. 375-387; Ho, D.C., Chan, W.E., Wong, N., Chan, M., Significant metrics for facilities management benchmarking in the Asia Pacific region (2000) Facilities, 18 (13-14), pp. 545-555; Hoffman, D., (2004) The hierarchy of supply chain metrics: diagnosing your supply chain health, , Boston, Massachusetts: AMR Research; Hulland, J., Wade, M., Antia, K.D., The impact of capabilities and prior investments on online channel commitment and performance (2007) Journal of Management Information Systems, 23 (4), pp. 109-142; (2013) India Retail News, , http://www.indiaretailnews.com/, January 24, Retrieved January 27, 2013, from Emergence of Retailing Sector in India: Challenges and Opportunities; Johnson, H.T., Toward a new understanding of nineteenth-century cost accounting (1981) Accounting Review, 56 (3), pp. 510-518; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Harvard Business School Press; Jones, R.M., Towill, D.R., Total cycle time compression and the agile supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 61-73; Kaplan, R.S., Yesterday's accounting undermines production (1984) Harvard Business Review, 62 (4), pp. 95-101; Kaplan, R.S., Norton, D.P., The balance Scorecard-Measures That Drive performance (1992) Harvard Business Review, , January; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50. , June; Koh, L.S., Demirbag, M., Bayraktar, E., Tatoglu, E., Zaim, S., The impact of supply chain management practices on performance of SMEs (2007) Industrial Management & Data Systems, 107 (1), pp. 103-124; (2011) Issues Monitor: Sharing knowledge on topical issues in Retail Industry, , July, KPMG; Kulp, S.C., Lee, H.L., Ofek, E., Manufacturer Benefits from Information Integration with Retail Customers (2004) Management Science, 50 (4), pp. 431-444; Lai, K.H., Ngai, E.T., Cheng, T.E., Measures for evaluating supply chain performance in transport logistics (2002) Transp Res Part E, 38 (6), pp. 439-456; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, pp. 1-19; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Lee, H.L., Billington, C., Material Management in Decentralized Supply Chains (1993) Operations Research, 41 (5), pp. 835-847; Lee, H.L., Padmanabhan, V., Whang, S., Information distortion in a supply chain: The bullwhip effect (1997) Management Science, 43 (4), pp. 546-558; Lee, H.L., Padmanabhan, V., Whang, S., The Bullwhip Effect in Supply Chains (1997) Sloan Management Review, pp. 93-101; Levy, D.L., Lean production in an international supply chain (1997) Sloan Management Review, pp. 94-102. , Winter; Li, S., Ragu-Nathanb, B., Ragu-Nathanb, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega International Journal of Management Science, 34 (2), pp. 107-124; Lin, C., Influencing factors on the innovation in logistics technologies for logistics service providers in Taiwan (2006) The Journal of American Academy of Business, 9 (2), pp. 257-263; Mabert, V.A., Venkataramanan, M.A., Special research focus on supply chain linkages: Challenges for design and management in the 21st century (1998) Decision Sciences, 29 (3), pp. 537-552; Marvick, D., White, J., Distribution operations: Managing distribution facilities for strategic advantage (1998) Strategic Supply Chain Alignment, , J. G. Gattorna (Ed.); McCarthy, T.M., Golicic, S.L., Implementing collaborative forecasting to improve supply chain performance (2002) International Journal of Physical Distribution & Logistics Management, 32 (6), pp. 431-454; McKinnon, A.C., (1996) The development of retail logistics in the UK: A position paper, Technology Foresight: Retail and Distribution Panel, , Edinburgh: Heriot-Watt University; Melville, N., Kraemer, K., Gurbaxani, V., Information technology and organizational performance: An integrative model of IT business value (2004) Management Information Systems Quarterly, 28 (2), pp. 283-322; Mentzer, J.T., Foggin, J.H., Golicic, S.L., Collaboration: the enablers, impediments, and benefits (2000) Supply Chain Management Review, , September/October; Moberg, C.R., Whipple, T.W., Cutler, B.D., Speh, T.W., Do the Management Components of Supply Chain Management Affect Logistics Performance? (2004) The International Journal of Logistics Management, 15 (2), pp. 15-30; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testingaprocess-based approach (1995) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Neely, A.D., (1998) Measuring Business Performance, , London: Economist Books; Noble, S.M., Haytko, D.L., Phillips, J., What drives college-age Generation Y consumers? (2009) Journal of Business Research, 62 (6), pp. 617-628; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management & Data Systems, 110 (9), pp. 1297-1318; (2009) Rajya Sabha, , Ninetienth Report on Foreign and Domestic Investment in Retail Sector; Pitt, M., Tucker, M., Performance measurement in facilities managementdriving innovation? (2008) Property Management, 26 (4), pp. 241-254; Pohlen, T.L., Lambert, D.M., Supply Chain Metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Raman, A., DeHoratius, N., Ton, Z., The Achilles heel of supply chain management (2001) Harvard Business Review, pp. 136-152; Ramdas, K., Spekman, R.E., Chain or Shackles: Understanding What Drives Supply-Chain Performance (2000) Interfaces, 30 (4), pp. 3-21; Rao, B.P., Improving retail effectiveness through technology: A survey of analytical tools for physical and on-line retailers (2000) Technology in Society, 22, pp. 111-122; Ray, G., Muhanna, W.A., Barney, J.B., Information technology and the performance of the customer service process: A resource-based analysis (2005) Management Information Systems Quarterly, 29 (4), pp. 625-651; Rosenzweig, E.D., Roth, A.V., Dean, J.W., Jr., The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21 (4), pp. 437-456; Rungtusanatham, M., Salvador, F., Forza, C., Choi, T.Y., Supply-chain linkages and operational performance: A resource-based-view perspective (2003) International Journal of Operations & Production Management, 23 (9), pp. 1084-1099; Rushton, A., Oxley, J., Croucher, P., (2001) The handbook of logistics and distribution management, , (2nd ed.). London, UK: Academic Press; Sahin, F., Robinson, E.P., Flow coordination and information sharing in supply chains: Review, implications, and directions for future research (2002) Decision Sciences, 33 (4), pp. 505-535; Sanders, N.R., Pattern of information technology use: The impact on buyer-suppler coordination and performance (2008) Journal of Operations Management, 26 (3), pp. 349-367; Schonberger, R.J., (1990) Building a chain of customers, , London: The Free Press; Shah, J., (2009) Supply chain management, , Delhi: Pearson Education; Shore, B., Venkatachalam, A., Evaluating the information sharing capabilities of supply chain partners: A fuzzy logic model (2003) International Journal of Physical Distribution & Logistics Management, 33 (9), pp. 804-824; Sinclair, D., Zairi, M., Effective process management through performance measurement: Part III - An integrated model of totalquality-based performance measurement (1995) Business Process Management Journal, 1 (3), pp. 50-65; Slack, N., Flexibility as a manufacturing objective (1983) International Journal of Operations & Production Management, 3 (3), pp. 4-13; Spohrer, J., Maglio, P.P., Bailey, J., Gruhl, D., Steps Toward a Science of Service Systems (2007) Computer, 40 (1), pp. 71-77; Stank, T.P., Davis, B.R., Fugate, B.S., A strategic framework for supply chain oriented logistics (2005) Journal of Business Logistics, 26 (2), pp. 27-45; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Storey, J., Emberson, C., Godsell, J., Harrison, A., Supply chain management: Theory, practice and future challenges (2006) International Journal of Operations & Production Management, 26 (7), pp. 754-774; Tan, K.-C., Kannan, V.R., Handfied, R.B., Ghosh, S., Supply chain management: An empirical study of its impact on performance (1999) International Journal of Operations & Production Management, 9 (10), pp. 1034-1052; Tan, K.C., Kannan, V.R., Hanfield, R.B., Supply Chain Management: Supplier Performance and Firm Performance (1998) International Journal of Purchasing and Materials Management, 34 (3), pp. 2-9; Tanriverdi, H., Information technology relatedness, knowledge management capability and performance of multibusiness firms (2005) Management Information Systems Quarterly, 29 (2), pp. 311-334; (2013) India Overview, , http://www.worldbank.org/en/country/india/overview, Retrieved January Saturday, 2013; Thomas, D.J., Griffin, P.M., Coordinated supply chain management (1996) European Journal of Operational Research, 94 (1), pp. 1-15; Towers, N., Burnes, B., A composite framework of supply chain management and enterprise planning for small and medium-sized manufacturing enterprises (2008) Supply Chain Management: An International Journal, 13 (5), pp. 349-355; Tracey, M., Lim, J.-S., Vonderembse, M.A., The impact of supply-chain management capabilities on business performance (2005) Supply Chain Management: An International Journal, 10 (3), pp. 179-191; van der Vorst, J.A., (2000) Effective food supply chains Generating, modelling and evaluating supply chain scenarios, , (PhD thesis). Wageningen University; Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships (2003) Journal of Operations Management, 21 (5), pp. 523-539; Wallace, D.W., Giese, J.L., Johnson, J.L., Customer retailer loyalty in the context of multiple channel strategies (2004) Journal of Retailing, 80 (4), pp. 249-263; Walters, P.G., Adding value in global B2B supply chains: Strategic directions and the role of the internet as a driver of competitive advantage (2008) Industrial Marketing Management, 37 (1), pp. 59-68; Whipple, J.M., Frankel, R., Daugherty, P.J., Information support for alliances: performance implications (2002) Journal of Business Logistics, 23 (2), pp. 67-82; Wortmann, J.C., Evolution of ERP systems (1998) Proceedings of International conference of the manufacturing value chain, , Troon: Kluwer Academic Publishing; Zhao, X., Xie, J., Wei, J.C., The impact of forecast errors on early order commitment in a supply chain (2002) Decision Sciences, 33 (2), pp. 251-280},
correspondence_address1={Grover, N.; University of Petroleum and Energy StudiesIndia},
publisher={IGI Global},
isbn={9781522518389; 1522518371; 9781522518372},
language={English},
abbrev_source_title={Decis. Manag.: Concepts, Methodol., Tools, and Appl.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Edmond2017557,
author={Edmond, J. and Soliman, F.},
title={The impact of balance score card implementation on supply chain firms},
journal={Decision Management: Concepts, Methodologies, Tools, and Applications},
year={2017},
volume={1-4},
pages={557-575},
doi={10.4018/978-1-5225-1837-2.ch026},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021354283&doi=10.4018%2f978-1-5225-1837-2.ch026&partnerID=40&md5=ae36f1d67f7a29825119075c0eebfdeb},
affiliation={University of Technology, Sydney, Australia},
abstract={The objective of this chapter is to understand the level of impact that implementing a balanced score card can have on organisations, and the factors that contribute to such impact; the research agrees that the BSC is an effective performance measurement system. The result of the research suggests that the quality of the knowledge and the understanding of the BSC within an organisation is a major determinant to the level of positive impact that implementing the BSC can have on an organisation. This research concluded that the same principle applies to any performance measurement and performance improvement initiatives, and that non BSC implemented firms are not necessary worse-off than BSC implemented firms from a performance perspective. Only few numbers of organisations were sampled. These sampled organisations are not a complete representation of the rest. The research acknowledges that the sources of information under which conclusions and assumptions were made were not based on primary sources. It is possible these data may not fully reflect the current state of all sampled organisations in this research. © 2017, IGI Global. All rights reserved.},
references={Akkermans, H., Van Oorschot, K., Relevance assumed: A case study of balanced scorecard development using system dynamics (2005) The Journal of the Operational Research Society, 56 (8), pp. 931-941; Al-najjar, S.M., Kalaf, K.H., Designing a balanced scorecard to measure a bank's performance: A case study (2012) International Journal of Business Administration, 3 (4), pp. 44-52; Bolstorff, P., Putting it all together: how to integrate Six Sigma quality objectives with the SCOR model (2003) Logistics Today; Chavan, M., The balanced scorecard: A new challenge (2007) Journal of Management Development, 28 (5), pp. 393-406; Christesen, D.A., (2008) The impact of balanced scorecard usage on organization performance (PhD thesis), , The University of Minnesota, MN; Cuganesan, S., A case study on organisational performance measurement systems for customer intimacy (2005) Australian Accounting Review, 75 (1), pp. 52-61; Gautreau, A., Kleiner, B.H., Recent trends in performance measurement systems (2001) Management Research News, 24 (3), pp. 153-156; Hult, G.T.M., Ketchen, D.J., Adams, G.L., Mena, J.A., Supply chain orientation and balanced scorecard performance (2008) Journal of Managerial Issues, 20 (4), p. 526; Kukreja, A., Ricks, J.M.J., Meyer, J.A., Using Six Sigma for performance improvement in business curriculum: A case study (2009) Performance Improvement, 48 (2), pp. 9-25; Lagrosen, Y., Chebl, R., Tuesta, M.R., Organisational learning and Six Sigma deployment readiness evaluation: A case study (2011) International Journal of Lean Six Sigma, 2 (1), pp. 23-40; Mcshane, S., Olekalns, M., Travaglione, T., (2010) Organisational behaviour: On the Pacific Rim, , (3rd ed.). Sydney: McGraw-Hill; Melymuka, K., Measuring intangible assets (2004) Computerworld, 38 (6), p. 42; Mensah, F.B., Dogbe, O.D., Performance-based pay as a motivational tool for achieving organisational performance: An exploratory case study (2011) International Journal of Business and Management, 6 (12), pp. 270-285; Muratoglu, K., Performance measurement of supply chain via balanced scorecard: The case of a brewing group (2008) The Business Review Cambridge, 70 (1), pp. 330-337; Neal, G.O., Elaine, F., Using the balanced scorecard to manage intangible asset in a sino foreign joint venture (2005) Australian Accounting Review, 75 (2), p. 22; Othman, R., Enhancing the effectiveness of the balanced scorecard with scenario planning (2008) International Journal of Productivity and Performance Management, 57 (3), pp. 259-266; Rompho, N., Why the balanced scorecard fails in SMEs: A case study (2011) International Journal of Business and Management, 6 (11), pp. 39-45; Stank, T.P., Keller, S.B., Closs, D.J., Performance benefits of supply chain logistical integration (2001) Transportation Journal, 47 (2), p. 32; Thuermer, K.E., Options abound as integration of the global supply chain becomes a top priority (2006) World Trade, 79 (6), p. 17; Troyer, C., Cooper, R., Smart moves in supply chain integration (1995) Transportation & Distribution, 36 (9), p. 55; Upadhyay, A.Y.A., Balanced Scorecard - fostering learning organizations towards achieving strategic results: A case study of an oil PSU in India (2012) Development and Learning, 26 (4), pp. 13-16; Youngblood, A.D., Collins, T.R., Addressing balanced scorecard trade-off issues between performance metrics using Multi-Attribute Utility Theory (2003) Engineering Management Journal, 75 (1), pp. 11-17; Zuckerman, A., Pushing ERP integration into the supply chain (2005) World Trade, 78 (3), p. 54; Zuckerman, A., Explosion of options in global supply chain integration (2007) World Trade, 20 (4), p. 42},
correspondence_address1={Edmond, J.; University of TechnologyAustralia},
publisher={IGI Global},
isbn={9781522518389; 1522518371; 9781522518372},
language={English},
abbrev_source_title={Decis. Manag.: Concepts, Methodol., Tools, and Appl.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Kasemsap2017658,
author={Kasemsap, K.},
title={The role of sustainable performance measurement system in global supply chain},
journal={Decision Management: Concepts, Methodologies, Tools, and Applications},
year={2017},
volume={2-4},
pages={658-676},
doi={10.4018/978-1-5225-1837-2.ch030},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021325043&doi=10.4018%2f978-1-5225-1837-2.ch030&partnerID=40&md5=89dfa96b6f418675e112751912128f2a},
affiliation={Suan Sunandha Rajabhat University, Thailand},
abstract={This chapter summarizes the role of sustainable performance measurement system (PMS) in global supply chain, thus illustrating the theoretical and practical concepts of sustainable PMS and strategic supply chain management (SCM); and the significance of sustainable PMS in global supply chain. Therefore, it is essential for organizations to evaluate their sustainable PMS applications, establish a strategic plan to consistently explore their improvements, and rapidly respond to the sustainable PMS needs of customers in global supply chain. For practitioners and researchers, this chapter provides managerial guidelines for sustainable PMS applications utilized in global supply chain. Applying the sustainable PMS will significantly enhance organizational performance and reach strategic goals in global supply chain. © 2017, IGI Global. All rights reserved.},
references={Aliei, M., Sazvar, A., Ashrafi, B., Assessment of information technology effects on management of supply chain based on fuzzy logic in Iran tail industries (2012) International Journal of Advanced Manufacturing Technology, 63 (1-4), pp. 215-223; Ambrose, E., Marshall, D., Lynch, D., Buyer supplier perspectives on supply chain relationships (2010) International Journal of Operations & Production Management, 30 (12), pp. 1269-1290; Anderson, E., Weitz, B.A., The use of pledges to build and sustain commitment in distribution channels (1992) JMR, Journal of Marketing Research, 29 (1), pp. 18-34; Antai, I., Supply chain vs. supply chain competition: A niche-based approach (2011) Management Research Review, 34 (10), pp. 1107-1124; Antai, I., Olson, H., Interaction: A new focus for supply chain vs. supply chain competition (2013) International Journal of Physical Distribution & Logistics Management, 43 (7), pp. 511-528; Ashby, A., Leat, M., Hudson-smith, M., Making connections: A review of supply chain management and sustainability literature (2012) Supply Chain Management: An International Journal, 17 (5), pp. 497-516; Attaran, M., Attaran, S., Collaborative supply chain management: The most promising practice for building efficient and sustainable supply chains (2007) Business Process Management Journal, 13 (3), pp. 390-404; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighbourhood rough sets (2012) International Journal of Production Research, 50 (9), pp. 2484-2500; Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Management: An International Journal, 19 (3), pp. 275-291; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Beloff, B., Tanzil, D., Lines, M., Sustainable development performance assessment (2004) Environment and Progress, 23 (4), pp. 271-276; Benton, W.C., Maloni, M., The influence of power driven buyer/seller relationships on supply chain satisfaction (2005) Journal of Operations Management, 23 (1), pp. 1-22; Bowersox, D.J., Closs, D.C., (1996) Logistical management: The integrated supply chain process, , New York, NY: McGraw-Hill; Bruce, M., Daly, L., Towers, N., Lean or agile: A solution for supply chain management in the textiles and clothing industry (2004) International Journal of Operations & Production Management, 24 (1-2), pp. 151-170; Cabral, I., Grilo, A., Cruz-machado, V., A decision-making model for lean, agile, resilient and green supply chain management (2012) International Journal of Production Research, 50 (17), pp. 4830-4845. , do i:10.1080/00207543.2012.657970; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2009) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Carvalho, H., Azevedo, S.G., Cruz-machado, V., Agile and resilient approaches to supply chain management: Influence on performance and competitiveness (2013) Logistics Research, 4 (1-2), pp. 49-62; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chang, C.C., Hung, S.W., Huang, S.Y., Evaluating the operational performance of knowledge-based industries: The perspective of intellectual capital (2013) Quality & Quantity, 47 (3), pp. 1367-1383; Chang, H.H., Hung, C.J., Wong, K.H., Lee, C.H., Using the balanced scorecard on supply chain integration performance-a case study of service businesses (2013) Service Business, 7 (4), pp. 539-561; Chen, I.J., Paulraj, A., Lado, A.A., Strategic purchasing, supply management, and firm performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Chen, S.H., Wang, P.W., Lee, H.T., Performance measurement considering the interdependence of evaluators and criteria (2012) Quality & Quantity, 46 (1), pp. 103-115; Christopher, M., (2005) Logistics and supply chain management: Creating value-adding networks, , London, UK: Pitman; Cocca, P., Alberti, M., A framework to assess performance measurement systems in SMEs (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 186-200; Corona, C., Dynamic performance measurement with intangible assets (2009) Review of Accounting Studies, 14 (2-3), pp. 314-348; Defee, C.C., Fugate, B.S., Changing perspective of capabilities in the dynamic supply chain era (2010) International Journal of Logistics Management, 21 (2), pp. 180-206; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M., Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing (2006) IEEE Transactions on Systems, Man, and Cybernetics, 36 (1), pp. 62-75; Dyer, J.H., Hatch, N.W., Using supplier network to learn faster (2004) MIT Sloan Management Review, 45 (3), pp. 57-63; Emrouznejad, A., Dey, P.K., Performance measurement in the health sector: Uses of frontier efficiency methodologies and multi-criteria decision making (2011) Journal of Medical Systems, 35 (5), pp. 977-979. , PMID:21072574; Epstein, M.J., Wisner, P.S., Using a balanced scorecard to implement sustainability (2001) Environmental Quality Management, 11 (2), pp. 1-10; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers, and bridges to effective supply chain management (2008) Supply Chain Management: An International Journal, 13 (1), pp. 35-48; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard: Linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11 (5), pp. 269-284; Franceschini, F., Turina, E., Quality improvement and redesign of performance measurement systems: An application to the academic field (2013) Quality & Quantity, 47 (1), pp. 465-483; Ganesan, S., Determinants of long-term orientation in buyer-seller relationships (1994) Journal of Marketing, 58 (2), pp. 1-19; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Gimenez, C., Tachizawa, E.M., Extending sustainability to suppliers: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (5), pp. 531-543; Godsell, J., Birtwistle, A., van Hoek, R., Building the supply chain to enable business alignment: Lessons from British American Tobacco (BAT) (2010) Supply Chain Management: An International Journal, 15 (1), pp. 10-15; Godsell, J., Harrison, A., Emberson, C., Storey, J., Customer responsive supply chain strategy: An unnatural act? (2006) International Journal of Logistics: Research and Applications, 9 (1), pp. 47-56; Grimm, J.H., Hofstetter, J.S., Sarkis, J., Critical factors for sub-supplier management: A sustainable food supply chains perspective (2013) International Journal of Production Economics, 152, pp. 159-173; Gummesson, E., Relationship marketing and the imaginary organisation: A synthesis (1996) European Journal of Marketing, 30 (2), pp. 31-44; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Hakansson, H., Snehota, I., (1995) Developing business relationships, , London, UK: Routledge; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hines, P., Holwe, M., Rich, N., Learning to evolve: A review of contemporary lean thinking (2004) International Journal of Operations & Production Management, 24 (10), pp. 994-1011; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement system (2001) International Journal of Operations & Production Management, 21 (8), pp. 1096-1115; Hult, G.T.M., Ketchen, D.J., Arrfelt, M., Strategic supply chain management: Improving performance through a culture of competitiveness and knowledge development (2007) Strategic Management Journal, 28 (10), pp. 1035-1052; Ireland, R.D., Webb, J.W., A multi-theoretic perspective on trust and power in strategic supply chains (2007) Journal of Operations Management, 25 (2), pp. 482-497; Jakhar, S.K., Designing the green supply chain performance optimisation model (2014) Global Journal of Flexible Systems Management, 15 (3), pp. 235-259; Jalalvand, F., Teimoury, E., Makui, A., Aryanezhad, M.B., Jolai, F., A method to compare supply chains of an industry (2011) Supply Chain Management: An International Journal, 16 (2), pp. 82-97; Kaplan, R.S., Norton, D.P., (1996) The balanced scorecard: Translating strategy into action, , Boston, MA: Harvard Business School Press; Kasemsap, K., The role of lean production on organizational performance (2014) Handbook of research on design and management of lean production systems, pp. 358-388. , V. Modrak & P. Semanco (Eds.), Hershey, PA: IGI Global; Keating, B., Quazi, A., Kriz, A., Coltman, T., In pursuit of a sustainable supply chain: Insights from Westpac Banking Corporation (2008) Supply Chain Management: An International Journal, 13 (3), pp. 175-179; Kizer, K.W., Kirsh, S.R., The double edged sword of performance measurement (2012) Journal of General Internal Medicine, 27 (4), pp. 395-397. , PMID:22271270; Kocaoglu, B., Gulsun, B., Tanyas, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Kotabe, M., Martin, X., Domoto, H., Gaining from vertical partnerships: Knowledge transfer, relationship duration and supplier performance improvements in the U.S. and Japanese automotive industries (2003) Strategic Management Journal, 24 (4), pp. 293-316; Kotzab, H., Grant, D.B., Teller, C., Halldorsson, A., Supply chain management and hypercompetition (2009) Logistics Research, 1 (1), pp. 5-13; La Londe, B.J., Masters, J.M., Emerging logistics strategies: Blueprints for the next century (1994) International Journal of Physical Distribution & Logistics Management, 24 (7), pp. 35-47; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Levitt, T., Marketing myopia (1960) Harvard Business Review, 38 (4), pp. 45-56. , PMID:15252891; Lin, L.C., Li, T.S., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Software Quality Journal, 18 (3), pp. 387-406; Manning, L., Baines, R., Chadd, S., Benchmarking the poultry meat supply chain (2008) Benchmarking: An International Journal, 15 (2), pp. 148-165; Meadows, M., Pike, M., Performance management for social enterprises (2010) Systemic Practice and Action Research, 23 (2), pp. 127-141; Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Mentzer, J.T., Gundlach, G., Exploring the relationship between marketing and supply chain management: Introduction to the special issue (2010) Journal of the Academy of Marketing Science, 38 (1), pp. 1-4; Merli, G., (1991) Co-makership: The new supply strategy for manufacturers, , Cambridge, MA: Productivity Press; Meybodi, M.Z., Benchmarking performance measures in traditional and just-in-time companies (2009) Benchmarking: An International Journal, 16 (1), pp. 88-102; Moffett, S., Anderson-gilliespie, K., McAdam, R., Benchmarking and performance measurement: A statistical analysis (2008) Benchmarking: An International Journal, 15 (4), pp. 368-381; Molla, A., Identifying IT sustainability performance drivers: Instrument development and validation (2013) Information Systems Frontiers, 15 (5), pp. 705-723; Möller, A., Schaltegger, S., The sustainability balanced scorecard as a framework for ecoefficiency analysis (2005) Journal of Industrial Ecology, 9 (4), pp. 73-83; Morali, O., Searcy, C., A review of sustainable supply chain management practices in Canada (2013) Journal of Business Ethics, 117 (3), pp. 635-658; Morgan, R.M., Hunt, S.D., The commitment-trust theory of relationship marketing (1994) Journal of Marketing, 58 (3), pp. 20-38; Nadler, S.S., Kros, J.F., An assessment of supply chain managers' trust in online auctions (2010) Industrial Management & Data Systems, 110 (6), pp. 805-822; Najmi, M., Rigas, J., Fan, I.S., A framework to review performance measurement systems (2005) Business Process Management Journal, 11 (2), pp. 109-122; Neely, A., Adams, C., Kennerley, M., (2002) The performance prism: The scorecard for measuring and managing stakeholder relationship, , London, UK: Financial Times-Prentice Hall; Obal, M., Lancioni, R.A., Maximizing buyer-supplier relationships in the digital era: Concept and research agenda (2013) Industrial Marketing Management, 42 (6), pp. 851-854; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Oztemel, E., Tekez, E.K., Interactions of agents in performance based supply chain management (2009) Journal of Intelligent Manufacturing, 20 (2), pp. 159-167; Palmatier, R.W., Dant, R.P., Grewal, D., A comparative longitudinal analysis of theoretical perspectives of interorganizational relationship performance (2007) Journal of Marketing, 71 (4), pp. 172-194; Parisi, C., The impact of organisational alignment on the effectiveness of firms' sustainability strategic performance measurement systems: An empirical analysis (2013) Journal of Management & Governance, 17 (1), pp. 71-97; Porter, M.E., (1985) Competitive advantage: Creating and sustaining superior performance, , New York, NY: Free Press; Ritzman, L.P., Krejewski, L.J., Klassen, R.D., (2004) Foundations of operations management, , Upper Saddle River, NJ: Prentice-Hall; Rugman, A.M., Li, J., Oh, C.H., Are supply chains global or regional? (2009) International Marketing Review, 26 (4-5), pp. 384-395; Saberi, S., Nookabadi, A.S., Hejazi, S.R., Applying agent-based system and negotiation mechanism in improvement of inventory management and customer order fulfilment in multi echelon supply chain (2012) Arabian Journal for Science and Engineering, 37 (3), pp. 851-861; Sarkis, J., (2006) Greening the supply chain, , Berlin, Germany: Springer-Verlag; Schone, R., Molka, D., Integrating performance analysis and energy efficiency optimizations in a unified environment (2014) Computer Science - Researchfor Development, 29 (3-4), pp. 231-239; Schwarz, J., Beloff, B., Beaver, E., Use sustainability metrics to guide decision-making (2002) Chemical Engineering Progress, 98 (7), pp. 58-63; Searcy, C., Karapetrovic, S., McCartney, D., Application of a systems approach to sustainable development performance measurement (2008) International Journal of Productivity and Performance Management, 57 (2), pp. 182-197; Seppanen, R., Blomqvist, K., Sundqvist, S., Measuring inter-organizational trust: A critical review of the empirical research in 1990-2003 (2007) Industrial Marketing Management, 36 (2), pp. 249-265; Shank, J.K., Govindarajan, V., (1993) Strategic cost management, , New York, NY: Free Press; Sharma, M.K., Bhagwat, R., Dangayach, G.S., Practice of performance measurement: Experience from Indian SMEs (2005) International Journal of Globalisation and Small Business, 1 (2), pp. 183-213; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shi, X., Liao, Z., The mediating effects of interfirm business process integration and joint teamwork on firm performance in supply chains (2013) Asia Pacific Journal of Management, 30 (4), pp. 1243-1264; Shoushtari, K.D., Redesigning a large supply chain management system to reduce the government administration: A socio-functional systems approach (2013) Systemic Practice and Action Research, 26 (2), pp. 195-216; Singh, A., Narain, R., Yadav, R.C., Benchmarking and performance measurement of supply chain management practices: A survey of Indian organizations (2006) International Journal of Services and Operations Management, 2 (4), pp. 313-334; Soni, G., Kodali, R., Internal benchmarking for assessment of supply chain performance (2010) Benchmarking: An International Journal, 17 (1), pp. 44-76; Srai, J.S., Gregory, M., A supply network configuration perspective on international supply chain development (2008) International Journal of Operations & Production Management, 28 (5), pp. 386-411; Stock, J.R., Boyer, S.L., Harmon, T., Research opportunities in supply chain management (2010) Journal of the Academy of Marketing Science, 38 (1), pp. 32-41; Stonebraker, P.W., Afifi, R., Toward a contingency theory of supply chains (2004) Management Decision, 42 (9), pp. 1131-1144; Strecker, S., Frank, U., Heise, D., Kattenstroth, H., MetricM: A modeling method in support of the reflective design and use of performance measurement systems (2012) Information Systems and e-Business Management, 10 (2), pp. 241-276; Sun, S., Hsu, M., Hwang, W.-J., The impact of alignment between supply chain strategy and environmental uncertainty on SCM performance (2009) Supply Chain Management: An International Journal, 14 (3), pp. 201-244; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40 (16), pp. 4257-4269; Tan, K.C., Kannan, V.R., Handfield, R.B., Ghosh, S., Supply chain management: An empirical study of its impact on performance (1999) International Journal of Operations & Production Management, 19 (10), pp. 1034-1052; Taticchi, P., Balachandran, K., Tonelli, F., Performance measurement and management systems: State of the art, guidelines for design and challenges (2012) Measuring Business Excellence, 16 (2), pp. 41-54; Taticchi, P., Tonelli, F., Cagnazzo, L., A decomposition and hierarchical approach for business performance measurement and management (2009) Measuring Business Excellence, 13 (4), pp. 47-57; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains: A literature review and a research agenda (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 782-804; Vachon, S., Halley, A., Beaulieu, M., Aligning competitive priorities in the supply chain: The role of interactions with suppliers (2009) International Journal of Operations & Production Management, 29 (4), pp. 322-340; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain (2006) Supply Chain Management: An International Journal, 11 (4), pp. 317-327; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, J.P., Designing supply chains: Towards theory development (2006) International Journal of Production Economics, 100 (2), pp. 223-238; Westphal, I., Thoben, K.D., Seifert, M., Managing collaboration performance to govern virtual organizations (2010) Journal of Intelligent Manufacturing, 21 (3), pp. 311-320; Wolf, J., The relationship between sustainable supply chain management, stakeholder pressure and corporate sustainability performance (2014) Journal of Business Ethics, 119 (3), pp. 317-328; Womack, J., Jones, D.T., (1996) Lean thinking: Banish waste and create wealth in your corporation, , London, UK: Simon and Schuster; Womack, J., Jones, D.T., (2005) Lean solutions: How companies and customers can create value and wealth together, , London, UK: Simon and Schuster; Womack, J., Jones, D.T., Roos, D., (1990) The machine that changed the world, , New York, NY: Rawson Associates; Zhu, Q., Dou, Y., Sarkis, J., A portfolio-based analysis for green supplier management using the analytical network process (2010) Supply Chain Management: An International Journal, 15 (4), pp. 306-319; Zokaei, K., Hines, P., Achieving consumer focus in supply chains (2007) International Journal of Physical Distribution & Logistics Management, 37 (3), pp. 223-247; Zokaei, K., Simons, D.W., Value chain analysis in improvement of customer focus: A case study of UK red meat industry (2006) International Journal of Logistics Management, 17 (2), pp. 141-162; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Bai, C., Sarkis, J., Evaluating supplier development programs with a grey based rough set methodology (2011) Expert Systems with Applications, 38 (11), pp. 13505-13517; Bentes, A.V., Carneiro, J., da Silva, J.F., Kimura, H., Multidimensional assessment of organizational performance: Integrating BSC and AHP (2012) Journal of Business Research, 65 (12), pp. 1790-1799; Bjorklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Bruzzone, A.A.G., Anghinolfi, D., Paolucci, M., Tonelli, F., Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops (2012) CIRP Annals - Manufacturing Technology, 61 (1), pp. 459-462; Carbone, V., Moatti, V., Esposito Vinzi, V., Mapping corporate responsibility and sustainable supply chains: An exploratory perspective (2012) Business Strategy and the Environment, 21 (7), pp. 475-494; Colicchia, C., Melacini, M., Perotti, S., Benchmarking supply chain sustainability: Insights from a field study (2011) Benchmarking: An International Journal, 18 (5), pp. 705-732; Dinter, B., Success factors for information logistics strategy: An empirical investigation (2013) Decision Support Systems, 54 (3), pp. 1207-1218; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70 (6), pp. 1088-1100; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; La Rovere, E.L., Soares, J.B., Oliveira, L.B., Lauria, T., Sustainable expansion of electricity sector: Sustainability indicators as an instrument to support decision-making (2010) Renewable & Sustainable Energy Reviews, 14 (1), pp. 422-429; Lee, K.H., Saen, R.F., Measuring corporate sustainability management: A data envelopment analysis approach (2012) International Journal of Production Economics, 140 (1), pp. 219-226; Lehtinen, J., Ahola, T., Is performance measurement suitable for an extended enterprise? (2010) International Journal of Operations & Production Management, 30 (2), pp. 181-204; Manning, L., Corporate and consumer social responsibility in the food supply chain (2013) British Food Journal, 115 (1), pp. 9-29; Mirhedayatian, S.M., Azadi, M., Saen, R.F., A novel network data envelopment analysis model for evaluating green supply chain management (2014) International Journal of Production Economics, 147, pp. 544-554; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decision Support Systems, 54 (4), pp. 1513-1520; Tonelli, F., Evans, S., Taticchi, P., Industrial sustainability: Challenges, perspectives, actions (2013) International Journal of Business Innovation Research, 7 (2), pp. 143-163; Wang, H.F., Hsu, H.W., A closed-loop logistic model with a spanning-tree based genetic algorithm (2010) Computers & Operations Research, 37 (2), pp. 376-389; Yakovleva, N., Sarkis, J., Sloan, T., Sustainable benchmarking of supply chains: The case of the food industry (2012) International Journal of Production Research, 50 (5), pp. 1297-1317},
correspondence_address1={Kasemsap, K.; Suan Sunandha Rajabhat UniversityThailand},
publisher={IGI Global},
isbn={9781522518389; 1522518371; 9781522518372},
language={English},
abbrev_source_title={Decis. Manag.: Concepts, Methodol., Tools, and Appl.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Marwah20172222,
author={Marwah, A.K. and Thakar, G. and Gupta, R.C.},
title={A new approach to supply chain performance measurement: An empirical study of manufacturing organizations},
journal={Decision Management: Concepts, Methodologies, Tools, and Applications},
year={2017},
volume={4-4},
pages={2222-2239},
doi={10.4018/978-1-5225-1837-2.ch105},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021322030&doi=10.4018%2f978-1-5225-1837-2.ch105&partnerID=40&md5=d7aba09ae37b00c314e67f0bbab23812},
affiliation={SGSITS, India},
abstract={Existing research work has established that many of today's manufacturing organizations have failed to develop a comprehensive supply chain performance measures. In this chapter, the authors intend to empirically assess the effects of supplier buyer relations and human metrics on supply chain performance in the context of Indian manufacturing organizations. After rigorous literature review, total 18 variables have been identified which are later on reduced in number by factor analysis. As a pilot study, primary data is collected from 100 manufacturing organizations by means of a questionnaire and a scale is developed. On a sample size of 100, the proposed hypotheses are tested by applying two-tailed tests. t-test and factor analysis resulted in 5 factors, 2 related to supplier-buyer relations and 3 related to human metrics. The overall reliability of the scale comes out to be 0.697. The research work provides a new approach to the manufacturing organizations to understand the factors affecting supply chain performance. The present study is limited to Indian manufacturing organizations. © 2017, IGI Global. All rights reserved.},
references={Autry, C.W., Susan, G., Evaluating buyer-supplier relationship-performance spirals-A longitudinal study (2010) Journal of Operations Management, 24 (2), pp. 87-100; Beamon, B.M., Measuring supply chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Chandra, K., Kumar, S., Supply chain management in theory and practice: A passing fad or a fundamental change? (2000) Industrial Management & Data Systems, 100 (3), pp. 100-113; Chen, J., Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships (2008) Journal of Operations Management, 26 (1), pp. 45-64; Dangayach, G., Deshmukh, S., Evidence of manufacturing strategies in Indian industry: A survey (2003) International Journal of Production Economics, 83 (3), pp. 279-298; A Report on the Summit on Indian Manufacturing Competitiveness (2007) Deloitte Research: Competing in Global Manufacturing and Service Networks, , http://www.deloitte.com/research; Fawcett, S.E., Ogden, J.A., Magnan, G.M., Cooper, M.B., Organisational commitment and governance for supply chain success (2006) International Journal of Physical Distribution & Logistics Management, 36 (1), pp. 22-35; Gunasekaran, A., Supply Chain management: Theory and applications (2004) European Journal of Operational Research, 159 (2), pp. 265-268; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain managements review ofrecent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.A., Framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hair, J., Andeson, R., Black, B., Barry, B., Tatham, R., (2007) Multivariate Data Analysis, , (5th ed.). New York, NY: Macmillan Publishing Company; Humphrey, P.K., Li, W.L., Chan, L.Y., The impact of supplier development on buyer-supplier performance (2004) International Journal of Management Sciences, 32 (2), pp. 131-143; Lee, C.W., Kwon, I.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer (2007) Supply Chain Management: An International Journal, 12 (6), pp. 444-452; Liang, L., Yang, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; Marwah, A.K., Thakar, G., Gupta, R.C., An Empirical Study of Supplier-Buyer Relations as a Key Determinant of Supply Chain Performance of Indian Manufacturing Organizations (2013) Proceedings of the International Conference on Managing Supply Chain for Global Competitiveness, , IIIE; Marwah, A.K., Thakar, G., Gupta, R.C., A Confirmatory Study of Supply Chain Performance and Competitiveness of Indian Manufacturing Organizations (2014) International Journal of Quality Research, 8 (1), pp. 23-38; Mello, J.E., Stank, T.P., Linking firm culture and orientation to supply chain success (2005) International Journal of Physical Distribution & Logistics Management, 35 (8), pp. 542-554; Robinson, C.J., Malhotra, M.K., Defining the concept of supply chain quality management and its relevance to academic and industrial practice (2005) International Journal of Production Economics, 96 (3), pp. 315-337; Sekaran, U., (2003) Research methods for business: A skill-building approach, , John Wiley & Sons; Spekman, R.E., Kamauff, J.W., Jr., Myhr, N., An empirical investigation into supply chain management: A perspective on partnerships (1998) Supply Chain Management: An International Journal, 3 (2), pp. 53-67; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance Measurement and Management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Tony, W., Kevin, H., The human factors in managing China supply chain (2007) Proceedings of the 92nd Annual International Supply Management Conference, pp. 1-6. , Nokia (China) Investment Co. Ltd. Shanghai Branch; Van Hoek, R.I., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (40), pp. 187-192; Wisner, J.D., Fawcett, S.E., Linking firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, 32 (4), pp. 5-11; Wouters, M., A developmental approach to performance measures-results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78; Bititci, U.S., Mendibil, M., Nudurupati, S., Turner, T., Garengo, P., Dynamics of Performance measurement and organizational culture (2006) International Journal of Operations & Production Management, 26 (12), pp. 1325-1350; Cigolini, R., Cozzi, M., Perona, M., A New Framework for Supply Chain Management: Conceptual Model and Empirical Test (2004) International Journal of Operations & Production Management, 24 (1), pp. 7-14; Ellram, L.M., Cooper, M.C., Supply Chain Management, Partnerships, and the Shipper-Third-Party Relationship (1990) The International Journal of Logistics Management, 1 (2), pp. 1-10; Kalwani, M., Narayandas, N., Long-Term Manufacturing-Supplier Relationships: Do They Pay-Off For Supplier Firms? (1995) Journal of Marketing, 59 (1), pp. 1-16; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Lambert, D.M., Cooper, M.C., Issues in Supply Chain Management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Li, S., Rao, S., Ragunathan, T., Ragunathan, B., Development and Validation of A Measurement Instrument for Studying Supply Chain Management Practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Neely, A., Gregory, M., Platts, K., Performance measurement system design-a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Powell, S., The challenges of performance measurement (2004) Management Decision, 42 (8), pp. 1017-1023; Sahay, B.S., Gupta, J.N.D., Mohan, R., Managing Supply Chains For Competitiveness: The Indian Scenario (2006) Supply Chain Management: An International Journal, 11 (1), pp. 15-24; Sahay, B.S., Mohan, R., Supply Chain Management Practices In Indian Industry (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 582-606},
correspondence_address1={Marwah, A.K.; SGSITSIndia},
publisher={IGI Global},
isbn={9781522518389; 1522518371; 9781522518372},
language={English},
abbrev_source_title={Decis. Manag.: Concepts, Methodol., Tools, and Appl.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Singh20171316,
author={Singh, R. and Singh, A.P. and Metri, B.A.},
title={Key performance indicators for the organized farm products retailing in India},
journal={Decision Management: Concepts, Methodologies, Tools, and Applications},
year={2017},
volume={3-4},
pages={1316-1329},
doi={10.4018/978-1-5225-1837-2.ch061},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021311685&doi=10.4018%2f978-1-5225-1837-2.ch061&partnerID=40&md5=a04aac9c7be0f60781a87004a7be65fb},
affiliation={International Management Institute, India; Defense University, Ethiopia},
abstract={The Non-livestock products include Horticulture products (flowers, fruits, nuts, vegetables and medicinal plants) and Agriculture products (Crops like; rice, cotton, wheat). These items share the maximum sale of the farm products. Unfortunately, the farm production in India has witnessed a huge wastage. It has attracted the attention of many practitioners and policy makers. Witnessing the opportunity many organized retail players have entered the arena to sell farm products. However, the supply chain (SC) performance measurement has remained the major challenge as "No measurement no improvement". Many organizations are searching for an efficient SC performance measurement system. Our study recommends that the SC performance shall be improved by developing a SC strategy based on a limited set of key performance indicators (KPI). Otherwise, managers shall waste time and resources on the undesirable performance indicators. We have identified and classified the KPI for non-livestock retailing SC management into five groups. These are 1) Customer Attraction Metrics (product quality, product personality, process quality); 2) Inventory Metrics (fill rate, customer response time, return adjustment, spoilage adjustment, and Vendor managed inventory); 3) Attractiveness Metrics (inventory cost, distribution cost, Return on investment, stakeholder value, sales profit and channel flexibility); 4) Transportation Metrics (shipping errors, and volume flexibility); and 5) Customer Metrics (lead time, delivery flexibility, and backorder flexibility). This grouping shall help the practitioners to focus on a limited set of KPI for better management of supply chains. © 2017, IGI Global. All rights reserved.},
references={Abbas, F., Mitchell, L., Mehmet, C.K., Streamlining Supply Chain Management with EBusiness (2002) Proceedings:38th MBAA Annual Meeting; Anand, N., Grover, N., Measuring retail supply chain performance: Theoretical model using key performance indicators (KPIs) (2015) Benchmarking International Journal (Toronto, Ont.), 22 (1), pp. 135-166; Angulo, A., Nacthmann, H., Waller, M.A., Supply chain information sharing in a vendor managed inventory partnership (2004) Journal of Business Logistics, 25 (1), pp. 101-120; Aramyan, L., (2006) Performance Indicators in Agri-Food Production Chains, , Springer; Arpita, K., Supply Chain Management: Improving the efficiency (2006) Synergy Journal of ITS; Artiuch, P., Kornstein, S., (2012) Sustainable approaches to reduce agriculture waste in India, , http://web.mit.edu/CoLab/pdf/papers/Reducing_Food_Waste_India.pdf, MIT; Beamon, B.M., Supply Chain Design and Analysis: Models and Methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring Supply chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B.M., Measuring Supply chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Blenkinsop, S.A., Burns, N., Performance measurement revisited (1992) International Journal of Operations & Production Management, 12 (10), pp. 16-25; Bookbinder, J.H., Gumus, M., Jewkes, E.M., Calculating the benefits of vendor managed inventory in a manufacturer-retailer system (2010) International Journal of Production Research, 48 (19), pp. 5549-5571; Brown, N., Laverick, S., Measuring Corporate Performance (1994) Long Range Planning, 27 (4), pp. 89-98; Bunte, F.M., Mulder, F., van Tongerenen, J.J., (1998) Meting van de performance van agrarische produktiekolommen, , Onderzoekverslag 163. LEI; Caplice, C., Sheffi, Y., A review and evaluation of logistic metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; Chapman, R., Soosay, C., Kandampully, J., Innovation in Logistics Services and the New Business Model: A Conceptual Framework (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 630-650; (1995) World class Logistics: The Challenge of Managing Continuous Change, , Oak Books, IL: CLM; Cronabach, L., (1990) Essentials of Psychological Testing, , New York: Harper and Row; Gates, S., (1999) Aligning Strategic Performance Measures and Results, , New York, NY: The Conference Board; Gilmour, P.A., Strategic Audit Framework to Improve Supply Chain Performance (1999) Journal of Business & Industrial Marketing, 14 (5-6), pp. 355-363; Hwang, Y.M., Rho, J.J., RFID system for centralized reverse supply chain in the apparel industry (2008) Proceedings of the 22nd International Symposium and Workshop on Global Supply Chain, Intermodal Transportation and Logistics, , https://www.utoledo.edu/research/ututc/docs/Proceedings_Busan_Symposium_20.pdf; (2015), http://www.ibef.org/industry/food-industry-presentation; Ittner, C.D., Larcker, D.F., Quality strategy, strategic control systems, and organizational performance (1997) Accounting, Organizations and Society, 22 (3-4), pp. 293-314; Ittner, C.D., Larcker, D.F., Are non-financial measures leading indicators of financial performance? An analysis of customer satisfaction (1998) Journal of Accounting Research, 36, pp. 1-35; Ittner, C.D., Larcker, D.F., Assessing empirical research in managerial accounting: A value-based management perspective (2001) Journal of Accounting and Economics, 32 (1), pp. 349-410; Ittner, C.D., Larcker, D.F., Determinants of performance measure choices in worker incentive plans (2002) Journal of Labor Economics, 2, pp. S58-S90; Ittner, C.D., Larcker, D.F., Measuring the impact of quality initiatives on firm financial performance (1996) Advances in the Management of Organizational Quality, p. 37; Ittner, C.D., Larcker, D.F., Rajan, M.V., The choice of performance measures in annual 44 bonus contracts (1997) Accounting Review, 72, pp. 231-255; Ittner, C.D., Larcker, D.F., Randall, T., (2002) Performance implications of strategic performance measurement in financial services firms, , Wharton School Working Paper; Kaplan, R., Norton, D., Using the Balance Scorecard as Strategic Management System (1996) Harvard Business Review, pp. 75-85. , Jan-Feb; Kaplan, R., Norton, D., (2001) The Strategy-Focused Organisation, , Harvard Business School Press; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79. , PMID:10119714; Krajewski, L., Ritzman, L., (2002) Operations Management: Strategy and Analysis, , (6th ed.). New York, NY: Prentice-Hall; Lee, H., Billington, C., Managing supply Chain Inventory: Pitfalls and Opportunities (1992) Sloan Management Review, 3, pp. 65-73; Lipe, M., Salterio, S., The balanced scorecard: Judgmental effects of common and unique performance measures (2000) Accounting Review, 75 (3), pp. 283-298; Lipe, M., Salterio, S., A note on the judgmental effects of the balanced scorecards information organization (2002) Accounting, Organizations and Society, 27 (6), pp. 531-540; Luning, P.A., Marcelis, W.J., Jongen, W.M.F., (2002) Food Quality Management: A techno-managerial approach, , Wageningen: Wageningen Pers; Mohd, N.F., Banwet, D.K., Ravi, S., Information risks management in supply chains: An assessment and mitigation framework (2007) Journal of Enterprise Information Management, 20 (6), pp. 677-699; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Pramod Kumar Mishra, B., Consumer behaviour, customer satisfaction vis-a-vis brand performance: An empirical study of dairy food supply chain in India (2013) International Journal of Indian Culture and Business Management, 7 (3), pp. 399-412. , January; Purushottam, L., Measuring satisfaction in buyer supplier relationship from supplier-sperspective (2012) International Journal of Business Performance and Supply Chain Modelling, 4 (1), pp. 60-74. , January; Saab, C., Jr., Bowers, Optimising performance with distributor managed inventory in a FMCG supply chain (2008) International Journal of Business Excellence, 1 (1-2), pp. 121-138; Shahin, A., Jaferi, F., The shortest route for transportation in supply chain by minimum spanning tree (2015) International Journal of Logistics Systems and Management, 22 (1), pp. 43-54. , January; Simon, H.A., Information Processing Models of Cognition (1979) Annual Review of Psychology, 30 (1), pp. 363-396. , PMID:18331186; Simons, R., (2000) Performance Measurement & Control Systems for Implementing Strategy: Text & Cases, , Upper Saddle River, NJ: Prentice Hall; Song, L., (2008) Supply Chain Management with Demand Substitution, , (PhD Thesis). University of Tennessee, Knoxville, TN; Speakman, R., An Empirical investigation into Supply Chain Management: A perspective on partnerships (1998) International Journal of Physical Distribution & Logistics Management, 28 (8), pp. 630-650; Suhong, L., The impact of supply chain management practices on competitive advantage and organizational performance (2004) International Journal of Management Sciences, 11, pp. 112-119; (2015) Retail Operations Benchmarking and Excellence Survey 2015, Measuring Supply Chain Performance, , httpwww.tcs.com/SiteCollectionDocuments/White%20Papers/Retail-Operations-BenchmarkingExcellence-Survey-Measuring-Supply-Chain-Performance-0215-1.pdf; India wastes 21 million tonnes of wheat every year: Report http://timesofindia.indiatimes.com/india/India-wastes-21-million-tonnes-of-wheat-every-year-Report/article-show/17969340.cms; van der Vorst, J.G.A.J., (2000) Effective food supply chains: Generating, modeling and evaluating supply chain scenarios, , Proefschrift Wageningen; Van Hoek, R., Measuring the measurable-measuring and improving performance in supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Venkatraman, N., Ramanuja, V., Measurement of business performance in strategy research: A comparison of approaches (1986) Academy of Management Review, 11 (4). , July/October; Verville, J., Taskin, N., Law, S., Buyer-supplier relationships in supply chain management: Relationship, trust, supplier involvement, and performance (2011) International Journal of Agile Systems and Management, 4 (3), pp. 203-221. , January; Wu, J., Li, J., Chen, J., Zhao, Y., Wang, S., Risk management in supply chains (2011) International Journal of Revenue Management, 5 (2-3), pp. 157-204. , January},
correspondence_address1={Singh, R.; International Management InstituteIndia},
publisher={IGI Global},
isbn={9781522518389; 1522518371; 9781522518372},
language={English},
abbrev_source_title={Decis. Manag.: Concepts, Methodol., Tools, and Appl.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Vishwasrao20171723,
author={Vishwasrao, R. and Sabri, E.},
title={Supply chain performance measurement and organizational alignment},
journal={Decision Management: Concepts, Methodologies, Tools, and Applications},
year={2017},
volume={4-4},
pages={1723-1749},
doi={10.4018/978-1-5225-1837-2.ch081},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021310002&doi=10.4018%2f978-1-5225-1837-2.ch081&partnerID=40&md5=48f2435198ab4f2a1f392865a6d0db3c},
affiliation={JDA Software, United States; University of Texas - Dallas, United States},
abstract={The authors describe how organizations can leverage a maturity model approach in conjunction with foundational concepts of perspective-based performance evaluation models like Balanced Scorecard (BSC) to define comprehensive performance measurement framework. Maturity models are essentially a reflection of theories about how the capabilities of an organization evolve. Therefore, by design, they provide a roadmap to the next level of performance. The authors propose using maturity models as a structured way of identifying current capability or maturity level of any supply chain. They then provide guidance on selecting the right "causal linkages" between supply chain objectives and performance measures and define a mechanism for specifying more granular definitions of measures linked to strategic objectives for progressive levels of maturity. The authors survey widely used business process maturity models, current practices for measuring operational metrics, and then present a tiered framework for operational metric alignment and KPI governance, based on perspective-based design principles. © 2017, IGI Global. All rights reserved.},
references={Akyuz, G., Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Analytics, J., (2009) White paper: A guide to creating dashboards people love to use, , http://goo.gl/QGGHty; Bell, C., Higgs, R., Vickers, S., Toncinich, S., Haslett, T., (2003) Using system modelling to understand the dynamics of supply chains, , Monash University. Working Paper 70/03; Bernard, M., (2012) Key performance indicators (KPIs): The 75 measures every manager needs to know, , http://goo.gl/v7r4hZ, FT Press; Camerinelli, E., Cantu, A., Measuring the value of the supply chain: A framework (2006) Supply Chain Practice, 8 (2), pp. 40-59; Campuzano Bolarín, F., Lisec, A., Esteban, F., Inventory cost consequences of variability demand process with a multi-echelon supply chain (2008) Logistics & Sustainable Transport, 1 (3), pp. 1-12; Davenport, T., Competing on analytics (2006) Harvard Business Review, , January; Dashboards demystified, , http://goo.gl/OriOSu; Dykes, B., (2011) Web analytics action hero: Using analysis to gain insight and optimize your business, , http://goo.gl/OWZSKn, Adobe Press; Eckerson, W., (2009) Performance management strategies: How to create and deploy effective metrics, , TDWI Best Practices Report Q1 2009; (2012) White paper: Business intelligence platforms for mid-size organizations: Comparing Birst, MicroStrategy, Oracle and SAP Business Objects, , http://goo.gl/lm2XRC; Fettke, P., Loos, P., Zwicker, J., Business process reference models: Survey and classification (2005) Paper presented at the Third International Conference on Business Process Management (BPM), , http://goo.gl/TQILgk, Nancy, France; Geethan, K.A.V., Jose, S., Koshy, A., Samuel, A.B., Methodology for study of characteristics and time reduction in reverse supply chain using system dynamics (2011) Proceedings of International Conference on Sociality and Economics Development, , IPEDR; What is analytics?, , http://goo.gl/odWhqS; Kaplan, R.S., (2010) Conceptualfoundations of the balanced scorecard, , http://ssrn.com/abstract=1562586, Harvard Business School Accounting & Management Unit Working Paper No. 10-074; Melnyk, S., Stewart, D., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-217; Melnyk, S.A., Calantone, R.J., Luft, J., Stewart, D.M., Zsidisin, G.A., Hanson, J., Burns, L., An empirical investigation of metrics alignment process (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 312-324; Mulcahy, R., Business intelligence definitions and solutions (2010) CIO Magazine, , http://goo.gl/VA8Wzx; Najmi, A., Gholamian, M., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) Journal of Operations and Supply Chain Management, 6 (2), pp. 94-113; Netland, T., Alfnes, E., A practical tool for supply chain improvement - Experiences with the supply chain maturity assessment test (SCMAT) (2008) Paper presented at the EurOMA/POMS/JOMSA Conference, , Tokyo, Japan; Netland, T.H., Alfnes, E., Fauske, H., How mature is your supply chain? A supply chain maturity assessment test (2007) Paper presented at the EurOMA Conference, , Ankara, Turkey; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Porter, M.E., What is strategy? (1996) Harvard Business Review, p. 4. , (November - December); Pruyt, E., (2013) Small system dynamics models for big issues: Triple jump towards real - world complexity. Delft: TU Delft Library, , http://goo.gl/ge98Wt; Röglinger, M., Pöppelbuß, J., Becker, J., Maturity models in business process management (2012) Business Process Management Journal, 18 (2), pp. 328-346; Sabri, E., Enduring transformation (2012) APICS Magazine, pp. 45-47. , September-October; Sun, H., Ren, Y., Yeo, K., Capability maturity model for supply chain management (2005) Paper presented at the International Conference on Management Science and Applications, , Chengdu, China; (2014) Supply Chain Council (SCC), , https://supply-chain.org/; Thakkar, J., Kanda, A., Deshmukh, S., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking International Journal (Toronto, Ont.), 16 (5), pp. 702-723; van Looy, A., (2013) BPMM sample (N=69), , http://goo.gl/dbiVkQ; van Looy, A., Which business process maturity model best fits your organization? (2013) BPTrends, , http://goo.gl/1yCkEA; van Looy, A., (2014) Business process maturity: A comparative study on a sample of business process maturity models, , Spinger; Wienberger, D., The problem with the data-information-knowledge-wisdom hierarchy (2010) HBR Blog Network, , http://goo.gl/tDtQQD; Wilson, G., Designing and building great dashboards - 6 golden rules to successful dashboard design (2012) Geckoboard Blog, , http://goo.gl/cJLCLK},
correspondence_address1={Vishwasrao, R.; JDA SoftwareUnited States},
publisher={IGI Global},
isbn={9781522518389; 1522518371; 9781522518372},
language={English},
abbrev_source_title={Decis. Manag.: Concepts, Methodol., Tools, and Appl.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Ozcan20172805,
author={Ozcan, S. and Ozyazici, M.S. and Ozerdem, M.B.},
title={Academic performance management policy for changing roles of universities in innovation systems},
journal={PICMET 2016 - Portland International Conference on Management of Engineering and Technology: Technology Management For Social Innovation, Proceedings},
year={2017},
pages={2805-2809},
doi={10.1109/PICMET.2016.7806533},
art_number={7806533},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016199845&doi=10.1109%2fPICMET.2016.7806533&partnerID=40&md5=b9453e1fbd0562dd74c95a0bdc68abf0},
affiliation={Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey; Faculty of Engineering and Computer Science, Izmir University of Economics, Izmir, Turkey},
abstract={The purpose of this study is to establish a new performance measurement method for academic actors for their changing roles in innovation systems. The widely accepted triple helix and systems of innovation models show changing and overlapping roles of academic, industrial and governmental actors. In previous innovation systems, universities were not focused on applied research and technology transfer as much as they are now. Current literature shows a changing role of universities and importance of their involvement in innovation systems. Although academic organizations' roles have changed in innovation systems, academic performance measurement systems (APMS) are not adapted to examine innovation related performance factors. Many APMS focus on key performance indicators (KPIs) such as; publications, research projects and patents. However, the new APMS needs to assess the activities and processes that are related to innovation, such as; technology transfer processes, collaborative innovation activities, consultancies and academic spin-offs. For this study a new APMS is applied according to the needs of universities by using a synthetic data based on an engineering department's KPIs. APMS scores are calculated based on the cumulative metric of all research and innovation activities and, weighted according to the needs and considerations of the university. The results of this study show that many of those academicians who have great performance in publications and academic research do not necessarily have high-level performance in innovative activities. In fact the results show that those who had high points in some measurements have very low performance in others. For the management point of view, it may be more effective to position academicians for different roles and assess their performance accordingly as innovation-targeted, teaching-targeted and research-targeted academicians. © 2016 Portland International Conference on Management of Engineering and Technology, Inc.},
keywords={Benchmarking;  Education;  Patents and inventions;  Societies and institutions;  Supply chain management;  Technology transfer, Academic performance;  Collaborative innovation;  Engineering department;  Innovation activity;  Innovative activities;  Key performance indicators;  Performance factors;  Performance measurements, Innovation},
references={Gulbrandsen, M., Smeby, J.C., Industry funding and university professors' research performance (2005) Research Policy, 34, pp. 932-950; Glenna, L.L., Welsh, R., Ervin, D., Lacy, W.B., Biscotti, D., Commercial science, scientists' values, and university biotechnology research agendas (2011) Research Policy, 40, pp. 957-968; Van Looy, B., Ramga, M., Callaert, J., Debackere, K., Zimmermann, E., Combining entrepreneurial and scientific performance in academia: Towards a compounded and reciprocal Matthew-effect (2004) Research Policy, 33, pp. 425-441; Etzkowitz, H., Leydesdorff, L., The dynamics of innovation: From National Systems and, Mode 2 to a Triple Helix of university-industry-government relations (2000) Research Policy, 29, pp. 109-123; Amabile, T.M., Motivational synergy: Toward new conceptualizations of intrinsic and extrinsic motivation in the workplace (1993) Human Resource Management Review, 3 (3), pp. 185-220; Harackiewicz, J.M., (2000) Intrinsic and Extrinsic Motivation: The Search for Optimal Motivation and Performance, , Academic Press; Frey, B.S., Osterloh, M., Successful management by motivation: Balancing intrinsic and extrinsic incentives (2001) Springer Science & Business Media; Deci, E.L., Koestner, R., Ryan, R.M., A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation (1999) Psychological Bulletin, 125 (6), p. 627; Wiersma, U.J., The effects of extrinsic rewards in intrinsic motivation: A meta-analysis (1992) Journal of Occupational and Organizational Psychology, 65 (2), pp. 101-114; Saracli, S., Keskinturk, T., Cicek, M., Gazeloglu, C., Examining the effects of organizational commitment on intrinsic and extrinsic motivation via structural equation modelling: Turkish academicians case (2014) Fen Bilimleri Dergisi, 2 (2); Berkeley, U.C., (2010) Guide to Managing Human Resources; Neely, A., (2008) Does the Balance Scorecard Work: An Empirical Investigation; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal OfBusiness Performance Management, 5 (1), pp. 1-24; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationships, , Financial Times/Prentice Hall, London; (2003) EFQM Homepage, , www.efqm.org, EFQM; Striteska, M., Spickova, M., Review and comparison of performance measurement systems (2012) Journal of Organizational Management Studies, 2012, p. 1; Neely, A., (2007) Business Performance Measurement: Unifying Theory and Integrating Practice, , Cambridge University Press; Al-Turki, U., Duffuaa, S., Performance measures for academic departments (2003) International Journal of Educational Management, 17 (7), pp. 330-338; Masron, T.A., Ahmad, Z., Rahim, N.B., Key performance indicators vs key intangible performance among academic staff: A case study of a public university in Malaysia (2012) Procedia-Social and Behavioral Sciences, 56, pp. 494-503},
editor={Anderson T.R., Kocaoglu D.F., Niwa K., Perman G., Kozanoglu D.C., Daim T.U.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781509035953},
language={English},
abbrev_source_title={PICMET - Portland Int. Conf. Manage. Eng. Technol.: Technol. Manage. Soc. Innov., Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Serrou201764,
author={Serrou, D. and Abouabdellah, A.},
title={Proposal for a multidimensional approach for supply chain performance measurement: Application to the hospital sector in Morocco [Proposition d’une approche multidimensionnelle pour la mesure de performance de la chaîne logistique: Application au secteur hospitalier au Maroc]},
journal={Journal of Decision Systems},
year={2017},
volume={26},
number={1},
pages={64-92},
doi={10.1080/12460125.2016.1232531},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991205098&doi=10.1080%2f12460125.2016.1232531&partnerID=40&md5=2df20b883d3a4d2f324454f60e01f26b},
affiliation={Laboratoire génie des systèmes Equipe, MOSIL ENSA, Kénitra, Morocco},
abstract={Logistics allows the hospital to reduce the financial impact of the consumption of the products, reduce inventory, reduce waste and provide better inventory tracking and product traceability services. In this paper, we focus on the development of an approach for measuring the performance of hospital supply chain via dimensions Cost, Safety, Quality, Delay. The first part of the article shows the definition and value of hospital logistics in hospital. The second part present the problems of hospital logistics and the literature on the evaluation of performance. The last part explains the contribution and the steps of our approach through the implementation of our support system the decision to hospital in Morocco. © 2016 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={Centralization;  Dashboard;  hospital logistics, Functional analysis, performance indicators, Indicators;  lead time;  quality;  security},
keywords={Image quality;  Quality control;  Supply chains, Centralization;  Dashboard;  Leadtime;  Performance indicators;  security, Hospitals},
references={Abouabdellah, A., Cherkaoui, A., Decision support system for predicting the degree of a cancer patient’s empowerment (2014) Journal of Theoretical and Applied Information Technology, 60. , 28th February; Abu Bakar, A.H., Lukman Hakim, I., Chong, S.C., Lin, B., Measuring supply chain performance among public hospital laboratories (2010) International Journal of Productivity and Performance Management, 59, pp. 75-97; Aloui, S., (2007) Contribution à la modélisation et l’analyse du rique dans une organisation de santé au moyen d’une approche système.thèse Ecoles des Mines de paris; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) International Journal of Business Performance and Supply Chain Modelling, 1, pp. 115-128; (2002) Résultats de l’enquête nationale “, Colloque : “ l’hôpital et la fonction logistique “, 21 mai 2002, Hôpital Expo; Barney, J., Organizational Culture: Can it be a Source of Sustained Competitive Advantage? (1986) The Academy of Management Review, 11, pp. 656-665; Bhagwat, R., Chan, F.T.S., Sharma, M.K., Performance measurement model for supply chain management in SMEs (2008) International Journal of Globalisation and Small Business, 2, pp. 428-445; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning & Control, 20, pp. 678-690; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28, pp. 249-260; Brewer, P., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logitics, 21, pp. 75-93; Chow, G., Heaver, T., Henriksson, L., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution and Logistics Management, 24; (1996) EHCR, Efficient Healthcare Consumer Response: Improving the Efficiency of the Healthcare Supply Chain; Delomenie, P., Yahiel, M., (1996) Dépenses de médicaments à l’hôpital, la documentation française, , Paris:; Di Martinelly, C., Guinet, A., Riane, R., (2005) Chaîne logistique en milieu hospitalier : modélisation des processus de distribution de la pharmacie, , Proceedings of the International Industrial Engineering Conference, Besançon-France:; Donabedian, A., (1980) Explorations in quality assessment and monitoring, vol. 1, The definition of quality and approaches to its assessment, Health Administration Press; (2009) la logistique hospitalière, , http://www.insset.u-picardie.fr/file/rapport_sdl/La%20Logistique%20Hospitalie%CC%80re.pdf; Giuliani Citton, M.-A., Grandhaye, J.P., Abouabdellah, A., Cherkaoui, A., The empowerment of patients factoring: Reporting to a Holonic approach Conference on Service Systems and Service Management; ICSSSM’06. Volume: 1, On page (s): 398-403. ISBN: 1-4244-0450-9. INSPEC Accession Number: 9398031. Digital Object Identifier: 10.1109/ICSSSM.2006.320647; Groene, O., Klazinga, N., The World Health OrganizationPerformance Assessment Tool for Quality Improvement in Hospitals (PATH): an analysis of the pilot implementation in 37 hospitals (2008) Int J Qual Health Care, 20, pp. 155-161; Hassan, T., (2006) Logistique hospitalière : organisation de la chaîne logistique pharmaceutique aval et optimisation des flux de consommables et des matériels à usage unique; Henning, W.K., The financial impact of materials management (1980) Healthcare Financial Management, 34, pp. 36-42; Housley, C.E., (1978) Hospital Materiel Management, p. 353. , Germantown: Aspen Systems Corporation; Hwang, Y., Lin, Y., Lyu, J., Jr., The performance evaluation of SCOR sourcing process–the case study of Taiwans TFT-LCD industry (2008) International Journal of Production Economics, 115, pp. 411-423; Ibn El Farouk, I., Talbi, A., Jawab, F., (2012) Modélisation des processus logistiques : quel rôle pour l’évaluation de la performance ? Application du modèle SCOR Laboratoire Productique, Energie et Développement Durable (LPE2D) Laboratoire Management International, Techniques de Décision et Logistique (MIDLOG) Logistiqua; Jean-phillipe, C., DHAEVERS Véléri. RIANE Fouad (2008), « l’art du pilotage de la performance vers une démarche instrumentalisée pour le milieu hospitalier » GISEH, actes sur CD ROM; Kaplan, R.S., Norton, D.P., (1996) Le tableau de bord prospectif, traduit de l’américain « The Balanced Scorecard »; Landry, S., Beaulieu, M., en collaboration avec Terry Friel et Claude R. Duguay Étude internationale des meilleures pratiques de logistique hospitalière, Montréal, Cahier de recherche no 00-05 Avril 2000 Cahier 00-05, 118 p. Un projet financé par la société SIBN. ISSN : 1485-5496; Landry, S., Beaulieu, M., (2003) Healthcare Logistics in Japan, , Montréal, Groupe de, recherche CHAÎNE, cahier de recherche n° 03-06e; Macron, M., (2008) gestion et performance des systèmes hospitaliers, LAVOISIE; Marie-Hélène, J., Beaulieu, M., Boivin, A., (2003) gérer la performance de la logistique hospitalière, « cahier de recherche n° 03-02 », Ecole des HEC MONTEREAL, série des cahiers de recherche du groupe CHAINE; Marty, C., Merlin, H., mesurer la performance des hôpitaux publics en France. Fondement méthodologiques, proposition d’un modèle d’évaluation, enjeux (2007) cahier de recherche, HEC PARIS; Matthieu, L., Méthodes de diagnostic et d’évaluation de performance pour la gestion de chaîne logistique : application à la coopération maison-mère-filiales internationales dans un groupe pharmaceutique et cosmétique”, thèse de doctorat présentée au centre de génie industriel de l’Ecole des Mines d’Albi-Garmaux, Institut National Polytechnique de Toulouse (INPT), soutenue le 07 Juillet 2004 (2004) N d’ordre, 2119, p. 192P; Outils d’aide à la décision basés sur la simulation pour la logistique hospitalière, application à un nouvel hôpital (2008) « Thèse » Université Blaise Pascal–Clermont-Ferrand II; Mebrek, F., (2008) Outils d’aide à la décision basés sur la simulation pour la logistique hospitalière, application à un nouvel hôpital, , https://tel.archives-ouvertes.fr/tel-00731433; Morgan, J., How effective are your measurement systems (2000) Purchasing, 129, pp. 26-28; (2003) Clinical Outcomes Group.Clinical Outcomes Indicators; Pierre, V., (2006) tableaux de bord de gestion et indicateurs de performance, , Presse de l’université de Québec; Pons, S., Petit, A., Carbonel, V., Pierrefitle, S., (2005) Gestion liés à l’achat des dispotifs médicaux : ITBM-RBM 26 (2005) 163-169, , Elsevier; Rakotondranaivo, A., Augustin, M.D., Supplychain et innovation : quelles perspectives pour l’organisation des flux des médicaments anticancéreux de l’hôpital à domicile, , GISEH’2012, Québec, Canada:; Rivard-Royer, H., Landry, S., Beaulieu, M., (2002) Hybrid Stockless: A case study, lessons from health-care Sypply Chain Integration, international Journal of operations and Production Management, 22, pp. 412-424; Roussel, P., Guez, M.C., (2008) Moll, methods and tools for risk management in heathcare organisations, , Elsevier; Serrou, D., Abouabdellah, A., Measuring the financial performance of the stock in the process of grouping pharmacies (2015) International Journal of Applied Engineering Research, 10; Serrou, D., Abouabdellah, A., Mesure de la performance de la chaîne logistique hospitalière en intégrant les dimensions coûts, Sécurité et Qualité : Application en cas du regroupement des pharmacies”. Conception et production intégrée (CPI, 2015) 2–4 décembre 2015, , Maroc: Tanger; Serrou, D., Abouabdellah, A., (2015) Study of fiscal improved performance of hospital supply chain pharmacies by centralizing, , University of Lorraine, Metz, France:; Serrou, D., Abouabdellah, A., (2015), Study pharmacies grouping impact on the performance of the hospital supply chain the 6th IESM Conference, Seville, Spain:; Serrou, D., Abouabdellah, A., Logistics in the hospital: Methodology for measuring performance (2016) ARPN Journal of Engineering and Applied Sciences, 11; Serrou, D., Abouabdellah, A., Management of hospital supply chain: New methodology for improving the performance of the maintenance of medical devices ARPN (2016) Journal of Engineering and Applied Sciences, 15; Serrou, D., Abouabdellah, A., Mharzi, H., Analysis of the pharmaceutical supply chain by the FMEA method: Case hospital Ibn Sina child-Rabat (2014) International Journal of Scientific & Engineering Research, 5; Serrou, D., Abouabdellah, A., Mharzi, H., Proposed an approach for measuring the performance of hospital logistics systems by integrating quality, safety and environment (2015) International Journal of Scientific Engineering and Technology, 1; Smith, C., « Analyse des performances pour l’amélioration des systèmes de santé : expériences, défis et perspectives » (2008) conférence ministérielle européenne de l’OMS sur les systèmes de santé 25-27 Juin 2008; Zambo, N., (2003) « l’évaluation de la performance : aspects conceptuels » CAFRAD/ACBF Séminaire sur l’Evaluation de la Performance et le Développement du Secteur Public},
correspondence_address1={Serrou, D.; Laboratoire génie des systèmes Equipe, Morocco; email: d.serrou@gmail.com},
publisher={Taylor and Francis Ltd.},
issn={12460125},
language={English},
abbrev_source_title={J. Decis. Syst.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Adomavičiūtė201745,
author={Adomavičiūtė, D. and Daujotaitė, D.},
title={Business Performance Assessment in the Customs Administrations Activity and Trade Facilitation Measures},
journal={Eurasian Studies in Business and Economics},
year={2017},
volume={6},
pages={45-55},
doi={10.1007/978-3-319-50164-2_3},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087645558&doi=10.1007%2f978-3-319-50164-2_3&partnerID=40&md5=55b50f3cb9e43e3c8947307ccf823a27},
affiliation={Faculty of Economics and Finance Management, Mykolas Romeris University, Vilnius, Lithuania},
abstract={Nowadays customs underlines the importance of investment as a means of making a considerable contribution to economic development and the protection of society from security threats. Customs administrations are implementing trade facilitation measures and began assessing the activities of the business companies, pursuing to contract the status of the authorized economic operator. In order to capture the overall view and variations on customs’ capacities the need of the exploration of business performance appeared. Trade costs are growing in the value chain at every stage, when goods crossed state’s borders multiple times. Reviewing the empirical literature of business performance assessment issues, it can be said that emphasize different researches aspects. This paper analyses the role of customs administrations to secure and facilitate legitimate trade in international supply chain and seeks to encourage a debate to consider the fundamental elements of business performance measurement systems. © 2017, Springer International Publishing AG.},
author_keywords={AEO program;  Business performance assessment;  Customs;  Trade facilitation},
references={Anderson, J.E., van Wincoop, E., Trade costs (2004) Journal of Economic Literature, 42 (3), pp. 691-751; Arvis, J.-F., Duval, Y., Shepherd, B., Utoktham, C., (2013) Trade Costs in the Developing World: 1995–2010, , (Policy Research Working Paper No. 6309, Washington, DC: World Bank; Atkinson, A.A., Strategic performance measurement and incentive compensation (1998) European Management Journal, 16 (5), pp. 552-561; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations and Production Management, 17 (5-6), pp. 522-534; (1992) Council Regulation (EEC) No 2913/92, European Commission, , http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:1992R2913:20050511:LT, [online]. Accessed August 14, 2015, from; (2015) Facts and Figures. EK, , http://ec.europa.eu/taxation_customs/resources/documents/customs/policy_issues/facts_and_figures/facts_figures_en.pdf, Accessed August 23, 2015, from; AEO Guidelines, , zhttp://ec.europa.eu/taxation_customs/resources/documents/customs/policy_issues/customs_security/aeo_guidelines2012_en.pdf, [pdf]. Accessed May 26, 2015, from; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Towards a definition of a business performance measurement system (2007) International Journal of Operations and Production Management, 27 (8), pp. 784-801; Kaplan, R.S., Norton, D.P., (2001) The Strategy-Focused Organization, , Boston, MA: Harvard Business School Press; Melitz, M.J., The impact of trade on intra-industry reallocations and aggregate industry productivity (2003) Econometrica, 71 (6), pp. 1695-1725; Meyer, M.W., Finding performance: The new discipline of management (2002) Business Performance Measurement: Theory and Practice, , Cambridge: Cambridge University Press; Ndonga, D., (2015) Single Windows and Trade Facilitation. a Tool for Development, , Leiden: Wolters Kluwer; Novy, D., Gravity redux: Measuring international trade costs with panel data (2013) Economic Inquiry, 51 (1), pp. 101-121; Otley, D.T., Performance management: A framework for management control systems research (1999) Management Accounting Research, 10 (4), pp. 363-382; (2002) What is EVA?, , http://www.sternstewart.com/evaabout/whatis.shtml, Accessed November 21, 2014, from; Widdowson, D., Preece, R., (2011) Post Clearance Audit: Reference and Implementation Guide, , http://siteresources.worldbank.org/INTRANETTRADE/Resources/239054-1305664393028/PostClearanceAudit_web.pdf, Washington, DC: World Bank. Accessed May 26, 2015, from; (2012) In the 21St Century, Economic Globalization Remains the Main Theme for World Economic Development. WCO News, N° 69 [Pdf]., , http://www.wcoomd.org/en/media/wco-news-magazine/~/media/CA4C731ECE724D46A3A99BA69F0CB13B.pdf, Accessed June 07, 2016, from; Framework of standards to secure and facilitate global trade (2005) Brussels: World Customs Organization; Framework of standards to secure and facilitate global trade (2015) Brussels: World Customs Organization; (2015) Speeding up Trade: Benefits and Challenges of Implementing the WTO Trade Facilitation, , https://www.wto.org/english/res_e/booksp_e/world_trade_report15_e.pdf, [pdf]. Accessed November 21, 2015, from; Yi, K.-M., Can multistage production explain the home bias in trade? (2010) American Economic Review, 100 (1), pp. 364-393},
correspondence_address1={Daujotaitė, D.; Faculty of Economics and Finance Management, Lithuania; email: dalidauj@takas.lt},
publisher={Springer Science and Business Media B.V.},
issn={23645067},
language={English},
abbrev_source_title={Eurasian. Stud. Bus. Econ.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Gawankar2017668,
author={Gawankar, S. and Kamble, S. and Gawankar, S. and Kamble, S.},
title={Effect of supply chain performance measures on supply chain profitability: An empirical investigation using structural equation modelling in Indian retail sector},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2017},
volume={2017},
number={JUL},
pages={668-669},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85066972802&partnerID=40&md5=0eae56dacbef38ca88d1385423e3a273},
affiliation={Robins School of Business University of Richmond, 28 Westhampton Way, Richmond, VA  23173, United States; National Institute of Industrial Engineering (NITIE), Vihar Lake Marg Mumbai, Maharashtra, 400087, India},
abstract={The purpose of the research is to investigate relationship between two major constructs (supply chain performance measures and supply chain profitability) as supply chain has a key role to play in the expansion and profitability of retailers and it also provides a recognized foundation for retail industry success, the most significant challenge that impedes by R-SCM is the development of an efficient and modern retail sector is an underdeveloped supply chain, thus this research provides a model using rigorous empirical method to validate the instrument scale for measuring the validity and reliability of the identified constructs. Additionally the paper further tests the relationship between supply chain performance measures and supply chain profitability using structural equation modelling (SEM). The data was compiled and collected from 213 Operations and Supply Chain heads from leading retail stores in India. Confirmatory factor analysis (CFA) was used to test the validity of the proposed measurement scale and the relationship is tested using structural equation modelling (SEM). The results exhibit that all the SCPM items have significant loadings to their corresponding second-order construct, similarly all the SCP items have significant loadings to their corresponding first-order construct. The relationship between the second order construct SCPM and SCP was also found to be significant. The results of the structural equation model analysis supports the developed hypothesis, which states that, higher level of SCM Performance leads to higher level of SC Profitability. It was found that standardized coefficient was statistically significant at (p < 0.01). The hypothesis is accepted which states that Organizations implementing SCM have obtained improved performance, cost savings, increased revenues, and the reduction of defects in products are some of the chief advantages of introducing supply chain management. It has been demonstrated that business profitability is closely associated with performance measures. Overall, the model has a satisfactory fit with CMIN/DF 2.648, RMR 0.078, RMSEA 0.088, GFI 0.813, NFI 0.810 and CFI 0.872. From the results it can be mentioned that the selected measures provide a good guide to managerial decision making processes. Another significant contribution of the research lies in the fact that there are a plethora of studies identifying different dimensions of SC performance; however, there is no study identifying important dimensions for managers. This study identifies important SC performance measures beneficial to managers, which deserve their immediate attention. The research contributes a model to the knowledge of theories on supply chain management by suggesting to obsolete old performance measures to eradicated and new set of tradition and relationship measures should be implemented, so that the downstream and upstream SCM happens smoothly in the organized retail sector in India. © 2017 IEEE.},
author_keywords={And Indian retail sector;  Structural equation modelling;  Supply chain performance measures;  Supply chain profitability},
correspondence_address1={Kamble, S.; National Institute of Industrial Engineering (NITIE), Vihar Lake Marg Mumbai, India; email: Sachinnitie06@gmail.com},
publisher={IEOM Society},
issn={21698767},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Smith2017401,
author={Smith, A.D.},
title={Operational strategies associated with RFID applications in healthcare systems},
journal={Health Care Delivery and Clinical Science: Concepts, Methodologies, Tools, and Applications},
year={2017},
pages={401-419},
doi={10.4018/978-1-5225-3926-1.ch020},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041637416&doi=10.4018%2f978-1-5225-3926-1.ch020&partnerID=40&md5=4dc77fa2cd426de1751a443d6c683bb7},
affiliation={Robert Morris University, United States},
abstract={An effective information system is essential for a business or industry to be successful in today’s highly competitive market. Perhaps the most compelling case for RFID-embedded technologies in the healthcare field has been increased efficiency in supply chain performance measurements, which generally consist of financial and non-financial indicators. Many research studies have assumed that these efficiency measures are transferrable in the medical services field. Such optimism is fuelled by the expectations that such supply chain measures will result in equally impressive results in the healthcare field. Although this transfer may be somewhat flawed and imperfectly applied, research has verified certain elements of operational optimism. There are still a number of technical, ethical, and legal issues or hurdles that surround RFID applications in the healthcare industry that must be successfully overcome. However, few can successfully argue against freeing hospital staff from the routine duties associated with traditional inventory so that they may be free to serve patients. With recent governmental regulations and the concern for increased access to universal medical care and its astronomical costs, these issues need to be addressed. © 2018, IGI Global.},
references={Bhakoo, V., Singh, P., Sohal, A., Collaborative management of inventory in Australian hospital supply chains: Practices and issues (2012) Supply Chain Management, 17 (2), pp. 217-230; Chen, Z., Dubinsky, A.J., A conceptual model of perceived customer value in e-commerce:A preliminary investigation (2003) Psychology and Marketing, 20 (4), pp. 323-347; Chiou, J.S., The antecedents of customer loyalty toward Internet service providers (2004) Information & Management, 41 (6), pp. 685-695; Collier, J.E., Bienstock, C.C., Measuring service quality in e-retailing (2006) Journal of Service Research, 8 (3), pp. 260-275; Condea, C., Thiessse, F., Fleisch, E., RFID-enabled shelf replenishment with backroom monitoring in retail stores (2012) Decision Support Systems, 52 (4), pp. 839-849; Cowles, D.L., Kiecker, P., Little, M.W., Using key informant insights as a foundation for e-retailing theory development (2002) Journal of Business Research, 55 (8), pp. 629-636; de Mast, J., Kemper, B., Doe, R.J., Mandjes, M., van der Bijl, Y., Process improvement in healthcare: Overall resource efficiency (2011) Quality and Reliability Engineering International, 27 (8), pp. 1095-1106; Gaukler, G.M., Özer, O., Hausman, W.H., Order progress information: Improved dynamic emergency ordering policies (2008) Production and Operations Management, 17 (6), pp. 599-614; Hu, G., Wang, L., Fetch, S., Bidanda, B., A multi-objective model for project portfolio selection to implement lean and Six Sigma concepts (2008) International Journal of Production Research, 46 (23), pp. 6611-6648; Jain, V., Benyoucef, L., Deshmukh, S.G., What’s the buzz about moving from ‘lean’ to ‘agile’ integrated supply chains? A fuzzy intelligent agent-based approach (2008) International Journal of Production Research, 46 (23), pp. 6649-6678; Kamhawi, E.M., System characteristics, perceived benefits, individual differences and use intentions:A survey of decision support tools of ERP systems (2008) Information Resources Management Journal, 21 (4), pp. 66-83; Kennedy, F.A., Widener, S.K., A control framework: Insights from evidence on lean accounting (2008) Management Accounting Research, 19 (4), pp. 301-319; Koong, L., Lin, C., Evaluating the decision to adopt RFID systems using Analytic Hierarchy Process (2007) Journal of American Academy of Business, 11 (1), pp. 72-77; Kumar, S., Livermont, G., McKewan, G., Stage implementation of RFID hospitals (2010) Technology and Health Care, 18 (1), pp. 31-46; Scanlon, D.P., Swaminathan, S., Lee, W., Chernew, M., Does competition improve health care quality? (2008) Health Services Research, 43 (6), pp. 1931-1944; Scherrer-Rathje, M., Boyle, T.A., Deflorin, P., Lean, take two! Reflections from the second attempt at lean implementation (2009) Business Horizons, 52 (1), pp. 79-85; Seidman, S.J., Brockman, R., Lewis, B.M., Guag, J., Shein, M.J., Clement, W.J., In vitro tests reveal sample radio frequency identification readers inducing clinically significant electromagnetic interference to implantable pacemakers and implantable cardioverter-defibrillators (2010) Heart Rhythm:The Official Journal of the Heart Rhythm Society, 7 (1), pp. 99-107; Smith, A.D., (2005) VINE: The Journal of Information and Knowledge Management, 35 (3), pp. 166-181; Smith, A.D., Exploring the inherent benefits of RFID and automated self-serve Checkouts in a B2C environment (2005) International Journal of Business Information Systems, 1 (1-2), pp. 149-183; Smith, A.D., Evolution and acceptability of medical applications of RFID implants among early users of technology (2008) Health Marketing Quarterly, 24 (1-2), pp. 121-155; Smith, A.D., Marketing and reputation aspects of neonatal safeguards and hospital-security systems (2009) Health Marketing Quarterly, 26 (2), pp. 117-144; Smith, A.D., Offodile, O.F., Exploring forecasting and project management characteristics of supply chain management (2007) International Journal of Logistics and Supply Management, 3 (2), pp. 174-214; Smith, A.D., Offodile, O.F., Gauging web portals impact on supply chain management concepts (2008) International Journal of Logistics and Management, 4 (2), pp. 184-206; Smith, A.D., Offodile, O.F., The perceived importance of major RFID-related technology initiatives among retail store managers (2009) International Journal of Services and Operations Management, 5 (4), pp. 520-547; Smith, A.D., Rupp, W.T., Supply supplier integration, procurement, and outsourcing: Case study of SCM social capital benefits (2013) International Journal of Logistics Systems and Management, 14 (2), pp. 221-241; Smith, A.D., Rupp, W.T., Data quality and knowledge/information management in service operations management: Regional supermarket case study (2013) International Journal of Knowledge-Based Organizations, 3 (3), pp. 35-52; Swaminathan, S., Chernew, M., Scanlon, D.P., Persistence of HMO performance measures (2008) Health Services Research, 43 (6), pp. 2033-2051; Ustundag, A., Evaluating RFID investment on a supply chain using tagging cost sharing factor (2010) International Journal of Production Research, 48 (9), pp. 2549-2562; Wamba, S.F., RFID-enabled healthcare applications, issues and benefits: An archival analysis (1997-2011) (2012) Journal of Medical Systems, 36 (6), pp. 3393-3398; Wyld, D.C., RFID 101: The next big thing for management (2006) Management Research News, 29 (4), pp. 154-173; Yao, A., Carlson, J., The impact of real-time data communication on inventory management (1999) International Journal of Production Economics, 59 (1), pp. 213-219; Yao, W., Chu, C.-H., Li, Z., The adoption and implementation of RFID Technologies in healthcare: A literature review (2012) Journal of Medical Systems, 36 (6), pp. 3507-3525},
correspondence_address1={Smith, A.D.; Robert Morris UniversityUnited States},
publisher={IGI Global},
isbn={9781522539285; 1522539263; 9781522539261},
language={English},
abbrev_source_title={Health Care Delivery and Clinical Science: Concepts, Methodologies, Tools, and Applications},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Pimentel2017,
author={Pimentel, C. and Azevedo, S.},
title={Supply chain 4.0 and performance measurement},
journal={Proceedings of International Conference on Computers and Industrial Engineering, CIE},
year={2017},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040916008&partnerID=40&md5=383c7e6f05f308f40d48d07c1e063f7a},
affiliation={GOVCOPP, Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT), University of Aveiro, Aveiro, Portugal; CEFAGE-UBI and UNIDEMI Research Units, Management and Economics Department, University of Beira Interior, Covilhã, Portugal},
abstract={Industry 4.0 is triggering major changes and innovations on industry. The combination of different kinds of information technologies and operational technologies, developed over the last years, are seen as key factors for making the 4th revolution possible. Although originated in the manufacturing world, due to its importance and implications on the supply chain processes and activities this concept is spreading to the Supply Chain (SC) world, giving rise to the term Supply Chain 4.0 (SC 4.0). So, in this paper the SC 4.0 concept is explored as well as its implications on the key supply chain business processes. Furthermore, this new context will foster the need to rethink the way supply chain business processes and activities are managed and controlled along the SC, leading also to the need to study the influences of the SC 4.0 on the SC performance measurement. Thus, new SC performance metrics and models should be deployed. In this paper we present an ongoing research aimed at characterizing the SC 4.0 and suggesting a conceptual model to illustrate its influence along the core SC business processes as well as on the performance of the SC 4.0.},
author_keywords={Conceptual Model;  Performance Measurement;  Performance Measurement Systems;  SC Business Processes;  Supply Chain 4.0},
keywords={Engineering;  Industry, Business Process;  Conceptual model;  Operational technologies;  Performance measurement system;  Performance measurements;  Performance metrics;  Supply chain process, Supply chains},
references={Pfohl, H.-C., Yahsi, B., Kurnaz, T., The impact of industry 4.0 on the supply chain (2015) Innov. Strateg. Logist. Supply Chain., pp. 31-58; Mallik, S., (2010) The Handbook of Technology Management: Supply Chain Management, Marketing and Advertising, and Global Management, , Wiley; Wong, C., Skipworth, H., Godsell, J., Achimugu, N., Towards a theory of supply chain alignment enablers: A systematic literature review (2012) Supply Chain Manag. An Int. J., 17 (4), pp. 419-437; Butner, K., The smarter supply chain of the future (2010) Strategy and Leadership, 38 (1), pp. 22-31; Schuster, E., Allen, S., Brock, D., (2007) Global RFID: The Value of the EPC Global Network for Supply Chain Management, , Springer; Kuglin, F., Rosenbaum, B., (2001) The Supply Chain Network@ Internet Speed, , American Management Association; Kache, F., Seuring, S., Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management (2017) Int. J. Oper. Prod. Manag., 37 (1), pp. 10-36; Wu, L., Yue, X., Jin, A., Yen, D., Smart supply chain management: A review and implications for future research (2016) Int. J. Logist. Manag., 27 (2), pp. 395-417; Zhu, X., Mukhopadhyay, S., Kurata, H., A review of RFID technology and its managerial applications in different industries (2012) J. Eng. Technol. Manag., 29, pp. 152-167; Lambert, D., Cooper, M., Pagh, J., Supply chain management: Implementation issues and research opportunities (1998) Int. J. Logist. Manag., 9 (2), pp. 1-18; Pant, S., Sethia, R., Bhandari, M., Making sense of the e-supply chain landscape: An implementation framework (2003) Int. J. Inf. Manage., 23, pp. 201-221; Kumar, R., Pugazhendhi, S., Information sharing in supply chains: An overview (2012) Procedia Eng., 38, pp. 2147-2154; Chui, M., Loffler, M., Roberts, R., No TitleThe internet of things (2010) McKinsey Q., 2, pp. 1-9; Oudenhoven, J., Vullers, R., Schaijk, R., A review of the present situation and future developments of micro-batteries for wireless autonomous sensor systems (2012) Int. J. Energy Res., 36 (12), pp. 1139-1150; Shibi, N., Enhancing operational efficiencies with mobile devices (2011) MHD Supply Chain Solut., pp. 26-28. , Jan./Fev; McCrea, B., Wireless evolution: Getting closer (2012) Logist. Manag., pp. 60-62. , August; Akyuz, G., Rehan, M., Requirements for forming an 'e-supply chain (2009) Int. J. Prod. Res., 47 (12), pp. 3265-3287; Kloch, C., Kristensen, J., Bilstrup, B., Future scenarios: What are the future services and applications? (2010) Wirel. Pers. Commun., 53, pp. 315-327; Ma, H., Internet of things: Objectives and scientific challenges (2011) J. Comput. Sci. Technol., 26 (6), pp. 919-924; Evans, P., Annunziata, M., (2012) Industrial Internet: Pushing the Boundaries of Minds and Machines, pp. 1-37. , Technical Report; Montreuil, B., Toward a physical internet: Meeting the global logistics sustainability grand challenge (2011) Logist. Res., 3, pp. 71-87; Hessman, T., (2013) The Dawn of the Smart Factory, pp. 15-19. , Ind. Week, February; Gimenez, C., Lourenco, H., (2004) E-Supply Chain Management: Review, Implications and Directions for Future Research, pp. 1-46. , Technical Report; Tien, J., The next industrial revolution: Integrated services and goods (2012) J. Syst. Sci. Syst. Eng., 21 (3), pp. 257-296; Foundations for innovation: Strategic R&D opportunities for 21st century cyber-physical systems (2013) Progressive Management, , U. S. Government and NIST; Gubbi, J., Buyya, R., Marusic, S., Internet of Things (IoT): A vision, architectural elements, and future directions (2013) Futur. Gener. Comput. Syst., 29, pp. 1645-1660; Sundmaeker, H., Guillemin, P., Friess, P., Woelfflé, S., (2010) Vision and Challenges for Realizing the Internet of Things, , European Commission; Yang, C., Shen, W., Wang, X., Applications of internet of things in manufacturing (2016) The 20th IEEE International Conference on Computer Supported Cooperative Work in Design, pp. 670-675; Borgia, E., The internet of things vision: Key features, applications and open issues (2014) Comput. Commun., 54, pp. 1-31; Banker, S., Warehouse control in the age of the internet of things (2014) Supply Chain Manag. Rev., 18 (5), pp. 26-29; Weiser, R., Gold, J., The origins of ubiquitous computing research at PARC in the late 1980s (1999) IBM Syst. J., 38 (4), pp. 693-696; Ballou, R., The evolution and future of logistics and supply chain management (2007) Eur. Bus. Rev., 19 (4), pp. 332-348; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) Int. J. Oper. Prod. Manag., 15 (4), pp. 80-116; Chan, F., Qi, H., An innovative performance measurement method for supply chain management (2003) Supply Chain Manag. An Int. J., 8 (3), pp. 209-223; Melnyk, S., Stewart, D., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) J. Oper. Manag., 22, pp. 209-217; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) Int. J. Prod. Econ., 183, pp. 299-315; Mondragon, A., Lalwani, C., Mondragon, C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Manag. An Int. J., 16 (1), pp. 43-56; Ramdas, K., Spekman, R., Chain or shackles: Understanding what drives supply-chain performance (2000) Interfaces, 30 (4), pp. 3-21; Balfaqih, H., Nopiah, Z., Saibani, N., Al-Nory, M., Review of supply chain performance measurement systems: 1998-2015 (2016) Comput. Ind., 82, pp. 135-150; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Bullinger, H., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) Int. J. Prod. Res., 40 (15), pp. 3533-3543; Beamon, B., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3), pp. 275-292; Lambert, D., Cooper, M., Issues in supply chain management (2000) Ind. Mark. Manag., 29, pp. 65-83},
correspondence_address1={Pimentel, C.; GOVCOPP, Portugal; email: carina.pimentel@ua.pt},
editor={Dessouky M., Grilo A., Cruz-Machado V.},
publisher={Curran Associates Inc.},
issn={21648689},
isbn={9780000000002},
language={English},
abbrev_source_title={Proc. Int. Conf. Comput. Ind. Eng., CIE},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Munik2017,
author={Munik, J. and Moura, L.F. and Treinta, F.T. and Cestari, J.M.A.P. and Deschamps, F. and Lima, E.P.D. and Costa, S.E.G.D.},
title={Bibliometric analysis of performance measurement systems in Non-Profit organizations: Cocitation analysis},
journal={2017 International Annual Conference of the American Society for Engineering Management, ASEM 2017},
year={2017},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040076679&partnerID=40&md5=e3af1f83813e73b2d6e287ce40f652f5},
affiliation={University of Parana, Brazil; University of Technology, Parana, Brazil; Pontifical Catholic University of Parana, Opet College, Brazil},
abstract={Non-Profit Organizations (NPOs) are endowed with a relevant characteristic, which is the restriction for financial profits generation. Studies show a steady growth of this sector and, for increases the competitive advantages, these organizations come to use tools and business strategy methods in their decision makings. These methods can contribute to increased competitiveness when they are supported by a Performance Measurement System (PMS). PMS measures the results through performance indicators, providing more accurate controls over the decision-making processes, as an example, when actions involve internal and external environment aspects, such as Cost Measurement, Quality and Stakeholders evaluate and satisfaction. This articles presents a Bibliometric Analysis of a study and Author Cocitations for a sample of scientific articles, developed in parallel with a Systematic Literature Review (SLR) involving disciplines relating to NPOs and PMS. A Systematic Review adopts judicious methods, with a more consistent basis for the quality of information for a set of scientific publications. This information may present biases, through a systematic research, there is a delimitation of the area, the field of investigation, and it excludes the observation of subjective data. The results contribute to the identification of future investigations, generating a research agenda. © 2017 American Society for Engineering Management.},
author_keywords={Bibliometric Analysis Cocitation;  Nonprofit Organizations;  Performance Measurement Systems},
keywords={Competition;  Decision making;  Profitability;  Societies and institutions;  Supply chain management, Cocitation;  Decision making process;  Non profit organizations;  Performance indicators;  Performance measurement system;  Quality of information;  Scientific publications;  Systematic literature review (SLR), Quality control},
references={Abbasi, A., Hossain, L., Leydesdorff, L., Betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks (2012) Journal of Informetrics, 6 (3), pp. 403-412; Arena, M., Azzone, G., Process based approach to select key sustainability indicators for steel companies (2010) Ironmaking and Steelmaking, 37, pp. 437-444; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Choong, K.K., The fundamentals of performance measurement systems: A systematic approach to theory and a research agenda (2014) International Journal of Productivity and Performance Management, 63 (7), pp. 879-922; Deschamps, F., (2013) Proposal for the Systematization of Enterprise Engineering Contributions: Guidelines for Enterprise Engineering Initiatives, , Pontifical Catholic University of Paraná, Curitiba; Eom, S.B., Author cocitation analysis using custom bibliographic databases: An exploratory tool for digging up reference disciplines (2004) European Conference On Information Systems; Glass, G., Primary, secondary and meta-analysis of research (1976) Educational Researcher, 5 (9), pp. 3-8; Gmür, M., Cocitation analysis and the search for invisible colleges: A methodological evaluation (2003) Scientometrics, 57 (1), pp. 27-57; Gonzalez, L.S., Rubio, F.G., Gonzalez, F.R., Velthui, N.P., Measurement in business processes: A systematic review (2010) Business Process Management Journal, 16 (1), pp. 114-134; Grácio, M.C.C., Oliveira, E.F.T., (2013) Estudos de Análise de Cocitacąõ de Autores: Uma Abordagem Teórica Metodologica Para a Compreensao de Um Dominio, , XIV Encontro Nacional de Pesquisa em Ciência da Informacąõ (ENANCIB 2013) GT 7: Producąõ e Comunicacąõ da Informacąõ em CT&I; Hanneman, R.A., Riddle, M., (2005) Introduction to Social Network Methods, , Riverside: University of California. Acessed on April 1st 2017:; Jeong, Y.K., Song, M., Ding, Y., Content-based author cocitation analysis (2014) Journal of Informetrics, 8 (1), pp. 197-211; Keathley, H.R., (2016) Empirical Investigation of Factors That Affect the Successful Implementation of Performance Measurement Systems, , Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University, Blacksburg, VA, USA; Kitchenham, B., Charters, S.C., (2007) Guidelines for Performing Systematic Literature Reviews in SE; Lacerda, R.T., Ensslin, L., Ensslin, S.R., A bibliometric analysis of strategy and performance measurement (2012) Gestaõ e Producąõ, 19 (1). , Saõ Carlos, SP 2012; Newman, M., (2010) Networks: An Introduction, , New York: Oxford University Press 2010; Tranfield, D., David, D., Palminder, S., Towards a methodology for developing Evidence-Informed management knowledge by means of systematic review (2003) British Journal of Management, 14, pp. 207-222; Waal, A.De., Goedegebuure, R., Geradts, P., The impact of performance management on the results of a non-profit organization (2011) International Journal of Productivity and Performance Management, 60 (8), pp. 778-796; Wasserman, S., Faust, K., (1994) Social Network Analysis: Methods and Applications, , Cambridge: Cambridge University Press},
publisher={American Society for Engineering Management},
language={English},
abbrev_source_title={Int. Annu. Conf. Am. Soc. Eng. Manag., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Sarkis201735,
author={Sarkis, J. and Hervani, A.A.},
title={Sustainable supply chain performance measurement: Concerns and directions},
journal={Strategic Performance Management: New Concepts and Contemporary Trends},
year={2017},
pages={35-48},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034791724&partnerID=40&md5=5213e8c187a7c73e9fffe31f0c258ee3},
affiliation={Worcester Polytechnic Institute, Foisie Business School, Worcester, CA, United States; Department of Economics, Armstrong State University, Savannah, GA, United States},
abstract={This chapter overviews issues related to performance measurement for a sustainable supply chain environment. Sustainability is defined as a triple-bottom-line set of dimensions related to economic, environmental, and social performance of organizations. In addition, the supply chain and its dimensions are integrated. The integration of multiple organizations across the supply chain, additional performance dimensions, and multiple stakeholders make the performance measurement of sustainability in supply chains relatively complex. Some dimensions and issues facing performance measurement are introduced. The reader is also provided some identification of emergent and future research directions related to this topic. © 2017 by Nova Science Publishers, Inc. All rights reserved.},
author_keywords={Performance measurement;  Supply chain;  Sustainability},
references={Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Management: An International Journal, 19 (3), pp. 275-291; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach (2014) Production Planning & Control, 25 (8), pp. 698-714; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for sustainable supply chain management: Developments and directions (2014) European Journal of Operational Research, 233 (2), pp. 299-312; Correia, E., Carvalho, H., Azevedo, S.G., Govindan, K., Maturity Models in Supply Chain Sustainability: A Systematic Literature Review (2017) Sustainability, 9 (1), p. 64; Elkington, J., (1998) Cannibals with Forks: The Triple Bottom Line of 21St Century Business, , Gabriola Island, Canada: New Society Publishers; Epstein, M.J., Roy, M.J., Sustainability in action: Identifying and measuring the key performance drivers (2001) Long Range Planning, 34 (5), pp. 585-604; Ferreira, L.M.D., Silva, C., Azevedo, S.G., An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM) (2016) Benchmarking: An International Journal, 23 (6), pp. 1398-1422; Friedman, M., (1962) Capitalism and Freedom, , Chicago: University of Chicago Press; Hahn, R., ISO 26000 and the standardization of strategic management processes for sustainability and corporate social responsibility (2013) Business Strategy and the Environment, 22 (7), pp. 442-455; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Upper Saddle River, NJ: Prentice Hall; Lambert, D.M., Enz, M.G., We Must Find the Courage to Change (2015) Journal of Business Logistics, 36 (1), pp. 9-17; Lambert, D.M., Enz, M.G., Issues in Supply Chain Management: Progress and potential (2017) Industrial Marketing Management, 62, pp. 1-16; Maestrini, V., Luzzini, D., Maccarrone, P., Caniato, F., Supply chain performance measurement systems: A systematic review and research agenda (2017) International Journal of Production Economics, 183, pp. 299-315; Mc Williams, A., Siegel, D., Corporate social responsibility: A theory of the firm perspective (2001) Academy of Management Review, 26 (1), pp. 117-127; Mishra, D., Gunasekaran, A., Papadopoulos, T., Hazen, B., Green supply chain performance measures: A review and bibliometric analysis (2017) Sustainable Production and Consumption, 10, pp. 85-99; Papadopoulos, T., Gunasekaran, A., Dubey, R., Fosso Wamba, S., Childe, S.J., World Class Sustainable Supply Chain Management: Critical review and further research directions (2017) International Journal of Logistics Management, , Forthcoming; Porter, M.E., (1985) Competitive Advantage: Creating and Sustaining Superior Performance, p. 1985. , New York: FreePress; Sarkis, J., Helms, M.M., Hervani, A.A., Reverse logistics and social sustainability (2010) Corporate Social Responsibility and Environmental Management, 17 (6), pp. 337-354; Trapp, A.C., Sarkis, J., Identifying Robust portfolios of suppliers: A sustainability selection and development perspective (2016) Journal of Cleaner Production, 112, pp. 2088-2100; Traverso, M., Bell, L., Saling, P., Fontes, J., Towards social life cycle assessment: A quantitative product social impact assessment (2016) The International Journal of Life Cycle Assessment, pp. 1-10; Wang, G., Hackett, R.D., Conceptualization and measurement of virtuous leadership: Doing well by doing good (2016) Journal of Business Ethics, 137 (2), pp. 321-345; White, M.A., Sustainability: I know it when I see it (2013) Ecological Economics, 86, pp. 213-217; (1987) Our Common Future, , Oxford University Press, New York},
correspondence_address1={Sarkis, J.; Worcester Polytechnic Institute, United States; email: jsarkis@wpi.edu},
publisher={Nova Science Publishers, Inc.},
isbn={9781536126822; 9781536126815},
language={English},
abbrev_source_title={Strategic Perform. Management: New Concepts and Contemporary Trends},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Wasusri2017464,
author={Wasusri, T. and Pongcheep, B.},
title={Using VMI to improve supply chain performance of a public enterprise in Thailand},
journal={International Journal of Logistics Systems and Management},
year={2017},
volume={28},
number={4},
pages={464-485},
doi={10.1504/IJLSM.2017.087788},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033212941&doi=10.1504%2fIJLSM.2017.087788&partnerID=40&md5=a19296bfedc544e0f72a70d5d508736a},
affiliation={School of Management and Innovation, King Mongkut's University of Technology Thonburi, 126 Pracha Uthit Rd, Bang Mod, Thung Khru, Bangkok, 10140, Thailand},
abstract={Supply chain management is well recognised and implemented in private sectors, but the beneficial of supply chain management in public sector is limited shown in the literature. This paper presents a comprehensive supply chain simulation study for a real case of Thai public sector focusing on its material supply network. The supply network consists of four stages in inventory system: head office, regional warehouse, main warehouse and service warehouse. This study aims to propose three different strategies according to the combination of purchasing structure and vendor managed inventory (VMI) with targeting to reduce lead time and cost. The discrete event simulation (DES) is a powerful tool used to reveal the supply chain performances. The three strategies result in shorter lead time and cost reduction. The decentralised purchasing with VMI on each echelon to support continuous replenishment offers the best solution. The managerial implication is provided to enable the enterprise to improve the supply chain performance measures. © 2017 Inderscience Enterprises Ltd.},
author_keywords={Discrete event simulation;  Performance;  Public procurement;  Public sector;  Purchasing;  Supply chain; distribution;  Supply network;  Vendor managed inventory;  VMI},
references={Achabal, D.D., McIntyre, S.H., Smith, S.A., Kalyanam, K., A decision support system for vendor managed inventory (2000) Journal of Retailing, 76 (4), pp. 430-454; Arlbjørn, J.S., Freytag, P.V., Public procurement vs private purchasing: Is there any foundation for comparing and learning across the sectors? (2012) International Journal of Public Sector Management, 25 (3), pp. 203-220; Arlbjørn, J.S., Freytag, P.V., De Haas, H., Service supply chain management: A survey of lean application in the municipal sector (2011) International Journal of Physical Distribution and Logistics Management, 41 (3), pp. 277-295; Blackhurst, J., Craighead, C.W., Handfield, R.B., Towards supply chain collaboration: An operation audit of VMI initiatives in the electronics industry (2006) International Journal of Integrated Supply Management 2006, 2 (1-2), pp. 91-105; Borade, A.B., Bansod, S.V., Study of vendor-managed inventory practices in Indian industries (2010) Journal of Manufacturing Technology Management, 21 (8), pp. 1013-1038; Callender, G., Alignment of inter-Agency supply chains to enhance public sector performance management (2011) International Journal of Productivity and Performance Management, 60 (1), pp. 9-23; Carvalho, H., Barroso, A.P., Machado, V.H., Azevedo, S., Cruz-Machado, V., Supply chain redesign for resilience using simulation (2012) Computers and Industrial Engineering, 62 (1), pp. 329-341; Chatzipanagioti, M., Theodori, V., Vlachos, D., VMI modelling in global and electronic markets (2007) International Journal of Logistics Systems and Management 2007, 3 (3), pp. 271-287; Christopher, M., (2011) Logistics and Supply Chain Management, , 4th ed Pearson Education Limited, Great Britain; Claassen, M.J.T., Van Weele, A.J., Van Raaij, E.M., Performance outcomes and success factors of vendor managed inventory (VMI) (2008) Supply Chain Management: An International Journal, 13 (6), pp. 406-414; Cox, A., Chicksand, D., Ireland, P., Sub-optimality in NHS sourcing in the UK: Demand-side constraints on supply-side improvement (2005) Public Administration, 83 (2), pp. 367-392; Danese, P., The extended VMI for coordinating the whole supply network (2006) Journal of Manufacturing Technology Management, 17 (7), pp. 888-907; De Toni, A., Zamolo, E., From a traditional replenishment system to vendor-managed inventory: A case study from the household electrical appliances sector (2005) International Journal of Production Economics, 96 (1), pp. 63-79; Dev, N.K., Caprihan, R., Swami, S., A case study on redesign of supply chain network of a manufacturing of a manufacturing organization (2011) Journal of Advances in Management Research, 8 (2), pp. 195-212; Disney, S.M., Towill, D.R., The effect of vendor managed inventory (VMI) dynamics on the bullwhip effect in supply chains (2013) International Journal of Production Economics, 85 (2), pp. 199-215; Dorling, K., Scott, J., Deakins, E., Determinants of successful vendor managed inventory relationships in oligopoly industries (2006) International Journal of Physical Distribution and Logistics Management, 36 (3), pp. 176-191; Handfield, R.B., Monczka, R.M., Giunipero, L.C., Patterson, J.L., (2009) Sourcing and Supply Chain Management, , 4th ed., South-Western Cengage Learning, USA; Hesping, F.H., Schiele, H., Purchasing strategy development: A multi-level review (2015) Journal of Purchasing and Supply Management, 21 (2), pp. 138-150; Johnsen, T.E., Howard, M., Miemczyk, J., (2014) Purchasing and Supply Chain Management: A Sustainability Perspective, , Routledge, London; Johnson, P.F., Leenders, M.R., McCue, C., Public procurement organization: A comparison of purhasings organizational roles and responsibilities in the public and private sector (2009) International Handbook of Public Procurement, , Thai, K.V. (Ed) Taylor and Francis Group, LLC., USA; Kauremaa, J., Smaros, J., Holmström, J., Patterns of vendor-managed inventory: Findings from a multiple-case study (2009) International Journal of Operations and Production Management, 29 (11), pp. 1109-1139; Kelton, W.D., Sadowski, R.P., Swets, N.B., (2010) Simulation with Arena, , 5th ed McGraw Hill, New York; Lysons, K., Farrington, B., (2006) Purchasing and Supply Chain Management, , 7th ed Pearson Education Limited, Great Britain; Northcott, D., Taulapapa, T.M., Using the balanced scorecard to management performance in public sector organizations: Issues and challenges (2012) International Journal of Public Sector Management, 25 (3), pp. 166-191; Pohlen, T., Goldsby, T., VMI and SMI programs: How economic value added can help sell the change (2003) International Journal of Physical Distribution and Logistics Management, 33 (7), pp. 565-581; Ramanathan, U., Performance of supply chain collaboration: A simulation study (2014) Expert Systems and Applications, 41 (1), pp. 210-220; Ross, D.F., (2015) Distribution Planning and Control: Managing in the Era of Supply Chain Management, pp. 531-564. , Springer Science+Business Media, New York; Sari, K., Exploring the benefits of vendor managed inventory (2007) International Journal of Physical Distribution and Logistics Management, 37 (7), pp. 529-545; Sari, K., On the benefits of CPFR and VMI: A comparative simulation study (2008) International Journal of Production Economics, 113 (2), pp. 575-586; Schapper, P.R., Viega Malta, J., Gilbert, D.L., Analytical framework for the management and reform of public procurement (2009) International Handbook of Public Procurement, , Taylor and Francis Group, LLC., USA, in Thai, K.V. (Ed; Son, J.Y., Sheu, C., The impact of replenishment policy deviations in a decentralized supply chain (2008) International Journal of Production Economics, 113 (2), pp. 785-804; Stanger, S.H.W., Vendor managed inventory in the blood supply chain in Germany (2013) Strategic Outsourcing: An International Journal, 6 (1), pp. 25-47; Thai, K.V., International public procurement: Concepts and practices (2009) International Handbook of Public Procurement, , Thai, K.V. (Ed) Taylor and Francis Group, LLC., USA; Trautmann, G., Bals, L., Hartmann, E., Global sourcing in integrated network structures: The case of hybrid purchasing organizations (2009) Journal of International Management, 15 (2), pp. 194-208; Walter, D., (2009) Supply Chain Management: An Introduction to Logistics, 2nd Ed, , Palgrave Macmillan, Great Britain; Wisner, J.D., Tan, K.C., Leong, G.K., (2012) Supply Chain Management: A Balanced Approach, , 3rd ed., South-Western Cengage Learning, USA; Zammori, F., Braglia, M., Frosolini, R., A standard agreement for vendor managed inventory (2009) Strategic Outsourcing: An International Journal, 2 (2), pp. 165-186},
correspondence_address1={Wasusri, T.; School of Management and Innovation, 126 Pracha Uthit Rd, Bang Mod, Thailand; email: thananya.was@kmutt.ac.th},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Moura20171759,
author={Moura, L.F. and De Lima, E.P. and Van Aken, E. and Deschamps, F. and Treinta, F.T. and Da Costa, S.E.G.},
title={The performance measurement in nonprofit organizations - A case study},
journal={67th Annual Conference and Expo of the Institute of Industrial Engineers 2017},
year={2017},
pages={1759-1764},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030999216&partnerID=40&md5=4a9b0d7731e453c2bd14c71b082e4a4c},
affiliation={Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba, Brazil; Federal University of Technology, Parana, Curitiba, Brazil; Department of Mechanical Engineering, Federal University of Parana, Curitiba, Brazil; Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, United States},
abstract={Nonprofit organizations have specific and complex characteristics that influence their performance measurement and differentiate it from others business models. So, it is important to know what are the factors, as motivations, barriers, and drivers, that influence the design of a performance measurement system to develop a complete system with all organizational issues, as social aspects, accountability, and legitimacy. A systematic literature review was previously performed and a set of factors that influence the design of performance measurement system for nonprofit organizations were identified. This paper presents a case study conducted in a Fire Department in the United States with the purpose investigate the set of factors in the practice. The data were collected using four techniques: interviews, documents and archival records, and observation. The findings show that the influence of the factors can vary depending on the type of organization and the paper indicates some reasons for that. Therefore, understanding the organization and these factors is a challenge for the performance measurement area to provide tools and frameworks that contribute to the organizational management and research area advance.},
author_keywords={Design;  Enterprise engineering;  Not-for-profit organizations;  Performance measurement systems},
keywords={Design;  Engineers;  Social aspects;  Supply chain management, Complex characteristics;  Enterprise engineering;  Non profit organizations;  Not-for-profit organization;  Organizational management;  Performance measurement system;  Performance measurements;  Systematic literature review, Societies and institutions},
references={De Waal, A., Goedegebuure, R., Geradts, P., The impact of performance management on the results of a non-profit organization (2011) Int. J. Product. Perform. Manag., 60 (8), pp. 778-796; Moxham, C., Performance measurement: Examining the applicability of the existing body of knowledge to nonprofit organisations (2009) Int. J. Oper. Prod. Manag., 29 (7), pp. 740-763; Arena, M., Azzone, G., Bengo, I., Performance measurement for social enterprises (2015) Volunt. Int. J. Volunt. Nonprofit Organ., 26 (2), pp. 649-672; Ebrahim, A., Rangan, V.K., What impact? A framework for measuring the scale and scope of social performance (2014) Calif. Manage. Rev., 56 (3), pp. 118-141; Moura, L.F., De Lima, E.P., Van Aken, E., Deschamps, F., Da Costa, S.E.G., Factors for design of performance measurement systems for not-forprofit organizations (2016) Proceedings of the 2016 Industrial and Systems Engineering Research Conference; Moura, L.F., De Lima, E.P., Da Costa, S.E.G., Deschamps, F., Van Aken, E., Identifying the factors that influence the design of performance measurement systems in not-for-profit organizations (2016) Proceedings of the American Society for Engineering Management 2016 International Annual Conference; Straub, A., Koopman, M., Van Mossel, H.-J., Systems approach and performance measurement by social enterprises (2010) Facilities, 28 (5-6), pp. 321-331; Mouchamps, H., Weighing elephants with kitchen scales: The relevance of traditional performance measurement tools for social enterprises (2014) Int. J. Product. Perform. Manag., 63 (6), pp. 727-745. , Jul; Moura, L.F., Munik, J., Leite, L.R., De Lima, E.P., Fernando Deschamps, E.M.V.A., Da Costa, S.E.G., Performance measurement systems in nonprofit organizations: A systematic literature review (2015) Proceedings of the American Society for Engineering Management 2015 International Annual Conference},
editor={Nembhard H.B., Coperich K., Cudney E.},
publisher={Institute of Industrial Engineers},
isbn={9780983762461},
language={English},
abbrev_source_title={Annu. Conf. Expo.Inst. Ind. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Hatzigeorgiou2017153,
author={Hatzigeorgiou, A. and Manoliadis, O.},
title={Assessment of performance measurement frameworks supporting the implementation of lean construction},
journal={IGLC 2017 - Proceedings of the 25th Annual Conference of the International Group for Lean Construction},
year={2017},
pages={153-160},
doi={10.24928/2017/0073},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029595931&doi=10.24928%2f2017%2f0073&partnerID=40&md5=c6a7996c9c7b84d40ad24ecbde7fc93b},
affiliation={School of Science and Technology, Hellenic Open University, Patra, Greece; Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece},
abstract={Construction companies can perceive significant benefits of Lean Construction management, although cannot clearly identify the extent and origin of the obtained benefits. Frameworks, consisting structured tools suitable for business management, find wide application in the field of Performance Measurement, in most of the industries. The role of contemporary, process-oriented, Performance Measurement frameworks, like the European Foundation for Quality Management Excellence Model, the Balanced Scorecard and Key Performance Indicators, in supporting Lean Construction is examined in this study. The contribution of Performance Measurement Systems developed upon such frameworks, in the implementation of basic Lean Construction principles, like waste/variability reduction, operation simplification and benchmarking, is demonstrated by reviewing a number of related studies from the literature and identifying their basic characteristics regarding the adopted Performance Measurement frameworks and the involved Lean Construction principles. The presented studies indicate that the use of these frameworks, and especially Key Performance Indicators, can assist the application of specific Lean Construction principles, and most of all benchmarking.},
author_keywords={Lean Construction principles;  Performance Measurement frameworks},
keywords={Construction industry;  Lean production;  Project management;  Quality management;  Supply chain management, Basic characteristics;  Construction companies;  European foundation for quality management excellence models;  Key performance indicators;  Lean construction;  Performance measurement frameworks;  Performance measurement system;  Performance measurements, Benchmarking},
references={Alarcon, L.F., Grillo, A., Freire, J., Diethelm, S., Learning from collaborative benchmarking in the construction industry (2001) Proc. 9th Ann. Conf. of Thelnt'l. Group for Lean Construction, , 6-8 August, Singapore, Singapore; Bassioni, H.A., Price, A.D.F., Hassan, T.M., Performance measurement in construction (2004) ASCE J. of Manage.in Eng., 20 (2), pp. 42-50; Beatham, S., Anumba, C., Thorpe, T., Hedges, I., KPIs: A critical appraisal of their use in construction (2004) Benchmarking: An Inti J., 11 (1), pp. 93-117; Cha, H.S., Kim, C.K., Quantitative approach for project performance measurement on building construction in South Korea (2011) KSCE J. of Civil Eng., 15 (8), pp. 1319-1328; Chan, A.P.C., Chan, A.P.L., Key performance indicators for measuring construction success (2004) Benchmarking: An Inti J., 11 (2), pp. 203-221; (2006) Industry Performance Report 2006, , Constructing Excellence, CE, London, U. K; (2001) CII Benchmarking & Metrics Data Report 2001, , Construction Industry Institute, CII, Austin, Texas; Costa, D.B., Formoso, C.T., Kagioglou, M., Alarcon, L.F., Caldas, C.H., Benchmarking initiatives in the construction industry: Lessons learned and improvement opportunities (2006) ASCE J. of Manage, in Eng., 22 (4), pp. 158-167; Cox, R.F., Issa, R.R.A., Ahrens, D., Management's perception of key performance indicators for construction (2003) ASCE J. Constr. Eng. Manage., 129 (2), pp. 142-151; (1999) The EFQM Excellence Model (Blue Book), , European Foundation for Quality Management, EFQM, Brussels, Belgium. British Quality Foundation, London, UK; Egan, S.J., (1998) Rethinking Construction, , Construction Task Force, Norwich, UK; Howell, G.A., What is lean construction - 1999 (1999) Proc. 7th Ann. Conf. of the Int'l. Group for Lean Construction, pp. 1-10. , 26-28 July, Berkeley, USA; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Koskela, L., (1992) Application of the New Production Philosophy to Construction, , Technical Report #72, Center for Integrated Facility Engineering, Department of Civil Engineering, Stanford University; Lantelme, E., Formoso, C.T., Improving performance though measurement: The application of lean production & organisational learning principles (2000) Proc. 8th Ann. Conf. of the Int'l. Group for Lean Construction, , 17-18 July, Brighton, UK; Latham Sir, M., (1994) Constructing the Team, , Joint Review of Procurement & Contractual Arrangements in the U. K. Construction Industry, London, UK: HMSO; Manoliadis, O., Labour productivity benchmarking in Greek projects (2011) Organ., Tech. and Manage, in Constr. - An Int'l. J., 3 (1), pp. 221-226; Neely, A., The performance revolution: Why now and what next? (1999) Int'l. J. of Operat. and Product. Manage., 19 (2), pp. 205-228; Sarhan, S., Fox, A., Performance measurement in the UK construction industry and its role in supporting the application of lean construction concepts (2013) Australasian J. of Constr. Econ. and Build., 13 (1), pp. 23-35; Thomas, H.R., Horman, M.J., De Souza, U.E.L., Azvrski, I., Reducing variability to improve performance as a lean construction principle (2002) ASCE J. of Constr. Eng. and Manage., 128 (2), pp. 144-154; Yang, H., Yeung, J.F.Y., Chan, A.P.C., Chiang, Y.H., Chan, D.W.M., A critical review of performance measurement in construction (2010) J. of Facil. Manage., 8 (4), pp. 269-284; Yeung, J.F.Y., Chan, A.P.C., Chan, D.W.M., Developing a performance index for relationship-based construction projects in Australia: Delphi study (2009) ASCE J. of Manage, in Eng., 25 (2), pp. 59-68; Yeung, J.F.Y., Chan, A.P.C., Chan, D.W.M., Chiang, Y.H., Yang, H., Developing a benchmarking model for construction projects in Hong Kong (2013) ASCE J. of Constr. Eng. and Manage., 139 (6), pp. 705-716; Yeung, J.F.Y., Chan, A.P.C., Chan, D.W.M., Li, L.K., Development of a partnering performance index (PPI) for construction projects in Hong Kong: A delphi study (2007) Constr. Manage, and Econ., 25 (12), pp. 1219-1237; Yu, I., Kim, K., Jung, Y., Chin, S., Comparable performance measurement system for construction companies (2007) ASCE J. of Manage, in Eng., 23 (3), pp. 131-139},
editor={Brilakis I., Walsh K., Sacks R.},
publisher={The International Group for Lean Construction},
language={English},
abbrev_source_title={IGLC - Proc. Annu. Conf. Int. Group Lean Constr.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Candido2017243,
author={Candido, L.F. and De Paula Barros Neto, J.},
title={Features, roles and processes of performance measurement in lean construction},
journal={IGLC 2017 - Proceedings of the 25th Annual Conference of the International Group for Lean Construction},
year={2017},
pages={243-250},
doi={10.24928/2017/0290},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029584322&doi=10.24928%2f2017%2f0290&partnerID=40&md5=13eec63d25e2c6cce1d426320d84836e},
affiliation={Christus University Center (UNICHRISTU), Brazil; Federal University of Ceara (UFC), Brazil},
abstract={The development of an adequate performance measurement system represents a challenge for all modern companies, including those applying Lean Construction (LC) principles. LC companies adopt a broader scope that focuses not only on traditional financial performance but also on process improvement and value creation. Thus, management should evolve to embrace different performance criteria and related indicators. However, in spite of advances in other industries, in the construction sector, as well as LC, it is slow. Thus, this paper aims to analyse the performance measurement process in lean construction context through a multiple case study. This paper is expected to contribute to efforts undertaken by practitioners and academics because the framework and the gaps identified provide guidelines to what works and to what does not work when implementing performance measurement systems especially in LC projects.},
author_keywords={Lean construction;  Metrics;  Performance indicators;  Performance measurement;  Strategy},
keywords={Construction industry;  Supply chain management, Lean construction;  Metrics;  Performance indicators;  Performance measurements;  Strategy, Lean production},
references={Åhlström, P., Sequences in the implementation of lean production (1998) European Management Journal, 16 (3), pp. 327-334; Alarcón, L.F., Salvatierra, J.L., Letelier, J.A., Using last planner indicators to identify early signs of project performance (2014) 22nd Annual Conference of the International Group for Lean Construction, pp. 547-558. , Kalsaas, B. T., Koskela, L. & Saurin, T. A., Oslo, Norway, 25-27 Jun. 2014; Ballard, G., Production system design in construction (2001) Annual Conference of the International Group for Lean Construction, 9, pp. 1-15; Bassioni, H.A., Price, A.D.F., Hassan, T.M., Performance measurement in construction (2004) Journal of Management in Engineering, 20 (2), pp. 42-50; Candido, L.F., Heineck, L.F.M., Neto, J.D.P.B., Critical analysis on earned value management (EVM) technique in building construction (2014) 22nd Annual Conference of the International Group for Lean Construction, pp. 159-170. , Kalsaas, B. T., Koskela, L. & Saurin, T. A., Oslo, Norway, 25-27 Jun. 2014; Cândido, L.F., De Lima, S.H.O., De Barros Neto, J.P., Análise de Sistemas de Medição de Desempenho na Indústria da Construção (2016) Ambiente Construido, 16 (2), pp. 189-208; Chavez, R., Internal lean practices and operational performance: The contingency perspective of industry clockspeed (2013) International Journal of Operations & Production Management, 33 (5), pp. 562-588; Choong, K.K., Has this large number of performance measurement publications contributed to its better understanding? A systematic review for research and applications (2013) International Journal of Production Research, 52 (14), pp. 4174-4197; Choong, K.K., Understanding the features of performance measurement system: A literature review (2013) Measuring Business Excellence, 17 (4), pp. 102-121; Costa, D.B., Benchmarking initiatives in the construction industry: Lessons learned and improvement opportunities (2006) Journal of Management in Engineering, 22 (4), pp. 158-167; Deng, F., Smyth, H., Anvuur, A., A critical review of PMS in construction: Towards a research agenda (2012) Annual ARCOM Conference, 28, pp. 807-816. , September; Esparia, F., Tsao, C.C., Hauser, M., Driving continuous improvement by developing and leveraging lean key performance indicators (2012) 20th Annual Conference of the International Group for Lean Construction, , Tommelein, I. D. & Pasquire, C. L., San Diego, USA, 18-20 Jul. 2012; Flapper, S.D.F., Fortuin, L., Stoop, P.P.M., Towards consistent perfromance management system (2006) International Journal of Operations & Production Management, 16 (7), pp. 27-37; Franco-Santos, M., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: A review of their consequences and a framework for research (2012) Management Accounting Research, 23 (2), pp. 79-119; Fullerton, R.R., Wempe, W.F., Lean manufacturing, non-financial performance measures, and financial performance (2009) International Journal of Operations & Production Management, 29 (3), pp. 214-240; Horman, M., Kenley, R., The application of lean production to project management (1996) 4th Annual Conference of the International Group for Lean Construction, pp. 1-8. , Birmingham, UK; Horstman, A., Witteveen, W., Performance indicators in the best value approach (2013) Advanced of Performance Information & Value, 5 (2), pp. 59-78; Jin, Z., Practical framework for measuring performance of international construction firms (2013) Journal of Construction Engineering and Management, 139 (9), pp. 1154-1167; Kagioglou, M., Cooper, R., Aquad, G., Performance management in construction: A conceptual framework (2001) Construction Management and Economics, 19, pp. 85-95; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Havard Business Review, 70 (1), pp. 70-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Korb, S., "Respect for people" and lean construction: Has the boat been missed? (2016) 24th Annual Conference of the International Group for Lean Construction, , Boston, USA, 20-22 Jul. 2016; Korde, T., Li, M., Russell, A.D., State-of-the-art review of construction performance models and factors (2005) Construction Research Congress 2005, pp. 1-14. , San Diego, E. U. A: ASCE; Koskela, L., (1992) Application of the New Production Philosophy to Construction, , Stanford: Techniccal Repport 72. Center for Integrated Facility Engineering. Department of Civil Engineering. Stanford Univeristy; Kueng, P., Andreas, M., Wttstein, T., Performance measurement systems must be engineered (2001) Communications of the Association for Information Systems, 7 (3), pp. 1-27; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1), pp. 23-35; Li, S., Wu, X., Hu, S., Hu, B., Performance evaluation of lean construction projects based on balanced scorecard (2015) 23rd Annual Conference of the International Group for Lean Construction, pp. 753-762. , Seppanen, O., Gonzalez, V. A. & Arroyo, P., Perth, Australia, 29-31 Jul. 2015; Micheli, P., Mari, L., The theory and practice of performance measurement (2013) Management Accounting Research, 25 (2), pp. 147-156; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Nudurupati, S., Arshad, T., Turner, T., Performance measurement in the construction industry: An action case investigating manufacturing methodologies (2007) Computers in Industry, 58 (7), pp. 667-676; Nudurupati, S.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Sacks, R., Construction flow index: A metric of production flow quality in construction (2017) Construction Management and Economics, 6193, pp. 1-19. , February; Sink, D.S., Tuttle, T.C., (1993) Planejamento e Medicao para Performance, , Rio de Janeiro, RJ: Qualitymark; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Jounal of Operations & Production Management, 21 (1-2), pp. 46-70; Wegelius-Lehtonen, T., Performance measurement in construction logistics (2001) International Journal of Production Economics, 69 (1), pp. 107-116; Yang, H., A critical review of performance measurement in construction (2010) Journal of Facilities Management, 8 (4), pp. 269-284; Yin, R.K., (2010) Estudo de Caso: Planejamento e Metodos, , 4th ed., Porto Alegre: Bookman},
editor={Brilakis I., Walsh K., Sacks R.},
publisher={The International Group for Lean Construction},
language={English},
abbrev_source_title={IGLC - Proc. Annu. Conf. Int. Group Lean Constr.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Jardioui2017553,
author={Jardioui, M. and Garengo, P. and El Alami, S.},
title={The impact of organizational culture on performance measurement system design, implementation and use: Evidence from Moroccan SMEs},
journal={IFIP Advances in Information and Communication Technology},
year={2017},
volume={513},
pages={553-559},
doi={10.1007/978-3-319-66923-6_65},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029357665&doi=10.1007%2f978-3-319-66923-6_65&partnerID=40&md5=6c8d19bda570009d4c1c5c582d4af5de},
affiliation={Laboratory IM2I, Hassan I University, Settat, Morocco; Department of Industrial Engineering, University of Padua, Padua, Italy},
abstract={Several earlier studies have viewed organizational culture (OC) as a key factor for performance measurement systems (PMS), yet its role is not well understood and the reviewed literature indicates a gap in articles describing the relationship between various types of OC and PMS. Thus, the following study sets out to explore how OC type impacts PMS design, implementation and use. The investigation is carried out using the case study methodology in two Moroccan manufacturing SMEs. Our findings suggest that there is a relationship between OC type and PMS life cycle. © IFIP International Federation for Information Processing 2017.},
author_keywords={Organizational culture;  Performance measurement system;  Semi-structured interviews;  Small and medium enterprises},
keywords={Industrial management;  Life cycle;  Manufacture, Case study methodologies;  Key factors;  Organizational cultures;  Performance measurement system;  Semi structured interviews;  Small and medium enterprise, Supply chain management},
references={Cocca, P., Alberti, M., A framework to assess performance measurement systems in SMEs (2010) Int. J. Prod. Perform. Manag., 59 (2), pp. 186-200; Garengo, P., Sharma, M.K., Performance measurement system contingency factors: A cross analysis of Italian and Indian SMEs (2014) Prod. Plan. Control, 25 (3), pp. 220-240; Bourne, M., Researching performance measurement system implementation: The dynamics of success and failure (2005) Prod. Plan. Control, 16 (2), pp. 101-113; Jwijati, I.M., Bititci, U.S., Exploring the impact of national culture on performance measurement (2014) APMS 2014. IAICT, 440, pp. 425-432. , Grabot, B., Vallespir, B., Gomes, S., Bouras, A., Kiritsis, D. (eds.), Springer, Heidelberg; Hofstede, G., (1984) Culture’s Consequences: International Differences in Work-Related Values, 5. , Sage, London; Kandula, S.R., (2006) Performance Management: Strategies, Interventions, , Drivers. PHI Learning Pvt. Ltd., New Delhi; Garengo, P., Bititci, U., Towards a contingency approach to performance measurement: An empirical study in Scottish SMEs (2007) Int. J. Oper. Prod. Manag., 27 (8), pp. 802-825; Jwijati, I., Bititci, U.S., The impact of organizational culture on the design and use of performance management systems (2015) Proceeding of the 22Th International Annual Euroma Conference, Neuchâtel, Switzerland, 26 June–1, July; Franco-Santos, M., Bourne, M., An examination of the literature relating to issues affecting how companies manage through measures (2005) Prod. Plan. Control, 16 (2), pp. 114-124; Bourne, M., Kennerley, M., Franco-Santos, M., Managing through measures: A study of impact on performance (2005) J. Manuf. Technol. Manag., 16 (4), pp. 373-395; Bititci, U.S., Dynamics of performance measurement and organisational culture (2006) Int. J. Oper. Prod. Manag., 26 (12), pp. 1325-1350; Bititci, U.S., The interplay between performance measurement, organizational culture and management styles (2004) Meas. Bus. Excell., 8 (3), pp. 28-41; Bourne, M., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1288-1310; Assiri, A., Zairi, M., Eid, R., How to profit from the balanced scorecard: An implementation roadmap (2006) Ind. Manag. Data Syst., 106 (7), pp. 937-952; De Waal, A.A., Counet, H., Lessons learned from performance management systems implementations (2009) Int. J. Prod. Perform. Manag., 58 (4), pp. 367-390; Henri, J.-F., Organizational culture and performance measurement systems (2006) Acc. Organ. Soc., 31 (1), pp. 77-103; Mendibil, K., Macbryde, J., Factors that affect the design and implementation of team-based performance measurement systems (2006) Int. J. Product. Perform. Manag., 55 (2), pp. 118-142; Garengo, P., Nudurupati, S., Bititci, U., Understanding the relationship between PMS and MIS in SMEs: An organizational life cycle perspective (2007) Comput. Ind., 58 (7), pp. 677-686; Cameron, K.S., Quinn, R.E., (1999) Diagnosing and Changing Organizational Culturebased on the Competing Values Framework, , Addison-Wesley Longman, Reading; Bourne, M., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20 (7), pp. 754-771},
correspondence_address1={Jardioui, M.; Laboratory IM2I, Morocco; email: m.jardioui@uhp.ac.ma},
editor={Riedel R., Thoben K.-D., von Cieminski G., Kiritsis D., Lodding H.},
publisher={Springer New York LLC},
issn={18684238},
isbn={9783319669229},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Man2017301,
author={Man, J.C.D. and Andersen, B.Sø. and Strandhagen, J.O.},
title={Addressing the Performance of Order Acceptance},
journal={Procedia CIRP},
year={2017},
volume={63},
pages={301-306},
doi={10.1016/j.procir.2017.03.101},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028687680&doi=10.1016%2fj.procir.2017.03.101&partnerID=40&md5=b19f172ff4d3835af58b675d6c8573d2},
affiliation={Department of Production and Quality Engineering, Norwegian University of Science and Technology, S. P. Andersens veg 5, Trondheim, 7031, Norway},
abstract={While there are many mathematical approaches to order acceptance (OA), the performance of OA in practice is overlooked. This paper assesses current OA methods and performance measures that could measure OA. The outcome is a conceptual performance measurement system for OA that measures performance through utilization of resources, output and flexibility in a diagnostic and predictive manner. This paper found a close relationship between OA and S&OP, as they both match supply and demand. The contribution of this paper is that it detaches OA from mathematical models and proposes a method to assess the value creation of OA in practice. © 2017 The Authors. Published by Elsevier.},
author_keywords={Due dates;  Order Acceptance;  Performance Measurement;  Pricing;  S&OP;  Scheduling;  Supply Chain Management;  Value Creation},
keywords={Costs;  Economics;  Manufacture;  Scheduling, Due dates;  Mathematical approach;  Order acceptance;  Performance measure;  Performance measurement system;  Performance measurements;  Utilization of resources;  Value creation, Supply chain management},
references={Kalantari, M., Rabbani, M., Ebadian, M., A decision support system for order acceptance/rejection in hybrid MTS/MTO production systems (2011) Applied Mathematical Modelling, 35, pp. 1363-1377; Slotnick, S.A., Order acceptance and scheduling: A taxonomy and review (2011) European Journal of Operational Research, 212, pp. 1-11; Ebben, M.J.R., Hans, E.W., Olde Weghuis, F.M., Workload based order acceptance in job shop environments (2005) OR Spectrum, 27, pp. 107-122; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15, pp. 80-116; Harbour, J.L., (2009) The Basics of Performance Measurement, , CRC Press; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 275-292; Wacker, J.G., A definition of theory: Research guidelines for different theory-building research methods in operations management (1998) Journal of Operations Management, 16, pp. 361-385; Hemmati, S., Ebadian, M., Nahvi, A., A new decision making structure for managing arriving orders in MTO environments (2012) Expert Systems with Applications, 39, pp. 2669-2676; Ten Kate, H.A., Towards a better understanding of order acceptance (1994) International Journal of Production Economics, 37, pp. 139-152; Zijm, W., Towards intelligent manufacturing planning and control systems (2000) OR-Spektrum, 22, pp. 313-345; Thomé, A.M.T., Sales and operations planning: A research synthesis (2012) International Journal of Production Economics, 138, pp. 1-13; Thomé, A.M.T., Sales and operations planning and the firm performance (2012) International Journal of Productivity and Performance Management, 61, pp. 359-381; Choong, K.K., Understanding the features of performance measurement system: A literature review (2013) Measuring Business Excellence, 17, pp. 102-121; (2012) Supply Chain Operations Reference Model. Revision 11.0, , Supply Chain Council; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21, pp. 71-87; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55, pp. 242-258; Van Hoek, R.I., "measuring the immeasurable" - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3, pp. 187-192; Ittner, C.D., Larcker, D.F., Coming up Short on Nonfinancial Performance Measurement (2003) Harvard Business Review, 81, pp. 88-95 and 139},
correspondence_address1={Man, J.C.D.; Department of Production and Quality Engineering, S. P. Andersens veg 5, Norway; email: hans.deman@ntnu.no},
editor={Wang Y., Tseng M.M., Tsai H.-Y.},
publisher={Elsevier B.V.},
issn={22128271},
language={English},
abbrev_source_title={Procedia CIRP},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Anjomshoae2017194,
author={Anjomshoae, A. and Hassan, A. and Kunz, N. and Wong, K.Y. and de Leeuw, S.},
title={Toward a dynamic balanced scorecard model for humanitarian relief organizations’ performance management},
journal={Journal of Humanitarian Logistics and Supply Chain Management},
year={2017},
volume={7},
number={2},
pages={194-218},
doi={10.1108/JHLSCM-01-2017-0001},
note={cited By 53},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023623915&doi=10.1108%2fJHLSCM-01-2017-0001&partnerID=40&md5=4c7c0755092bbd7694ddf57f9c27dc75},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia; Coggin College of Business, University of North Florida, Jacksonville, FL, United States; Department of Information Systems and Logistics, VU University Amsterdam, Amsterdam, Netherlands; Department of Management, Nottingham Business School, Nottingham Trent University, Nottingham, United Kingdom},
abstract={Purpose: In recent years, the balanced scorecard (BSC) has received considerable interest among practitioners for managing their organization’s performance. Unfortunately existing BSC frameworks, particularly for humanitarian supply chains, lack causal relationships among performance indicators, actions, and outcomes. They are not able to provide a dynamic perspective of the organization with factors that drive the organization’s behavior toward its mission. Lack of conceptual references seems to hinder the development of a performance measurement system toward this direction. The paper aims to discuss these issues. Design/methodology/approach: The authors formulate the interdependencies among key performance indicators (KPIs) in terms of cause-and-effect relationships based on published case studies reported in international journals from 1996 to 2017. Findings: This paper aims to identify the conceptual interdependencies among KPIs and represent them in the form of a conceptual model. Research limitations/implications: The study was solely based on relevant existing literature. Therefore further practical research is needed to validate the interdependencies of performance indicators in the strategy map. Practical implications: The proposed conceptual model provides the structure of a dynamic balanced scorecard (DBSC) in the humanitarian supply chain and should serve as a starting reference for the development of a practical DBSC model. The conceptual framework proposed in this paper aims to facilitate further research in developing a DBSC for humanitarian organizations (HOs). Originality/value: Existing BSC frameworks do not provide a dynamic perspective of the organization. The proposed conceptual framework is a useful reference for further work in developing a DBSC for HOs. © 2017, © Emerald Publishing Limited.},
author_keywords={Dynamic balanced scorecard;  Humanitarian supply chain;  Key performance indicator;  Performance measurement;  System dynamics},
references={Abidi, H., Scholten, K., Applicability of performance measurement systems to humanitarian supply chains (2015) Humanitarian Logistics and Sustainability, pp. 235-260. , Klumpp, M., de Leeuw, S., Regattieri, A. and de Souza, R.and (Eds), Springer, Cham; Abidi, H., de Leeuw, S., Klumpp, M., Humanitarian supply chain performance management: a systematic literature review (2014) Supply Chain Management – An International Journal, 19 (5-6), pp. 592-608; Acimovic, J., Goentzel, J., Models and metrics to assess humanitarian response capacity (2016) Journal of Operations Management, 45 (July), pp. 11-29; Akhtar, P., Marr, N.E., Garnevska, E.V., Coordination in humanitarian relief chains: chain coordinators (2012) Journal of Humanitarian Logistics and Supply Chain Management, 2 (1), pp. 85-103; Akkermans, H.A., van Oorschot, K.E., Relevance assumed: a case study of balanced scorecard development using system dynamics (2005) Journal of the Operational Research Society, 56 (8), pp. 931-941; Allen, A.M., Kovács, G., Masini, A., Vaillancourt, A., Wassenhove, L.V., Exploring the link between the humanitarian logistician and training needs (2013) Journal of Humanitarian Logistics and Supply Chain Management, 3 (2), pp. 129-148; Apte, A., Goncalves, P., Yoho, K., Capabilities and competencies in humanitarian operations (2016) Journal of Humanitarian Logistics and Supply Chain Management, 6 (2), pp. 240-258; Balcik, B., Beamon, B.M., Facility location in humanitarian relief (2008) International Journal of Logistics Research and Applications, 11 (2), pp. 101-121; Balcik, B., Beamon, B.M., Krejci, C.C., Muramatsu, K.M., Ramirez, M., Coordination in humanitarian relief chains: practices, challenges and opportunities (2010) International Journal of Production Economics, 126 (1), pp. 22-34; Barlas, Y., Formal aspects of model validity and validation in system dynamics (1996) System Dynamics Review, 12 (3), pp. 183-210; Barnabè, F., A ‘system dynamics‐based balanced scorecard’ to support strategic decision making: insights from a case study (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 446-473; Barnabè, F., Busco, C., The causal relationships between performance drivers and outcomes (2012) Journal of Accounting & Organizational Change, 8 (4), pp. 528-538; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Berenguer, G., Modeling approaches and metrics to evaluate nonprofit operations (2016) Advances in Managing Humanitarian Operations, pp. 9-31. , Zobel, C.W., Altay, N. and Haselkorn, M.P (Eds), Springer International Publishing, Cham; Besiou, M., Stapleton, O., Van Wassenhove, L.N., System dynamics for humanitarian operations (2011) Journal of Humanitarian Logistics and Supply Chain Management, 1 (1), pp. 78-103; Bianchi, C., Montemaggiore, G.B., Enhancing strategy design and planning in public utilities through ‘dynamic’ balanced scorecards: insights from a project in a city water company (2008) System Dynamics Review, 24 (2), pp. 175-213; Bianchi, C., Rivenbark, W.C., Performance management in local government: the application of system dynamics to promote data use (2014) International Journal of Public Administration, 37 (13), pp. 945-954; Blecken, A., Supply chain process modelling for humanitarian organizations (2010) International Journal of Physical Distribution & Logistics Management, 40 (8-9), pp. 675-692; Blecken, A., Hellingrath, B., Dangelmaier, W., Schulz, S.F., A humanitarian supply chain process reference model (2009) International Journal of Services Technology and Management, 12 (4), pp. 391-413; Bölsche, D., (2012) Performance measurement in humanitarian logistics – a process-oriented perspective, , paper presented at the Proceedings from the 2nd International HumLog Workshop, Essen, September 24; Brown, E., Slivinski, A., Nonprofit organizations and the market (2006) The Nonprofit Sector: A Research Handbook, pp. 140-158. , Powell, W.W. and Steinberg, R.and (Eds), Yale University Press, New Haven, CT; Brown, P.H., Minty, J.H., Media coverage and charitable giving after the 2004 Tsunami (2008) Southern Economic Journal, 75 (1), pp. 9-25; Burkart, C., Besiou, M., Wakolbinger, T., The funding – humanitarian supply chain interface (2017) Surveys in Operations Research and Management Science, 21 (2), pp. 31-45; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) The International Journal of Logistics Management, 6 (1), pp. 61-74; Careem, M., De Silva, C., De Silva, R., Raschid, L., Weerawarana, S., (2006) Sahana: overview of a disaster management system, , paper presented at the International Conference on Information and Automation, ICIAColombo, December 15-17; Charles, A., Lauras, M., An enterprise modelling approach for better optimisation modelling: application to the humanitarian relief chain coordination problem (2011) OR Spectrum, 33 (3), pp. 815-841; Charles, F., (1996) Reputation: Realizing Value from the Corporate Image, , Harvard Business School Press, Boston, MA; Cheung, C.K., Chan, C.M., Social-cognitive factors of donating money to charity, with special attention to an international relief organization (2000) Evaluation and Program Planning, 23 (2), pp. 241-253; Currion, P., Silva, C., Van de Walle, B., Open source software for disaster management (2007) Communications of the ACM, 50 (3), pp. 61-65; D’Haene, C., Verlinde, S., Macharis, C., Measuring while moving (humanitarian supply chain performance measurement – status of research and current practice) (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (2), pp. 146-161; Darcy, J., Hofmann, C., (2003) According to need?, , Humanitarian Policy Group Report 5, p. 15; Daugirdas, K., Reputation and the responsibility of international organizations (2014) European Journal of International Law, 25 (4), pp. 991-1018; Davidson, A.L., (2006) Key performance indicators in humanitarian logistics, , Master thesis, Massachusetts Institute of Technology, Cambridge, MA; De Leeuw, S., Towards a reference mission map for performance measurement in humanitarian supply chains (2010) Proceedings of the 11th Working Conference on Virtual Enterprises, pp. 166-172. , Camarinha-Matos, L.M. and Boucher, X (Eds), Berlin Springer Verlag, Berlin; De Leeuw, S., (2016) Towards sector-wide supply chain KPIs, , paper presented at the 27th Annual POMS Conference, Orlando, FL, May 6-9; Dubey, R., Gunasekaran, A., Altay, N., Childe, S.J., Papadopoulos, T., Understanding employee turnover in humanitarian organizations (2016) Industrial and Commercial Training, 48 (4), pp. 208-214; Ford, D.N., Sterman, J.D., Expert knowledge elicitation to improve formal and mental models (1998) System Dynamics Review, 14 (4), pp. 309-340; (2008) Helios overview; Galaskiewicz, J., Colman, M.S., Collaboration between corporations and nonprofit organizations (2006) The Nonprofit Sector: A Research Handbook, pp. 180-204. , Powell, W.W. and Steinberg, R.and (Eds), Yale University Press, New Haven, CT; Galindo, G., Batta, R., Prepositioning of supplies in preparation for a hurricane under potential destruction of prepositioned supplies (2013) Socio-Economic Planning Sciences, 47 (1), pp. 20-37; Gerdin, M., Chataigner, P., Tax, L., Kubai, A., von Schreeb, J., Does need matter? Needs assessments and decision-making among major humanitarian health agencies (2014) Disasters, 38 (3), pp. 451-464; Howden, M., (2009) How humanitarian logistics information systems can improve humanitarian supply chains: a view from the field, , paper presented at the 6th International ISCRAM Conference, Gothenburg; Hunt, S.D., Morgan, R.M., The comparative advantage theory of competition (1995) Journal of Marketing, 59 (2), pp. 1-15; Jahre, M., Heigh, I., Does the current constraints in funding promote failure in humanitarian supply chains? (2008) Supply Chain Forum: An International Journal, 9 (2), pp. 44-54; Jahre, M., Jensen, L.M., Listou, T., Theory development in humanitarian logistics: a framework and three cases (2009) Management Research News, 32 (11), pp. 1008-1023; Jassbi, J., Mohamadnejad, F., Nasrollahzadeh, H., A fuzzy DEMATEL framework for modeling cause and effect relationships of strategy map (2011) Expert Systems with Applications, 38 (5), pp. 5967-5973; Kabra, G., Ramesh, A., Arshinder, K., Identification and prioritization of coordination barriers in humanitarian supply chain management (2015) International Journal of Disaster Risk Reduction, 13 (September), pp. 128-138; Kaplan, R.S., (1990) Measures for Manufacturing Excellence, , Harvard Business Press, Boston, MA; Kaplan, R.S., Strategic performance measurement and management in Nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 353-370; Kaplan, R.S., Norton, D.P., Balanced scorecard: measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Having trouble with your strategy? Then map it (2000) Harvard Business Review, 78 (5), pp. 167-176; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: part I (2001) Accounting Horizons, 15 (1), pp. 87-104; Khakbaz, S.B., Hajiheydari, N., Proposing a basic methodology for developing balanced scorecard by system dynamics approach (2015) Kybernetes, 44 (6-7), pp. 1049-1066; Korff, V.P., Balbo, N., Mills, M., Heyse, L., Wittek, R., The impact of humanitarian context conditions and individual characteristics on aid worker retention (2015) Disasters, 39 (3), pp. 522-545; Kovacs, G., Spens, K.M., Knowledge sharing in relief supply chains (2010) International Journal of Networking and Virtual Organisations, 7 (2-3), pp. 222-239; Kovács, G., Spens, K.M., Trends and developments in humanitarian logistics – a gap analysis (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 32-45; Kovács, G., Tatham, P., What is special about a humanitarian logistician? A survey of logistic skills and performance (2010) Supply Chain Forum: An International Journal, 11 (3), pp. 32-41; Kovács, G., Tatham, P., Larson, P.D., What skills are needed to be a humanitarian logistician? (2012) Journal of Business Logistics, 33 (3), pp. 245-258; Kruke, B.I., Olsen, O.E., Knowledge creation and reliable decision-making in complex emergencies (2012) Disasters, 36 (2), pp. 212-232; Kunz, N., Reiner, G., Gold, S., Investing in disaster management capabilities versus pre-positioning inventory: a new approach to disaster preparedness (2014) International Journal of Production Economics, 157 (November), pp. 261-272; Kunz, N., Wassenhove, L.N.V., McConnell, R., Hov, K., Centralized vehicle leasing in humanitarian fleet management: the UNHCR case (2015) Journal of Humanitarian Logistics and Supply Chain Management, 5 (3), pp. 387-404; Leete, L., Work in the nonprofit sector (2006) The Nonprofit Sector: A Research Handbook, pp. 159-179. , Powell, W.W. and Steinberg, R (Eds), Yale University Press, New Haven, CT; Linard, K., Dvorsky, L., (2001) People – not human resources: the system dynamics of human capital accounting, , paper presented at the Operations Research Society Conference, University of Bath, September; Loquercio, D., Hammersley, M., Emmens, B., (2006) Understanding and addressing staff turnover in humanitarian agencies: overseas development institute (ODI), , Humanitarian practice network (HPN); Lu, Q., Goh, M., Souza, R., A SCOR framework to measure logistics performance of humanitarian organizations (2016) Journal of Humanitarian Logistics and Supply Chain Management, 6 (2), pp. 222-239; Marr, B., Schiuma, G., Business performance measurement – past, present and future (2003) Management Decision, 41 (8), pp. 680-687; Martin, J.A., Disasters and donations: the conditional effects of news attention on charitable giving (2013) International Journal of Public Opinion Research, 25 (4), pp. 547-560; Maxwell, D., Bailey, S., Harvey, P., Walker, P., Sharbatke-Church, C., Savage, K., Preventing corruption in humanitarian assistance: perceptions, gaps and challenges (2012) Disasters, 36 (1), pp. 140-160; Medina-Borja, A., Triantis, K., A conceptual framework to evaluate performance of non-profit social service organisations (2006) International Journal of Technology Management, 37 (1-2), pp. 147-161; Meijer, M.-M., The effects of charity reputation on charitable giving (2009) Corporate Reputation Review, 12 (1), pp. 33-42; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced scorecard for natural disaster management projects (2007) Disaster Prevention and Management, 16 (5), pp. 785-806; Moke, M., Rüther, M., Media and humanitarian action (2015) The Humanitarian Challenge, pp. 253-263. , Gibbons, P. and Heintze, H.and (Eds), Springer, Cham; Morecroft, J.D., (2015) Strategic Modelling and Business Dynamics: A Feedback Systems Approach, , Wiley, Chichester; Nørreklit, H., The balance on the balanced scorecard a critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Nørreklit, H., The balanced scorecard: what is the score? A rhetorical analysis of the balanced scorecard (2003) Accounting, Organizations and Society, 28 (6), pp. 591-619; Oloruntoba, R., Gray, R., Humanitarian aid_ an agile supply chain? (2006) Supply Chain Management: An International Journal, 11 (2), pp. 115-120; Oloruntoba, R., Gray, R., Customer service in emergency relief chains (2009) International Journal of Physical Distribution & Logistics Management, 39 (6), pp. 486-505; Olsen, G.R., Carstensen, N., Høyen, K., Humanitarian crises: what determines the level of emergency assistance? Media coverage, donor interests and the aid business (2003) Disasters, 27 (2), pp. 109-126; Oosterhof, L., Heuvelman, A., Peters, O., Donation to disaster relief campaigns: underlying social cognitive factors exposed (2009) Evaluation and Program Planning, 32 (2), pp. 148-157; Othman, R., Balanced scorecard and causal model development: preliminary findings (2006) Management Decision, 44 (5), pp. 690-702; Pedraza-Martinez, A.J., Van Wassenhove, L.N., Vehicle replacement in the international committee of the red cross (2013) Production and Operations Management, 22 (2), pp. 365-376; Perkins, M., Grey, A., Remmers, H., What do we really mean by ‘balanced scorecard’? (2014) International Journal of Productivity and Performance Management, 63 (2), pp. 148-169; Pettit, S., Beresford, A., Critical success factors in the context of humanitarian aid supply chains (2009) International Journal of Physical Distribution & Logistics Management, 39 (6), pp. 450-468; Qiang, P., Nagurney, A., A bi-criteria indicator to assess supply chain network performance for critical needs under capacity and demand disruptions (2012) Transportation Research Part A: Policy and Practice, 46 (5), pp. 801-812; Reyes, P.M., Meade, L.M., Improving reverse supply chain operational performance: a transshipment application study for not‐for‐profit organizations (2006) Journal of Supply Chain Management, 42 (1), pp. 38-48; Santarelli, G., Abidi, H., Klumpp, M., Regattieri, A., Humanitarian supply chains and performance measurement schemes in practice (2015) International Journal of Productivity and Performance Management, 64 (6), pp. 784-810; Sargeant, A., Charitable giving: towards a model of donor behaviour (1999) Journal of Marketing Management, 15 (4), pp. 215-238; Sargeant, A., Ford, J.B., West, D.C., Perceptual determinants of nonprofit giving behavior (2006) Journal of Business Research, 59 (2), pp. 155-165; Sarstedt, M., Schloderer, M.P., Developing a measurement approach for reputation of non-profit organizations (2009) International Journal of Nonprofit and Voluntary Sector Marketing, 15 (3); Schiffling, S., Piecyk, M., Performance measurement in humanitarian logistics: a customer-oriented approach (2014) Journal of Humanitarian Logistics and Supply Chain Management, 4 (2), pp. 198-221; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Management Research News, 32 (11), pp. 1038-1049; Senge, P.M., (1990) The Art and Practice of the Learning Organization, , Doubleday, New York, NY; Sloper, P., Linard, K.T., Paterson, D., (1999) Towards a dynamic feedback framework for public sector performance management, , paper presented at the International Conference of the System Dynamics & ANZSYS, July 20-23; Smith, D.H., Shen, C., Factors characterizing the most effective nonprofits managed by volunteers (1996) Nonprofit Management and Leadership, 6 (3), pp. 271-289; Sterman, J.D., (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World, 19. , Irwin/McGraw-Hill, Boston, MA; Tatham, P., Hughes, K., Humanitarian logistics metrics: where we are and how we might improve (2011) Humanitarian Logistics: Meeting the Challenge of Preparing for and Responding to Disasters, pp. 65-84. , Christopher, K. and Tatham, P.and (Eds), Kogan Page, London; Tatham, P., Spens, K., Towards a humanitarian logistics knowledge management system (2011) Disaster Prevention and Management: An International Journal, 20 (1), pp. 6-26; Taupiac, C., (2001) Humanitarian and development procurement: a vast and growing market, (4), pp. 7-10. , paper presented at the International Trade Forum; Telford, J., Cosgrave, J., The international humanitarian system and the 2004 Indian ocean earthquake and tsunamis (2007) Disasters, 31 (1), pp. 1-28; Thomas, A., (2003) Humanitarian Logistics: Enabling Disaster Response, , www.fritzinstitute.org/pdfs/whitepaper/enablingdisasterresponse.pd, Fritz Institute, San Francisco, CA; Thomas, A.S., Kopczak, L.R., From logistics to supply chain management: the path forward in the humanitarian sector (2005) Fritz Institute, pp. 1-15. , www.fritzinstitute.org/pdfs/whitepaper/fromlogisticsto.pdf, March 15, 2016, San Francisco, CA: :, (accessed; Tomasini, R., Van Wassenhove, L., (2009) Humanitarian Logistics, , Palgrave Macmillan, Basingstoke; Tomasini, R., Stapleton, O., Van Wassenhove, L.N., (2010) The challenges of matching private sector donations to the humanitarian needs and the role of brokers, , Working Papers Collection, 41, INSEAD, Fontainebleau, 1-25; (2011) Surge in demand for humanitarian assistance in high-risk environments informs general assembly debate on strengthening UN disaster relief assistance (Publication No. GA/11038), , www.un.org/News/Press/docs/2010/ga11038.doc.htm, June 25, 2016, :, (accessed; Van der Laan, E., De Brito, M., Vergunst, D., Performance measurement in humanitarian supply chains (2009) International Journal of Risk Assessment and Management, 13 (1), pp. 22-45; Van der Laan, E.A., De Brito, M.P., Van Fenema, P.C., Vermaesen, S., Managing information cycles for intra-organisational coordination of humanitarian logistics (2009) International Journal of Services Technology and Management, 12 (4), pp. 362-390; Van Wassenhove, L.N., Humanitarian aid logistics: supply chain management in high gear† (2006) Journal of the Operational Research Society, 57 (5), pp. 475-489; Vennix, J.A., Group model-building: tackling messy problems (1999) System Dynamics Review, 15 (4), p. 379; Warren, K., (2002) Competitive Strategy Dynamics, , Wiley, Chichester; Warren, K., Strategy dynamics for nonprofit organizations (2007) Next Generation Business Handbook, pp. 431-449. , Chowdhury, S (Ed.), Wiley-Interscience, Hoboken, NJ; Warren, K., (2008) Strategic Management Dynamics, , Wiley, Chichester; Widera, A., Hellingrath, B., (2011) Performance measurement systems for humanitarian logistics, , paper presented at the Proceedings of the 23rd Annual NOFOMA Conference, Harstad; Widera, A., Hellingrath, B., Making performance measurement work in humanitarian logistics: the case of an IT-supported balanced scorecard (2016) Supply Chain Management for Humanitarians: Tools for Practice, pp. 339-352. , Kovács, G., Spens, K.Haavisto, I.and and (Eds), Kogan Page Publishers; Willems, J., Jegers, M., Faulk, L., Organizational effectiveness reputation in the nonprofit sector (2016) Public Performance & Management Review, 39 (2), pp. 454-475; Wynter, A., (2005) Humanitarian media coverage in the digital age in the digital age, , Red Cross Red Crescent World Disasters Report, Kumarian Press Inc., Broomfield, CT, 126-149; Yang, M.-C., Tung, Y.-C., Using path analysis to examine causal relationships among balanced scorecard performance indicators for general hospitals: the case of a public hospital system in Taiwan (2006) Health Care Management Review, 31 (4), pp. 280-288; Zhang, D., Zhou, L., Nunamaker, J.F., Jr., A knowledge management framework for the support of decision making in humanitarian assistance/disaster relief (2002) Knowledge and Information Systems, 4 (3), pp. 370-385},
correspondence_address1={Anjomshoae, A.; Department of Manufacturing and Industrial Engineering, Malaysia; email: aali5@live.utm.my},
publisher={Emerald Group Publishing Ltd.},
issn={20426747},
language={English},
abbrev_source_title={J. Humanit. Logist. Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Liebetruth2017539,
author={Liebetruth, T.},
title={Sustainability in Performance Measurement and Management Systems for Supply Chains},
journal={Procedia Engineering},
year={2017},
volume={192},
pages={539-544},
doi={10.1016/j.proeng.2017.06.093},
note={cited By 19},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85022205424&doi=10.1016%2fj.proeng.2017.06.093&partnerID=40&md5=2287655a86f91204c27b9778e7214df0},
affiliation={OTH Regensburg, Seybothstraße 2, Regensburg, 93053, Germany},
abstract={Aspects of sustainability - understood as the ability to manage economic, social and environmental performance at the same time - are becoming more important in Supply Chain Management. This is a challenge as sustainability adds less quantifiable aspects to Supply Chain Management than classic process aspects. On the other side measuring sustainability is crucial for the implementation of modern Supply Chains Management and to manage sustainably in the daily business. This contribution discusses the integration of sustainability in performance measurement and management systems (PMMS) for Supply Chain Management. Therefore in the paper firstly an overview of definitions and developments in performance measurement and management systems and a structure for PMMS are given. Secondly guidelines for good and modern PMMS are discussed. Thirdly existing approaches for Supply Chain Management PMMS (e. g. KPIs, TCO, value driver trees and balanced scorecards and maturity assessments) are presented and the suitability for Supply Chain Management as well as the possibility to integrate aspects of sustainability are examined. Lastly the fulfilment of the requirements and the ability to cope with the challenges of the approaches is discussed. © 2017 The Authors. Published by Elsevier Ltd.},
author_keywords={Balanced Scorecard;  Key Perfomance Indicators;  Maturity Assessment;  Performance Management Systems;  Performance Measurement Systems;  Supply Chain Management;  Sustainablility;  Total Cost of Ownership},
keywords={Costs;  Environmental management;  Sustainable development, Balanced scorecards;  Maturity assessments;  Perfomance;  Performance management systems;  Performance measurement system;  Sustainablility;  Total cost of ownership, Supply chain management},
references={Anthony, R.N., Govindarajan, V., (2000) Management Control Systems, , 10. Edition, New York; Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A Stakeholder Approach to Strat. Perf. Measurement (1997) Sloan Mgmt. Rev., Spring, pp. 25-37; Braz, R., Scavarda, L., Martins, R., Reviewing and improving performance measurement systems (2011) Int. J. Prod. Econ., 133, pp. 751-760; Bredrup, H., Performance Measurement (1995) Performance Management, pp. 169-190. , A. Rolstadas (Ed.) London; Brewer, P.C., Speh, T.W., Using the Balanced Scorecard to measure Supply Chain Performance (2000) J. of Business Logistics, (1), pp. 75-93; Caplice, C., Sheffi, Y., A Review and Evaluation of Logistics Performance Measurement Systems (1995) Int. J. of Log. Mgmt, (1), pp. 61-74; Forslund, H., Performance management in supply chains: Logistics service providers' perspective Int. J. of Physical Distribution & Logistics Management, 42 (3), pp. 296-311; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Prod. Pl. & Contr., 16 (6), pp. 531-544; Globerson, S., Issues in developing a performance criteria system for an organization (1985) Int. J. of Production Research, (4), pp. 639-646; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. of Operations & Production Management, (1-2), pp. 71-87; Hassini, E., Surti, C., Searcy, C.A., Literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. of Production Economics, 140, pp. 69-82; Hieber, R., (2002) Supply Chain Management: A Collaborative Approach, , Zürich: Vdf Wirtschaft; Kaplan, R.S., Norton, D.P., The Balanced Scorecard - Measures That Drive Performance (1992) Harvard Business Rev., 70 (1), pp. 71-79; Keebler, J.S., The State of Logistics Measurement (2000) The Supply & Logistics Journal, 3 (2), pp. 1-7; Kim, S., Adriaens, P., (2013) Correlating Sustainability and Financial Performance - What Measures Matter? A Study in the Pulp and Paper Industry, , Ross School of Business Working Paper No. 1192, Michigan: University of Michigan, July; Lakri, S., Jemai, Z., Performance Measurement and Management Systems of Supply chains: A Review of the Challenges they Raise (2015) Proceedings of the 6th IESM Conference, , October Seville, Spain; Lebas, M.J., Performance measurement and performance management (1995) Int. J. of Production Economics, (41), pp. 23-35; Liebetruth, T., (2005) Die Informationsbasis des Supply Chain Controllings, , Köln: Kölner Wissenschaftsverlag; Liebetruth, T., Melneck, W., Pilsl, A., Process maturity-assessments in strategic procurement (2015) Supply Management Research 2015, pp. 273-291. , R. Bogaschewsky, M. Eßig, R. Lasch, W. Stölzle (Ed.) Springer Gabler, Wiesbaden; Linton, J.D., Klassen, R., Jayaram, V., Sustainable supply chains: An introduction (2007) J. of Operations Management, 25, pp. 1075-1082; Mentzer, J.T., Konrad, B.P., An Efficiency/Effectiveness Approach to Logistics Performance Measurement (1991) J. of Bus. Log., (1), pp. 33-62; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) Int. J. of Operations & Production Mgmt, (4), pp. 80-116; Seuring, S., A review of modeling approaches for sustainable SCM (2013) Decision Support Systems, 54, pp. 1513-1520; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable SCM (2008) J. of Cleaner Prod., 16, pp. 1699-1710; Slater, S.F., Olson, E.M., Reddy, V.K., Strategy-Based Performance Measurement (1997) Business Horizons, (4), pp. 37-44; Spangenberg, H., (1994) Understanding and Implementing Performance Management, , Juta Limited; Verdecho, M.J., Alfaro-Saiz, J.J., Rodriguez-Rodriguez, R., Performance measurement framework for monitoring supply chain sustainability (2012) 6th Int. Conference on Industrial Engineering and Industrial Management, , Vigo: July 18-20; Weber, J., Bacher, A., Groll, M., Konzeption einer Balanced Scorecard (2002) Kostenrechnungspraxis, 46 (3), pp. 133-141; Weber, J., Wallenburg, C.M., Bühler, A., Singh, M., (2012) Logistik-Controlling mit Kennzahlensystemen, , Koblenz: Görres-Verlag GmbH; Wisner, J.D., Tan, K.-C., Leong, G., (2012) Supply Chain Management - A Balanced Approach, , 3rd Ed. South-Western CENGAGE Learning; Zairi, M., Benchmarking: The Best Tool for Measuring Competitiveness Benchmarking for QM & Technology, 1 (1), pp. 11-24},
correspondence_address1={Liebetruth, T.; OTH Regensburg, Seybothstraße 2, Germany},
publisher={Elsevier Ltd},
issn={18777058},
language={English},
abbrev_source_title={Procedia Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Marimin201760,
author={Marimin and Adhi, W. and Darmawan, M.A.},
title={Decision support system for natural rubber supply chain management performance measurement: A sustainable balanced scorecard approach},
journal={International Journal of Supply Chain Management},
year={2017},
volume={6},
number={2},
pages={60-74},
note={cited By 25},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021755282&partnerID=40&md5=67eb192f43946f10b5288c63a6a18e0a},
affiliation={Department of Agroindustrial Technology, Faculty of Agricultural Technology, Bogor Agricultural University, Bogor, Indonesia; Supply and Demand Planner, Salim Ivomas Pratama Co., Jakarta, Indonesia},
abstract={Performance evaluation and decision making application on natural rubber agro industry productivity improvement along its supply chain is a complex and uncertain process which needs decision support system (DSS). The research objectives were: (1) to analyse natural rubber supply chain decisionmaking models; (2) to formulate performance measurement model by using sustainable balanced scorecard approach; and (3) to develop decision support system prototype using systems approach. The DSS model consisted of four sub-models, i.e. supply chain performance measurement using sustainable balanced scorecard approach and fuzzy analytical hierarchy process (F-AHP); latex quality improvement strategy selection using F-AHP; prospective product selection using exponential comparison technique; and supply chain performance improvement strategy selection using strengths, weaknesses, opportunities and threats (SWOT) analysis. The developed DSS was implemented and tested by using real cases at Indonesia natural rubber industries. This research gave promising result in improving the natural rubber industry supply chain management performance. © 2017 ExcelingTech Pub, UK.},
author_keywords={DSS;  Fuzzy AHP;  Natural rubber;  Supply chain;  Sustainable balanced scorecard},
references={(2014) Rubber Plant Technical Development Guidelines 2014, , ", Plantation General Directorate, Indonesia Ministry of Agriculture, Indonesia; Marimin, Darmawan, M.A., Machfud, Putra, M.P.F., Wiguna, B., Value chain analysis for green productivity improvement in the natural rubber supply chain: a case study (2014) , Journal of Cleaner Production, 85, pp. 201-211; Septiani, W., Marimin, Herdiyeni, Y., Haditjaroko, L., Method and approach mapping for agri-food supply chain risk management: a literature review (2016) , International Journal of Supply Chain Management, 5 (2); Suharjito, Marimin, Risks balancing model of agri-supply chain using fuzzy risks utility regression (2012) , Journal of Theoretical and Applied Information Technology, 41, pp. 13-23; Pujawan, I.N., (2005) Supply Chain Management, , ", Guna Widya, Indonesia; Kaplan, R.S., Norton, D.P., (2000) Balanced Scorecard: Implementing Strategy into Action, , ", Erlangga, Indonesia; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., (2001) Sustainability balanced scorecard, wertorientiertes nachhaltigkeits managementmit der balanced scorecard, , ", Center for Sustainability Management, L" uneburg; Cramer, J., From financial to sustainable profit (2002) , Corporate Social Responsibility and Environmental Management, 9, pp. 99-106; Marimin, D.T., Suharjito Hidayat, S., Utama, D.N.U., Astuti, R., Martini, S., (2013) Techniques and Fuzzy Decision Analysis in Supply Chain Management", , IPB Press, Indonesia; Kreng, V.B., Wu, C., "Evaluation of knowledge portal development tools using a fuzzy AHP approach: The case of Taiwanese stone industry (2007) European Journal of Operation Research, 176, pp. 1795-1810; Bozbura, F.T., Beskete, A., "Prioritization of organizational capital measurement indicators using fuzzy AHP" (2007) International Journal of Approximate Reasoning, 44, pp. 124-127; Hidayat, S., Marimin, Agent based modeling for investment and operational risk considerations in palm oil supply chain (2014), 3 (1); (2003) Systems Science: Improving the Quality and Effectiveness of Management, , ", IPB Press, Indonesia; Hunger, J.D., "Principles of Strategic Management (2010) " Tehran: Cultural Research Bureau; Riadi, F., Machfud, Bantacut, T., Sailah, I., Integrated natural rubber agroindustry development model (2011) , Jurnal Teknologi Industri Pertanian, 21 (3), pp. 146-153; Hervani, A.A., Helms, M.M., Performance measurement for green supply chain management (2005) , Benchmarking: An International Journal, 12 (4), pp. 330-353; Srivastava, S.K., Green supply-chain management: A state-of the-art literature review (2007) , International Journal of Management Reviews, 9 (1), pp. 53-80; Aramyan, L.H., Lansink, A.G.J.M.O., van der Vorst, J.G.A.J., van Kooten, O., Performance Measurement in Agri-Food Supply Chains: A Case Study (2007) , Supply Chain Management: An International, 12, pp. 304-315; Bhagwat, R., Sharma, K.M., Performance measurement of supply chain management: A balanced scorecard approach (2007) , Computers and Industrial Engineering, 53, pp. 43-62; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) , International Journal of Production Economics, 87, pp. 333-347; Neto, J.Q.F., Walther, G., Bloemhof, J., Nunen, J.A.E.E., Spengler, T., From closed loop to sustainable supply chains, the WEEE case (2010) , International Journal of Production Research, 48 (15); Bounif, E.M., Bourahla, M., Decision support technique for supply chain management (2013) , Journal of Computing and Information Technology-CIT, 21 (4), pp. 255-266; Filip, G.F., Duta, L., "Decision support system in reverse supply chain management" (2015) Procedia Economics and Finance, 22, pp. 154-159; Vayvay, O., Ozcan, Y., Cruz-Cunha, M., ERP consultant selection problem using AHP, fuzzy AHP and ANP: A case study in Turkey (2012) , Journal of Business Management and Economics, 3 (3), pp. 106-117; Kong, F., Liu, H., Applying fuzzy analytical hierarchy process to evaluate success factors of E-commerce (2005) . International, Journal of Information and Systems Sciences, 1 (3-4), pp. 406-412; Cebeci, U., Fuzzy AHP-based decision support system for selecting ERP system in textile industry by using balanced scorecard (2009) , Expert System with Application, 36, pp. 8900-8909; Gaspersz, V., (2013) "All in One 150 Key Performance Indicators and Balanced Scorecard, Malcom Baldrige, Lean Six Sigma Supply Chain Management, , , Tri Al Bros Publishing, Indonesia; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard - linking sustainability management to business strategy (2002) , Business Strategy and the Environment, Wiley Inter Science, Bus. Strat. Env, 11, pp. 269-284; Kaplan, R.S., Norton, D., (2001) "The strategy-focused organization: how balanced scorecard companies thrive in the new business environment, , Harvard Business School Press, Boston (USA); Bieker, (2013) Sustainability management with the balanced scorecard, pp. 17-34. , In Karner, S., Oehme, I. and Seebacher, U. (Eds). Corporate Sustainability, 5th International Summer Academy on Technology Studies, Deutschlandsberg, Austria, July 13-19, Conference Proceedings; Huang, K., Matthew, P., Graham, B., (2010) "Implementing sustainability balanced scorecard dashboard "approach " to assess organizational legitimacy, , University of Wollongong, New South Wales; Wills, B., (2009) Green Intentions: Creating a Green Value Stream to Compete and Win", , Productivity Press, USA; Astuti, R., Marimin, M., Arkeman, Y., Poerwanto, R., Meuwissen, M.P.M., (2013) Risks and risks mitigations in the supply chain of mangosteen: a case study, 6 (1), pp. 11-25. , Operations and Supply Chain Management; Ayag, Z., Ozdemir, R.G., (2006) "A Fuzzy AHP Approach to Evaluating Machine Tool Alternative, , ", Springer S; Ayag, Z., A fuzzy AHP-based simulation approach to concept evaluation in a NPD environment (2005) , IIE Transaction, 37, pp. 827-842; Nepal, B., Yadav, P., Murat, A., A fuzzy-AHP approach to prioritization of CS attributes in target planning for automotive product development (2010) , Expert Systems with Applications, Elsevier, 37, pp. 6775-6786; Kendall, K.E., Kendall, J.E., (2011) "Systems Analysis and Design 8th Edition, , Prentice Hall; Ian, S., (2011) "Software Engineering, Ninth Edition, , Addison-Wesley, California, USA},
correspondence_address1={Marimin, M.; Department of Agroindustrial Technology, Indonesia; email: marimin@ipb.ac.id},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Genovese20171290,
author={Genovese, A. and Morris, J. and Piccolo, C. and Koh, S.C.L.},
title={Assessing redundancies in environmental performance measures for supply chains},
journal={Journal of Cleaner Production},
year={2017},
volume={167},
pages={1290-1302},
doi={10.1016/j.jclepro.2017.05.186},
note={cited By 29},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020915122&doi=10.1016%2fj.jclepro.2017.05.186&partnerID=40&md5=cbf540072979f6e766a86d344b223ce1},
affiliation={Management School, University of Sheffield, Sheffield, United Kingdom; Department of Industrial Engineering, University of Naples “Federico II”, Naples, Italy},
abstract={Incorporating environmental sustainability into production systems and supply chain management perspectives is a growing issue; this requires thorough efforts in measuring the environmental performance of such systems and benchmarking these against industry standards, through the usage of appropriate indicators. The usage of environmental indicators in order to monitor and manage sustainability issues is an ongoing topic of debate and deliberation in the scientific community, which has generated the development of several methodological and conceptual approaches, often incorporated into Life Cycle Assessment frameworks, enabling the evaluation and monitoring of cumulative polluting impacts resulting across the whole product supply chain. In this field, the main challenge is to identify indicators to be employed in environmental assessments, in such a way that a precise account of sustainability issues is given without overloading end-users with overly complex and redundant information. By utilising well-established environmental indicators measuring the sustainability performance of supply chains, this paper aims at critically assessing the amount of redundancy embedded in current performance measurement systems, also identifying the subset of environmental indicators that, if employed, could cover a wide amount of environmental impact categories without redundancies and providing decision-makers with a clear perspective. © 2017 Elsevier Ltd},
author_keywords={Environmental indicators;  Environmental sustainability;  Principal component analysis},
keywords={Benchmarking;  Embedded systems;  Environmental impact;  Environmental management;  Life cycle;  Principal component analysis;  Redundancy;  Supply chain management, Environmental assessment;  Environmental indicators;  Environmental performance;  Environmental sustainability;  Life Cycle Assessment (LCA);  Product supply chains;  Sustainability issues;  Sustainability performance, Sustainable development},
references={Bare, J.C., Gloria, T., Norris, G.A., Development of the method and U.S. normalization database for life cycle impact assessment and sustainability metrics (2007) Environ. Sci. Technol., 40 (16), pp. 5108-5115; Boesch, M.E., Hellweg, S., Huijbregts, M.A.J., Frischknecht, R., Applying cumulative exergy demand (CExD) indicators to the ecoinvent database (2007) Int. J. Life Cycle Assess., 12 (3), pp. 181-190; Brockhoff, D., Zitzler, E., On objective conflicts and objective reduction in multiple criteria optimization (2006) Peabody J. Educ., 81, pp. 180-202; Bruno, G., Esposito, E., Genovese, A., Gwebu, K.L., A critical analysis of current indexes for digital divide measurement (2010) Inf. Soc., 27 (1), pp. 16-28; Campos, L.M., de Melo Heizen, D.A., Verdinelli, M.A., Miguel, P.A.C., Environmental performance indicators: a study on ISO 14001 certified companies (2015) J. Clean. Prod., 99, pp. 286-296; Cherchye, L., Moesen, W., Rogge, N., Van Puyenbroeck, T., Saisana, M., Saltelli, A., Liska, R., Tarantola, S., Creating composite indicators with DEA and robustness analysis: the case of the technology achievement index (2008) J. Operational Res. Soc., 59, pp. 239-251; Chichilnisky, G., Economic theory and the global environment (2012) Econ. Theory, 49 (2), pp. 217-225; Dale, V.H., Beyeler, S.C., Challenges in the development and use of ecological indicators (2001) Ecol. Indic., 1 (1), pp. 3-10; Despotis, D.K., A reassessment of the human development index via data envelopment analysis (2005) J. Operational Res. Soc., 56 (8), pp. 969-980; Dobbie, M.J., Dail, D., Robustness and sensitivity of weighting and aggregation in constructing composite indices (2013) Ecol. Indic., 29, pp. 270-277; Frischknecht, R., Büsser Knöpfel, S., Swiss eco-factors 2013 according to the ecological scarcity method. Methodological fundamentals and their application in Switzerland (2013) Environ. Stud., p. 1330; Frischknecht, R., Jungbluth, N., Althaus, H.-J., Doka, G., Dones, R., Heck, T., Hellweg, S., Spielmann, M., The ecoinvent database: overview and methodological framework (2005) Int. J. Life Cycle Assess., 10, pp. 3-9; Frischknecht, R., Wyss, F., Knöpfel, S.B., Lützkendorf, T., Balouktsi, M., Cumulative energy demand in LCA: the energy harvested approach (2015) Int. J. Life Cycle Assess., 20 (7), pp. 957-969; Gaussin, M., Hu, G., Abolghasem, S., Basu, S., Shankar, M.R., Bidanda, B., Assessing the environmental footprint of manufactured products: a survey of current literature (2013) Int. J. Prod. Econ., 146 (2), pp. 515-523; Genovese, A., Lenny Koh, S.C., Kumar, N., Tripathi, P.K., Exploring the challenges in implementing supplier environmental performance measurement models: a case study (2014) Prod. Plan. Control, 25 (13-14), pp. 1198-1211; Guillén-Gosálbez, G., A novel MILP-based objective reduction method for multi-objective optimization: application to environmental problems (2011) Comput. Chem. Eng., 35 (8), pp. 1469-1477; Hansmann, R., Mieg, H., Frischknecht, P., Principal sustainability components: empirical analysis of synergies between the three pillars of sustainability (2012) Int. J. Sustain. Dev. World Ecol., 19 (5), pp. 451-459; Hauschild, M., Wenzel, H., Environmental Assessment of Products (1997) Scientific Background, 2. , Chapman & Hall London, Weinheim, New York; Huijbregts, M.A.J., Hellweg, S., Frischknecht, R., Hungerbühler, K., Hendriks, A.J., Ecological footprint accounting in the life cycle assessment of products (2006) Ecol. Econ., 64 (4), pp. 798-807; Jenkins, S.P., Cappellari, L., Summarizing Multiple Deprivation Indicators (2007) Inequality and Poverty Re-examined, pp. 166-184. , John Micklewright Oxford University Press Oxford, UK; Jollands, N., Lermit, J., Patterson, M., Aggregate eco-efficiency indices for New Zealand—a principal components analysis (2004) J. Environ. Manag., 73 (4), pp. p293-305; Jolliet, O., Margni, M., Charles, R., Humbert, S., Payet, J., Rebitzer, G., Rosenbaum, R., IMPACT 2002+: a new life cycle impact assessment methodology (2003) Int. J. Life Cycle Assess., 8 (6), pp. 324-330; Koh, S.C.L., Genovese, A., Acquaye, A., Barratt, P., Rana, N., Kuylenstierna, J., Gibbs, D., Decarbonising product supply chains: design and development of an integrated evidence-based decision support system: the supply chain environmental analysis tool (SCEnAT) (2013) Int. J. Prod. Res., 51 (7), pp. 2092-2109; Köllner, T., Scholz, R., Assessment of land use impact on the natural environment: Part 1: an analytical framework for pure land occupation and land use change (2007) Int. J. Life Cycle Assess., 12 (1), pp. 16-23; Köllner, T., Scholz, R.W., Assessment of land use impacts on the natural environment (2008) Int. J. Life Cycle Assess., 13 (1), pp. 32-48; Lorenz, C.M., Gilbert, A.J., Cofino, W.P., Indicators for transboundary river basin management (1999) Environmental Indices: System Analysis Approach, pp. 313-328. , Y.A. Pykh D.E. Hyatt R.J.M. Lenz EOLSS Publishers Co. Ltd. Oxford, UK; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental performance indicators for integrated supply chains: the case of Xerox Ltd (1998) Supply Chain Manag. Int. J., 3 (3), pp. 149-156; Pozo, C., Ruíz-Femenia, R., Caballero, J., Guillén-Gosálbez, G., Jiménez, L., On the use of Principal Component Analysis for reducing the number of environmental objectives in multi-objective optimization: application to the design of chemical supply chains (2012) Chem. Eng. Sci., 69 (1), pp. 146-158; Rahdari, A.H., Rostamy, A.A.A., Designing a general set of sustainability indicators at the corporate level (2015) J. Clean. Prod., 108, pp. 757-771; Rosenbaum, R.K., Bachmann, T.M., Gold, L.S., Huijbregts, M.A., Jolliet, O., Juraske, R., Koehler, A., McKone, T.E., USEtox—the UNEP-SETAC toxicity model: recommended characterisation factors for human toxicity and freshwater ecotoxicity in life cycle impact assessment (2008) Int. J. Life Cycle Assess., 13 (7), pp. 532-546; Saisana, M., Tarantola, S., Saltelli, A., Uncertainty and sensitivity techniques as tools for the analysis and validation of composite indicators (2005) J. R. Stat. Soc., 168 (2), pp. 1-17; Sundarakani, B., De Souza, R., Goh, M., Wagner, S.M., Manikandan, S., Modeling carbon footprints across the supply chain (2010) Int. J. Prod. Econ., 128 (1), pp. 43-50; Veleva, V., Ellenbecker, M., Indicators of sustainable production: framework and methodology (2001) J. Clean. Prod., 9 (6), pp. 519-549; Zhou, P., Ang, B.W., Zhou, D.Q., Weighting and aggregation in composite indicator construction: a multiplicative optimization approach (2010) Soc. Indic. Res., 96, pp. 169-181},
correspondence_address1={Genovese, A.; Management School, United Kingdom; email: a.genovese@sheffield.ac.uk},
publisher={Elsevier Ltd},
issn={09596526},
coden={JCROE},
language={English},
abbrev_source_title={J. Clean. Prod.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Zackrisson2017457,
author={Zackrisson, M. and Kurdve, M. and Shahbazi, S. and Wiktorsson, M. and Winroth, M. and Landström, A. and Almström, P. and Andersson, C. and Windmark, C. and Öberg, A.E. and Myrelid, A.},
title={Sustainability Performance Indicators at Shop Floor Level in Large Manufacturing Companies},
journal={Procedia CIRP},
year={2017},
volume={61},
pages={457-462},
doi={10.1016/j.procir.2016.11.199},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020043898&doi=10.1016%2fj.procir.2016.11.199&partnerID=40&md5=bbfcae5c057af365e7ab799abcb8bdd8},
affiliation={Swerea IVF AB, Sweden; School of Innovation, Design and Engineering, Mälardalen University, Sweden; Department of Technology Management and Economics, Chalmers University of Technology, Sweden; Department of Production and Materials Engineering, Lund University, Sweden; Volvo Construction Equipment AB, Sweden; GKN Aerospace Engine Systems AB, Sweden},
abstract={This article investigates sustainability in the performance measurement systems of Swedish manufacturing companies. It builds on a previous study that documents relatively few direct environmental indicators at shop floor level, which raises questions about possible indirect links between existing indicators and the environment that could be used to improve the environmental aspect of company's sustainability ambitions. A method for identifying and categorizing indirect links to sustainability issues was defined and used. The results suggest that at shop floor level 90% of the indicators have at least an indirect relation to one or more of the sustainability dimensions economy, environment and social, of which 26% are at least indirectly related to the environmental dimension. Despite the many indirect connections, participating companies perceive a need to improve sustainability indicators and some ideas are suggested. © 2017 The Authors.},
author_keywords={Environmental management;  Performance Measurement System;  Sustainability;  Sustainability Performance Indicator},
keywords={Benchmarking;  Environmental management;  Floors;  Life cycle;  Manufacture;  Supply chain management;  Waste disposal, Environmental aspects;  Environmental indicators;  Manufacturing companies;  Performance measurement system;  Sustainability dimensions;  Sustainability indicators;  Sustainability issues;  Sustainability performance, Sustainable development},
references={Srimai, S., Radford, J., Wright, C., Evolutionary paths of performance measurement: An overview of its recent development (2011) International Journal of Productivity and Performance Management, 60 (7), pp. 662-687; Kaplan, R.S., Norton, D.P., (1992) The Balanced Scorecard - Measures That Drive Performance, pp. 71-79. , Harvard Business Review, January-February; Landström, A., Almström, P., Winroth, M., Andersson, C., Windmark, C., Ericsson Öberg, A., Myrelid, A., Present state analysis of business performance measurement systems in large manufacturing companies (2016) Proceedings of the 10th Conference of the Performance Measurement Association (PMA), , Edinburgh, UK, June 26-29; Åhlström, P., Karlsson, C., Change processes towards lean production the role of the management accounting system (1996) International Journal of Operations & Production Management, 16 (11), p. 42; Modig, N., Åhlström, P., (2012) This Is Lean: Resolving the Efficiency Paradox, , Halmstad, Sweden: Rheologica Publishing, Stockholm, Sweden; Amrina, E., Yusof, M.S., Key performance indicators for sustainable manufacturing evaluation in automotive companies (2011) IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), , Singapore, Dec 6-9; Garza-Reyes, J.A., Lean and green - A systematic review of the state of the art literature (2015) Journal of Cleaner Production, 102, pp. 18-29; Johansson, G., Winroth, M., Introducing environmental concerns in manufacturing strategies - Implications for the decision criteria (2010) Management Research Review, 33 (9), pp. 877-899; Kurdve, M., Zackrisson, M., Wiktorsson, M., Harlin, U., Lean and green integration into production system models - Experiences from Swedish industry (2014) Journal of Cleaner Production, 85, pp. 180-190; Veleva, V., Hart, M., Greiner, T., Crumbley, C., (2001) Indicators of Sustainable Production, Clean Technologies and Environmental Policy, 9; Labuschagne, C., Brent, A.C., Van Erck, R.P.G., Assessing the sustainability performances of industries (2005) Journal of Cleaner Production, 13, pp. 373-385; Čucek, L., Klemes, J.J., Kravanja, Z., A Review of Footprint analysis tools for monitoring impacts on sustainability (2012) Journal of Cleaner Production, 34, pp. 9-20; Miles, M.B., Huberman, A.M., (1994) Qualitative Data Analysis: An Expanded Sourcebook, , SAGE Publications; (1987) Our Common Future, , World Commission on Environment and Development (WCED) Oxford; Cato, S., (2009) Green Economics, , London: Earthscan; Adams, W.M., (2006) The Future of Sustainability: Re-thinking Environment and Development in the Twenty-first Century, , Report of the IUCN Renowned Thinkers Meeting; (2015) Transforming Our World: The 2030 Agenda for Sustainable Development, pp. 1-29. , UN; (2010) ISO 26000 Guidance on Social Responsibility, , ISO ISO/TMB/WG SR N 172; (2015), https://www.unglobalcompact.org, Global Compact in September 2015; (2014) G4 Sustainability Reporting Guidelines, , https://www.globalreporting.org/standards/g4/Pages/default.aspx, GRI in September 2015; Chen, D., Thiede, S., Schudeleit, T., Herrmann, C., A holistic and rapid sustainability assessment tool for manufacturing SMEs (2014) CIRP Annals-Manufacturing Technology, 63 (1), pp. 437-440; (2015) Miljömålen - Årlig Uppföljning Av Sveriges Miljökvalitetsmål och Etappmål, 2015, , http://www.naturvardsverket.se/Documents/publikationer6400/978-91-620-6661-1.pdf?pid=15081, Naturvårdsverket September 2015; Galbraith, J.R., (2014) Designing Organizations, , Jossey-Bass, San Francisco; Salloum, M., Explaining the Evolution of Performance Measures-A Dual Case-Study Approach (2013) Journal of Engineering, Project, and Production Management, 3 (2), p. 99; Roca, L.C., Searcy, C., An analysis of indicators disclosed in corporate sustainability reports (2012) Journal of Cleaner Production, 20 (1), pp. 103-118; Kurdve, M., Hanarp, P., Chen, X., Qiu, X., Zhang, Y., Stahre, J., Laring, J., Use of environmental value stream mapping and environmental loss analysis in lean manufacturing work at Volvo (2011) Proceedings of the 4th Swedish Production Symposium, , Lund, Sweden, May 3-5},
correspondence_address1={Zackrisson, M.; Swerea IVF ABSweden; email: mats.zackrisson@swerea.se},
editor={Umeda Y., Kondoh S., Takata S.},
publisher={Elsevier B.V.},
issn={22128271},
language={English},
abbrev_source_title={Procedia CIRP},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Butzer2017251,
author={Butzer, S. and Schötz, S. and Petroschke, M. and Steinhilper, R.},
title={Development of a Performance Measurement System for International Reverse Supply Chains},
journal={Procedia CIRP},
year={2017},
volume={61},
pages={251-256},
doi={10.1016/j.procir.2016.11.264},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020008966&doi=10.1016%2fj.procir.2016.11.264&partnerID=40&md5=9863bd95e80c0eaf724147765ac63dea},
affiliation={University of Bayreuth, Department of Manufacturing and Remanufacutring Technology, Universitaetsstrasse 9, Bayreuth, 95447, Germany; Fraunhofer Project Group Process Innovation, Universitaetsstrasse 9, Bayreuth, 95447, Germany},
abstract={In times of globally connected production networks, supply chain management is a key discipline of modern living. Due to increasing commodity prizes and a greater awareness of resource efficiency, the relevance of international reverse supply chains is increasing. Unfortunately, there is a lack of knowledge when it comes to the assessment of international reverse supply chains. To close this lack, scientists from Bayreuth defined a performance measurement system to assess international reverse supply chains. The aim of this paper is to support the Circular Economy and the remanufacturing industry with an approach to optimize international reverse supply chains and thus to become more sustainable. © 2017 The Authors.},
author_keywords={Performance Measurement;  Performance Measurement System;  Reverse Logistics;  Reverse Supply Chain;  Supply Chain Management},
keywords={Life cycle;  Logistics, Circular economy;  Performance measurement system;  Performance measurements;  Production network;  Resource efficiencies;  Reverse logistics;  Reverse supply chains, Supply chain management},
references={Asdecker, B., Statistiken Retouren Deutschland Definition, , http://www.retourenforschung.de/definition_statistiken-retouren-deutschland.html, Otto-Friedrich-Universität Bamberg checked at 25.08.2018; Elektro-und Elektronikgeräte in Deutschland: Daten 2013 Zur Erfassung, Behandlung und Wiederverwendung, , http://www.bmub.bund.de/themen/wasser-abfall-boden/abfallwirtschaft/statistiken/elektro-und-elektronikaltgeraete/, Bundesministerium für Umwelt, Naturschutz, Bau und Reaktorsicherheit (BMUB) checked at 25.08.2016; Seitz, M., (2006) Remanufacturing, Supply Chain Management and Sustainability, , Dissertation. In: Friedwart Lender. editor. Hofer Akademische Schriften Losgitik, Edition 6. Hof: Fachhochschule Hof; Sink, D., Scott, T., Thomas, C., (1989) Planning and Measurment in Your Oranization of the Future, , US: Industrial Engineering and Management Press; Janse, B., Schuur, P., Brito, M.P., A reverse logistics diagnostic tool the case of the consumer electronics industry (2010) International Journal of Advanced Manufacturing Technology, 47 (5-8), pp. 495-513; Shaik, M., (2015) Comprehensive Performance Measurement Methodology for Reverse Logistics Enterprise, , http://scholar.uwindsor.ca/etd/5247, Electronic Theses and Dissertations. Paper 5247 Windsor, Ontario: University of Windsor; Rogers, D.S., Tibben-Lembke, R.S., Going backwards (1999) Reverse Logistics Trends and Practices, , Reno, Nev.: Reverse Logistics Executive Council; Guimarães Da Silveira, J.L., Salomon, V.A.P., ANP Applied to the Evaluation of Performance Indicators of Reverse Logistics in Footwear Industry (2015) Procedia Computer Science, 55, pp. 139-148; Guide, D., Van Wassenhove, L., The Reverse Supply Chain (2002) Smart Manufacturers Are Designing Efficient Processes for Reusing Their Products. In: Harvard Business Review (80/2), pp. p1-p3; Larson, P.D., Poist, R.F., Halldósson, Á., Perspectives on logistics vs. SCM: A survey of scm professionals (2007) Journal of Business Logistics, 28 (1), pp. 1-24; Guide, D., Harrison, T., Van Wassenhove, L., The Challenge of Cloosed-Loop Supply Chains (2003) Insead. Working Paper Series, , https://flora.insead.edu/fichiersti_wp/inseadwp2003/2003-62.pdf; Lambert, S., Riopel, D., Abdul-Kader, W., (2011) A Reverse Logistics Decisions Conceptual Framework. In: Computers & Industrial Engineering, 61 (3), pp. 561-581; Scott, C.D., Lundgren, H., Thompson, P., (2011) Guide to Supply Chain Management, , Springer Berlin, New York; Gupta, S.M., Reverse Supply Chains (2013) Issues and Analysis, , Hoboken: CRC., Press; Rogers, D.S., Tibben-Lembke, R.S., Going backwards (1999) Reverse Logistics Trends and Practices, , Reno, Nev.: Reverse Logistics Executive Council; Tibben-Lembke, R., Rogers, S., Dale, S., (2002) Differences between Forward and Reverse Logistics in A Retail Environment. In: Suppy Chain Management, 5, pp. 271-282; Tibben-Lembke, R.S., (2002) Life after Death Reverse Logistics and the Product Life Cycle. In: Int Jnl Phys Dist & Log Manage, 32 (3), pp. 223-244; Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., Van Der Laan, E., Van Nunen Jo, A.E.E., Van Wassenhove, L., Quantitative models for reverse logistics. A review (1997) European Journal of Operational Research, 103 (1), pp. 1-17; Klaus, P., Krieger, W., Gabler lexikon logistik (2009) Management Logistischer Netzwerke und Flüsse, , 4. Edition. Wiesbaden: Gabler Verlag /Springer Fachmedien Wiesbaden; Zanjirani, F.R., Supply chain and logistics in national, international and governmental environment (2009) Concepts and Models, , Heidelberg: Physica Verlag; MacDonald, A., The Difference between Managing a Global vs (2006) Domestic Supply Chain Is A Matter of Degree. In: World Trade, 19 (7), pp. 66-74; Agrawal, S., Singh, R.K., Murtaza, Q., A literature review and perspectives in reverse logistics (2015) Resources, Conservation and Recycling, 97, pp. 76-92; Verweij, H., Dang, N., Bonney, G., Janse, B., Reverse Logistics How to Realise An Agile and Efficient Reverse Chain Within the Consumer Electronics Industry, , http://www.pwc.nl/nl/assets/documents/pwc-reverse-logistics.pdf, checked at 25.08.2016; Neely, A., Gregory, M., Platts, K., (1995) Performance Measurement System Design. In: International Journal of Operations & Production Management, 15 (4), pp. 80-116; Giese, A., (2011) Differenziertes Performance Mesurement in Supply Chains. Dissertation, , FernUniversität in Hagen Springer Gabler; (2008) SCOR Supply Chain Operations Reference Model, , The Supply Chain Council Version 9; Richert, J., Performance measurement in supply chains (2006) Balanced Scorecard in Wertschöpfungsnetzwerken, , 1. Edition. Wiesbaden: Gabler; Luger, T., Herrmann, C., (2010) Referenzprozessbasierte Gestaltung und Bewertung von Reverse Supply Chains. In: Uwf 18, 2, pp. 91-99; Kaplan, R.S., Norton, D., (2005) The Balanced Score Card Measures That Drive Performance. In: Harvard Business Review, 83, pp. 172-180; Saaty, R.W., (1987) The Analytic Hierarchy Process - What It Is and How It Is Used. In: Mathematical Modelling, 9 (3-5), pp. 161-176; Yang, J., On the construction and implementation methods for performance measurement of reverse supply chain (2010) 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 899-903. , Yantai, China; Hung, L.K., Wang, Y., Reverse logistics in the electronic industry of China. A case study (2009) Suppy Chain Management, 14 (6), pp. 447-465; Shaik, M., Abdul-Kader, W., Performance measurement of reverse logistics enterprise. A comprehensive and integrated approach (2012) Measuring Business Excellence, 16 (2), pp. 23-34; Simatupang, T.M., Sridharan, R., (2012) The Collaborative Supply Chain. In: International Journal of Logistics Management, 13 (1), pp. 15-30; Bansia, M., Varkey, J.K., Agrawal, S., Development of a Reverse Logistics Performance Measurement System for a Battery Manufacturer (2014) Procedia Materials Science, 6, pp. 1419-1427; Genchev, S.E., Reverse logistics program design. A company study (2009) Business Horizons, 52 (2), pp. 139-148; Genchev, S.E., Glenn Richey, R., Gabler, C.B., Evaluating reverse logistics programs. A suggested process formalization (2011) International Journal Logistics Management, 22 (2), pp. 242-263; Bai, C., Sarkis, J., Flexibility in reverse logistics. A framework and evaluation approach (2013) Journal of Cleaner Production, 47, pp. 306-318; Beamon, B.M., (1999) Measuring Supply Chain Performance. In: International Journal of Operations & Production Management, 19 (3), pp. 275-292},
correspondence_address1={Butzer, S.; University of Bayreuth, Universitaetsstrasse 9, Germany; email: steffen.butzer@uni-bayreuth.de},
editor={Umeda Y., Kondoh S., Takata S.},
publisher={Elsevier B.V.},
issn={22128271},
language={English},
abbrev_source_title={Procedia CIRP},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Sopadang20171079,
author={Sopadang, A. and Wichaisri, S. and Banomyong, R.},
title={Sustainable supply chain performance measurement a case study of the sugar industry},
journal={Proceedings of the International Conference on Industrial Engineering and Operations Management},
year={2017},
pages={1079-1080},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018963041&partnerID=40&md5=78e3078742c63455e457d6081368177b},
affiliation={Excellence Center in Logistics and Supply Chain Management, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand; Centre for Logistics Research, Department of International Business Logistics and Transport, Thammasat Business School, Thammasat University, Bangkok, 10200, Thailand},
abstract={The purpose of this paper is to introduce a framework to measure the sustainable performance of supply chains. The supply chain stakeholders in this study are suppliers, a manufacturer, customer and the surrounding community. The proposed sustainable supply chain performance measurement consists of three dimensions with 14 indicators. In order to assess overall sustainability of the studied supply chain, a composite index is developed to combine all indicators to reflect the sustainability performance of the supply chain. The case of the sugar industry in Thailand is selected as an illustrative case. There are 4 steps to create the composite index which are (1) measuring value (2) normalization method (3) weighting of indicators (4) aggregation method. The numerical example, based on obtained data of the Thai sugar industry, is provided in order to explain how to calculate the composite index. The findings show that the overall sustainable supply chain score of the manufacturer is 55.61% while suppliers, customers and the community, achieved a composite index score of 80.97% and 75.00% respectively. The major contribution of this paper is the development of sustainable supply chain indicators between supply chain stakeholders and an approach to measure overall sustainable supply chain performance via the development of a composite index. © IEOM Society International.},
author_keywords={Numerical example;  Performance measurement;  Sugar industry;  Supply chain;  Sustainable},
references={Adel El-Baz, M., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38, pp. 6681-6688; Azapagic, A., Perdan, S., Indicators of Sustainable Development for Industry: A General Framework (2000) Process Safety and Environmental Protection, 78 (4), pp. 243-261; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Journal Computers and Industrial Engineering, 53 (1), pp. 43-62; Bouchery, Y., Ghaffari, A., Jemai, Z., Key performance indicators for sustainable distribution supply chains: set building methodology and application (2010) Laboratoire Génie Industriel, , Ecole Centrale Paris; Chen, M.Y., Huang, M.J., Cheng, Y.C., Measuring knowledge management performance using a competitive perspective: An empirical study (2009) Expert Systems with Applications, 36, pp. 8449-8459; Dey, A., LaGuardia, P., Srinivasan, M., Building sustainability in logistics operations: a research agenda (2011) Management Research Review, 34 (11), pp. 1237-1259; Dossi, A., Patelli, L., You Learn From What You Measure: Financial and Non-financial Performance Measures in Multinational Companies (2010) Strategic Performance Measurement, 43 (4), pp. 498-562; Erol, I., Cakar, N., Erel, D., Sari, R., Sustainability in the Turkish Retailing Industry (2009) Sustainable Development, 17, pp. 49-67; Freeman, R., Labout Productivity Indicators: Comparison of Two OECD Databases Productivity Differentials and The Balassa-Samuelson Effect (2008), Paris: OECD Statistics Directorate, July; Forberg, A., Saukkoriipi, L., Measurement of Waste and Productivity in Relation to Lean Thinking (2007) Proceeding of IGLC, , Michigan, USA July 15; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement of the after-sales service network-Evidence from the automotive industry (2007) Computers in Industry, 58, pp. 698-708; Gheewala, S.H., Bonnet, S., Prueksakorn, K., Nilsalab, P., Sustainability Assessment of a Biorefinery Complex in Thailand" (2011) Sustainability, 3, pp. 518-530; http://www.globalreporting.org, (accessed 15 September 2016); Gopalakrishnan, K., Yusuf, Y.Y., Musa, A., Abubakar, T., Ambursa, H.M., Sustainable supply chain management: A case study of British Aerospace (BAe) Systems (2012) International Journal of Production Economics, 140, pp. 193-203; Grigoroudis, E., Orfanoudaki, E., Zopounidis, C., Strategic performance measurement in a healthcare organisation: A multiple criteria approach based on balanced scorecard (2012) Omega, 40, pp. 104-119; Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2013) Journal of Cleaner Production, 47, pp. 345-354; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 8 (3), pp. 333-347; Gupta, S., Kumar, V., Sustainability as corporate culture of a brand for superior performance (2013) Journal of World Business, 48, pp. 311-320; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140, pp. 69-82; Krajnc, D., Glavic, P., Indicators of sustainable production (2003) Clean Technologies and Environmental Policy, 5, pp. 279-288; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156, pp. 267-286; Markley, M.J., Davis, L., Exploring future competitive advantage through sustainable supply chains (2007) International Journal of Physical Distribution & Logistics Management, 37 (9), pp. 763-774; Saaty, T.L., A Scaling Method for Priorities in Hierarchical Structures (1977) Journal of Mathematical Psychology, 15, pp. 234-281; Schmidberger, S., Bals, L., Hartmann, E., Jahns, C., Ground handling services at European hub airports: development of a performance measurement system for benchmarking (2009) International Journal of Production Economics, 117, pp. 104-111; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., Development of composite sustainability performance index for steel industry (2007) Ecological Indicators, 7, pp. 565-588; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., An overview of sustainability assessment methodologies (2009) Ecological Indicators, 9, pp. 189-212; Soosay, C.A., Cahpman, R.L., An empirical examination of performance measurement for managing continuous innovation in logistics (2006) Special Issue: Continuous Innovation, Performance and Knowledge Management, 13 (3), pp. 192-205; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16, pp. 1699-1710; Tan, H.X., Yeoa, Z., Nga, R., Tjandraa, T.B., Song, B., A sustainability indicator framework for Singapore small and medium-sized manufacturing enterprises (2015) Procedia CIRP, 29, pp. 132-137; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains-A literature review and a research agenda, (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 782-804; Vinodh, S., Arvind, K.R., Somanaathan, M., Tools and techniques for enabling sustainability through lean initiatives (2011) Clean Technologies and Environmental Policy, 13, pp. 469-479; Yee, R.W.Y., Yeung, A.C.L., Cheng, T.C.E., An empirical study of employee loyalty (2010) service quality and firm performance in the service industry, International Journal of Production Economics, 124, pp. 109-120; Yeung, A.C.L., Strategic supply management (2008) quality initiatives, and organizational performance, Journal of Operations Management, 26, pp. 490-502},
correspondence_address1={Banomyong, R.; Centre for Logistics Research, Thailand; email: Banomyong.ruth@gmail.com},
publisher={IEOM Society},
issn={21698767},
isbn={9780985549763},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Oper. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Rezaei2017688,
author={Rezaei, M. and Shirazi, M.A. and Karimi, B.},
title={IoT-based framework for performance measurement A real-time supply chain decision alignment},
journal={Industrial Management and Data Systems},
year={2017},
volume={117},
number={4},
pages={688-712},
doi={10.1108/IMDS-08-2016-0331},
note={cited By 44},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018751735&doi=10.1108%2fIMDS-08-2016-0331&partnerID=40&md5=ff1d745294a5aad527868168b290072d},
affiliation={Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran},
abstract={Purpose - The purpose of this paper is to develop an Internet of Things (IoT)-based framework for supply chain (SC) performance measurement and real-time decision alignment. The aims of the proposed model are to optimize the performance indicator based on integrated supply chain operations reference metrics. Design/methodology/approach - The SC multi-dimensional structure is modeled by multi-objective optimization methods. The operational presented model considers important SC features thoroughly such as multi-echelons, several suppliers, several manufacturers and several products during multiple periods. A multi-objective mathematical programming model is then developed to yield the operational decisions with Pareto efficient performance values and solved using a well-known meta-heuristic algorithm, i.e., non-dominated sorting genetic algorithm II. Afterward, Technique for Order of Preference by Similarity to Ideal Solution method is used to determine the best operational solution based on the strategic decision maker's idea. Findings - This paper proposes a dynamic integrated solution for three main problems: strategic decisions in high level, operational decisions in low level and alignment of these two decision levels. Originality/value - The authors propose a human intelligence-based process for high level decision and machine intelligence-based decision support systems for low level decision using a novel approach. High level and low level decisions are aligned by a machine intelligence model as well. The presented framework is based on change detection, event driven planning and real-time decision alignment. © Emerald Publishing Limited.},
author_keywords={Decision alignment;  Internet of Things (IoT);  NSGAII;  SCOR model;  Supply chain performance measurement},
keywords={Alignment;  Artificial intelligence;  Decision making;  Decision support systems;  Genetic algorithms;  Heuristic algorithms;  Mathematical programming;  Multiobjective optimization;  Optimization;  Pareto principle;  Supply chains, Design/methodology/approach;  Internet of Things (IOT);  Multi-dimensional structure;  Multi-objective mathematical programming;  Non-dominated sorting genetic algorithm - ii;  NSGA-II;  SCOR models;  Supply chain performance measurements, Internet of things},
references={Agami, N., Saleh, M., Rasmy, M., A hybrid dynamic framework for supply chain performance improvement (2012) IEEE Systems Journal, 6 (3), pp. 469-478; Agami, N., Saleh, M., Rasmy, M., An innovative fuzzy logic based approach for supply chain performance management (2014) IEEE Systems Journal, 8 (2), pp. 336-342; Alomar, M., Pasek, Z.J., Linking supply chain strategy and processes to performance improvement (2014) Procedia CIRP, 17, pp. 628-634; Anagnostopoulos, K., Mamanis, G., A portfolio optimization model with three objectives and discrete variables (2010) Computers & Operations Research, 37 (7), pp. 1285-1297; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Ashton, K., That 'Internet of things' thing (2009) Journal of Radio Frequency Identification, 22 (7), pp. 97-114; Badri, H., Ghomi, S.F., Hejazi, T., A two-stage stochastic programming model for value-based supply chain network design (2016) Scientia Iranica. Transaction E, Industrial Engineering, 23 (1), p. 348; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Blanco, V., A mathematical programming approach to the computation of the omega invariant of a numerical semigroup (2011) European Journal of Operational Research, 215 (3), pp. 539-550; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (3), pp. 512-521; Cai, Z., Wang, Y., Xiao, R., Xiao, J., A multi-agent-driven closed-loop quality chain model and coordinated optimization (2013) Communications in Information Science and Management Engineering, 3 (11), p. 524; Celik, N., Nageshwaraniyer, S.S., Son, Y.-J., Impact of information sharing in hierarchical decision-making framework in manufacturing supply chains (2012) Journal of Intelligent Manufacturing, 23 (4), pp. 1083-1101; Council, S.C., (2012) Supply Chain Operations Reference Model: Revision 11.0, , technical report, Supply Chain Council, Cypress, CA; Damodaran, A., (2008) Applied Corporate Finance: A User's Manual, , John Wiley & Sons Inc., New York, NY; Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6 (2), pp. 182-197; Elgazzar, S., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain operations' performance to the company's financial strategy: A case study of an Egyptian natural bottled water company (2011) European Journal of Operational Research, 223 (1), pp. 276-289; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Francis, J., (2009) Supply Chain Management & Business Financial Performance; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Subramanian, N., Tiwari, M.K., Information technology governance in internet of things supply chain networks (2016) Industrial Management & Data Systems, 116 (7); Huang, G.Q., Zhang, Y., Jiang, P., RFID-based wireless manufacturing for walking-worker assembly islands with fixed-position layouts (2007) Robotics and Computer-Integrated Manufacturing, 23 (4), pp. 469-477; Huang, G.Q., Zhang, Y., Jiang, P., RFID-based wireless manufacturing for real-time management of job shop WIP inventories (2008) The International Journal of Advanced Manufacturing Technology, 36 (7-8), pp. 752-764; Huang, G.Q., Zhang, Y., Chen, X., Newman, S.T., RFID-enabled real-time wireless manufacturing for adaptive assembly planning and control (2008) Journal of Intelligent Manufacturing, 19 (6), pp. 701-713; Ivanov, D., An adaptive framework for aligning (re) planning decisions on supply chain strategy, design, tactics, and operations (2010) International Journal of Production Research, 48 (13), pp. 3999-4017; Jiang, P., Fu, Y., Zhu, Q., Zheng, M., Event-driven graphical representative schema for job-shop-type material flows and data computing using automatic identification of radio frequency identification tags (2011) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 226 (2), pp. 339-352; Juttner, U., Christopher, M., Godsell, J., A strategic framework for integrating marketing and supply chain strategies (2010) The International Journal of Logistics Management, 21 (1), pp. 104-126; Kocaoǧlu, B., Gulsun, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Lapide, L., What about measuring supply chain performance (2000) Achieving Supply Chain Excellence Through Technology, 2 (2), pp. 287-297; Levin, S., Complex adaptive systems: Exploring the known, the unknown and the Unknowable (2003) Bulletin of the American Mathematical Society, 40 (1), pp. 3-19; Li, G., Yang, H., Sun, L., Ji, P., Feng, L., The evolutionary complexity of complex adaptive supply networks: A simulation and case study (2010) International Journal of Production Economics, 124 (2), pp. 310-330; Li, L., Su, Q., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) International Journal of Production Research, 49 (1), pp. 33-57; Liu, S., Papageorgiou, L.G., Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry (2013) Omega, 41 (2), pp. 369-382; Melo, M.T., Nickel, S., Saldanha-Da-Gama, F., Facility location and supply chain management - A review (2009) European Journal of Operational Research, 196 (2), pp. 401-412; Mohamed, M.A.-E., El-Baz, M.A., Elmedany, T.T., A critical review of supply chain performance evaluation (2015) International Journal of Engineering Research and Technology, 4 (6); Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) Journal of Operations and Supply Chain Management, 6 (2), pp. 94-113; Ntabe, E.N., Lebel, L., Munson, A.D., Santa-Eulalia, L.-A., A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues (2015) International Journal of Production Economics, 169, pp. 310-332; Olugu, E.U., Wong, K.Y., An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry (2012) Expert Systems with Applications, 39 (1), pp. 375-384; Oztemel, E., Tekez, E.K., Interactions of agents in performance based supply chain management (2009) Journal of Intelligent Manufacturing, 20 (2), pp. 159-167; Peng Wong, W., Yew Wong, K., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51; Puigjaner, L., Laínez, J.M., Capturing dynamics in integrated supply chain management (2008) Computers & Chemical Engineering, 32 (11), pp. 2582-2605; Ramaa, A., Rangaswamy, T., Subramanya, K., A review of literature on performance measurement of supply chain network (2009) 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET), IEEE, pp. 802-807; Rooeinfar, R., Azimi, P., Pourvaziri, H., Multi-echelon supply chain network modelling and optimization via simulation and metaheuristic algorithms (2016) Scientia Iranica, 23 (1), pp. 330-347; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28 (5), pp. 581-598; Skipworth, H., Godsell, J., Wong, C.Y., Saghiri, S., Julien, D., Supply chain alignment for improved business performance: An empirical study (2015) Supply Chain Management: An International Journal, 20 (5), pp. 511-533; Schaltegger, S.P.R.B., Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Management: An International Journal, 19 (3), pp. 275-291. , Dr; Stevenson, W.J., (2005) Operations Management, , McGraw-Hill, New York, NY; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2014) International Journal of Production Research, 53 (21), pp. 6473-6494; Teran, H., Hernandez, J.C., Vizán, A., Ríos, J., Performance measurement integrated information framework in e-manufacturing (2014) Enterprise Information Systems, 8 (6), pp. 607-629; Tseng, M., Lim, M., Wong, W.P., Sustainable supply chain management: A closed-loop network hierarchical approach (2015) Industrial Management & Data Systems, 115 (3), pp. 436-461; Wang, W.Y., Chan, H.K., Pauleen, D.J., Aligning business process reengineering in implementing global supply chain systems by the SCOR model (2010) International Journal of Production Research, 48 (19), pp. 5647-5669; Wong, J.-T., DSS for 3PL provider selection in global supply chain: Combining the multi-objective optimization model with experts' opinions (2012) Journal of Intelligent Manufacturing, 23 (3), pp. 599-614; Zang, C., Fan, Y., Complex event processing in enterprise information systems based on RFID (2007) Enterprise Information Systems, 1 (1), pp. 3-23; Zhang, W., Reimann, M., Towards a multi-objective performance assessment and optimization model of a two-echelon supply chain using SCOR metrics (2013) Central European Journal of Operations Research, 22 (4), pp. 591-622; Zhang, Y., Jiang, P., Huang, G., RFID-based smart kanbans for just-in-time manufacturing (2008) International Journal of Materials and Product Technology, 33 (1-2), pp. 170-184; Zhang, Y., Huang, G.Q., Qu, T., Ho, O., Agent-based workflow management for RFID-enabled real-time reconfigurable manufacturing (2010) International Journal of Computer Integrated Manufacturing, 23 (2), pp. 101-112; Zhang, Y., Qu, T., Ho, O.K., Huang, G.Q., Agent-based smart gateway for RFID-enabled real-time wireless manufacturing (2011) International Journal of Production Research, 49 (5), pp. 1337-1352; Zhang, Y., Wang, W., Liu, S., Xie, G., Real-time shop-floor production performance analysis method for the internet of manufacturing things (2014) Advances in Mechanical Engineering, 6, pp. 270-749; Zhang, Y., Huang, G.Q., Qu, T., Ho, O., Sun, S., Agent-based smart objects management system for real-time ubiquitous manufacturing (2011) Robotics and Computer-Integrated Manufacturing, 27 (3), pp. 538-549},
correspondence_address1={Shirazi, M.A.; Department of Industrial Engineering, Iran; email: akbarpour@aut.ac.ir},
publisher={Emerald Group Publishing Ltd.},
issn={02635577},
coden={IMDSD},
language={English},
abbrev_source_title={Ind Manage Data Sys},
document_type={Article},
source={Scopus},
}

@ARTICLE{Nawi2017246,
author={Nawi, M.N.M. and Songappenm, M. and Nadarajan, S. and Ibrahim, S.H. and Mustapha, R.},
title={Procurement performance and supplier management measurement issues: A case of Malaysian private company},
journal={International Journal of Supply Chain Management},
year={2017},
volume={6},
number={1},
pages={246-253},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016578860&partnerID=40&md5=d709404dd21e3aae6743c56d994295b3},
affiliation={School of Technology Management and Logistics, Universiti Utara Malaysia, Sintok, Kedah, Malaysia; Koyama Medical Sdn. Bhd., Penang, Malaysia; Faculty of Civil Engineering, Universiti Malaysia Sarawak, Sarawak, Malaysia; Faculty of Technical and Vocational Education, Sultan Idris Education University, Tanjong Malim, 35900, Malaysia},
abstract={The rising material and operational cost for manufacturers in medical device industry is of concern to many organizations. As such, it is imperative to measure the procurement and supplier's performance for competitive advantage and provide a framework to stakeholders for continuous improvement. This paper develops a procurement and supplier performance measurement system at a firm by conducting a review of literature in procurement performance measurement and supplier performance measurement. Through the key components of supply chain together with the resources, procedures and output, a model was developed. Additionally, a system was established with a set of generic measures and six perspectives. The case study conducted at the firm applies to the model developed to describe the procurement department within the supply chain activities. Results indicate that supply chain department has already made a good progress in measuring procurement process through the implementation of supplier development program. Few areas that needed to be measured include cycle time of delivery, order processing time, effectiveness, efficiency and reliability. While a lot of hard work was involved, the advantages of establishing a measurement system outweigh the costs and efforts involved in its implementation. Results of key performance index measurements provide stakeholders with critical information on efficiency and effectiveness of the procurement department's work. © ExcelingTech Pub, UK.},
author_keywords={Manufacturing;  Measurement;  Performance;  Procurement;  Supply chain},
references={Goksoy, A., Ozsoy, B., Vayvay, O., "Business process reengineering: strategic tool for managing organizational change an application in a multinational company" (2012) International Journal of Business and Management, 7 (2), p. 89; Leflar, R.B., "Health Care Reform: Treatment Effectiveness Information Nationwide" (2013) UALR L. Rev, 36, p. 606; Sangwa, N.R., Choudhary, K., Sangwan, K.S., "Performance Evaluation Framework for Lean Manufacturing-A Review" (2015) National Conference on Sustainable Manufacturing National Conference on Sustainable Manufacturing, 2015, p. 3. , DMS, MNIT Jaipur, India; Vijay, B., Shetkar, S., "Cost management techiniques used in Sugar industry" (2015) International Journal of Research in Finance and Marketing, 7, pp. 82-89; Kumar, A., Ozdamar, L., Peng Ng, C., "Procurement performance measurement system in the health care industry" (2005) International journal of health care quality assurance, 18 (2), pp. 152-166; Miller, S., John, R., "An interval type-2 fuzzy multiple echelon supply chain model" (2010) Knowledge-Based Systems, 23 (4), pp. 363-368; Hong, P., Kwon, H.B., "Emerging issues of procurement management: a review and prospect" International Journal of Procurement Management, 5 (4), pp. 452-469; Van Weele, A.J., (2000) Purchasing and Supply Chain Management, , Thomson Learning, Boston, MA; Akintoye, A., Hardcastle, C., Beck, M., Chinyio, E., Asenova, D., "Achieving best value in private finance initiative project procurement" Construction Management and Economics, 21 (5), pp. 461-470; Kenyon, G.N., Sen, K.C., The Dimensions of Supply Chain Quality (2015) The Perception of Quality, pp. 141-159. , Springer London; Nawi, M.N.M., Roslan, S., Salleh, N.A., Zulhumadi, F., Harun, A.N., "The Benefits and Challenges of E-procurement Implementation: A Case Study of Malaysian Company" (2016) International Journal of Economics and Financial Issues, 6 (7), pp. 329-332},
correspondence_address1={Nawi, M.N.M.; School of Technology Management and Logistics, Malaysia; email: nasrun@uum.edu.my},
publisher={ExcelingTech},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Moharamkhani2017115,
author={Moharamkhani, A. and Bozorgi-Amiri, A. and Mina, H.},
title={Supply chain performance measurement using SCOR model based on interval-valued fuzzy TOPSIS},
journal={International Journal of Logistics Systems and Management},
year={2017},
volume={27},
number={1},
pages={115-132},
doi={10.1504/IJLSM.2017.083225},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016258466&doi=10.1504%2fIJLSM.2017.083225&partnerID=40&md5=47f05f2c84264b1ca139d304e4136081},
affiliation={Department of Industrial and Systems Engineering, University of Tehran, Iran},
abstract={Performance measurement is known to be the best way of investigating the supply chains' success. In this regard, managers can identify the root causes of weakness points and improve supply chain's performance through analysing and solving these problems. Concerning this problem, various supply chain performance evaluation models have been presented in literature. Through all of the models, this paper used supply chain operations reference (SCOR) model for performance measurement of three Iranian automotive supply chains. First SCOR model is employed to define the performance criteria. Afterwards, technique for order of preference by similarity to ideal solution (TOPSIS) is used to determine the supply chain that performs best. In this paper, expert's judgment is used to determine the criteria's value and weight but uncertainties in expert's judgment was unavoidable, also experts cannot reach an agreement on the method of defining linguistic variables based on fuzzy sets. So, paper used interval-valued fuzzy set to solve these problems. To the best of authors' knowledge, this is the first study that have applied interval-valued fuzzy TOPSIS in group decision-making in order to evaluate and improve the performance of supply chains on the basis of SCOR model. Copyright © 2017 Inderscience Enterprises Ltd.},
author_keywords={Interval-valued fuzzy TOPSIS;  Performance measurement;  SCOR model;  Supply chain},
references={Anisseh, M., Piri, F., Shahraki, M.R., Agamohamadi, F., Fuzzy extension of TOPSIS model for group decision making under multiple criteria (2012) Artificial Intelligence Review, 38 (4), pp. 325-338; Ashtiani, B., Haghighirad, F., Makui, A., Montazer, G.A., Extension of fuzzy TOPSIS method based on interval-valued fuzzy sets (2009) Applied Soft Computing, 9 (2), pp. 457-461; Aydin, S.D., Eryuruk, S.H., Kalaoglu, F., Evaluation of the performance attributes of retailers using the SCOR model and AHP: A case study in the Turkish clothing industry (2014) Fibres & Textiles in Eastern Europe, 5 (107), pp. 14-19; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Chan, F.T., Qi, H., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chen, C.T., Extensions of the TOPSIS for group decision-making under fuzzy environment (2000) Fuzzy Sets and Systems, 114 (1), pp. 1-9; Chen, S.M., Lee, L.W., Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method (2010) Expert Systems with Applications, 37 (4), pp. 2790-2798; Chen, T.Y., Comparative analysis of SAW and TOPSIS based on interval-valued fuzzy sets: Discussions on score functions and weight constraints (2012) Expert Systems with Applications, 39 (2), pp. 1848-1861; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , Prentice-Hall, London; Cornelis, C., Deschrijver, G., Kerre, E., Advances and challenges in interval-valued fuzzy logic (2006) Fuzzy Sets and Systems, 157 (5), pp. 622-627; (2013), http://www.cscmp.se/CLMSite/CLM_logistik.htm, (accessed 1 October 2013); Elgazzar, S., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Incorporating fuzzy AHP in SCOR model for measuring supply chain operations performance: A case study of an Egyptian natural bottled water company (2010) Proceedings of the 15th Annual Logistics Research Network Conference (LRN 2010), pp. 180-187. , Harrogate, UK; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., A SW application system for measuring supply chain operations' performance using SCOR FAHP technique (2011) International Proceedings of Economics Development and Research, the 2011 International Conference on Business and Economics Research, 16, pp. 37-41. , Cairo, Egypt; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes' performance to a company's financial strategic objectives (2012) European Journal of Operational Research, 223 (1), pp. 276-289; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Ganga, G.M., Carpinetti, L.C., Politano, P.R., A fuzzy logic approach to supply chain performance management (2011) International Journal of Production Economics, 134 (4), pp. 177-187; Gorzalczany, M., A method of inference in approximate reasoning based on interval-valued fuzzy sets (1987) Fuzzy Sets and Systems, 21 (1), pp. 1-17; Grzegorzewski, P., Distances between intuitionistic fuzzy sets and/or interval valued fuzzy sets based on the Hausdorff metric (2004) Fuzzy Set and Systems, 148 (2), pp. 319-328; Hong, D.H., Lee, S., Some algebraic properties and a distance measure for interval valued fuzzy numbers (2002) Information Sciences, 148 (1-4), pp. 1-10; Hwang, C.L., Yoon, K., (1981) Multiple Attributes Decision Making Methods and Applications, , Springer-Verlag, Berlin Heidelberg, New York; Janvier-James, A.M., A new introduction to supply chains and supply chain management: Definitions and theories perspective (2012) International Business Research, 5 (1), pp. 194-207; Jothimani, D., Sarmah, S.P., Supply chain performance measurement for third party logistics (2014) Benchmarking: An International Journal, 21 (6), pp. 944-963; Karnik, N., Mendel, J., Operations on type-2 fuzzy sets (2001) Fuzzy Sets and Systems, 122 (2), pp. 327-348; Kocaoǧlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a bench markable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Kuo, M.S., Liang, G.S., A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers (2012) Applied Soft Computing, 12 (1), pp. 476-485; Lima-Junior, F.R., Carpinetti, L.C., Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management (2016) International Journal of Production Economics, 174, pp. 128-141; Mensah, P., Merkuryev, Y., The role of ICT in the supply chain resilience (2013) International Conference on Applied Information and Communication Technologies (AICT2013), pp. 129-135. , Jelgava, Latvia; Min, H., Zhou, G., Supply chain modeling: Past, present and future (2002) Computers & Industrial Engineering Journal, 43 (1-2), pp. 231-249; Ntabe, E.N., Lebel, L., Munson, D.A., Santa-Eulalia, L.A., A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues (2015) International Journal of Production Economics, 169 (1), pp. 310-332; Opricovic, S., Tzeng, G.H., Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS (2004) European Journal of Operational Research, 156 (2), pp. 445-455; Öztayşi, B., Sürer, O., Supply chain performance measurement using a SCOR based fuzzy VIKOR approach (2014) Supply Chain Management Under Fuzziness, 313, pp. 199-224; Sangari, M.S., Razmi, J., Zolfaghari, S., Developing a practical evaluation framework for identifying critical factors to achieve supply chain agility (2015) Measurement, 62 (1), pp. 205-214; Savino, M.M., (2015) Performance Measurement and Management in a Supply Chain: An Integrated Approach Based on the SCOR Model, 1 (1), pp. 1-16. , Department of Engineering, University of Sannio; (2012) Supply Chain Council SCOR Model Version 11.00, , http://docs.huihoo.com/scm/supply-chain-operations-reference-model-r11.0.pdf, SCC; Senvar, O., Tuzkaya, U.R., Kahraman, C., Supply chain performance measurement: An integrated DEMATEL and fuzzy-ANP approach (2014) Supply Chain Management Under Fuzziness, 313 (1), pp. 143-165; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies, , Irwin McGraw-Hill, New York; Stefanovic, D., Stefanovic, N., Methodology for modeling and analysis of supply networks (2008) Journal of Intelligent Manufacturing, 19 (4), pp. 485-503; Theeranuphattana, A., Tang, J.C., A conceptual model of performance measurement for supply chains (2007) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Theeranuphattana, A., Tang, J.C., Khang, D.B., An integrated approach to measuring supply chain performance (2012) Industrial Engineering & Management Systems, 11 (1), pp. 54-69; Torabi, A., Aghabagloo, M., Meisami, A.H., (2012) A Framework for Performance Measurement of Humanitarian Relief Chains: A Combined Fuzzy DEMATEL-ANP Approach, 1 (1), pp. 1-10. , Production and Operations Management Society; Turksen, I., Interval-valued strict preference with Zadeh triples (1996) Fuzzy Sets and Systems, 78 (2), pp. 183-195; Vahdani, B., Hadipouri, H., Sadaghiani, J.S., Amiri, M., Extension of VIKOR method based on interval-valued fuzzy sets (2010) The International Journal of Advanced Manufacturing Technology, 47 (9), pp. 1231-1239; Wang, Y.M., Elhag, T.S., Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment (2006) Expert Systems with Applications, 31 (2), pp. 309-319; Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8 (3), pp. 338-353},
correspondence_address1={Moharamkhani, A.; Department of Industrial and Systems Engineering, Iran; email: a_moharamkhani@ut.ac.ir},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Oey2017497,
author={Oey, E. and Mulianti, N.},
title={Performance measurement system using balanced score card and tracking tool - A case study in a pharmaceutical company},
journal={International Journal of Logistics Systems and Management},
year={2017},
volume={26},
number={4},
pages={497-514},
doi={10.1504/IJLSM.2017.082615},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014740767&doi=10.1504%2fIJLSM.2017.082615&partnerID=40&md5=42efc4ef4af0c8c15e94d8d7d6abe7c2},
affiliation={International Business and Management, School of Business and Management, Bina Nusantara University, Jl. KH Syahdan No. 9 Palmerah, Jakarta Barat, Indonesia},
abstract={Every organisation needs to measure where they are before they can make further improvement. In supply chain management, where suppliers and customers involved, it is important to have a comprehensive and aligned performance measurement system. The study is based on a multinational pharmaceutical company in Indonesia. It aims to refine the performance measurement system (PMS) for its export department. The export activities involve multiple logistic service providers as their suppliers and several inter-market customers. The PMS is done based on balanced score card (BSC) concept. The study gives practical approach on implementation of PMS in middle and part of organisation, aligning its internal needs and external environment, without losing BSC integrity. A tracking tool is also developed to assist in measuring and monitoring the KPIs. © Copyright 2017 Inderscience Enterprises Ltd.},
author_keywords={Balanced score card;  BSC;  KPI;  Performance management system;  Tracking tool},
references={Aitken, M., Machin, C., Troein, P., (2014) Understanding the Pharmaceutical Value Chain, , USA; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bulsara, H.P., Qureshi, M.N., Patel, H., Supply chain performance measurement - An exploratory study (2014) Int. J. of Logistics Systems and Management, 18 (2), pp. 231-249; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) Int. J. of Logistics Systems and Management, 18 (2), pp. 216-230; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning, and Operation, , 5th ed., Pearson Education Inc., England; Cooper, D.J., Ezzamel, M., Globalization discourses and performance measurement systems in a multinational firm (2013) Accounting, Organizations and Society, 38 (4), pp. 288-313; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Ebrahimnejad, P.S.S., An integrated approach for measuring the performance of suppliers in the pharmaceutical industry: A case study (2015) Int. J. of Logistics Systems and Management, 22 (3), pp. 267-295; El Sayed, H., Supply chain key performance indicators analysis (2013) International Journal of Application or Innovation in Engineering and Management, 2 (1), pp. 201-210; Forslund, H., Performance management in supply chains: Logistics service providers' perspective (2012) International Journal of Physical Distribution and Logistics Management, 42 (3), pp. 296-311; Horstman, A., Witteveen, W., Performance indicators in the best value approach (2013) Journal for the Advancement of Performance Information and Value, 5 (2), pp. 59-78; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Kurien, G.P., Qureshi, M.N., Performance measurement systems for green supply chains using modified balanced score card and analytical hierarchical process (2012) Scientific Research and Essays, 7 (36), pp. 3149-3161; Kurien, G.P., Qureshi, M.N., Analysis and measurement of supply chain flexibility (2015) Int. J. of Logistics Systems and Management, 21 (1), pp. 70-91; Mandal, S., An empirical-relational investigation on supply chain responsiveness (2015) International Journal of Logistics Systems and Management, 20 (1), pp. 59-82; Mehralian, G., Zarenezhad, F., Ghatari, A., Developing a model for an agile supply chain in pharmaceutical industry (2013) International Journal of Pharmaceutical and Healthcare Marketing, 9 (1), pp. 74-91; Mirzabeiki, V., Sjöholm, V.M.P., Sharing tracking and tracing data to improve operations of supply chain actors (2012) Int. J. of Logistics Systems and Management, 13 (1), pp. 81-95; Moynihan, G.P., Wang, S., Web-based decision support system for integrated supply chain management (2015) Int. J. of Logistics Systems and Management, 21 (3), pp. 269-283; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Pauwels, K., Dashboards as a service (2009) Journal of Service Research, 12 (2-89), pp. 175-189; Pettersson, A.I., Segerstedt, A., Measurements of excellence in a supply chain (2012) International Journal of Logistics Systems and Management, 13 (1), p. 65; Pidun, T., Felden, C., The role of performance measurement systems between assessment tool and knowledge repository (2013) 2013 46th Hawaii International Conference on System Sciences (HICSS), pp. 3426-3435; Pujara, A.A., Kant, P.R., Information sharing enablement of supply chain: A conceptual framework (2013) Int. J. of Logistics Systems and Management, 14 (3), pp. 298-314; Silva, T.F.G., Gonçalves, A.T.P., Leite, M.S.A., Logistics cost management: Insights on tools and operations (2014) Int. J. of Logistics Systems and Management, 19 (3), pp. 329-346; Singh, R.K., Acharya, P., Performance evaluation of supply chain management systems: A critical review of literature (2014) International Journal of Procurement Management, 7 (2), pp. 201-218; Sousa, R.T., Global supply chain planning for pharmaceuticals (2011) Chemical Engineering Research and Design, 89 (11), pp. 2396-2409; Striteska, M., Spickova, M., Review and comparison of performance measurement systems (2012) The Journal of Organizational Management Studies, pp. 1-13; Sundarakani, B., Tan, A.W.K., Van Over, D., Enhancing the supply chain management performance using information technology: Some evidence from UAE companies (2012) Int. J. of Logistics Systems and Management, 11 (3), pp. 306-324; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Tian, C., Performance management in supply chain (2003) SMC'03 Conference Proceedings: 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No. 03CH37483), 5, pp. 4934-4939; Zolfagharinia, H., Haughton, M.A., The benefit of information sharing in a logistics outsourcing context (2012) Int. J. of Logistics Systems and Management, 13 (2), pp. 187-208},
correspondence_address1={Oey, E.; International Business and Management, Jl. KH Syahdan No. 9 Palmerah, Indonesia; email: eliaoey@binus.ac.id},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dweekat2017267,
author={Dweekat, A.J. and Hwang, G. and Park, J.},
title={A supply chain performance measurement approach using the internet of things: Toward more practical SCPMS},
journal={Industrial Management and Data Systems},
year={2017},
volume={117},
number={2},
pages={267-286},
doi={10.1108/IMDS-03-2016-0096},
note={cited By 66},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014644985&doi=10.1108%2fIMDS-03-2016-0096&partnerID=40&md5=63fa8916dace9e30bd505d7540aaef06},
affiliation={Department of Industrial Engineering, Seoul National University, Seoul, South Korea},
abstract={Purpose - The purpose of this paper is to introduce a more practical approach for supply chain performance measurement (SCPM) and to approve the promising role of internet of things (IoT) technologies in SCPM systems. Design/methodology/approach - This is a conceptual paper that includes literature review analysis, designing a new approach for SCPM, and a case study scenario for proving its applicability. Findings - The case study scenario shows that IoT can enhance SCPM, as it has the capability to enable real-time data collection, increase data efficiency as long as enable real-time communication within the supply chain (SC). Practical implications - The proposed approach can help to develop performance measurement systems and applications enabled by IoT technologies. These systems can be used to monitor, manage, and control the overall SC in real time and in a more integrated and cooperative manner. Originality/value - This paper provides a structured systems building approach tailored to show how to employ IoT technologies in the field of SCPM. This approach could help in establishing new performance measurement applications, and it is believed that both practitioners and researchers will benefit from it. © 2017 Emerald Publishing Limited.},
author_keywords={IoT;  Performance measurement;  SCOR model;  Supply chain},
keywords={Data acquisition;  Real time systems;  Supply chain management;  Supply chains, Design/methodology/approach;  Internet of Things (IOT);  Performance measurement system;  Performance measurements;  Real time data collections;  Real-time communication;  SCOR models;  Supply chain performance measurements, Internet of things},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: Review and classification (2012) Journal of Organizational Management Studies, pp. 1-20. , http://ibimapublishing.com/articles/JOMS/2012/872753/872753.pdf; Ashton, K., That 'internet of things' thing (2009) RFiD Journal, 22 (7), pp. 97-114; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bolstorff, P., Rosenbaum, R., (2012) Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , AMACOM, New York, NY; Burkitt, F., (2015) A Strategist's Guide to the Internet of Things, , www.strategy-business.com/article/00294?gko=a9303, (accessed January 7, 2016); Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chan, F.T., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chen, S.-J.G., Huang, E., A systematic approach for supply chain improvement using design structure matrix (2007) Journal of Intelligent Manufacturing, 18 (2), pp. 285-299; Cluster, F., (2012) Future Internet Enterprise Systems: Research Roadmap 2025, , final report, Version 2; Da Xu, L., He, W., Li, S., Internet of things in industries: A survey (2014) IEEE Transactions on Industrial Informatics, 10 (4), pp. 2233-2243; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Fisher, M.L., (1997) What Is the Right Supply Chain for Your Product?, , https://hbr.org/1997/03/what-is-the-right-supply-chain-for-your-product, (accessed January 12, 2016); Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hall, D.C., Saygin, C., Impact of information sharing on supply chain performance (2012) The International Journal of Advanced Manufacturing Technology, 58 (1-4), pp. 397-409; Hwang, Y.-D., Lin, Y.-C., Lyu, J., The performance evaluation of SCOR sourcing process - The case study of Taiwan's TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Jara, A.J., Ladid, L., Gómez-Skarmeta, A.F., The internet of everything through IPv6: An analysis of challenges, solutions and opportunities (2013) Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 4 (3), pp. 97-118; Kaplan, R.S., Norton, D.P., (2006) Alignment: Using the Balanced Scorecard to Create Corporate Synergies, , Harvard Business School Press, Boston, MA; Kocaoǧlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Kueng, P., Meier, A., Wettstein, T., Performance measurement systems must be engineered (2001) Communications of the Association for Information Systems, 7 (3), pp. 1-27; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Lapide, L., What about measuring supply chain performance (2000) Achieving Supply Chain Excellence Through Technology, 2 (2), pp. 287-297; Lauras, M., Lamothe, J., Pingaud, H., A business process oriented method to design supply chain performance measurement systems (2011) International Journal of Business Performance Management, 12 (4), pp. 354-376; Lockamy, A., III, McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; McCormack, K., Bronzo Ladeira, M., Paulo Valadares-De-Oliveira, M., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Marwah, A.K., Thakar, G., Gupta, R., Implications of performance measurement approaches on supply chain performance (2012) National Conference on Emerging Challenges for Sustainable Business, , Haridwar, June 1-2; Moisescu, M., Sacala, I., Towards the development of interoperable sensing systems for the future enterprise (2016) Journal of Intelligent Manufacturing, 27 (1), pp. 33-54; Myles, J.F., (2008) Discovering Critical Success Factors for Implementing An Automated Performance Measurement System: A Case Study Approach, , PhD thesis, Edith Cowan University; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) Journal of Operations and Supply Chain Management, 6 (2), pp. 94-113; O'Halloran, D., Kvochko, E., Industrial internet of things: Unleashing the potential of connected products and services (2015) World Economic Forum; Ramaa, A., Rangaswamy, T., Subramanya, K., A review of literature on performance measurement of supply chain network (2009) Second International Conference on Emerging Trends in Engineering & Technology, , http://doi.ieeecomputersociety.org/10.1109/ICETET.2009.18, IEEE, December 16-18, (accessed January 18, 2016); Robinson, C.J., Malhotra, M.K., Defining the concept of supply chain quality management and its relevance to academic and industrial practice (2005) International Journal of Production Economics, 96 (3), pp. 315-337; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; (2012) Supply Chain Operations Reference Model: Version 11.0; Sepehri, M., A grid-based collaborative supply chain with multi-product multi-period production-distribution (2012) Enterprise Information Systems, 6 (1), pp. 115-137; Shapiro, J., (2001) Modeling the Supply Chain, , Wadsworth Group, Duxbury, MA; Stadtler, H., Kilger, C., Meyr, H., (2015) Supply Chain Management and Advanced Planning, , Springer, Berlin; Stephens, S., Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; Stock, J.R., Lambert, D.M., (2001) Strategic Logistics Management, , 4th ed., McGraw-Hill/Irwin, Boston, MA; Teimoury, E., Fathian, M., Chambar, I., Automation of the supply chain performance measurement based on multi-agent system (2013) International Journal of Agile Systems and Management, 6 (1), pp. 25-42; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Vermesan, O., Friess, P., (2014) Internet of Things - From Research and Innovation to Market Deployment, , River Publishers, Aalborg; Wang, L., Da Xu, L., Bi, Z., Xu, Y., Data cleaning for RFID and WSN integration (2014) IEEE Transactions on Industrial Informatics, 10 (1), pp. 408-418; Wang, X.F., Research on security issues of the internet of things (2014) Advanced Materials Research, pp. 4261-4264. , Cai, S.Z., Zhang, Q.F., Xu, X.P., Hu, D.H. and Qu, Y.M. (Eds), Trans Tech Publ., Zurich; (2016) GS1 General Specifications Version 16, , www.gs1.org/barcodes-epcrfid-idkeys/gs1-general-specifications, GS1, (accessed February 8, 2016); Kevin, A., (2009) That 'Internet of Things' Thing: In the Real World Things Matter More Than Ideas, , www.rfidjournal.com/articles/view?4986, (accessed December 15, 2015)},
correspondence_address1={Park, J.; Department of Industrial Engineering, South Korea; email: autofact@snu.ac.kr},
publisher={Emerald Group Publishing Ltd.},
issn={02635577},
coden={IMDSD},
language={English},
abbrev_source_title={Ind Manage Data Sys},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tyagi2017392,
author={Tyagi, M. and Kumar, P. and Kumar, D.},
title={Modelling and analysis of barriers for supply chain performance measurement system},
journal={International Journal of Operational Research},
year={2017},
volume={28},
number={3},
pages={392-414},
doi={10.1504/IJOR.2017.081912},
note={cited By 15},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011843853&doi=10.1504%2fIJOR.2017.081912&partnerID=40&md5=45dc306ef8be6f1da10dc9b519d90ab8},
affiliation={Department of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, Uttrakhand (UK), 247667, India},
abstract={During this competitive environment, the behaviour of market is changing abruptly and generating interference for industries, to sustain their stability in the market place. Therefore, most of all industries are in race to improve their supply chain performance (SCP) system. The objective of this research is to develop relationship among the considered barriers of SCP system. To achieve the objective, fifteen barriers have been considered and analysed using kappa statistics and interpretive structural modelling (ISM) approach, to understand the mutual relationship among them. The analysis reveals that barriers namely: insufficient focus on customers and competitors, lack of visibility of true customer demand, lack of collaborative relationships, lack of relevant performance measures and less accurate information are the desired output, those should be strongly emphasised in order to achieve a better SCP. The findings of this research may enable managers to make accurate strategies-based decisions through driving and dependence power of considered barriers, for an effective implementation of SCP system. Copyright © 2017 Inderscience Enterprises Ltd.},
author_keywords={Barriers;  Interpretive structural modelling;  ISM;  Kappa statistics;  Performance system;  SCM;  Supply chain management},
references={Aich, S., Tripathy, S., An interpretive structural model of green supply chain management in Indian computer and its peripheral industries (2014) International Journal of Procurement Management, 7 (3), pp. 239-256; Amer, Y., Luong, L., Ashraf, M.A., Lee, S.H., A systems approach to order fulfilment (2010) International Journal of Operational Research, 9 (4), pp. 443-465; Babazadeh, R., Razmi, J., Ghodsi, R., Facility location in responsive and flexible supply chain network design (SCND) considering outsourcing (2013) International Journal of Operational Research, 17 (3), pp. 295-310; Barratt, M., Oliveria, A., Exploring the experiences of collaborative planning initiatives (2001) International Journal of Physical Distribution & Logistics Management, 31 (4), pp. 266-289; Beamon, B.M., Measuring SCP (1999) International Journal of Operations & Production Management, 19 (3-4), pp. 275-292; Blackhurst, J., Wu, T., O'Grady, P., Network-based approach to modeling uncertainty in a supply chain (2004) International Journal of Production Research, 42 (8), pp. 1639-1658; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3-4), pp. 209-223; Chan, F.T.S., Nayak, A., Raj, R., Chong, A.Y.L., Manoj, T., An innovative supply chain performance measurement system incorporating research and development (R&D) and marketing policy (2014) Computers & Industrial Engineering, 69, pp. 64-70. , March; Chander, M., Jain, S.K., Shankar, R., Modeling of information security management parameters in Indian organizations using ISM and MICMAC approach (2013) Journal of Modelling in Management, 8 (2), pp. 171-189; Chandra, C., Kumar, S., Supply chain management in theory and practice: A passing fad or a fundamental change? (2000) Industrial Management & Data System, 100 (3), pp. 100-114; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among variables of SCP measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Costs and Improving Services, , 2nd ed., Pitman Publishing, London; Cirtita, H., Daniel, A.G.S., Measuring downstream supply chain performance (2012) Journal of Manufacturing Technology Management, 23 (3), pp. 299-314; Cohen, J., Weighted Kappa: Nominal scale agreement provision for scaled disagreement or partial credit (1968) Psychological Bulletin, 70 (4), pp. 213-220; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; DeGroote, S.E., Marx, T.G., The impact of IT on supply chain agility and firm performance: An empirical investigation (2013) Information Journal of Information Management, 33 (6), pp. 909-916; Droge, C., Vickery, S.K., Jacobs, M.A., Does supply chain integration mediate the relationships between product/process strategy and service performance? An empirical study (2012) International Journal of Production Economics, 137 (2), pp. 250-262; Ellram, L.M., Supply management's involvement in the target costing process (2002) European Journal of Purchasing & Supply Management, 8 (4), pp. 235-244; Faisal, M.N., Banwet, D.K., Shankar, R., Supply chain risk mitigation: Modelling the enablers (2006) Business Process Management Journal, 12 (4), pp. 535-552; Fynes, B., Voss, C., De Burca, S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354; Gorane, S.J., Kant, R., Supply chain management: Modelling the enablers using ISM and fuzzy MICMAC approach (2013) International Journal of Logistics Systems and Management, 16 (2), pp. 147-166; Gunasekaran, A., Ngai, E.W.T., Information systems in supply chain integration and management (2004) European Journal of Operational Research, 159 (2), pp. 269-295; Gunasekaran, A., Patel, C., McGaughey, E., A framework for SCP measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Holmberg, S., A systems perspective on supply chain measurement (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Ibrahim, S.E., Ogunyemi, O., The effect of linkages and information sharing on supply chain and export performance: An empirical study of Egyptian textile manufacturers (2012) Journal of Manufacturing Technology Management, 23 (4), pp. 441-463; Jharkharia, S., Shankar, R., Supply chain management: Some sectoral dissimilarities in the Indian manufacturing industry (2006) Supply Chain Management: An International Journal, 11 (4), pp. 345-352; Jones, C., Moving beyond ERP: Making the missing link (1998) Logistics Focus, 6 (7), pp. 2-7; Kannan, G., Haq, N.A., Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment (2007) International Journal of Production Research, 45 (17), pp. 1-22; Kannan, G., Jannan, D., Haq, N.A., Analysing supplier development criteria for an automobile industry (2010) Industrial Management and Data Systems, 110 (1), pp. 43-62; Kannan, G., Pokharel, S., Sasi Kumar, P., A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider (2009) Resources, Conservation and Recycling, 54 (1), pp. 28-36; Kumar, D., Kumar, D., Modelling hospital inventory management using interpretive structural modelling approach (2014) International Journal Logistics Systems and Management (in Press); Kumar, D., Rahman, Z., Modelling relationship marketing strategies for sustainability adoption (2013) International Journal of Intercultural Information Management, 3 (4), pp. 277-291; Lalonde, B.J., Pohlen, T.L., Issues in supply chain costing (1996) International Journal of Logistics Management, 7 (1), pp. 1-12; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Landis, J.R., Koch, G.G., A one-way component of variance model for categorical data (1977) Biometrics, 33 (4), pp. 671-679; Laura, X.X.X., Bin, M., Roland, L., AHP based SCP measurement system (2007) The 12th IEEE International Conference on Emerging Technologies and Factory Automation, , Greece, 25-28 September; Lee, H., Kim, M.S., Kim, K.K., Interorganizational information systems visibility and supply chain performance (2014) International Journal of Information Management, 34 (2), pp. 285-295; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Liao, S.H., Kuo, F.I., The study of relationships between the collaboration for supply chain, supply chain capabilities and firm performance: A case of the Taiwan's TFT-LCD industry (2014) International Journal of Production Economics, 156, pp. 295-304. , October; Liu, H., Ke, W., Wei, K.K., Hua, Z., The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility (2013) Decision Support Systems, 54 (3), pp. 1452-1462; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system design: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Luthra, S., Kumar, V., Kumar, S., Haleem, A., Barriers to implement green supply chain management in automobile industry using interpretive structural modelling technique - An Indian perspective (2011) Journal of Industrial Engineering and Management and Data Systems, 4 (2), pp. 231-257; Macrosson, W., (1998) Human Factors and Productivity: Social Aspects of Computer Integrated Manufacture, Internal Course Material 54316, , n A. S. Carrie Ed., DMEM University of Strathclyde, UK; Malhotra, M.K., Grover, V., An assessment of survey research in POM: From constructs to theory (1998) Journal of Operations Management, 16 (4), pp. 407-425; Malone, D.W., An introduction to the application of interpretive structural modeling (1975) Proceedings of IEEE, 63 (3), pp. 397-404; Mandal, A., Deshmukh, S.G., Vendor selection using interpretive structural modeling (ISM) (1994) International Journal of Operations & Production Management, 14 (6), pp. 52-59; Mangla, S., Madaan, J., Chan, F.T.S., Analysis of flexible decision strategies for sustainability-focused green product recovery system (2013) International Journal of Production Research, 51 (11), pp. 3428-3442; Mangla, S., Madaan, J., Sarma, P.R.S., Gupta, M.P., Multi-objective decision modelling using interpretive structural modelling for green supply chains (2014) International Journal of Logistics Systems and Management, 17 (2), pp. 125-142; Mathiyazhagan, K., Govindan, K., Haq, N.A., Geng, Y., An ISM approach for the barrier analysis in implementing green supply chain management (2013) Journal of Cleaner Production, 47, pp. 283-297. , May; McNurlin, B.C., Sprague, R.H., (2002) Information Systems Management in Practice, , 5th ed., Prentice Hall, New Jersey,: NJ; Meekings, A., Unlocking the potential of performance measurement: A practical implementation guide (1995) Public Money & Management, 15 (4), pp. 5-12; Morris, M., Carter, C.R., Relationship marketing and supplier logistics performance: An extension of the key mediating variables model (2005) The Journal of Supply Chain Management, 41 (4), pp. 32-43; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; New, C., Szwejczewski, M., Performance measurement and the focused factory: Empirical evidence (1995) International Journal of Operations & Production Management, 15 (4), pp. 63-79; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Parthiban, P., Zubar, A.H., Ganesh, K., Nagarajan, S., MASS: An analytical model for assessment of supply chain entity (2013) International Journal of Operational Research, 18 (3), pp. 318-345; Pramod, V.R., Banwet, D.K., ISM for the inhibitors of service supply chain: A case study in a safety health environment and risk consultancy service sector (2010) International Journal of Logistics Economics and Globalisation, 2 (2), pp. 151-175; Qureshi, M.N., Kumar, D., Kumar, P., Modeling the logistics outsourcing relationship variables to enhance shippers' productivity and competitiveness in logistical supply chain (2007) International Journal of Productivity and Performance Management, 56 (8), pp. 689-714; Raj, T., Shankar, R., Suhaib, M., An ISM approach to analyse interaction between barriers of transition to flexible manufacturing system (2009) International Journal of Manufacturing Technology and Management, 16 (4), pp. 417-438; Ramanathan, U., Performance of supply chain collaboration - A simulation study (2014) Expert Systems with Applications, 41 (1), pp. 210-220; Ravi, V., Shankar, R., Tiwari, M.K., Productivity improvement of a computer-hardware supply chain (2005) International Journal of Productivity and Performance Management, 54 (4), pp. 239-255; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28 (5), pp. 581-590; Sage, A.P., (1977) Interpretive Structural Modeling: Methodology for Large-scale Systems, , McGraw-Hill, New York, NY; Samvedi, A., Jain, V., A study on the interactions between supply chain risk management criteria using fuzzy DEMATEL method (2013) International Journal of Operational Research, 18 (3), pp. 255-271; Shapiro, J.F., (2001) Modeling the Supply Chain, , Duxbury Pacific Grove, California; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2000) Designing and Managing the Supply Chain, , 1st ed., McGraw-Hill, New York, NY; Singh, R.K., Developing the framework for coordination in supply chain for SMEs (2011) Business Process Management Journal, 17 (4), pp. 619-638; Stadtler, H., Supply chain management and advanced planning: Basics, overview and challenges (2005) European Journal of Operations Research, 163 (3), pp. 575-588; Surti, C., Ritchie, P., Rowcroft, J., Supply chain contracting with product substitution and partial vertical integration (2014) International Journal of Operational Research, 19 (4), pp. 435-456; Thakkar, J., Kanda, A., Deshmukh, S.G., SCP measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Thien, N.P., Luong, H.T., Khang, D.B., Sukhotu, V., Revenue sharing contract with two-way penalties (2014) International Journal of Operational Research (in Press); Tyagi, M., Kumar, P., Kumar, D., Selecting alternatives for improvement in IT enabled supply chain performance (2014) International Journal of Procurement Management, 7 (2), pp. 168-182; Tyagi, M., Kumar, P., Kumar, D., Analysis of interactions among the drivers of green supply chain management (2014) International Journal of Business Performance and Supply Chain Modeling (in Press); Upadhye, N., Deshmukh, S.G., Garg, S., An interpretive structure modelling of implementation issues for lean manufacturing system (2011) International Journal of Modelling in Operations Management, 1 (4), pp. 311-343; Waddell, D., Sohal, A.S., Resistance: A constructive tool for change management, management decision (1998) MCB University Press, 36 (8), pp. 543-548; Waggoner, D., Neely, A., Kennerley, M., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61 (1), pp. 53-60; Warfield, J.N., (1973) An Assault on Complexity, , Battelle Memorial Institute, Columbus; Warfield, J.W., Developing interconnected matrices in structural modeling (1974) IEEE Transcript on Systems Men and Cybernetics, 4 (1), pp. 51-81},
correspondence_address1={Tyagi, M.; Department of Mechanical and Industrial Engineering, India; email: mohitmied@gmail.com},
publisher={Inderscience Publishers},
issn={17457645},
language={English},
abbrev_source_title={Int. J. Oper. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dörnhöfer2017102,
author={Dörnhöfer, M. and Günthner, W.A.},
title={A research and industry perspective on automotive logistics performance measurement},
journal={International Journal of Logistics Management},
year={2017},
volume={28},
number={1},
pages={102-126},
doi={10.1108/IJLM-06-2015-0105},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010807469&doi=10.1108%2fIJLM-06-2015-0105&partnerID=40&md5=5563bcec15ddce77f5f0a4e6ce8c3024},
affiliation={Lehrstuhl für Fördertechnik Materialfluss Logistik, Technische Universität München, Munich, Germany},
abstract={Purpose - The purpose of this paper is to assess the current state of performance measurement (PM) in academic literature and in the automotive supply chain (SC). The research question is to what extent PM systems (PMS) in academic literature and in industry are applicable in automotive logistics. Design/methodology/approach - The authors use a combination of a systematic literature review (SLR) and an industry survey. The SLR is used to identify literature on design aspects for PMS and current PMS reported in academic literature. The survey adds an industry perspective and is used to validate the design aspects found in literature. The synthesis aims to deliver both, current research gaps and needs in industry. Findings - The findings from SLR indicate, there is a broad base of literature regarding design aspects for PMS. Literature on PMS in context of automotive logistics is nevertheless rare. The industry survey leads to the conclusion that conformity with design aspects from literature increases added value of PMS. Nevertheless, current PMS show improvement needs in automotive context. Academic literature lacks detailed process orientation, transparency and is seen as too high level to be helpful for practitioners. For systems in industry, the SC perspective can be improved. In addition, applicability for continuous improvement is identified as research need in industry and literature. Research limitations/implications - Due to the selected survey approach, additional research is required to obtain more generalizable results. To increase reliability and validity, a selection of the findings should be further analyzed, e.g. by focusing on regions outside Western Europe or a more in depth analysis of selected aspects. In addition insights into selected research topics highlighted in the paper should be assessed in detail by applying different methodologies (e.g. case studies) to validate the findings. Originality/value - The findings add to the research on PMS. Latest academic literature is reviewed. The findings are combined with an industry perspective. The combination of SLR and a broad survey enables us to draw conclusions which are relevant for researchers and practitioners alike. Where academic research should focus more on the practical applicability, industry can benefit from the findings by integrating a more holistic perspective into their PMS, especially with a SC wide evaluation. © 2017 Sabet, Yazdani, De Leeuw.},
author_keywords={Automotive;  Logistics;  Performance management;  Performance measurement system;  Supply chain;  Supply chain management},
references={Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Engineering Costs and Production Economics, 17 (1-4), pp. 253-262; Azevedo, S., Carvalho, H., Duarte, S., Cruz-Machado, V., Influence of green and lean upstream supply chain management practices on business sustainability (2012) IEEE Transactions on Engineering Management, 59 (4), pp. 753-765; Bagchi, P.K., Chun Ha, B., Skjoett-Larsen, T., Boege Soerensen, L., Supply chain integration: A European survey (2005) The International Journal of Logistics Management, 16 (2), pp. 275-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Behrouzi, F., Wong, K.Y., A study on lean supply chain performance measures of SMEs in the automotive industry (2011) IEEE International Conference on Industrial Engineering and Engineering Management, , Singapore, 23 October; Bendoly, E., Rosenzweig, E., Stratman, J., Performance metric portfolios: A framework and empirical analysis (2007) Production and Operations Management, 16 (2), pp. 257-276; De, B.A., Wagner, S.M., Small sample surveys: Increasing rigor in supply chain management research (2012) International Journal of Physical Distribution & Logistics Management, 42 (7), pp. 615-639; Bititci, U.S., Modelling of performance measurement systems in manufacturing enterprises (1995) International Journal of Production Economics, 42 (2), pp. 137-147; Cabral, I., Grilo, A., Cruz-Machado, V., A decision-making model for lean, agile, resilient and green supply chain management (2012) International Journal of Production Research, 50 (17), pp. 4830-4845; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) The International Journal of Logistics Management, 6 (1), pp. 61-74; Chia, A., Goh, M., Hum, S.-H., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking, 16 (5), pp. 605-620; Clottey, T., Grawe, S., Non-response bias assessment in logistics survey research: Use fewer tests? (2014) International Journal of Physical Distribution & Logistics Management, 44 (5), pp. 412-426; Danese, P., Romano, P., Relationship between downstream integration, performance measurement systems and supply network efficiency (2012) International Journal of Production Research, 50 (7), pp. 2002-2013; De Leeuw, S., Beekman, L., Supply chain-oriented performance measurement for automotive spare parts (2008) International Journal of Automotive Technology and Management, 8 (1), pp. 56-70; Dey, P., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (2013) Production Planning and Control, 24 (8-9), pp. 702-720; Evans, J.R., Mathur, A., The value of online surveys (2005) Internet Research, 15 (2), pp. 195-219; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Forslund, H., Jonsson, P., Selection, implementation and use of ERP systems for supply chain performance management (2010) Industrial Management and Data Systems, 110 (8), pp. 1159-1175; Gligor, D.M., Holcomb, M.C., Understanding the role of logistics capabilities in achieving supply chain agility: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 438-453; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Grando, A., Belvedere, V., Exploiting the balanced scorecard in the operations department: The ducati motor holding case (2008) Production Planning and Control, 19 (5), pp. 495-507; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Williams, H., McGaughey, R., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: A case study from the packaging industry (2009) Production Planning & Control, 20 (1), pp. 68-81; Holmberg, S., A system perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Janeš, A., Empirical verification of the balanced scorecard (2014) Industrial Management and Data Systems, 114 (2), pp. 203-219; Joshi, D., Nepal, B., Rathore, A.P.S., Sharma, D., On supply chain competitiveness of Indian automotive component manufacturing industry (2013) International Journal of Production Economics, 143 (1), pp. 151-161; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard, , Harvard Business School Press, Boston, MA; Kocaoǧlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Management Accounting Research, 25 (2), pp. 173-186; Mentzer, J.T., Flint, D.J., Validity in logistics research (1997) Journal of Business Logistics, 18 (1), pp. 199-216; Neely, A., Gregory, M., Platts, K., Performance measurement system design. A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Neto, M.S., Pires, S., Performance measurement in supply chains: A study in the automotive industry (Medição de desempenho em cadeias de suprimentos: Um estudo na indústria automobilística) (2012) Gestao e Producao, 19 (4), pp. 733-746; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management and Data Systems, 110 (9), pp. 1297-1318; Pilbeam, C., Alvarez, G., Wilson, H., The governance of supply networks: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 358-376; Pinheiro De-Lima, E., Gouvea Da-Costa, S., Angelis, J., Munik, J., Performance measurement systems: A consensual analysis of their roles (2013) International Journal of Production Economics, 146 (2), pp. 524-542; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Said, A.A., Hassabelnaby, H.R., Wier, B., An empirical investigation of the performance consequences of nonfinancial measures (2003) Journal of Management Accounting Research, 15 (1), pp. 193-223; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89 (2), pp. 231-243; Seuring, S., Gold, S., Conducting content-analysis based literature reviews in supply chain management (2012) Supply Chain Management: An International Journal, 17 (5), pp. 544-555; Stainer, A., Logistics-A productivity and performance perspective (1997) Supply Chain Management, 2 (2), pp. 53-62; Taylor, A., Taylor, M., Antecedents of effective performance measurement system implementation: An empirical study of UK manufacturing firms (2013) International Journal of Production Research, 51 (18), pp. 5485-5498; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidenceinformed management knowledge by means of systematic review (2003) British Journal of Managment, 14 (3), pp. 207-222; Van Der-Vaart, T., Van Donk, D., A critical review of survey-based research in supply chain integration (2008) International Journal of Production Economics, 111 (1), pp. 42-55; Wagner, S., Kemmerling, R., Handling nonresponse in logistics research (2010) Journal of Business Logistics, 31 (2), pp. 357-381; Wang, X.-Y., Zhang, J.-H., System measurement research on supply chain (2010) Proceedings-3rd International Conference on Information Management, Innovation Management and Industrial Engineering, Kunming, ICIII, 4. , November 26-28; Wilderom, C., Wouters, M., Van Brussel, J., Balanced leadership, professionalism, and team trust predict positive attitudes toward performance measurement (2007) Academy of Management 2007 Annual Meeting: Doing Well by Doing Good, , AOM, Philadelphia, PA; Azevedo, S., Carvalho, H., Cruz MacHado, V., The influence of green practices on supply chain performance: A case study approach (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 850-871; Meissner, S., Controlling just-in-sequence flow-production (2010) Logistics Research, 2 (1), pp. 45-53; Shaik, M.N., Abdul-Kader, W., Transportation in reverse logistics enterprise: A comprehensive performance measurement methodology (2013) Production Planning and Control, 24 (6), pp. 495-510},
correspondence_address1={Dörnhöfer, M.; Lehrstuhl für Fördertechnik Materialfluss Logistik, Germany; email: m.doernhoefer@gmx.de},
publisher={Emerald Group Publishing Ltd.},
issn={09574093},
language={English},
abbrev_source_title={Int. J. Logist. Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Gawankar2017257,
author={Gawankar, S.A. and Kamble, S. and Raut, R.},
title={An investigation of the relationship between supply chain management practices (SCMP) on supply chain performance measurement (SCPM) of Indian retail chain using SEM},
journal={Benchmarking},
year={2017},
volume={24},
number={1},
pages={257-295},
doi={10.1108/BIJ-12-2015-0123},
note={cited By 32},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009477183&doi=10.1108%2fBIJ-12-2015-0123&partnerID=40&md5=d97d74add3efffa3a9c21c75072a3ebe},
affiliation={Department of Operations Management, National Institute of Industrial Engineering (NITIE), Mumbai, India},
abstract={Purpose: The purpose of this paper is to investigate the relationship between two major constructs (supply chain management practices (SCMP) and supply chain performance measures), which determines the efficiency and efficacy of retail-supply chain management, using a rigorous empirical method to validate the instrument scale for measuring the validity and reliability of the identified constructs. Additionally, the paper further tests the relationship between SCMP and supply chain performance measures using structural equation modeling (SEM). Design/methodology/approach: Data were compiled and collected from 213 operations and supply chain (SC) heads from leading retail stores in India. Confirmatory factor analysis was used to test the validity of the proposed measurement scale and the relationship is tested using SEM. Findings: The results of the research will help the decision makers in the SC/procurement field to understand the importance of the association between SCMP and supply chain performance measures. Statistical tests show that the implementation of SCMP are associated with supply chain performance measures, which leads to overall improvements; moreover, there is a statistically significant association between the five SCMP and eight SCPM. Research limitations/implications: This research is also needed to provide more understanding about the SCMP along with the supply chain performance measures and the positive association among them. Overall, this research provides an additional insight into the growing field of the relationships between SCMP and SCPM. Clearly, the field has ample space to grow in terms of research and practice. Originality/value: This research paper contributes to the literature on supply chain performance measurement. © 2017, © Emerald Publishing Limited.},
author_keywords={Indian retail sector;  Supply chain management;  Supply chain management practices;  Supply chain performance measurement},
references={Aggarwal, S., Flexibility management: the ultimate strategy (1997) Industrial Management, 39 (1), pp. 26-31; Ahn, J., Khandelwal, A., Wei, S.J., The role of intermediaries in facilitating trade (2011) Journal of International Economics, 84, pp. 73-85; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: a literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Alvarado, U.Y., Kotzab, H., Supply chain management: the integration of logistics in marketing (2001) Industrial Marketing Management, 30 (2), pp. 183-198; Alvarez-Gil, M.J., Capital budgeting and flexible manufacturing (1994) International Journal of Production Economics, 36 (1), pp. 109-128; Angeles, R., Corritore, C.L., Basu, S.C., Nath, R., Success factors for domestic and international electronic data interchange (EDI) implementation for US firms (2001) International Journal of Information Management, 21 (5), pp. 329-347; Arnold, S.J., Lessons learned from the world’s best retailers (2002) International Journal of Retail and Distribution Management, 30 (11-12), pp. 562-570; Azadegan, A., Pankaj, C., Patel, P.C., Zangoueinezhad, A., Linderman, K., The effect of environmental complexity and environmental dynamism on lean practices (2013) Journal of Operations Management, 31 (4), pp. 193-212; Azar, A., Kahnali, R.A., Taghavi, A., Relationship between supply chain quality management practices and their effects on organisational performance (2010) Singapore Management Review, 32 (1), pp. 45-68; Bagchi, P.K., Skjoett-Larsen, T., Supply chain integration: a European survey (2005) The International Journal of Logistics Management, 16 (2), pp. 275-294; Bagozzi, R.P., (1980) Causal Model in Marketing, , John Wiley and Sons, New York, NY; Bagozzi, R.P., Fornell, C., Theoretical concepts, measurements and meaning (1982) A Second Generation of Multivariate Analysis, 2 (2), pp. 5-23; Bagozzi, R.P., Yi, Y., Phillips, L.W., Assessing construct validity in organizational research (1991) Administrative Science Quarterly, 36 (3), pp. 421-458; Baihaqi, I., Sohal, A.S., The impact of information sharing in supply chains on organizational performance: an empirical study (2012) Production Planning and Control: The Management of Operations, 24 (8-9), pp. 743-758; Bala, H., The effects of IT-enabled supply chain process change on job and process outcomes: a longitudinal investigation (2013) Journal of Operations Management, 31 (6), pp. 450-473; Balsmeier, P.W., Voisin, W.J., A supply chain management: a time-based strategy (1996) Industrial Management, 38 (5), pp. 24-27; Banfield, E., (1999) Harnessing Value in the Supply Chain, , Wiley, New York, NY; Barringer, B.R., Harrison, J.S., Walking a tightrope: creating value through interorganizational relationships (2000) Journal of Management, 26 (3), pp. 367-403; Battezzati, L., Magnani, R., Supply chain for FMCG and industrial products in Italy practices and the advantages of postponement (2000) International Journal of Physical Distributions and Logistic Management, 30 (5), pp. 413-424; Beamon, B.M., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Behrouzi, F., Wong, K.Y., An investigation and identification of lean supply chain performance measures in the automotive SMEs (2011) Scientific Research and Essays, 6 (24), pp. 5239-5252; Berry, D., Towill, D.R., Wadsley, N., Supply chain management in the electronics products industry (1994) International Journal of Physical Distribution and Logistics Management, 24 (10), pp. 20-32; Blome, C., Schoenherr, T., Eckstein, D., The impact of knowledge transfer and complexity on supply chain flexibility: a knowledge-based view (2013) International Journal of Production Economics, 147 (B), pp. 307-316. , Part; Boddy, D., Macbeth, D., Wagner, B., Implementing collaboration between organizations: an empirical study of supply chain partnering (2000) Journal of Management Studies, 37 (7), pp. 1003-1018; Bommer, M., O’Neil, B., Treat, S., Strategic assessment of the supply chain interface: a beverage industry case study (2001) International Journal of Physical Distribution and Logistics Management, 31 (1), pp. 11-25; Bordonaba, V., Cambra, J., Managing supply chain in the context of SMEs: a collaborative and customized partnership with the suppliers as the key for success (2009) Supply Chain Management: An International Journal, 14 (5), pp. 393-402; Bowersox, D.J., The strategic benefits of logistics alliances (1990) Harvard Business Review, 68 (4), pp. 36-43; Bowersox, D.J., Morash, E.A., The integration of marketing flows in channels of distribution (1989) European Journal of Marketing, 23 (20), pp. 58-67; Brill, D., Mandelbaum, M., On measures of flexibility in manufacturing systems (1989) International Journal of Production Research, 27 (5), pp. 747-756; Burgess, R., Avoiding supply chain management failure: lesson from business process reengineering (1998) International Journal of Logistics Management, 9 (1), pp. 15-23; Buzzell, R., Gale, B., Bradley, T., Sultan, R., (1975), “Market share – a key to profitability”, Harvard Business Review, 53, January, 97-106; Cadden, T., Marshall, D., Cao, G., Opposites attract: organisational culture and supply chain performance (2013) Supply Chain Management: An International Journal, 18 (1), pp. 86-103; Campbell, D.T., Fiske, D.W., Convergent and discriminant validation by the multitrait – multi method matrix (1959) Psychological Bulletin, 56 (2), pp. 81-105; Cao, M., Zhang, Q., Supply chain collaboration: impact on collaborative advantage and firm performance (2011) Journal of Operations Management, 20 (3), pp. 163-180; Cagliano, R., Caniato, F., Spina, G., Business strategy: how companies are shaping their supply chain through the internet (2003) International Journal of Operations & Production Management, 23 (10), pp. 1142-1162; Chadee, D., Raman, R., Michailova, S., Sources of competitiveness of offshore IT service providers in India: towards a conceptual framework (2011) Competition and Change, 15 (3), pp. 196-220; Chen, I.J., Paulraj, A., Lado, A.A., Strategic purchasing, supply management, and firm performance (2004) Journal of Operations Management, 22, pp. 505-523; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: the constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-151; Chen, I.J., Paulraj, A., Understanding supply chain management: critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Chen, Y.S., Rungtusanatham, M.J., Goldstein, S.M., Koerner, A.S., Theorizing through metaphorical transfer in OM/SCM research: divorce as a metaphor for strategic buyer-supplier relationship dissolution (2013) Journal of Operations Management, 31 (7-8), pp. 579-586; Childhouse, P., Towill, D.R., Simplified material flow holds the key to supply chain integration (2003) Omega – International Journal of Management Sciences, 31 (1), pp. 17-27; Chitakornkijsil, P., Moral meaning in green marketing and socially responsible marketing (2012) International Journal of Organizational Innovation, 4 (3), pp. 113-128; Choi, K., Narasimhan, R., Kim, S.W., Postponement strategy for international transfer of products in a global supply chain: a system dynamics examination (2012) Journal of Operations Management, 30 (3), pp. 167-179; Chong, A.Y.L., Chan, F.T.S., Ooi, K.B., Sim, J.J., Can Malaysian firms improve organizational/innovation performance via SCM? (2011) Industrial Management and Data Systems, 111 (3), pp. 410-431; Chong Felix, T.S., Alain, Y.L., Chan, K.B., Sim, O.J.J., Can Malaysian firms improve organizational/innovation performance via SCM? (2011) Industrial Management and Data Systems, 111 (3), pp. 410-431; Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, Planning, and Operation, , 3rd ed., Prentice Hall Inc., NJ; Christopher, A.B., Christopher, W.C., Hanna, J.B., Postponement: an evolving supply chain concept (2007) International Journal of Physical Distribution and Logistics Management, 37 (8), pp. 594-611; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , Financial Times, London; Christy, D.P., Grout, J.R., Safeguarding supply chain relationships (1994) International Journal of Production Economics, 36 (3), pp. 233-242; Churchill, G.A., Jr., A paradigm for developing better measures of marketing constructs (1979) Journal of Marketing Research, 16 (1), pp. 64-73; Claro, D.P., Hagelaar, G., Omta, O., The determinants of relational governance and performance: how to manage business relationships? (2003) Industrial Marketing Management, 32 (8), pp. 703-716; Claycomb, C., Droge, C., Germain, R., The effect of just-in-time with customers on organizational design and performance (1999) International Journal of Logistics Management, 10 (1), pp. 37-58; Claycomb, C., Droge, C., Germain, R., Applied customer knowledge in a manufacturing environment: flexibility for industrial firms (2005) Industrial Marketing Management, 34 (6), pp. 629-640; Cook, L.S., Heiser, D.R., Sengupta, K., The moderating effect of supply chain role on the relationship between supply chain practices and performance: an empirical analysis (2011) International Journal of Physical Distribution and Logistics Management, 41 (2), pp. 104-134; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships (2008) International Journal of Operations and Production Management, 28 (3), pp. 238-250; Cox, A., The art of the possible: relationship management in power regimes and supply chains (2004) Supply Chain Management: An International Journal, 9 (5), pp. 346-356; Cuthbertson, R., Piotrowicz, W., Supply chain best practices Ŕ identification and categorization of measures and benefits (2008) Journal of Productivity and Performance Management, 57 (5), pp. 389-404; Dadzie, K.Q., Winston, E., Consumer response to stock-out in the online supply chain (2007) International Journal of Physical Distribution and Logistics Management, 37 (1), pp. 19-42; Danese, P., Romano, P., Formentini, M., The impact of supply chain integration on responsiveness: the moderating effect of using an international supplier network (2013) Logistics and Transportation Review, 49 (1), pp. 125-140; Day, G.S., Managing market relationships (2000) Journal of the Academy of Marketing Science, 28 (1), pp. 55-66; Defee, C.C., Randall, W.S., Gibson, B.J., Roles and capabilities of the retail supply chain organization (2009) Journal of Transportation Management, 21 (2), pp. 31-48; Deshpande, A., Supply chain management dimensions, supply chain performance and organizational performance: an integrated framework (2012) International Journal of Business and Management, 7 (8), pp. 1819-1833; Devaraj, S., Krajewski, L., Wei, J.C., Impact of ebusiness technologies on operational performance: the role of production information integration in the supply chain (2007) Journal of Operations Management, 25 (6), pp. 1199-1216; Donlon, J.P., Maximizing value in the supply chain (1996) Chief Executive, 117 (1), pp. 54-63; Ellram, L.M., Supply-chain management: the industrial organisation perspective (1991) International Journal of Physical Distribution and Logistics Management, 21 (1), pp. 13-22; Elmaghraby, W.J., Supply contract competition and sourcing policies (2000) Manufacturing and Service Operations Management, 2 (4), pp. 350-371; Fawcett, S.E., Magnan, G.M., The rhetoric and reality of supply chain integration (2002) International Journal of Physical Distribution and Logistics Management, 32 (5), pp. 339-361; Feitzinger, E., Lee, L.H., Mass customization at Hewlett-Packard: the power of postponement (1997) Harvard Business Review, 75 (1), pp. 116-121; Feldmann, M., Müller, S., An incentive scheme for true information providing in supply chains (2003) Omega, 31 (2), pp. 63-73; Fiala, P., Information sharing in supply chains (2005) The International Journal of Management Science, 33 (5), pp. 419-423; Fisher, M., What is the right supply chain for your product? A simple framework – can you figure out the answer? (1997) Harvard Business Review, 75 (2), pp. 105-116; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: a contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Forslund, H., Jonsson, P., The impact of forecast information quality on supply chain performance (2007) International Journal of Operations and Production Management, 27 (1), pp. 90-107; Fynes, B., de Búrca, S., Marshall, D., Environmental uncertainty, supply chain relationship quality and performance (2004) Journal of Purchasing and Supply Management, 10 (4-5), pp. 179-190; Fynes, B., de Búrca, S., Voss, C., Supply chain partnership quality: the competitive environment and performance (2005) International Journal of Production Research, 43 (16), pp. 3303-3320; Ganesan, S., George, M., Jap, S., Palmatier, R.W., Weitz, B., Supply chain management and retailer performance: emerging trends, issues, and implications for research and practice (2009) Journal of Retailing, 85 (1), pp. 84-94; Garver, M.S., Mentzer, J.T., Logistics research methods: employing structural equation modeling to test for construct validity (1999) Journal of Business Logistics, 20 (1), pp. 33-57; Garvin, D.A., (1988) Managing Quality: The Strategic and Competitive Edge, , Free Press, New York, NY; Garvin, D.A., What does product quality really mean? (1984) Sloan Management Review, 1 (3), pp. 25-43; Gavirneni, S., Price fluctuations, information sharing, and supply chain performance (2006) European Journal of Operational Research, 174 (3), pp. 1651-1663; Gawankar, S., Kamble, S., Verma, R., Development, measurement and validation of supply chain management practices scale in Indian retail sector (2013) International Journal Procurement Management, 6 (5), pp. 495-522; Gawankar, S., Kamble, S., Retail revolution and Indian customers: the relationships dynamics (2014) Banaras Hindu University Management Review, 4 (Special Issue), pp. 20-25. , July; Gharakhani, D., Mavi, R.M., Hamidi, N., Impact of supply chain management practices on innovation and organizational performance in Iranian companies (2012) African Journal of Business Management, 6 (19), pp. 5939-5949; Gimenez, C., Vaart, T.V.D., Donk, D.P.V., Supply chain integration and performance: the moderating effect of supply complexity (2012) International Journal of Operations & Production Management, 32 (5), pp. 583-610; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Gosling, J., Naim, M., Towill, D., A supply chain flexibility framework for engineer-to-order systems (2013) Production Planning and Control: The Management of Operations, 24 (7), pp. 552-566; Gray, J., Harvey, T., (1992) Quality Value Banking: Effective Management Systems that Increase Earnings, Lower Costs, and Provide Competitive Customer Service, , John Wiley and Sons Inc., New York, NY; Griffith, D.A., Harvey, M.G., A resource perspective of global dynamic capabilities (2001) Journal of International Business Studies, 32 (3), pp. 597-606; Gunasekaran, A., Chung, W.C., Supply chain management for the 21st century organizational competitiveness (2004) International Journal of Production Economics, 87 (3), pp. 209-212; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measure and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gupta, S., Zeithaml, V., Customer metrics and their impact on financial performance (2006) Marketing Science, 25 (6), pp. 718-739; Hair, J.F., Jr., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , 5th ed., Prentice Hall, NJ; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2010) Multivariate Data Analysis, , Seventh Edition., Prentice Hall, Upper Saddle River, NJ; Hallgren, M., Olhager, J., Lean and agile manufacturing: external and internal drivers and performance outcomes (2009) International Journal of Operations and Production Management, 29 (10), pp. 976-999; Hammer, M., Reengineering work: don’t automate, obliterate (1990) Harvard Business Review, 68 (4), pp. 104-112; Handfield, R., Nichols, E., (1999) Introduction to Supply Chain Management, , Prentice Hall Inc., NJ; Hoek, R.I.V., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 126-147; Holmberg, S., A system perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Holweg, M., The three dimensions of responsiveness (2005) International Journal of Operations and Production Management, 25 (7), pp. 603-622; Hsu, L.-L., Chiu, C.-M., Chen, J.C., Liu, C.-C., The impacts of supply chain management systems on information sharing and integrated-performance (2009) Human Systems Management, 28 (3), pp. 101-121; Iacobucci, D., Structural equations modeling: fit indices, sample size, and advanced topics (2010) Journal of Consumer Psychology, 20 (2), pp. 90-99; Ibrahim, S.E., Ogunyemi, O., The effect of linkages and information sharing on supply and export performance: an empirical study of Egyptian textile manufacturers (2012) Journal of Manufacturing Technology Management, 23 (4), pp. 441-463; Iyer, K.N.S., Germain, R., Claycomb, C., B2B e-commerce supply chain integration and performance: a contingency fit perspective on the role of environment (2009) Information and Management, 46 (6), pp. 313-322; Jain, A., FDI in multi-brand retailing in India: comparative study with China (2013) International Journal of Management and Information Technology, 3 (1), pp. 54-61; James, L.R., Mulaik, S.A., Brett, J.M., (1982) Causal Analysis: Assumptions, Models, and Data, , Sage, Beverly Hills, New York, NY; Jap, S.D., Anderson, E., Safeguarding interorganizational performance and continuity under ex post opportunism (2003) Management Science, 49 (12), pp. 1684-1701; Jarrell, J.L., Supply chain economics (1998) World Trade, 11 (11), pp. 58-61; Jia, F., Lamming, R., Cultural adaptation in Chinese‐Western supply chain partnerships: dyadic learning in an international context (2013) International Journal of Operations & Production Management, 33 (5), pp. 528-561; Johansson, H.J., McHugh, P., Pendlebury, A.J., Wheeler, W.A., (1993) Business Process Reengineering: Breakpoint Strategies for Market Dominance, , Wiley, Chichester; Johnston, D.A., McCutcheon, D.M., Stuart, F.I., Kerwood, H., Effects of supplier trust on performance of cooperative supplier relationships (2004) Journal of Operations Management, 22 (1), pp. 23-38; Jones, C., Hesterly, W., Borgatti, S., A general theory of network governance: exchange conditions and social mechanisms (1998) Academy of Management Review, 22 (4), pp. 911-945; Jöreskog, K.G., Testing structural equation models (1993) Testing Structural Equation Models, pp. 294-316. , Bollen, K.A. and Long, J.S (Eds), Sage, Thousand Oaks, CA; Kalakota, R., Robinson, M., (1999) E-Business: Roadmap for Success, , Addison-Wesley, Reading, MA; Kamauff, J.W., Smith, D.B., Extended enterprise metrics: the key to achieving synthesized effectiveness (2004) Journal of Business and Economics Research, 2 (5), pp. 39-52; Kamble, S.S., Raut, R.D., Bhasin, H., Analysing the effect of uncertain environmental factors on supplier-buyer strategic partnership (SBSP) by using structural equation model (SEM) (2012) International Journal of Procurement Management, 5 (2), pp. 202-228; Kashyap, A., Impact of ERP implementation on supply chain management (2011) International Journal of Computer Applications in Engineering Sciences, 1 (4), pp. 474-479; Ketchen, D.J., Jr., Hult, G.T.M., Bridging organization theory and supply chain management: the case of best value supply chains (2007) Journal of Operations Management, 25, pp. 573-580; Kiefer, A.W., Novack, R.A., An empirical analysis of warehouse measurement systems in the context of supply chain implementation (1999) Transportation Journal, 38 (3), pp. 18-27; Kim, D.Y., Kumar, V., Uma Kumar, U., Relationship between quality management practices and innovation (2012) Journal of Operations Management, 30 (4), pp. 295-315; Koh, S., Demirbag, M., Bayraktar, E., Tatoglu, E., Zaim, S., The impact of supply chain management practices on performance of SMEs (2007) Industrial Management and Data Systems, 107 (1), pp. 103-124; Kotabe, M., Martin, X., Domoto, H., Gaining from vertical partnerships: knowledge transfer, relationship duration, and supplier performance improvement in the US and Japanese automotive industries (2003) Strategic Management Journal, 24 (4), pp. 293-316; Koufteros, X.A., Vonderembse, M.A., Doll, W.J., Developing measures of time based manufacturing (1998) Journal of Operations Management, 16 (1), pp. 21-41; Krause, D.R., Scannell, T.V., Calantone, R.J., A structural analysis of the effectiveness of buying firm’s strategies to improve supplier performance (2000) Decisions Sciences, 31, pp. 33-55; Lahiri, S., Kedia, B.L., The effects of internal resources and partnership quality on firm performance: an examination of Indian BPO providers (2009) Journal of International Management, 15 (2), pp. 209-224; Lalonde, B.J., Building a supply chain relationship (1998) Supply Chain Management Review, 2 (2), pp. 7-8; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lamming, R.C., Squaring lean supply with supply chain management (1996) International Journal of Operations and Production Management, 16 (2), pp. 183-196; Larson, P.D., Kulchitsky, J.D., The promise of information sharing and the peril of information overload (2008) International Journal of Logistics Systems and Management, 4 (4), pp. 423-436; Lee, D., (2012), www.bbc.co.uk/news/business-20668700?print=true, December 10, 2012, “From Apple to polka dots, technology can help supply chains react”, BBC News, :, (accessed; Lee, H.L., Billington, C., Managing supply chain inventory: pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lee, H.L., Whang, S., Winning the last mile of e-commerce (2001) Sloan Management Review, 42 (4), pp. 54-62; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lee, J., Kim, Y., Effect of partnership quality on IS outsourcing: conceptual framework and empirical validation (1999) Journal of Management Information Systems, 15 (4), pp. 26-61; Lee, J.N., The impact of knowledge sharing, organizational capability and partnership quality on IS outsourcing success (2001) Information and Management, 38, pp. 323-335; Lemke, F., Goffin, K., Szwejczewski, M., Investigating the meaning of supplier-manufacturer partnerships: an exploratory study (2003) International Journal of Physical Distribution and Logistics Management, 33 (1), pp. 12-35; Li, D., O’Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 20 (1-3), pp. 147-157; Li, S., Lin, B., Accessing information sharing and information quality in supply chain management (2006) Decision Support Systems, 42 (3), pp. 1641-1656; Li, S., Subba Rao, S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a management instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Li, X., Sawhney, R., Arendt, E.J., Ramasamy, K., A comparative analysis of management accounting systems’ impact on lean implementation (2012) International Journal of Technology Management, 57 (1), pp. 33-48; Lummus, R.R., Vokurka, R.J., Duclos, L.K., Delphi study on supply chain flexibility (2005) International Journal of Production Research, 43 (13), pp. 2687-2708; Lyons, A., Coleman, J., Kehoe, D., Coronado, A., Performance observation and analysis of an information re-engineered supply chain: a case study of an automotive firm (2004) Industrial Management and Data Systems, 104 (8), pp. 658-666; McAdam, R., McCormack, D., Integrating business processes for global alignment and supply chain management (2001) Business Process Management Journal, 7 (2), pp. 113-130; McIvor, R., Lean supply: the design and cost reduction dimensions (2001) European Journal of Purchasing and Supply Management, 7, pp. 227-242; Mabert, V.A., Venkataramanan, M.A., Special research focus on supply chain linkages: challenges for design and management in the 21st century (1998) Decision Sciences, 29 (1), pp. 537-552; Magretta, J., The power of virtual integration: an interview with Dell computers’ Michael Dell (1998) Harvard Business Review, 76 (2), pp. 72-84; Malhotra, M.K., Mackelprang, A.W., Are internal manufacturing and external supply chain flexibilities complementary capabilities? (2012) Journal of Operations Management, 30 (3), pp. 180-200; Mandal, P., El-Houbi, A., Business practices and information strategy in performance improvement (2009) International Journal of Productivity and Quality Management, 4 (5-6), pp. 715-729; Marksberry, P., Investigating ‘The Way’ for Toyota suppliers: a quantitative outlook on Toyota’s replicating efforts for supplier development (2012) Benchmarking: An International Journal, 19 (2), pp. 277-298; Mason-Jones, R., Towill, D.R., Information enrichment: designing the supply chain for competitive advantage (1997) Supply Chain Management, 2 (4), pp. 137-148; Mathew, J., Retail supply chain: challenges and prospects (2012) International Journal of Physical Distribution and Logistics Management, 38 (4), pp. 296-310; Mentzer, J.T., Foggin, J.H., Golicic, S.L., Collaboration: the enablers, impediments and benefits (2000) Supply Chain Management Review, 5 (4), pp. 52-58; Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Metters, R., Quantifying the bullwhip effect in supply chains (1997) Journal of Operations Management, 15 (1), pp. 89-100; Miguel, P.L.D.S., Brito, L.A.L., Supply chain management measurement and its influence on operational performance (2013) Journal of Operations and Supply Chain Management, 4 (2), pp. 56-70; Min, S., Mentzer, J.T., Developing and measuring supply chain concepts (2004) Journal of Business Logistics, 25 (1), pp. 63-99; Moberg, C.R., Cutler, B.D., Gross, A., Speh, T.W., Identifying antecedents of information exchange with in supply chain (2002) International Journal of Physical Distribution and Logistics Management, 32 (9), pp. 755-770; Modi, S.B., Mabert, V.M., Supplier development: improving supplier performance through knowledge transfer (2007) Journal of Operations Management, 25 (1), pp. 42-64; Molnár, A., Gellynck, X., Chain governance structures: the European traditional food sector (2009) British Food Journal, 111 (8), pp. 762-775; Monczka, R.M., Morgan, J., What’s wrong with supply chain management (1997) Purchasing, 122 (1), pp. 69-72; Monczka, R.M., Morgan, J., Trent, R.J., Handfield, B., (1998) Purchasing and Supply Chain Management, , Thomas Rennie Cengage Learning, South-Western, Cincinnati, OH; Moon, K.K.L., Yi, C.Y., Ngai, E.W.T., An instrument for measuring supply chain flexibility for the textile and clothing companies (2012) European Journal of Operational Research, 222 (2), pp. 191-203; Nahm, A.Y., Vonderembse, M.A., Koufteros, X.A., The impact of organizational structure on time-based manufacturing and plant performance (2003) Journal of Operations Management, 21 (3), pp. 281-306; Narasimhan, R., Das, A., An empirical investigation of the contribution of strategic sourcing to manufacturing flexibilities and performance (1999) Decision Sciences, 30 (3), pp. 683-718; Narasimhan, R., Das, A., An empirical examination of sourcing’s role in developing manufacturing flexibilities (2000) International Journal of Production Research, 38 (4), pp. 875-893; Narasimhan, R., Jayaram, J., Causal linkages in supply chain management: an exploratory study of North American manufacturing firms (1998) Decision Sciences, 29 (3), pp. 579-605; Narayandas, D., Rangan, K., Building and sustaining buyer-seller relationships in mature industrial markets (2004) Journal of Marketing, 68 (3), pp. 63-77; Naylor, J.B., Naim, M.M., Berry, D., Legality: integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1), pp. 107-118; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Realizing strategy through measurement (1994) International Journal of Operations and Production Management, 14 (3), pp. 140-152; Ngai, E.W.T., Lai, K.H., Cheng, T.C.E., Logistics information systems: the Hong Kong experience (2008) International Journal of Production Economics, 113 (1), pp. 223-234; Noble, D., Purchasing and supplier management as a future competitive edge (1997) Logistics Focus, 5 (5), pp. 23-27; Noble, S., Shenkan, A.G., Shi, C., (2009) The Promise of Multichannel Retailing, pp. 1-4. , McKinsey; Novack, R.A., Langley, C.J., Jr., Rinehart, L.M., (1995) Creating Logistics Value: Themes for the Future, , Council of Logistics Management, Oak Brook, IL; Nunnally, J.C., (1978) Psychometric, , 2nd ed., McGraw-Hill, New York, NY; Ogunyemi, O., Ibrahim, S.E., The effect of linkages and information sharing on supply and export performance: an empirical study of Egyptian textile manufacturers (2012) Journal of Manufacturing Technology Management, 23 (4), pp. 441-463; Oh, L.B., Teo, H.H., Sambamurthy, V., The effects of retail channel integration through the use of information technologies on firm performance (2012) Journal of Operations Management, 30 (5), pp. 368-381; Oliva, R., Watson, N.H., Managing functional biases in organizational forecasts: a case study of consensus forecasting in supply chain planning (2009) Production and Operations Management, 18 (2), pp. 138-151; Oliver, R.L., A cognitive model of the antecedents and consequences of satisfaction decisions (1980) Journal of Marketing Research, 17 (4), pp. 460-469; Parmigiani, A., Klassen, R.D., Russo, M.V., Efficiency meets accountability: performance implications of supply chain configuration, control, and capabilities (2011) Journal of Operations Management, 29 (3), pp. 212-223; Patel, P.C., Jayaram, J., The antecedents and consequences of product variety in new ventures: an empirical study (2013) Journal of Operations Management, 32 (1-2), pp. 34-50; Paulraj, A., Lado, A., Chen, J., Inter-organizational communication as a relational competency: antecedents and performance outcomes in collaborative buyer-supplier relationships (2008) Journal of Operations Management, 26 (1), pp. 45-64; Pereira, J.V., The new supply chain’s frontier: information management (2009) International Journal of Information Management, 29 (5), pp. 372-379; Popli, G.S., Kumari, S., Impact of multi brand foreign direct investment in retail sector in India (2013) International Journal of Research in Commerce and Management, 4 (11), pp. 1-16; Prahinski, C., Benton, W.C., Supplier evaluations: communication strategies to improve supplier performance (2013) Journal of Operations Management, 22 (1), pp. 39-62; Prajogo, D., Olhager, J., Supply chain integration and performance: the effects of long-term relationships, information technology and sharing, and logistics integration (2011) International Journal Production Economics, 136, pp. 123-130; Radas, S., Božić, L., The antecedents of SME innovativeness in an emerging transition economy (2009) Technovation, 29 (6-7), pp. 438-450; Rai, A., Patnayakuni, R., Seth, N., Firm performance impacts of digitally enabled supply chain integration capabilities (2006) MIS Quarterly, 30 (2), pp. 225-246; Raisinghani, M.S., Meade, L.L., Strategic decisions in supply-chain intelligence using knowledge management: an analytic-network-process framework (2005) Supply Chain Management: An International Journal, 10 (2), pp. 114-121; Raman, R., Chadee, D., Roxas, B., Michailova, S., Effects of partnership quality, talent management, and global mindset on performance of offshore IT service providers in India (2013) Journal of International Management, 19 (4), pp. 333-346; Ranjan, J., Bhatnagar, V., A framework for analytical CRM: a data mining perspective (2010) International Journal of Business Excellence, 3 (1), pp. 1-18; Reichhart, A., Holweg, M., Lean distribution: concepts, contributions, conflicts (2007) International Journal of Production Research, 45 (16), pp. 3699-3722; Rexhausen, D., Pibernik, R., Kaiser, G., Customer-facing supply chain practices – the impact of demand and distribution management on supply chain success (2012) Journal of Operations Management, 30 (4), pp. 269-281; Ro, Y.K., Liker, J.K., Fixson, S.K., Evolving models of supplier involvement indesign: the deterioration of the Japanese model in US auto (2008) IEEE Transactions on Engineering Management, 55 (2), pp. 359-377; Ryu, S., Park, J.E., Min, S., Factors of determining long-term orientation in interfirm relationships (2007) Journal of Business Research, 60 (12), pp. 1225-1233; Salhieh, L., An exploratory study of the relationship between supply chain management practices and technical efficiency of Jordanian manufacturing companies (2011) International Journal of Business and Management, 6 (12), pp. 126-134; Sahay, B.S., Gupta, J.N.D., Mohan, R., Managing supply chains for competitiveness: the Indian scenario (2006) Supply Chain Management: An International Journal, 11 (1), pp. 15-24; Scannell, T.N., Vickery, S.K., Droge, C.L., Upstream supply chain management and competitive performance in the automotive supply industry (2000) Journal of Business Logistics, 21 (1), pp. 23-48; Schermelleh-Engel, K., Moosbrugger, H., Evaluating the fit of structural equation models: tests of significance and descriptive goodness-of-fit measures (2003) Methods of Psychological Research, 8 (2), pp. 23-74; Scott, C., Westbrook, R., New strategic tools for supply chain management (1991) International Journal of Physical Distribution and Logistics Management, 21 (1), pp. 177-187; Sheridan, J.H., The supply-chain paradox (1998) Industry Week, 247 (3), pp. 20-29; Sheu, C., Yen, H.R., Chae, B., Determinants of supplier-retailer collaboration: evidence from an international study (2006) International Journal of Operations and Production Management, 26 (1), pp. 24-49; Shin, H., Collier, D.A., Wilson, D.D., Supply management orientation and supplier/buyer performance (2000) Journal of Operations Management, 18 (3), pp. 317-333; Simchi-Levi, D., Kaminsky, P., Simichi-Levi, E., Designing and managing the supply chain-concepts (2003) Strategies and Case Studies, pp. 259-261. , Simchi-Levi, D., and (Ed.), McGraw-Hill Publishing, New York, NY; Singh, R.K., Garg, S.K., Deshmukh, S.G., Strategy development by Indian SSIs (2010) Industrial Management and Data Systems, 110 (7), pp. 1073-1093; Spekman, R.E., Kamauff, J.W., Jr., Myrh, N., An empirical investigation into supply chain management: a perspective on partnerships (1998) International Journal of Physical Distribution and Logistics Management, 28 (8), pp. 630-650; Srinivasan, M., Mukherjee, D., Gaur, A.S., Buyer-supplier partnership quality and supply chain performance: moderating role of risks, and environmental uncertainty (2011) European Management Journal, 29 (4), pp. 260-271; Stein, T., Sweat, J., Killer supply chains (1998) Information Week, 708 (9), pp. 36-46; Stevens, G.C., Integrating the supply chain (1989) International Journal of Physical Distribution and Logistics Management, 19 (8), pp. 3-8; Stevens, G.C., Successful supply-chain management (1990) Management Decision, 28 (8), pp. 25-28; Stevenson, M., Spring, M., Flexibility from a supply chain perspective: definition and review (2007) International Journal of Operations & Production Management, 27 (7), pp. 685-713; Stock, G.N., Greis, N.P., Kasarda, J.D., Logistics, strategy and structure (1998) International Journal of Operations and Production Management, 18 (1), pp. 37-52; Stouthuysen, K., Slabbinck, H., Roodhooft, F., Controls, service type and perceived supplier performance in interfirm service exchanges (2012) Journal of Operations Management, 30 (5), pp. 423-435; Stuart, F.I., McCutcheon, D.M., Sustaining strategic supplier alliances: profiling the dynamic requirements for continued development (1996) International Journal of Production and Operations Management, 16 (10), pp. 5-22; Suarez, F.F., Cusumano, M.A., Fine, C.H., An empirical study of flexibility in printed circuit board assembly (1996) Operations Research, 44 (1), pp. 223-240; Sukatia, I., Hamida, A.B., Baharuna, R., Yusoffa, R., The study of supply chain management strategy and practices on supply chain performance (2012) Procedia – Social and Behavioral Sciences, 40 (1), pp. 225-233; Sundram, V.P.K., Ibrahim, A.R., Govindaraju, C.V.G.R., Supply chain management practices in the electronics industry in Malaysia: consequences for supply chain performance (2011) Benchmarking: An International Journal, 18 (6), pp. 834-855; Talluri, S., Narasimhan, R., A methodology for strategic sourcing (2004) European Journal of Operational Research, 154 (1), pp. 236-250; Tan, K.C., Kannan, V.R., Handfield, R.B., Supply chain management: supplier performance and firm performance (1998) International Journal of Purchasing and Materials Management, 34 (3), pp. 2-9; Tan, K.C., Kannan, V.R., Handfield, R.B., Supply chain management: an empirical study of its impact on performance (1999) International Journal of Operations and Production Management, 19 (10), pp. 1034-1052; Tan, K.C., Lyman, S.B., Wisner, J.D., Supply chain management: a strategic perspective (2002) International Journal of Operations and Production Management, 22 (6), pp. 614-631; Taticchi, P., Balachandran, K.R., Forward performance measurement and management integrated frameworks (2008) International Journal of Accounting and Information Management, 16 (2), pp. 140-154; Taylor, D.H., Supply chain improvement: the lean approach (1999) Logistics Focus, 7 (1), pp. 14-20; Thatte, A.A., (2007) Competitive advantage of a firm through supply chain responsiveness and SCM practices, , doctoral dissertation, University of Toledo, Toledo, OH; Towill, D.R., The seamless supply chain – the predator’s strategic advantage (1997) International Journal of Technology Management, 13 (1), pp. 37-56; Trkman, P., McCormack, K., de Oliveira, M.P.V., Ladeira, M.B., The impact of business analytics on supply chain performance (2010) Decision Support Systems, 49 (3), pp. 318-327; Tung, J., A study of product innovation on firm performance (2012) The International Journal of Organizational Innovation, 4 (3), pp. 84-97; Turner, J.R., Integrated supply chain management: what’s wrong with this picture (1993) Industrial Engineering, 25 (12), pp. 52-55; Twede, D., Clarke, R.H., Tait, J.A., Packaging postponement: a global packaging strategy (2000) Packaging Technology Science, 13 (3), pp. 105-115; van der Vaart, T., van Donk, D.P., A critical review of survey-based research in supply chain integration (2008) International Journal of Production Economics, 111 (1), pp. 42-55; Van Der Vorst, J.G.A.J., Performance measurement in agri-food supply-chain networks (2006) Logistics and Operations Research Group, pp. 14-24. , Ondersteijn, C.J.M., Wijnands, J.H.M., Huirne, R.B.M. and Van Kooten, O (Eds)., Wageningen University; Van Hoek, R.I., Measuring the unmeasurable, measuring and improving performance in the supply chain (1998) Supply Chain Management, An International Journal, 3 (4), pp. 187-192; Van Hoek, R.I., van Dierdonck, R., (1997) Postponed manufacturing as a supplementary service, , EUROMA Conference Proceedings on Managing Service Operations, Lessons from the Service and the Manufacturing Operations, Barcelona, June; Venkatraman, N., Ramanujan, V., Measurement of business economic performance: an examination of method convergence (1987) Journal of Management, 13 (1), pp. 109-122; Vickery, S., Calantone, R., Droge, C., Supply chain flexibility: an empirical study (1999) The Journal of Supply Chain Management, 35 (1), pp. 16-24; Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: an analysis of direct versus indirect relationships (2003) Journal of Operations Management, 21 (5), pp. 523-539; Wallace, D.W., Giese, J.L., Johnson, J.L., Customer retailer loyalty in the context of multiple channel strategies (2004) Journal of Retailing, 80 (4), pp. 249-263; Waller, M.A., Dabholkar, P.A., Gentry, J.J., Postponement, product customization, and market-oriented supply chain management (2000) Journal of Business Logistics, 21 (2), pp. 133-160; White, A., Daniel, E.M., Mohdzain, M., The role of emergent information technologies and systems in enabling supply chain agility (2005) International Journal of Information Management, 25 (5), pp. 396-410; Wilson, D.T., Vlosky, R.P., Inter-organizational information system technology and buyer-seller relationships (1998) Journal of Business and Industrial Marketing, 13 (3), pp. 215-234; Wines, L., High order strategy for manufacturing (1996) The Journal of Business Strategy, 17 (4), pp. 32-33; Wisner, J.D., A structural equation model of supply chain management strategies and firm performance (2003) Journal of Business Logistics, 24 (1), pp. 1-27; Womack, J.P., Jones, D.T., (1996) Lean Thinking, , Simon and Schuster, New York, NY; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51; Wood, A., Extending the supply chain: strengthening links with IT (1997) Chemical Week, 159 (25), pp. 25-26; Wu, F., Yeniyurt, S., Kim, D., Cavusgil, S.R.T., The impact of information technology on supply chain capabilities and firm performance: a resource-based view (2006) Industrial Marketing Management, 35 (4), pp. 493-504; Xue, L., Ray, G., Sambamurthy, V., The impact of supply-side electronic integration on customer service performance (2013) Journal of Operations Management, 31 (6), pp. 363-375; Yang, B., Burn, N., Implications of postponement for the supply chain (2003) International Journal of Production Research, 41 (9), pp. 2075-2090; Yang, B., Yang, Y., Wijngaard, J., Postponement: an inter-organizational perspective (2007) International Journal of Production Research, 45 (4), pp. 971-988; Yeung, J.H.Y., Selen, W., Deming, Z., Min, Z., Postponement strategy from a supply chain perspective: cases from China (2007) International Journal of Physical Distribution and Logistics Management, 37 (4), pp. 331-356; Youn, S.G., Yang Hong, M., Hong, P., Park, K., Strategic supply chain partnership, environmental supply chain management practices, and performance outcomes: an empirical study (2011) Journal of Cleaner Production, pp. 1-10; Zaheer, A., Venkatraman, N., Relational governance as an Interorganizational strategy: an empirical test of the role of trust in economic exchange (1995) Strategic Management Journal, 16 (5), pp. 373-392; Zhao, X., Xie, J., Zhang, W.J., The impact of information sharing and ordering co-ordination on supply chain performance (2002) Supply Chain Management: An International Journal, 7 (1), pp. 24-40; Zinn, W., Bowersox, D.J., Planning physical distribution with the principle of postponement (1988) Journal of Business Logistics, 9 (2), pp. 117-136; Zografose, K.G., Giannouli, I.M., Development and application of a methodological framework for assessing SCM trends (2001) International Journal of Logistics: Research and Applications, 4 (2), pp. 153-190; Ahn, T., Ryu, S., Han, I., The impact of web quality and playfulness on user acceptance of online retailing (2007) Information and Management, 44 (3), pp. 263-275; Ansari, A., Strategies for the implementation of JIT purchasing (1986) International Journal of Physical Distribution and Logistics Management, 17 (1), pp. 5-12; Berry, T., Ahmed, A., The consequences of interfirm supply chains for management accounting (1997) Management Accounting, 75 (10), pp. 74-75; Braganza, A., Enterprise integration: creating competitive capabilities (2002) Integrated Manufacturing Systems, 13 (8), pp. 562-572; Cagliano, R., Caniato, F., Spina, G., The linkage between supply chain integration and manufacturing improvement programmes (2006) International Journal of Operations & Production Management, 26 (3), pp. 282-299; Chong, A.Y.L., Ooi, K.B., Adoption of inter organizational system standards in supply chains (2008) Industrial Management and Data Systems, 108 (4), pp. 529-547; Chong, A.Y.L., Ooi, K.B., Lin, B., Raman, M., Factors affecting the adoption level of C-commerce: an empirical study (2009) Journal of Computer Information Systems, 50 (2), pp. 13-22; Fisher, A.G., (1999) Assessment of Motor and Process Skills, , 3rd ed., Three Star Press, Fort Collins, CO; Freeland, J.R., A survey of just-in-time purchasing practices in the United States (1991) Production and Inventory Management Journal, 32 (2), pp. 43-49; Giunipero, L.C., Hooker, R.E., Joseph-Matthews, S., Yoon, T.E., Brudvig, S., A decade of SCM literature: past, present and future implications (2008) Journal of Supply Chain Management, 44 (4), pp. 66-86; Hamister, J.W., Supply chain management practices in small retailers (2012) International Journal of Retail and Distribution Management, 40 (6), pp. 427-450; Hendricks, K.B., Singhal, V.R., Stratman, J.K., The impact of enterprise systems on corporate performance: a study of ERP, SCM, and CRM system implementations (2007) Journal of Operations Management, 25 (1), pp. 65-82; Kaynak, H., Hartley, J.L., Using replication research for just-in-time purchasing construct development (2006) Journal of Operations Management, 24 (6), pp. 868-892; Krause, D.R., Handfield, R.B., Tyler, B.B., The relationships between supplier development, commitment, social capital accumulation and performance improvement (2007) Journal of Operations Management, 25 (2), pp. 528-545; Lamming, R., Caldwell, N., Harrison, D., Developing the concept of transparency for use in supply relationships (2013) British Journal of Management, 15 (4), pp. 291-302; Narasimhan, R., Das, A., An empirical investigation of the contribution of strategic sourcing to manufacturing flexibilities and performance (2007) Decision Sciences, 30 (3), pp. 683-718; Pagell, M., Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics (2004) Journal of Operations Management, 22 (5), pp. 459-487; Hallgren, M., Olhager, J., Lean and agile manufacturing: external and internal drivers and performance outcomes (2009) International Journal of Operations and Production Management, Production Management, 29 (10), pp. 976-999; Schonberger, R., Gilbert, J., Just-in-time purchasing: a challenge for US industry (1983) California Management Review, 26 (1), pp. 54-68},
correspondence_address1={Gawankar, S.A.; Department of Operations Management, India; email: gawankar.shradha@gmail.com},
publisher={Emerald Group Publishing Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Maestrini2017299,
author={Maestrini, V. and Luzzini, D. and Maccarrone, P. and Caniato, F.},
title={Supply chain performance measurement systems: A systematic review and research agenda},
journal={International Journal of Production Economics},
year={2017},
volume={183},
pages={299-315},
doi={10.1016/j.ijpe.2016.11.005},
note={cited By 211},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997666888&doi=10.1016%2fj.ijpe.2016.11.005&partnerID=40&md5=a435ad5705ad7e22a57c8aeba10554fd},
affiliation={Management, Economics and Industrial Engineering, Politecnico di Milano, via Lambruschini 4/b, Milano, Italy; Information Systems and Supply Chain Management, Audencia Nantes School of Management, Nantes, France; MIT-Zaragoza International Logistics Program, Zaragoza Logistics Center, Edificio Náyade 5, C/Bari 55 - PLAZA, Zaragoza, 50197, Spain},
abstract={Supply chain performance measurement systems (SCPMSs) are experiencing a new life in business practice thanks to new technologies that allow the collecting, integrating and sharing of information among multiple supply chain partners. However, studies that truly investigate performance measurement beyond a single firm's boundaries are still limited. With the purpose of revamping SCPMSs as a relevant research topic, we conduct a systematic review of the literature. To this end, we firstly provide a clear and up-to-date definition of SCPMSs, which sets the boundary of the study. Secondly, we perform a systematic review of academic articles published in peer-reviewed international journals, mostly in the domains of supply chain management and performance measurement. A final sample of 92 articles published from 1998 to 2015 constitutes the knowledge base of the study. The results show the publication pattern over time and provide evidence about the journals, the methodology adopted and the content elements (the SCPMS frameworks presented and the scope and phase of the measurement process). We synthesize the research state of the art and present a detailed research agenda for future scientific contributions. In particular, we envisage a theory-testing approach concerning the relatively more mature component of SCPMSs (i.e. supplier PMSs) from a life cycle perspective as well as an exploratory/theory-building approach concerning the other under-investigated components (i.e. customer PMSs, multi-tier SCPMSs and many-to-many SCPMSs). © 2016 Elsevier B.V.},
author_keywords={Performance measurement;  Supply chain management;  Systematic literature review},
keywords={Knowledge based systems;  Life cycle, Business practices;  International journals;  Life cycle perspectives;  Measurement process;  Performance measurements;  Scientific contributions;  Supply chain performance measurements;  Systematic literature review, Supply chain management},
references={Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach (2006) Eur. J. Oper. Res., 173 (1), pp. 211-225; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) J. Clean. Prod., 86, pp. 360-377; Akyuz & Erkan, Supply chain performance measurement: a literature review (2010) Int. J. Prod. Res., 48, pp. 5137-5155; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decis. Support Syst., 42 (1), pp. 283-301; Barber, E., How to measure the “value” in value chains (2008) Int. J. Phys. Distrib. Logist. Manag., 38 (9), pp. 685-698; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3), pp. 275-292; Berrah, L., Cliville, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Comput. Ind., 58, pp. 709-719; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Comput. Ind. Eng., 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Prod. Plan. Control, 20 (8), pp. 678-690; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2014) Prod. Plan. Control, 25 (8), pp. 698-714; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure SC performance (2000) J. Bus. Logist., 21 (1), pp. 75-93; Bryceson, K.P., Slaughter, G., Alignment of performance metrics in a multi-enterprise agribusiness: achieving integrated autonomy? (2010) Int. J. Product. Perform. Manag., 59 (44), pp. 325-350; Bullinger, H., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) Int. J. Prod. Res., 40 (15), pp. 3533-3543; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decis. Support Syst., 46, pp. 512-521; Carr, A.S., Pearson, J.N., Strategically managed buyer–supplier relationships and performance outcomes (1999) J. Oper. Manag., 17, pp. 497-519; Chae, B.K., Developing key performance indicators for supply chain: an industry perspective (2009) Supply Chain Manag.: Int. J., 14 (6), pp. 422-428; Chan, A conceptual model of performance measurement for supply chains (2003) Manag. Decis., 41, pp. 635-642; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) Int. J. Bus. Perform. Manag., 8 (2-3), pp. 110-131; Chan, F.T.S., Nayak, A., Raj, R., Chong, A.Y., Manoj, T., An innovative supply chain performance measurement system incorporating research and development and marketing policy (2014) Comput. Ind. Eng., 69, pp. 64-70; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: a process-based approach and measures (2003) Integr. Manuf. Syst., 14 (3), pp. 179-190; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Bus. Process Manag. J., 14 (4), pp. 512-529; Chia, A., Goh, M., Hum, S.-H., Performance measurement in supply chain entities: balanced scorecard perspective (2009) Benchmark.: Int. J., 16 (5), pp. 605-620; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput. Ind. Eng., 62 (3), pp. 801-818; Clift, R., Metrics for supply chain sustainability (2003) Clean. Technol. Environ. Policy, 5 (3), pp. 240-247; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer–supplier relationships: the mediating role of socialization mechanisms (2008) Int. J. Oper. Prod. Manag., 28 (3), pp. 238-258; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: a framework for contextual analysis (2011) Int. J. Product. Perform. Manag., 60 (6), pp. 583-602; Dey & Cheffy, Green SC performance measurement using the AHP: a comparative analysis of manufacturing organizations (2013) Prod. Plan. Control, 24 (8), pp. 701-720; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes' performance to a company's financial strategic objectives (2012) Eur. J. Oper. Res., 223 (1), pp. 276-289; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecol. Econ., 70, pp. 1088-1100; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2010) Int. J. Prod. Econ., 142 (2), pp. 247-258; Fine, C.H., Vardan, R., Pethick, R., El-Hout, J., Rapid response capability in value chain design (2002) Sloan Manag. Rev., 43 (2), pp. 69-75; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int. J. Prod. Econ., 134 (1), pp. 177-187; Ganji Jamehshooran, B., Shaharoun, M., Norehan Haron, H., Assessing supply chain performance through applying the SCOR model (2015) Int. J. Supply Chain Manag., 4, p. 1; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Manag.: Int. J., 12 (6), pp. 400-411; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000–2011 (2012) Int. J. Product. Perform. Manag., 61, pp. 518-547; Govindan, K., Khodaverdi, R., Jafarian, A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2013) J. Clean. Prod., 47, pp. 345-354; Gulledge, T., Chavusholu, T., Automating the construction of supply chain key performance indicators (2008) Ind. Manag. Data Syst., 108 (6), pp. 750-774; Gunasekaran, A., Kobu, B., Performance measures and metrics in SCM: a review of recent literature (1995–2004) and applications (2007) Int. J. Prod. Res., 45, pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag., 21 (1), pp. 71-87; Hald, K.S., Ellegaard, C., Supplier evaluation processes: the shaping and reshaping of supplier performance (2011) Int. J. Oper. Prod. Manag., 31 (8), pp. 888-910; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Hervani, A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmark.: Int. J., 12 (4), pp. 330-353; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: a case study from the packaging industry (2009) Prod. Plan. Control, 20 (1), pp. 68-81; Holmberg, S., A systems perspective on supply chain measurements (2000) Int. J. Phys. Distrib. Logist. Manag., 30 (10), pp. 847-868; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Manag.: Int. J., 9 (1), pp. 23-29; Jakhar, S.K., Barua, M.K., An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP (2014) Prod. Plan. Control, 25 (11), pp. 938-957; Kannan, V.R., Tan, K.C., Supplier selection and assessment: Their impact on business performance (2002) J. Supply Chain Manag., 38 (3), pp. 11-21; Kim, D.Y., Kumar, V., Kumar, U., Performance assessment framework for supply chain partnership (2010) Supply Chain Manag.: Int. J., 15 (3), pp. 187-195; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) J. Oper. Res. Soc., 54 (5), pp. 507-514; Kocaoğlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2011) J. Intell. Manuf., 24 (1), pp. 113-132; Lai, K., Ngai, E.W., Cheng, T.C., Measures for evaluating supply chain performance in transport logistics (2002) Transp. Res. Part E: Logist. Transp. Rev., 38 (6), pp. 439-456; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int. J. Logist. Manag., 12 (1), pp. 1-19; Li, X., Gu, X.J., Liu, Z.G., A strategic performance measurement system for firms across supply and demand chains on the analogy of ecological succession (2009) Ecol. Econ., 68 (12), pp. 2918-2929; Lin, L.C., Li, T.S., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Softw. Qual. J., 18 (33), pp. 387-406; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: an empirical analysis (2014) J. Purch. Supply Manag., 20 (2), pp. 113-129; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: a survey in the mines (2006) Manag. Account. Res., 17 (3), pp. 315-339; Martin, P.R., Patterson, J.W., On measuring company performance within a supply chain (2009) Int. J. Prod. Res., 47 (9), pp. 2449-2460; Mondragon, A.E.C., Lalwani, C., Mondragon, C.E.C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Manag.: Int. J., 16 (1), pp. 43-56; Morgan, C., Supply network performance measurement: future challenges? (2007) Int. J. Logist. Manag., 18 (2), pp. 255-273; Morgan, C., Dewhurst, A., Using SPC to measure a national supermarket chain's suppliers' performance (2007) Int. J. Oper. Prod. Manag., 27 (8), pp. 874-900; Muralidharan, C., A multi-criteria group decision making for supplier rating (2002) J. Supply Chain Manag., 38 (3), pp. 22-35; Muralidharan, C., Anantharaman, N., Deshmukh, S.G., Vendor rating in purchasing scenario: a confidence interval approach (2001) Int. J. Oper. Prod. Manag., 21 (10), pp. 1305-1325; Najmi, A., Makui, A., A conceptual model for measuring supply chain performance (2012) Prod. Plan. Control, 23 (9), pp. 694-706; O'Toole, T., Donaldson, B., Relationship performance dimensions of buyer–supplier exchanges (2002) Eur. J. Purch. Supply Manag., 8 (4), pp. 197-207; De Oliveira, M.P.V., McCormack, K., Trkman, P., Business analytics in supply chains – The contingent effect of business process maturity (2012) Expert Syst. Appl., 39 (5), pp. 5488-5498; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour. Conserv. Recycl., 55 (6), pp. 567-579; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) Eur. J. Oper. Res., 144 (2), pp. 306-320; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) Eur. J. Inf. Syst., 14 (4), pp. 335-346; Prahinski, C., Benton, W., Supplier evaluations: communication strategies to improve supplier performance (2004) J. Oper. Manag., 22 (1), pp. 39-62; Prahinski, C., Fan, Y., Supplier evaluations: the role of communication quality (2007) J. Supply Chain Manag., 43 (3), pp. 16-28; Purdy, L., Safayeni, F., Strategies for supplier evaluation: a framework for potential advantages and limitations (2000) IEEE Trans. Eng. Manag., 47 (4), pp. 435-443; Ramanathan, U., Gunasekaran, A., Subramanian, N., Supply chain collaboration performance metrics: a conceptual framework (2011) Benchmark.: Int. J., 18 (6), pp. 856-872; Saad, M., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmark.: Int. J., 13, pp. 36-53; Schmitz, J., Platts, K.W., Roles of supplier performance measurement: indication from a study in the automotive industry (2003) Manag. Decis., 41 (8), pp. 711-721; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: indications from a study in the automotive industry (2004) Int. J. Prod. Econ., 89 (2), pp. 231-243; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) Int. J. Prod. Res., 53 (16), pp. 4917-4926; Shafiee, M., Lotfi, F.H., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Appl. Math. Model., 38 (21), pp. 5092-5112; Sharif, A.M., Irani, Z., Lloyd, D., Information technology and performance management for build-to-order supply chains (2007) Int. J. Oper. Prod. Manag., 27 (11), pp. 1235-1253; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmark.: Int. J., 17, pp. 320-339; Sheperd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) Int. J. Product. Perform. Manag., 55, pp. 242-258; Sillanpää, I., Empirical study of measuring supply chain performance (2015) Benchmark.: Int. J., 22 (2), pp. 290-308; Simatupang, T.M., Sridharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmark.: Int. J., 11 (1), pp. 9-30; Simatupang, T.M., Sridharan, R., The collaboration index: a measure for supply chain collaboration (2005) Int. J. Phys. Distrib. Logist. Manag., 35 (1), pp. 44-62; Simpson, P.M., Siguaw, J.A., White, S.C., Measuring the performance of suppliers: an analysis of evaluation processes (2002) J. Supply Chain Manag., 38 (4), pp. 29-41; Stefanović, N., Stefanović, D., Supply chain performance measurement system based on scorecards and web portals (2011) Comput. Sci. Inf. Syst., 8 (1), pp. 167-192; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmark.: Int. J., 16 (5), pp. 702-723; Van Hoek, R.I., Measuring the unmeasurable – Measuring and improving performance in the supply chain (1998) J. Supply Chain Manag., 3, pp. 187-192; Van Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) Int. J. Oper. Prod. Manag., 21 (1), pp. 126-148; Wickramatillake, C.D., Koh, S.C.L., Gunasekaran, A., Arunachalam, S., Measuring performance within the supply chain of a large scale project (2007) Supply Chain Manag.: Int. J., 12 (1), pp. 52-59; Widyaningrum, D., Masruroh, N.A., Development of the sea fishery supply chain performance measurement system: a case study (2013) Int. J. Supply Chain Manag., 1 (3), pp. 68-80; Zhang, L., Wang, S., Li, F., Wang, H., Wang, L., Tan, W., A few measures for ensuring supply chain quality (2011) Int. J. Prod. Res., 49 (1), pp. 87-97; Association of Business School, 2015. Academic Journal Guide, 1–54; Barratt, M., Barratt, R., Exploring internal and external supply chain linkages: evidence from the field (2011) J. Oper. Manag., 29 (5), pp. 514-528; Barratt, M., Oke, A., Antecedents of supply chain visibility in retail supply chains: a resource-based theory perspective (2007) J. Oper. Manag., 25, pp. 1217-1233; Beske-Janssen, P., Johnson, M.P., Schaltegger, S., 20 years of performance measurement in sustainable supply chain management – what has been achieved? (2015) Supply Chain Manag.: Int. J., 20 (6), pp. 664-680; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: challenges for tomorrow (2012) Int. J. Manag. Rev., 14 (3), pp. 305-327; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organizational culture (2006) Int. J. Oper. Prod. Manag., 26 (12), pp. 1325-1350; Blome, C., Schoenherr, T., Kaesser, M., Ambidextrous governance in supply chains: the impact on innovation and cost performance (2013) J. Supply Chain Manag., 49 (4), pp. 59-80; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20 (7), pp. 754-771; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: a literature review (2003) Int. J. Bus. Perform. Manag., 5 (1), pp. 22-24; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: an action research (2011) Int. J. Prod. Econ., 133, pp. 751-760; Bryman, A., Social Research Methods (2012), Oxford University Press USA; Cecere, L., Building business-to-business supply chain networks (2014) Supply Chain Insights, pp. 1-30; Caglio, A., Ditillo, A., A review and discussion of management control in inter-firm relationships: achievements and future directions (2008) Account. Organ. Soc., 33, pp. 865-898; Cavinato, J.L., A total cost/value model for supply chain competitiveness (1992) J. Bus. Logist., 13 (2), pp. 285-301; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: the constructs and measurements (2004) J. Oper. Manag., 23, pp. 618-641; Chenhall, R., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: an exploratory study (2005) Account. Organ. Soc., 30, pp. 395-442; Choong, K.K., Has this large number of performance measurement publications contributed to its better understanding? A systematic review for research and applications (2013) Int. J. Prod. Res., 52 (14), pp. 4174-4197; Cooper, M.D., Lambert, J.P., Supply chain management: More than a new name for logistics (1997) Int. J. Logist. Manag., 8 (1), pp. 1-13; Dotson, M.J., Dava, D.S., Miller, S.E., Desired skills and competencies of prospective supply chain managers: an empirical investigation of US supply chain executives (2015) Int. J. Logist. Syst. Manag., 22 (1), pp. 55-66; Ellram, L.M., Feitzinger, E., Using total profit analysis to model supply chain decisions (1997) J. Cost. Manag., 8 (1), pp. 1-14; Foerstl, K., Reuter, C., Hartmann, E., Blome, C., Managing supplier sustainability risks in a dynamically changing environment – Sustainable supplier management in the chemical industry (2010) J. Purch. Supply Manag., 16 (2), pp. 118-130; FrancoSantos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: a review of their consequences and a framework for research (2012) Manag. Account. Res., 23 (2), pp. 79-119; Free, C., Walking the talk? Supply chain accounting and trust among UK supermarkets and suppliers (2008) Account. Organ. Soc., 33, pp. 629-662; Giunipero, L.C., Hooker, R.E., Yoon, T.O.M.E., Brudvig, S., A decade of SCM literature: past, present and future implications (2008) J. Supply Chain Manag., 44 (4), pp. 66-86; Gutierrez, D.M., Scavarda, L.F., Fiorencio, L., Martins, R.A., Evolution of the performance measurement system in the logistics department of a broadcasting company: an action research (2015) Int. J. Prod. Econ., 160, pp. 1-12; Hamprecht, J., Corsten, D., Noll, M., Meier, E., Controlling the sustainability of food supply chains (2005) Supply Chain Manag.: Int. J., 10 (1), pp. 7-10; Hart, C., Doing a Literature Review (1998), Thousand Oaks, CA, Sage Publications Limited; Hartmann, E., Kerkfeld, D., Henke, M., Top and bottom line relevance of purchasing and supply management (2012) J. Purch. Supply Manag., 18 (1), pp. 22-34; Henri, J.F., Management control systems and strategy: a resource-based perspective (2006) Account. Organ. Soc., 31 (6), pp. 529-558; Huang, S., Keskar, H., Comprehensive and configurable metrics for supplier selection (2007) Int. J. Prod. Econ., 105 (2), pp. 510-523; Igarashi, M., de Boer, L., Fet, A.M., What is required for greener supplier selection? A literature review and conceptual model development (2013) J. Purch. Supply Manag., 19 (4), pp. 247-263; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance (1992) Harv. Bus. Rev., 70, pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harv. Bus. Rev., 74 (1), pp. 75-85; Ketchen, D.J., Giunipero, L.C., The intersection of strategic management and supply chain management (2004) Ind. Mark. Manag., 33 (1), pp. 51-56; Ketchen, D.J., Hult, G.T.M., Bridging organization theory and supply chain management: the case of best value supply chains (2007) J. Oper. Manag., 25 (2), pp. 573-580; Li, S., Nathan, B.R., Nathan, T.S., Rao, S.S., Development and validation of a measurement instrument for studying supply chain management practices (2005) J. Oper. Manag., 23, pp. 618-623; Liu, Y., Huang, Y., Luo, Y., Zhao, Y., How does justice matter in achieving buyer–supplier relationship performance? (2012) J. Oper. Manag., 30 (5), pp. 355-367; Lorentz, H., Töyli, J., Solakivi, T., Ojala, L., Priorities and determinants for supply chain management skills development in manufacturing firms (2013) Supply Chain Manag.: Int. J., 18 (4), pp. 358-375; Luzzini, D., Ronchi, S., Cinderella purchasing transformation: linking purchasing status to purchasing practices and business performance (2016) Prod. Plan. Control, pp. 1-10; Maestrini, V., Luzzini, D., 2015 Supply chain performance measurement system lifecycle. Acad. Manag. Proc., , 1, 1, 1530; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res., 25 (2), pp. 173-186; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: dealing with the metrics maze (2004) J. Oper. Manag., 22, pp. 209-217; Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) J. Bus. Logist., 22 (2), pp. 1-24; Micheli, P., Manzoni, J.F., Strategic performance measurement: benefits, limitations and paradoxes (2010) Long Range Plan., 43 (4), pp. 465-476; Neely, A., The performance measurement revolution: why now and what next? (1999) Int. J. Oper. Prod. Manag., 19 (2), pp. 205-228; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) Int. J. Oper. Prod. Manag., 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Performance measurement system design: developing and testing a process-based approach (2000) Int. J. Oper. Prod. Manag., 20, pp. 1119-1145; Neely, A., The evolution of performance measurement research: developments in the last decade and a research agenda for the next (2005) Int. J. Oper. Prod. Manag., 25 (12), pp. 1264-1277; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Comput. Ind. Eng., 60 (2), pp. 279-290; Raj, S., Sharma, A., supply chain management in the cloud: how can cloud based computing make supply chains more competitive? (2015) Accent. Rep., pp. 1-11; Schaltegger, S., Burritt, R., Measuring and managing sustainability performance of supply chains (2014) Supply Chain Manag.: Int. J., 19 (3), pp. 232-241; Spina, G., Caniato, F., Luzzini, D., Ronchi, S., Past, present and future trends of purchasing and supply management: an extensive literature review (2013) Ind. Mark. Manag., 42, pp. 1202-1212; Van der Rhee, B., Verma, R., Plaschka, G., Understanding trade-offs in the supplier selection process: the role of flexibility, delivery, and value-added services/support (2009) Int. J. Prod. Econ., 120 (1), pp. 30-41; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) Int. J. Oper. Prod. Manag., 22 (2), pp. 195-219; Wong, C.Y., Boonitt, S., Wong, C.W.Y., The contingency effects of environmental uncertainty on the relationship between supply chain integration and performance (2011) J. Oper. Manag., 29, pp. 604-615; Zhou, H., Benton, W.C., Supply chain practice and information sharing (2007) J. Oper. Manag., 25 (6), pp. 1348-1365},
correspondence_address1={Maestrini, V.; Management, via Lambruschini 4/b, Italy},
publisher={Elsevier B.V.},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Review},
source={Scopus},
}

@ARTICLE{Balfaqih20172,
author={Balfaqih, H. and Al-Nory, M.T. and Nopiah, Z.M. and Saibani, N.},
title={Environmental and economic performance assessment of desalination supply chain},
journal={Desalination},
year={2017},
volume={406},
pages={2-9},
doi={10.1016/j.desal.2016.08.004},
note={cited By 32},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994358511&doi=10.1016%2fj.desal.2016.08.004&partnerID=40&md5=228c3ab13333283bee9d0fcf04d593ce},
affiliation={Faculty of Engineering and Built Environment, National University of Malaysia (UKM), Bangi, Selangor  43600, Malaysia; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA  03129, United States; College of Engineering, Effat University, Jeddah, Saudi Arabia},
abstract={The interest in desalination has been growing steadily for the recent decades due to the global increased demand for fresh water. Viewing the desalination network as a supply chain permits decision makers to evaluate the performance of desalination processes end to end. However, there are few articles considered evaluating the desalination supply chain as a whole. Thus, the purpose of this study is to develop performance measurement criteria and metrics that is designed to evaluate the efficiency of desalination supply chain concerning the economic and environmental factors. Different desalination and supply chain performance measures related to economic, and environmental perspective have been reviewed. The performance measures and metrics have been further refined by a panel of experts using Delphi technique. By adopting the analytic hierarchy process technique, a weight factor has been assigned to each perspective and metric according to its significant in desalination supply chain. It is believed that both academics and practitioners would benefit from the framework developed. Finally, it must be realized that desalination is one part of the solution. Only with a synergistic approach involving desalination, water management, consumption reduction and water reuse could efficiently solve the water demand challenge in future. © 2016 Elsevier B.V.},
author_keywords={AHP;  Delphi;  desalination;  performance measurement;  Supply chain},
keywords={Decision making;  Supply chains;  Wastewater reclamation;  Water conservation;  Water management, Analytic hierarchy;  Consumption reductions;  Delphi;  Environmental and economic performance;  Environmental factors;  Performance measure;  Performance measurements;  Supply chain performance, Desalination, analytical hierarchy process;  Delphi analysis;  desalination;  economics;  environmental assessment;  performance assessment},
references={Alegre, H., Baptista, J.M., Cabrera, E., Cubillo, F., Duarte, P., Hirner, W., Merkel, W., Parena, R., Performance indicators for water supply services (2013) Water Intell. Online, 12. , (9781780405292); Al-Nory, M.T., Brodsky, A., Bozkaya, B., Graves, S.C., Desalination supply chain decision analysis and optimization (2014) Desalination, 347, pp. 144-157; Al-Nory, M.T., Graves, S.C., Water desalination supply chain modeling and optimization: case of Saudi Arabia (2013) IDA J. Desalin. Water Reuse, 5 (2), pp. 64-74; Al-Shammiri, M., Safar, M., Multi-effect distillation plants: state of the art (1999) Desalination, 126 (1), pp. 45-59; Balfaqih, H., Nopiah, Z.M., Saibani, N., Al-Nory, M.T., Review of supply chain performance measurement systems: 1998–2015 (2016) Comput. Ind., 82, pp. 135-150; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5/6), pp. 249-260; Chen, Z., Chen, W., Chen, Q., Group decision making based on the LCLR method (2010) Kybernetes, 39 (6), pp. 881-887; Coday, B.D., Life cycle and economic assessments of engineered osmosis and osmotic dilution for desalination of Haynesville shale pit water (2015) Desalination, 369, pp. 188-200; Das, R., Ali, M.E., Hamid, S.B.A., Ramakrishna, S., Chowdhury, Z.Z., Carbon nanotube membranes for water purification: a bright future in water desalination (2014) Desalination, 336, pp. 97-109; Lee, E.J., Criddle, C.S., Bobel, P., Freyberg, D.L., Assessing the scale of resource recovery for centralized and satellite wastewater treatment (2013) Environ. Sci. Technol., 47, pp. 10762-10770; El-Dessouky, H., Shaban, H.I., Al-Ramadan, H., Steady-state analysis of multi-stage flash desalination process (1995) Desalination, 103 (3), pp. 271-287; Ghaffour, The challenge of capacity-building strategies and perspectives for desalination for sustainable water use in MENA (2009) Desalin. Water Treat., 5, pp. 48-53; Ghaffour, N., Missimer, T.M., Amy, G.L., Technical review and evaluation of the economics of water desalination: current and future challenges for better water supply sustainability (2013) Desalination, 309, pp. 197-207; Gopal, P.R.C., Thakkar, J., Sustainable supply chain practices: an empirical investigation on Indian automobile industry (2016) Prod. Plan. Control, 27 (1), pp. 49-64; Raluy, G., Serra, L., Uche, J., Life cycle assessment of MSF, MED, and RO desalination technologies (2006) Energy, 31 (13), pp. 2361-2372; Hamid Abu Bakar, A., Measuring supply chain performance among public hospital laboratories (2009) Int. J. Product. Perform. Manag., 59 (1), pp. 75-97; Muga, H.E., Mihelcic, J.R., Sustainability of wastewater treatment technologies (2008) J. Environ. Manag., 88, pp. 437-447; Jana, P.K., Saha, I., Correlation of greenhouse molecules with global and surface temperature and its effect on environment (2011) Indian J. Phys., 85 (5), pp. 667-682; Bare, J.C., Norris, G.A., Pennington, D.W., McKone, T., TRACI: the tool for the reduction and assessment of chemical and other environmental impacts (2003) J. Ind. Ecol., 6, pp. 49-78; Sadhwani, J.J., Veza, J.M., Carmelo, S., Case studies on environmental impact of seawater desalination (2005) Desalination, 185 (1-3), pp. 1-8; Khalili-Damghani, K., Taghavi-Fard, M., Abtahi, A.-R., A fuzzy two-stage DEA approach for performance measurement: real case of agility performance in dairy supply chains (2012) Int. J. Appl. Decis. Sci., 5 (4), pp. 293-317; Mezher, T., Fath, H., Abbas, Z., Khaled, A., Techno-economic assessment and environmental impacts of desalination technologies (2011) Desalination, 266 (1), pp. 263-273; Houghton, J.T., Filho, L.G.M., Callendar, B.A., Harris, N., Kattenberg, A., Maskell, K., Book review: climate change 1995: the science of climate change (1997) Int. J. Climatol., 17, p. 904; Pochampally, K.K., Gupta, S.M., Strategic planning of a reverse supply chain network (2005) Int. J. Integr. Supply Manag., 1 (4); Lin, S., Elimelech, M., Staged reverse osmosis operation: Configurations, energy efficiency, and application potential (2015) Desalination, 366, pp. 9-14; Lipovetsky, S., An interpretation of the AHP eigenvector solution for the lay person (2010) Int. J. Anal. Hierarchy Process, 2 (2); Magni, C.A., The internal rate of return approach and the AIRR paradigm: a refutation and a corroboration (2013) Eng. Econ., 58 (2), pp. 73-111; Jenkin, M.E., Hayman, G.D., Photochemical ozone creation potentials for oxygenated volatile organic compounds: sensitivity to variations in kinetic and mechanistic parameters (1999) Atmos. Environ., 33, p. 12751293; Hepbasli, A., Alsuhaibani, Z., A key review on present status and future directions of solar energy studies and applications in Saudi Arabia (2011) Renew. Sust. Energ. Rev., 15 (9), pp. 5021-5050; Maurer, M., Specific net present value: an improved method for assessing modularisation costs in water services with growing demand (2009) Water Res., 43, pp. 2121-2130; Meneses, M., Pasqualino, J.C., Céspedes-Sánchez, R., Castells, F., Alternatives for reducing the environmental impact of the main residue from a desalination plant (2010) J. Ind. Ecol., 14 (3), pp. 512-527; Modigliani, F., Miller, M.H., The cost of capital, corporation finance and the theory of investment (1958) Am. Econ. Rev., 48 (3), pp. 261-297; Shahabi, M.P., Anda, M., Ho, G., Influence of site-specific parameters on environmental impacts of desalination (2014) Desalin. Water Treat., 1-7; Poustie, M.S., Deletic, A., Brown, R.R., Wong, T., de Haan, F.J., Skinner, R., Sustainable urban water futures in developing countries: the centralised, decentralised or hybrid dilemma (2014) Urban Water J., 1-16; Mukhtar, M., Issues in the design and evaluation of e-supply chains (2014) Handbook of Strategic e-Business Management, pp. 255-279. , Springer Berlin Heidelberg; Pecchia, L., Bath, P.A., Pendleton, N., Bracale, M., Analytic hierarchy process (AHP) for examining healthcare professionals’ assessments of risk factors (2011) Methods Inf. Med., 50 (5), pp. 435-444; Einav, R., Lokiec, F., Environmental aspects of a desalination plant in Ashkelon (2003) Desalination, 156, pp. 79-85; Raluy, R.G., Serra, L., Uche, J., Valero, A., Life-cycle assessment of desalination technologies integrated with energy production systems (2004) Desalination, 167, pp. 445-458; Raluy, R.G., Serra, L., Uche, J., Life cycle assessment of water production technologies: part1. Life cycle assessment of different commercial desalination technologies (MSF, MED, RO) (2004) Int. J. Life Cycle Assess., 10 (5), pp. 1-9; Clark, R.M., Stevie, R.G., A water supply cost model incorporating spatial variables (1981) Land Econ., 57, pp. 18-32; Robb, D.J., Xie, B., Arthanari, T., Supply chain and operations practice and performance in Chinese furniture manufacturing (2008) Int. J. Prod. Econ., 112 (2), pp. 683-699; Saaty, T.L., Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process (2000), 6. , Rws Publications; Lattemann, S., El Habr, H., UNEP resource and guidance manual for environmental impact assessment of desalination projects (2009) Desalin. Water Treat., 3, pp. 217-228; Liu, S., Konstantopoulou, F., Gikas, P., Papageorgiou, L.G., A mixed integer optimisation approach for integrated water resources management (2011) Comput. Chem. Eng., 35, pp. 858-875; Hopner, T., A procedure for environmental impact assessments (EIA) for seawater desalination plants (1999) Desalination, 124, pp. 1-12; Morales-Pinzón, T., Lurueña, R., Rieradevall, J., Gasol, C.M., Gabarrell, X., Financial feasibility and environmental analysis of potential rainwater harvesting systems: a case study in Spain (2012) Resour. Conserv. Recycl., 69, pp. 130-140; Wibowo, M.A., Sholeh, M.N., The analysis of supply chain performance measurement at construction project (2015) Procedia Eng., 125, pp. 25-31; WMO, Scientific Assessment of Ozone Depletion, Global Ozone Research and Monitoring Project (1999), p. 1998. , World Meteorological Organization Geneva; World Health Organization, Guidelines for Safe Recreational Water Environments: Coastal and Fresh Waters (2003), 1. , World Health Organization; Saif, Y., Almansoori, A., Design and operation of water desalination supply chain using mathematical modelling approach (2014) Desalination, 351, pp. 184-201; Zarenezhada, F., Mehralianb, G., RajabzadehGhataric, A., Developing a model for agile pharmaceutical distribution: evidence from Iran (2013) J. Basic Appl. Sci. Res., 3 (1), pp. 161-172; Zhou, J., Chang, V.W.-C., Fane, A.G., Environmental life cycle assessment of reverse osmosis desalination: the influence of different life cycle impact assessment methods on the characterization results (2011) Desalination, 283, pp. 227-236; Zhou, J., Chang, V.W.-C., Fane, A.G., Life cycle assessment for desalination: a review on methodology feasibility and reliability (2014) Water Res., 61, pp. 210-223; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347},
correspondence_address1={Balfaqih, H.; Faculty of Engineering and Built Environment, Malaysia; email: h.balfaqih@siswa.ukm.edu.my},
publisher={Elsevier B.V.},
issn={00119164},
coden={DSLNA},
language={English},
abbrev_source_title={Desalination},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chiarini201798,
author={Chiarini, A.},
title={Environmental Policies for Evaluating Suppliers' Performance Based on GRI Indicators},
journal={Business Strategy and the Environment},
year={2017},
volume={26},
number={1},
pages={98-111},
doi={10.1002/bse.1907},
note={cited By 33},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941796942&doi=10.1002%2fbse.1907&partnerID=40&md5=c70b613bae356f28b19ab2d6d653f63c},
affiliation={University of Ferrara and Chiarini & Associates, Italy},
abstract={Many companies use a performance measurement system (PMS) to evaluate their suppliers' environmental performance (SEP). To do this a company can establish its own set of measures or adopt indicators borrowed from specific standards and guidelines such as the Global Reporting Initiative (GRI). However, the results of a literature review indicate that the weights of the different environmental performance indicators within an overall SEP rating are unclear. The aim of this paper is to understand what the environmental policies adopted by European manufacturing companies to evaluate their suppliers really are. In order to reach this objective, a quantitative model based on multiple linear regression has been developed. The model correlates different environmental aspects taken from the GRI set of indicators with the overall SEP rating. Interestingly, results show that performances on aspects such as transport and ISO 14001 certification do not count towards the final ranking, whereas compliance with laws and regulations is the cornerstone of the evaluation of the supplier. Other performance indicators can in some ways be linked to compliance with laws and regulations. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment},
author_keywords={environmental sustainable supply chain management;  GRI;  multi-regression model;  supplier environmental performances},
keywords={environmental policy;  manufacturing;  regression analysis;  supply chain management, Europe},
references={Akili, E.A., Green supplier selection criteria (2009) Supply Chain Systems, 36 (4), pp. 7917-7927; Alonso-Almeida, M., Llach, J., Marimon, F., A closer look at the ‘global reporting initiative’ sustainability reporting as a tool to implement environmental and social policies: a worldwide sector analysis (2014) Corporate Social Responsibility and Environmental Management, 21 (6), pp. 318-335; Awasthi, A., Chauhan, S.S., Goyal, S.K., A fuzzy multicriteria approach for evaluating environmental performance of suppliers (2010) International Journal of Production Economics, 126 (2), pp. 370-378; Baskaran, V., Nachiappan, S., Rahman, S., Indian textile suppliers' sustainability evaluation using the Grey approach (2012) International Journal of Production Economics, 135 (2), pp. 647-658; Boys, K.A., Grant, J.H., ISO 14000 standards: voluntary environmental governance as a trade facilitation strategy? (2010) Proceedings of the Agricultural & Applied Economics Association 2010 AAEA, CAES, & WAEA Joint Annual Meeting, Denver, Colorado, pp. 25-27. , July 25-27, 201; Brammer, S., Hoejmose, S., Marchant, K., Environmental management in SMEs in the UK: practices, pressures and perceived benefits (2012) Business Strategy and the Environment, 21 (7), pp. 423-434; Chen, C.C., Incorporating green purchasing into the frame of ISO 14000 (2005) Journal of Cleaner Production, 13 (9), pp. 927-933; Chiarini, A., Designing an environmental sustainable supply chain through ISO 14001 standard (2012) Management of Environmental Quality, 24 (1), pp. 16-33; Chiarini, A., Strategies for developing an environmentally sustainable supply chain: differences between manufacturing and service sectors (2013) Business Strategy and the Environment; Chiarini, A., Differences between Six Sigma applications in manufacturing and the service industry (2013) International Journal of Productivity and Quality Management, 13 (3), pp. 345-360; Chiarini, A., Sustainable manufacturing-greening processes using specific Lean Production tools: an empirical observation from European motorcycle component manufacturers (2014) Journal of Cleaner Production, 85 (2014), pp. 226-233; Cho, C.H., Guidry, R.P., Hageman, A.M., Patten, D.M., Do actions speak louder than words? An empirical investigation of corporate environmental reputation (2012) Accounting, Organizations and Society, 37 (1), pp. 14-25; Curkovic, S., Sroufe, R., Using ISO 14001 to promote a sustainable supply chain strategy (2011) Business Strategy and the Environment, 20 (2), pp. 71-93; Curkovic, S., Sroufe, R., Melnyk, S., Identifying the factors which affect the decision to attain ISO 14000 (2005) Energy, 30 (8), pp. 1387-1407; Delmas, M., Montiel, I., Greening the supply chain: when is customer pressure effective? (2009) Journal of Economics and Management Strategy, 18 (1), pp. 171-201; Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., Lautenbach, S., Collinearity: a review of methods to deal with it and a simulation study evaluating their performance (2013) Ecography, 36 (1), pp. 27-46; Draper, N.R., Smith, H., (1981) Applied Regression Analysis, , 2nd edition, Wiley, New Yor; Fielding, J., Gilbert, N., (2000) Understanding Social Statistics, , Sage, Londo; Florida, R., Davidson, D., Why do firms adopt environmental practices (and do they make a difference)? (2001) Regulating from the Inside: Can Environmental Management Systems Achieve Policy Goals?, 4, p. 82. , In, Coglianese C, Nash J, (eds)., Resources for the Future, Washington, DC, Chapter, p; Fu, X., Zhu, Q., Sarkis, J., Evaluating green supplier development programs at a telecommunications systems provider (2012) International Journal of Production Economics, 140 (1), pp. 357-367; (2011) Indicator Protocols Set Environment (EN), , https://www.globalreporting.org/reporting/G3andg3-1/guidelines-online/G3Online/StandardDisclosures/Environmental/Pages/default.aspx, [30 September 2013]; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gupta, M.C., Environmental management and its impact on the operations function (1995) International Journal of Operations and Production Management, 15 (8), pp. 34-51; Hamschmidt, J., (2000) Economic and Ecological Impacts of Environmental Management Systems in Companies: Experiences from Switzerland, , Aalborg, EURO Environmen; Handfield, R., Sroufe, R., Walton, S., Integrating environmental management and supply chain strategies (2005) Business Strategy and the Environment, 14 (1), pp. 1-19; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: a study in the application of the Analytical Hierarchy Process (2002) European Journal of Operational Research, 141 (1), pp. 70-87; Harms, D., Hansen, E.G., Schaltegger, S., Strategies in sustainable supply chain management: an empirical investigation of large German companies (2013) Corporate Social Responsibility and Environmental Management, 20 (4), pp. 205-218; Heras-Saizarbitoria, I., Boiral, O., ISO 9001 and ISO 14001: towards a research agenda on management system standards (2013) International Journal of Management Reviews, 15 (1), pp. 47-65; Heras-Saizarbitoria, I., Molina-Azorín, J.F., Dick, G.P., ISO 14001 certification and financial performance: selection-effect versus treatment-effect (2011) Journal of Cleaner Production, 19 (1), pp. 1-12; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353; Humphreys, P., Mc Closkey, A., Mc Ivor, R., Maguire, L., Glackin, C., Employing dynamic fuzzy membership functions to assess environmental performance in the supplier selection process (2006) International Journal of Production Research, 44 (12), pp. 2379-2419; Humphreys, P., Mc Ivor, R., Chan, F., Using case-based reasoning to evaluate supplier environmental management performance (2003) Expert Systems with Applications, 25 (2), pp. 141-153; (2004) Environmental Management Systems – Requirements with Guidance for Use, , ISO, Genev; Kehbila, A.G., Ertel, J., Brent, A.C., Corporate sustainability, ecological modernization and the policy process in the South African automotive industry (2010) Business Strategy and the Environment, 19 (7), pp. 453-465; Kriegler, E., O'Neill, B.C., Hallegatte, S., Kram, T., Lempert, R.J., Moss, R.H., Wilbanks, T., The need for and use of socio-economic scenarios for climate change analysis: a new approach based on shared socio-economic pathways (2012) Global Environmental Change, 22 (4), pp. 807-822; Lee, A.H., Kang, H.Y., Hsu, C.F., Hung, H.C., A green supplier selection model for high-tech industry (2009) Expert Systems with Applications, 36 (4), pp. 7917-7927; Legendre, S., Coderre, F., Determinants of GRI G3 application levels: the case of the Fortune Global 500 (2013) Corporate Social Responsibility and Environmental Management, 20 (3), pp. 182-192; Lewisbeck, M.S., Bryman, A., Liao, T.F., (2004) Research Methods. The Sage Encyclopedia of Social Science Research Methods, , Sage, Londo; Link, S., Naveh, E., Standardization and discretion: does the environmental standard ISO 14001 lead to performance benefits? (2006) IEEE Transactions on Engineering Management, 53 (4), pp. 508-519; Lintukangas, K., Hallikas, J., Kähkönen, A.K., The role of green supply management in the development of sustainable supply chain (2013) Corporate Social Responsibility and Environmental Management; Miles, J., Shevlin, M., (2001) Applying Regression and Correlation. A Guide for Students and Researchers, , Sage, Londo; Min, H., Galle, W.P., Green purchasing practices of US firms (2001) International Journal of Operations and Production Management, 21 (9), pp. 1222-1238; Morrow, D., Rondinelli, D., Adopting corporate environmental management systems: motivations and results of ISO 14001 and EMAS certification (2002) European Management Journal, 20 (2), pp. 159-171; Naud, M., Quaddus, M.A., Rowe, A., Nowak, M., Reporting on sustainability standards in Australia: focus on ISO 14001 (2012) Journal of Corporate Citizenship, 2012 (47), pp. 27-54; Nawrocka, D., Brorson, T., Lindhqvist, T., ISO 14001 in environmental supply chain practices (2009) Journal of Cleaner Production, 17 (16), pp. 1435-1443; Noci, G., Designing ‘green’ vendor rating systems for the assessment of a supplier's environmental performance (1997) European Journal of Purchasing and Supply Management, 3 (2), pp. 103-114; Pérez-López, D., Moreno-Romero, A., Barkemeyer, R., Exploring the relationship between sustainability reporting and sustainability management practices (2013) Business Strategy and the Environment; Pryshlakivsky, J., Searcy, C., Fifteen years of ISO 14040: a review (2013) Journal of Cleaner Production; Rondinelli, D., Vastag, G., Panacea, common sense, or just a label?: The value of ISO 14001 environmental management systems (2000) European Management Journal, 18 (5), pp. 499-510; Roxas, B., Lindsay, V., Social desirability bias in survey research on sustainable development in small firms: an exploratory analysis of survey mode effect (2012) Business Strategy and the Environment, 21 (4), pp. 223-235; Schnittfeld, N.L., Busch, T., Sustainability management within supply chains – a resource dependence view (2015) Business Strategy and the Environment; Seuring, S., Supply chain management for sustainable products – insights from research applying mixed methodologies (2011) Business Strategy and the Environment, 20 (7), pp. 471-484; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A., A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences (2013) Resources, Conservation and Recycling, 74, pp. 170-179; Smith, A.C., Koper, N., Francis, C.M., Fahrig, L., Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation (2009) Landscape Ecology, 24 (10), pp. 1271-1285; Stevenson, T.H., Barnes, F.C., Fourteen years of ISO 9000: impact, criticisms, costs, and benefits (2001) Business Horizons, 44 (3), pp. 45-51; Steyn, B., De Beer, E., Strategic role of public relations in the process of ‘integrated reporting’ – an exploratory study (2012) Sinergie -The Italian Journal of Management, 88 (May–August), pp. 53-72; Tuzkaya, G., Ozgen, A., Ozgen, D., Tuzkaya, U.R., Environmental performance evaluation of suppliers: a hybrid fuzzy multi-criteria decision approach (2009) International Journal of Environmental Science and Technology, 6 (3), pp. 477-490; Walton, S.V., Handfield, R.B., Melnyk, S.A., The green supply chain: integrating suppliers into environmental management processes (1998) Journal of Supply Chain Management, 34 (2), pp. 2-11; Weber, C.A., Current, J.R., Benton, W.C., Vendor selection criteria and methods (1991) European Journal of Operational Research, 50 (1), pp. 75-84; Zhu, Q., Sarkis, J., The moderating effects of institutional pressures on emergent green supply chain practices and performance (2007) International Journal of Production Research, 45 (18-19), pp. 4333-4355},
correspondence_address1={Chiarini, A.; University of Ferrara and Chiarini & AssociatesItaly; email: andrea.chiarini@chiarini.it},
publisher={John Wiley and Sons Ltd},
issn={09644733},
language={English},
abbrev_source_title={Bus. Strategy Environ.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Janamanchi2016,
author={Janamanchi, B. and Burns, J.R.},
title={Performance metric optimization advocates CPFR in supply chains: A system dynamics model based study},
journal={Cogent Business and Management},
year={2016},
volume={3},
number={1},
doi={10.1080/23311975.2016.1139440},
art_number={1139440},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020547228&doi=10.1080%2f23311975.2016.1139440&partnerID=40&md5=034518702510790ed1156d5814159cdb},
affiliation={Division of International Business and Technology Studies, Texas A&M International University, 5201 University Blvd, Laredo, TX  78041-1900, United States; Operations Management and Information Technology Department, Rawls College of Business Administration, Texas Tech University, ISQS area, P.O. Box 42101, Lubbock, TX  79409-2101, United States},
abstract={Background: Supply Chain partners often find themselves in rather helpless positions, unable to improve their firm’s performance and profitability because their partners although willing to share production information do not fully collaborate in tackling customer order variations as they don’t seem to appreciate the benefits of such collaboration. Methods: We use a two-player (supplier-manufacturer) System Dynamics model to study the dynamics to assess the impact and usefulness of supply chain partner collaboration on the supply chain performance measures. Results: Simulation results of supply chain metrics under varied customer order patterns viz., basecase, random normal, random uniform, random upwardtrend, and random downwardtrend under (a) basecase, (b) independent optimization by manufacturer, and (c) collaborative optimization by manufacturer and supplier, are obtained to contrast them to develop useful insights. Conclusions: Focus on obtaining improved inventory turns with optimization techniques provides some viable options to managers and makes a strong case for increased collaborative planning forecasting and replenishment (CPFR) in supply chains. Despite the differences in the inventory management practices that it was contrasted with, CPFR has proven to be beneficial in a supply chain environment for all SC partners. © 2016 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.},
author_keywords={CPFR;  optimization;  simulation modeling;  supply chain performance metrics;  systems dynamics},
references={Akkermans, H., Dellaert, N., (2005) System Dynamics Review, 21, pp. 173-186; Boone, T., Ganeshan, R., Stenger, A.J., (2001) The impact of CPFR on supply chain performance: A simulation study, , https://mason.wm.edu/faculty/ganeshan_r/documents/cpfr_paper_stenger.pdf, Research paper). Retrieved January 23, 2015, from; Burns, J.R., Janamanchi, B., Strategies for reducing inventory costs and mitigating the bullwhip effect in supply chains: A simulation study (2006) Proceedings of the SWDSI Annual Conference, , Oklahoma City, OK:, &,. In; Croson, R., Donohue, K., The impact of POS data sharing on supply chain management; an experimental study (2003) Production and Operations Management, 12, pp. 1-11; Croson, R., Donohue, K., Upstream versus downstream information and its impact on bullwhip effect (2005) System Dynamics Review, 21, pp. 249-260; Forrester, J.W., Industrial dynamics: A major breakthrough for decision makers (1958) Harvard Business Review, 36, pp. 37-66; Forrester, J.W., (1961) Industrial dynamics, , Cambridge, MA (now available from Pegasus Communications, Waltham, MA): MIT Press; Janamanchi, B., Burns, J., Strategies to tackle trends in customer orders in a supply chain: A system dynamics model based Study (2010) Proceedings of the DSI Annual Conference, , San Diego, CA:, &, November) In; Janamanchi, B., Burns, J.R., Customer order forecasting in supply chains: A simulation study (2007) Proceedings of the SWDSI Annual Conference, , San Diego, CA:, &,. In; Janamanchi, B., Burns, J.R., Reducing bullwhip oscillation in a supply chain: a system dynamics model-based study (2007) International Journal of Information Systems and Change Management, 2, pp. 350-371; Janamanchi, B., Burns, J.R., Simulation studies of the effects of safety stock and related policies upon Bullwhip oscillation in supply chains (2008) International Journal of Information Systems and Change Management, 3, pp. 171-187; Little, J.D.C., A proof for the queuing formula: L = λ W (1961) Operations Research, 9, pp. 383-387; Russell, R.S., Taylor, B.W., (2011) Operations management, pp. 437-438. , Hoboken, NJ: Wiley; Senge, P.M., (1990) The fifth discipline, The art & practice of the learning organization, Double day currency, , 1st, Chapter 4, ed; Sterman, J.D., (2000) Business dynamics-systems thinking and modeling for a complex world, , Boston, MA: McGrawHill; (2013) Ventana Systems Inc, , http://vensim.com/vensim-software/, Retrieved January 23, 2015, from; (2010) Linking CPFR and S&OP: A roadmap to integrated business planning, , http://www.gs1us.org/DesktopModules/Bring2mind/DMX/Download.aspx?Command=Core_Download&EntryId=1375&PortalId=0&TabId=785, Version 1.0, September) Retrieved January 23, 2015, from},
correspondence_address1={Janamanchi, B.; Division of International Business and Technology Studies, 5201 University Blvd, United States; email: bjanamanchi@tamiu.edu},
publisher={Cogent OA},
issn={23311975},
language={English},
abbrev_source_title={Cogent Bus. Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Maestrini2016289,
author={Maestrini, V. and Luzzini, D. and Shani, A.B. and Canterino, F.},
title={The action research cycle reloaded: Conducting action research across buyer-supplier relationships},
journal={Journal of Purchasing and Supply Management},
year={2016},
volume={22},
number={4},
pages={289-298},
doi={10.1016/j.pursup.2016.06.002},
note={cited By 24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995960795&doi=10.1016%2fj.pursup.2016.06.002&partnerID=40&md5=b12aa0eeb244cbfaeae940ec6ee2878e},
affiliation={Politecnico di Milano School of Management, Via Raffaele Lambruschini 4, Milan, 20156, Italy; Audencia Nantes School of Management, 8 Route de la Jonelière, Nantes, 44312, France; California Polytechnic State University – Orfalea College of Business, 1 Grand Avenue, San Luis Obispo, CA  93407-0300, United States},
abstract={Buyer-supplier relationships in purchasing and supply chain management practice are instrumental and often “messy”. Indeed, the buyer and the supplier generally interact while pursuing their own interest, which are often subject to change over time. The action research method can help address the complexity of buyer-supplier relationships, generating important theoretical insights and relevant managerial implications. First, action research helps the researcher to better understand the problem by integrating diverse perspectives. Second, it allows the researcher to influence the buyer-supplier relationship directly, providing mutually beneficial solutions. This study proposes action research as a suitable interactive method that could complement other methodologies in the field of purchasing and supply chain management, as well as in other fields. An expanded action research framework – the “action research cycle reloaded” – is proposed and the role of the action researcher in the buyer-supplier context is discussed. The framework is applied to study the design and implementation of a supplier performance measurement system in the banking industry. © 2016 Elsevier Ltd},
author_keywords={Action research;  Buyer-supplier relationship;  Purchasing;  Supply chain},
references={Adler, N., Shani, A.B., (Rami), Styhre, A., Collaborative Research in Organizations (2004), Sage Thousand Oaks, CA; Ambrose, E., Marshall, D., Lynch, D., Buyer supplier perspectives on supply chain relationships (2010) Int. J. Oper. Prod. Manag., 30 (12), pp. 1269-1290; Avella, L., Alfaro, J.A., Spanish University Business Chairs used to increase the deployment of Action Research in Operations Management: a case study and analysis (2014) Action Res., 12 (2), pp. 194-208; Bartunek, J.M., Rynes, S.L., Academics and practitioners are alike and unlike: the paradoxes of academic–practitioner relationships (2014) J. Manag., 40 (5), pp. 1181-1201; Bartunek, J.M., Rynes, S.L., Ireland, R.D., What makes management research interesting, and why does it matter? (2006) Acad. Manag. J., 49 (1), pp. 9-15; Bartunek, J.M., What Has Happened to Mode 2? (2011) Br. J. Manag., 22 (3), pp. 555-558; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: an action research (2011) Int. J. Prod. Econ., 133 (2), pp. 751-760; Bushe, G.R., Marshak, R.J., Dialogic organization development (2014) The NTL Handbook of Organization Development and Change, , B.B. Jones M. Brazzel Wiley San Francisco, CA; Caniato, F., Luzzini, D., Ronchi, S., Purchasing performance management systems: an empirical investigation (2014) Prod. Plan. Control, 25 (7), pp. 616-635; Canterino, F., Shani, A.B., Coghlan, D., Brunelli, M.S., Collaborative management research as a modality of action research: learning from a merger-based study (2016) J. Appl. Behav. Sci., 52 (2), pp. 157-186; Carter, C.R., Ethical issues in international buyer–supplier relationships: a dyadic examination (2000) J. Oper. Manag., 18 (2), pp. 191-208; Chicksand, D., Watson, G., Walker, H., Radnor, Z., Johnston, R., Theoretical perspectives in purchasing and supply chain management: an analysis of the literature (2012) Supply Chain Manag.: Int. J., 17 (4), pp. 454-472; Cirella, S., Guerci, M., Shani, A.R., A process model of collaborative management research: the study of collective creativity in the luxury industry (2012) Syst. Pract. Action Res., 25 (3), pp. 281-300; Coghlan, D., Action research: exploring perspectives on a philosophy of practical knowing (2011) Acad. Manag. Ann., 5 (1), pp. 53-87; Coghlan, D., Shani, A.B., Creating action research quality in organization development: Rigorous, reflective and relevant (2014) Syst. Pract. Action Res., 27 (6), pp. 523-536; Coghlan, D., Brannick, T., Doing Action Research in Your Own Organization (2001), Sage London; Cooke, B., Wolfram Cox, J., Fundamentals of Action Research (2005), Sage Publications Thousand Oaks; Coughlan, P., Coghlan, D., Action research for operations management (2002) Int. J. Oper. Prod. Manag., 22 (2), pp. 220-240; Cousins, P.D., The alignment of appropriate firm and supply strategies for competitive advantage (2005) Int. J. Oper. Prod. Manag., 25 (5), pp. 403-428; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships: the mediating role of socialization mechanisms (2008) Int. J. Oper. Prod. Manag., 28 (2), pp. 238-258; Dickens, L., Watkins, K., Action research: rethinking Lewin (1999) Manag. Learn., 30 (2), pp. 127-140; Eden, C., Huxham, C., Researching organizations using action research (2006) Handbook of Organization Studies, pp. 388-408. , W.R. Nord Sage Beverly Hills; Eggert, A., Ulaga, W., Schultz, F., Value creation in the relationship life cycle: a quasi-longitudinal analysis (2006) Ind. Mark. Manag., 35 (1), pp. 20-27; Eltantawy, R., Paulraj, A., Giunipero, L., Naslund, D., Thute, A.A., Towards supply chain coordination and productivity in a three echelon supply chain (2015) Int. J. Oper. Prod. Manag.; Fincham, R., Clark, T., Introduction: can we bridge the rigor-relevance gap? (2009) J. Manag. Stud., 46, pp. 510-515; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: a contingency and configuration approach (2010) J. Oper. Manag., 28 (1), pp. 58-71; Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., Trow, M., The new production of knowledge: the dynamics of science and research in contemporary societies (1994), Sage Thousands Hoaks; Gibbons, M., Mode 2 society and the emergence of context-sensitive science (2000) Sci. Public Policy, 27 (3), pp. 159-163; González-Benito, J., A theory of purchasing's contribution to business performance (2007) J. Oper. Manag., 25 (4), pp. 901-917; Gopinath, C., Hoffman, R.C., The Relevance of Strategy Research: practitioner and academic viewpoints (1995) J. Manag. Stud., 32 (5), pp. 575-594; Greenwood, D., Levin, M., Introduction to Action Research (1998), Sage Thousand Oaks, CA; Gummesson, E., Qualitative Methods in Management Research (2000), 2nd ed. Sage London; Hald, K.S., Ellegaard, C., Supplier evaluation processes: the shaping and reshaping of supplier performance (2011) Int. J. Oper. Prod. Manag., 31 (8), pp. 888-910; Hesping, F.H., Schiele, H., Purchasing strategy development: a multi-level review (2015) J. Purch. Supply Manag., 21 (2), pp. 138-150; Hodgkinson, G.P., Rousseau, D.M., Bridging the rigour-relevance gap in management research: It's already happening! (2009) J. Manag. Stud., 46, pp. 534-546; Hodgkinson, G.P. (Ed.), 2001. Facing the future: the nature and purpose of management research reassessed, Br. J. Manag., vol. 12(special issue), pp. S1–S80; Hoss, M., Ten Caten, C.S., Lean schools of thought (2013) Int. J. Prod. Res., 51 (11), pp. 3270-3282; Kieser, A., Leiner, L., Why the rigour-relevance gap in management research is unbridgeable (2009) J. Manag. Stud., 46, pp. 516-533; Kieser, A., Nicolai, A., Seidl, D., The practical relevance of management research: turning the debate on relevance into a rigorous scientific research program (2015) Acad. Manag. Ann., 9 (1), pp. 143-233; Kim, K.K., Park, S.H., Ryoo, S.Y., Park, S.K., Inter-organizational cooperation in buyer–supplier relationships: Both perspectives (2010) J. Bus. Res., 63 (8), pp. 863-869; Koplin, J., Seuring, S., Mesterharm, M., Incorporating sustainability into supply management in the automotive industry–the case of the Volkswagen AG (2007) J. Clean. Prod., 15 (11), pp. 1053-1062; LaGanga, L.R., Lean service operations: reflections and new directions for capacity expansion in outpatient clinics (2011) J. Oper. Manag., 29 (5), pp. 422-433; March, J., Exploration and exploitation in organizational learning (1991) Annu. Rev. Sociol., 14, pp. 319-340; Lewin, K., Frontiers in group dynamics II. Channels of group life; social planning and action research (1947) Hum. Relat., 1 (2), pp. 143-153; Luzzini, D., Caniato, F., Ronchi, S., Spina, G., A transaction costs approach to purchasing portfolio management (2012) Int. J. Oper. Prod. Manag., 32 (9), pp. 1015-1042; Luzzini, D., Caniato, F., Spina, G., Designing vendor evaluation systems: an empirical analysis (2014) J. Purch. Supply Manag., 20 (2), pp. 113-129; MacLean, D., MacIntosh, R., Grant, S., Mode 2 management research (2002) Br. J. Manag., 13, pp. 187-207; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: a survey in the mines (2006) Manag. Account. Res., 31 (3), pp. 315-339; Mirvis, P.H., Academic-practitioners learning forums: a new model for interorganizational research (2008) Handbook of collaborative management research, pp. 567-582. , A.B. Shani S.A. Mohrman W.A. Pasmore B. Stymne N. Adler Sage Thousand Oaks, CA; Näslund, D., Kale, R., Paulraj, A., Action research in supply chain management – a framework for relevant and rigorous research (2010) J. Bus. Logist., 31 (2), pp. 331-355; Oosterhuis, M., Molleman, E., Van der Vaart, T., Differences in buyers' and suppliers' perceptions of supply chain attributes (2013) Int. J. Prod. Econ., 142 (1), pp. 158-171; Ottmann, G., Laragy, C., Allen, J., Feldman, P., Coproduction in practice: participatory action research to develop a model of community aged care (2011) Syst. Pract. action Res., 24 (5), pp. 413-427; Pasmore, W.A., Woodman, R., Simmons, A.L., Toward a more rigorous, reflective, and relevant science of collaborative management research (2008) Handbook of Collaborative Management Research, pp. 567-582. , A.B. Shani S.A. Mohrman W.A. Pasmore B. Stymne N. Adler Sage Thousand Oaks, CA; Paulraj, A., Chen, I., Flynn, J., Levels of strategic purchasing: Impact on supply integration and performance (2006) J. Purch. Supply Manag., 12 (3); Radaelli, G., Guerci, M., Cirella, S., Shani, A.B., Intervention research as management research in practice: learning from a case in the fashion design industry (2014) Br. J. Manag., 25 (2), pp. 335-351; Raelin, J., Seeking conceptual clarity in the action modalities (2009) Action Learn.: Res. Pract., 6 (1), pp. 17-24; Rapaport, R., Three dilemmas in action research (1970) Hum. Relat., 23 (60), pp. 499-513; Reason, P., Choice and quality in action research practice (2006) J. Manag. Inq., 15 (2), pp. 187-203; Reason, P., Bradbury, H., Handbook of Action Research (2008), 2nd ed. Sage London; Schein, E.H., The Clinical Perspective in Fieldwork (1987), Sage Thousand Oaks, CA; Schein, E.H., Process consultation, action research and clinical inquiry, are they the same? (1995) J. Manag. Psychol., 10 (6), pp. 14-19; Schein, E.H., (1999) Process Consultation Revisited, Building the Helping Relationship, , Addison-Wesley, Reading. MA; Schoenherr, T., Tummala, V.R., Harrison, T.P., Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the offshoring decision by a US manufacturing company (2008) J. Purch. Supply Manag., 14 (2), pp. 100-111; Seuring, S., Supply chain management for sustainable products–insights from research applying mixed methodologies (2011) Bus. Strategy Environ., 20 (7), pp. 471-484; Shani, A.B., Coghlan, D., Cirella, S., Action research and collaborative management research: more than meets the eye? (2012) Int. J. Action Res., 8 (1), pp. 45-67; Shani, A.B.R., Pasmore, W.A., Organization inquiry: towards a new model of the action research process (1985) Contemporary Organization Development: Current Thinking and Applications Contemporary Organization Development, pp. 438-448. , D.D. Warrick; Spina, G., Caniato, F., Luzzini, D., Ronchi, S., Past, present and future trends of purchasing and supply management: an extensive literature review (2013) Ind. Mark. Manag., 42 (8), pp. 1202-1212; Starkey, K., Madan, P., Bridging the Relevance Gap: aligning stakeholders in the future of management research (2001) Br. J. Manag., 12 (special issue), pp. S3-S26; Stringer, E., Action Research (2007), 3rd ed. Sage Thousand Oaks, CA; Terpend, R., Tyler, B.B., Krause, D.R., Handfield, R.B., Buyer-supplier relationships: derived value over two decades (2008) J. Supply Chain Manag., 44 (2), pp. 28-55; Tranfield, D., Starkey, K., The nature, social organization and promotion of management research: towards policy (1998) Br. J. Manag., 9 (4), pp. 341-353; Waring, T.S., Alexander, M., Innovations in inpatient flow and bed management: an action research project in a UK acute care hospital (2015) Int. J. Oper. Prod. Manag., 35 (5), pp. 751-781; Wengerm, E., Snyder, W., Communities of practice: the organizational frontier (2000) Harv. Bus. Rev., 78, pp. 139-145; Wynstra, F., What did we do, who did it and did it matter? A review of fifteen volumes of the (European) Journal of Purchasing and Supply Management (2010) J. Purch. Supply Manag., 16 (4), pp. 279-292; Zhang, W., Levenson, A., Crossley, C., Move your research from the Ivy Tower to the board room: a primer on action research for academics, consultants, and business executives (2015) Hum. Resour. Manag., 54 (1), pp. 151-174},
correspondence_address1={Maestrini, V.; Politecnico di Milano School of Management, Via Raffaele Lambruschini 4, Italy; email: vieri.maestrini@polimi.it},
publisher={Elsevier Ltd},
issn={14784092},
language={English},
abbrev_source_title={J. Purch. Supply Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dörnhöfer2016,
author={Dörnhöfer, M. and Schröder, F. and Günthner, W.A.},
title={Logistics performance measurement system for the automotive industry},
journal={Logistics Research},
year={2016},
volume={9},
number={1},
doi={10.1007/s12159-016-0138-7},
art_number={11},
note={cited By 22},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977138198&doi=10.1007%2fs12159-016-0138-7&partnerID=40&md5=221e34d047e4ab58f0101c4206906e38},
affiliation={Lehrstuhl für Fördertechnik Materialfluss Logistik, Technische Universität München, Garching, Germany; Volkswagen AG, Konzernlogistik, Wolfsburg, Germany},
abstract={Logistics can be seen as a key competitive factor in the automotive industry due to the rising number of model variants and options. With the increasing importance of logistics (Gunasekaran et al. in Int J Prod Econ 87(3):333–347, 2004), the evaluation of logistics effectiveness and efficiency is gaining increased attention. Logistics performance management (PM) is the key to quantifying the current state and improvement potentials within logistics. To account for the increasing importance of a supply chain, logistics PM needs to start at the supplier and reach at least until the original equipment manufacturer’s (OEM) assembly line. Furthermore, logistics PM needs to be in line with the latest logistics concepts, mainly based on lean logistics. In contrast to the great importance of logistics PM, the literature analysis shows a limited availability of logistics performance measurement systems (PMS), which are actually applicable to industry within a lean logistics context. The systems in the literature are either too high level to be useful to practitioners (e.g. supply chain-orientated systems) or too narrow in focus, and therefore do not cover the supply chain and lean perspectives. In the following paper, a logistics PMS is developed which allows for assessing the effectiveness and efficiency of current logistics processes. The developed approach incorporates the latest logistics concepts in the automotive industry, integrates a process orientation with a supply chain perspective, and is defined with the specificity required to enable the implementation within a specific industry context and triggers continuous improvement. The suggested framework is evaluated in an automotive context, presenting a short case study on the implementation of the proposed framework at two sites of a German automotive OEM. Furthermore, future application potentials and development needs are summarised. The paper’s contribution to the literature is in the field of logistics PM, specifically in the automotive industry. It offers a new approach, applicable to automotive logistics, which follows lean principles. For industry, this paper provides specific suggestions for a PMS, as well as performance indicators to holistically monitor the logistics chain. While being generic in terms of its definition, it can be seen as specific enough to be applicable in industry with limited adjustments. It provides practitioners with answers to the question of which performance indicators to use in today’s automotive logistics chain and which indicators serve as a base for continuous improvement. © 2016, The Author(s).},
author_keywords={Automotive;  Logistics;  Management;  Performance;  Supply chain;  System},
references={Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) Int J Prod Econ, 87 (3), pp. 333-347; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag Account Res, 25, pp. 173-186; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: indications from a study in the automotive industry (2004) Int J Prod Econ, 89 (2), pp. 231-243; Forslund, H., Jonsson, P., Selection, implementation and use of ERP systems for supply chain performance management (2010) Ind Manag Data Syst, 110 (8), pp. 1159-1175; Danese, P., Romano, P., Relationship between downstream integration, performance measurement systems and supply network efficiency (2012) Int J Prod Res, 50 (7), pp. 2002-2013; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) Int J Prod Res, 45 (12), pp. 2819-2840; de Leeuw, S., Beekman, L., Supply chain-oriented performance measurement for automotive spare parts (2008) Int J Automot Technol Manag, 8 (1), pp. 56-70; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) Int J Logist Manag, 5 (2), pp. 11-28; Neto, M.S., Pires, S., Performance measurement in supply chains: a study in the automotive industry [Medição de desempenho em cadeias de suprimentos: Um estudo na indústria automobilística] (2012) Gestao e Producao, 19 (4), pp. 733-746; Gunasekaran, A., Williams, H., McGaughey, R., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Jones, D.T., Hines, P., Rich, N., Lean logistics (1997) Int J Phys Distrib Logist Manag, 27 (3), pp. 153-173; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: should process based approaches be adopted? (1996) Int J Prod Econ, 46-47, pp. 423-431; Behrouzi, F., Wong, K.Y., A study on lean supply chain performance measures of SMEs in the automotive industry (2011) In: IEEE international conference on industrial engineering and engineering management; Womack, J.P., Jones, D.T., Roos, D., (1991) The machine that changed the world. How Japan’s secret weapon in the global auto wars will revolutionize western industry, , HarperPerennial, New York; Folan, P., Browne, J., A review of performance measurement: towards performance management (2005) Comput Ind, 56 (7), pp. 663-680; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: indications from a study in the automotive industry (2004) Int J Prod Econ, 89, pp. 231-243; Stainer, A., Logistics: a productivity and performance perspective (1997) Supply Chain Manag, 2 (2), pp. 53-62; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) Int J Product Perform Manag, 59 (2), pp. 163-185; Kaplan, R.S., Norton, D.P., (1996) The balanced scorecard, , Harvard Business School Press, Boston; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int J Oper Prod Manag, 21 (1), pp. 71-87; Bendoly, E., Rosenzweig, E., Stratman, J., Performance metric portfolios: a framework and empirical analysis (2007) Prod Oper Manag, 16 (2), pp. 257-276; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Eng Costs Prod Econ, 17 (1-4), pp. 253-262; Bititci, U.S., Modelling of performance measurement systems in manufacturing enterprises (1995) Int J Prod Econ, 42, pp. 137-147; Agus, A., Hajinoor, M.S., Lean production supply chain management as driver towards enhancing product quality and business performance (2012) Int J Qual Reliab Manag, 29 (1), pp. 92-121; Globerson, S., Issues in developing a performance criteria system for an organization (1985) Int J Prod Res, 23 (4), pp. 639-646; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) Int J Logist Manag, 6 (1), pp. 61-74; Seuring, S.A., Müller, M., Westhaus, M., Morana, R., Conducting a literature review: the example of sustainability in supply chains (2005) Research methodologies in supply chain management, pp. 91-106. , Kotzab H, Westhaus M, (eds), Physica-Verlag, Heidelberg; Bentz, T.K., Über Kennzahlen zur Planung, Steuerung und Kontrolle des Materialflusses für den optimalen Produktionsablauf (1974) Berlin; Syska, A., (1990) Kennzahlen für die Logistik, , Springer, Berlin; Karlsson, C., Ahlstrom, P., Assessing changes towards lean production (1996) Int J Oper Prod Manag, 16 (2), pp. 24-41; Soriano-Meier, H., Forrester, P.L., A model for evaluating the degree of leanness of manufacturing firms (2002) Int J Integr Manuf Syst, 13 (2), pp. 104-109; Arif-Uz-Zaman, K., Nazmul, A.M.M., Lean supply chain performance measurement (2014) Int J Product Perform Manag, 63 (5), pp. 588-612; Wee, H.M., Wu, S., Lean supply chain and its effect on product cost and quality: a case study on ford motor company (2009) Supply Chain Manag Int J, 14 (5), pp. 335-341; Wan, H., Chen, F.F., Rivera, L., Leanness score of value stream maps (2007) In: Proceedings of the 2007 industrial engineering research conference; Fullerton, R.R., Wempe, W.F., Lean manufacturing, non-financial performance measures, and financial performance (2009) Int J Oper Prod Manag, 29 (3), pp. 214-240; Karim, A., Arif-Uz-Zaman, K., A methodology for effective implementation of lean strategies and its performance evaluation in manufacturing organizations (2013) Bus Process Manag J, 19 (1), pp. 169-196; Leung, S., Lee, W.B., Strategic manufacturing capability pursuance: a conceptual framework (2004) Benchmarking, 11 (2), pp. 156-174; Monden, Y., (2012) Toyota production system. An integrated approach to just-in-time, 4th edn, , CRC Press, Boca Raton; Forslund, H., Measuring information quality in the order fulfilment process (2007) Int J Qual Reliab Manag, 24 (5), pp. 515-524; Bullinger, H., Kühner, M., van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) Int J Prod Res, 40 (15), pp. 3533-3543; Wouters, M., Wilderom, C., Developing performance-measurement systems as enabling formalization: a longitudinal field study of a logistics department (2008) Acc Organ Soc, 33 (4-5), pp. 488-516; Seuring, S.A., Müller, M., Westhaus, M., Morana, R., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Int J, 13 (2), pp. 128-137; Craighead, C.W., Hanna, J.B., Gibson, B.J., Meredith, J.R., Research approaches in logistics (2007) Int J Logist Manag, 18 (1), pp. 22-40; Göpfert, I., (2011) Einführung in die Logistik. Supply Chain Management und Logistikcontrolling, , Vahlen, Franz; Chan, F., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Manag, 8 (3), pp. 209-223; Cabral, I., Grilo, A., Cruz-Machado, V., A decision-making model for lean, agile, resilient and green supply chain management (2012) Int J Prod Res, 50 (17), pp. 4830-4845; Supply Chain Council, (2008) SCOR. Supply chain operations reference model, , Supply Chain Council, USA; Verein Deutscher Ingenieure e.V (2012) Düsseldorf 03.100, p. 50; Lohmann, C., Fortuin, L., Wouters, M., Designing a performance measurement system: a case study (2004) Eur J Oper Res, 156, pp. 267-286; Annual Meeting: Doing Well by Doing Good (2007) AOM, p. 2007; McCutcheon, D., Meredith, J.R., Conducting case study research in operations management (1993) J Oper Manag, 11, pp. 239-256; Wang, X., Zhang, J., System measurement research on supply chain (2010) Proceedings—3rd international conference on information management, innovation management and industrial engineering, ICIII, 2010, p. 4; Supplier manual logistics (2014) Global logistics standards and processes of continental automotive, , http://www.conti-online.com/www/download/automotive_de_en/general/download/supplier_manual_logistics_en.pdf; Kayakutlu, G., Buyukozkan, G., Assessing performance factors for a 3PL in a value chain (2011) Int J Prod Econ, 131 (2), pp. 441-452; Klug, F., Aktuelle trends in der Automobillogistik (2011) Zeitschrift für die gesamte Wertschöpfungskette der Automobilwirtschaft (ZfAW), 3, pp. 60-68; Göbel, W., Effiziente Fertigfahrzeuglogistik im Spannungsfeld von Nachfrageverschiebungen, ökologischen Ansprüchen und Wirtschaftlichkeit (2014) Garching bei München; Günthner, W.A., Dörnhöfer, M., Einsatz von Kennzahlensystemen in der Automobillogistik. Aktueller Entwicklungsstand und Handlungsbedarf (2015) Lehrstuhl für Fördertechnik Materialfluß Logistik (fml) TU München, , Garching b, München; Forslund, H., Jonsson, P., Obstacles to supply chain integration of the performance management process in buyer-supplier dyads: the buyer’s perspective (2009) Int J Oper Prod Manag, 29 (1), pp. 77-95},
correspondence_address1={Dörnhöfer, M.; Lehrstuhl für Fördertechnik Materialfluss Logistik, Germany; email: doernhoefer@fml.mw.tum.de},
publisher={Springer Verlag},
issn={1865035X},
language={English},
abbrev_source_title={Logist. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Balaman20161179,
author={Balaman, Ş.Y.},
title={Investment planning and strategic management of sustainable systems for clean power generation: An ε-constraint based multi objective modelling approach},
journal={Journal of Cleaner Production},
year={2016},
volume={137},
pages={1179-1190},
doi={10.1016/j.jclepro.2016.07.202},
note={cited By 37},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991475227&doi=10.1016%2fj.jclepro.2016.07.202&partnerID=40&md5=d11d8e057efebb3c0dab1ae4c3ead1f2},
affiliation={Department of Industrial Engineering, Dokuz Eylul University, Tinaztepe Yerleskesi, Buca, Izmir, 35160, Turkey},
abstract={Biomass based energy production has been considered as a part of the solution to energy crisis, which is mainly caused by diminishing fossil fuel resources and environmental pollution from traditional fossil fuel based energy production systems. Therefore, it is important to design sustainable and effective systems for biomass based energy production to provide competitive advantage on fossil fuel resourced systems. This study develops a novel optimization model to aid investment planning and strategic management of biomass based clean power generation systems. The model integrates the location, capacity and technology decisions to find the optimal combination of bioenergy production systems to meet electricity demand of particular regions and accounts for multiple biomass types and power technologies. The modelling approach and data analysis are presented to outline the important characteristics of the problem for minimization of the supply chain cost and minimization of the greenhouse gas (GHG) emissions simultaneously. To handle the multi objective problem efficiently, an integrated approach based on fuzzy decision making and ε-constraint method is proposed and used, considering both sustainability aspects and uncertainties in the system parameters. The viability of the proposed approach is explored on a case study of İzmir region in Turkey. Different supply chain configuration alternatives are provided for the case study region considering various weights for objective functions representing relative importance of each objective. Corresponding supply chain performance measures in terms of total cost and GHG emissions are proposed and discussed for each configuration alternative. Further enviro-economic analyses denote that discounted investment cost and GHG emissions associated with energy production activities receive the biggest shares in the total cost and in the total GHG emissions, respectively. The government and private investors can employ the model and solution algorithm to design the most cost effective and environment friendly supply chain, to monitor the economic and environmental performance of the current biomass based supply chains and identify policies to support a viable, profitable and eco-friendly energy industry. © 2016 Elsevier Ltd},
author_keywords={Bio-based supply chains;  Clean power generation;  Investment planning;  Multiobjective optimization;  Strategic management},
keywords={Biomass;  Competition;  Cost benefit analysis;  Cost effectiveness;  Costs;  Decision making;  Economic analysis;  Economics;  Electric power system planning;  Energy policy;  Environmental impact;  Environmental management;  Fossil fuel deposits;  Fossil fuels;  Fuels;  Gas emissions;  Greenhouse gases;  Multiobjective optimization;  Optimization;  Planning;  Privatization;  Strategic planning;  Supply chains;  Sustainable development, Bio-based;  Clean power generations (CPG);  Economic and environmental performance;  Energy production systems;  Epsilon-constraint method;  Investment planning;  Strategic management;  Supply chain configuration, Investments},
references={Ahmadi, A., Ahmadi, M.R., Nezhad, A.E., A lexicographic optimization and augmented ε-constraint technique for short-term environmental/economic combined heat and power scheduling (2014) Elect. Pow. Comp. Syst., 42 (9), pp. 945-958; Andersen, F., Iturmendi, F., Espinosa, S., Diaz, M.S., Optimal design and planning of biodiesel supply chain with land competition (2012) Comput. Chem. Eng., 47, pp. 170-182; Awudu, I., Zhang, J., Uncertainties and sustainability concepts in biofuel supply chain management: a review (2012) Renew. Sustain. Energy Rev., 16, pp. 1359-1368; Aviso, K.B., Tan, R.R., Culaba, A.B., Cruz, J.B., Jr., Fuzzy input–output model for optimizing eco-industrial supply chains under water footprint constraints (2011) J. Clean. Prod., 19, pp. 187-196; Chankong, V., Haimes, Y.Y., Multiobjective Decision Making: Theory and Methodology (1983), North-Holland New York; Delivand, M.K., Cammerino, A.R.B., Garofalo, P., Monteleone, M., Optimal locations of bioenergy facilities, biomass spatial availability, logistics costs and GHG (greenhouse gas) emissions: a case study on electricity productions in South Italy (2015) J. Clean. Prod., 99, pp. 129-139; Do, T.X., Techno-economic analysis of power plant via circulating fluidized-bed gasification from woodchips (2014) Energy, 70, pp. 547-560; (2009), ec.europa.eu/clima/policies/ets/index_en.html, Emissions Trading System (). (); Esmaili, M., Amjady, N., Shayanfar, H.A., Multi-objective congestion management by modified augmented e-constraint method (2011) Appl. Energy, 88 (3), pp. 755-756; (2000), ec.europa.eu/clima/policies/eccp/index_en.html, European Climate Change Programme (). (); Faaij, A.P.C., Bio-energy in Europe: changing technology choices (2006) Energy Policy, 34, pp. 322-342; Giarola, S., Zamboni, A., Bezzo, F., Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries (2011) Comput. Chem. Eng., 35 (9), pp. 1782-1797; Giarola, S., Zamboni, A., Bezzo, F., Environmentally conscious capacity planning and technology selection for bioethanol supply chains (2012) Renew. Energy, 43, pp. 61-72; Giarola, N., Shah, A., Bezzo, F., A comprehensive approach to the design of ethanol supply chains including carbon trading effects (2012) Biores. Technol., 107, pp. 175-185; Haimes, Y.Y., Lasdon, L.S., Wismer, D.A., On a bicriterion formulation of the problems of integrated system identification and system optimization (1971) IEEE Trans. Syst. Man. Cybern. SMC, 1 (3), pp. 296-297; Kindt, V.T., Billaut, J.-C., Multicriteria scheduling problem: a survey (2001) RAIRO Oper. Res., 35 (2), pp. 143-163; (1997), unfccc.int/kyoto_protocol/items/2830.php, Kyoto Protocol (). (); Lam, H.L., Ng, W.P.Q., Ng, R.T.L., Green strategy for sustainable waste-to-energy supply chain (2013) Energy, 57, pp. 4-16; Li, Q., Hu, G., Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification (2014) Energy, 74, pp. 576-584; Lin, T., Rodríguez, L.F., Shastri, Y.N., Hansen, A.C., King, K.C., Integrated strategic and tactical biomass-biofuel supply chain optimization (2014) Bioresour. Technol., 156, pp. 256-266; Lu, X., Withers, M.R., Seifkar, N., Biomass logistics analysis for large scale biofuel production: case study of loblolly pine and switchgrass (2015) Biores. Technol., 183, pp. 1-9; Marufuzzaman, M., Li, X., Yu, F., Zhou, F., Supply chain design and management for syngas production (2016) ACS Sustain. Chem. Eng., 4 (3), pp. 890-900; Marvin, W.A., Schmidt, L.D., Benjaafar, S., Optimization of a lignocellulosic biomass-to-ethanol supply chain (2012) Chem. Eng. Sci., 67 (1), pp. 68-79; Marufuzzaman, M., Ekşioğlu, S.D., Hernandez, R., Environmentally friendly supply chain planning and design for biodiesel production via wastewater sludge (2014) Transp. Sci., pp. 1-20. , Articles in Advance; Mavrotas, G., Effective implementation of the e-constraint method in Multi-Objective mathematical Programming problems (2009) Appl. Math. Comput., 213, pp. 455-465; Miettinen, K., Nonlinear Multiobjective Optimization. USA (1998), Kluwer Boston, MA; Moghaddam, A.A., Seifi, A., Niknam, T., Multi-operation management of a typical micro-grids using particle swarm optimization: a comparative study (2012) Renew. Sustain. Energy Rev., 16 (2), pp. 1268-1281; Moghaddam, A.A., Seifi, A., Niknam, T., Pahlavani, M.R., Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source (2011) Energy, 36 (11), pp. 6490-6507; Reza Norouzi, M., Ahmadi, A., Esmaeel, Nezhad, A., Ghaedi, A., Mixed integer programming of multi-objective security constrained hydro/thermal unit commitment (2014) Renew. Sustain. Energy Rev., 29, pp. 911-923; Rezvani, A., Gandomkar, M., Izadbakhsh, M., Ahmadi, A., Environmental/economic scheduling of a micro-grid with renewable energy resources (2015) J. Clean. Prod., 87, pp. 216-226; Santibañez-Aguilar, J.E., Optimal planning of a biomass conversion system considering economic and environmental aspects (2011) Ind. Eng. Chem. Res., 50 (14), pp. 8558-8570; Searcy, E., Flynn, P., Ghafoori, E., Kumar, A., The relative cost of biomass energy transport (2007) Appl. Biochem. Biotecnol., 136-140, pp. 639-652; Shabani, N., Sowlati, T., A hybrid multi-stage stochastic programming-robust optimization model for maximizing the supply chain of a forest-based biomass power plant considering uncertainties (2016) J. Clean. Prod., 112, pp. 3285-3293; Sharifzadeh, M., Garcia, M.C., Shah, N., Supply chain network design and operation: systematic decisionmaking for centralized, distributed, and mobile biofuel production using mixed integer linear programming (MILP) under uncertainty (2015) Biomass Bioenergy, 81, pp. 401-414; UK Department of Energy & Climate Change, Government emission conversion factors for greenhouse gas company reporting: Conversion factors 2015 - full set (2015); Xie, F., Huang, Y., Eksioglu, S., Integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California (2014) Biores. Technol, 152, pp. 15-23; Yılmaz, S., Selim, H., A review on the methods for biomass to energy conversion systems design (2013) Renew. Sustain. Energy Rev., 25, pp. 420-430; You, F., Wang, B., Life cycle optimization of biomass-to-liquid supply chains with distributed–centralized processing networks (2011) Ind. Eng. Chem. Res., 50 (17), pp. 10102-10127; Zhang, L., Hu, G., Supply chain design and operational planning models for biomass to drop-in fuel production (2013) Biomass Bioenergy, 58, pp. 238-250; Zhang, Y., Wright, M.M., Product selection and supply chain optimization for fast pyrolysis and biorefinery system (2014) Ind. Eng. Chem. Res., 53 (51), pp. 19987-19999},
publisher={Elsevier Ltd},
issn={09596526},
coden={JCROE},
language={English},
abbrev_source_title={J. Clean. Prod.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tippayawong2016,
author={Tippayawong, K.Y. and Niyomyat, N. and Sopadang, A. and Ramingwong, S.},
title={Factors affecting green supply chain operational performance of the Thai auto parts industry},
journal={Sustainability (Switzerland)},
year={2016},
volume={8},
number={11},
doi={10.3390/su8111161},
art_number={1161},
note={cited By 22},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85001099644&doi=10.3390%2fsu8111161&partnerID=40&md5=efd869e21b01c9629b5e7845e214da71},
affiliation={Center of Excellence in Logistics and Supply Chain Management, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand},
abstract={In this work, operational performance in the green supply chain management (SCM) of the Thai auto parts industry was investigated. A green supply chain performance measurement (GSPM) model was developed from the combination of various concepts including an SCM logistics scorecard, a supply chain operations reference model, a balance scorecard, and green supply chain management. The GSPM has been designed for use as a self-evaluation tool focusing on five decisive areas, or factors, and 28 sub-factors. A factor analysis was conducted using the survey results of the GSPM in order to identify significant factors that represent the green supply chain operation performance. Grouped as three major factors, namely green procurement, green transportation, and green manufacturing; reverse logistics and eco-design; and reuse and recycle of manufacturing, their significance and impact on the auto parts industry in Thailand were highlighted. Specifically, the factor of green procurement, green transportation, and green manufacturing, as major factor 1, in relation with the factor of reverse logistics and eco-design, as major factor 2, were found to have a strong positive relationship with the asset turnover ratio.. © 2016 by the authors.},
author_keywords={Factor analysis;  Financial performance;  Supply chain management;  Sustainable logistics},
keywords={automobile industry;  design method;  financial system;  logistics;  performance assessment;  supply chain management;  sustainability, Thailand},
references={Clemens, B., Douglas, T.J., Does coercion drive firms to adopt "voluntary" green initiatives? Relationships among coercion, superior firmresources, and voluntary green initiatives (2006) J. Bus. Res, 59, pp. 483-491; Vachon, S., Klassen, R., Green project partnership in the supply chain: The case of the package printing industry (2006) J. Clean. Prod, 14, pp. 661-671; Arimura, T.H., Darnall, N., Katayama, H., Is ISO 14001 a gateway to more advanced voluntary action? The case of green supply chain management (2011) J. Environ. Econ. Manag, 61, pp. 170-182; Lun, Y., Lai, K.H., Ng, C.T., Wong, C., Cheng, T.C.E., Editorial: Research in shipping and transport logistics (2011) Int. J. Shipp. Transp. Logist, 3, pp. 1-5; Carter, C.R., Kale, R., Grimm, C.M., Environmental purchasing and firmperformance: An empirical investigation (2000) Transp. Res. Part E Logist. Transp. Rev, 36, pp. 219-228; Wook Kim, S., Effects of supply chain management practices, integration and competition capability on performance (2006) Supply Chain Manag. Int. J, 11, pp. 241-248; Jiang, Y., Zhou, L.-J., Study on green supply chain management based on circular economy (2012) Phys. Procedia, 25, pp. 1682-1688; Zhu, Q., Sarkis, J.B., Lai, K., Green supply chain management: Pressures, practices and performance within the Chinese automobile industry (2007) J. Clean. Prod, 15, pp. 1041-1052; Eltayeba, T., Zailani, S., Ramayah, T., Green supply chain initiatives among certified companies in Malaysia and environmental sustainability: Investigating the outcomes (2011) J. Resour. Conserv. Recycl, 55, pp. 495-506; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) J. Oper. Manag, 25, pp. 1075-1082; Sakis, J., Zhu, Q., Lai, K., An organizational theoretic review of green supply chain management literature (2011) Int. J. Prod. Econ, 130, pp. 1-15; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) Int. J. Manag. Rev, 9, pp. 53-80; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Manag. Int. J, 9, pp. 23-29; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ, 87, pp. 333-347; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng, 53, pp. 43-62; Wiersma, E., Forwhich purposes domanagers use balanced scorecards? (2009) An empirical study. Manag. Account. Res, 20, pp. 239-251; Yaibuathet, K., Enkawa, T., Suzuki, S., Supply chain operational performance and its influential factors: Cross national analysis (2007) J. Jpn. Ind. Manag. Assoc, 57, pp. 473-482; Arashida, K., Enkawa, T., Hamasaki, A., Suzuki, S., Developing the SCM logistics scorecard and analyzing its relation to the managerial performance (2004) J. Jpn. Ind. Manag. Assoc, 15, pp. 95-103; Bhateja, A.K., Babbar, R., Singh, S., Sachdeva, A., Study of green supply chain management in the Indian manufacturing industries: A literature review cum an analytical approach for the measurement of performance (2011) Int. J. Comput. Eng. Manag, 13, pp. 84-99; Ninlawan, C., Seksan, P., Tossapol, K., Pilada, W., The implementation of green supply chain management practices in electronics industry (2010) Proceedings of the International Multi-conference of Engineers and Computer Scientists (IMECS), pp. 17-19. , Hong Kong, China, 17-19 March; D'Avanzo, R., von Lewinski, H., van Wassenhove, L.N., The link between supply chain and financial performance (2003) Supply Chain Manag. Rev, 7, pp. 40-47; Reiner, G., Hofmann, P., Efficiency analysis of supply chain processes (2006) Int. J. Prod. Res, 44, pp. 5065-5087; Hendricks, K.B., Singhal, V.R., Stratman, J.K., The impact of enterprise systems on corporate performance: A study of ERP, SCM, and CRM system implementations (2007) J. Oper. Manag, 25, pp. 65-82; Zhu, Q., Geng, Y., Fujita, T., Hashimoto, S., Green supply chain management in leading manufacturers: Case studies in Japanese large companies (2010) Manag. Res. Rev, 33, pp. 380-392; Tracey, M., Lim, J.S., Vonderembse, M.A., The impact of supply-chain management capabilities on business performance (2005) Supply Chain Manag. Int. J, 10, pp. 179-191; Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships (2003) J. Oper. Manag, 21, pp. 523-539; Raymond, P.C., Lopez, J., Marche, S., Perron, G.M., Wright, R., Influences practices and opportunities for environmental supply chain management in Nova Scotia SMEs (2008) J. Clean. Prod, 16, pp. 1561-1570; Chien, M.K., Shih, L.H., An empirical study of the implementation of green supply chain management practices in the electrical and electronic industry and their relation to organizational performances (2007) Int. J. Environ. Sci. Technol, 4, pp. 383-394; Nishitani, K., An empirical study of the initial adoption of ISO 14001 in Japanese manufacturing firms (2009) Ecol. Econ, 68, pp. 669-679; Chiou, T., Chan, H., Lettice, F., Chung, S., The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan (2011) Transp. Res. Part E, 47, pp. 822-836; Dehning, B., Richardson, V.J., Zmud, R.W., The financial performance effects of IT-based supply chain management systems in manufacturing firms (2007) J. Oper. Manag, 25, pp. 806-824},
correspondence_address1={Tippayawong, K.Y.; Center of Excellence in Logistics and Supply Chain Management, Thailand; email: korrakot@eng.cmu.ac.th},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Morioka2016123,
author={Morioka, S.N. and Carvalho, M.M.},
title={Measuring sustainability in practice: exploring the inclusion of sustainability into corporate performance systems in Brazilian case studies},
journal={Journal of Cleaner Production},
year={2016},
volume={136},
pages={123-133},
doi={10.1016/j.jclepro.2016.01.103},
note={cited By 77},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962089346&doi=10.1016%2fj.jclepro.2016.01.103&partnerID=40&md5=e82b478d9c9d804ce73d199bc0259031},
affiliation={Polytechnic School – University of São Paulo, Production Engineering, Av. Prof. Almeida Prado, 128 Tr.2 Biênio 2o. andar, São Paulo, SP  05508-900, Brazil; Polytechnic School – University of São Paulo, Brazil},
abstract={Given the challenge of measuring corporate sustainability in practice, the aim of the present exploratory research is to investigate the incorporation of sustainability in corporate performance measurement systems, towards sustainability performance measurement system (SPMS). More specifically, the research seeks to explore the factors that affect the interaction between sustainability indicators regarding their relative priority for decision making and to investigate how firms include sustainability indicators into their corporate performance measurement systems. To address these objectives, cross-sector case studies were conducted in five firms located in Brazil. Data were collected based on semi-structured interviews and triangulated with published reports and internal documents. The results show four performance measurement systems that encompass sustainability indicators: (a) periodic performance measurement system of a specific area/department; (b) individual performance assessment; (c) sustainability reporting; and (d) project assessment. The paper value lies in bringing a structured view of integrating sustainability in a corporate performance measurement system, based on empirical evidence. However, further research is still needed to develop a more integrative and dynamic SPMS encompassing both leading and lagging indicators to better understand the priorities, interactions and tradeoffs between sustainability indicators. © 2016 Elsevier Ltd},
author_keywords={Case study;  Corporate sustainability;  Performance measurement system;  Sustainability indicators;  Tradeoffs},
keywords={Commerce;  Decision making;  Environmental protection;  Supply chain management, Corporate-sustainability;  Individual performance;  Performance measurement system;  Semi structured interviews;  Sustainability indicators;  Sustainability performance;  Sustainability reporting;  Tradeoffs, Sustainable development},
references={Angell, L.C., Klassen, R.D., Integrating environmental issues into the mainstream: an agenda for research in operations management (1999) J. Oper. Manag., 17, pp. 575-598; Azapagic, A., Developing a framework for sustainable development indicators for the mining and minerals industry (2004) J. Clean. Prod., 12, pp. 639-662; Azapagic, A., Perdan, S., Indicators of sustainaible development for industry: a general framework (2000) Process Saf. Environ. Prot., 78, pp. 243-261; Azzone, G., Masella, C., Bertele, H., Design of performance measures for time-based companies (1991) Int. J. Oper. Prod. Manag., 11, pp. 77-85; Balezentis, A., Balezentis, T., Framework of strategic management model for strategy Europe 2020: diachronic analysis and proposed guidelines (2011) Eng. Econ., 22, pp. 271-282; Bititci, U.S., Turner, U., Begemann, C., Turner, T., Dynamics of performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20, pp. 692-704; Bocken, N.M.P., Short, S.W., Rana, P., Evans, S., A literature and practice review to develop sustainable business model archetypes (2014) J. Clean. Prod., 65, pp. 42-56; Bolis, I., Brunoro, C.M., Sznelwar, L.I., Mapping the relationships between work and sustainability and the opportunities for ergonomic action (2014) Appl. Ergon., 45, pp. 1225-1239; Bolis, I., Morioka, S.N., Sznelwar, L.I., When sustainable development risks losing its meaning. Delimiting the concept with a comprehensive literature review and a conceptual model (2014) J. Clean. Prod., 83, pp. 7-20; Boons, F., Lüdeke-Freund, F., Business models for sustainable innovation: state-of-the-art and steps towards a research agenda (2013) J. Clean. Prod., 45, pp. 9-19; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) Int. J. Oper. Prod. Manag., 20, pp. 754-771; Brones, F., Carvalho, M.M.D., From 50 to 1: integrating literature toward a systemic ecodesign model (2014) J. Clean. Prod., 96, pp. 44-57; Brones, F., Carvalho, M.M.D., Zancul, E.D.S., Ecodesign in project management: a missing link for the integration of sustainability in product development? (2014) J. Clean. Prod., 80, pp. 106-118; Callan, S.J., Thomas, J.M., Corporate financial performance and corporate social performance: an update and reinvestigation (2009) Corp. Soc. Responsib. Environ. Manag., 16, pp. 61-78; Chen, C., Chiou, I., Remediation of heavy metal-contaminated farm soil using management framework (2008) Environ. Eng. Sci., 25, pp. 11-32; Chen, C.-H., Liu, W.-L., Leu, H.-G., Sustainable water quality management framework and a strategy planning system for a river basin (2006) Environ. Manag, 38, pp. 952-973; Cooper, R.G., Edgett, S.J., Kleinschmidt, E.J., New product portfolio management: practices and performance (1999) J. Prod. Innov. Manag., 16; Crittenden, V.L., Crittenden, W.F., Ferrell, L.K., Ferrell, O.C., Pinney, C.C., Market-oriented sustainability: a conceptual framework and propositions (2011) J. Acad. Mark. Sci., 39, pp. 71-85; Dowell, G., Hart, S., Yeung, B., Do corporate global environmental standards create or destroy market value? (2000) Manag. Sci., 46, pp. 1059-1074; Eisenhardt, K.M., Building theories from case study research (1989) Acad. Manag. Rev., 14, p. 532; Eisenhardt, K.M., Graebner, M.E., Theory building from cases: opportunities and challenges (2007) Acad. Manag. J., 50, pp. 25-32; Elkington, J., Cannibals with Forks: the Triple Bottom Line of the 21st Century Business (1997), New Society Publishers Oxford; Epstein, M.J., Making Sustainability Work: Best Practices in Managing and Measuring Corporate Social, Environmental and Economic Impacts (2008), 1st ed. Greenleaf Publishing Sheffield; Epstein, M.J., Widener, S.K., Facilitating sustainable development decisions: measuring stakeholder reactions (2011) Bus. Strateg. Environ., 123, pp. 107-123; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecol. Econ., 70, pp. 1088-1100; Figge, F., Hahn, T., Sustainable value added: measuring corporate contributions to sustainability beyond eco-efficiency (2004) Ecol. Econ., 48, pp. 173-187; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard: linking sustainability management to business strategy (2002) Bus. Strateg. Environ., 11, pp. 269-284; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard: theory and application of a tool for value-based (2002) Sustain. Manag., pp. 1-32; Fitzgerald, L., Johnston, R., Brignall, T.J., Silvestro, R., Voss, C., Performance Measurement in Service Businesses (1991), The Chartered Institute of Management Accountants London; Flynn, B.B., Kakibara, S.S., Schroeder, R.G., Bates, K.A., Flynn, E.J., Empirical research methods in operations management (1990) J. Oper. Manag, 9, pp. 250-284; Freeman, R.E., Strategic Management: a Stakeholder Approach (1984), Pitman Boston; Glavič, P., Lukman, R., Review of sustainability terms and their definitions (2007) J. Clean. Prod., 15, pp. 1875-1885; Gonzalez-Benito, J., Gonzalez-Benito, O., An analysis of the relationship between environmental motivations and ISO14001 certification (2005) Br. J. Manag., 16, pp. 133-148; Hahn, T., Wagner, M., Sustainability Balanced Scorecard (2001); Hardin, G., The tragedy of commons (1968) Science, 162, pp. 1243-1248. , (80-.); Hauser, J.R., Katz, G.M., Metrics: you are what you measure! (1998) Eur. Manag. J., 16, pp. 516-528; Hillman, A.M.Y.J., Keim, G.D., Shareholder value, stakeholder management, and social issues: what's the bottom line? (2001) Strateg. Manag. J., 22, pp. 125-139; Holling, C.S., The resilience of terrestrial ecosystems: local surprise and global change (1986) Sustainable Development of the Biosphere1, pp. 67-109. , W.C. Clark R.E. Munn Cambridge University Press Cambridge; Hopwood, B., Mellor, M., O'Brien, G., Sustainable development: mapping different approaches (2005) Sustain. Dev., 13, pp. 38-52; Hubbard, G., Measuring organizational performance: beyond the triple bottom line (2009) Bus. Strateg. Environ., 19, pp. 177-191; Jiménez, J.B., Lorente, J.J.C., Environmental performance as an operations objective (2001) Int. J. Oper. Prod. Manag., 21, pp. 1553-1572; Jorgensen, T.H., Towards more sustainable management systems: through life cycle management and integration (2008) J. Clean. Prod., 16, pp. 1071-1080; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance (1992) Harv. Bus. Rev., 70, pp. 71-79; Karwan, K.R., Markland, R.E., Integrating service design principles and information technology to improve delivery and productivity in public sector operations: the case of the South Carolina DMV (2006) J. Oper. Manag., 24, pp. 347-362; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Manag. Acc., 70, pp. 45-50; Ketokivi, M., Choi, T., Renaissance of case research as a scientific method (2014) J. Oper. Manag, 32, pp. 232-240; Kleindorfer, P., Singhal, K., van Wassenhove, L.N., Sustainable operations management (2005) Prod. Oper. Manag., 14, pp. 482-492; Labuschagne, C., Brent, A.C., van Erck, R.P.G., Assessing the sustainability performances of industries (2005) J. Clean. Prod., 13, pp. 373-385; Lee, K.-H., Saen, R.F., Measuring corporate sustainability management: a data envelopment analysis approach (2012) Int. J. Prod. Econ., 140, pp. 219-226; Lindsey, T.C., Sustainable principles: common values for achieving sustainability (2011) J. Clean. Prod., 19, pp. 561-565; Lo, S., Sheu, H., Is corporate sustainability a value- increasing strategy for business? (2007) Corp. Gov., 15, pp. 345-358; López, M.V., Garcia, A., Rodriguez, L., Sustainable development and corporate performance: a study based on the Dow Jones Sustainability Index (2007) J. Bus. Ethics, 75, pp. 285-300; Lozano, R., Envisioning sustainability three-dimensionally (2008) J. Clean. Prod., 16, pp. 1838-1846; Lozano, R., Towards better embedding sustainability into companies’ systems: an analysis of voluntary corporate initiatives (2012) J. Clean. Prod., 25, pp. 14-26; Lundberg, K., Balfors, B., Folkeson, L., Framework for environmental performance measurement in a Swedish Public Sector Organization (2009) J. Clean. Prod., 17, pp. 1017-1024; Lynch, R.I., Cross, F.K., Measure Up: the Essential Guide to Measuring Business Performance (1991), Mandarin London; Marcus, J., Kurucz, E.C., Colbert, B.A., Conceptions of the business-society-nature interface: implications for management scholarship (2010) Bus. Soc., 49, pp. 402-438; Meadows, D.H., Meadows, D.L., Randers, J., Behrens, W.W., III, The Limits to Growth: a Report for the Club of Rome's Project on the Predicament of Mankind (1972), fifth ed. Universe Books New York; Meyar-Naimi, H., Vaez-Zadeh, S., Developing a DSR-HNS policy making framework for electric energy systems (2012) Energy Policy, 42, pp. 616-627; Miles, M.B., Huberman, A.M., Qualitative Data Analysis (1994), second ed. Sage Publications London; Morioka, T., Saito, O., Yabar, H., The pathway to a sustainable industrial society: initiative of the Research Institute for Sustainability Science (RISS) at Osaka University (2006) Sustain. Sci., 1, pp. 65-82; Neely, A., Measurement of Business Performance: Why, What and How (1998), Economist Books London; Neely, A., The evolution of performance measurement research: developments in the last decade and a research agenda for the next (2005) Int. J. Oper. Prod. Manag., 25, pp. 1264-1277; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Meas. Bus. Excel., 5, pp. 6-12; Neely, A., Gregory, M., Platts, K., A literature review and research agenda: performance measurement system design (1995) Int. J. Oper. Prod. Manag., 15, pp. 80-116; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: developing and testing a process-based approach (2000) Int. J. Oper. Prod. Manag., 20, pp. 1119-1145; Nidumolu, R., Prahalad, C.K., Rangaswami, M.R., Why sustainability is now the key driver of innovation (2009) Harv. Bus. Rev., 87, pp. 1-10; Nielsen, P.H., Wenzel, H., Integration of environmental aspects in product development: a stepwise procedure based on quantitative life cycle assessment (2002) J. Clean. Prod., 10, pp. 247-257; Core Set of Indicators for Environmental Performance Reviews (1993), (Paris); Peloza, J., Shang, J., How can corporate social responsibility activities create value for stakeholders? a systematic review (2011) J. Acad. Mark. Sci., 39, pp. 117-135; Perrini, F., Tencati, A., Sustainability and Stakeholder Management: the need for new corporate performance evaluation and reporting systems (2006) Bus. Strateg. Environ., 15, pp. 296-308; Porter, M., Kramer, M., Strategy and society (2006) Harv. Bus. Rev., pp. 4-6; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) Int. J. Oper. Prod. Manag., 25, pp. 898-916; Rudd, M.A., An institutional framework for designing and monitoring ecosystem-based fisheries management policy experiments (2004) Ecol. Econ., 48, pp. 109-124; Savitz, A.W., Weber, K., The Triple Bottom Line: How Today's Best-run Companies Are Achieving Economic, Social and Environmental Success – and How You Can Too (2006), Jossey-Bass San Francisco; Savitz, A.W., Weber, K., The sustainability sweet spot (2007) Environ. Qual. Manag., 17, pp. 17-28; Schneider, A., Meins, E., Two dimensions of corporate sustainability assessment: towards a comprehensive framework (2012) Bus. Strateg. Environ., 21, pp. 211-222; Searcy, C., Corporate sustainability performance measurement systems: a review and research agenda (2012) J. Bus. Ethics, 107, pp. 239-253; Silveira, G.D., Slack, N., Exploring the trade-off concept (2001) Int. J. Oper. Prod. Manag., 21, pp. 949-964; Sprengel, D.C., Busch, T., Stakeholder engagement and environmental strategy – the case of climate change (2011) Bus. Strateg. Environ., 20, pp. 351-364; Thierry, M., Salomon, M., Nunen, J.V., Wassenhove, L.V., Strategic issues in product recovery management (1995) Calif. Manag. Rev., 37, pp. 114-135; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) Int. J. Oper. Prod. Manag., 31, pp. 1287-1310; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain (2008) Int. J. Prod. Econ., 111, pp. 299-315; Valdes-Vasquez, R., Klotz, L.E., Social sustainability considerations during planning and design: framework of processes for construction projects (2013) J. Constr. Eng. Manag., 139, pp. 80-89; van Bommel, H.W.M., A conceptual framework for analyzing sustainability strategies in industrial supply networks from an innovation perspective (2011) J. Clean. Prod., 19, pp. 895-904; Veleva, V., Ellenbecker, M., Indicators of sustainable production: framework and methodology (2001) J. Clean. Prod., 9, pp. 519-549; Vergragt, P.J., Quist, J., Backcasting for sustainability: introduction to the special issue (2011) Technol. Forecast. Soc. Change, 78, pp. 747-755; Vitezić, N., Correlation between social responsibility and efficient performance in croatian enterprises (2011) Zb. Rad. Ekon. Fak. Rij, 29, pp. 423-442; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) Int. J. Oper. Prod. Manag., 22, pp. 195-219; WCED, Report of the World Commission on Environment and Development: Our Common Future (1987), (United Nations, Geneva); Yongvanich, K., Guthrie, J., An extended performance reporting framework for social and environmental accounting (2006) Bus. Strateg. Environ., 15, pp. 309-321},
correspondence_address1={Morioka, S.N.; Polytechnic School – University of São Paulo, Av. Prof. Almeida Prado, 128 Tr.2 Biênio 2o. andar, Brazil; email: sanmorioka@gmail.com},
publisher={Elsevier Ltd},
issn={09596526},
coden={JCROE},
language={English},
abbrev_source_title={J. Clean. Prod.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Balfaqih2016135,
author={Balfaqih, H. and Nopiah, Z.M. and Saibani, N. and Al-Nory, M.T.},
title={Review of supply chain performance measurement systems: 1998–2015},
journal={Computers in Industry},
year={2016},
volume={82},
pages={135-150},
doi={10.1016/j.compind.2016.07.002},
note={cited By 104},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032070431&doi=10.1016%2fj.compind.2016.07.002&partnerID=40&md5=9ef48bdecbdec482d7df94619cf18d7c},
affiliation={Faculty of Engineering and Built Environment, National University of Malaysia (UKM), BangiSelangor  43600, Malaysia; College of Engineering, Effat University, Jeddah, Saudi Arabia; Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA  02139-4307, United States},
abstract={In today's competitive business environment, supply chain performance is one of the most critical issues in various industries. It is argued that supply chain performance measurement is fundamental to efficient supply chain management. Over the past two decades, several frameworks and systems have been developed to meet this need. This study reviews the literature in the field of supply chain performance measurement and assembles an overview of those systems, approaches, techniques and criteria. For this purpose, 83 of 374 related articles from 1998 to 2015 were selected for final review using the Scopus and ISI databases. Findings disclose that performance measurement in supply chain contexts is still a fruitful area of research. The study also provides an overview of the performance measures employed in supply chain systems. These findings present a solid basis for future academic and practitioner work in the field of supply chain performance measurement. © 2016 Elsevier B.V.},
author_keywords={Frameworks;  Models;  Performance measurement systems;  Supply chain management},
keywords={Chains;  Models, Competitive business;  Frameworks;  Performance measure;  Performance measurement system;  Performance measurements;  Supply chain performance;  Supply chain performance measurements;  Supply chain systems, Supply chain management},
references={Abu Bakar, A.H., Lukman Hakim, I., Chong, S.C., Lin, B., Measuring supply chain performance among public hospital laboratories (2010) Int. J. Product. Perform. Manage., 59 (1), pp. 75-97; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2015) J. Clean. Prod., 86, pp. 360-377; Algren, C., Kotzab, H., State of the art of supply chain performance measurement in Danish industrial companies (2011) In 23rd Annual NOFOMA Conference; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decis. Support Syst., 42, pp. 283-301; Aramyan, L.H., Oude Lansink, A.G., Van Der Vorst, J.G., Van Kooten, O., Performance measurement in agri-food supply chains: a case study (2007) Supply Chain Manage.: Int. J., 12 (4), pp. 304-315; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: a literature review (2010) Int. J. Prod. Res., 48 (17), pp. 5137-5155; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) Int. J. Bus. Perform. Supply Chain Modell., 1 (2), pp. 115-128; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighbourhood rough sets (2012) Int. J. Prod. Res., 50 (9), pp. 2484-2500; Balfaqih, H., Yunus, B., Supply chain performance in electronics manufacturing industry (2014) Appl. Mech. Mater., 554, pp. 633-637; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Manage.: Int. J., 16 (1), pp. 20-31; Bass, F.M., Comments on a new product growth for model consumer durables the bass model (2004) Manage. Sci., 50, pp. 1833-1840. , (12_supplement); Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage., 19 (3), pp. 275-292; Berrah, L., Clivillé, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Comput. Ind., 58 (7), pp. 709-719; Berrah, L., Vernadat, F., Towards a system-based model for overall performance evaluation in a supply chain context (2012) Open Ind. Manuf. Eng. J., 5 (1525-1874), pp. 8-18; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: a balanced scorecard approach (2010) Facilities, 28 (5/6), pp. 249-260; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: a collaborative decision-making approach (2014) Prod. Plan. Control, 25 (8), pp. 698-714; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Comput. Ind. Eng., 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Prod. Plan. Control, 18 (8), pp. 666-680; Bhagwat, R., Chan, F.T., Sharma, M.K., Performance measurement model for supply chain management in SMEs (2008) Int. J. Glob. Small Bus., 2 (4), pp. 428-445; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Prod. Plann. Control, 20 (8), pp. 678-690; Bolstorff, P., Rosenbaum, R.G., Supply Chain Excellence: a Handbook for Dramatic Improvement Using the Scor Model (2007), AMACOM Div Am. Mgmt. Assn; Brans, J.P., Vincke, P., Note—a preference ranking organisation method: (the PROMETHEE method for multiple criteria decision-Making) (1985) Manage. Sci., 31 (6), pp. 647-656; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logist., 21 (1), p. 75; Bullinger, H.J., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) Int. J. Prod. Res., 40 (15), pp. 3533-3543; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decis. Support Syst., 46 (2), pp. 512-521; Chae, B., Developing key performance indicators for supply chain: an industry perspective (2009) Supply Chain Manage.: Int. J., 14 (6), pp. 422-428; Chan, F.T., Nayak, A., Raj, R., Chong, A.Y.L., Manoj, T., An innovative supply chain performance measurement system incorporating research and development (R&D) and marketing policy (2013) Comput. Ind. Eng., 69, pp. 64-70; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Manage.: Int. J., 8 (3/4), pp. 209-223; Chan, F.T., Qi, H.J., Feasibility of performance measurement system for supply chain: a process-based approach and measures (2003) Integr. Manuf. Syst., 14 (3), pp. 179-190; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) Int. J. Logist. Syst. Manage., 18 (2), pp. 216-230; Charnes, A., Cooper, W.W., Rhodes, E., A Data Envelopment Analysis Approach to Evaluation of the Program Follow Through Experiment in US Public School Education (No. MSRR-432) (1978), Carnegie-Mellon Univ Pittsburgh Pa Management Sciences Research Group; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Chen, Y.W., Larbani, M., Simulating the performance of supply chain with various alliances (2005) Int. J. Adv. Manuf. Technol., 25 (7-8), pp. 803-810; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: balanced scorecard perspective (2009) Benchmarking: Int. J., 16 (5), pp. 605-620; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Comput. Ind. Eng., 62 (3), pp. 801-818; Clivillé, V., Berrah, L., Overall performance measurement in a supply chain: towards a supplier-prime manufacturer based model (2012) J. Intell. Manuf., 23 (6), pp. 2459-2469; Critcher, C., Gladstone, B., Utilizing the Delphi technique in policy discussion: a case study of a privatized utility in Britain (1998) Public Adm., 76 (3), pp. 431-449; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: a framework for contextual analysis (2011) Int. J. Product. Perform. Manage., 60 (6), pp. 583-602; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Bus. Excell., 14 (3), pp. 355-366; David, K.G., Shalle, N., An assessment of the effects of reverse logistics adoption on supply chain performance in the manufacturing sector in kenya: a case of Hewlett-Packard Kenya (2014) Eur. J. Bus. Manage., 2 (1), pp. 161-173; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations (2013) Prod. Plann. Control, 24 (8-9), pp. 702-720; Drzymalski, J., Odrey, N.G., Wilson, G.R., Aggregating performance measures of a multi-echelon supply chain using the analytical network and analytical hierarchy process (2010) Int. J. Services Econ. Manage., 2 (3), pp. 286-306; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Syst. Appl., 38 (6), pp. 6681-6688; Eskafi, S., Roghanian, E., Jafari-Eskandari, M., Designing a performance measurement system for supply chain using balanced scorecard, path analysis, cooperative game theory and evolutionary game theory: a case study (2015) Int. J. Ind. Eng. Comput., 6 (2), pp. 157-172; Galasso, F., Ducq, Y., Lauras, M., Gourc, D., Camara, M., A method to select a successful interoperability solution through a simulation approach (2014) J. Intell. Manuf., pp. 1-13; Galankashi, M.R., Memari, A., Anjomshoae, A., Ma'aram, A., Helmi, S.A., Selection of supply chain performance measurement frameworks in electrical supply chains (2014) Int. J. Ind. Eng. Manage., 3 (5), pp. 131-137; Gallear, D., Ghobadian, A., Li, Y., O'Regan, N., Childerhouse, P., Naim, M., An environmental uncertainty-based diagnostic reference tool for evaluating the performance of supply chain value streams (2013) Prod. Plan. Control, 25 (13-14), pp. 1182-1197; Gong, K., Yan, H., Performance measurement of logistics service supply chain using bijective soft set (2015) J. Adv. Manuf. Syst., 14 (1), pp. 23-40; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000–2011 (2012) Int. J. Product. Perform. Manage., 61 (5), pp. 518-547; Gopal, P.R.C., Thakkar, J., Sustainable supply chain practices: an empirical investigation on Indian automobile industry (2015) Prod. Plan. Control, 27 (1), pp. 49-64; Guide, V.D.R., Jayaraman, V., Linton, J.D., Building contingency planning for closed-loop supply chains with product recovery (2003) J. Oper. Manage., 21 (3), pp. 259-279; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Gunasekaran, A., Patelb, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87, pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manage., 21 (1/2), pp. 71-87; Hall, D.C., Saygin, C., Impact of information sharing on supply chain performance (2012) Int. J. Adv. Manuf. Technol., 58 (1-4), pp. 397-409; Hasson, F., Keeney, S., McKenna, H., Research guidelines for the Delphi survey technique (2000) J. Adv. Nurs., 32 (4), pp. 1008-1015; Hernandez, J.E., Lyons, A.C., Zarate, P., Dargam, F., Collaborative decision-making and decision support systems for enhancing operations management in industrial environments (2014) Prod. Plan. Control, 25 (8), pp. 636-638; Ho, C.-J., Measuring system performance of an ERP-based supply chain (2007) Int. J. Prod. Res., 45 (6), pp. 1255-1277; Hwang, Y.D., Lin, Y.C., Lyu, J., The performance evaluation of SCOR sourcing process—the case study of Taiwan's TFT-LCD industry (2008) Int. J. Prod. Econ., 115 (2), pp. 411-423; Isaai, M.T., Kanani, A., Tootoonchi, M., Afzali, H.R., Intelligent timetable evaluation using fuzzy AHP (2011) Expert Syst. Appl., 38 (4), pp. 3718-3723; Kaplan, R., Norton, D., The Balanced Scorecard Measures That Drive Performance, Harvard Business Review, January–February 1992; Khalili-Damghani, K., Taghavi-Fard, M., Abtahi, A.R., A fuzzy two-stage DEA approach for performance measurement: real case of agility performance in dairy supply chains (2012) Int. J. Appl. Decis. Sci., 5 (4), pp. 293-317; Kotzab, H., Grant, D.B., Friis, A., Supply chain management implementation and priority strategies in Danish organizations (2006) J. Bus. Logist., 27 (2), pp. 273-300; Kusrini, E., Subagyo, Masruroh, N.A., Combining Balanced Scorecard and Data Envelopment Analysis to Design Performance Measurement for Supply Chain Actor and Regulator: A Case Study in Innovative Product in Indonesia. Industrial Engineering, Management Science and Applications (2015), pp. 551-562. , Springer Berlin Heidelberg; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transp. Res. E: Logist. Transp. Rev., 38 (6), pp. 439-456; Lapide, L., True measures of supply chain performance (2000) Supply Chain Manage. Rev., 4 (3), pp. 25-28; Lauras, M., Lamothe, J., Pingaud, H., A business process oriented method to design supply chain performance measurement systems (2011) Int. J. Bus. Perform. Manage., 12 (4), pp. 354-376; Leung, L.C., Lam, K.C., Cao, D., Implementing the balanced scorecard using the analytic hierarchy process & the analytic network process (2006) J. Oper. Res. Soc., 57 (6), pp. 682-691; Liang, Y.H., Performance measurement of interorganizational information systems in the supply chain (2015) Int. J. Prod. Res., pp. 1-16. , (ahead-of-print); Liepina, L., Kirikova, M., SCOR based ISS requirements identification (2011) Business Information Systems Workshops, pp. 232-243. , Springer Berlin Heidelberg; Lin, L.C., Li, T.S., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Softw. Qual. J., 18 (3), pp. 387-406; Lockamy, A., McCormack, K., Linking the SCOR planning practices to supply chain performance (2004) Int. J. Oper. Prod. Manage., 24 (11/12), pp. 1192-1218; McCormack, K., Bronzo Ladeira, M., Paulo Valadares de Oliveira, M., Supply chain maturity and performance in Brazil (2008) Supply Chain Manage.: Int. J., 13 (4), pp. 272-282; Mishra, P., Sharma, R.K., Benchmarking SCM performance and empirical analysis: a case from paint industry (2014) Logist. Res., 7 (1), pp. 1-16; Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: a case study of an auto industry supply chain (2011) Res. Conserv. Recycl., 55 (6), pp. 593-603; Najmi, A., Gholamian, M.R., Makui, A., Supply chain performance models: a literature review on approaches, techniques, and criteria (2013) J. Oper.Supply Chain Manage., 6 (2), pp. 94-113; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) Eur. J. Oper. Res., 144 (2), pp. 306-320; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Ind. Manage. Data Syst., 110 (9), pp. 1297-1318; Parkan, C., Wang, J., Gauging the performance of a supply chain (2007) Int. J. Product. Qual. Manage., 2 (2), pp. 141-176; Peng Wong, W., Yew Wong, K., Supply chain performance measurement system using DEA modeling (2007) Ind. Manage. Data Syst., 107 (3), pp. 361-381; Perea, E., Dynamic modeling and classical control theory for supply chain management (2000) Comput. Chem. Eng., 24 (2), pp. 1143-1149; Pramod, V.R., Banwet, D.K., Performance measurement of SHER service supply chain: a balanced score card—ANP approach (2011) Int. J. Bus. Excell., 4 (3), pp. 321-345; Persson, F., Olhager, J., Performance simulation of supply chain designs (2002) Int. J. Prod. Econ., 77 (3), pp. 231-245; Ptak, C.A., Schragenheim, E., ERP: Tools, Techniques, and Applications for Integrating the Supply Chain (2003), CRC Press; Ralston, P.M., Blackhurst, J., Cantor, D.E., Crum, M.R., A structure-conduct-performance perspective of how strategic supply chain integration affects firm performance (2015) J. Supply Chain Manage., 51 (2), pp. 47-64; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Emerging Trends in Engineering and Technology (ICETET), 2009 2nd International Conference, IEEE, pp. 802-807; Robb, D.J., Xie, B., Arthanari, T., Supply chain and operations practice and performance in Chinese furniture manufacturing (2008) Int. J. Prod. Econ., 112 (2), pp. 683-699; Rodriguez, R.R., Saiz, J.J.A., Bas, A.O., Quantitative relationships between key performance indicators for supporting decision-making processes (2009) Comput. Ind., 60 (2), pp. 104-113; Saaty, R.W., The analytic hierarchy process—what it is and how it is used (1987) Math. Modell., 9 (3), pp. 161-176; Saaty, T.L., (1996) Decision Making with Dependence and Feedback: The Analytic Network Process, 4922. , RWS publications Pittsburgh; Santiteerakul, S., Sekhari, A., Bouras, A., Sopadang, A., Sustainability performance measurement framework for supply chain management (2015) Int. J. Prod. Dev., 20 (3), pp. 221-238; Sellitto, M.A., Pereira, G.M., Borchardt, M., da Silva, R.I., Viegas, C.V., A SCOR-based model for supply chain performance measurement: application in the footwear industry (2015) Int. J. Prod. Res., 53 (16), pp. 4917-4926; Shafiee, M., Lotfi, F.H., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Appl. Math. Modell., 38 (21), pp. 5092-5112; Shah, J., Singh, N., Benchmarking internal supply chain performance: development of a framework (2001) J. Supply Chain Manage., 37 (4), pp. 37-47; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2011) Behavioral Operations in Planning and Scheduling, pp. 105-121. , Springer Berlin Heidelberg; Soni, G., Kodali, R., Internal benchmarking for assessment of supply chain performance (2010) Benchmarking: Int. J., 17 (1), pp. 44-76; Stock, G.N., Greis, N.P., Kasarda, J.D., Enterprise logistics and supply chain structure: role of fit (2000) J. Oper. Manage., 18 (5), pp. 531-547; Sumsion, T., The Delphi technique: an adaptive research tool (1998) Br. J. Occup. Ther., 61 (4), pp. 153-156; Tavana, M., Kaviani, M.A., Di Caprio, D., Rahpeyma, B., A two-stage data envelopment analysis model for measuring performance in three-level supply chains (2015) Measurement, 78, pp. 322-333; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: Int. J., 16 (5), pp. 702-723; Theeranuphattana, A., Tang, J.C.S., A conceptual model of performance measurement for supply chains alternative considerations (2008) J. Manuf. Technol. Manage., 19 (1), pp. 125-148; Trivedi, A., Rajesh, K., A framework for performance measurement in supply chain using balanced score card method: a case study (2013) Int. J. Recent Trends Mech. Eng., 4 (1), pp. 20-23; Tyagi, M., Kumar, P., Kumar, D., Assessment of critical enablers for flexible supply chain performance measurement system using fuzzy DEMATEL approach (2015) Global J. Flexible Syst. Manage., 16 (2), pp. 115-132; Ravi, V., Shankar, R., Tiwari, M.K., Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach (2005) Comp. Ind. Eng., 48 (2), pp. 327-356; Van Hoek, R., Measuring the unmeasurable—measuring and improving performance in the supply chain (1998) Supply Chain Manage., 3 (4), pp. 187-192; Varma, S., Wadhwa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance: application of analytical hierarchy process to balanced scorecard (2008) Asia Pac. J. Mark. Logist., 20 (3), pp. 343-356; Wang, Y.M., Chin, K.S., Some alternative DEA models for two-stage process (2010) Expert Syst. Appl., 37 (12), pp. 8799-8808; Wibowo, M.A., Sholeh, M.N., The analysis of supply chain performance measurement at construction project (2015) Procedia Eng., 125, pp. 25-31; Wong, W.P., Performance evaluation of supply chain in stochastic environment: using a simulation based DEA framework (2009) Int. J. Bus. Perform. Supply Chain Modell., 1 (2), pp. 203-228; Wong, W.P., Jaruphongsa, W., Lee, L.H., Supply chain performance measurement system: a Monte Carlo DEA-based approach (2008) Int. J. Ind. Syst. Eng., 3 (2), pp. 162-188; Wong, W.P., Jaruphongsa, W., Lee, L.H., Wong, K.Y., A preliminary study on using Data Envelopment Analysis (DEA) in measuring supply chain efficiency (2007) Int. J. Appl. Syst. Stud., 1 (2), pp. 188-207; Xu, J., Li, B., Wu, D., Rough data envelopment analysis and its application to supply chain performance evaluation (2009) Int. J. Prod. Econ., 122 (2), pp. 628-638; Yang, J., Integrative performance evaluation for supply chain system based on logarithm triangular fuzzy number-AHP method (2009) Kybernetes, 38 (10), pp. 1760-1770; Yao, K.H., Liu, C.C., An integrated approach for measuring supply chain performance (2006) J. Mod. Acc. Audit., 2 (10), p. 17; Yeh, D.Y., Cheng, C.H., Chi, M.L., A modified two-tuple FLC model for evaluating the performance of SCM: By the Six Sigma DMAIC process (2007) Appl. Soft Comput., 7 (3), pp. 1027-1034; Zarenezhada, F., Mehralianb, G., Ghataric, A.R., Developing a model for agile pharmaceutical distribution: evidence from Iran (2013) J. Basic Appl. Sci. Res., 3 (1), pp. 161-172},
correspondence_address1={Balfaqih, H.; National University of Malaysia (UKM), Bangi, Malaysia; email: h.balfaqih@siswa.ukm.edu.my},
publisher={Elsevier B.V.},
issn={01663615},
coden={CINUD},
language={English},
abbrev_source_title={Comput Ind},
document_type={Review},
source={Scopus},
}

@ARTICLE{Laihonen20165607,
author={Laihonen, H. and Pekkola, S.},
title={Impacts of using a performance measurement system in supply chain management: a case study},
journal={International Journal of Production Research},
year={2016},
volume={54},
number={18},
pages={5607-5617},
doi={10.1080/00207543.2016.1181810},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965077476&doi=10.1080%2f00207543.2016.1181810&partnerID=40&md5=add4a35c6ebe6aced3b2ad7ca2c1290d},
affiliation={School of Management (JKK), University of Tampere, Tampere, Finland; Rautakesko Ltd., Vantaa, Finland},
abstract={The study examines how the utilisation of a new performance measurement system (PMS) influences supply chain management (SCM) and what kind of impacts the new system has on the performance of the supply chain. The paper utilises a longitudinal research setting where the data was gathered in two semi-structured interview studies after an action research project in which a new measurement system was designed and developed. The findings show how a PMS serves as a catalyst of inter-organisational knowledge transfer and promotes shared learning. The results also show how knowledge transfer and shared learning led to improved performance of the supply chain. The paper contributes by opening up the practical mechanisms through which performance measurement provides value for SCM and by showing how inter-organisational transfer of performance information catalyses learning and performance improvement. The paper looks beyond the design of performance measures and provides a view on the practice of supply chain performance management. The paper illustrates how sharing performance information amongst supply chain companies improves managers’ awareness of shared targets and the status of network operations. This also resulted in improved performance in the studied case network. Overall, the results encourage organisations to engage in network-level performance measurement and share performance information with their network partners. © 2016 Informa UK Limited, trading as Taylor & Francis Group.},
author_keywords={knowledge management;  performance analysis;  performance information;  performance measures;  supply chain management},
keywords={Graphical user interfaces;  Information dissemination;  Information management;  Knowledge management, Performance analysis;  performance information;  Performance measure;  Performance measurement system;  Performance measurements;  Semi structured interviews;  Supply chain managements (SCM);  Supply chain performance, Supply chain management},
references={Albino, V., Garavelli, A.C., Schiuma, G., Knowledge Transfer and Inter-firm Relationships in Industrial Districts: The Role of the Leader Firm (1999) Technovation, 19 (1), pp. 53-63; Arzu Akyuz, G.A., Erman Erkan, T.E., Supply Chain Performance Measurement: A Literature Review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Banomyong, R., Supatn, N., Developing a Supply Chain Performance Tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Bititci, U., Turner, T., Mackay, D., Kearney, D., Parung, J., Walters, D., Managing Synergy in Collaborative Enterprises (2007) Production Planning & Control, 18 (6), pp. 454-465; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance Measurement: Challenges for Tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Chan, F.T.S., Qi, H.J., An Innovative Performance Measurement Method for Supply Chain Management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A Conceptual Model of Performance Measurement for Supply Chains (2003) Management Decision, 41 (7), pp. 635-642; Chen, I.J., Paulraj, A., Towards a Theory of Supply Chain Management: The Constructs and Measurements (2004) Journal of Operations Management, 22, pp. 119-150; Cohen, W.M., Levinthal, D.A., Absorptive Capacity: A New Perspective on Learning and Innovation (1990) Administrative Science Quarterly, 35 (1), pp. 128-152; Collins, J.D., Hitt, M.A., Leveraging Tacit Knowledge in Alliances: The Importance of Using Relational Capabilities to Build and Leverage Relational Capital (2006) Journal of Engineering and Technology Management, 23, pp. 147-167; Cousins, P.D., Lawson, B., Squire, B., Performance Measurement in Strategic Buyer–Supplier Relationships: The Mediating Role of Socialization Mechanisms (2008) International Journal of Operations & Production Management, 28 (3), pp. 238-258; Easterby-Smith, M., Lyles, M.A., Tsang, E.W.K., Inter-organizational Knowledge Transfer: Current Themes and Future Prospects (2008) Journal of Management Studies, 45 (4), pp. 677-690; Eisenhardt, K.M., Graebner, M.E., Theory Building From Cases: Opportunities and Challenges (2007) Academy of Management Journal, 50 (1), pp. 25-32; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary Performance Measurement Systems: A Review of Their Consequences and a Framework for Research (2012) Management Accounting Research, 23, pp. 79-119; Grosvold, J., Hoejmose, S.U., Roehrich, J.K., Squaring the Circle: Management, Measurement and Performance of Sustainability in Supply Chains (2014) Supply Chain Management: An International Journal, 19 (3), pp. 292-305; Gummesson, E., (2000) Qualitative Methods in Management Research, , 2nd ed, Thousand Oaks, CA: Sage; Gunasekaran, A., Kobu, B., Performance Measures and Metrics in Logistics and Supply Chain Management: A Review of Recent Literature (1995–2004) for Research and Applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A Framework for Supply Chain Performance Measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Hutzschenreuter, T., Horstkotte, J., Knowledge Transfer to Partners: A Firm Level Perspective (2010) Journal of Knowledge Management, 14 (3), pp. 428-448; Inkpen, A.C., Tsang, E.W.K., Social Capital, Networks, and Knowledge Transfer (2005) Academy of Management Review, 30 (1), pp. 146-165; Jiang, X., Li, Y., An Empirical Investigation of Knowledge Management and Innovative Performance: The Case of Alliances (2009) Research Policy, 38, pp. 358-368; Kaplan, R.S., Norton, D.P., Rugelsjoen, B., Managing Alliances with the Balanced Scorecard (2010) Harvard Business Review, 88 (1), pp. 68-75; Kulmala, H.I., Lonnqvist, A., Performance Measurement of Networks: Towards a Non-financial Approach (2006) International Journal of Networking and Virtual Organisations, 3 (3), pp. 299-316; Laihonen, H., Jääskeläinen, A., Pekkola, S., Measuring Performance of a Service System – From Organizations to Customer-perceived Performance (2014) Measuring Business Excellence, 18 (3), pp. 73-86; Lane, P.J., Salk, J.E., Lyles, M.A., Absorptive Capacity, Learning, and Performance in International Joint Ventures (2001) Strategic Management Journal, 22, pp. 1139-1161; Lönnqvist, A., Laihonen, H., Welfare Service System Productivity: The Concept and Its Application (2012) International Journal of Productivity and Performance Management, 61 (2), pp. 128-141; Mahama, H., Management Control Systems, Cooperation and Performance in Strategic Supply Relationships: A Survey in the Mines (2006) Management Accounting Research, 17 (3), pp. 315-339; Martinkenaite, I., Antecedents and Consequences of Inter‐Organizational Knowledge Transfer (2011) Baltic Journal of Management, 6 (1), pp. 53-70; May, A., Anslow, A., Wu, Y., Ojiako, U., Chipulu, M., Marshall, A., Prioritisation of Performance Indicators in Air Cargo Demand Management: An Insight from Industry (2014) Supply Chain Management: An International Journal, 19 (1), pp. 108-113; Mazloomi Khamseh, H.M., Jolly, D.R., Knowledge Transfer in Alliances: Determinant Factors (2008) Journal of Knowledge Management, 12 (1), pp. 37-50; McCormack, K., Bronzo Ladeira, M.B., Paulo Valadares de Oliveira, M.P., Supply Chain Maturity and Performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Meier, M., Knowledge Management in Strategic Alliances: A Review of Empirical Evidence (2011) International Journal of Management Reviews, 13, pp. 1-23; Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining Supply Chain Management (2001) Journal of Business Logistics, 22, pp. 1-25; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the Art Literature Review on Performance Measurement (2011) Computers & Industrial Engineering, 60, pp. 279-290; Parung, J., Bititci, U.S., A Metric for Collaborative Networks (2008) Business Process Management Journal, 14 (5), pp. 654-674; Pekkola, S., Managing a Network by Utilizing Performance Measurement Information (2013) Measuring Business Excellence, 17 (1), pp. 72-79; Pekkola, S., Rantanen, H., Utilisation of Performance Measurement Information in Management: Top Manager Perspective (2014) International Journal of Business Performance Management, 15 (1), pp. 23-34; Phelps, C., Heidl, R., Wadhwa, A., Knowledge, Networks, and Knowledge Networks: A Review and Research Agenda (2012) Journal of Management, 38 (4), pp. 1115-1166; Selviaridis, K., Norrman, A., Performance-based Contracting in Service Supply Chains: A Service Provider Risk Perspective (2014) Supply Chain Management: An International Journal, 19 (2), pp. 153-172; Shepherd, C., Günter, H., Measuring Supply Chain Performance: Current Research and Future Directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Simons, R., How New Top Managers Use Control Systems as Levers of Strategic Renewal (1994) Strategic Management Journal, 15 (3), pp. 169-189; de Waal, A.A., Stimulating Performance‐Driven Behaviour to Obtain Better Results (2004) International Journal of Productivity and Performance Management, 53 (4), pp. 301-316; Waggoner, D., Neely, A., Kennerley, M., The Forces That Shape Organisational Performance Measurement Systems (1999) International Journal of Production Economics, 60-61, pp. 53-60; van Wijk, R., Jansen, J.P., Lyles, M.A., Inter- and Intra-organizational Knowledge Transfer: A Meta-analytic Review and Assessment of Its Antecedents and Consequences (2008) Journal of Management Studies, 45 (4), pp. 815-838; Yin, R.K., (2009) Case Study Research – Design and Methods, , 4th ed, Thousand Oaks, CA: Sage},
correspondence_address1={Laihonen, H.; School of Management (JKK), Finland; email: Harri.laihonen@uta.fi},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ferreira20161398,
author={Ferreira, L.M.D.F. and Silva, C. and Azevedo, S.G.},
title={An environmental balanced scorecard for supply chain performance measurement (Env_BSC_4_SCPM)},
journal={Benchmarking},
year={2016},
volume={23},
number={6},
pages={1398-1422},
doi={10.1108/BIJ-08-2013-0087},
note={cited By 44},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979519599&doi=10.1108%2fBIJ-08-2013-0087&partnerID=40&md5=ddf2b0b579b645035a51d1bc679c78fa},
affiliation={Department of Economics, Management and Industrial Engineering, University of Aveiro, Aveiro, Portugal; Department of Mechanical Engineering, University of Coimbra, Coimbra, Portugal; Department of Management and Economics, University of Beira Interior, Covilhã, Portugal},
abstract={Purpose – Companies need to excel in many areas to achieve a competitive advantage. This, together with pressure imposed by regulators and customers regarding sustainability concerns, leads companies to address sustainability in an integrated fashion across all management processes. The purpose of this paper is to suggest a model for the assessment of the environmental performance of a supply chain, based on four perspectives used in the balanced scorecard. Performance indicators are proposed based on the literature, as well as on the ISO 14031 and GRI standards, and were validated by a panel of experts. Design/methodology/approach – Based on a literature review on models for environmental performance management a novel model to assess the environmental performance of the supply chains (Env_BSC_4_SCPM) is proposed. Data collected from the first tier suppliers of an automotive industry case study are used to test the proposed model. Findings – The model developed was tested in a case study company, showing it ability to benchmark the company first tiers suppliers and products. The model is also useful as a decision support tool to define actions to be taken in order to improve the global environment performance of the supply chain. Research limitations/implications – The proposed model was developed to evaluate the environmental performance of supply chains. Nevertheless, the case study only takes account of the first tier suppliers, due to difficulties associated to data collecting for the other elements in the supply chain. Widening the frontiers, the next phase may include the application of this model to second, third and lower tier suppliers, as well as the final customer. Improvements in the model could also include the construction of a composite index to measure the environmental supply chain performance. Practical implications – The paper provides a model that can be used by practitioners to evaluate the environmental performance of their supply chain and to decide on actions to be taken to improve it. Originality/value – As stated by several authors, there has been limited research conducted in the field of environmental evaluation of supply chains. This paper proposes a novel model for the environmental performance of the supply chain and tests it using industrial empirical data. © 2016, © Emerald Group Publishing Limited.},
author_keywords={Balanced scorecard;  Benchmarking;  Environmental performance;  Performance;  Supply chain management;  Sustainability},
references={Alfalla-Luque, R., Medina-López, C., Supply chain management: unheard of in the 1970s, core to today’s company (2009) Business History, 51 (2), pp. 202-221; Azevedo, S., Carvalho, H., Cruz-Machado, V., The influence of green practices on supply chain performance: a case study approach (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 850-871; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Braithwaite, A., Knivett, D., (2008) Evaluating a supply chain carbon footprint – a methodology and case example of carbon-to-serve, pp. 323-328; Campos, L.M.S., Melo, D.A., Indicadores de desempenho dos Sistemas de Gestão Ambiental (SGA): uma pesquisa teórica (2008) Produção, 18 (3), pp. 540-555; Carter, C.R., Easton, P.L., Sustainable supply chain management: evolution and future directions (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 46-62; Chan, F.T., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chen, C., Incorporating green purchasing into the frame of ISO 14000 (2004) Journal of Cleaner Production, 13 (9), pp. 927-933; Christopher, M., (1998) Logistics and Supply Chain Management – Strategies for Reducing Cost and Improving Service, , Prentice Hall/Financial Times; Cohen, S., Roussel, J., (2004) Strategic Supply Chain Management: The Five Disciplines for Top Performance, , McGraw-Hill Education, New Yo; Colicchia, C., Melacini, M., Perotti, S., Benchmarking supply chain sustainability: insights from a field study (2011) Benchmarking: An International Journal, 18 (5), pp. 705-732; Corbett, C.J., Klassen, R.D., Extending the horizons: environmental excellence as key to improving operations (2006) Manufacturing and Service Operations Management, 8 (1), pp. 5-22; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations (2012) Production Planning & Control: The Management of Operations, 24 (8-9), pp. 702-720; Dias-Sardinha, I., Reijnders, L., Environmental performance evaluation and sustainability performance evaluation of organizations: an evolutionary framework (2001) Eco – Management and Auditing, 8 (2), pp. 71-79; El Saadany, A.M.A., Jaber, M.Y., Bonney, M., Environmental performance measures for supply chains (2011) Management Research Review, 34 (11), pp. 1202-1221; Epstein, M.J., Wisner, P.S., Measuring and managing social and environmental impacts (2001) Handbook of Cost Management, , Shank, J. (Ed.), Warren, Gorham & Lamont, New Yo; Fandel, G., Stammen, M., A general model for extended strategic supply chain management with emphasis on product life cycles including development and recycling (2004) International Journal of Production Economics, 89 (3), pp. 293-308; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard – linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11 (5), pp. 269-283; Fine, C., (1998) Clockspeed: Winning Industry Control in the Age of Temporary Advantage, , Basic Books, New Yo; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organizational resources: a literature review (2010) Corporate Social Responsibility and Environmental Management, 17 (4), pp. 230-245; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547. , http://dx.doi.org/10.1108/17410401211232957; Griffith, A., Bhutto, K., Better environmental performance: a framework for integrated management systems (IMS) (2009) Management of Environmental Quality, 20 (5), pp. 566-580; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Production and Operations Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., Ronald, E., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-348; Handfield, R., Sroufe, R., Walton, S., Integrating environmental management and supply chain strategies (2005) Business Strategy and the Environment, 14 (1), pp. 1-19. , http://onlinelibrary.wiley.com/doi/10.1002/bse.v14:1/issuetoc; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice Hall, Upper Saddle Riv; Hervani, A., Helms, M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hong, P., Roh, J.J., Rawski, G., Benchmarking sustainability practices: evidence from manufacturing firms (2012) Benchmarking: An International Journal, 19 (4-5), pp. 634-648; Hsu, C.W., Hu, A.H., Chiou, C.Y., Chen, T.C., Using the FDM and ANP to construct a sustainability balanced scorecard for the semiconductor industry (2011) Expert Systems with Applications, 38 (10), pp. 12891-12899; Hsu, Y.L., Liu, C.C., Environmental performance evaluation and strategy management using balanced scorecard (2010) Environmental Monitoring and Assessment, 170 (1-4), pp. 599-607; Hutchins, M.J., Sutherland, J.W., An exploration of measures of social sustainability and their application to supply chain decisions (2008) Journal of Cleaner Production, 16 (15), pp. 1688-1698; Jiménez, J.B., Lorente, J.L., Environmental performance as an operations objective (2001) International Journal of Operations & Production Management, 21 (12), pp. 1553-1572; Johnson, S.D., Identification and selection of environmental performance indicators: application of the balanced scorecard approach (1998) Corporate Environmental Strategy, 5 (4), pp. 34-41; Kaplan, R.S., Norton, D.P., The balanced scorecard: measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Länsiluoto, A., Järvenpää, M., Environmental and performance management forces: integrating ‘greenness’ into balanced scorecard (2008) Qualitative Research in Accounting and Management, 5 (3), pp. 184-206; Lau, K.H., Benchmarking green logistics performance with a composite index (2011) Benchmarking: An International Journal, 18 (6), pp. 873-896; Lehtinen, J., Ahola, T., Is performance measurement suitable for an extended enterprise? (2010) International Journal of Operations & Production Management, 30 (2), pp. 181-204; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: an introduction (2007) Journal of Operations Management, 25 (6), pp. 1075-1082; Markley, M.J., Davis, L., Exploring future competitive advantage through sustainable supply chains (2007) International Journal of Physical Distribution & Logistics Management, 37 (9), pp. 763-774; Morhardt, J., Baird, S., Freeman, K., Scoring corporate environmental and sustainability reports using GRI 2000, ISO 14031 and other criteria (2002) Corporate Social Responsibility and Environmental Management, 9 (4), pp. 215-233; Naini, S.G., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental SCM using evolutionary game theory and balanced scorecard – a case study of an auto industry supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 593-603; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Perotto, E., Canziani, R., Marchesi, R., Butelli, P., Environmental performance, indicators and measurement uncertainty in EMS context: a case study (2008) Journal of Cleaner Production, 16 (4), pp. 517-530; Pettigrew, A.M., Longitudinal field research on change: theory and practice (1990) Organisation Science, 1 (3), pp. 267-292; Saunders, M., Lewis, P., Thornhill, A., (2003) Research Methods for Business Students, , Prentice Hall; Seuring, S., Case study research in supply chains: an outline and three examples (2005) Research Methodologies in Supply Chain Management, pp. 235-250. , Kotzab, H., Seuring, S., Muller, M., Reiner, G. (Eds), Physica, Heid; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339; Soni, G., Kodali, R., Internal benchmarking for assessment of supply chain performance (2010) Benchmarking: An International Journal, 17 (1), pp. 44-76; Srivasta, S.K., Green supply-chain management: a state of the art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Stonebraker, P.W., Goldhar, J., Nassos, G., Weak links in the supply chain: measuring fragility and sustainability (2009) Journal of Manufacturing Technology Management, 20 (2), pp. 161-177; Tóth, G., Evaluation of environmental performance of companies (2003) Society and Economy, 25 (3), pp. 383-402; Vachon, S., Klassen, R.D., Extending green practices across the supply chain. The impact of upstream and downstream integration (2006) International Journal of Operations & Production Management, 26 (7), pp. 795-821; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain (2006) Supply Chain Management: An International Journal, 11 (4), pp. 317-327; Webster, S., Mitra, S., Competitive strategy in remanufacturing and the impact of take-back laws (2007) Journal of Operations Management, 25 (6), pp. 1123-1140; Winter, M., Knemeyer, A.M., Exploring the integration of sustainability and supply chain management: current state and opportunities for future inquiry (2013) International Journal of Physical Distribution & Logistics Management, 43 (1), pp. 18-38; Yang, M., Hong, P., Modi, S., Impact of lean manufacturing and environmental management on business performance: an international study of manufacturing firms (2011) International Journal of Production Economics, 129 (2), pp. 251-261; Yin, R.K., (2003) Case Study Research – Design and Methods, , 3rd ed., Sage, Thous Oa; Yu, X., Li-Ping Tang, T., Sustainability in supply chain management: suggestions for the auto industry (2011) Management Decision, 49 (4), pp. 495-512; Brewer, P.C., Speh, T., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56; Clift, R., Metrics for supply chain sustainability (2003) Clean Technologies and Environmental Policy, 5 (3), pp. 240-247; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Bost; Linton, J.D., Jayaraman, V., A conceptual framework for product life extension (2005) International Journal of Production Research, 43 (9), pp. 1807-1829; Porter, M., van der Linde, C., Green and competitive: ending the stalemate (1995) Harvard Business Review, 73 (5), pp. 120-133; Sroufe, R., Effects of environmental management systems on environmental management practices and operations (2004) Production and Operations Management, 12 (3), pp. 416-431; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657},
correspondence_address1={Ferreira, L.M.D.F.; Department of Economics, Portugal; email: lmferreira@ua.pt},
publisher={Emerald Group Publishing Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Dweekat2016,
author={Dweekat, A.J. and Park, J.},
title={Internet of things-enabled supply chain performance measurement model},
journal={ICIMSA 2016 - 2016 3rd International Conference on Industrial Engineering, Management Science and Applications},
year={2016},
doi={10.1109/ICIMSA.2016.7504014},
art_number={7504014},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84980369566&doi=10.1109%2fICIMSA.2016.7504014&partnerID=40&md5=b4cef500572e2819796eeab0426f7c2c},
affiliation={Departement of Industrial Engineering, Automation and Systems Research Institute, Seoul National University, Seoul, South Korea},
abstract={Internet of Things is expected to offer promising solution to transform the operation and the role of many existing industrial systems. It is also increasingly seen as an engine of development in many systems and fields. Supply chain performance measurement is one of these systems that might be significantly affected by Internet of Things evolution. In the last few decades, many standards and models have been developed to measure supply chain performance. However, it has been argued that these systems still have some problems, such as being static, short term, lack of holistic approach, and data collection difficulties, etc.[1-3] [1]. To overcome these problems, this research has introduced a new Internet of Things-Enabled model to collect information and communicate things for the sleek of supply chain performance measurement. Supply Chain Operation Reference model and ISA-95 standards have been used to analyze supply chains and identify performance metrics' data identification queries. The model also can be used to show the promising and essential role of Internet of Things technologies in the next generation performance measurement systems that can be employed to monitor, manage and control the overall supply chain in real-time and in more integrated and cooperated way. © 2016 IEEE.},
author_keywords={Internet of things (IoT);  ISA-95;  Performance metrics (PM);  SCOR;  Supply chain performance management SCPM},
keywords={Internet;  Internet of things;  Management science;  Real time systems, Internet of Things (IOT);  ISA-95;  Performance metrics;  SCOR;  Supply chain performance, Supply chains},
references={Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45, pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: Review and classification (2012) Journal of Organizational Management Studies, 2012, pp. 1-20; Burkitt, F., A strategist's guide to the Internet of Things (2015) Strategy+Business, , http://www.strategybusiness.com/article/00294?gko=a9303, 7 Jan; Kevin, A., That'Internet of Things' Thing, in the real world things matter more than ideas (2009) RFID Journal, , http://www.rfidjournal.com/articles/view?4986, 15 Dec; Da Xu, L., He, W., Li, S., Internet of things in industries: A survey (2014) Industrial Informatics, IEEE Transactions on, 10, pp. 2233-2243; Moisescu, M., Sacala, I., Towards the development of interoperable sensing systems for the future enterprise (2016) Journal of Intelligent Manufacturing, 27, pp. 33-54; Cluster, F., Future internet enterprise systems: Research roadmap 2025 (2012) Final Report, Version, 2; Sepehri, M., A grid-based collaborative supply chain with multi-product multi-period production-distribution (2012) Enterprise Information Systems, 6, pp. 115-137; Tao, F., Zhang, L., Venkatesh, V., Luo, Y., Cheng, Y., Cloud manufacturing: A computing and serviceoriented manufacturing model (2011) The Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, , presented at; Shapiro, J., (2001) Modeling the Supply Chain, , Duxbury: Wadsworth Group; Council, S.-C., (2012) Supply-Chain Operations Reference Model: Version 11.0, , ed; Stadtler, H., Kilger, C., Meyr, H., (2015) Supply Chain Management and Advanced Planning, , Berlin: Springer; GS1, (2016) GS1 General Specifications Version, 16; Jara, A.J., Ladid, L., Gómez-Skarmeta, A.F., The internet of everything through IPv6: An analysis of challenges, solutions and opportunities (2013) JoWUA, 4, pp. 97-118},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781509016716},
language={English},
abbrev_source_title={ICIMSA - Int. Conf. Ind. Eng., Manag. Sci. Appl.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Cheng2016150,
author={Cheng, M.C.B. and Wang, J.J.},
title={An integrative approach in measuring hub-port supply chain performance: Potential contributions of a logistics and transport data exchange platform},
journal={Case Studies on Transport Policy},
year={2016},
volume={4},
number={2},
pages={150-160},
doi={10.1016/j.cstp.2016.03.001},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962016882&doi=10.1016%2fj.cstp.2016.03.001&partnerID=40&md5=8ca93804d6b6261251a3033c60ec3422},
affiliation={Geography Department, University of Hong Kong, Hong Kong},
abstract={The widespread globalization of production corporations in the late 20th century, as well as the subsequent trade facilitation policies of state authorities, generates an imperative need to extend the scope of supply chain management. Apart from the evaluation and monitoring of networked operations in different parts of the world, the effectiveness of trade policy measures is also included. This paper examines the potential contributions of a logistics and transport data exchange platform in measuring the performance of supply chain stakeholders of a hub port at different stages along the chain. A case study demonstrates how the integrative characteristics of a logistics and transport data exchange platform can be exploited to measure performances of different parties along the supply chain of a firm. More importantly, the authors explain how such a platform can be transformed into a system that evaluates the integrative supply chain activities of firm, the infrastructure, and the institutional stakeholders. A series of new supply chain performance metrics, including specific monitor end-to-end processes, can be captured by the proposed system. The paper concludes that in view of the significant policy implications of a platform-based supply chain performance measurement (SCPM) system, state intervention is critical to foster a high participation rate of the logistics and transport data exchange platform and develop its SCPM functions. © 2016 World Conference on Transport Research Society.},
author_keywords={Case study;  DTTN;  Hub port;  Performance measurement;  Supply chain},
references={Arvis, J., Saslavsky, D., Ojala, L., Shepherd, B., Busch, C., Raj, A., (2014) The Logistics Performance Index, , The World Bank; Barros, C., Dieke, P., Measuring the economic efficiency of airports: a Simar-Wilson methodology analysis (2008) Transp. Res. Part E: Logist. Transp. Rev., 44 (6), pp. 1039-1051; Bichou, K., Gray, R., A logistics and supply chain management approach to port performance measurement (2004) Marit. Policy Manage., 31 (1), pp. 47-67; Bichou, K., Review of port performance approaches and a supply chain framework to port performance benchmarking (2006) Devolution, Port Governance and Port Performance, 17, pp. 567-598. , Research in Transportation Economics; Bukhsh, F., Weigand, H., E-government controls in service-oriented auditing perspective: beyond single window (2012) Int. J. Electron. Gov. Res., 8 (4), pp. 34-53; (2015) CBSA Service Standards, , http://www.cbsa-asfc.gc.ca/services/serving-servir/standards-normes-eng.html, Canada Border Services Agency, (accessed 04.02.15.); Capineri, C., Leinbach, T., Globalization, e-economy and trade (2004) Transp. Rev., 24 (6), pp. 645-663; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains (2011) Int. J. Prod. Perform. Manage., 60 (6), pp. 583-602; Danese, P., Designing CPFR collaboration: insights from seven case studies (2007) Int. J. Oper. Prod. Manage., 27 (2), pp. 181-204; Dicken, P., (2011) Global Shift: Mapping the Changing Contours of the World Economy, , Guilford Press, New York; (2015) The Unique Approach, , http://www.hk-dttn.com/services/english/ouruniqueapproach.html, (accessed 04.02.15.); (2011) Key Factors In Establishing Single Windows for Handling Import/Export Procedures and Formalities: Trade Facilitation and the Single Window, , United Nation, New York; Frederick, S., Gereffi, G., Upgrading and restructuring in the global apparel value chain: why China and Asia are outperforming Mexico and Central America (2011) Int. J. Technol. Learn. Innov. Dev., 4 (1-3), pp. 67-95; Gadde, L., Snehota, I., Making the most of supplier relationships (2000) Ind. Market. Manage., 29 (4), pp. 305-316; Gereffi, G., Fernandez-Stark, K., (2011) Global Value Chain Analysis: A Primer, , Center on Globalization, Governance & Competitiveness; Grosvold, J., Hoejmose, S.U., Roehrich, J.K., Squaring the circle (2014) Supply Chain Manage.: Int. J., 19 (3), pp. 292-305; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manage., 21 (1-2), pp. 71-87; Guthrie, R., Austin, L., Competitive implications of the internet (1996) Inf. Syst. Manage., 13 (3), pp. 90-92; Hassan, R., (2008) The Information Society: Cyber Dreams and Digital Nightmares, , Polity Press, Cambridge; Holmberg, S., A system perspective on supply chain measurement (2000) Int. J. Phys. Distrib. Logist. Manage., 30 (10), pp. 847-868; (2015) Performance Standards, , http://www.hactl.com/en-US/Services/Performance-Standards.aspx#.VRS15SEcSUk, Hong Kong Air Cargo Terminals Limited, (accessed 05.02.15.); Kaplan, R., Norton, D., (1996) Using the Balanced Scorecard as a Strategic Management System, , Harvard Business Review, January-February; Langen, P., Nijdam, M., Horst, M., New indicators to measure port performance (2007) J. Marit. Res., 4 (1), pp. 23-36; Mann, C.L., Supply chain logistics, trade facilitation and international trade: a macroeconomic policy view (2012) J. Supply Chain, 48 (3), pp. 7-14; Mentzer, J., DeWitt, W., Keebler, J., Min, S., Nix, N., Smith, C., Zacharia, Z., Defining supply chain management (2001) J. Bus. Logist., 22 (2), pp. 1-25; Moise, E., Trade facilitation (2003) Regionalism and the Multilateral Trading System, pp. 87-96. , OECD, Paris, OECD (Ed.); (2015) Nippon Automated Cargo and Port Consolidated System, , http://www.naccs.jp/e/aboutnaccs/aboutnaccs.html, (accessed 05.02.15.); (2005) The Economic Impact of Trade Facilitation. TD/TC/WP12/Final, p. 2005. , OECD, Paris; (2015) Port of Singapore Authority, , https://www.singaporepsa.com/our-commitment/innovation, (accessed 05.02.15.); Porter, M., (2001) Strategy and the Internet, , Harvard Business Review, March; Pugliatti, L., Cloud single window: legal implications of a new model of cross-border window (2011) World Customs J., 5 (2), pp. 3-20; Schaltegger, S., Burritt, R., Measuring and managing sustainability performance of supply chains (2014) Supply Chain Manage.: Int. J., 19 (3), pp. 232-241; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2006) Int. J. Prod. Perform. Manage., 55 (3-4), pp. 242-258; Sillanpaa, I., Empirical study of measuring supply chain performance (2015) Benchmark.: Int. J., 22 (2), pp. 290-308; Sink, D.S., Tuttle, T.C., (1989) Planning and Measurement in Your Organization of the Future, , Industrial Engineering and Management Press; (2015) Services Overview, , https://www.tradexchange.gov.sg/tradexchange/content/services.html, (accessed 05.02.15.); Wang, J., Cheng, M., From a hub port city to a global supply chain management center: a case study of Hong Kong (2010) J. Transp. Geogr., 18 (1), pp. 104-115; Wang, J., Cheng, M., Mature hub ports in the free trade environment, the way forward from a global supply chain perspective: an Asian case (2015) Marit. Policy Manage., 42 (5), pp. 436-458; Wong, T.N., Chow, P.S., Sculli, D., An e-logistics system for sea-freight forwarding (2010) J. Int. Technol. Inf. Manage., 19 (2), pp. 29-42; (2005) Safe Framework of Standards, , World Customs Organization; Yin, R., (2003) Case Study Research: Design and Method, , Sage, Thousand Oaks, CA, (Web refferences)},
correspondence_address1={Cheng, M.C.B.; Department of Geography, Hong Kong; email: cbcheng@netvigator.com},
publisher={Elsevier Ltd},
issn={2213624X},
language={English},
abbrev_source_title={Case Stud. Transp. Policy},
document_type={Article},
source={Scopus},
}

@ARTICLE{Moreira20162345,
author={Moreira, M. and Tjahjono, B.},
title={Applying performance measures to support decision-making in supply chain operations: A case of beverage industry},
journal={International Journal of Production Research},
year={2016},
volume={54},
number={8},
pages={2345-2365},
doi={10.1080/00207543.2015.1076944},
note={cited By 23},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939209827&doi=10.1080%2f00207543.2015.1076944&partnerID=40&md5=1283c28197a0213e73b44f2e2e5250cc},
affiliation={Supply Chain Research Centre, Cranfield School of Management, Cranfield University, Cranfield, United Kingdom},
abstract={Performance measurement systems (PMS) have commonly been applied to evaluate and reward performances at managerial levels, especially in the context of supply chain management. However, evidence suggests that the effective use of PMS can also positively influence the behaviour and improve performance at an operational level. The motivation is to accomplish organisational goals, namely to increase supply chain flexibility by responding to evermore-varying customer demands in a timely manner. The purpose of the study described in this paper was to develop a conceptual framework that adopts performance measures for ex-ante decision-making at an operational level within the supply chain. To guide the research, five questions were asked and subsequently key gaps have been identified. In an attempt to fill the gaps, a case study at a major global brand beverage company has been carried out, and as a result, a conceptual framework of the PMS has been developed. Overall, the research offers a foundation of the applicability and impact of PMS in the supply chain and provides a framework that attends to some of the potential uses of PMS that so far have not been practically applied. The outcomes from the testing indicate that the initial gaps identified in the literature have been addressed and that the framework is judicious with scope for practical applicability. The framework is deemed worthy of further testing in different operational contexts of the supply chain. © 2015 Taylor & Francis.},
author_keywords={decision-making;  flexibility;  performance measurement systems;  performance measures;  supply chain operations},
keywords={Beverages;  Supply chain management, Beverage industry;  Conceptual frameworks;  flexibility;  Improve performance;  Performance measure;  Performance measurement system;  Supply chain flexibility;  Supply chain operation, Decision making},
references={Arzu Akyuz, G., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Beamon, M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Berglund, M., Karltun, J., Human, technological and organizational aspects influencing the production scheduling process (2007) International Journal of Production Economics, 110 (12), pp. 160-174; Bitton, M., (1990) Méthode de Conception et Dimplantation de Systèmes de Measure de Performances pour Organisations Industrielles [Method of Design and Implementation of Performance Measurement Systems for Industrial Organizations], , Thèse dautomatique, Université de Bordeaux I, France; Bongsug, C., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Braz, R., Scavarda, L., Martins, R., Reviewing and improving performance measurement systems: An action research (2011) International Journal of Production Economics, 133 (2), pp. 751-760; Burgess, T.F., Ong, T.S., Shaw, N.E., Traditional or contemporary? the prevalence of performance measurement system types (2007) International Journal of Productivity and Performance Management, 56 (7), pp. 583-602; Chapman, C., Kihn, L.A., Information system integration, enabling control, and performance (2009) Accounting, Organizations and Society, 34 (2), pp. 151-169; Chiarini, A., Vagnoni, E., World-class Manufacturing by Fiat. Comparison with Toyota Production System from a Strategic Management, Management Accounting, Operations Management and Performance Measurement Dimension (2015) International Journal of Production Research, 53 (2), pp. 590-606; Collins, T., Harris, H., Productivity measurement: A shifting paradigm in purchasing (1992) NAPM Insights, 2, pp. 10-11; Danese, P., Romano, P., Relationship between downstream integration, performance measurement systems and supply network efficiency (2012) International Journal of Production Research, 50 (7), pp. 2002-2013; Dixon, J., Nanni, A., Vollmann, T., (1990) The New Performance Challenge: Measuring Operations for World-class Competition, , Homewood, IL: Business One Irwin; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: A review of their consequences and a framework for research (2012) Management Accounting Research, 23 (2), pp. 79-119; Gomes, C., Yasin, M., Lisboa, J., Performance measurement practices in manufacturing firms revisited (2011) International Journal of Operations & Production Management, 31 (1), pp. 5-30; Grafton, J., Lillis, A., Widener, S., The role of performance measurement and evaluation in building organizational capabilities and performance (2010) Accounting, Organizations and Society, 35 (7), pp. 689-706; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., (2001) International Journal of Operations & Production Management, 21 (12), p. 71. , Performance Measures and Metrics in a Supply Chain Environment. 87; Hall, M., The effect of comprehensive performance measurement systems on role clarity, psychological empowerment and managerial performance (2008) Accounting, Organizations and Society, 33 (23), pp. 141-163; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Ilgen, D.R., Fisher, C.D., Taylor, M.S., Consequences of individual feedback on behavior in organizations (1979) Journal of Applied Psychology, 64 (4), pp. 349-371; Kaplan, R., Norton, D., The balanced scorecard -measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kiker, G., Bridges, T., Varghese, A., Seager, T., Linkov, I., Application of multicriteria decision analysis in environmental decision making (2005) Integrated Environmental Assessment and Management, 1 (2), pp. 95-108; Kolehmainen, K., Dynamic strategic performance measurement systems: Balancing empowerment and alignment (2010) Long Range Planning, 43 (4), pp. 527-554; Koufteros, X., Verghese, A., Lucianetti, L., The effect of performance measurement systems on firm performance: A cross-sectional and a longitudinal study (2014) Journal of Operations Management, 32, pp. 313-336; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Laudon, K., Laudon, J., (2014) Management Information Systems: Managing the Digital Firm, , 13th ed. Harlow: Pearson Education Limited; Manikas, I., Terry, L., A case study assessment of the operational performance of a multiple fresh produce distribution centre in the UK (2010) British Food Journal, 112 (6), pp. 653-667; McDaniels, T., Gregory, R., Fields, D., Democratizing risk management: Successful public involvement in an electric utility water management decision (1999) Risk Analysis, 19 (3), pp. 491-504; Moullin, M., Performance measurement definitions (2007) International Journal of Health Care Quality Assurance, 20 (3), pp. 181-183; Mundy, J., Creating dynamic tensions through a balanced use of management control systems (2010) Accounting Organizations and Society, 35 (5), pp. 499-523; Najmi, A., Makui, A., A conceptual model for measuring supply chains performance (2012) Production Planning & Control: The Management of Operations, 23 (9), pp. 694-706; Neely, A., Mills, J., Gregory, M., Platts, K., Performance measurement system design -A literature review and research agenda (2005) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Pavlov, A., Bourne, M., Explaining the effects of performance measurement on performance: An organizational routines perspective (2011) International Journal of Operations & Production Management, 31 (1), pp. 101-122; Pinheiro De Lima, E.P., Gouvea Da Costa, S.E.G., Angelis, J.J., Munik, J., Performance measurement systems: A consensual analysis of their roles (2013) International Journal of Production Economics, 146 (2), pp. 524-542; Poon, T., Choy, K., Chan, F., Lau, H., A real-Time production operations decision support system for solving stochastic production material demand problems (2011) Expert Systems with Applications, 38 (5), pp. 4829-4838; Saaty, T.L., How to make a decision: The analytic hierarchy process (1990) European Journal of Operational Research, 48, pp. 9-26; Schuff, D., Paradice, D., Burstein, F., Power, D., Sharda, R., (2010) Decision Support: An Examination of the DSS Discipline, , Stillwater, OK: Springer; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (34), pp. 242-258; Tang, C., Tomlin, B., The power of flexibility for mitigating supply chain risks (2007) International Journal of Production Economics, 116 (1), pp. 12-27; Tang, C., Tomlin, B., The power of flexibility for mitigating supply chain risks (2008) International Journal of Production Economics, 116 (1), pp. 12-27; Taticchi, P., Garengo, P., Nudurupati, S.S., Tonelli, F., Pasqualino, R., A review of decision-support tools and performance measurement and sustainable supply chain management (2014) International Journal of Production Research, pp. 1-22. , In press; Tayler, W., The balanced scorecard as a strategy-evaluation tool: The effects of implementation involvement and a causal-chain focus (2010) The Accounting Review, 85 (3), pp. 1095-1117; Taylor, A., Taylor, M., Factors influencing effective implementation of performance measurement systems in small and medium-sized enterprises and large firms: A perspective from contingency theory (2014) International Journal of Production Research, 52 (3), pp. 847-866; Van Veen-Dirks, P., Different uses of performance measures: The evaluation versus reward of production managers (2010) Accounting, Organizations and Society, 35 (2), pp. 141-164; Verdecho, J., Alfaro, J., Rodriguez-Rodriguez, R., Foundations for collaborative performance measurement (2009) Production Planning & Control, 20 (3), pp. 193-205; Witcher, B.J., Chau, V.S., Balanced scorecard and hoshin kanri: Dynamic capabilities for managing strategic fit (2007) Management Decision, 45 (3), pp. 518-538; Wouters, M., Wilderom, C., Developing performance-measurement systems as enabling formalization: A longitudinal field study of a logistics department (2008) Accounting, Organizations and Society, 33, pp. 488-516; Zhaofang, M., Li, X., Fu, J., A new approach of supply network performance measurement based on supporting level and operational level (2006) Proceedings of the Technology and Innovation Conference, 2006, pp. 1537-1545. , ITIC 2006, November 6-7, 2006},
correspondence_address1={Tjahjono, B.; Supply Chain Research Centre, United Kingdom; email: b.tjahjono@cranfield.ac.uk},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Searcy2016120,
author={Searcy, C.},
title={Measuring Enterprise Sustainability},
journal={Business Strategy and the Environment},
year={2016},
volume={25},
number={2},
pages={120-133},
doi={10.1002/bse.1861},
note={cited By 84},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958897876&doi=10.1002%2fbse.1861&partnerID=40&md5=7d12ed60ac2e127fb5e6f68d1a8fbdbc},
affiliation={Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, ON, Canada},
abstract={The purpose of this paper is to identify the key requirements for measuring enterprise sustainability. The paper argues that measuring enterprise sustainability requires the explicit consideration of a focal firm, its supply chain and the sustainability context within which the firm operates. Building on this notion, original definitions of enterprise sustainability and enterprise sustainability performance measurement systems (ESPMSs) are proposed. The definitions provide the basis for the development of a novel conceptual framework. The framework is used to identify seven key requirements and 35 associated sub-requirements for an ESPMS. Overall, the requirements highlight that sustainability performance measurement requires a systematic, structured and integrated approach that considers all aspects of enterprise sustainability. The framework presented in this paper is a conceptual model. In recognition of this point, the paper provides discussions on the potential application of the framework and guidance for further research. The academic, managerial and societal implications of the paper are also discussed. © 2016 John Wiley & Sons, Ltd and ERP Environment.},
author_keywords={Corporate sustainability;  Enterprise sustainability;  Performance measurement;  Supply chains;  Sustainability context},
keywords={corporate strategy;  industrial enterprise;  performance assessment;  supply chain management;  sustainability},
references={Ahi, P., Searcy, C., A comparative literature analysis of definitions of green and sustainable supply chain management (2013) Journal of Cleaner Production, 52, pp. 329-341; Akyuz, G.R., Erkan, T.E., Supply chain performance measurement: a literature review (2010) International Journal of Production Research, 48, pp. 5137-5155; Angus-Leppan, T., Benn, S., Young, L., A sensemaking approach to trade-offs and synergies between human and ecological elements of corporate sustainability (2010) Business Strategy and the Environment, 19, pp. 230-244; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: an International Journal, 17, pp. 78-92; Bansal, P., Roth, K., Why companies go green: a model of ecological responsiveness (2000) Academy of Management Journal, 43, pp. 717-736; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 275-292; Beare, D., Buslovich, R., Searcy, C., Linkages between corporate sustainability reporting and public policy (2013) Corporate Social Responsibility and Environmental Management, , in press; Bititci, U., Garengo, P., Dorfler, V., Nudurupati, S., Performance measurement: challenges for tomorrow (2012) International Journal of Management Reviews, 14, pp. 305-327; Bjorklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17, pp. 29-39; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20, pp. 754-771; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: a literature review (2003) International Journal of Business Performance Management, 5, pp. 1-24; Byggeth, S., Hochschorner, E., Handling trade-offs in ecodesign tools for sustainable product development and procurement (2006) Journal of Cleaner Production, 14, pp. 1420-1430; Chee Tahir, A., Darton, R.C., The process analysis method of selecting indicators to quantify the sustainability performance of a business operation (2010) Journal of Cleaner Production, 18, pp. 1598-1607; Colicchia, C., Strozzi, F., Supply chain risk management: a new methodology for a systematic literature review (2012) Supply Chain Management: An International Journal, 17, pp. 403-418; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: a framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60, pp. 583-602; Dyllick, T., Hockerts, K., Beyond the business case for corporate sustainability (2002) Business Strategy and the Environment, 11, pp. 130-141; Edgeman, R., Eskildsen, J., Modeling and assessing sustainable enterprise excellence (2014) Business Strategy and the Environment, 23, pp. 173-187; Elkington, J., (1998) Cannibals with Forks: the Triple Bottom Line of 21st Century Business, , New Society: Stony Creek, CT; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, pp. 105-116. , March-April:; Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., van der Laan, E., van Nunen, J.A.E.E., Van Wassenhove, L.N., Quantitative models for reverse logistics: a review (1997) European Journal of Operational Research, 103, pp. 1-17; Foley, K.J., Meta Management: a Stakeholder/Quality Management Approach to Whole-of-Enterprise Management, Standards Australia Handbook 265-2005 (2005), Sydney; Freeman, R.E., (1984) Strategic Management: a Stakeholder Approach, , Pitman: Boston, MA; Gibson, R.B., Sustainability assessment: basic components of a practical approach (2006) Impact Assessment and Project Appraisal, 24, pp. 170-182; Gimenez, C., Tachizawa, E.M., Extending sustainability to suppliers: a systematic literature review (2012) Supply Chain Management: An International Journal, 17, pp. 531-543; (2013) G4 Sustainability Reporting Guidelines, , GRI: Amsterdam; Goldberg, E., (2002) Aggregated Environmental Indices, , OECD: Paris, France; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61, pp. 518-547; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45, pp. 2819-2840; Hahn, T., Figge, F., Pinkse, J., Preuss, L., Trade-offs in corporate sustainability: you can't have your cake and eat it (2010) Business Strategy and the Environment, 19, pp. 217-229; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: a study in the application of the Analytical Hierarchy Process (2002) European Journal of Operational Research, 141, pp. 70-87; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12, pp. 330-353; Huang, S.H., Keskar, H., Comprehensive and configurable metrics for supplier selection (2007) International Journal of Production Economics, 105, pp. 510-523; Ilgin, M.A., Gupta, S.M., Environmentally conscious manufacturing and product recovery (ECMPRO): a review of the state of the art (2010) Journal of Environmental Management, 91, pp. 563-591; (1992) Business Strategies for Sustainable Development, , IISD: Winnipeg, Canada; Jain, V., Benyoucef, L., Deshmukh, S.G., Strategic supplier selection: some emerging issues and challenges (2009) International Journal of Logistics Systems and Management, 5, pp. 61-88; Kaptein, M., Wempe, J., Sustainability management: balancing conflicting economic, environmental, and social responsibilities (2001) Journal of Corporate Citizenship, 2, pp. 91-106; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22, pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23, pp. 213-229; Kleindorfer, P.R., Singhal, K., Van Wassenhove, L.N., Sustainable operations management (2005) Production and Operations Management, 14, pp. 482-492; Kocabasoglu, C., Prahinski, C., Klassen, R.D., Linking forward and reverse supply chain investments: the role of business uncertainty (2007) Journal of Operations Management, 25, pp. 1141-1160; Kranjc, D., Glavic, P., How to compare companies on relevant dimensions of sustainability (2005) Ecological Economics, 55, pp. 551-563; Lau, K.H., Benchmarking green logistics performance with a composite index (2011) Benchmarking: An International Journal, 18, pp. 873-896; Lenzen, M., Dey, C.J., Murray, S.A., Historical accountability and cumulative impacts: the treatment of time in corporate sustainability reporting (2004) Ecological Economics, 51, pp. 237-250; Lozano, R., Envisioning sustainability three-dimensionally (2008) Journal of Cleaner Production, 16, pp. 1838-1846; McElroy, M.W., Jorna, R.J., van Engelen, J., Social quotients and the social footprint (2008) Corporate Social Responsibility and Environmental Management, 15, pp. 223-234; McElroy, M.W., van Engelen, J.M.L., (2012) Corporate Sustainability Management: the Art and Science of Managing Non-financial Performance, , Earthscan: London; Miemczyk, J., Johnsen, T.E., Macquet, M., Sustainable purchasing and supply management: a structured literature review of definitions and measures at the dyad, chain and network levels (2012) Supply Chain Management: An International Journal, 17, pp. 478-496; Mitchell, R.K., Agle, B.R., Wood, D.J., Towards a theory of stakeholder identification and salience: defining the principle of who and what really counts (1997) Academy of Management Review, 22, pp. 853-886; Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., (2008) Handbook on Constructing Composite Indicators: Methodology and User Guide, , OECD Publication 302008251E1. OECD-European Commission Joint Research Centre: Ispra, Italy; Neely, A., (1998) Measuring Business; Performance, , The Economist Books: London; Pagell, M., Wu, Z., Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars (2009) Journal of Supply Chain Management, 45, pp. 37-56; Palme, U., Tillman, A.M., Sustainable development indicators: how are they used in Swedish water utilities? (2008) Journal of Cleaner Production, 16, pp. 1346-1357; Parmigiani, A., Klassen, R.D., Russo, M.V., Efficiency meets accountability: performance implications of supply chain configuration, control, and capabilities (2011) Journal of Operations Management, 29, pp. 212-223; Rockstrom, J., A safe operating space for humanity (2009) Nature, 461, pp. 472-475; Sarkis, J., Helms, M.M., Hervani, A.A., Reverse logistics and social sustainability (2010) Corporate Social Responsibility and Environmental Management, 17, pp. 337-354; Sarkis, J., Meade, L.M., Presley, A.R., Incorporating sustainability into contractor evaluation and team formation in the built environment (2012) Journal of Cleaner Production, 31, pp. 40-53; Schneider, L., Wallenburg, C.M., Implementing sustainable sourcing - does purchasing need to change? (2012) Journal of Purchasing and Supply Management, 18, pp. 243-257; Searcy, C., Setting a course for corporate sustainability performance measurement (2009) Measuring Business Excellence, 13, pp. 49-57; Searcy, C., Corporate sustainability performance measurement systems: a review and research agenda (2012) Journal of Business Ethics, 107, pp. 239-253; Searcy, C., Karapetrovic, S., McCartney, D., Designing sustainable development indicators: analysis for a case utility (2005) Measuring Business Excellence, 9, pp. 33-41; Searcy, C., Karapetrovic, S., McCartney, D., Integrating sustainable development indicators with existing business infrastructure (2006) International Journal of Innovation and Sustainable Development, 1, pp. 389-411; Searcy, C., McCartney, D., Karapetrovic, S., Sustainable development indicators for the transmission system of an electric utility (2007) Corporate Social Responsibility and Environmental Management, 14, pp. 135-151; Searcy, C., McCartney, D., Karapetrovic, S., Identifying priorities for action in corporate sustainable development indicator programs (2008) Business Strategy and the Environment, 17, pp. 137-148; Seghezzo, L., The five dimensions of sustainability (2009) Environmental Politics, 18, pp. 539-556; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decision Support Systems, 54, pp. 1513-1520; Seuring, S., Gold, S., Sustainability management beyond corporate boundaries: from stakeholders to performance (2013) Journal of Cleaner Production, 56, pp. 1-6; Slawinski, N., Bansal, P., Short on time: the role of time in business sustainability (2009) Proceedings of the 69th Annual Meeting of the Academy of Management, pp. 1-6. , In . Chicago: USA; Spangenberg, J.H., Pfahl, S., Deller, K., Towards indicators for institutional sustainability: lessons from an analysis of Agenda 21 (2002) Ecological Indicators, 2, pp. 61-77; Srivastava, S., Green supply-chain management: a state-of-the-art literature review (2007) International Journal of Management Reviews, 9, pp. 53-80; Steurer, R., Langer, M.E., Konrad, A., Martinuzzi, A., Corporations, stakeholders and sustainable development I: a theoretical exploration of business-society relations (2005) Journal of Business Ethics, 61, pp. 263-281; Tangen, S., Analysing the requirements of performance measurement systems (2005) Measuring Business Excellence, 9, pp. 46-54; Van Marrewijk, M., Concepts and definitions of CSR and corporate sustainability: between agency and communion (2003) Journal of Business Ethics, 44, pp. 95-105; Whiteman, G., Walker, B., Perego, P., Planetary boundaries: ecological foundations for corporate sustainability (2013) Journal of Management Studies, 50 (2), pp. 307-336; Winn, M., Pinske, J., Illge, L., Case studies on trade-offs in corporate sustainability (2012) Corporate Social Responsibility and Environmental Management, 19, pp. 63-68; Wittstruck, D., Teuteberg, F., Integrating the concept of sustainability into the partner selection process: a fuzzy-AHP-TOPSIS approach (2012) International Journal of Logistics Systems and Management, 12, pp. 195-226; (1987), Our Common Future. Oxford University Press: Oxford; Wouters, M., Sportel, M., The role of existing measures in developing and implementing performance measurement systems (2005) International Journal of Operations and Production Management, 25, pp. 1062-1082},
correspondence_address1={Searcy, C.; Department of Mechanical and Industrial Engineering, Canada; email: cory.searcy@ryerson.ca},
publisher={John Wiley and Sons Ltd},
issn={09644733},
language={English},
abbrev_source_title={Bus. Strategy Environ.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gawankar201625,
author={Gawankar, S. and Kamble, S. and Raut, R.},
title={Development, measurement and validation of supply chain performance measurement (SCPM) scale in Indian retail sector},
journal={Benchmarking},
year={2016},
volume={23},
number={1},
pages={25-60},
doi={10.1108/BIJ-06-2013-0068},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953219205&doi=10.1108%2fBIJ-06-2013-0068&partnerID=40&md5=32a73953ace144667e69fd3e80cc3856},
affiliation={Operations & Supply Chain Management, National Institute of Industrial Engineering (NITIE), Mumbai, India},
abstract={Purpose – The purpose of this paper is to develop a scale with a high degree of reliability, validity and dimensionality which help to determine appropriate supply chain performance measurement (SCPM). Design/methodology/approach – The data were compiled and collected from 213 operations and supply chain heads from leading retail stores in India. Confirmatory factor analysis was used to test the validity of the proposed measurement scale. Findings – The major contribution of the present study is the development of SCPM constructs as well as a rigorously validated measurement instrument. The confirmation process is according to the typical standards of scale development. Research limitations/implications – This paper strives to contribute to the literature on the SCPM in Indian retail industry. This paper tests the validity of the measurement scales which will enable the managers to determine the appropriate SCPM. Practical implications – In the long run a good set of identified SCPM and their implementation would inevitability enables in deriving meticulous supply chain strategies. Social implications – SCM managers in evaluating their current supply chain performance. This can help the managers to identify the strengths and weaknesses of their supply chain. Originality/value – This study proved that the SCPM can be used from different perspectives, all of which are integrated into one exclusive assessment instrument, applied to the SCPM in this case. © 2016, Emerald Group Publishing Limited.},
author_keywords={Performance measurement;  Supply chain management},
references={Agrawal, D.K., (2010) Supply Chain Management, Strategy Cases and Best Practices, , Macmillan Publishers India LimitedNew Delhi:; Agus, A., Mohd Hajinoor, S., Lean production supply chain management as driver towards enhancing product quality and business performance: case study of manufacturing companies in Malaysia (2012) International Journal of Quality & Reliability Management, 29 (1), pp. 92-121. , )“”; Ahire, S.L., Golhar, D.Y., Waller, M.A., Development and validation of TQM implementation constructs (1996) Decision Sciences, 27 (1), pp. 23-56. , )“”; Ahn, J., Khandelwal, A., Wei, S.J., The role of intermediaries in facilitating trade (2011) Journal of International Economics, 84 (1), pp. 73-85. , )“”; Amaratunga, D., Baldry, D., Sarshar, M., Process improvement through performance measurement: the balanced scorecard methodology (2001) Work Study, 50 (5), pp. 179-189. , )“”; Anderson, J.C., Gerbing, D.W., Structural equation modeling in practice: a review and recommended two step approach (1988) Psychological Bulletin, 103 (3), pp. 411-423. , )“”; Arawati, A., (2011) Supply chain management, product quality and business performance, , )“” 98-102; Arnold, S.J., Lessons learned from the world’s best retailers (2002) International Journal of Retail and Distribution Management, 30 (11-12), pp. 562-570. , )“”; Bagchi, P.K., Skjoett-Larsen, T., Supply chain integration: a European survey (2005) The International Journal of Logistics Management, 16 (2), pp. 275-294. , )“”; Bagozzi, R.P., (1980) Causal Model in Marketing, , John Wiley SonsNew YorkNY:; Bagozzi, R.P., Fornell, C., Theoretical concepts, measurements, and meaning (1982) A Second Generation of Multivariate Analysis, pp. 24-38. , )“”New YorkNY:; Bagozzi, R.P., Yi, Y., Phillips, L.W., Assessing construct validity in organizational research (1991) Administrative Science Quarterly, 36 (3), pp. 421-458. , )“”; Barnabe, F., Busco, C., The causal relationships between performance drivers and outcomes reinforcing balanced scorecards’ implementation through system dynamics models (2012) Journal of Accounting and Organizational Change, 8 (4), pp. 528-538. , )“”; Beamon, B.M., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , )“”; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342. , a)“”; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3-4), pp. 275-292. , b)“”; Behrouzi, F., Wong, K.Y., An investigation and identification of lean supply chain performance measures in the automotive SMEs (2011) Scientific Research and Essays, 6 (24), pp. 5239-5252. , )“”; Bentler, P.M., (1992) EQS Structural Equation Program Manual, , BMDP Statistical SoftwareLos AngelesCA:; Bernardes, E.S., Zsidisin, G.A., An examination of strategic supply management benefits and performance implications (2008) Journal of Purchasing and Supply Management, 14 (4), pp. 209-219. , )“”; Berrah, L., Clivillé, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Computers in Industry, 58 (7), pp. 709-719. , )“”; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62. , a)“”; Bhasin, S., Lean and performance measurement (2008) Journal of Manufacturing Technology Management, 19 (5), pp. 670-684. , )“”; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations and Production Management, 26 (12), pp. 1325-1350. , )“”; Bowersox, D.J., The strategic benefits of logistics alliances (1990) Harvard Business Review, 68 (4), pp. 36-43. , )“”; Bukh, P.N., Malmi, T., Re-examining the cause-and-effect principle of the balanced scorecard (2005) Accounting in Scandinavia: The Northern Lights, pp. 87-113. , JönssonS.MouritsenJ.)“”in(Eds)LiberMalmö:; Butterman, G., Germain, R., Iyer, K.N.S., Contingency theory ‘fit’ as gestalt: an application to supply chain management (2008) Transportation Research Part E: Logistics and Transportation Review, 44 (6), pp. 955-969. , )“”; Buzzell, R.D., Gale, B.T., Sultan, R.G.M., Market share – a key to profitability (1975) Harvard Business Review, 53, pp. 97-106. , )“”; Byrne, B.M., (1994) Structural Equation Modeling with EQS and EQS/Windows, , SageNewbury ParkCA:; Cai, Z.-Y., Xiao, R.-B., Tan, Y., Gong, F.-M., Fuzzy adaptive production plan dispatching of cycle supply chain under uncertainty conditions (2008) Control and Decision, 23 (5), pp. 525-529. , )“”; Campbell, D.T., Fiske, D.W., Convergent and discriminant validation by the multitrait-multimethod matrix (1959) Psychological Bulletin, 56 (2), pp. 81-105. , )“”; Cao, M., Zhang, Q., Supply chain collaboration: impact on collaborative advantage and firm performance (2011) Journal of Operations Management, 20 (3), pp. 163-180. , )“”; Cavinato, J.L., A total cost/value model for supply chain competitiveness (1992) Journal of Business Logistics, 13 (2), pp. 285-301. , )“”; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548. , )“”; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223. , a)“”; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: a process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190. , b)“”; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642. , )“”; Chari, A., Raghavan, T.C.A.M., Foreign direct investment in India’s retail bazaar: opportunities and challenges (2012) The World Economy, 35 (1), pp. 79-90. , )“”; Chavez, R., Fynes, B., Gimenez, C., Wiengarten, F., Assessing the effect of industry clockspeed on the supply chain management practice-performance relationship (2012) Supply Chain Management: An International Journal, 17 (3), pp. 235-248. , )“”; Chen, I.J., Paulraj, A., Understanding supply chain management: critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-163. , )“”; Chitakornkijsil, P., Business performing social responsibility activities and corporate social responsibility issues (2012) The International Journal of Organizational Innovation, 5 (1), pp. 309-323. , )“”; Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: definition and measurement (1994) International Journal of Physical Distribution and Logistics Management, 24 (1), pp. 17-28. , )“”; Christopher, M., Logistics and supply chain management: strategies for reducing cost and improving service (1998) Financial Times, , )“”London:; Christy, D.P., Grout, J.R., Safeguarding supply chain relationships (1994) International Journal of Production Economics, 36 (3), pp. 233-242. , )“”; Cirtita, H., Daniel, A., Segura, G., Measuring downstream supply chain performance (2012) Journal of Manufacturing Technology Management, 23 (3), pp. 299-314. , )“”; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships (2008) International Journal of Operations and Production Management, 28 (3), pp. 238-250. , )“”; Cuthbertson, R., Piotrowicz, W., Supply chain best practices – identification and categorisation of measures and benefits (2008) International Journal of Productivity and Performance Management, 57 (5), pp. 389-404. , )“”; Deshpande, A., Supply chain management dimensions, supply chain performance and organizational performance: an integrated framework (2012) International Journal of Business and Management, 7 (8), pp. 1819-1833. , )“”; Dey, P.K., Hariharan, S., Clegg, B.T., Measuring the operational performance of intensive care units using the analytic hierarchy process approach (2006) International Journal of Operations and Production Management, 26 (8), pp. 849-865. , )“”; Ding, L., Velicer, W.F., Harlow, L.L., Effects of estimation methods, number of indicators per factor, and improper solution on structural equation modeling fit indices (1995) Structural Equation Modeling, 2 (2), pp. 119-144. , )“”; Ellram, L.M., Feitzinger, E., Using total profit analysis to model supply chain decisions (1997) Journal of Cost Management, 11, pp. 12-21. , )“”; Finne, S., Sivonen, H., (2008) The Retail Value Chain: How to Gain Competitive Advantage Through Efficient Consumer Response (ECR) Strategies, pp. 54-56. , Kogan Page Ltd; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: a contingency and configuration approach (2010) Journal of Operations Management, 28 (4), pp. 58-71. , a)“”; Flynn, B.B., Wu, S.J., Melnyk, S., Operational capabilities: hidden in plain view (2010) Business Horizons, 53, pp. 247-256. , b)“”; Flynn, B.B., Sakakibara, S., Schroede, R., Bates, K., Flynn, J., Empirical research methods in operations management (1990) Journal of Operational Management, 9 (2), pp. 250-284. , )“”; (2006), www.forbes.com/fdc/logistics/glossary_d.shtml, )“Logistics glossary”:(accessed 1 July 2011); Fynes, B., de Búrca, S., Marshall, D., Environmental uncertainty, supply chain relationship quality and performance (2004) Journal of Purchasing and Supply Management, 10 (4-5), pp. 179-190. , )“”; Fynes, B., de Burca, S., Voss, C., Supply chain partnership quality: the competitive environment and performance (2005) International Journal of Production Research, 43 (16), pp. 3303-3320. , )“”; Garvin, D.A., (1988) Managing Quality, , New YorkNY:; Geiger, S.W., Ritchie, W.J., Marlin, D., Strategy/structure fit and firm performance (2006) Organization Development Journal, 24 (2), pp. 10-22. , )“”; Gimenez, C., van der Vaart, T., van Donk, D.P., Supply chain integration and performance: the moderating effect of supply complexity (2012) International Journal of Operations and Production Management, 32 (5), pp. 583-610. , )“”; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547. , )“”; Gorane, S.J., Kant, R., Singh, M.D., Functional benchmarking of organisations on the basis of supply chain performance measures (2012) Management of Innovation and Technology (ICMIT), pp. 146-151. , )“”; Goyal, P., Supply chain management integration performance measurement and evaluation (2012) International Journal of Research in Management, Economics and Commerce, 2 (1), pp. 100-113. , )“”; Gray, J., Harvey, T., (1992) Quality Value Banking: Effective Management Systems that Increase Earnings, Lower Costs, and Provide Competitive Customer Service, , John Wiley SonsInc.New YorkNY:; Guangliang, Z., (2011) Research on the supporting role of information integration to supply chain management in large retail enterprises, , )“” 1-5; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , )“”; Gunasekaran, A., Ngai, E.W.T., Modelling and analysis of build-to-order supply chains (2009) European Journal of Operational Research, 195, pp. 319-334. , )“”; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87. , )“”; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (4), pp. 333-347. , )“”; Hair, J., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , 5th ed.Prentice-HallNJ:; Hergert, M., Morris, D., Accounting data for value chain analysis (1989) Strategic Management Journal, 10 (2), pp. 175-188. , )“”; Hewitt, F., Information technology mediated business process management – lessons from the supply chain (1999) International Journal of Technology Management, 17 (1-2), pp. 37-53. , )“”; Ho, C.J., Measuring system performance of an ERP-based supply chain (2007) International Journal of Production Research, 45 (6), pp. 12-30. , )“”; Hoek, R.I., Measuring the unmeasurable” – measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192. , )“”; Hou, J., Zeng, A.Z., Zhao, L., Coordination with a backup supplier through buy-back contract under supply disruption (2011) Transportation Research, 46 (6), pp. 881-895. , )“”; Hwang, Y., Lin, Y., Lyu, J., The performance evaluation of SCOR sourcing process – the case study of Taiwan’s TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423. , )“”; Ibrahim, S.E., Ogunyemi, O., The effect of linkages and information sharing on supply chain and export performance: an empirical study of Egyptian textile manufacturers (2012) Journal of Manufacturing Technology Management, 23 (4), pp. 441-463. , )“”; Janeš, A., Faganel, A., Instruments and methods for the integration of company’s strategic goals and key performance indicators (2013) Kybernetes, 42 (6), pp. 928-942. , )“”; Janvier-James, A.M., A new introduction to supply chains and supply chain management: definitions and theories perspective (2012) International Business Research, 5 (1), pp. 194-207. , )“”; Jap, S.D., Anderson, E., Safeguarding interorganizational performance and continuity under ex post opportunism (2003) Management Science, 49 (12), pp. 1684-1701. , )“”; Johansson, H.J., McHugh, P., Pendlebury, A.J., Wheeler, W.A., (1993) Business Process Reengineering: Breakpoint Strategies for Market Dominance, , WileyChichester:; Johnston, D.A., McCutcheon, D.M., Stuart, F.I., Kerwood, H., Corrigendum to effects of supplier trust on performance of cooperative supplier relationships (2004) Journal of Operations Management, 22 (1), pp. 23-38. , a)“”; Johnston, D.A., McCutcheon, D.M., Stuart, F.I., Kerwood, H., Effects of supplier trust on performance of cooperative supplier relationships (2004) Journal of Operations Management, 22 (1), pp. 23-38. , b)“”; Jöreskog, K.G., Sörbom, D., (1989) Lisrel 7: A Guide to the Program and Applications, , 2nd ed.SPSSChicagoIL:; Jöreskog, K.G., Sörbom, D., (1993) LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language, , Scientific Software InternationalChicagoIL:; Kanji, G., Wong, A.K., Business excellence model for supply chain management (1999) Total Quality Management, 10 (1), pp. 1147-1168. , )“”; Kiefer, A.W., Novack, R.A., An empirical analysis of warehouse measurement systems in the context of supply chain implementation (1999) Transportation Journal, 38 (3), pp. 18-27. , )“”; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83. , )“”; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19. , )“”; Lee, H.L., Billington, C., Supply chain management: pitfalls and opportunities (1992) Sloan Management Review, 33 (5), pp. 65-73. , )“”; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102. , )“”; Lee, J., Kim, Y., Effect of partnership quality on IS outsourcing: conceptual framework and empirical validation (1999) Journal of Management Information Systems, 15 (4), pp. 26-61. , )“”; Lemke, F., Goffin, K., Szwejczewski, M., Investigating the meaning of supplier-manufacturer partnerships: an exploratory study (2003) International Journal of Physical Distribution and Logistics Management, 33 (1), pp. 12-35. , )“”; León-Soriano, R., Muñoz-Torres, M.J., Chalmeta-Rosaleñ, R., Methodology for sustainability, strategic planning and management (2010) Industrial Management and Data Systems, 110 (2), pp. 249-268. , )“”; Li, D., O’Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 20 (1-3), pp. 147-157. , )“”; Li, S., Lin, B., Accessing information sharing and information quality in supply chain management (2006) Decision Support Systems, 42 (3), pp. 1641-1656. , )“”; Lockamy, A.I.I.I., McCormack, K., Linking SCOR planning practices to supply chain performance: an exploratory study (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218. , )“”; Lummus, R.R., Vokurka, R.J., Duclos, L.K., Delphi study on supply chain flexibility (2005) International Journal of Production Research, 43 (13), pp. 2687-2708. , )“”; Lyons, A.C., Mondragon, A.E.C., Piller, F., Poler, R., Glass pipelines: the role of information systems in supporting customer-driven supply chains (2012) Decision Engineering, pp. 45-70. , )“”London; McCormack, K., Lockamy, A., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278. , )“”; McCormack, K., Ladeira, M.B., de Oliveira, M.P.V., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282. , )“”; Manrodt, K.B., Abott, J., Visatek, K., (2005) Understanding the Lean Supply Chain: Beginning the Journey 2005 Report on Lean Practices in the Supply Chain, , APICSGeorgia Southern UniversityOracle Supply Chain Visions; Marksberry, P., Investigating ‘the way’ for Toyota suppliers: a quantitative outlook on Toyota’s replicating efforts for supplier development (2012) Benchmarking: An International Journal, 19 (2), pp. 277-298. , )“”; Martin, P.R., Patterson, J.W., On measuring company performance within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460. , )“”; Meixell, M.J., Gargeya, V.B., Global supply chain design: a literature review and critique (2005) Transportation Research, 41 (6), pp. 531-550. , )“”; Molnár, A., Gellynck, X., Felföldi, J., (2007) Towards the development of an innovative supply chain performance measurement instrument for the fruit and vegetable sector, , )“” 42-50; Motwani, J., Kumar, A., Youssef Mohamed, A., Essam, M., Forecasting quality of Indian manufacturing organizations: an exploratory analysis (1997) Total Quality Management, 8 (6), pp. 361-373. , )“”; Narasimhan, R., Jayaram, J., Causal linkage in supply chain management: an exploratory study of North American manufacturing firms (1998) Decision Science, 29 (3), pp. 579-605. , )“”; Naylor, J.B., Naim, M.M., Berry, D., Legality: integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1), pp. 107-118. , )“”; Neely, A., Adams, C., Crowe, P., The performance prism in practice measuring excellence (2001) The Journal of Business Performance Management, 5 (2), pp. 6-12. , )“”; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , )“”; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Realizing strategy through measurement (1994) International Journal of Operations and Production Management, 14 (3), pp. 140-152. , )“”; Nørreklit, H., The balance on the balanced scorecard a critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88. , )“”; Norton, D.P., Russell, R.H., The office of strategy management – the state of the art, 2011 (2011) Balanced Scorecard Report, 13 (1), pp. 1-6. , )“”; Nunnally, J.C., (1978) Psychometric, , 2nd ed.McGraw-HillNew YorkNY:; Perea, E., Grossmann, I., Ydstie, E., Tahmassebi, T., Dynamic modeling and classical control theory for supply chain management (2000) Computers and Chemical Engineering, 24 (2-7), pp. 1143-1149. , )“”; Pittiglio, R., Todd, T., McGrath, M., Integrated supply chain performance measurement: a multi-industry consortium recommendation (1994) Conference Proceedings, Oak Brook, IL, pp. 1-16. , )“”; Puigjaner, L., Lainez, J.M., Capturing dynamics in integrated supply chain management (2008) Computers and Chemical Engineering, 33 (11), pp. 9556-9570. , )“”; Raghunathan, B., Raghunathan, T.S., Tu, Q., Dimensionality of the strategic grid framework: the construct and its measurement (1999) Information System Research, 10 (4), pp. 343-355. , )“”; Risch, E., (1991) Retail Merchandising, , Merrill Publishing Co.ColumbusOH:; Robinson, C.J., Malhotra, M.K., Defining the concept of supply chain quality management and its relevance to academic and industrial practice (2005) International Journal of Production Economics, 96 (3), pp. 315-337. , )“”; Rolstadas, A., (1995) Performance Management: A Business Process Benchmarking Approach, , Chapman HallLondon:; Ryu, S., Park, J.E., Min, S., Factors of determining long-term orientation in interfirm relationships (2007) Journal of Business Research, 60 (12), pp. 1225-1233. , )“”; Sethi, V., King, W.R., Development of measures to assess the extent to which an information technology application provides competitive advantage (1994) Management Science, 40 (12), pp. 1601-1627. , )“”; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , )“”; Shin, H., Collier, D.A., Wilson, D.D., Supply management orientation and supplier/buyer performance (2000) Journal of Operations Management, 18 (3), pp. 317-333. , )“”; Spekman, R.E., Kamauff, J., Myhr, N., An empirical investigation into supply chain management: a perspective on partnerships (1998) Supply Chain Management: An International Journal, 3 (2), pp. 53-67. , )“”; Srinivasan, M., Mukherjee, D., Gaur, A.S., Buyer-supplier partnership quality and supply chain performance: moderating role of risks, and environmental uncertainty (2011) European Management Journal, 29 (4), pp. 260-271. , )“”; Stevens, G., Successful supply chain management (1990) Management Decision, 28 (8), pp. 25-30. , )“”; Stock, G.N., Greis, N.P., Kasarda, J.D., Logistics, strategy and structure: a conceptual framework (1998) International Journal of Operations and Production Management, 18 (1), pp. 37-52. , )“”; Stock, G.N., Greis, N.P., Kasarda, J.D., Enterprise logistics and supply chain structure: the role of fit (2000) Journal of Operations Management, 18 (5), pp. 531-547. , )“”; Stouthuysen, K., Slabbinck, H., Roodhooft, F., Controls, service type and perceived supplier performance in interfirm service exchanges (2012) Journal of Operations Management, 30 (5), pp. 423-435. , )“”; Supasansanee, L., Kasiphongphaisan, P., (2009), )“Logistics management in retail industry – a case study of 7-eleven in Thail”master thesisInternational Logistics Supply Chain ManagementJönköping International Business SchoolJönköping University 1-72; Swafford, P.M., Ghosh, S., Murthy, N.N., Achieving supply chain agility through IT integration and flexibility (2008) International Journal of Production Economics, 116 (2), pp. 288-297. , )“”; Tan, K.C., Kannan, V.J., Handeld, R.B., Ghosh, S., Supply chain management: an empirical study of its impact on firm performance (1999) International Journal of Operations and Production Management, 19 (10), pp. 1034-1052. , )“”; Tangen, S., Performance measurement: from philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737. , )“”; Taticchi, P., (2008), )“Business performance measurement management: implementation of principles in SMEs enterprise networks”PhD thesisUniversity of PerugiaPerugia; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains: a literature review and a research agenda (2013) International Journal of Productivity and Performance Management, 62 (8), pp. 782-804. , )“”; Tung, J., A study of product innovation on firm performance (2012) The International Journal of Organizational Innovation, 4 (3), pp. 84-97. , )“”; Van Der Vorst, J.G.A.J., Performance measurement in agri-food supply-chain networks (2006) Logistics and Operations Research Group, pp. 14-24. , )“”Wageningen University; Venkatraman, N., Ramanujam, V., Measurement of business economic performance: an examination of method convergent (1987) Journal of Management, 13 (1), pp. 109-122. , )“”; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, J.P., Designing supply chains: towards theory development (2006) International Journal of Production Economics, 100 (2), pp. 223-238. , )“”; Whitten, G.D., Green, K.W.J., Zelbst, P.J., Triple-a supply chain performance (2012) International Journal of Operations & Production Management, 32 (1), pp. 28-48. , )“”; Wilson, D.T., Vlosky, R.P., Inter-organizational information system technology and buyer-seller relationships (1998) Journal of Business and Industrial Marketing, 13 (3), pp. 215-234. , )“”; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51. , )“”; Wood, A., Extending the supply chain: strengthening links with IT (1997) Chemical Week, 159 (25), pp. 25-26. , )“”; Wouters, M., A developmental approach to performance measures-results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78. , )“”; Yao, K., Liu, C., An integrated approach for measuring supply chain performance (2006) Journal of Modern Accounting and Auditing, 2 (10), pp. 17-22. , )“”; Zaheer, A., Venkatraman, N., Relational governance as an interorganizational strategy: an empirical test of the role of trust in economic exchange (1995) Strategic Management Journal, 16 (5), pp. 373-392. , )“”; Zelbst, P.J., Green, K., Sower, V.E., Reyes, P., Impact of supply chain linkages on supply chain performance (2009) Industrial Management and Data Systems, 109 (5), pp. 665-682. , )“”; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (1), pp. 512-521. , )“”; Flynn, B.B., Schroeder, R.C., Sakakibara, S., A framework for quality management research and an associated measurement instrument (1994) Journal of Operations Management, 11 (4), pp. 339-366. , )“”; Ganesan, S., George, M., Jap, S., Palmatier, R.W., Weitz, B., Supply chain management and retailer performance: emerging trends, issues, and implications for research and practice (2009) Journal of Retailing, 85 (1), pp. 84-94. , )“”; Garvin, D.A., What does product quality really mean? (1984) Sloan Management Review, pp. 25-43. , )“”; Gunasekaran, A., Ngai, N.W.T., Build-to-order supply chain management: literature review and framework for development (2005) Journal of Operations Management, 23 (5), pp. 423-451. , )“”; Grant, R., Clarke, R.J., Kyriazis, E., Research needs for assessing online value creation in complex consumer purchase process behaviour (2010) Journal of Retailing and Consumer Services, 17 (1), pp. 53-60. , )“”; Kauffman, R.J., Mohtadi, H., Proprietary and open systems adoption in e-procurement: a risk-augmented transaction cost perspective (2004) Journal of Management Information Systems, 21 (1), pp. 137-166. , )“”; Lummus, R.R., Krumwiede, D., Vokurka, R.J., The relationship of logistics to supply chain management: developing a common industry definition (2001) Industrial Management and Data Systems, 101 (8), pp. 426-432. , )“”; Lyons, A., Coleman, J., Kehoe, D., Coronado, A., Performance observation and analysis of an information re-engineered supply chain: a case study of an automotive firm (2004) Industrial Management and Data Systems, 104 (8), pp. 658-666. , )“”; Manrodt, K.B., Abott, J., Vitasek, K., (2008) Lean Practices in the Supply Chain. Report on Lean Practices in the Supply Chain, , www.apics.org, APICSChicagoIL:; Neely, A.D., Mills, J.F., Gregory, M.J., Richards, A.H., Platts, K.W., Bourne, M.C.S., (1996) Getting the Measure of Your Business, , Findlay PublicationsHorton Kirby:; Randall, W.S., Gibson, B.J., Defee, C.C., Williams, B.D., Retail supply chain management: key priorities and practices (2011) The International Journal of Logistics Management, 22 (3), pp. 390-402. , )“”; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18. , )“”; Whitten, D., Inman, R.A., Sale, R.S., Green, K., Agile manufacturing: relation to JIT, operational performance and firm performance (2011) Journal of Operations Management, 29 (4), pp. 343-355. , )“”; Zelbst, P.J., Green, K., Sower, V.E., Gary, B., RFID utilization and information sharing: the impact on supply chain performance (2010) Journal of Business and Industrial Marketing, 25 (8), pp. 582-589. , )“”},
correspondence_address1={Gawankar, S.; Operations & Supply Chain Management, India; email: gawankar.shradha@gmail.com},
publisher={Emerald Group Publishing Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Jardioui2016338,
author={Jardioui, M. and Alami, S. and Okar, C.},
title={What are the critical success factors for the implementation of supply chain performance measurement systems in SMEs},
journal={Proceedings of 2015 International Conference on Industrial Engineering and Systems Management, IEEE IESM 2015},
year={2016},
pages={338-343},
doi={10.1109/IESM.2015.7380180},
art_number={7380180},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971668152&doi=10.1109%2fIESM.2015.7380180&partnerID=40&md5=3f6d64da113fd42311a0d0434d9912f0},
affiliation={Laboratory of Mechanical Engineering, Industrial and Innovation, Faculty of Science and Technology, Hassan i University, Km 3, road of Casablanca, Settat, Morocco; Laboratory of Systems Analysis and Modeling for Decision Support, College of Technology, Hassan i University, Berrchid, Morocco},
abstract={In an attempt to cope with an economic environment that doesn't seem to slow its development and while under the pressure of being more competitive, every company takes in a new strategy enabling it to be in better conditions in its business sector. Research has shown that performance measurement systems, especially those deriving from the mission of the supply chain and the process of strategic planning, have a key role in the development of the companies' management and performance. There is a growing literature concerned with the design and the implementation of supply chain performance measurement (SCPMS) systems and plenty of them mention the difficulties of their implementation especially in SMEs. Using a critical success factor (CSF) approach and based on the analysis of the existing literature, we investigate this crucial issue in-depth and we put forward and define CSFs that are crucial for implementing SCPM systems in SMEs throughout this article. The empirical investigation is still ongoing and it will be carried out by using critical thinking, inductive reasoning and in-depth interviews in Moroccan small and medium enterprises (SMEs) © 2015 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.},
author_keywords={critical success factors;  performance measurement system;  small and medium enterprises;  Supply chain;  Supply chain performance measurement system},
keywords={Enterprise resource planning;  Supply chain management;  Supply chains, Critical success factor;  Economic environment;  Empirical investigation;  In-depth interviews;  Inductive reasoning;  Performance measurement system;  Small and medium enterprise;  Supply chain performance measurements, Research and development management},
references={Garengo, P., Biazzo, S., Bernardi, G., Design and implementing a performance measurement system in SMEs: A bottom up approach (2007) 14th International Annual EurOMA Conference Managing Operations in Expanding Europe; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) InEmerging Trends in Engineering and Technology (ICETET), 2009 2nd International Conference on, pp. 802-807. , December IEEE; Cocca, P., Alberti, M., A framework to assess performance measurement systems in SMEs (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 186-200; Bourne, M., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Liu, J., Zhang, S., Hu, J., A case study of an inter-enterprise workflow-supported supply chain management system (2005) Information &Management, 42 (3), pp. 441-454; Christensen, W.J., Germain, R.N., Birou, L., Variance vs average: Supply chain lead-time as a predictor of financial performance (2007) Supply Chain Management: An International Journal, 12 (5), pp. 349-357; Tangen, T., Insights from research: Improving the performance of a performance measure (2005) Measuring Business Excellence, 9 (2), pp. 4-11; Kazemkhanlou, H., Ahadi, H.R., Study of Performance Measurement Practices in Supply Chain Management; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations &Production Management, 21 (1-2), pp. 71-87; Elisa, K.S., Masruroh, N.A., Good criteria for supply chain performance measurement (2014) J Eng Bus Manag, 6 (9); Storey, D.J., Understanding the small business sector (1994) University of Illinois at Urbana-Champaign's Academy for Entrepreneurial Leadership Historical Research Reference in Entrepreneurship; Welsh, J.A., White, J.F., A small business is not a little big business (1981) Harvard Business Review, 59 (4), p. 18; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T., State of the art literature review on performance measurement (2011) Computers &Industrial Engineering, 60 (2), pp. 279-290; Bititci, U., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Ates, A., Garengo, P., Cocca, P., Bititci, U., The development of sme managerial practice for effective performance management (2013) Journal of Small Business and Enterprise Development, 20 (1), pp. 28-54; Avots, I., Why does project management fail?(Project management systems failure analysis, discussing cost, products quality and project objectives) (1969) California Management Review, 12, pp. 77-82; Pinto, J.K., Covin, J.G., Critical factors in project implementation: A comparison of construction and R & D projects (1989) Technovation, 9 (1), pp. 49-62; Slevin, D.P., Pinto, J.K., Balancing strategy and tactics in project implementation (1986) Sloan Management Review, 29 (1), pp. 33-41; Shahin, A., Jamshidian, M., Critical success Factors in Project Management: A Comprehensive Review (2006) International Project Management Conference, 2; Milosevic, D., Patanakul, P., Standardized project management may increase development projects success (2005) International Journal of Project Management, 23 (3), pp. 181-192; Olszak, C.M., Ziemba, E., Critical success factors for implementing business intelligence systems in small and medium enterprises on the example of upper Silesia, Poland (2012) Interdisciplinary Journal of Information, Knowledge, and Management, 7, pp. 129-150; Turban, E., McLean, E., Wetherbe, J., Information technology for management (2001) Improving Quality and Productivity, , New Jersey: John Wiley &Sons; Fortune, J., White, D., Framing of project critical success factors by a systems model (2006) International Journal of Project Management, 24 (1), pp. 53-65; Khang, D.B., Moe, T.L., Success criteria and factors for international development projects: A life-cycle-based framework (2008) Project Management Journal, 39 (1), pp. 72-84; Bourne, M., Researching performance measurement system implementation: The dynamics of success and failure (2005) Production Planning &Control, 16 (2), pp. 101-113; Morgan, C., Supply network performance measurement: Future challenges (2007) The International Journal of Logistics Management, 18 (2), pp. 255-273; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Taylor, A., Taylor, M., Antecedents of effective performance measurement system implementation: An empirical study of UK manufacturing firms (2013) International Journal of Production Research, 51 (18), pp. 5485-5498; Okar, C., Beidouri, Z., Mssassi, S., Barrijal, S., A maturity model for SCPMS project: An empirical investigation in large sized Moroccan companies (2011) International Journal of Computer Science Issues, 8 (2), pp. 203-212; Bourne, L., Walker, D.H.T., Project relationship management and the Stakeholder Circle™ (2009) International Journal of Managing Projects in Business, 1 (1), pp. 125-130; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Kueng, P., Krahn, A.J., Building a process performance measurement system some early experiences (1999) Journal of Scientific &Industrial Research, 58, pp. 149-159; Bititci, U.S., Mendibil, K., Nudurupati, S., Turner, T., Garengo, P., The interplay between performance measurement organizational culture and management styles (2004) Measuring Business Excellence, 8, pp. 28-41; Wouters, M., Sportel, M., The role of existing measures in developing and implementing performance measurement systems (2005) International Journal of Operations &Production Management, 25, pp. 1062-1082; Choong, K.K., Has this large number of performance measurement publications contributed to its better understanding (2014) A Systematic Review for Research and Applications. International Journal of Production Research, 52 (14), pp. 4174-4197},
editor={Framinan J.M., Perez Gonzalez P., Artiba A.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9782960053265},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Syst. Manag., IEEE IESM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Rosado2016788,
author={Rosado, J.O. and Relvas, S.},
title={Integral supply chain performance management system design and implementation},
journal={Proceedings of 2015 International Conference on Industrial Engineering and Systems Management, IEEE IESM 2015},
year={2016},
pages={788-802},
doi={10.1109/IESM.2015.7380248},
art_number={7380248},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965118027&doi=10.1109%2fIESM.2015.7380248&partnerID=40&md5=e5b2d11553ee611b14c37580e4d55c47},
affiliation={Instituto Superior Técnico, Universidade de Lisboa, Portugal; CEG-IST - Centre for Management Studies, Instituto Superior Técnico, Universidade de Lisboa, Portugal},
abstract={Supply chain versus supply chain is the new mantra for contemporary competitive businesses. To reach the desired competitive advantage and sustainability, companies are acknowledging the importance of supply chain management (SCM) and the benefits of improving it by a performance management system (PMS). Since one cannot manage without measure, a performance measurement system (PMeS) is a basic requirement for any PMS. Combining SCM and PMS into a supply chain performance management system (SCPMS) should enable enterprise quest in becoming business differentiators. There is a large gap in literature when it comes to merging these two concepts into the SCPMS, so this research aims at bridging this gap, providing a case study within the high tech industry. A SCMPS framework is developed, together with a supply chain performance measurement system (SCPMeS) and the intent is to create awareness for further validation and development. This research is still in an exploratory level and will be followed by an implementation period in a high tech industry firm. © 2015 International Institute for Innovation, Industrial Engineering and Entrepreneurship - I4e2.},
author_keywords={high tech industry;  performance management system;  performance measurement system;  supply chain management;  supply chain performance},
keywords={Competition, Competitive advantage;  Design and implementations;  High tech industry;  Performance management systems;  Performance measurement system;  Supply chain managements (SCM);  Supply chain performance;  Supply chain performance measurements, Supply chain management},
references={The future of business: Supply chains (2014) The Economist, , The Economist Intelligence Unit Limited; Global supply chain survey (2013) Next Generation Supply Chains, , Efficient, fast and tailored. PwC; Ellinger, A.E., Improving marketing/logistics cross-functional collaboration in the supply chain (2000) Industrial Marketing Management Journal, 29 (1), pp. 85-96; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41, pp. 23-35; Coveney, M., Performance management vs. Performance measurement (2010) FSN: Business Systems News and Analysis for Finance and IT Professionals; Tseng, F.M., Chiu, Y.J., Chen, J.S., Measuring business performance in the high tech manufacturing industry: A case study of Taiwan's largesized TFT-LCD panel companies (2007) International Journal of Management Science, 37, pp. 686-697; Wang, R., Wang, K.Y., Franchisor-franchisee supply chain cooperation: Sharing of demand forecast information in high tech industries (2012) International Journal of Industrial Marketing Management, 41 (7), pp. 1164-1173; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Accenture consulting, technology, outsourcing (2012) Semiconductor Supply Chains: And Urgent Need for Change; Forster, C., Aelker, M.Z.J., Westkamper, E., Bauernhansl, T., Collaborative value chain management between automotive and semiconductor industry: An analysis of differences and improvement measures (2013) 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 12, pp. 312-317; Huethorst, K., (2011) Sultans of Swing: The Persistence of the Bullwhip Effect, , Tilburg University Press, Tilburg School of Economics and Management; Forster, C., Zapp, M., Cross-company collaboration between automotive and semiconductor industry-alignment of orders and capacity in complex supply networks (2011) International Journal of Industrial Engineering: 16th Annual International Conference on Industrial Engineering Theory, Applications and Practice, p. 164; Poiger, M., (2010) Improving Performance Supply Chain Processes by Reducing Variability, , in PhD Dissertation, Vienna University of Economics and Business; Mentzer, J.T., DeWitt, W., Keebler, J.S., Nix, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Giunipero, L.C., Hooker, R.E., Joseph-Matthews, S., Yoon, T.E., Brudvig, S., A decade of SCM literature: Past, present and future implications (2008) Journal of Supply Chain Management: A Global Review of Purchasing &Supply, 44 (4), pp. 66-86; Akdogan, A.A., Demirtas, O., Managerial role in strategic supply chain management (2014) Procedia-Social and Behavioral Sciences, 150, pp. 1020-1029; Barratt, M., Barratt, R., Exploring internal and external supply chain linkages: Evidence from the field (2011) Journal of Operations Management, 29, pp. 514-528; Prajogo, D., Olhanger, J., Supply chain integration and performance: The effect of long-term relationships, information sharing, and logistics integration (2012) International Journal of Production Economics, 135 (1), pp. 514-522; Martin, C., The agile supply chain: Competing in volatile markets (2000) Industrial Marketing Management, 29 (1), pp. 37-44; Huang, Z., Li, S.X., Mahajan, V., Analysis of manufacturer-retailer supply chain coordination in cooperative advertising (2002) Decision Sciences, 33 (3), pp. 469-494; Jacoby, D., Guide to supply chain management: How getting it right boosts corporate performance (2010) Profile Books LTD, , London; Qrunfleh, S., Tarafdar, M., Supply chain information systems strategy: Impacts on supply chain performance and firm performance (2014) International Journal of Production Economics, 147, pp. 340-350; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, J.P., Designing supply chains: Towards theory development (2006) International Journal of Production Economics, 100, pp. 223-238; Lee, H.L., Aligning supply chain strategies with product uncertainties (2002) California Management Review, 44, pp. 105-120; Ozkir, V., Demirel, T., A comprehensive analysis for the metrics of supply chain design strategies (2011) The 15th International Research/Expert Conference on Trends in the Development of Machinery and Associated Technology, Prague; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operations Research, 173, pp. 211-225; Gunasekaran, A., Lai, K., Cheng, E., Responsive supply chain: A competitive strategy in a networked economy (2008) Omega, 36 (4), pp. 549-564; Radnor, Z.J., Barnes, D., Historical analysis of performance measurement and management in operations management (2007) International Journal of Productivity and Performance Management, 56 (5-6), pp. 384-396; Ferreira, A., Otley, D., The design and use of performance management systems: An extended framework for analysis (2009) Management Accounting Research, 20, pp. 263-282; Otley, D., Performance management: A framework for management control systems research (1999) Management Accounting Research, 10 (4), pp. 363-382; Mutingi, M., Mapfaira, H., Monageng, R., Developing performance management systems for the green supply chain (2014) Journal of Remanufacturing, 4 (6), pp. 1-20; Cai, J., Zhang, D., Li, D., (2007) Business Performance Management: Concepts. Methods, and Applications, , Tsinghua University Press, China; Ding, H., Guo, B., Liu, Z., Information sharing and profit allotment based on supply chain cooperation (2011) International Journal of Production and Economics, 133 (1), pp. 70-79; Yu, M.-M., Ting, S.-C., Chen, M.-C., Evaluating the cross-efficiency of information sharing in supply chains (2010) Expert Systems with Applications, 37, pp. 2891-2897; Barlow, A., Lee, F., Online value network linkages: Integration, information sharing and flexibility (2005) Electronic Commerce Research and Applications, 4, pp. 100-112; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2013) Management Accounting Research, 25, pp. 173-186; Rao, K.S., Supply chain management metrics (2011) CII Institute of Logistics, India; Neely, A.D., Performance measurement system design (1995) International Journal of Operations &Production Management, 20 (10), pp. 1264-1145; Lohman, C., Fortuin, L., Wouter, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Neely, A.D., Adams, C., Kennerley, M.P., (2002) Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationship, , Financial Times/Prentice Hall, London; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 134-147; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations &Production Management, 20 (6), pp. 692-704; Lynch, R.L., Cross, K.F., (1995) Measure Up! How to Measure Corporate Performance, , Second Edition, Blackwell Business; Stefanovic, N., Stefanovic, D., Supply chain performance measurement system based on scorecards and web portals (2011) ComSIS, 8 (1), pp. 167-192; Gutierrez, D.M., Scavarda, L.F., Florencio, L., Martins, R., Evolution of the performance measurement systems in the logistics department of a broadcasting company: An action research (2014) International Journal of Production Economics, 160, pp. 1-12; Eckerson, W.W., Research Q and A performance management strategies (2009) Business Intelligence Journal, 14 (1), pp. 24-27; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future direction (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-293; Keebler, J.S., Manrodt, K.B., Durtsche, D.A., Ledyard, D.M., Keeping score (1999) Oakbrook, II: Council of Logistics Management; Van Hoek, R.I., Measuring the unmeasurable: Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bolstorff, P., Measuring the impact of supply chain performance (2003) CLO/Chief Logistics Officers, 12, pp. 5-11; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3-4), pp. 209-223; Bartlett, P.A., Julien, D.M., Baines, T.S., Improving supply chain performance through improved visibility (2007) International Journal of Logistics Management, 18 (2), pp. 294-313; Gustin, C.M., Daugherty, P.J., Stank, T.P., The effects of information availability on logistics integration (1995) Journal of Business and Logistics, 16 (1), pp. 1-21; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42, pp. 283-301; Tse, Y.K., Tan, K.H., Managing product quality risk visibility in multi-layer supply chain (2012) International Journal of Production and Economics, 139 (1), pp. 49-57; Clark, T.H., Hammond, J., Reengineering channel reordering processes to improve total supply chain performance (1997) Production and Operations Management, 6 (1), pp. 248-265; Lee, H.L., Wang, S., Information sharing in a supply chain International Journal of Manufacturing and Technology Management, 1 (1), pp. 79-93; Lee, H.L., Padmanabhan, V., Whang, S., Information distortion in a supply chain: The bullwhip effect (1997) Management Sciences, 43 (4), pp. 546-558; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lee, H.L., Padmanabhan, V., Whang, S., Comments on: Information distortion in a supply chain: The bullwhip effect Management Sciences, 50 (12), pp. 1887-1893; Yu, Z., Yan, H., Cheng, T.C.E., Benefits of information sharing with supply chain partners (2001) Industrial Management Systems, 101 (3), pp. 114-119; Disney, S.M., Towill, D.R., The effect of vendor managed inventory dynamics on the bullwhip effect in supply chains (2003) International Journal of Production Economics, 85 (2), pp. 199-215; Disney, S.M., Towill, D.R., Vendor managed inventory and bullwhip reduction in a two-level supply chains (2003) International Journal of Operations and Production Management, 23 (6), pp. 625-650; Handfiels, R.B., Bechtel, C., The role of trust and relationship structure in improving supply chain responsiveness (2002) Industrial Marketing Management Journal, 31 (4), pp. 367-382; Jayaram, J., Vickery, S.K., Droge, C., An empirical study of timebased competition in north American automotive supplier industry (1999) International Journal of Operations Production and Management, 19 (10), pp. 1010-1033},
editor={Framinan J.M., Perez Gonzalez P., Artiba A.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9782960053265},
language={English},
abbrev_source_title={Proc. Int. Conf. Ind. Eng. Syst. Manag., IEEE IESM},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kopczak201679,
author={Kopczak, L.R. and Matthews, G.},
title={Measuring markets that supply cash-based humanitarian interventions},
journal={International Series in Operations Research and Management Science},
year={2016},
pages={79-109},
doi={10.1007/978-3-319-24418-1_5},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085958260&doi=10.1007%2f978-3-319-24418-1_5&partnerID=40&md5=870dc83ae269157f2c931635634ee483},
affiliation={The MIT-Zaragoza Masters in International Logistics Program, Zaragoza, Spain; NYU Wagner School of Public Service, New York, NY, United States; IRC (International Rescue Committee), New York, NY, United States},
abstract={The humanitarian sector is rapidly shifting to a new business model that combines a high reliance on cash-based interventions with traditional goods distributions for humanitarian response. While one reason that agencies are shifting to “cash” is to contribute to recovery, growth and resilience of local markets, measurement of the effect of interventions on markets is not commonly done. This chapter focuses on how to harness lessons from commercial and global health supply chain performance measurement models to better inform how humanitarian actors measure markets and/or supply chains that support new cash-based initiatives in emergencies. We approach this question by first describing the shift to cash and reviewing recent literature on measurement of supply chains and markets. Next we review three existing models of supply chains/markets that may be relevant to this discussion. We then examine and contrast several case studies of cash-based market interventions used in emergency responses in the Philippines, Jordan, Lebanon, Haiti, and the Sahel. Finally, we pull these threads together by revisiting the three supply chain models and providing insights on measures for markets that supply cash-based interventions. © Springer International Publishing Switzerland 2016.},
author_keywords={Case studies;  Cash programs;  Cash-based interventions;  EMMA analysis;  Humanitarian logistics;  KPIs;  Market systems;  Performance measures;  Value for money},
references={Abidi, H., De Leeuw, S., Measuring success in humanitarian supply chains (2013) Int J Bus Manage Innov, 2 (8); Andersen, M.B., (1999) Do No Harm: How Aid Can Support Peace Or War, , Lynne Rienner Publishers, Boulder; Austin, L., Chessex, S., (2013) Minimum Requirements for Analysis of Markets in Emergencies, , Cash Learning Partnership, Oxford; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) Int J Public Sect Manage, 21 (1), pp. 4-25; Brady, C., (2012) An Emergency Market Mapping Analysis Case Study: Changing Responses to the Haiti Earthquake, , Jan, Oxfam. Oxford, UK; (2015) Scoping Study-Emergency Cash Transfer Programming in the Wash and Shelter Sectors, , www.cashlearning.org; Cuny, F.C., Hill, R.B., (1999) Famine, Conflict and Response, , Kumarian Press, West Hartford; De Leeuw, S., Matos, X.B., Afsarmanesh, H., (2010) Towards a Reference Mission Map for Performance Measurement in Humanitarian Supply Chains. in Collaborative Networks for a Sustainable World, pp. 181-188. , edited by L.M. Camarinha- and , 11th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2010, St. Etienne, France, October 11-13, 2010. Proceedings; Detoni, A., Tonchia, S., Performance measurement systems (2001) Int J Oper Prod Mgt, 21, pp. 46-70; (2011) Department of International Development, , https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/67479/DFID-approach-value-money.pdf, DFID’s Approach to Value for Money (VfM), July, Accessed 18 Nov 2014; Eisenhardt, K.M., Building theories from case study research (1989) Acad Manage Rev, 14 (4), pp. 532-550; (2010), http://emma-toolkit.org/get/download/, Accessed 23 Nov 2014; Frost, L.J., Reich, M.R., (2008) Access: How Do Good Health Technologies Get to Poor People in Poor Countries, Harvard Center for Population and Development Studies, , www.accessbook.org, distributed by Harvard University Press; Gatignon, A., Van Wassenhove, L.N., Charles, A., The Yogyakarta earthquake: Humanitarian relief through IFRC’s decentralized supply chain (2010) Int J Product Econ, 126, pp. 102-110; (2015) Unlocking Cash and Market-Based Programmes, , http://logcluster.org, Global Logistics Cluster; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) Int J Product Res, 45 (12), pp. 2819-2840; Holguin-Veras, J., On the appropriate objective function for post-disaster logistics models (2013) J Oper Manage, 31, pp. 262-280; Komrska, J., Kopczak, L.R., Swaminathan, J., When supply chains save lives (2013) Supply Chain Manage Rev, 17, pp. 42-49; Lee, H.L., The triple-A supply chain (2004) Harv Bus Rev, 82, pp. 1-10; (2014) The Supply Chain in Cash and Voucher Programmes, , https://www.youtube.com/watch?v=WXCCFt8QemQ, Logistics Cluster, youtube video, Accessed 23 Nov 2014; Oloruntoba, R., Gray, R., Humanitarian aid: An agile supply chain (2006) Int J Supply Chain Manage, 11 (2), pp. 115-120; (2013) Water Market System in Balqa, , Zarqa informal settlements of Amman and the Jordan Valley—August-September 2013; (2014) Emergency Mapping and Analysis Assessment: Corrugated Galvanised Iron (CGI) Market System, , Jan 14-18; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Manage Res News, 32 (11), pp. 1038-1049; Sodhi, M.S., Tang, C.S., (2014) Buttressing Supply Chains against Floods in Asia for Humanitarian Relief and Economic Recovery, , http://ssrn.com/abstract=2234478, Production and Operations Management, Forthcoming. SSRN; Tatham, P., Hughes, K., Humanitarian logistics metrics: Where we are and how we might improve (2011) Humanitarian Logistics. Kogen Page Limited, pp. 65-84. , In: Christopher M, Tatham P; (2014) Emergency Economies: The Impact of Cash Assistance in Lebanon: An Impact Evaluation of The 2013-2014 Winter Cash Assistance Program for Syrian Refugees in Lebanon, , http://www.rescue.org/sites/default/files/resource-file/Emergency%20Economies%20Evaluation%20Report%20FINAL%2009.09.14%20%282%29.pdf, The IRC, Aug, Accessed 23 Nov 2014; Tomasini, R., Van Wassenhove, L.N., (2009) Humanitarian Logistics, , INSEAD Business Press, London; (2013) Annual Statistical Report on United Nations Procurement, , Marmorvej 51, 2100 Copenhagen. Denmark (2014); (2013) Sahel Regional Strategy 2013, , http://reliefweb.int/sites/reliefweb.int/files/resources/SahelStrategy2013_Dec2012.pdf, Accessed 23 Nov 2014; (2014) Healthy Markets for Global Health: A Market Shaping Primer, , http://www.usaid.gov/sites/default/files/documents/1864/healthymarkets_primer.pdf, Accessed 23 Nov 2014; (2013) Famine Prevented: A Success Story in the Sahel, , http://wfpusa.org/sites/default/files/u-6876/Sahel%20success%20story_FINAL.pdf, Accessed 23 Nov 2014},
correspondence_address1={Kopczak, L.R.; The MIT-Zaragoza Masters in International Logistics ProgramSpain; email: kopczak@stanfordalumni.org},
publisher={Springer},
issn={08848289},
language={English},
abbrev_source_title={Int. Ser. Oper. Res.Manag. Sci.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Ferreira2016113,
author={Ferreira, L.M.D.F. and Silva, C.},
title={Integrating Sustainability Metrics in the Supply Chain Performance Measurement System},
journal={Innovation, Technology and Knowledge Management},
year={2016},
pages={113-132},
doi={10.1007/978-3-319-29677-7_8},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044576921&doi=10.1007%2f978-3-319-29677-7_8&partnerID=40&md5=8d0052a5f06b7a1640f987956cdf2946},
affiliation={Department of Economics, Management and Industrial Engineering, University of Aveiro, Campus de Santiago, Aveiro, 3810-193, Portugal; Mechanical Engineering Department, University of Coimbra, R. Luís Reis Santos—Pinhal de Marrocos, Coimbra, 3030-788, Portugal},
abstract={Sustainability impact of business activities, taking in account the three dimensions of the triple-bottom-line (economical, environmental and social), has become an important issue in the last years due to growing public awareness, and the introduction of legislations mainly in developed countries. From an industrial perspective, sustainable development must be extended beyond organizational boundaries to incorporate a supply chain approach. This paper aims to contribute to the theoretical body of literature by proposing a five step model to supply chain sustainability performance assessment. The model is based on the Balanced Scorecard framework to define the company sustainability strategy and uses Global Reporting Initiative (GRI) and ISO 14031 indicators to measure the sustainability performance of it upstream supply chain. The proposed model helps companies in the analysis of trade-offs among economic, environmental and social performance of supply chains, providing directions to improve each of the considered indicators. © 2016, Springer International Publishing Switzerland.},
author_keywords={Corporate Sustainability;  Global Reporting Initiative;  Supply Chain;  Supply Chain Management;  Sustainability Performance},
references={Ahi, P., Searcy, C., Assessing sustainability in the supply chain: A triple bottom line approach (2015) Appl Math Model, 39, pp. 2882-2896; Alfalla-Luque, R., Medina-López, C., Supply chain management: Unheard of in the 1970s, core to today’s company (2009) Bus Hist, 51 (2), pp. 202-221; Azapagic, A., Developing a framework for sustainable development indicators for the mining and minerals industry (2004) J Clean Prod, 12, pp. 639-662; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput Ind Eng, 53, pp. 43-62; Bitici, U., Garengo, P., Dorfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) Int J Manage Rev, 14, pp. 305-327; Bjorklund, M., Martinsen, U., Abrahamssson, M., Performance measurement in the greening of supply chains (2012) Supply Chain Manage, 17 (1), pp. 29-39; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for sustainable supply chain management: Developments and directions (2014) Eur J Oper Res, 233 (2), pp. 299-312; Burritt, R., Schaltegger, S., Accounting towards sustainability in production and supply chains (2014) Brit Account Rev, 46 (4), pp. 327-343; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2009) Int J Prod Perform Manage, 59 (2), pp. 163-185; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) Int J Phys Distrib Log Manage, 41 (1), pp. 46-62; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) Int J Phys Distrib Log Manage, 38 (5), pp. 360-387; Christopher, M., (1998) Logistics and Supply Chain management—strategies for Reducing Cost and Improving Service, , Prentice Hall/Financial Times, New Jersey; Clift, R., Metrics for supply chain sustainability (2003) Clean Technol Environ Policy, 5, pp. 240-247; Collier, D., Evans, J., Operations management: Goods (2007) Services and Value Chains, , Thomson South-Western, Mason; Cohen, S., Roussel, J., (2004) Strategic Supply Chain Management: The Five Disciplines for Top Performance, , Mc Graw Hill; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (2012) Product Plann Contr: Manage Oper, 24 (8-9), pp. 702-720; Dias-Sardinha, I., Reijnders, L., Environmental performance evaluation and sustainability performance evaluation of organizations: An evolutionary framework (2001) Ecomanage Audit, 8 (2), pp. 71-79; Dowse, J., Making CR measurement meaningful to the rest of the business (2005) Corp Responsib Manage, 1, pp. 14-21; Epstein, M.J., Wisner, P.S., Measuring and managing social and environmental impacts” (2001) Handbook of Cost Management. Warren, , Shank J, Gorham & Lamont, New York; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecol Econ, 70, pp. 1088-1100; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard—linking sustainability management to business strategy (2002) Bus Strat Environ, 11 (5), pp. 269-283; Fine, C., (1998) Clockspeed: Winning Industry Control in the Age of Temporary Advantage, , Basic Books, New York; Geibler, J., Kristof, K., Bienge, K., Sustainability assessment of entire forest value chains: Integrating stakeholder perspectives and indicators in decision support tools (2010) Ecol Model, 221 (18), pp. 2206-2214; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organizational resources: A literature review (2010) Corp Soc Responsib Environ Manage, 17, pp. 230-245; Gopalakrishnan, K., Yusuf, Y.Y., Musa, A., Abubakar, T., Ambursa, H.M., Sustainable supply chain management—a case study of British Aerospace (BAe) systems (2012) Int J Prod Econ, 140 (1), pp. 193-203; Grosvold, J., Hoejmose, S., Roehrich, J., Squaring the circle: Management, measurement and performance of sustainability in supply chains (2014) Supply Chain Manage, 19 (3), pp. 292-305; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int J Prod Oper Manage, 21, pp. 71-87; Gunasekaran, A., Patel, C., Ronald, E., McGaughey, R., A framework for supply chain performance measurement (2004) Int J Prod Econ, 87 (3), pp. 333-348; Handfield, R., Sroufe, R., Walton, S., Integrating environmental management and supply chain strategies (2005) Bus Strat Environ, 14 (1), pp. 1-19; Hansen, E., Schaltegger, S., The sustainability balanced scorecard: A systematic review of architectures (2014) J Bus Ethics, 133 (2), pp. 193-221; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Inter J Prod Econ, 140 (1), pp. 69-82; Hervani, A., Helms, M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353; Hong, P., Roh, J.J., Rawski, G., Benchmarking sustainability practices: Evidence from manufacturing firms (2012) Benchmarking, 19 (4-5), pp. 634-648; Hsu, C.W., Hu, A.H., Chiou, C.Y., Chen, T.C., Using the FDM and ANP to construct a sustainability balanced scorecard for the semiconductor industry (2011) Expert Syst Appl, 38, pp. 12891-12899; Hsu, Y.L., Liu, C.C., Environmental performance evaluation and strategy management using balanced scorecard (2010) Environ Monit Assess, 170 (1-4), pp. 599-607; Hubbard, G., Measuring organizational performance: Beyond the triple bottom line (2009) Bus Strateg Environ, 18, pp. 177-191; Jensen, M., Value Maximization, stakeholder theory and the corporate objective function (2001) Euro Fin Manage, 7 (3), pp. 297-317; Jiménez, J.B., Lorente, J.L., Environmental performance as an operations objective (2001) Int J Oper Prod Manage, 21 (12), pp. 1553-1572; Johnson, S.D., Identification and selection of environmental performance indicators: Application of the balanced scorecard approach (1998) Corp Environ Strategy, 5 (4), pp. 34-41; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Bus Rev, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., (2001) The Strategy Focused Organization, , Harvard Business Press, Boston; Keeble, J.J., Topiol, S., Berkely, S., Using indicators to measure sustainability performance at a corporate and project level (2003) J Bus Ethics, 44, pp. 149-158; Kleindorfer, P.A., Singhal, K., van Wassenhove, L.N., Sustainable operations management (2005) Prod Oper Manage, 14 (4), pp. 482-492; Corporate environmental responsibility in the supply chain (2008) J Clean Prod, 16, pp. 1571-1578; Krajnc, D., Glavic, P., A model for integrated assessment of sustainable development (2005) Resour Conserv Recycl, 43, pp. 189-208; Krajnc, D., Glavic, P., Indicators of sustainable development (2003) Clean Technol Environ Policy, 5, pp. 279-288; Labuschagne, C., Brent, A.C., van Erck, R.P.G., Assessing the sustainability performance of industries (2004) J Clean Prod, 13, pp. 373-385; Länsiluoto, A., Järvenpää, M., Environmental and performance management forces: Integrating “greenness” into balanced scorecard (2008) Qual Res Acc Manage, 5 (3), pp. 184-206; Lau, K.H., Benchmarking green logistics performance with a composite index (2011) Benchmarking, 18 (6), pp. 873-896; Lehtinen, J., Ahola, T., Is performance measurement suitable for an extended enterprise? (2010) Int J Oper Prod Manage, 30 (2), pp. 181-204; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) J Oper Manage, 25 (6), pp. 1075-1082; Markley, M.J., Davis, L., Exploring future competitive advantage trough sustainable supply chains (2007) Int J Phys Distrib Log Manage, 37 (9), pp. 763-774; Naini, S.G., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental SCM using evolutionary game theory and balanced scorecard—a case study of an auto industry supply chain (2011) Resour Conserv Recycl, 55, pp. 593-603; Nikolaou, I.E., Tsalis, T.A., Development of a sustainable balanced scorecard framework (2013) Ecol Indic, 34, pp. 76-86; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour Conserv Recycl, 55, pp. 567-579; Pagell, M., Shevchenko, A., Why research in sustainable supply chain management should have no future (2013) J Supply Chain Manage, 50 (1), pp. 44-55; Pullman, M.E., Maloni, M.J., Carter, C.R., Food for thought: Social versus environmental sustainability practices and performance outcomes (2009) J Supply Chain Manage, 45 (4), pp. 38-54; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: Design and development guidelines (2013) Int J Prod Perform Manage, 62 (8), pp. 805-826; Ross, A., Parker, H., Benavides, M., Sustainability and supply chain infrastructure development (2012) Manage Decis, 50 (10), pp. 1891-1910; Schaltegger, S., Burritt, R., Measuring and managing sustainability performance of supply chains. Review and sustainability supply chain management framework (2014) Supply Chain Manage, 19 (3), pp. 232-241; Schaltegger, S., Wagner, M., Integrative management of sustainability performance, measurement and reporting (2006) Int J Account Audit Perfor Eval, 3 (3), pp. 1-19; Searcy, C., Corporate sustainability performance measurement systems: A review and research agenda (2012) J Bus Ethics, 107, pp. 239-253; Searcy, C., Karapetrovic, S., McCartney, D., Designing sustainable development indicators: Analysis for a case utility (2005) Meas Bus Excell, 9, pp. 33-41; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decis Support Syst, 54 (4), pp. 1513-1520; Seuring, S., Gold, S., Sustainability management beyond corporate boundaries: From stakeholders to performance (2013) J Clean Prod, 56, pp. 1-6; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J Clean Prod, 16 (15), pp. 1699-1710; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking, 17 (3), pp. 320-339; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2005) Int J Prod Perform Manage, 55 (3-4), pp. 242-258; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., An overview of sustainability assessment methodologies (2009) Ecol Indic, 9, pp. 189-212; Stonebraker, P.W., Goldhar, J., Nassos, G., Weak links in the supply chain: Measuring fragility and sustainability (2009) J Manuf Technol Manage, 20 (2), pp. 161-177; Taticchi, P., Tonelli, F., Pasqualino, R., Performance measurement of sustainable supply chains: A literature review and a research agenda (2013) Int J Prod Perform Manage, 62 (8), pp. 782-804; Thakkar, J., Kanda, A., Deshemukh, S., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking, 16 (5), pp. 702-723; Vachon, S., Klassen, R.D., Extending green practices across the supply chain. The impact of upstream and downstream integration (2006) Int J Oper Prod Manage, 26 (7), pp. 795-821; van Marrewijk, M., A value based approach to organization types: Toward a coherent set of stakeholder-oriented management tools (2004) J Bus Ethics, 55 (2), pp. 147-158; Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Manage, 19 (3), pp. 242-257; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain (2006) Supply Chain Manage, 11 (4), pp. 317-327; Veleva, V., Ellenbecker, M., Indicators of sustainable production: Framework and methodology (2001) J Clean Prod, 9, pp. 519-549; Walker, H., Jones, N., Sustainable supply chain management across the UK private sector (2012) Supply Chain Manag, 17 (1), pp. 15-28; Winter, M., Knemeyer, A.M., Exploring the integration of sustainability and supply chain management: Current state and opportunities for future inquiry (2013) Int J Phys Distrib Log Manage, 43 (1), pp. 18-38; Wong, C.Y., Wong, C., Boon-Itt, S., Integrating environmental management into supply chains: A systematic literature review and theoretical framework (2015) Int J Phys Distrib Log Manage, 45 (1-2), pp. 43-68; Wu, Z., Pagell, M., Balancing priorities: Decision-making in sustainable supply chain management (2011) J Oper Manage, 29 (6), pp. 577-590; Yu, X., Li-Ping Tang, T., Sustainability in supply chain management: Suggestions for the auto industry (2011) Manage Decis, 49 (4), pp. 495-512; Zingales, F., O’Rourke, A., Hockerts, K., (2002) Balanced Scorecard and Sustainability: State of the Art Review (2002/65/CMER), , INSEAD, Paris},
correspondence_address1={Ferreira, L.M.D.F.; Department of Economics, Campus de Santiago, Portugal; email: lmferreira@ua.pt},
publisher={Springer},
issn={21975698},
language={English},
abbrev_source_title={Innov. Technol. Knowl. Manag.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Hosseini20163213,
author={Hosseini, M. and El-Diraby, T. and Shalaby, A.},
title={A standard lexicon to measure the level of service of public transportation services through online transit-oriented discussions},
journal={Proceedings, Annual Conference - Canadian Society for Civil Engineering},
year={2016},
volume={4},
pages={3213-3223},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030866354&partnerID=40&md5=138c8a6091277328d9cd9100c058b827},
affiliation={University of TorontoON, Canada},
abstract={Public involvement in operation and planning of transit services is becoming a major focus of public transportation agencies. With the growing population and diversification of ideas, distilling knowledge from public opinion to incorporate into decision support systems is a major challenge for transit agencies. The common practice is to collect information from customers via Customer Satisfaction Surveys (CSat). Although such surveys provide useful insight for transit agencies, their design, data collection, and interpretation is expensive and time consuming. Because of the qualitative nature of the questions in these surveys, inconsistency in respondents' perception of the survey questions is another challenge. Furthermore, for management and decision-making purposes, the results of surveys should be quantified into various key performance indicators. Lack of a standard quantification system causes some biases in the reported results. The advent of online social media has introduced a new bidirectional communication system. Developing a linguistic-based system to interpret customer discussions in social media about the transit system and to transform the discussions into useful information for the agency could overcome some deficiencies existing in CSat. The current study develops a standard lexical resource to categorize online discussions on Twitter into different Level of Service (LOS) indicators.},
author_keywords={Customer Satisfaction;  Level of Service;  Lexicon;  Performance Measurement System;  Social Media},
keywords={Artificial intelligence;  Benchmarking;  Customer satisfaction;  Decision making;  Decision support systems;  Mass transportation;  Sales;  Social aspects;  Social networking (online);  Supply chain management;  Transportation, Bi-directional communication;  Customer satisfaction survey;  Key performance indicators;  Level of Service;  Lexicon;  Performance measurement system;  Public transportation services;  Social media, Surveys},
references={(2009) AASHTO Transportation Glossary. 4th Ed. American Association of State Highway and Transportation Officials (AASHTO), , Washington, D.C., USA; Atkinson, A.A., Strategic Performance Measurement and Incentive Compensation (1998) European Management Journal, 16 (5), pp. 552-561; Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A Stakeholder Approach to Strategic Performance Measurement (1997) Sloan Management Review, 38 (3), pp. 25-37; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated Performance Measurement Systems: A Development Guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Bourne, M.C.S., Neely, A.D., Mills, J.F., Platts, K.W., Implementing Performance Measurement Systems: A Literature Review (2003) International Journal of Business Performance, pp. 1-24; Bregman, S., (2012) Uses of Social Media in Public Transportation, Transportation Research Board of the National Academies, , Washington, D.C., USA; Cox, L., Butcher, L., Byrne, S., Meier, A., (2013) Australian Transport Index Thesaurus, , ARRB Group Ltd. Melbourne, Australia; Forza, C., Salvador, F., Assessing Some Distinctive Dimensions of Performance Feedback Information in High Performing Plants (2000) International Journal of Operations & Production Management, 20 (3), pp. 359-385; Franco-Santos, M., Towards a Definition of a Business Performance Measurement System (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Gates, S., (1999) Aligning Strategic Performance Measures and Results, , The Conference Board Trusted Insights for Business Worldwide, New York, USA; Gupta, P., Goel, A., Lin, J., Sharma, A., Wang, D., Zadeh, R., (2013) WTF: The Who to Follow Service at Twitter Committee (IW3C2). International World Wide Web Conference, , Rio de Janeiro, Brazil; Ittner, C.D., Larcker, D.F., Randall, T., Performance Implications of Strategic Performance Measurement in Financial Services Firms (2003) Accounting, Organizations and Society, 28 (7-8), pp. 715-741; Kaplan, R.S., Norton, D.P., Linking the Balanced Scorecard to Strategy (1996) California Management Review, 39 (1), pp. 53-79; Kerssens-Van Drongelen, I.C., Fisscher, O.A., Ethical Dilemmas in Performance Measurement (2003) Journal of Business Ethics, 45 (1), pp. 51-63; (2013) Transit Capacity and Quality of Service Manual, Transportation Research Board of the National Academies, , Washington, D.C., USA; (2003) A Guidebook for Developing A Transit Performance-Measurement System, , Transportation Research Board of the National Academies, Washington, D.C., USA; Lebas, M.J., Performance Measurement and Performance Management (1995) International Journal of Production Economics, 42 (1-3), pp. 23-35; Lynch, R.L., Cross, K.F., (1991) Measure Up!: The Essential Guide to Measuring Business Performance, , Mandarin, London, UK; Maisel, L.S., (2001) Performance Measurement Practices Survey Results, American Institute of Certified Public Accountants, , USA; McGee, J., (1992) What Is Strategic Performance Measurement?, Ernst & Young Center for Business Innovation, , Boston, USA; (2001) Transportation Research Thesaurus and User's Guide, , National Academy Press, Washington, D.C., USA; (2010) Highway Capacity Manual, , 5th ed. Transportation Research Board of the National Academies, Washington, D.C., USA; Neely, A.D., (1998) Measuring Business Performance: Why, What and How, the Economist and Profile Books Ltd, , London, UK; Neely, A.D., Gregory, M.J., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (1995) International Journal of Operations &. Production Managment, 15 (4), pp. 80-116; Neely, A.D., Gregory, M.J., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Otley, D., Performance Management: A Framework for Management Control Systems Research (1999) Management Accounting Research, 10 (4), pp. 363-382; Spiteri, L., A simplified model for facet analysis: Ranganathan 101 (1998) Canadian Journal of Information and Library Science, 23, pp. 1-30},
publisher={Canadian Society for Civil Engineering},
isbn={9781510843592},
coden={PCSEE},
language={English},
abbrev_source_title={Proc Annu Conf Can Soc Civ Eng},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Habib20161,
author={Habib, M.M.},
title={Supply chain management: Applications for manufacturing and service industries},
journal={Supply Chain Management: Applications for Manufacturing and Service Industries},
year={2016},
pages={1-269},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021976385&partnerID=40&md5=1d5351ab339f314d9911172578c353ba},
affiliation={Asia Graduate School of Business (AGSB), UNITAR International University, Malaysia},
abstract={Supply Chain Management (SCM) has been widely researched in numerous application domains in manufacturing and service industries during the last decades. There are several attempts made by researchers and practitioners to appropriately define SCM and its applications. This book entitled Supply Chain Management: Applications for Manufacturing and Service Industry is comprised of twelve chapters. The introductory chapter, the first chapter of this book, addresses an empirical model entitled ITESCM (Integrated Tertiary Educational Supply Chain Management) for better Supply Chain Management in Universities, one of the service industries. This study encompasses education supply chain, research supply chain and educational management as major constituents of ITESCM. This empirical model evaluation by the actual implementation is suggested for prospective investors or current university administrators. Chapter Two presents a recent and exhaustive review on important integrated supply chain management problems and the current research on integration between different supply chain management functions. Chapter Three illustrates a comprehensive multi criteria decision making (MCDM) methodology for the selection of a third party logistics service provider. Chapter Four explains the capabilities of Google Spreadsheet as a tool, using its tool inventory distribution management, risk pooling and transshipment at retailer levels for integrated operation of the supply chain. Chapter Five describes the agent-based modelling approach to simulate the interaction and negotiation behavior of palm oil supply chain (POSC) actors, which attempt to obtain highest added-value, but must consider overall supply chain profitability and sustainability. Chapter Six intends to signify the performance measurement practices in the industries and also presents the need of supply chain performance measurement in textile industry with a case study. In Chapter Seven, a generic method has been developed in order to assist and assess complex supply chain network design and analysis considering different types of uncertainty factors. Chapter Eight proposes a mathematical model to determine the optimal location for a warehouse. Several factors like locations of distributors, nature of demand etc. have been considered to formulate the optimization model, which has been solved using differential evolution. Chapter Nine is a study of sport-related location and layout strategies as they related to the important questions that management needs to answer by focusing on location of a firm (i.e., intermediate variables/functions, superior firm performance). Chapter Ten illustrates the various approaches to implement the green principles by different researchers. The main objective of the green supply chain management (GSCM) approach is to eliminate/minimize the impact of the GHG emissions into the environment throughout the supply chain process. Chapter Eleven focuses on increasing the supply chain value by looking into the color, image and typography of water product in influencing customers’ intention to purchase. Finally, Chapter Twelve aims to investigate scenarios for the new setting of the Center of Sterilized Material (CSMs) health system of Rio de Janeiro, Brazil. To build and evaluate scenarios for the CSMs, an exploratory methodology based on gravity center approach for location of storage facilities and distribution of materials was adopted. © 2016 by Nova Science Publishers, Inc. All rights reserved.},
keywords={Autonomous agents;  Computational methods;  Decision making;  Evolutionary algorithms;  Greenhouse gases;  Location;  Manufacture;  Optimization;  Outsourcing;  Palm oil;  Sales;  Sustainable development;  Textile industry;  Uncertainty analysis, Differential Evolution;  Educational management;  Green supply chain management;  Integrated supply chain management;  Multi-criteria decision making;  Performance measurements;  Supply chain managements (SCM);  Supply chain performance measurements, Supply chain management},
correspondence_address1={Habib, M.M.; Asia Graduate School of Business (AGSB), Malaysia; email: mamunhabib@gmail.com},
publisher={Nova Science Publishers, Inc.},
isbn={9781634850131; 9781634849876},
language={English},
abbrev_source_title={Supply Chain Management: Applications for Manufacturing and Service Industries},
document_type={Book},
source={Scopus},
}

@BOOK{Charkha2016141,
author={Charkha, P.G. and Jaju, S.B.},
title={Performance measurement system for supply chain management: Case of a textile industry in India},
journal={Supply Chain Management: Applications for Manufacturing and Service Industries},
year={2016},
pages={141-167},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021877312&partnerID=40&md5=0be79120219be666de22bd7f75694ef1},
affiliation={Department of Mechanical Engineering, Datta Meghe Institute of Engineering, Technology and Research, Wardha, India; Department of Mechanical Engineering, G. H. Raisoni College of Engineering, Nagpur, India},
abstract={Nowadays supply chain (SC) performance measurement is one of the complex problems in most of enterprises. Number of frameworks and systems has been proposed to undertake the problem. In this chapter, the literature is reviewed for various approaches, techniques and criteria for SC performance measurement. For this reason, work of numerous authors published in international journals, conferences and other practicing forum have been collected, studied and analyzed. This chapter also presents a case of supply chain performance measurement system usage in small and medium scale textile industry. Hence, the chapter tries to contribute to following question: (I) Which SC approaches were generally used? (II) Which techniques were used to make possible to develop the performance measurement system? (III) Which are the most important criteria used? The result of the chapter is the compilation of comprehensive approaches for supply chain performance measurement. The other objective of this research is to aid the researchers and practitioners in applying the approaches, techniques and criteria effectively. Contribution of this chapter is also that, a case study of textile industry is presented. © 2016 by Nova Science Publishers, Inc. All rights reserved.},
author_keywords={Balanced scorecard (BSC);  Distribution;  Performance measurement system (PMS);  Procurement;  Production;  Supply chain management (SCM);  Textile industry;  Textile supply chain},
keywords={Production;  Textile industry;  Textiles, Balanced scorecards;  Distribution;  Performance measurement system;  Procurement;  Supply chain managements (SCM);  Textile supply chain, Supply chain management},
references={Beamon, B.M., Measuring supply chain performance (1999) Intl Jrnl of operation and production management, 19 (3), pp. 275-292. , 7; Bedi, J.S., Report on Assessing the prospects for India’s Textile and Clothing Sector (2009) National Council of Applied Economic Research, , June; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engg, 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain using the analytical hierarchy process (2007) Production Planning and Control: The Management of Operation, 18 (8), pp. 666-680; Bhagwat, R., Chan, F.T.S., Sharma, M.K., Performance measurement model for supply chain management in SMEs (2008) Intl. Jrnl. of Globalization and Small Business, 2 (4), pp. 428-444. , 2008; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning and Control: The Management of Operation, 20 (8), pp. 678-690; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities-Emerald Publication, 28 (5-6), pp. 249-260; Bititci, C.U.S., Cieminski, G., Implementation of performance measurement system: Public and private sector (2005) Prod. Planning and Control: The Management of Operation, 16 (2), pp. 99-100; Bourne, M., Neely, A., Implementing performance measurement system: A literature review (2003) Intl. Jrnl. of Business Performance Management, 5 (1), pp. 1-23; Browne, J., Devlin, J., Rolstadas, A., Andersen, B., Performance Measurement: The ENAPS Approach (1998) Intl. Jrnl. Business Transform, 1 (2), pp. 73-84; Cao, N., Zhang, Z., To, M.K., Ng, K.P., How are supply chain coordinated? - An empirical observation in textile-apparel businesses (2008) Journal of Fashion Marketing and Management, 12 (3), pp. 384-397; Chan, Y.-C.L., An analytic hierarchy framework for evaluating balanced scorecards of healthcare organizations (2006) Canadian Journal of Administrative Sciences, 23 (2), pp. 85-104; Chan, F.T.S., Performance measurement in a supply chain (2003) Intl. Jrnl. of Adv. Manuf. Technol., 21, pp. 534-548; Chandra, C., Kumar, S., An application of system analysis methodology to manage logistic in textile supply chain (2000) Supply chain management: An International Journal, 5 (5), pp. 234-245; Chandra, P., (2006) The textile and apparel industry in India, , Oxford companion to economies in India, 2006; Charkha, P.G., Jaju, S.B., Supply chain performance measurement system: An overview (2014) Intl. Jrnl. Business Performance and Supply Chain Modeling, 6 (1), pp. 40-60; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) Int. J. Logistics Systems and Management, 6 (1), pp. 216-230; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-13; Dalalah, D., Oqla-Al, F., Hayajneh, M., Application of Analytical hierarchy process in multi criteria analysis of selection of cranes (2010) Jordan Journal of Mechanical and Industrial Engineering, 4 (5), pp. 567-578. , ISSN: 1995-6665; Digalwar, A.K., Sangwan, K.S., An overview of existing performance measurement frameworks in the context of world class manufacturing performance measurement (2011) Int. J. Services and Operations Management, 9 (1), pp. 60-82; Gopal, P.R.C., Thakkar, J., A review of supply chain performance measures and metrics: 2000-2011 (2012) Intl. Jrnl. of Productivity and Performance Management, 61 (5), pp. 518-547; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in supply chain environment (2001) Intl. Jrnl. of Production and Operation Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) Intl. Jrnl. of Production Economics, 87 (3), pp. 333-348; Gunasekaran, A., Kobu, P., (2007) Performance measures and metrics in logistics and supply chain management: Review of recent literature (1995-2004) for research and application, 45, pp. 2819-2840; Hall, D.C., Can, S., Impact of information sharing on supply chain performance (2011) International Journal of Advanced Manufacturing Technology, 58 (1-4), pp. 397-409. , 2011; Lam, J.K.C., Textile and Apparel supply chain management in Hongkong (2006) Intl. Jrnl. of Clothing Science and Technology, 18 (4); Kaplan, R.S., Nortan, D.P., (1992) The Balanced Scorecard-Measures that Drive Performance, , Harvard Business School, January-February 1992; Kaplan, R.S., Nortan, D.P., (2007) Using the BSC as a strategic management system, , Best of HBR Managing for Long term, July-August 2007; Lapiede, L., True measures of supply chain performance (2000) Supply Chain Management Review, 4 (2), pp. 25-28; Mathur, A., Dangayach, G.S., Mittal, M.L., Sharma, M.K., Performance measurement in automated manufacturing (2011) Measuring Business Excellence, 15 (1), pp. 77-91. , 2011; Moon, K.K.L., Yi, C.Y., Ngai, E.W.T., An instrument for measuring supply chain flexibility for the textile and clothing companies (2012) European Journal of Operational Research, 22 (2), pp. 191-203; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Bradford, 10 (5), p. 522; Shah, N., Process Industry Supply Chains: Advances and Challenges (2005) Computer and Chemical Engineering, 29 (2005), pp. 1225-1235; Parker, C., Performance measurement (2000) Work Study, 49 (2), pp. 63-66; Saaty, T.L., How to make a decision: The Analytic Hierarchy Process (1990) European Journal of Operation Research, 48, pp. 9-26; Saaty, T.L., (1980) The analytic hierarchy process: Planning, priority setting, resource allocation, , US: McGraw-Hill; 1980; Sanders, N.R., (2011) Supply Chain Management: A Global Perspective, , New York: John Wiley and Sons, ISBN-978-0-470-91395-6, November 2011; Sardar, S., Lee, Y.H., Recent Researches and future Research Directions in Textile Supply Chain Management (2012) UTCC-Intl. Jrnl. of Business and Economics, 4 (2). , December 2012; Searcy, D.L., Aligning the BSC and firms strategy using AHP (2004) Management Accounting Quarterly, 5, pp. 1-10. , 2004; Shah, N., Process Industry Supply Chains: Advances and Challenges (2005) Computer and Chemical Engineering, 29 (2005), pp. 1225-1235; Singh, R.K., Sharma, M.K., Prioritizing the alternatives for flexibility in supply chain (2014) Production Planning and Control: The management of operation, 25 (2), pp. 176-192; Singh, R.K., Sharma, M.K., Selecting competitive supply chain using fuzzy AHP and extent analysis (2015) Journal of Industrial and Production Engineering, 31 (8), pp. 524-538; Sharma, M.K., Bhagwat, R., Performance measurement system: Case studies from SMEs in India (2007) Intl. Jrnl. of Production and Quality Management, 2 (4), pp. 475-509; Swaminathan, J.M., Managing supply chain operation in India (2006) Building Supply Chain Excellence in Emerging Economies, pp. 137-154; Teng, S.G., Jaramillo, H., A model for evaluation and selection of suppliers in global textile and apparel supply chains (2005) International Journal of Physical Distribution and Logistics Management, 35 (7), pp. 503-523; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Turner, T.J., Bititci, U.S., Nudurupati, S., Implementation and impact of performance measures in two SMEs in Central Scotland (2005) Production Planning and Control, 16 (2), pp. 135-151; Varma, S., Wadhwa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance: Application of analytical hierarchy process to balanced scorecard (2008) Asia Pacific Journal of Mktg. and Logistics, 20 (3), pp. 343-356; Wong, J.K., Li, H., Application of analytical hierarchy process (AHP) in multicriteria analysis of the selection of intelligent building system (2007) Building and Environment, p. 2007; Xia, L., Xiao, X.U., Lim, R., Bin, M.A., (2007) AHP based Supply Chain Performance Measurement System, , 1-4244-0826-1/07, 2007, IEEE; Yadav, V., Sharma, M.K., An application of hybrid data envelopment analytical hierarchy process approach for supplier selection (2015) Journal of Enterprise Information Management, 28 (2), pp. 218-242},
publisher={Nova Science Publishers, Inc.},
isbn={9781634850131; 9781634849876},
language={English},
abbrev_source_title={Supply Chain Management: Applications for Manufacturing and Service Industries},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Moura2016,
author={Moura, L.F. and De Lima, E.P. and Da Costa, S.E.G. and Deschamps, F. and Van Aken, E.},
title={Identifying the factors that influence the design of performance measurement systems in not-for-profit organizations},
journal={2016 International Annual Conference of the American Society for Engineering Management, ASEM 2016},
year={2016},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014129172&partnerID=40&md5=3f7f4ef6c74bfbe68183c1a574f6684e},
affiliation={Pontifical Catholic University of Parana, Brazil; Virginia Polytechnic Institute and State University, United States; Federal University of Technology, Parana, Brazil},
abstract={Not-for-profit organizations have complex characteristics related to their social goals. Legitimacy, accountability, as well as efficient and effective use of resources are examples of performance requirements from community, government and regulatory bodies. Their particularities in terms of social demand make performance measurement a difficult task. In this context, the purpose of this paper is to identify and to classify the factors that influence the design of performance measurement system in not-for-profit organizations. The paper describes the results of a systematic literature review used to synthesize a conceptual framework for studying the factors that influence the design of performance measurement systems in not-for-profit organizations. The systematic literature review results show that performance measurement systems in for-profit organizations are incomplete for supporting not-for-profit organizations because characteristics such as accountability and intangible results are usually not properly considered. It is noteworthy that in some cases in the literature, performance measurement systems were adaptations derived from for-profit models, but some authors indicate that this type of adaptation is not recommended for other not-for-profit organizations. The conceptual model presented in this paper can assist practitioners in developing design processes, observing the role that the factors play and their influence in the design of not-for-profit organizations' performance measurement systems. © 2016 American Society for Engineering Management.},
author_keywords={Conceptual framework;  Design;  Not-for-profit organizations;  Performance measurement systems;  Systematic literature review},
keywords={Design;  Societies and institutions;  Supply chain management, Complex characteristics;  Conceptual frameworks;  Not-for-profit organization;  Performance measurement system;  Performance measurements;  Performance requirements;  Regulatory bodies;  Systematic literature review, Profitability},
references={Arena, M., Azzone, G., Bengo, I., Performance measurement for social enterprises (2015) VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 26 (2), pp. 649-672; Arvidson, M., Lyon, F., Social impact measurement and non-profit organisations: Compliance, resistance, and promotion (2014) VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 25 (4), pp. 869-886; Chenhall, R.H., Hall, M., Smith, D., Performance measurement, modes of evaluation and the development of compromising accounts (2013) Accounting, Organizations and Society, 38 (4), pp. 268-287; Clark, C., Brennan, L., Entrepreneurship with social value: A conceptual model for performance measurement (2012) Academy of Entrepreneurship Journal, 18 (2), pp. 17-40; Connolly, C., Kelly, M., Understanding accountability in social enterprise organisations: A framework (2011) Social Enterprise Journal, 7 (3), pp. 224-237; Cordery, C., Sinclair, R., Measuring performance in the third sector (2013) Qualitative Research in Accounting & Management, 10 (3-4), pp. 196-212; Dobmeyer, T.W., Woodward, B., Olson, L., Factors supporting the development and utilization of an outcome-based performance measurement system in a chemical health case management program (2002) Administration in Social Work, 26 (4), pp. 25-44; Duque-Zuluaga, L.C., Schneider, U., Market orientation and organizational performance in the nonprofit context: Exploring both concepts and the relationship between them (2008) Journal of Nonprofit & Public Sector Marketing, 19 (2), pp. 25-47; Ebrahim, A., Rangan, V.K., What impact a framework for measuring the scale and scope of social performance (2014) California Management Review, 56 (3), pp. 118-141; Fernandes, A.J., Introducing social evaluation for improved project performance: A suggested checklist approach (1990) Project Appraisal, 5 (1), pp. 11-18. , http//doi.org/10.1080/02688867.1990.9726744; Kong, E., Innovation processes in social enterprises: An IC perspective (2010) Journal of Intellectual Capital, 11 (2), pp. 158-178; Mehrotra, S., Verma, S., An assessment approach for enhancing the organizational performance of social enterprises in India (2015) Entrepreneurship in Emerging Economies, 7 (1), pp. 35-54; Mouchamps, H., Weighing elephants with kitchen scales: The relevance of traditional performance measurement tools for social enterprises (2014) International Journal of Productivity and Performance Management, 63 (6), pp. 727-745; Moura, L.F., De Lima, E.P., Van Aken, F., Deschamps, E., Da, G.E.C.S., Factors for design of performance measurement systems for not-forprofit organizations (2016) Proceedings of the 2016 Industrial and Systems Engineering Research Conference; Moxham, C., Performance measurement: Examining the applicability of the existing body of knowledge to nonprofit organisations (2009) International Journal of Operations & Production Management, 29 (7), pp. 740-763; Moxham, C., Understanding third sector performance measurement system design: A literature review (2014) International Journal of Productivity and Performance Management, 63 (6), pp. 704-726; Taylor, M., Taylor, A., Performance measurement in the Third Sector: The development of a stakeholder-focussed research agenda (2014) Production Planning & Control, 25 (16), pp. 1370-1385; Valentinov, V., The meaning of nonprofit organization: Insights from classical institutionalism (2011) Journal of Economic Issues, (4), pp. 901-916},
editor={Long S., Downing C., Ng E.H., Nepal B.},
publisher={American Society for Engineering Management},
language={English},
abbrev_source_title={Int. Annu. Conf. Ame. Soc. Eng. Manag., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Grijalva2016,
author={Grijalva, P. and Darrow, L. and Mirdad, W.},
title={Balance scorecard approach in assessing social impact performance measures},
journal={2016 International Annual Conference of the American Society for Engineering Management, ASEM 2016},
year={2016},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85014071131&partnerID=40&md5=15cbf4e29c10d90c03486f6ba2b60ba3},
affiliation={Bhuvanamalini Karaikudi Ramesh, United States; Oregon State University, United States},
abstract={One of the issues in putting the Triple Bottom Line (TBL) into practice is the measurement of each of its three categories: social, environmental and economic. The social aspect of TBL lacks a uniform conceptual framework for assessment-i.e. identification and measurement of social impact. In the literature, there are relatively few articles that discuss how to evaluate the social impact of projects. In this paper, a uniform system of measures is suggested to help scholars and practitioners complete effective benchmarking. The study includes a holistic comparison between different social impact performance measurement systems discussed in the literature. This study is a first step toward building an integrated performance measurement system that utilizes the advantages of the primary models developed in the literature. A path is identified for scholars to better understand social impact performance measurement systems to build on for future research. Additionally, implementation guidelines are provided for the practitioner seeking to implement a social impact performance measurement system in an effective and efficient way. This work investigates a strategy for an organization's social assessment by identifying causal relationships between different social impact performance measures categories, using Decision Making Trial Evaluation Laboratory method (DEMATEL). © 2016 American Society for Engineering Management.},
keywords={Decision making;  Social aspects;  Supply chain management, Balance scorecard;  Causal relationships;  Conceptual frameworks;  Laboratory methods;  Performance measurement system;  Social assessment;  Three categories;  Triple Bottom Line, Economic and social effects},
references={(2016) Root Capital: Growing Rural Prosperity, pp. 1-13. , http//www.rootcapital.org/our-impacthttp//www.rootcapital.org/our-impact, A Roadmap for Impact; Arvidson, M., Lyon, F., Social impact measurement and non-profit organisations: Compliance, resistance, and promotion (2014) VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 25 (4), pp. 869-886; Burdge Rabel, J., Social impact assessment model and the planning process (1987) Environmental Impact Assessment Review, 7, pp. 141-150; Burdge Rabel, J., (1995) A Community Guide to Social Impact Assessment, , Middleton, Wisconsin: Social Ecology Press; Kim, D., Corporate citizenship: A stakeholder approach for defining corporate social performance and identifying measures for assessing it (2000) Business & Society, 39 (2), pp. 210-219; Bandura, A., Social cognitive theory for personal and social change by enabling media (2004) Entertainment-education and Social Change: History, Research, and Practice, pp. 75-96; Florman, M., Klingler-Vidra, R., Facada, M., A critical evaluation of social impact assessment methodologies and a call to measure economic and social impact holistically through the External Rate of Return platform (2016) LSE Enterprise; Fontes, J., Bogaers, B.A.K., Saling, P., Van Gelder, R., Traverso, M., Gupta, J.D., Van Der Merwe, R., Handbook for product social impact assessment (2014) PRé Roundtable for Product Social Metrics; Daniel, F., Vanclay, F., Social impact management plans: Innovation in corporate and public policy (2013) Environmental Impact Assessment Review, 43, pp. 40-48; Freudenburg, W.R., Social impact assessment (1986) Annual Review of Sociology, 12, pp. 451-478; Gramling, R., Freudenburg, W.R., Opportunitythreat, development, and adaptation: Toward a comprehensive framework for social impact assessment (1992) Rural Sociology, 57 (2), pp. 216-234; Grieco, C., Michelini, L., Iasevoli, G., Measuring value creation in social enterprises a cluster analysis of social impact assessment models (2015) Nonprofit and Voluntary Sector Quarterly, 44 (6), pp. 1173-1193; Hall, T.J., The triple bottom line: What is it and how does it work (2011) Indiana Business Review, 86 (1), p. 4; Guidelines and principles for social impact assessment (1995) Environmental Impact Assessment Review, 15 (1), pp. 11-43. , Interorganizational Committee on Guidelines; (2016) Social Impact Objectives (OD6247). V4.0, , IRIS; Juslèn, J., Social impact assessment: A look at Finnish experiences (1995) Project Appraisal, 10 (3), pp. 163-170; Kaplan, R.S., Strategic performance measurement and management in nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 353-370; Maas, K.E., Social impact measurement: Towards a guideline for managers (2008) EMAN-EU 2008 Conference: Sustainability and Corporate Responsibility Accounting Measuring and Managing Business Benefits, pp. 75-78. , October; Nguyen, L., Szkudlarek, B., Seymour, R.G., Social impact measurement in social enterprises: An interdependence perspective (2015) Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 32 (4), pp. 224-237; Nicholls, J., Lawlor, E., Neitzert, E., Goodspeed, T., (2009) A Guide to Social Return on Investment, , http//1068899683.n263075.test.prositehosting.co.uk/wp-content/uploads/2013/01/SROI-Guide-2009.pdf; Niven, P.R., (2011) Balanced Scorecard: Step-by-step for Government and Nonprofit Agencies, , John Wiley & Sons; Norman, W., MacDonald, C., Getting to the bottom of "triple bottom line (2004) Business Ethics Quarterly, 14 (2), pp. 243-262; Parrish, R.G., Measuring population health outcomes (2010) Prev Chronic Dis, 7 (4). , http//www.cdc.gov/pcd/issues/2010/jul/10_0005.htm; Stevenson, N., Taylor, M., Lyon, F., Rigby, M., (2010) Social Impact Measurement (SIM) Experiencing and Future Directions for the Third Sector Organisations in the East of England; Taber, T.D., Walsh, J.T., Cooke, R.A., Developing a community-based program for reducing the social impact of a plant closing (1979) The Journal of Applied Behavioral Science, 15 (2), pp. 133-155; (2013) CDFI Fund: Capacity Building Initiative, , https//www.cdfifund.gov/Documents/4%20Social%20Impact_Training%20Deck.pdf#search=impact%20assessment, Training Module: Telling Your Story Through Social Impact Data Collection and Reporting; Ullman Arieh, A., Data in search of a theory: A critical examination of the relationships among social performance, social disclosure, and economic performance of U.S. Firms (1985) The Academy of Management Review, 10 (3), pp. 540-557; Vanclay, F., Bronstein, D.A., (1995) Environmental and Social Impact Assessment, , John Wiley & Sons; Waddock, S.A., Graves, S.B., The corporate social performance-financial performance link (1997) Strategic Management Journal, 18 (4), pp. 303-319; Henriques, A., Richardson, J., The triple bottom line: Does it all add up (2013) Routledge; Shen, L., Wang, Z.H., Huang, Y.L., The establishment and analysis of the variable system for assessing the social impact of large-scale projects in China (2015) Open Cybernetics & Systemics Journal, 9, pp. 734-741; Hoek, H.W., Bartelds, A.I., Bosveld, J.J., Van Der Graaf, Y., Impact of urbanization on detection rates of eating disorders (1995) The American Journal of Psychiatry, 152 (9), p. 1272},
editor={Long S., Downing C., Ng E.H., Nepal B.},
publisher={American Society for Engineering Management},
language={English},
abbrev_source_title={Int. Annu. Conf. Ame. Soc. Eng. Manag., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ahmad20163694,
author={Ahmad, K. and Redahe, N.N.M. and Zabri, S.M.},
title={Lean strategy implementation and the roles of performance measurement},
journal={Proceedings of the 28th International Business Information Management Association Conference - Vision 2020: Innovation Management, Development Sustainability, and Competitive Economic Growth},
year={2016},
pages={3694-3703},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013959681&partnerID=40&md5=3218d963fba01668c5efecd27dbb0f2b},
affiliation={Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia},
abstract={In recent years, the implementation of lean has been one of the influential development in the production domain of manufacturing firms due to highly competitive market. Therefore building a relevant performance measurement system is crucial in achieving the objectives of lean manufacturing. This research was carried out to identify the extent of lean implementation and the roles of performance measurements in supporting lean implementation. The research was based on the qualitative approach where data were collected using semi-structured interview among three Malaysian companies from Electrical and Electronic (E&E) sector. The results show that respondents have focused on a certain type of lean techniques in realizing lean strategies for eliminating wastes. The most common lean techniques are customer value, continuous flow & pull system and continuous improvement. In meeting the objective of lean manufacturing, the findings suggest that performance measurement system has assisted in identifying problems and defects, meeting requirement of customers and improving the engineering techniques. In addition, the practice of lean strategies have increased the efficiency of production processes and enhanced quality towards achieving firms' targeted goals. Overall the results suggest that performance measurement system is crucial and informational towards improving lean manufacturing implementation.},
author_keywords={And key performance indicators (KPI);  Electric & electronics sector;  Lean techniques;  Performance measurement},
keywords={Agile manufacturing systems;  Benchmarking;  Information management;  Innovation;  Lean production;  Manufacture;  Supply chain management;  Sustainable development, Electronics sectors;  Key performance indicators;  Lean techniques;  Manufacturing implementations;  Performance measurement system;  Performance measurements;  Semi structured interviews;  Strategy implementations, Economics},
references={Ahmad, K., Mohamed Zabri, S., The effect of non-financial performance measurement system on firm performance (2016) International Journal of Economics and Financial Issues, 6 (S6), pp. 50-53; Arbulo-Lopez, P.R., Fortuny-Santos, J., Cuatrecasas-Arbos, L., Lean manufacturing: Costing the value stream (2012) Industrial Management & Data Systems, 113 (5), pp. 647-668; Bhuiyan, N., Baghel, A., An overview of continuous improvement: From the past to the present (2005) Management Decision, 43 (5), pp. 761-771; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Burnard, P., Gill, P., Treasure, E., Chadwick, B., Analysing and presenting qualitative data (2008) British Dental Journal, 204, pp. 429-432; Chow, C.W., Van Der Stede, W.A., The use and usefulness of non-financial performance measures (2006) Management Accounting Quarterly, 7 (3), pp. 1-8; Cumbo, D., Kline, D.E., Bumgardner, M.S., Benchmarking performance measurement and lean manufacturing in the rough mill (2005) Forest Products Journal, 56 (6), pp. 25-30; Fullerton, R.R., Kennedy, F., Widener, S.K., Lean manufacturing and firm performance: The incremental contribution of lean management accounting practices (2014) Journal of Operations Management, 32, pp. 414-428; Ferreira, A., Otley, D., The design and use of performance management systems: An extended framework for analysis (2009) Journal of Management Accounting Research, 20 (4), pp. 263-283; Fryer, K., Antony, J., Ogden, S., Performance management in the public sector (2009) International Journal of Public Sector Management, 22 (6), pp. 478-498; Kuhi, K., Kaare, K.K., Koppel, O., Using probabilistic models for missing data prediction in network industries performance measurement systems (2015) Procedia Engineering, 100, pp. 1348-1353; Kennedy, F.A., Widener, S.K., A control framework: Insights from evidence on lean accounting (2008) Management Accounting Research, 19, pp. 301-323; Li, X., Sawhney, R., Arendt, E.J., Ramasamy, K., A comparative analysis of management accounting systems' impact on lean implementation (2012) International Journal of Technology Management, 57 (1-3), pp. 33-48; Maskell, B.H., Baggaley, B.L., Lean accounting: What's it all about? (2006) Target Magazine, 22 (1), pp. 35-43; Melton, T., The benefits of lean manufacturing what lean thinking has to offer the process industries (2005) Chemical Engineering Research and Design, 83 (A6), pp. 662-673; Neely, A., Gregory, M., Platts, K., Performance measurement system design (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Ofileanu, D., Topor, D.L., Lean accounting - An ingenious solution for cost optimization (2014) International Journal of Academic Research in Business and Social Sciences, 4 (4), pp. 342-352; Shah, R., Ward, P.T., Lean manufacturing context, practice bundles, and performance (2003) Journal of Operations Management, 21 (2), pp. 129-149; Senapati, A., Mishra, P.C., Routra, B.C., Biswas, A., An extensive literature review on lead time reduction in inventory control (2012) International Journal of Engineering and Advanced Technology, 1 (6), pp. 104-111; Sundar, R., Balaji, A.N., Kumar, R.M.S., A review on lean manufacturing implementation techniques (2014) Procedia Engineering, 97, pp. 1875-1885; Taggart, P., Kienhofer, F., The effectiveness of lean manufacturing audits in measuring operational performance improvements (2013) The South African Journal of Industrial Engineering, 24 (2), pp. 140-154; Tillema, S., Van Der Steen, M., Co-existing concepts of management control: The containment of tensions due to the implementation of lean production (2015) Management Accounting Research, 27, pp. 67-83},
editor={Soliman K.S.},
publisher={International Business Information Management Association, IBIMA},
isbn={9780986041983},
language={English},
abbrev_source_title={Proc. Int. Bus. Inf. Manag. Assoc. Conf. - Vis. : Innov. Manag., Dev. Sustain., Compet. Econ. Growth},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Sager201661,
author={Sager, B. and Hawer, S. and Reinhart, G.},
title={A Performance Measurement System for Global Manufacturing Networks},
journal={Procedia CIRP},
year={2016},
volume={57},
pages={61-66},
doi={10.1016/j.procir.2016.11.012},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006965647&doi=10.1016%2fj.procir.2016.11.012&partnerID=40&md5=8873db6095fd2889ad9cad873470513b},
affiliation={Institute for Machine Tools and Industrial Management, Technical University of Munich, Munich, Germany},
abstract={New developments coming along with globalisation increasingly force companies to realize efficient global manufacturing networks (GMN). Current research offers abundant methods aiming at the configuration of GMNs. However, less attention is paid to identifying the need for adapting existing networks and the comparison of enhanced network configurations. In other fields, like for example logistics, performance measurement systems (PMS) are applied to accomplish these tasks. This paper therefore seeks to support the improvement of network configurations by providing a PMS for GMNs. In the course of this research existing PMS are reviewed and a multidimensional evaluation is carried out. The system with the best fit is chosen and transferred to the field of GMNs. Subsequently, performance attributes are deduced from a strategic and operational point of view based on a literature review as well as the application of concepts known from life-cycle-management and systems theory. The proposed PMS is validated by an industrial case study. The results of the multidimensional evaluation show that the concept of selective key figures that is known from the field of logistics has the best fit to serve as a basis for a novel PMS for GMNs. The transferred PMS consists of metrics evaluating the strategic success factors of GMNs like flexibility and delivery reliability on the one hand and possible operational bottlenecks like complexity on the other hand. The validation of the PMS in a real life environment shows that it contributes to overcoming the identified gap in the literature and supports practitioners in the process of enhancing GMNs. © 2016 The Authors.},
author_keywords={Global Manufacturing Network;  Performance Measurement System;  Strategic Success Factors},
keywords={Complex networks;  Life cycle;  Supply chain management, Global Manufacturing Networks;  Industrial case study;  Life-cycle management;  Multi-dimensional evaluations;  Network configuration;  Operational bottlenecks;  Performance measurement system;  Success factors, Manufacture},
references={Mourtzis, D., Doukas, M., Psarommatis, F., Manufacturing Network Design for Mass Customisation using a Genetic Algorithm and an Intelligent Search Method (2013) Procedia CIRP, 7, pp. 37-42; Wiendahl, H.P., Lutz, S., Production in Networks (2002) CIRP Annals - Manufactur- Ing Technology, 51 (2), pp. 573-586; Liebeck, T., (2009) Markt- Und Ressourcenorientierter Ansatz Zur Strategischen Gestaltung Globaler Produktionsnetzwerke, , Aachen: Shaker; Shepherd, C., Gnter, H., Measuring Supply Chain performance: Current re- search and future directions (2006) International Journal of Productivity and Per- Formance Management, 55 (3-4), pp. 242-258; Laux, H., Gillenkirch, R., Schenk-Mathes, H.Y., (2014) Entscheidungstheorie, , Berlin: Springer Gabler; (2007) A Structured Approach to Developing and Exploit- Ing Manufacturing Footprint Strategy, , University of Cambridge Institute of Manufacturing. Making the right things in the right places Cambridge: University of Cambridge Institute for Manufacturing; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations. & Production Management, 15 (4), pp. 80-116; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Selto, F.H., Malina, M.A., Choice and Change of Measures in Performance Measurement Models (2004) SSRN Journal, 15, pp. 441-469; Varandani, R.M., (2014) Managementkomplexitt Als Gestaltungsgre Kostenopti- Mierter Globaler Produktionsnetzwerke, , Aachen: Appriumus Verlag; Herm, M., (2006) Konfiguration Globaler Wertschpfungsnetzwerke Auf Basis von Business Capabilities, , Aachen: Shaker; De Toni, A., Tonchia, S., Performance measurement systems - Models, char- acteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Ude, J., (2010) Entscheidungsuntersttzung Fr Die Konfiguration Globaler Wertschp- Fungsnetzwerke, , Aachen. Shaker; Krebs, P., (2011) Bewertung Vernetzter Produktionsstandorte Unter Bercksichtigung Multidimensionaler Unsicherheiten, , Mnchen: Herbert Utz; Fts, C., Performance Measurement in a Supply Chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain man- agement: A balanced scorecard approach (2007) Computers & Industrial Engi- Neering, 53 (1), pp. 43-62; Richert, J., (2006) Performance Measurement in Supply Chains, , Wiesbaden: Gabler Verlag; Giese, A., (2012) Differenziertes Performance Measurement in Supply Chains, , Wiesbaden: Springer Gabler; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Productions Management, 19 (3), pp. 275-292; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strate- gic and operational planning in supply chain design (2000) Omega, 28 (5), pp. 581-598; Kaplan, R.S., (1993) Balanced Scorecard. Boston Massachusetts: Harvard Business School Management Productions; Hon, K.K.B., Performance and Evaluation of Manufacturing Systems (2005) CIRP Annals - Manufacturing Technology, 54 (2), pp. 139-154; (2012) The Supply Chain Council. SCOR Supply Chain Operations Reference Model: Revision 11.0, , Chicago: The Supply Chain Council; Weber, J., Kummer, S., Groklaus, A., Nippel, H., Warnke, D., Methodik zur Generierung von Logistik-Kennzahlen (1997) Betriebswirtschaftliche Forschung und Praxis, 49 (4), pp. 438-454; Weber, J., Schaeffer, U., (2006) Einfuehrung in das Controlling, , 11th ed. Stuttgart:. Schaeffer-Poeschel; Lelke, F., (2005) Kennzahlensysteme in Konzerngebundenen Dienstleistungsun- Ternehmen Unter Besonderer Bercksichtigung der Entwicklung Eines Wis- Sensbasierten Kennzahlengenerators, , Duisburg-Essen: Universitaet; Saad, K.N., Roussel, P.A., Tiby, C., (1991) Management der F& E-Strategie, , Wies- baden: Gabler Verlag; Friedli, T., Mundt, A., Thomas, S., (2014) Strategic Management of Global Manufac- Turing Networks, , Heidelberg: Springer},
correspondence_address1={Sager, B.; Institute for Machine Tools and Industrial Management, Germany; email: benedikt.sager@iwb.tum.de},
editor={Bauernhansl T., Westkamper E.},
publisher={Elsevier B.V.},
issn={22128271},
language={English},
abbrev_source_title={Procedia CIRP},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Bühler2016694,
author={Bühler, A. and Wallenburg, C.M. and Wieland, A.},
title={Accounting for external turbulence of logistics organizations via performance measurement systems},
journal={Supply Chain Management},
year={2016},
volume={21},
number={6},
pages={694-708},
doi={10.1108/SCM-02-2016-0040},
note={cited By 15},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85003914611&doi=10.1108%2fSCM-02-2016-0040&partnerID=40&md5=e328522ef20517d2d02212dfe3b34e28},
affiliation={Supply Chain Management Group, WHU – Otto Beisheim School of Management, Düsseldorf, Germany; Department of Operations Management, Copenhagen Business School, Frederiksberg, Denmark},
abstract={Purpose: This paper aims to investigate the role of upper management in designing performance measurement systems (PMS) that account for external turbulence of the organization and to show how this PMS design for turbulence impacts organizational resilience and distribution service performance. Design/methodology/approach: Hypotheses are developed by integrating management accounting and strategic management perspectives into supply chain management and subsequently tested based on data from 431 logistics organizations (i.e. both logistics companies and internal logistics departments of manufacturing and retailing companies). Findings: Attention focusing usage type of the PMS by the upper management fosters incorporating the element of risk into the PMS of the company. Further, PMS design for turbulence enhances organizational resilience, and, indirectly, this also leads to improved distribution service performance. Originality/value: This paper is the first to introduce the concept of PMS design for turbulence to the literature and to show that it is relevant for supply chain risk management by fostering the capabilities and the performance of logistics organizations. Further, it is shown that a seemingly detached issue such as the general PMS use focus of the upper management impacts supply chain risk management. © 2016, © Emerald Group Publishing Limited.},
author_keywords={Logistic service industry;  Performance measurement;  Resilience;  Risk management;  Structural equation model;  Supply chain management},
references={Abrahamsson, M., Christopher, M., Stensson, B.I., Mastering supply chain management in an era of uncertainty at SKF (2015) Global Business and Organizational Excellence, 34 (6), pp. 6-17; Ambulkar, S., Blackhurst, J., Grawe, S., Firm’s resilience to supply chain disruptions: scale development and empirical examination (2015) Journal of Operations Management, 33-34, pp. 111-122. , Vols; Amit, R., Schoemaker, P.J., Strategic assets and organizational rent (1993) Strategic Management Journal, 14 (1), pp. 33-46; Armstrong, J.S., Overton, T.S., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Atkinson, A.A., Balakrishnan, R., Booth, P., Cote, J.M., Groot, T., Uliana, E., Wu, A., New directions in management accounting research (1997) Journal of Management Accounting Research, 9, pp. 79-108; Barney, J.B., Firm resources and sustainable competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Barney, J.B., Purchasing, supply chain management and sustained competitive advantage: the relevance of resource-based theory (2012) Journal of Supply Chain Management, 48 (2), pp. 3-6; Björklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Blome, C., Schoenherr, T., Supply chain risk management in financial crises: a multiple case-study approach (2011) International Journal of Production Economics, 134 (1), pp. 43-57; Bollen, K.A., (1989) Structural Equations with Latent Variables, , Wiley, New York, NY; Braunscheidel, M.J., Suresh, N.C., The organizational antecedents of a firm’s supply chain agility for risk mitigation and response (2009) Journal of Operations Management, 27 (2), pp. 119-140; Brislin, R.W., Comparative research methodology: cross-cultural studies (1976) International Journal of Psychology, 11 (3), pp. 215-229; Burchell, S., Clubb, C., Hopwood, A., Hughes, J., Nahapiet, J., The roles of accounting in organizations and society (1980) Accounting, Organizations and Society, 5 (1), pp. 5-27; Calantone, R., Garcia, R., Droege, C., The effects of environmental turbulence on new product development strategy planning (2003) Journal of Product Innovation Management, 20 (2), pp. 90-103; Carpenter, M.A., Geletkanycz, M.A., Sanders, W.G., Upper echelons research revisited: antecedents, elements, and consequences of top management team composition (2004) Journal of Management, 30 (6), pp. 749-778; Chan, H.K., Wang, W.Y., Luong, L.H., Chan, F.T., Flexibility and adaptability in supply chains: a lesson learnt from a practitioner (2009) Supply Chain Management: An International Journal, 14 (6), pp. 407-410; Chang, S.J., van Witteloostuijn, A., Eden, L., Common method variance in international business research (2010) Journal of International Business Studies, 41 (2), pp. 178-184; Christopher, M., Holweg, M., Supply chain 2.0: managing supply chains in the era of turbulence (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 63-82; Christopher, M., Mena, C., Khan, O., Yurt, O., Approaches to managing global sourcing risk (2011) Supply Chain Management: An International Journal, 16 (2), pp. 67-81; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: more than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-14; Coutu, D.L., How resilience works (2002) Harvard Business Review, 80 (5), pp. 46-55; Daugherty, P., Chen, H., Mattioda, D., Grawe, S., Marketing/logistics relationships: influence on capabilities and performance (2009) Journal of Business Logistics, 30 (1), pp. 1-18; Dempster, A., Laird, N., Rubin, D., Maximum likelihood from incomplete data via the EM algorithm (1977) Journal of the Royal Statistical Society: Series B (Methodological), 39 (1), pp. 1-38; Durach, C.F., Wieland, A., Machuca, J.A.D., Antecedents and dimensions of supply chain robustness: a systematic literature review (2015) International Journal of Physical Distribution & Logistics Management, 45 (1-2), pp. 118-137; Ellinger, A.E., Daugherty, P.J., Keller, S.B., The relationship between marketing/logistics interdepartmental integration and performance in US manufacturing firms: an empirical analysis (2000) Journal of Business Logistics, 21 (1), pp. 1-22; Evans, J., An exploratory study of performance measurement systems and relationships with performance results (2004) Journal of Operations Management, 22 (3), pp. 219-232; Feldman, M.S., March, G.J., Information in organizations as signal and symbol (1981) Administrative Science Quarterly, 26 (2), pp. 171-186; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Garver, M.S., Mentzer, J.T., Logistics research methods: employing structural equation modeling to test for construct validity (1999) Journal of Business Logistics, 20 (1), pp. 33-58; Gerbing, D., Anderson, J., An updated paradigm for scale development incorporating unidimensionality and its assessment (1988) Journal of Marketing Research, 25 (2), pp. 186-192; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Grant, R.M., The resource-based theory of competitive advantage: implications for strategy formulation (1991) California Management Review, 33 (3), pp. 114-135; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Hall, E.T., Hall, M.R., (2000) Understanding Cultural Differences: Germans, French and Americans, , Intercultural Press, Yarmouth, ME; Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.M., Tuominen, M., Risk management processes in supplier networks (2004) International Journal of Production Economics, 90 (1), pp. 47-58; Hamel, G., Välikangas, L., The quest for resilience (2003) Harvard Business Review, 81 (9), pp. 52-63; Harman, H.H., (1967) Modern Factor Analysis, , University of Chicago Press, Chicago, IL; Hendricks, K.B., Singhal, V.R., The effect of supply chain glitches on shareholder wealth (2003) Journal of Operations Management, 21 (5), pp. 501-522; Henri, J., Organizational culture and performance measurement systems (2006) Accounting, Organizations and Society, 31 (1), pp. 77-103; Henri, J., Taxonomy of performance measurement systems (2008) Advances in Management Accounting, 17, pp. 247-288; Henseler, J., Ringle, C.M., Sarstedt, M., A new criterion for assessing discriminant validity in variance-based structural equation modeling (2015) Journal of the Academy of Marketing Science, 43 (1), pp. 115-135; Hofstede, G., The poverty of management control philosophy (1978) Academy of Management Review, 3 (3), pp. 450-461; Ignatiadis, I., Nandhakumar, J., The impact of enterprise systems on organizational resilience (2006) Journal of Information Technology, 22 (1), pp. 36-43; Ittner, C.D., Larcker, D.F., Innovations in performance measurement: trends and research implications (1998) Journal of Marketing Research, 10 (2), pp. 205-238; Johnson, N., Elliott, D., Drake, P., Exploring the role of social capital in facilitating supply chain resilience (2013) Supply Chain Management: An International Journal, 18 (3), pp. 324-336; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Kilfoyle, E., Richardson, A.J., MacDonald, L.D., Vernacular accountings: bridging the cognitive and the social in the analysis of employee-generated accounting systems (2013) Accounting, Organizations and Society, 38 (5), pp. 382-396; Koufteros, X., Verghese, A., Lucianetti, L., The effect of performance measurement systems on firm performance: a cross-sectional and a longitudinal study (2014) Journal of Operations Management, 32 (6), pp. 313-336; McCann, J., Selsky, J., Lee, J., Building agility, resilience, and performance in turbulent environments (2009) People and Strategy, 32 (3), pp. 44-51; Malik, Y., Niemeyer, A., Ruwadi, B., Building the supply chain of the future (2011) McKinsey Quarterly, (1), pp. 62-71; Malmi, T., Brown, D.A., Management control systems as a package: opportunities, challenges and research directions (2008) Management Accounting Research, 19 (4), pp. 287-300; Manuj, I., Mentzer, J.T., Global supply chain risk management (2008) Journal of Business Logistics, 29 (1), pp. 133-155; Melnyk, S.A., Closs, D.J., Griffis, S.E., Zobel, C.W., Macdonald, J.R., Understanding supply chain resilience (2014) Supply Chain Management Review, 18 (1), pp. 34-41; Menon, A., Varadarajan, P.R., A model of marketing knowledge use within firms (1992) Journal of Marketing, 56 (4), pp. 53-71; Mentzer, J.T., Flint, D.J., Hult, G.T., Logistics service quality as a segment-customized process (2012) Journal of Marketing, 65 (4), pp. 82-104; Narver, J.C., Slater, S.F., The effect of a market orientation on business profitability (1990) Journal of Marketing, 54 (4), pp. 20-35; Neely, A., Gregory, M., Platts, K., Performance measurement system design (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Otley, D., Performance management: a framework for management control systems research (1999) Management Accounting Research, 10 (4), pp. 363-382; Phillips, L.W., Assessing measurement error in key informant reports: a methodological note on organizational analysis in marketing (1981) Journal of Marketing Research, 18 (4), pp. 395-415; Podsakoff, P.M., MacKenzie, S.B., Lee, Y.-L., Podsakoff, N.P., Common method biases in behavioral research: a critical review of the literature and recommended remedies (2003) Journal of Applied Psychology, 88 (5), pp. 879-903; Ponomarov, S.Y., Holcomb, M.C., Understanding the concept of supply chain resilience (2009) The International Journal of Logistics Management, 20 (1), pp. 124-143; Rasid, S.Z.A., Golshan, N.M., Ismail, W.K.W., Ahmad, F.S., (2012) Risk management, performance measurement and organizational performance: a conceptual framework, pp. 1702-1715. , Proceedings of 3rd International Conference on Business and Economic Research; Rhea, M.J., Shrock, D.J., Measuring the effectiveness of physical distribution customer service programs (1987) Journal of Business Logistics, 8 (1), pp. 31-45; Ridgway, V.F., Dysfunctional consequences of performance measurements (1956) Administrative Science Quarterly, 1 (2), pp. 240-247; Ritchie, B., Brindley, C., Supply chain risk management and performance: a guiding framework for future development (2007) International Journal of Operations & Production Management, 27 (3), pp. 303-322; Schaeffer, U., Steiners, D., Zur Nutzung von controlling information (2003) Management Accounting, (9), pp. 1-36; Scholten, K., Sharkey-Scott, P., Fynes, B., Mitigation processes – antecedents for building supply chain resilience (2014) Supply Chain Management: An International Journal, 19 (2), pp. 211-228; Sheffi, Y., Rice, J.B., Jr., A supply chain view of the resilient enterprise (2005) MIT Sloan Management Review, 47 (1), pp. 40-48; Simchi-Levi, D., Schmidt, W., Wei, Y., From superstorms to factory fires: managing unpredictable supply-chain disruptions (2014) Harvard Business Review, 92 (1-2), pp. 96-101; Simon, H.A., Guetzkow, H., Kozmetsky, G., Tyndall, G., (1954) Centralization vs Decentralization in Organizing the Controllers’ Department, , Controllership Foundation, New York, NY; Simons, R., The role of management control systems in creating competitive advantage: new perspectives (1990) Accounting, Organizations and Society, 15 (1-2), pp. 127-143; Sodhi, M.S., Son, B.G., Tang, C.S., Researchers’ perspectives on supply chain risk management (2012) Production and Operations Management, 21 (1), pp. 1-13; Springinklee, M., Wallenburg, C.M., Improving distribution service performance through effective production and logistics integration (2012) Journal of Business Logistics, 33 (4), pp. 309-323; Stank, T.P., Keller, S.B., Daugherty, P.J., Supply chain collaboration and logistical service performance (2001) Journal of Business Logistics, 22 (1), pp. 29-48; Tang, C.S., Perspectives in supply chain risk management (2006) International Journal of Production Economics, 103 (2), pp. 451-488; Taplin, R., (2005) Risk Management and Innovation in Japan, Britain, and the United States, , Routledge, New York, NY; Tomer, J., (1987) Organizational Capital, , Praeger Publishing, New York, NY; Trkman, P., McCormack, K., Supply chain risk in turbulent environments: a conceptual model for managing supply chain network risk (2009) International Journal of Production Economics, 119 (2), pp. 247-258; van Hoek, R.I., The purchasing and control of supplementary third-party logistics services (2000) Journal of Supply Chain Management, 36 (3), pp. 14-26; Vandenbosch, B., An empirical analysis of the association between the use of executive support systems and perceived organizational competitiveness (1999) Accounting, Organizations and Society, 24 (1), pp. 77-92; Voorhees, C.M., Brady, M.K., Calantone, R., Ramirez, E., Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies (2016) Journal of the Academy of Marketing Science, 44 (1), pp. 119-134; Wagner, S.M., Bode, C., An empirical investigation into supply chain vulnerability (2006) Journal of Purchasing and Supply Management, 12 (6), pp. 301-312; Wagner, S.M., Bode, C., An empirical examination of supply chain performance along several dimensions of risk (2008) Journal of Business Logistics, 29 (1), pp. 307-325; Weick, K.E., Sutcliffe, K.M., Obstfeld, D., Organizing for high reliability: processes of collective mindfulness (1999) Research in Organizational Behavior, 21, pp. 13-81; Wieland, A., Wallenburg, C.M., Dealing with supply chain risks: linking risk management practices and strategies to performance (2012) International Journal of Physical Distribution & Logistics Management, 42 (10), pp. 887-905; Wieland, A., Wallenburg, C.M., The influence of relational competencies on supply chain resilience: a relational view (2013) International Journal of Physical Distribution & Logistics Management, 43 (4), pp. 300-320; Wieland, A., Handfield, R., Durach, C.F., Mapping the landscape of future research themes in supply chain management (2016) Journal of Business Logistics, 37 (3), pp. 1-8; Wong, C., Skipworth, H., Godsell, J., Achimugu, N., Towards a theory of supply chain alignment enablers: a systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 419-437; Zacharia, Z.G., Mentzer, J.T., Logistics salience in a changing environment (2004) Journal of Business Logistics, 25 (1), pp. 187-210},
correspondence_address1={Wieland, A.; Department of Operations Management, Denmark; email: awi.om@cbs.dk},
publisher={Emerald Group Publishing Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Fernandes20161113,
author={Fernandes, C.W.N. and Taglialenha, S.L.D.S. and Silva, V.M.D.},
title={Performance measures to humanitarian logistics: The perspective of the humanitarian assistance chain},
journal={Advances in Transdisciplinary Engineering},
year={2016},
volume={4},
pages={1113-1120},
doi={10.3233/978-1-61499-703-0-1113},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994036047&doi=10.3233%2f978-1-61499-703-0-1113&partnerID=40&md5=9e6f9bec0ed4275ceb3372098d464967},
affiliation={Mobility Enginneering Department, Joinville Campus, Federal University of Santa Catarina, Brazil},
abstract={Earthquakes, hurricanes, floods, tsunamis and other emergency situations have demanded a special logistic treatment named Humanitarian Logistics, has currently been studied in several countries around the world. The goal of this article is to compare the performance measurement in the humanitarian supply chain to the traditional performance measurement in the commercial supply chain, to develop performance indicators in the humanitarian supply chain and present a structure that can be used as a basis for the development of a performance measurement system for the humanitarian logistics. © 2016 The authors and IOS Press.},
author_keywords={Humanitarian assistance chain;  Humanitarian logistics;  Logistics;  Performance measures},
keywords={Logistics;  Supply chain management, Emergency situation;  Humanitarian assistances;  Humanitarian logistics;  Performance indicators;  Performance measure;  Performance measurement system;  Performance measurements, Supply chains},
references={Beamon, B.M., (2004) Humanitarian Relief Chains: Issues and Challenges, 34th International Conference on Computers and Industrial Engineering, , San Francisco, CA, USA; Thomas, A., (2004) Elevating Humanitarian Logistics, International Aid & Trade Review, pp. 102-106. , www.fritzinstitute.org/PDFs/InTheNews/2004/AidandTrade_0104.pdf, Accessed: 15 July, 2016; Beamon, B.M., Kotleba, S.K., Inventory management support systems for emergency humanitarian relief operations in South Sudan (2006) The International Journal of Logistics Management, 17 (2), pp. 187-212; Nogueira, C.W., Gonçalves, M.B., Novaes, A.G., Humanitarian logistics and corporate logistics: Relationships, concepts and challenges (2007) Records of the XXI Congress of Research and Education in Transports, , Rio de Janeiro; Boin, A., Kelle, P., Whybark, C., Editorial: Resilient supply chains for extreme situations: Outlining a new field of study (2010) Int. J. Production Economics, 126, pp. 1-6; Ballou, R., (1993) Corporate Logistics, , Atlas São Paulo; Bowersox, D.J., (1996) Logistical Management: The Integrated Supply Chain Process, , McGraw-Hill, New York; Meirim, H., (2007) Humanitarian Logistics and Corporate Logistics, Mmrbrasil, , www.mmrbrasil.com.br, Accessed: 27 May 2016; Moore, M.H., Managing for value: Organizational strategy in for-profit, nonprofit, and governmental organizations (2000) Nonprofit and Voluntary Sector Quarterly, 29 (1), pp. 183-204; Carvalho, H., Maleki, M., Cruz-Machado, V., The links between supply chain disturbances and resilience strategies (2012) Int. J. Agile Systems and Management, 5 (3), pp. 203-234; (2007) International Federation of Red Cross, , www.cvb.org.br, Accessed on: 20 Dec. 2015; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Thomas, A., (2003) Humanitarian Logistics: Enabling Disaster Response, , www.fritzinstitute.org/pdfs/whitepaper/enablingdisasterresponse.pdf, White paper, The Fritz Institute Accessed on: 30 May 2016; Neely, A., Adams, B., Perspectives on Performance: The performance prism (1995) Handbook of Performance Measurement, , In: M. Bourne (ed.) Gee Publishing, London; Rouse, P., Puteteril, M., Towards a general managerial framework for performance measurement: A comprehensive highway maintenance application (1997) Journal of Productivity Analysis, 8 (2), pp. 127-149; Sawhill, J.C., Williamson, D., Mission impossible? Measuring success in nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 371-386; Lindenberg, M., Bryant, C., (2001) Going Global: Transforming Relief and Development NGOs, , Kumarian Press, Bloomfield; Stevenson, M., The role of services in flexible supply chains: An exploratory study (2013) Int. J. Agile Systems and Management, 6 (4), pp. 307-323; Fizzanty, T., Russell, I., Collins, R., Learning from failed supply chains: The application of complex adaptive systems and a modified SAFE framework in evaluating proposed system improvements (2013) Int. J. Agile Systems and Management, 6 (3), pp. 232-258; Poister, T.H., (2003) Measuring Performance in Public and Nonprofit Organizations, , Jossey-Bass, San Francisco; Kaplan, R.S., Strategic performance measurement and management in nonprofit organization (2001) Nonprofit Management and Leadership, 11 (3), pp. 353-370; Henderson, D.A., Chase, B.W., Woodson, B.M., Performance measures for NPOs (2002) Journal of Accountancy, 193 (1), pp. 63-68},
correspondence_address1={Fernandes, C.W.N.; Mobility Enginneering Department, Brazil; email: christiane.fernandes@ufsc.br},
editor={Verhagen W.J.C., Peruzzini M., Borsato M., Stjepandic J., Wognum N.},
publisher={IOS Press BV},
isbn={9781614997023},
language={English},
abbrev_source_title={Adv. Transdiscipl. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Voltolini20161093,
author={Voltolini, A.O. and Pinheiro De Lima, E. and Gouvea Da Costa, S.-E.},
title={Performance measurement for supply chain management: A systematic literature review},
journal={Advances in Transdisciplinary Engineering},
year={2016},
volume={4},
pages={1093-1102},
doi={10.3233/978-1-61499-703-0-1093},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84993995132&doi=10.3233%2f978-1-61499-703-0-1093&partnerID=40&md5=7d5fdb85af1a2d01e21c9f2f0e69f232},
affiliation={Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Federal University of Technology, Parana, Brazil},
abstract={Performance measurement models are evolving fast in recent years, many research studies have been done regarding the nature and the methodologies of measuring performance in organizations. The present global economic environment of continuous change is demanding new business models and competitive strategies. These new models are being characterized by integration, and new technologies adoption, their operations are being forced to look not only in individual company, but also in their entire set of operations networks. The present challenge is to extend the performance management and measurement models developed for isolated companies to supply chains. This article aims to systematically review the literature on supply chain performance management and measurement in order to map the trends and behavior of scientific production developed in the field. © 2016 The authors and IOS Press.},
author_keywords={Performance measurement;  Supply chain management;  Supply chain performance measurement systems;  Systematic literature review},
keywords={Behavioral research, Competitive strategy;  Measuring performance;  New business models;  Performance management;  Performance measurements;  Supply chain performance;  Supply chain performance measurements;  Systematic literature review, Supply chain management},
references={Najmi, A., Makui, A., A conceptual model for measuring supply chain's performance (2012) Prod. Plan. Control, 23 (9), pp. 694-706; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Measurement, P., Challenges for tomorrow∗ (2012) Int. J. Manag. Rev, 14 (3), pp. 305-327; Aramyan, L.H., Oude Lansink, A.G.J.M., Van Der Vorst, J.G.A.J., Van Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Manag. An Int. J, 12 (4), pp. 304-315; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Manag. Decis, 41 (7), pp. 635-642; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manag, 21 (1-2), pp. 71-87; Kevin Chae, B., Developing key performance indicators for supply chain: An industry perspective, supply chain manag (2009) An Int. J, 14 (6), pp. 422-428; Rosenthal, R., Dimatteo, M.R., Meta-analysis: Recent developments in quantitative methods for literature reviews (2001) Annu Rev Psychol, 52, pp. 59-82; Choong, K.K., The fundamentals of performance measurement systems: A systematic approach to theory and a research agenda (2014) Int. J. Product. Perform. Manag, 63 (7), pp. 879-922; González, L.S., Rubio, F.G., González, F.R., Velthuis, M.P., Measurement in business processes: A systematic review (2010) Bus. Process Manag. J, 16 (1), pp. 114-134; Vanti, N.A.P., Da bibliometria à webometria: uma exploração conceitual dos mecanismos utilizados para medir o registro da informação e a difusão do conhecimento (2002) Ciência da Informação, 31 (2), pp. 369-379; Treinta, F.T., De Farias Filho, J.R., Sant'Anna, A.P., Rabelo, L.M., Metodologia de pesquisa bibliográfica com a utilização de método multicritério de apoio à decisão (2014) Produção, 24 (3), pp. 508-520; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res, 25 (2), pp. 173-186; Wong, C., Skipworth, H., Godsell, J., Achimugu, N., Towards a theory of supply chain alignment enablers: A systematic literature review (2012) Supply Chain Manag. An Int. J, 17 (4), pp. 419-437; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) Int. J. Prod. Res, 42 (1), pp. 131-163; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) Int. J. Prod. Res, 48 (17), pp. 5137-5155; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Prod. Plan. Control, 20 (8), pp. 678-690; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ, 87 (3), pp. 333-347; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) Int. J. Prod. Res, 45 (12), pp. 2819-2840; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) Int. J. Prod. Econ, 55 (3), pp. 281-294; Ahi, P., Searcy, C., An analysis of metrics used to measure performance in green and sustainable supply chains (2014) J. Clean. Prod, 86, pp. 360-377; Bhattacharya, A., Mohapatra, P., Kumar, V., Dey, P.K., Brady, M., Tiwari, M.K., Nudurupati, S.S., Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach (2014) Prod. Plan. Control, 25 (8), pp. 698-714; Chin, T.A., Tat, H.H., Sulaiman, Z., Green supply chain management environmental collaboration and sustainability performance (2015) Procedia CIRP, 26, pp. 695-699; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour. Conserv. Recycl, 55 (6), pp. 567-579; Byrd, T.A., Davidson, N.W., Examining possible antecedents of IT impact on the supply chain and its effect on firm performance (2003) Inf Manag, 41 (2), pp. 243-255; Liu, H., Ke, W., Wei, K.K., Hua, Z., The impact of IT capabilities on firm performance: The mediating roles of absorptive capacity and supply chain agility (2013) Decis. Support Syst, 54 (3), pp. 1452-1462; Baihaqi, I., Sohal, A.S., The impact of information sharing in supply chains on organisational performance: An empirical study (2012) Prod. Plan. Control, 24 (8-9), pp. 743-758; Ambe, I.M., Key indicators for optimising supply chain performance: The case of light vehicle manufacturers in South Africa (2014) J. Appl. Bus. Res, 30 (1), pp. 277-289; Terpend, R., Ashenbaum, B., The intersection of power, trust and supplier network size: Implications for supplier performance (2012) J. Supply Chain Manag, 48 (3), pp. 52-77; Guiffrida, A.L., Nagi, R., Cost characterizations of supply chain delivery performance (2006) Int. J. Prod. Econ, 102 (1), pp. 22-36; Bhattacharyya, K., Guiffrida, A.L., An optimization framework for improving supplier delivery performance (2015) Appl. Math. Model, 39 (13), pp. 3771-3783; Vachon, S., Klassen, R.D., An exploratory investigation of the effects of supply chain complexity on delivery performance (2002) IEEE Trans. Eng. Manag, 49 (3), pp. 218-230; Kurien, G.P., Qureshi, M.N., Analysis and measurement of supply chain flexibility (2015) Int. J. Logist. Syst. Manag, 21 (1), p. 70; Moon, K.K.-L., Yi, C.Y., Ngai, E.W.T., An instrument for measuring supply chain flexibility for the textile and clothing companies (2012) Eur. J. Oper. Res, 222 (2), pp. 191-203; Agami, N., Saleh, M., Rasmy, M., A hybrid dynamic framework for supply chain performance improvement (2012) IEEE Syst. J, 6 (3), pp. 469-478; Irfan, D., Xiaofei, X., Chun, D.S., A SCOR reference model of the supply chain management system in an enterprise (2008) Int. Arab J. Inf. Technol, 5 (3), pp. 288-295; Hwang, Y., Wen, Y., Chen, M., A study on the relationship between the PDSA cycle of green purchasing and the performance of the SCOR model (2010) Total Qual. Manag. Bus. Excell, 21 (12), pp. 1261-1278; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int. J. Prod. Econ, 134 (1), pp. 177-187; Li, L., Su, Q., Chen, X., Ensuring supply chain quality performance through applying the SCOR model (2011) Int. J. Prod. Res, 49 (1), pp. 33-57; Sellitto, M.A., Pereira, G.M., Borchardt, M., Da Silva, R.I., Viegas, C.V., Inácio, R., Viegas, C.V., A SCOR-based model for supply chain performance measurement: Application in the footwear industry (2015) Int. J. Prod. Res, 53 (16), pp. 4917-4926; Thunberg, M., Persson, F., Using the SCOR model's performance measurements to improve construction logistics (2013) Prod. Plan. Control, 25 (13-14), pp. 1065-1078; Kim, J., Rhee, J., An empirical study on the impact of critical success factors on the balanced scorecard performance in Korean green supply chain management enterprises (2012) Int. J. Prod. Res, 50 (9), pp. 2465-2483; Jalali Naini, S.G., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resour. Conserv. Recycl, 55 (6), pp. 593-603; Kusrini, E., Subagyo, N.A., Masruroh, N.A., A new approach to design supply chain key performance indicator for actors and regulator: A case study in innovative product in Indonesia (2016) Int. J. Bus. Perform. Manag, 17 (1), p. 1; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harv. Bus. Rev, 70 (1), pp. 71-79; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng, 53 (1), pp. 43-62; Reefke, H., Trocchi, M., Balanced scorecard for sustainable supply chains: Design and development guidelines (2013) Int. J. Product. Perform. Manag, 62 (8), pp. 805-826},
correspondence_address1={Voltolini, A.O.; Industrial and Systems Engineering Graduate Program, Brazil; email: amanda.voltolini@pucpr.br},
editor={Verhagen W.J.C., Peruzzini M., Borsato M., Stjepandic J., Wognum N.},
publisher={IOS Press BV},
isbn={9781614997023},
language={English},
abbrev_source_title={Adv. Transdiscipl. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Tarasewicz20161433,
author={Tarasewicz, R.},
title={Integrated Approach to Supply Chain Performance Measurement - Results of the Study on Polish Market},
journal={Transportation Research Procedia},
year={2016},
volume={14},
pages={1433-1442},
doi={10.1016/j.trpro.2016.05.216},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84991325483&doi=10.1016%2fj.trpro.2016.05.216&partnerID=40&md5=9fe64d7c3f8f773a26602f2513d6793c},
affiliation={Department of Logistics, Warsaw School of Economics, Al. Niepodległości 128, Warsaw, 02-554, Poland},
abstract={The purpose of this study is to examine the integration level of supply chain performance measurement among the largest production and commercial companies in Poland. Modern advanced measurement systems should analyze both the performance of individual organizations in the supply chains and the performance of a supply chain as a whole (integrated approach). Many international researches show that lack of information about the situation in the entire supply chain and lack of methods to analyze the entire network of partners is one of the most important barriers to improve the performance. A questionnaire survey was carried out within the research project 'Measuring the performance of supply chains in the largest production and commercial companies in Poland'. The survey involved a sample of 79 senior level managers responsible for logistics/supply chain management of the companies listed on the 'Ranking of 500 largest companies in Poland by Rzeczpospolita Daily'. It was carried out with the use of CATI (Computer Assisted Telephone Interviewing) method. The final form of the questionnaire was adopted after conducting a pilot study with logistics / supply chain directors of three leading companies in Poland. Methods of supply chain performance measurement used on Polish market do not take into account to a sufficient degree all the cooperating partners in the extended supply chain (from suppliers' suppliers to the final consumer), what has a negative impact on the performance of these chains. The research results indicate that the main barriers to the implementation of advanced measurement systems include: insufficient awareness of managers concerning the role of supply chains management in building the real value, low tendency to invest in advanced tools that support such measurement, fear of increase in costs, as well as reluctance to share information with partners. The study shows a potential to improve existing achievements, which should arouse interest of companies. Awareness among managers about potential for improving the performance of supply chains by using integrated measurement and adequate management information on the entire system should have an impact on tendency to invest in tools to support such measurement. The study fills the gap in the existing literature on logistics and supply chain management. This is the only research that treats this topic comprehensively on Polish market and is a base for benchmarking studies in Europe and worldwide. © 2016 The Authors. Published by Elsevier B.V.},
author_keywords={empirical study;  Logistics;  performance measurement;  supply chain management},
references={Aramyan, L.H., Oude Lansink, O., Van Der Vorst, J., Van Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Banomyong, R., Measuring the time conversion cycle in an international supply chain (2005) Annual Logistics Research Network Conference Proceedings, , http://www.bus.tu.ac.th, Sept. Plymouth, UK, (accessed 20 February 2014) 2005; Bello, D.C., Gilliland, D.I., The effects of output controls, process controls, and flexibility on export channel performance (1997) Journal of Marketing, 61 (1), pp. 22-38; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Bowersox, D.J., Closs, D., (1996) Logistical Management. The Integrated Supply Chain Processs, , McGraw-Hill Companies, New York; Bowersox, D.J., Closs, D., Stank, T.P., 21st century logistics: Making supply chain integration a reality (1999) Council of Logistics Management, Michigan State University, East Lansing; Brennan, N., Conell, B., Intellectual capital: Current issues and policy implications (2000) Journal of Intellectual Capital, 1 (3), pp. 206-240; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance management systems (1995) International Journal of Logistics Management, 6 (1), pp. 61-74; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Christopher, M., Logistics and supply chain management: Strategies for reducing cost and improving service (1998) Prentice Hall, Harlow; Cook, M., Hagey, R., Why companies flunk their supply chain (2003) Journal of Business Strategy, 24 (4), pp. 35-42; Elliff, S., (2010) Supply Chain Management: Looking at the Whole Picture, , www.ccmiservices.com, (accessed 28 July 2014); Fawcett, S.E., Clinton, S.R., Enhancing logistics performance to improve the competitiveness of manufacturing organization (1996) Production and Inventory Management, 37, pp. 40-46; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hagerty, J., Cecere, L., Souza, J., How best measure your supply chain today (2005) AMR Research, , www.amrresearch.com, (accessed 20 April 2012); Hofman, D., The hierarchy of supply chain metrics (performance) (2004) Supply Chain Management Review, , http://www.highbeam.com/doc/1G1-122408969.html, (accessed 6 May 2010); Kaplan, R.S., Norton, D.P., The Balanced Scorecard - Measures that drive performance (1992) Harvard Business Review, January-February, pp. 71-79; Kisperska-Moroń, D., (2006) "Pomiar funkcjonowania łańcuchów dostaw"["Measurement in supply chain management"], , Akademia Ekonomiczna, Katowice; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lambert, D.M., Pohlen, R.L., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lapide, L., What about measuring supply chain performance (2000) AMR Research, Available At: Lapide.ascet.com. (Accessed 1 March 2010); Lapide, L., Not-so-perfect order (2007) Supply Chain Management Review, July-August, pp. 7-8; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, Spring, pp. 65-73; Morgan, C., Structure, speed and salience: Performance management in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Rutkowski, K., (2008) "Najlepsze praktyki w zarzadzaniu łańcuchem dostaw. Wyjść naprzeciw wyzwaniom społecznej odpowiedzialności biznesu"["Best practices in supply chain management. Meeting the challenges of corporate social responsibility"], , Warsaw School of Economics Publishing House, Warsaw; Shen, T., Lapide, L., Supply Chain 2020 Report. Working paper (2005) MIT Centre for Transportation and Logistics, August, pp. 21-23; Stephens, S., Supply Chain Council and Supply Chain Operations Reference Model overview (2000) Supply Chain Council, Pittsburg; Van Weele, A., Purchasing and supply chain management, 6th edition (2014) Cengage Learning, Andover, UK; Wijnands, J.H., Ondersteijn, C., (2006) Quantifying the Agri-food Supply Chain, , Springer Dordrecht; Wilding, R., Waller, A., Rossi, S., Geldard, C., Mayhew, S., Cigolini, R., Metcalfe, C., Supply chain strategy in the boardroom (2010) Cranfield University & Solving Efeso, Cranfield; Zapato, L., (1997) Lord Kelvin Quotations, , http://zapatopi.net/kelvin/quotes, (accessed 10 November 2013)},
correspondence_address1={Tarasewicz, R.; Department of Logistics, Al. Niepodległości 128, Poland; email: Rafal.Tarasewicz@dbschenker.com},
publisher={Elsevier B.V.},
issn={23521457},
language={English},
abbrev_source_title={Transp. Res. Procedia},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kumar2016271,
author={Kumar, R.},
title={Supply chain performance measurement of olive oil industry with KPIs},
journal={International Journal of Value Chain Management},
year={2016},
volume={7},
number={3},
pages={271-284},
doi={10.1504/IJVCM.2016.079212},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84989876336&doi=10.1504%2fIJVCM.2016.079212&partnerID=40&md5=16e8f62aefab43aa0e80aeba99e975fa},
affiliation={Symbiosis Institute of Operations Management (SIOM), Nashik, Maharashtra, 422008, India; Constituent of Symbiosis International University, Pune, India; CEG-IST, Department of Engineering and Management, Instituto Superior Técnico, Universidade de Lisboa, A. Rovisco Pais, 1, Lisboa, 1049-001, Portugal},
abstract={Supply chain performance of a particular type of supply chain can be determined using certain key performance indicators (KPIs) of that industry. This paper aims to present a supply chain performance index for Portuguese olive oil industry on the basis of identifying performance indicators. The indicators were identified on the basis of survey and informal discussion with the management team of olive mills in Portugal. A questionnaire was developed for getting responses from mill managers, heads, engineers, and other employees of different departments of olive mills using a five-point Likert scale. Further, the key performance index of the particular olive mill is calculated using the proposed formula by Cleveland et al. (1989) on the basis of three weight criteria. This index can be used to make a suitable benchmark for the best olive industries which will help managers of other supply chains to measure and compare their supply chain performance with benchmarked supply chains. The result shows the weak and strong performance indicators of the olive mill in order to know their fallback situation and manage their on-time production requirements. Additionally, each performance indicator can be improved with the use of suggestive IT solutions. © 2015 Inderscience Enterprises Ltd.},
author_keywords={IT solutions;  Key performance indicators;  KPIs;  Olive fruits;  Olive oil industry;  supply chain performance index},
references={Afonso, O., Silva, L.N., Morgado, A., Barata, M., (2011) Water Footprint in Portugal [Online], , http://assets.panda.org/downloads/pegadahidrica_2011.pdf, (accessed 27 February 2015); Agrimonti, C., Vietina, M., Pafundo, S., Marmiroli, N., The use of food genomics to ensure the traceability of olive oil (2011) Trends in Food Science & Technology, 22 (5), pp. 237-244; Bordons, C., Núñez-Reyes, A., Model based predictive control of an olive oil mill (2008) Journal of Food Engineering, 84 (1), pp. 1-11; Cano, M., Roales, J., Castillero, P., Medoza, P., Calero, A.M., Jiménez-Ot, C., Pedrosa, J.M., Improving the training and data processing of an electronic olfactory system for the classification of virgin olive oil into quality categories (2011) Sensors and Actuators B: Chemical, 160 (1), pp. 916-922; Carbone, A., Galli, F., Caswell, J.A., Sorrentino, A., The performance of protected designations of origin: An ex post multi-criteria assessment of the Italian cheese and olive oil sectors (2014) Journal of Agricultural & Food Industrial Organization, 12 (1), pp. 121-140; Carmona-Torres, C., Parra-López, C., Hinojosa-Rodríguez, A., Sayadi, S., Farm-level multifunctionality associated with farming techniques in olive growing: An integrated modeling approach (2014) Agricultural Systems, 127 (1), pp. 97-114; (2011) EU Legislation: Olive Oil [Online], , http://www.paltrade.org/upload/multimedia/admin/2014/06/53a02794abdea.pdf, CBI (accessed 20 March 2015); Cleveland, G., Schroeder, R.G., Anderson, J.C., A theory of production competence (1989) Decision Sciences, 20 (4), pp. 655-668; Cristóvão, A., Tibério, M.L., Teixeira, M.S., (2015) WP5-Azeite de Trás-Os-Montes PDO: Case Study Synthesis [Online], , http://www.cetrad.info/static/docs/documentos/16.pdf, (accessed 15 March 2015); Dangayach, G.S., Deshmukh, S.G., Manufacturing strategy: Experiences from Indian manufacturing companies (2001) Production Planning and Control: The Management of Operations, 12 (8), pp. 775-786; Dermeche, S., Nadour, M., Larroche, C., Moulti-Mati, F., Michaud, P., Olive mill wastes: Biochemical characterizations and valorization strategies (2013) Process Biochemistry, 48 (10), pp. 1532-1552; Escuderos, M.E., Uceda, M., Sánchez, S., Jiménez, A., Instrumental technique evolution for olive oil sensory analysis (2007) European Journal of Lipid Science and Technology, 109 (5), pp. 536-546; (2004) European Union Citizens and Agriculture from 1995 to 2003 [Online], , http://ec.europa.eu/public_opinion/archives/ebs/ebs_112_agri_rep_en.pdf, European Commission (accessed 24 February 2015); (2012) Economic Analysis of the Olive Oil Sector [Online], , http://ec.europa.eu/agriculture/olive-oil/economic-analysis_en.pdf, European Commission (accessed 24 February 2015); Fragoso, R.M.S., Planning marketing channels: Case of the olive oil agribusiness in Portugal (2013) Journal of Agricultural & Food Industrial Organization, 11 (1), pp. 51-67; García-González, D.L., Aparicio, R., Research in olive oil: Challenges for the near future (2010) Journal of Agricultural and Food Chemistry, 58 (24), pp. 12569-12577; Harwood, J., Aparicio, R., (2000) Handbook of Olive Oil: Analysis and Properties, , Aspen Publishers Inc, Gaithersburg, Maryland; Iraldo, F., Testa, F., Bartolozzi, I., An application of life cycle assessment (LCA) as a green marketing tool for agricultural products: The case of extra-virgin olive oil in Val di Cornia, Italy (2014) Journal of Environmental Planning and Management, 57 (1), pp. 78-103; Kaabia, M.B., Gil, J.M., The EU demand for imports of virgin olive oil (2007) Proceedings of the 103rd EAAE Seminar of i Mediterranean Conference, Agro-Food Social Scientists; Adding Value to the Agro-Food Supply Chain in the Future Euromediterranean Space, , http://ageconsearch.umn.edu/bitstream/9390/1/sp07ka07.pdf, Barcelona, Spain, 23-25 April 2007, European Association of Agricultural Economists, Research in Agricultural & Applied Economics, 2007 [online] (accessed 20 March 2015); Krystallis, A., Ness, M., Motivational and cognitive structures of Greek consumers in the purchase of quality food products (2004) Journal of International Consumer Marketing, 16 (2), pp. 7-36; Kumar, R., Agrawal, R., Sharma, V., IT Enablement in sugar supply chain: An approach for farmers (2015) International Journal of Business Performance and Supply Chain Modelling, 7 (4), pp. 360-381; Kumar, R., Nath, V., Agrawal, R., Sharma, V., Performance measurement of sugar supply chain (2013) Journal of Management Research, 1 (1), pp. 3-14; Lahyani, R., Coelho, L.C., Khemakhem, M., Laporte, G., Semet, F., A multi-compartment vehicle routing problem arising in the collection of olive oil in Tunisia (2015) Omega, 51 (1), pp. 1-10; Lastra, C.A., Barranco, M.D., Motilva, V., Herrerías, J.M., Mediterranean diet and health: Biological importance of olive oil (2001) Current Pharmaceutical Design, 7 (10), pp. 933-950; Marques, C.A.F., (2013) Internationalisation Strategy for the Agro-Food Systems of Olive Oil and Wine of the Alentejo Region of Portugal, , http://hdl.handle.net/10174/10874, ADRAL/PACMAn project, 2013 [online] (accessed 15 March 2015); Mohamad, R.S., Verrastro, V., Cardone, G., Bteich, M.R., Favia, M., Moretti, M., Roma, R., Optimization of organic and conventional olive agricultural practices from a life cycle assessment and life cycle costing perspectives (2014) Journal of Cleaner Production, 70 (1), pp. 78-89; Núñez-Reyes, A., Scheffer-Dutra, C.B., Bordons, C., Comparison of different predictive controllers with multi-objective optimization: Application to an olive oil mill (2002) Proceedings of the 2002 IEEE International Conference on Control Applications, pp. 1242-1247. , Glasgow, Scotland, U.K; Parra-López, C., Calatrava-Requenaa, J., De-Haro-Giménez, T., A systemic comparative assessment of the multifunctional performance of alternative olive systems in Spain within an AHP-extended framework (2008) Ecological Economics, 64 (4), pp. 820-834; Sabbatini, V., The supply function of olive oil: A case study of Italy (2014) Procedia Economics and Finance, 14 (1), pp. 553-558; Salomone, R., Ioppolo, G., Environmental impacts of olive oil production: A life cycle assessment case study in the province of Messina (Sicily) (2012) Journal of Cleaner Production, 28 (1), pp. 88-100; Veillet, S., Tomao, V., Bornard, I., Ruiz, K., Chemat, F., Chemical changes in virgin olive oils as a function of crushing systems: Stone mill and hammer crusher (2009) Comptes Rendus Chimie, 12 (8), pp. 895-904; Vlyssides, A.G., Loizides, M., Karlis, P.K., Integrated strategic approach for reusing olive oil extraction by-products (2004) Journal of Cleaner Production, 12 (6), pp. 603-611; Vossen, P., Olive oil production (2005) Olive Production Manual, 2nd Ed., 161p. , Sibbett, G.S., Ferguson, L., Coviello, J.L. and Lindstrand, M. (Eds.) ANR Publications, Oakland, California, USA; Zaferatos, N.C., Sustaining the ancient olive community: Strategies for Mediterranean sustainable development (2011) Journal of Sustainable Development, 4 (1), pp. 23-35},
correspondence_address1={Kumar, R.; Symbiosis Institute of Operations Management (SIOM)India; email: scholar.rupesh@gmail.com},
publisher={Inderscience Publishers},
issn={17415357},
language={English},
abbrev_source_title={Int. J. Value Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Selviaridis2016513,
author={Selviaridis, K.},
title={Who’s to blame or praise? Performance attribution challenges in outsourced service provision in supply chains},
journal={Supply Chain Management},
year={2016},
volume={21},
number={5},
pages={513-533},
doi={10.1108/SCM-11-2015-0439},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988000727&doi=10.1108%2fSCM-11-2015-0439&partnerID=40&md5=df37f0f16a560f868cded3487e863a77},
affiliation={Department of Management Science, Lancaster University Management School, Lancaster, United Kingdom},
abstract={Purpose: The aim of this paper is to understand the antecedents and effects of performance attribution challenges arising in the provision of business-to-business (B2B) services in supply chains. Design/methodology/approach: The study draws on three in-depth case studies of logistics service providers (LSPs) offering supply chain solutions to their clients in Sweden. The analysis of performance attribution challenges and their antecedents and effects is based on 38 semi-structured interviews and review of 43 documents, including contracts and performance monitoring records. Findings: Three key antecedents of performance attribution challenges are stressed. Two of these, the inseparability and contestability of service inputs, are closely related to the notion of service co-production. The third antecedent is the limited provider capability in performance data collection and analysis. Performance attribution challenges may result in provider aversion to performance-related risk and have a harmful effect on client relationships, for example, in terms of provider perceptions of opportunism and unfair allocation of gains. These effects can be mitigated through contracting, interventions in performance measurement system design and deployment of relational mechanisms. Research limitations/implications: The paper extends the service management literature that emphasises on service co-production by suggesting that inputs of the client firm and its supply chain partners may not only vary in quality but also can be inseparable from provider inputs and highly contestable. It also empirically demonstrates how performance attribution challenges and their antecedents and effects manifest themselves in B2B service provision, as opposed to supply chain settings where the main user of logistics services is the consumer. Practical implications: LSP managers should contract for performance based on high-quality and incontestable external inputs they rely upon. Contractual specifications (performance indicators and related incentives) should explicate and consider the inputs required by clients and their supply chain partners to minimise their contestability. Originality/value: The study proposes an empirically based framework of the antecedents and effects of performance attribution challenges, an issue that has received scant attention in logistics outsourcing research and the business services literature more broadly. © 2016, © Emerald Group Publishing Limited.},
author_keywords={Case studies;  Contracts;  Performance management;  Service delivery;  Supply-chain management},
references={Alchian, A.A., Demsetz, H., Production, information costs, and economic organization (1972) American Economic Review, 62 (5), pp. 777-795; Barratt, M., Choi, T., Li, M., Qualitative case studies in operations management: trends, research outcomes and future research implications (2011) Journal of Operations Management, 29 (4), pp. 329-342; Büyüközkan, G., Feyzioglu, O., Ersoy, M.S., Evaluation of 4PL operating models: A decision making approach based on 2-additive Choquet integral (2009) International Journal of Production Economics, 121 (1), pp. 112-120; Doerr, K., Lewis, I., Eaton, D., Measurement issues in performance-based logistics (2005) Journal of Public Procurement, 5 (2), pp. 164-186; Eisenhardt, M.K., Agency theory: an assessment and review (1989) Academy of Management Review, 14 (1), pp. 57-74; Evangelista, P., Mogre, R., Perego, A., Raspagliesi, A., Sweeney, E., A survey based analysis of IT adoption and 3PLs’ performance (2012) Supply Chain Management: An International Journal, 17 (2), pp. 172-186; Fayezi, S., O’Loughlin, A., Zutshi, A., Agency theory and supply chain management: a structured literature review (2012) Supply Chain Management: An International Journal, 17 (5), pp. 556-570; Feldman, E., Montgomery, C., Are incentives without expertise sufficient? Evidence from Fortune 500 firms (2015) Strategic Management Journal, 36 (1), pp. 113-122; Flyvbjerg, B., Five misunderstandings about case-study research (2006) Qualitative Inquiry, 12 (12), pp. 219-245; Folkes, V.S., Consumer reactions to product failure: an attributional approach (1984) Journal of Consumer Research, 10 (4), pp. 398-409. , ), “,”; Forslund, H., Performance management in supply chains: logistics service provider’s perspective (2012) International Journal of Physical Distribution and Logistics Management, 42 (3), pp. 296-311; Foss, N., Stea, D., Putting a realistic theory of mind into agency theory: implications for reward design and management in principal-agent relations (2014) European Management Review, 11 (1), pp. 101-116; Gibbert, M., Ruigrok, W., Wicki, B., What Passes as a rigorous case study? (2008) Strategic Management Journal, 29 (13), pp. 1465-1474; Gotzamani, K., Longinidis, P., Vouzas, F., The logistics services outsourcing dilemma: quality management and financial performance perspectives (2010) Supply Chain Management: An International Journal, 15 (6), pp. 438-453; Grawe, S., Daugherty, P., Ralston, P., Enhancing dyadic performance through boundary spanners and innovation: an assessment of service provider–customer relationships (2015) Journal of Business Logistics, 36 (1), pp. 88-101; Gunasekaranan, A., Iranib, Z., Choyc, K., Filippid, L., Papadopoulos, T., Performance measures and metrics in outsourcing decisions: a review for research and applications (2015) International Journal of Production Economics, 161 (2), pp. 153-166; Halldórsson, A., Hsuan, J., Kotzab, H., Complementary theories to supply chain management revisited – from borrowing theories to theorizing (2015) Supply Chain Management: An International Journal, 20 (6), pp. 574-586; Hartmann, E., de Grahl, A., Logistics outsourcing interfaces: the role of customer partnering behavior (2012) International Journal of Physical Distribution and Logistics Management, 42 (6), pp. 526-543; Hess, R.L., Ganesan, S., Klein, N.M., Service failure and recovery: the impact of relationship factors on customer satisfaction (2003) Journal of Academy of Marketing Science, 31 (2), pp. 127-145; Hingley, M., Lindgreen, A., Grant, D.B., Kane, C., Using fourth-party logistics management to improve horizontal collaboration among grocery retailers (2011) Supply Chain Management: An International Journal, 16 (5), pp. 316-327; Hsiao, H., Kemp, R., Van der Vorst, J., Omta, S., A classification of logistics outsourcing levels and their impact on service performance: evidence from the food processing industry (2010) International Journal of Production Economics, 124 (1), pp. 75-86; Huo, B., Ye, Y., Zhao, X., The impacts of trust and contracts on opportunism in the 3PL industry: the moderating role of demand uncertainty (2015) International Journal of Production Economics, 170 (1), pp. 160-170; Hwang, B.N., Chen, T., Lin, J., 3PL selection criteria in integrated circuit manufacturing industry in Taiwan (2016) Supply Chain Management: An International Journal, 21 (1), pp. 103-124; Jayaram, Y., Tan, K.C., Supply chain integration with third-party logistics providers (2010) International Journal of Production Economics, 125 (2), pp. 262-271; Jensen, M., Meckling, W., Theory of the firm: managerial behavior, agency costs, and ownership structure (1976) Journal of Financial Economics, 3 (1), pp. 305-360; Jin, T., Tian, Z., Xie, M., A game-theoretical approach for optimizing maintenance, spares and service capacity in performance contracting (2015) International Journal of Production Economics, 161 (3), pp. 31-43; Kayakutlu, G., Buyukozkan, G., Assessing performance factors for a 3PL in a value chain (2011) International Journal of Production Economics, 131 (2), pp. 441-452; Ketokivi, M., Choi, T., Renaissance of case research as a scientific method (2014) Journal of Operations Management, 32 (1), pp. 232-240; Kim, S.H., Cohen, M., Netessine, S., Performance contracting in after-sales service supply chains (2007) Management Science, 53 (12), pp. 1843-1858; Krakovics, F., Leal, J., Mendes, P., Santos, R.L., Defining and calibrating performance indicators of a 4PL in the chemical industry in Brazil (2008) International Journal of Production Economics, 115 (2), pp. 502-514; Lai, K.H., Ngai, E., Cheng, T.C.E., An empirical study of supply chain performance in transport logistics (2004) International Journal of Production Economics, 87 (3), pp. 321-331; Leung, L., Wong, W.H., Van Hui, Y., Wan, Y., Managing third-party logistics under uncertainty: a decision scheme and managerial implications (2013) International Journal of Production Economics, 145 (2), pp. 630-644; Liu, W., Wang, Y., Quality control game model in logistics service supply chain based on different combinations of risk attitude (2015) International Journal of Production Economics, 161 (3), pp. 181-191; Logan, M.S., Using agency theory to design successful outsourcing relationships (2000) International Journal of Logistics Management, 11 (2), pp. 21-32; Lukassen, P., Wallenburg, C.M., Pricing third party logistics services: integrating insights from the logistics and industrial services literature (2010) Transportation Journal, 49 (2), pp. 24-43; Marasco, A., Third-party logistics: a literature review (2008) International Journal of Production Economics, 113 (1), pp. 127-147; Marshall, C., Rossman, G.B., (1999) Designing Qualitative Research, , 3rd ed., Sage, Thousand Oaks; Martikainen, A., Niemi, P., Pekkanen, P., Developing a service offering for a logistics service provider: case of local food supply chain (2014) International Journal of Production Economics, 157 (2), pp. 318-326; Miles, M.B., Huberman, A.M., (1994) Qualitative Data Analysis: An Expanded Sourcebook, , Sage, Newbury Park; Min, H., Joo, S.J., Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis (2006) Supply Chain Management: An International Journal, 11 (3), pp. 259-265; Ng, I.C.L., Maull, R., Yip, N., Outcome-based contracts as a driver for systems thinking and service-dominant logic in service science: evidence from the defence industry (2009) European Management Journal, 27 (6), pp. 377-387; Oflac, B.S., Sullivan, U.Y., Baltacioglu, T., An attribution approach to consumer evaluations in logistics customer service failure situations (2012) Journal of Supply Chain Management, 48 (4), pp. 51-71; Patton, M., (1990) Qualitative Evaluation and Research Methods, , 2nd ed., Sage, Newbury Park, CA; Prockl, G., Pflaum, A., Kotzab, H., 3PL factories or lernstatts? Value creation models for 3PL service providers (2012) International Journal of Physical Distribution and Logistics Management, 42 (6), pp. 544-561; Rajesh, R., Pugazhendhi, S., Ganesh, K., Ducq, Y., Lenny Koh, S., Generic balanced scorecard framework for third party logistics service provider (2012) International Journal of Production Economics, 140 (1), pp. 269-282; Randall, W., Pohlen, T., Hanna, J., Evolving a theory of performance-based logistics using insights from service dominant logic (2010) Journal of Business Logistics, 31 (2), pp. 35-61; Randall, W.S., Nowicki, D., Hawkins, T., Explaining the effectiveness of performance based logistics: a quantitative investigation (2011) International Journal of Logistics Management, 22 (3), pp. 324-338; Rossi, S., Colicchia, C., Cozzolino, A., Christopher, M., The logistics service providers in eco-efficiency innovation: an empirical study (2013) Supply Chain Management: An International Journal, 18 (6), pp. 583-603; Rossiter-Hofer, A., Knemeyer, M., Murphy, P.R., The roles of procedural and distributive justice in logistics outsourcing relationships (2012) Journal of Business Logistics, 33 (3), pp. 196-209; Rossiter-Hofer, A., Knemeyer, M., Murphy, P.R., Achieving and exceeding logistics outsourcing expectations in Brazil: a replication study (2015) Transportation Journal, 54 (3), pp. 339-367; Sampson, S., Customer-supplier duality and bidirectional supply chains in service organisations (2000) International Journal of Service Industry Management, 11 (4), pp. 348-364; Sampson, S.E., Froehle, C.M., Foundations and implications of a proposed unified services theory (2006) Production and Operations Management, 15 (2), pp. 329-343; Sampson, S.E., Spring, M., Customer roles in the service supply chain and opportunities for innovation (2012) Journal of Supply Chain Management, 48 (4), pp. 30-50; Sanchez Rodrigues, V., Harris, I., Mason, R., Horizontal logistics collaboration for enhanced supply chain performance: an international retail perspective (2015) Supply Chain Management: An International Journal, 20 (6), pp. 631-647; Schwenk, C.R., The use of participant recollection in the modeling of organizational decision processes (1985) Academy of Management Review, 10 (3), pp. 496-503; Selviaridis, K., Norrman, A., Performance-based contracting in service supply chains: a service provider risk perspective (2014) Supply Chain Management: An International Journal, 19 (2), pp. 153-172; Selviaridis, K., Norrman, A., Performance-based contracting for advanced logistics services: challenges in its adoption, design and management (2015) International Journal of Physical Distribution & Logistics Management, 45 (6), pp. 592-617; Selviaridis, K., Spring, M., Third party logistics: a literature review and research agenda (2007) International Journal of Logistics Management, 18 (1), pp. 125-150; Selviaridis, K., Matopoulos, A., Szamosi, L., Psychogios, A., Reverse resource exchanges in service supply chains: the case of returnable transport packaging (2016) Supply Chain Management: An International Journal, 21 (3), pp. 381-397; Shi, Y., Yang, A., Arthanari, T., Liu, Y., Cheng, T., Third party purchase: an empirical study of third-party logistics providers in China (2016) International Journal of Production Economics, 171 (1), pp. 189-200; Strauss, A., Corbin, J., (1990) Basics of Qualitative Research: Grounded Theory Procedures and Techniques, , Sage, Newbury Park, CA; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party alliances in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 15-25; Van Hoek, R.I., Chong, I., Epilogue: UPS Logistics - practical approaches to the e-supply chain (2001) International Journal of Physical Distribution and Logistics Management, 31 (6), pp. 463-468; Vargo, S.L., Lusch, R.F., Evolving to a new dominant logic for marketing (2004) Journal of Marketing, 68 (1), pp. 1-17; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 195-219; Wagner, S.M., Sutter, R., A qualitative investigation of innovation between third-party logistics providers and customers (2012) International Journal of Production Economics, 140 (2), pp. 944-958; Wallenburg, C.M., Schäffler, T., The interplay of relational governance and formal control in horizontal alliances: a social contract perspective (2014) Journal of Supply Chain Management, 50 (2), pp. 41-58; Whipple, J.M., Roh, J., Agency theory and quality fade in buyer-supplier relationships (2010) International Journal of Logistics Management, 21 (3), pp. 338-352; Wu, Q., Mu, Y., Feng, Y., Coordinating contracts for fresh product outsourcing logistics channels with power structures (2015) International Journal of Production Economics, 160 (3), pp. 94-105; Wynstra, F., Spring, M., Schoenherr, T., Service triads: A research agenda for buyer-supplier-customer triads in business services (2015) Journal of Operations Management, 35 (1), pp. 1-20; Yang, Q., Zhao, X., Are logistics outsourcing partners more integrated in a volatile environment? (2015) International Journal of Production Economics, 171 (2), pp. 211-220; Yang, Q., Zhao, X., Yeung, H.Y.J., Liu, Y., Improving logistics outsourcing performance through transactional and relational mechanisms under transaction uncertainties: evidence from China (2016) International Journal of Production Economics, 175 (3), pp. 12-23. , and; Yin, R.K., (2003) Case Study Research: Design and Methods, , Sage, Thousand Oaks; Zhang, Y., Lawrence, B., Anderson, C., An agency perspective on service triads: linking financial and operational performance (2015) Journal of Operations Management, 35 (1), pp. 56-66},
correspondence_address1={Selviaridis, K.; Department of Management Science, United Kingdom; email: k.selviaridis@lancaster.ac.uk},
publisher={Emerald Group Publishing Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@BOOK{Marwah2016111,
author={Marwah, A.K. and Thakar, G. and Gupta, R.C.},
title={A new approach to supply chain performance measurement: An empirical study of manufacturing organizations},
journal={Innovative Solutions for Implementing Global Supply Chains in Emerging Markets},
year={2016},
pages={111-128},
doi={10.4018/978-1-4666-9795-9.ch007},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981513498&doi=10.4018%2f978-1-4666-9795-9.ch007&partnerID=40&md5=c8841140da1e3c721c112427c125cffa},
affiliation={Department of Industrial and Production Engineering, Shri Govindram Seksaria Institute of Technology and Science, Indore, India; SGSITS, India},
abstract={Existing research work has established that many of today's manufacturing organizations have failed to develop a comprehensive supply chain performance measures. In this chapter, the authors intend to empirically assess the effects of supplier buyer relations and human metrics on supply chain performance in the context of Indian manufacturing organizations. After rigorous literature review, total 18 variables have been identified which are later on reduced in number by factor analysis. As a pilot study, primary data is collected from 100 manufacturing organizations by means of a questionnaire and a scale is developed. On a sample size of 100, the proposed hypotheses are tested by applying two-tailed tests. t-test and factor analysis resulted in 5 factors, 2 related to supplier-buyer relations and 3 related to human metrics. The overall reliability of the scale comes out to be 0.697. The research work provides a new approach to the manufacturing organizations to understand the factors affecting supply chain performance. The present study is limited to Indian manufacturing organizations. © 2016 by IGI Global. All rights reserved.},
references={Autry, C.W., Susan, G., Evaluating buyer-supplier relationship-performance spirals-A longitudinal study (2010) Journal of Operations Management, 24 (2), pp. 87-100; Beamon, B.M., Measuring Supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Chandra, K., Kumar, S., Supply chain management in theory and practice: A passing fad or a fundamental change? (2000) Industrial Management & Data Systems, 100 (3), pp. 100-113; Chen, J., Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships (2008) Journal of Operations Management, 26 (1), pp. 45-64; Dangayach, G., Deshmukh, S., Evidence of manufacturing strategies in Indian industry: A survey (2003) International Journal of Production Economics, 83 (3), pp. 279-298; A Report on the Summit on Indian Manufacturing Competitiveness (2007) Deloitte Research: Competing in Global Manufacturing and Service Networks, , http://www.deloitte.com/research, Retrieved from; Fawcett, S.E., Ogden, J.A., Magnan, G.M., Cooper, M.B., Organisational commitment and governance for supply chain success (2006) International Journal of Physical Distribution & Logistics Management, 36 (1), pp. 22-35; Gunasekaran, A., Supply Chain management: Theory and applications (2004) European Journal of Operational Research, 159 (2), pp. 265-268; Gunasekaran, A., Kobu, B., (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.A., Framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hair, J., Andeson, R., Black, B., Barry, B., Tatham, R., (2007) Multivariate Data Analysis, , (5th ed.). New York, NY: Macmillan Publishing Company; Humphrey, P.K., Li, W.L., Chan, L.Y., The impact of supplier development on buyer-supplier performance (2004) International Journal of Management Sciences, 32 (2), pp. 131-143; Lee, C.W., Kwon, I.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer (2007) Supply Chain Management: An International Journal, 12 (6), pp. 444-452; Liang, L., Yang, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; Marwah, A.K., Thakar, G., Gupta, R.C., An Empirical Study of Supplier-Buyer Relations as a Key Determinant of Supply Chain Performance of Indian Manufacturing Organizations (2013) Proceedings of the International Conference on Managing Supply Chain for Global Competitiveness., , IIIE; Marwah, A.K., Thakar, G., Gupta, R.C., A Confirmatory Study of Supply Chain Performance and Competitiveness of Indian Manufacturing Organizations (2014) International Journal of Quality Research, 8 (1), pp. 23-38; Mello, J.E., Stank, T.P., Linking firm culture and orientation to supply chain success (2005) International Journal of Physical Distribution & Logistics Management, 35 (8), pp. 542-554; Robinson, C.J., Malhotra, M.K., Defining the concept of supply chain quality management and its relevance to academic and industrial practice (2005) International Journal of Production Economics, 96 (3), pp. 315-337; Sekaran, U., (2003) Research methods for business: A skill-building approach, , John Wiley & Sons; Spekman, R.E., Kamauff, J.W., Jr., Myhr, N., An empirical investigation into supply chain management:A perspective on partnerships (1998) Supply Chain Management: An International Journal, 3 (2), pp. 53-67; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Tony, W., Kevin, H., The human factors in managing China supply chain (2007) Proceedings of the 92nd Annual International Supply Management Conference, pp. 1-6. , Nokia (China) Investment Co. Ltd. Shanghai Branch; Van Hoek, R.I., Measuring the unmeasurable-Measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (40), pp. 187-192; Wisner, J.D., Fawcett, S.E., Linking firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, 32 (4), pp. 5-11; Wouters, M., A developmental approach to performance measures-results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78; Bititci, U.S., Mendibil, M., Nudurupati, S., Turner, T., Garengo, P., Dynamics of Performance measurement and organizational culture (2006) International Journal of Operations & Production Management, 26 (12), pp. 1325-1350; Cigolini, R., Cozzi, M., Perona, M., A New Framework for Supply Chain Management: Conceptual Model and Empirical Test (2004) International Journal of Operations & Production Management, 24 (1), pp. 7-14; Ellram, L.M., Cooper, M.C., Supply Chain Management, Partnerships, and the Shipper-Third-Party Relationship (1990) The International Journal of Logistics Management, 1 (2), pp. 1-10; Kalwani, M., Narayandas, N., Long-Term Manufacturing-Supplier Relationships: Do They Pay-Off For Supplier Firms? (1995) Journal of Marketing, 59 (1), pp. 1-16; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Li, S., Rao, S., Ragunathan, T., Ragunathan, B., Development and Validation of A Measurement Instrument for Studying Supply Chain Management Practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Neely, A., Gregory, M., Platts, K., Performance measurement system design-a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Powell, S., The challenges of performance measurement (2004) Management Decision, 42 (8), pp. 1017-1023; Sahay, B.S., Gupta, J.N.D., Mohan, R., Managing Supply Chains For Competitiveness: The Indian Scenario (2006) Supply Chain Management: An International Journal, 11 (1), pp. 15-24; Sahay, B.S., Mohan, R., Supply Chain Management Practices In Indian Industry (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 582-606},
correspondence_address1={Marwah, A.K.; Department of Industrial and Production Engineering, India},
publisher={IGI Global},
isbn={9781466697966; 1466697954; 9781466697959},
language={English},
abbrev_source_title={Innov. Solutions for Implement. Glob. Supply Chains in Emerg. Mark.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Grover2016212,
author={Grover, N.},
title={Performance measurement: Measuring retail supply chain performance},
journal={Innovative Solutions for Implementing Global Supply Chains in Emerging Markets},
year={2016},
pages={212-241},
doi={10.4018/978-1-4666-9795-9.ch015},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981501426&doi=10.4018%2f978-1-4666-9795-9.ch015&partnerID=40&md5=c3ac2299c06137a8297015d286814283},
affiliation={University of Petroleum and Energy Studies, India},
abstract={The purpose of this chapter is to study what supply chain managers of a retail supply chain measure. In other words aim here to identify the key indicators for measuring retail supply chain performance. A qualitative approach is adopted. Published literature from refereed journals on supply chain performance measurement has been considered. Besides the need for organizations to adopt a holistic approach, firms remain focused on traditional financial measures (gross profit margin, Interest coverage, and debt and equity ratios). The chapter identifies key indicators for performance measurement and classifies them into four major categories: transport optimization, information technology optimization, inventory optimization and resource optimization. These key indicators are arranged precisely for retail industry. From a supply chain perspective, the non financial measures such as on-time delivery, training of employees, warehouse layout, etc. are also important aspects of measuring supply chain performance. Further research can be carried out to validate the relevance and applicability of identified indicators. The study can be further conducted to measure the interrelationships between the KPIs and their impact on financial performance of the firm. In this chapter the author attempts to identify the performance indicators specifically for retail supply chain. The identified measures are further categorized based on its operations. © 2016 by IGI Global. All rights reserved.},
references={Adams, N.D., Brown, T.W., Firth, R.V., (1996) Warehouse and Distribution Automation Handbook, , New York: McGraw-Hill; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Aviv, Y., On the benefits of collaborative forecasting partnerships between retailers and manufacturers (2007) Management Science, 53 (5), pp. 777-794; Ballou, R.H., Gilbert, S.M., Mukherjee, A., New managerial challenges from supply chain opportunities (2000) Industrial Marketing Management, 29 (1), pp. 7-18; Barratt, M., Oke, A., Antecedents of supply chain visibility in retail supply chains: A resourebased theory perspective (2007) Journal of Operations Management, 25 (6), pp. 1217-1233; Barua, A., Konana, P., Whinston, A.B., Yin, F., An empirical investigation of net-enabled business value (2004) Management Information Systems Quarterly, 28 (4), pp. 585-620; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bechtel, C., Jayaram, J., Supply chain management: a strategic perspective (1997) The International Journal of Logistics Management, pp. 15-34; Bourland, K.E., Powell, S.G., Pyke, D.K., Exploiting timely demand information to reduce inventories (1996) European Journal of Operational Research, 92 (2), pp. 239-253; Bower, J.L., Hout, T.M., Fast cycle capability for competitive power (1988) Harvard Business Review, pp. 110-118; Bowersox, D., Rodrigues, A., Calantone, R., Estimation of Global and National Logistics Expenditures: 2002 Data Update (2005) Journal of Business Logistics, 26 (2), pp. 1-16; Bowersox, D.J., Closs, D.J., Stank, T.P., Keller, S.B., How supply chain competency leads to business success (2000) Supply Chain Management Review, 4 (4), pp. 70-78; Cachon, G., Exact evaluation of batch-ordering policies in two-echelon supply chains with periodic review (2001) Operations Research, 49 (1), pp. 79-98; Cachon, G.P., Fisher, M., Supply chain inventory management and the value of shared information (2001) Management Science, 46 (8), pp. 1032-1048; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, pp. 612-621. , 2009; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Cannon, J.P., Perreault, W.D., Jr., Buyer-seller relationships in business markets (1999) JMR, Journal of Marketing Research, 36 (4), pp. 439-460; Chae, B.K., Developing key performance indicators for supply chain: An industry perspective (2009) Supply chain Management. International Journal (Toronto, Ont.), 14 (6), pp. 422-428; Chan, F.T., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chang, T., Fu, H., Lee, W., Lin, Y., Hsueh, H., A study of an augmented CPFR model for the 3C retail industry (2007) Supply Chain Management: An International Journal, 12 (3), pp. 200-209; Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D., Quantifying the bullwhip effect in a simple supply chain:the impact of forecasting, lead times, and information (2000) Management Science, 46 (3), pp. 436-443; Chen, I.J., Paulraj, A., Lado, A., Strategic purchasing, supply management and firm performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Christopher, M., (1992) Logistics and Supply Chain Management: Strategies for Reducing Costs and Improving Services., , London: Financial Times/Pitman; Closs, D.J., Goldsby, T.J., Clinton, S.R., Information technology influences on world class logistics capability (1997) International Journal of Physical Distribution & Logistics Management, 27 (1), pp. 4-17; Corsten, D., Gruen, T., Seeking on-shelf availability-an examination of the extent, the causes and the efforts to address retail out-of-stocks (2003) International Journal of Retail & Distribution Management, 31 (12), pp. 605-716; Coyle, J.J., Bardi, E.J., Langley, C.J., (2003) The Management of Business Logistics: A Supply Chain Perspective, , (7th ed.). Mason, OH: South-Western Thomson Learning; Davenport, T.H., (2000) Mission critical - realizing the promise of enterprise systems, , Boston, MA: Harvard Business School Press; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge- Measuring Operations for World-class Competition, , Homewood, IL: Dow Jones-Irwin; Dobler, D.W., Burt, D.N., (1996) Purchasing and Supply Management., , New York, NY: The McGraw-Hill Companies; Douglas, J., Building performance and its relevance to facilities management (1996) Facilities, 14 (3-4), pp. 23-32; Drucker, P.F., The economy's dark continent (1962) Fortune, pp. 265-270. , April; (2004) Differentiation for performance excellence in logistics 2004, , Hamburg, Germany: Deutscher Verkehrs-Verlag GmbH; Ellinger, A.E.D.P., The Relationship between Marketing/Logistics Interdepartmental Integration and Performance in U.S. Manufacturing Firms: An Empirical Study (2000) Journal of Business Logistics, 21 (2), pp. 1-22; Ellram, L.M., La Londe, B.J., Weber, M.M., Retail Logistics (1989) International Journal of Physical Distribution & Materials Management, 19 (12), pp. 29-39; Ernst, Young, Doing (2011) Business India; Ferguson, M., Ketzenberg, M., Sharing Information to Improve Retail Product Freshness of Perishables (2006) Production and Operations Management, 15 (1), pp. 57-73; Fiala, P., Information sharing in supply chains (2005) Omega, 33 (5), pp. 419-423; Fisher, M.L., What is the Right Supply Chain for your product (1997) Harvard Business Review; Flynn, B.B., Huo, B., Zhao, X., The impact of supplychain integration on performance: A contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Forrester, J.W., (1961) Industrial Dynamics, , Cambridge, MA: MIT Press; Forslund, H., Jonsson, P., The impact of forecast information quality on supply chain performance (2007) International Journal of Operations & Production Management, 27 (1), pp. 90-107; Frazelle, E.H., (2002) Supply Chain Strategy: The Logistics of Supply Chain Management, , New York: McGraw-Hill; Froehle, C., Roth, A., A Resource-Process Framework of New Service Development (2007) Production and Operations Management, 16 (2), pp. 169-188; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2001) Journal of Operations Management, 19 (2), pp. 185-200; Fynes, B., Voss, C., Búrca, S., The impact of supply chain relationship dynamics on manufacturing performance (2005) International Journal of Operations & Production Management, 25 (1), pp. 6-19; Fynes, B., Voss, C., de Búrca, S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354; Gavirneni, S., Kapuscinski, R., Tayur, S., Value of Information in Capacitated Supply Chains (1999) Management Science, 45 (1), pp. 16-24; Gelders, L., Mannaert, P., Maes, J., Manufacturing strategy performance indicators and improvement programs (1994) International Journal of Production Research, 32 (4), pp. 797-805; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Gopal, P.R., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Green, K.W., Jr., Whitten, D., In, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply Chain Management: An International Journal, 13 (4), pp. 317-327; Green, K.W., Jr., Whitten, D., Inman, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply chain management. International Journal (Toronto, Ont.), 13 (4), pp. 317-327; Gumbus, A., Introducing the balanced scorecard: Creating metrics to measure performance (2005) Journal of Management Education, 29 (4), pp. 617-630; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekarana, A., Patelb, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 84 (3), pp. 333-347; Gunasekran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Guruswamy, M., Sharma, K., Jos, M.M., (2007) FDI in Retail - III Implications of Wal Mart's Backdoor Entry, , Centre for Policy Alternatives; Gustin, C.M., Daugherty, P.J., Stank, T.P., The effects of information availability on logistics integration (1995) Journal of Business Logistics, 16 (1), pp. 1-21; Harrington, L., Untapped savings abound (1996) Industry Week, pp. 53-58. , July 15; Hayes, R.H., Abernathy, W.J., Managing our way to economic decline (1980) Harvard Business Review, 62, pp. 95-101; Hill, C.A., Scudder, G.D., The use of electronic data interchange for supply chain coordination in the food industry (2002) Journal of Operations Management, 20 (4), pp. 375-387; Ho, D.C., Chan, W.E., Wong, N., Chan, M., Significant metrics for facilities management benchmarking in the Asia Pacific region (2000) Facilities, 18 (13-14), pp. 545-555; Hoffman, D., (2004) The hierarchy of supply chain metrics: diagnosing your supply chain health, , Boston, Massachusetts: AMR Research; Hulland, J., Wade, M., Antia, K.D., The impact of capabilities and prior investments on online channel commitment and performance (2007) Journal of Management Information Systems, 23 (4), pp. 109-142; (2013) India Retail News, , http://www.indiaretailnews.com/, January 24, Retrieved January 27, 2013, from Emergence of Retailing Sector in India: Challenges and Opportunities; Johnson, H.T., Toward a new understanding of nineteenth-century cost accounting (1981) Accounting Review, 56 (3), pp. 510-518; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Harvard Business School Press; Jones, R.M., Towill, D.R., Total cycle time compression and the agile supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 61-73; Kaplan, R.S., Yesterday's accounting undermines production (1984) Harvard Business Review, 62 (4), pp. 95-101; Kaplan, R.S., Norton, D.P., The balance Scorecard- Measures That Drive performance (1992) Harvard Business Review, , January; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50. , June; Koh, L.S., Demirbag, M., Bayraktar, E., Tatoglu, E., Zaim, S., The impact of supply chain management practices on performance of SMEs (2007) Industrial Management & Data Systems, 107 (1), pp. 103-124; (2011) Issues Monitor: Sharing knowledge on topical issues in Retail Industry, , July, KPMG; Kulp, S.C., Lee, H.L., Ofek, E., Manufacturer Benefits from Information Integration with Retail Customers (2004) Management Science, 50 (4), pp. 431-444; Lai, K.H., Ngai, E.T., Cheng, T.E., Measures for evaluating supply chain performance in transport logistics (2002) Transp Res Part E, 38 (6), pp. 439-456; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, pp. 1-19; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Lee, H.L., Billington, C., Material Management in Decentralized Supply Chains (1993) Operations Research, 41 (5), pp. 835-847; Lee, H.L., Padmanabhan, V., Whang, S., Information distortion in a supply chain: The bullwhip effect (1997) Management Science, 43 (4), pp. 546-558; Lee, H.L., Padmanabhan, V., Whang, S., The Bullwhip Effect in Supply Chains (1997) Sloan Management Review, pp. 93-101; Levy, D.L., Lean production in an international supply chain (1997) Sloan Management Review, pp. 94-102. , Winter; Li, S., Ragu-Nathanb, B., Ragu-Nathanb, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega International Journal of Management Science, 34 (2), pp. 107-124; Lin, C., Influencing factors on the innovation in logistics technologies for logistics service providers in Taiwan (2006) The Journal of American Academy of Business, 9 (2), pp. 257-263; Mabert, V.A., Venkataramanan, M.A., Special research focus on supply chain linkages: Challenges for design and management in the 21st century (1998) Decision Sciences, 29 (3), pp. 537-552; Marvick, D., White, J., Distribution operations: Managing distribution facilities for strategic advantage (1998) Strategic Supply Chain Alignment, , In J. G. Gattorna (Ed.); McCarthy, T.M., Golicic, S.L., Implementing collaborative forecasting to improve supply chain performance (2002) International Journal of Physical Distribution & Logistics Management, 32 (6), pp. 431-454; McKinnon, A.C., (1996) The development of retail logistics in the UK:A position paper, Technology Foresight: Retail and Distribution Panel, , Edinburgh: Heriot-Watt University; Melville, N., Kraemer, K., Gurbaxani, V., Information technology and organizational performance: An integrative model of IT business value (2004) Management Information Systems Quarterly, 28 (2), pp. 283-322; Mentzer, J.T., Foggin, J.H., Golicic, S.L., Collaboration: the enablers, impediments, and benefits (2000) Supply Chain Management Review, , September/October; Moberg, C.R., Whipple, T.W., Cutler, B.D., Speh, T.W., Do the Management Components of Supply Chain Management Affect Logistics Performance? (2004) The International Journal of Logistics Management, 15 (2), pp. 15-30; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testingaprocess-based approach (1995) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Neely, A.D., (1998) Measuring Business Performance, , London: Economist Books; Noble, S.M., Haytko, D.L., Phillips, J., What drives college-age Generation Y consumers? (2009) Journal of Business Research, 62 (6), pp. 617-628; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management & Data Systems, 110 (9), pp. 1297-1318; (2009) Rajya Sabha, , Ninetienth Report on Foreign and Domestic Investment in Retail Sector; Pitt, M., Tucker, M., Performance measurement in facilities management:driving innovation? (2008) Property Management, 26 (4), pp. 241-254; Pohlen, T.L., Lambert, D.M., Supply Chain Metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Raman, A., DeHoratius, N., Ton, Z., The Achilles heel of supply chain management (2001) Harvard Business Review, pp. 136-152; Ramdas, K., Spekman, R.E., Chain or Shackles: Understanding What Drives Supply-Chain Performance (2000) Interfaces, 30 (4), pp. 3-21; Rao, B.P., Improving retail effectiveness through technology: A survey of analytical tools for physical and on-line retailers (2000) Technology in Society, 22, pp. 111-122; Ray, G., Muhanna, W.A., Barney, J.B., Information technology and the performance of the customer service process: A resource-based analysis (2005) Management Information Systems Quarterly, 29 (4), pp. 625-651; Rosenzweig, E.D., Roth, A.V., Dean, J.W., Jr., The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21 (4), pp. 437-456; Rungtusanatham, M., Salvador, F., Forza, C., Choi, T.Y., Supply-chain linkages and operational performance: A resource-based-view perspective (2003) International Journal of Operations & Production Management, 23 (9), pp. 1084-1099; Rushton, A., Oxley, J., Croucher, P., The handbook of logistics and distribution management (2001), 2nd ed., London, UK: Academic Press; Sahin, F., Robinson, E.P., Flow coordination and information sharing in supply chains: Review, implications, and directions for future research (2002) Decision Sciences, 33 (4), pp. 505-535; Sanders, N.R., Pattern of information technology use: The impact on buyer-suppler coordination and performance (2008) Journal of Operations Management, 26 (3), pp. 349-367; Schonberger, R.J., (1990) Building a chain of customers, , London: The Free Press; Shah, J., (2009) Supply chain management, , Delhi: Pearson Education; Shore, B., Venkatachalam, A., Evaluating the information sharing capabilities of supply chain partners: A fuzzy logic model (2003) International Journal of Physical Distribution & Logistics Management, 33 (9), pp. 804-824; Sinclair, D., Zairi, M., Effective process management through performance measurement: Part III - An integrated model of totalquality-based performance measurement (1995) Business Process Management Journal, 1 (3), pp. 50-65; Slack, N., Flexibility as a manufacturing objective (1983) International Journal of Operations & Production Management, 3 (3), pp. 4-13; Spohrer, J., Maglio, P.P., Bailey, J., Gruhl, D., Steps Toward a Science of Service Systems (2007) Computer, 40 (1), pp. 71-77; Stank, T.P., Davis, B.R., Fugate, B.S., A strategic framework for supply chain oriented logistics (2005) Journal of Business Logistics, 26 (2), pp. 27-45; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Storey, J., Emberson, C., Godsell, J., Harrison, A., Supply chain management: Theory, practice and future challenges (2006) International Journal of Operations & Production Management, 26 (7), pp. 754-774; Tan, K.-C., Kannan, V.R., Handfied, R.B., Ghosh, S., Supply chain management: An empirical study of its impact on performance (1999) International Journal of Operations & Production Management, 9 (10), pp. 1034-1052; Tan, K.C., Kannan, V.R., Hanfield, R.B., Supply Chain Management: Supplier Performance and Firm Performance (1998) International Journal of Purchasing and Materials Management, 34 (3), pp. 2-9; Tanriverdi, H., Information technology relatedness, knowledge management capability and performance of multibusiness firms (2005) Management Information Systems Quarterly, 29 (2), pp. 311-334; (2013) India Overview, , http://www.worldbank.org/en/country/india/overview, Retrieved January Saturday, 2013, from; Thomas, D.J., Griffin, P.M., Coordinated supply chain management (1996) European Journal of Operational Research, 94 (1), pp. 1-15; Towers, N., Burnes, B., A composite framework of supply chain management and enterprise planning for small and medium-sized manufacturing enterprises (2008) Supply Chain Management: An International Journal, 13 (5), pp. 349-355; Tracey, M., Lim, J.-S., Vonderembse, M.A., The impact of supply-chain management capabilities on business performance (2005) Supply Chain Management: An International Journal, 10 (3), pp. 179-191; van der Vorst, J.A., (2000) Effective food supply chains Generating, modelling and evaluating supply chain scenarios., , (PhD thesis). Wageningen University; Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships (2003) Journal of Operations Management, 21 (5), pp. 523-539; Wallace, D.W., Giese, J.L., Johnson, J.L., Customer retailer loyalty in the context of multiple channel strategies (2004) Journal of Retailing, 80 (4), pp. 249-263; Walters, P.G., Adding value in global B2B supply chains: Strategic directions and the role of the internet as a driver of competitive advantage (2008) Industrial Marketing Management, 37 (1), pp. 59-68; Whipple, J.M., Frankel, R., Daugherty, P.J., Information support for alliances:performance implications (2002) Journal of Business Logistics, 23 (2), pp. 67-82; Wortmann, J.C., Evolution of ERP systems (1998) Proceedings of Internatinal conference of the manufacturing value chain., , Troon: Kluwer Academic Publishing; Zhao, X., Xie, J., Wei, J.C., The impact of forecast errors on early order commitment in a supply chain (2002) Decision Sciences, 33 (2), pp. 251-280},
correspondence_address1={Grover, N.; University of Petroleum and Energy StudiesIndia},
publisher={IGI Global},
isbn={9781466697966; 1466697954; 9781466697959},
language={English},
abbrev_source_title={Innov. Solutions for Implement. Glob. Supply Chains in Emerg. Mark.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Singh2016246,
author={Singh, R.K. and Rastogi, S. and Aggarwal, M.},
title={Analyzing the factors for implementation of green supply chain management},
journal={Competitiveness Review},
year={2016},
volume={26},
number={3},
pages={246-264},
doi={10.1108/CR-06-2015-0045},
note={cited By 33},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84979009926&doi=10.1108%2fCR-06-2015-0045&partnerID=40&md5=32b4282c70e32c10fa0499214f50c64d},
affiliation={Management Development Institute Gurgaon, Gurgaon, India; Indian Institute of Foreign Trade, New Delhi, India; Amity University, Delhi, India},
abstract={Purpose: In the present context of a globalised economy, market requirements are changing regularly in term of product cost, delivery time, safety and environmental issues. Apart from conventional measures, environmental factors have become an integral part of performance measurement system. This study aims to analyse major barriers and factors in green supply chain (GSC) management implementation. Design/methodology/approach: The present study has tried to identify barriers and factors for green supply chain management (GSCM) based on literature review. Factors for GSCM have been modelled by interpretive structural modelling (ISM) approach. Findings: Based on literature review, 12 factors have been identified. It has been observed that top management commitment, integration among supply chain members, vendors’ development, environmental friendly packaging and transportation, reverse logistic management and development of a green performance measurement system are major drivers for successful implementation of GSCM. Research Limitations/implications: Organisations need to follow a proactive approach for taking GSC initiatives. For generalisation of findings, empirical study and some case studies need to be carried out. Originality/value: Findings of this study will help organisations in strategy formulation for GSCM and successful implementation for sustainable competitiveness. © 2016, © Emerald Group Publishing Limited.},
author_keywords={Barriers;  Green supply chain management;  Performance;  Sustainability},
references={Agrawal, S., Singh, R.K., Murtaza, Q., Forecasting product returns for recycling in Indian electronics industry (2014) Journal of Advances in Management Research, 11 (1), pp. 102-114; Agrawal, S., Singh, R.K., Murtaza, Q., A literature review and perspectives in reverse logistics (2015) Resources, Conservation & Recycling, 97 (1), pp. 76-92; Bai, C., Sarkis, J., Green supplier development: analytical evaluation using rough set theory (2010) Journal of Cleaner Production, 18 (12), pp. 1200-1210; Bansal, A., Singh, R.K., Issar, S., Varkey, J., Evaluation of vendors ranking by EATWOS approach (2014) Journal of Advances in Management Research, 11 (3), pp. 290-311; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Bhakoo, V., Chan, C., Collaborative implementation of e-business processes within the health-care supply chain: the Monash Pharmacy Project (2011) Supply Chain Management: An International Journal, 16 (3), pp. 184-193; Bowen, F.E., Cousins, P.D., Lamming, R.C., Faruk, A.C., Horse for courses: explaining the gap between the theory and practice of green supply (2001) Greener Management International, 35 (1), pp. 41-60; Buyukozkan, G., Cifci, G., Evaluation of green supply chain management practices: a fuzzy ANP approach (2012) Production Planning & Control, 23 (6), pp. 405-418; Clark, G., Developing better systems for communications: environmental best practice in small business (2000) Small and Medium-sized Enterprises and the Environment, , Hillary, R (Ed.), Greenleaf, Sheffield; Cox, J., Blackstone, J., (1998) APICS Dictionary, , www.apics.org, Ninth Edition, APICS, Falls Church, VA; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organisations (2013) Production Planning and Control, 24 (8-9), pp. 702-720; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers, and bridges to effective supply chain management (2008) Supply Chain Management: An International Journal, 13 (1), pp. 35-48; Green, K., Morton, B., New, S., Green purchasing and supply policies: do they improve companies’ environmental performance? (1998) Supply Chain Management, 3 (2), pp. 89-95; Green, K., Zelbst, P., Meacham, J., Bhadauria, V., Green supply chain management practices: impact on performance (2012) Supply Chain Management: An International Journal, 17 (3), pp. 1-44; Jayaram, J., Avittathur, B., Insights into India (2012) Supply Chain Management Review, 16 (4), pp. 34-41; Hall, J., Environmental supply chain dynamics (2000) Journal of Cleaner Production, 8 (6), pp. 455-471; Hamprecht, J., Corsten, D., Noll, M., Mei, E., Controlling the sustainability of food supply chains (2005) Supply Chain Management: An International Journal, 10 (1), pp. 7-10; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice-Hall, NJ; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hines, F., Jones, R., (2001) Environmental supply chain management: evaluating the use of environmental mentoring through supply chain, , Bangkok., Greening of Industry Network Conference Proceedings; Hsu, C.C., Tan, K.C., Zailani, S.H.M., Jayaraman, V., Supply chain drivers that foster the development of green initiatives in an emerging economy (2013) International Journal of Operations & Production Management, 33 (6), pp. 656-688; Hugo, A., Pistikopoulos, E.N., Environmentally conscious long-range planning and design of supply chain networks (2005) Journal of Cleaner Production, 13 (15), pp. 1471-1491; Ireland, R.D., Webb, J.W., A multi-theoretic perspective on trust and power in strategic supply chains (2007) Journal of Operations Management, 25 (2), pp. 482-497; Klassen, R.D., Vachon, S., Collaboration and evaluation in the supply chain: the impact on plant-level environmental investment (2003) Production and Operations Management, 12 (3), pp. 336-352; Kannan, D., Jabbour, A.B.L., Jabbour, C.J.C., Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS (2014) European Journal of Operational Research, 233 (2), pp. 432-447; Lubin, D.A., Esty, D.C., The sustainability imperative (2010) Harvard Business Review, 88 (5), pp. 42-50; Lamming, R., Hampson, J., The environment as a supply chain issue (1996) British Journal of Management, 7 (1), pp. 45-62; Laosirihongthong, T., Adebanjo, D., Tan, K.C., Green supply chain management practices and performance (2013) Industrial Management & Data Systems, 113 (8), pp. 1088-1109; Lee, K., Why and how to adopt green management into business organizations? (2009) Management Decision, 47 (7), pp. 1101-1121; Lee, S.M., Kim, S.T., Choi, D., Green supply chain management and organizational performance (2012) Industrial Management & Data Systems, 112 (8), pp. 1148-1180; Mangla, S.K., Kumar, P., Barua, M.K., Risk analysis in green supply chain using fuzzy AHP approach: a case study (2015) Resources, Conservation and Recycling, 21 (6); Mentzer, J.T., Myers, M.B., Stank, T.P., (2007) Handbook of Global Supply Chain Management, , Sage, California; Narasimhan, R., Carter, J.R., (1998) Environmental Supply Chain Management, , The Center for Advanced Purchasing Studies Arizona State University, Tempe, AZ; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2010) Resources Conservation and Recycling, 65 (6), pp. 567-579; Qian, W., Burritt, R., Monroe, G., Environmental management accounting in local government: a case of waste management (2011) Accounting, Auditing & Accountability Journal, 24 (1), pp. 93-128; Rao, P., Greening the supply chain: a new initiative in South East Asia (2002) International Journal of Operations & Production Management, 22 (6), pp. 632-655; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations & Production Management, 25 (9), pp. 898-916; Reza, G., Nasiri, D.H., Karimi, B., The impact of integrated analysis on supply chain management: a coordinated approach for inventory control policy (2010) Supply Chain Management: An International Journal, 15 (4), pp. 277-289; Rosen, C.M., Bercovitz, J., Beckman, S., Environmental supply-chain management in the computer industry: a transaction cost economics perspective (2000) Journal of Industrial Ecology, 4 (4), pp. 83-103; Subramanian, N., Gunasekaran, A., Cleaner supply-chain management practices for twenty- first-century organizational competitiveness: practice-performance framework and research propositions (2015) International Journal of Production Economics, 164 (1), pp. 216-233; Sarkis, J., A strategic decision framework for green supply chain management (2003) Journal of Cleaner Production, 11 (4), pp. 397-409; Sarkis, J., A boundaries and flows perspective of green supply chain management (2012) Supply Chain Management: An International Journal, 17 (2), pp. 202-216; Seuring, S., Industrial ecology, life cycles, supply chains: differences and interrelations (2004) Business Strategy and the Environment, 13 (5), pp. 306-319; Sheu, J.B., Chou, Y.H., Hu, C.C., An integrated logistics operational model for green-supply chain management (2005) Translational Research, 41 (4), pp. 287-313; Singh, R.K., Green supply chain management: an overview (2008) Productivity Promotion, 11 (40), pp. 32-40; Singh, R.K., Analyzing the factors for VMI implementation: a framework (2013) Global Business Review, 14 (1), pp. 169-186; Singh, R.K., Prioritizing the factors for coordinated supply chain using analytic hierarchy process (AHP) (2013) Measuring Business Excellence, 17 (1), pp. 80-98; Singh, R.K., Modelling of critical factors for responsiveness in supply chain (2015) Journal of Manufacturing and Technology Management, 26 (6), pp. 868-888; Singh, R.K., Sharma, M.K., Selecting competitive supply chain using Fuzzy-AHP and extent analysis (2015) Journal of Industrial and Production Engineering, 31 (8), pp. 524-538; Stonebraker, P.W., Afifi, R., Towards a contingency theory of supply chains (2004) Management Decision, 42 (9), pp. 1131-1144; Teuteberg, D., Wittstruck, F., Understanding the success factors of sustainable supply chain management: empirical evidence from the electrics and electronics industry (2012) Corporate Social Responsibility and Environmental Management, 19 (3), pp. 141-158; Thakkar, J., Kanda, A., Deshmukh, S.G., Mapping of supply chain learning: a framework for SMEs (2011) Learning Organization, 18 (4), pp. 313-332; Theyel, G., Customer and supplier relations for environmental performance (2001) Greener Management International, 2001 (35), pp. 61-69; Vachon, S., Green supply chain practices and the selection of environmental technologies (2007) International Journal of Production Research, 45 (18), pp. 4357-4379; Van Hock, R.I., Erasmus, From reversed logistics to green supply chains (2000) Logistics Solutions, 2 (1), pp. 28-33; Van Hoek, R.I., From reversed logistics to green supply chains (1999) Supply Chain Management, 4 (3), pp. 129-134; Van Hoek, R.I., Case studies of greening the automotive supply chain through technology and operations (2002) International Journal of Technology Management, 23 (2-3), pp. 89-112. , 1; Walker, H., Di Sisto, L., McBain, D., Drivers and barriers to environmental supply chain management practices: lessons from the public and private sectors (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 69-85; Warfield, J., (1976) Societal Systems: Planning, Policy and Complexity, , John Wiley & Sons, New York, NY; Wiersma, G.B., (2009) Environmental management and assessment; Wycherley, I., Greening supply chains: the case of the Body Shop International (1999) Business Strategy and the Environment, 8 (2), pp. 120-127; Zhu, Q., Cote, R., Integrating green supply chain management into an embryonic eco-industrial development: a case study of the Guitang Group (2004) Journal of Cleaner Production, 12 (8-10), pp. 1025-1035; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) Journal of Operations Management, 22 (3), pp. 265-289; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: drivers and practices (2006) Journal of Cleaner Production, 14 (5), pp. 472-486; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: pressures, practices and performance (2005) International Journal of Operations & Production Management, 25 (5), pp. 449-468; Zhu, Q., Sarkis, J., Lai, K., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111 (2), pp. 261-273; Zhu, Q., Sarkis, J., Lai, K.H., Examining the effects of green supply chain management practices and their mediations on performance improvements (2012) International Journal of Production Research, 50 (5), pp. 1377-1394},
correspondence_address1={Rastogi, S.; Indian Institute of Foreign TradeIndia; email: srastogi@iift.ac.in},
publisher={Emerald Group Publishing Ltd.},
issn={10595422},
language={English},
abbrev_source_title={Compet. Rev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{RaghuKumar2016311,
author={Raghu Kumar, B.R. and Agarwal, A. and Sharma, M.K.},
title={Lean management – a step towards sustainable green supply chain},
journal={Competitiveness Review},
year={2016},
volume={26},
number={3},
pages={311-331},
doi={10.1108/CR-05-2015-0040},
note={cited By 31},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978997867&doi=10.1108%2fCR-05-2015-0040&partnerID=40&md5=e7d2c67004c6c709131cad1b1b1f486e},
affiliation={Indian Armed Forces, Bangalore, India; School of Engineering and Technology, Indira Gandhi National Open University (IGNOU), Maidan Garhi, New Delhi, India; Department of Production and Industrial Engineering, M.B.M. Engineering College, Jodhpur, India; Faculty of Engineering, J.N.V. University, Jodhpur, India},
abstract={Purpose: The purpose of this paper is to identify important factors which contribute to the evolution of Green Supply Chain (GSC) initiatives framework as a result of sustained lean strategies predominantly but not confined to aviation sector. Design/methodology/approach: This research is exploratory in nature and studies lean management implementation environment with focus on developing a framework for a GSC established through a path of identifying waste minimization techniques which automatically contribute to green initiatives in supply chain design invariably qualifying an ordinary supply chain as GSC. The approach is of identifying the framework parameters for both carbon footprint analysis and lean management. The focus of study is cross-sectional, within and outside aviation industry based on the information collected through survey methodology. Findings: The paper identifies the key factors that are responsible to make a “green” supply chain and presents a framework to establish the same through rigorous evaluation while not losing sight of lean management requirements so that operational management perspective is not lost. Research limitations/implications: The target of the study is the aviation industry and its supportive upstream and downstream beneficiaries. The study has implications for managers in all types of industrial environment, especially in aviation, in the era of globalized lean supply chain establishment which helps them to convert their present lean management initiatives to GSC. Practical implications: Inputs obtained are from both literary and industrial research in a live environment and hence impacts lean management outlook and its benefits to industries. Social implications: This paper has vast impact on the social well being with the focus on techno – environmental green initiatives factor identification through simple implemented and available procedures with no additional cost implication. Originality/value: This paper presents unique inputs regarding conversion of lean implementation scenario in aviation and other industries to a GSC model, with suitable incorporation of recognized factors which are outcome of complicated internal processes. It also tries to establish factors relevant for any organization in assessing lean initiatives. © 2016, © Emerald Group Publishing Limited.},
author_keywords={Carbon footprint analysis;  Green supply chain management;  Lean management;  Performance measurement system},
references={Aivazidou, E., Iakovou, E., Vlachos, D., Keramydas, C., A methodological framework for supply chain carbon footprint management (2013) Chemical Engineering Transactions, 35 (1), pp. 313-318; Al-Aomar, R., Weriakat, D., (2012) A framework for a green and lean supply chain: a construction project application, , July., Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Istanbul; Allenby, B., Supporting environmental quality: developing an infrastructure for design (1993) Total Quality Environmental Management, 2 (3), pp. 303-308; Ana Beatriz, L.S.J., Charbel, J.C.J., Are supplier selection criteria going green? Case studies of companies in Brazil (2009) Industrial Management & Data Systems, 109 (4), pp. 477-495; Anbumozhi, V., Kanada, Y., (2005) Greening the production and supply chains in Asia: is there a role for voluntarily initiatives?, , www.iges.or.jp, IGES Kansai Research Center Discussion Paper, KRC-2005, 6E; Azevedo, S.G., Carvalho, H., Machado, V.C., The influence of green practices on supply chain performance: a case study approach (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 850-871; Azzone, G., Noci, G., Identifying effective PMSs for the deployment of green manufacturing strategies (1998) International Journal of Operations & Production Management, 18 (4), pp. 308-335; Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Benjaafar, S., Li, Y., Daskin, M., Carbon footprint and the management of supply chains: insights from simple models (2013) IEEE Transactions on Automation Science and Engineering, 10 (1); Bowen, F.E., Cousins, P.D., Lamming, R.C., Faruk, A.C., Horses for courses: explaining the gap between the theory and practice of green supply (2001) Greener Management International, 35 (1), pp. 41-60; Carter, C.R., Ellram, L.M., Thirty-five years of the journal of supply chain management: where have we been, where are we going? (2003) Journal of Supply Chain Management, 39 (2), pp. 27-39; Carter, C.R., Jennings, M.M., Logistics social responsibility: an integrative framework (2002) Journal of Business Logistics, 23 (1), pp. 145-180; Carvalho, H., Duarte, S., Machado, V.C., Lean, agile, resilient and green: divergencies and synergies (2011) International Journal of Lean Six Sigma, 2, pp. 151-179; Charles, D.R., Xiaoqiu, Z., Judd, H.M., Janice, K.W., The lean index: operational lean metrics for the wood products industry (2006) Wood and Fiber Science, 38 (2), pp. 238-255; Christopher, M., (2005) Logistics & Supply Chain Management: Creating Value-Adding Networks, , 3rd ed., Pearson Education, Harlow; Corbett, C.J., Klassen, R.D., Extending the horizons: environmental excellence as key to improving operations (2006) Manufacturing & Service Operations Management, 8 (1), pp. 5-22; Craig, R.C., Dale, S.R., A framework of sustainable supply chain management: moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Dayna, F.S., Damien, J.P., Use the supply relationship to develop lean and green suppliers (2005) Supply Chain Management: An International Journal, 10 (1), pp. 60-68; Dyer, J., Nobeoka, K., Creating and managing a high-performance knowledge-sharing network: the Toyota case (2000) Strategic Management Journal, 21 (3), pp. 345-367; Eltayeb, T.K., Zailani, S., Going green through green supply chain initiatives towards environmental sustainability (2009) Operations and Supply Chain Management, 2 (2), pp. 93-110; Emiliani, M.L., Lean behaviors (1998) Management Decision, 36 (9), pp. 615-631; Flapper, S.D.P., Van Nunen, J.A., Van Wassenhove, L.N., (2005) Managing Closed-Loop Supply Chains, , Springer-Verlag, New York, NY; Florida, R., Lean and green: the move to environmentally conscious manufacturing (1996) CA Management Review, 39 (1), pp. 80-105; (2000) The future of lean construction: a brave new world, pp. 1-11. , Proceedings of the 8th Annual Conference of the International Group for Lean Construction, Brighton; Guide, V.D.R., Jr., Van Wassenhove, L.N., Closed-loop supply chains: an introduction to the feature issue (part 1) (2006) Production and Operations Management, 15 (3), pp. 345-350; Guide, V.D.R., Jr., Van Wassenhove, L.N., The evolution of closed-loop supply chain research (2009) Operations Research, 57 (1), pp. 10-18; Gupta, M., Environmental management and its impact on the operations function (1995) International Journal of Operations & Production Management, 15 (8), pp. 34-51; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: a study in the application of the analytical hierarchy process (2002) European Journal of Operational Research, 141 (1), pp. 70-87; Harland, C., Supply chain management: relationships, chains and networks (1996) British Journal of Management, 7 (1), pp. 63-80; Harms, D., Environmental sustainability and supply chain management – a framework of cross-functional integration and knowledge transfer (2011) Journal of Environmental Sustainability, 1 (1), pp. 121-141; Hart, S., A natural-resource based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1000; Haslam, C., Williams, K., Johal, S., Williams, J., A fallen idol? Japanese management in the 1990s (1996) Asia Pacific Management Review, 2 (4), pp. 21-43; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-335; Hopp, W.J., Spearman, M.L., To pull or not to pull: what is the question? (2004) Manufacturing & Service Operations Management, 6 (2), pp. 133-148; Hu, A.H., Hsu, C.W., (2006) Empirical study in the critical factors of green supply chain management (GSCM) practice in the Taiwanese electrical and electronics industries, pp. 853-857. , Singapore, IEEE International Conference on Management of Innovation and Technology; Huang, X., Tan, B.L., Ding, X., (2015) Journal of Manufacturing Technology Management, 26 (1), pp. 80-103; Humphreys, P.K., Wong, Y.K., Chan, F.T.S., Integrating environmental criteria into the supplier selection process (2003) Journal of Materials Processing Technology, 138 (1-3), pp. 349-356; Kenny, M., Florida, R., (1993) Beyond Mass Production, p. 410. , p., Oxford University Press, Oxford and New York, NY; Kent, J.L., Jr., Flint, D.J., Perspectives on the evolution of logistics thought (1997) Journal of Business Logistics, 18 (2), pp. 15-29; King, A.A., Lenox, M.J., Lean and green? An empirical examination of the relationship between lean production and environmental performance (2001) Production and Operations Management, 10 (3), pp. 244-256; Klassen, R.D., McLaughlin, C.P., The impact of environmental management of firm (1996) Performance Management Science, 42 (8), pp. 1199-1215; Kleindorfer, P.R., Singhal, K., Van Wassenhove, L.N., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Koplin, J., Seuring, S., Mesterharm, M., Incorporating sustainability into supply management in the automotive industry and the case of the Volkswagen AG (2007) Journal of Cleaner Production, 15 (11), pp. 1053-1062; Korchi, A.E., Conditions of emergence of OEM’s reverse supply chains (2014) Journal of Remanufacturing, 4 (3); Krause, D., Scannell, T., Calantone, R., A structural analysis of the effectiveness of buying firms’ strategies to improve supplier performance (2000) Decision Sciences, 31 (1), pp. 33-55; Lamming, R., (1993) Beyond Partnership: Strategies for Innovation and Lean Supply, , Prentice-Hall, New York, NY; Lamming, R., Hampson, J., The environment as a supply chain management issue (1996) British Journal of Management, 7 (1), pp. 45-62; Lash, J., Wellington, F., Competitive advantage on a warming planet (2007) Harvard Business Review, 85 (3), pp. 95-102; Lee, H., Don’t tweak your supply chain – Rethink it end to end (2010) Harvard Business Review, 88 (10), pp. 62-69; Lin, B., Jones, C.A., Hsieh, C., Environmental practices and assessment: a process perspective (2001) Industrial Management & Data Systems, 101 (2), pp. 71-79; Linton, J.D., Klassen, R., Jayaraman, J., Sustainable supply chains: an introduction (2007) Journal of Operations Management, 25 (6), pp. 1078-1082; Manuj, I., Mentzer, J.T., Global supply chain risk management (2008) Journal of Business Logistics, 29 (1), pp. 133-155; Matos, S., Hall, J., Integrating sustainable development in the supply chain: the case of life cycle assessment in oil and gas and agricultural biotechnology (2007) Journal of Operations Management, 25 (1), pp. 1083-1102; Melynk, S.A., Handfield, R.B., May you live in interesting times: the emergence of theory-driven empirical research (1998) Journal of Operations Management, 16 (4), pp. 321-329; Melnyk, S.A., Sroufe, R.P., Calantone, R.J., Assessing the impact of environmental management systems on corporate and environmental performance (2003) Journal of Operations Management, 21 (3), pp. 329-351; Mentzer, J.T., Kahn, K.B., A framework for logistics research (1995) Journal of Business Logistics, 16 (1), pp. 231-250; Meredith, J., Theory building through conceptual methods (1993) International Journal of Operations & Production Management, 13 (5), pp. 3-11; Min, H., Zhou, G., Supply chain modelling: past present and future (2002) Computers and Industrial Engineering, 43 (1), pp. 231-249; Mutingi, M., Mapfaira, H., Monageng, R., An analysis of remanufacturing practices in Japan (2014) Journal of Remanufacturing, 4 (6). , www.journalofremanufacturing.com/4/1/6; Mutingi, M., Mapfaira, H., Monageng, R., Developing performance management systems for the green supply chain (2014) Journal of Remanufacturing, 4 (6). , www.journalofremanufacturing.com/4/1/6; Mollenkopf, D., Wendy, H.S., Tate, L., Ueltschy, M., Green, lean, and global supply chains (2010) International Journal of Physical Distribution & Logistics Management, 40 (1-2), pp. 14-41; Mollenkopf, D.A., (2006) Environmental Sustainability: Examining the Case for Environmentally-sustainable Supply Chains, , Council of Supply Chain Management Professionals, Lombard, IL; Monden, Y., (1997) Toyota Production System: An Integrated Approach to Just-in-Time, , Engineering and Management Press, Norcross, GA; Murman, E., Allen, T., Bozdogan, K., Cutcher-Gershenfeld, J., McManus, H., Nightingale, D., Rebentisch, E., Widnall, S., (2002) Lean Enterprise Value, , Palgrave, London; Murman, E., Allen, T., Bozdogan, K., Cutcher-Gershenfeld, J., McManus, H., Nightingale, D., Rebentisch, E., Widnall, S., (2002) Lean Enterprise Value: Insights from MIT’s Lean Aerospace Initiative, , Palgrave, Hampshire, New York, NY; Ofori, G., Greening the construction supply chain in Singapore (2000) European Journal of Purchasing and Supply Management, 6 (3-4), pp. 195-206; Oliver, R., Webber, M., Supply-chain management: logistics catches up with strategy (1982) Logistics: The Strategic Issues, pp. 63-75. , Christopher, M.and (Ed.), Champmon, London; Parry, P., Martha, J., Grenon, G., The energy-efficient supply chain (2007) Strategy Bus, 47 (1), pp. 22-26; Piercy, N., Rich, N., The relationship between lean operations and sustainable operations (2015) International Journal of Operations & Production Management, 35 (2), pp. 282-315; The greening of Walmart’s supply chain (2007) Supply Chain Management Review, 11 (5), pp. 18-25; Porter, M., Van der Linde, C., Green and competitive: ending the stalemate (1995) Harvard Business Review, 1 (1), pp. 120-134; Pratt, K.M., Environmental standards could govern trade (1997) Transportation and Distribution, 38 (1), pp. 68-76; Raghu Kumar, B.R., Sharma, M.K., Agarwal, A., An experimental investigation of lean management in aviation (2015) Journal of Manufacturing Technology Management, 26 (2), pp. 231-260; Rawabdeh, I.A., A model for the assessment of waste in job shop environments (2005) International Journal of Operations & Production Management, 25 (8), pp. 800-822; Rothenberg, S., Pil, F., Maxwell, J., Lean, green and the quest for superior environmental performance (2001) Production and Operations Management, 10 (3), pp. 228-243; Sarkis, J., Rasheed, A., Greening the manufacturing function (1995) Business Horizons, 38 (5), pp. 17-27; Schmidt, G., Wilhelm, W.E., Strategic, tactical and operational decisions in multi-national logistics networks: a review and discussion of modelling issues (2000) International Journal of Production Research, 38 (7), pp. 1501-1523; Shultz, C.J., II, Holbrook, M.B., Marketing and tragedy of the commons: a synthesis commentary and analysis for action (1999) Journal of Public Policy and Marketing, 18 (2), pp. 218-229; Simpson, D., Power, D., Use the supply relationship to develop lean and green suppliers (2005) Supply Chain Management: An International Journal, 10 (1), pp. 60-68; Spear, S.J., Learning to lead at Toyota (2004) Harvard Business Review, 82 (5), pp. 78-86; Srivastava, S.K., Green supply-chain management: a state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Sundarakani, B., Goh, M., de Souza, R., Shun, C., Measuring carbon footprints across the supply chain (2008) Proceeding of the 13th International Symposium on Logistics (ISL2008): Integrating the Global Supply Chain, pp. 555-562. , Nottingham University Business School, Nottingham; Theyel, G., Management practices for environmental innovation and performance (2000) International Journal of Operations & Production Management, 20 (2), pp. 249-266; Wacker, J.G., A definition of theory: research guidelines for different theory-building research methods in operations management (1998) Journal of Operations Management, 16 (4), pp. 361-385; Walton, S.V., Handfield, R.B., Melnyk, S.A., The green supply chain: integrating suppliers into environmental management process (1998) International Journal of Purchasing and Materials Management, 34 (2), pp. 2-11; Winsemius, P., Guntram, U., Responding to the environmental challenge (1992) Business Horizons, 35 (2), pp. 12-20; Womack, J., Jones, D., Roos, D., (1990) The Machine that Changed the World, , Rawson Associates, New York, NY; Womak, J.P., Jones, D.T., (1996) Lean Thinking: Banish Waste and Create Wealth in Your Corporation, , Simon and Schuster, New York, NY; Yang, M.G., Hong, P., Modi, S.B., Impact of lean manufacturing and environmental management on business performance: an empirical study of manufacturing firms (2011) International Journal of Production Economics, 129 (2), pp. 251-261; Zhu, Q.H., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: drivers and practice (2006) Journal of Cleaner Production, 14 (5), pp. 471-486; Carter, P., (2011) 7 wastes, , http://cisystem-ltd.blogspot.com/2011/03/7-wastestransportation.html, accessed, 2 March 2012; Chu, W., MacDuffie, J.P., Nobeoka, K., Pil, F.K., (2002) Management of the extended enterprise”, Project Report to International Motor Vehicle Program (IMVP), M.I.T., , https://dspace.mit.edu/bitstream/handle/1721.1/747/Asystudy.pdf?sequence=1, ), “, International Assembly Plant Study; (2009) Europeans’ attitudes towards the issue of sustainable consumption and production, , ec.europa.eu/public_opinion/flash/fl_256_en.pdf, accessed, 14 2 2013; Grenon, G., Martha, J., Turner, M., (2008) How Big Is Your Carbon Footprint? Before You Can Build an Energy-Efficient Supply Chain, You Need to Understand Your Company’s Carbon Emissions, , Booz Allen Hamilton, Virginia; Grieco, D.M., (1749) Automotive Approaches For General Aviation Aircraft, , dgrieco@munroassoc, Munro and Associates, Northwood Drive, Troy; Simchi, D., Levi, P.K., Simchi, E.L., Shankar, R., (2008) Designing and Managing the Supply Chain – Concepts,Strategies and Case Studies, , Third Edition, Tata McGraw Hill Education Private, New Delhi; Suo, S., (2012) A conceptual framework for adopting sustainability in the supply chain, , www.researchgate.net/profile/Stuart_So/publication/235405694_A_conceptual_framework_for_adopting_sustainability_in_the_supply_chain/links/09e415114b88ed4063000000.pdf; Wayne, N., MacDonald, C., Getting to the bottom of ‘Triple Bottom Line’ (2004) Business Ethics Quarterly, pp. 1-19. , http://isites.harvard.edu/fs/docs/icb.topic549945.files/Canadian%20Paper.pdf, April, pp; Welford, R., Starkey, R., (1996) Business and the Environment, , Earthscan Publications, London; Emiliani, M.L., Stec, D.J., Leaders lost in transformation (2004) Leadership and Organization Development Journal, 26 (5), pp. 370-387; (2000) The lean and green supply chain, , United States Environmental Protection Agency, EPA 742-R-00-001, WA; Gunasekaran, A., Patel, C., Tirtirogulu, E., Performance measures and metrics in supply chain environment (2001) International Journal of Operations & Production Management, 21 (2), pp. 71-87; Zhu, Q., Sarkis, J., The moderating effects of institutional pressures on emergent green supply chain practices and performance (2007) International Journal of Production Research, 45 (18-19), pp. 4333-4355; Zhu, Q., Sarkis, J., Lai, K.-H., Green supply chain management implications for closing the loop (2008) Transportation Research Part E: Logistics and Transportation Research, 44 (1), p. 18; www.journalofremanufacturing.com/4/1/6},
correspondence_address1={Raghu Kumar, B.R.; Indian Armed ForcesIndia; email: rkbrmysore@gmail.com},
publisher={Emerald Group Publishing Ltd.},
issn={10595422},
language={English},
abbrev_source_title={Compet. Rev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Panjehfouladgaran2016178,
author={Panjehfouladgaran, H. and Yusuff, R.},
title={Fuzzy performance measurement for supply chain management in Malaysian rubber glove manufacturer},
journal={International Journal of Logistics Systems and Management},
year={2016},
volume={24},
number={2},
pages={178-199},
doi={10.1504/IJLSM.2016.076471},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969872754&doi=10.1504%2fIJLSM.2016.076471&partnerID=40&md5=078e9b9968378018c816fd98250a8044},
affiliation={Mechanical and Manufacturing Department, Faculty of Engineering, Universiti Putra Malaysia, Selangor, 43400, Malaysia},
abstract={Companies are trying to increase their market share due to globalisation in recent years. Various types of products and customers faced different challenges for companies to achieve market share. Hence, to accomplish this goal, successful companies have to concentrate on the efficiency and effectiveness of their activities. Supply chain involves all activities that fulfil customers' demands and transfer materials through downstream into upstream of the chain. Since, the supply chain creates value for customers and companies simultaneously, monitoring supply chain activities is essential. Numerous performance measurement systems and frameworks in the supply chain have been developed in recent years. This paper discusses on the development of a framework for measuring qualitative metrics in supply chain. Qualitative metrics in supply chain management were identified and then experts selected the most common metrics between them. Fuzzy logic controller was applied to translate verbal expression into numerical variables. Finally, a framework was programmed in the MATLAB® software for easy utilisation and evaluation of the qualitative performances in the supply chain. Copyright © 2016 Inderscience Enterprises Ltd.},
author_keywords={FLC;  Fuzzy logic controller;  Performance measurement;  Performance measurement system;  PMS;  Qualitative metrics;  SCPM;  Supply chain performance measurement},
references={Adel El-Baz, M., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38 (6), pp. 6681-6688; Aramyan, L.H., Lansink, A.G.O., Van Der Vorst, J.G., Van Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning and Control, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Braz, R.G.F., Scavarda, L.F., Martins, R.A., Reviewing and improving performance measurement systems: An action research (2011) International Journal of Production Economics, 133 (2), pp. 751-760; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F.T., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.T., Qi, H., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Nayak, A., Raj, R., Chong, A.Y.-L., Manoj, T., An innovative supply chain performance measurement system incorporating Research and Development (R&D) and marketing policy (2014) Computers and Industrial Engineering, 69, pp. 64-70; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers and Industrial Engineering, 62 (3), pp. 801-818; Cirtita, H., Glaser-Segura, D.A., Measuring downstream supply chain performance (2012) Journal of Manufacturing Technology Management, 23 (3), pp. 299-314; Cuthbertson, R., Piotrowicz, W., Supply chain best practices-identification and categorisation of measures and benefits (2008) International Journal of Productivity and Performance Management, 57 (5), pp. 389-404; De Toni, A., Tonchia, S., Performance measurement systems-models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 46-71; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: A contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations and Production Management, 27 (8), pp. 784-801; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Griffis, S.E., Cooper, M., Goldsby, T.J., Closs, D.J., Performance measurement: Measure selection-based upon firm goals and information reporting needs (2004) Journal of Business Logistics, 25 (2), pp. 95-118; Grosswiele, L., Röglinger, M., Friedl, B., A decision framework for the consolidation of performance measurement systems (2013) Decision Support Systems, 54 (2), pp. 1016-1029; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Jain, V., Tiwari, M., Chan, F., Evaluation of the supplier performance using an evolutionary fuzzy-based approach (2004) Journal of Manufacturing Technology Management, 15 (8), pp. 735-744; Kleijnen, J.P., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Lau, H., Pang, W.K., Wong, C.W., Methodology for monitoring supply chain performance: A fuzzy logic approach (2002) Logistics Information Management, 15 (4), pp. 271-280; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1), pp. 23-35; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; McCormack, K., Ladeira, M.B., De Oliveira, M.P.V., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Ohdar, R., Ray, P.K., Performance measurement and evaluation of suppliers in supply chain: An evolutionary fuzzy-based approach (2004) Journal of Manufacturing Technology Management, 15 (8), pp. 723-734; Otley, D., Performance management: A framework for management control systems research (1999) Management Accounting Research, 10 (4), pp. 363-382; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management and Data Systems, 110 (9), pp. 1297-1318; Pedrycz, W., (1995) Fuzzy Sets Engineering, , CRC Press, Boca Rayton, FL; Pettersson, A.I., Segerstedt, A., Performance measurements in supply chains within Swedish industry (2011) International Journal of Logistics Systems and Management, 9 (1), pp. 69-88; Ramanathan, U., Performance of supply chain collaboration - A simulation study (2014) Expert Systems with Applications, 41 (1), pp. 210-220; Ross, T.J., (2009) Fuzzy Logic with Engineering Applications, , John Wiley & Sons, New York; Schmitz, J., Platts, K., Roles of supplier performance measurement: Indication from a study in the automotive industry (2003) Management Decision, 41 (8), pp. 711-721; Schnsleben, P., (2004) Integral Logistics Management: Planning and Control of Comprehensive Business Practices, , CRC Press, Boca Rayton, FL; Shahabuddin, S., Supply chain management and its effect on company's performance (2011) International Journal of Logistics Systems and Management, 8 (1), pp. 101-117; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Tangen, S., An overview of frequently used performance measures (2003) Work Study, 52 (7), pp. 347-354; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Unahabhokha, C., Platts, K., Tan, K.H., Predictive performance measurement system: A fuzzy expert system approach (2007) Benchmarking: An International Journal, 14 (1), pp. 77-91; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381; Yap, J., (2010) OSK Rubber Gloves: Demand Still Ahead of Supply, , OSK, Malaysia},
correspondence_address1={Panjehfouladgaran, H.; Mechanical and Manufacturing Department, Malaysia; email: hamidfouladgaran@gmail.com},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jaipuria2016208,
author={Jaipuria, S. and Mahapatra, S.S.},
title={A system dynamic approach to study the behaviour of serial supply chain under uncertain environment},
journal={International Journal of Services and Operations Management},
year={2016},
volume={24},
number={2},
pages={208-234},
doi={10.1504/IJSOM.2016.076504},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969856071&doi=10.1504%2fIJSOM.2016.076504&partnerID=40&md5=b4cdc2ca5d8c58fe9ba7c53a35d506f4},
affiliation={Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, India},
abstract={In order to gain a competitive edge, currently it is being emphasised to improve the performance of the whole supply chain instead of individual entities within it. However, uncertainties allied along the supply chain causes an adverse effect on performance measures such as the bullwhip effect (BWE) and total cost leading to decrease in the service level and profitability of an organisation. Therefore, in this work, a four level serial supply chain employed with (s, S) replenishment policy is modelled though a system dynamics approach to mimic the behaviour of the supply chain. Performance measures of the system are estimated under the influence of stochastic variation of lead time and demand through simulations approach. The effect of ordering decision parameter (e.g., target inventory) on supply chain performance has been studied under the above uncertainties. A design of experiment (DOE) approach has been implemented to generate the experimental scenarios and study the influence of individual and interaction effect of system parameters on performance. Best parametric combination that simultaneously optimises two performance measures such as BWE and total cost has been reported. Copyright © 2016 Inderscience Enterprises Ltd.},
author_keywords={Bullwhip effect;  BWE;  Design of experiment;  DOE;  Grey relational analysis;  Supply chain performance;  System dynamics},
references={Aji, Y., Hariga, M., An AHP-DEA-based vendor selection approach for an online trading platform (2013) International Journal of Applied Decision Sciences, 6 (1), pp. 66-82; Arabzad, S.M., Kamali, A., Naji, B., Ghorbani, M., DEA and TOPSIS techniques for purchasing management: The case of aircraft manufacturing industry (2013) International Journal of Logistics Systems and Management, 14 (2), pp. 242-260; Bilen, C., Ding, F.Y., Stoner, A.P., Selecting a third party logistics partner for operating a materials service centre: A data envelopment analysis approach (2011) International Journal of Logistics Systems and Management, 9 (3), pp. 280-295; Boute, R.N., Lambrecht, M.R., Exploring the bullwhip effect by mean of spread sheet simulation (2009) Informs Transaction on Education, 10 (1), pp. 1-9; Campuzano, F., Mula, J., Peidro, D., Fuzzy estimations and system dynamics for improving supply chains (2010) Fuzzy Sets and Systems, 161 (11), pp. 1530-1542; Campuzano-Bolarín, F., Mula, J., Peidro, D., Fuzzy estimations and system dynamics for improving manufacturing orders in VMI supply chains (2014) Supply Chain Management under Fuzziness, 313, pp. 227-241. , Springer, Berlin Heidelberg; Caydas, U., Hascalik, A., Use of grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics (2008) Optics and Laser Technology, 40 (7), pp. 987-994; Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D., Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information (2000) Management Science, 46 (3), pp. 436-443; Chen, M.-F., Ho, Y.-S., Hsiao, W.-T., Wu, T.-H., Tseng, S.-F., Huang, K.-C., Optimized laser cutting on light guide plates using grey relational analysis (2011) Optics and Lasers in Engineering, 49 (2), pp. 222-228; Chopra, S., Meindle, P., Kalra, D.V., (2006) Supply Chain Management Strategy, Planning and Operation, , 3rd ed., Pearson Education, Inc., New Delhi, India; Christopher, M.G., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London, UK; Cooper, M.C., Ellram, L.M., Characteristics of supply chain management and the implication for purchasing and logistics strategy (1993) The International Journal of Logistics Strategy, 4 (2), pp. 13-24; Coppini, M., Rossignoli, C., Rossi, T., Strozzi, F., Bullwhip effect and inventory oscillations analysis using the beer game model (2010) International Journal of Production Research, 48 (13), pp. 3943-3956; Datta, S., Bandyopadhyay, A., Pal, P.K., Desirability function approach for solving multi-objective optimization problem in submerged arc welding (2006) International Journal for Manufacturing Science and Production, 7 (2), pp. 127-135; Datta, S., Bandyopadhyay, A., Pal, P.K., Grey based Taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding (2008) International Journal of Advanced Manufacturing Technology, 39 (11-12), pp. 1136-1143; Davis, T., Effective supply chain management (1993) Sloan Management Review, Summer, 34, pp. 35-46; Dejonckheere, J., Disney, S.M., Lambrecht, M.R., Towill, D.R., Measuring and avoiding the bullwhip effect: A control theoretic approach (2003) European Journal of Operational Research, 147 (3), pp. 567-590; Disney, S.M., Towill, D.R., On the bullwhip and inventory variance produced by an ordering policy (2003) International Journal of Management Science-Omega, 31 (3), pp. 157-167; Fan, C.-Y., Fan, P.-S., Chang, P.-C., A system dynamics modeling approach for a military weapon maintenance supply system (2010) International Journal of Production Economics, 128 (2), pp. 457-469; Forrester, J.W., (1961) Industrial Dynamics, , Cambridge MIT Press and John Wiley and Sons, Inc, New York; Ge, Y., Yang, J.-B., Proudlove, N., Spring, M., System dynamics modelling for supply-chain management: A case study on a supermarket chain in the UK (2004) International Transactions in Operational Research, 11 (5), pp. 495-509; Georgiadis, P., Vlachos, D., Lakovou, E., A system dynamics modelling framework for the strategic supply chain management of food chains (2005) Journal of Food Engineering, 70 (3), pp. 351-364; Helo, P.T., Dynamic modelling of surge effect and capacity limitation in supply chains (2000) International Journal of Production Research, 38 (17), pp. 4521-4533; Huang, J.T., Liao, Y.S., Optimization of machining parameters of wire-EDM based on grey relational and statistical analyses (2003) International Journal of Production Research, 41 (8), pp. 1707-1720; Huang, M., Yang, M., Zhang, Y., Liu, B., System dynamics modelling-based study of contingent sourcing under supply disruptions (2012) Systems Engineering Procedia, 4, pp. 290-297; Hussain, M., Drake, P.R., Analysis of the bullwhip effect with order batching in multi-echelon supply chains (2011) International Journal of Physical Distribution and Logistics Management, 41 (10), pp. 972-990; Hwarng, H.B., Xie, N., Understanding supply chain dynamics: A chaos perspective (2008) European Journal of Operational Research, 184 (3), pp. 1163-1178; Kara, S., Kayis, B., Proactive logistics risk management - A system dynamics modelling approach (2008) International Journal of Risk Assessment and Management, 10 (3), pp. 224-237; Kim, J.G., Chatfield, D.T., Harrison, P., Hayya, J.C., Quantifying the bullwhip effect in a supply chain with stochastic lead time (2006) European Journal of Operational Research, 173 (2), pp. 617-636; Kumar, A.S., Jeyapaul, R., Haq, A.N., Analysing the performance of supply chain design (2009) International Journal of Business Performance Management, 11 (1-2), pp. 72-95; Kumar, S., Nigmatullin, A., A system dynamics analysis of food supply chains-case study with non-perishable products (2011) Simulation Modelling Practice and Theory, 19 (10), pp. 2151-2168; Kuo, Y., Yang, T., Huang, G.-W., The use of grey-based Taguchi method for optimizing multiple-response simulation problem (2011) Engineering Optimization, 40 (6), pp. 517-528; La, L.B.J., Masters, J.M., Emerging logistics strategy: Blueprints for the next century (1994) International Journal of Physical Distribution and Logistics Management, 24 (7), pp. 35-47; Lambert, D.M., Stock, J.R., Ellram, L.M., (1998) Fundamental of Logistics Management, , Irwin/McGraw-Hill, Burr Ridge, Illinois; Lee, C.-F., Chung, C.-P., An inventory model for deteriorating items in a supply chain with system dynamics analysis (2012) Procedia-Social and Behavioral Sciences, 40, pp. 41-51; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lee, H.L., Padmanabhan, V., Whang, S., Information distortion in a supply chain: The bullwhip effect (1997) Management Science, 43 (4), pp. 546-558; Liu, T.Z., Chou, H.C., Exploring important factors affecting implementation of restaurant information system into traditional Chinese restaurant (2012) International Journal of Enterprise Network Management, 5 (1), pp. 3-16; Mahapatra, S.S., Panda, B.N., Benchmarking of rapid prototyping systems using grey relational analysis (2013) International Journal of Services and Operations Management, 16 (4), pp. 460-477; Makui, A., Madadi, A., The bullwhip effect and Lyapunov exponent (2007) Applied Mathematics and Computation, 189 (1), pp. 35-40; Mason-Jones, R., Towill, D.-R., Coping with uncertainty: Reducing 'bullwhip' behaviour in global supply chains (2000) Supply Chain Forum: An International Journal, 1 (1), pp. 40-45; Minegishi, S., Thiel, D., System dynamics modeling and simulation of a particular food supply chain (2000) Simulation Practice and Theory, 8 (5), pp. 321-339; Monczka, R.M., Handfield, R.B., Guinipero, L.C., (2002) Purchasing and Supply Chain Management, , 2nd ed., Thomson Learning, Singapore; Montogomory, D.C., (2001) Design and Analysis of Experiments, , 5th ed., John Wiley and Sons, Singapore; O'Donnell, T., Maguire, L., McIvor, R., Humphreys, P., Minimizing the bullwhip effect in a supply chain using genetic algorithms (2006) International Journal of Production Research, 44 (8), pp. 1523-1543; Owens, S.F., Levary, R.R., Evaluation of impact of electronic data interchange on the ingredient supply chain of a food processing company (2002) Supply Chain Management: A International Journal, 7 (4), pp. 200-211; Ozbayrak, M., Papadopoulou, T.C., Akgun, M., System dynamics modelling of a manufacturing supply chain system (2007) Simulation Modelling Practice and Theory, 15 (10), pp. 1338-1355; Pan, L.K., Wang, C.C., Wei, S.L., Sher, H.F., Optimizing multiple quality characteristics via Taguchi method based grey analysis (2007) Journal of Material Processing Technology, 182 (1-3), pp. 107-116; Petrovic, D., Simulation of supply chain behaviour and performance in an uncertain environment (2001) European Journal of Operational Research, 71 (1-3), pp. 429-438; Petrovic, D., Roy, R., Petrovic, R., Modelling and simulation of a supply chain in an uncertain environment (1998) European Journal of Operational Research, 109 (2), pp. 299-309; Petrovic, D., Roy, R., Petrovic, R., Supply chain modelling using fuzzy sets (1999) European Journal of Operational Research, 59 (1-3), pp. 443-453; Ramakrishnan, S., Tsai, P.-F., Srihari, K., Foltz, C., Using design of experiments and simulation modelling to study the facility layout for a server assembly process (2008) Proceedings of the 2008 Industrial Engineering Research Conference, pp. 601-606. , in Fowler, J. and Mason, S. (Eds.); Ranganathan, S., Senthilvelan, T., Multi-response optimization of machining parameters in hot turning using grey analysis (2011) International Journal of Advanced Manufacturing Technology, 56 (5-8), pp. 455-462; Saysel, A.K., Barlas, Y., Yenigun, O., Environmental sustainability in an agricultural development project: A system dynamics approach (2002) Journal of Environmental Management, 64 (3), pp. 247-260; Silver, E.A., Pyke, D.F., Peterson, R., (1998) Inventory Management and Production Planning and Scheduling, , 3rd ed., John Wiley and Sons, New York; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Managing the Supply Chain the Definitive Guide for the Business Professional, , 1st ed., McGraw-Hill Companies, New York; Sirikasemsuk, K., Luong, H.T., Measure of bullwhip effect - A dual sourcing model (2014) International Journal of Operational Research, 20 (4), pp. 396-426; Sterman, J.D., (2000) Business Dynamics: System Thinking and Modelling for Complex World, , McGraw-Hill, New York; Tu, Y., Luh, P.B., Feng, W., Narimatsu, K., Supply chain performance evaluation: A simulation study (2003) Proceedings of the 2003 IEEE International Conference on Robotics and Automation, pp. 14-19. , Taipei, Taiwan, September; Vlachos, D., Georgiadis, P., Lakovou, E., A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains (2007) Computers and Operations Research, 34 (2), pp. 367-394; Walia, R.S., Shan, H.S., Kumar, P., Multi-response optimization of CFAAFM process through Taguchi method and utility concept (2006) Materials and Manufacturing Processes, 21 (8), pp. 907-914; Wangphanich, P., Kara, S., Kayis, B., Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems - A simulation approach (2010) International Journal of Production Research, 48 (15), pp. 4501-4517; Xie, Y., Petrovic, D., Burnham, K., A heuristic procedure for the two-level control of serial supply chains under fuzzy customer demand (2006) International Journal of Production Economics, 102 (1), pp. 37-50},
correspondence_address1={Mahapatra, S.S.; Department of Mechanical Engineering, India; email: mahapatrass2003@yahoo.com},
publisher={Inderscience Publishers},
issn={17442370},
language={English},
abbrev_source_title={Int. J. Serv. Oper. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bulsara2016476,
author={Bulsara, H.P. and Qureshi, M.N. and Patel, H.},
title={Green supply chain performance measurement: An exploratory study},
journal={International Journal of Logistics Systems and Management},
year={2016},
volume={23},
number={4},
pages={476-498},
doi={10.1504/IJLSM.2016.075210},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962598078&doi=10.1504%2fIJLSM.2016.075210&partnerID=40&md5=07984d73752472352d5839ae8e8bb435},
affiliation={Management Section, Applied Mathematics and Humanities Department, S.V. National Institute of Technology, Ichchhanath, Surat, Gujarat  395 007, India; Industrial Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia; Faculty of Technology and Engineering, MS University of Baroda, Vadodara, Gujarat  390 001, India; S.V. National Institute of Technology, Surat, Gujarat, India; S.R. Luthra Institute of Management, Athwalines, Surat, Gujarat  395001, India},
abstract={The existing performance measurement tool set for green operations is growing but seems inadequate to gauge green supply chain measures (GSCM) comprehensively. Several tools, like multi criteria decision making (MCDM), activity-based costing (ABC), balance scorecard (BSC) and life cycle analysis (LCA) are being used. In view of this, a systematic literature review has been carried out with keywords of performance measurement, green supply chain, supply chain performance measurement and green supply chain performance measurement, which yielded 1,432. Later on, 112 research papers, proceedings are shortlisted having high significance and citation index. Three-tier classification is adopted to encompass scope, methodology and focus. Since prevailing performance measurement tool set for environmental operations is inadequate to assess GSC comprehensively, need for single tool for GSCPM for planning, execution, assessment and management is identified. The present study classifies set of variables into operational, economic and environmental. It also provides systematic research guidelines and managerial implications. © Copyright 2016 Inderscience Enterprises Ltd.},
author_keywords={Green supply chain;  Green supply chain performance measurement;  GSCPM;  SCPM;  Supply chain performance measurement},
references={Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable supply management: An empirical study (2012) International Journal of Production Economics, 140 (1), pp. 168-182; Agndal, H., Nilsson, U., Different open book accounting practices for different purchasing strategies (2010) Management Accounting Research, 21 (3), pp. 147-166; Ashley, S., Designing for the environment (1993) Mechanical Engineering, 115 (3), pp. 52-54; Awasthi, A., Chauhan, S.S., Goyal, S.K., A fuzzy multi-criteria approach for evaluating environmental performance of suppliers (2010) International Journal of Production Economics, 126 (2), pp. 370-378; Azzone, G., Masella, C., Bertelè, U., Design of performance measures for time-based companies (1991) International Journal of Operations and Production Management, 11 (3), pp. 77-85; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Banerjee, S.B., Managerial perceptions of corporate environmentalism: Interpretations from industry and strategic implications for organizations (2001) Journal of Management Studies, 38 (4), pp. 489-513; Banerjee, S.B., Iyer, E.S., Kashyap, R.K., Corporate environmentalism: Antecedents and influence of industry type (2003) Journal of Marketing, 67 (2), pp. 106-122; Beamon, B.M., Performance measures in supply chain management (1996) Proceedings of the Conference on Agile and Intelligent Manufacturing Systems, , Rensselaer Polytechnic Institute, Troy, NewYork, NY; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bhateja, A.K., Babbar, R., Singh, S., Sachdeva, A., Study of green supply chain management in the Indian Manufacturing industries: A literature review cum an analytical approach for the measurement of performance (2011) International Journal of Computational Engineering & Management, 13 (1), pp. 84-99; Bond, T.C., The role of performance measurement in continuous improvement (1999) International Journal of Operations and Production Management, 19 (12), pp. 1318-1334; Boonitt, S., Pongpanarat, C., Measuring service supply chain management processes: The application of the Q-sort technique (2011) International Journal of Innovation, Management and Technology, 2 (3), pp. 217-221; Bowen, F.E., Cousins, P.D., Lamming, R.C., Faruk, A.C., Horses for courses: Explaining the gap between the theory and practices of green supply (2001) Greener Management International, 35 (1), pp. 41-60; Brimson, J.A., (1991) Activity Accounting, , Editions, John Wiley and Sons, New York; Bulsara, H., Qureshi, M.N., Patel, H., Supply chain performance measurement: An exploratory study (2014) International Journal of Logistics Systems and Management, 18 (2), pp. 231-249; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 46-62; Carter, C.R., Ellram, L.M., Reverse logistics: A review of the literature and framework for future investigation (1998) Journal of Business Logistics, 19 (1), pp. 85-102; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Chan, F.-T.-S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.-T.-S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management - An International Journal, 8 (4), pp. 209-223; Charkha, P.G., Jaju, S.B., Designing innovative framework for supply chain performance measurement in textile industry (2014) International Journal of Logistics Systems and Management, 18 (2), pp. 216-230; Chen, Y.S., The driver of green innovation and green image - Green core competence (2008) Journal of Business Ethics, 81 (3), pp. 531-543; Chen, Y.S., Lai, S.B., Wen, C.T., The influence of green innovation performance on corporate advantage in Taiwan (2006) Journal of Business Ethics, 67 (4), pp. 331-339; Chiou, T.Y., Chqn, H.K., Lettice, F., Chung, S.H., The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan (2011) Transportation Research, Part e, 47 (6), pp. 822-836; Christopher, M., Towill, D.R., An integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution and Logistics Management, 31 (4), pp. 235-246; Clemens, B., Three phases of environmental strategies (2001) Journal of Environmental Management, 62 (2), pp. 221-231; Clemens, B., Economic incentives and small firms: Does it pay to be green? (2006) Journal of Business Research, 59 (4), pp. 492-500; Cramer, J., Experiences with integrated chain management in Dutch industry (1996) Business Strategy and the Environment, 5 (2), pp. 38-47; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organizations (2012) Production Planning & Control: The Management of Operations, 24 (8-9), pp. 702-720; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge - Measuring Operations for World Class Competition, , DowJones-Irwin, Homewood, IL; Drumwright, M., Socially responsible organizational buying: Environmental concern as a noneconomic buying criterion (1994) Journal of Marketing, 58 (3), pp. 1-19; Dummett, K., Drivers of corporate environmental responsibility (2006) Environment Development and Sustainability, 8 (3), pp. 375-389; Etzion, D., Research on organizations and the natural environment, 1992-present: A review (2007) Journal of Management, 33 (4), pp. 637-664; Faith-Ell, C., Balfors, B., Folkeson, L., The application of environmental requirements in Swedish road maintenance contracts (2006) Journal of Cleaner Production, 14 (2), pp. 163-171; Fiala, P., Information sharing in supply chains (2005) Omega, 33 (5), pp. 419-423; Forrester, J.W., (1961) Industrial Dynamics, , MIT Press, Cambridge, MA; Fortuin, L., Performance indicators - Why, where and how? (1988) European Journal of Operational Research, 34 (1), pp. 1-9; Globerson, S., Issues in developing a performance criteria system for an organisation (1985) International Journal of Production Research, 23 (4), pp. 639-646; Goold, M., Strategic control in the decentralised firm (1991) Sloan Management Review, 32 (2), pp. 69-81; Goold, M., Quinn, J.J., The paradox of strategic controls (1990) Strategic Management Journal, 11 (1), pp. 43-57; Gorane, S.J., Kant, R., Supply chain practices: Study and framework for future development (2014) International Journal of Logistics Systems and Management, 17 (1), pp. 83-103; Green, K., Morton, B., New, S., Purchasing and environmental management: Interactions, policies and opportunities (1996) Business Strategy and the Environment, 5 (3), pp. 188-197; Guide, V.D.R., Jr., Jayaraman, V., Srivastava, R., Benton, W.C., Supply-chain management for recoverable manufacturing systems (2000) Interfaces, 30 (3), pp. 125-142; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., McNeil, R., Singh, D., Activity-based management in a small company: A case study (2000) Production Planning & Control, 11 (4), pp. 391-399; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gupta, M., Environmental management and its impact on the operations function (1995) International Journal of Operations and Production Management, 15 (8), pp. 34-51; Handfiled, R., Walton, S., Sorufe, R., Applying environmental criteria to supplier assessment: A study of the application of the analytical hierarchy process (2002) European Journal of Operational Research, 41 (1), pp. 70-87; Hart, S.L., A natural-resource-based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1014; Hervani, A.-A., Helms, M.-M., Sarkis, J., Performance measurement for green supply chain management', Benchmarking (2005) An International Journal, 12 (4), pp. 330-353; Hoffman, A.J., Integrating environmental and social issues into corporate practice (2000) Environment, 42 (5), pp. 22-33; Hoffman, A.J., Business decisions and the environment: Significance, challenges, and momentum of an emerging research field (2005) Decision Making for the Environment: Social and Behavioral Science Research Priorities, pp. 200-229. , Brewer, G. and Stern, P. (Eds. National Research Council, National Academies Press, Washington, DC; Holmberg, S., A system perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Hsu, C.W., Hu, A.H., The green supply chain management in the electronic industry (2008) International Journal of Environment Science Technology, 5 (2), pp. 205-216; Hughes, A., ABC/ABM - Activity-based costing and activity-based management: A profitability model for SMEs manufacturing clothing and textiles in the UK (2005) Journal of Fashion Marketing and Management, 9 (1), pp. 8-19; Hui, I.K., Chan, A.H.S., Pun, K.F., A study of the environmental management system implementation practices (2001) Journal of Cleaner Production, 9 (3), pp. 269-276; Ilgin, M.-A., Gupta, M., Environmental conscious manufacturing and product recovery (ECMPRO): A review of the state of the art (2010) Journal of Environmental Management, 91 (3), pp. 563-591; Ittner, C.D., Larcker, D.F., Nagar, V., Rajan, M.V., Supplier selection, monitoring practices and firm performance (1999) Journal of Accounting and Public Policy, 18 (3), pp. 253-281; Kaplan, R., Norton, D., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-99; Karna, A., Heiskanen, E., The challenge of 'product chain' thinking for product development and design: The example of electrical and electronics products (1998) Journal of Sustainable Product Design, 4 (1), pp. 26-36; King, A.J., Lenox, M., Exploring the locus of profitable pollution reduction (2002) Management Science, 48 (2), pp. 289-299; Kumar, S., Chattopadhyaya, S., Sharma, V., Green supply chain management: A case study from Indian electrical and electronics industry (2012) International Journal of Soft Computing and Engineering, 1 (6), pp. 275-281; Kurien, G.P., Qureshi, M.N., Performance measurement systems for green supply chains using modified balanced score card and analytical hierarchical process (2012) Scientific Research and Essays, 7 (36), pp. 3149-3161; Lea, R., Parker, B., The JIT spiral of continuous improvement (1989) International Material Data System, 89 (4), pp. 10-13; Liang, L., Yang, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; Linton, J.D., Jayaraman, V., A conceptual framework for product life extension (2005) International Journal of Production Research, 43 (9), pp. 1807-1829; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) Journal of Operations Management, 25 (6), pp. 1075-1082; Lu, L.Y.Y., Wu, C.W., Kuo, T.-C., Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis (2007) International Journal of Production Research, 45 (18-19), pp. 4317-4331; Luthra, S., Qadri, M.A., Garg, D., Haleem, A., Identification of critical success factors to achieve high green supply chain management performances in Indian automobile industry (2014) International Journal of Logistics Systems and Management, 18 (2), pp. 170-199; Lynch, R.L., Cross, K.F., (1991) Measure Up the Essential Guide to Measuring Business Performance, , Mandarin, London; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: A survey in the mines (2006) Management Accounting Research, 17 (3), pp. 315-339; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Cambridge, MA; Mudgal, R.K., Shankar, R., Talib, P., Raj, T., Greening the supply chain practices: An Indian perspective of enablers relationship (2009) International Journal of Advanced Operations Management, 1 (2-3), pp. 151-176; Mudgal, R.K., Shankar, R., Talib, P., Tilak, R., Modeling the barriers of green supply chain practices: An Indian perspective (2010) International Journal of Logistics Systems and Management, 7 (1), pp. 81-107; Naim, M.M., Potter, A.T., Mason, R.J., Bateman, N., The role of transport flexibility in logistics provision (2006) The International Journal of Logistics Management, 17 (3), pp. 297-311; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Ninlawan, C., Seksan, P., Tossapol, K., Pilada, W., The implementation of green supply chain management practices in electronics industry (2010) Proceeding of the International MultiConference of Engineer and Computer Scientists, 3, pp. 1563-1568. , IMECS 2010, 17-19 March 2010, Hong Kong; Olugu, E.-U., Wong, K.-Y., Shaharoun, A.-M., Development of key performance measures for the automobile green supply chain (2010) Resources Conservation and Recycling, 65 (6), pp. 567-579; Pak, S., A review of the literature and a framework for green supply chain management (2013) Proceeding of 2013 IBEA (International Conference on Business, Economics, and Accounting), , March, Bangkok, Thailand; Park-Poaps, H., Rees, K., Stakeholder forces of socially responsible supply chain management orientation (2010) Journal of Business Ethics, 92 (2), pp. 305-322; Pava, M.L., Krausz, J., The association between corporate social-responsibility and financial performance: The paradox of social cost (1996) Journal of Business Ethics, 15 (1), pp. 321-357; Pennington, D.W., Potting, J., Finnveden, G., Lindeijer, E., Jolliet, O., Rydberg, T., Rebitzer, G., Life cycle assessment part 2 current impact assessment practice (2004) Environment International, 30 (5), pp. 721-739; Porter, M.E., Van Der Linde, K., Green and competitive (1995) Harvard Business Review, 73 (5), pp. 120-134; Ramudhin, A., Chaabane, A., Carbon market sensitive sustainable supply chain network design (2010) International Journal of Management Science and Engineering Management, 5 (1), pp. 30-38; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916; Rebitzer, G., Ekvall, T., Frischknecht, R., Hunkeler, D., Norris, G., Rydberg, T., Schmidt, W.-P., Pennington, D.W., Life cycle assessment. Part 1. Framework, goal and scope definition, inventory analysis, and applications (2004) Environmental International, 30 (5), pp. 701-720; Rondinelli, D.A., Berry, M.A., Corporate environmental management and public policy: Bridging the gap (2000) Am Behaviour Science, 44 (2), pp. 168-187; Rothenberg, S., Schenck, B., Maxwell, J., Lessons from benchmarking environmental performance at automobile assembly plants (2005) Benchmarking: An International Journal, 12 (1), pp. 5-15; Saaty, T.L., (2001) Analytic Hierarchy Process - Decision Making for Leaders, 2, p. 315. , AHP Series RWS Publication (new edition), Pittsburgh; Sarkis, J., A strategic decision framework for green supply chain management (2003) Journal of Cleaner Production, 11 (4), pp. 397-409; Sarmiento, R., Thomas, A., Identifying improvement areas when implementing green initiatives using a multitier AHP approach (2010) Benchmarking: An International Journal, 17 (3), pp. 452-463; Sarode, A.D., Bhaskarwar, V.S., Development and evaluation of performance measure for the environmental management in Indian industries (2011) Industrial Engineering Journal, 2 (26), pp. 31-34; Schoeneborn, F., Linking balanced scorecard to system dynamics (2003) Proceeding 21st International Conference of the System Dynamics Society, , New York; Shang, K.C., Lu, C.S., Li, S., A taxonomy of green supply chain management capability among electronic related manufacturing firms in Taiwan (2010) Journal of Environmental Management, 91 (5), pp. 1218-1226; Sharma, M.J., Yu, S.J., Fuzzy analytic hierarchy process-based decision tree in identifying priority attributes for supply chain coordination (2014) International Journal of Logistics Systems and Management, 17 (1), pp. 46-65; Shrivastava, P., Environmental technologies and competitive advantage (1995) Strategic Management Journal, 16 (1), pp. 183-200; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Managing the Supply Chain: The Definitive Guide for the Business Professional, , McGraw-Hill New York; Simpson, D., Power, D., Samson, D., Greening the automotive supply chain: A relationship perspective (2007) International Journal of Operations & Production Management, 27 (1), pp. 28-48; Smith, M., Crotty, J., Environmental regulation and innovation driving ecological design in the UK automotive industry (2008) Business Strategy and Environment, 17 (6), pp. 341-349; Srivastava, S., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 63 (1), pp. 231-241; Szwejczewski, M., Lemke, F., Goffin, K., Manufacturer-supplier relationships: An empirical study of German manufacturing companies (2005) International Journal of Operations and Production Management, 25 (9-10), pp. 875-897; Tachizawa, E.M., Thomsen, C.G., Drivers and sources of supply flexibility: An exploratory study (2007) International Journal of Operations and Production Management, 27 (10), pp. 1115-1136; Tan, A.W.K., Yu, W.S., Kumar, A., Improving the performance of a computer company in supporting its reverse logistics operations in the Asia-Pacific region (2003) International Journal of Physical Distribution and Logistics Management, 33 (1), pp. 59-74; Toke, L.K., Gupta, R.C., Dandekar, M., An empirical study of green supply chain management in Indian perspective (2012) International Journal of Applied Sciences and Engineering Research, 1 (2), pp. 372-383; Tsaia, W.-H., Hung, S.-J., A fuzzy goal programming approach for green supply chain optimisation under activity-based costing and performance evaluation with a value-chain structure (2009) International Journal of Production Research, 47 (18), pp. 4991-5017; Tsoulfas, G., Pappis, C., Environmental principles applicable to supply chains design and operation (2006) Journal of Cleaner Production, 14 (18), pp. 1593-1602; Tsoulfas, G., Pappis, C., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Van Hoek, R.I., Case studies of greening the automotive supply chain through technology and operations (2002) International Journal of Technology Management, 23 (1-3), pp. 89-112; Wang, X., Chan, H.K., Yee, R.W.Y., Diaz-Rainey, I., A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain (2012) International Journal of Production Economics, 135 (2), pp. 595-606; Wang, Y.-F., Chen, S.-P., Lee, Y.-C., Tsai, C.-T., Developing green management standards for restaurants: An application of green supply chain management (2013) International Journal of Hospitality Management, 34 (1), pp. 263-273; Yan, L., Research on the performance measurement of green supply chain management in China (2011) Journal of Sustainable Development, 4 (3), pp. 101-107; Zhang, X., Zhiwei, Z., Study of green supply chain and its performance based on fuzzy AHP and measurement system (2009) International Conference on E-Business and Information System Security, pp. 1-5. , Wuhan, China, 23-24 May; Zhou, F., Study on the implementation of green supply chain management in textile enterprises (2009) Journal of Sustainable Development, 2 (1), pp. 75-79; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) Journal of Operations Management, 22 (1), pp. 265-289; Zhu, Q., Sarkis, J., Lai, K.-H., Green supply chain management: Pressures, practices and performance within the Chinese automobile industry (2007) Journal of Cleaner Production, 15 (11-12), pp. 1041-1052; Zhu, Q., Sarkis, J., Lai, K.-H., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) Journal of Environmental Management, 85 (1), pp. 179-189; Zhu, Q., Sarkis, J., Lai, K.-H., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111 (2), pp. 261-273; Zhu, Q., Sarkis, J., Lai, K.-H., Green supply chain management implications for 'closing the loop' (2008) Transportation Research, Part e, 44 (1), pp. 1-18},
correspondence_address1={Bulsara, H.P.; Management Section, Ichchhanath, India; email: hemantbulsara@gmail.com},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Vugalter2016162,
author={Vugalter, M. and Even, A.},
title={The impact of enhancing a performance measurement system on the Israeli police},
journal={Lecture Notes in Business Information Processing},
year={2016},
volume={243},
pages={162-178},
doi={10.1007/978-3-319-30528-8_10},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961138206&doi=10.1007%2f978-3-319-30528-8_10&partnerID=40&md5=79db4fade6190293070728d2971cfc50},
affiliation={Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, POB 653, Beersheba, 8410501, Israel},
abstract={The Performance Measurement System (PMS) explored in the study was implemented by public police forces, using advanced Business Intelligence (BI) technologies. The study examines the impact of enhancing that PMS, through analysis of metric results over an 8-year period that covered a transition between two major system versions. The analysis results indeed show a significant impact of transitioning to the new PMS in most (75 %) performance metrics. A noticeable impact of the transition is the temporary performance decline, followed by some improvement that can be attributed in part to the redefinition of some metrics. Further, the results confirmed the preliminary assumptions that the improvement in the measured performance is positively and significantly associated with human-resource allocation; however, with some mediation effects of the crime category and the organization unit. © Springer International Publishing Switzerland 2016.},
author_keywords={Business intelligence (BI);  IS adoption;  Key performance indicators (KPI);  Performance measurement systems (PMS)},
keywords={Benchmarking;  Information analysis;  Information systems;  Law enforcement;  Supply chain management, IS adoption;  Key performance indicators;  Performance measurement system;  Performance metrics;  Police forces;  System version, Information management},
references={Bourne, M., Performance management: Learning from the past and projecting the future (2008) Measur. Bus. Excellence, 12, pp. 67-72; Bourne, M., Neely, A., Implementing performance measurements systems: A literature review (2003) Bus. Perform. Manage, 5 (1), pp. 1-24; Brynjolfsson, E., Hitt, L., Kim, H., Strength in numbers: How does data-driven decision-making affect firm performance? (2011) Proceedings of the 32nd International Conference on Information Systems, , Shanghai, China. Paper 13; Yadav, N., Sagar, M., Performance measurement and management frameworks-research trends to the last two decades (2013) Bus. Process Manage. J, 19 (6), pp. 947-970; Vuksic, V., Bach, M., Popvic, A., Supporting PM with business performance and business intelligence: A case analysis of integration and orchestration (2013) Int. J. Inf. Manage, 33, pp. 613-619; Micheli, P., Kennerley, M., Performance measurement frameworks in public and non-profit sectors (2005) Prod. Plann. Control, 16 (2), pp. 125-134; Franco-Santos, M., Bourne, M., An examination of the literature relating to issues affecting how companies manage through measures (2005) Prod. Plann. Control, 16 (2), pp. 114-124; Elbashir, M.Z., Collier, P.A., Davern, M.J., Measuring the effects of business intelligence systems: The relationship between business process and organizational performance (2008) Int. J. Acc. Inf. Syst, 9 (3), pp. 135-153; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: A review of their consequences and a framework for research (2012) Manage. Acc. Res, 23 (2), pp. 79-119; Fisher, T., Business Productivity measurement using standard cost accounting information (1990) Int. J. Oper. Prod. Manage, 10 (8), pp. 61-69; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T., State of the art literature review on performance measurement (2011) Comput. Ind. Eng, 60 (2), pp. 279-290; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) Intl. J. Manage. Rev, 7 (1), pp. 25-47; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) Int. J. Oper. Prod. Manage, 28 (7), pp. 663-686; Neely, A., Gregory, M., Platts, K., Richards, H., Kennerley, M., Bourne, M., Performance measurement system design: Developing and testing a process-based approach (2000) Int. J. Oper. Prod. Manage, 20 (10), pp. 1119-1145; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) Int. J. Oper. Prod. Manage, 31 (12), pp. 1287-1310; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harv. Bus. Rev, 70, pp. 71-79; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measur. Bus. Excellence, 5, pp. 6-13; Norreklit, H., The balance on the balanced scorecard a critical analysis of some of its assumptions (2000) Manage. Acc. Res, 11 (1), pp. 65-88},
correspondence_address1={Even, A.; Department of Industrial Engineering and Management, POB 653, Israel; email: adireven@bgu.ac.il},
editor={Ziemba E.},
publisher={Springer Verlag},
issn={18651348},
isbn={9783319305271},
language={English},
abbrev_source_title={Lect. Notes Bus. Inf. Process.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{DosSantos2016298,
author={Dos Santos, T.F. and Leite, M.S.A.},
title={Performance measurement system in supply chain management: Application in the service sector},
journal={International Journal of Services and Operations Management},
year={2016},
volume={23},
number={3},
pages={298-315},
doi={10.1504/IJSOM.2016.074816},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959335321&doi=10.1504%2fIJSOM.2016.074816&partnerID=40&md5=211df3a2672c76a5302ff90b64a63423},
affiliation={Federal University of Piaui, Campus Universitário Ministro Petrônio Portella, Teresina-PI, 64049-550, Brazil; Federal University of Paraiba, Cidade Universitária, João Pessoa-PB, 58051-970, Brazil},
abstract={This study aims at proposing a performance measurement system (PMS) to assist in the supply chain management. This paper followed these steps: 1) conceptual theoretical structure's definition; 2) focal company choice; 3) mapping of current and beneath supply chains; 4) performance indicators' definition; 5) performance metric's definition; 6) checking for the significance of processes and metrics with AHP. The study proposes a PMS based on the SCOR model and it was applied to a company in the service sector and a supplier. Starting from the conducted analysis it was found that the most important processes in decreasing order were: source, planning, delivering, making and returning. For metrics, the most important were: perfect order fulfillment, stock cost, average answer time to a service request, deliveries carried out without failures, and customer complaint. According to the results, it is possible to settle the most important metrics between focal company and supplier, expecting the relationship's performance to be improved. Copyright © 2016 Inderscience Enterprises Ltd.},
author_keywords={AHP;  Analytic hierarchy process;  Performance measurement;  Performance measurement system;  PMS;  SCOR;  Service sector;  Supply chain;  Supply chain management;  Supply chain performance},
references={Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Beamon, B.M., Measuring supply chain performance (1999) International Journal Operations & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 53-62; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Brignall, S., Ballantine, J., Performance measurement in service businesses revisited (1996) International Journal of Service Industry Management, 7 (1), pp. 6-31; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chan, F.T.S., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (3), pp. 534-548; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The Internacional Journal of Logistics Management, 8 (1), pp. 1-14; Cravens, K., Piercy, N., Cravens, D., Assessing the performance of strategic alliances: Matching metrics to strategies (2000) European Management Journal, 18 (5), pp. 529-541; Estampe, D.K., Lamouri, S., Paris, J., Djelloul, S., A framework ofr analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Gawankar, S., Kamble, S.C., Verma, R., Nasekaran, A., Effect of supply chain management practices on supply chain profitability: An empirical investigation using strctural equation modelling in Indian retail sector (2013) Internacional Journal of Service and Operation Management, 16 (2), pp. 145-173; Giannakis, M., Management of service supply chains with a service oriented reference model: The case of management consulting (2011) Supply Chain Management: An International Journal, 16 (5), pp. 346-361; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain enviroment (2001) Internacional Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hausman, W.H., Supply chain metrics (2002) The Practice of Supply Chain Management: Where Theory and Application Converge, , Springer Science & Business, New York; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 10 (10), pp. 847-868; Huang, H.-C., Lai, M.-C., Lin, L.-H., Developing strategic measurement and improvement for the biopharmaceutical firm: Using the BSC hierarchy (2011) Expert Systems with Applications, 38 (5), pp. 4875-4881; Jajja, M.S.S., Brah, S.A., Hassan, S.Z., Supply chain strategy and organisational performance: Role of core operational fuctions (2014) International Journal of Services and Operations Management, 17 (3), pp. 330-349; Rajagopal, Bridging sales and service quality functions in retaling high - Technology consumer products (2010) International Journal of Service and Operations Management, 7 (2), pp. 177-199. , Dr; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Parasuraman, A., Zeithaml, V.A., Berry, L.L., SERVQUAL: Multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; Parasuraman, A., Zeithaml, V.A., Malhotra, A., E-S-QUAL: A multiple-item scale for assessing electronic service quality (2005) Journal of Service Research, 7 (3), pp. 213-233; Saaty, T.L., How to make a decision: The analytic hierarchy process (1990) European Journal of Operational Research, 48 (1), pp. 9-26; Saaty, T.L., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; Sellitto, M.A., Mendes, L.W., Avaliação comparativa do desempenho de três cadeias de suprimento em manufatura (2006) Produção, 16 (3), pp. 552-568; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) Internacional Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; (2010) Supply-Chain Operations Reference Model: SCOR Version 10.0 Overview [online], , https://supply-chain.org/f/SCOR-Overview-Web.pdf, accessed February 2013; Tezza, R., Bornia, A.C., Vey, I.H., Sistemas de medição de desempenho: Uma revisão e classificação da literatura (2010) Gestão & Produção, 17 (1), pp. 75-93; Uzkurt, C., Customer participation in the service process: A model and research propositions (2010) International Journal of Services and Operations Management, 6 (1), pp. 17-37; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 15-29; Wedding, G.C., Crawford-Brown, D., Measuring site-level success in brownfield redevelopments: A focus on sustainability and green building (2007) Journal of Environmental Management, 85 (2), pp. 483-495; Yang, C.-L., Chuang, S.-P., Huang, R.-H., Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry (2009) Expert Systems with Applications, 36 (8), pp. 11369-11377},
correspondence_address1={Dos Santos, T.F.; Federal University of Piaui, Brazil; email: talitafloriano@gmail.com},
publisher={Inderscience Publishers},
issn={17442370},
language={English},
abbrev_source_title={Int. J. Serv. Oper. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kusrini20161,
author={Kusrini, E. and Subagyo and Masruroh, N.A.},
title={A new approach to design supply chain key performance indicator for actors and regulator: A case study in innovative product in Indonesia},
journal={International Journal of Business Performance Management},
year={2016},
volume={17},
number={1},
pages={1-29},
doi={10.1504/IJBPM.2016.073315},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84948772362&doi=10.1504%2fIJBPM.2016.073315&partnerID=40&md5=2150296ef10ed18e17e18a1713312aa2},
affiliation={Department of Mechanical and Industrial Engineering, Faculty of Engineering, Gadjah Mada University, Jl. Grafika No. 2, Yogyakarta, 55281, Indonesia; Department of Industrial Engineering, Faculty of Industrial Technology, Islamic University of Indonesia, Jl. Kaliurang Km 14.5, Sleman, Yogyakarta, 55584, Indonesia},
abstract={Nowadays, supply chain performance measurements are focused on the performance measurement from the internal side of supply chain actors. Meanwhile, the concern of this issue is becoming important for the government (regulator) especially with regard to public sector policy. The regulator needs to recognise the key indicators to measure the effectiveness of their programs. This paper aims to identify the supply chain key performance indicator based on strategic objectives of supply chain's actors and regulator simultaneously. The integrated model of balanced scorecard (BSC) that combined with supply chain operation reference (SCOR) and regulator contribution were proposed to identify these indicators. This integrated model was validated based on behaviour data of innovative product in Yogyakarta, Indonesia using structural equation modelling (SEM). This research found that there were 31 valid key indicators consisting 19 indicators to measure actors's performance and 12 indicators to measure regulator's performance. Copyright © 2016 Inderscience Enterprises Ltd.},
author_keywords={Balanced scorecard;  BSC;  Indonesia;  Performance measurement;  Regulator;  Regulator's contribution;  SCM;  SCOR;  Supply chain actors;  Supply chain management;  Supply chain operation reference},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: Review and classification (2012) IBIMA Publishing Journal of Organizational Management Studies, pp. 1-20; Anthoula, K., Alexandros, H., Designing a balanced scorecard for the evaluation of a local authority organization (2011) European Research Studies, 14 (2), pp. 65-80; Arbuckle, J.L., (1995) Amos™ 18 User's Guide, , Amos Development Corporation, USA; Baldrige National Quality Program, (2007) Criteria for Performance Excellence, , http://www.baldrige.nist.gov, Administration Building, Gaithersburg [online] (accessed 18 November 2011); Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Barber, E., How to measure the "value" in value chains (2008) International Journal of Physical Distribution & Logistics Management, 38 (9), pp. 685-698; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operation & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18 (8), pp. 666-680; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Bititci, U.S., Mendibil, K., Measuring and managing performance in extended enterprises (2005) International Journal of Operation and Production Management, 25 (4), pp. 333-353; Bolívar, M.P.R., Hernandez, A.M.L., Rodríguez, D.O., Implementing the balanced scorecard in public sector agencies: An experience in municipal sport services (2010) Academia, Revista Latinoamericana de Administración, 45, pp. 116-139; Brewer, P.C., Speh, T.W., Using balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bulinger, H., Kuhner, M., Hoof, A., Analyzing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Carpinetti, L.C.R., Galdámez, E.V.C., Gerolamo, M.C., A measurement system for managing performance of industrial clusters a conceptual model and research cases (2008) International Journal of Productivity and Performance Management, 57 (5), pp. 405-419; Carpinetti, L.C.R., Gerolamo, M.C., Galdámez, E.V.C., Continuous innovation and performance management of SME clusters (2007) Creativity and Innovation Management, 16 (4), pp. 376-385; Chae, K., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advance Manufacturing Technology, 21, pp. 534-548; Chan, Y.-C.L., Performance measurement and adoption of balanced scorecards: A survey of municipal governments in the USA and Canada (2004) The International Journal of Public Sector Management, 17 (3), pp. 204-221; Chang, H.H., An empirical study of evaluating supply chain management integration using balanced score card in Taiwan (2009) The Service Industries Journal, 29 (2), pp. 185-201; Chen, H., Papazafeiropoulou, A., Wu, C., An e-government initiative to support supply chain integration for small to medium sized enterprises: Successes and challenges (2011) The DATA BASE for Advances in Information Systems, 42 (4), pp. 63-80; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains :A framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Espinoza, O., Bond, B.H., Kline, E., Supply chain measures of performance for wood products manufacturing (2010) Forest Production Journal, 60 (7-8), pp. 700-708; Farneti, F., Guthrie, J., Italian and Australian local governments: Balanced scorecard practices: A research note (2008) Journal of Human Resource Costing & Accounting, 12 (1), pp. 4-13; Farris, M.T., Hutchison, P.D., Cash-to-cash: The new supply chain management metric (2002) International Journal of Physical Distribution & Logistics Management, 32 (4), pp. 288-298; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production and Planning Control, 16 (6), pp. 531-544; Georgise, F.B., Thoben, K.D., Seifert, M., Adapting the SCOR model to suit the different scenarios: A literature review & research agenda (2012) International Journal of Business and Management, 7 (6), pp. 1-17; (2007) Value Links Manual-The Methodology of Value Chain Promotion, , http://www2.gtz.de/wbf/4tDx9kw63gma/ValueLinks_Manual.pdf, [online] (accessed 22 July 2012); Gunasekaran, A., Kobu, B., Performance measures and metric in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal Production Economy, 87, pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operation & Production Management, 21 (1-2), pp. 71-87; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , 5th ed., Prentice Hall, Englewood, New Jersey; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: A case study from the packaging industry (2009) Production Planning & Control, 20 (1), pp. 68-81; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management, 9 (1), pp. 23-29. , ABI/INFORM; Hum, S.-H., Parlar, M., Measurement and optimization of supply chain responsiveness (2014) IIE Transactions, 46 (1), pp. 1-22. , Taylor & Francis Group, Philadelphia; Johnsen, A., Balanced scorecard: Theoritical perspectives and public management implications (2001) Managerial Auditing Journal, 16 (6), pp. 319-330; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Keating, B., Managing ethic in tourism supply chain: The case of Chinese travel to Australia (2009) International Journal of Tourism Research, 11, pp. 403-408; Kellogg Foundation, (2004) Using Logic Models to Bring Together Planning, Evaluation, and Action Logic Model Development Guide, pp. 49017-54058. , http://www.wkkf.org, One East Michigan Avenue, East Battle Creek, Michigan [online] (accessed 2 September 2012); Kouzmin, A., Benchmarking and performance measurement in public sectors towards learning for agency effectiveness (1999) The International Journal of Public Sector Management, 12 (2), pp. 121-144; Lambert, D., Stock, J., Ellram, L., (1998) Fundamental of Logistics Management, , McGraw-Hill Company, New York, USA; Lin, W.C., Balanced scorecard and IPA enables public service in township management: Local government performance (2013) Lex Localis-Journal of Local Self-Government, 11 (1), pp. 21-32; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (11-12), pp. 1192-1217; Maheshwarkar, M., Sohani, N., Combined AHP-TOPSIS based approach for the evaluation of knowledge sharing capabilities of supply chain partners (2013) Management Science and Engineering, 7 (3), pp. 27-32; Mann, I.J.S., Murphy, S.A., Kumar, V., Unit of analysis: A case for performance measurement in supply chain management (2009) Journal of Supply Chain Management, 6 (3-4), pp. 41-56; Marayuma, G.M., (1998) Basic of Structural Equation Modeling, , SAGE Publication Inc, California, USA; Marimin, M., Feifi, D., Martini, S., Astuti, R., Suharjito, S., Hidayat, S., Added value and performance analyses of edamame soybean supply chain : A case study (2010) Operational and Supply Chain Management, 3 (3), pp. 148-163; Marthin, P.R., Patterson, J.W., On measuring company within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; Mawardi, M.K., Choi, T., Perera, N., The factors of SME cluster developments in a developing country: The case of Indonesian clusters (2011) ICSB World Conference Proceedings, pp. 1-28. , International Council for Small Business (ICSB), Washington; Millett, S.M., Zelman, S.T., Case study scenario analysis and a logic model of public education in Ohio (2005) Strategy & Leadership, 33 (2), pp. 33-40. , Emerald Group Publishing Limited; Muratoglu, K., Performance measurement of supply chain via balanced scorecard: The case of a brewing group (2008) The Business Review, 10 (1), pp. 330-337; Nakagawa, R., The policy approach in promoting small and medium sized enterprises in Japan (2012) International Business & Economics Research Journal, 11 (10), pp. 1087-1098; Nakano, M., Collaborative forecasting and planning in supply chain: The impact on performance in Japanese manufacturer (2009) International Journal of Physical Distribution & Logistics Management, 39 (2), pp. 84-105; National Performance Management Advisory Commission, (2010) A Performance Management Framework for State and Local Government: From Measurement and Reporting to Management and Improving, , National Performance Management Advisory Commission, 203 North LaSalle Street, Suite 2700 Chicago, Illinois 60601; Neely, A., Adam, C., Kennerly, M., (2002) The Performance Prism-The Scorecard for Measuring and Managing Business Success, , Pearson Education Limited, London; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-11; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operation and Production Management, 25 (12), pp. 1228-1263; Northcott, D., Taulapapa, T.M., Using the balanced scorecard to manage performance in public sector organizations issues and challenges (2012) International Journal of Public Sector Management, 25 (3), pp. 166-191; Olugu, E.U., Wong, K.Y., Supply chain performance evaluation: Trends and challenges (2009) American Journal of Engineering and Applied Sciences, 2 (1), pp. 202-211; Park, J.H., Lee, J.S., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14, pp. 335-346; Parrilli, M.D., Collective efficiency, policy inducement and social embeddedness: Drivers for the development of industrial district (2009) Entrepreneurship & Regional Development, 21 (1), pp. 1-24; Parrilli, M.D., Sacchetti, S., Linking learning with governance in networks and clusters: Key issues for analysis and policy (2008) Entrepreneurship & Regional Development, 20 (4), pp. 37-408. , July; Pohlen, T.L., Coleman, B.J., Evaluating internal operations and supply chain performance using EVA and ABC (2005) S.A.M. Advanced Management Journal, 70 (2), pp. 45-58. , ABI/INFORM Global; Porter, M., Cluster and the new economics of competition (1998) Harvard Business Review, 7 (6), pp. 6-15; Rusko, R.T., Kylänen, M., Saari, R., Supply chain in tourism destinations: The case of Levi resort in Finnish Lapland (2009) International Journal of Tourism Research, 11, pp. 71-87; Saiz, J.J.A., Bas, A.O., Rodriguez, R.R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; (2001) Supply Chain Operation Reference Model, , http://www.supply-chain.org, [online] (accessed 14 December 2010); Tambunan, T., Promoting small and medium enterprises with a clustering approach: A policy experience from Indonesia (2005) Journal of Small Business Management, 43 (2), pp. 138-154; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Theeranuphattana, A., Tang, J.C.S., A conceptual model of performance measurement for supply chains alternative considerations (2008) Thailand Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Zulkiffli, S.N.A., The impact of supply chain operational capabilities on business performance of small and medium enterprises in Malaysia: A preliminary analysis (2010) International Journal of Business and Management Science, 3 (2), pp. 147-168},
correspondence_address1={Kusrini, E.; Department of Mechanical and Industrial Engineering, Jl. Grafika No. 2, Indonesia; email: elisakusrini@uii.ac.id},
publisher={Inderscience Publishers},
issn={13684892},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Ahmad201564,
author={Ahmad, A.R. and Kim Soon, N.},
title={Balanced scorecard in higher education institutions: What should be consider?},
journal={2nd International Symposium on Technology Management and Emerging Technologies, ISTMET 2015 - Proceeding},
year={2015},
pages={64-68},
doi={10.1109/ISTMET.2015.7359002},
art_number={7359002},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84962326717&doi=10.1109%2fISTMET.2015.7359002&partnerID=40&md5=df88ae0a88351314a43d3f72d1fccba5},
affiliation={Faculty of Technology Management and Business, Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia},
abstract={The purpose of this concept paper is to explore factors influence BSC implementation in higher education institutions. Changes in educational systems in today's competitive global environment have prompted several countries to restructure their strategy aligns with the demands for more accountability through financial planning and actual performance. In general, the BSC perspective includes of financial, customer, internal business process and learning and growth perspective. BSC is an innovative performance management system that has been widely applied in large organizations in the world. In fact, Higher education institutions are already using this performance measurement system effectively. BSC may assist organization to monitor the overall performance while simultaneously monitoring the excellent results. © 2015 IEEE.},
author_keywords={Balanced Scorecard;  key performance indicators;  performance management system},
keywords={Benchmarking;  Industrial management;  Planning;  Societies and institutions;  Supply chain management, Balanced scorecards;  Educational systems;  Higher education institutions;  Innovative performance;  Key performance indicators;  Large organizations;  Performance management systems;  Performance measurement system, Education},
references={Al-Zwyalif, I.M., The Possibility of Implementing Balanced Scorecard in Jordanian Private Universities (2012) International Business Research, 5 (11), pp. 113-120; Ambras, A., Tamošinas, T., Institution strategic management according to balanced scorecard: Šiauliai university social sciences faculty case (2010) The Th International Scientific Conference Business and Management2010, pp. 529-537. , Selected Papers; Biden, W., Mziu, H., Suhaimi, M.A., Employing the Balanced Scorecard (BSC) to Measure Performance in Higher Education: Malaysia (2014) International Journal of Information and Communication Technology Research, 4 (1), pp. 38-44; Carlotta Del Sordoa, R.L., (2012) Assessing Global Performance in Universities: An Application of Balance Scorecard, pp. 4793-4797; Farid, D., Nejati, M., Mirfakhredini, H., Balanced scorecard application in universities and higher education institutes: Implementation guide in an Iranian context (2008) Annals of University of Bucharest, Economic and Administrative Series, 2, pp. 31-45; Karuhanga, B.N., Werner, A., Challenges impacting performance management implementation in public universities: A case of Uganda (2013) African Journal of Economic and Management Studies, 4 (2), pp. 223-243; Kassahun, T., (2010) Rethinking Institutional Excellence in Ethiopia: Adapting and Adopting the Balanced Scorecard (BSC) Model, 2 (1), pp. 22-53; Karpagam, P.L., Suganthi, L.L., A strategy map of balanced scorecard in academic institutions for performance improvement (2012) IUP Journal of Business Strategy, 9 (3), pp. 7-16; Sayed, N., Ratify, reject or revise: Balanced scorecard and universities (2013) International Journal of Educational Management, 27 (3), pp. 203-220; Sudirman, I., Implementing balanced scorecard in higher education management: Hasanuddin university of Indonesia (2012) International Journal of Business and Social Science, 3 (18), pp. 199-204; Taylor, J., Baines, C., Performance management in UK universities: Implementing the Balanced Scorecard (2012) Journal of Higher Education Policy & Management, 34 (2), pp. 111-124; Wu, H.-Y., Performance evaluation of extension education centers in universities based on the balanced scorecard (2011) Evaluation and Program Planning, 34 (1), pp. 37-50; Zangoueinezhad, A., Moshabaki, A., Measuring university performance using a knowledgebased balanced scorecard (2011) International Journal of Productivity and Performance Management, 60 (8), pp. 824-843},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781479917235},
language={English},
abbrev_source_title={Int. Symp. Technol. Manag. Emerg. Technol., ISTMET},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Chow201585,
author={Chow, P.-S. and Wang, Y. and Choi, T.-M. and Shen, B.},
title={An experimental study on the effects of minimum profit share on supply chains with markdown contract: Risk and profit analysis},
journal={Omega (United Kingdom)},
year={2015},
volume={57},
pages={85-97},
doi={10.1016/j.omega.2013.11.007},
note={cited By 24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84941099051&doi=10.1016%2fj.omega.2013.11.007&partnerID=40&md5=80d14bec8eba26e7d1fa4be07b0a7d02},
affiliation={Business Division, Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; Department of Logistics and Maritime Studies, Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; Glorious Sun School of Business and Management, Donghua University, Yan An Road (West) 1882, Shanghai, 200051, China},
abstract={Supply chain performance measures include both profit and risk. In this study, we examine the effect of retailers' minimum profit share concerns on supply chain system performance through laboratory experimental and analytical modeling approaches. In the experiment, each retailer's minimum profit share, which partially reflects her self-serving fairness concern, is measured with a parameter defined as the minimum profit share ratio (MPSR), which is the ratio of the retailer's profit to the whole supply chain profit. We specifically consider a two-stage supply chain in which a supplier offers a take-it-or-leave-it markdown contract to a retailer who has an MPSR concern. In our laboratory experiment, the role of the supplier is played by human subjects who are practitioners in the fashion industry; to ensure that the MPSR concept is fully implemented, the role of the retailer is played by the computer. Mirroring the observed industrial practice, the markdown price is defined as a fixed percentage of the wholesale price, and the supplier needs to decide on a wholesale price. Our empirical results show that when the MPSR increases, the supplier's average profit and absolute risk decreases, whereas those of the retailer increase. As for the whole supply chain, our experiments suggest there is an inverse U-shaped relationship between the supply chain profit and the MPSR; thus the presence of an MPSR concern leads to a higher supply chain risk (both in absolute and relative terms). We also observe that when the retailer tends to split the supply chain profit equally with the supplier (MPSR=0.5; in this case, neither party faces disadvantageous inequality), the whole supply chain achieves the best performance, and the supply chain profit is close to the theoretically optimal one (the centralized supply chain profit). In other words, a fair retailer helps to create a sense of cooperation between the supplier and herself. © 2014 Elsevier Ltd.},
author_keywords={Behavioral experiment;  Markdown contract;  Minimum profit share ratio (MPSR);  Multi-methodological study;  Self-serving fairness;  Supply chain risk management},
references={Adams, J.S., Inequity in social exchange (1965) Advances in Experimental Social Psychology, 2, pp. 267-299. , Academic Press, New York, London; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bernstein, F., Marx, L., Reservation profit levels and the division of supply chain profit, Working paper, Duke University (2005); Binmore, K., Morgan, P., Shaked, A., Sutton, J., Do people exploit their bargaining power? An experimental study (1991) Games and Economic Behavior, 3, pp. 295-322; Bolton, G.E., Ockenfels, A., ERC: a theory of equity, reciprocity and competition (2000) American Economic Review, 90, pp. 166-193; Cachon, G., Supply chain coordination with contracts. Handbooks in operations research and management science: supply chain management (2003), editor Steve Gravesand Ton de Kok, North Holland; Cachon, G., Kok, G., Competing manufacturers in a retail supply chain: on contractual form and coordination (2010) Management Science, 56 (3), pp. 571-589; Carr, A.S., Pearson, J.N., Strategically managed buyer-seller relationships and performance outcomes (1999) Journal of Operations Management, 17 (5), pp. 497-519; Chiu, C.H., Choi, T.M., Supply chain risk analysis with mean-variance models: a technical review (2013) Annals of Operations Research; Choi, T.M., Li, D., Yan, H.M., Mean-variance analysis of a single supplier and retailer supply chain under a returns policy (2008) European Journal of Operational Research, 184 (1), pp. 356-376; Choi, T.Y., Hartley, J.L., An exploration of supplier selection practices across the supply chain (1996) Journal of Operations Management, 14 (4), pp. 333-343; Corbett, C.J., Fransoo, J.C., Entrepreneurs and newsvendors: do small businesses follow the newsvendor logic when making inventory decisions? (2007), Working paper, University of California, Los Angeles; Cui, T.H., Raju, J.S., Zhang, Z.J., Fairness and channel coordination (2007) Management Science, 53 (8), pp. 1303-1314; Dahl, G.B., Ransom, M.R., Does where you stand depend on where you sit? Tithing donations and self-serving beliefs (1999) American Economic Review, 89, pp. 703-727; Davis, A.M., Katok, E., Santamaria, N., Push, pull, or both? A behavioral study of inventory risk on channel efficiency (2013) Management Science, 60 (11), pp. 2666-2683; (2012), http://www.dc-nice.com/en/about_designic/join_us/index.asp, Join in Desinice;; Fehr, E., Schmidt, K.M., A theory of fairness, competition and cooperation (1999) Quarterly Journal of Economics, 114 (3), pp. 817-868; Feng, T., Keller, L.R., Zheng, X., Decision making in the newsvendor problem: a cross-national laboratory study (2011) OMEGA-The International Journal of Management Science, 39 (1), pp. 41-50; Haruvy, E., Katok, E., Pavlov, V., Can coordinating contracts improve channel efficiency? (2012), Working paper, the University of Texas at Dallas; Ho, T.H., Su, X.M., Wu, Y.Z., Distributional and peer-induced fairness in supply chain contract design (2012), Working paper. University of California, Berkeley; Hong, Z., Lee, C.K.M., Nie, X., Proactive and reactive purchasing planning under dependent demand, price, and yield risks (2013), OR Spectrum, published online; Kaplan, T.R., Ruffle, B.J., Self-serving bias (1998) Journal of Economic Perspectives, 12, pp. 243-244; Katok, E., Olsen, T., Pavlov, V., Wholesale pricing under mild and privately known concerns for fairness (2012) Production and Operations Management Production and Operations Management, forthcoming, 23 (2), pp. 285-302; Katok, E., Pavlov, V., Fairness in supply chain contracts: a laboratory study (2013) Journal of Operations Management, 31 (3), pp. 129-137; Katok, E., Wu, D.Y., Contracting in supply chains: a laboratory investigation (2009) Management Science, 55 (12), pp. 1953-1968; Kalkanci, B., Chen, K.Y., Erhun, F., Contract complexity and performance under asymmetric demand information: an experimental evaluation (2011) Management Science, 57 (4), pp. 689-704; Keser, C., Paleologo, G., Experimental investigation of retailer-supplier contracts: the wholesale price contract (2004), Working paper. IBM Research, New York; Lau, H., Lau, A.H.L., Manufacturer's pricing strategy and return policy for a single-period commodity (1999) European Journal of Operational Research, 116 (2), pp. 291-304; Levy, M., Grewal, D., Kopalle, P.K., Hess, J.D., Emerging trends in retail pricing practice: implications for research (2004) Journal of Retailing, 80, pp. xiii-xxi; Li, X., Li, Y., Cai, X., Double marginalization and coordination in the supply chain with uncertain supply (2013) European Journal of Operational Research, 226, pp. 228-236; Li, Y., Liu, Y., Li, M., Wu, H., Transformational offshore outsourcing: empirical evidence from alliances in China (2008) Journal of Operations Management, 26 (2), pp. 257-274; (2012), http://www.livex.com.cn/?page=join, Join in Livex; Milgrom, P., North, D., Weingast, B., The role of institutions in the revival of trade: the law merchant, private judges, and the champagne fairs (1990) Economics and Politics, 2 (1), pp. 1-23; Mukhopadhyay, S.K., Zhu, X., Yue, X., Optimal contract design for mixed channels under information asymmetry (2008) Production and Operations Management, 17 (6), pp. 641-650; Narasimhan, R., Nair, A., Griffith, D.A., Arlbjørn, J.S., Bendoly, E., Lock-in situations in supply chains: a social exchange theoretic study of sourcing arrangements in buyer-supplier relationships (2009) Journal of Operations Management, 27 (5), pp. 374-389; Nowak, M.A., Page, K.M., Sigmund, K., Fairness versus reason in the ultimatum game (2000) Science, 289 (5485), pp. 1773-1775; Olson, D.L., Wu, D.D., Risk management models for supply chain: a scenario analysis of outsourcing to China (2011) Supply Chain Management, 16 (6), pp. 401-408. , (An International Journal); Pavlov, V., Katok, E., Fairness and coordination failures in supply chain contracts (2012), working paper, University of Texas at Dallas; Primo, M.A.M., Amundson, S.D., An exploratory study of the effects of supplier relationships on new product development outcomes (2002) Journal of Operations Management, 20 (1), pp. 33-52; Ren, Y., Croson, R., Overconfidence in newsvendor orders: Ananexperimental study (2013) Management Science, 59 (11), pp. 2502-2517; Rozhon, T., (2005) First the markdown, then the showdown, p. 25. , New York Times, (February); Scheer, L.K., Kumar, N., Steenkamp, J.B.E.M., Reactions to perceived in equity in U.S. and Dutch interorganizational relationships (2003) Academy of Management Journal, 46 (3), pp. 303-316; Schiffels, S., Fügener, A., Kolischa, R., Brunner, O.J., (2014) On the assessment of costs in a newsvendor environment: insights from an experimental study, 43, pp. 1-8; Schweitzer, M., Cachon, G., Decision bias in the newsvendor problem: experimental evidence (2000) Management Science, 46 (3), pp. 404-420; Shen, B., Choi, T.M., Wang, Y., Lo, C.K.Y., The coordination of fashion supply chains with a risk averse supplier under the markdown money policy (2013) IEEE Transactions on Systems, Man, and Cybernetics: Systems, 42 (3), pp. 266-276; Siegel, S., (1956) Nonparametric statistics for behavioral sciences, , McGraw-Hill, New York; Tomlin, B., On the value of mitigation and contingency strategies for managing supply chain disruption risks (2006) Management Science, 52 (5), pp. 639-657; Tsay, A.A., Managing retail channel overstock: markdown money and return policy (2001) Journal of Retailing, 77 (4), pp. 457-492; Wu, D.D., Olson, D., Enterprise risk management: a DEA VaR approach in vendor selection (2010) International Journal of Production Research, 48 (16), pp. 4919-4932; Wu, D., Olson, D.L., Enterprise risk management: coping with model risk in a large bank (2010) Journal of the Operational Research Society, 61 (2), pp. 179-190; Wu, D., Olson, D.L., Birge, J.R., Introduction to special issue on "Enterprise risk Management in Operations" (2011) International Journal of Production Economics, 134 (1), pp. 1-2; Zhang, M.L., Revolution! Revolution! The era of zero inventory is coming, assortment upgrades (2010), Garment Dealers. July 7th; Zhu, X., Mukhopadhyay, S.K., Yue, X., Role of forecast effort on supply chain profitability under various information sharing scenarios (2011) International Journal of Production Economics, 129 (2), pp. 284-291},
correspondence_address1={Shen, B.; Glorious Sun School of Business and Management, Donghua University, Yan An Road (West) 1882, China},
publisher={Elsevier Ltd},
issn={03050483},
coden={OMEGA},
language={English},
abbrev_source_title={Omega},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tajbakhsh201574,
author={Tajbakhsh, A. and Hassini, E.},
title={A data envelopment analysis approach to evaluate sustainability in supply chain networks},
journal={Journal of Cleaner Production},
year={2015},
volume={105},
pages={74-85},
doi={10.1016/j.jclepro.2014.07.054},
note={cited By 121},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028199087&doi=10.1016%2fj.jclepro.2014.07.054&partnerID=40&md5=ae87ca2cba47b29c55f80ba9544166a9},
affiliation={DeGroote School of Business, McMaster University, Hamilton, ON  L8S 4L8, Canada},
abstract={Benefiting from a performance measurement system is an inevitable necessity for any supply chain management to direct the business operations towards the maximal efficiency. In particular, there has been an increasing interest in incorporating sustainability practices evaluation in supply chain operations in the last years due to various stakeholders' requirements. The sustainability paradigm calls for balancing economic, environmental, and social needs. This paper focuses on the evaluation of supply chain operations that maximize economic returns, minimize environment impacts, and meet social expectations. In order to design a supervisory framework to measure the performance of these operations, this study develops a multi-stage data envelopment analysis model that is apt to evaluate the sustainability of a chain of business partners. In addition, the proposed mathematical model is applied to two case studies; one in the manufacturing sector and the other in the banking sector. © 2014 Elsevier Ltd. All rights reserved.},
author_keywords={Centralized framework;  Multi-stage data envelopment analysis;  Performance measurement;  Sustainable supply chain management},
keywords={Data envelopment analysis;  Sustainable development, Centralized framework;  Data envelopment analysis models;  Multi stage;  Performance measurement system;  Performance measurements;  Supply chain operation;  Sustainability practices;  Sustainable supply chains, Supply chain management},
references={Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) J. Clean. Prod., 52, pp. 329-341; Amirteimoori, A., Khoshandam, L., A data envelopment analysis approach to supply chain efficiency (2011) Adv. Decis. Sci., 2011, pp. 1-8; Aoki, S., Naito, A., Gejima, R., Inoue, K., Tsuji, H., Data envelopment analysis for a supply chain (2010) Artif. Life Robotics, 15 (2), pp. 171-175; Bai, C., Sarkis, J., Performance measurement and evaluation for sustainable supply chains using rough set and data envelopment analysis (2012) Sustain. Supply Chains, pp. 223-241; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Manag. Sci., 30 (9), pp. 1078-1092; Banker, R.D., Morey, R.C., Efficiency analysis for exogenously fixed inputs and outputs (1986) Oper. Res., 34 (4), pp. 513-521; Belu, C., Ranking corporations based on sustainable and socially responsible practices. A data envelopment analysis (DEA) approach (2009) Sustain. Dev., 17 (4), pp. 257-268; Blancard, S., Hoarau, J.F., A new sustainable human development indicator for small island developing states: A reappraisal from data envelopment analysis (2013) Econ. Model., 30, pp. 623-635; Castelli, L., Pesenti, R., Ukovich, W., DEA-like models for the efficiency evaluation of hierarchically structured units (2004) Eur. J. Oper. Res., 154 (2), pp. 465-476; Cetinkaya, B., Cuthbertson, R., Ewer, G., Klaas-Wissing, T., Piotrowicz, W., Tyssen, C., (2011) Sustainable Supply Chain Management: Practical Ideas for Moving Towards Best Practice, , Springer Germany; Chang, D.S., Kuo, L.C.R., Chen, Y.T., Industrial changes in corporate sustainability performance - An empirical overview using data envelopment analysis (2013) J. Clean. Prod., 56, pp. 147-155; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Eur. J. Oper. Res., 2 (6), pp. 429-444; Charnes, A., Cooper, W.W., Rhodes, E., Short communication: Measuring the efficiency of decision making units (1979) Eur. J. Oper. Res., 3 (4), p. 339; Charnes, A., Cooper, W.W., Rhodes, E., Evaluating program and managerial efficiency: An application of data envelopment analysis to program follow through (1981) Manag. Sci., 27 (6), pp. 668-697; Chen, Y., Liang, L., Yang, F., A DEA game model approach to supply chain efficiency (2006) Ann. Oper. Res., 145 (1), pp. 5-13; Chen, Y., Liang, L., Yang, F., Zhu, J., Evaluation of information technology investment: A data envelopment analysis approach (2006) Comput. Oper. Res., 33 (5), pp. 1368-1379; Chen, Y., Liang, L., Zhu, J., Equivalence in two-stage DEA approaches (2009) Eur. J. Oper. Res., 193 (2), pp. 600-604; Chen, Y., Zhu, J., Measuring information technology's indirect impact on firm performance (2004) Inf. Technol. Manag., 5 (1-2), pp. 9-22; Chen, C., Zhu, J., Yu, J.Y., Noori, H., A new methodology for evaluating sustainable product design performance with two-stage network data envelopment analysis (2012) Eur. J. Oper. Res., 221 (2), pp. 348-359; Chilingerian, J.A., Sherman, H.D., Health-care applications: From hospitals to physicians, from productive efficiency to quality frontiers (2011) Handbook on Data Envelopment Analysis, pp. 445-493. , 5(1-2); Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning and Operation, , Pearson Higher Education USA; Cook, W.D., Hababou, M., Sales performance measurement in bank branches (2001) Omega, 29 (4), pp. 299-307; Cook, W.D., Hababou, M., Tuenter, H.J., Multicomponent efficiency measurement and shared inputs in data envelopment analysis: An application to sales and service performance in bank branches (2000) J. Prod. Anal., 14 (3), pp. 209-224; Cook, W.D., Liang, L., Zhu, J., Measuring performance of two-stage network structures by DEA: A review and future perspective (2010) Omega, 38 (6), pp. 423-430; Cook, W.D., Seiford, L.M., Data envelopment analysis (DEA) - Thirty years on (2009) Eur. J. Oper. Res., 192 (1), pp. 1-17; Cooper, W.W., Seiford, L.M., Tone, K., (2007) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-solver Software, , Springer USA; Elkington, J., (1997) Cannibals with Forks: The Triple Bottom Line of 21st Century Business, , Capstone UK; Emrouznejad, A., Parker, B.R., Tavares, G., Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA (2008) Socio Econ. Plan. Sci., 42 (3), pp. 151-157; Färe, R., Grosskopf, S., Whittaker, G., (2007) Network DEA. Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, 209-240; Farrell, M.J., The measurement of productive efficiency (1957) J. R. Stat. Soc. Ser. Gen., 120 (3), pp. 253-290; Golany, B., Hackman, S.T., Passy, U., An efficiency measurement framework for multi-stage production systems (2006) Ann. Oper. Res., 145 (1), pp. 51-68; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Kao, C., Efficiency decomposition in network data envelopment analysis: A relational model (2009) Eur. J. Oper. Res., 192 (3), pp. 949-962; Kao, C., Efficiency measurement for parallel production systems (2009) Eur. J. Oper. Res., 196 (3), pp. 1107-1112; Kao, C., Hwang, S.N., Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan (2008) Eur. J. Oper. Res., 185 (1), pp. 418-429; Koopmans, T., (1951) Activity Analysis of Production and Allocation, , John Wiley & Sons USA; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Manage. Rev., 33 (3), pp. 65-73; Lewis, H.F., Sexton, T.R., Network DEA: Efficiency analysis of organizations with complex internal structure (2004) Comput. Oper. Res., 31 (9), pp. 1365-1410; Liang, L., Cook, W.D., Zhu, J., DEA models for two-stage processes: Game approach and efficiency decomposition (2008) Nav. Res. Logist., 55 (7), pp. 643-653; Liang, L., Yang, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Ann. Oper. Res., 145 (1), pp. 35-49; Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) J. Bus. Logist., 22 (2), pp. 1-25; Mincer, J., The color of money: Sustainability has become more than a buzzword among corporations. It has become smart business (2008) Wall Str. J., , http://online.wsj.com/article/SB122305414262702711.html, Retrieved from this online source; Mirhedayatian, S.M., Azadi, M., Farzipoor Saen, R., A novel network data envelopment analysis model for evaluating green supply chain management (2014) Int. J. Prod. Econ., 147, pp. 544-554; Nouri, J., Hosseinzadeh Lotfi, F., Atabi, F., Sadeghzadeh, S.M., Moghaddas, Z., An analysis of the implementation of energy efficiency measures in the vegetable oil industry of Iran: A data envelopment analysis approach (2013) J. Clean. Prod., 52, pp. 84-93; Paradi, J.C., Rouatt, S., Zhu, H., Two-stage evaluation of bank branch efficiency using data envelopment analysis (2011) Omega, 39 (1), pp. 99-109; Pareto, V., (1927) Manuel d'Economie Politique. Paris, France; Qingmin, Y., Lipeng, W., A study on green supply chain achievements evaluation based on DEA model (2009) Management and Service Science, International Conference, pp. 1-3. , IEEE; Saisana, M., Tarantola, S., (2002) State-of-the-Art Report on Current Methodologies and Practices for Composite Indicator Development, , European Commission, Joint Research Centre, Institute for the Protection and the Security of the Citizen Italy; Sarica, K., Or, I., Efficiency assessment of Turkish power plants using data envelopment analysis (2007) Energy, 32 (8), pp. 1484-1499; Sarkis, J., The adoption of environmental and risk management practices: Relationships to environmental performance (2006) Ann. Oper. Res., 145 (1), pp. 367-381; Sarkis, J., Weinrach, J., Using data envelopment analysis to evaluate environmentally conscious waste treatment technology (2001) J. Clean. Prod., 9 (5), pp. 417-427; Seiford, L.M., Zhu, J., Profitability and marketability of the top 55 US commercial banks (1999) Manag. Sci., 45 (9), pp. 1270-1288; Sexton, T.R., Lewis, H.F., Two-stage DEA: An application to major league baseball (2003) J. Prod. Anal., 19 (2-3), pp. 227-249; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) Int. J. Prod. Perform. Manag., 55 (3-4), pp. 242-258; Sueyoshi, T., Goto, M., Environmental assessment by DEA radial measurement: US coal-fired power plants in ISO (Independent System Operator) and RTO (Regional Transmission Organization) (2012) Energy Econ., 34 (3), pp. 663-676; Tajbakhsh, A., Hassini, E., Performance measurement of sustainable supply chains: A review and research questions (2013) Int. J. Prod. Perform. Manag., , (under the third review); Talluri, S., Baker, R.C., A multi-phase mathematical programming approach for effective supply chain design (2002) Eur. J. Oper. Res., 141 (3), pp. 544-558; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) Int. J. Prod. Res., 40 (16), pp. 4257-4269; Troutt, M.D., Ambrose, P.J., Chan, C.K., Optimal throughput for multistage input-output processes (2001) Int. J. Oper. Prod. Manag., 21 (1-2), pp. 148-158; United Nations, (2012) A Guidebook to the Green Economy, Issue 1, , United Nations, Division for Sustainable Development USA; United Nations, (2012) A Guidebook to the Green Economy, Issue 2, , United Nations, Division for Sustainable Development USA; United Nations, (2012) A Guidebook to the Green Economy, Issue 3, , United Nations, Division for Sustainable Development USA; Vázquez-Rowe, I., Villanueva-Rey, P., Iribarren, D., Teresa Moreira, M., Feijoo, G., Joint life cycle assessment and data envelopment analysis of grape production for vinification in the Rias Baixas appellation (NW Spain) (2012) J. Clean. Prod., 27, pp. 92-102; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Ind. Manag. Data Syst., 107 (3), pp. 361-381; Xue, Y., Performance evaluation of green supply chain (2010) E-Business and Information System Security, International Conference, pp. 1-4. , IEEE; Yang, F., Wu, D., Liang, L., Bi, G., Wu, D.D., Supply chain DEA: Production possibility set and performance evaluation model (2011) Ann. Oper. Res., 185 (1), pp. 195-211; Zhang, B., Bi, J., Fan, Z., Yuan, Z., Ge, J., Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach (2008) Ecol. Econ., 68 (1), pp. 306-316; Zhou, P., Ang, B.W., Poh, K.L., A survey of data envelopment analysis in energy and environmental studies (2008) Eur. J. Oper. Res., 189 (1), pp. 1-18; Zhu, J., Multi-factor performance measure model with an application to Fortune 500 companies (2000) Eur. J. Oper. Res., 123 (1), pp. 105-124; Zhu, J., (2009) Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, , Springer USA},
correspondence_address1={Tajbakhsh, A.; DeGroote School of Business, Canada; email: alirezt@mcmaster.ca},
publisher={Elsevier Ltd},
issn={09596526},
coden={JCROE},
language={English},
abbrev_source_title={J. Clean. Prod.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Seo2015292,
author={Seo, Y.-J. and Dinwoodie, J. and Roe, M.},
title={Measures of supply chain collaboration in container logistics},
journal={Maritime Economics and Logistics},
year={2015},
volume={17},
number={3},
pages={292-314},
doi={10.1057/mel.2014.26},
note={cited By 20},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939165824&doi=10.1057%2fmel.2014.26&partnerID=40&md5=3084fab2bca7791ec9ace2c4c74fa560},
affiliation={International Shipping and Logistics Group, Faculty of Business, Plymouth Graduate School of Management, Plymouth University, MAST House, Drake Circus, Plymouth, PL4 8AA, United Kingdom},
abstract={Why do container ports and related maritime logistics organisations collaborate? Despite increasing research into supply chain collaboration (SCC), few valid and reliable instruments are available to measure accurately and objectively the multiple dimensions of SCC in maritime logistics contexts. This research aims to introduce SCC into maritime logistics as an intermediary supply chain process. It adopts comprehensive, rigorous and systematic procedures including item review, Q-sorting, pilot testing and confirmatory factor analysis (CFA) to develop and validate SCC measures. Practical instruments comprised 23 measures derived from a large-scale survey of 178 South Korean maritime logistics professionals. Empirical results indicate that a proposed model of SCC is statistically consistent, reliable and valid. Information sharing, knowledge creation, goal similarity, decision harmonisation and joint supply chain performance measurement were identified as factors that underpin SCC between ports and port users such as shipping lines, inland transport companies, freight forwarders, ship management companies and third-party logistics companies. This work extends the concept of SCC into maritime logistics, and offers managers insights into a self-diagnostic tool to shape effective collaborative relationships. © 2015 Macmillan Publishers Ltd.},
author_keywords={containers;  maritime logistics;  measurement development;  port supply chain;  supply chain collaboration},
references={Anderson, J.C., Gerbing, D.W., Structural equation modeling in practice: A review and recommended two-step approach (1988) Psychological Bulletin, 103 (3), pp. 411-423; Angeles, R., Nath, R., Partner congruence in electronic data interchange (EDI) enabled relationships (2001) Journal of Business Logistics, 22 (2), pp. 109-127; Armstrong, J.S., Overton, T.S., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Bagozzi, R.P., Yi, Y., Phillips, L.W., Assessing construct validity in organizational research (1991) Administrative Science Quarterly, 36 (3), pp. 421-458; Bichou, K., Gray, R., A logistics and supply chain management approach to port performance measurement (2004) Maritime Policy & Management, 31 (1), pp. 47-67; Carbone, V., De Martino, M., The changing role of ports in supply-chain management: An empirical analysis (2003) Maritime Policy & Management, 30 (4), pp. 305-320; Carbone, V., Gouvernal, E., (2007) Supply Chain and Supply Chain Management: Appropriate Concepts for Maritime Studies, pp. 11-26. , In: J. Wang., D. Olivier., T. Notteboom and B. Slack (eds.) Ports, Cities, and Global Supply Chains. Aldershot, UK: Ashgate; Cheng, T.C.E., Choy, P.W.C., Measuring success factors of quality management in the shipping industry (2007) Maritime Economics & Logistics, 9 (3), pp. 234-253; Cheng, T.C.E., Choy, P.W.C., A study of the relationships between quality management practices and organizational performance in the shipping industry (2013) Maritime Economics & Logistics, 15 (1), pp. 1-31; Corbett, C.J., Blackburn, J.D., Van Wassenhove, L.V., Partnerships to improve supply chains (1999) Sloan Management Review, 40 (4), pp. 71-82; De Martino, M., Morvillo, A., Activities, resources and inter-organizational relationships: Key factors in port competitiveness (2008) Maritime Policy & Management, 35 (6), pp. 571-589; Ferrari, C., Parola, F., Morchio, E., Southern European ports and the spatial distribution of EDCs (2006) Maritime Economics & Logistics, 8 (1), pp. 60-81; Fornell, C., Larcker, D.F., Structural equation models with unobservable variables and measurement errors: Algebra and statistics (1981) Journal of Marketing Research, 18 (3), pp. 382-388; Frankel, E., The economics of total trans-ocean supply chain management (1999) International Journal of Maritime Economics, 1 (1), pp. 61-69; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2010) Multivariate Data Analysis, , New York: Prentice Hall; Harland, C., Zheng, J., Johnsen, T., Lamming, R., A conceptual model for researching the creation and operation of supply networks (2004) British Journal of Management, 15 (1), pp. 1-21; Heaver, T.D., Coordination in multi-actor logistics operations: Challenges at the port interface (2011) Integrating Seaports and Trade Corridors, pp. 155-170. , In: P. Hall, R.J. McCalla, C. Comtois and B. Slack (eds.). Surrey, UK: Ashgate; Heaver, T.D., (2014) Increased Collaborative Relationships in International Logistics: Canadian and Other National and Corporate Examples, , Maritime Policy & Management Forthcoming; Islam, D.M.Z., Dinwoodie, J., Roe, M., Towards supply chain integration through multimodal transport in developing economies: The case of Bangladesh (2005) Maritime Economics & Logistics, 7 (4), pp. 382-399; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lee, T.-W., Park, N.-K., Lee, D.-W., A simulation study for the logistics planning of a container terminal in view of SCM (2003) Maritime Policy & Management, 30 (3), pp. 243-254; Marsh, H.W., Hocevar, D., Application of confirmatory factor analysis to the study of selfconcept: First and higher-order factor models and their invariance across groups (1985) Psychological Bulletin, 97 (3), pp. 562-582; Min, S., Roath, A.S., Daugherty, P.J., Genchev, S.E., Chen, H., Arndt, A.D., Supply chain collaboration: What's happening? (2005) The International Journal of Logistics Management, 16 (2), pp. 237-256; Nam, H.-S., Song, D.-W., Defining maritime logistics hub and its implication for container port (2011) Maritime Policy & Management, 38 (3), pp. 269-292; Paixão, A.C., Marlow, P.B., Fourth generation ports-A question of agility? (2003) International Journal of Physical Distribution & Logistics Management, 33 (4), pp. 355-376; Panayides, P.M., Maritime logistics and global supply chains: Towards a research agenda (2006) Maritime Economics & Logistics, 8 (1), pp. 3-18; Panayides, P.M., Song, D.-W., Port integration in global supply chains: Measures and implications for maritime logistics (2009) International Journal of Logistics: Research and Applications, 12 (2), pp. 133-145; Panayides, P.M., Song, D.-W., Maritime logistics as an emerging discipline (2013) Maritime Policy & Management, 20 (3), pp. 295-308; Seo, Y.-J., Dinwoodie, J., Roe, M., Co-opetition for collaborative advantage in supply chains involving maritime organisations (2013) Paper Presented at the International Association of Maritime Economists (IAME) Annual Conference, , 3-5 July, Marseille, France; Simatupang, T.M., Sridharan, R., The collaborative index: A measure for supply chain collaboration (2005) International Journal of Physical Distribution & Logistics Management, 35 (1), pp. 44-62; Slack, D., Pawns in the game: Ports in a global transportation system (1993) Growth and Change, 24 (4), pp. 579-588; Song, D.-W., Lee, E.-S., Coopetitive networks, knowledge acquisition and maritime logistics value (2012) International Journal of Logistics: Research and Applications, 15 (1), pp. 1-21; Stank, T.P., Keller, S.B., Daugherty, P.J., Supply chain collaboration and logistical service performance (2001) Journal of Business Logistics, 22 (1), pp. 29-48; Talley, W.K., Port performance: An economics perspective (2007) Devolution, Port Governance and Port Performance, pp. 499-516. , In: M.R. Brookes and K. Cullinane (eds.). Oxford: Elsevier; UNCTAD, (2004) Assessment of A Seaport Land Interface: An Analytical Framework, , UNCTAD/SDTE/TLB/MISC/2004/3. New York: United Nations; UNCTAD, (2013) Review of Maritime Transport. UNCTAD/RMT/2013, , New York: United Nations; Van Der Horst, M.R., De Langen, P.W., Coordination in hinterland transport chains: A major challenge for the seaport community (2008) Maritime Economics and Logistics, 10 (1-2), pp. 108-129; Vitsounis, T.K., Pallis, A.A., Port value chains and the role of interdependencies (2012) Maritime Logistics: Contemporary Issues, pp. 155-173. , In: D.-W. Song and P.M. Panayides (eds.). Bingley, UK: Emerald Group; Wiengarten, F., Humphreys, P., Cao, G., Fynes, B., McKittrick, A., Collaborative supply chain practices and performance: Exploring the key role of information quality (2010) Supply Chain Management: An International Journal, 15 (6), pp. 463-473; Woo, S.-H., Pettit, S., Beresford, A.K.C., Port evolution and performance in changing logistics environments (2011) Maritime Economics & Logistics, 13 (3), pp. 250-277; Xia, W., Lee, G., Complexity of information systems development projects: Conceptualization and measurement development (2005) Journal of Management Information System, 22 (1), pp. 45-83},
publisher={Palgrave Macmillan},
issn={14792931},
language={English},
abbrev_source_title={Marit. Econ. Logist.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dubey20155207,
author={Dubey, R. and Gunasekaran, A. and Chakrabarty, A.},
title={World-class sustainable manufacturing: Framework and a performance measurement system},
journal={International Journal of Production Research},
year={2015},
volume={53},
number={17},
pages={5207-5223},
doi={10.1080/00207543.2015.1012603},
note={cited By 109},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937252189&doi=10.1080%2f00207543.2015.1012603&partnerID=40&md5=b5b98f300678fa67e42f9602ded1ba75},
affiliation={Symbiosis Institute of Operations Management (Constituent of Symbiosis International University), New Nashik, India; Charlton College of Business, University of Massachusetts Dartmouth, North Dartmouth, MA, United States},
abstract={Sustainable manufacturing has been the subject of debate in the last few years, amongst both researchers and industrialists. Manufacturing activities have been regarded as amongst the most important factors which have caused major damage to planet. The aim of this paper is to scientifically develop a world-class sustainable manufacturing (WCSM) framework. The study uses a structured questionnaire, which was developed using extant literature and pretested to check content validity. The data was collected following a modified version of Dillmans total design method to improve the quality of response. The psychometric properties of the instrument including reliability and construct validity were assessed using confirmatory factor analysis and multiple regression analysis for hypotheses testing, and partial least squares approach for testing goodness of fit. We find that the scale and items of our theoretical framework meet the criteria. The multiple regression analysis output suggests that all our hypotheses are supported, which further supports the extant literature. Our present study is unique in terms of scope and its contribution to the theory and practice of supply chain management. This study has empirically tested the research calls of various researchers, extended them to WCSM practices and further developed a scale for the measurement of the framework. © 2015 Taylor & Francis.},
author_keywords={confirmatory factor analysis;  goodness of fit;  PLS;  world class manufacturing;  world class sustainable manufacturing},
keywords={Factor analysis;  Instrument testing;  Least squares approximations;  Manufacture;  Multivariant analysis;  Regression analysis;  Reliability analysis;  Supply chain management, Confirmatory factor analysis;  Goodness of fit;  Manufacturing activities;  Multiple regression analysis;  Partial least square (PLS);  Performance measurement system;  Sustainable manufacturing;  World class manufacturing, Engineering education},
references={Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable Supply Management: An Empirical Study (2012) International Journal of Production Economics, 140 (1), pp. 168-182; Armstrong, J.S., Overton, T.S., Estimating Nonresponse Bias in Mail Surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Azevedo, S.G., Carvalho, H., Cruz Machado, V., The Influence of Green Practices on Supply Chain Performance: A Case Study Approach (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 850-871; Azzone, G., Noci, G., Identifying Effective PMSs for the Deployment of 'Green' Manufacturing Strategies (1998) International Journal of Operations & Production Management, 18 (4), pp. 308-335; Bai, C., Sarkis, J., Green Supplier Development: Analytical Evaluation Using Rough Set Theory (2010) Journal of Cleaner Production, 18 (12), pp. 1200-1210; Bendoly, E., Croson, R., Goncalves, P., Schultz, K., Bodies of Knowledge for Research in Behavioral Operations (2010) Production and Operations Management, 19 (4), pp. 434-452; Bendoly, E., Donohue, K., Schultz, K.L., Behavior in Operations Management: Assessing Recent Findings and Revisiting Old Assumptions (2006) Journal of Operations Management, 24 (6), pp. 737-752; Bhateja, A.K., Babbar, R., Singh, S., Sachdeva, A., Study of the Critical Factor Finding's regarding Evaluation of Green Supply Chain Performance of Indian Scenario for Manufacturing Sector (2012) International Journal of Computational Engineering & Management, 15 (1), pp. 74-80; Blome, C., Schoenherr, T., Eckstein, D., The Impact of Knowledge Transfer and Complexity on Supply Chain Flexibility: A Knowledge-based View (2014) International Journal of Production Economics, 147, pp. 307-316; Boudreau, M.C., Robey, D., Enacting Integrated Information Technology: A Human Agency Perspective (2005) Organization Science, 16 (1), pp. 3-18; Braunscheidel, M.J., Suresh, N.C., The Organizational Antecedents of a Firm's Supply Chain Agility for Risk Mitigation and Response (2009) Journal of Operations Management, 27 (2), pp. 119-140; Brown, S., Squire, B., Blackmon, K., The Contribution of Manufacturing Strategy Involvement and Alignment to World-class Manufacturing Performance (2007) International Journal of Operations & Production Management, 27 (3), pp. 282-302; Carter, C.R., Rogers, D.S., A Framework of Sustainable Supply Chain Management: Moving toward New Theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Cetinkaya, B., Developing a Sustainable Supply Chain Strategy (2011) Sustainable Supply Chain Management, pp. 17-55. , edited by . Cetinkaya, R. Cuthbertson, G. Ewer, T. Klaas-Wissing, W. Piotrowicz, and C. Tyssen Springer Berlin Heidelberg; Chen, I.J., Paulraj, A., Towards a Theory of Supply Chain Management: The Constructs and Measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Chen, I.J., Paulraj, A., Lado, A.A., Strategic Purchasing, Supply Management, and Firm Performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Chien, M.K., Shih, L.H., An Empirical Study of the Implementation of Green Supply Chain Management Practices in the Electrical and Electronic Industry and Their Relation to Organizational Performances (2007) International Journal of Environmental Science and Technology, 4 (3), pp. 383-394; Chin, W.W., The Partial Least Squares Approach to Structural Equation Modeling (1998) Modern Methods for Business Research, 295 (2), pp. 295-336. , edited by G. A. Marcoulides New York: Psychology Press; Christopher, M., (2005) Logistics and Supply Chain Management: Creating Value-Adding Networks. 3rd Ed., , Harlow: Financial Times/ Prentice Hall; Chun, Y., Bidanda, B., Sustainable Manufacturing and the Role of the International Journal of Production Research (2013) International Journal of Production Research, 51 (23-24), pp. 7448-7455; Churchill, G.A., Jr., A Paradigm for Developing Better Measures of Marketing Constructs (1979) Journal of Marketing Research, 16 (1), pp. 64-73; Cohen, J., Cohen, P., West, S.G., Aiken, L.S., (2013) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3rd Ed., , Hillsdale, NJ: Erlbaum; Curran, P.J., West, S.G., Finch, J.F., The Robustness of Test Statistics to Nonnormality and Specification Error in Confirmatory Factor Analysis (1996) Psychological Methods, 1 (1), pp. 16-29; Deif, A.M., A System Model for Green Manufacturing (2011) International Journal of Cleaner Production, 19 (14), pp. 1553-1559; Despeisse, M., Ball, P.D., Evans, S., Levers, A., Industrial Ecology at Factory Level - A Conceptual Model (2012) Journal of Cleaner Production, 31 (3-4), pp. 30-39; Dillman, D.A., (2007) Mail and Internet Surveys: The Tailored Design, , [2007 Update]. Hoboken: John Wiley; DiMaggio, P.J., Powell, W.W., The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields (1983) American Sociological Review, 48 (2), pp. 147-160; Dües, C.M., Tan, K.H., Lim, M., Green as the New Lean: How to Use Lean Practices as a Catalyst to Greening Your Supply Chain (2013) Journal of Cleaner Production, 40, pp. 93-100; Farish, M., Plants That Are Green [Toyota's Lean Manufacturing] (2009) Engineering & Technology, 4 (3), pp. 68-69; Flynn, B.B., Schroeder, R.G., Flynn, E.J., World Class Manufacturing: An Investigation of Hayes and Wheelwright's Foundation (1999) Journal of Operations Management, 17 (3), pp. 249-269; Fornell, C., Larcker, D.F., Evaluating Structural Equation Models with Unobservable Variables and Measurement Error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Franchetti, M., Bedal, K., Ulloa, J., Grodek, S., Lean and Green-industrial Engineering Methods Are Natural Stepping Stones to Green Engineering (2009) Industrial Engineer, 41 (9), pp. 24-29; Garbie, I.H., DFSME: Design for Sustainable Manufacturing Enterprises (an Economic Viewpoint) (2013) International Journal of Production Research, 51 (2), pp. 479-503; Garbie, I.H., An Analytical Technique to Model and Assess Sustainable Development Index in Manufacturing Enterprises (2014) International Journal of Production Research, 52 (16), pp. 4876-4915; Garbie, I.H., A Methodology for the Reconfiguration Process in Manufacturing Systems (2014) Journal of Manufacturing Technology Management, 25 (6), pp. 891-915; Garbie, I.H., Shikdar, A., Analysis and Estimation of Complexity Level in Industrial Firms (2011) International Journal of Industrial and Systems Engineering, 8 (2), pp. 175-197; Garetti, M., Mummolo, G., Taisch, M., Special Issue on 'Sustainable Manufacturing' (2012) Production Planning & Control, 23 (2-3), pp. 79-82; Garetti, M., Taisch, M., Sustainable Manufacturing: Trends and Research Challenges (2012) Production Planning & Control, 23 (2-3), pp. 83-104; Gavronski, I., Paiva, E.L., Teixeira, R., De Andrade, M.C.F., ISO 14001 Certified Plants in Brazil - Taxonomy and Practices (2013) Journal of Cleaner Production, 39, pp. 32-41; Geffen, D., Straub, D.W., Bourdreau, M.C., Structural Equation Modeling and Regression: Guidelines for Research Practice (2000) Communications of the Association for Information Systems, 4 (7), pp. 1-79; Gerbing, D.W., Anderson, J.C., An Updated Paradigm for Scale Development Incorporating Unidimensionality and Its Assessment (1988) Journal of Marketing Research, 25 (2), pp. 186-192; Gino, F., Pisano, G., Toward a Theory of Behavioral Operations (2008) Manufacturing & Service Operations Management, 10 (4), pp. 676-691; Götz, O., Liehr-Gobbers, K., Krafft, M., Evaluation of Structural Equation Models Using the Partial Least Squares (PLS) Approach (2010) Handbook of Partial Least Squares, pp. 691-711. , edited by V. E. Vinzi, W. W. Chin, J. Henseler, and H. Wang Berlin: Springer; Gunasekaran, A., Irani, Z., Papadopoulos, T., Modelling and Analysis of Sustainable Operations Management: Certain Investigations for Research and Applications (2013) Journal of the Operational Research Society, 65 (6), pp. 806-823; Gunasekaran, A., Spalanzani, A., Sustainability of Manufacturing and Services: Investigations for Research and Applications (2012) International Journal of Production Economics, 140 (1), pp. 35-47; Gunasekaran, A., Yusuf, Y.Y., Agile Manufacturing: A Taxonomy of Strategic and Technological Imperatives (2002) International Journal of Production Research, 40 (6), pp. 1357-1385; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , Upper Saddle River, NJ: Prentice-Hall; Hallgren, M., Olhager, J., Lean and Agile Manufacturing: External and Internal Drivers and Performance Outcomes (2009) International Journal of Operations & Production Management, 29 (10), pp. 976-999; Hayes, R.H., Wheelwright, S.C., (1984) Restoring Our Competitive Edge: Competing through Manufacturing, 8. , New York: Wiley; Hong, P., Roh, J.J., Rawski, G., Benchmarking Sustainability Practices: Evidence from Manufacturing Firms (2012) Benchmarking: An International Journal, 19 (4-5), pp. 634-648; Van Hoof, B., Lyon, T.P., Cleaner Production in Small Firms Taking Part in Mexico's Sustainable Supplier Program (2013) Journal of Cleaner Production, 41, pp. 270-282; Hsu, C.W., Hu, A.H., Green Supply Chain Management in the Electronic Industry (2008) International Journal of Science and Technology, 5 (2), pp. 205-216; Hsu, C.W., Hu, A.H., Applying Hazardous Substance Management to Supplier Selection Using Analytic Network Process (2009) Journal of Cleaner Production, 17 (2), pp. 255-264; (2013), http://trade.gov/competitiveness/sustainablemanufacturing/how_doc_defines_SM.asp, Accessed September 17, 2014; Jayaraman, V., Singh, R., Anandnarayan, A., Impact of Sustainable Manufacturing Practices on Consumer Perception and Revenue Growth: An Emerging Economy Perspective (2012) International Journal of Production Research, 50 (5), pp. 1395-1410; Jovane, F., Yoshikawa, H., Alting, L., Boër, C.R., Westkamper, E., Williams, D., Tseng, M., The Incoming Global Technological and Industrial Revolution towards Competitive Sustainable Manufacturing (2008) CIRP Annals-Manufacturing Technology, 57 (2), pp. 641-659; Kaebernick, H., Kara, S., Sun, M., Sustainable Product Development and Manufacturing by Considering Environmental Requirements (2003) Robotics and Computer-Integrated Manufacturing, 19 (6), pp. 461-468; Kannegiesser, M., Günther, H.O., Sustainable Development of Global Supply Chains - Part 1: Sustainability Optimization Framework (2014) Flexible Services and Manufacturing Journal, 26 (1-2), pp. 24-47; Kim, J., Park, K., Hwang, Y., Park, I., Sustainable Manufacturing: A Case Study of the Forklift Painting Process (2010) International Journal of Production Research, 48 (10), pp. 3061-3078; Ku, C.Y., Chang, C.T., Ho, H.P., Global Supplier Selection Using Fuzzy Analytic Hierarchy Process and Fuzzy Goal Programming (2010) Quality & Quantity, 44 (4), pp. 623-640; Lambert, D.M., Harrington, T.C., Measuring Nonresponse Bias in Customer Service Mail Surveys (1990) Journal of Business Logistics, 11 (2), pp. 5-25; Law, K.M., Gunasekaran, A., Sustainability Development in High-tech Manufacturing Firms in Hong Kong: Motivators and Readiness (2012) International Journal of Production Economics, 137 (1), pp. 116-125; Liu, S., Leat, M., Smith, M.H., State-of-the-art Sustainability Analysis Methodologies for Efficient Decision Support in Green Production Operations (2011) International Journal of Sustainable Engineering, 4 (3), pp. 236-250; Luthra, S., Kumar, V., Kumar, S., Haleem, A., Barriers to Implement Green Supply Chain Management in Automobile Industry Using Interpretive Structural Modeling Technique: An Indian Perspective (2011) Journal of Industrial Engineering and Management, 4 (2), pp. 231-257; MacCallum, R.C., Browne, M.W., Sugawara, H.M., Power Analysis and Determination of Sample Size for Covariance Structure Modeling (1996) Psychological Methods, 1 (2), pp. 130-149; Malhotra, M.K., Grover, V., An Assessment of Survey Research in POM: From Constructs to Theory (1998) Journal of Operations Management, 16 (4), pp. 407-425; Mani, M., Madan, J., Lee, J.H., Lyons, K.W., Gupta, S.K., Sustainability Characterisation for Manufacturing Processes (2014) International Journal of Production Research, 52 (20), pp. 5895-5912; Mathiyazhagan, K., Govindan, K., Noorul Haq, A., Pressure Analysis for Green Supply Chain Management Implementation in Indian Industries Using Analytic Hierarchy Process (2014) International Journal of Production Research, 52 (1), pp. 188-202; Meena, P.L., Sarmah, S.P., Development of a Supplier Satisfaction Index Model (2012) Industrial Management & Data Systems, 112 (8), pp. 1236-1254; Mizruchi, M.S., Fein, L.C., The Social Construction of Organizational Knowledge: A Study of the Uses of Coercive, Mimetic, and Normative Isomorphism (1999) Administrative Science Quarterly, 44 (4), pp. 653-683; Noci, G., Designing 'Green' vendor Rating Systems for the Assessment of a Supplier's Environmental Performance (1997) European Journal of Purchasing & Supply Management, 3 (2), pp. 103-114; Pochampally, K.K., Gupta, S.M., Govindan, K., Metrics for Performance Measurement of a Reverse/Closed-loop Supply Chain (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (1), pp. 8-32; Pusavec, F., Krajnik, P., Kopac, J., Transitioning to Sustainable Production-Part I: Application on Machining Technologies (2010) Journal of Cleaner Production, 18 (2), pp. 174-184; Rahimifard, S., Clegg, A.J., Aspects of Sustainable Design and Manufacture (2007) International Journal of Production Research, 45 (18-19), pp. 4013-4019; Roth, A.V., Giffi, C.A., Seal, G.M., Operating Strategies for the 1990s: Elements Comprising World-class Manufacturing (1992) Manufacturing Strategy. Process and Content, pp. 133-165. , edited by C. Voss London: Chapman & Hall; Sánchez, A.M., Pérez, M.P., Lean Indicators and Manufacturing Strategies (2001) International Journal of Operations & Production Management, 21 (11), pp. 1433-1452; Sarkis, J., Zhu, Q., Lai, K.H., An Organizational Theoretic Review of Green Supply Chain Management Literature (2011) International Journal of Production Economics, 130 (1), pp. 1-15; Schoenherr, T., Mabert, V.A., The Use of Bundling in B2B Online Reverse Auctions (2008) Journal of Operations Management, 26 (1), pp. 81-95; Schonberger, R.J., (1986) World Class Manufacturing: The Lessons of Simplicity Applied, , New York: Free Press; Schroeder, R.G., Flynn, B.B., (2002) High Performance Manufacturing: Global Perspectives, , New York: Wiley; Seman, N.A.A., Zakuan, N., Jusoh, A., Arif, M.S.M., Saman, M.Z.M., Green Supply Chain Management: A Review and Research Direction (2012) International Journal of Managing Value & Supply Chains, 3 (1), pp. 1-18; Shah, R., Ward, P.T., Defining and Developing Measures of Lean Production (2007) Journal of Operations Management, 25 (4), pp. 785-805; Sharma, M., Kodali, R., Development of a Framework for Manufacturing Excellence (2008) Measuring Business Excellence, 12 (4), pp. 50-66; Singh, A., Singh, B., Dhingra, A.K., Drivers and Barriers of Green Manufacturing Practices: A Survey of Indian Industries (2012) International Journal of Engineering Sciences, 1 (1), pp. 5-19; Steudel, H.J., Desruelle, P., (1992) Manufacturing in The'90s: How to Become a Mean, Lean World-class Competitor, , New York: Van Nostrand Reinhold Company; Tan, K.H., Rae, R.H., Uncovering the Links between Regulation and Performance Measurement (2009) International Journal of Production Economics, 122 (1), pp. 449-457; Tenenhaus, M., Vinzi, V.E., Chatelin, Y.M., Lauro, C., PLS Path Modeling (2005) Computational Statistics & Data Analysis, 48 (1), pp. 159-205; Testa, F., Iraldo, F., Shadows and Lights of GSCM (Green Supply Chain Management): Determinants and Effects of These Practices Based on a Multi-National Study (2010) Journal of Cleaner Production, 18 (10-11), pp. 953-962; Tsoulfas, G.T., Pappis, C.P., Environmental Principles Applicable to Supply Chains Design and Operation (2006) Journal of Cleaner Production, 14 (18), pp. 1593-1602; Vachon, S., Green Supply Chain Practices and the Selection of Environmental Technologies (2007) International Journal of Production Research, 45 (18-19), pp. 4357-4379; Vachon, S., Klassen, R.D., Green Project Partnership in the Supply Chain: The Case of the Package Printing Industry (2006) Journal of Cleaner Production, 14 (6-7), pp. 661-671; Westkämper, E., Manufuture and Sustainable Manufacturing (2008) Manufacturing Systems and Technologies for the New Frontier, pp. 11-14. , edited by M. Mitsuishi, K. Ueda, and F. Kimura London: Springer; Wetzels, M., Odekerken-Schroder, G., Van Oppen, C., Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration (2009) Management Information Systems Quarterly, 33 (1), pp. 177-195; Yang, M.G.M., Hong, P., Modi, S.B., Impact of Lean Manufacturing and Environmental Management on Business Performance: An Empirical Study of Manufacturing Firms (2011) International Journal of Production Economics, 129 (2), pp. 251-261; Zhu, Q., Sarkis, J., Geng, Y., Green Supply Chain Management in China: Pressures, Practices and Performance (2005) International Journal of Operations & Production Management, 25 (5), pp. 449-468; Zsidisin, G.A., Melnyk, S.A., Ragatz, G.L., An Institutional Theory Perspective of Business Continuity Planning for Purchasing and Supply Management (2005) International Journal of Production Research, 43 (16), pp. 3401-3420},
correspondence_address1={Gunasekaran, A.; Charlton College of Business, United States; email: agunasekaran@umassd.edu},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sellitto20154917,
author={Sellitto, M.A. and Pereira, G.M. and Borchardt, M. and Da Silva, R.I. and Viegas, C.V.},
title={A SCOR-based model for supply chain performance measurement: Application in the footwear industry},
journal={International Journal of Production Research},
year={2015},
volume={53},
number={16},
pages={4917-4926},
doi={10.1080/00207543.2015.1005251},
note={cited By 87},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84932198608&doi=10.1080%2f00207543.2015.1005251&partnerID=40&md5=44d33230cdede924c1abba756c568f68},
affiliation={Production and Systems Graduate Program, UNISINOS, São Leopoldo, Brazil},
abstract={Supply Chain Operations Reference (SCOR) is a widely employed model for SC performance assessment, regardless its generic nature. This article presents a SCOR-based model for performance measurement in supply chains (SC) and apply it in the context of Brazilian footwear industry. The model has two dimensions: SCOR processes (source, make, deliver and return) and performance standards adapted from original SCOR (cost, quality, delivery and flexibility). This structure delivers a 4 × 4 matrix, with each component assessed under analytical hierarchy process. Using focus groups, SC's experts weighted each component of the matrix regarding their relevance. Thereafter, SC's managers indicated respective results. The SC's overall performance was obtained by adding the performance of all indicators. The model application embraced one focal footwear manufacturer, four suppliers, three distribution channels and a return channel, with 85 indicators assessed. The achieved performance for the whole SC is 75.29%. The main gaps were found in deliver process (12.78 percentual points of difference between relevance and achieved proportions) and in flexibility performance (9.82). Further application is recommended in order to find consolidated results. © 2015 Taylor & Francis.},
author_keywords={performance measurement;  SCOR model;  strategic priorities;  supply chain performance},
keywords={Chains, Analytical Hierarchy Process;  Performance assessment;  Performance measurements;  SCOR models;  strategic priorities;  Supply chain operations references;  Supply chain performance;  Supply chain performance measurements, Supply chains},
references={Agarwal, A., Shankar, R., Tiwari, M., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Akyuz, A., Erman, E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Alomar, M., Pasek, Z.J., Linking supply chain strategy and processes to performance improvement (2014) Procedia 47th Conference on Manufacturing Systems CIRP, 17, pp. 628-634; Aramyan, L., Lansink, A., Van Der-Vorst, J., Van Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Azevedo, S., Carvalho, H., Cruz-Machado, V., A proposal of LARG supply chain management practices and a performance measurement system (2011) International Journal of E-Education, E-Business, E-Management and E-Learning, 1 (1), pp. 7-14; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighbourhood rough sets (2012) International Journal of Production Research, 50 (9), pp. 2484-2500; Bechtel, C., Jayaram, J., Supply chain management: A strategic perspective (1997) The International Journal of Logistics Management, 8 (1), pp. 15-34; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bhagwat, R., Sharma, M., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18 (8), pp. 666-680; Bourne, M., Neely, A., Mills, J., Platts, K., Wilcox, M., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548. , 2003; Clivillé, V., Berrah, L., Overall performance measurement in a supply chain: Towards a supplier-prime manufacturer based model (2012) Journal of Intelligent Manufacturing, 23 (6), pp. 2459-2469; Elgazzar, S., Nicoleta, T., Hubbard, N., Leach, D.L., Linking supply chain processes' performance to a Company's financial strategic objectives (2012) European Journal of Operational Research, 223 (1), pp. 276-289; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142, pp. 247-258; Forman, E.H., Facts and fictions about the analytic hierarchy process (1993) Mathematical and Computer Modelling, 17 (4-5), pp. 19-26; Fu, H.P., Chu, K.K., Lin, C.W., Chen, C.R., A study on factors for retailers implementing CPFR - A fuzzy AHP analysis (2010) Journal of Systems Science and Systems Engineering, 19 (2), pp. 192-209; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hausman, W., Supply chain performance metrics (2003) The Practice of Supply Chain Management: Where Theory and Application Converge, pp. 61-73. , edited by T. Harrison, H. Lee, and J. Neale, Amsterdam: Kluwer Academic; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Huang, S.H., Keskar, H., Comprehensive and configurable metrics for supplier selection (2007) International Journal of Production Economics, 105, pp. 510-523; Kleijnen, J., Smits, M., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54, pp. 507-514; Lockamy, A., III, McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Lu, T.P., Trappey, A.J., Chen, Y.K., Chang, Y.D., Collaborative design and analysis of supply chain network management key processes model (2013) Journal of Network and Computer Applications, 36 (6), pp. 1503-1511; Mafakheri, S., Breton, M., Ghoniem, A., Supplier selection-order allocation: A two-stage multiple criteria dynamic programming approach (2011) International Journal of Production Economics, 132, pp. 52-57; Naesens, K., Gelders, L., Pintelon, L., A swift response framework for measuring the strategic fit for a horizontal collaborative initiative (2009) International Journal of Production Economics, 121, pp. 550-561; Navas-Alemán, L., The impact of operating in multiple value chains for upgrading: The case of the Brazilian furniture and footwear industries (2011) World Development, 39 (8), pp. 1386-1397; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60, pp. 279-290; Özgen, D., Önüt, S., Gülsün, B., Tuzkaya, U.R., A two-phase possibilistic linear programming methodology for multi-objective supplier evaluation and order allocation problems (2008) Information Sciences, 178, pp. 485-500; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management & Data Systems, 110 (9), pp. 1297-1318; Rabelo, L., Eskandari, H., Shaalan, T., Helal, M., Value chain analysis using hybrid simulation and AHP (2007) International Journal of Production Economics, 105, pp. 536-547; Ramanathan, U., Performance of supply chain collaboration - A simulation study (2014) Expert Systems with Applications, 41, pp. 210-220; Saaty, T., (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, , New York: McGraw-Hill; Sanayei, A., Mousavi, S.F., Abdi, M.R., Mohaghar, A., An integrated group decision-making process for supplier selection and order allocation using multi-attribute utility theory and linear programming (2008) Journal of the Franklin Institute, 345, pp. 731-747; (1996) What is SCOR?, , http://supply-chain.org/scor, Accessed January 2013; Sen, A., The US fashion industry: A supply chain review (2008) International Journal of Production Economics, 114, pp. 571-593; Simon, H., The architecture of complexity (1999) Facets of Systems Sciences, pp. 457-476. , edited by G. Klin (org.), New York: Plenum Press; Subramanian, N., Ramanathan, R., A review of applications of analytic hierarchy process in operations management (2012) International Journal of Production Economics, 138, pp. 215-241; Swafford, P.M., Soumen, G., Murthy, N., The antecedents of supply chain agility of a firm: Scale development and model testing (2006) Journal of Operations Management, 24, pp. 170-188; Vijayvargiya, A., Dey, A., An analytical approach for selection of a logistics provider (2010) Management Decision, 48 (3), pp. 403-418; Vinodh, S., Devadasan, S.R., Vimala, K.E.K., Kumar, D., Design of agile supply chain assessment model and its case study in an Indian automotive components manufacturing organization (2013) Journal of Manufacturing Systems, 32, pp. 620-631; Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteria decision-making methodology (2004) International Journal of Production Economics, 91, pp. 1-15; Ward, P., McCreery, J., Ritzman, L., Deven, S., Competitive priorities in operations management (1998) Decision Sciences, 29 (4), pp. 1035-1046; Xu, J., Li, B., Wu, D., Rough data envelopment analysis and its application to supply chain performance evaluation (2009) International Journal of Production Economics, 122, pp. 628-638},
correspondence_address1={Sellitto, M.A.; Production and Systems Graduate Program, Brazil},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lu2015121,
author={Lu, X. and Shang, J. and Wu, S.-Y. and Hegde, G.G. and Vargas, L. and Zhao, D.},
title={Impacts of supplier hubris on inventory decisions and green manufacturing endeavors},
journal={European Journal of Operational Research},
year={2015},
volume={245},
number={1},
pages={121-132},
doi={10.1016/j.ejor.2015.02.051},
note={cited By 29},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927959377&doi=10.1016%2fj.ejor.2015.02.051&partnerID=40&md5=43c7236817267547430d0ee0015dd550},
affiliation={College of Management and Economics, Tianjin University, Tianjin, 300072, China; Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA  15260, United States; W. P. Carey School of Business, Arizona State University, Tempe, AZ  85287, United States},
abstract={The paper studies the impact of a green supplier's overconfidence on inventory decisions in a supply chain consisting of a supplier facing effort-dependent stochastic demand and a rational retailer. The overconfident supplier may overestimate the product demand due to carbon-reduction green efforts or underestimate the variability of the stochastic demand. We characterize these two kinds of overconfidence as ability-based and precision-based overconfidence, for which we develop optimal models for three supply chain systems: integrated, vendor managed inventory, and retailer managed inventory. Extensive comparative studies are conducted to highlight the impacts of supplier's overconfidence on the inventory decisions and on different green-supply chain performance measures. We find that, under certain conditions, supplier's overconfidence prompts the supplier to exert more efforts on green manufacturing, and enhances the profits of the retailer and of the entire supply chain. Managerial insights are provided for various scenarios and propositions. © 2015 Elsevier B.V. All rights reserved.},
author_keywords={Effort-dependent demand;  Green manufacturing efforts;  Supplier's overconfidence;  Supply chain management;  Vendor or retailer managed inventory},
keywords={Sales;  Stochastic systems;  Supply chain management, Comparative studies;  Effort-dependent demand;  Green supply chain;  Inventory decisions;  Supplier's overconfidence;  Supply chain systems;  Vendor managed Inventory;  Vendor or retailer managed inventory, Green manufacturing},
references={Augstein, V., New carbon trust research underlines need for corporate leadership on carbon reduction (2011) Air Quality & Climate Change, 45 (3), pp. EI-2; Axsäter, S., A note on stock replenishment and shipment scheduling for vendor-managed inventory systems (2001) Management Science, 47 (9), pp. 1306-1310; Banker, R.D., Khosla, I., Sinha, K.K., Quality and competition (1998) Management Science, 44 (9), pp. 1179-1192; Bernstein, F., Chen, F., Federgruen, A., Coordinating supply chains with simple pricing schemes: The role of vendor-managed inventories (2006) Management Science, 52 (10), pp. 1483-1492; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for sustainable supply chain management: Developments and directions (2014) European Journal of Operational Research, 233 (2), pp. 299-312; Buzzel, R.D., Ortmeyer, G., Channel partnerships streamline distribution (1995) Sloan Management Review, 36 (3), pp. 85-96; Cetinkaya, S., Lee, C.-Y., Stock replenishment and shipment scheduling for vendor-managed inventory systems (2000) Management Science, 46 (2), p. 217; Cheung, K.L., Lee, H.L., The Inventory benefit of shipment coordination and stock rebalancing in a supply chain (2002) Management Science, 48 (2), pp. 300-306; Cooke, J.A., VMI: Very mixed impact (1998) Logistics Management, 37 (12), pp. 51-53; Cooper, A.C., Woo, C.Y., Dunkelberg, W.C., Entrepreneurs' perceived chances for success (1988) Journal of Business Venturing, 3 (2), pp. 97-108; Corbett, C.J., Stochastic inventory systems in a supply chain with asymmetric information: Cycle stocks, safety stocks, and consignment stock (2001) Operations Research, 49 (4), pp. 487-500; Craig, A., Measuring Supply Chain Carbon Efficiency: A Carbon Label Framework, , http://hdl.handle.net/1721.1/78481, (MIT Ph.D. thesis); Darwish, M.A., Odah, O.M., Vendor managed inventory model for single-vendor multi-retailer supply chains (2010) European Journal of Operational Research, 204 (3), pp. 473-484; Dong, Y., Xu, K., A supply chain model of vendor managed inventory? (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (2), pp. 75-95; Elofsson, K., Matsdotter, E., Arntyr, J., (2014) Got Green Milk, , Swedish University of Agricultural Sciences, Department of Economics. (Working paper series 2014:02); El Saadany, A.M.A., Jaber, M.Y., Bonney, M., Environmental performance measures for supply chains (2011) Management Research Review, 34 (11), pp. 1202-1221; Galasso, A., Simcoe, T.S., CEO overconfidence and innovation (2011) Management Science, 57 (8), pp. 1469-1484; Gerchak, Y., Wang, Y., Revenue-sharing vs. Wholesale-price contracts in assembly systems with random demand (2004) Production & Operations Management, 13 (1), pp. 23-33; Gervais, S., Heaton, J.B., Odean, T., Overconfidence, compensation contracts, and capital budgeting (2011) Journal of Finance, 66 (5), pp. 1735-1777; Kim, H.-S., Revisiting retailer vs. Vendor-managed inventory and brand competition (2008) Management Science, 54 (3), pp. 623-626; Hayward, M.L.A., Hambrick, D.C., Explaining the premiums paid for large acquisitions: Evidence of CEO hubris (1997) Administrative Science Quarterly, 42 (1), pp. 103-127; Hirshleifer, D., Low, A., Teoh, S.H., Are overconfident CEOs better innovators? (2012) Journal of Finance, 67 (4), pp. 1457-1498; Inman, J., Nikolova, H., (2014) Healthy Choice: The Effect of Simplified POS Nutritional Information on Consumer Choice Behavior, , Katz Graduate School of Business, University of Pittsburgh. (Working paper); Jammernegg, W., Kischka, P., The price-setting newsvendor with service and loss constraints (2013) Omega-International Journal of Management Science, 41 (2), pp. 326-335; Johnson, D.D.P., Fowler, J.H., The evolution of overconfidence (2011) Nature, 477 (7364), pp. 317-320; Karmarkar, U., Pitblado, R.U., Quality, class, and competition (1997) Management Science, 43 (1), pp. 27-39; Kraiselburd, S., Narayanan, V.G., Raman, A., Contracting in a supply chain with stochastic demand and substitute products (2004) Production & Operations Management, 13 (1), pp. 46-62; Leopold-Wildburger, U., Weber, G.W., Zachariasen, M., OR for better management of sustainable development (2009) European Journal of Operational Research, 193 (3), pp. 647-648; Li, J.T., Tang, Y., CEO hubris and firm risk taking in China: The moderating role of managerial discretion (2010) Academy of Management Journal, 53 (1), pp. 45-68; Lin, Y.H., Hu, S.Y., Chen, M.S., Managerial optimism and corporate investment: Some empirical evidence from Taiwan (2005) Pacific Basin Finance Journal, 13 (5), pp. 523-546; Mahar, H., (2003) Why Are There so Few Prenuptial Agreements?, , Harvard Law School John M. Olin Center for Law, Economics and Business. (Discussion paper 436); Malmendier, U., Tate, G., CEO overconfidence and corporate investment (2005) Journal of Finance, 60 (6), pp. 2661-2700; Malmendier, U., Tate, G., Does overconfidence affect corporate investment? CEO overconfidence measures revisited (2005) European Financial Management, 11 (5), pp. 649-659; Malmendier, U., Tate, G., Yan, J., Overconfidence and early-life experiences: The effect of managerial traits on corporate financial policies (2011) Journal of Finance, 66 (5), pp. 1687-1733; Mishra, B.K., Raghunathan, S., Retailer- vs. Vendor-managed inventory and brand competition (2004) Management Science, 50 (4), pp. 445-457; Moore, D.A., Healy, P.J., The trouble with overconfidence (2008) Psychological Review, 115 (2), pp. 502-517; Mueller, J., Death by hubris? (2012) The Catastrophic Decision That Could Bankrupt A Great American Manufacturer, , http://www.forbes.com/sites/joannmuller/2012/08/02/death-by-hubris-the-catastrophic-decision-that-could-bankrupt-a-great-american-manufacturer/; Oberlechner, T., Osler, C., Survival of overconfidence in currency markets (2012) Journal of Financial and Quantitative Analysis, 47 (1), pp. 91-113; Odean, T., Volume, volatility, price, and profit when all traders are above average (1998) Journal of Finance, 53 (6), pp. 1887-1934; Polonsky, M.J., Vocino, A., Grau, S.L., Garma, R., Ferdous, A.S., The impact of general and carbon-related environmental knowledge on attitudes and behaviour of US consumers (2012) Journal of Marketing Management, 28 (34), pp. 238-263; Quariguasi Frota Neto, J., Walther, G., Bloemhof, J., Van Nunen, J.A.E.E., Spengler, T., A methodology for assessing eco-efficiency in logistics networks (2009) European Journal of Operational Research, 193 (3), pp. 670-682; Roberts, J.H., Urban, G.L., Modeling multiattribute utility, risk, and belief dynamics for new consumer durable brand choice (1988) Management Science, 34 (2), pp. 167-185; Roland, B., Tirole, J., Self-confidence and personal motivation (2002) The Quarterly Journal of Economics, 117 (3), pp. 871-915; Roll, R., The hubris hypothesis of corporate takeovers (1986) The Journal of Business, 59 (2), pp. 197-216; Russo, J.E., Schoemaker, P.J.H., Managing overconfidence (1992) Sloan Management Review, 33 (2), pp. 7-17; Steen, E.V., Rational overoptimism (and other biases) (2004) The American Economic Review, 94 (4), pp. 1141-1151; Trevelyan, R., Overconfidence and effort in new venture development (2008) Frontiers of Entrepreneurship Research, 28 (5), pp. 1-15; Trevelyan, R., Optimism, overconfidence and entrepreneurial activity (2008) Management Decision, 46 (7), pp. 986-1001; Tyan, J., Wee, H.-M., Vendor managed inventory: A survey of the Taiwanese grocery industry (2003) Journal of Purchasing and Supply Management, 9 (1), pp. 11-18; Vanclay, J.K., Shortiss, J., Aulsebrook, S., Gillespie, A.M., Howell, B.C., Johanni, R., Customer response to carbon labelling of groceries (2011) Journal of Consumer Policy, 34 (1), pp. 153-160; Van Den Steen, E., Overconfidence by Bayesian-rational agents (2011) Management Science, 57 (5), pp. 884-896; Voros, J., Product balancing under conditions of quality inflation, cost pressures and growth strategies (2002) European Journal of Operational Research, 141 (1), pp. 153-166; Xu, X.L., Cai, X.Q., Chen, Y.H., Unimodality of price-setting newsvendor's objective function with multiplicative demand and its applications (2011) International Journal of Production Economics, 133 (2), pp. 653-661; Yao, Y., Evers, P.T., Dresner, M.E., Supply chain integration in vendor-managed inventory (2007) Decision Support Systems, 43 (2), pp. 663-674; Yu, Y.G., Huang, G.Q., Liang, L., Stackelberg game-theoretic model for optimizing advertising, pricing and inventory policies in vendor managed inventory (VMI) production supply chains (2009) Computers & Industrial Engineering, 57 (1), pp. 368-382},
correspondence_address1={Shang, J.; Katz Graduate School of Business, United States; email: shang@katz.pitt.edu},
publisher={Elsevier B.V.},
issn={03772217},
coden={EJORD},
language={English},
abbrev_source_title={Eur J Oper Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Skipworth2015511,
author={Skipworth, H. and Godsell, J. and Wong, C.Y. and Saghiri, S. and Julien, D.},
title={Supply chain alignment for improved business performance: An empirical study},
journal={Supply Chain Management},
year={2015},
volume={20},
number={5},
pages={511-533},
doi={10.1108/SCM-06-2014-0188},
note={cited By 48},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938060879&doi=10.1108%2fSCM-06-2014-0188&partnerID=40&md5=65f733314e34b5d43614e6fe2919989b},
affiliation={Supply Chain Research Centre, Cranfield School of Management, Cranfield, United Kingdom; Warwick Manufacturing Group, University of Warwick, Coventry, United Kingdom; Leeds University Business School, University of Leeds, Leeds, United Kingdom},
abstract={Purpose – This study aims to explain how supply chain alignment, which remains a major challenge for supply chains, can be achieved and its implications for business performance (BP) by testing the strengths of the relationships between previously identified enablers, supply chain alignment and BP. Design/methodology/approach – A literature review develops hypotheses on the relationships between enablers, alignment and BP. A survey of medium-to-large UK manufacturing companies was conducted where the sample comprised 151 randomly selected companies, and the response rate was 56 per cent. Partial least square regression was used to test the hypothesis. Findings – Two types of supply chain alignment are defined – shareholder and customer – but only customer alignment (CA) has a direct positive impact on BP, while shareholder alignment (SA) is its antecedent. Top management support was shown to be an enabler of both shareholder and CA, while organisation structure, information sharing and performance measurement system enabled SA, while internal relational behaviour enabled CA. Research limitations/implications – Supply chain management research lacks knowledge on exactly how supply chain alignment can be achieved and what BP implications it has. This research provides a tested conceptual model to address this gap. Practical implications – The refined conceptual model provides precise guidance to practitioners on how to improve BP through supply chain alignment. Originality/value – Whilst the strategic management literature emphasizes the importance of SA, this study reveals another crucial alignment – CA – and shows its direct positive impact on BP. © Emerald Group Publishing Limited.},
author_keywords={Alignment;  Business performance;  Customer;  Enablers;  Shareholder;  Supply chain},
references={Ahire, S.L., Golhar, D.Y., Waller, M.A., Development and validation of TQM implementation constructs (1996) Decision Science, 27 (1), pp. 23-56; Ahmed, N.U., Montagno, R.V., Firenze, R.J., Operations strategy and organizational performance an empirical study (1996) International Journal of Operations and Production Management, 16 (5), pp. 41-53; Akkermans, H., Bogerd, P., Vos, B., Virtuous and vicious cycles on the road towards international supply chain management (1999) International Journal of Operations and Production Management, 19 (5-6), p. 565; Anderson, J.C., Narus, J.A., A model of distribution firm and manufacturer firm working partnerships (1990) Journal of Marketing, 54 (1), pp. 42-58; Armstrong, J., Overton, T., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Ashenbaum, B., Maltz, A., Ellram, L., Barratt, M., Organisational alignment and supply chain governance structure: Introduction and construct validation (2009) The International Journal of Logistics Management, 20 (2), pp. 169-186; Auramo, J., Tanskanen, K., Smaros, J., Increasing operational efficiency through improved customer service process maintenance case (2004) International Journal of Logistics: Research and Applications, 7 (3), pp. 167-180; Bagchi, P.K., Skjoett-Larsen, T., Organizational integration in supply chains: A contingency approach (2002) Global Journal of Flexible Systems Management, 3 (1), p. 1; Baier, C., Hartman, E., Moser, R., Strategic alignment and purchasing efficacy: An exploratory analysis of their impact on financial performance (2008) Journal of Supply Chain Management, 44 (4), pp. 36-52; Barratt, M., Positioning the role of collaborative planning in grocery supply chains (2003) International Journal of Logistics Management, 14 (2), p. 53; Barratt, M., Oliveira, A., Exploring the experiences of collaborative planning initiatives (2001) International Journal of Physical Distribution &Amp; Logistics Management, 31 (4), p. 266; Beth, S., Burt, D.N., Capacino, W., Gopal, C., Lee, H.L., Lynch, R.P., Morris, S., Supply chain challenges: Building relationships (2003) Harvard Business Review, 81 (7), pp. 64-73; Bourguignon, A., Management accounting and value creation: The profit and loss of reification (2005) Critical Perspectives on Accounting, 16 (4), p. 353; Bourland, K.E., Powell, S.G., Pyke, D.F., Exploiting timely demand information to reduce inventories (1996) European Journal of Operational Research, 92 (2), pp. 239-253; Brown, C.V., Magill, S.L., Alignment of the IS functions with the enterprise: Toward a model of antecedents (1994) MIS Quarterly, 18 (4), pp. 371-403; Brown, S., Squire, B., Blackmon, K., The contribution of manufacturing strategy involvement and alignment to world class manufacturing performance (2007) International Journal of Operations Management and Production Management, 27 (3), pp. 282-302; Buhner, R., Increasing shareholder value through human asset management (1997) Long Range Planning, 30 (5), p. 710; Burgelman, R.A., Doz, Y.L., The power of strategic integration (2001) MIT Sloan Management Review, 42 (3), pp. 28-38; Caniëls, M.C., Gehrsitz, M.H., Semeijn, J., Participation of suppliers in greening supply chains: An empirical analysis of German automotive suppliers (2013) Journal of Purchasing and Supply Management, 19 (3), pp. 134-143; Cao, Q., Baker, J., Hoffman, J.J., The role of the competitive environment in studies of strategic alignment: A meta-analysis (2012) International Journal of Production Research, 50 (2), pp. 567-580; Carlile, P.R., Christensen, C.M., (2004) The Cycles of Theory Building in Management Research, , Harvard Business School Working Paper 05-057, Harvard Business School; Boston, M.A., Chin, W.W., The partial least squares approach for structural equation modeling (1998) Modern Methods for Business Research, Erlbaum, pp. 295-336. , in Marcoulides, G.A. (Ed.), Hillsdale, New Jersey, NJ; Christopher, M., Gattorna, J., Supply chain cost management and value-based pricing (2005) Industrial Marketing Management, 34 (2), p. 115; Christopher, M., Lowson, R., Peck, H., Creating agile supply chains in the fashion industry (2004) International Journal of Retail &Amp; Distribution Management, 32 (8), p. 367; Christopher, M., Ryals, L., Supply chain strategy: Its impact on shareholder value (1999) International Journal of Logistics Management, 10 (1), pp. 1-10; Churchill, G.A., A paradigm for developing better measures of marketing constructs (1979) Journal of Marketing Research, 16 (1), pp. 64-73; Claassen, M.J., Van Weele, A.J., Van Raaij, E.M., Performance outcomes and success factors of vendor managed inventory (VMI) (2008) Supply Chain Management: An International Journal, 13 (6), pp. 406-414; Clark, T.H., Croson, D.C., Schiano, W.T., A hierarchical model of supply-chain integration: Information sharing and operational interdependence in the US grocery channel (2001) Information Technology and Management, 2 (3), p. 261; Cleland, A.S., Bruno, A.V., Building customer and shareholder value (1997) Strategy &Amp; Leadership, 25 (3), pp. 22-28; Clemons, E.K., Weber, B.W., Segmentation, differentiation, and flexible pricing: Experiences with information technology and segment-tailored strategies (1994) Journal of Management Information Systems, 11 (2), p. 9; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-13; Copulsky, W., Balancing the needs of customers and shareholders (1991) Journal of Business Strategy, 12 (6), pp. 44-45; Cornelius, I., Davies, M., (1997), Shareholder Value, FT Financial Publishing, London; Davenport, T.H., (1993) Process Innovation: Reengineering Work through Information Technology, , Harvard Business School Press, Boston, MA; Day, G.S., The capabilities of market-driven organizations (1994) Journal of Marketing, 58 (4), pp. 37-52; Day, G., Managing market relationships (2000) Academy of Marketing Science, 28 (1), pp. 24-30; Day, G.S., Fahey, L., Putting strategy into shareholder value analysis (1990) Harvard Business Review, 68 (2), p. 156; De Giovanni, P., Do internal and external environmental management contribute to the triple bottom line? (2012) International Journal of Operations &Amp; Production Management, 32 (3), pp. 265-290; Deming, W.E., Transformation of western style of management (1985) Interfaces, 15 (3), pp. 6-11; Doyle, P., (1994) Marketing Management and Strategy, , Prentice Hall International (UK), Hemel Hempstead; Drucker, P.F., (1974) Management: Tasks, Responsibilities and Practices, , Butterworth-Heinemann; Drucker, P.F., What is our business?” (2001) Executive Excellence, 18 (6), p. 3; Dubin, R., (1969) Theory Building, , Free Press, New York, NY; Eggert, A., Ulaga, W., Schultz, F., Value creating in the relationship life-cycle: A quasi-longitudinal analysis (2006) Industrial Marketing Management, 35 (1), pp. 20-27; Ettlie, J.E., Reza, E., Organizational integration and process innovation (1992) Academy of Management Journal, 35 (4), pp. 795-827; Faul, F., Erdfelder, E., Lang, A.G., Buchner, A., GPower 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences (2008) Behavior Research Methods, 39 (2), pp. 175-191; Feurer, R., Chaharbaghi, K., Defining competitiveness: A holistic approach (1994) Management Decision, 32 (2), p. 49; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance a contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Freudenthal, H., (1961) The Concept and the Role of the Model in Mathematics and Natural and Social Sciences, , Reidel Publishing, Dordrecht; Frohlich, M.T., E-integration in the supply chain: Barriers and performance (2002) Decision Sciences, 33 (4), p. 537; Gattorna, J.L., (1998) Strategic Supply Chain Alignment, , Gower, Surrey; Gerbing, D.W., Hamilton, J.G., Freeman, E.B., A large-scale second-order structural equation model of the influence of management participation on organizational planning benefits (1994) Journal of Management, 20 (4), pp. 859-885; Godsell, J., Harrison, A., Emberson, C., Storey, J., Customer responsive supply chain strategy: An unnatural act? (2006) International Journal of Logistics: Research and Applications, 9 (1), pp. 47-56; Godsell, J., Birtwistle, A., Van Hoek, R., Building the supply chain to enable business alignment: Lessons from British American Tobacco (BAT) (2010) Supply Chain Management: An International Journal, 15 (1), pp. 10-15; Godsell, J., Towill, D., Christopher, M., Clemmow, C., Diefenbach, T., Enabling supply chain segmentation through demand profiling (2011) International Journal of Physical Distribution and Logistics Management, 41 (3), pp. 142-147; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations &Amp; Production Management, 21 (1-2), pp. 71-87; Hair, J.F., Jr., Hult, G., Ringle, C., Sarstedt, M., (2014) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), , Sage Publications, London; Handfield, R.B., Melnyk, S.A., The scientific theory-building process: A primer using the case of TQM (1998) Journal of Operations Management, 16 (4), pp. 321-339; Hayes, A.F., Scharkow, M., The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis does method really matter? (2013) Psychological Science, 24 (10), pp. 1918-1927; Hendricks, K., Singhal, V., The effect of supply chain glitches on shareholder wealth (2003) Journal of Operations Management, 21 (5), pp. 501-522; Hill, T., (1985) Manufacturing Strategy: The Strategic Management of The Manufacturing Function, , 1st ed., Macmillan, Basingstoke; Holweg, M., An investigation into supplier responsiveness: Empirical evidence from the automotive industry (2005) International Journal of Logistics Management, 16 (1), p. 96; Houlihan, J.B., International supply chain management (1985) International Journal of Physical Distribution and Materials Management, 15 (1), pp. 22-38; Jaworski, B.J., Kohli, A.K., Market orientation: Antecedents and consequences (1993) Journal of Marketing, 57 (3), pp. 53-70; Jeong, J.S., Hong, P., Customer orientation and performance outcomes in supply chain management (2007) Journal of Enterprise Information Management, 20 (5), pp. 578-594; Johnson, G., Scholes, K., (1999) Exploring Corporate Strategy: Text and Cases, , 5th ed., Prentice Hall; Hempstead, H., Joshi, M.P., Kathuria, R., Porth, S.J., Alignment of strategic priorities and performance an integration of operations and strategic management perspectives (2003) Journal of Operations Management, 21 (3), pp. 353-369; Kaplan, A., (1964) The Conduct of Inquiry, , Chandler Publishing, San Francisco, CA; Kaplan, R.S., Norton, D.P., The strategy map: Guide to aligning intangible assets (2004) Strategy &Amp; Leadership, 32 (5), p. 10; Kathuria, R., Joshi, M.P., Porth, S.J., Organizational alignment and performance past, present and future (2007) Management Decision, 45 (3), pp. 503-517; Kovács, G., Spens, K.M., Abductive reasoning in logistics research (2005) International Journal of Physical Distribution &Amp; Logistics Management, 35 (2), pp. 132-144; Laitamaki, J., Kordupleski, R., Building and deploying profitable growth strategies based on the waterfall of customer value added (1997) European Management Journal, 15 (2), p. 158; Lambert, D.M., García-Dastugue, S.J., Croxton, K.L., An evaluation of process-oriented supply chain management frameworks (2005) Journal of Business Logistics, 26 (1), p. 25; Lawrence, P., Lorsch, J., (1967) Organization and Environment: Managing Differentiation and Integration, , Harvard Business School, Irwin, IL; Lee, H., The triple-a supply chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Lee, H., Whang, S., Information sharing in supply chain (2000) International Journal of Technology Management, 20 (3-4), pp. 373-387; Leong, G.K., Snyder, D.L., Ward, P.T., Research in the process and content of manufacturing strategy (1990) Omega, 18 (2), p. 109; Lewis, M., Slack, N., (2003) Operations Management: Critical Perspectives on Business Management, , Routledge, London; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Listou, T., “A critical realism perspective on logistics research (1998) NOFOMA 1998 Conference, , Helsinki; Luftman, J., Brier, T., Achieving and sustaining business-IT alignment (1999) California Management Review, 42 (1), pp. 109-122; McAdam, R., Brown, L., Strategic alignment and the supply chain for the steel stockholder sector: An exploratory case study analysis (2001) Supply Chain Management: An International Journal, 6 (2), pp. 83-94; McCormack, K., Ladeira, M.B., De Oliveira, M., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; McQuitty, S., Statistical power and structural equation models in business research (2004) Journal of Business Research, 57 (2), pp. 175-183; Malhotra, M.K., Singhal, C., Shang, G., Ployhart, R.E., A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations management research (2014) Journal of Operations Management, 32 (4), pp. 127-137; Marquez, A.C., Blanchard, C., A decision support system for evaluating operations investments in high-technology business (2006) Decision Support Systems, 41 (2), p. 472; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), p. 209; Mentzer, J.T., Kahn, K.B., A framework of logistics research (1995) Journal of Business Logistics, 16 (1), pp. 231-250; Meredith, J., Theory building through conceptual methods (1993) International Journal of Operations &Amp; Production Management, 13 (5), pp. 3-11; Meredith, J., Building operations management theory through case and field research (1998) Journal of Operations Management, 16 (4), pp. 441-454; Meredith, J.R., Hopes for the future of operations management (2001) Journal of Operations Management, 19 (4), pp. 397-402; Meredith, J.R., Raturi, A., Amoako-Gyampah, K., Kaplan, B., Alternative research paradigms in operations (1989) Journal of Operations Management, 8 (4), pp. 297-326; Morgan, C., Structure, speed and salience performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), p. 522; Nadler, D., Tushman, M., (1988) Strategic Organization Design: Concepts, Tools and Processes, , Scott Foresman. Nunnally, J. (1978), Psychometric Theory, McGraw-Hill, New York, NY; O’leary-Kelly, S., Flores, B.E., The integration of manufacturing and marketing/sales decisions: Impact on organizational performance (2002) Journal of Operations Management, 20 (3), pp. 221-240; O’Regan, N., Ghobadian, A., The importance of capabilities for strategic direction and performance (2004) Management Decision, 42 (2), pp. 292-312; Pagell, M., Understanding the factors that enable and inhibit the integration of operations and logistics (2004) Journal of Operations Management, 22 (5), p. 459; Parasuraman, A., Reflections on gaining competitive advantage through customer value (1997) Academy of Marketing Science Journal, 25 (2), pp. 154-161; Popper, K., (1959) The Logic of Scientific Discovery, , Routledge, London. Porter, M.E. (1985), Competitive Advantage: Creating and Sustaining Superior Performance, Free Press, a division of Macmillan; Powell, T.C., Organizational alignment as competitive advantage (1992) Strategic Management Journal, 13 (2), pp. 119-134; Rappaport, A., Linking competitive strategy and shareholder value analysis (1987) Journal of Business Strategy, 7 (4), p. 58; Ringle, C.M., Sven, J.M., Smart PLS 2.0, SmartPLS, Hamburg. Rosenzweig, E.D. (2009), “A contingent view of e-collaboration and performance in manufacturing” (2005) Journal of Operations Management, 27 (6), pp. 462-478; Rungtusanatham, M., Miller, J.W., Boyer, K.K., Theorizing, testing, and concluding for mediation in SCM research: Tutorial and procedural recommendations (2014) Journal of Operations Management, 32 (3), pp. 99-113; Sahin, F., Robinson, E.P., Flow coordination and information sharing in supply chains: Review, implications, and directions for future research (2002) Decision Sciences, 33 (4), p. 505; Schmenner, R.W., Swink, M.L., On theory in operations management (1998) Journal of Operations Management, 17 (2), pp. 97-113; Schmenner, R.W., Vollmann, T.E., Performance measures: Gaps, false alarms and the ‘usual suspects (1994) International Journal of Operations &Amp; Production Management, 14 (12), pp. 58-69; Schneiderjans, M.J., Cao, Q., Alignment of operations strategy, information strategic orientation and performance an empirical study (2009) International Journal of Production Research, 47 (10), pp. 2535-2563; Skinner, W., Manufacturing: Missing link in corporate strategy (1969) Harvard Business Review, 47, pp. 136-145; Slater, S.F., Narver, J.C., Market orientation, customer value, and superior performance (1994) Business Horizons, 37 (2), p. 22; Srivastava, R.K., Shervani, T.A., Fahey, L., Marketing, business processes, and shareholder value: An organizationally embedded view of marketing activities and the discipline of marketing (1999) Journal of Marketing, 63 (4), p. 168; Stock, J.R., Applying theories from other disciplines to logistics (1997) International Journal of Physical Distribution &Amp; Logistics Management, 27 (9-10), pp. 515-539; Stock, J.R., A research view of supply chain management: Developments and topics for exploration (2009) Orion, 25 (2), pp. 147-160; Storey, J., Emberson, C., Reade, D., The barriers to customer responsive supply chain management (2005) International Journal of Operations &Amp; Production Management, 25 (3-4), p. 242; Storey, J., Emberson, C., Godsell, J., Harrison, A., Supply chain management: Theory, practice and future challenges (2006) International Journal of Operations &Amp; Production Management, 26 (7), p. 754; Straub, D., Carlson, C.L., Validating instruments in MIS research (1989) MIS Quarterly, 13 (2), pp. 147-169; Sun, S.Y., Hsu, M.H., Hwang, W.J., The impact of alignment between supply chain strategy and environmental uncertainty on SCM performance (2009) Supply Chain Management: An International Journal, 14 (3), pp. 201-212; Svensson, G., Processes of substantiations and contributions through theory building towards theory in business research (2013) European Business Review, 25 (5), pp. 466-480; Tamas, M., Mismatched strategies: The weak link in the supply chain? (2000) Supply Chain Management and International Journal, 5 (4), pp. 171-175; Tarigan, R., An evaluation of the relationship between alignment of strategic priorities and manufacturing performance (2005) International Journal of Management, 22 (4), pp. 586-597; Thompson, J., (1967) Organizations in Action, , McGraw-Hill, New York, NY; Tracey, M., Lim, J., Vonderembse, M.A., “The impact of supply-chain management capabilities on business performance (2005) Supply Chain Management, 10 (3-4), p. 179; Tsikriktsis, N., A review of techniques for treating missing data in OM survey research (2005) Journal of Operations Management, 24 (1), pp. 53-62; Van De Ven, A.H., (2007) Engaged Scholarship: A Guide for Organizational and Social Research, , Oxford University Press, Oxford, New York, NY; Van Hoek, R.I., Mitchell, A.J., The challenge of internal misalignment (2006) International Journal of Logistics: Research and Applications, 9 (3), pp. 269-281; Venkatraman, N., The concept of fit in strategy research: Toward verbal and statistical correspondence (1989) The Academy of Management Review, 14 (3), pp. 423-444; Wacker, J., A definition of theory: Research guidelines for different theory-building research methods in operations management (1998) Journal of Operations Management, 16 (4), pp. 361-385; Wheelwright, S.C., Manufacturing strategy: Defining the missing link (1984) Strategy Management Journal, 5 (1), pp. 77-91; Wong, C., Skipworth, H., Godsell, J., Achimugu, N., Towards a theory of supply chain alignment enablers: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 419-437; Yin, R.K., (2014) Case Study Research: Design and Methods, , 5th ed., Sage Publications, Thousand Oaks, CA; Zhong, L., Godsell, J., Johnson, M., Whats the logic?: An empirical exploration of theory de‘velopment in operations and supply chain management” (2015) Proceedings of 22Nd Euroma Conference, Neuchatel},
correspondence_address1={Skipworth, H.; Supply Chain Research Centre, United Kingdom; email: heather.skipworth@cranfield.ac.uk},
publisher={Emerald Group Holdings Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{D’Haene2015146,
author={D’Haene, C. and Verlinde, S. and Macharis, C.},
title={Measuring while moving (humanitarian supply chain performance measurement – status of research and current practice)},
journal={Journal of Humanitarian Logistics and Supply Chain Management},
year={2015},
volume={5},
number={2},
pages={146-161},
doi={10.1108/JHLSCM-04-2013-0016},
note={cited By 24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958742525&doi=10.1108%2fJHLSCM-04-2013-0016&partnerID=40&md5=7531386de92a6ba995ca5db2860d61ba},
affiliation={Médecins Sans Frontières, Brussels, Belgium; Research Group MOBI (Mobility, Logistics and Automotive Technology), Vrije Universiteit Brussel, Brussels, Belgium},
abstract={Purpose – Raising standards are leading humanitarians to become increasingly interested in measuring the performance of their supply chain. A few researchers have addressed this topic, building on classical measurement theories and trying to identify the salient features of the humanitarian sector. This young body of literature must now be tested against current practice. The paper aims to discuss these issues. Design/methodology/approach – The practitioners’ perspective is explored through a multiple case study, based on qualitative evidence, within three major humanitarian organizations. Results are discussed using a situation-actor-process – learning-action-performance model of inquiry. Preceding this investigation, a literature review delves into research on humanitarian supply chain performance measurement and presents the most relevant pieces in a general framework. Findings – Humanitarians have elaborated measures whose essential focus is laid on service level. The more comprehensive approach advocated by researchers has received some consideration but is still poorly implemented. An issue that is given more priority by humanitarians is the accelerated supply chain integration they are going through. Originality/value – In addition to an unprecedented literature review, this paper offers a comparative study of humanitarian organizations’ practices in the field of supply chain performance measurement. Humanitarian procurement centres, virtually ignored in literature until now, are scrutinized at a turning point of their evolution. © 2015, © Emerald Group Publishing Limited.},
author_keywords={Humanitarian logistics;  Humanitarian supply chain;  Performance measurement;  Supply chain integration},
references={(2010) The State of the Humanitarian System: Assessing Performance and Progress, a Pilot Study, , Overseas Development Institute, London:; Arshinder, K.A., Desmukh, S.G., Supply chain coordination issues: an SAP-LAP framework (2007) Asia Pacific Journal of Marketing and Logistics, 19 (3), pp. 240-264. , “”; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , “”; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector, 21 (1), pp. 4-25. , “”; Blecken, A., Hellingrath, B., Dangelmaier, W., Schulz, S.F., A humanitarian supply chain process reference model (2009) International Journal of Services Technology and Management, 12 (4), pp. 391-413. , “”; Bölsche, D., (2012) Performance measurement in humanitarian logistics – a process-oriented perspective, , “”; Bourne, M., Kennerley, M., Franco-Santos, M., Managing through measures: a study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), pp. 373-395. , “”; (2004), “Literature review on performance measurement management”, prepared for The IDeA Audit Commission Performance Management, Measurement Information (PMMI) Project, CBP, Cranfield University, Cranfield; Charan, P., Supply chain performance issue in an automobile company: a SAP-LAP analysis (2012) Measuring Business Excellence, 16 (1), pp. 67-86. , “”; Davidson, A.L., (2006), “Key performance indicators in humanitarian logistics”, master of engineering in logistics thesis, Massachusetts Institute of Technology, Cambridge, MA; De Leeuw, S., Towards a reference mission map for performance measurement in humanitarian supply chains (2010) International Federation for Information Processing, Advances in Information and Communication Technology Series, 336, pp. 181-188. , “”; Greiling, D., (2011) Performance measurement systems in non-profit organizations as management tool or an option for strategic responses?, , www.feb.ugent.be/accoeco/Papers_Cigar2011/72, “” Ghent, Performance Measurement Systems in Non-profit Organisations.pdf, June 9-10, :. (accessed March 29, 2013); Guyoton, S., Muirheid, B., (2002), “Building a balanced scorecard for the IFRC”, unpublished master thesis, INSEAD Business School, Fontainebleau; Kanter, R.M., Summers, D.S., Doing good while doing well: dilemmas in performance measurement in nonprofit organizations and the need for a multiple constituency approach (1987) The Nonprofit Sector: A Research Handbook, pp. 154-166. , Powell, W.W.), “”, in(Ed.) Yale University Press, New Haven, CT:; Kaplan, R.S., Norton, D.P., (2000), “Having trouble with your strategy? Then map it” September-October, 167-176; Kaplan, R.S., Norton, D.P., (1992), “The balanced scorecard: measures that drive performance” January-February, 71-79; Kent, R.C., International humanitarian crises: two decades before and two decades beyond (2004) International Affairs (Royal Institute of International Affairs 1944-), 80 (5), pp. 851-869. , “”; Kim, D.Y., Relationship between supply chain integration and performance (2013) Operations Management Research, 6 (1-2), pp. 74-90. , “”; Kovács, G., Tatham, P., Responding to disruptions in the supply network – from dormant to action (2009) Journal of Business Logistics, 30 (2), pp. 215-229. , “”; Lee, H.L., (2004), “The triple-a supply chain” October, 102-112; Lijo, J., Ramesh, A., Humanitarian supply chain management in India: a SAP-LAP framework (2012) Journal of Advances in Management Research, 9 (2), pp. 217-235. , “”; McLachlin, R., Larson, P.D., Khan, S., Not-for-profit supply chains in interrupted environments. the case of a faith-based humanitarian relief organization (2009) Management Research News, 32 (11), pp. 1050-1064. , “”; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced scorecard for natural disaster management projects (2007) Disaster Prevention and Management, 16 (5), pp. 785-806. , “”; Neely, A.D., Powell, S., The challenges of performance measurement: Andy Neely in conversation with Sarah Powell (2004) Management Decision, 42 (7-8), pp. 1017-1023. , “”; (2011), http://fts.unocha.org/pageloader.aspx?page=Trend-TrendAnalysis, “Funding trend analysis”, :(accessed March 29, 2013); Oloruntoba, R., Gray, R., Humanitarian aid_ an agile supply chain? (2006) Supply Chain Management: An International Journal, 11 (2), pp. 115-120. , “”; Patton, M.Q., (1980) Qualitative Evaluation Methods, , Sage Publications, Beverly Hills, CA:; Salomons, D., (2012), www.sipa.columbia.edu/academics/concentrations/ha/ghsi/index.html, “Global Humanitarian Studies Index”, :(accessed March 29, 2013); Scholten, K., Sharkey-Scott, P., Fynes, B., (Le)agility in humanitarian aid (NGO) supply chains (2010) International Journal of Physical Distribution and Logistics Management, 40 (8-9), pp. 623-635. , “”; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Management Research News, 32 (11), pp. 1038-1049. , “”; Sofaer, S., Qualitative research methods: what are they and why use them? (1999) Health Services Research, 34 (5), pp. 1101-1118. , “”; Stewart, G., Supply-chain operations reference model (SCOR): the first cross-industry framework for integrated supply-chain management (1997) Logistics Information Management, 10 (2), pp. 62-67. , “”; Sushil, S., SAP-LAP models of inquiry (2000) Management Decision, 38 (5), pp. 347-353. , “”; Tatham, P.H., Hughes, K., Humanitarian logistic metrics: where we are, and how we might improve (2011) Humanitarian Logistics: Meeting the Challenge of Preparing for and Responding to Disasters, pp. 65-84. , Christopher, M.Tatham, P.), “”, in(Eds) Kogan Page, London:; Thomas, A.S., Kopczak, L.R., (2005) From Logistics to Supply Chain Management: The Path Forward in the Humanitarian Sector, , Fritz Institute, San Francisco, CA:; Tomasini, R., Van Wassenhove, L., (2009) Humanitarian Logistics, , Palgrave Macmillan, Basingstoke:; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14 (3), pp. 207-222. , “”; Van der Laan, E.A., de Brito, M.P., Vergunst, D.A., Performance measurement in humanitarian supply chains (2009) International Journal of Risk Assessment and Management, 13 (1), pp. 22-45. , “”; Walker, P., Russ, C., Fit for purpose: the role of modern professionalism in evolving the humanitarian endeavour (2011) International Review of the Red Cross, 93 (884), pp. 1193-1210. , “”; Whetten, D.A., What constitutes a theoretical contribution? (1989) Academy of Management Review, 14 (4), pp. 490-495. , “”; Yin, R.K., The case study crisis: some answers (1981) Administrative Science Quarterly, 26 (1), pp. 58-65. , “”},
publisher={Emerald Group Publishing Ltd.},
issn={20426747},
language={English},
abbrev_source_title={J. Humanit. Logist. Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tajbakhsh2015744,
author={Tajbakhsh, A. and Hassini, E.},
title={Performance measurement of sustainable supply chains: A review and research questions},
journal={International Journal of Productivity and Performance Management},
year={2015},
volume={64},
number={6},
pages={744-783},
doi={10.1108/IJPPM-03-2013-0056},
note={cited By 92},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936971232&doi=10.1108%2fIJPPM-03-2013-0056&partnerID=40&md5=8004ca93f441c420c760b92d23859efd},
affiliation={DeGroote School of Business, McMaster University, Hamilton, Canada},
abstract={Purpose – One of the hurdles to the adoption of sustainable practices across supply chains is the lack of pan-chain performance measurements and their related information and organizational structures. The authors review the literature on performance measurement of sustainable supply chains with a focus on comprehensive measures that include multiple supply chain partners as well as different sustainability aspects. The purpose of this paper is to analyze the reviewed literature and propose some research questions. Design/methodology/approach – The authors reviewed 140 journal articles, cases and reports that appeared since 1994. Findings – The authors classify the reviewed literature according to seven sustainability dimensions (economical, environmental, social, reputable, valuable, equitable and sustainable) as well as the type of industry and methodology used. In addition the authors synthesize the available performance measurements into a comprehensive framework that incorporates different stages of the supply chain operations and decision-making processes. Social implications – The results of this study can be used by researchers to focus on research that may have more implications on supply chains. Practitioners can use the authors proposed performance measurement framework for developing practical and comprehensive measures for their respective industries. Originality/value – The work is original in the way the authors integrate sustainability (seven dimensions) across the supply chain taking into account the type of operational decisions. The framework can be used by researchers and practitioners to develop practical sustainability performance measurement systems for supply chains. © Emerald Group Publishing Limited.},
author_keywords={Green issues;  Measurement;  Performance measurement;  Supply chain;  Sustainability},
references={Abeysundra, U.G., Babel, S., Gheewala, S., Sharp, A., Environmental, economic and social analysis of materials for doors and windows in Sri Lanka (2007) Building and Environment, 42 (5), pp. 2141-2149; Adivar, B., Atan, T., Oflaç, B.S., Örten, T., Improving social welfare chain using optimal planning model (2010) Supply Chain Management: An International Journal, 15 (4), pp. 290-305; Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) Journal of Cleaner Production, Vol. 52, August, pp. 329-341; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Allen, M.W., Walker, K.L., Brady, R., Sustainability discourse within a supply chain relationship mapping convergence and divergence (2012) Journal of Business Communication, 49 (3), pp. 210-236; Amoako-Gyampah, K., Acquaah, M., Manufacturing strategy, competitive strategy and firm performance: an empirical study in a developing economy environment (2008) International Journal of Production Economics, 111 (2), pp. 575-592; Azapagic, A., Developing a framework for sustainable development indicators for the mining and minerals industry (2004) Journal of Cleaner Production, 12 (6), pp. 639-662; Azevedo, S.G., Govindan, K., Carvalho, H., Cruz-Machado, V., Ecosilient index to assess the greenness and resilience of the upstream automotive supply chain (2013) Journal of Cleaner Production, 56, pp. 131-146; Bai, C., Sarkis, J., Green supplier development: Analytical evaluation using rough set theory (2010) Journal of Cleaner Production, 18 (12), pp. 1200-1210; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Balon, V., Sharma, A.K., Barua, M.K., Katiyar, R., A performance measurement of green supply chain management in Indian auto industries (2012) National Conference on Emerging Challenges for Sustainable Business, pp. 1577-1583; Baresel-Bofinger, A., Ketikidis, P.H., Koh, S., Cullen, J., Innovative measures for green supply chain management in South-East Europe (2007) International Conference on Supply Chain Management and Information Systems, pp. 1-10; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations &Amp; Production Management, 19 (3), pp. 275-292; Beske, P., Koplin, J., Seuring, S., The use of environmental and social standards by German first-tier suppliers of the Volkswagen AG (2008) Corporate Social Responsibility and Environmental Management, 15 (2), pp. 63-75; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers &Amp; Industrial Engineering, 53 (1), pp. 43-62; Björklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Bolstorff, P., Rosenbaum, R.G., (2007) Supply Chain Excellence: a Handbook for Dramatic Improvement Using the SCOR Model, , Amacom Books, New York, NY; Bos-Brouwers, H., Corporate sustainability and innovation in SMEs: Evidence of themes and activities in practice (2010) Business Strategy and the Environment, 19 (7), pp. 417-435; Burgess, T.F., Heap, J., Creating a sustainable national index for social, environmental and economic productivity (2012) International Journal of Productivity and Performance Management, 61 (4), pp. 334-358; Cao, D., Chen, Z., (2010) Evaluation of Green Supply Chain Performance Based on Fuzzy Method and Grey Incidence Analysis, pp. 858-861. , Fuzzy Systems and Knowledge Discovery, International Conference; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) International Journal of Physical Distribution &Amp; Logistics Management, 41 (1), pp. 46-62; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution &Amp; Logistics Management, 38 (5), pp. 360-387; Cetinkaya, B., Cuthbertson, R., Ewer, G., Klaas-Wissing, T., Piotrowicz, W., Tyssen, C., (2011) Sustainable Supply Chain Management: Practical Ideas for Moving Towards Best Practice, , Springer, Heidelberg; Chae, B.K., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chen, J., Weng, Y., Zhao, S., (2009) Performance Evaluation of Green Supply Chain Based on Entropy Weight Grey System Model, pp. 474-478. , Service Systems and Service Management, International Conference; Chopra, S., Meindl, P., (2013) Supply Chain Management: Strategy, Planning and Operation, , Pearson Higher Education, Boston; Clemens, B., Economic incentives and small firms: Does it pay to be green? (2006) Journal of Business Research, 59 (4), pp. 492-500; Clift, R., Metrics for supply chain sustainability (2003) Clean Technologies and Environmental Policy, 5 (3), pp. 240-247; Cobb, G.B., (2011) Model for Sustainable Business Performance Measures for Supply Chain Integration, , graduate dissertation, Appalachian State University, Boone, NC; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-14; Darnall, N., Jolley, G.J., Handfield, R., Environmental management systems and green supply chain management: Complements for sustainability? (2008) Business Strategy and the Environment, 17 (1), pp. 30-45; De Brito, M.P., Carbone, V., Blanquart, C.M., Towards a sustainable fashion retail supply chain in Europe: Organisation and performance (2008) International Journal of Production Economics, 114 (2), pp. 534-553; De Carvalho, J.F., Measuring economic performance, social progress and sustainability using an index (2011) Renewable and Sustainable Energy Reviews, 15 (2), pp. 1073-1079; De Felice, F., Petrillo, A., Cooper, O., Multicriteria analysis to evaluate influence of green practices on supply chain performance (2012) Science Journal of Business Management, 2012 (2), pp. 1-12; Dehghanian, F., Mansoor, S., Nazari, M., A framework for integrated assessment of sustainable supply chain management (2011) Industrial Engineering and Engineering Management, International Conference, pp. 279-283; Diabat, A., Govindan, K., An analysis of the drivers affecting the implementation of green supply chain management (2011) Resources, Conservation and Recycling, 55 (6), pp. 659-667; Dobrzykowski, D., Hong, P., Kim, S., Saboorideilami, V., Supply chain research in healthcare: A literature review and research directions (2012) International Supply Chain Management Symposium and Workshop, pp. 1-19; El Saadany, A., Jaber, M.Y., Bonney, M., Environmental performance measures for supply chains (2011) Management Research Review, 34 (11), pp. 1202-1221; Elkington, J., (1997) Cannibals with Forks: The Triple Bottom Line of 21St Century Business, , Capstone, Oxford; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70 (6), pp. 1088-1100; Esty, D.C., Levy, M., Srebotnjak, T., De Sherbinin, A., (2005) Environmental Sustainability Index: Benchmarking National Environmental Stewardship, , Yale Center for Environmental Law & Policy, Yale University, New Haven, CT; Facanha, C., Horvath, A., Environmental assessment of logistics outsourcing (2005) Journal of Management in Engineering, 21 (1), pp. 27-37; Farrell, A., (1996) Making Decisions about Sustainability: Joining Social Values with Technical Expertise, pp. 188-197. , Technology and Society Technical Expertise and Public Decisions, International Symposium; Fortes, J., Green supply chain management: A literature review (2009) Otago Management Graduate Review, 7, pp. 51-62; Fynes, B., De Burca, S., Voss, C., Supply chain relationship quality, the competitive environment and performance (2005) International Journal of Production Research, 43 (16), pp. 3303-3320; Gao, Y., Li, J., Song, Y., Performance evaluation of green supply chain management based on membership conversion algorithm (2009) Computing, Communication, Control, and Management, International Colloquium, pp. 237-240; Gates, S., Germain, C., The integration of sustainability measures into strategic performance measurement systems: An empirical study (2010) Management Accounting Quarterly, 11 (3), pp. 1-7; (2011) Sustainability Reporting Guidelines: GRI Ver. 3.1, , Global Reporting Initiative, Global Reporting Initiative, Amsterdam; Gomez, A., Rodriguez, M.A., The effect of ISO 14001 certification on toxic emissions: An analysis of industrial facilities in the north of Spain (2011) Journal of Cleaner Production, 19 (9), pp. 1091-1095; Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2013) Journal of Cleaner Production, 47, pp. 345-354; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations &Amp; Production Management, 21 (1-2), pp. 71-87; (1995) Environmental Indicators: A Systematic Approach to Measuring and Reporting on Environmental Policy Performance in the Context of Sustainable Development, , Hammond, A.L. and World Resources Institute, World Resources Institute, Washington; Harzing, A.W., A preliminary test of Google Scholar as a source for citation data: A longitudinal study of Nobel Prize winners (2013) Scientometrics, 94 (3), pp. 1057-1075; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hausman, W.H., Supply chain performance metrics (2004) The Practice of Supply Chain Management: Where Theory and Application Converge, 62, pp. 61-73; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hickey, G.M., Evaluating sustainable forest management (2008) Ecological Indicators, 8 (2), pp. 109-114; Holt, D., Ghobadian, A., An empirical study of green supply chain management practices amongst UK manufacturers (2009) Journal of Manufacturing Technology Management, 20 (7), pp. 933-956; Holton, I., Glass, J., Price, A., Managing for sustainability: Findings from four company case studies in the UK precast concrete industry (2010) Journal of Cleaner Production, 18 (2), pp. 152-160; Hooper, P.D., Greenall, A., Exploring the potential for environmental performance benchmarking in the airline sector (2005) Benchmarking: An International Journal, 12 (2), pp. 151-165; Huang, S.H., Keskar, H., Comprehensive and configurable metrics for supplier selection (2007) International Journal of Production Economics, 105 (2), pp. 510-523; Hussey, D.M., Eagan, P.D., Pojasek, R.B., (2002) A Performance Model for Driving Environmental Improvement down the Supply Chain, pp. 107-112. , Electronics and the Environment, International Symposium; Hutchins, M.J., Sutherland, J.W., An exploration of measures of social sustainability and their application to supply chain decisions (2008) Journal of Cleaner Production, 16 (15), pp. 1688-1698; Hutchins, M.J., Gierke, J.S., Sutherland, J.W., Decision making for social sustainability: A life-cycle assessment approach (2009) Technology and Society, International Symposium, pp. 1-5; Jain, R., Sustainability: Metrics, specific indicators and preference index (2005) Clean Technologies and Environmental Policy, 7 (2), pp. 71-72; Jalali Naini, S.G., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 593-603; Jasch, C., Environmental performance evaluation and indicators (2000) Journal of Cleaner Production, 8 (1), pp. 79-88; Jiansheng, Z., Wei, T., The study of performance evaluation index of green supply chain and comprehensive evaluation model (2010) E-Product, E-Service and E-Entertainment, International Conference, pp. 1-4; Kang, S., Juanmei, Y., Study on the performance evaluation of green supply chain based on the balance scorecard and fuzzy theory (2010) Information Management and Engineering, International Conference, pp. 242-246; Kehbila, A.G., Ertel, J., Brent, A.C., Corporate sustainability, ecological modernization and the policy process in the South African automotive industry (2010) Business Strategy and the Environment, 19 (7), pp. 453-465; Kim, I., Min, H., Measuring supply chain efficiency from a green perspective (2011) Management Research Review, 34 (11), pp. 1169-1189; Klang, A., Vikman, P.-Å., Brattebø, H., Sustainable management of demolition waste – an integrated model for the evaluation of environmental, economic and social aspects (2003) Resources, Conservation and Recycling, 38 (4), pp. 317-334; Klassen, R.D., McLaughlin, C.P., The impact of environmental management on firm performance (1996) Management Science, 42 (8), pp. 1199-1214; Kleijnen, J., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Krajnc, D., Glavič, P., Indicators of sustainable production (2003) Clean Technologies and Environmental Policy, 5 (3), pp. 279-288; Krajnc, D., Glavič, P., A model for integrated assessment of sustainable development (2005) Resources, Conservation and Recycling, 43 (2), pp. 189-208; Krajnc, D., Glavič, P., How to compare companies on relevant dimensions of sustainability (2005) Ecological Economics, 55 (4), pp. 551-563; Labuschagne, C., Brent, A., Social indicators for sustainable project and technology life cycle management in the process industry (2006) The International Journal of Life Cycle Assessment, 11 (1), pp. 3-15; Labuschagne, C., Brent, A.C., Van Erck, R., Assessing the sustainability performances of industries (2005) Journal of Cleaner Production, 13 (4), pp. 373-385; Larson, P.D., Halldórsson, Á., Logistics versus supply chain management: An international survey (2004) International Journal of Logistics: Research and Applications, 7 (1), pp. 17-31; Lee, A., Kang, H.-Y., Hsu, C.-F., Hung, H.-C., A green supplier selection model for high-tech industry (2009) Expert Systems with Applications, 36 (4), pp. 7917-7927; Lin, R.-J., Using fuzzy DEMATEL to evaluate the green supply chain management practices (2013) Journal of Cleaner Production, 40, pp. 32-39; Lisa, S., Indicators of environment and sustainable development theories and practical experience (2002) The International Bank for Reconstruction and Development, 2002, pp. 1-61; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental performance indicators for integrated supply chains: The case of Xerox Ltd (1998) Supply Chain Management: An International Journal, 3 (3), pp. 149-156; McKay, H., Environmental, economic, social and political drivers for increasing use of woodfuel as a renewable resource in Britain (2006) Biomass and Bioenergy, 30 (4), pp. 308-315; Mayer, A.L., Strengths and weaknesses of common sustainability indices for multidimensional systems (2008) Environment International, 34 (2), pp. 277-291; Mayyas, A.T., Qattawi, A., Mayyas, A.R., Omar, M., Quantifiable measures of sustainability: A case study of materials selection for eco-lightweight auto-bodies (2013) Journal of Cleaner Production, 40, pp. 177-189; Mentzer, J.T., Dewitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Metta, H., Badurdeen, F., A framework for coordinated sustainable product and supply chain design (2009) Value Chain Sustainability, International Conference, pp. 20-25; Mincer, J., The color of money: Sustainability has become more than a buzzword among corporations. It has become smart business (2008) Wall Street Journal, , http://online.wsj.com/article/SB122305414262702711.html, October 6; Molina-Azorín, J.F., Claver-Cortés, E., Pereira-Moliner, J., Tarí, J.J., Environmental practices and firm performance: an empirical analysis in the Spanish hotel industry (2009) Journal of Cleaner Production, 17 (5), pp. 516-524; Munda, G., Nijkamp, P., Rietveld, P., Qualitative multicriteria evaluation for environmental management (1994) Ecological Economics, 10 (2), pp. 97-112; Nardo, M., Saisana, M., Saltelli, A., Tarantola, S., (2005) Tools for Composite Indicators Building, European Commission, , Joint Research Centre, Institute for the Protection and the Security of the Citizen, Ispra; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations &Amp; Production Management, 15 (4), pp. 80-116; Nikolaou, I.E., Evangelinos, K.I., Allan, S., A reverse logistics social responsibility evaluation framework based on the triple bottom line approach (2013) Journal of Cleaner Production, 56, pp. 173-184; Norman, W., Macdonald, C., Getting to the bottom of‘ triple bottom line (2004) Business Ethics Quarterly, 14 (2), pp. 243-262; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F., State of the art literature review on performance measurement (2011) Computers &Amp; Industrial Engineering, 60 (2), pp. 279-290; (2008) Handbook on Constructing Composite Indicators: Methodology and User Guide, , OECD, Technical Report No. ISBN 978-92-64-04345-9, Organisation for Economic Co-operation and Development, Paris; Orti, P.S., Cavenaghi, V., Albino, J.P., Sustainability and performance management systems (2010) Production and Operation Management Society, Annual Conference, pp. 1-20; Patlitzianas, K.D., Doukas, H., Kagiannas, A.G., Psarras, J., Sustainable energy policy indicators: Review and recommendations (2008) Renewable Energy, 33 (5), pp. 966-973; Paulraj, A., Chen, I.J., Environmental uncertainty and strategic supply management: A resource dependence perspective and performance implications (2007) Journal of Supply Chain Management, 43 (3), pp. 29-42; Perrini, F., Tencati, A., Sustainability and stakeholder management: The need for new corporate performance evaluation and reporting systems (2006) Business Strategy and the Environment, 15 (5), pp. 296-308; Petrie, J., Cohen, B., Stewart, M., Decision support frameworks and metrics for sustainable development of minerals and metals (2007) Clean Technologies and Environmental Policy, 9 (2), pp. 133-145; Piplani, R., Pujawan, N., Ray, S., Sustainable supply chain management (2008) International Journal of Production Economics, 111 (2), pp. 193-194; Qingmin, Y., Lipeng, W., A study on green supply chain achievements evaluation based on DEA model (2009) Management and Service Science, International Conference, pp. 1-3; Rehman, M., Rakesh, L.S., An innovative approach to evaluate green supply chain management (GSCM) drivers by using interpretive structural modeling (ISM) (2011) International Journal of Innovation and Technology Management, 8 (2), pp. 315-336; Rogers, M., Ryan, R., The triple bottom line for sustainable community development (2001) Local Environment, 6 (3), pp. 279-289; Russo, M.V., Fouts, P.A., A resource-based perspective on corporate environmental performance and profitability (1997) Academy of Management Journal, 40 (3), pp. 534-559; Saisana, M., Tarantola, S., (2002) State-Of-The-Art Report on Current Methodologies and Practices for Composite Indicator Development, European Commission, , Joint Research Centre, Institute for the Protection and the Security of the Citizen, Ispra; Salzmann, O., Ionescu-Somers, A., Steger, U., The business case for corporate sustainability: Literature review and research options (2005) European Management Journal, 23 (1), pp. 27-36; Samuel, V.B., Agamuthu, P., Hashim, M.A., Indicators for assessment of sustainable production: A case study of the petrochemical industry in Malaysia (2013) Ecological Indicators, 24, pp. 392-402; Sarkis, J., The adoption of environmental and risk management practices: Relationships to environmental performance (2006) Annals of Operations Research, 145 (1), pp. 367-381; Sarkis, J., Talluri, S., A synergistic framework for evaluating business process improvements (2002) International Journal of Flexible Manufacturing Systems, 14 (1), pp. 53-71; Sarkis, J., Talluri, S., Evaluating and selecting e-commerce software and communication systems for a supply chain (2004) European Journal of Operational Research, 159 (2), pp. 318-329; Sarkis, J., Zhu, Q., Lai, K., An organizational theoretic review of green supply chain management literature (2011) International Journal of Production Economics, 130 (1), pp. 1-15; Schwab, K., Sala-I-martin, X., (2012) The Global Competitiveness Report 2012-2013, , World Economic Forum, Geneva; (2009) Green Supply Chain Management Manufacturing – a Canadian Perspective, , SCL, Technical Report No. ISBN 978-1-100-13651-6, Supply Chain and Logistics Association, Toronto; Searcy, C., Karapetrovic, S., McCartney, D., Designing sustainable development indicators: Analysis for a case utility (2005) Measuring Business Excellence, 9 (2), pp. 33-41; Searcy, C., Karapetrovic, S., McCartney, D., Designing corporate sustainable development indicators: Reflections on a process (2009) Environmental Quality Management, 19 (1), pp. 31-42; Searcy, C., McCartney, D., Karapetrovic, S., Sustainable development indicators for the transmission system of an electric utility (2007) Corporate Social Responsibility and Environmental Management, 14 (3), pp. 135-151; Searcy, C., McCartney, D., Karapetrovic, S., Identifying priorities for action in corporate sustainable development indicator programs (2008) Business Strategy and the Environment, 17 (2), pp. 137-148; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339; Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A., A fuzzy multi criteria approach for evaluating green suppliers performance in green supply chain with linguistic preferences” (2013) Resources, Conservation and Recycling, 74, pp. 170-179; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shuaib, M., Metta, H., Lu, T., Badurdeen, F., Jawahir, I.S., Goldsby, T., Design and performance evaluation of sustainable supply chains: Approach and methodologies (2011) Advances in Sustainable Manufacturing, Global Conference, pp. 347-352; Shuwang, W., Lei, Z., Zhifeng, L., Guangfu, L., Zhang, H.C., Study on the performance assessment of green supply chain (2005) Systems, Man and Cybernetics, International Conference, pp. 942-947; Simchi-Levi, D., Kaminsky, P., Edith, S.-L., (2007) Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies, , McGraw-Hill, New York, NY; Smeets, E., Lewandowski, I.M., Faaij, A., The economical and environmental performance of miscanthus and switchgrass production and supply chains in a European setting (2009) Renewable and Sustainable Energy Reviews, 13 (6), pp. 1230-1245; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Svensson, G., Aspects of sustainable supply chain management (SSCM): Conceptual framework and empirical example (2007) Supply Chain Management: An International Journal, 12 (4), pp. 262-266; Tajbakhsh, A., Hassini, E., A multi-stage DEA approach to evaluate sustainability in multi-partner supply chain networks (2014) Journal of Cleaner Production, , (in press); Tajbakhsh, A., Hassini, E., (2014) Two-Stage Data Envelopment Analysis with Heterogeneous Intermediate Values, , DeGroote School of Business, McMaster University, (working paper); Taylor, D.A., (2004) Supply Chains: A Manager’s Guide, , Addison-Wesley Professional, Boston; Toke, L.K., Gupta, R.C., Dandekar, M., An empirical study of green supply chain management in Indian perspective (2012) International Journal of Applied Science and Engineering Research, 1 (2), pp. 372-383; Tseng, M.-L., Green supply chain management with linguistic preferences and incomplete information (2011) Applied Soft Computing, 11 (8), pp. 4894-4903; Tseng, M.-L., Modeling sustainable production indicators with linguistic preferences (2013) Journal of Cleaner Production, 40, pp. 46-56; Tseng, M.-L., Chiu, A., Evaluating firms green supply chain management in linguistic preferences” (2013) Journal of Cleaner Production, 40, pp. 22-31; Tseng, M.-L., Wang, R., Chiu, A., Geng, Y., Lin, Y.H., Improving performance of green innovation practices under uncertainty (2013) Journal of Cleaner Production, 40, pp. 71-82; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; (2007) Indicators of Sustainable Development: Guidelines and Methodologies, , United Nations, United Nations, Economic & Social Affairs; (2012) A Guidebook to the Green Economy, (1). , United Nations, United Nations, Division for Sustainable Development; (2012) A Guidebook to the Green Economy, (2). , United Nations, United Nations, Division for Sustainable Development; (2012) A Guidebook to the Green Economy, (3). , United Nations, nited Nations, Division for Sustainable Development; Vachon, S., Green supply chain practices and the selection of environmental technologies (2007) International Journal of Production Research, 45 (18-19), pp. 4357-4379; Vachon, S., Klassen, R.D., Supply chain management and environmental technologies: The role of integration (2007) International Journal of Production Research, 45 (2), pp. 401-423; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Vachon, S., Mao, Z., Linking supply chain strength to sustainable development: A country-level analysis (2008) Journal of Cleaner Production, 16 (15), pp. 1552-1560; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain (2006) Supply Chain Management: An International Journal, 11 (4), pp. 317-327; Veleva, V., Ellenbecker, M., Indicators of sustainable production: Framework and methodology (2001) Journal of Cleaner Production, 9 (6), pp. 519-549; Veleva, V., Hart, M., Greiner, T., Crumbley, C., Indicators of sustainable production (2001) Journal of Cleaner Production, 9 (5), pp. 447-452; Wang, F., Research on performance measurement of green supply chain management (2012) Economics, Trade and Development, International Conference, pp. 111-114; Welford, R., Frost, S., Corporate social responsibility in Asian supply chains (2006) Corporate Social Responsibility and Environmental Management, 13 (3), pp. 166-176; Wolfslehner, B., Vacik, H., Evaluating sustainable forest management strategies with the analytic network process in a pressure-state-response framework (2008) Journal of Environmental Management, 88 (1), pp. 1-10; Xue, Y., Performance evaluation of green supply chain (2010) E-Business and Information System Security, International Conference, pp. 1-4; Yakovleva, N., Measuring the sustainability of the food supply chain: A case study of the UK (2007) Journal of Environmental Policy &Amp; Planning, 9 (1), pp. 75-100; Yakovleva, N., Flynn, A., Innovation and sustainability in the food system: A case of chicken production and consumption in the UK (2004) Journal of Environmental Policy &Amp; Planning, 6 (3-4), pp. 227-250; Yan, L., Xia, L.H., Study on performance measurement for green supply chain management (2011) Cyber Technology in Automation, Control, and Intelligent Systems, International Conference, pp. 293-297; Yao, F., Zhang, Y., A performance evaluation model of green supply chain based on balanced scorecard (2011) Information Technology, Computer Engineering and Management Sciences, International Conference, pp. 34-37; Yusuf, Y.Y., Gunasekaran, A., Musa, A., El-Berishy, N.M., Abubakar, T., Ambursa, H.M., The UK oil and gas supply chains: An empirical analysis of adoption of sustainable measures and performance outcomes (2013) International Journal of Production Economics, 146 (2), pp. 501-514; Zhang, Z.H., (2011) Designing Sustainable Supply Chain Networks, , graduate dissertation, Concordia University, Montreal; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) Journal of Operations Management, 22 (3), pp. 265-289; Zhu, Q., Geng, Y., Sarkis, J., Lai, K., Evaluating green supply chain management among Chinese manufacturers from the ecological modernization perspective (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 808-821; Vachon, S., Klassen, R.D., Supply chain management and environmental technologies: The role of integration (2007) International Journal of Production Research, 45 (2), pp. 401-423; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Vachon, S., Mao, Z., Linking supply chain strength to sustainable development: A country-level analysis (2008) Journal of Cleaner Production, 16 (15), pp. 1552-1560; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain (2006) Supply Chain Management: An International Journal, 11 (4), pp. 317-327; Veleva, V., Ellenbecker, M., Indicators of sustainable production: Framework and methodology (2001) Journal of Cleaner Production, 9 (6), pp. 519-549; Veleva, V., Hart, M., Greiner, T., Crumbley, C., Indicators of sustainable production (2001) Journal of Cleaner Production, 9 (5), pp. 447-452; Wang, F., Research on performance measurement of green supply chain management (2012) Economics, Trade and Development, International Conference, pp. 111-114; Welford, R., Frost, S., Corporate social responsibility in Asian supply chains (2006) Corporate Social Responsibility and Environmental Management, 13 (3), pp. 166-176; Wolfslehner, B., Vacik, H., Evaluating sustainable forest management strategies with the analytic network process in a pressure-state-response framework (2008) Journal of Environmental Management, 88 (1), pp. 1-10; Xue, Y., Performance evaluation of green supply chain (2010) E-Business and Information System Security, International Conference, pp. 1-4; Yakovleva, N., Measuring the sustainability of the food supply chain: A case study of the UK (2007) Journal of Environmental Policy &Amp; Planning, 9 (1), pp. 75-100; Yakovleva, N., Flynn, A., Innovation and sustainability in the food system: A case of chicken production and consumption in the UK (2004) Journal of Environmental Policy &Amp; Planning, 6 (3-4), pp. 227-250; Yan, L., Xia, L.H., (2011) Study on Performance Measurement for Green Supply Chain Management, Cyber Technology in Automation, pp. 293-297. , Control, and Intelligent Systems, International Conference; Yao, F., Zhang, Y., A performance evaluation model of green supply chain based on balanced scorecard (2011) Information Technology, Computer Engineering and Management Sciences, International Conference, pp. 34-37; Yusuf, Y.Y., Gunasekaran, A., Musa, A., El-Berishy, N.M., Abubakar, T., Ambursa, H.M., The UK oil and gas supply chains: An empirical analysis of adoption of sustainable measures and performance outcomes (2013) International Journal of Production Economics, 146 (2), pp. 501-514; Zhang, Z.H., (2011) Designing Sustainable Supply Chain Networks, , graduate dissertation, Concordia University, Montreal; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) Journal of Operations Management, 22 (3), pp. 265-289; Zhu, Q., Geng, Y., Sarkis, J., Lai, K., Evaluating green supply chain management among Chinese manufacturers from the ecological modernization perspective (2011) Transportation Research Part E: Logistics and Transportation Review, 47 (6), pp. 808-821},
correspondence_address1={Hassini, E.; DeGroote School of Business, Canada; email: hassini@mcmaster.ca},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Santarelli2015784,
author={Santarelli, G. and Abidi, H. and Klumpp, M. and Regattieri, A.},
title={Humanitarian supply chains and performance measurement schemes in practice},
journal={International Journal of Productivity and Performance Management},
year={2015},
volume={64},
number={6},
pages={784-810},
doi={10.1108/IJPPM-11-2013-0185},
note={cited By 42},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936933891&doi=10.1108%2fIJPPM-11-2013-0185&partnerID=40&md5=1690b253cac87388bd79f051767efee9},
affiliation={Department of Management and Engineering, University of Padua, Vicenza, Italy; Institute for Logistics and Service Management, FOM University of Applied Sciences, Essen, Germany; Department of Information, Logistics and Innovation, VU University Amsterdam, Amsterdam, Netherlands; Department of Industrial Engineering, University of Bologna, Bologna, Italy},
abstract={Purpose – The purpose of this paper is to provide a system for the performance evaluation of humanitarian supply chains. The system will help humanitarian organizations (HOs) to make better decisions, improve their performance, and provide accountability, as well as helping them measure their operational performance during disaster response and reconstruction projects. Design/methodology/approach – A holistic and implementable performance measurement system was developed to measure the operational performance of HOs during disaster response and reconstruction projects. The system is divided into five categories with several key performance indicators (KPIs) being identified for each; identifiers are qualitative and quantitative, as well as financial and non-financial. Findings – Empirical testing of the system demonstrated the low use of existing performance indicators in humanitarian supply chains and the possible added value for HOs to improve their operational efficiency. Social implications – The performance measurement system could help HOs improve their operations and resource allocation. Originality/value – The paper is one of the first research contributions in which a sample of developed KPIs is empirically tested in various HOs. Prior to this paper, a limited number of articles examined the performance of HOs, and no defined system of KPIs existed. By analyzing real results in the field of the humanitarian projects and applying the proposed performance measurement system, it will be possible to define a set of best approaches to improve the effectiveness and efficiency of humanitarian supply chains. © Emerald Group Publishing Limited.},
author_keywords={Experimental evidences;  Humanitarian supply chains;  Key performance indicators;  Performance measurement system},
references={Abidi, H., De Leeuw, S., Klumpp, M., Measuring success in humanitarian supply chains (2013) International Journal of Business and Management Invention, 2 (1), pp. 31-39; Altay, N., Green, W.G., OR/MS research in disaster operations management (2006) European Journal of Operational Research, 175 (1), pp. 475-493; Apte, A., (2010) Humanitarian Logistics: A New Field of Research and Action, , Now Publisher Inc., Boston, MA; Balcik, B., Beamon, B.M., Facility location in humanitarian relief (2008) International Journal of Logistics: Research and Applications, 11 (2), pp. 101-121; Balcik, B., Beamon, B.M., Smilowitz, K., Last mile distribution in humanitarian relief (2008) Journal of Intelligent Transportation Systems: Technology, Planning and Operations, 12 (2), pp. 51-63; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector, 21 (1), pp. 4-25; Blecken, A.F., (2010) Humanitarian Logistics: Modelling Supply Chain Process of Humanitarian Organizations, , Haupt Verlag AG, Bern; Blecken, A.F., Hellingrath, B., Dangelmaier, W., A humanitarian supply chain process reference model (2009) International Journal of Services Technology and Management, 12 (4), pp. 391-413; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Chang, S.E., Nojima, N., Measuring post-disaster transportation system performance: the 1995 Kobe earthquake in comparative perspective (2001) Transportation Research Part A: Policy and Practice, 35 (6), pp. 475-494; Cohen, R., The worlds 100 best NGOs, according to global journal” (2012) Non-Profit Quarterly, pp. 1-37. , January/February; Davidson, A.L., (2006) Key Performance Indicators in Humanitarian Logistics, , master thesis, Massachusetts Institute of Technology, Boston, MA; De Leeuw, S., Towards a reference mission map for performance measurement in humanitarian supply chains (2010) IFIP International Federation for Information Processing, pp. 181-188; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Productions Research, 45 (12), pp. 2819-2840; Helbing, D., Kühnert, C., Assessing interaction networks with applications to catastrophe dynamics and disaster management (2003) Physica A: Statistical Mechanics and Its Applications, 328 (3), pp. 584-606; Jahre, M., Jensen, L.M., Listou, T., Theory development in humanitarian logistics – a framework and three cases (2009) Management Research Review, 32 (11), pp. 1008-1023; Kaplan, R.S., (1990) Measures for Manufacturing Excellence, , Harvard Business School Press, Boston, MA; Kovács, G., Spens, K., Trends and developments in humanitarian logistics – a gap analysis (2011) International Journal of Physical Distribution and Logistics Management, 41 (1), pp. 32-45; Kovács, G., Spens, K.M., Humanitarian logistics in disaster relief operations (2007) International Journal of Physical Distribution &Amp; Logistics Management, 37 (2), pp. 99-114; Kovács, G., Tatham, P., What is special about a humanitarian logistician? A survey of logistics skills and performance (2010) Supply Chain Forum an International Journal, 11 (3), pp. 32-41; Lin, Y.H., Batta, R., Rogerson, P.A., Blatt, A., Flanigan, M., Application of a humanitarian relief logistics model to an earthquake disaster (2009) TRB’s 89Th Annual Meeting, , paper presented at, January 10-14, Washington, DC; Medina-Borja, A., Pasupathy, K.S., Triantis, K., Large-scale data envelopment analysis (DEA) implementation: A strategic performance management approach (2007) The Journal of the Operational Research Society, 58 (8), pp. 1084-1098; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced scorecard for natural disaster management (2007) Disaster Prevention and Management, 16 (5), pp. 785-806; Mwenja, D., Lewis, A., Exploring the impact of the board of directors on the performance of not-for-profit organizations (2009) Business Strategy Series, 10 (6), pp. 359-365; Neely, A., Gregory, M., Platts, K., Performance measurement systems design: A literature review and research agenda (1995) International Journal of Operations &Amp; Production Management, 15 (4), pp. 80-116; Oloruntoba, R., Gray, R., Humanitarian aid: An agile supply chain? (2006) International Journal of Supply Chain Management, 11 (2), pp. 115-120; Pettit, S., Beresford, A., Critical success factors in the context of humanitarian aid supply chains (2009) International Journal of Physical Distribution &Amp; Logistics Management, 39 (6), pp. 450-468; Rongier, C., Galasso, F., Lauras, M., Gourc, D., A method to define a performance indicator system for the control of a crisis (2010) 8Th International Conference of Modelling and Simulation, , paper presented at the, May 10-12, Hammamet; Scholten, S., Sharkey Scott, P., Fynes, B., (Le)agility in humanitarian aid (NGO) supply chains (2010) International Journal of Physical Distribution &Amp; Logistics Management, 40 (8-9), pp. 623-635; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Management Research News, 32 (11), pp. 1038-1049; Thomas, A., Mizushima, M., Logistics training: Necessity or luxury? (2005) Forced Migration Review, 22 (1), pp. 60-61; Thomas, A.S., (2003) Humanitarian Logistics: Enabling Disaster Response, , Fritz Institute, San Francisco, CA; Thomas, A.S., Kopczak, L.R., (2005) From Logistics to Supply Chain Management: The Path Forward in The Humanitarian Sector, , Fritz Institute, San Francisco, CA; Tomasini, R., Van Wassenhove, L.N., From preparedness to partnerships: Case study research on humanitarian logistics (2009) International Transaction in Operational Research, 16 (5), pp. 549-559; Van Der Laan, E.A., De Brito, M.P., Vergunst, D.A., Performance measurement in humanitarian supply chains (2009) International Journal of Risk Assessment and Management, 13 (1), pp. 22-45; Zhang, J., Fu, S., An effective DEA-AHP algorithm for evaluation of emergency logistics performance (2012) Advances in Information Sciences and Service Sciences, 4 (12), pp. 1-8},
correspondence_address1={Regattieri, A.; Department of Industrial Engineering, Italy; email: alberto.regattieri@unibo.it},
publisher={Emerald Group Holdings Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dubey2015752,
author={Dubey, R. and Ali, S.S.},
title={Exploring antecedents of extended supply chain performance measures: An insight from Indian green manufacturing practices},
journal={Benchmarking},
year={2015},
volume={22},
number={5},
pages={752-772},
doi={10.1108/BIJ-04-2013-0040},
note={cited By 30},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84931394895&doi=10.1108%2fBIJ-04-2013-0040&partnerID=40&md5=21d88312b0ca60a30f819f79c63270b7},
affiliation={Department of Operations Management, Symbiosis International University, Nashik, India; New Delhi Institute of Management, New Delhi, India},
abstract={Purpose – The purpose of this paper is to explore the antecedents of Indian firms practicing green manufacturing practices and their impact on extended supply chain performance. Design/methodology/approach – In this paper the authors have adopted systematic literature review (SLR) to derive research objectives and questions. In order to answer research questions the authors have proposed a theoretical framework and derived research hypotheses. The authors identified constructs and items through extant literatures and developed a structured questionnaire which was pretested before using for final survey. The data were collected in two phases and also performed wave analysis to check non-response bias to avoid any significant impact of non-response bias on statistical analysis. The data has been used to conduct exploratory factor analysis using varimax rotation which reduces variables into five parsimonious and orthogonal factors. The factor analysis output was further used as an input of regression analysis. Findings – The factor analysis output has further validated the findings from literature review. The factor analysis output suggests that total quality management (TQM), supplier relationship management (SRM), R&D and technology and lean manufacturing practices are important determinants of Indian firms practicing green manufacturing practices which impact extended supply chain performance. The regression analysis output has further established that TQM and R&D and technology are strong determinants of extended supply chain performance. However, present study does not support SRM and lean manufacturing practices from respondent’s perspective. However, it further needs to be explored. Research limitations/implications – The present study is limited to medium-sized manufacturing firms. Second, the conclusive model explains only 31.9 percent of the total extended supply chain performance. The study provides an insight to managers that those companies which have implemented TQM and green technology have realized superior performance in comparison to those which have not successfully implemented. Originality/value – The present study is toward understanding Indian green manufacturing practices. © Emerald Group Publishing Limited.},
author_keywords={Manufacturing;  Performance management;  Supply chain management},
references={Ahmad, A., Mehra, S., Pletcher, M., The perceived impact of JIT implementation on firms financial/growth performance” (2004) Journal of Manufacturing Technology Management, 15 (2), pp. 118-130; Antony, J., Desai, D.A., Assessing the status of six sigma implementation in the Indian industry: Results from an exploratory empirical study (2009) Management Research News, 32 (5), pp. 413-423; Armstrong, J.S., Overton, T.S., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Azzone, G., Noci, G., Identifying effective PMSs for the deployment of ‘green manufacturing strategies” (1998) International Journal of Operations and Production Management, 8 (6), pp. 308-335; Bai, C., Sarkis, J., Green supplier development: Analytical evaluation using rough set theory (2010) Journal of Cleaner Production, 18 (12), pp. 1200-1210; Baines, T., Brown, S., Benedettini, O., Ball, P., Examining green production and its role within the competitive strategy of manufacturers (2012) Journal of Industrial Engineering and Management, 5 (1), pp. 53-87. , http://dx.doi.org/10.3926/jiem.405; Bakker, E.F., Kamann, D.F., Perception and social factors as influencing supply management: A research agenda (2007) Journal of Purchasing and Supply Management, 13 (4), pp. 304-316; Balan, K., Introduction to green manufacturing (2008) The Shot Peener, 22 (3), pp. 4-6; Billesbach, T.J., Hayen, R., Long term impact of just-in-time on inventory performance measures (1994) Production &Amp; Inventory Management Journal, 35 (1), pp. 62-67; Björklund, M., Martinsen, U., Abrahamsson, M., Performance measurement in greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Bower, J.L., Christensen, C.M., Disruptive technologies: Catching the wave (1995) Harvard Business Review, 73 (1), pp. 43-53; Boyd, L.H., Gupta, M.C., Constraints management: What is the theory? (2004) International Journal of Operations &Amp; Production Management, 24 (4), pp. 350-371; Brah, S.A., Chong, W.K., Relationship between total productive maintenance and performance (2004) International Journal of Production Research, 42 (15), pp. 2383-2401; Camp, R.C., (1989) Benchmarking: The Search for Industry Best Practices that Lead to Superior Performance, , ASQC Quality Press, Milwaukee, WI; Camp, R.C., (1995) Business Process Benchmarking: Finding and Implementing Best Practices, , ASQC Quality Press, Milwaukee, WI; Chan Kim, W., Mauborgne, R., Blue ocean strategy (2004) Harvard Business Review, 82 (10), pp. 76-84; Deif, A., A system model for green manufacturing (2011) Advances in Production Engineering &Amp; Management, 6 (1), pp. 27-36; Dess, G.G., Robinson, R.B., Jr, Measuring organizational performance in the absence of objective measures: The case of the privately-held firm and conglomerate business unit (1984) Strategic Management Journal, 5 (3), pp. 265-273; Dragolea, L., Cotirlea, D., Benchmarking: A valid strategy for the long term (2009) Annales Universitatis Apulensis Series Oeconomica, 11 (2), pp. 813-826; Dubey, R., Bag, S., Ali, S.S., Venkatesh, V.G., Green purchasing is key to superior performance: An empirical study (2013) International Journal of Procurement Management, 6 (2), pp. 187-210; Dües, C.M., Tan, K.H., Lim, M., Green as the new lean: How to use lean practices as a catalyst to greening your supply chain (2013) Journal of Cleaner Production, 40, pp. 93-100; Ellram, L.M., Stanley, L.L., Integrating strategic cost management with a 3DCE environment: Strategies, practices and benefits (2008) Journal of Purchasing and Supply Management, 14 (3), pp. 180-191; Essig, M., Amman, M., Supplier satisfaction: Conceptual basics and explorative findings (2009) Journal of Purchasing &Amp; Supply Management, 15 (2), pp. 103-113; Farish, M., Plants that are green: Toyotas lean manufacturing” (2009) Engineering and Technology, 4 (3), pp. 68-69; Feng, C., Yuan, C., The impact of information and communication technologies on logistics management (2006) International Journal of Management, 23 (4), pp. 909-926; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Franchetti, M., Bedal, K., Ulloa, J., Grodek, S., Lean and green: Industrial engineering methods are natural stepping stones to green engineering (2009) Industrial Engineer: IE, 41 (9), pp. 24-29; Fullerton, D., Leicester, A., Smith, S., (2008) Environmental Taxes, , www.ifs.org.uk/mirrleesreview/reports/environment.pdf, prepared for the report of a Commission on Reforming the Tax System for the 21st Century, Chaired by Sir James Mirrlees, accessed June 13, 2013; Gavronski, I., Paiva, E.L., Teixeira, R., Andrade, M., ISO 14001 certified plants in Brazil e taxonomy and practices (2013) Journal of Cleaner Production, 39, pp. 32-41; Giannakis, M., The role of procurement in the management of supplier relationships (2012) International Journal of Procurement Management, 5 (3), pp. 368-408; Giovanni, P.D., Do internal and external environmental management contribute to the triple bottom line? (2012) International Journal of Operations and Production Management, 32 (3), pp. 265-290; Gonzalez, M.E., Quesada, G., Bahill, A.T., Improving product design using quality function deployment: The school furniture case in developing countries (2003) Quality Engineering, 16 (1), pp. 47-58; Gupta, M., Boyd, L., An excel-based dice game: An integrative learning activity in operations management (2011) International Journal of Operations and Production Management, 31 (6), pp. 608-630; Hair, J.F., (1995) Multivariate Data Analysis, , Prentice-Hall International Inc., New Jersey, NJ; Hayes, R.H., Pisano, G.P., Beyond world-class: The new manufacturing strategy (1994) Harvard Business Review, 72 (1), pp. 77-84; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hill, C., Jones, G.R., (2001) Strategic Management: An Integrated Approach, , 5th ed., Houghton Mifflin Company, Boston, MA; Hiltrop, J.M., Just-in-time manufacturing: Implications for the management of human resources (1992) European Management Journal, 10 (1), pp. 49-55; Hitt, M.A., Irelend, R.D., Corporate distintive competence, strategy, industry and performance (1985) Strategic Management Journal, 6 (3), pp. 273-293; Hitt, M.A., Irelend, R.D., Stadter, G., Functional importance and company performance: Moderating effects of grand strategy and industry type (1982) Strategic Management Journal, 3 (4), pp. 315-330; Hoof, B.V., Lyon, T.P., Cleaner production in small firms taking part in Mexicos sustainable supplier program” (2013) Journal of Cleaner Production, 41, pp. 270-282; Hsu, C.W., Hu, A.H., Applying hazardous substance management to supplier selection using analytic network process (2009) Journal of Cleaner Production, 17 (2), pp. 255-264; Huson, M., Nanda, D., The impact of just-in-time manufacturing on firm performance in the U.S (1995) Journal of Operations Management, 9 (1), pp. 5-14; Johansson, G., Winroth, M., Lean vs green manufacturing: Similarities and differences (2009) Proceedings of the 16Th International Annual Euroma Conference, Implementation Realizing Operations Management Knowledge, , Göteborg, June 14-17; Kaynak, H., The relationship between total quality management practices and their effects on firm performance (2003) Journal of Operations Management, 21 (4), pp. 405-435; Koning, H., Mast, J., A rational reconstruction of Six-Sigmas breakthrough cookbook” (2006) International Journal of Quality &Amp; Reliability Management, 23 (7), pp. 766-787; Large, R.O., Konig, T., A gap model of purchasings internal service quality: Concept, case study and internal survey” (2009) Journal of Purchasing and Supply Management, 15 (1), pp. 24-32; Liker, J., (2004) The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer, , McGraw-Hill, New York, NY; Markley, M.J., Davis, L., Exploring future competitive advantage through sustainable supply chains (2007) International Journal of Physical Distribution &Amp; Logistics Management, 37 (9), pp. 763-774; Mehra, S., Current issues and emerging trends in supply chain management: An editorial perspective (2005) International Journal of Production Research, 43 (16), pp. 3299-3302; Micheli, G., Cagno, E., Giulio, A.D., Reducing the total cost of supply through risk-efficiency-based supplier selection in EPC industry (2009) Journal of Purchasing and Supply Management, 15 (3), pp. 166-177; Nollet, J., Calvi, R., Audet, E., Côté, M., When excessive cost savings measurement drowns the objectives (2008) Journal of Purchasing and Supply Management, 14 (2), pp. 125-135; Parast, M.M., The effect of six-sigma projects on innovation and firm performance (2011) International Journal of Project Management, 29 (1), pp. 45-55; Pereira-Moliner, J., Claver-Cortes, E., Molina-Azorin, J., Tari, J., Quality management, environmental management and firm performance: Direct and mediating effects in the hotel industry (2012) Journal of Cleaner Production, 37, pp. 82-92; Pisano, G.P., Shih, W.C., Restoring american competitiveness, Harvard Business Review, Vol (2009) 87 Nos, 7 (8), pp. 114-125; Porter, M.E., (1985) Competitive Advantage: Creating and Sustaining Superior Performance, , The Free Press, New York, NY; Porter, M.E., Van Der Linde, C., Towards a new conception of the environment competitiveness relationship (1995) Journal of Economic Perspectives, 9 (4), pp. 97-118; Prajogo, D., Chowdhury, M., Yeung, A., Cheng, T., The relationship between supplier management and firms operational performance: A multi-dimensional perspective” (2012) International Journal Production Economics, 136 (1), pp. 123-130; Ramsay, J., Wagner, B.A., Organisational supplying behaviour: Understanding supplier needs wants and preferences (2009) Journal of Purchasing and Supply Management, 15 (2), pp. 127-138; Schonberger, R.J., (1986) The World Class Manufacturing: The Lessons of Simplicity Applied, , Free Press, New York, NY; Schroeder, R.G., Linderman, K., Liedtke, C., Choo, A.S., Six Sigma: Definition and underlying theory (2008) Journal of Operations Management, 26 (4), pp. 536-554; Sharma, M., Ammons, J., Hartman, J., Asset management with reverse product flows and environmental considerations (2007) Computers &Amp; Operations Research, 34 (2), pp. 464-480; Sikdar, S.K., Howell, S.G., On developing cleaner organic unit processes (1998) Journal of Cleaner Production, 6 (3), pp. 253-259; Stefanovic, N., Stefanovic, D., Supply chain performance measurement system based on scorecards and web portals (2011) Comsis, 8 (1), pp. 167-192; Thakkar, J., Kanda, A., Deshmukh, S.G., Interpretive structural modeling (ISM) of IT-enablers for Indian manufacturing SMEs (2008) Information Management &Amp; Computer Security, 16 (2), pp. 113-136; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14 (3), pp. 207-222; (2011) UNEP Year Book, , www.unep.org/yearbook/2011/, UNEP Year Book, UNEP, Nairobi; (2012) Statistical Year Book 2010 Fifty-Fifth Issue, , United Nation Statistics, United Nations, New York; Vachon, S., Klassen, R.D., Green project partnership in the supply chain: The case of the package printing industry (2006) Journal of Cleaner Production, 14 (6-7), pp. 661-671; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Wagner, S.M., Grosse-Ruyken, P.T., Erhum, F., The link between supply chain fit and financial performance of the firm (2012) Journal of Operations Management, 29 (1-2), pp. 340-353; Wayhan, V.B., McCallum, M.F., Golyer, N.M., TQM and financial performance: Are findings of direct effects methodical artefacts? (2013) Total Quality Management &Amp; Business Excellence, 24 (1-2), pp. 171-187; (1987) Our Common Future, , World Commission on Environment and Development, Oxford University Press, Oxford; Xu, L., Beamon, B.M., Supply chain coordination and cooperation mechanisms: An attribute-based approach (2006) Journal of Supply Chain Management, 42 (1), pp. 4-12; Yusuf, Y., Gunasekaran, A., Dan, D., Implementation of TQM in China and organisation performance: An empirical investigation (2007) Total Quality Management &Amp; Business Excellence, 18 (5), pp. 509-530; Zhang, G.P., Hoekstra, A.Y., Mathews, R.E., Water Footprint Assessment (WFA) for better water governance and sustainable development (2013) Water Resources and Industry, 1 (2), pp. 1-6; Zhu, Q., Sarkis, J., Lai, K.-H., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111 (2), pp. 261-273; Zhu, Q., Sarkis, J., Lai, K.-H., Examining the effects of green supply chain management practices and their mediation on performance improvements (2012) International Journal of Production Research, 50 (5), pp. 1377-1394; Bernardes, E.S., Zsidisin, G.A., An examination of strategic supply management benefits and performance implications (2008) Journal of Purchasing and Supply Management, 14 (4), pp. 209-219; Chen, Z., Tan, K.H., The perceived impact of JIT implementation on operations performance: Evidence from Chinese firms (2011) Journal of Advances in Management Research, 8 (2), pp. 213-235; Kim, S.K., Lee, B.G., Park, B.S., Oh, K.S., (2011) The Effect of R&Amp;D, Technology, Commercialization Capabilities and Innovation Performance, 17 (4), pp. 563-578; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: Pressure, practices and performance (2005) International Journal of Operations and Production Management, 25 (5), pp. 449-468},
correspondence_address1={Dubey, R.; Department of Operations Management, India; email: rameshwardubey@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Siham2015211,
author={Siham, L. and Jean-Claude, B. and Laurent, G. and Yves, D. and Zied, J.},
title={Designing supply chain performance measurement and management systems: A systemic perspective},
journal={2015 4th IEEE International Conference on Advanced Logistics and Transport, IEEE ICALT 2015},
year={2015},
pages={211-216},
doi={10.1109/ICAdLT.2015.7136625},
art_number={7136625},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946133170&doi=10.1109%2fICAdLT.2015.7136625&partnerID=40&md5=22c54f34a4517904c55234ff55f44292},
affiliation={Industrial Engineering Laboratory, Ecole Centrale Paris, Châtenay-Malabry, France; OASIS - ENIT, University of Tunis El Manar, Tunis, Tunisia},
abstract={In the recent years, supply chain has been recognized as a key factor of companies' success and particularly one of the best means by which companies can achieve a goal of paramount importance: enhance customer satisfaction. That is why systems to measure and manage supply chain performance are becoming fundamental. However, establishing and implementing such systems remain a particular challenging task. As the literature review led highlights it, challenges related to those systems are multiple and interrelated. We suggest tackling this issue based on systems thinking and value creation concepts. This research paper proposed a systemic-based method to aid in the designing process of a performant system of measurement and management of supply chain performance or aid in the assessment and analysis of the performance of an existing system. The strengths of the method described are mainly the capacity to consider the supply chain performance measurement and management system as a complex system, to identify key values expected, to ensure their robust creation, and to highlight the relationships of dependence between values and resources via the elaboration of processes. © 2015 IEEE.},
author_keywords={performance management;  Performance measurement;  supply chain performance;  systemic approach;  value creation},
keywords={Customer satisfaction;  Design;  Supply chains, Performance management;  Performance measurements;  Supply chain performance;  Systemic approach;  Value creation, Supply chain management},
references={Fouin, C., (2008) Comment Securiser Sa Supply Chain, , Ed. Techniques Ingenieur; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ, 142 (2), pp. 247-258. , Apr; Kaplan, R.S., Norton, D.P., The Balanced Scorecard: Measures That Drive Performance (1992) Harv. Bus. Rev, pp. 71-79; SCOR Framework - The APICS Supply Chain Council, , http://www.apics.org/sites/apics-supply-chain-council/frameworks/scor, Supply Chain Council; Eccles, R., The performance measurement manifesto. (1990) Harv. Bus. Rev, 69 (1), pp. 131-137. , Dec; Ittner, C.D., Larcker, D.F., Are nonfinancial measures leading indicators of financial performance? An analysis of customer satisfaction (1998) J. Account. Res, 36, pp. 1-35. , Jan; Kaplan, R.S., Norton, D.P., (2000) The Strategy-focused Organization I: How Balanced Scorecard Companies Thrive in the New Business Environment, , Boston, Mass: Harvard Business School Press; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) J. Oper. Manag, 22 (3), pp. 209-218. , Jun; Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., Is performance measurement and management fit for the future? (2014) Manag. Account. Res, 25 (2), pp. 173-186. , Jun; Franco-Santos, M., Lucianetti, L., Bourne, M., Contemporary performance measurement systems: A review of their consequences and a framework for research (2012) Manag. Account. Res, 23 (2), pp. 79-119. , Jun; Lima De E.Pinheiro, Gouvea da Costa, S.E., Angelis, J.J., Munik, J., Performance measurement systems: A consensual analysis of their roles (2013) Int. J. Prod. Econ, 146 (2), pp. 524-542. , Dec; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ, 87 (3), pp. 333-347. , Feb; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) Int. J. Prod. Res, 48 (17), pp. 5137-5155. , Sep; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) Int. J. Prod. Res, 45 (12), pp. 2819-2840. , Jun; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) Int. J. Prod. Econ, 46-47, pp. 423-431. , Dec; Neely, A., Performance measurement system design: Developing and testing a process-based approach (2000) Int. J. Oper. Prod. Manag, 20 (10), pp. 1119-1145. , Oct; Berrah, J., (2002) L'Indicateur de Performancel: Concepts et Applications, , Editions Cepadues; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing: A Model for American Companies, , Productivity Press; Agami, N., Supply Chain Performance Measurement Approaches Review and Classification (2012) J. Organ. Manag. Stud; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Com Put. Ind, 56 (7), pp. 663-680. , Sep; Cuthbertson, R., Piotrowicz, W., Supply chain best practices - Identification and categorisation of measures and benefits (2008) Int. J. Product. Perform. Manag, 57 (5), pp. 389-404. , Jun; Lakri, S., Dallery, Y., Measurement and management of supply chain performance: A benchmarking study (2014) ThelOth International Conference on Modeling, Optimization and Simulation - MOSIM' 14, , Nancy, France; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.I.S., State of the art literature review on performance measurement (2011) Comput. Ind. Eng, 60 (2), pp. 279-290. , Mar; Hansen, M.I., Birkinshaw, J., The innovation value chain (2007) Harv. Bus. Rev, 85, pp. 121-130; Pisano, G.P., Verganti, R., Which kind of collaboration is right for you? (2008) Harv. Bus. Rev, 86, pp. 1-8; Patrizia Garengo, U.B., Towards a contingency approach to performance measurement: An empirical study in Scottish SMEs (2007) Int. J Oper. Prod. Manag, 27 (8), pp. 802-825; Hudson Smith, M., Smith, D., Implementing strategically aligned performance measurement in small firms (2007) Int. J. Prod. Econ, 106 (2), pp. 393-408. , Apr; Neely, A., The servitization of manufacturing: An analysis of global trends (2007) Proceeding of the 14th European Operations Management Association Conference, , Ankara, Turkey; White, A., Stoughton, M., Feng, J., (1999) Servicing: The Quiet Transition to Extended Product Responsibility, , Boston Tel/us Inst; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives (2002) Int. J. Oper. Prod. Manag, 22 (11), pp. 1288-1310. , Nov; Marchand, M., Raymond, L., Researching performance measurement systems (2008) Int. J. Oper. Prod. Manag, 28 (7), pp. 663-686. , Jun; Holmberg, S., A systems perspective on supply chain measurements (2000) Int. J. OfPhy. Dist. Log. Manag; Agrell, P.J.J., Hatami-Marbini, A., Frontier-based performance analysis models for supply chain management: State of the art and research directions (2013) Comput. Ind. Eng, 66 (3), pp. 567-583. , Nov; Bititci, U.S., Turner, U., Begemann, C., Dynamics of performance measurement systems (2000) Int. J. Oper. Prod. Manag, 20 (6), pp. 692-704. , Jun; Bertalanffy, L.V., (1968) General System Theory: Foundations, Development, Applications, , New York: George Braziller Inc; Le Moigne, J.J.J., (1999) La Modelisation des Systemes Complexes, , Dunod; Bocquet, J.-C., Patay, E., Schindler, A., Dudezert, A., How to build a design product and its end-product system? An original approach called SCOS (2007) Proceedings of the International Conference on Engineering Design; Dixon, J.R., Nanni, A.J., Volmann, I.E., (1990) New Performance Challenge: Measuring Operations for World-Class Competition, , Homewood III: Irwin Professional Publishing; Lebas, M.J., Performance measurement and performance management (1995) Int. J. Prod. Econ, 41 (1-3), pp. 23-35; Schindler, A., Bocquet, J.-C., Dudezert, A., Systemic approach as a multi-criteria design method: Healthcare R&D centre application (2007) ICED '07, , Paris, France; Le Moigne, J.-L.L., (1985) La Theorie du Systeme General, , Presses Universitaires de France (PUF)},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781479984008},
language={English},
abbrev_source_title={IEEE Int. Conf. Adv. Logist. Transp., IEEE ICALT},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Tavassoli2015381,
author={Tavassoli, M. and Saen, R.F. and Faramarzi, G.R.},
title={Developing network data envelopment analysis model for supply chain performance measurement in the presence of zero data},
journal={Expert Systems},
year={2015},
volume={32},
number={3},
pages={381-391},
doi={10.1111/exsy.12097},
note={cited By 19},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930817433&doi=10.1111%2fexsy.12097&partnerID=40&md5=15e8b47cdd4fa67577e19763f2107066},
affiliation={Department of Industrial Engineering, Faculty of Engineering, Islamic Azad University, Khorramabad, Iran; Department of Industrial Management, Faculty of Management and Accounting, Islamic Azad University, PO Box: 31485-313, Karaj, Iran; Young Researchers and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran},
abstract={Performance measurement of supply chain (SC) has a vital impact on SC management and can increase efficiency of whole system and also brings competitive advantages for companies. Conventional data envelopment analysis (DEA) models treat the supply chain as a black box and do not deal with interactions of components within supply chain. Existence of zero data in supply chains can be a new assumption in performance evaluation. The main objective of this paper is to propose a new network DEA (NDEA) model in the presence of zero data. In addition, to rank supply chains, this paper proposes a novel super-efficiency formulation of NDEA using input saving and output surplus concepts. To demonstrate applicability of the proposed model, a case study is presented. © 2014 Wiley Publishing Ltd.},
author_keywords={network data envelopment analysis;  performance measurement;  supply chain management;  zero data},
keywords={Competition;  Data envelopment analysis;  Efficiency;  Information management, Black boxes;  Competitive advantage;  Network data;  Network DEA;  Performance measurements;  Super efficiency;  Supply chain performance measurements;  zero data, Supply chain management},
references={Andersen, P., Petersen, N.C., A procedure for ranking efficient units in data envelopment analysis (1993) Management Science, 39, pp. 1261-1264; Azadeh, A., Alem, S.M., A flexible deterministic, stochastic and fuzzy data envelopment analysis approach for supply chain risk and vendor selection problem: Simulation analysis (2010) Expert Systems with Applications, 37, pp. 7438-7448; Azadi, M., Farzipoor Saen, R., A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors (2011) Expert Systems with Applications, 38, pp. 12231-12236; Azadi, M., Farzipoor Saen, R., Developing a new chance-constrained DEA model for suppliers selection in the presence of undesirable outputs (2012) International Journal of Operational Research, 13, pp. 44-66; Azadi, M., Farzipoor Saen, R., Tavana, M., Supplier selection using chance-constrained data envelopment analysis with non-discretionary factors and stochastic data (2012) International Journal of Industrial and Systems Engineering, 10, pp. 167-196; Barbot, C., Costa, A., Sochirca, E., Airlines performance in the new market context: A comparative productivity and efficiency analysis (2008) Journal of Air Transport Management, 14, pp. 270-274; Banker, R., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Management Science, 30, pp. 1078-1092; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 275-292; Bernroider, E.W.N., Stix, V., Profile distance method-A multi-attribute decision making approach for information system investment (2006) Decision Support Systems, 42, pp. 988-998; Bhadra, D., Race to the bottom or swimming upstream: Performance analysis of US airlines (2009) Journal of Air Transport Management, 15, pp. 227-235; Bilsel, R.U., Ravindran, A., A multiobjective chance constrained programming model for supplier selection under uncertainty (2011) Transportation Research Part B, 45, pp. 1284-1300; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 23, pp. 429-444; Chen, Y., Measuring super-efficiency in DEA in the presence of infeasibility (2005) European Journal of Operational Research, 161, pp. 545-551; Chen, C.M., A network-DEA model with new efficiency measures to incorporate the dynamic effect in production networks (2009) European Journal of Operational Research, 194, pp. 687-699; Chen, C., Yan, H., Network DEA model for supply chain performance evaluation (2011) European Journal of Operational Research, 213, pp. 147-155; Cheng, J.C.P., Law, K.H., Bjornsson, H., Jones, A., Sriram, R.D., Modeling and monitoring of construction supply chains (2010) Advanced Engineering Informatics, 24, pp. 435-455; Chiou, Y.C., Lan, L.W., Yen, B.T.H., A joint measurement of efficiency and effectiveness for non-storable commodities: Intenerated data envelopment analysis approaches (2010) European Journal of Operational Research, 201, pp. 477-489; Cook, W.D., Liang, L., Zha, Y., Zhu, J., A modified super-efficiency DEA model for infeasibility (2009) Journal of Operational Research Society, 60, pp. 276-281; Cook, D., Zhu, J., Bi, G., Yang, F., Network DEA: Additive efficiency decomposition (2010) European Journal of Operational Research, 207, pp. 1122-1129; Farrell, M.J., The measurement of productive efficiency (1957) Journal of Royal Statistical Society, Series A, 120, pp. 253-290; Farzipoor Saen, R., Supplier selection by the new AR-IDEA model (2008) International Journal of Advanced Manufacturing Technology, 39, pp. 1061-1070; Farzipoor Saen, R., Using super-efficiency analysis for ranking suppliers in the presence of volume discount offers (2008) International Journal of Physical Distribution & Logistics Management, 38, pp. 637-651; Farzipoor Saen, R., Technology selection in the presence of imprecise data, weight restrictions, and nondiscretionary factors (2009) International Journal of Advanced Manufacturing Technology, 41, pp. 827-838; Farzipoor Saen, R., A decision model for ranking suppliers in the presence of cardinal and ordinal data, weight restrictions, and nondiscretionary factors (2009) Annals of Operations Research, 172, pp. 177-192; Farzipoor Saen, R., Supplier selection by the pair of nondiscretionary factors-imprecise data envelopment analysis models (2009) Journal of the Operational Research Society, 60, pp. 1575-1582; Farzipoor Saen, R., Restricting weights in supplier selection decisions in the presence of dual-role factors (2010) Applied Mathematical Modelling, 34, pp. 2820-2830; Farzipoor Saen, R., Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data (2010) International Journal of Advanced Manufacturing Technology, 51, pp. 1243-1250; Ghodsypour, S.H., O'Brien, C., A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming (1998) International Journal of Production Economics, 5657, pp. 199-212; Greer, M.R., Are the discount carriers actually more efficient than the legacy carriers? A data envelopment analysis (2006) International Journal of Transport Economics, 33, pp. 37-55; Greer, M.R., Nothing focuses the mind on the productivity quite like the fear of liquidation: Changes in airline productivity in the United States, 2000-2004 (2008) Transportation Research Part A, 42, pp. 414-426; Ha, S.H., Krishnan, R., A hybrid approach to supplier selection for the maintenance of a competitive supply chain (2008) Expert System with Application, 34, pp. 1303-1311; Hong, G.-H., Ha, S.H., Evaluating supply partner's capability for seasonal products using machine learning techniques (2008) Computers & Industrial Engineering, 54, pp. 721-736; Kao, C., Hwang, S.N., Efficiency measurement for network systems: IT impact on firm performance (2010) Decision Support Systems, 48, pp. 437-446; Kuo, R.J., Wang, Y.C., Tien, F.C., Integration of artificial neural network and MADA methods for green supplier selection (2010) Journal of Cleaner Production, 18, pp. 1161-1170; Lee, H.S., Zhu, J., Super-efficiency infeasibility and zero data in DEA (2012) European Journal of Operational Research, 216, pp. 429-433; Liang, L., Yang, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145, pp. 35-49; Lockamy, A., Benchmarking supplier risks using Bayesian networks (2011) Benchmarking: An International Journal, 18, pp. 409-427; Mahdiloo, M., Noorizadeh, A., Farzipoor Saen, R., Optimal direct mailing modelling based on data envelopment analysis (2014) Expert Systems, 31, pp. 101-109; Mentzer, J.T., Dewitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22, pp. 1-25; Oliver, R.K., Webber, M.D., Supply-chain management: Logistics catches up with strategy (1982) Outlook, Booz, Allen and Hamilton Inc. Reprinted (1992) in Logistics: The Strategic Issues, pp. 63-75. , In M. Christopher (Ed.), Chapman Hall: London,  ; Oum, T.H., You, C., A productivity comparison of the world's major airlines (1995) Journal of Air Transport Management, 2, pp. 181-195; Paksoy, T., Pehlivan, N.Y., A fuzzy linear programming model for the optimization of multi-stage supply chain networks with triangular and trapezoidal membership functions (2012) Journal of the Franklin Institute, 349, pp. 93-109; Scheraga, C.A., Operational efficiency versus financial mobility in the global airline industry: A data envelopment and Tobit analysis (2004) Transportation Research Part A, 38, pp. 384-404; Seiford, L., Zhu, J., Sensitivity and stability of the classifications of returns to scale in data envelopment analysis (1999) Journal of Productivity Analysis, 12, pp. 55-75; Shabani, A., Farzipoor Saen, R., Torabipour, S.M.R., A new benchmarking approach in Cold Chain (2012) Applied Mathematical Modelling, 36, pp. 212-224; Talluri, S., Baker, R.C., A multi-phase mathematical programming approach for effective supply chain design (2002) European Journal of Operational Research, 141, pp. 544-558; Tavassoli, M., Faramarzi, G.R., Farzipoor Saen, R., A joint measurement of efficiency and effectiveness for the best supplier selection using integrated data envelopment analysis approach (2014) International Journal of Mathematics in Operational Research, 6, pp. 70-83; Vinodh, S., Anesh Ramiya, R., Gautham, S.G., Application of fuzzy analytic network process for supplier selection in a manufacturing organization (2011) Expert Systems with Applications, 38, pp. 272-280; Wu, D., Supplier selection: A hybrid model using DEA, decision tree and neural network (2009) Expert Systems with Applications, 36, pp. 9105-9112; Wu, D., Olson, D.L., Supply chain risk, simulation, and vendor selection (2008) International Journal of Production Economics, 114, pp. 646-655; Zhu, J., Super-efficiency and DEA sensitivity analysis (2001) European Journal of Operational Research, 129, pp. 443-455},
issn={02664720},
coden={EXSYE},
language={English},
abbrev_source_title={Expert Syst},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tyagi2015115,
author={Tyagi, M. and Kumar, P. and Kumar, D.},
title={Assessment of critical enablers for flexible supply chain performance measurement system using fuzzy DEMATEL approach},
journal={Global Journal of Flexible Systems Management},
year={2015},
volume={16},
number={2},
pages={115-132},
doi={10.1007/s40171-014-0085-6},
note={cited By 35},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928322262&doi=10.1007%2fs40171-014-0085-6&partnerID=40&md5=56e2d159ddc643b9f1362d8838afa261},
affiliation={Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand (UK)  247667, India},
abstract={In the competitive world of today, each organization has a desire to sustain in the marketplace with the implementation of their healthier and flexible supply chain performance measurement (SCPM) system. For the successful implementation, needs to know the significant set of enablers. This study identifies a set of important enablers based on literature review and discussion with field experts of automobile manufacturing industries located in the National Capital Region of India. The vagueness and impreciseness of field expert’s judgements has been reduced using fuzzy decision making trial and evaluation laboratory (fuzzy DEMATEL) approach and analyzed the enablers in order to implement a flexible SCPM system. The findings of this research advocate that enabler ‘higher customer satisfaction’ comes in picture with highest value of ‘Prominence’ (6.4272) and ‘Relation’ (1.0354), therefore seems as a most significant and influencing enabler, while on other side the enabler ‘proper capacity utilization’ is considered as ample influencing enabler, because it has lowest Prominence’ (4.4735) and ‘Relation’ (minus 0.9680) values. This research discussed the categorization into the cause and effect group, degree of interaction and inter-relationship of considered enablers. The outcomes of this study may provide an aid to the managers to implement an effective and flexible SCPM system through which overall profitability of an organization may be improved. © 2014, Global Institute of Flexible Systems Management.},
author_keywords={Enablers;  Fuzzy DEMATEL;  Kappa statistics;  Performance measurement system;  Supply chain management},
references={Anderson, E.W., Fornell, C., Lehmann, D.R., Customer satisfaction, market share, and profitability: Findings from Sweden (1994) Journal of Marketing, 58 (7), pp. 53-66; Anderson, M.F., Katz, P.B., Strategic sourcing (1998) International Journal of Logistics Management, 9 (1), pp. 1-13; Aviv, Y., On the benefits of collaborative forecasting partnerships between retailers and manufacturers (2007) Management Science, 53 (5), pp. 777-794; Barut, M., Faisst, W., Kanet, J.J., Measuring supply chain coupling: an information system perspective (2002) European Journal of Purchasing & Supply Management, 8 (3), pp. 161-171; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bowersox, D., Closs, D., Cooper, M., (2010) Supply chain logistics management, , McGraw-Hill, New York:; Brown, S., Squire, B., Blackmon, K., The contribution of manufacturing strategy involvement and alignment to world-class manufacturing performance (2007) International Journal of Operations and Production Management, 27 (3), pp. 282-302; Chan, F.T.S., Nayak, A., Raj, R., Chong, A.Y.L., Tiwari, M., An innovative supply chain performance measurement system incorporating research and development (R&D) and marketing policy (2014) Computers & Industrial Engineering, 69, pp. 64-70; Chang, B., Chang, C.W., Wu, C.H., Fuzzy DEMATEL method for developing supplier selection criteria (2011) Expert Systems with Applications, 38 (3), pp. 1850-1858; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Chen, J.K., Chen, S., Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education (2010) Expert Systems with Applications, 37 (3), pp. 1981-1990; Chiadamrong, N., Prasertwattana, K., A comparative study of supply chain models under the traditional centralized and coordinating policies with incentive schemes (2006) Computers & Industrial Engineering, 50 (4), p. 367; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Christopher, M., Logistics and supply chain management: Strategies for reducing costs and improving services (1998) Financial times, , Pitman Publishing, London:; Cohen, J., Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit (1968) Psychological Bulletin, 70 (4), pp. 213-220; Cooper, R.G., Kleinschmidt, E.J., Benchmarking the firm’s critical success factors in new project development (1995) Journal of Product Innovation Management, 12 (5), pp. 374-391; Cousins, P.D., The alignment of appropriate firm and supply strategies for competitive advantage (2005) International Journal of Operations & Production Management, 25 (5), pp. 403-428; Das, A., Narasimhan, R., Talluri, S., Supplier integration: Finding an optimal configuration (2006) Journal of Operations Management, 24 (5), pp. 563-582; Daugherty, P.J., Ellinger, A.E., Rogers, D.S., Information accessibility: Customer responsiveness and enhanced performance (1995) International Journal of Physical Distribution and Logistics Management, 25 (1), pp. 4-17; Dawes, J., The relationship between subjective and objective company performance measures in market orientation research: Further empirical evidence (1999) Marketing Bulletin, 10, pp. 65-75; Demirbag, M., Koh, S.C.L., Tatoglu, E., Zaim, S., TQM and market orientation’s impact on SMEs’ performance (2006) Industrial Management & Data Systems, 106 (8), pp. 1206-1228; Dreyer, D.E., Performance measurement: A practitioner’s perspective (2000) Supply Chain Management Review, 4 (4), pp. 30-36; Fawcett, S.E., Cooper, M.B., Logistics performance measurement and customer success (1998) Industrial Marketing Management, 27 (4), pp. 341-357; Fawcett, S.E., Myers, M.B., Product and employee development in advanced manufacturing: Implementation and impact (2001) International Journal of Production Research, 39 (1), pp. 65-79; Flynn, B.B., Flynn, E.J., Synergies between supply chain management and quality management: Emerging implications (2005) International Journal of Production Research, 43 (16), pp. 3421-3436; Frohlich, M.T., Westbrook, R., Arcs of integration: an international study of supply chain strategies (2001) Journal of Operations Management, 19 (2), pp. 185-200; Fynes, B., de Búrca, S., Marshall, D., Environmental uncertainty, supply chain relationship quality and performance (2004) Journal of Purchasing and Supply Management, 10 (4-5), pp. 179-190; Gabus, A., Fontela, E., (1973) Perceptions of the world problematique: Communication procedure, communicating with those bearing collective responsibility, , Geneva: Battelle Geneva Research Center. (DEMATEL Report No. 1); Giannakis, M., Management of service supply chains with a service oriented reference model: The case of management consulting source (2011) Supply Chain Management, 16 (5), pp. 346-361; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) International Journal of Purchasing and Materials Management, 30 (4), pp. 13-18; Green, K.W., Jr., Inman, R.A., Using a just-in-time selling strategy to strengthen supply chain linkages (2005) International Journal of Production Research, 43 (16), pp. 3437-3453; Green, K.W., Jr., Inman, R.A., Brown, G., Willis, T.H., Market orientation: Relation to structure and performance (2005) Journal of Business & Industrial Marketing, 20 (6), pp. 276-284; Griffin, A., PDMA research on new product development practices: Updating trends and benchmarking best practices (1997) Journal of Product Innovation Management, 14 (6), pp. 429-458; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Ngai, E.W.T., Adoption of e-procurement in Hong Kong: An empirical research (2008) International Journal of Production Economics, 113 (1), pp. 159-175; Gunasekaran, A., Patel, C., McGaughey, R.E., A Framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gupta, A.K., Govindarajan, V., Knowledge flows within multinational corporations (2000) Strategic Management Journal, 21 (4), pp. 473-496; Handfield, R.B., Nichols, E.L., Jr., (1999) Introduction to supply chain management, , Upper Saddle River, Prentice-Hall:; Harrtman, R.V., Bengtsson, L., Manufacturing competence: A key to success-full supplier integration (2009) International Journal of Manufacturing Technology and Management, 16 (3), pp. 283-299; Heizer, J., Render, B., (2006) Operations management, , Upper Saddle River, Pearson Education New Jersey:; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: A case study from the packaging industry (2009) Production Planning and Control, 20 (1), pp. 68-81; Holweg, M., Disney, S., Holmstrom, J., Smaros, J., Supply chain collaboration: Making sense of the strategy continuum (2005) European Management Journal, 23 (2), pp. 170-181; Hsu, C.Y., Chen, Y.H., Tzeng, G.H., FMCDM with Fuzzy DEMATEL Approach for customers’ choice behavior model (2007) International Journal of Fuzzy Systems, 9 (4), pp. 236-246; Hu, F., Lim, C.C., Lu, Z., Coordination of supply chains with a flexible ordering policy under yield and demand uncertainty (2013) International Journal of Production Economics, 146, pp. 686-693; Hurley, R.F., Hult, G.T.M., Innovation, market orientation, and organisational learning: An integration and empirical examination (1998) Journal of Marketing, 62, pp. 42-54; Ittner, C.D., Larcker, D.F., Product development cycle time and organizational performance (1997) Journal of Marketing Research, 34 (1), pp. 13-23; Jakhar, S.K., Barua, M.K., An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP (2014) Production Planning and Control, 25 (11), pp. 938-957; Johnson, J.L., Strategic integration in distribution channels: Managing the inter-firm relationship as a strategic asset (1999) Academy of Marketing Science Journal, 27 (1), pp. 4-18; Kaufmann, A., Gupta, M.M., (1991) Introduction to fuzzy arithmetic: theory and application, , Van Nostrand Reinhold, New York:; Kaynak, H., The relationship between total quality management practices and their effects on firm performance (2003) Journal of Operations Management, 21 (4), pp. 405-435; Ketokivi, M.A., Schroeder, R.G., Strategic, structural contingency and institutional explanations in the adoption of manufacturing practices (2004) Journal of Operations Management, 22 (1), pp. 63-89; Klein, S., Roth, V.J., Satisfaction with international marketing channels (1993) Journal of the Academy of Marketing Science, 21 (1), pp. 39-44; Koh, S.C.L., Saad, S.M., Arunachalam, S., Competing in the 21st century supply chain through supply chain management and enterprise resource planning integration (2006) International Journal of Physical Distribution and Logistics Management, 36 (6), pp. 455-465; Kohli, A.K., Jaworski, B.J., Market orientation: The construct, research propositions, and managerial implications (1990) Journal of Marketing, 54 (2), pp. 1-18; Kumar, P., Deshmukh, S.G., A model for flexible supply chain through flexible manufacturing (2006) Global Journal of Flexible Systems Management, 7 (3), pp. 17-24; Kwon, I.-W.G., Suh, T., Factors affecting the level of trust and commitment in supply chain relationships (2004) Journal of Supply Chain Management, 40 (2), pp. 4-14; Labahn, D.W., Ali, A., Krapfel, R., New product development cycle time: The influence of project and process factors in small manufacturing companies (1997) Journal of Business Research, 36 (2), pp. 179-188; Lalonde, B.J., Building a supply chain relationship (1998) Supply Chain Management Review, 2 (2), pp. 7-8; Landis, J.R., Koch, G.G., A one-way component of variance model for categorical data (1977) Biometrics, 33 (4), pp. 671-679; Lawson, B., Potter, A., Determinants of knowledge transfer in inter-firm new product development projects (2012) International Journal of Operations & Production Management, 32 (10), pp. 1228-1247; Lee, H.I., Kang, H.Y., Hsu, C.F., Hung, H.C., A green supplier selection model for high-tech industry (2009) Expert System Application, 36 (4), pp. 7917-7927; Li, C.W., Tzeng, G.H., Identification of a threshold value for the DEMATEL method using the maximum mean de-entropy algorithm to find critical services provided by a semiconductor intellectual property mall (2009) Expert Systems with Applications, 8 (1), pp. 9891-9898; Lin, R.J., Using fuzzy DEMATEL to evaluate the green supply chain management practices (2013) Journal of Cleaner Production, 40, pp. 32-39; Lin, H.T., Chang, W.L., Order selection and pricing methods using flexible quantity and fuzzy approach for buyer evaluation (2008) European Journal of Operational Research, 187 (2), pp. 415-428; Lin, C., Tseng, H., Identifying the pivotal role of participation strategies and information technology application for supply chain excellence (2006) Industrial Management & Data Systems, 106 (5), pp. 739-756; Lin, Y.T., Yang, Y.H., Kang, J.S., Yu, H.C., Using DEMATEL method to explore the core competences and causal effect of the IC design service company: An empirical case study (2011) Expert Systems with Applications, 38 (5), pp. 6262-6268; Lings, I., Internal market orientation construct and consequences (2004) Journal of Business Research, 57 (4), pp. 405-413; Little, D., Kenworthy, J., Jarvis, P., Porter, K., Scheduling across the supply chain (1995) Logistics Information Management, 8 (1), pp. 42-48; Lockstrom, M., Schadel, J., Harrison, N., Moser, R., Malhotra, M.K., Antecedents to supplier integration in the automotive, industry: A multiple-case study of foreign subsidiaries in China (2010) Journal of Operations Management, 28 (3), pp. 240-256; Malhotra, M.K., Grover, V., An assessment of survey research in POM: From constructs to theory (1998) Journal of Operations Management, 16 (4), pp. 407-425; Mangla, S. K., Kumar, P., & Barua, M. K. (2014). Flexible decision approach for analyzing performance of sustainable supply chains under risks/uncertainty. Global Journal of Flexible Systems Management, 15(2), 113–130; Mavi, R.K., Kazemi, S., Najafabadi, A.F., Mousaabadi, H.B., Identification and assessment of logistical factors to evaluate a green supplier using the fuzzy logic DEMATEL method (2013) Polish journal of environmental studies, 22 (2), pp. 445-455; Mishra, A.A., Shah, R., In union lies strength: Collaborative competence in new product development and its performance effects (2009) Journal of Operations Management, 27 (4), pp. 324-338; Morgan, J., Monczka, R.M., Supplier integration: A new level of supply chain management (1996) Purchasing, 120, pp. 110-113; Narasimhan, R., Jayaram, J., Causal linkages in supply chain management: an exploratory study of North American manufacturing firms (1998) Decision Sciences, 29 (3), pp. 579-605; Neely, A., Gregory, M.J., Platts, K., Performance measurement system design, a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Nyaga, G., Whipple, J., Lynch, D., Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ? (2010) Journal of Operations Management, 28 (2), pp. 101-114; Oke, A., Prajogo, D.I., Jayaram, J., Strengthening the innovation chain: The role of internal innovation climate and strategic relationships with supply chain partners (2013) Journal of Supply Chain Management, 49 (4), pp. 43-58; Opricovic, S., Tzeng, G.H., Defuzzification within a multi-criteria decision model (2003) International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11 (5), pp. 635-652; Paulraj, A., Lado, A.A., Chen, J.J., Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships (2008) Journal of Operations Management, 26 (1), pp. 45-64; Petersen, K., Handfield, R., Ragatz, G., Supplier integration into new product development: coordinating product, process, and supply chain design (2005) Journal of Operations Management, 23 (3-4), pp. 371-388; Qureshi, M.N., Kumar, D., Kumar, P., An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers (2008) Asia Pacific Journal of Marketing and Logistics, 20 (2), pp. 227-249; Rabelo, L., Eskandari, H., Shaalan, T., Helal, M., Value chain analysis using hybrid simulation and AHP (2007) International Journal of Production Economics, 105, pp. 536-547; Ragatz, G.L., Handfield, R.B., Scannell, T.V., Success factors for integrating suppliers into new product development (1997) Journal of Product Innovation Management, 14 (3), pp. 190-202; Ravi, V., Shankar, R., Tiwari, M.K., Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach (2005) Computers & Industrial Engineering, 48 (2), pp. 327-356; Saad, M., Patel, B., An investigation of supply chain performance measurement in Indian automotive sector (2006) Benchmarking, 13 (1-2), pp. 36-53; Shieh, J.I., Wu, H.H., Huang, K.K., A DEMATEL method in identifying key success factors of hospital service quality (2010) Knowledge-Based Systems, 23 (3), pp. 277-282; Sidola, A., Kumar, P., Kumar, D., System dynamics investigation of information technology in small and medium enterprise supply chain (2012) Journal of Advances in Management Research, 9 (2), pp. 199-207; Simatupang, T.M., Sridharan, R., The collaborative supply chain (2002) International Journal of Logistics Management, 13 (1), pp. 15-30; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations management, , Pitman Publishing, London:; Sushil, Multiple perspectives of flexible systems management (2012) Global Journal of Flexible Systems Management, 13 (1), pp. 1-2; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking, 16 (5), pp. 702-723; Thome, A.M.T., Scavarda, L.F., Pires, S.R.I., Ceryno, P., Klingebiel, K., A multi-tier study on supply chain flexibility in the automotive industry (2014) International Journal of Production Economics, 158, pp. 91-105; Tompkins, J., Ang, D., What are your greatest challenges related to supply chain performance measurement? (1999) IIE Solutions, 31 (6), p. 66; Turner, J.R., Integrated supply chain management: What’s wrong with this picture? (1993) Industrial Engineering, 25 (12), pp. 52-55; Tyagi, M., Kumar, P., Kumar, D., Selecting alternatives for improvement in IT enabled supply chain performance (2014) International Journal of Procurement Management, 7 (2), pp. 168-182; Tzeng, G.H., Chiang, C.H., Li, C.W., Evaluating intertwined effects in e-learning programs: a novel hybrid MCDM model based on factor analysis and DEMATEL (2007) Expert Systems with Applications, 32 (4), pp. 1028-1044; Ulaga, W., Chacour, S., Measuring customer-perceived value in business markets (2001) Industrial Marketing Management, 30 (6), pp. 525-540; Van der Vorst, J.G.A.J., Beulens, A.J.M., Identifying sources of uncertainty to generate supply chain redesign strategies (2002) International Journal of Physical Distribution & Logistics Management, 32 (6), pp. 409-430; Vanderhaeghe, A., de Treville, S., How to fail at flexibility (2003) Supply Chain Forum, 4 (1), pp. 64-67; Wadhwa, S., Madaan, J., Avneet, S., Need for flexibility and innovation in healthcare management systems (2007) Global Journal of Flexible Systems Management, 8 (1-2), pp. 45-54; Waggoner, D., Neely, A., Kennerley, M., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60; Womack, J.P., Jones, D.T., (2005) Lean solutions: How companies and customers can create wealth together, , Simon & Schuster, New York:; Wu, W.W., Lee, Y.T., Developing global managers’ competencies using the fuzzy DEMATEL method (2007) Expert Systems with Applications, 32 (2), pp. 499-507; Yang, Y.P., Shieh, H.M., Leu, J.D., Tzeng, G.H., A novel hybrid MCDM model combined with DEMATEL and ANP with applications (2008) International Journal Operational Research, 5 (3), pp. 160-168; Yeh, C.H., Deng, H., A practical approach to fuzzy utilities comparison in fuzzy multi-criteria analysis (2004) International Journal of Approximate Reasoning, 35 (2), pp. 179-194; Zadeh, L.A., Fuzzy Set (1965) Information and Control, 8 (3), pp. 338-353},
correspondence_address1={Tyagi, M.; Department of Mechanical and Industrial Engineering, India; email: mohitmied@gmail.com},
publisher={Global Institute of Flexible Systems Management},
issn={09722696},
language={English},
abbrev_source_title={Global J. Flexible Syst. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Narawita201564,
author={Narawita, U.N. and Layangani, L.D.C.S.},
title={How the performance measurement of supply chain contributes in achieving organisational objectives of Sri Lankan apparel organisations},
journal={MERCon 2015 - Moratuwa Engineering Research Conference},
year={2015},
pages={64-69},
doi={10.1109/MERCon.2015.7112322},
art_number={7112322},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84936768507&doi=10.1109%2fMERCon.2015.7112322&partnerID=40&md5=4f52b426dbd509bc3c1faa9937c25c71},
affiliation={Department of transport and Logistics Management, University of Moratuwa, Sri Lanka},
abstract={Half way through the evolution of supply chain management, the measurement of performance became an important component to the successful implementation of its concepts and practices. The intense competition in the apparel manufacturing industry has encouraged the players to focus not only on the profitability of their businesses but also on the elements such as supplier - customer relationship management and optimization of every single echelon along the supply chain. This research presents a detailed study and an analysis carried out to determine the impact of the performance measurement systems established in apparel companies on their organizational objectives in Sri Lankan context. The apparel manufacturing companies involve in export trade that are registered under Export Development Board were considered as the population and the apparel exporting organisations which are registered under Sri Lanka Apparel Exporters' Association were considered as the sample for this research. All the data is gathered via an online questionnaire and focus respondents are the senior level managers in these apparel manufacturing organizations. In the data analysis phase, the Cronbach's alpha, the principal component analysis and regression analysis became extensively useful. The analysis and the conclusions are solely based on the data collected from the apparel organizations in the export industry and not having even a single representation from non-exporting small scale apparel organisations is seen as a main limitation of this study. This research brought into light that measurement of organisational performance has favourable as well as antagonistic impact on organisational performances, therefore organisations should identify the tradeoffs and set division targets accordingly. © 2015 IEEE.},
author_keywords={apparel organisations;  organizational objectives;  Performance measurement},
keywords={Clothes;  Competition;  Engineering research;  Industrial research;  Manufacture;  Public relations;  Regression analysis, Apparel manufacturing;  Online questionnaire;  Organisational objectives;  Organisational performance;  organizational objectives;  Performance measurement system;  Performance measurements;  Supplier-customer relationships, Supply chain management},
references={Lewis, H.T., Culliton, J.W., Steele, J.D., The role of air freight in physical distribution (1956) Division of Research, Graduate School of Business Administration, , Harvard University Boston, MA; Ballou, R.H., The evolution and future of logistics and supply chain management (2007) European Business Review, pp. 332-348; Cooper, M.C., Douglas, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) International Journal of Logistics Management, 8 (1), pp. 1-14; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation and research opportunities (1998) International Journal of Logistics Management, 9 (2), pp. 1-19; Hoek, R.I.V., Measuring the unmeasurable"-measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Bower, J.L., Hout, T.M., Fast cycle capability for competitive power (1988) Harvard Business Review, November-December, pp. 110-118; Thomas, D.J., Griffin, P.M., Co-ordinated supply chain management (1996) European Journal of Operational Research, 94 (3), pp. 1-15; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , Pitman Publishing, London; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Eljabri, S.S., (2013) New Statistical Models for Extreme Values School of Mathematics, University of Manchester; Gliem, J.A., Gliem, R.R., (2003) Calculating, Interpreting, and Reporting Cronbach's Alpha Reliability Coefficient for Likert-Type Scales", Midwest Research to Practice Conference in Adult, Continuing, and Community Education; Kaiser, H.F., An index of factorial simplicity (1974) Psychometrika, 39, pp. 31-36; Webster, M., Lane, E., Supply system structure, management and performance: A conceptual model (2002) International Journal of Management Reviews, 4, pp. 353-369; Advance Your Supply Chain with SCOR from, , http://www.apics.org/sites/apics-supply-chain.council/framework/scor, APICS Supply Chain Council n.d; Ballou, R.H., Revenue estimation for logistics customer service offerings (2006) International Journal of Logistics Management, 17 (1), pp. 21-37; Heskett, J.L., Ivie, R.M., Glaskowsky, N.A., Jr., (1964) Business Logistics: Management of Physical Supply and Distribution, , The Ronald Press, New York, NY; Heskett, J.L., Glaskowsky, N.A., Jr., Ivie, R.M., (1973) Business Logistics, pp. 14-21. , 2nd ed, The RonaldPress, New York, NY; Kobayashi, I., Management of physical distribution cost (1973) Proceedings of International Physical Distribution Conference, Tokyo, p. 9; Stephenson, A.R., A Regional Survey of Physical Distribution Problems and Practice (1975) Productivity Promotion Council of Australia, Melbourne, pp. 7-10; Lalonde, B.J., Zinzer, P.H., (1976) Customer Service: Meaning and Measurement", National Council of Physical Distribution Management, , Chicago, IL; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities",Sloan Management Review (1992) Spring, pp. 65-73; Murphy, G.J., Transport and Distribution (1972) Business Books, London, p. 7; (2014) Sri Lanka Apparel Exporters Association, , http://www.srilanka-apparel.com/current-membership/members, Export Performance; Timms, H.L., Pohlen, M.F., (1970) Production Function in Business, p. 7. , 3rd ed, Richard D. Irwin, Inc., Homewood IL; Van Dierdonck, R., Supply chain management (1997) Management Jaarboek Trends, , Roularta Books, Zellik},
correspondence_address1={Narawita, U.N.; Department of transport and Logistics Management, Sri Lanka; email: narawitau@gmail.com},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781479917402},
language={English},
abbrev_source_title={MERCon - Moratuwa Eng. Res. Conf.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{DeFelice2015741,
author={De Felice, F. and Petrillo, A.},
title={Optimization of manufacturing system through world class manufacturing},
journal={IFAC-PapersOnLine},
year={2015},
volume={28},
number={3},
pages={741-746},
doi={10.1016/j.ifacol.2015.06.171},
note={cited By 16},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953854850&doi=10.1016%2fj.ifacol.2015.06.171&partnerID=40&md5=1a37ce5a52d550e614814b7b5fc756d7},
affiliation={University of Cassino and Southern Lazio, Cassino, Italy; University of Naples Parthenope, Napoli, Italy},
abstract={Each company struggles with the same question: How can I provide - at the lowest possible costs and with an acceptable delivery time - products or services that add maximum value for my customers? Firms must develop strategic objectives which result in a competitive advantage in the market place. There are many different methods which address this problem: Lean (the value adding organization), Six Sigma (the perfect organization), TOC (the unlimited organization), TPM (the smooth organization), RCM (the reliable organization) and QRM (the cellular organization). In addition, combinations of these methods exist, like Lean Six Sigma (value adding and perfect organization) and World Class (value adding and perfect and smooth organization). The aim of this work is to present establishments of the basic model of WCM for the logistics system in the automotive industry in order to improve the work standards. The result of this research was to develop principles on strategic objectives, performance measurement systems and performance measurement system linkages for improved organizational coordination. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
author_keywords={Control processes;  Efficiency;  Measurement;  Optimization;  World class manufacturing},
keywords={Automotive industry;  Competition;  Efficiency;  Manufacture;  Measurement;  Optimization;  Process engineering;  Process monitoring;  Silicon;  Six sigma;  Work simplification;  Logistics;  Measurements;  Supply chain management, Cellular organization;  Competitive advantage;  Control process;  Logistics system;  Performance measurement system;  Strategic objectives;  Work standards;  World class manufacturing;  Lean Six Sigma, Engineering education},
references={Barnes, J., Morris, M., Staying alive in the global automotive industry: What can developing economies learn from South Africa about linking into global automotive value chains? (2008) The European Journal of Development Research, 20 (1), pp. 31-55; Black, J.T., Design for system success (2002) Journal of Manufacturing Systems, 20 (6), pp. 77-82; Bruzzone, A.G., Longo, F., An advanced system for supporting the decision process within large-scale retail stores (2010) Simulation, 86, pp. 742-762; Chan, F.T.C., Design of material handling equipment selection system an integration of expert system with analytic hierarchy approach (2002) Integrated Manufacturing Systems, 113 (1), pp. 58-68. , (2002); De Felice, F., Petrillo, A., Simulation approach for the optimization of the layout in a manufacturing firm (2013) 24th IASTED International Conference on Modelling and Simulation, pp. 152-161. , MS 2013; Banff, AB; Canada; 17 July 2013 through 19 July 2013; De Felice, F., Petrillo, A., Key success factors for organizational innovation in the fashion industry (2013) International Journal of Engineering Business Management, 5, pp. 47-57; De Felice, F., Petrillo, A., Monfreda, S., Improving operations performance with world class manufacturing technique: A case in automotive industry (2013) Chapter 1 - Operations Management, 240p. , Edited by Massimiliano M. Schiraldi Hard cover Publisher: InTech, Published: March 13, 2013; Digalwar, A., Sangwan, K.S., Role of knowledge management in world class manufacturing: An empirical investigation (2011) Proceedings of IEEE IEEM; Francisco, M., Roy, R., Wegen, B., Steele, A., A framework to create key performance indicators for knowledge management solutions (2003) J. Knowledge Management, 7 (2), pp. 46-62; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) Int. J. Production & Performance Management, 54 (5-6), pp. 419-437; Gunn, T.G., (1987) Manufacturing for Competitive Advantage: Becoming a World Class Manufacturer, pp. 28-29. , Cambridge, Mass: Ballinger Pub; Kangis, P., Williams, D., Organizational climate and corporate performance: An empirical investigation (2000) Management Decision, 38 (8), pp. 531-540; Kodali, R.B., Sangwan, K.S., Sunnapwar, V.K., Performance value analysis for the justification of world-class manufacturing systems (2004) J. Advanced Manufacturing Systems, 3 (1), pp. 85-102; Longo, F., Operational strategies and internal logistic costs analysis in a real warehouse based on modeling & simulation (2011) International Journal of Computer Science Issues, 8, pp. 39-47; Mosey, S., Understanding new-to-market product development in SMEs (2005) Int. J. Operations & Production Management, 25 (2), pp. 114-130; Nachiappan, R.M., Anantharaman, N., Performance measurement of world class manufacturing system: An overview (2006) The Icfai Journal of Operations Management, 21 (6), pp. 29-53; Nandi, S., Banwet, D., Benchmarking for world class manufacturing - Concept, framework and applications (2000) Productivity, 41 (2), pp. 189-200; Palucha, K., World class manufacturing model in production management (2012) Archives of Materials Science and Engineering, 58 (2), pp. 227-234; Schonberger, R.J., World class manufacturing (1986) The Lesson of Simplicity Applied, , Macmillan, New York, 1986},
editor={Dolgui A., Sasiadek J., Zaremba M.},
issn={24058963},
language={English},
abbrev_source_title={IFAC-PapersOnLine},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Vishwasrao2015148,
author={Vishwasrao, R. and Sabri, E.},
title={Supply chain performance measurement and organizational alignment},
journal={Optimization of Supply Chain Management in Contemporary Organizations},
year={2015},
pages={148-174},
doi={10.4018/978-1-4666-8228-3.ch006},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84955082471&doi=10.4018%2f978-1-4666-8228-3.ch006&partnerID=40&md5=374b4432211484d15387406c0bcd61f3},
affiliation={JDA Software, United States; University of Texas, Dallas, United States},
abstract={The authors describe how organizations can leverage a maturity model approach in conjunction with foundational concepts of perspective-based performance evaluation models like Balanced Scorecard (BSC) to define comprehensive performance measurement framework. Maturity models are essentially a reflection of theories about how the capabilities of an organization evolve. Therefore, by design, they provide a roadmap to the next level of performance. The authors propose using maturity models as a structured way of identifying current capability or maturity level of any supply chain. They then provide guidance on selecting the right "causal linkages" between supply chain objectives and performance measures and define a mechanism for specifying more granular definitions of measures linked to strategic objectives for progressive levels of maturity. The authors survey widely used business process maturity models, current practices for measuring operational metrics, and then present a tiered framework for operational metric alignment and KPI governance, based on perspective-based design principles. © 2015 by IGI Global. All rights reserved.},
author_keywords={Balanced scorecard;  KPI governance framework;  Maturity models;  Operational metrics;  Performance metrics;  SCOR;  System dynamics},
keywords={Benchmarking, Balanced scorecards;  KPI governance framework;  Maturity model;  Operational metrics;  Performance metrics;  SCOR;  System Dynamics, Supply chains},
references={Akyuz, G., Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Analytics, J., (2009) White paper: A guide to creating dashboards people love to use, , http://goo.gl/QGGHty, Retrieved from; Bell, C., Higgs, R., Vickers, S., Toncinich, S., Haslett, T., (2003) Using system modelling to understand the dynamics of supply chains, , Monash University. Working Paper 70/03; Bernard, M., (2012) Key performance indicators (KPIs): The 75 measures every manager needs to know, , http://goo.gl/v7r4hZ, FT Press. Retrieved from; Camerinelli, E., Cantu, A., Measuring the value of the supply chain: A framework (2006) Supply Chain Practice, 8 (2), pp. 40-59; Campuzano Bolarã-n, F., Lisec, A., Esteban, F., Inventory cost consequences of variability demand process with a multi-echelon supply chain (2008) Logistics & Sustainable Transport, 1 (3), pp. 1-12; Davenport, T., (2006) Competing on analytics, , http://goo.gl/OriOSu, Harvard Business Review. Dundas. (n.d.). Dashboards demystified. Retrieved from, January; Dykes, B., (2011) Web analytics action hero: Using analysis to gain insight and optimize your business, , http://goo.gl/OWZSKn, Adobe Press. Retrieved from:; Eckerson, W., (2009) Performance management strategies: How to create and deploy effective metrics, , TDWI Best Practices Report Q1 2009; (2012) White paper: Business intelligence platforms for mid-size organizations: Comparing Birst, MicroStrategy, Oracle and SAP BusinessObjects, , http://goo.gl/lm2XRC, Retrieved from, Enterprise Management Associates; Fettke, P., Loos, P., Zwicker, J., (2005) Business process reference models: Survey and classification, , http://goo.gl/TQILgk, Paper presented at the Third International Conference on Business Process Management (BPM), Nancy, France. Retrieved from:; Geethan, K.A.V., Jose, S., Koshy, A., Samuel, A.B., (2011) Methodology for study of characteristics and time reduction in reverse supply chain using system dynamics, , http://goo.gl/odWhqS, In Proceedings of International Conference on Sociality and Economics Development. IPEDR. INFORMS. (n.d.). What is analytics? Retrieved from:; Kaplan, R.S., (2010) Conceptual foundations of the balanced scorecard, , http://ssrn.com/abstract=1562586, Harvard Business School Accounting & Management Unit Working Paper No. 10-074. Retrieved from:; Melnyk, S., Stewart, D., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-217; Melnyk, S.A., Calantone, R.J., Luft, J., Stewart, D.M., Zsidisin, G.A., Hanson, J., Burns, L., An empirical investigation of metrics alignment process (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 312-324; Mulcahy, R., (2010) Business intelligence definitions and solutions, , http://goo.gl/VA8Wzx, CIO Magazine. Retrieved from:; Najmi, A., Gholamian, M., Makui, A., Supply chain performance models: A literature review on approaches, techniques, and criteria (2013) Journal of Operations and Supply Chain Management, 6 (2), pp. 94-113; Netland, T., Alfnes, E., (2008) A practical tool for supply chain improvement - Experiences with the supply chain maturity assessment test (SCMAT), , Paper presented at the EurOMA/POMS/JOMSA Conference, Tokyo, Japan; Netland, T.H., Alfnes, E., Fauske, H., (2007) How mature is your supply chain? A supply chain maturity assessment test, , Paper presented at the EurOMA Conference, Ankara, Turkey; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Porter, M.E., (1996) What is strategy, , Harvard Business Review, (November - December), 4; Pruyt, E., (2013) Small system dynamics models for big issues: Triple jump towards real - world complexity, , http://goo.gl/ge98Wt, Delft: TU Delft Library. Retrieved from; Röglinger, M., Pöppelbuaÿ, J., Becker, J., Maturity models in business process management (2012) Business Process Management Journal, 18 (2), pp. 328-346; Sabri, E., (2012) Enduring transformation, pp. 45-47. , APICS Magazine, September-October; Sun, H., Ren, Y., Yeo, K., (2005) Capability maturity model for supply chain management, , Paper presented at the International Conference on Management Science and Applications, Chengdu, China; (2014) Supply Chain Council (SCC), , https://supply-chain.org/, Retrieved from:; Thakkar, J., Kanda, A., Deshmukh, S., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking International Journal Toronto, 16 (5), pp. 702-723; van Looy, A., (2013) BPMM sample (N=69), , http://goo.gl/dbiVkQ, Retrieved from:; van Looy, A., (2013) Which business process maturity model best fits your organization? BPTrends, , http://goo.gl/1yCkEA, Retrieved from:; van Looy, A., (2014) Business process maturity: A comparative study on a sample of business process maturity models, , Spinger. doi:10.1007/978-3- 319-04202-2; Wienberger, D., (2010) The problem with the data-information-knowledge-wisdom hierarchy, , http://goo.gl/tDtQQD, HBR Blog Network. Retrieved from; Wilson, G., (2012) Designing and building great dashboards - 6 golden rules to successful dashboard design, , http://goo.gl/cJLCLK, Geckoboard Blog. Retrieved from},
correspondence_address1={Vishwasrao, R.; JDA SoftwareUnited States},
publisher={IGI Global},
isbn={9781466682306; 9781466682290},
language={English},
abbrev_source_title={Optim. of Supply Chain Manag. in Contemp. Organ.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Gong201523,
author={Gong, K. and Yan, H.},
title={Performance measurement of logistics service supply chain using bijective soft set},
journal={Journal of Advanced Manufacturing Systems},
year={2015},
volume={14},
number={1},
pages={23-40},
doi={10.1142/S0219686715500031},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928476520&doi=10.1142%2fS0219686715500031&partnerID=40&md5=abb52b2ef5dbc30769c8d3191414318c},
affiliation={School of Management, Chongqing Jiaotong University, Chongqing, 400074, China},
abstract={In the logistics service supply chain (LSSC), performance measurement is an important approach that can improve competitiveness of the overall supply chain. In the complicated measurement system, it includes various quantitative and qualitative indices with uncertain problems. However, classical measurement methods were based on subjective suggestion to weigh each index in the measurement system; moreover, there is no well-established performance measurement system (PMS) for evaluation of LSSC. For those reasons, this paper aims to present Bijective soft set approach to deal with the performance measurement of LSSC, which ensures the objective of the weight of each index and gets the core indices of the PMS. Based on the overall LSSC operation, customer service, and corporate management - the three parts in the proposed system of LSSC - the indices, and consistency of the metrics have been selected. Finally, an empirical analysis of developed framework for performance measurement is conducted and tested. © 2015 World Scientific Publishing Company.},
author_keywords={bijective soft set;  discernibility matrix;  Logistics service supply chain;  performance measurement},
keywords={Petroleum reservoir evaluation;  Set theory, Corporate management;  Discernibility matrix;  Logistics service supply chain;  Measurement methods;  Performance measurement system;  Performance measurements;  Qualitative indices;  Soft sets, Supply chains},
references={Choy, K.L., Managing uncertainty in logistics service supply chain (2007) International Journal of Risk Assessment and Management, 7 (1), pp. 19-43; Liu, W.H., Xie, D., Quality decision of the logistics service supply chain with service quality guarantee (2013) International Journal of Production Research, 51 (5), pp. 1618-1634; Liu, W.H., Xu, X.C., Study on the quantity coordination of capability collaboration for multi-period-oriented two-echelon logistics service supply chain (2012) African Journal of Business Management, 6 (11), pp. 3920-3929; Liu, W.H., Xu, X.C., Ren, Z.X., Peng, Y., An emergency order allocation model based on multi-provider in two-echelon logistics service supply chain, Supply Chain Management (2011) An International Journal, 16 (6), pp. 391-400; Liu, W.H., Xie, D., Xu, X.C., Research on the procedure joint process and synthesized performance evaluation of logistics service supply chain (2012) African Journal of Business Management, 6 (3), pp. 908-923; Li, L., Assessing the relational benefits of logistics services proceived by manufacturers in supply chain (2011) International Journal of Production Economics, 132 (1), pp. 58-67; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Reseach, 48 (17), pp. 5137-5155; Bullinger, H.-J., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Kim, J., Rhee, J., An empirical study on the impact of critical success factors on the balanced scorecard performance in korean green supply chain management enterprises (2012) International Journal of Production Research, 50 (9), pp. 2465-2483; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 5 (3), pp. 43-63; Punniyamoorthy, M., Murali, R., Balanced score for the balanced scorecard a benchmarking tool, Benchmarking (2008) An International Journal, 15 (4), pp. 420-443; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities balanced scorecard perspective, Benchmarking (2009) An International Journal, 15 (5), pp. 605-620; Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 593-603; Swenson, D., The benefits of activity-based cost (1995) Journal of Management Accounting Research, 7, pp. 167-180; Parasuraman, A., Servqual: A multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64, pp. 13-40; Bouwman, H., Bejar, A., Nikou, S., Mobile services put in context: A q-sort analysis (2012) Telematics and Informatics, 29 (1), pp. 66-81; Niranjan, T.T., Weaver, M., A unifying view of goods and services supply chain management (2011) The Service Industries Journal, 31 (14), pp. 2391-2410; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., Computers & Industrial Engineering (2012) A Framework for Measuring the Performance of Service Supply Chain Management, 62 (3), pp. 801-818; Murata, K., Katayama, H., A study of the performance evaluation of the visual management case-base: Development of an integrated model by quantification theory category iii and ahp (2013) International Journal of Production Research, 51 (2), pp. 380-394; Pramod, V.R., Performance measurement of sher service (2011) International Journal of Business Excellence, 4 (3), pp. 321-345; Uysal, F., An integrated model for sustainable performance measurement in supply chain (2012) Procedia - Social and Behavioral Sciences, 62, pp. 689-694; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Wong, W.P., Wong, K.Y., Industrial Management & Data Systems (2007) Supply Chain Performance Measurement System Using DEA Modeling, 107 (3), pp. 361-381; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighbourhood rough sets (2012) International Journal of Production Research, 50 (9), pp. 2484-2500; Huang, C.T., Yeh, T.M., Lin, W.T., Lee, B.T., A fuzzy ahp-based performance evaluation model for implementing spc in the taiwanese lcd industry (2009) International Journal of Production Research, 47 (18), pp. 5163-5183; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design should process based approaches be adopted? (1996) International Journal of Production Economics, 46 (47), pp. 423-431; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Martin, F., A performance technologist's approach to process performance improvement (2008) Performance Improvement, 47 (2), pp. 30-40; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Lin, L.C., Li, T.S., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Software Quality Journal, 18 (3), pp. 387-406; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain manegement (2003) Supply Chain Management: An International Journal, 8, pp. 209-223; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Stank, T.P., Goldsby, T.J., Vicitery, S.K., Logistics service performance estimating its influence on market share (2003) Journal of Business Logistics, 24 (1), pp. 27-55; Kilibarda, M., Zecevíc, S., Vidovíc, M., Measuring the quality of logistic service as an element of the logistics provider offering (2012) Total Quality Management & Business Excellence, 23 (11-12), pp. 1345-1361; Cross, K.F., Lynch, R.L., The smart way to define and sustain success (1988) National Productivity Review, 8 (1), pp. 22-23; Molodtsov, D., Soft set theory-first results (1999) Computer & Mathematics with Applications, 37, pp. 19-31; Gong, K., Xiao, Z., Zhang, X., The bijective soft set with its operations (2010) Computers & Mathematics with Applications, 60 (8), pp. 2270-2278; Maji, P.K., Biswas, R., Roy, A.R., Soft set theory (2003) Computers & Mathematics with Application, 45, pp. 555-562; Gong, K., Wang, P., Xiao, Z., Bijective soft set decision system based parameters reduction under fuzzy environments (2013) Applied Mathematical Modelling, 37 (6), pp. 4474-4485; Xiao, Z., Gong, K., Xia, S., Zou, Y., Exclusive disjunctive soft sets (2010) Computers & Mathematics with Applications, 59 (6), pp. 2128-2137; Miao, B., Wei, W., The parameter reduction algorithm and its application in decision-making based on the bijective soft set (2012) System Engineering, 30 (2), pp. 115-119},
publisher={World Scientific},
issn={02196867},
language={English},
abbrev_source_title={J. Adv. Manuf. Syst.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sillanpää2015290,
author={Sillanpää, I.},
title={Empirical study of measuring supply chain performance},
journal={Benchmarking},
year={2015},
volume={22},
number={2},
pages={290-308},
doi={10.1108/BIJ-01-2013-0009},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924085916&doi=10.1108%2fBIJ-01-2013-0009&partnerID=40&md5=cf97bdf8ae699e1e31f87050627cdb2a},
affiliation={Department of Strategic Management, University of Vaasa, Vaasa, Finland},
abstract={Purpose – Supply chain (SC) performance measurement – the process of qualifying the efficiency and effectiveness of the SC. The purpose of this paper is to create a SC measurement framework for manufacturing industry, define which data should be measured and verify the measurement framework in the case company’s SC. Design/methodology/approach – There is a review of the current understanding of supply chain management and literature related to SC performance measurement and the study creates a framework for SC measurement. This research is qualitative case study research. Findings – This study presents the main theoretical framework of SC performance measurement. The key elements for the measurement framework were defined as time, profitability, order book analysis and managerial analysis. The measurement framework is tested by measuring case SC performance. Research limitations/implications – In the study, a performance measurement framework was created for the needs of manufacturing industry. Suggestions for future research are multiple case study in different manufacturing industry areas and positivistic-based SC performance research. Practical implications – The measurement framework in this study offers guidelines for measuring the SC in manufacturing industry but the measurement framework could be used in different areas of industry as well. Originality/value – The SC performance measurement framework is tested and a valid framework for SC performance measurement in manufacturing industry. ©Emerald Group Publishing Limited},
author_keywords={Manufacturing industry;  Supply chain;  Supply chain performance measurement},
references={Arbnor, I., Bjerke, B., (1997) Methodology for Creating Business Knowledge, , 2nd ed., SAGE, Thousand Oaks, CA; Balsmeier, P.W., Voisin, W.J., Supply chain management: A time-based strategy (1996) Industrial Management, 38 (5), pp. 24-27; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations &Amp; Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers &Amp; Industrial Engineering, 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning and Control, 20 (8), pp. 678-690; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56; Burrell, G., Morgan, G., (1979) Sociological Paradigms and Organisational Analysis: Elements of the Sociology of Corporate Life, , Heinemann, London; Burrell, G., Morgan, G., (1998) Sociological Paradigms and Organisational Analysis: Elements of the Sociology of Corporate Life, , Ashgate, Aldershot; Caridi, M., Crippa, L., Perego, A., Sianesi, A., Tumino, A., Measuring visibility to improve supply chain performance: A quantitative approach (2010) Benchmarking, 17 (4), pp. 593-615; Chan, A., Chan, A., Key performance indicators for measuring construction success (2004) Benchmarking, 11 (2), pp. 203-221; Chan, F., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chia, A., Goh, M., Hum, S.-H., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking, 16 (5), pp. 605-620; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-13; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management and Business Excellence, 14 (3), pp. 355-366; De Toni, A., Tonchia, S., Performance measurement systems models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 46-71; Dixon, J.R., (1990) The New Performance Challenge: Measuring Operations for Word-Class Competition, , Business One Irwin; Dror, S., The balanced scorecard versus quality award models as strategic frameworks (2008) Total Quality Management and Business Excellence, 19 (6), pp. 583-593; Eisenhardt, K.M., Building theories from case study research (1989) Academy of Management Review, 14 (4), pp. 532-550; Fynes, B., Voss, C., De Búrca, S., The impact of supply chain relationship dynamics on manufacturing performance (2005) International Journal of Operations and Production Management, 25 (1), pp. 6-19; Ghauri, P.N., Grønhaug, K., (2005) Research Methods in Business Studies: A Practical Guide, , 3rd ed., Financial Times Prentice Hall, Harlow; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations &Amp; Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Williams, H.J., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353; Hieber, R., (2002) Supply Chain Management: A Collaborative Performance Measurement Approach, , vdf Hochschulverlag AG; Hoek, R., Measuring the unmeasurable - measuring and improving performance in the supply chain” (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Ittner, C.D., Larcker, D.F., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, 81 (11), pp. 88-95+139; Ittner, C.D., Larcker, D.F., Randall, T., (2003) Performance implications of strategic performance measurement in financial services firms, Accounting, Organizations and Society, 28 (7-8), pp. 715-741; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part I (2001) Accounting Horizons, 15 (1), pp. 87-104; Kasanen, E., Lukka, K., Siitonen, A., The constructive approach in management accounting research (1993) Journal of Management Accounting Research, 5 (1), pp. 241-264; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, 12, pp. 45-50; Kessler, E.H., Chakrabarti, A.K., Innovation speed: A conceptual model of context, antecedents, and outcomes (1996) Academy of Management Review, 21 (4), pp. 1143-1191; Labro, E., Tuomela, T., On bringing more action into management accounting research: Process considerations based on two constructive case studies (2003) European Accounting Review, 12 (3), pp. 409-442; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-19; Lawrie, G., Cobbold, I., Third-generation balanced scorecard: Evolution of an effective strategic control tool (2004) International Journal of Productivity and Performance Management, 53 (7), pp. 611-623; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Li, Z., Xu, X., Kumar, A., Supply chain performance evaluation from structural and operational levels (2007) IEEE Symposium on Emerging Technologies and Factory Automation, pp. 1131-1140. , ETFA; Lin, L., Li, T., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Software Quality Journal, 18 (3), pp. 387-406; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218; Lukka, K., (2000) The Key Issues of Applying the Constructive Approach to Field Research, pp. 113-128. , Publications of Turku School of Economics and Business Administration, Turku; Mehrjerdi, Y.Z., Excellent supply chain management (2009) Assembly Automation, 29 (1), pp. 52-60; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Olsen, E.O., Zhou, H., Lee, D., Ng, Y., Chong, C.C., Padunchwit, P., Performance measurement system and relationships with performance results: A case analysis of a continuous improvement approach to PMS design (2007) International Journal of Productivity and Performance Management, 56 (7), pp. 559-582; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) 2009 2Nd International Conference on Emerging Trends in Engineering and Technology, 802p. , ICETET 2009, 16 December 2009 through 18 December 2009; Ramanathan, U., Gunasekaran, A., Subramanian, N., Supply chain collaboration performance metrics: A conceptual framework (2011) Benchmarking, 18 (6), pp. 856-872; Ren, T., Application of supply chain performance measurement based on SCOR model (2008) 2008 International Conference on Wireless Communications, Networking and Mobile Computing, , WiCOM 2008, Dalian, 12 October 2008 through 14 October 2008; Sampson, S.E., Customer-supplier duality and bidirectional supply chains in service organizations (2000) International Journal of Service Industry Management, 11 (4), pp. 348-364; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking, 17 (3), pp. 320-339; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Sink, D.S., Tuttle, T.C., Planning and measurement in your organization of the future. Stalk, G. (1988), “Time - the next source of competitive advantage” (1989) Harvard Business Review, 66 (4), pp. 41-51; Stephens, S., Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; (2005) Supply Chain Operations Reference (SCOR) Model: Overview -Version 10.0, , http://supply-chain.org/f/SCOR-Overview-Web.pdf, Supply Chain Council; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64 (1), pp. 231-241; Tangen, S., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Tapinos, E., Dyson, R.G., Meadows, M., The impact of performance measurement in strategic planning (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 370-384; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking, 16 (5), pp. 702-723; Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R., Development of a balanced scorecard: An integrated approach of interpretive structural modeling (ISM) and analytic network process (ANP) (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 25-59; Theeranuphattana, A., Tang, J., A conceptual model of performance measurement for supply chains: Alternate considerations (2008) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 15-29; Vesey, J.T., Time-to-market: Put speed in product development (1992) Industrial Marketing Management, 21 (2), pp. 151-158; Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteria decision-making methodology (2004) Int J Prod Econ, 91 (1), pp. 1-15; Xu, J., A six sigma-based methodology for performance measurement of a supply chain (2008) 2008 International Conference on Wireless Communications, Networking and Mobile Computing, , WiCOM 2008, Dalian, 12 October 2008 through 14 October 2008; Xu, X., Li, C., The supply chain performance evaluations indicator system based on benchmark balanced scorecard (2008) International Conference on Wireless Communications, , Networking and Mobile Computing, WiCOM; Yin, R.K., (2009) Case Study Research: Design and Methods, , 4th ed., Sage Publications, Thousand Oaks, CA},
correspondence_address1={Sillanpää, I.; Department of Strategic Management, Finland; email: ilkka.j.sillanpaa@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Baptista2015739,
author={Baptista, G.L. and Vanalle, R.M. and Salles, J.A.A.},
title={A Software Development Process Model Integrated to a Performance Measurement System},
journal={IEEE Latin America Transactions},
year={2015},
volume={13},
number={3},
pages={739-745},
doi={10.1109/TLA.2015.7069099},
art_number={7069099},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927158754&doi=10.1109%2fTLA.2015.7069099&partnerID=40&md5=157950758d8f826e92ca9655588164ed},
affiliation={Universidade Nove de Julho (UNINOVE), São Paulo, São Paulo, Brazil},
abstract={Software development process has been considered engineering for less than 50 years. During its evolution, many papers were presented proposing software development models that allow the adaptation of Software Engineering basis to the reality of the companies. It is also known that this industry has been suffering to attend cost, deadlines and/or functionalities. Besides, there are more companies requiring tight targets and the use of performance measurement systems becomes important to enable control, evaluation, prevention and understanding about processes, products and services. Focusing these two issues, this study was conducted, having as a goal the creation of a software development process model that integrates performance measurement systems concepts. As a result of this research, the GMQA Model, acronym from Brazilian Portuguese to Management, Measurement, Quality, and Documentation, is presented. This research details how the model was design and evolved based on a set of presentations to companies from the software industry. © 2003-2012 IEEE.},
author_keywords={Lean;  Performance Measurement System;  Software Engineering;  Stage-gates},
keywords={Software engineering;  Supply chain management, Lean;  Performance measurement system;  Products and services;  Software development models;  Software development process;  Software industry;  Stage gates, Software design},
references={Dijkstra, E.W., The humble programmer (1972) Communications of the ACM, , ACM; Humphrey, W., A discipline for software engineering (1995) SEI Series in Software Engineering, , Pittsburgh: Addison-Wesley; Sommerville, I., (2011) Engenharia de Software, 9ª Ed., , São Paulo: Pearson Prentice Hall; (2009) CHAOS Summary 2009, , Boston; Magdaleno, A.M., Werner, C.M.L., Araujo, R.M.D., Reconciling software development models: A quasi-systematic review (2011) The Journal of Systems and Software; Petersen, K., Wohlin, C., Software process improvement through the Lean Measurement (SPI-LEAM) method (2010) The Journal of Systems and Software; Poppendieck, M., Poppendieck, T., (2011) Implementando o Desenvolvimento Lean de Software: Do Conceito Ao Dinheiro, , Porto Alegre: Bookman; Fagan, M.E., Advances in Software Inspections (1986) IEEE Transactions on Software Engineering; Pressman, R.S., (2011) Engenharia de Software: Uma Abordagem Profissional, 7ª Ed., , Porto Alegre: AMGH; ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS, (2008) ABNT NBR ISO 9001:2008 - Sistemas de Gestão da Qualidade - Requisitos, , Rio de Janeiro; ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS, (2009) ABNT NBR ISO/IEC 12207:2009 Tecnologia da Informação - Processos de Ciclo de Vida de Software, , Rio de Janeiro; ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS, (2003) ABNT NBR ISO/IEC 9126-1:2003 Engenharia de Software - Qualidade de Produto Parte 1: Modelo de Qualidade, , Rio de Janeiro; ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS, (2008) ABNT NBR ISO/IEC 15504-5:2008 Tecnologia da Informação - Avaliação de Processo - Parte 5: Um Exemplo de Modelo de Avaliação de Processo, , Rio de Janeiro; Software Engineering Institute, (2010) CMMI® for Development V1.3, , Pittsburgh; (2011) MPS.BR - Melhoria de Processo Do Software Brasileiro: Guia Geral, , Campinas; Cordeiro, A.G., Freitas, A.L.P., O cenário atual da qualidade de software XV Simpósio de Engenharia de Produção, Bauru, 2008; Anacleto, A., Von Wangenheim, C.G., Salviano, C.F., Um Método de Avaliação de Processos de Software em Micro e Pequenas Empresas Simpósio Brasileiro de Qualidade de Software, Porto Alegre, 2005; Pino, F.J., Pardo, C., Garc, F., Assessment methodology for software process improvement in small organizations (2010) Information and Software Technology; Al-Tarawneh, M.Y., Abdullah, M.S., Ali, A.B.M., A Proposed Methodology for Establishing Software Process Development Improvement for Small Software Development Firms (2011) Procedia Computer Science, 3; (2010) Pesquisa de Qualidade No Setor de Software Brasileiro 2009, , Brasília; Eralp Ö., (2004) Design and Implementation of a Software Development Process Measurement System, , Ankara; Ganssle, J., Developing a good bedside manner (2009) Embedded Systems Design; Jones, C., (2008) Applied Software Measurement: Global Analysis of Productivity and Quality, , New York: McGraw-Hill; NATO, (1969) Report on a Conference Sponsored by the NATO Science Committee, , Garmisch, Germany; Booch, G., Leaving Kansas (1998) IEEE Software; Boehm, B.W., A Spiral Model of Software Development and Enhancement (1988) IEEE Compute; Royce, W.W., Managing the Development of Large Software Systems (1970) IEEE WESCON, , WESCON; Kruchten, P., (2003) The Rational Unified Process - An Introduction, , the U.S.A.: Addison-Wesley; Beck, K., Beedle, M., Van Bennekum, A., Cockburn, A., Cunningham, W., Fowler, M., Grenning, J., Kern, J., (2001), http://agilemanifesto.org/, [Online]. Available Acesso em 09 06 2012; Schwaber, K., Sutherland, J., (2011) The Scrum Guide: The Definitive Guide to Scrum, , the U.S.A.: Scrum.org; Berrocal, J., Alonso, J.G., Chicote, C.V., Murillo, J.M., A Model-Driven Approach for Documenting Business and Requirements Interdependencies for Architectural Decision Making (2014) IEEE Latin America Transactions; Dyba, T., Dingsøyr, T., Empirical studies of agile software development: A systematic review (2008) Information and Software Technology; Staats, B.R., Brunner, D.J., Upton, D.M., Lean principles, learning, and knowledge work: Evidence from a software services provider (2010) Journal of Operations Management; Slack, N., Chambers, S., Johnston, R., (2009) Administração da Produção, , São Paulo: Atlas; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering; Humphrey, W.S., (1989) Managing the Software Process, , Pittsburgh: Addison-Wesley Professional; Kaplan, R.S., Norton, D.P., (1997) A Estratégia em Ação - Balanced Scorecard, , Rio de Janeiro: Campus; ASSOCIAÇÃO BRASILEIRA DE NORMAS TÉCNICAS, (2009) ABNT NBR ISO/IEC 15939:2009 Engenharia de Sistemas e de Software - Processo de Medição, , Rio de Janeiro; Melhorias nos processos de software (2010) Computação Brasil, (14), pp. 14-16; Solingen, R., Berghout, E., (1999) The Goal/Question/Metric Method: A Practical Guide for Quality Improvement of Software Development, , London: McGraw-Hill; Cooper, R.G., (2007) Doing It Right: Winning with New Products, , Hamilton; Karlström, D., Runeson, P., Integrating agile software development into stage-gate managed product development (2006) Empirical Software Engineering; Cutovoi, I.T.M., (2012) Metodologia de Desenvolvimento de Produtos Com Foco na Logística de Abastecimento Aplicada a Uma Montadora de Motores, , São Paulo; Miguel, P.A.C., Estudo de caso na engenharia de produção: Estruturação e recomendações para sua condução (2007) Produção, 17 (1); Severino, A.J., (2007) Metodologia Do Trabalho Científico, 23ª Ed., , São Paulo: Cortez; Alexandre, J.W.C., Andrade, D.F.D., Vasconcelos, A.P.D., Araujo, A.M.S.D., Batista, M.J., Análise do número de categorias da escala de Likert aplicada à gestão pela qualidade total através da teoria da resposta ao item XXIII Encontro Nac. de Eng. de Produção, Ouro Preto, 2003; Sutherland, J., Schwaber, K., (2011) The Scrum Papers: Nut, Bolts, and Origins of an Agile Framework, , Paris: ScrumInc; Beck, K., Embracing Change with Extreme Programmin (1999) IEEE Computer; Atkinson, H., Strategy implementation: A role or the balanced scorecard? (2006) Management Decision, pp. 1441-1460; Macedo, M.A.S., Barbosa, A.C.T.D.A.M., Cavalcante, G.T., Desempenho de agências bancárias no Brasil: Aplicando análise envoltória de dados (DEA) a indicadores relacionados às perspectivas do BSC (2009) Revista Economia e Gestão, 9 (19); Salviano, C.F., (2009) Projetos da CE-21-007-10 e Novidades da ISO/IEC 15504 (SPICE), , São Paulo; Li, E.Y., Chen, H.-G., Cheung, W., Total Quality Management in Software Development Process (2000) The Journal of Quality Assurance Institute; Koscianski, A., Soares, M., (2007) Qualidade de Software: Aprenda As Metodologias e Técnicas Mais Modernas para O Desenvolvimento de Software, 2ª Ed., , São Paulo: Novatec; Barbosa, A., Mendes, L., Bottoli, M., Análise Comparativa de Metodologias e Padrões de Qualdiade Com Foco No Gerenciamento de Projetos de Software (2010) Revista Ciência e Tecnologia; Project Management Institute, (2004) Um Guia Do Conjunto de Conhecimentos em Gerenciamento de Projetos (PMBOK), , Newtown Square; Nogueira, M., Abe, J.M., Paradigmas da Engenharia de Software como Fatores críticos de Sucesso para a Gestão de Projetos de Software No Contexto brasileiro XVIII Simpósio de Engenharia de Produça&tild;o, Bauru, 2010},
publisher={IEEE Computer Society},
issn={15480992},
language={English},
abbrev_source_title={IEEE. Lat. Am. Trans.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Eskafi2015157,
author={Eskafi, S.H. and Roghanian, E. and Jafari-Eskandari, M.},
title={Designing a performance measurement system for supply chain using balanced scorecard, path analysis, cooperative game theory and evolutionary game theory: A Case Study},
journal={International Journal of Industrial Engineering Computations},
year={2015},
volume={6},
number={2},
pages={157-172},
doi={10.5267/j.ijiec.2014.12.003},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921750696&doi=10.5267%2fj.ijiec.2014.12.003&partnerID=40&md5=5d3ec985e0448ac4dfed658ebd33927c},
affiliation={Department of Industrial Engineering, Khaje Nasir university of Technology, Tehran, Iran; Department of Industrial Engineering, Payame Noor university, Tehran, Iran},
abstract={In recent years, supply chain management is known as the key factor for achieving competitive advantage. Better customer service, revenue improvement and cost reduction are the results of this philosophy. Organizations can manage the performance of their firms by appropriate goal setting, identifying criteria and continuous performance measurement, which creates a good view for the business circumstances. Developing and defining appropriate indicators at different levels of chain is necessary for implementing a performance measurement system. In this study, we propose a new method to determine the measurement indicators and strategies of the company in term of balanced scorecard. The study is a combination of balanced scorecard, path analysis, evolutionary game theory and cooperative game theory for strategic planning. The study offers an appropriate program for future activities of organizations and determines the present status of the firm. The implementation of the proposed method is introduced for a food producer and the results are analyzed. © 2014 Growing Science Ltd. All rights reserved.},
author_keywords={Balanced scorecard;  Cooperative game theory;  Evolutionary game theory;  Path analysis;  Performance measurement;  Shapely value},
references={Abu Turab Rizvi, S., Aumann's and Schelling's game theory: The Nobel Prize in Economic Science, 2005 (2007) Review of Political Economy, 19 (3), pp. 297-316; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Burney, L.L., Henle, C.A., Widener, S.K., A path model examining the relations among strategic performance measurement system characteristics, organizational justice, and extra-and inrole performance (2009) Accounting, Organizations and Society, 34 (3), pp. 305-321; Cardemil-Katuranic, G., Shadbolt, N., The Balanced Scorecard as a spontaneous framework in an agricultural hybrid cooperative under strategic change: A case study in the New Zealand kiwifruit industry (2006) In World Food and Agribusiness Congress, Buenos Aires, , Argentina; Cebeci, U., Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard (2009) Expert Systems with Applications, 36 (5), pp. 8900-8909; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Gilles, R.P., (2010) The cooperative game theory of networks and Hierarchies, 44. , Springer; Gumbus, A., Introducing the balanced scorecard: Creating metrics to measure performance (2005) Journal of Management Education, 29 (4), pp. 617-630; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Huang, C.F., Hsueh, S.L., A Study on the relationship between intellectual capital and business performance in the engineering consulting industry: A path analysis (2007) Journal of Civil Engineering and Management, 13 (4), pp. 265-271; Jafari, M., Tootooni, M., Shahanaghi, K., Eskandari, M.J., Determining the relationship between science and technology development indicators in private sector industries of Iran via path analysis based on cooperative game theory (2013) Life Science Journal, 10 (3); Jalali Naini, S.G., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 593-603; Kanji, G.K., Performance measurement system (2002) Total Quality Management, 13 (5), pp. 715-728; Kanji, G.K., E Sá, P.M., Kanji's business scorecard (2002) Total Quality Management, 13 (1), pp. 13-27; Kaplan, R.S., Norton, D.P., The Balanced Scorecard oe Measures That Drive Performance-, in (1992) Harvard Business Review, , January-February 1992; Kaplan, R.S., Norton, D.P., Using the Balancesd scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management accounting, 70 (12), pp. 45-50; Kohlberg, E., Mertens, J.F., On the strategic stability of equilibria (1986) Econometrica: Journal of the Econometric Society, pp. 1003-1037; Kreps, D.M., Wilson, R., Reputation and imperfect information (1982) Journal of Economic Theory, 27 (2), pp. 253-279; Kuhn, H.W., Tucker, A.W., John von Neumann's work in the theory of games and mathematical economics (1958) Bulletin of the American Mathematical Society, 64 (3), pp. 100-122; Lee, A.H., Chen, W.C., Chang, C.J., A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan (2008) Expert Systems with Applications, 34 (1), pp. 96-107; Nash, J.F., Jr., Equilibrium points in N-person games. Proceedings of the National Academy of Sciences of the USA, Vol. 36, pp. 48= 49. Two-Person Cooperative Games (1953) Econometrica, 21, pp. 128-140; Nash, J., Non-cooperative games (1951) Annals of Mathematics, pp. 286-295; Nash, J.F., Equilibrium points in n-person games (1950) Proceedings of the national academy of sciences, 36 (1), pp. 48-49; Razmi, J., Ghaderi, S.F., Zairi, M., Keyno, H.S., Compiling and prioritizing strategies for electrical energy production from fossil fuel with support of benchmarking (2008) Benchmarking: An International Journal, 15 (6), pp. 794-804; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339; Tijs, S.H., (2003) Introduction to game theory; Yang, M.C., Tung, Y.C., Using path analysis to examine causal relationships among balanced scorecard performance indicators for general hospitals: The case of a public hospital system in Taiwan (2006) Health Care Management Review, 31 (4), pp. 280-288},
correspondence_address1={Eskafi, S.H.; Department of Industrial Engineering, Iran; email: hootaneskafi@gmail.com},
publisher={Growing Science},
issn={19232926},
language={English},
abbrev_source_title={Int. J. Ind. Eng. Comput.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Callado2015288,
author={Callado, A.A.C. and Jack, L.},
title={Balanced scorecard metrics and specific supply chain roles},
journal={International Journal of Productivity and Performance Management},
year={2015},
volume={64},
number={2},
pages={288-300},
doi={10.1108/IJPPM-05-2014-0071},
note={cited By 22},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923320967&doi=10.1108%2fIJPPM-05-2014-0071&partnerID=40&md5=934f99bc95432aedb2d790f546827ec5},
affiliation={Department of Management, Federal Rural University of Pernambuco, Recife, Brazil; Portsmouth Business School, University of Portsmouth, Portsmouth, United Kingdom},
abstract={Purpose – The overarching question here is whether common balanced scorecards (BSCs) are possible between partners in supply networks. The purpose of this paper is to form four independent samples of Brazilian input suppliers, producers, distributors and retailers from the agri-food industry to identify how many of the metrics used in BSCs can be related to specific supply chain (SC) roles. Design/methodology/approach – A survey of 121 agribusiness companies was undertaken. Usage of indicators was identified through percentages, while the groups of performance indicators for the SC roles considered were identified through two reference values. Findings – Customer satisfaction was the single metric present within the BSC framework for all SC roles. Different SC roles showed different compositions of indicators used. Research limitations/implications – The research findings are descriptive and based on responses provided by senior managers. A new perspective of the use of specific performance metrics by different SC participants is seen. Practical implications – Performance measurement within SCs needs to consider the specific measures used by different roles within the SC. The data suggest that any implementation of performance measurement systems for supply networks should consider performance indicators that are common to the role-type and specific to the constituent companies. Originality/value – The findings contribute to the debate on whether a common set of measures in a scorecard can be used between SC partners. The findings suggest that it may be very difficult to achieve a BSC framework that is common and practical for all SC participants and that other alternatives should be investigated. © Emerald Group Publishing Limited},
author_keywords={Balanced scorecard;  Food industry;  Performance;  Supply chain},
references={Abu-Suleiman, A., Boardman, B., Priest, J.W., A framework for an integrated supply chain performance management system (2003) Proceedings of the 36Th Hawaii International Conference on System Sciences, Hawaii, pp. 1-10; Angerhofer, J.B., Angelides, C.M., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Beamon, B., Supply chain and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computer &Amp; Industrial Engineering, 53 (1), pp. 43-62; Bigliardi, B., Bottani, E., (2010) Performance measurement in the food supply chain: A balanced scorecard approach, Facilities, 28 (5-6), pp. 249-260; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Callado, A., Mendes, E., Callado, A., Um estudo empirico da significancia das relates entre a elaboragao de metas estrategicas e o uso de indicadores de desempenho (2013) Revista Iberoamericana De Contabilidad De Gestion, 11 (21), pp. 1-15; Chan, F., Qi, H.J., Chan, H.K., Lau, H., Ip, R., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Chavan, M., The balanced scorecard: A new challenge (2009) Journal of Management Development, 28 (5), pp. 393-406; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting, Organization and Society, 30, pp. 395-422; Chizzotti, A., (1991) A Pesquisa Em Ciencias Humanas E Sociais, , Cortez, Sao Paulo; Christopher, M., (2005) Logistics and Supply Chain Management, , Prentice Hall, London; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analyzing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Fawcett, S.E., Magnan, G.M., The rhetoric and reality of supply chain integration (2002) The International Journal of Physical Distribution &Amp; Logistics Management, 32 (5), pp. 339-361; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Gunasekaran, A., Patel, C., Mc Gaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environment (2001) International Journal of Operations &Amp; Production Management, 21 (1-2), pp. 71-87; Harland, C., Supply chain operational performance roles (1997) Integrated Manufacturing Systems, 8 (2), pp. 70-78; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution &Amp; Logistics Management, 30 (10), pp. 847-868; Hsieh, M.-H., Tsai, K.-H., Wang, J.-R., The moderating effects of market orientation and launch proficiency on the product advantage-performance relationship (2008) Industrial Marketing Management, 37 (5), pp. 580-592; Jack, L., Ramon Jeronimo, J., Florez Lopez, R., Performance measurement and risk management in intermediary food supply chain businesses (2012) Technical Report, , Chartered Institute of Management Accountants, London; Johanson, U., Skoog, M., Backlund, A., Almqvist, R., Balancing dilemmas of the balanced scorecard (2006) Accounting, Auditing &Amp; Accountability Journal, 19 (6), pp. 842-857; Kleijnen, J., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54, pp. 507-514; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lawrie, G., Cobbold, I., Third-generation balanced scorecard: Evolution of an effective strategic control tool (2004) International Journal of Productivity and Performance Management, 53 (7), pp. 611-623; Levin, J., (1987) Estatistica Aplicada a Ciencias Humanas, , 2nd ed., Harbra, Sao Paulo; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Matopoulos, A., Vlachopoulou, M., Folinas, D., Manthou, V., Information architecture framework for agri-food networks (2004) Dynamics in Chains and Networks: Proceedings of the Sixth International Conference on Chain and Network Management in Agribusiness and the Food Industry, , in Bremmers, H., Omta, S.W.F., Trienekens, J.H. and Wubben, E.F.M, Wageningen Academic Publishers, Ede; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22, pp. 209-217; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14, pp. 335-346; Prieto, V.C., Pereira, F., Carvalho De, M.M., Laurindo, F., Fatores criticos na implementagao do balanced scorecard (2006) Gestao &Amp; Produgao, 13 (1), pp. 81-92; Punniyamoorthy, M., Murali, R., Balanced score for the balanced scorecard: A benchmarking tool (2008) Benchmark: An International Journal, 15 (4), pp. 420-443; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Rajesh, R., Pugazhendhi, S., Ganesh, K., Ducq, Y., Koh, S., Generic balanced scorecard framework for third party logistics service provider (2012) International Journal of Production Economics, 140 (1), pp. 269-282; Savaris, C.E., Voltolini, E., Modelo de aplicagao do balanced scorecard para cadeia de suprimentos (2004) Revista Da FAE, 7 (2), pp. 59-72; Silver, M., (2000) Estatistica Para Administragao, , Atlas, Sao Paulo; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Trainor, K.J., Parr, A., Beitelspacher, L.S., Schillewaert, N., Integrating information technology and marketing: An examination of the drivers and outcomes of e-marketing capability (2011) Industrial Marketing Management, 40 (1), pp. 162-174; Van Der Vorst, J., Performance measurement in agri-food supply-chain networks: An overview (2006) Quantifying the Agri-Food Supply Chain, , in Ondersteijn, C.J.M., Wijnands, J.H.M., Huirne, R.B.M. and van Kooten, O, Springer, Dordrecht; Van Hoek, R.I., Measuring the unmeasurable - measuring and improving performance in the supply chain” (1998) Supply Chain Management, 3 (4), pp. 187-192; Van Veen-Dirks, P., Wijn, M., Strategic control: Meshing critical success factors within the balanced scorecard (2002) Long Range Planning, 35, pp. 407-427; Varma, S., Wadhwa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance: Application of analytical hierarchy process to balanced scorecard (2008) Asia Pacific Journal of Marketing and Logistics, 20 (3), pp. 343-356; Zago, C.A., Abreu, L.F., Grzebielukas, C., Bornia, A.C., Modelo de avaliagao de desempenho logistico com base no balanced scorecard (BSC): Proposta para uma pequena empresa (2008) Revista Da Micro E Pequena Empresa, 2 (1), pp. 19-37; Chang, H.H., An empirical study of evaluating supply chain management integration using the balanced scorecard in Taiwan (2009) The Service Industries Journal, 29 (2), pp. 185-202; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Gil, A.C., (2002) Como Elaborar Projetos De Pesquisa, , 4th ed., Atlas, Sao Paulo; Marconi, M., Lakatos, E.M., (1999) Tecnicas De Pesquisa, , 4th ed., Atlas, Sao Paulo},
correspondence_address1={Jack, L.; Portsmouth Business School, United Kingdom; email: Lisa.Jack@port.ac.uk},
publisher={Emerald Group Publishing Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Anand2015135,
author={Anand, N. and Grover, N.},
title={Measuring retail supply chain performance: Theoretical model using key performance indicators (KPIs)},
journal={Benchmarking},
year={2015},
volume={22},
number={1},
pages={135-166},
doi={10.1108/BIJ-05-2012-0034},
note={cited By 68},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921310665&doi=10.1108%2fBIJ-05-2012-0034&partnerID=40&md5=83d6d8887a73dee70a62ed1cefba1f3c},
affiliation={College of Management and Economics Studies (LSCM), University of Petroleum and Energy Studies, Dehradun, India},
abstract={Purpose – A growing body of literature has begun in the direction of supply chain performance measurement. However, selecting the appropriate set of key performance indicators (KPIs) for measuring supply chain performance have always remained a challenge. The purpose of this paper is to identify the KPIs and categorize them specifically for measuring retail supply chain performance. Design/methodology/approach – A qualitative approach, based on literature has been adopted. Published literature from refereed journals on supply chain performance measurement has been considered and various approaches for developing KPIs have been studied to develop a theoretical framework for performance measurement in retail supply chain. Findings – The paper identifies key indicators for performance measurement and classifies them into fourmajor categories: transport optimization, information technology optimization, inventory optimization and resource optimization. These key indicators are arranged precisely for retail industry. A theoretical framework is proposed to link the performance of these constructs on financial performance of the firm. Research limitations/implications – Future research can be carried out to validate the relevance and applicability of identified indicators. The study can be further conducted to measure the interrelationships between the KPIs and their impact on financial performance of the firm. Practical implications – This study proposes a list of indicators for retail industry, which are presented in appropriate categories so that it can be used by the focussed teams for further improvement. Originality/value – To the best of authors’ knowledge, no other study has categorized the KPIs into groups, specifically for measuring retail supply chain performance. The researcher also intends to carry out further empirical study to test the proposed theoretical framework. ©Emerald Group Publishing Limited.},
author_keywords={Information technology optimization;  Inventory optimization;  Resource optimization;  Return on assets;  Supply chain performance measurement;  Transport optimization},
references={Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Akkermans, H., Bogerd, P., Vos, B., Virtuous and vicious cycles on the road towards international supply chain management (1999) International Journal of Operations & Production Management, Vol. 19 Nos, 5 (6), pp. 565-581; Altiok, T., Raghav, R., Multi-stage, pull-type production/inventory systems (1995) IIE Transactions, 27, pp. 190-200; Amaratunga, D., Baldry, D., A conceptual framework to measure facilities management performance (2003) Property Management, 21 (2), pp. 171-189; Amaratunga, D., Baldry, D., Sarshar, M., (2000) A Assessment of facilities management performance – what next?, Facilities, Vol Nos, 1 (2), pp. 66-75; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42, pp. 283-301; Aramyan, L.H., Lansink, A.O., Vorst, J., Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management International Journal, 12 (4), pp. 304-315; Arntzen, B.C., Gerald, B.G., Terry, H.P., Linda, T.L., Global supply chain management at digital equipment corporation (1995) INTERFACES, 25, pp. 69-93; Atkin, B., Brooks, A., (2000) Total Facilities Management, , Blackwell Science, Oxford; Augenbroe, G., Park, C.S., Quantification methods of technical building performance (2005) Building Research and Information, 33 (2), pp. 159-172; Aviv, Y., On the benefits of collaborative forecasting partnerships between retailers and manufacturers (2007) Management Science, 53 (5), pp. 777-794; Bailey, J.E., Pearson, S.W., Development of a tool for measuring and analyzing computer user satisfaction (1983) Management Science, 29 (5), pp. 530-545; Baldwin, L.H., Camm, F., Moore, N.Y., Strategic Sourcing – Measuring and Managing Performance, Project Air Force (2000) Research and Development (RAND) Corporation, , Santa Monica, CA; Ballou, R.H., Gilbert, S.M., Mukherjee, A., New managerial challenges from supply chain opportunities (2000) Industrial Marketing Management, 29 (1), pp. 7-18; Barratt, M., Oliveira, A., Exploring the experiences of collaborative planning initiatives (2001) International Journal of Physical Distribution & Logistics Management, 31 (4), pp. 266-289; Beamon, B.M., Performance measures in supply chain management (1996) Proceedings of the Conference on Agile and Intelligent Manufacturing Systems, Troy, NY; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Blumberg, D.R., Strategic benchmarking of service and logistic support operations (1994) Journal of Business Logistics, 15 (2), pp. 89-119; Bolstorff, P., Measuring the impact of supply chain performance (2003) CLO/ Chief Logistics Officers, 12, pp. 5-11; Bourland, K.E., Powell, S.G., Pyke, D.K., Exploiting timely demand information to reduce inventories (1996) European Journal of Operational Research, 44 (2), pp. 239-253; Bower, J.L., Hout, T.M., (1988) Fast cycle capability for competitive power, pp. 110-118. , Harvard Business Review, November-December; Bowersox, D., Rodrigues, A., Calantone, R., Estimation of global and national logistics expenditures: 2002 data update (2005) Journal of Business Logistics, 26 (2), pp. 1-16; Bowersox, D.J., (1997) Integrated supply chain management: a strategic imperative, Annual Conference Proceedings, pp. 181-189. , Council of Logistics Management, Chicago, IL; Bradley, P., Thomas, J., Gooley, T., Cooke, J.A., Future competition: Supply vs supply chain (1999) Logistics Management & Distribution Report, 38 (3), pp. 20-21; Brown, J.R., Dant, R., On what makes a significant contribution to the retailing literature (2008) Journal of Retailing, 84 (2), pp. 131-136; Cachon, G., Exact evaluation of batch-ordering policies in two-echelon supply chains with periodic review (2001) Operations Research, 49 (1), pp. 79-98; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 612-621; Chae, B.K., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F.T., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chang, T., Fu, H., Lee, W., Lin, Y., Hsueh, H., A study of an augmented CPFR model for the 3C retail industry (2007) Supply Chain Management: An International Journal, 12, pp. 200-209; Chen, I.J., Paulraj, A., Lado, A., Strategic purchasing, supply management and firm performance (2004) Journal of Operations Management, 22, pp. 505-523; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: Balance scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Chopra, S., Meindl, P., Kalra, D.V., Supply Chain Management: Strategy, Planning and Operation, Pearson Education, New Delhi. Christensen, C. and Raynor, M. (2003), Why hard-nosed executives should care about management theory (2007) Harvard Business Review, 81 (9), pp. 67-74; Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London; Christopher, M., Logistics and Supply Chain Management, Richard D. Irwin Inc (1994) Financial Times, , New York, NY; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , 2nd ed., Financial Times/Prentice Hall, Harlow, Essex; Christopher, M., (2005) Logistics & Supply Chain Management: Creating Value-Adding Networks, , FT/Prentice-Hall, Harlow; Christy, D.P., Grout, J.R., Safeguarding supply chain relationships (1994) International Journal of Production Economics, 36, pp. 233-242; Closs, D.J., Mollenkopf, D.A., A global supply chain framework (2004) Industrial Marketing Management, 33, pp. 37-44; Closs, D.J., Goldsby, T.J., Clinton, S.R., Information technology influences on world class logistics capability (1997) International Journal of Physical Distribution & Logistics Management, 27 (1), pp. 4-17; Cohen, M.A., Moon, S., Impact of production scale economies, manufacturing complexity, and transportation costs on supply chain facility networks (1990) Journal of Manufacturing and Operations Management, 3, pp. 269-292; Corsten, D., Gruen, T., Seeking on-shelf availability-an examination of the extent, the causes and the efforts to address retail out-of-stocks (2003) International Journal of Retail and Distribution Management, 31 (12), pp. 605-716; Cox, A., A research agenda for supply chain and business management thinking (1999) Supply Chain Management, 4 (4), pp. 209-211; D’avanzo, R.L., Starr, C.E., Von Lewinski, H., Supply chain and the bottom line:A critical link (2004) Outlook: Accenture, 1, pp. 39-45; Davis, S., Albright, T., An investigation of the effect of balanced scorecard implementation on financial performance (2004) Management Accounting Research, 15, pp. 135-153; Davis, T., Effective supply chain management (1993) Sloan Management Review, pp. 35-46; De Toni, A., Tonchia, S., Performance measurement systems (2001) International Journal of Operations & Production Management, Vol. 21 Nos, 1 (2), pp. 46-70; Dixon, J.R., Nanni, A.J., (1990) Vollmann, , T.E, The New Performance Challenge- Measuring Operations for World-class Competition; Dow Homewood Dong, J.I.I.L.M., Chen, F.F., Performance modeling and analysis of integrated logistic chains: An analytic framework (2005) European Journal of Operational Research, 162, pp. 83-98; Douglas, J., Building performance and its relevance to facilities management, Facilities, Vol (1996) Nos, 3 (4), pp. 23-32; Dyapur, K.R., Patnaik, K.K., (2005) Transaction oriented computing, pp. 879-882. , HIVE computing) using GRAM-soft&#x201D;, Computational Science; Ellinger, A.E., The relationship between marketing/logistics interdepartmental integration and performance in US manufacturing firms: An empirical study (2000) Journal of Business Logistics, 21 (2), pp. 1-22; Ellram, L.M., Liu, B., The financial impact of supply management (2002) Supply Chain Management Review, 6 (6), pp. 30-37; Ellram, L.M., Londe, J.L., Weber, M.M., Retail logistics (1999) International Journal of Physical Distribution & Logistics Management, Vol. 29 No, 7 (8), pp. 477-494; Ellram, L.M., Tate, W.L., Billington, C., Understanding and managing the services supply chain (2004) Journal of Supply Chain Management, 40 (4), pp. 17-32; Emmet, S., Crocker, B., (2006) The Relationship – Driven Supply Chain, , Gower, Aldershot; Farris, M.T.I., Hutchison, P.D., Cash-to-cash: The new supply chain management metric (2002) International Journal of Physical Distribution & Logistics Management, 32 (4), pp. 288-298; Flynn, B.B., Flynn, E.J., Synergies between supply chain management and quality management: Emerging implications (2005) International Journal of Production Research, 43 (16), pp. 3421-3436; Forme, F.G., Genoulaz, V.B., Campagne, J.P., A framework to analyse collaborative performance (2007) Computers in Industry, 58, pp. 687-697; Forslund, H., Jonsson, P., The impact of forecast information quality on supply chain performance (2007) International Journal of Operations & Production Management, 27, p. 90; Friscia, T., O’marah, K., Souza, J., The AMR Research Supply Chain Top 25 and the New Trillion-Dollar Opportunity (2004) AMR Research Inc, , Boston, MA; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2001) Journal of Operations Management, 19, pp. 185-200; Fynes, B., Voss, C.A., A The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354; Ganesan, S., Morris, G., Sandy, J., Robert, W., Supply chain management and retailer performance:emerging trends, issues and implications for research and practice (2009) Journal of Retailing, 85, pp. 84-94; Gavirneni, S.R.K.S.T., A Value of information in capacitated supply chains (1999) Management Science, 45 (1), pp. 16-24; Gelders, L., Mannaert, P., Maes, J., Manufacturing strategy performance indicators and improvement programs (1994) International Journal of Production Research, 32 (4), pp. 797-805; Ginsberg, A., Venkatraman, N., Contingency perspective of organizational strategy: A critical review of the empirical research (1985) Academy of Management Review, 10 (3), pp. 421-434; Globerson, S., Issues in developing a performance criteria system for an organisation (1985) International Journal of Production Research, 23 (4), pp. 639-646; Gopal, P.R., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547; Green, K.W., Jr, Whitten, D., Inman, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply Chain Management: An International Journal, 13 (4), pp. 317-327; Gumbus, A., Introducing the balanced scorecard: Creating metrics to measure performance (2005) Journal of Management Education, 29 (4), pp. 617-630; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Gunasekarana, A., Patelb, C., Mc gaugheyc, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 84, pp. 333-347; Gustin, C.M., Daugherty, P.J., Stank, T.P., The effects of information availability on logistics integration (1995) Journal of Business Logistics, 16 (1), pp. 1-21; Hieber, R., (2002) Supply Chain Management: A Collaborative Performance Measurement Approach, , Verlag der Fachvereine Hochschulverlag AG an der ETH Zurich, Zurich; Hinks, J., Mc nay, P., The creation of a management-by-variance tool for facilities management performance assessment, Facilities, Vol (1999) Nos, 1 (2), pp. 31-53; Hitchcock, R.J., (2002) High Performance Commercial Building Systems Program, Element 2 – Project 2.1 – Task 2.1, p. 2. , Standardized Building Performance Metrics, Final Report, Building Technology Department, Lawrence Berkeley National Laboratory, Berkeley, CA; Ho, D., Au, K., Newton, E., Empirical research on supply chain management: A critical review and recommendations (2002) International Journal of Production Research, 40 (17), pp. 4415-4430; Ho, D.C., Chan, W.E., Wong, N., Chan, M., Significant metrics for facilities management benchmarking in the Asia Pacific region, Facilities, Vol (2000) Nos, 13 (14), pp. 545-555; Hofer, C., Towards a contingency theory of business strategy (1975) Academy of Management Journal, 18 (4), pp. 784-810; Hoffman, D., (2004) The Hierarchy of Supply Chain Metrics: Diagnosing your Supply Chain Health, AMR Research, Boston, MA; Huang, S.H., Sheoran, S.K., Keskar, H., (2005) Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model, Comput Ind Eng, 48, pp. 377-394; Ireland, R.K., Crum, C., Supply Chain Collaboration: How to Implement CPFR and Other Best Collaborative Practices, J (2005) Ross Publishing Inc, , Plantation, FL; Ishii, K., Takahashi, K., Muramatsu, R., Integrated production, inventory and distribution systems (1988) International Journal of Production Research, 26 (3), pp. 473-482; Johnson, H.T., Kaplan, R.S., Relevance Lost: The Rise and Fall of Management Accounting (1987) Harvard Business School Press, , Boston, MA; Johnson, J.B., Randolph, S., Brief: Making alliances work–using a computer-based management system to integrate the supply chain, JPT (1995) Journal of Petroleum Technology, 47 (6), pp. 512-513; Johnson, M., Templar, S., The relationship between supply chain and firm performance, the development and testing of a unified proxy (2011) International Journal of Physical Distribution & Logistics Management, 41 (2), pp. 88-103; Jones, R.M., Towill, D.R., Information enrichment: Designing the supply chain for competitive advantage (1997) Supply Chain Management: An International Journal, 2 (4), pp. 137-148; Kaplan, R.S., Norton, D.P., The balance scorecard- measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, 71, pp. 45-50; Kim, B., Oh, H., The impact of decision-making sharing between supplier and manufacturer on their collaboration performance, Supply Chain Management, Vol (2005) Nos, 3 (4), pp. 223-236; Kincaid, D.G., Measuring performance in facility management (1994) Facilities, 12 (6), pp. 24-27; (2011) Issues Monitor: Sharing Knowledge on Topical Issues in Retail Industry, Vol, 10. , KPGM, Amstelveen, July; Kulp, S.C., Lee, H.L., Ofek, E., Manufacturer benefits from information integration with retail customers (2004) Management Science, 50 (4), pp. 431-444; Lai, K.H., Ngai, E.T., Cheng, T.E., Measures for evaluating supply chain performance in transport logistics (2002) Transp Res Part E, 38, pp. 439-456; Lalonde, B.J., Making finance take notice (2000) Supply Chain Management Review, 4 (5), pp. 11-12; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lapide, L., What about measuring supply chain performance? (2000) ASCET, 2 (15), pp. 287-297; Lavy, S., Garcia, J.A., Dixit, M.K., Establishment of KPIs for facility performance measurement: Review of literature (2010) Facilities, 28 (9), pp. 440-464; Lawrence, P.R., Lorsch, W.J., Differentiation and integration in complex organization (1967) Administration, pp. 1-47; Lebas, M.J., Performance measurement and performance management (1995) Int. J. Product. Econ, 41 (1-3), pp. 23-35; Lee, H.L., The triple-a supply chain (2004) Harvard Business Review, 82 (10), pp. 102-113; Lee, H.L., Billington, C., Material management in decentralized supply chains (1993) Operations Research, 41 (5), pp. 835-847; Lee, H.L., Feitzinger, E., (1995) Product configuration and postponement for supply chain efficiency, pp. 43-48. , Institute of Industrial Engineers, Fourth Industrial Engineering Research Conference Proceedings; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Li, D., O’brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 59 (1-3), pp. 147-157; Li, S., Ragu-Nathanb, B., Ragu-Nathanb, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega International Journal of Management Science, 34 (2), pp. 107-124; Lin, C., Influencing factors on the innovation in logistics technologies for logistics service providers in Taiwan (2006) The Journal of American Academy of Business, 9 (2), pp. 257-263; Lockamy, A., Mc cormack, K., Linking SCOR planning practices to supply chain performance: an exploratory study (2004) International Journal of Operations & Production Management, Vol. 24 Nos, 11 (12), pp. 1192-1218; Mc carthy, T.M., Golicic, S.L., Implementing collaborative forecasting to improve supply chain performance (2002) International Journal of Physical Distribution & Logistics Management, 32 (6), pp. 431-454; Mapes, J., New, C., Szwejczewski, M., Performance trade-offs in manufacturing plants (1997) International Journal of Operations & Production Management, 17 (10), pp. 1020-1033; Maskell, B., Performance Measurement for World Class Manufacturing: A Model for American Companies (1991) Productivity Press, , Cambridge, MA; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Moseng, B., (1995) Productivity Measurement: Methods And Tools Developed in TOPP: Benchmarking Theory and Practice, , Chapman&#x0026;Hall, London; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testingaprocess-based approach (1995) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Neely, A.D., The performance measurement revolution: Why now and where next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228; Newhart, D.D., Stott, K.L., Vasko, F.J., Consolidating product sizes to minimize inventory levels for a multi-stage production and distribution systems (1993) Journal of the Operational Research Society, 44 (7), pp. 637-644; Nicoll, A.D., Integrating logistics strategies (1994) Annual International Conference Proceedings – American Production and Inventory Control Society, pp. 590-594; O’sullivan, D.T., Keane, M.M., Kelliher, D., Hitchcock, R.J., Improving building operation by tracking performance metrics throughout the building lifecycle (BLC) (2004) Energy and Buildings, 36 (11), pp. 1075-1090; Prahalad, C.K., Hamel, G., The core competence of the corporation (1990) Harvard Business Review, 68 (3), pp. 79-91; Prahalad, C.K., Krishnan, M.S., (2008) The New Age of Innovation: Driving Co-Created Value Through Global Networks, , McGraw Hill, NewYork, NY; Pyke, D.F., Cohen, M.A., Performance characteristics of stochastic integrated production-distribution systems (1993) European Journal of Operational Research, 68 (1), pp. 23-48; Pyke, D.F., Cohen, M.A., Multi-product integrated production distribution systems (1994) European Journal of Operational Research, 74 (1), pp. 18-49; Raghunathan, S., Information sharing in a supply chain: A note on its value when demand is non stationary (2001) Management Science, 47 (4), pp. 605-610; Rahman, S., Theory of constraints- a review of the philosophy and its application (1998) International Journal of Operations and Production Management, 18 (4), pp. 336-355; Rajaguru, R., Matanda, M.J., Influence of inter-organisational integration on business performance: the mediating role of organisational-level supply chain functions (2009) Journal of Enterprise Information Management, 22 (4), pp. 456-467; Raman, A., Dehoratius, N., Ton, Z., The achilles heel of supply chain management (2001) Harvard Business Review, pp. 136-152; Ramdas, K., Spekman, R.E., Chain or shackles: Understanding what drives supplychain performance (2000) Interfaces, 30 (4), pp. 3-21; Ramesh, A., Banwet, D.K., Shankar, R., Modeling the enablers of supply chain collaboration (2008) International Journal of Logistics Systems and Management, pp. 373-383; Rosenzweig, E.D., Roth, A.V., Dean, J.W., The influence of an integration strategy on competitive capabilities and business performance: an exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21 (4), pp. 437-456; Rowat, C., Collaboration for improved product availability (2006) Logistics and Transport Focus, 8 (3), pp. 18-20; Rungtusanatham, M., Salvador, F., Forza, C., Choi, T.Y., Supply-chain linkages and operational performance: a resource-based-view perspective (2003) International Journal of Operations & Production Management, 23 (9), pp. 1084-1099; Sambasivan, M., Mohamed, Z.A., Nandan, T., Performance measures and metrics for e-supply chains (2009) Journal of Enterprise Information Management, 22 (3), pp. 346-360; Schefcyzk, M., Industrial benchmarking: A case study of performance analysis techniques (1993) International Journal of Production Economics, 32 (1), pp. 1-11; Schonberger, R.J., (1990) Building a Chain of Customers, , The Free Press, London; Schoonhoven, C., Problems with contingency theory: Testing assumptions hidden within the language of contingency theory (1981) Administrative Science Quarterly, 26, pp. 349-377; Shah, J., (2009) Supply Chain Management, , Pearson Education, Delhi; Shahin, A., Mahbod, M.A., Prioritization of key performance indicators. An integration of analythical hierarchy process and goal setting (2007) International Journal of Productivity and Performance Management, 56 (3), pp. 226-240; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions a (2006) International journal of Productivity and Performance Measurement, 55 (34), pp. 242-258; Simatupang, T.M., Sridharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmarking: An International Journal, 11 (1), pp. 9-29; Simchi-Levi, D., Zhao, Y., Safety stock positioning in supply chains with stochastic lead times (2005) Manufacturing & Service Operations Management, 7, pp. 295-318; Slack, N., Chambersm, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , Pitman Publishing, London; Soni, G., Kodali, R., Performance value analysis for the justification of the leagile supply chain (2009) International Journal of Business Performance Management, Vol. 11 Nos, 1 (2), pp. 96-133; Sousa, S.D., Aspinwall, E.M., Rodrigues, A.G., Performance measures in english small and medium enterprises: Survey results (2006) Benchmarking: An International Journal, Vol. 13 Nos, 1 (2), pp. 120-134; Stank, T.P., Davis, B.R., Fugate, B.S., A strategic framework for supply chain oriented logistics (2005) Journal of Business Logistics, 26 (2), pp. 27-45; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Swanson, E.B., Measuring user attitudes in MIS research: A review (1982) OMEGA, 10 (2), pp. 157-165; Talluri, S., Sarkis, J., A computational geometry approach for benchmarking (2001) International Journal of Operations & Production Management, 21 (1), pp. 210-222; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Towers, N., Burnes, B., A composite framework of supply chain management and enterprise planning for small and medium-sized manufacturing enterprises (2008) Supply Chain Management: An International Journal, 13 (5), pp. 349-355; Van Der Vorst, J.A., Effective food supply chains generating, modelling and evaluating supply chain scenarios (2000) PhD thesis, , Wageningen University, Wageningen; Van Donk, P., Van Der Vaart, T., (2005) A critical discussion on the theoretical and methodological advancements in supply chain integration research, pp. 31-46. , in Kotzab, H., Seuring, S., M&#x00FC;ller, M. and Reiner, G. (Eds), Research Methodologies in Supply Chain Management, H. Physica-Verlag; Voudouris, V.T., Mathematical programming techniques to debottleneck the supply chain of fine chemical industries (1996) Computers and Chemical Engineering, 20, pp. 1269-1274; Walters, P.G., Adding value in global B2B supply chains: Strategic directions and the role of the internet as a driver of competitive advantage (2008) Industrial Marketing Management, 37 (1), pp. 59-68; Whipple, J.M., Frankel, R., Daugherty, P.J., Information support for alliances: Performance implications (2002) Journal of Business Logistics, 23 (2), pp. 67-82; Yang, C.C., Chen, B.S., Supplier selection using combined analytical hierarchy process and grey relational analysis (2006) Journal of Manufacturing Technology, pp. 926-941; Barratt, M., Oke, A., Antecedents of supply chain visibility in retail supply chain: A resource-based theory perspective (2007) Journal of Operations Management, 25, pp. 1217-1233; Beierlein, J.G., Miller, C.A., (2000) Performance Measures, and Measurement in Supply Chains in the Food System, Pennsylvania State University, World Food & Agribusiness Forum, p. 25. , Chicago, June; Bernon, M., Rossi, S., Cullen, J., Retail reverse logistics: A call and grounding framework (2011) International Journal of Physical Distribution & Logistics Management, 41 (5), pp. 484-510; Chavez, R., Brian, F., Gimenez, C., Wiengarten, F., Assessing the effect of Industry clockspeed on the supply chain management practice- performance relationship (2012) Supply Chain Management: An International Journal, 17 (3), pp. 290-305; Churchill, G., A paradign for developing better measures of marketing constructs (1979) Journal of Marketing Research, 12 (2), pp. 64-73; Modeling retail phenomena (2010) Journal of Retailing, 86 (2), pp. 117-124; Forker, L.B., Mendex, D., Hershauer, J.C., Total quality management in the supply chain: What is its impact on performance? (1997) International journal of Production Research, 35 (6), pp. 1681-1701; Ganesan, S., George, M., Jap, S., Palmatier, R.W., Weitz, B., Supply chain management and retailer performance: emerging trends, issues, and implications for research and practice (2009) Journal of Retailing, 85 (1), pp. 84-94; Gomez, I.M., Mc laughlin, E.W., Wittink, D.R., Customer satisfaction and retail sales performance: an empirical investigation (2004) Journal of Retailing, 80, pp. 265-278; Gregory, M.J., Integrated performance measurement: A review of current practice and emerging trends (1993) International Journal of Production Economics, 30 (31), pp. 281-296; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , Prentice-Hall, Englewood Cliffs, NJ; Hamister, J.W., Supply chain management practices in small retailers (2012) International Journal of Retail & Distribution Management, 40 (6), pp. 427-450; Hofer, C., Jin, H., Swanson, R.D., Matthew, W.A., Williams, B.D., The impact of key retail accounts on supplier performance: a collaborative perspective of resource dependency theory (2012) Journal of Retailing; Hsiao, J.-M.M., (2006) The Impact of Retailer-Supplier Cooperation and Decision-Making Uncertainty on Supply Chain Performance, Institute of Transport and Logistics Studies, Faculty of Economics and Business, The University of Sydney, pp. 2-17. , Jakate, D. (2007), &#x201C;Retail supply chain&#x201D;, The 2007 BPC World Conference Proceedings, Mumbai, pp; Khan, K., Shah, A., Understanding performance measurement through the literature (2011) African Journal of Business Management, 35 (5), pp. 13410-13418; Lee, H.L., Creating value through supply chain integration (2000) Supply Chain Management Review, 4 (4), pp. 30-36; Lewis, A.T., (2006) The Effects of Information Sharing, Organizational Capability and Relationship Characteristics on Outsourcing Performance in the Supply Chain: An Empirical Study, , The Ohio State University, Columbus, OH; Lindner, C., Suppl y Chain Per formance Measurement (2009) A Research of Occurring Problems and Challenges, , J&#x00F6;nk&#x00F6;ping University, J&#x00F6;nk&#x00F6;ping; Moberg, C.R., Whipple, T.W., Cutler, B.D., Speh, T.W., Do the management components of supply chain management affect logistics performance? (2004) The International Journal of Logistics Management, 15 (2), pp. 15-30; Nunnally, J., (1979) Psychometric Theory, , Mc-Graw-Hill, London; Oh, L.-B., Teo, H.-H., Sambamurthy, V., The effects of retail channel integration through the use of information technologies on firm performance (2012) Journal of Operations Management, 30, pp. 368-381; Ou, C.S., Liu, F.C., Hung, Y.C., Yen, C.D., A structural model of supply chain management on firm performance (2010) International Journal of Operations & Production Management, 30 (5), pp. 526-545; Rao, B.P., Mproving retail effectiveness through technology: A survey of analytical tools for physical and on-line retailers (2000) Technology in Society, 22, pp. 111-122; Robinson, J.P., Shaver, P.R., Wrightsman, L.S., Criteria for scale selection and evaluation (1991) Measures of Personality and Social Psychological Attitudes, 1, pp. 1-16. , Academic Press, San Diego, CA; Shaw, S., Grant, D.B., Developing environmental supply chain performance measures (2012) Benchmarking: An International Journal, 17 (3), pp. 320-339; Watch, E., (2010) Conclusion on retail industry in India, , www.economywatch.com/business-and-economy/conclusion.html, June 30, available at, accessed June 4, 2012; Watch, E., (2010) Indian retail industry, , www.economywatch.com/business-and-economy/indian-retail-industry.html, June 30, available at, accessed June 4, 2012; Watch, E., (2010) Size of indian retail industry and present scenario, , www.economywatch.com/business-and-economy/indian-retail-industry-size.html, June 30, available at, (accessed June 4, 2012; Watch, E., (2010) Top companies in the Indian retail industry, , www.economywatch.com/business-and-economy/top-companies-india-retail-industry.html, June 30, available at, accessed June 4, 2012; Whitten, G.D., Green, W.K., Jr, Zelbst, P.J., A Triple-a supply chain performance (2012) International Journal of Operations & Production Management, 32 (1), pp. 28-48},
correspondence_address1={Anand, N.; College of Management and Economics Studies (LSCM), India; email: neha.grover10@gmail.com},
publisher={Emerald Group Holdings Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Abidi2015427,
author={Abidi, H. and Klumpp, M. and de Leeuw, S.},
title={Modelling Impact of Key Success Factors in Humanitarian Logistics},
journal={Lecture Notes in Logistics},
year={2015},
pages={427-443},
doi={10.1007/978-3-319-13177-1_33},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85102010132&doi=10.1007%2f978-3-319-13177-1_33&partnerID=40&md5=d4197dac2af4998871798df18487423e},
affiliation={FOM ild, Research Institute for Logistics and Service Management, Essen, 45141, Germany; VU University Amsterdam, 1081 HV, Amsterdam, Netherlands},
abstract={Recently the authors have explored strategic, tactical and operative key success factors for the humanitarian supply chain. Moreover, they have developed and tested a holistic and applicable performance measurement system for the humanitarian supply chain. Based on these results the impact of key success factors on performance is investigated: An impact model of key success factors on key performance indicators is developed and formulated. The evaluation of the model will be based on sequential qualitative system analysis for a first testing and analytical insights. Findings reveal that performance measurement in humanitarian logistics and humanitarian supply chains is still an open area of research, especially compared to the commercial logistics and supply chain sector. Moreover, it highlights how to identify and measure success in humanitarian supply chains. The results help humanitarian logistics and humanitarian supply chain actors to conduct further research in this area and to develop key performance indicators and measurement frameworks that suit the humanitarian logistics sector. © 2015, Springer International Publishing Switzerland.},
author_keywords={Humanitarian Logistics;  Humanitarian Organization;  Impact Matrix;  Performance Measurement System;  Supply Chain},
references={Abidi, H., Klumpp, M., De Leeuw S (2013) Measuring Success in Humanitarian Supply Chains (2013) Proceedings of the 24Th Annual Production and Operations Management Society Conference, pp. 03-06. , Schoenherr T, Denver, Colorado, May; Albers, O., Broux, A., (1999), Weinheim; Altay, N., Green, W.G., OR/MS research in disaster operations management (2006) Eur J Oper Res, 175 (1), pp. 475-493; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) Int J Pub Sect, 21 (1), pp. 4-25; Blecken, A.F., Hellingrath, B., Dangelmaier, W., A humanitarian supply chain process reference model (2009) Int J Serv Technol Manage, 12 (4), pp. 391-413; Chang, S.E., Nojima, N., Measuring post-disaster transportation system performance: the 1995 Kobe earthquake in comparative perspective (2001) Transport Res Part a Policy Pract, 35 (6), pp. 475-494; de Leeuw, S., Towards a reference mission map for performance measurement in humanitarian supply chains (2010) IFIP International Federation for Information Processing, pp. 181-188. , , pp; Dupont, A., Pearman, G., Heating up the planet: Climate change and security (2006) Lowy Institute Paper 12. the Lowy Institute, , Double Bay; Geschka, H., Hammer, R., Die Szenario-Technik in der strategischen Unternehmensplanung (1997) Strategische Unternehmensplanung, pp. 464-489. , Hahn D, Taylor B, Physica-Verlag, Heidelberg, pp; Grosskurth, J., Rotmans, J., The SCENE model: Getting a grip on sustainable development in policy making (2005) Environment, Development and Sustainability, pp. 135-151. , , pp; Guha-Sapir, D., Vos, F., Below, R., Ponserre, S., (2012) Annual Disaster Statistical Review 2011: The Numbers and Trends; Gunasekaran, A., Ngai, E.W.T., The successful management of a small logistics company (2003) Int J Phys Distrib Logistics Manage, 33 (9), pp. 825-842; Helbing, D., Kühnert, C., Assessing interaction networks with applications to catastrophe dynamics and disaster management (2003) Physica A, 328 (3-4), pp. 584-606; Holguín-Veras, J., Jaller, M., Wachtendorf, T., Comparative performance of alternative humanitarian logistics structures after the Port-au-Prince earthquake: ACEs, PIEs, and CANs (2012) Transp Res Part A, 46, pp. 1623-1640; Komrska, J., Kopczak, L., Swaminathan, J.M., (2013) When Supply Chains save Lives, , http://www.unicef.org/supply/files/SCMR1301_Unicef.pdf, available at; Kovács, G., Spens, K.M., Humanitarian logistics in disaster relief operations (2007) Int J Phys Distrib Logistics Manage, 37, pp. 99-114; Lang, D.J., Binder, C.R., Scholz, R.W., Schleiss, K., Staubli, B., Impact factors and regulatory mechanisms for material flow management: Integrating stakeholder and scientific perspectives (2006) —the Case of Biowaste Delivery. Resour Conserv Recycl, 47, pp. 101-132; Lin, Y.H., Batta, R., Rogerson, P.A., Blatt, A., Flanigan, M., Application of a humanitarian relief logistics model to an earthquake disaster (2009) Transport Res Record J Transport Res Board, pp. 1-14. , , pp; Lu, D.-K., Pettit, S., Beresford, A., Critical success factors for emergency relief logistics (2006) WHAMPOA Interdisc J, 51, pp. 177-184; Medina-Borja, A., Pasupathy, K.S., Triantis, K., Large-scale data envelopment analysis (DEA) implementation: A strategic performance management approach (2007) J Oper Res Soc, 58 (8), pp. 1084-1098; Mingers, J., Rosenhead, J., Problem structuring methods in action (2004) Eur J Oper Res, 152, pp. 530-554; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced scorecard for natural disaster management (2007) Disaster Prev Manage, 16 (5), pp. 785-806; Mwenja, D., Lewis, A., Exploring the impact of the board of directors on the performance of not-for-profit organizations (2009) Bus Strat Ser, 10 (6), pp. 359-365; Neely, A., Gregory, M., Platts, K., Performance measurement systems design: A literature review and research agenda (1995) Int J Oper Prod Manage, 15 (4), pp. 80-116; Oloruntoba, R., A wave of destruction and the waves of relief: Issues, challenges and strategies (2005) Disaster Prev Manage, 14 (4), pp. 506-521; Oloruntoba, R., An analysis of the cyclone larry emergency relief chain: Some key success factors (2010) Int J Prod Econ, 126, pp. 85-101; Oloruntoba, R., Gray, R., Humanitarian aid: An agile supply chain? (2006) Int J Supply Chain Manage, 11 (2), pp. 115-120; Pettit, S., Beresford, A., Critical success factors in the context of humanitarian aid supply chains (2009) Int J Phys Distrib Logistics Manage, 39 (6), pp. 450-468; Power, D.J., Sohal, A.S., Rahman, S.U., Critical success factors in agile supply chain management (2001) Int J Phys Distrib Logistics Manage, 31 (4), pp. 247-265; Razzaque, M.A., Cheng, C.C., Outsourcing of logistics functions: A literature survey (1998) Int J Phys Distrib Logistics Manage, 28 (2-3), pp. 89-107; Reibnitz, U., (1991) Szenario-Technik: Instrumente für Die Unternehmerische Und persönliche Erfolgsplanung, , Wiesbaden; Rongier, C., Galasso, F., Lauras, M., Gourc, D., A method to define a performance indicator system for the control of a crisis (2010) 8Th International Conference of Modeling and Simulation, , Hammamet; Santarelli, G., Abidi, H., Regattieri, A., Klumpp, M., A performance measurement system for the evaluation of humanitarian supply chain (2013) Proceedings of the 24Th Annual Production and Operations Management Society Conference, Denver, 03–06 May, p. 2013. , Schoenherr T; Scholz, R., Tietje, O., (2002) Embedded Case Study Methods: Integrating Quantitative and Qualitative Knowledge, , SAGE, Thousand Oaks; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Manage Res News, 32 (1), pp. 1038-1049; Thomas, A., Kopczak, L.R., (2007) Life-Saving Supply chain–challenges and the Path Forward, pp. 93-111. , In: Lee HL, Lee CY (eds) Building supply chain excellence in emerging economies. Springer Science & Business Media, New York, pp; Tomasini, R., van Wassenhove, L.N., From preparedness to partnerships: Case study research on humanitarian logistics (2009) Int Trans Oper Res, 16, pp. 549-559; Nations, U., (2006) Strengthening of the Coordination of Emergency Humanitarian Assistance of the United Nations, , http://www.un.org/docs/ecosoc/documents/2006/reports/ECOSOC%202006%20-%20Humanitarian%20Coordination.pdf, United Nations. Available at; van Der Laan, E.A., de Brito, M.P., Vergunst, D.A., Performance measurement in humanitarian supply chains (2009) Int J Risk Assess Manag, 13 (1), pp. 22-45; van Wassenhove, L.N., Humanitarian aid logistics: Supply chain management in high gear (2006) J Oper Res Soc, 57 (5), pp. 475-484; Wiek, A., Lang, D., Siegrist, M., Qualitative system analysis as a means for sustainable governance of emerging technologies: The case of nanotechnology (2008) J Clean Prod, 16 (8-9), pp. 988-999},
correspondence_address1={Abidi, H.; FOM ild, Germany; email: hella.abidi@fom-ild.de},
publisher={Springer Science and Business Media B.V.},
issn={21948917},
language={English},
abbrev_source_title={Lect. Notes Logist.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Abidi2015235,
author={Abidi, H. and Scholten, K.},
title={Applicability of Performance Measurement Systems to Humanitarian Supply Chains},
journal={Lecture Notes in Logistics},
year={2015},
pages={235-260},
doi={10.1007/978-3-319-15455-8_13},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023596422&doi=10.1007%2f978-3-319-15455-8_13&partnerID=40&md5=206e4c9badcecf25b5aade641804987b},
affiliation={FOM Ild, Essen, Germany; VU University, Amsterdam, Netherlands; University of Groningen, Groningen, Netherlands},
abstract={Recently performance measurement in humanitarian supply chains has become a key driver to ensure efficiency and effectiveness of operations and overall sustainability. The purpose of this paper is to evaluate current performance measurement systems from the commercial sector for their applicability to the specificities of the humanitarian supply chain sector. In doing so, we adapt the evaluation criteria for an ‘appropriate’ performance measurement system from Caplice and Sheffi (1995) to the humanitarian supply chain context and apply them to well-known performance measurement framework from the commercial sector (Balanced Score Card, SCOR Model and Performance Prism). Findings indicate that the analyzed performance measurement frameworks have some potential to enable effective and efficient performance measurement of supply chains in the humanitarian aid sector in light of the established criteria. © 2015, Springer International Publishing Switzerland.},
author_keywords={Balance Scorecard;  Humanitarian Setting;  Performance Measurement System;  Supply Chain;  Supply Chain Management},
references={Abidi, H., Klumpp, M., Performance measurement in humanitarian logistics: A literature review (2013) NOFOMA Conference Proceedings, , Göteborg, Sweden; Balcik, B., Beamon, B.M., Krejci, C.C., Muramatsu, K.M., Ramirez, M., Coordination in humanitarian relief chains: Practices, challenges and opportunities (2010) Int J Prod Econ, 126 (1), pp. 22-34; Beamon, B.M., Humanitarian relief chains: Issues and challenges (2004) Proceedings of the 34Th International Conference on Computers and Industrial Engineering, pp. 77-82. , San Francisco, CA, pp; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) Int J Publ Sect Manage, 21 (1), pp. 4-25; Beamon, B.M., Measuring supply chain performance (1999) Int J Oper Prod Manage, 19 (3), pp. 275-292; Bititci, U., Mendibili, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) Int J Oper Prod Manage, 25 (4), pp. 333-353; Blecken, A., (2010) Humanitarian Logistics: Modelling Supply Chain Processes of Humanitarian Organisations, , Haupt Verlag, Bern et al; Blecken, A., Hellingrath, B., Dangelmeier, W., Schulz, S.F., A humanitarian supply chain process reference model (2009) Int J Serv Technol Manage, 12 (4), pp. 391-413; Bourne, M.C.S., Neely, A.D., Mills, J.F., Platts, K., Implementing performance measurement systems: A literature review (2003) Int J Bus Perform Manage, 5 (1), pp. 1-24; Buckmaster, N., Associations between outcome measurement, accountability and learning for non-profit organizations (1999) Int J Publ Sect Manage, 12 (2), pp. 186-197; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) Int J Logistics Manage, 6 (1), pp. 61-74; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) Int J Bus Perfor Manage, 8 (2-3), pp. 110-131; Davidson, A.L., (2006) Key Performance Indicators in Humanitarian Logistics, , Master thesis, Boston; de Bruijn, H., van Helden, J., A plea for dialogue driven performance-based management systems: Evidence from the Dutch public sector (2006) Financ Acc Manage, 22 (4), pp. 405-423; de Leeuw, S., Towards a reference mission map for performance measurement in humanitarian supply chains (2010) IFIP International Federation for Information Processing, pp. 181-188. , , pp; Dees, J.G., Enterprising nonprofit (1998) Harvard Bus Rev, 76 (1), pp. 54-67. , http://search.ebscohost.com/login.aspx?direct=true&db=buh&AN=16924&site=ehost-live, Retrieved from; Erdmann, M.-K., Supply chain performance measurement. Operative und strategische Management-und Controllingansätze (English translation: Supply chain performance measurement. Operational and strategic management and controlling approaches). Dissertation. Josef Eul Verlag (2003) Lohmar, K01 (Produktionswirtschaft und Industriebetriebslehre. Bd. 11); Ernst, R., The academic side of commercial logistics and the importance of this special issue (2003) Forced Migr Rev, 18, p. 5; Fitzgerald, L., Johnston, R., Brignall, T.J., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business. the Chartered Institute of Management Accounts, , London; Gammelgaard, B., Vesth, H., The SCOR model—a critical review (2004) Proceedings of Operations Management as a Change Agent Conference, INSEAD, pp. 233-241. , , pp; Gatignon, A., van Wassenhove, L.N., Charles, A., The Yogyakarta earthquake: Humanitarian relief through IFRC’s decentralized supply chain (2010) Int J Prod Econ, 126 (1), pp. 102-110; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications (2007) Int J Prod Res, 45 (12), pp. 2819-2840; Hatten, M.L., Strategic management in not-for-profit organizations (1982) Strateg Manage J, 3, pp. 89-104; Hofmann, C.A., Roberts, L., Shoham, J., Harvey, P., (2004) Measuring the Impact of Humanitarian Aid: A Review of Current Practice. Humanitarian Policy Group (HPG) Research Report, Report, p. 17. , June, Overseas Development Institute, London, UK; IFRC (2014) Syria crisis. Retrieved from http://www.ifrc.org/syria-crisis. Accessed 16 Jan 2014; (2002) World Disasters report— Focus on Reducing Risk. Retrieved From, , http://www.ifrc.org/Global/Publications/disasters/WDR/32600-WDR2002.pdf, Accessed 18 Feb 2014; Jahre, M., Heigh, I., Does failure the current constrains in funding promote failure in humanitarian supply chains? Int (2008) J Supply Chain Forum, 9 (2), pp. 44-45; Jahre, M., Jensen, L.-M., Listou, T., Theory development in humanitarian logistics—a framework and three cases (2009) Manage Res News, 32 (11), pp. 1008-1023; Jensen, L.-M., Humanitarian cluster leads: Lessons from 4PLs (2012) J Humanitarian Logistics Supply Chain Manage, 2, pp. 148-160; Kaplan, R.S., Norton, D.P., The balanced scorecard—measures that drive performance (1992) Harvard Bus Rev, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Bus Rev, 74, pp. 75-85; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1998) The Economic Impact of Knowledge, pp. 315-324. , Neef D, Siesfeld GA, Cefola J, Butterworth-Heinemann, Woburn, pp; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Manage Accounting, 70, pp. 45-50; Kennerly, M., Neely, A., Measuring performance in a changing business environment (2002) Int J Oper Prod Manage, 23 (2), pp. 213-229; Kovács, G., Spens, K.M., Humanitarian logistics in disaster relief operations (2007) Int J Phys Distrib Logistics Manage, 37 (2), pp. 99-114; Kovács, G., Spens, K.M., Humanitarian logistics and supply chain management: The start of a new journal (2011) J Humanitarian Logistics Supply Chain Manage, 1 (1), pp. 5-14; Kovács, G., Spens, K.M., Trends and developments in humanitarian logistics—a gap analysis (2011) Int J Phys Distrib Logistics Manage, 41 (1), pp. 32-45; Logistics Cluster (2014) “Meeting minutes” and “situation report”. Retrieved from http://logcluster.org/ops/syr12a. Accessed on 12 Dec 2014; Long, D.C., Logistics for disaster relief: Engineering on the run (1997) IIE Solutions, 29 (6), pp. 26-29; Long, D.C., Wood, D.F., The logistics of famine relief (1995) J Bus Logistics, 16 (1), pp. 213-229; Lynch, R.L., Cross, K.F., (1991) Measure up—the Essential Guide to Measuring Business Performance, , Economist Books, London; Manville, G., Broad, M., Changing times for charities: Performance management in a third sector housing association (2013) Publ Manage Rev, 15 (7), pp. 992-1010; Maon, F., Lindgreen, A., Vanhamme, J., Developing supply chains in disaster relief operations through cross-sector socially oriented collaborations: A theoretical model (2009) Supply Chain Manage Int J, 14 (2), pp. 149-164; McAdam, R., Hazlett, S.A., Casey, C., Performance management in the UK public sector: Addressing multiple stakeholder complexity (2005) Int J Publ Sect Manage, 18 (3), pp. 256-273; McEntire, D.A., Development, disasters and vulnerability: A discussion of divergent theories and the need for their integration (2004) Disaster Prev Manage, 13 (3), pp. 193-198; McLachlin, R., Larson, P.D., Khan, S., Not-for-profit supply chain in interrupted environments: The case of a faith-based humanitarian relief organization (2009) Manage Res News, 32 (11), pp. 1050-1064; Micheli, P., Kennerly, M., Performance measurement frameworks in public and non-profit sectors (2005) Prod Plann Control, 16 (2), pp. 125-134; Moe, T.L., Gehbauer, F., Senitz, S., Mueller, M., Balanced score-card for natural disaster management (2007) Disaster Prev Manage, 16 (5), pp. 785-806; Moullin, M., (2002) Delivering Excellence in Health and Social Care, , Open University Press, Buckingham; Moxham, C., Performance measurement (2009) Examining the Applicability of the Existing Body of Knowledge to Nonprofit Organizations. Int J Oper Prod Manage, 29 (7), pp. 740-763; Moxham, C., Boaden, R., The impact of performance measurement in the voluntary sector: Identification of contextual and processual factors (2007) Int J Oper Prod Manage, 27 (8), pp. 826-845; Neely, A., Gregory, M., Platts, K., Performance measurement systems design: A literature review and research agenda (1995) Int J Oper Prod Manage, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Richards, H., Bourne, M., Performance measurement system design: Developing and testing a process-based approach (2000) Int J Oper Prod Manage, 20 (10), pp. 1119-1145; Neely, A.D., Adams, C., Crowe, P., The performance prism in practice (2001) Measur Bus Excellence, 5 (2), pp. 6-12; Neely, A.D., Bourne, M.C.S., Why measurement initiatives fail (2000) Measur Bus Excellence, 4 (4), pp. 3-6; Oloruntoba, R., Gray, R., Humanitarian aid: An agile supply chain? (2006) Supply Chain Manage Int J, 11, pp. 115-120; Oloruntoba, R., Gray, R., Customer service in emergency relief chains (2009) Int J Phys Distrib Logistics Manage, 39 (6), pp. 486-505; Pettit, S., Beresford, A., Critical success factors in the context of humanitarian aid supply chains (2009) Int J Phys Distrib Logistics Manage, 39 (6), pp. 450-468; Powell, S., The challenges of performance measurement (2004) Manage Decis, 42 (8), pp. 1017-1023; Purbey, S., Mukherjee, K., Bhar, C., Performance measurement system for healthcare processes (2007) Int J Prod Perform Manage, 56 (3), pp. 241-251; Schulz, S.F., Heigh, I., Logistics performance management in action within a humanitarian organization (2009) Manage Res News, 32 (11), pp. 1038-1049; Srimai, S., Radord, J., Wright, C., Evolutionary paths of performance measurement (2011) Int J Prod Perfor Manage, 60 (7), pp. 662-687; Stephenson, M., Making humanitarian relief networks more effective: Operational coordination, trust and sense making (2005) Disasters, 29 (4), pp. 337-350; Striteska, M., Spickova, M., Review and comparison of performance measurement systems (2012) J Organ Manage Stud, 19 (3), pp. 277-294; (2007) SCOR 8.0 Overview Booklet, Supply Chain Council Editions. Retrieved From, , http://www.supply-chain.org/cs/root/home, Accessed 11 Mar 2013; Tangen, S., (2004) Evaluation and Revision of Performance Measurement Systems, , Doctoral thesis, Stockholm, Sweden; Tatham, P., Hughes, K., Humanitarian logistics metric where we are and how we might improve (2011) Humanitarian Logistics: Meeting the Challenge of Preparing for and Responding to Disasters, pp. 249-263. , Christopher M, Tatham P, Philadelphia, London, pp; Theeranuphattana, A., Tang, J., A conceptual model of performance measurement for supply chains: Alternative considerations (2008) J Manuf Technol Manage, 19 (1), pp. 125-148; Thévenaz, C., Resodihardjo, S.L., All the best laid plans conditions impeding proper emergency response (2010) Int J Prod Econ, 126 (1), pp. 7-21; Thomas, A.S., Kopczak, L.R., (2005) From Logistics to Supply Chain Management: The Path Forward in The Humanitarian Sector, , Fritz Institute, San Francisco, CA; Tomasini, R., van Wassenhove, L.N., From preparedness to partnerships: Case study research on humanitarian logistics (2009) Int Trans Oper Res, 16, pp. 549-559; van Der Laan, E.A., de Brito, M.P., Vergunst, D.A., Performance measurement in humanitarian supply chains (2009) Int J Risk Assess Manage, 13 (1), pp. 22-45; van Wassenhove, L.N., Blackett memorial lecture humanitarian aid logistics: Supply chain management in high gear (2006) J Oper Res Soc, 57, pp. 475-489; Wainwright, S., (2003) Measuring impact—a Guide to Resources, , NCVO Publications, London; Weidinger, C., Platts, K., Evaluating the effectiveness of performance measurement systems (2012) Seventeenth International Working Seminar on Production Economics, , Innsbruck, Austria; Widera, A., Hellingrath, B., Performance measurement systems for humanitarian logistics (2011) Proceedings of the 23Rd Annual NOFOMA Conference, pp. 1327-1342. , Harstad, Norway, pp; Williams, W., Lewis, D., Strategic management tools and public sector management: The challenge of context specificity (2008) Publ Manage Rev, 10 (5), pp. 653-671; Wisniewski, M., Derek, S., Performance measurement for stakeholders: The case of Scottish local authorities (2004) Int J Publ Sect Manage, 17 (3), pp. 222-233},
correspondence_address1={Abidi, H.; FOM IldGermany; email: hella.abidi@fom-ild.de},
publisher={Springer Science and Business Media B.V.},
issn={21948917},
language={English},
abbrev_source_title={Lect. Notes Logist.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Shin2015348,
author={Shin, J. and Choi, J. and Kim, I.},
title={Development of BIM performance measurement system for architectural design firms},
journal={Communications in Computer and Information Science},
year={2015},
volume={527},
pages={348-365},
doi={10.1007/978-3-662-47386-3_19},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983341694&doi=10.1007%2f978-3-662-47386-3_19&partnerID=40&md5=51cedd3afc68e8cceccb3b0740017207},
affiliation={Kyung Hee University, Seoul, South Korea},
abstract={Despite the effort of Korean government to vitalize BIM adoption in AEC industry, the domestic adoption of BIM is still in its initial step. Particular in design field where medium and small firms being the majority, shows lower level of BIM adoption. The primary reason for this can be considered as lacking of necessities caused by uncertain benefits of BIM. Therefore, it is time to develop the objectives, quantifiable and qualitative measurement system of BIM performances. The purpose of this study is to suggest the BIM Performance Measurement System for architectural design firms. In achieving this, the authors have developed Balanced Scorecard (BSC) and validated its appropriateness by questionnaire survey with experts and performing statistical analysis. This development can be contributed to the voluntary BIM adoption by visualizing the detailed benefit of BIM and to the improvement of enterprise competitiveness by facilitating management of design process and estimating future outcome. © Springer-Verlag Berlin Heidelberg 2015.},
author_keywords={Balanced scorecard (BSC);  BIM adoption;  BIM benefit;  Building information modeling (BIM);  Critical success factors (CSF);  Key performance indicators (KPI);  Performance measurement system (PMS)},
keywords={Benchmarking;  Competition;  Design;  Supply chain management;  Surveys, Balanced scorecards;  BIM adoption;  BIM benefit;  Building Information Model - BIM;  Critical success factor;  Key performance indicators;  Performance measurement system, Architectural design},
references={Kim, W., (2012) Survey and Implications of Utilizing BIM in Construction Company, , Construction & Economy Research Institute of Korea, Korea; Becerik-Gerber, B., Rice, S., The percieved value of building information modeling in the U.S. building industry (2010) Itcon, 25, pp. 185-201; Radhkrishnan, A., A process-oriented perspective on differential business value creation by information technology: A empirical investment (2008) Omega, 36, pp. 1105-1125; Song, M., BSC based measurement of satisfaction degree for based BIM construction project (2011) Korean J. Constr. Eng. Manag, 12, pp. 117-129; Lee, J., Lee, J., A study on BIM capability evaluation for design organizations (2011) J. Archit. Inst. Korea Planning Design, 27 (6), pp. 257-266; Seo, J., Choo, S., A study on cost-benefit analysis of BIM-based integrated design process in Korean architectural offices (2010) Int. J. CAD/CAM, 15, pp. 261-270; Barlish, K., Sullivan, K., How to measure the benefits of BIM-a case study approach. Autom (2012) Constr, 24, pp. 149-159; Kang, T., A Study on the Development Direction of a BIM Performance Assessment Tool. Korean (2013) J. Space Inf, 21, pp. 53-62; Im, J., (2006) Study on Using BSC Framework to Develop CSF & KPI for IT Performance Measure on IT Organization and Examining the Relation of Cause and Effect in Indicators, , Master dissertation, Yonsei University; Grembergen, W., (2001) Information Technology Governance through the Balanced Scorecard, pp. 199-211. , IDEA Group Publishing, Hershey; Lee, Y., (2014) IT Performance of Small and Medium Firms : A Comparison between Manufacturers and Non-Manufacturers, Master Dissertation, , Hankuk University of Foreign Studies; Olve, N., (2000) Performance Drivers : A Practical Guide to Using the Balanced Scorecard, , Haneon Books, Seoul; Choi, D., (2004) (Easy to Know) BSC, , KOMIT Publishing, Seoul; Min, J., Performance evaluation of knowledge workers in knowledge-based organization (2000) Korean J. Manag. Sci, 25, pp. 137-154; Kim, H., (2008) A Case Study on the Establishing Public IT organization’s Performance Measurement Model through the IT BSC, , Master dissertation, Soongsil University; Kaplan, S., Noton, P., Using the Balanced Scorecard as a Strategic Management System (1996) Harvard Business Review, pp. 75-85. , January-February; Seo, J., The development of an evaluation model for the work environment of the BIM-based architectural design firms (2012) J. Archit. Inst. Korea Planning Design, 28 (5), pp. 95-105; Cha, S., Kim, T., Developing measurement system for key performance indicators on building construction projects (2008) Korean J. Constr. Eng. Manag, 9, pp. 120-130; Kim, E., A Study on Building the Work Environment to Utilize BIM for Domestic Architectural Firms (2014) J. Archit. Inst. Korea Planning Design, 30 (3), pp. 121-128; Kim, J., Lee, Y., Kim, S., Perceived IT performance and contextual factor of small firms in Korea: An explorative study (2004) J. Inf. Prof., pp. 23-41; (2000), pp. 36-129. , Hanun, Seoul; Won, T., Jung, S., (2010) Statistical Research SPSS PASW STATISTICS 18.0, pp. 343-357. , Hanara Academi, Seoul},
correspondence_address1={Choi, J.; Kyung Hee UniversitySouth Korea; email: junsikchoi@khu.ac.kr},
publisher={Springer Verlag},
issn={18650929},
language={English},
abbrev_source_title={Commun. Comput. Info. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Dissanayake2015615,
author={Dissanayake, C.K.},
title={Fuzzy logic applications in supply chain performance measurement},
journal={International Annual Conference of the American Society for Engineering Management 2015, ASEM 2015},
year={2015},
pages={615-621},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963758851&partnerID=40&md5=17b8d77835a300de5bb63fc15f8cdddc},
affiliation={Texas Tech. University, United States; Industrial Engineering Department, United States},
abstract={Supply chain, undoubtedly, is an area of wide research and application. It apparently is an incredible network, which comes in to existence with the formation of every organization and, becomes automatically complex as the organization progresses. There are many aspects in supply chain that interest researchers, out of which, development of a supply chain performance measurement method, is having a lot of avenues still left to explore. Fuzzy logic techniques, on the other hand, has wide applications across many fields and, is known to be one of the best means for dealing with uncertainty issues found in data sets. This study is aimed at exploring instances where fuzzy logic technique has been applied to measure supply chain performance. The objective of this study is to capture milestones of fuzzy logic application in supply chain performance measurement, in order to support the interested researchers and practitioners on their further research, and potential real world applications. The findings are anticipated to be of value for academia working on supply chain performance measurement, who are particularly looking at improving the accuracy of results. © American Society for Engineering Management, 2015.},
author_keywords={Fuzzy logic;  Supply chain performance measurement},
keywords={Complex networks;  Computer circuits;  Reconfigurable hardware;  Supply chains, Fuzzy logic applications;  Fuzzy logic techniques;  Real-world;  Research and application;  Supply chain performance;  Supply chain performance measurements, Fuzzy logic},
references={Alex, R., Fuzzy point estimation and its application on fuzzy supply chain analysis (2007) Fuzzy Sets and Systems, 158 (14), pp. 1571-1587; Büyüközkan, G., Çifçi, G., A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers (2012) Expert Systems with Applications, 39 (3), pp. 3000-3011; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chan, F.T., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., A fuzzy basis channel-spanning performance measurement method for supply chain management (2002) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 216 (8), pp. 1155-1167; Chan, F.T., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T., Qi, H.J., Chan, H., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Chen, J.J., Wang, C.L., Using fuzzy cognitive maps to model performance measurement system of internet-based supply chain (2006) Journal of Chongqing University, 5 (4), pp. 212-217; Chen, Y.W., Larbani, M., Fuzzy multiple criteria decision making for supply chain management (2014) Supply Chain Management Under Fuzziness, pp. 103-141. , Springer Berlin Heidelberg; Raut, D., Bhasin, V., Kamble, S., Evaluation of supplier selection criteria by combination of AHP and fuzzy DEMATEL method (2011) International Journal of Business Innovation and Research, 5 (4), pp. 359-392. , R. H. S; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Kurien, G.P., Qureshi, M.N., Performance measurement systems for green supply chains using modified balanced score card and analytical hierarchical process (2012) Scientific Research and Essays, 7 (36), pp. 3149-3161; Moritz, B.B., (2010) Cognition and Heterogeneity in Supply Chain Planning: A Study of Inventory Decision Making, , (Doctoral dissertation, UNIVERSITY OF MINNESOTA); Olugu, E.U., Wong, K.Y., Supply chain performance evaluation: Trends and challenges (2009) American Journal of Engineering and Applied Sciences, 2 (1), p. 202; Öztayşi, B., Sürer, O., Supply chain performance measurement using a SCOR based fuzzy VIKOR approach (2014) Supply Chain Management Under Fuzziness, pp. 199-224. , Springer Berlin Heidelberg; Sari, I.U., Ugurlu, S., Kahraman, C., Prioritization of supply chain performance measurement factors by a fuzzy multi-criteria approach (2014) Applications of Multi-Criteria and Game Theory Approaches, pp. 161-180. , Springer London; Senvar, O., Tuzkaya, U.R., Kahraman, C., Supply chain performance measurement: An integrated DEMATEL and fuzzy-ANP approach (2014) Supply Chain Management Under Fuzziness, pp. 143-165. , Springer Berlin Heidelberg; Spitzer, D.R., (2007) Transforming Performance Measurement: Rethinking the Way We Measure and Drive Organizational Success, , AMACOM Div American Mgmt Assn; Theeranuphattana, A., Tang, J.C., A conceptual model of performance measurement for supply chains: Alternative considerations (2007) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Van Laarhoven, P.J.M., Pedrycz, W., A fuzzy extension of saaty's priority theory (1983) Fuzzy Sets and Systems, 11 (1), pp. 199-227; Xu, X.X.L., Ma, B., Lima, R., AHP based supply chain performance measurement system (2007) IEEE Proceedings of the Conference on Emerging Technologies and Factory Automation, pp. 1308-1315; Yeh, D.Y., Cheng, C.H., Chi, M.L., A modified two-tuple FLC model for evaluating the performance of SCM: By the six sigma DMAIC process (2007) Applied Soft Computing, 7 (3), pp. 1027-1034; Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8 (3), pp. 338-353},
correspondence_address1={Dissanayake, C.K.; Texas Tech. UniversityUnited States; email: kalpani.dissanayake@ttu.edu},
editor={Ng E.-H., Long S., Squires A.},
publisher={American Society for Engineering Management},
isbn={9781510816022},
language={English},
abbrev_source_title={Int. Annu. Conf. Am. Soc. Eng. Manag., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wibowo201525,
author={Wibowo, M.A. and Sholeh, M.N.},
title={The analysis of supply chain performance measurement at construction project},
journal={Procedia Engineering},
year={2015},
volume={125},
pages={25-31},
doi={10.1016/j.proeng.2015.11.005},
note={cited By 47},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960927660&doi=10.1016%2fj.proeng.2015.11.005&partnerID=40&md5=5b09f893b892a7ac0330fc323a92005e},
affiliation={Civil Engineering Department, Diponegoro University, Jl. Prof. Soedarto, SH Tembalang, Semarang, 50275, Indonesia},
abstract={During recent years, supply chain has become a major subject of management research and manufacturing theory. The impact of various supply chain management practices on project performance has been measured through a combination of a survey and the development of simulation models. Supply chain performance measurement on the road project has not been much discussed much today. As it is known that the road project is one of the very high construction project roles. Performance measurement using the Supply Chain Operations References (SCOR) will analyze how the supply chain management of contractor. The aim of the study is to analyze supply chain performance in road projects. This measurement using the SCOR model as a Key Performance Indicator (KPI) is calculated using the weighted criteria Analytical Hierarchy Process (AHP). Weighting and measurement results were analyzed using Objectives Matrix (OMAX) and traffic light. The results of the analysis and the measurement of supply chain performance are medium score or good enough for road projects. The factors that leads to the success of this supply chain are the readiness of the contractor, supplier partners, supply chain strategy, and the ability of labors. © 2015 The Authors. Published by Elsevier Ltd.},
author_keywords={Construction;  Performance measurement;  Road;  SCOR model;  Supply chain},
keywords={Benchmarking;  Construction;  Contractors;  Highway administration;  Highway planning;  Industrial research;  Roads and streets;  Supply chains;  Transportation, Analytical Hierarchy Process;  Key performance indicators;  Performance measurements;  Road;  SCOR models;  Supply chain management practices;  Supply chain operations references;  Supply chain performance measurements, Supply chain management},
references={Lawrence, F.B., Varma, A., Integrated supply: Supply chain management in materials management and procurement (1999) Production and Inventory Management Journal, pp. 1-5; Irfan Xiaofe, D., X, Chun, D.S., A SCOR reference model of the supply chain management system in an enterprise (2008) The International Arab Journal of Information Technology, 5, pp. 288-295. , July; Halman, J.I.M., Voordijk, J.T., Balanced framework for measuring performance of supply chains in house building (2012) Journal of Construction Engineering and Management © ASCE, pp. 1444-1450. , December; Obrien, W.J., Formoso, C.T., Vrijhoef, R., London, K.A., (2009) Construction Supply Chain Management, London, , CRC Press; Vrijhoef, R., Koskela, L., The four roles of supply chain management in construction (2000) European Journal of Purchasing & Supply Management, 6, pp. 169-178; Hatmoko, J.U.D., Scott, S., Simulating the impact of supply chain management practice on the performance of medium-sized (2010) Construction Management and Economics, 28, pp. 35-49. , January; Meng, X., Sun, M., Jones, M., Maturity model for supply chain relationships in construction (2011) Journal of Management in Engineering, pp. 97-105; Minchin, R.E., Cui, S., Walters, R.C., Issa, R., Pan, J., Sino-American opinions and perceptions of counterfeiting in the construction supply chain (2013) Journal of Construction Engineering and Management, pp. 1-8. , January; Emuze, F.A., (2009) Thesis: The Impact of Construction Supply Chain Management on Value on Projects; Keung, C.C.W., Shen, L.-Y., Measuring the networking performance for contractors in practicing construction management (2013) Jornal of Management in Engineering © ASCE, pp. 400-406. , October; Said, H., El-Rayes, K., Optimizing material procurement and storage on construction sites (2011) Journal of Construction Engineering and Management © ASCE, 137, pp. 421-431. , June; Supply-Chain-Council, (2010) Supply Chain Operations Reference (SCOR®) Model Overview - Version 10, , supply-chain.org/scor; Pujawan, I.N., (2005) Supply Chain Management, , Surabaya: Guna Widya; Georgis, F.B., Thoben, K.-D., Seifert, M., Adapting the SCOR model to suit the different scenarios: A literature review & research agenda (2012) International Journal of Business and Management, 7. , March; Lin, S.C.J., Ali, A.S., Alias, A.B., Analytic hierarchy process decision-making framework for procurement strategy selection in building maintenance work (2014) Journal of Performance of Constructed Facilities, , July; Young, K.D., Kibler, D.F., Benham, B.L., Loganathan, G.V., Application of the analytical hierarchical process for improved selection of storm-water bmps (2009) Journal of Water Resources Planning and Management, 135, pp. 264-275. , July; Sun, L., Gu, W., Pavement condition assessment using fuzzy logic theory and analytic hierarchy process (2011) Journal of Transportation Engineering, 137, pp. 648-655. , September; Saaty, T.L., (1980) Analytic Hierarchy Process, , McGraw-Hill, New York; Agustina, F., Riana, N.A., Analisis produktivitas dengan metode objective matrix (OMAX) di PT. X (2011) Jurnal Teknik Dan Manajemen Industri, 6, pp. 150-158; Sari, A.P., (2013), Thesis. Pengukuran Kinerja Rantai Pasok Pada PT Emera Adyan Raya dengan Pendekatan SCOR Model, Semarang: Fakultas Teknik Program Studi Teknik Industri; Hinkka, V., Tätilä, J., RFID tracking implementation model for the technical trade and construction supply chains (2013) Automation in Construction, pp. 405-414; Creswell, J.W., (2009) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, , third ed., CA: Sage Publications},
correspondence_address1={Wibowo, M.A.; Civil Engineering Department, Jl. Prof. Soedarto, SH Tembalang, Indonesia; email: agungwibowo360@gmail.com},
editor={Antoni, Muljati I., Hardjito D.},
publisher={Elsevier Ltd},
issn={18777058},
language={English},
abbrev_source_title={Procedia Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Afonso2015270,
author={Afonso, H. and Cabrita, M.D.R.},
title={Developing a lean supply chain performance framework in a SME: A perspective based on the balanced scorecard},
journal={Procedia Engineering},
year={2015},
volume={131},
pages={270-279},
doi={10.1016/j.proeng.2015.12.389},
note={cited By 32},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960495904&doi=10.1016%2fj.proeng.2015.12.389&partnerID=40&md5=adeac83b326a6faa665d679f902fca17},
affiliation={DEMI, Department of Mechanical and Industrial Engineering, Faculty of Science and Technology, FCT, Universidade Nova de Lisboa, Caparica, 2829-516, Portugal; UNIDEMI, Department of Mechanical and Industrial Engineering, Faculty of Science and Technology, FCT, Universidade Nova de Lisboa, Caparica, 2829-516, Portugal},
abstract={To keep ahead of the completion, organizations look for continuous improvement in Supply Chain Management (SCM). Lean paradigm connected to SCM is a strategy based on cost and time reduction to improve the effectiveness. At the operational level, Lean Supply Chain (LSC) is focused on optimizing the processes of all supply chain, searching for simplification, reducing waste and reducing activities that do not add value. A well-defined lean supply chain measurement system increases the chance for success because it enables managers to see areas where supply chain performance can be improved, so they can focus their attention, and obtain higher levels of performance. There are a number of conceptual frameworks and discussions on supply chain performance measurements in the literature, however there is a lack of empirical analysis and case studies on performance metrics and measurements in the supply chain environment of Small and Medium Sized Enterprises (SMEs). This research aims to develop a conceptual framework for managing LSC, integrating both financial and non-financial performance dimensions and so it expands the existent knowledge and provides indication of how LSC performance can be assessed and improved in this and other kinds of organizations. The proposed framework has been implemented in a Portuguese SME operating in the food manufacturing sector. A case study was developed to better understand the suitability of this tool. © 2015 The Authors. Published by Elsevier Ltd.},
author_keywords={Analytic hierarchy process;  Balanced scorecard;  Lean supply chain;  Performance;  Supply chain management},
keywords={Analytic hierarchy process;  Knowledge based systems;  Problem solving, Balanced scorecards;  Continuous improvements;  Lean supply chains;  Performance;  Small and medium-sized enterprise;  Supply chain managements (SCM);  Supply chain performance;  Supply chain performance measurements, Supply chain management},
references={Behrouzi, F., Wong, K.Y., Lean performance evaluation of manufacturing systems: A dynamic and innovative approach (2011) Procedia Computer Science, 3, pp. 388-395; Behrouzi, F., Wong, K.Y., An investigation and identification of lean supply chain performance measures in the automotive SMEs (2011) Scientific Research and Essays, 6, pp. 5239-5252; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21, pp. 75-93; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45, pp. 2819-2840; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61, pp. 1-23; Cunningham, J.E., Fiume, O.J., Adams, E., (2003) Real Numbers: Management Accounting in A Lean Organization, , Durham: Managing Times Press; Kaplan, R., Norton, D., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70, pp. 71-99; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 5, pp. 605-620; Seyedhosseini, S.M., Teleghani, A.E., Bakhsha, A., Partovi, S., Extracting leanness criteria by employing the concept of balanced scorecard (2011) Expert Systems with Applications, 38, pp. 10454-10461; Saaty, T.L., Decision making with the analytic hierarchy process (2008) International Journal Services Sciences, 1, pp. 83-98; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18, pp. 666-680; Toni, A.D., Tonchia, S., Lean organization, management by process and performance measurement (1996) International Journal of Operations Management, 16, pp. 221-236; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62, pp. 107-118; Gurumurthy, A., Kodali, R., A multi-criteria decision-making model for the justification of lean manufacturing systems (2008) International Journal of Management Science and Engineering Management, 3, pp. 100-118; Maskell, B., Baggaley, B., (2004) Practical Lean Accounting - A Proven System for Measuring and Managing the Lean Enterprise, , New York: Productivity Press; Ray, C.D., Zuo, X., Michael, J.H., Wiedenbeck, J.K., The lean index (2006) Wood and Fiber Science, 38, pp. 238-255; Plenert, G., (2007) Reiventing Lean: Introducing Lean Management into the Supply Chain, , Oxford: Elsevier; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach the analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521; Bhasin, S., Performance of organisations treating lean as an ideology (2011) Business Process Management Journal, 17, pp. 986-1011; McIvor, R., Lean supply the design and cost reduction dimensions (2001) European Journal of Purchasing & Supply Management, 7, pp. 227-242; Gunasekaran, A., Patel, C., Tirtioglu, E., Performance measures and metrics in a supply chain environment (2011) International Journal of Operations & Production Management, 21, pp. 71-87; Narasimhan, R., Swink, M., Kim, S.W., Disentangling leanness and agility: An empirical investigation (2006) Journal of Operations Management, 24, pp. 449-457; Zylstra, K.D., (2006) Lean Distribution: Applying Lean Manufacturing to Distribution, Logistics, and Supply Chain, , New Jersey: John Wiley & Sons},
correspondence_address1={Afonso, H.; DEMI, Portugal},
editor={Cavallucci D., Duflou J., Livotov P., Vaneker T., Cascini G., Cascini G., Livotov P., Cavallucci D., Duflou J., Vaneker T.},
publisher={Elsevier Ltd},
issn={18777058},
language={English},
abbrev_source_title={Procedia Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Famurewa201549,
author={Famurewa, S.M. and Parida, A. and Kumar, U.},
title={Application of maintenance performance measurement for continuous improvement in railway infrastructure management},
journal={International Journal of COMADEM},
year={2015},
volume={18},
number={1},
pages={49-58},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959308053&partnerID=40&md5=fd4c84fdd24fb1dd6e4768e59f90b588},
affiliation={Luleå University of Technology, Luleå, 97187, Sweden; Luleå Railway Research Centre, Luleå, 97187, Sweden},
abstract={Railway transport infrastructure is a linearly distributed asset that requires an effective performance management system to meet sectional and overall business objectives. In particular, an effective performance measurement system with relevant analysis technique in an ongoing manner is necessary to facilitate continuous improvement. Maintenance performance measurement (MPM) is essential to quantify the impact of past maintenance decisions and actions and also to support new decisions. This article presents the challenges of implementing and using MPM systems for maintenance decisions in the railway industry. Thereafter, a risk matrix with maintenance performance indicators is introduced as a complementary analysis tool to identify weak links on a railway line. A case study of a section on the heavy haul line of the Swedish Transport Administration railway network is presented to demonstrate the application of the risk matrix tool for continuous improvement. The results identified the bottlenecks on the line section, which are improvement opportunities for maintenance performance in terms of service quality and capacity target of the infrastructure manager.},
author_keywords={Bottleneck;  Constraints;  Continuous improvement;  Improvement potential;  Maintenance performance indicators;  Quality of service;  Railway capacity;  Risk matrix},
keywords={Benchmarking;  Maintenance;  Quality of service;  Railroad plant and structures;  Railroads;  Risk assessment;  Supply chain management;  Transportation, Bottleneck;  Constraints;  Continuous improvements;  Improvement potential;  Maintenance performance;  Railway capacity;  Risk matrix, Railroad transportation},
references={Bititci, U.S., Integrated performance measurement systems: A development guide (1997) International Journal of Operations Production Management 1997, 17, pp. 522-534; Kennerley, M., Neely, A., Performance measurement frameworks: A review (2002) Business Performance Measurement: Theory and Practice, pp. 145-155. , Neely, A., Ed. Cambridge University Press: Cambridge; Neely, A., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations Production Management, 25, pp. 1228-1263; (2007) EN-15341 Maintenance - Maintenance Key Performance Indicators, , European Standard; Masaaki, Gemba, I., Kaizen, A., (1997) Commonsense Low-cost Approach to Management; http://asq.org/leam-about-quality/continuous-improvement/overview/overview.html, (accessed 02/10,2014); Dale, B.G., Van Der Wiele, A., Van Iwaarden, (2007) Journal of Managing Quality, , Blackwell Publishers: Oxford; Klefsjö, B., Wiklund, H., Edgeman, R.L., Six sigma seen as a methodology for total quality management (2001) Measuring Business Excellence, 5, pp. 31-35; Dettmer, H.W., (1997) Goldratt's Theory of Constraints: A Systems Approach to Continuous Improvement, , ASQ Quality Press: Wisconsin, USA; Rahman, S., Theory of constraints: A review of the philosophy and its applications (1998) International Journal of Operations & Production Management, 18, pp. 336-355; Nave, D., How to compare six sigma, lean and the theory of constraints (2002) Qual. Prog., 35, pp. 73-80; Smith, R., Hawkins, B., (2004) Lean Maintenance: Reduce Costs, Improve Quality, and Increase Market Share, , Butterworth-Heinemann: Burlington; Balanced Scorecard Institute Balanced Score Card Basics, , http://balancedscorecard.org/Resources/AbouttheBalancedScorecard/tabid/55/Default.aspx, (accessed 02/10, 2014); Parida, A., Chattopadhyay, G., Development of a multi-criteria hierarchical framework for maintenance performance measurement (MPM) (2007) Journal of Quality in Maintenance Engineering, 13, pp. 241-258; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part 1 (2001) Accounting Horizons, 15, pp. 87-104; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Prodution Economy, 48, pp. 207-225; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations & Production Management, 20, pp. 520-533; Stenström, C., Parida, A., Galar, D., Performance indicators of railway infrastructure (2014) International Journal of Railway Technology, 1, pp. 1-18; Andersson, R., Eriksson, H., Torstensson, H., Similarities and differences between TQM, six sigma and lean (2006) The TQM Magazine, 18, pp. 282-296; Tsang, A.H.C., A strategic approach to managing maintenance performance (1998) Journal of Quality in Maintenance Engineering, 4, pp. 87-94; Ellingsen, H., Kumar, U., Management of assets, resources and maintenance by using a balanced scorecard based performance framework (2002) Proceedings of the 16th International Maintenance Conference: Euromaintenance, pp. 203-211. , 2002; Åhrén, T., Parida, A., Maintenance performance indicators (MPIs) for benchmarking the railway infrastructure: A case study (2009) Benchmarking: An International Journal, 16, pp. 247-258; Carretero, J., Pérez, J.M., Garcia-Carballeira, F., Calderón, A., Fernández, J., Garcia, J.D., Lozano, A., Prete, P., Applying RCM in large scale systems: A case study with railway networks (2003) Reliability Engineering and System Safety, 82, pp. 257-273; (1999) EN 50126 Railway Applications - The Specification and Demonstration of Reliability, Availability, Maintainability and Safety (RAMS), , European Standard; (2009) IEC 31010 Risk Management - Risk Assessment Techniques, , European Standard; MIL-STD-1629A procedures for performing a failure mode, effect and criticality analysis (1980) Departement of Defense, , USA; Yuan, J., (2006) Stochastic Modeling of Train Delays and Delay Propagation in Stations, , Delft University of Technology, Faculty of Civil Engineering and Geosciences, Department of Transport and Planning, 2006; Söderholm, P., Norrbin, P., A risk based dependability approach to link maintenance performance measurement and management to overall objectives - A case study within the swedish railway (2013) Journal of Quality in Maintenance Engineering, 19, pp. 316-329},
correspondence_address1={Famurewa, S.M.; Luleå University of TechnologySweden; email: stefam@ltu.se},
publisher={COMADEM International},
issn={13637681},
language={English},
abbrev_source_title={Int. J. COMADEM},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Vugalter20151513,
author={Vugalter, M. and Even, A.},
title={Organizational impacts of enhancing a BI-supported performance measurement system on the Israeli police},
journal={Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015},
year={2015},
pages={1513-1519},
doi={10.15439/2015F221},
art_number={2015F221},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958074111&doi=10.15439%2f2015F221&partnerID=40&md5=fc5ce4e35b59ff05865ca13c154fa2a7},
affiliation={Ben-Gurion University of the Negev, Department of Industrial Engineering and Management, POB 653, Beer-Sheva, 8410501, Israel},
abstract={Performance Measurement Systems (PMS) have long captured the attention of organizational behavior and information systems (IS) research. The PMS in the study was implemented by public police forces, using advanced Business Intelligence (BI) technologies. The study examines the impact of enhancing that PMS, through analysis of the metric results over an 8-year time period that covered a transition between two major system versions. The analysis results indeed show a significant impact of transitioning to the new PMS in most (75%) performance metrics. A noticeable impact of the transition is the temporary performance decline, followed by some improvement that can be attributed in part to the redefinition of some metrics. Further, the results confirmed the preliminary assumptions that the improvement in the measured performance is positively and significantly associated with humanresource allocation; however, with some mediation effects of the crime category and the organization unit. © 2015, IEEE.},
keywords={Information systems;  Supply chain management, Organizational behavior;  Organizational impacts;  Performance measurement system;  Performance metrics;  Police forces;  System version;  Time-periods, Law enforcement},
references={Bourne, M., Performance Management: Learning from the past and projecting the future (2008) Measuring Business Excellence, 12, pp. 67-72; Yadav, N., Sagar, M., Performance measurement and management frameworks-research trends to the last two decades (2013) Business Process Management Journal, 19 (6), pp. 947-970; Vuksic, V., Bach, M., Popvic, A., Supporting PM with business performance and Business Intelligence: A case analysis of integration and orchestration (2013) International Journal of Information Management, 33, pp. 613-619; Bititci, U., Garengo, P., Dorfler, V., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, pp. 327-1305; Micheli, P., Kennerley, M., Performance measurement frameworks in public and non-profit sectors (2005) Production Planning & Control, 16 (2), pp. 134-1125; Rantanen, H.K., Performance measurement systems in the Finnish public sector (2007) International Journal of Public Sector Management, 20 (5); Boland, T., Fowler, A., A system prespective of performance management in public sector organizations (2000) The International Journal of Public Sector Management, 13 (5), pp. 417-446; Lijphart, A., Corporatism and consensus democracy in eighteen countries: Conceptual and empirical linkages (1991) British Journal of Political Science, pp. 246-1235; Batjargal, B., Social capital and entrepreneurial performance in Russia: A longitudinal study (2003) Organization Studies, pp. 535-556; Kaplan, R.S., Norton, D.P., The balanced Scorecard-measures that drive performance (1992) Harvard Busisness Review, pp. 71-79},
editor={Paprzycki M., Maciaszek L., Ganzha M., Maciaszek L.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9788360810651},
language={English},
abbrev_source_title={Proc. Fed. Conf. Comput. Sci. Inf. Syst., FedCSIS},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kuhi2015536,
author={Kuhi, K. and Kaare, K.K. and Koppel, O.},
title={A concept for performance measurement and evaluation in network industries [Võrguettevõtete tulemuslikkuse mõõtmise ja hindamise kontseptsioon]},
journal={Proceedings of the Estonian Academy of Sciences},
year={2015},
volume={64},
number={4S},
pages={536-542},
doi={10.3176/proc.2015.4S.01},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954477980&doi=10.3176%2fproc.2015.4S.01&partnerID=40&md5=d7414da82a130c7267ae2efc94764534},
affiliation={Department of Logistics and Transport, Tallinn University of Technology, Akadeemia tee 15A, Tallinn, 12616, Estonia},
abstract={The structure and functioning of network industries have a great effect on the well-being of society, being also prerequisites for economic growth and productivity. Social goals, industry trends, ownership structures, and the economic environment play an important role in the development of such networks. Most network industries enjoy a dominant position on the market. Their performance is steered by national regulatory authorities via price control and quality requirements. For this the regulatory authorities need besides financial indicators feedback on the technical performance of the provided services. The vast development in sensing, data transmission, and collection technologies, as well as in analytical methods, has made it possible and feasible to acquire the needed feedback. Such comprehensive data enable to construct a performance measurement system to regulate, develop, and administer the networks. This paper explores the possibility of developing an overall technical performance index and presents a relevant concept. The suggested overall index would be an additional regulatory tool to evaluate the performance of network industries and their compliance with consumers’ requirements. The aim of the proposed concept is to establish empirically verifiable feedback between a given state of technology, state of institutional governance, and the performance of network industries. © 2015, Estonian Academy Publishers. All rights reserved.},
author_keywords={Industrial engineering;  Network industries;  Performance indicators;  System architecture},
keywords={Economics;  Feedback;  Industrial engineering;  Quality control;  Regulatory compliance;  Supply chain management, Economic environment;  Network industries;  Performance indicators;  Performance measurement and evaluation;  Performance measurement system;  Regulatory authorities;  System architectures;  Technical performance, Industrial economics},
references={Oswald, M., Li, Q., McNeil, S., Trimbath, S., Measuring infrastructure performance: Development of a national infrastructure index (2011) Public Works Management & Policy, 16 (4), pp. 373-394; Göttinger, H., (2003) Economies of Network Industries, , Routledge, London; (2002) A Methodological Note for the Horizontal Evaluation of Services of General Economic Interest, , European Commission, Brussels; Uukkivi, R., Ots, M., Koppel, O., Systematic approach to economic regulation of network industries in Estonia (2014) Trames: J. Humanit. Soc. Sci, 18 (3), pp. 221-241; Brignall, S., Modell, S., An institutional perspective on performance measurement and management in the 'new public sector' (2000) Manage. Account. Res, 11, pp. 281-306; Kõrbe Kaare, K., (2013) Performance Measurement of a Road Network: A Conceptual and Technological Approach for Estonia, , TUT Press, Tallinn; Hagerty, J., Hofman, D., Defining a Measurement Strategy, Part III (2006) BI Review, p. 8; (1998) Performance Measurement Fundamentals, , U.S. Federal Highway Administration, Washington; De Toni, A., Tonchia, S., Performance measurement systems. Models, characteristics and measures (2001) Int. J. Oper. Prod. Man, 21 (1-2), pp. 46-71; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) Int. J. Oper. Prod. Man, 16 (8), pp. 63-80; (1995) How to Measure Performance. A Handbook of Techniques and Tools, , U.S. Department of Energy, Washington; Sinclair, D., Zairi, M., An empirical study of key elements of total quality-based performance measurement systems: A case study approach in the service industry sector (2001) Total Qual. Manage, 12 (4), pp. 535-550; Zheng, J., Garrick, N.W., Atkinson-Palombo, C., McCahill, C., Marshall, W., Guidelines on developing performance metrics for evaluating transportation sustainability (2013) Research in Transportation Business & Management, 7, pp. 4-13; Kaare, K.K., Koppel, O., Performance measurement data as an input in national transportation policy (2013) Proc. XXVIII Int. Baltic Road Conf, pp. 1-9. , Baltic Road Association, Vilnius,, [CD-ROM]; Ismail, M.A., Sadiq, R., Soleymani, H.R., Tesfamariam, S., Developing a road performance index using a Bayesian belief network model (2011) J. Franklin Inst, 348, pp. 2539-2555; Litzka, J., Leben, B., La Torre, F., (2008) The Way Forward for Pavement Performance Indicators Across Europe. COST Action 354 Final Report, , Austrian Transportation Research Association, Vienna; de Lurdes Antunes, M., (2011) Framework for Implementation of Environment Key Performance Indicators. EVITA (Environmental Indicators for the Total Road Infrastructure Assets), Deliverable D4. 1, , Institut Français des Sciences et des Technologies des Transports, de l'Aménagement et des Réseaux, and PMS-Consult; Kuhi, K., Kõrbe Kaare, K., Koppel, O., Performance measurement in network industries: Example of power distribution and road networks (2014) Proc. 9th Int. Conf. DAAAM Baltic Industrial Engineering, pp. 115-120. , (Otto, T., ed.). TUT Press, Tallinn; Davey, E., Rail Traffic Management Systems (TNS) (2012) IET Professional Development Course on Railway Signalling and Control Systems (RSCS), pp. 126-143. , IET, London; Corriere, F., Di Vincenzo, D., The rail quality index as an indicator of the "global comfort" in optimizing safety, quality and efficiency in railway rails (2012) Procedia-Social and Behavioral Sciences, 53, pp. 1090-1099; Tsang, A.H.C., A strategic approach to managing maintenance performance (1998) J. Qual. Mainten. Eng, 4 (2), pp. 87-94; Åhrén, T., Parida, A., Maintenance performance indicators (MPIs) for benchmarking the railway infrastructure (2009) Benchmarking, 16 (2), pp. 247-258; Parida, A., (2006) Development of a Multi-criteria Hierarchical Framework for Maintenance Performance Measurement: Concepts, Issues and Challenges, , Luleå University of Technology; Kumar, U., Galar, D., Parida, A., Stenström, C., Berges, L., Maintenance performance metrics: A stateof-the-art review (2013) J. Qual. Mainten. Eng, 19 (3), pp. 233-277; de Queiroz, L.M.O., (2012) Assessing the Overall Performance of Brazilian Electric Distribution Companies, , The George Washington University, Washington; (2011) Electricity Network Performance Report 2011/2012, , Ausgrid; Gosbell, V.J., Perera, B.S.P., Herath, H.M.S.C., Unified Power Quality Index (UPQI) for continuous disturbances (2002) In 10th Int. Conf. Harmonics and Quality of Power, 1, pp. 316-321; Meldorf, M., Tammoja, H., Treufeldt, Ü., Kilter, J., (2007) Jaotusvõrgud [Distribution Networks], , TUT Press, Tallinn, (in Estonian); Markiewicz, H., Klajn, A., (2004) Standard EN 50160-Voltage Characteristics in Public Distribution Systems, , European Copper Institute, Brussels; (2012) IEEE Guide for Electric Power Distribution Reliability Indices, , IEEE, New York; (1999) Reliability Benchmarking Application Guide for Utility/Customer PQ Indices, , Electric Power Research Institute, Palo Alto; Billinton, R., Li, W., (1994) Reliability Assessment of Electrical Power Systems Using Monte Carlo Methods, , Plenum Press, New York; Waverman, L., Koutroumpis, P., Benchmarking telecoms regulation-the Telecommunications Regulatory Governance Index (TRGI) (2011) Telecommun. Policy, 35, pp. 450-468; Matteson, S., Methods for multi-criteria sustainability and reliability assessments of power systems (2014) Energy, 71, pp. 130-136; Shukla, V., Beyond data handling (2014) Int. J. Scientific Research And Education, 2 (9), pp. 1929-1935; Kaisler, S., Armour, F., Espinosa, J.A., Money, W., Big data: Issues and challenges moving forward (2013) 46th Hawaii Int. Conf. System Sciences (HICSS), pp. 995-1004. , IEEE, Wailea; (2014) Big Data Analytics Guidebook. Unleashing Business Value in Big Data, , TM Forum, Morristown; Yang, H., Dasdan, A., Hsiao, R.-L., Parker, D.S., Map-reduce-merge: Simplified relational data processing on large clusters (2007) Proc. ACM SIGMOD Int. Conf. Management of Data, pp. 1029-1040. , ACM, New York; Wu, E., Diao, Y., Rizvi, S., High-performance complex event processing over streams (2006) Proc. ACM SIGMOD Int. Conf. Management of Data, pp. 407-418. , ACM, New York},
correspondence_address1={Kuhi, K.; Department of Logistics and Transport, Akadeemia tee 15A, Estonia; email: kristjan.kuhi@gmail.com},
publisher={Estonian Academy Publishers},
issn={17366046},
language={English},
abbrev_source_title={Proc. Est. Acad. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sriram2015263,
author={Sriram, P.K. and Andersen, B. and Alfnes, E.},
title={Designing a performance measurement system for materials management under engineering change situations in ETO environment},
journal={IFIP Advances in Information and Communication Technology},
year={2015},
volume={460},
pages={263-270},
doi={10.1007/978-3-319-22759-7_31},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951941158&doi=10.1007%2f978-3-319-22759-7_31&partnerID=40&md5=a44bf039925c79b94db0e62f04d430f8},
affiliation={Norwegian University of Science and Technology, Trondheim, Norway},
abstract={In this study we aim to understand how performance indicators should be designed and implemented across various phases and levels in materials management in the order fulfilment process under engineering chan- ges. The paper address questions such as how can such systems help managers to handle and manage materials management under engineering change situa- tions? How do we convince potential users and obtain their support when starting to develop such a system? How can we aggregate performance indi- cators? How do we present results? Then using the literature review and the results of the empirical study from a Norwegian company operating in ETO product delivery strategy, we develop a framework. © IFIP International Federation for Information Processing 2015.},
author_keywords={Case studies;  Engineer-to-order;  Engineering change;  Performance measurement},
keywords={Inventory control;  Materials handling;  Supply chain management;  Sustainable development, Aggregate performance;  Case-studies;  Engineer to orders;  Engineering changes;  Materials management;  Performance indicators;  Performance measurement system;  Performance measurements, Industrial management},
references={Andersen, B., Fagerhaug, T., Eight steps to a new performance measurement system (2002) Qual. Prog, 35, p. 1125; Armstrong, M., Baron, A., (2005) Managing Performance: Performance Management in Action, , CIPD publishing, London; Bell, L.C., Stukhart, G., Attributes of materials management systems (1986) J. Constr. Eng. Manag, 112, pp. 14-21; Bertrand, J.W.M., Muntslag, D.R., Production control in engineer-to-order firms (1993) Int. J. Prod. Econ, 30-31, pp. 3-22; De Leeuw, S., Van Den Berg, J.P., Improving operational performance by influencing shopfloor behavior via performance management practices (2011) J. Oper. Manag, 29, pp. 224-235; Denscombe, M., (2014) The Good Research Guide: For Small-Scale Social Research Projects, , McGraw-Hill Education, UK; Douwe, P., Flapper, S., Fortuin, L., Stoop, P.P., Towards consistent performance management systems (1996) Int. J. Oper. Prod. Manag, 16, pp. 27-37; Gelders, L.F., Production control in an ‘engineer-to-order’ environment (1991) Prod. Plan. Control, 2, pp. 280-285; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) Int. J. Prod. Res, 45, pp. 2819-2840; Hicks, C., Braiden, P.M., Computer-aided production management issues in the engineer-to-order production of complex capital goods explored using a simulation approach (2000) Int. J. Prod. Res, 38, pp. 4783-4810; Hicks, C., Pongcharoen, P., Dispatching rules for production scheduling in the capital goods industry (2006) Int. J. Prod. Econ, 104, pp. 154-163; Jiao, J., Zhang, L., Pokharel, S., Process platform planning for variety coordination from design to production in mass customization manufacturing (2007) IEEE Trans. Eng. Manag, 54, pp. 112-129; Jonsson, P., Mattsson, S.-A., Inventory management practices and their implications on perceived planning performance (2008) Int. J. Prod. Res, 46, pp. 1787-1812; Jonsson, P., Mattsson, S.A., The implications of fit between planning environments and manufacturing planning and control methods (2003) Int. J. Oper. Prod. Manag, 23, pp. 872-900; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part I (2001) Acc. Horiz, 15, pp. 87-104; Lin, G., Shen, Q., Measuring the performance of value management studies in construction: Critical review (2007) J. Manag. Eng, 23, pp. 2-9; Otley, D., Performance management: A framework for management control systems research (1999) Manag. Acc. Res, 10, pp. 363-382; Parthiban, P., Goh, M., An integrated model for performance management of manufacturing units (2011) Benchmarking Int. J, 18, pp. 261-281; Persona, A., Regattieri, A., Romano, P., An integrated reference model for production planning and control in SMEs (2004) J. Manuf. Technol. Manage, 15 (7), pp. 626-640; Sjøbakk, B., Bakås, O., Designing an engineer-to-order performance measurement system: A case study (2014) Advances in Production Management Systems, Part III. IFIP AICT, 440, pp. 473-480. , Grabot, B., Vallespir, B., Gomes, S., Bouras, A., Kiritsis, D. (eds.), Springer, Heidelberg; Stevenson, M., Hendry, L.C., Kingsman†, B.G., A review of production planning and control: The applicability of key concepts to the make-to-order industry (2005) Int. J. Prod. Res, 43, pp. 869-898; Theeranuphattana, A., Tang, J.C., A conceptual model of performance measurement for supply chains: Alternative considerations (2007) J. Manuf. Technol. Manag, 19, pp. 125-148; Vollmann, T.E., Berry, W.L., Whybark, D.C., (2005) Manufacturing Planning and Control Systems for Supply Chain Management, , McGraw-Hill, New York; Wänström, C., Jonsson, P., The impact of engineering changes on materials planning (2006) J. Manuf. Technol. Manag, 17, pp. 561-584; Yin, R.K., (2009) Case Study Research: Design and Methods, , Sage Publications, Thousand Oaks},
editor={Mizuyama H., Hibino H., Kiritsis D., Umeda S., von Cieminski G., Nakano M.},
publisher={Springer New York LLC},
issn={18684238},
isbn={9783319227580},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Schuh2015144,
author={Schuh, G. and Stich, V. and Hocken, C. and Schenk, M.},
title={Design of an integrated model for the real-time disturbance management in transportation supply networks},
journal={IFIP Advances in Information and Communication Technology},
year={2015},
volume={459},
pages={144-151},
doi={10.1007/978-3-319-22756-6_18},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950134332&doi=10.1007%2f978-3-319-22756-6_18&partnerID=40&md5=f99bd7646c9e888a1a282fb29b2c03f6},
affiliation={Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany; Institute for Industrial Management (FIR), RWTH Aachen University, Aachen, Germany},
abstract={In recent years supply chain participants are increasingly suffering the effects of disturbances in transportation supply chains. Both, dynamics in consumer demands and global supply chains lead to a growth in unplanned supply chain events. These can cause from rather manageable disturbances through to complete break-downs of transportation chains, resulting in high follow-up and penalty costs. Consequently, concepts for an efficient supply chain disturbance management are needed, preferably with a real-time identification and reaction to disturbance events. Therefore in the following paper the research results of the German research project Smart Logistic Grids with the focus on designing an integrated model for the real-time disturbance management in transportation supply networks are presented. This includes the introduction of elaborated classification models for disturbances and action patterns as well as an associated costs and performance measurement system. Finally, a procedure model for the disturbance management is presented. © IFIP International Federation for Information Processing 2015.},
author_keywords={Disturbance management;  Supply chain disturbances;  Supply chain event management;  Supply chain management;  Supply chain resilience},
keywords={Industrial management;  Transportation, Classification models;  Disturbance managements;  Global supply chain;  Integrated modeling;  Performance measurement system;  Real-time identification;  Supply chain event managements;  Supply chain resiliences, Supply chain management},
references={Deloitte, (2012) Supply Chain Resilience: A Risk Intelligent Approach to Managing Global Supply Chains, 7p. , Deloitte, New York; (2011) Supply Chain Resilience 2011-An International Survey of More Than 550 Organizations from over 60 Countries, Which Considers the Causes and Consequences of Disruption, the Techniques and Approaches to Identify Key Supply Chains, and Methods to Gain Assurance of Resilience Capability, , The Business Continuity Institute, Reading; Henke, M., Lasch, R., Eckstein, D., Neumüller, C., Blome, C., (2012) Supply Chain Agility, 10p. , Strategische Anpassungsfähigkeit im Supply Chain Management, Bundesvereinigung Logistik (BVL) e. V., Bundesverband Materialwirtschaft, Einkauf und Logistik e. V. (BME), Bremen; Rice, J.B., Caniato, F., Building a secure and resilient supply network (2003) Supply Chain Manage. Rev, 7 (5), pp. 22-30; Schenk, M., Stich, V., Managing supply chain disturbances-review and synthesis of existing contributions (2014) Advances in Production Management Systems. Innovative and Knowledge-Based Production Management in a Global-Local World, pp. 262-269. , Grabot, B., Vallespir, B., Gomes, S., Bouras, A., Kiritsis, D. (eds.),. Springer, Heidelberg; Prestifilippo, G., Hocken, C., Schmitz, S., Schenk, M., Smart logistic grids: Entwicklungeines Risikomanagementsystems (2013) Anpassungsfähige multimodale Logistiknetzwerke durch integrierte Logistikplanung und-regelung. Unternehmen der Zukunft, 14 (2), pp. 32-34; Schuh, G., Stich, V., (2012) Produktionsplanung und-Steuerung 1, , Grundlagen der PPS. Springer, Berlin; Fischäder, H., (2007) Störungsmanagement in netzwerkförmigen Produktionssystemen, , Deutscher Universitäts-Verlag, Ilmenau},
correspondence_address1={Hocken, C.; Institute for Industrial Management (FIR), Germany; email: christian.hocken@fir.rwth-aachen.de},
editor={Kiritsis D., von Cieminski G., Hibino H., Umeda S., Nakano M., Mizuyama H.},
publisher={Springer New York LLC},
issn={18684238},
isbn={9783319227559},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Vattulainen20153,
author={Vattulainen, M.},
title={Improving the predictive power of business performance measurement systems by constructed data quality features? Five cases},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2015},
volume={9165},
pages={3-16},
doi={10.1007/978-3-319-20910-4_1},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84950121446&doi=10.1007%2f978-3-319-20910-4_1&partnerID=40&md5=987a1cc8f33473c7a68c3b270c321158},
affiliation={University of Tampere, Tampere, Finland},
abstract={Predictive power is an important objective for current business performance measurement systems and it is based on metrics design, collection and preprocessing of data and predictive modeling. A promising but less studied preprocessing activity is to construct additional features that can be interpreted to express the quality of data and thus provide predictive models not only data points but also their quality characteristics. The research problem addressed in this study is: can we improve the predictive power of business performance measurement systems by constructing additional data quality features? Unsupervised, supervised and domain knowledge approaches were used to operationalize eight features based on elementary data quality dimensions. In the case studies five corporate datasets Toyota Material Handling Finland, Innolink group, 3stepIt, Papua Merchandising and Lempesti constructed data quality features performed better than minimally processed data sets in 29/38 and equally in 9/38 tests. Comparison to a competing method of preprocessing combinations with the first two datasets showed that constructed features had slightly lower prediction performance, but they were clearly better in execution time and easiness of use. Additionally, constructed data quality features helped to visually explore high dimensional data quality patterns. Further research is needed to expand the range of constructed features and to map the findings systematically to data quality concepts and practices. © Springer International Publishing Switzerland 2015.},
author_keywords={Business performance measurement system;  Data quality;  Feature construction;  Predictive classification;  Preprocessing},
keywords={Clustering algorithms;  Commerce;  Data mining;  Data reduction;  Electric power system measurement;  Materials handling;  Supply chain management, Business performance;  Data quality;  Data quality dimensions;  Feature construction;  Pre-processing of data;  Prediction performance;  Preprocessing;  Quality characteristic, Data visualization},
references={Abdul-Rahmana, S., Abu Bakara, A., Hussein, B., Zeti, A., An intelligent data pre-processing of complex datasets (2012) Intell. Data Anal, 16, pp. 305-325; Bellman, R.E., (1957) Dynamic Programming, , Rand Corporation, Princeton University Press, New Jersey; Berthold, M.R., Borgelt, C., Höppner, F., Klawonn, F., (2010) Guide to Intelligent Data Analysis–How to Intelligently Make Sense of Real Data, , Springer, London; Breiman, L., Random forests (2001) Mach. Learn, 45, pp. 5-32; Breunig, M.M., Kriegel, H.-P., Ng, R.T., Sander, J., LOF: Identifying density-based local outliers (2000) Proceedings of ACM SIGMOD 2000 International Conference on Management of Data, pp. 93-104; Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A., Ensemble selection for libraries of models (2004) Proceedings of ICML, 18p; Chandola, V., Banerjee, A., Kumar, V., Anomaly detection: A survey (2009) ACM Comput. Surv, 41 (3), p. 15; Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R., (2000) Crisp-Dm 1.0 Step by Step Data Mining Guide, , Crisp-DM Consortium; Crone, S.F., Lessmann, S., Stahlbock, R., The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing (2005) Eur. J. Oper. Res, 173 (3), pp. 781-800; Engel, J., Gerretzen, J., Szymanka, E., Jeroen, J.J., Downey, G., Blanchet, L., Buydens, L., Breaking with trends in preprocessing (2013) Trac Trends in Analytical Chemistry, 50, pp. 96-106; Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., The KDD process for extracting useful knowledge from volumes of data (1996) Commun. ACM, 39 (11), pp. 27-34; Filzmoser, P., Maronna, R., Werner, M., Outlier identification in high dimensions (2008) Comput. Stat. Data Anal, 52 (3), pp. 1694-1711; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) Int. J. Oper. Prod. Manag, 27 (8), pp. 784-801; Freund, Y., Schapire, R.E., A decision-theoretic generalization of on-line learning and an application to boosting (1995) J. Comput. Syst. Sci, 55 (1), pp. 119-139; Guyon, I., Elisseeff, A., An introduction to variable and feature selection (2003) J. Mach. Learn. Res, 3, pp. 1157-1182; Han, J., Kamber, M., Pei, J., (2012) Data Mining: Concepts and Techniques, , Morgan Kaufmann, San Francisco; Hsu, C.-W., Chang, C.-C., Lin, C.-J., (2010) A Practical Guide to Support Vector Classification, , Taiwan National University, Taipei; Hodge, V.J., Austin, J., A survey of outlier detection methodologies (2004) Artif. Intell. Rev, 22 (2), pp. 85-126; Hu, M.-X., Salvucci, S., (1991) A Study of Imputation Algorithms, , Institure of Education Science, NCES, New York; Järvinen, P., (2012) On Research Methods, , Opinpajan kirja, Tampere; Kaplan, R.S., Norton, D.P., The balanced scorecard–measures that drive performance (1992) Harvard Bus. Rev, 71 (1), pp. 71-79; Kira, K., Rendell, L.A., A practical approach to feature selection (1992) Proceedings of the Ninth International Workshop on Machine Learning, pp. 249-256; Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., Linkman, S., Systematic literature reviews in software engineering-a systematic literature review (2009) J. Inf. Softw. Technol, 51 (1), pp. 7-15; Kriegel, H.-P., Borgwardt, K.M., Kröger, P., Pryakhin, A., Schubert, M., Zimek, A., Future trends in data mining (2007) Data Min. Knowl. Disc, 15 (1), pp. 87-97; Kriegel, H.-P., Kröger, P., Zimek, A., Outlier detection techniqes (2010) 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, , Washington, DC; Kuhn, M., Johnson, K., (2013) Applied Predictive Modeling, , Springer, New York; Kochanski, A., Perzyk, M., Klebczyk, M., (2012) Knowledge in Imperfect Data in Advances in Knowledge Representation, , http://www.intechopen.com/books/advances-inknowledge-representation/knowledge-in-imperfect-data, Ramirez, C. (ed); Kohavi, R., John, G.H., Wrappers for feature subset selection (1997) Artif. Intell, 97, pp. 273-324; Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E., Data preprocessing for supervised learning (2006) Int. J. Comput. Sci, 2, pp. 111-117; Ludmila, K., (2004) Combining Pattern Classifiers: Methods and Algorithms, , Wiley-Interscience, New Jersey; Longadge, R., Dongre, S.S., Malik, L., Class imbalance problem in data mining: Review (2013) Int. J. Comput. Sci. Netw, 2 (1); March, S., Smith, G., Design and natural science research on information technology (1995) J. Decis. Support Syst, 15 (4), pp. 251-266; Nørreklit, H., The balance on the balanced scorecard—a critical analysis of some of its assumptions (2000) Manag. Acc. Res, 11 (1), pp. 65-88; Peltonen, J., (2014) Dimensionality Reduction, , Lecture Series, University of Tampere; Pyle, D., (2003) Data Preparation for Data Mining, , Morgan Kauffman, San Francisco; Quinlan, J.R., (1993) C4.5: Programs for Machine Learning, , Morgan Kauffman, San Francisco; Sadiq, S., Khodabandehloo, Y.N., Induska, M., 20 Years of data quality research: Themes, trends and synergies (2011) ADC 2011 Proceedings of the Twenty-Second Australasian Database Conference, 115, pp. 153-162; Torgo, L., (2010) Data Mining with R: Learning with Case Studies, , CRC Press, Boca Raton; Vattulainen, M., A method to improve the predictive power of a business performance measurement system by data preprocessing combinations: Two cases in predictive classification of service sales volume from balanced data (2014) Proceedings of the 37th Information Systems Research Seminar in Scandinavia (IRIS 37), pp. 10-13. , In: Ghazawneh, A., Nørbjerg, J., Pries-Heje, J. (eds.), Ringsted, Denmark; Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G., Steinberg, D., Top 10 algorithms in data mining (2008) Knowl. Inf. Syst, 14 (1), pp. 1-37; Wand, Y., Wang, R., Anchoring data quality dimensions in ontological foundations (1996) Commun. ACM, 39 (11), pp. 86-95; Wu, X., Zhu, X., Wu, G.-Q., Ding, W., Data mining with big data (2013) IEEE Trans. Kowl. Disc. Data Eng, 26 (1), pp. 97-107; Wolpert, D., Stacked generalization (1992) Neural Netw, 5, pp. 241-259; Yang, Q., Wu, X., 10 Challenging problems in data mining research (2006) Int. J. Inf. Technol. Decis. Mak, 5 (4), pp. 597-604; Zhao, H., Sudra, R., Entity identification for heterogenous database integration —a multiple classifier system approach and empirical evaluation (2005) Inf. Syst, 30 (2), pp. 119-132},
correspondence_address1={Vattulainen, M.; University of TampereFinland; email: markus.vattulainen@gmail.com},
editor={Perner P.},
publisher={Springer Verlag},
issn={03029743},
isbn={9783319209098},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Charkha201550,
author={Charkha, P.G. and Jaju, S.B.},
title={Identification of performance measures for textile supply chain: Case of small & medium size enterprise},
journal={International Journal of Supply Chain Management},
year={2015},
volume={4},
number={3},
pages={50-58},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949787991&partnerID=40&md5=21ec885c3a2e13e6fd5df975651deb31},
affiliation={Department of Mechanical Engineering, G. H. Raisoni College of Engineering, Nagpur, Maharashtra, India; Department of Mechanical Engineering, Datta Meghe Institute of Engg., Technology and Research, Maharashtra, India},
abstract={Identifying the performance measures for textile supply chain network is rapidly growing multi criteria decision making problem and so the task of performance measurement due to large number of parameter's involvement. Selection & analysis of appropriate performance measures is critical to achieve success for textile industry in today's global competitive market. With this paper, we tried to overcome it by recognizing three areas; cyclic processes of supply chain network (procurement-production-distribution), measures under three decisions making levels & considering balanced scorecard (BSC) perspectives. Developed a framework for supply chain performance measurement and analyzed using analytical hierarchy process. © ExcelingTech Pub, UK.},
author_keywords={Analytical hierarchy process;  Balanced scorecard;  Performance measurement;  Supply chain management;  Supply chain network (procurementproduction-distribution)},
references={Allahdad, Ban, S., Naveed Akhtar, M., A Supply chain made for SMEs in the Weaving sector of Punjab, Pakistan (2012) Far East Journal of Marketing & Management, 2 (2), pp. 60-72. , August; Haque, K.M.A., Hssain, M.M., Performance evaluation of Bangadeshi Apparel & textile supply chain network, A case study (2012) World Review of Business Research, 2 (2), pp. 211-218. , August; Hwang, H.J., Seruga, J., An Intelligent supply chain management system to enhance collaboration in textile industry (2011) Intl. Jrnl. of U & E services, science & technolog, 4 (4), pp. 47-62. , December; Fernie, J., Azuma, N., The changing nature of Japanese fashion: Can Quick Response Improve Supply Chain Efficiency? (2004) European Journal of Marketing, 38 (7), pp. 790-808; Gunasekaran, A., Patel, C., Tirtiroglu, E., Perfrmance measures and metrics in supply chain environment (2001) Intl. Jrnl. of operation & production management, 21 (2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Intl. Jrnl. of Prodcution Economics, 87, pp. 333-347; Kacagu, B., Gulsun, B., Tanyas, M., A SCOR based approach for measuring a benchmarkabe supply chain (2013) Jrnl. of Intell Manuf, 24, pp. 113-132; Varma, S., Wadhawa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance (2008) Asia Pacific Journal of Marketing, 2 (3), pp. 343-356; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computer & Industrial Engineering, 53, pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using anaytical hierarchy process (2007) PPC: The management of operations, 18 (8), pp. 666-680. , December; Chan, F.T.S., Performance measurement in a supply chain (2003) Intl. Jrnl. f Manuf Technol, 21, pp. 534-548; Sarode, A., Khodke, P., A framework for performance measurement system of supply chain management (2011) Intl. Jrnl. Advanced Engg. Technologies, 2 (4), pp. 182-190. , December; Hong, Y., Zhong-Hua, Y., Supply chain dynamic performance measurement based on BSC and SVM (2013) Intl. Jrnl. of Computer Science Issues, 10 (1), pp. 271-277. , Jan, No 2; Chandra, C., Kumar, S., An application of a system analysis methodology to manage logistics in a textile supply chain (2000) Supply chain management: An international journal, 5 (5), pp. 234-245; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Managemant, 9 (3), pp. 275-292; Chan, F.T.S., Qi, H.J., (2) An Innovative performance measurement method for supply chain management (2003) Supply chain management: An internatinal journal, 8 (3-4), pp. 209-223; Kaplan, R.S., Norton, D.P., The Balance Scorecard: Measures that drive the performance (1992) Harvard Business Review, pp. 71-79. , January-February; Lambert, D., Terrance, L.P., Supply chain metrics The international journal of logistics management, 1 (1), pp. 1-19; Lin, C., Wing, C.S., Madhu, N., Kuei, C.H., Yu, P., A structural equation modeling of supply chain quaity management and organizatinal performance Intl Jrnl. of production ecanomics, 96, pp. 355-365; Sahin, H., Collier, D.A., Wilson, D.D., Supply chain management orientation and supplier/ buyer performance (2000) Jrnl., of operation management, 18 (3), pp. 317-333; Vishwanadham, N., Analysis & design of Manufacturing Enterprise (1999), kluwer Academic publisher; Zheng, P., Li, K.K., A rough set approach on supply chain dynamic performance measurement LNAI 4953, pp. 312-322; Parida, A., Chattopadhyay, G., Kumar, U., Multi criteria maintenance performance: a conceptual model (2005) Prceedings of 18th Intl. Congress of COMADEM, pp. 349-356. , Cranefield, 31 August; Satty, T.L., How to make a decisin: The Analytical Hierarchy Process (1990) European Journal of operation research, 48, pp. 9-26; Charkha, P.G., Jaju, S.B., Supply chain performance measurement: an overview (2014) Intl. Jrnl of Buss Performance & Supply chain modeling, 6 (1), pp. 40-60; Charkha, P.G., Jaju, S.B., Developing Innovative framework for supply chain performace measurement in textile indsutry (2014) Intl. Jrnl of logistics sytem & management, 18 (2), pp. 216-230},
correspondence_address1={Charkha, P.G.; Department of Mechanical Engineering, India; email: pgcharkha@gmail.com},
publisher={ExcelingTech Publishers},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jamehshooran20151,
author={Jamehshooran, B.G. and Shaharoun, Awaluddin.M. and Haron, H.N.},
title={Assessing supply chain performance through applying the SCOR model},
journal={International Journal of Supply Chain Management},
year={2015},
volume={4},
number={1},
pages={1-11},
note={cited By 29},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949755951&partnerID=40&md5=a5d8b51d80a1902205633a327884ff80},
affiliation={Department of Manufacturing and Industrial, Engineering, Faculty of Mechanical Engineering, University Technology Malaysia, Kuala Lumpur, Malaysia; Department of Engineering, Razak School of Engineering and Advanced Technology, University Technology Malaysia, Kuala Lumpur, Malaysia},
abstract={In recent years, supply chain performance measurement has received much attention from researchers and practitioners. Effective supply chain performance through supply chain antecedents such as business analytics has become a potentially valuable way of securing competitive advantage and improving supply chain performance. This study addressed the lack of the empirical studies by developing a comprehensive model to examine the effect of business analytics on supply chain performance. A quantitative methodology using a cross-sectional survey method was used to investigate the relationship between variables. Data were collected from automotive companies in Iran. The relationships between variables were examined using structural equation modelling (SEM) technique and partial least squares (PLS) software was used. The results revealed there is a significant positive relationship between business analytics and supply chain performance. The study combined resourcebased theory, resource dependence theories to develop a new theoretical framework to demonstrate the importance of businesses analytics; in improving supply chain performance. © ExcelingTech Pub, UK.},
author_keywords={Business Analytics (BA);  Supply Chain Operations Reference (SCOR) model;  Supply chain performance (scp)},
references={Bidgoli, H., The Handbook of Technology Management: Supply Chain Management, Marketing and Advertising, and Global Management (2010), Wiley; Cetinkaya, B., (2011) Sustainable Supply Chain Management: Practical Ideas for Moving Towards Best Practice, , Springer; Mandal, S., Supply Chain Performance: Review of Empirical Literature. Romanian Review of Social Sciences (2012), (3); Liang, L., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; Wisner, J.D., (2011) Principles of Supply Chain Management: A Balanced Approach, , South-Western; Stephens, S., Supply chain operations reference model version 5 0: a new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Cirtita, H., Glaser-Segura, D.A., Measuring downstream supply chain performance (2012) Journal of Manufacturing Technology Management, 23 (3), pp. 299-314; Bose, R., Advanced analytics: opportunities and challenges (2009) Industrial Management & Data Systems, 109 (2), pp. 155-172; Dehning, B., Richardson, V.J., Zmud, R.W., The financial performance effects of IT-based supply chain management systems in manufacturing firms (2007) Journal of Operations Management, 25 (4), pp. 806-824; Sahay, B.S., Ranjan, J., Real time business intelligence in supply chain analytics (2008) Information Management & Computer Security, 16 (1), pp. 28-48; Elbashir, M.Z., Collier, P.A., Davern, M.J., Measuring the effects of business intelligence systems: The relationship between business process and organizational performance (2008) International Journal of Accounting Information Systems, 9 (3), pp. 135-153; Wixom, B.H., Continental Airlines Continues to Soar with Business Intelligence (2008) Information Systems Management, 25 (2), pp. 102-112; Cai, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Cadez, S., Guilding, C., An exploratory investigation of an integrated contingency model of strategic management accounting, Accounting (2008) Organizations and Society, 33 (7-8), pp. 836-863; Azvine, B., Nauck, D., Cui, Z., Towards realtime business intelligence (2005) BT Technology, 23 (3), pp. 214-225; Hoole, R., Five ways to simplify your supply chain Supply Chain (2005) Management An International Journal, 10 (1), pp. 3-6; Hedgebeth, D., (2007) Data-driven decision making for the enterprise: an overview of business intelligence applications, 37 (4), pp. 414-420; Li, S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega, 34 (2), pp. 107-124; Shang, J., A decision support system for managing inventory at GlaxoSmithKline (2008) Decision Support Systems, 46 (1), pp. 1-13; Ranjan, J., Business justification with business intelligence (2008) The journal of information and knowledge management systems, 38 (4), pp. 461-475; Curtis, B., The Case for Quantitative Process Management (2008) IEEE Software, 25 (3), pp. 24-28; Stadtler, H., Supply chain management and advanced planning--basics, overview and challenges (2005) European Journal of Operational Research, 163 (3), pp. 575-588; Burgess, K., Singh, P., Koroglu, R., Supply chain management: a structured literature review and implications for future research (2006) International Journal of Operations & Production Management, 26 (7), pp. 703-729; Bolstorff, P., Rosenbaum, R., (2003) Supply Chain Excellence: a handbook for dramatic improvement using the SCOR model, , New York, Amacom; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management -An International Journal, 9 (1), pp. 23-29; Lee, C.K.M., Design and development of agent-based procurement system to enhance business intelligence (2009) Expert Systems with Applications, 36 (1), pp. 877-884; Trkman, P., Process approach to supply chain integration (2007) Supply Chain Management -An International Journal, 12 (2), pp. 116-128; Reyes, P.M., Logistics networks: A game theory application for solving the transshipment problem (2005) Applied Mathematics and Computation, 168 (2), pp. 1419-1431; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of management, 17 (1), pp. 99-120; Barney, J.B., Wright, P.M., On becoming a strategic partner: The role of human resources in gaining competitive advantage (1998) Human resource management, 37 (1), pp. 31-46; Newbert, S.L., Value, rareness, competitive advantage, and performance: a conceptual-level empirical investigation of the resource-based view of the firm (2008) Strategic Management Journal, 29 (7), pp. 745-768; Takeuchi, R., , An empirical examination of the mechanisms mediating between high-performance work systems and the performance of Japanese organizations (2007) Journal of Applied Psychology, 92 (4), pp. 1069-1082; Wright, P.M., Dunford, B.B., Snell, S.A., Human resources and the resource based view of the firm (2001) Journal of management, 27 (6), pp. 701-721; Allen, M.R., Wright, P.M., Strategic management and HRM (2006) CAHRS Working Paper Series, p. 404; Wernerfelt, B., A resource-based view of the firm (1984) Strategic Management Journal, 5 (2), pp. 171-180; Sachs, S., Rühli, E., (2011) Stakeholders matter: A new paradigm for strategy in society, , Cambridge University Press; Kumar, B.A., (2013) Studies in Accounting and Finance: Contemporary Issues and Debates, , Pearson Education India; Carter, C., Kornberger, M., Schweitzer, J., Strategy: Theory and practice (2011) Sage; Lorca, P., García-Diez, J., The relation between firm survival and the achievement of balance among its stakeholders: An analysis (2004) International Journal of Management, 21 (1), pp. 93-99; Emerson, R.M., (1962) Power-dependence relations American sociological review, pp. 31-41; Pfeffer, J., Salancik, G.R., (2003) The external control of organizations: A resource dependence perspective, , Stanford University Press; Pfeffer, J., Salancik, (1978) The External Control of Organizations: A Resource Dependence Perspective, , New York: Harper & Row; De Vos, A., Research at grass roots: A primer for the caring professions (1998), JL van Schaik Pretoria; Thabane, L., A tutorial on pilot studies: the what, why and how (2010) BMC medical research methodology, 10 (1), p. 1; Diamantopoulos, A., Siguaw, J.A., Introducing, Lisrel, A guide for the uninitiated (2000) Sage; Malhotra, N.K., Birks, D.F., (2000) E.I.S.Inc. Marketing research: an applied approach., , Financial Times Prentice Hall; Krieken, v.R., (2000) Sociology: Themes and perspectives, , New South Wales: Pearson Education Australia; Peterson, R.A., A meta-analysis of Cronbach's coefficient alpha (1994) Journal of consumer research, pp. 381-391; Nunnally, J.C., (1978) Psychometric theory, , (2nd ed). Tata McGraw-Hill Education; Kaiser, H.F., (1974) An index of factorial simplicity Psychometrika, 39 (1), pp. 31-36; Hair, J.F., (2009) Multivariate data analysis; Hair, J.F., An assessment of the use of partial least squares structural equation modeling in marketing research (2012) Journal of the Academy of Marketing Science, 40 (3), pp. 414-433; Niedergassel, B., (2011) Knowledge Sharing in Research Collaborations, , Understanding the Drivers and Barriers. Springer; Henseler, J., Fassott, G., Testing moderating effects in PLS path models: An illustration of available procedures (2010) Handbook of partial least squares, pp. 713-735. , Springer; Tabachnick, B.G., Fidell, L., (2012) Using Multivariate Statistics: International Edition, , Pearson; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error Journal of Marketing Research, 1981, pp. 39-50; Chin, W.W., Commentary: Issues and opinion on structural equation modeling (1998) MIS quarterly, pp. vii-vii10; Khan, R., Business analytics and supply chain performance: An Empirical Perspective (2013) International Journal of Operations and Logistics Management, 2 (3), pp. 43-56; Irfan, D., A SCOR Reference Model of the Supply Chain Management System in an Enterprise Int (2008) Arab J. Inf. Technol., 5 (3), pp. 288-295; Kharabsheh, R.A., Proceedings of the International Conference on Innovation and Entrepreneurship: ICIE 2013 (2013) Academic Conferences Limited},
publisher={ExcelingTech Publishers},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Okoshi2015,
author={Okoshi, C.Y. and Da Silva, E.H.D.R. and Pinheiro De Lima, E. and Kruger, E.M. and Oliveira Sant'Anna, Â.M. and Gouvêa Da Costa, S.E.},
title={An assessment of performance indicators of higher education teaching through the concept of post-graduate programs in Brazilian universities},
journal={23rd International Conference for Production Research, ICPR 2015},
year={2015},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949644229&partnerID=40&md5=f185307ad82876dbb70de92dd18cf26b},
affiliation={Pontifical Catholic University of Parana, Curitiba, Parana, Brazil; Federal University of Technology - Parana, Curitiba, Parana, Brazil; Federal University of Bahia, Salvador, Bahia, Brazil},
abstract={Nowadays society is becoming more demanding for graduate studies, requiring a multidisciplinary background and competences for managing change processes dynamically and systematically. Graduate or Post-Graduate studies could be studied as service operations; therefore operations strategy and performance measurement content models are interesting options for framing their processes and activities. A performance measurement system should improve operations performance, creating proper tools for analyzing their causalities. Brazilian Universities are assessed and evaluated by a National Agency called CAPES that every three years promote an assessment exercise. Data are organized in terms of performance objectives and decision areas, and factorial analysis, cluster and logistic regression are use for studying graduate programs performance in the last 12 years. The main result consist in explaining graduate studies service operations performance through the causalities presented in an operations strategy and performance measurement framework.},
author_keywords={Brazilian Universities performance measurement;  Multivariate statiscal analysis;  Service operations strategy},
keywords={Multivariant analysis;  Societies and institutions;  Supply chain management, Multivariate statiscal analysis;  Operations strategies;  Performance indicators;  Performance measurement frameworks;  Performance measurement system;  Performance measurements;  Performance objective;  Service operations strategy, Education},
references={Gonçalves, C.M., Colauto, R.D., Beuren, I.M., Proposta de indicadores para sistema de inteligência competitiva em instituição de ensino superior (2007) Revista Produção Online, 7; Leal Filho, W., Sustainability and university life (2000) International Journal of Sustainability in Higher Education, 1, pp. 168-181; Scholz, R.W., Higher education as a change agent for sustainability in different cultures and context (2008) International Journal of Sustainability in Higher Education, 9, pp. 317-338; Pinheiro De Lima, E., Gouvêa Da Costa, S.E., Angelis, J.J., Framing operations and performance strategic management system design process (2008) Brazilian Journal of Operations & Production Management, 5, pp. 23-46; Correa, H.L., Caon, M., (2002) Gestão de Serviços: Lucratividade Por Meio de Operações e de Satisfação Dos Clients, , São Paulo: Atlas; Neely, A., (1998) Measuring Business Performance: The Economics, , 3rd ed. London; Ign, G., Corrêa, H.L., (1996) Administração Estratégica de Serviços: Operações Para A Satisfação Do Cliente, , São Paulo: Atlas; Hill, T., (2000) Manufacturing Strategy: Text and Cases, , 3rd ed. Boston: McGraw-Hill; Hayes, R.H., Wheelwright, S.C., (1984) Restoring Our Competitive Edge: Competing Through Manufacturing, , New York: John Wiley & Sons; Skinner, W., Manufacturing: Missing link in corporate strategy (1969) Harvard Business Review, 47, pp. 136-145; Slack, N., Lewis, M., (2008) Operations Strategy, , 2 nd ed. Harlow Prentice Hall; Johnston, R., Service operations management: From the roots up (2005) International Journal of Operations and Production Management, 25, pp. 1298-1308; Johnston, R., Operations: From factory to service management (1994) International Journal of Service Industry Management, 5, pp. 49-63; Correa, H., Gianesi, I., (1994) Administração Estratégia de Serviços, , São Paulo: Fundação Vanzolini - Atlas; Michael, S.O., American higher education system: Consumerism versus professorialism (1997) International Journal of Educational Management, 11, pp. 117-130; Abreu, A.F., Gonçalves, C.M., Pagnozzi, L., Tecnologia da informação e educação corporativa: contribuições e desafios da modalidade de ensino-aprendizagem à distância no desenvolvimento de pessoas (2003) Revista PEC Programa de Educação Corporativa, 3, pp. 47-58; Branco, M., Lambiase, D., Mattioda, P., Oliveira, G., Teixeira, R., Co-produção de serviço na pós-graduação: uma análise do comportamento do mestrando no contexto brasileiro (2014) Revista Produção Online, 14, pp. 294-320; Skinner, W., The productivity paradox (1986) Management Review, 75, pp. 41-45; Keeney, R.L., (1992) Value-focused Thinking: A Path to Creative Decision Making, , Harvard University Press, London; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25, pp. 1228-1263; Fea, F., Rozenfeld, H., Medição de desempenho para a gestão do ciclo de vida de produtos: uma revisão sistemática da literatura (2012) Revista Produção Online, 12, pp. 159-184; Krüger, E.M., (2013) Proposta de Um Modelo de Estratégia de Operações de Serviço Para O Ensino Superior de Pós-graduação, 195p. , Curitiba: PUCPR, Dissertação (Mestrado) - Programa de Pós-Graduação em Engenharia de Produção e Sistemas, Pontifícia Universidade Católica do Paraná, Curitiba, 2013; Pac, M., Fleury, A., Nakano, D.N., Turrioni, J.B., Ho, L.L., Morabito, R., Martins, R.A., Pureza, V., (2012) Metodologia de Pesquisa em Engenharia de Produção e Gestão de Operações, , Rio de Janeiro: Elsevier; Vergara, S.C., (2007) Projetos e Relatórios de Pesquisa em Administração, , São Paulo: Atlas; Corrar, L.J., Paulo, E., Dias Filho, J.M., (2009) Análise Multivariada Para Os Cursos de Administração, Ciências Contábeis e Economia, , São Paulo: Atlas; Hair, F.J., Black, W.C., Babin, B., Anderson, R.E., Tathan, R.L., (2009) Multivariate Data Analysis, , 7th ed. New Jersey: Prentice Hall; Rodrigues, M.C.P., Potencial de desenvolvimento dos municípios Fluminenses: uma metodologia alternativa ao IQM com base na análise fatorial exploratória e na análise de clusters (2012) Caderno de Pesquisas em Administração, 9, pp. 75-89; Johnson, R., Wichern, D., (1992) Applied Multivariate Statistical Analysis, , New Jersey: Prentice Hall; Mns, A., Siqueira, A.L., Caiaffa, W.T., Ordinal logistic regression in epidemiological studies (2009) Rev Saúde Pública, 43, pp. 183-194; Pulkstenis, E., Robinson, T.J., Goodness-of-fit tests for ordinal response regression models (2004) Stat. Med., 23, pp. 999-1014; Hosmer, D.W., Lemeshow, S., (2000) Applied Logistic Regression, , 2nd ed. New York: John Wiley & Sons; Mccullagh, P., Nelder, J.A., (1989) Generalized Linear Models, , Chapman and Hall; Tabachnick, B.G., Fidell, L.S., (2007) Using Multivariate Statistics, , Washington: Perason Educational Inc},
correspondence_address1={Okoshi, C.Y.; Pontifical Catholic University of ParanaBrazil; email: cleina.okoshi@pucpr.br},
publisher={International Foundation for Production Research (IFPR)},
language={English},
abbrev_source_title={Int. Conf. Prod. Res., ICPR},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Giannakis2015,
author={Giannakis, M. and Mi, H.},
title={Sustainable supply chain performance evaluation: A multi-criteria decision analysis approach},
journal={23rd International Conference for Production Research, ICPR 2015},
year={2015},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949638764&partnerID=40&md5=1d1ac973c2cf166c1221a41b9b9bc978},
affiliation={Audencia Nantes Ecole de Management, 8 Route de la Joneliere, Nantes, 44312, France; University of Warwick, Coventry, CV4 7AL, United Kingdom},
abstract={This paper develops a sustainability performance measurement system using an advanced field of operations research, the multi-criteria decision analysis (MCDA), to assist companies in selecting sustainable suppliers according to their priorities/sustainability strategy. Through an extensive literature review, several sustainability performance metrics (environmental, societal and economic) related to supplier evaluation are selected. With the use of the Analytical Hierarchy Processing method, the most important metrics are determined by calculating their relative weights. This is done through pairwise comparisons of the perceived importance of each metric. Data is collected through a large survey amongst 500 supply chain professionals in the UK and France. Results show that the selected economic-related metrics carry the most relatively important role in supplier selection. Based on the findings of the study implications for theory and practice are discussed.},
author_keywords={Analytical hierarchy processing;  Multi criteria decision analysis;  Supplier selection;  Sustainability},
keywords={Decision making;  Operations research;  Processing;  Supply chains, Analytical hierarchies;  Multi-criteria decision analysis;  Pair-wise comparison;  Supplier Evaluations;  Supplier selection;  Sustainability performance;  Sustainable supply chains;  Theory and practice, Sustainable development},
references={Hassini, E., Suti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Global Reporting Initiative (G4), Guidelines, , https://www.globalreporting.org/reporting/g4/Pages/default.aspx, Last visit March 15, 2015; World Business Council for Sustainable Development, , http://www.wbcsd.org/publications-and-tools.aspx, Last visit on 15 March 2015; OECD, (2004) Measuring Sustainable Development: Integrated Economic, Environmental and Social Frameworks, , OECD, Paris; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Chen, C.-T., Lin, C.-T., Huang, S.-F., A fuzzy approach for supplier evaluation and selection in supply chain management (2006) International Journal of Production Economics, 102 (2), pp. 289-301; De Boer, L., Labro, E., Morlacchi, P., A review of methods supporting supplier selection (2001) European Journal of Purchasing and Supply Management., 7 (2), pp. 75-89; Giannakis, M., Nocera, G., Collective supply chain responsibility: The effect of suppliers' sustainability behaviour on firm's shareholder Value (2014) 23rd Annual IPSERA Conference, , Pretoria, SA; Singh, R.K., Murphy, H.N., Gupta, S.K., An overview of sustainability assessment methodologies (2009) Ecological Indicators., 9 (2), pp. 189-212; Büyüközkan, G., Çifçi, G., A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information (2011) Computers in Industry., 62 (2), pp. 164-174; Carter, R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution and Logistics Management, 38 (5), pp. 360-387; Szekely, F., Knirsch, M., Responsible leadership and corporate social responsibility: Metrics for sustainable performance (2005) European Management Journal., 23 (6), pp. 628-647; Schwarz, J., Beloff, B., Beaver, E., Use sustainability metrics to guide decision-making (2002) Chemical Engineering Progress., 8 (7), pp. 58-63; Ayres, R.U., Ayres, L.W., (2002) A Handbook of Industrial Ecology, , MPG books, UK; Jain, R., Sustainability: Metrics, specific indicators and preference index (2005) Clean Technologies and Environmental Policy., 7 (2), pp. 71-72; Searcy, C., Corporate sustainability performance measurement systems: A review and research agenda (2012) Journal of Business Ethics., 107 (3), pp. 239-253; Fiskel, J., Eason, T., Frederickson, H., A framework for sustainability indicators at EPA (2012) National Risk Management Research Laboratory Office of Research and Development US Environmental Protection Agency; Amindoust Ahmed, A.S., Saghafinia, A., Bahreininejad, A., Sustainable supplier selection: A ranking model based on fuzzy inference system (2012) Applied Soft Computing Journal., 12 (6), pp. 1668-1677; Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2013) Journal of Cleaner Production., 47, pp. 345-354; Triantaphyllou, E., (2000) Multi-Criteria Decision Making: A Comparative Study, , Dordrecht: Springer; Cavalaro, F., (2013) Assessment and Simulation Tools for Sustainable Energy Systems, , London: Springer; Grigoroudis, E., Orfanoudaki, E., Zopounidis, C., Strategic performance measurement in a healthcare organisation: A multiple criteira approach based on balanced scorecard (2012) Omega, 40 (1), pp. 104-119; Hutchins, M.J., Sutherland, J.W., An exploration of measures of social sustainabilityand their applicatin to supply chain decisions (2008) Journal of Cleaner Production., 16 (5), pp. 1688-1698; Saaty, T.L., (1980) The Analytic Hierarchy Process, , New York: McGraw-Hill; Dai, J., Blackhurst, J., A four-phase AHP-QFD approach for supplier assessment: A sustainability perspective (2012) International Journal of Production Research., 50 (19), pp. 5474-5490; Linton, J.D., Jayaraman, V., A conceptual framework for product life extension (2005) International Journal of Production Research., 43 (9), pp. 1807-1829; Basu, R., New criteria of performance management: A transition from enterprise to collaborative supply chain (2001) Measuring Business Excellence., 5 (4), pp. 7-12; Lee, H.L., Amaral, J., Continuous and sustainable improvement through supply chain performance management (2002) Stanford Global Supply Chain Management Forum SGSCMF, pp. 1-14},
correspondence_address1={Giannakis, M.; Audencia Nantes Ecole de Management, 8 Route de la Joneliere, France; email: mgiannakis@audencia.com},
publisher={International Foundation for Production Research (IFPR)},
language={English},
abbrev_source_title={Int. Conf. Prod. Res., ICPR},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lucci2015249,
author={Lucci, G. and Ferretti, M. and Giordano, F. and Schiraldi, M.M.},
title={Design a performance measurement system: Metrics to identify the most critical indicators according to operative policies},
journal={Proceedings of the Summer School Francesco Turco},
year={2015},
number={Industrial Systems Engineering},
pages={249-255},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949559535&partnerID=40&md5=67cff735740ac36f3a9d5e6651b64ae6},
affiliation={Department of Enterprise Engineering, Tor Vergata University of Rome, Via del Politecnico, 1, Rome, 00133, Italy},
abstract={Supply Chain Management (SCM) literature review shows a great research effort in analysing relationship between SCM strategies and Performance Measurement Systems. Indeed, one of the most common issue in Supply Chain Management is, not being able to find the right strategy for decision-makers' own organization features. In order to be effective, the process of SCM strategy definition, requires the identification and the measurement of a proper set of Performance Indicators. Those PIs can help stakeholders displaying system current state, which is the first step to define scenario goals and prioritization for the organization. Aims of this preliminary analysis is to study which criteria affect the choice of most fitting PIs for a manufacturing company. Thus, after the evaluation of criteria that could influence the relevance of some PIs of the Performance Measurement System, the paper will show how to choose a set of KPIs, through the ranking of PIs. The study will refer to the PI set proposed by the Supply Chain Operations Reference (SCOR), which is widely considered the most fitting performance model for manufacturing context. In order to describe the process of choosing the proper KPIs through the proposed approach, an industrial application for a fashion luxury company is presented. In particular, SCOR PIs were evaluated according to the company's product portfolio, considering novelty and collection belonging, which are considered two of the most relevant criteria for classifying products in the fashion industry.},
author_keywords={Critical indicators identification;  KPI;  Performance measuring systems design;  PMS},
references={Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-625; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521; Childerhouse, A.T., Analysis and design of focused demand chains (2002) Journal of Operations Management, 20 (6), pp. 675-689; Christopher, T., Developing market specific SC strategies (2002) International Journal of Logistics Management, 13 (1), pp. 1-14; Cigolini, R.C.M., A new framework for supply chain management: Conceptual model and empirical test (2004) International Journal of Operations and Production Management, 24 (1), pp. 7-41; Estampe, D., Lamouri, S., Paris, J., Djelloul, S., A framework for anlysing supply chain performance evaluation models (2013) International J Ournal of Production Economics, 142 (2), pp. 247-259; Falatoonitoosi, E.L.Z.S.S.S.M., Decision-making trial and evaluation laboratory (2013) Research Journal of Applied Sciences, Engineering and Technology, 5 (13), pp. 3476-3480; Fisher, M., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Gunasekaran, A., Ngai, E., Information systems in supply chain integration and management (2004) European Journal of Operational Research, 159 (2), pp. 269-295; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hoekstra, S., Romme, J., (1992) Integrated Logistics Structures: Developing Customer Oriented Goods Flow, , London, UK: McGraw-Hill; Hwang, Y., Lin, Y., Lyu, J., Jr., The performance evalutation of SCOR sourcing process - The case study of Taiwans TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, pp. 71-79. , January-February; Lamming, R., Johnsen, T., Zheng, J., Harland, C., An initial classification of supply networks (2000) International Journal of Operations and Production Management, 20 (6), pp. 675-691; Lee, H.L., Aligning supply chain strategies with product uncertainties (2002) California Management Review, 44 (3), pp. 105-119; Lockamy, A., McCormack, K., Linking the SCOR plannning practices to supply chain performance (2004) International Journal of Operations and Production Management, 24 (11-12), pp. 1192-1218; Masood, S., Jahanzaib, M., Akhtar, K., Key performance indicators prioritization in whole business process: A case of manufacturing industry (2013) Life Science Journal, 10 (4), pp. 195-201; McCormack, K., Ladeira, M., Oliviera, M., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Olhager, J., Strategic positioning of the order penetration point (2003) International Journal of Production Economics, 85, pp. 319-329; Olson, E.M., Slater, S.F., The balanced scorecard, competitive strategy, and performance (2002) Business Horizon, pp. 11-16. , May-June; Pagh, J., Cooper, M., Supply chain postponement and speculation strategies: How to choose the right strategy (1998) Journal of Business Logistics, 19 (2), pp. 13-33; Palma-Mendoza, J., Analytical hierarchy process and SCOR model to support supply chain re-design (2014) International Journal of Information Management, 34, pp. 634-638; Shahin, A., Mahbod, M.A., Prioritization of key performance indicators: An integration of analytical hierarchy process and goal setting (2007) International Journal of Productivity and Performance Management, 56 (3), pp. 226-240; Srivastava, M., Srivastava, A., Rai, S., Review of various supply chain performance measurement frameworks: A proposed framework for ITES industry (2013) Global Journal of Management and Business Studies, 3 (9), pp. 999-1006; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply chain management (1997) Logistic Information Management, 10 (2), pp. 62-67; The Supply Chain Council, (2010) SCOR: Supply Chain Operations Reference Model, , USA, s.n; Wu, H.-Y., Constructing a strategy map for banking institutions with key performance indicators of the balanced scorecard (2012) Evaluation and Program Planning, 35, pp. 303-320},
publisher={AIDI - Italian Association of Industrial Operations Professors},
issn={22838996},
language={English},
abbrev_source_title={Proc. Summer Sch. Francesco Turco},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Supeekit2015927,
author={Supeekit, T. and Somboonwiwat, T. and Kritchanchai, D.},
title={Linking hospital supply chain processes and performance to identify key performance indicator},
journal={Lecture Notes in Electrical Engineering},
year={2015},
volume={349},
pages={927-938},
doi={10.1007/978-3-662-47200-2_97},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946030860&doi=10.1007%2f978-3-662-47200-2_97&partnerID=40&md5=aa29eee38ad0a7489316e41571897a8e},
affiliation={Department of Production Engineering, King Mongkut’s University of Technology Thonburi, Bangmod, Thungkru, Bangkok, Thailand; Department of Industrial Engineering, Mahidol University Salaya, Phuttamonthon, Nakhonpathoom, Thailand},
abstract={Previous studies of hospital supply chain performance measurement normally focus on either the performance of the hospital supply chain to be measured and the framework/methodology for performance measurement, or the performance of certain activities/processes in the chain for improvement. There is limited number of studies that explore the entire hospital supply chain performance concurrently with the processes in the hospital supply chain. This paper attempts to explore the performance of hospital supply chain and link them to the activities of the hospital supply chain in order to see the relationship of performance on the hospital supply chain activities to eventually identify the key performance indicator for the particular process. The result of the study shows that relationship of hospital supply chain performance, the business process and the link between the two can yield the key performance indicator for the considered process. © Springer-Verlag Berlin Heidelberg 2015.},
keywords={Benchmarking;  Management science;  Supply chains, Business Process;  Key performance indicators;  Performance measurements;  Supply chain performance;  Supply chain performance measurements;  Supply chain process, Hospitals},
references={Kumar, A., Ozdamar, L., Ng, C.P., Procurement performance measurement system in the health care industry (2005) International Journal of Health Care Quality Assurance, 18 (2), pp. 152-166; Neely, A., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46, pp. 423-431; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 9 (3), pp. 275-292; Da Encarnação Filipe Amado, C.A., Dos Pereira Santos, S., Challenges for performance assessment and improvement in primary health care (2009) . Health Policy, 91 (1), pp. 43-56; Purbey, S., Mukherjee, K., Bhar, C., Performance measurement system for healthcare processes (2007) International Journal of Productivity and Performance Management, 56 (3), pp. 241-251; Abu Bakar, A.H., Measuring supply chain performance among public hospital laboratories (2010) International Journal of Productivity and Performance Management, 59 (1), pp. 75-97; Kumar, A., Ozdamar, L., Zhang, C.N., Supply chain redesign in the healthcare industry of Singapore (2008) Supply Chain Management: An International Journal, 13 (2), pp. 95-103; Aronovich, D., Measuring Supply Chain Performance: Guide to Key Performance Indicators for Public Health Managers (2010) USAID and T.O. DELIVER PROJECT, 52p. , Editors, U.S. Agency for International Development, Arlington, Va; (2009), H.S.C.M.W.G., Performance Measurement: a report, OntarioBuys, Editor. Treasury Board Office of the Ontario Ministry of Finance, Ontario; Dobrzykowski, D., Vonderembse, M.A., Healthcare supply chain and IS strategies for improved outcomes (2009) POMS 20Th Annual Conference, pp. 1-19. , Orlando, Florida; Parnaby, J., Towill, D.R., Engineering cellular organisation and operation for effective healthcare delivery supply chains (2009) The International Journal of Logistics Management, 20 (1), pp. 5-29; Singh, M., (2008) Chronic Care Driving a Fundamental Shift in Health Care Supply Chains, , MIT Research Report, MIT; Bhattacharjee, P., Ray, P.K., Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections (2014) Computers & Industrial Engineering; Kujala, J., Time-based management of patient processes (2006) Journal of Health Organization and Management, 20 (6), pp. 512-524; Rahimnia, F., Supply chain leagility in professional services: How to apply decoupling point concept in healthcare delivery system (2010) Supply Chain Management: An International Journal, 15 (1), pp. 80-91; Kosnik, L., Breakthrough Demand-Capacity Management Strategies to Improve Hospital Flow, Safety, and Satisfaction (2013) Patient Flow: Reducing Delay in Healthcare Delivery, , In: Hall, R. (ed.) , . Springer, New York; Swinehart, K.D., Smith, A.E., Internal supply chain performance measurement A health care continuous improvement implementation (2005) International Journal of Health Care Quality Assurance, 18 (7), pp. 533-542; Narayana, S.A., Kumar Pati, R., Vrat, P., Managerial research on the pharmaceutical supply chain-A critical review and some insights for future directions (2014) Journal of Purchasing and Supply Management, 20 (1), pp. 18-40; Mustaffa, N.H., Potter, A., Healthcare supply chain management in Malaysia: A case study (2009) Supply Chain Management: An International Journal, 14 (3), pp. 234-243; Cai, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Barnabè, F., A system dynamics-based Balanced Scorecard to support strategic decision making (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 446-473; Battini, D., Healthcare supply chain modelling: A conceptual framework (2009) POMS 20Th Annual Conference, , Orlando, Florida; Berrah, L., Clivillé, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Computers in Industry, 58 (7), pp. 709-719; Shafiee, M., Hosseinzadeh Lotfi, F., Saleh, H., Supply chain performance evaluation with data envelopment analysis and balanced scorecard approach (2014) Applied Mathematical Modelling; Goedhals-Gerber, L.L., The Measurement of Supply Chain Efficiency: Theoretical Considerations and Practical Criteria (2010) Logistics Management Program., , Stellenbosch University, Matieland; Pettersson, A., Measurements of Efficiency in a Supply Chain (2008) Logistics Management Program., , Luleå University of Technology, Stockholm; Babaie, K., Promoting patient safety: One company’s example (2006) The Case Manager, 17 (6), pp. 54-59; Hariharan, S., A new tool for measurement of processed-based performance of multispecialty tertiary care hospitals (2004) International Journal of Health Care Quality Assurance, 17 (6), pp. 302-312; McFadden, K.L., Henagan, S.C., Gowen, C.R., The patient safety chain: Transformational leadership’s effect on patient safety culture, initiatives and outcomes (2009) Journal of Operations Management, 27, pp. 390-404; Stock, G.N., McFadden, K.L., Gowen, I., C.R.: Organizational culture, critical success factors, and the reduction of hospital errors (2007) International Journal of Production Economics, 106, pp. 368-392; Allen, L.C., Role of a quality management system in improving patient safety-Laboratory aspects (2013) Clinical Biochemistry, 46, pp. 1187-1193; Ford, E.W., Scanlon, D.P., Promise and Problems with Supply Chain Management Approaches to Health Care Purchasing (2007) Health Care Management Review, 32 (3), pp. 192-202; Ross, J., Improving patient safety through quality indicators (2010) Journal of Perianesthesia Nursing, 25 (2), pp. 112-113; Tzeng, G.H., Chiang, C.H., Li, C.W., Evaluating intertwined effects in elearning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL (2007) Expert Systems with Applications, 32 (4), pp. 1028-1044; Shieh, J.-I., Wu, H.-H., Huang, K.-K., A DEMATEL method in identifying key success factors of hospital service quality (2010) Knowledge-Based Systems, 23 (3), pp. 277-282; Hu, H.-Y., Using BPNN and DEMATEL to modify importance-performance analysis model-A study of the computer industry (2009) Expert Systems with Applications, 36 (6), pp. 9969-9979; Supeekit, T., Somboonwiwat, T., Kritchanchai, D., Causal relationship of supply chain efficiency and patient safety in measuring internal hospital supply chain performance applying DEMATEL (2014) The 44Th International Conference on Computers & Industrial Engineering (CIE’ 44), , Istanbul, Turkey},
editor={Kim K.J., Gen M., Hiroshi Y., Huang X.},
publisher={Springer Verlag},
issn={18761100},
isbn={9783662471999},
language={English},
abbrev_source_title={Lect. Notes Electr. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wilberg2015203,
author={Wilberg, J. and Hollauer, C. and Omer, M.},
title={Supporting the performance assessment of Product-Service Systems during the use phase},
journal={Procedia CIRP},
year={2015},
volume={30},
pages={203-208},
doi={10.1016/j.procir.2015.02.119},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84939497659&doi=10.1016%2fj.procir.2015.02.119&partnerID=40&md5=5b29a45bd4307347aa35c8c0c9f57a89},
affiliation={Institute of Product Development, Technische Universität München, Boltzmannstraße 15, Garching, 85748, Germany},
abstract={Companies providing Product-Service Systems (PSS) can gain competitive advantages by addressing the customer needs in a more precise way than regular product manufacturer. However, achieving these benefits also increases the responsibility on the company's side because the value of a PSS is delivered during the use phase. In many cases a provider stays the owner of the PSS and is thus responsible for the performance during the use. Accordingly, the PSS provider acts not only as a manufacturer but also as a service provider, which requires a constant monitoring of the PSS performance (e.g., service availability or customer satisfaction). The implementation of a performance measurement system is an approach to cope with the additional responsibilities during the use phase. However, different publications exist that offer support for the development phase of PSS but the performance assessment during the use phase is not addressed sufficiently, even though the importance in mentioned by different authors. This paper thus proposes two generic processes to address the identified need for the performance assessment of PSS. The first process guides through the implementation of an entire performance measurement system, and the other process describes the development of PSS specific key performance indicators (KPIs). The proposed approach applies KPIs to a new context and supports PSS providers in implementing a performance measurement system, which allows them to initiate measure to increase the PSS performance. © 2015 The Authors. Published by Elsevier B.V This is an open access article under the CC BY-NC-ND license.},
author_keywords={Assessment of strategic goals;  Key performance indicators;  Use phase orientation},
keywords={Benchmarking;  Competition;  Customer satisfaction;  Manufacture;  Supply chains, Competitive advantage;  Key performance indicators;  Performance assessment;  Performance measurement system;  Product-service systems;  Productservice system (PSS);  Strategic goals;  Use Phase, Supply chain management},
references={Brandstotter, M., Haberl, M., Knoth, R., Kopacek, B., Kopacek, P., IT on demand-towards an environmental conscious service system for Vienna (AT) (2003) Environmentally Conscious Design and Inverse Manufacturing, 2003. EcoDesign'03. 2003 3rd International Symposium on Environmentally Conscious Design and Inverse Manufacturing, pp. 799-802; Tukker, A., Eight types of product-service system: Eight ways to sustainability? Experiences from SusProNet (2004) Business Strategy and the Environment, 13 (4), pp. 246-260; Andersen, J.B., McCloone, T.C., Garciai Mateu, A., Mougaard, K., Neugebauer, L., Husan, J., Ahm, L.T., (2013) PSS Business Models: A Workbook in the PROTEUS Series, PRO-07, , Technical University of Denmark; Baines, T.S., Lightfoot, H.W., Evans, S., Neely, A., Greenough, R., Peppard, J., Roy, R., Tiwari, A., State-of-theart in product-service systems (2007) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221 (10), pp. 1543-1552; Samsonowa, T., (2012) Industrial Research Performance Management: Key Performance Indicators in the ICT Industry, , Physica, Heidelberg; Oliva, R., Kallenberg, R., Managing the transition from products to services (2003) International Journal of Service Industry Management, 14 (2), pp. 160-172; Vasantha, G.V.A., Roy, R., Lelah, A., Brissaud, D., A review of product-service systems design methodologies (2012) Journal of Engineering Design, 23 (9), pp. 635-659; Tukker, A., Tischner, U., Product-services as a research field: Past, present and future. Reflections from a decade of research (2006) Journal of Cleaner Production, 14 (17), pp. 1552-1556; Tan, A., McAloone, T.C., Andreasen, M.M., What happens to integrated product development models with produc/service-system approaches? (2006) IPD 2006: Proceedings of the 6th Workshop on Integrated Product Development, , Magdeburg, Germany; Morelli, N., Developing new product service systems (PSS): Methodologies and operational tools (2006) Journal of Cleaner Production, 14 (17), pp. 1495-1501; Morelli, N., Product-service systems, a perspective shift for designers: A case study: The design of a telecentre (2003) Design Studies, 24 (1), pp. 73-99; Gebauer, H., Fleisch, E., Friedli, T., Overcoming the service paradox in manufacturing companies (2005) European Management Journal, 23 (1), pp. 14-26; Schendel, D., Hofer, C., (1979) Strategic Management: A New View of Business Policy and Planning, , Little Brown, Boston, MA; Avlonitis, V.H.J., McAloone, T.C., Garcia I Mateu, A., Andersen, J.B., Mougaard, K., Neugebauer, L.A., (2013) L. T PSS Readiness Manual: A Workbook in the PROTEUS Series, PRO-03, , Technical University of Denmark; Aurich, J., Fuchs, C., Wagenknecht, C., Life cycle oriented design of technical Product-Service systems (2006) Journal of Cleaner Production, 14 (17), pp. 1480-1494; Ramaswamy, R., (1996) Design and Management Service Processes: Keeping Customers for Life, , Addison Wesley, Boston, MA; Kerzner, H.R., (2013) Project Management Metrics, KPIs, and Dashboards: A Guide to Measuring and Monitoring Project Performance, , John Wiley & Sons, New York; Parmenter, D., (2010) Key Performance Indicators (KPI): Developing, Implementing, and Using Winning KPIs, , John Wiley & Sons, Hoboken, New Jersey; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Shahin, A., Mahbod, M.A., Prioritization of key performance indicators: An integration of analytical hierarchy process and goal setting (2007) International Journal of Productivity and Performance Management, 56 (3), pp. 226-240; Chan, A.P., Chan, A.P., Key performance indicators for measuring construction success (2004) Benchmarking: An International Journal, 11 (2), pp. 203-221; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Baines, T., Lightfoot, H., Peppard, J., Johnson, M., Tiwari, A., Shehab, E., Swink, M., Towards an operations strategy for product-centric servitization (2009) International Journal of Operations & Production Management, 29 (5), pp. 494-519; Freund, Y.P., Critical success factors (1988) Strategy & Leadership, 16 (4), pp. 20-23; Neely, A., Three modes of measurement: Theory and practice (1998) International Journal of Business Performance Management, 1 (1), pp. 47-64; Beatham, S., Anumba, C., Thorpe, T., Hedges, I., KPIs: A critical appraisal of their use in construction (2004) Benchmarking: An International Journal, 11 (1), pp. 93-117},
correspondence_address1={Wilberg, J.; Institute of Product Development, Boltzmannstraße 15, Germany; email: wilberg@pe.mw.tum.de},
editor={Brissaud D., Boucher X.},
publisher={Elsevier B.V.},
issn={22128271},
language={English},
abbrev_source_title={Procedia CIRP},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Loos20157,
author={Loos, M.J. and Rodriguez, C.M.T.},
title={Mapping the state of the art of lean logistic performance measurement [Mapeamento do estado da arte do tema avaliação de desempenho direcionado para a logística Lean]},
journal={Espacios},
year={2015},
volume={13},
number={14},
pages={7},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84938591447&partnerID=40&md5=a341185ee2cebe2bed68971f6c99fab7},
affiliation={Universidade Federal de Santa Catarina - UFSC, Brazil; Universidade Politécnica de Madrid, Spain},
abstract={The objective of the present study is to create a mapping of publications dealing with the topic of Lean Logistic Performance Measurement, and, based on that, select a bibliographic portfolio composed of the most relevant and applicable works in the field, as per the opinion of the authors of this study. Towards these ends, a bibliometric analysis was carried out with the goal of gaining insight as to the most-recurring topics: authors, articles, journals, and keywords falling under the topic. This work is characterized as exploratory-descriptive research; beside this, it employs mixed qualitative and quantitative approaches to data analysis by way of the Knowledge Development Process - Constructivist (ProknowC) intervention instrument. Resulting from this, 22 relevant -and 1391 reference- articles were selected, which went on to form the Bibliographic Portfolio. Upon completion of bibliometric analysis of the articles and references making up the Bibliographic Portfolio, the following was shown: the academic journals "International Journal of Production Economics" and "Journal of Operations Management"; the scholarly articles "Performance measures and metrics in a supply chain environment", "The Agile Supply Chain: Competing in Volatile Markets", "A framework for supply chain performance measurement" and "Towards a theory of supply chain management: the constructs and measurements"; the keywords "Supply chain management" and "Performance measurement"; and the most-cited authors Gunasekaran, A.; Patel, C.; Tirtiroglu, E.; Christopher, M.; McGaughey, R. E.; Chen, I. J.; Paulraj, A. These results are deemed singular, given the limitations set by this work's authors and the ample nature of the approach employed during research; regardless, they stand to contribute to the work of future researchers in this, or other, fields.},
author_keywords={Bibliometric analysis;  Lean logistic;  Performance assessment;  Performance evaluation;  Performance measurement},
references={Afonso, M.H.F., Souza, J.V., Ensslin, S.R., Ensslin, L., Como Construir Conhecimento Sobre o Tema de Pesquisa? Aplicação do Processo ProKnow-C na Busca de Literatura Sobre Avaliação do Desenvolvimento Sustentável (2011) Revista de Gestão Social e Ambiental, 5 (2), pp. 47-62. , mai./ago; Alavi, M., Carlson, P., A Review of MIS Research and Disciplinary Development (1992) Journal of Management Information Systems, 8 (4), pp. 45-62; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Araújo, C.A., Bibliometria: evolução histórica e questões atuais (2006) Em Questão, 12 (1), pp. 11-32. , http://revistas.univerciencia.org/index.php/revistaemquestao/article/viewFile/3707/3495, Porto Alegre. [Em linha], Disponível em, Cited: May 11, 2014; Azevedo, S.G., Carvalho, H., Cruz Machado, V., The influence of green practices on supply chain performance: a case study approach (2011) Transportation research part E: logistics and transportation review, 47 (6), pp. 850-871; Ballou, R.H., (2004) Business logistics/supply chain management, , (5th ed.). Upper Saddle River, New Jersey: Pearson-Prentice Hall; Bayraktar, E., A causal analysis of the impact of information systems and supply chain management practices on operational performance: evidence from manufacturing SMEs in Turkey (2009) International Journal of Production Economics, 122 (1), pp. 133-149; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bortoluzzi, S.C., A Avaliação de desempenho em redes de pequenas e médias empresas: estado da arte para as delimitações postas pelo pesquisador (2011) Revista Eletrônica de Estratégia & Negócios , Florianópolis, 4 (2), pp. 202-222. , jun./dez; Braunscheidel, M.J., Suresh, N.C., The organizational antecedents of a firm's supply chain agility for risk mitigation and response (2009) Journal of Operations Management, 27 (2), pp. 119-140; Cai, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Ensslin, L., Avaliação do desempenho de empresas terceirizadas com o uso da metodologia multicritério de apoio à decisão-construtivista (2010) Revista Pesquisa Operacional, 30 (1), pp. 125-152; Ensslin, L., Um estudo sobre segurança em estádios de futebol baseado na análise da literatura internacional (2012) Perspectivas em Ciências da Informação, 17 (2), pp. 71-91; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: the constructs and measurements (2004) Journal of operations management, 22 (2), pp. 119-150; Cho, D.W., A framework for measuring the performance of service supply chain management (2012) Computers & Industrial Engineering, 62 (3), pp. 801-818; Christopher, M., The agile supply chain: competing in volatile markets (2000) Industrial marketing management, 29 (1), pp. 37-44; Demeter, K., Matyusz, Z., The impact of lean practices on inventory turnover (2011) International Journal of Production Economics, 133 (1), pp. 154-163; Droge, C., Jayaram, J., Vickery, S.K., The effects of internal versus external integration practices on time-based performance and overall firm performance (2004) Journal of Operations Management, 22, pp. 557-573; Dües, C.M., Tan, K.H., Lim, M., Green as the new Lean: how to use Lean practices as a catalyst to greening your supply chain (2013) Journal of cleaner production, 40, pp. 93-100; Gil, A.C., (1999) Métodos e Técnicas de Pesquisa Social, , São Paulo: Atlas; Forme, G.L., France-Anne, G., Botta, V., Campagne, J.-P., A framework to analyse collaborative performance (2007) Computers in Industry, 58 (7), pp. 687-697; Gu, J., Goetschalckx, M., Mcginnis, L.F., Research on warehouse design and performance evaluation: A comprehensive review (2010) European Journal of Operational Research, 203 (3), pp. 539-549; Gunasekaran, A., Patel, C., Mcgaughey, R.E., A framework for supply chain performance measurement (2004) International journal of production economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International journal of operations & production Management, 21 (1-2), pp. 71-87; Hudson, M., Lean, J., Smart, P.A., Improving control through effective performance measurement in SMEs (2001) Production Planning and Control, 12 (8), pp. 804-813. , Hvolby; Iudicibus, S., (2004) Teoria da contabilidade, , São Paulo: Atlas; (2014), http://www.journals.elsevier.com/international-journal-of-production-economics, Disponível em Cited: November, 27; Johnson, A., Mcginnis, L., Performance measurement in the warehousing industry (2010) IIE Transactions, 43 (3), pp. 220-230; Kim, S.W., An investigation on the direct and indirect effect of supply chain integration on firm performance (2009) International Journal of Production Economics, 119 (2), pp. 328-346; Lacerda, R.T.O., Ensslin, L., Ensslin, S.R., Uma análise bibliométrica da literatura sobre estratégia e avaliação de desempenho (2012) Gestão & Produção, 19 (1); Lai, K.-H., Wong, C.W., Cheng, T.C.E., Bundling digitized logistics activities and its performance implications (2010) Industrial Marketing Management, 39 (2), pp. 273-286; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Okubo, Y., Bibliometric indicators and analysis of research systems: methods and examples (2014), www:http://dx.doi.org/10.1787/208277770603, OECD Science, Technology and Industry Working Papers. [Em linha]. N° 1 (1997). Disponível em, Cited: May 11; Richardson, R.J., (1999) Pesquisa Social: Métodos e Técnicas, , São Paulo: Atlas; Rosa, F.S., Ensslin, S.R., Ensslin, L., Lunkes, R.J., Gestão da Evidenciação Ambiental: Um Estudo Sobre as Potencialidades e Oportunidades do Tema (2011) Engenharia Sanitária e Ambiental, 16 (2), pp. 157-166. , abr./jun; Rosenzweig, E.D., Roth, A.V., Dean, J.W., Jr., The influence of an integration strategy on competitive capabilities and business performance: an exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21, pp. 437-456; Sá-Silva, J.R., Almeida, C.D., Guindani, J.F., Pesquisa Documental: Pistas Teóricas e Metodológicas (2009) Revista Brasileira de História & Ciências Sociais, (1); Sancho, R., Indicadores bibliométricos utilizados en la evaluación de la ciencia y la tecnologia: revisión bibliográfica (2002) Inteligencia competitiva: documentos de lecture, pp. 77-106. , www:http://www.tramasoft.com/documentos/I+D+i/UND2/Lecturas%20complementarias/79059.Inteligencia%2520Competitiva.Lecturas.pdf#page=77, [Em linha]. Barcelona: Fundació per a la Universitat Oberta de Catalunya, Disponível em, Cited: May 11, 2014; Schnetzler, M.J., Sennheiser, A., Schönsleben, P., A decomposition-based approach for the development of a supply chain strategy (2007) International Journal of Production Economics, 105 (1), pp. 21-42; SJR: Scimago journal & country rank (2007), http://www.scimagojr.com, Available from, Cited: May 11, 2014; Tasca, J.E., Ensslin, L., Ensslin, S.R., Alves, M.B.M., An Approach for Selecting a Theoretical Framework For The Evaluation Of Training Programs (2010) Journal of European Industrial Training, 34 (7), pp. 631-655; Hoek, V., Remko, I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 15-29; Vlajic, J.V., Van Der Vorst, J.G., Haijema, R., A framework for designing robust food supply chains (2012) International Journal of Production Economics, 137 (1), pp. 176-189; Journal citation reports: information for new users (2012), http://admin-apps.webofknowledge.com/JCR/help/h_info.htm#information, Available from, Cited: May 11, 2014; Zhou, H., Benton, W.C., Jr., Supply chain practice and information sharing (2007) Journal of Operations Management, 25 (6), pp. 1348-1365},
publisher={Revista Espacios},
issn={07981015},
language={Portuguese},
abbrev_source_title={Espacios},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jamehshooran201597,
author={Jamehshooran, B.G. and Shaharoun, A.M. and Haron, H.N.},
title={The moderating effect of web service on the relationship between business analytics and supply chain performance},
journal={Journal of Theoretical and Applied Information Technology},
year={2015},
volume={76},
number={1},
pages={97-108},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930700686&partnerID=40&md5=6b943f0769b279ce750832316dd29438},
affiliation={Department of Manufacturing and Industrial, Engineering, University Technology Malaysia, Malaysia; Department of Engineering, Razak School of Engineering and Advanced Technology, University Technology Malaysia, Kuala Lumpur, Malaysia},
abstract={In recent years, supply chain performance measurement has received much attention from researchers and practitioners. Effective supply chain performance through supply chain antecedents such as business analytics has become a potentially valuable way of securing competitive advantage and improving supply chain performance. Many organizations have begun to recognize that supply chain management is the key to building a sustainable competitive advantage for their products or services in an increasingly crowded marketplace. Despite the fact that, determining supply chain performance through its antecedents is considered as a unique methodology, theoretical and comprehensive studies on supply chain performance through the methodology is few and far between. This study addressed the lack of the empirical studies by developing a comprehensive model to examine the effect of plan analyse, source analyse, make analyse and delivery analyse on supply chain performance considering moderating effect of WS on the relationships. A quantitative methodology using a cross-sectional survey method was used to investigate the relationship between variables. Data were collected from automotive companies in Iran. The overall response rate was 86.72%. The relationships between variables were examined using structural equation modelling (SEM) technique and partial least squares (PLS) software was used to examine the proposed hypotheses. The results revealed there is a significant and positive relationship between four areas of business analytics namely plan analyse, source analyse, make analyse and delivery analyse and supply chain performance. Moreover, the results indicated that the aforementioned relationship is moderated by WS. The study combined resource- based theory, resource dependence theories to develop a new theoretical framework to demonstrate the importance of the four areas of SCOR approach businesses analytic; in improving supply chain performance. Moreover, the study has provided a new platform to effectively improve supply chain performance. © 2005 - 2015 JATIT & LLS. All rights reserved.},
author_keywords={Business analytics (BA);  Information system (IS);  Make analyse (MA) and delivery analyse (DA);  Plan analyse (PA);  Source analyse (SA);  Supply chain operations reference (SCOR) model;  Supply chain performance (SCP)},
references={Trkman, P., Process approach to supply chain integration (2007) Supply Chain Management - an International Journal, 12 (2), pp. 116-128; Trkman, P., The impact of business analytics on supply chain performance (2010) Decision Support Systems, 49, pp. 318-327; Magnusson, L., Performance Management Using SCOR, , Ericsson AB, Supply Chain Council; Marco, G., Taisch, M., A novel Industrial Services Reference Model. 14th International (2008) Conference on Concurrent Enterprising. 2010; Vijay, K., Systemic Assessment of SCOR for Modeling Supply Chains Proceeding of the 38Th Hawaii International Conference on System Sciences, 87p. , http://dx.doi.org/10.1109/HICSS.2005.574.2005; Stevens, G.C., Successful supply chain management (1990) Management Decision, 28 (8), pp. 25-30; Neely, A., Gregory, M., Platts, K., Performance measurement design (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; New, S.J., A framework for analyzing supply chain improvement (1996) International Journal of Operations and Production Management, 16 (4), pp. 19-34; Harland, C.M., Supply chain management: Relationships, chains and networks (1996) British Journal of Management, 7, pp. 63-80; Narasimhan, R., Jayaram, J., (1998) Causal Linkage in Supply Chain Management: An Exploratory Study of North American Manufacturing Firms Decision Science, 29 (3), pp. 579-605; Spekman, R.E., Kamauff, J., Myhr, N., An empirical investigation into supply chain management: A perspective on partnerships (1998) Supply Chain Management, 3 (2), pp. 53-67; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Van Hoek, R.I., Measuring the unmeasurable -Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Shah, J., Singh, N., Benchmarking internal supply chain performance: Development of a framework (2001) Journal of Supply Chain Management, 37 (1), pp. 37-47; Stewart, G., Supply-chain operations reference model (SCOR): The first crossindustry framework for integrated supplychain management (1997) Logistics Information Management, 10 (2), pp. 62-67; Lai, C.L., Ip, W.H., Lee, W.B., The system dynamics model for engineering services (2001) Managing Service Quality, 11 (3), pp. 191-199; Popovič, A., Coelho, P.S., Jaklič, J., (2009) The Impact of Business Intelligence System Maturity on Information Quality, 14, 417p; Brynjolfsson, E., (2010) The Four Ways IT is Revolutionizing Innovation.Mit Sloan Management Review, 20 (51), pp. 51-56; Cai, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Sahay, B.S., Ranjan, J., Real time business intelligence in supply chain analytics (2008) Information Management & Computer Security, 16 (1), pp. 28-48; Petrini, M., Pozzebon, M., Managing sustainability with the support of business intelligence: Integrating socio-environmental indicators and organisational context (2009) The Journal of Strategic Information Systems, 18, pp. 178-191; Williams, S., Williams, N., (2007) The Profit Impact of Business Intelligence, , Morgan Kaufmann, San Francisco; Ranjan, J., (2008) Business Justification with Business Intelligence the Journal of Information and Knowledge Management Systems, 38 (4), pp. 461-475; Fink, L., Neumann, S., Exploring the perceived business value of the flexibility enabled by information technology infrastructure (2009) Information & Management, 46, pp. 90-99; Muylle, S., Basu, A., Online support for business processes by electronic intermediaries (2008) Decision Support Systems, 5 (4), 845p; Borges, M., Hoppen, N., Luce, F.B., Information technology impact on market orientation in e-business (2009) Journal of Business Research, 62 (9); Cadez, S., Guilding, C., An exploratory investigation of an integrated contingency model of strategic management accounting, Accounting (2008) Organizations and Society, 33 (7-8), pp. 836-863; Dale Stoel, M., Muhanna, W.A., IT capabilities and firm performance: A contingency analysis of the role of industry and IT capability type (2009) Information & Management, 46 (3), pp. 181-189; Bardhan, I., Krishnan, V., Lin, S., A Model to Measure the Business Value of Information Technology: The Case of Project and Information Work Rady School of Management; Heeley, M.B., Jacobson, R., The recency of technological inputs and financial performance (2008) Strategic Management Journal, 29 (7), pp. 723-744; Carr, N., IT doesn't matter (2003) Harvard Business Review, 81 (5), pp. 41-49; Chae, B., Yen, H.R., Sheu, C., Information technology and supply chain collaboration: Moderating effects of existing relationships between partners (2005) IEEE Transactions on Engineering Management, 52 (4), pp. 440-448; Ravinchandran, T., Lertwongsatien, C., Effect of information systems resources and capabilities on firm performance: A resourcebased perspective (2005) Journal of Management Information Systems, 21 (4), pp. 237-276; Howson, C., (2008) Successful Business Intelligence: Secrets to Making BI a Killer App, , McGraw- Hill Osborne Media; Schiefer, J., Seufert, A., Management and controlling of time-sensitive business processes with sense, computational intelligence for modelling (2005) Control and Automation, pp. 77-82; Pramatari, K., Collaborative supply chain practices and evolving technological approaches, Supply Chain Management 39 (2007) An International Journal, 12 (3), pp. 210-220; Fairbank, J.F., Information processing design choices, strategy, and risk management performance (2006) Journal of Management Information Systems, 23 (1), pp. 293-319; Demirkan, H., Service-oriented technology and management: Perspectives on research and practice for the coming decade (2008) Electronic Commerce Research and Applications, 7 (4), pp. 356-376; Dos Santos, B., Justifying investments in new information technologies (1991) Management Information Systems Quarterly, 7 (4), pp. 71-86; Goodhue, D.L., Understanding user evaluations of information systems 44 (1995) Science Management Decision, 41 (12), pp. 1827-1844; De Vos, A., (1998) Research at Grass Roots: A Primer for the Caring Professions, , JL van Schaik Pretoria; Thabane, L., A tutorial on pilot studies: The what, why and how (2010) BMC Medical Research Methodology, 10 (1), 1p; Diamantopoulos, A., Siguaw, J.A., (2000) Introducing LISREL: A Guide for the Uninitiated, , Sage; Malhotra, N.K., Birks, D.F., (2000) E.I.S. Inc.. Marketing Research: An Applied Approach, , Financial Times, Prentice Hall; Krieken, V.R., Sociology: Themes and perspectives (2000) New South Wales: Pearson Education Australia; Peterson, R.A., A meta-analysis of Cronbach's coefficient alpha (1994) Journal of Consumer Research, pp. 381-391; Nunnally, J.C., (1978) Psychometric Theory, , Tata McGraw-Hill Education; Hair, J.F., An assessment of the use of partial least squares structural equation modeling in marketing research (2012) Journal of the Academy of Marketing Science, 40 (3), pp. 414-433; Niedergassel, B., (2011) Knowledge Sharing in Research Collaborations: Understanding the Drivers and Barriers, , Springer; Henseler, J., Fassott, G., Testing moderating effects in PLS path models: An illustration of available procedures (2010) Handbook of Partial Least Squares, pp. 713-735. , Springer; Tabachnick, B.G., Fidell, L., (2012) Using Multivariate Statistics, , International Edition, Pearson; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, pp. 39-50; Chin, W.W., Commentary: Issues and opinion on structural equation modeling (1998) MIS Quarterly, pp. 7-16; Khan, R., Business analytics and supply chain performance: An Empirical Perspective (2013) International Journal of Operations and Logistics Management, 2 (3), pp. 43-56; Irfan, D., A SCOR Reference Model of the Supply Chain Management System in an Enterprise (2008) Int. Arab J. Inf. Technol, 5 (3), pp. 288-295; Kharabsheh, R.A., Proceedings of the International Conference on Innovation and Entrepreneurship: ICIE 2013 (2013) Academic Conferences Limited},
publisher={Asian Research Publishing Network},
issn={19928645},
language={English},
abbrev_source_title={J. Theor. Appl. Inf. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Santiteerakul2015221,
author={Santiteerakul, S. and Sekhari, A. and Bouras, A. and Sopadang, A.},
title={Sustainability performance measurement framework for supply chain management},
journal={International Journal of Product Development},
year={2015},
volume={20},
number={3},
pages={221-238},
doi={10.1504/IJPD.2015.069325},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929623485&doi=10.1504%2fIJPD.2015.069325&partnerID=40&md5=61d4cec218589848fda8853e6cc87830},
affiliation={Excellence Center in Logistics and Supply Chain Management, Chiang Mai University, Chiang Mai, Thailand; DISP Laboratory, Université Lumière Lyon 2, Bron, 69676, France; Qatar University - IctQATAR Depart., Qatar University, Doha, Qatar},
abstract={Sustainability of supply chains has been recognised as a competitive indicator for business. The most widely used framework for implementing sustainability practices is the triple bottom line (TBL). However, the TBL is a holistic view for measuring sustainability performance. A good categorisation in each TBL dimension is one of the challenges in the sustainable supply chain management (sSCM) research field. Another challenge is dealing with a wide range of objectives, criteria and elements in the supply chain. A clear border of supply chain scope helps supply chains obtains a precise strategic direction and performance measurement system. This work fills these gaps by developing a conceptual framework, which transforms the TBL concept into three management levels of implementation by analysing the context of sustainable development and the engagement level of the supply chain. This framework can be applied to establish and construct sustainability metrics in supply chain management. Copyright © 2015 Inderscience Enterprises Ltd.},
author_keywords={Conceptual framework;  Performance measurement;  Product development;  Supply chain management;  Sustainability performance;  Sustainable supply chain},
keywords={Product development;  Sustainable development, Conceptual frameworks;  Performance measurement system;  Performance measurements;  Strategic direction;  Sustainability metrics;  Sustainability performance;  Sustainability practices;  Sustainable supply chains, Supply chain management},
references={Beamon, M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Dyllick, T., Hockerts, K., Beyond the business case for corporate sustainability (2002) Business Strategy and the Environment, 11, pp. 130-141; Elkington, J., (1998) Cannibals with Forks: The Triple Bottom Line of the 21st Century, , New Society Publishers, Stoney Creek; Giddings, B., Hopwood, B., O'Brien, G., Environment, economy and society: Fitting them together into sustainable development (2002) Sustainable Development, 10, pp. 187-196; Hutchins, M.J., Sutherland, J.W., An exploration of measures of social sustainability and their application to supply chain decisions (2008) Journal of Cleaner Production, 16, pp. 1688-1698; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) The International of Logistics Management, 9 (2), pp. 1-19; Maslow, A., A theory of human motivation (1943) Psychological Review, 50, pp. 370-396; Max-Neef, M., Development and human needs (1992) Real-Life Economics: Understanding Wealth Creation, pp. 197-213. , Ekins, P. and Max-Neef, M. (Eds): Routledge, London; Mentzer, J.T., Dewitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22, pp. 1-25; Mongsawad, P., Sufficiency economy: A contribution to economic development (2009) International Journal of Social Sciences, 4, pp. 144-151; Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16, pp. 1699-1710; Teuteberg, F., Wittstruck, D., A systematic review of sustainable supply chain management research: What is there and what is missing? (2010) Proceedings of MKWI, 2010, pp. 1001-1015; (1993) Report of the United Nations Conference on Environment and Development, , United Nations (UN), New York; Wood, D.J., Corporate social performance revisited (1991) The Academy of Management Review, 16 (6), pp. 691-718; (1987) Our Common Future, , World Commission on Environment and Development (WCED) UN},
correspondence_address1={Santiteerakul, S.; Excellence Center in Logistics and Supply Chain Management, Thailand; email: salinee@eng.cmu.ac.th},
publisher={Inderscience Publishers},
issn={14779056},
language={English},
abbrev_source_title={Int. J. Prod. Dev.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Balasubramaniam201591,
author={Balasubramaniam, C.P. and Thigarasu, V.},
title={Agent-based modeling in supply chain management using improved C4-5},
journal={Research Journal of Applied Sciences, Engineering and Technology},
year={2015},
volume={9},
number={2},
pages={91-97},
doi={10.19026/rjaset.9.1382},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925402384&doi=10.19026%2frjaset.9.1382&partnerID=40&md5=52a87de3d79b168c592f07d589d6899d},
affiliation={Karpagam University, Coimbatore, Tamil Nadu, 641021, India; Depertament of Computer Science, Gobi Arts and Science College, Gobichettipalayam, India},
abstract={The Supply chain can describe the activities that are involved in the chain, or the companies, or the different functions. In literature there are a lot of models describing the Supply chain from different perspectives. Currently supply chains performance measurement systems suffer from being too inward looking, ignoring external environmental factors that might affect the overall supply chain performance when setting new targets. The most efficient Supply chain is the one that has the lowest possible cost and at the same time meet the customer's expectations on service like delivery precision and lead-time. In this study decision based technique C4.5 is improved using correlation coefficient of Kendall for effective classification. The correlation coefficient of Kendall is adapted to improve the system. The C4.5 not only produce discrete attributes, but also continuous ones can be handled, handling incomplete training data with missing values and it is prune during the construction of trees to avoid over-fitting. The accuracy is calculated by sensitivity and specificity for the proposed and existing technique for the textile synthesis dataset. Obtained results will prove the efficiency of this proposed technique based on its accuracy. © Maxwell Scientific Organization, 2015.},
author_keywords={Decision trees;  Fuzzy classification;  Kendall's rank correlation coefficients;  Supply chain},
references={Anderson, P., Aronson, H., Storhagen, N.G., Measuring logistics performance (1989) Eng. Cost. Prod. Econ., 17, pp. 253-262; Chandra, C., Tumanyan, A., Organization and problem ontology for supply chain information support system (2007) Data Knowl. Eng., 61 (2), pp. 263-280; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: The constructs and measurements (2004) J. Oper. Manag., 22 (2), pp. 119-150; Christopher, M., Logistics and Supply Chain Management: Strategies for Reducing Costs and Improving Services (1998), 2nd Edn., Pitman, London; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) Int. J. Logist. Manage., 8 (1), pp. 1-14; Croom, S., Romano, P., Giannakis, M., Supply chain management: An analytical framework for critical literature review (2000) Eur. J. Purchas. Supply Manage., 6 (1), pp. 67-83; Estampe, D., Lamouri, S., Paris, J.L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ., 142 (2), pp. 247-258; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int. J. Prod. Econ., 134 (1), pp. 177-187; Gosain, S., Malhotra, A., El Sawy, O.A., Coordinating for flexibility in e-business supply chains (2004) J. Manage. Inform. Syst., 21 (3), pp. 7-45; Guarino, N., Formal ontology and information systems (1998) Proceeding of 1st International Conference on Formal Ontology (FOIS, 1998), , Trento, Italy; Gunasekaran, A., Ngai, E.W.T., Information systems in supply chain integration and management (2004) Eur. J. Oper. Res., 159 (2), pp. 269-295; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of Supply chain operations reference (SCOR) model (2004) Supply Chain Manag., 9 (1), pp. 23-29; Hult, G.T.M., Ketchen, D.J., Cavusgil, S.T., Calantone, R.J., Knowledge as a strategic resource in supply chains (2006) J. Oper. Manag., 24 (5), pp. 458-475; Higginson, J.K., Alam, A., Supply chain management techniques in medium-to-small manufacturing firms (1997) Int. J. Logist. Manage., 8 (2), pp. 19-32; Jain, V., Wadhwa, S., Deshmukh, S.G., Revisiting information systems to support a dynamic supply chain: Issues and perspectives (2009) Prod. Plan. Control Manage. Oper., 20 (1), pp. 17-29; Kendall, M.G., Rank Correlation Methods (1955), Hafner Publishing Co., New York; Kishore, R., Sharman, R., Ramesh, R., Computational ontologies and information systems I: Foundations (2004) Communi. Associat. Inform. Syst., 14, pp. 158-183; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Ind. Market. Manag., 29 (1), pp. 65-83; Murthy, S.K., Automatic construction of decision trees from data: A multi-disciplinary survey (1998) Data Min. Knowl. Disc., 2, pp. 345-389; Norman, M.S., Hildum, D.W., Kjenstad, D., Agent-based e-supply chain decision support (2003) J. Org. Comp. Elect. Com., 13 (3-4), pp. 225-241; Paulraj, A., Lado, A., Chen, I.J., Inter-organizational communication as a relational competency: Antecedents and performance outcomes in collaborative buyer-supplier relationships (2008) J. Oper. Manag., 26 (1), pp. 45-64; Quinlan, J.R., Discovering Rules by Induction from Large Collections of Examples (1979) Expert Systems in the Microelectronic Age, pp. 168-201. , In: Michie, D. (Ed.), Edinburg University Press, Edinburg; Quinlan, J.R., C4.5: Programs for Machine Learning (1993), Morgan Kaufmann, San Francisco; Rai, A., Patnayakuni, R., Seth, N., Firm performance impacts of digitally enabled supply chain integration capabilities (2006) MIS Quart., 30 (2), pp. 225-246; Selwyn, P., Machine learning for dynamic multi-product supply chain formation (2005) Expert Syst. Appl., 29 (4), pp. 985-990; Solvang, W.D., Architecture for supply chain analysis and methodology for quantitative measurement of supply chain flexibility (2001), Ph.D. Thesis, Norwegian University of Science and Technology, Trondheim; Sotiris, K., Panayotis, P., Predictive Data Mining: A Survey of Regression Methods (2009) The Encyclopedia of Information Science and Technology, 6, pp. 3105-3110. , In: Khosrow-Pour, M. (Ed.), 2nd Edn; Sotiris, Z., Supply chain management (2000) Report Produced for the EC Funded Project INNOREGIO: Dissemination of Innovation and Knowledge Management Techniques, , BPR Hellas, 2000; Stainer, A., Logistics-a productivity and performance perspective (1997) Supply Chain Manag., 2 (2), pp. 53-62; Svensson, G., Holistic and cross-disciplinary deficiencies in the theory generation of Supply chain management (2003) Supply Chain Manag., 8 (4), pp. 303-316; Sycara, K.P., Multiagent systems (1998) AI Mag., 19 (2), pp. 79-92; Tummala, V.M.R., Phillips, C.L.M., Johnson, M., Assessing supply chain management success factors: A case study (2006) Supply Chain Manag., 11 (2), pp. 179-192; Wooldridge, M., Intelligent Agents (1999) Multiagent Systems: A Modern Approach to Distribute Artificial Intelligence, 1, pp. 27-78. , In: Gerhard, W. (Ed.), The MIT Press, Cambridge; Zdravkovic, M., Panetto, H., Trajanovic, M., Aubry, A., An approach for formalizing the supply chain operations (2011) Lect. Notes Bus. Inf., 5 (4), pp. 401-421},
correspondence_address1={Balasubramaniam, C.P.; Karpagam UniversityIndia},
publisher={Maxwell Science Publications},
issn={20407459},
language={English},
abbrev_source_title={Res. J. Appl. Sci. Eng. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Jakhar2015391,
author={Jakhar, S.K.},
title={Performance evaluation and a flow allocation decision model for a sustainable supply chain of an apparel industry},
journal={Journal of Cleaner Production},
year={2015},
volume={87},
number={1},
pages={391-413},
doi={10.1016/j.jclepro.2014.09.089},
note={cited By 110},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922461195&doi=10.1016%2fj.jclepro.2014.09.089&partnerID=40&md5=07d2c93153f207568edb4d3a646731d5},
affiliation={Faculty Operations Management Area, Indian Institute of Management Rohtak, Haryana, India},
abstract={There is a growing concern that business enterprises and their supply chains focus primarily on economic activities and ignore their impact on the environment and society. This paper aims to help decision makers, managers, and practitioners to achieve economic growth, societal development, and environmental protection by developing sustainable supply chain performance measures and proposes a partner selection and flow allocation decision-making model. Survey data from 278 business organizations from the Indian apparel industry supply chain network were used, and an integrated method of structural equation modeling, fuzzy analytical hierarchy process, and fuzzy multi-objective linear programming was applied to the proposed model. The results of the structural equation modeling analysis indicate that the survey respondents considered sustainable production performance to be of prime importance, which thus indicates its significance in developing a sustainable supply chain for the apparel industry. To illustrate the use of the proposed model for partner selection and flow allocation decision making, real-time data from an apparel manufacturer are presented. Optimal results were obtained for two strategiesdsustainability and cost savingdto show a costebenefit trade-off when developing a sustainable supply chain. Using comparative performance, a decision maker can choose an appropriate strategy based on costebenefit analysis of the presented trade-offs. © 2014 Elsevier Ltd. All rights reserved.},
author_keywords={Flow optimization;  Fuzzy analytic hierarchy process;  Fuzzy multi-objective linear programming;  Performance measures;  Structural equation modeling;  Sustainable supply chain},
keywords={Chains;  Economic and social effects;  Economics;  Linear programming;  Multiobjective optimization;  Supply chains;  Surveys;  Sustainable development, Flow optimization;  Fuzzy analytic hierarchy process;  Fuzzy multi-objective linear programming;  Performance measure;  Structural equation modeling;  Sustainable supply chains, Decision making},
references={Ahi, P., Searcy, C., A comparative literature analysis of definitions for green and sustainable supply chain management (2013) J. Clean. Prod, 52, pp. 329-341; Azevedo, S.G., Carvalho, H., Cruz Machado, V., The influence of green practices on supply chain performance: A case study approach (2011) Transp. Res. Part E Logist. Transp. Rev., 47 (6), pp. 850-871; Baskaran, V., Nachiappan, S., Rahman, S., Indian textile suppliers' sustainability evaluation using the grey approach (2012) Int. J. Prod. Econ, 135 (2), pp. 647-658; Bektas, T., Laporte, G., The pollution-routing problem (2011) Transp. Res. Part B Methodol., 45 (8), pp. 1232-1250; Beltran, B., Carrese, S., Cipriani, E., Petrelli, M., Transit network design with allocation of green vehicles: A genetic algorithm approach (2009) Transp. Res. Part C Emerg. Technol, 17 (5), pp. 475-483; Benjaafar, S., Li, Y., Daskin, M., Carbon footprint and the management of supply chains: Insights from simple models (2013) Autom. Sci. Eng. IEEE Trans, 10 (1), pp. 99-116; Blauwens, G., Vandaele, N., Van De Voorde, E., Vernimmen, B., Witlox, F., Towards a modal shift in freight transport? A business logistics analysis of some policy measures (2006) Transp. Rev., 26 (2), pp. 239-251; Bloemhof, J., Van Der Laan, E.A., Beijer, C., Sustainable inland transportation (2011) Int. J. Bus. Insights Transform, 3 (3), pp. 26-33; Bonacich, E., Appelbaum, R., (2000) Behind the Label, , University of California Press, Berkeley, CA; Bowen, F.E., Cousins, P.D., Lamming, R.C., Farukt, A.C., The role of supply management capabilities in green supply (2001) Prod. Oper. Manag, 10 (2), pp. 174-189; Brickman, C., Ungerman, D., (2008) Climate Change and Supply Chain Management, pp. 1-2. , McKinsey Q; De Brito, M.P., Carbone, V., Blanquart, C.M., Towards a sustainable fashion retail supply chain in Europe: Organisation and performance (2008) Int. J. Prod. Econ, 114 (2), pp. 534-553; Buysse, K., Verbeke, A., Proactive environmental strategies: A stakeholder management perspective (2003) Strateg. Manag. J., 24 (5), pp. 453-470; Büyükozkan, G., Berkol Ç., Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment (2011) Expert Syst. Appl., 38 (11), pp. 13731-13748; Büyükozkan, G., Çifçi, G., A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers (2012) Expert Syst. Appl., 39 (3), pp. 3000-3011; Cachon, G.P., (2011) Supply Chain Design and the Cost of Greenhouse Gas Emissions, , The Wharton School. University of Pennsylvania working paper; Cachon, G., Gallino, S., Olivares, M., (2012) Severe Weather and Automobile Assembly Productivity, , The Wharton School. University of Pennsylvania working paper; Caldentey, R., Mondschein, S., Policy model for pollution control in the copper industry, including a model for the sulfuric acid market (2003) Oper. Res, 51 (1), pp. 1-16; Caniato, F., Caridi, M., Crippa, L., Moretto, A., Environmental sustainability in fashion supply chains: An exploratory case based research (2012) Int. J. Prod. Econ, 135 (2), pp. 659-670; Cannon, M., Kouvaritakis, B., Huang, G., Modelling and optimisation for sustainable development policy assessment (2005) Eur. J. Oper. Res., 164 (2), pp. 475-490; Cariou, P., Is slow steaming a sustainable means of reducing CO2 emissions from container shipping? (2011) Transp. Res. Part D Transp. Environ, 16 (3), pp. 260-264; Carmona, R., Fehr, M., Hinz, J., Porchet, A., Market design for emissions trading schemes (2010) SIAM Rev., 52, pp. 403-452; Caro, F., Corbett, C.J., Tan, T., Zuidwijk, R., Double counting in supply chain carbon footprinting (2013) Manuf. Serv. Oper. Manag, 15 (4), pp. 545-558; Carter, C.R., Ethical issues in international buyeresupplier relationships: A dyadic examination (2000) J. Oper. Manag, 18 (2), pp. 191-208; Carter, C.R., Purchasing social responsibility and firm performance: The key mediating roles of organizational learning and supplier performance (2005) Int. J. Phys. Distrib. Logist. Manag., 35 (3), pp. 177-194; Carter, C.R., Jennings, M.M., Social responsibility and supply chain relationships (2002) Transp. Res. Part E Logist. Transp. Rev., 38 (1), pp. 37-52; Chaabane, A., Ramudhin, A., Paquet, M., Designing supply chains with sustainability considerations (2011) Prod. Plan. Control, 22 (8), pp. 727-741; Chaabane, A., Ramudhin, A., Paquet, M., Design of sustainable supply chains under the emission trading scheme (2012) Int. J. Prod. Econ., 135 (1), pp. 37-49; Chamberlain, G., (2010) Gap, Next and M&S in New Sweatshop Scandal, , 8 August, Guardian 12; Chang, D.Y., Applications of the extent analysis method on fuzzy AHP (1996) Eur. J. Oper. Res, 95 (3), pp. 649-655; Chen, C., Design for the environment: A quality-based model for green product development (2001) Manag. Sci., 47 (2), pp. 250-263; Chen, C., Monahan, G.E., Environmental safety stock: The impacts of regulatory and voluntary control policies on production planning, inventory control, and environmental performance (2010) Eur. J. Oper. Res., 207 (3), pp. 1280-1292; Cholette, S., Venkat, K., The energy and carbon intensity of wine distribution: A study of logistical options for delivering wine to consumers (2009) J. Clean. Prod., 17 (16), pp. 1401-1413; Chopra, S., Meindl, P., Kalra, D.V., (2010) Supply Chain Management, Strategy, Planning and Operations, , Pearson Education, New Delhi; Christmann, P., Effects of "best practices" of environmental management on cost advantage: The role of complementary assets (2000) Acad. Manag. J, 43 (4), pp. 663-680; Corbett, C.J., DeCroix, G.A., Shared-savings contracts for indirect materials in supply chains: Channel profits and environmental impacts (2001) Manag. Sci., 47 (7), pp. 881-893; Corbett, C.J., Klassen, R.D., Extending the horizons: Environmental excellence as key to improving operations (2006) Manuf. Serv. Oper. Manag, 8 (1), pp. 5-22; Corbett, J.J., Wang, H., Winebrake, J.J., The effectiveness and costs of speed reductions on emissions from international shipping (2009) Transp. Res. Part D: Transp. Environ, 14 (8), pp. 593-598; Cruz, J.M., Dynamics of supply chain networks with corporate social responsibility through integrated environmental decision-making (2008) Eur. J. Oper. Res., 184 (3), pp. 1005-1031; Cruz, J.M., Modeling the relationship of globalized supply chains and corporate social responsibility (2013) J. Clean. Prod, 56, pp. 73-85; Delmas, M.D., Stakeholders and competitive advantage: The case of ISO 14001 (2001) Prod. Oper. Manag., 10 (3), pp. 343-358; Diabat, A., Govindan, K., An analysis of the drivers affecting the implementation of green supply chain management (2011) Resour. Conserv. Recycl, 55 (6), pp. 659-667; Diabat, A., Simchi-Levi, D., December. A carbon-capped supply chain network problem (2009) Industrial Engineering and Engineering Management, 2009, IEEM 2009, IEEE International Conference On IEEE, pp. 523-527; Diabat, A., Kannan, D., Mathiyazhagan, K., Analysis of enablers for implementation of sustainable supply chain management e a textile case (2014) J. Clean. Prod, 83, pp. 391-403. , http://dx.doi.org/10.1016/j.jclepro.2014.06.081; Dillman, D.A., (2000) Mail and Internet Surveys: The Tailored Design Method, 2. , Wiley, New York; Dekker, R., Bloemhof, J., Mallidis, I., Operations research for green logistics e an overview of aspects, issues, contributions and challenges (2012) Eur. J. Oper. Res., 219 (3), pp. 671-679; Dou, Y., Zhu, Q., Sarkis, J., Evaluating green supplier development programs with a grey-analytical network process-based methodology (2014) Eur. J. Oper. Res., 233 (2), pp. 420-431; Dowell, G., Hart, S., Yeung, B., Do corporate global environmental standards create or destroy market value? (2000) Manag. Sci., 46 (8), pp. 1059-1074; Drake, D.F., Carbon tariffs: Impacts on technology choice, regional competitiveness, and global emissions (2011) Working Paper, Harvard Business School, No. 12-029; Drake, D.F., Spinler, S., OM forum-sustainable operations management: An enduring stream or a passing fancy? (2013) Manuf. Serv. Oper. Manag, 15 (4), pp. 689-700; Drake, D.F., Kleindorfer, P., Van Wassenhove, L.N., Technology choice and capacity investment under emissions regulation (2010) INSEAD Working Paper; D'Agosto, M., Ribeiro, S.K., Eco-efficiency management program (EEMP)da model for road fleet operation (2004) Transp. Res. Part D Transp. Environ, 9 (6), pp. 497-511; Elhedhli, S., Merrick, R., Green supply chain network design to reduce carbon emissions (2012) Transp. Res. Part D Transp. Environ, 17 (5), pp. 370-379; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecol. Econ., 70 (6), pp. 1088-1100; Fagerholt, K., Laporte, G., Norstad, I., Reducing fuel emissions by optimizing speed on shipping routes (2010) J. Oper. Res. Soc, 61 (3), pp. 523-529; Faulin, J., De Paz, E., Lera-Lopez, F., Juan, A.A., Gil-Ramírez, I., Practice summaries: Distribution companies use the analytical hierarchy process for environmental assessment of transportation routes crossing the Pyrenees in Navarre, Spain (2013) Interfaces, 43 (3), pp. 285-287; Ferretti, I., Zanoni, S., Zavanella, L., Diana, A., Greening the aluminium supply chain (2007) Int. J. Prod. Econ, 108 (1), pp. 236-245; Figge, F., Hahn, T., Is green and profitable sustainable? Assessing the trade-off between economic and environmental aspects (2012) Int. J. Prod. Econ., 140 (1), pp. 92-102; Florida, R., Davison, D., Gaining from green management: Environmental management systems inside and outside the factory (2001) Calif. Manag. Rev., 43 (3); (1913) Ford's Assembly Line Starts Rolling, , http://www.history.com/this-day-in-history/fords-assembly-line-starts-rolling, Ford, (accessed 05.03.13); Fornell, C., Larcker, D., Evaluating structural equation models with unobservable variables and measurement error (1981) J. Mark. Res., 18 (1), pp. 39-50; (1916) Gantt Charts, , http://www.mbsportal.bl.uk/taster/subjareas/busmanhist/mgmtthinkers/gantt.aspx, Gantt, (accessed 05.03.13); (1922) Time and Motion Study, , http://en.wikipedia.org/wiki/Time_and_motion_study, Gilbreths, (accessed 05.03.13); Gimenez, C., Sierra, V., Rodon, J., Sustainable operations: Their impact on the triple bottom line (2012) Int. J. Prod. Econ., 140 (1), pp. 149-159; Goebel, P., Reuter, C., Pibernik, R., Sichtmann, C., The influence of ethical culture on supplier selection in the context of sustainable sourcing (2012) Int. J. Prod. Econ, 140 (1), pp. 7-17; Golini, R., Longoni, A., Cagliano, R., Developing sustainability in global manufacturing networks: The role of site competence on sustainability performance (2014) Int. J. Prod. Econ., 147, pp. 448-459; Gong, X., Zhou, S.X., Optimal production planning with emissions trading (2013) Oper. Res., 61 (4), pp. 908-924; Gonzalez-Benito, J., Gonzalez-Benito, O., Environmental proactivity and business performance: An empirical analysis (2005) Omega, 33 (1), pp. 1-15; Gonzalez-Benito, J., Gonzalez-Benito, O., The role of stakeholder pressure and managerial values in the implementation of environmental logistics practices (2006) Int. J. Prod. Res, 44 (7), pp. 1353-1373; Gopalakrishnan, K., Yusuf, Y.Y., Musa, A., Abubakar, T., Ambursa, H.M., Sustainable supply chain management: A case study of British aerospace (BAe) systems (2012) Int. J. Prod. Econ, 140 (1), pp. 193-203; Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2013) J. Clean. Prod, 47, pp. 345-354; Govindan, K., Azevedo, S.G., Carvalho, H., Cruz-Machado, V., Impact of supply chain management practices on sustainability (2014) J. Clean. Prod, 85, pp. 212-225. , http://dx.doi.org/10.1016/j.jclepro.2014.05.068; Govindan, K., Kaliyan, M., Kannan, D., Haq, A.N., Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process (2014) Int. J. Prod. Econ., 147, pp. 555-568; Green, K.W., Jr., Zelbst, P.J., Meacham, J., Bhadauria, V.S., Green supply chain management practices: Impact on performance (2012) Supply Chain Manag. Int. J., 17 (3), pp. 290-305; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , Prentice Hall, New Jersey, Upper Saddle River; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: A study in the application of the analytical hierarchy process (2002) Eur. J. Oper. Res., 141 (1), pp. 70-87; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Prod. Econ., 140 (1), pp. 69-82; Hoen, K.M.R., Tan, T., Fransoo, J.C., Van Houtum, G.J., Effect of carbon emission regulations on transport mode selection under stochastic demand (2014) Flex. Serv. Manuf. J., 26 (1-2), pp. 170-195; Hofer, C., Cantor, D.E., Dai, J., The competitive determinants of a firm's environmental management activities: Evidence from US manufacturing industries (2012) J. Oper. Manag, 30 (1), pp. 69-84; Hoffman, A.J., Bazerman, M.H., (2005) Changing Environmental Practice: Understanding and Overcoming the Organizational and Psychological Barriers; Hugo, A., Pistikopoulos, E.N., Environmentally conscious long-range planning and design of supply chain networks (2005) J. Clean. Prod, 13 (15), pp. 1471-1491; Islegen, O., Reichelstein, S., Carbon capture by fossil fuel power plants: An economic analysis (2011) Manag. Sci., 57 (1), pp. 21-39; Jacobs, B.W., Singhal, V.R., Subramanian, R., An empirical investigation of environmental performance and the market value of the firm (2010) J. Oper. Manag, 28 (5), pp. 430-441; Jakhar, S.K., Barua, M.K., An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP (2013) Prod. Plan. Control, pp. 1-20. , ahead-of-print; Jalali Naini, S.G., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resour. Conserv. Recycl., 55 (6), pp. 593-603; Janic, M., Assessing some social and environmental effects of transforming an airport into a real multimodal transport node (2011) Transp. Res. Part D, 16, pp. 137-149; Ji, G., Gunasekaran, A., Yang, G., Constructing sustainable supply chain under double environmental medium regulations (2014) Int. J. Prod. Econ., 147, pp. 211-219; Jin, B., Achieving an optimal global versus domestic sourcing balance under demand uncertainty (2004) Int. J. Oper. Prod. Manag., 24 (12), pp. 1292-1305; Jira, C., Toffel, M.W., Engaging supply chains in climate change (2013) Manuf. Serv. Oper. Manag., 15 (4), pp. 559-577; Kannan, D., Jabbour, A.B.L.D.S., Jabbour, C.J.C., Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company (2014) Eur. J. Oper. Res., 233 (2), pp. 432-447; Kassinis, G.I., Soteriou, A.C., Greening the service profit chain: The impact of environmental management practices (2003) Prod. Oper. Manag., 12 (3), pp. 386-403; Keskin, N., Plambeck, E.L., Greenhouse gas emissions accounting: Allocating emissions from processes to co-products (2011) Working Paper, Stanford Graduate School of Business; King, A.A., Lenox, M.J., Industry self-regulation without sanctions: The chemical industry's responsible care program (2000) Acad. Manag. J., 43 (4), pp. 698-716; King, A.A., Lenox, M.J., Lean and green? An empirical examination of the relationship between lean production and environmental performance (2001) Prod. Oper. Manag., 10 (3), pp. 244-256; King, A., Lenox, M., Exploring the locus of profitable pollution reduction (2002) Manag. Sci., 48 (2), pp. 289-299; Kitazawa, S., Sarkis, J., The relationship between ISO 14001 and continuous source reduction programs (2000) Int. J. Oper. Prod. Manag., 20 (2), pp. 225-248; Klassen, R.D., Just-in-time manufacturing and pollution prevention generate mutual benefits in the furniture industry (2000) Interfaces, 30 (3), pp. 95-106; Klassen, R.D., Vereecke, A., Social issues in supply chains: Capabilities link responsibility, risk (opportunity), and performance (2012) Int. J. Prod. Econ, 140 (1), pp. 103-115; Kolk, A., Pinkse, J., Market strategies for climate change (2004) Eur. Manag. J., 22 (3), pp. 304-314; Kraft, T., Zheng, Y., Erhun, F., The NGO's dilemma: How to influence firms to replace a potentially hazardous substance (2013) Manuf. Serv. Oper. Manag., 15 (4), pp. 649-669; Krajnc, D., Glavic, P., How to compare companies on relevant dimensions of sustainability (2005) Ecol. Econ., 55 (4), pp. 551-563; Krass, D., Nedorezov, T., Ovchinnikov, A., Environmental taxes and the choice of green technology (2013) Prod. Oper. Manag, 22 (5), pp. 1035-1055; Kumar, S., Teichman, S., Timpernagel, T., A green supply chain is a requirement for profitability (2012) Int. J. Prod. Res., 50 (5), pp. 1278-1296; Kumar, A., Jain, V., Kumar, S., A comprehensive environment friendly approach for supplier selection (2014) Omega, 42 (1), pp. 109-123; Lai, K.H., Wong, C.W., Green logistics management and performance: Some empirical evidence from Chinese manufacturing exporters (2012) Omega, 40 (3), pp. 267-282; Lapre, M.A., Mukherjee, A.S., Van Wassenhove, L.N., Behind the learning curve: Linking learning activities to waste reduction (2000) Manag. Sci., 46 (5), pp. 597-611; Larson, A.L., Teisberg, E.O., Johnson, R.R., Sustainable business: Opportunity and value creation (2000) Interfaces, 30 (3), pp. 1-12; Leal, I.C., Jr., D'Agosto, M.D.A., Modal choice evaluation of transport alternatives for exporting bio-ethanol from Brazil (2011) Transp. Res. Part D Transp. Environ, 16 (3), pp. 201-207; Lee, H.L., Creating value through supply chain integration (2000) Supply Chain Manag. Rev., 4 (4), pp. 30-36; Lee, K.H., Carbon accounting for supply chain management in the automobile industry (2012) J. Clean. Prod., 36, pp. 83-93; Lee, K.H., Farzipoor, S.R., Measuring corporate sustainability management: A data envelopment analysis approach (2012) Int. J. Prod. Econ, 140 (1), pp. 219-226; Lee, K.H., Kim, J.W., Current status of CSR in the realm of supply management: The case of the Korean electronics industry (2009) Supply Chain Manag. Int. J., 14 (2), pp. 138-148; Lee, S.Y., Klassen, R.D., Drivers and enablers that foster environmental management capabilities in small- And medium-sized suppliers in supply chains (2008) Prod. Oper. Manag., 17 (6), pp. 573-586; Lenzen, M., Murray, J., Sack, F., Wiedmann, T., Shared producer and consumer responsibilitydtheory and practice (2007) Ecol. Econ., 61 (1), pp. 27-42; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) J. Oper. Manag., 25 (6), pp. 1075-1082; Lo, S.M., Effects of supply chain position on the motivation and practices of firms going green (2013) Int. J. Oper. Prod. Manag., 34 (1), pp. 93-114; Loch, C.H., Wu, Y., Social preferences and supply chain performance: An experimental study (2008) Manag. Sci., 54 (11), pp. 1835-1849; Longoni, A., Golini, R., Cagliano, R., The role of new forms of work organization in developing sustainability strategies in operations (2014) Int. J. Prod. Econ, 147, pp. 147-160; Lovric, M., Li, T., Vervest, P., Sustainable revenue management: A smart card enabled agent-based modeling approach (2013) Decis. Support Syst, 54 (4), pp. 1587-1601; Lozano, R., Envisioning sustainability three-dimensionally (2008) J. Clean. Prod., 16 (17), pp. 1838-1846; Lozano, R., Towards better embedding sustainability into companies' systems: An analysis of voluntary corporate initiatives (2012) J. Clean. Prod., 25, pp. 14-26; Lozano, R., Sustainability inter-linkages in reporting vindicated: A study of European companies (2013) J. Clean. Prod., 51, pp. 57-65; Lozano, R., Huisingh, D., Inter-linking issues and dimensions in sustainability reporting (2011) J. Clean. Prod, 19 (2), pp. 99-107; Maden, W., Eglese, R., Black, D., Vehicle routing and scheduling with timevarying data: A case study (2010) J. Oper. Res. Soc., 61 (3), pp. 515-522; Mak, H.Y., Rong, Y., Shen, Z.J.M., Infrastructure planning for electric vehicles with battery swapping (2013) Manag. Sci., 59 (7), pp. 1557-1575; Malhotra, M.K., Grover, V., An assessment of survey research in POM: From constructs to theory (1998) J. Oper. Manag, 16 (4), pp. 407-425; Mallidis, I., Dekker, R., Vlachos, D., (2010) Greening Supply Chains: Impact on Cost and Design, pp. 1-38. , (No. EI 2010-39b). Econometric Institute research papers; Matos, S., Hall, J., Integrating sustainable development in the supply chain: The case of life cycle assessment in oil and gas and agricultural biotechnology (2007) J. Oper. Manag, 25 (6), pp. 1083-1102; Melnyk, S.A., Sroufe, R.P., Calantone, R., Assessing the impact of environmental management systems on corporate and environmental performance (2003) J. Oper. Manag, 21 (3), pp. 329-351; Miao, Z., Cai, S., Xu, D., Exploring the antecedents of logistics social responsibility: A focus on Chinese firms (2012) Int. J. Prod. Econ, 140 (1), pp. 18-27; Min, H., Kim, I., Green supply chain research: Past, present, and future (2012) Logist. Res, 4 (1-2), pp. 39-47; Mirhedayatian, S.M., Azadi, M., Farzipoor Saen, R., A novel network data envelopment analysis model for evaluating green supply chain management (2014) Int. J. Prod. Econ, 147, pp. 544-554; Mirzapour Al-E-Hashem, S.M.J., Baboli, A., Sazvar, Z., A stochastic aggregate production planning model in a green supply chain: Considering flexible lead times, nonlinear purchas e and shortage cost functions (2013) Eur. J. Oper. Res, 230 (1), pp. 26-41; Montabon, F., Sroufe, R., Narasimhan, R., An examination of corporate reporting, environmental management practices and firm performance (2007) J. Oper. Manag, 25 (5), pp. 998-1014; Mota, B., Gomes, M.I., Carvalho, A., Barbosa-Povoa, A.P., Towards supply chain sustainability: Economic, environmental and social design and planning (2014) J. Clean. Prod., , http://dx.doi.org/10.1016/j.jclepro.2014.07.052; Nagurney, A., Toyasaki, F., Supply chain super networks and environmental criteria (2003) Transp. Res. Part D Transp. Environ, 8 (3), pp. 185-213; Nusair, K., Hua, N., Comparative assessment of structural equation modeling and multiple regression research methodologies: E-commerce context (2010) Tour. Manag., 31 (3), pp. 314-324; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour. Conserv. Recycl., 55 (6), pp. 567-579; Orlitzky, M., Schmidt, F.L., Rynes, S.L., Corporate social and financial performance: A meta-analysis (2003) Organ. Stud., 24 (3), pp. 403-441; Pagell, M., Wu, Z., Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars (2009) J. Supply Chain Manag, 45 (2), pp. 37-56; Palmer, A., (2007) Development of An Integrated Routing and Carbon Dioxide Emissions Model for Goods Vehicles, , PhD thesis). Cranfield University, School of Management; Paulraj, A., Understanding the relationships between internal resources and capabilities, sustainable supply management and organizational sustainability (2011) J. Supply Chain Manag, 47 (1), pp. 19-37; Peloza, J., The challenge of measuring financial impacts from investments in corporate social performance (2009) J. Manag., 35 (6), pp. 1518-1541; Perotti, S., Zorzini, M., Cagno, E., Micheli, G.J., Green supply chain practices and company performance: The case of 3PLs in Italy (2012) Int. J. Phys. Distrib. Logist. Manag, 42 (7), pp. 640-672; Pil, F.K., Rothenberg, S., Environmental performance as a driver of superior quality (2003) Prod. Oper. Manag., 12 (3), pp. 404-415; Plambeck, E.L., OM forum-operations management challenges for some "Cleantech" firms (2013) Manuf. Serv. Oper. Manag., 15 (4), pp. 527-536; Plambeck, E., Wang, Q., Effects of e-waste regulation on new product introduction (2009) Manag. Sci., 55 (3), pp. 333-347; Porter, M.E., Kramer, M.R., The link between competitive advantage and corporate social responsibility (2006) Harv. Bus. Rev, 84 (12), pp. 78-92; Potoski, M., Prakash, A., Covenants with weak swords: ISO 14001 and facilities' environmental performance (2005) J. Policy Anal. Manag., 24 (4), pp. 745-769; Pullman, M.E., Maloni, M.J., Carter, C.R., Food for thought: Social versus environmental sustainability practices and performance outcomes (2009) J. Supply Chain Manag, 45 (4), pp. 38-54; Quariguasi Frota Neto, J.Q., Bloemhof-Ruwaard, J.M., Van Nunen, J.A.E.E., Van Heck, E., Designing and evaluating sustainable logistics networks (2008) Int. J. Prod. Econ, 111 (2), pp. 195-208; Quariguasi Frota Neto, J., Walther, G., Bloemhof, J., Van Nunen, J.A.E.E., Spengler, T., A methodology for assessing eco-efficiency in logistics networks (2009) Eur. J. Oper. Res., 193 (3), pp. 670-682; Rajaram, K., Corbett, C.J., Achieving environmental and productivity improvements through model-based process redesign (2002) Oper. Res, 50 (5), pp. 751-763; Raykov, T., Marcoulides, G.A., (2006) A First Course in Structural Equation Modelling, , Lawrence Erlbaum Associates Publisher, Mahwah, New Jersey; Richardson, B.C., Sustainable transport: Analysis frameworks (2005) J. Transp. Geogr., 13, pp. 29-39; Rothenberg, S., Pil, F.K., Maxwell, J., Lean, green, and the quest for superior environmental performance (2001) Prod. Oper. Manag., 10 (3), pp. 228-243; Russo, M.V., Harrison, N.S., Organizational design and environmental performance: Clues from the electronics industry (2005) Acad. Manag. J., 48 (4), pp. 582-593; Radulescu, M., Radulescu, S., Radulescu, C.Z., Sustainable production technologies which take into account environmental constraints (2009) Eur. J. Oper. Res., 193 (3), pp. 730-740; Saraph, J.V., Benson, P.G., Schroeder, R.G., An instrument for measuring the critical factors of quality management (1989) Decis. Sci., 20 (4), pp. 810-829; Sarkis, J., Gonzalez-Torre, P., Adenso-Diaz, B., Stakeholder pressure and the adoption of environmental practices: The mediating effect of training (2010) J. Oper. Manag, 28 (2), pp. 163-176; Schoenherr, T., The role of environmental management in sustainable business development: A multi-country investigation (2012) Int. J. Prod. Econ., 140 (1), pp. 116-128; Seuring, S., Gold, S., Sustainability management beyond corporate boundaries: From stakeholders to performance (2013) J. Clean. Prod., 56, pp. 1-6; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod, 16 (15), pp. 1699-1710; Shah, R., Goldstein, S.M., Use of structural equation modeling in operations management research: Looking back and forward (2006) J. Oper. Manag, 24 (2), pp. 148-169; Shaw, K., Shankar, R., Yadav, S.S., Thakur, L.S., Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain (2012) Expert Syst. Appl, 39 (9), pp. 8182-8192; Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A., A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences (2013) Resour. Conserv. Recycl., 74, pp. 170-179; Shewhart, W.A., (1931) Economic Control of Quality of Manufactured Product, 509. , ASQ Quality Press; Sheu, J.B., Chou, Y.H., Hu, C.C., An integrated logistics operational model for green-supply chain management (2005) Transp. Res. E: Logist. Transp. Rev, 41 (4), pp. 287-313; Shrivastava, P., The role of corporations in achieving ecological sustainability (1995) Acad. Manag. Rev., 20 (4), pp. 936-960; Sroufe, R.S., Effects of environmental management systems on environmental management practices and operations (2003) Prod. Oper. Manag, 12 (3), pp. 416-431; Steg, L., Gifford, R., Sustainable transportation and quality of life (2005) J. Transp. Geogr, 13 (1), pp. 59-69; Subramanian, R., Subramanyam, R., Key factors in the market for remanufactured products (2012) Manuf. Serv. Oper. Manag, 14 (2), pp. 315-326; Subramanian, R., Gupta, S., Talbot, B., Compliance strategies under permits for emissions (2007) Prod. Oper. Manag, 16 (6), pp. 763-779; Sundarakani, B., De Souza, R., Goh, M., Wagner, S.M., Manikandan, S., Modeling carbon footprints across the supply chain (2010) Int. J. Prod. Econ, 128 (1), pp. 43-50; Svensson, G., Aspects of sustainable supply chain management (SSCM): Conceptual framework and empirical example (2007) Supply Chain Manag. Int. J., 12 (4), pp. 262-266; Tang, C.S., Zhou, S., Research advances in environmentally and socially sustainable operations (2012) Eur. J. Oper. Res., 223 (3), pp. 585-594; Taylor, F.W., (1913) Principles of Scientific Management, , Harper and Brothers, New York; Tsai, W.H., Hung, S.J., A fuzzy goal programming approach for green supply chain optimisation under activity-based costing and performance evaluation with a value-chain structure (2009) Int. J. Prod. Res., 47 (18), pp. 4991-5017; Ubeda, S., Arcelus, F.J., Faulin, J., Green logistics at Eroski: A case study (2011) Int. J. Prod. Econ., 131 (1), pp. 44-51; Ülengin, F., Kabak, O., Onsel Ş., Ülengin, B., Aktas, E., A problem-structuring model for analyzing transportationeenvironment relationships (2010) Eur. J. Oper. Res., 200 (3), pp. 844-859; (2014) United Nations Commodity Trade Statistics Database, , http://comtrade.un.org/db/, accessed on 02.06.14; Vachon, S., Klassen, R.D., Extending green practices across the supply chain: The impact of upstream and downstream integration (2006) Int. J. Oper. Prod. Manag, 26 (7), pp. 795-821; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) Int. J. Prod. Econ., 111 (2), pp. 299-315; Van Der Vorst, J.G., Tromp, S.O., Zee, D.J.V.D., Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics (2009) Int. J. Prod. Res., 47 (23), pp. 6611-6631; Vanek, F.M., Morlok, E.K., Improving the energy efficiency of freight in the United States through commodity-based analysis: Justification and implementation (2000) Transp. Res. Part D Transp. Environ., 5 (1), pp. 11-29; Wang, F., Lai, X., Shi, N., A multi-objective optimization for green supply chain network design (2011) Decis. Support Syst., 51 (2), pp. 262-269; Wang, X., Chan, H.K., Yee, R.W., Diaz-Rainey, I., A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain (2012) Int. J. Prod. Econ., 135 (2), pp. 595-606; WBCSD, (1997) Annual Review 1997, WBCSD, 18p. , (World Business Council for Sustainable Development), Geneva; (1800) Pioneers of the Machine Shop, , https://www.eliwhitney.org/7/museum/about-eli-whitney/factory, accessed 05.03.13; Winebrake, J.J., Corbett, J.J., Falzarano, A., Hawker, J.S., Korfmacher, K., Ketha, S., Zilora, S., Assessing energy, environmental, and economic tradeoffs in intermodal freight transportation (2008) J. Air Waste Manag. Assoc., 58 (8), pp. 1004-1013; (1994) World Resources: A Guide to the Global Environment, , http://www.wri.org/sites/default/files/pdf/worldresources1994-95_bw.pdf, accessed on 21.03.13; Wu, C.C., Chang, N.B., Corporate optimal production planning with varying environmental costs: A grey compromise programming approach (2004) Eur. J. Oper. Res., 155 (1), pp. 68-95; Wu, Z., Pagell, M., Balancing priorities: Decision-making in sustainable supply chain management (2011) J. Oper. Manag., 29 (6), pp. 577-590; Wu, G.C., Ding, J.H., Chen, P.S., The effects of GSCM drivers and institutional pressures on GSCM practices in Taiwan's textile and apparel industry (2012) Int. J. Prod. Econ., 135 (2), pp. 618-636; Wu, T., Jim Wu, Y.C., Chen, Y.J., Goh, M., Aligning supply chain strategy with corporate environmental strategy: A contingency approach (2014) Int. J. Prod. Econ., 147, pp. 220-229; Xu, L., Mathiyazhagan, K., Govindan, K., Noorul Haq, A., Ramachandran, N.V., Ashokkumar, A., Multiple comparative studies of green supply chain management: Pressures analysis (2013) Resour. Conserv. Recycl, 78, pp. 26-35; Yakovleva, N., Sarkis, J., Sloan, T., Sustainable benchmarking of supply chains: The case of the food industry (2012) Int. J. Prod. Res, 50 (5), pp. 1297-1317; Yan, X.P., Ma, X.F., Huang, G.H., Wu, C.Z., An inexact transportation planning model for supporting vehicle emissions management (2010) J. Environ. Inf., 15 (2), pp. 87-98; Yoshida, Y., Matsuhashi, R., Evaluation of CO2 emission reduction in Japan utilizing the interregional repercussion model on freight transportation (2009) J. Environ. Inf., 14 (1), pp. 41-50; Zailani, S., Jeyaraman, K., Vengadasan, G., Premkumar, R., Sustainable supply chain management (SSCM) in Malaysia: A survey (2012) Int. J. Prod. Econ., 140 (1), pp. 330-340; Zeydan, M., Çolpan, C., Çobanoglu, C., A combined methodology for supplier selection and performance evaluation (2011) Expert Syst. Appl, 38 (3), pp. 2741-2751; Zhao, J., Hobbs, B.F., Pang, J.S., Long-run equilibrium modeling of emissions allowance allocation systems in electric power markets (2010) Oper. Res., 58 (3), pp. 529-548; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises (2004) J. Oper. Manag., 22 (3), pp. 265-289; Zhu, Q., Sarkis, J., The moderating effects of institutional pressures on emergent green supply chain practices and performance (2007) Int. J. Prod. Res., 45 (18-19), pp. 4333-4355; Zhu, Q., Sarkis, J., Cordeiro, J.J., Lai, K.H., Firm-level correlates of emergent green supply chain management practices in the Chinese context (2008) Omega, 36 (4), pp. 577-591; Zimmermann, H.-J., Fuzzy programming and linear programming with several objective functions (1978) Fuzzy Sets Syst., 1 (1), pp. 45-55},
correspondence_address1={Jakhar, S.K.; Faculty Operations Management Area, India; email: suresh.jakhar@iimrohtak.ac.in},
publisher={Elsevier Ltd},
issn={09596526},
coden={JCROE},
language={English},
abbrev_source_title={J. Clean. Prod.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Teimoury20141160,
author={Teimoury, E. and Chambar, I. and Gholamian, M.R. and Fathian, M.},
title={Designing an ontology-based multi-agent system for supply chain performance measurement using graph traversal},
journal={International Journal of Computer Integrated Manufacturing},
year={2014},
volume={27},
number={12},
pages={1160-1174},
doi={10.1080/0951192X.2013.874584},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926096890&doi=10.1080%2f0951192X.2013.874584&partnerID=40&md5=e5db9c7ff424a44c1328fc5e588874df},
affiliation={Industrial Engineering Department, Iran University of Science and Technology (IUST), Tehran, Iran},
abstract={Previous studies have not provided any information system (IS) solution for the supply chain (SC) performance measurement based on the Global Supply Chain Forum (GSCF) reference model due to the complexities of interoperability among the ISs in the SC. As an innovation, this paper has proposed an architecture that allows the SC Performance Measurement System (SCPMS) components to have multi-level interoperability by integrating the multi-agent technology as well as ontology technology. It has also proposed a method to develop the required ontologies for the PMS agents and developed the ontologies in the Web Ontology Language (OWL) to provide the semantic level interoperability. The syntax-level interoperability has been provided by a number of agent interaction protocols (AIPs). As another innovation, the authors have developed the SCPMS algorithms for the link analysis method by the Breadth-First Search (BFS) method and proved those algorithms. The authors have created the PMS agents based on those algorithms and prototyped a Multi-Agent System (MAS) for the SC of a tile manufacturer as a case study. The proposed multi-level architecture supports the GSCF model in the SC performance measurement, and improves the interoperability and the integration of the SCPMS. © 2014 © 2014 Taylor & Francis.},
author_keywords={Global Supply Chain Forum (GSCF);  Multi-Agent System (MAS);  Ontology;  Performance Measurement System (PMS)},
keywords={Interoperability;  Ontology;  Semantics;  Supply chains, Agent interaction protocols;  Global supply chain;  Multi-agent technologies;  Ontology-based multi-agent system;  Performance measurement system;  Performance measurements;  Supply chain performance measurements;  Web ontology language, Multi agent systems},
references={Ahmad, A., Mollaghasemi, M., Rabelo, L., Ontologies for supply chain management (2004) Proceedings of the 13th Annual Industrial Engineering Research Conference, , Houston, May; Bang-Jensen, J., Gutin, G.Z., (2008) Digraphs: Theory, Algorithms and Applications, , London: Springer; Barnard, J.H., (2006) A Multi-View Framework for Defining the Services Supply Chain Using Object Oriented Methodology, , PhD thesis, University of Central Florida, Orlando, FL; Bititci, U., Nudurupati, S., Turner, T., Creighton, S., Web enabled performance measurement systems: Management implications (2002) International Journal of Operations & Production Management, 22 (11), pp. 1273-1287; Blanc, S., Ducq, Y., Vallespir, B., Evolution management towards interoperable supply chains using performance measurement (2007) Computers in Industry, 58 (7), pp. 720-732; Caire, G., Coulier, W., Garijo, F., Gomez, J., Pavón, J., Leal, F., Chainho, P., Evans, R., Agent oriented analysis using message/uml (2002) Agent- Oriented Software Engineering, 2, pp. 119-135. , edited by M. Wooldridge, G. Wei, and P. Ciancarini, Berlin: Springer; Chandra, C., Tumanyan, A., Ontology-driven information system for supply chain management (2007) Ontologies, pp. 697-726. , edited by R. Sharman, R. Kishore, and R. Ramesh, New York: Springer; Chiu, H.Y., Sheng, C.C., Chen, A.P., Modeling agent-based performance evaluation for e-learning systems (2008) The Electronic Library, 26 (3), pp. 345-362; Cutting-Decelle, A., Das, B., Young, R., Case, K., Rahimifard, S., Anumba, C., Bouchlaghem, N., Building supply chain communication systems: A review of methods and techniques (2006) Data Science Journal, 5, pp. 29-51; Farshchi, M., Jusoh, Y.Y., Murad, M.A., Agent- based distributed performance measurement system for itsp projects (2010) Proceedings of International Conference on Electronics and Information Engineering (ICEIE), , Kyoto, August 1-3; Fayez, M., Rabelo, L., Mollaghasemi, M., Ontologies for supply chain simulation modeling (2005) Proceedings of the 37th Winter Simulation Conference, , Orlando, FL, December 4-7; Folan, P., Higgins, P., Browne, J., A communications framework for extended enterprise performance measurement (2006) International Journal of Computer Integrated Manufacturing, 19 (4), pp. 301-314; Grubic, T., Bastl, M., Fan, I.S., Harrison, A., Templar, S., Towards the integrative supply chain model (2010) International Journal of Logistics: Research and Applications, 13 (1), pp. 59-73; Hellingrath, B., Witthaut, M., Böhle, C., Brügger, S., An organizational knowledge ontology for automotive supply chains (2009) Holonic and Multi-Agent Systems for Manufacturing, HoloMAS '09, pp. 37-46. , edited by V. Marik, T. Strasser and A. Zoitl, New York: Springer-Verlag; Lambert, D.M., (2008) Supply Chain Management: Processes, Partnerships, Performance, , Sarasota, FL: Supply Chain Management Institute; Lin, Y., (2008) Semantic Annotation for Process Models: Facilitating Process Knowledge Management Via Semantic Interoperability, , PhD thesis, Norwegian University of Science and Technology; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Nurmi, A., Moyaux, T., Botta-Genoulaz, V., Towards semantic performance measurement systems for supply chain management (2010) On the Move to Meaningful Internet Systems: OTM 2010 Workshops, pp. 239-248. , edited by R. Meersman, T. Dillon, and P. Herrero, Berlin: Springer; Padgham, L., Winikoff, M., Prometheus: A methodology for developing intelligent agents (2003) Agent- Oriented Software Engineering, 3, pp. 174-185. , edited by F. Giunchiglia, J. Odell, and G. Weiß, Berlin: Springer; Pakonen, A., Tommila, T., Pirttioja, T., Seilonen, I., Owl based information agent services for process monitoring (2007) IEEE Conference on Emerging Technologies and Factory Automation (ETFA 2007), , Patras, September 25-28; Pavón, J., Gómez-Sanz, J., Agent oriented software engineering with ingenias (2003) Multi-Agent Systems and Applications, 3, pp. 394-403. , edited by V. Mařík, M. Pěchouček, and J. Müller, Berlin: Springer; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Proceedings of 2nd International Conference on Emerging Trends in Engineering and Technology (ICETET), , Nagpur, December 16-18; Saiz, J.J.A., Rodriguez, R.R., Bas, A.O., Verdecho, M.J., An information architecture for a performance management framework by collaborating smes (2010) Computers in Industry, 61 (7), pp. 676-685; Teimoury, E., Fathian, M., Chambar, I., Automation the supply chain performance measurement based on multi-agent system (2013) International Journal of Agile Systems and Management, 6 (1), pp. 25-42; Tran, Q.N.N., Low, G., MOBMAS: A methodology for ontology-based multi-agent systems development (2008) Information and Software Technology, 50 (7), pp. 697-722; Vegetti, M., Gonnet, S., Henning, G., Leone, H., Towards a supply chain ontology of information logistics within process industry environments (2005) Proceedings of the Fourth Mercosur Congress on Process Systems Engineering, , Rio de Janeiro, August 14-15; Wang, M., Wang, H., Vogel, D., Kumar, K., Chiu, D.K.W., Agent-based negotiation and decision making for dynamic supply chain formation (2009) Engineering Applications of Artificial Intelligence, 22 (7), pp. 1046-1055; Ye, Y., Yang, D., Jiang, Z., Tong, L., Ontology-based semantic models for supply chain management (2008) The International Journal of Advanced Manufacturing Technology, 37 (11), pp. 1250-1260; Zambonelli, F., Jennings, N.R., Wooldridge, M., Developing multiagent systems: The gaia methodology (2003) ACM Transactions on Software Engineering and Methodology (TOSEM), 12 (3), pp. 317-370; Zdravković, M., Panetto, H., Trajanović, M., Towards an approach for formalizing the supply chain operations (2010) Proceedings of the 6th International Conference on Semantic Systems, , Graz, September 6-8; Zdravković, M., Panetto, H., Trajanović, M., Aubry, A., An approach for formalising the supply chain operations (2011) Enterprise Information Systems, 5 (4), pp. 401-421},
correspondence_address1={Chambar, I.; Industrial Engineering Department, Iran},
publisher={Taylor and Francis Ltd.},
issn={0951192X},
coden={ICIME},
language={English},
abbrev_source_title={Int J Computer Integr Manuf},
document_type={Article},
source={Scopus},
}

@ARTICLE{Mutingi2014,
author={Mutingi, M. and Mapfaira, H. and Monageng, R.},
title={Developing performance management systems for the green supply chain},
journal={Journal of Remanufacturing},
year={2014},
volume={4},
number={1},
doi={10.1186/s13243-014-0006-z},
art_number={6},
note={cited By 33},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018219227&doi=10.1186%2fs13243-014-0006-z&partnerID=40&md5=1a6fb8983ce624fce28ea7cacc2f9f83},
affiliation={Mechanical Engineering Department, University of Botswana, Gaborone, P/Bag 0061 UB Post, Botswana; Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa},
abstract={As ‘green’ issues continue to become a global concern in the manufacturing supply chain, developing appropriate performance measurement systems for specific supply chains is imperative. Various green supply chain management strategies have been proposed in different contexts. On the other hand, a number of performance management systems (PMS) have been proposed. However, given the variations in the contexts of the available green strategies and the performance measurement approaches, selecting or developing suitable performance measures and the ensuing PMS under a given supply chain context is not trivial. The purpose of this study is to develop a structured taxonomic approach to developing PMS under various green supply chain conditions, contexts, and business objectives. Therefore, we (i) explore extant empirical studies on green supply chain activities and environmental management, (ii) develop a taxonomy of green supply chain strategies, (iii) derive a structured approach to developing green performance management systems, and (iv) provide a taxonomic performance measurement framework consisting of environmental, economic, and social performance metrics. Unlike past studies, the taxonomic framework forms a practical platform to assist decision makers when developing a suitable set of performance measures and the ultimate PMS while considering the particular context of specific green strategies under which the PMS is supposed to operate. © 2014, Mutingi et al.; licensee Springer.},
author_keywords={Environmental management;  Green supply chain;  Performance management systems;  Performance measurement},
references={Azzone, G., Noci, G., Identifying effective PMSs for the deployment of “green” manufacturing strategies (1998) Int. J. Operations Produc. Manag, 18 (4), pp. 308-335; Azzone, G., Bertele, U., Exploiting green strategies for competitive advantage (1994) Long Range Plan, 27, pp. 69-81; Bloom, G.S., Morton, M.S., Hazardous waste is every managers’ problem. Sloan Manage (1991) Rev., pp. 75-84; Noci, G., Supporting decision making for recycling based investments (1995) Bus. Strateg. Environ, 4 (2), pp. 62-71; Kumar, S., Yamaoka, T., System dynamics study of the Japanese automotive industry closed loop supply chain (2007) J. Manuf. Technol. Manag, 1 (2), pp. 115-138; Azzone, G., Bianchi, R., Mauri, R., Noci, G., Defining operating environmental strategies: programmes and plans within Italian industries (1997) Environ. Manag. Health, 8 (1), pp. 1-15; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking Int. J, 12 (4), pp. 330-353; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain (2001) Int. J. Operations Product. Manag, 21, pp. 71-87; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Manage Rev, 5 (2), p. 48; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Operations Product Manag, 19, pp. 275-292; Gray, R., Accounting and environmentalism: an exploration of the challenge of gently accounting for accountability, transparency and sustainability (1992) Acc. Organ. Soc, 17 (5), pp. 399-425; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Manag, 8, pp. 209-223; Hart, S.L., A natural-resource-based view of the firm (1995) Acad. Manag. Rev, 20 (4), pp. 986-1014; Hart, S.L., Beyond greening: strategies for a sustainable world (1997) Harv. Bus. Rev, 75 (1), pp. 66-76; Porter, M., Van Der Linde, C., Green and competitive: ending the stalemate (1995) Harv. Bus. Rev, 28 (6), pp. 120-134; Hu, A.H., Hsu, C.W., Empirical study in the critical factors of green supply chain management (GSCM) practice in the Taiwanese electrical and electronics industries (2006) IEEE Int. Conf. Manage. Innov. Tech., pp. 853-857; Hu, A.H., Hsu, C.W., Critical factors for implementing green supply chain management practice-an empirical study of electrical and electronics industries in Taiwan (2010) Manage. Res. Rev, 33 (6), pp. 586-608; Vlachos, D., Gaorrgiadis, P., Iakovou, E., A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains (2007) Comput. Oper. Res, 34, pp. 367-394; Simpson, D., Samson, D., Developing strategies for green supply chain management (2008) Decision line, 39 (4), pp. 12-15; Agarwal, G., Vijayvargy, L., Green supplier assessment in environmentally responsive supply chains through analytical network process (2012) Proceed. Int. Multi. Conference Eng. Computer Sci. Hong Kong, 2, pp. 14-16; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) Int. J. Prod. Econ, 101 (1), pp. 99-108; Zhu, Q., Sarkis, J., Lai, K., Confirmation of a measurement model for green supply chain management practices implementation (2008) Int J Product Econ, 111, pp. 261-273; Zhu, Q., Sarkis, J., Lai, K., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) J. Environ. Manag, 85, pp. 179-189; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., A comprehensive approach in assessing the performance of an automobile closed-loop supply chain (2010) Sustainability, 2, pp. 871-889; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities: balanced scorecard perspective (2009) Benchmarking Int. J, 16 (5), pp. 605-620; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) J. Clean. Prod, 16, pp. 1647-1657; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour. Conserv. Recycl, 55 (6), pp. 567-579; Eltayeb, T.K., Zailani, S., Ramayah, T., Green supply chain initiatives among certified companies in Malaysia and environmental sustainability: Investigating the outcomes (2011) Resour. Conserv. Recycl, 55, pp. 495-506; Li, Y., Research on the performance measurement of green supply chain management in China (2011) Int. J. Sustain. Dev, 4 (3), pp. 101-107; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) Int. J. Operations Product Manage, 25, pp. 898-916; Rao, P., Greening of the supply chain: a new initiative in South East Asia (2002) Int. J. Operations Product Manage, 22, pp. 632-655; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental performance indicators for integrated supply chains: the case of Xerox Ltd (1998) Supply Chain Manag, 3, pp. 149-156; Wu, J., Dunn, S., Forman, H., A study on green supply chain management practices among large global corporations (2012) J. Supply. Chain Operations Manag, 10 (1), pp. 182-194; Wang, H.F., Gupta, S.M., (2011) Green supply chain management-A product life cycle approach, , McGraw-Hill Prof. Med/Tech; Choudhary, M., Seth, N., Integration of green practices in supply chain environment - the practices of inbound, operational, outbound and reverse logistics (2011) Int. J. Eng. Sci. Technol, 3 (6), pp. 4993-4995; Mondragon, A.E.C., Lalwani, C., Measures for auditing performance and integration in closed-loop supply chain (2011) Supply Chain Manage. Int. J, 16 (1), pp. 43-56; Diabat, A., Govindan, K., An analysis of the drivers affecting the implementation of green supply chain management (2011) Resour. Conserv. Recycl, 55 (6), pp. 659-667; Olugu, E.U., Wong, K., A study on the validation of green supply chain performance measures in the automotive industry (2011) Commun. IBIMA, 2011, pp. 1-14; Gupta, M., Environmental management and its impact on the operations function (1995) Int. J. Operations Product. Manag, 15 (8), pp. 34-51; Allenby, B., Supporting environmental quality: developing an infrastructure for design (1993) Total Qual. Environ. Manag, 2 (3), pp. 303-308; Klassen, R.D., McLaughlin, C.P., The impact of environmental management of firm performance (1996) Manag. Sci, 42 (8), pp. 1199-1215; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: a study in the application of the analytical hierarchy process (2002) Eur. J. Oper. Res, 141, pp. 70-87; Winsemius, P., Guntram, U., Responding to the environmental challenge (1992) Bus. Horiz, 35 (2), pp. 12-20; Mutingi, M., Mbohwa, C., A taxonomic framework for developing green supply chain strategies (2012) Proceed. 2012 Int. Conference Ind. Eng. Operations Manag. Istanbul Turkey, 6, pp. 326-335; Korchi, A.E., Conditions of emergence of OEM’s reverse supply chains (2014) J. Remanufactur, 4 (3); Srivastava, S., Green supply-chain management: a state-of-the-art literature review (2007) Int. J. Manag. Rev, 9 (1), pp. 53-80; Tibben-Lembke, R.S., Life after death: reverse logistics and the product life cycle (2002) Int. J. Phys. Distr. Log. Manag, 32, pp. 223-244; Van Hoek, R.I., From reversed logistics to green supply chains (1999) Supply Chain Manag, 4, pp. 129-134; Schot, J., Fischer, K., (1993) Environmental Strategies for Industry: International Perspectives on Research Needs and Policy Implications, , Island Press, Washington D.C; Kollman, K., Prakash, A., EMS-based environmental regimes as club goods: examining variations in firm-level adoption of ISO 14001 and EMAS in U.K., U.S., and Germany (2002) Policy. Sci, 35 (1), pp. 43-67; (2014) The Centre for Remanufacturing and Reuse, , http://www.remanufacturing.org.uk/, Accessed 15 July 2014; Ninlawan, C., Seksan, P., Tossapol, K., Pilada, W., The implementation of green supply chain management practices in electronics industry. Proceed (2010) Int. Multi-Conference Eng. Computer Sci, p. 3; Azzone, G., Noci, G., Measuring the environmental performance of new products: an integrated approach (1996) Int. J. Prod. Res, 34 (11), pp. 3055-3078; Sarkis, J., Rasheed, A., Greening the manufacturing function (1995) Bus. Horiz, 38 (5), pp. 17-27; Diabat, A., Kannan, D., Mathiyazhagan, K., Analysis of enablers for implementation of sustainable supply chain management: a textile case study (2014) J. Clean. Prod, 14 (5), pp. 1-13; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: drivers and practices (2006) J. Clean. Prod, 14, pp. 472-486; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: pressures, practices and performance (2005) Int. J. Operations Product Manag, 25 (5), pp. 449-468; Braun, J., Guthrie, M., McCampbell, E., Sit, V., (1997) Chrysler Corporation: Innovations In Supply Chain Management, Case Study, , http://www-personal.umich.edu/~afuah/cases/case3.html, University of Michigan. Available from:. Accessed 03 August 2014; Beamon, B.M., Designing the green supply chain (1999) Logist. Inf. Manag, 12 (4), pp. 332-342; Lopes, C.P., (2010) Toyota motor European (TME) sustainable logistics: An example to Brazil, , POMS 21st Ann. Conf. Vancouver Canada; (2014) Vehicle Recycling, Sustainability Report, , http://www.toyota-global.com/sustainability/report/vehicle_recycling/pdf/vr_all.pdf, Accessed 07 August 2014; Lind, S., Olsson, D., Sundin, E., Exploring inter-organizational relationships in automotive component remanufacturing (2014) J. Remanufactur, 4, p. 5; Zhu, Q., Sarkis, J., Kai, K., Green supply chain management: practices and performance within the Chinese automobile industry (2007) J. Clean. Prod, 15, pp. 1041-1052},
correspondence_address1={Mutingi, M.; Mechanical Engineering Department, Botswana; email: mmutingi@gmail.com},
publisher={SpringerOpen},
issn={2210464X},
language={English},
abbrev_source_title={J. Remanuf.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Barroso2014924,
author={Barroso, A.P. and Machado, V.H. and Machado, V.C.},
title={Demand information sharing impact on supply chain management under demand uncertainty. A simulation model},
journal={IEEE International Conference on Industrial Engineering and Engineering Management},
year={2014},
pages={924-928},
doi={10.1109/IEEM.2013.6962547},
art_number={6962547},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914182361&doi=10.1109%2fIEEM.2013.6962547&partnerID=40&md5=838077e9d215d1b47c77cf8c5e7950c2},
affiliation={Department of Mechanical and Industrial Engineering, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Portugal},
abstract={The modern business environments are subject to uncertainties that can adversely influence the supply chain performance. Demand information sharing between supply chain entities can improve the supply chain performance. To encourage them to share information, the generated benefits need to be comprehensively recognized. Simulation allows doing so. This paper intends to quantify the impact of the demand information sharing on supply chain management under demand uncertainty. The performance measure used is total costs. A simulation model of the supply chain of a case study was developed in Arena software. Customer demand is modeled by a normal distribution. Four scenarios are simulated with two different standard deviations each one with and without demand information sharing between supply chain entities. The results analysis shows that an increase in the customer demand variability worsens the studied performance measure. However, the introduction of demand information sharing improves the supply chain performance measure. © 2013 IEEE.},
author_keywords={demand uncertainty;  information sharing;  simulation;  Supply chain management},
keywords={Computer software;  Information analysis;  Information dissemination;  Normal distribution, Business environments;  Demand information;  Demand uncertainty;  Information sharing;  Performance measure;  simulation;  Standard deviation;  Supply chain performance, Supply chain management},
references={Zhang, C., Zhang, C., Design and simulation of demand information sharing in a supply chain (2007) Simulation Modelling Practice and Theory, 15, pp. 32-46; Chan, H.K., Chan, F.T.S., Effect of information sharing in supply chains with flexibility (2009) International Journal of Production Research, 47, pp. 213-232; Acar, Y., Kadipasaoglu, S., Schipperijn, P., A decision support framework for global supply chain modelling: An assessment of the impact of demand, supply and lead-time uncertainties on performance (2010) International Journal of Production Research, 48, pp. 3245-3268; Datta, P.P., Christopher, M.G., Information sharing and coordination mechanisms for managing uncertainty in supply chains: A simulation study (2011) International Journal of Production Research, 49, pp. 765-803; Jespersen, B.D., Skjott-Sarsen, T., (2005) Supply Chain Management: In Theory and Practice, Copenhagen Business School Press; Suwanruji, P., Enns, S.T., Evaluating the effects of capacity constraints and demand patterns on supply chain replenishment strategies (2006) International Journal of Production Research, 44, pp. 4607-4629; Hung, W.Y., Samsatli, N.J., Shah, N., Object-oriented dynamic supply-chain modelling incorporated with production scheduling (2006) European Journal of Operational Research, 169, pp. 1064-1076; Merschmann, U., Thonemann, U.W., Supply chain flexibility, uncertainty and firm performance: An empirical analysis of german manufacturing firms (2011) International Journal of Production Economics, 130, pp. 43-53; Walker, W.E., Rotmans, J., Sluijs Der Van, J.P., Van Asselt, M.B.A., Janssen, P., Von Krauss, M.P.K., Defining uncertainty: A conceptual basis for uncertainty management in model-based decision support (2003) Integrated Assessment, 4, pp. 5-17; Papageorgiou, L.G., Supply chain optimisation for the process industries: Advances and opportunities (2009) Computers & Chemical Engineering, 33, pp. 1931-1938; Wangphanich, P., Kara, S., Kayis, B., Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems-A simulation approach (2010) International Journal of Production Research, 48, pp. 4501-4517; Chiang, W.K., Feng, Y., The value of information sharing in the presence of supply uncertainty and demand volatility (2007) International Journal of Production Research, 45, pp. 1429-1447; Iida, T., Coordination of cooperative cost-reduction efforts in a supply chain partnership (2012) European Journal of Operational Research, 222, pp. 180-190; Iannone, R., Miranda, S., Riemma, S., Supply chain distributed simulation: An efficient architecture for multimodel synchronization (2007) Simulation Modelling Practice and Theory, 15, pp. 221-236; Carvalho, H., Barroso, A.P., Machado, V.H., Azevedo, S., Cruz Machado, V., Supply chain redesign for resilience using simulation (2012) Computers & Industrial Engineering, 62, pp. 329-341},
publisher={IEEE Computer Society},
issn={21573611},
isbn={9781479909865},
language={English},
abbrev_source_title={IEEE Int. Conf. Ind. Eng. Eng. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Edmond2014240,
author={Edmond, J. and Soliman, F.},
title={The impact of balance score card implementation on supply chain firms},
journal={Business Transformation and Sustainability through Cloud System Implementation},
year={2014},
pages={240-257},
doi={10.4018/978-1-4666-6445-6.ch016},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945342235&doi=10.4018%2f978-1-4666-6445-6.ch016&partnerID=40&md5=f02cd5de3ac93a50330d37959265d607},
affiliation={UTS School of Business, University of Technology, Sydney, Australia},
abstract={The objective of this chapter is to understand the level of impact that implementing a balanced score card can have on organisations, and the factors that contribute to such impact; the research agrees that the BSC is an effective performance measurement system. The result of the research suggests that the quality of the knowledge and the understanding of the BSC within an organisation is a major determinant to the level of positive impact that implementing the BSC can have on an organisation. This research concluded that the same principle applies to any performance measurement and performance improvement initiatives, and that non BSC implemented firms are not necessary worse-off than BSC implemented firms from a performance perspective. Only few numbers of organisations were sampled. These sampled organisations are not a complete representation of the rest. The research acknowledges that the sources of information under which conclusions and assumptions were made were not based on primary sources. It is possible these data may not fully reflect the current state of all sampled organisations in this research. © 2015 by IGI Global. All rights reserved.},
keywords={Supply chains, Balanced scorecards;  Effective performance;  Performance improvement initiatives;  Performance measurements;  Primary sources;  Score cards;  Sources of informations, Societies and institutions},
references={Akkermans, H., Van Oorschot, K., Relevance assumed: A case study of balanced scorecard development using system dynamics (2005) The Journal of the Operational Research Society, 56 (8), pp. 931-941; Al-Najjar, S.M., Kalaf, K.H., Designing a balanced scorecard to measure a bank's performance: A case study (2012) International Journal of Business Administration, 3 (4), pp. 44-52; Bolstorff, P., Putting it all together: how to integrate Six Sigma quality objectives with the SCOR model (2003) Logistics Today.; Chavan, M., The balanced scorecard: A new challenge (2007) Journal of Management Development, 28 (5), pp. 393-406; Christesen, D.A., (2008) The impact of balanced scorecard usage on organization performance (PhD thesis), , The University of Minnesota, MN; Cuganesan, S., A case study on organisational performance measurement systems for customer intimacy (2005) Australian Accounting Review, 15 (1), pp. 52-61; Gautreau, A., Kleiner, B.H., Recent trends in performance measurement systems (2001) Management Research News, 24 (3), pp. 153-156; Hult, G.T.M., Ketchen, D.J., Adams, G.L., Mena, J.A., Supply chain orientation and balanced scorecard performance (2008) Journal of Managerial Issues, 20 (4), p. 526; Kukreja, A., Ricks, J.M.J., Meyer, J.A., Using Six Sigma for performance improvement in business curriculum: A case study (2009) Performance Improvement, 48 (2), pp. 9-25; Lagrosen, Y., Chebl, R., Tuesta, M.R., Organisational learning and Six Sigma deployment readiness evaluation: A case study (2011) International Journal of Lean Six Sigma, 2 (1), pp. 23-40; Mcshane, S., Olekalns, M., Travaglione, T., (2010) Organisational behaviour: On the Pacific Rim, , (3rd ed.). Sydney: McGraw-Hill; Melymuka, K., Measuring intangible assets (2004) Computerworld, 38 (6), p. 42; Mensah, F.B., Dogbe, O.D., Performance- based pay as a motivational tool for achieving organisational performance: An exploratory case study (2011) International Journal of Business and Management, 6 (12), pp. 270-285; Muratoglu, K., Performance measurement of supply chain via balanced scorecard: The case of a brewing group (2008) The Business Review Cambridge, 10 (1), pp. 330-337; Neal, G.O., Elaine, F., Using the balanced scorecard to manage intangible asset in a sino foreign joint venture (2005) Australian Accounting Review, 15 (2), p. 22; Othman, R., Enhancing the effectiveness of the balanced scorecard with scenario planning (2008) International Journal of Productivity and Performance Management, 57 (3), pp. 259-266; Rompho, N., Why the balanced scorecard fails in SMEs: A case study (2011) International Journal of Business and Management, 6 (11), pp. 39-45; Stank, T.P., Keller, S.B., Closs, D.J., Performance benefits of supply chain logistical integration (2001) Transportation Journal, 41 (2), p. 32; Thuermer, K.E., Options abound as integration of the global supply chain becomes a top priority (2006) World Trade, 19 (6), p. 17; Troyer, C., Cooper, R., Smart moves in supply chain integration (1995) Transportation & Distribution, 36 (9), p. 55; Upadhyay, A.Y.A., Balanced Scorecard - fostering learning organizations towards achieving strategic results: A case study of an oil PSU in India (2012) Development and Learning, 26 (4), pp. 13-16; Youngblood, A.D., Collins, T.R., Addressing balanced scorecard trade-off issues between performance metrics using Multi-Attribute Utility Theory (2003) Engineering Management Journal, 15 (1), pp. 11-17; Zuckerman, A., Pushing ERP integration into the supply chain (2005) World Trade, 18 (3), p. 54; Zuckerman, A., Explosion of options in global supply chain integration (2007) World Trade, 20 (4), p. 42},
correspondence_address1={Edmond, J.; UTS School of Business, Australia},
publisher={IGI Global},
isbn={9781466664463; 1466664452; 9781466664456},
language={English},
abbrev_source_title={Bus. Transform. and Sustain. through Cloud Syst. Implement.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Smith2014199,
author={Smith, A.D.},
title={Operational strategies associated with RFID applications in healthcare systems},
journal={RFID Technology Integration for Business Performance Improvement},
year={2014},
pages={199-217},
doi={10.4018/978-1-4666-6308-4.ch010},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946745019&doi=10.4018%2f978-1-4666-6308-4.ch010&partnerID=40&md5=9046bf5cd296a7348ab66e07a9a2e357},
affiliation={Department of Management and Marketing, Robert Morris University, Pittsburgh, PA, United States},
abstract={An effective information system is essential for a business or industry to be successful in today's highly competitive market. Perhaps the most compelling case for RFID-embedded technologies in the healthcare field has been increased efficiency in supply chain performance measurements, which generally consist of financial and non-financial indicators. Many research studies have assumed that these efficiency measures are transferrable in the medical services field. Such optimism is fuelled by the expectations that such supply chain measures will result in equally impressive results in the healthcare field. Although this transfer may be somewhat flawed and imperfectly applied, research has verified certain elements of operational optimism. There are still a number of technical, ethical, and legal issues or hurdles that surround RFID applications in the healthcare industry that must be successfully overcome. However, few can successfully argue against freeing hospital staff from the routine duties associated with traditional inventory so that they may be free to serve patients. With recent governmental regulations and the concern for increased access to universal medical care and its astronomical costs, these issues need to be addressed. © 2015 by IGI Global. All rights reserved.},
keywords={Efficiency;  Laws and legislation;  Supply chains, Competitive markets;  Embedded technology;  Financial indicator;  Governmental regulations;  Health-care system;  Healthcare industry;  Operational strategies;  Supply chain performance measurements, Health care},
references={Bhakoo, V., Singh, P., Sohal, A., Collaborative management of inventory in Australian hospital supply chains: Practices and issues. (2012) Supply Chain Management, 17 (2), pp. 217-230; Chen, Z., Dubinsky, A.J., A conceptual model of perceived customer value in e-commerce: A preliminary investigation. (2003) Psychology and Marketing, 20 (4), pp. 323-347; Chiou, J.S., The antecedents of customer loyalty toward Internet service providers. (2004) Information & Management, 41 (6), pp. 685-695; Collier, J.E., Bienstock, C.C., Measuring service quality in e-retailing. (2006) Journal of Service Research, 8 (3), pp. 260-275; Condea, C., Thiessse, F., Fleisch, E., RFID-enabled shelf replenishment with backroom monitoring in retail stores. (2012) Decision Support Systems, 52 (4), pp. 839-849; Cowles, D.L., Kiecker, P., Little, M.W., Using key informant insights as a foundation for e-retailing theory development. (2002) Journal of Business Research, 55 (8), pp. 629-636; de Mast, J., Kemper, B., Doe, R.J., Mandjes, M., van der Bijl, Y., Process improvement in healthcare: Overall resource efficiency. (2011) Quality and Reliability Engineering International, 27 (8), pp. 1095-1106; Gaukler, G.M., Özer, O., Hausman, W.H., Order progress information: Improved dynamic emergency ordering policies. (2008) Production and Operations Management, 17 (6), pp. 599-614; Hu, G., Wang, L., Fetch, S., Bidanda, B., A multi-objective model for project portfolio selection to implement lean and Six Sigma concepts. (2008) International Journal of Production Research, 46 (23), pp. 6611-6648; Jain, V., Benyoucef, L., Deshmukh, S.G., What's the buzz about moving from 'lean' to 'agile' integrated supply chains? A fuzzy intelligent agent-based approach. (2008) International Journal of Production Research, 46 (23), pp. 6649-6678; Kamhawi, E.M., System characteristics, perceived benefits, individual differences and use intentions: A survey of decision support tools of ERP systems. (2008) Information Resources Management Journal, 21 (4), pp. 66-83; Kennedy, F.A., Widener, S.K., A control framework: Insights from evidence on lean accounting. (2008) Management Accounting Research, 19 (4), pp. 301-319; Koong, L., Lin, C., Evaluating the decision to adopt RFID systems using Analytic Hierarchy Process. (2007) Journal of American Academy of Business, 11 (1), pp. 72-77; Kumar, S., Livermont, G., Mckewan, G., Stage implementation of RFID hospitals. (2010) Technology and Health Care, 18 (1), pp. 31-46. , PMID:20231801; Scanlon, D.P., Swaminathan, S., Lee, W., Chernew, M., Does competition improve health care quality? (2008) Health Services Research, 43 (6), pp. 1931-1944. , PMID:18793214; Scherrer-Rathje, M., Boyle, T.A., Deflorin, P., Lean, take two! Reflections from the second attempt at lean implementation. (2009) Business Horizons, 52 (1), pp. 79-85; Seidman, S.J., Brockman, R., Lewis, B.M., Guag, J., Shein, M.J., Clement, W.J., In vitro tests reveal sample radio frequency identification readers inducing clinically significant electromagnetic interference to implantable pacemakers and implantable cardioverter-defibrillators. (2010) Heart Rhythm: The Official Journal of the Heart Rhythm Society, 7 (1), pp. 99-107. , PMID:20129290; Smith, A.D., Reverse logistics and their affects on CRM and online behavior. (2005) VINE: The Journal of Information and Knowledge Management, 35 (3), pp. 166-181; Smith, A.D., Exploring the inherent benefits of RFID and automated self-serve Checkouts in a B2C environment. (2005) International Journal of Business Information Systems, 1 (1-2), pp. 149-183; Smith, A.D., Evolution and acceptability of medical applications of RFID implants among early users of technology. (2008) Health Marketing Quarterly, 24 (1-2), pp. 121-155. , PMID:19042524; Smith, A.D., Marketing and reputation aspects of neonatal safeguards and hospital-security systems. (2009) Health Marketing Quarterly, 26 (2), pp. 117-144. , PMID:19408180; Smith, A.D., Offodile, O.F., Exploring forecasting and project management characteristics of supply chain management. (2007) International Journal of Logistics and Supply Management, 3 (2), pp. 174-214; Smith, A.D., Offodile, O.F., Gauging web portals impact on supply chain management concepts. (2008) International Journal of Logistics and Management, 4 (2), pp. 184-206; Smith, A.D., Offodile, O.F., The perceived importance of major RFID-related technology initiatives among retail store managers. (2009) International Journal of Services and Operations Management, 5 (4), pp. 520-547; Smith, A.D., Rupp, W.T., Supply supplier integration, procurement, and outsourcing: Case study of SCM social capital benefits. (2013) International Journal of Logistics Systems and Management, 14 (2), pp. 221-241; Smith, A.D., Rupp, W.T., Data quality and knowledge/information management in service operations management: Regional supermarket case study. (2013) International Journal of Knowledge-Based Organizations, 3 (3), pp. 35-52; Swaminathan, S., Chernew, M., Scanlon, D.P., Persistence of HMO performance measures. (2008) Health Services Research, 43 (6), pp. 2033-2051. , PMID:18783460; Ustundag, A., Evaluating RFID investment on a supply chain using tagging cost sharing factor. (2010) International Journal of Production Research, 48 (9), pp. 2549-2562; Wamba, S.F., RFID-enabled healthcare applications, issues and benefits: An archival analysis (1997-2011). (2012) Journal of Medical Systems, 36 (6), pp. 3393-3398. , PMID:22109670; Wyld, D.C., RFID 101: The next big thing for management. (2006) Management Research News, 29 (4), pp. 154-173; Yao, A., Carlson, J., The impact of real-time data communication on inventory management. (1999) International Journal of Production Economics, 59 (1), pp. 213-219; Yao, W., Chu, C.-H., Li, Z., The adoption and implementation of RFID Technologies in healthcare: A literature review. (2012) Journal of Medical Systems, 36 (6), pp. 3507-3525. , PMID:22009254; Acharyulu, G., RFID in the healthcare supply chain: Improving performance through greater visibility. (2007) ICFAI Journal of Management Research, 6 (11), pp. 32-45; Al-Sakran, H., Agent and radio frequency identification based architecture for supermarket information system. (2013) Journal of Computer Science, 9 (6), pp. 699-707; Chao, C., Yang, J.M., Jen, W., Determining technology trends and forecasts of RFID by a historical review and bibliometric analysis from 1991 to 2005. (2007) Technovation, 27 (5), pp. 268-279; Chen, C.C., Wu, J., Su, Y.S., Yang, S.C., Key drivers for the continued use of RFID technology in the emergency room. (2008) Management Research News, 31 (4), pp. 273-288; Chen, C.-I., Liu, C.-Y., Li, Y.-C., Chao, C.-C., Liu, C.-T., Chen, C.-F., Kuan, C.-F., Pervasive observation medicine: The application of RFID to improve patient safety in observation unit of hospital emergency department (2005) Studies in Heath Technology Information, pp. 311-315. , http://www.ncbi.nlm.nih.gov/pubmed/16160277, [Online]. Retrieved December 20, 2013 from; Chen, C.-L., Wu, C.-Y., Using RFID yoking proof protocol to enhance inpatient medication safety. (2012) Journal of Medical Systems, 36 (5), pp. 2849-2864. , PMID:21811800; Chien, H.-Y., Yang, C.-C., Wu, T.-C., Lee, C.-F., Two RFID based solutions to enhance inpatient medication safety. (2011) Journal of Medical Systems, 35 (3), pp. 369-375. , PMID:20703553; Condea, C., Thiessse, F., Fleisch, E., RFID-enabled shelf replenishment with backroom monitoring in retail stores. (2012) Decision Support Systems, 52 (4), pp. 839-849; Devaraj, S., Fan, M., Kohli, R., Antecedents of B2C channel satisfaction and preference: Validating e-commerce metrics. (2002) Information Systems Research, 13 (3), pp. 316-333; Dutta, A., Lee, H.L., Whang, S., RFID and operations management: Technology, value and incentives. (2007) Production and Operations Management, 16 (5), pp. 646-655; Erdem, E., Zeng, H., Zhou, J., Shi, J., Wells, D.L., Investigation of RFID tag readability for pharmaceutical products at item level. (2009) Drug Development and Industrial Pharmaceuticals, 35 (11), pp. 1312-1324. , PMID:19832631; Ferguson, R.B., RFID loses reception: high tag costs are still putting the kibosh on returns on investment (2006) e-Week, 23 (10), pp. 11-12; Fisher, J.A., Monahan, T., Tracking the social dimensions of RFID systems in hospitals. (2008) International Journal of Medical Informatics, 77 (3), pp. 176-183. , PMID:17544841; Green, H., Khermouch, G., Barcodes better watch their backs (2005) BusinessWeek., , http://www.businessweek.com/magazine/content/03_28/b3841063.htm, Retrieved December 8, 2012, from; Huang, H.-H., Ku, C.-Y., A RFID grouping proof protocol for medication safety of inpatient. (2009) Journal of Medical Systems, 33 (6), pp. 467-474. , PMID:20052898; Kamhawi, E.M., System characteristics, perceived benefits, individual differences and use intentions: A survey of decision support tools of ERP systems. (2008) Information Resources Management Journal, 21 (4), pp. 66-83; Mehrjerdi, Y.Z., RFID-enabled healthcare systems: Risk-benefit analysis. (2010) International Journal of Pharmaceutical and Healthcare Marketing, 4 (3), pp. 282-300; Min, D., Yih, Y., Fuzzy logic-based approach to detecting a passive RFID tag in an outpatient clinic. (2011) Journal of Medical Systems, 35 (3), pp. 423-432. , PMID:20703549; Ohashi, K., Ota, S., Ohno-Machado, L., Tanaka, H., Comparison of RFID systems for tracking clinical interventions at the bedside (2008) AMIA Annual Symposium Proceedings, pp. 525-529; Ranji, U., Lundy, J., Salganicoff, A., (2010) US healthcare costs: A background brief, , http://www.kaiseredu.org/topics_im.asp?imID=1&parentID=61&id=358, Retrieved October 27, 2013, from; Smith, A.A., Smith, A.D., Baker, D.J., Inventory management shrinkage and employee anti-theft approaches. (2011) International Journal of Electronic Finance, 5 (3), pp. 209-234; Smith, A.D., Corporate social responsibility in the healthcare insurance industry: A cause-branding approach. (2010) International Journal of Electronic Healthcare, 5 (3), pp. 284-302. , PMID:20643642; Smith, A.D., Operational effectiveness and quality assurance mechanisms with stochastic demand of blood supply: Blood bank case study. (2011) International Journal of Electronic Healthcare, 6 (2-4), pp. 174-191. , PMID:22189177; Smith, A.D., Case studies of RFIDrelated applications in the healthcare and voicerecognition industries. (2012) International Journal of Knowledge-Based Organizations, 2 (3), pp. 48-63; Smith, A.D., Operational efficiencies and ethical issues associated with microchip implants. (2012) International Journal of Mobile Communications, 10 (3), pp. 281-302; Smith, A.D., Quality and technologybased management initiatives within a hospital environment: Benchmarking case study. (2013) International Journal of Procurement Management, 6 (6), pp. 621-648. , PM.2013.056746; Smith, A.D., Competitive uses of information and knowledge management tools: Case study of supplier-side management. (2013) International Journal of Knowledge-Based Organizations, 3 (1), pp. 71-87; Smith, A.D., Perceived value and strategic importance of supply chain options: Multi-firm case study. (2013) International Journal of Logistics Systems and Supply Management, 13 (2), pp. 244-267; Ting, S.L., Kwok, S.K., Tsang, A.H.C., Lee, W.B., Critical elements and lessons learnt from the implementation of an RFID-enabled healthcare management system in a medical organization. (2009) Journal of Medical Systems, 35 (4), pp. 657-669. , PMID:20703523; van der Togt, R., Jan van Lieshout, E., Hensbroek, R., Beinat, E., Binnekade, J.M., Bakker, P.J.M., Electromagnetic interference from radio frequency identification inducing potentially hazardous incidents in critical care medical equipment. (2008) Journal of the American Medical Association, 299 (24), pp. 2884-2890. , PMID:18577733; Wickboldt, A.-K., Piramuthu, S., Patient safety through RFID: Vulnerabilities in recently proposed grouping protocols. (2012) Journal of Medical Systems, 36 (2), pp. 431-435. , PMID:20703708; Zang, C., Fan, Y., Complex event processing in enterprise information systems based on RFID. (2007) Enterprise Information Systems, 1 (1), pp. 3-23},
correspondence_address1={Smith, A.D.; Department of Management and Marketing, United States},
publisher={IGI Global},
isbn={9781466663091; 1466663081; 9781466663084},
language={English},
abbrev_source_title={RFID Technol. Integr. for Bus. Perform. Improv.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Franceschini2014783,
author={Franceschini, F. and Galetto, M. and Turina, E.},
title={Impact of performance indicators on organisations: A proposal for an evaluation model},
journal={Production Planning and Control},
year={2014},
volume={25},
number={9},
pages={783-799},
doi={10.1080/09537287.2012.756128},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901237680&doi=10.1080%2f09537287.2012.756128&partnerID=40&md5=53752cb0cd23d41b581729801e43e774},
affiliation={DIGEP, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy},
abstract={This work aims at giving some guidelines to assess the impact of performance indicators on organisations. Performance measurement systems are usually introduced into organisations in order to monitor goal achievement, to allocate resources and to implement a strategy. However, the implementation of performance indicators may generate an alteration in the rational behaviour of the monitored structure. The risk of this impact is always present, and must be considered very carefully in order to preserve the organisation from a counter-productive effect. In the present analytical work, a reference model is proposed as a first step. This model considers all the organisational dimensions on which an indicator may exert its influence. The method makes use of the four Kaplan and Nortons balanced scorecard perspectives in order to identify the dimensions on which an indicator may exert its impact. Previous works reported in scientific literature do not provide operational models for impact analysis, whereas, the proposed model is linked to an operative procedure in order to support management to make this analysis. The aim is to make the impact analysis less complex by structuring it in a sequence of predefined steps. The proposal is supported by some practical examples. © 2014 Copyright Taylor & Francis Group, LLC.},
author_keywords={balanced scorecard;  impact assessment;  manufacturing engineering;  performance indicators;  performance management;  quality management},
keywords={Benchmarking;  Quality management;  Societies and institutions, Balanced scorecards;  Impact assessments;  Manufacturing engineering;  Performance indicators;  Performance management, Supply chain management},
references={Alfaro, J., Ortiz, A., Poler, R., Performance measurement system for business processes (2007) Production Planning & Control, 18 (8), pp. 641-654; Andersen, E.S., Jessen, S.A., Project maturity in organizations (2003) International Journal of Project Management, 21, pp. 457-461; Atkinsons, A., Strategic performance measurement and compensation (1998) European Management Journal, 16 (5), pp. 552-561; Azadeh, A., Ghaderi, S.F., Partovi, M.Y., Ebrahimipour, V., Suzuki, K., An integrated framework for continuous assessment and improvement of manufacturing systems (2007) Applied Mathematics and Computation, 186, pp. 1216-1233; (2011) Baldrige Foundation, 7. , http://www.baldrigepe.org/America-Needs-Baldrige/Index.html, Accessed November; Barnetson, B., Cutright, M., Performance indicators as conceptual technologies (2000) Higher Education, 40, pp. 277-292; Bhagwat, R., Sharma, M.K., An application of the integrated ahp-pgp model for performance measurement of supply chain management (2009) Production Planning & Control, 20 (8), pp. 678-690; Borda, J.C., Mémoire sur les élections au scrutin [Comptes Rendus de l'Académie des Sciences] (1781) Translated by Alfred de Grazia As Mathematical Derivation of An Election System, Isis, 44, pp. 42-51; Bourne, M., Kennerly, M., Franco-Santos, M., Managing through measures: A study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), pp. 373-395; Cameron, K.S., Quinn, R.E., Diagnosis and changing organizational culture (1999) Based on the Competing Values Framework, , Reading, MA: Addison Wesley; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; (2011) European Foundation for Quality Management, , http://www.efqm.org, EFQM Accessed November 7, 2011; Evans, J.R., An exploratory study of performance measurement systems and relationship with performance results (2004) Journal of Operations Management, 22, pp. 219-232; Finkelstein, L., Handbook of measurement science (1982) Theoretical Fundamentals, 1. , edited by P. H. Sydenham New York, NY: John Wiley; Franceschini, F., Brondino, G., Galetto, M., Vicario, G., Synthesis maps for multivariate ordinal variables in manufacturing (2006) International Journal of Production Research, 44 (20), pp. 4241-4255; Franceschini, F., Galetto, M., Maisano, D., Classification of performance and quality indicators in manufacturing (2006) International Journal of Services and Operations Management, 2 (3), pp. 294-311; Franceschini, F., Galetto, M., Maisano, D., Management by measurement (2007) Designing Key Indicators and Performance Measurements, , Berlin: Springer; Franceschini, F., Galetto, M., Maisano, D., Viticchi, L., The condition of uniqueness in manufacturing process representation by performance/quality indicators (2006) Quality and Reliability Engineering International, 22, pp. 567-580; Franceschini, F., Galetto, M., Turina, E., Service quality monitoring by performance indicators: A proposal for a structured methodology (2009) International Journal of Services Operations Management, 5 (2), pp. 251-273; Franceschini, F., Galetto, M., Varetto, M., Ordered samples control charts for ordinal variables (2005) Quality and Reliability Engineering International, 21 (2), pp. 177-195; Ghalayini, A.M., Noble, J.S., Crowe, T., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48, pp. 207-225; Gilmour, P., Customer service: Differentiating by market segment (1977) International Journal of Physical Distribution, 7 (3), pp. 141-148; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: A framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437; Hauser, J., Katz, G., Metrics: You are what you measure! (1998) European Management Journal, 16 (5), pp. 517-528; Henry, J.F., Organizational culture and performance measurement systems (2006) Accounting Organizations and Society, 31, pp. 77-103; Heo, J., Han, I., Performance measure of information systems (is) in evolving computing environments: An empirical investigation (2003) Information & Management, 40, pp. 243-256; Hoque, Z., James, W., Linking balanced scorecard measures to size and market factors: Impact on organizational performance (2000) Journal of Management Accounting Research, 12, pp. 1-17; Hudson, M., Lean, J., Smart, P.A., Improving control trough effective performance measures in smes (2001) Production Planning and Control, 12 (8), pp. 804-813; Hutchinson, W.M., Stolle, J.F., How to manage customer service (1968) Harvard Business Review, 46, pp. 85-96; Jackson Jr., D.W., Keith, J.E., Burdick, R.K., Examining the relative importance of physical distribution service elements (1986) Journal of Business Logistics, 7 (2), pp. 14-31; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard, , Cambridge, MA: Harvard Business School Press; Kaplan, R.S., Norton, D.P., The strategy focused organisation (2001) How Balanced Scorecard Companies Thrive in the New Business Environment, , Boston, MA: Harvard Business School Press; Kennerly, M., Neely, A., A framework of the factors affecting the evolution of performance measurement system (2002) International Journal of Operations and Management, 22 (11), pp. 1222-1245; Kennerly, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Kerssens-Van Drongelen, I.C., Fisscher, O.A.M., Ethical dilemmas in performance measurement (2003) Journal of Business Ethics, 45, pp. 51-63; Levy, M.R., Toward an optimal customer service package (1981) Journal of Business Logistics, 2 (2), pp. 87-109; Lipe, M.G., Salterio, S., A note on judgmental effects of the balanced scorecard's information organization (2002) Accounting Organization and Society, 27, pp. 531-540; Micheli, P., Kennerley, M., Performance measurement frameworks in public and non-profit sectors (2005) Production Planning & Control, 16 (2), pp. 125-134; Nahm, A., Vonderembse, M.A., Koufteros, X.A., The impact of organizational structure on time-based manufacturing and plant performance (2003) Journal of Operations Management, 21, pp. 281-306; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations and Production Management, 4, pp. 80-116; The performance-based management handbook (2001) Establishing An Integrated Performance Measurement System, 2. , Performance Based Special Interest Group (PMB SIG). Oak Ridge Institute for Science and Education (ORISE), US Department of Energy; Perrault Jr., W.D., Russ, F.A., Physical distribution service: A neglected aspect of marketing management (1974) MSU Business Topics, 22, pp. 37-45; Perrault Jr., W.D., Russ, F.A., Physical distribution service in industrial purchase decisions (1976) Journal of Marketing, 40, pp. 3-10; Roberts, F.S., (1979) Measurement Theory, , Massachusetts: Addison-Wesley; Roy, B., (1996) Multicriteria Methodology for Decision Aiding, , Dordrecht: Kluwer Academic; Saari, D.G., (1995) Basic Geometry of Voting, , Berlin: Springer (doi: 3-540-60064-7; Schein, E.H., (2004) Organizational Culture and Leadership, , San Francisco, CA: Jossey-Bass; Stephenson, P., Willett, R.P., Selling with physical distribution service: How logistics services affect product demand (1968) Business Horizons, 11 (6), pp. 75-85; Tuck, N., Zaleski, G., Criteria for developing performance measurement systems in the public sector (1994) OPMA Working Paper Series, , edited by us department of the treasury; (2010) Objective Standards of Performance, , http://www.ucop.edu/labs/labprimecontracts/LBNL/appendices/archives/ apndx_f_lbnl_m345.pdf, University of California Laboratory Administration Office, Appendix F Accessed 31 December; Vincke, P., (1992) MultiCriteria Decision Aid, , Chichester: John Wiley; Wainwright, S., (2002) Measuring Impact: A Guide to Resources, , London: NCVO; Yager, R., Filev, D.P., (1994) Essentials in Fuzzy Modeling and Control, , 4th ed. New York, NY: John Wiley},
correspondence_address1={Franceschini, F.; DIGEP, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy; email: fiorenzo.franceschini@polito.it},
publisher={Taylor and Francis Ltd.},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dey2014,
author={Dey, P.K. and Cheffi, W. and Nunes, B.},
title={Erratum: Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (Production Planning and Control (2014) 25:9 DOI: 10.1080/09537287.2012.751186)},
journal={Production Planning and Control},
year={2014},
volume={25},
number={9},
pages={i},
doi={10.1080/09537287.2012.751186},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901190007&doi=10.1080%2f09537287.2012.751186&partnerID=40&md5=f277d84ce71ebc6d694b933935683125},
affiliation={Aston Business School, Aston University, Birmingham, B4 7ET, United Kingdom; Rouen Business School, 1, rue du Maréchal Juin - BP 215, Mont Saint Aignan, 76825, France},
correspondence_address1={Cheffi, W.; Rouen Business School, 1, rue du Maréchal Juin - BP 215, Mont Saint Aignan, 76825, France},
publisher={Taylor and Francis Ltd.},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Erratum},
source={Scopus},
}

@ARTICLE{Bhattacharya2014698,
author={Bhattacharya, A. and Mohapatra, P. and Kumar, V. and Dey, P.K. and Brady, M. and Tiwari, M.K. and Nudurupati, S.S.},
title={Green supply chain performance measurement using fuzzy ANP-based balanced scorecard: A collaborative decision-making approach},
journal={Production Planning and Control},
year={2014},
volume={25},
number={8},
pages={698-714},
doi={10.1080/09537287.2013.798088},
note={cited By 191},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897916891&doi=10.1080%2f09537287.2013.798088&partnerID=40&md5=ff30d75bfe9577768450d9e4be76987c},
affiliation={Dublin City University Business School, Dublin City University, Dublin, Ireland; Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, India; Aston Business School, Aston University, Birmingham, United Kingdom; Business School, Manchester Metropolitan University, Manchester, United Kingdom},
abstract={The purpose of this paper is to delineate a green supply chain (GSC) performance measurement framework using an intra-organisational collaborative decision-making (CDM) approach. A fuzzy analytic network process (ANP)-based green-balanced scorecard (GrBSc) has been used within the CDM approach to assist in arriving at a consistent, accurate and timely data flow across all cross-functional areas of a business. A green causal relationship is established and linked to the fuzzy ANP approach. The causal relationship involves organisational commitment, eco-design, GSC process, social performance and sustainable performance constructs. Sub-constructs and sub-sub-constructs are also identified and linked to the causal relationship to form a network. The fuzzy ANP approach suitably handles the vagueness of the linguistics information of the CDM approach. The CDM approach is implemented in a UK-based carpet-manufacturing firm. The performance measurement approach, in addition to the traditional financial performance and accounting measures, aids in firms decision-making with regard to the overall organisational goals. The implemented approach assists the firm in identifying further requirements of the collaborative data across the supply-cain and information about customers and markets. Overall, the CDM-based GrBSc approach assists managers in deciding if the suppliers performances meet the industry and environment standards with effective human resource. © 2013 © 2013 Taylor & Francis.},
author_keywords={Collaborative decision-making;  Fuzzy ANP;  Green-balanced scorecard;  Performance measurement;  Supply chain},
keywords={Fuzzy logic;  Supply chains, Balanced scorecards;  Collaborative decision making;  Financial performance;  Fuzzy analytic network process;  Fuzzy ANP;  Performance measurement frameworks;  Performance measurements;  Sustainable performance, Decision making},
references={Alfalla-Luque, R., Medina-Lopez, C., Dey, P.K., Supply chain integration framework using literature review (2012) Production Planning & Control, , doi: 10.1080/ 09537287.2012.666870; Alfaro, J., Ortiz, A., Poler, R., Performance measurement system for business processes (2007) Production Planning and Control, 18 (8), pp. 641-654. , DOI 10.1080/09537280701599772, PII 785680113; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Banker, R.D., Chang, H., Janakiraman, S.N., Konstans, C., A balanced scorecard analysis of performance metrics (2004) European Journal of Operational Research, 154 (2), pp. 423-436; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Bhagwat, R., Sharma, M.K., An application of the integrated ahp-pgp model for performance measurement of supply chain management (2009) Production Planning & Control, 20 (8), pp. 678-690; Bhattacharya, A., Geraghty, J., Young, P., Supplier selection paradigm: An integrated hierarchical qfd methodology under multiple-criteria environment (2010) Applied Soft Computing, 10 (4), pp. 1013-1027; Björklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Braam, G.J.M., Nijssen, E.J., Performance effects of using the Balanced Scorecard: A note on the Dutch experience (2004) Long Range Planning, 37 (4), pp. 335-349. , DOI 10.1016/j.lrp.2004.04.007, PII S0024630104000640; Butler, J.B., Henderson, S.C., Raiborn, C., Sustainability and the balanced scorecard: Integrating green measures into business reporting (2011) Management Accounting Quarterly, 12 (2), pp. 1-10; Camarinha-Matos, L.M., Abreu, A., Performance indicators for collaborative networks based on collaboration benefits (2007) Production Planning and Control, 18 (7), pp. 592-609. , DOI 10.1080/09537280701546880, PII 781885865; Chan, F.T.S., Kumar, N., Tiwari, M.K., Lau, H.C.W., Choy, K.L., Global supplier selection: A fuzzy- ahp approach (2008) International Journal of Production Research, 46 (14), pp. 3825-3857; Cheffi, W., Dey, P.K., Green supply chain performance measurement using balance scorecard in the manufacturing industry (2012) Proceedings of the 10th International Conference on Manufacturing Research, 2, pp. 811-817. , edited by T. Baines, B. Clegg, and D. Harrison, Birmingham, September 11-13; Cohen, Y., A new technique for evaluating the balanced scorecard dashboard values (2011) Problems and Perspectives in Management, 9 (1), pp. 78-84; Cuthbertson, R., Piotrowicz, W., Supply chain best practices - Identification and categorisation of measures and benefits (2008) International Journal of Productivity and Performance Management, 57 (5), pp. 389-404; Dey, P.K., Cheffi, W., Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations (2012) Production Planning & Control, , doi: 10.1080/09537287.2013.798088; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard - Linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11 (5), pp. 269-284; Gunasekaran, A., Cheng, T.C.E., Special issue on logistics: New perspectives and challenges (2008) Omega - The International Journal of Management Science, 36 (4), pp. 505-508; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353. , DOI 10.1108/14635770510609015; Hitchcock, T., Low carbon and green supply chains: The legal drivers and commercial pressures (2012) Supply Chain Management: An International Journal, 17 (1), pp. 98-101; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: A case study from the packaging industry (2009) Production Planning & Control, 20 (1), pp. 68-81; Hsu, C.-W., Hu, A.H., Chiou, C.-Y., Chen, T.-C., Using the fdm and anp to construct a sustainability balanced scorecard for the semiconductor industry (2011) Expert Systems with Applications, 38 (10), pp. 12891-12899; Hult, G.T.M., Ketchen Jr., D.J., Cavusgil, S.T., Calantone, R.J., Knowledge as a strategic resource in supply chains (2006) Journal of Operations Management, 24 (5), pp. 458-475. , DOI 10.1016/j.jom.2005.11.009, PII S0272696305001531; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston, MA: Harvard Business School Press; Ketokivi, M., Heikkilä, J., A strategic management system for manufacturing: Linking action to performance (2003) Production Planning & Control, 14 (6), pp. 487-496; Länsiluoto, A., Järvenpää, M., Greening the balanced scorecard (2010) Business Horizons, 53 (4), pp. 385-395; Lapide, L., What about measuring supply chain performance? White paper (2000) ASCET, 2 (15), pp. 287-297. , http://ftp.gunadarma.ac.id/idkf/idkfwireless/aplikasi/e-commerce/lapide. pdf, Accessed January 3, 2013; Lee, A.H.I., Chen, W.-C., Chang, C.-J., A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan (2008) Expert Systems with Applications, 34 (1), pp. 96-107. , DOI 10.1016/j.eswa.2006.08.022, PII S0957417406002582; Leung, L.C., Lam, K.C., Cao, D., Implementing the balanced scorecard using the analytic hierarchy process & the analytic network process (2006) Journal of the Operational Research Society, 57 (6), pp. 682-691. , DOI 10.1057/palgrave.jors.2602040, PII 2602040; Maccarthy, B.L., Jayarathne, P.G.S.A., Sustainable collaborative supply networks in the international clothing industry: A comparative analysis of two retailers (2012) Production Planning & Control, 23 (4), pp. 252-268; Malmi, T., Balanced scorecards in Finnish companies: A research note (2001) Management Accounting Research, 12 (2), pp. 207-220. , DOI 10.1006/mare.2000.0154, PII S1044500500901549; Martinsons, M., Davison, R., Tse, D., The balanced scorecard: A foundation for the strategic management of information systems (1999) Decision Support Systems, 25 (1), pp. 71-88; Mendes, P., Santos, A.C., Perna, F., Teixeira, M.R., The balanced scorecard as an integrated model applied to the portuguese public service: A case study in the waste sector (2012) Journal of Cleaner Production, 24 (MARCH), pp. 20-29; Mettanen, P., Design and implementation of a performance measurement system for a research organization (2005) Production Planning and Control, 16 (2), pp. 178-188. , DOI 10.1080/09537280512331333075, PII P30454660410; Moller, A., Schaltegger, S., The sustainability balanced scorecard as a framework for eco-efficiency analysis (2005) Journal of Industrial Ecology, 9 (4), pp. 73-83. , http://www.mitpressjournals.org/doi/pdf/10.1162/108819805775247927, DOI 10.1162/108819805775247927; Olugu, E.-U., Wong, K.-Y., Shaharoun, A.-M., Development of key performance measures for the automobile green supply chain (2010) Resources Conservation and Recycling, 65 (6), pp. 567-579; Rao, P., Greening the supply chain: A new initiative in south east asia (2002) International Journal of Operations & Production Management, 22 (6), pp. 632-655; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916. , DOI 10.1108/01443570510613956; Ravi, V., Shankar, R., Tiwari, M.K., Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach (2005) Computers and Industrial Engineering, 48 (2), pp. 327-356. , DOI 10.1016/j.cie.2005.01.017, PII S0360835205000185; Saaty, T.L., (1980) The Analytic Hierarchy Process, , New York: McGraw-Hill; Saaty, T.L., (1996) Decision Making with Dependence and Feedback: The Analytic Network Process, , Pittsburgh, PA: RWS; Sawalqa, F.A., Holloway, D., Alam, M., Balanced scorecard implementation in jordan (2011) International Journal of Electronic Business Management, 9 (3), pp. 196-210; Shi, V.G., Koh, S.C.L., Baldwin, J., Cucchiella, F., Natural resource based green supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 54-67; Stwart, R.A., Mohamed, S., Utilizing the balanced scorecard for it/is performance evaluation in construction (2001) Construction Innovation, 1 (3), pp. 147-163; Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R., Development of a balanced scorecard: An integrated approach of Interpretive Structural Modeling (ISM) and Analytic Network Process (ANP) (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 25-59. , DOI 10.1108/17410400710717073; Thipparat, T., Evaluation of construction green supply chain management (2011) International Conference on Innovation Manage and Service, 14, pp. 209-213; Tseng, M.-L., Implementation and performance evaluation using the fuzzy network balanced scorecard (2010) Computers & Education, 55 (1), pp. 188-201; Tsoulfas, G., Pappis, C., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; Ulbrich, S., Troitzsch, H., Anker Den F.Van, Plüss, A., Huber, C., How teams in networked organisations develop collaborative capability: Processes, critical incidents and success factors (2011) Production Planning & Control, 22 (5-6), pp. 488-500; Van Hoek, R.I., From reversed logistics to green supply chains (1999) Supply Chain Management: An International Journal, 4 (3), pp. 129-135; Verdecho, M.J., Alfaro, J.J., Rodriguez-Rodriguez, R., Foundations for collaborative performance measurement (2009) Production Planning & Control, 20 (3), pp. 193-205; Wu, H.-Y., Tzeng, G.-H., Chen, Y.-H., A fuzzy mcdm approach for evaluating banking performance based on balanced scorecard (2009) Expert Systems with Applications, 36 (6), pp. 10135-10147; Wynder, M., Chemico: Evaluating performance based on the balanced scorecard (2010) Journal of Accounting Education, 28 (3-4), pp. 221-236; Yang, J., A structural model of supply chain performance in an emerging economy (2012) International Journal of Production Research, 50 (14), pp. 3895-3903; Yuan, F.-C., Chiu, C., A hierarchical design of case-based reasoning in the balanced scorecard application (2009) Expert Systems with Applications, 36 (1), pp. 333-342; Yüksel, Ì., Daǧdeviren, M., Using the fuzzy analytic network process (anp) for balanced scorecard (bsc): A case study for a manufacturing firm (2010) Expert Systems with Applications, 37 (2), pp. 1270-1278; Zhu, Q., Sarkis, J., Lai, K.-h., Green supply chain management: Pressures, practices and performance within the Chinese automobile industry (2007) Journal of Cleaner Production, 15 (11-12), pp. 1041-1052. , DOI 10.1016/j.jclepro.2006.05.021, PII S0959652606002411; Zhu, Q., Sarkis, J., Lai, K.-h., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) Journal of Environmental Management, 85 (1), pp. 179-189. , DOI 10.1016/j.jenvman.2006.09.003, PII S0301479706002465; Zhu, Q., Sarkis, J., Lai, K.-H., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111 (2), pp. 261-273. , DOI 10.1016/j.ijpe.2006.11.029, PII S0925527307001855},
correspondence_address1={Kumar, V.; Dublin City University Business School, , Dublin, Ireland; email: vikas.kumar@dcu.ie},
publisher={Taylor and Francis Ltd.},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cucchiella20141268,
author={Cucchiella, F. and Dadamo, I. and Gastaldi, M. and Koh, S.C.L.},
title={Implementation of a real option in a sustainable supply chain: An empirical study of alkaline battery recycling},
journal={International Journal of Systems Science},
year={2014},
volume={45},
number={6},
pages={1268-1282},
doi={10.1080/00207721.2012.761458},
note={cited By 34},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897927518&doi=10.1080%2f00207721.2012.761458&partnerID=40&md5=1a48f46e07bad8bec702b0b42c6a8ae3},
affiliation={Department of Electric and Information Engineering, Faculty of Engineering, University of l'Aquila, L'Aquila, Italy; Logistics and Supply Chain Management Research Group, Management School, University of Sheffield, Sheffield, United Kingdom},
abstract={Green supply chain management (GSCM) has emerged as a key approach for enterprises seeking to become environmentally sustainable. This paper aims to evaluate and describe the advantages of a GSCM approach by analysing practices and performance consequences in the battery recycling sector. It seeks to integrate works in supply chain management (SCM), environmental management, performance management and real option (RO) theory into one framework. In particular, life cycle assessment (LCA) is applied to evaluate the environmental impact of a battery recycling plant project, and life cycle costing (LCC) is applied to evaluate its economic impact. Firms, also understanding the relevance of GSCM, have often avoided applying the green principles because of the elevated costs that such management involved. Such costs could also seem superior to the potential advantages since standard performance measurement systems are internally and business focused; for these reasons, we consider all the possible value deriving also by uncertainty associated to a green project using the RO theory. This work is one of the few and pioneering efforts to investigate GSCM practices in the battery recycling sector. © 2013 Taylor and Francis.},
author_keywords={alkaline batteries;  real options;  recycling;  sustainable supply chain},
keywords={Alkaline batteries;  Green supply chain management;  Life Cycle Assessment (LCA);  Performance management;  Real Options;  Standard performance;  Supply chain managements (SCM);  Sustainable supply chains, Electric batteries;  Environmental impact;  Industry;  Life cycle;  Recycling;  Supply chain management, Environmental management},
references={Antonioli, B., Filippini, M., Optimal size in the waste collection sector (2002) Review of Industrial Organization, 20 (3), pp. 239-252. , DOI 10.1023/A:1015043524679; Bennett, M., James, P., Environmental-related management accounting: Current practice and future trends (1997) Greener Management International, 17, pp. 33-51; Broer, S., Titheridge, H., Enabling low-carbon living in new UK housing developments (2010) Management of Environmental Quality, 21, pp. 90-107; Busch, T., Hoffmann, V.H., Sustainability related uncertainties and real options (2006) September, 28-30. , Paper presented at the IFSAM 8th World Congress Berlin, Germany; Buttol, P., Masoni, P., Bonoli, A., Goldoni, S., Belladonna, V., Cavazzuti, C., LCA of integrated MSW management systems: Case study of the Bologna District (2007) Waste Management, 27 (8), pp. 1059-1070. , DOI 10.1016/j.wasman.2007.02.010, PII S0956053X07000748, Life Cycle Assessment in Waste Management; Caputo, A.C., Cucchiella, F., Fratocchi, L., Pelagagge, P.M., An integrated framework for e-supply networks analysis (2005) Supply Chain Management, 10 (2), pp. 84-95. , DOI 10.1108/13598540510589160; Carter, C.R., Kale, R., Grimm, C.M., Environmental purchasing and firm performance: An empirical investigation (2000) Transportation Research Part E: Logistics and Transportation Review, 36 (3), pp. 219-228. , DOI 10.1016/S1366-5545(99)00034-4, PII S1366554599000344; Cucchiella, F., D'Adamo, I., Feasibility study of developing photovoltaic power projects in italy: An integrated approach (2012) Renewable & Sustainable Energy Reviews, 16, pp. 1562-1576; Cucchiella, F., D'Adamo, I., Estimation of the energetic and environmental impacts of a roof-mounted building-integrated photovoltaic systems (2012) Renewable and Sustainable Energy Reviews, 16, pp. 5245-5259; Cucchiella, F., D'Adamo, I., Gastaldi, M., Highway project management through real option (2008) Journal of European Economy, 7, pp. 38-54; Cucchiella, F., D'Adamo, I., Gastaldi, M., Green supply chain and the energy recovery plant in abruzzo (2011) Procedia-Social and Behavioral Sciences, 25, pp. 54-72; Cucchiella, F., Gastaldi, M., Risk management in supply chain: A real option approach (2006) Journal of Manufacturing Technology Management, 17 (6), pp. 700-720. , DOI 10.1108/17410380610678756, e and hyphen;Technology and manufacturing enterprise competitiveness; Day, J.M., Danielwright, P., Schoenherr, T., Venkataramanan, M., Gaudette, K., Improving routing and scheduling decisions at a distributor of industrial gasses (2009) Omega, 37, pp. 227-237; Deane, J.K., Craighead, C.W., Ragsdale, C.T., Mitigating environmental and density risk in global sourcing (2009) International Journal of Physical Distribution & Logistics Management, 39, pp. 861-883; Defee, C.C., Esper, T., Mollenkopf, D., Leveraging closed-loop orientation and leadership for environmental sustainability (2009) Supply Chain Management, 14, pp. 87-98; Dorini, G., Kapelan, Z., Azapagic, A., Managing uncertainty in multiple-criteria decision making related to sustainability assessment (2011) Clean Technologies and Environmental Policy, 13, pp. 133-139; Driouchi, T., Leseure, M., Bennett, D., A robustness framework for monitoring real options under uncertainty (2009) Omega, 37, pp. 698-710; Elkington, J., Towards the sustainable corporation: Win-win-win business strategies for sustainable development (1994) California Management Review, 36, pp. 90-100; (2008) Stagnation of the Quantities of Used Portable Batteries Recycled in 2008, , European Battery Recycling Association (EBRA) Press release EBRA 010909. Berchem: EBRA; Ferella, F., De Michelisa, I., Veglìo, F., Process for the recycling of alkaline and zinc-carbon spent batteries (2008) Journal of Power Sources, 183, pp. 805-811; Ferella, F., Furlani, G., Navarra, M., De Michelis, I., Pagnanelli, F., Beolchini, F., Toro, L., Veglìo, F., Hydrometallurgical plant to recycle alkaline and zn-c spent batteries: Process and economic analysis (2008) WasteEng08:2nd International Conference on Engineering for Waste Valorisation, , Paper presented at the June 3-5 Patras, Greece; Flint, D.J., Golicic, S.L., Searching for competitive advantage through sustainability: A qualitative study in the new zealand wine industry (2009) International Journal of Physical Distribution & Logistics Management, 39, pp. 841-860; Freitas, M.B.J.G., Pegoretti, V.C., Pietre, M.K., Recycling manganese from spent Zn-MnO2 primary batteries (2007) Journal of Power Sources, 164 (2), pp. 947-952. , DOI 10.1016/j.jpowsour.2006.10.050, PII S0378775306021550; Green Jr., K.W., Whitten, D., Inman, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply Chain Management, 13, pp. 317-327; Handfield, R., Sroufe, R., Walton, S., Integrating environmental management and supply chain strategies (2005) Business Strategy and the Environment, 14 (1), pp. 1-19. , DOI 10.1002/bse.422; Hasna, A.M., Dimensions of sustainability (2007) Journal of Engineering for Sustainable Development: Energy, Environment, and Health, 2, pp. 47-57; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353. , DOI 10.1108/14635770510609015; Holt, D., Ghobadian, A., An empirical study of green supply chain management practices amongst uk manufacturers (2009) Journal of Manufacturing Technology Management, 20, pp. 933-956; Hunkeler, D., Rebitzer, G., Life cycle costing - Paving the road to sustainable development? (2003) International Journal of Life Cycle Assessment, 8 (2), pp. 109-110; Kish, R.J., Weng, W., Valuing investment projects with expansion options (2005) International Journal of Managerial Finance, 1 (3), pp. 164-186. , DOI 10.1108/17439130510619622; Lee, S.Y., Drivers for the participation of small and medium-sized suppliers in green supply chain initiatives (2008) Supply Chain Management, 13, pp. 185-198; Liamsanguan, C., Gheewala, S.H., LCA: A decision support tool for environmental assessment of MSW management systems (2007) Journal of Environmental Management, 1, pp. 132-138; Markley, M.J., Davis, L., Exploring future competitive advantage through sustainable supply chains (2007) International Journal of Physical Distribution & Logistics Management, 37, pp. 763-774; Piplani, R., Pujawan, N., Ray, S., Sustainable supply chain management (2008) International Journal of Production Economics, 111 (2), pp. 193-194. , DOI 10.1016/j.ijpe.2007.05.001, PII S0925527307001806; Ravn, M.O., Mazzenga, E., International business cycles: The quantitative role of transportation costs (2004) Journal of International Money and Finance, 23 (4), pp. 645-671. , DOI 10.1016/j.jimonfin.2004.03.001, PII S0261560604000178; Sarkis, J., Tamarkin, M., Real options analysis for "green trading": The case of greenhouse gases (2005) Engineering Economist, 50 (3), pp. 273-294. , DOI 10.1080/00137910500227208, PII J5127013033X3; Shi, V.G., Koh, S.C.L., Baldwin, J., Cucchiella, F., Natural resource based green supply chain management (2012) Supply Chain Management An International Journal, 17, pp. 54-67; Shukla, A.C., Deshmukh, S.G., Kanda, A., Environmentally responsive supply chains: Learnings from the indian auto sector (2009) Journal of Advances in Management Research, 6, pp. 154-171; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9, pp. 53-80; Toro, L., Veglìo, F., Beolchini, F., Paganelli, F., Zanetti, M., Furlani, G., (2004) Process and Plant for the Treatment of Run-down Batteries, , European Patent Application EP1684369; Tsai, W.-H., Hung, S.-J., Dynamic pricing and revenue management process in internet retailing under uncertainty: An integrated real options approach (2009) Omega, 37, pp. 471-481; Vatistas, N., Bartolozzi, M., Arras, S., The dismantling of the spent alkaline zinc manganese dioxide batteries and the recovery of the zinc from the anodic material (2001) Journal of Power Sources, 101 (2), pp. 182-187. , DOI 10.1016/S0378-7753(01)00710-8, PII S0378775301007108; Visich, J.K., Li, S., Khumawala, B.M., Reyes, P.M., Empirical evidence of RFID impacts on supply chain performance (2009) International Journal of Operations&Production Management, 29, pp. 1290-1315; Wycherley, I., Greening supply chains: The case of the body shop international (1999) Business Strategy and the Environment, 8 (2), pp. 120-127; Zhu, Q., Sarkis, J., Cordeiro, J.J., Lai, K.-H., Firm-level correlates of emergent green supply chain management practices in the chinese context (2008) Omega, 36, pp. 577-591},
correspondence_address1={Cucchiella, F.; Department of Electric and Information Engineering, , L'Aquila, Italy; email: federica.cucchiella@univaq.it},
issn={00207721},
coden={IJSYA},
language={English},
abbrev_source_title={Int J Syst Sci},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wong20141306,
author={Wong, D.W.C. and Choy, K.L. and Chow, H.K.H. and Lin, C.},
title={Assessing a cross-border logistics policy using a performance measurement system framework: The case of Hong Kong and the pearl river delta region},
journal={International Journal of Systems Science},
year={2014},
volume={45},
number={6},
pages={1306-1320},
doi={10.1080/00207721.2012.761468},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897920604&doi=10.1080%2f00207721.2012.761468&partnerID=40&md5=b84b6df2aaa13c955d48e16815ad0d06},
affiliation={Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong; Faculty of Management and Administration, Macau University of Science and Technology, Taipa , Macau, Macau},
abstract={For the most rapidly growing economic entity in the world, China, a new logistics operation called the indirect cross-border supply chain model has recently emerged. The primary idea of this model is to reduce logistics costs by storing goods at a bonded warehouse with low storage cost in certain Chinese regions, such as the Pearl River Delta (PRD). This research proposes a performance measurement system (PMS) framework to assess the direct and indirect cross-border supply chain models. The PMS covers four categories including cost, time, quality and flexibility in the assessment of the performance of direct and indirect models. Furthermore, a survey was conducted to investigate the logistics performance of third party logistics (3PLs) at the PRD regions, including Guangzhou, Shenzhen and Hong Kong. The significance of the proposed PMS framework allows 3PLs accurately pinpoint the weakness and strengths of it current operations policy at four major performance measurement categories. Hence, this helps 3PLs further enhance the competitiveness and operations efficiency through better resources allocation at the area of warehousing and transportation. © 2013 Taylor and Francis.},
author_keywords={case study;  cross-border supply chain;  logistics operations policy;  performance measurement system;  third party logistics},
keywords={Costs;  Outsourcing;  Supply chains, Cross-border;  Direct and indirect models;  Logistics operations;  Operations efficiencies;  Pearl River Delta region;  Performance measurement system;  Performance measurements;  Third party logistics, Warehouses},
references={Berglund, M., Van Laarhoven, P., Sharman, G., Wandel, S., Third party logistics: Is there a future? (1999) International Journal of Logistics Management, 10, pp. 59-70; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , DOI 10.1108/01443570010330739; Brah, S.A., Lim, H.Y., The effects of technology and TQM on the performance of logistics companies (2006) International Journal of Physical Distribution and Logistics Management, 36 (3), pp. 192-209. , DOI 10.1108/09600030610661796; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1990) The International Journal of Logistics Management, 6, pp. 61-74; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2002) Integrated Manufacturing Systems, 14, pp. 179-190; Chow, H.K.H., Choy, K.L., Lee, W.B., Lau, K.C., Design of a RFID case-based resource management system for warehouse operations (2006) Expert Systems with Applications, 30 (4), pp. 561-576. , DOI 10.1016/j.eswa.2005.07.023, PII S0957417405001454; Chow, T., Trevor Heaver G, D., Lennart, E., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution and Logistics Management, 24, pp. 17-28; Christopher, M., Magrill, L., Wills, G., Educational development for marketing logistics (1998) International Journal of Physical Distribution and Logistics Management, 28, pp. 234-241; Cook, T.J., Vansant, J., Stewart, L., Adrian, J., Performance measurement: Lessons learned for development management (1995) World Development, 23 (8), pp. 1303-1315. , DOI 10.1016/0305-750X(95)00050-M; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1989) National Production Review, 8, pp. 23-33; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge-Measuring Operating for World-Class Competition, , Homewood IL: Dow Jones-Irwin; Eccles, R.G., Pyburn, P.J., Creating a comprehensive system to measure performance (1992) Management Accounting (US), 74, pp. 41-44; Enright, M.J., Scott, E.E., Chang, K.M., (2005) The Greater Pearl River Delta and the Risk of China, , Singapore: John Wiley & Sons; Fawcett, S.E., Cooper, M.B., Logistics Performance Measurement and Customer Success (1998) Industrial Marketing Management, 27 (4), pp. 341-357. , PII S0019850197000783; Gunasekaran, A.P.C., Tirtiroglu, E., Performance measurement metrics in a supply chain management (2001) International Journal of Operation and Production Management, 21, pp. 71-87; Hall, R., (1991) Organizations: Structures, Processes and Outcomes, p. 267. , New York and London: Prentice-Hall; Hsiao, H.I., Kemp, R.G.M., Van Der Vorst, J.G.A.J., Omta, S.W.F., A classification of logistic outsourcing levels and their impact on service performance: Evidence from the food processing industry (2010) International Journal of Production Economics, 124, pp. 75-86; Langley, J.C., Allen, R.G., Tyndall, G.R., (2001) Third-Party-Logistics Services: View from the Customers, , Atlanta, GA: Georgia Institute of Technology with Cap Gemini, Ernst & Young and Ryder System, Inc; Lai, K.-H., Cheng, T.C.E., Supply Chain Performance in Transport Logistics: An Assessment by Service Providers (2003) International Journal Of Logistics, 6 (3), pp. 151-164. , DOI 10.1080/367556031000123115; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., An empirical study of supply chain performance in transport logistics (2004) International Journal of Production Economics, 87, pp. 321-331; Lin, C.L., Lyons, A.C., An analysis of third-party logistics performance and service provision (2011) Transportation Research Part e, 47, pp. 547-570; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Keung, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Quality Management, 11, pp. 67-85; Krauth, E., Moonen, H., Popova, V., Schut, M., Performance measurement and control in logistics service providing (2005) Proceedings of 7th International Conference on Enterprise Information Systems, ICEIS, pp. 239-247; Leung, S.C.H., Wu, Y., Lai, K.K., Cross-border logistics with fleet management: A goal programming approach (2006) Computers and Industrial Engineering, 50 (3), pp. 263-272. , DOI 10.1016/j.cie.2006.04.003, PII S0360835206000313; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22, pp. 209-217; Mentzer, J.T., Konard, P.B., An efficiency effectiveness approach to logistics performance analysis (1991) Journal of Business Logistics, pp. 33-62; Moberg, C.R., Speh, T.W., Evaluating the relationship between questionable business practices and the strength of supply chain (2004) Journal of Business Logistics, 3, pp. 12-17; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations and Production Management, 17, pp. 1131-1152; Panayides, P.M., The impact of organizational learning on relationship orientation, logistics service effectiveness and performance (2007) Industrial Marketing Management, 36 (1), pp. 68-80. , DOI 10.1016/j.indmarman.2005.07.001, PII S001985010500115X; Rose, K.H., A performance measurement method (1995) Quality Progress, 28, pp. 63-66; Shen, J., Wong, K.-Y., Feng, Z., State-sponsored and spontaneous urbanization in the Pearl River Delta of South China, 1980-1998 (2002) Urban Geography, 23 (7), pp. 674-694; Sink, H.L., Langley, C.J., Gibson, B.J., Buyer observations of theus third-party logistics market (1996) International Journal of Physical Distribution and Logistics Management, 26, pp. 38-46; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations and Production Management, 21, pp. 15-29; Vitale, M., Vitale, S.C., Hauser, M., New process/financial scorecard: A strategic performance measurement system (1994) Planning Review, 22, pp. 12-16; Wisner, J.D., Fawcett, S.E., Link firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, Third Quarter, pp. 5-11; Yeung, J.H.Y., Selen, W., Sum, C., Huo, B., Linking financial performance to strategic orientation and operational priorities: An empirical study of third-party logistics providers (2006) International Journal of Physical Distribution and Logistics Management, 36 (3), pp. 210-230. , DOI 10.1108/09600030610661804; Zhang, Q., Vonderembse, M.A., Lim, J.-S., Logistics flexibility and its impact on customer satisfaction (2005) International Journal Of Logistics Management, pp. 71-95},
correspondence_address1={Choy, K.L.; Department of Industrial and Systems Engineering, , Hong Kong, Hong Kong; email: klchoy@inet.polyu.edu.hk},
publisher={Taylor and Francis Ltd.},
issn={00207721},
coden={IJSYA},
language={English},
abbrev_source_title={Int J Syst Sci},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bai2014275,
author={Bai, C. and Sarkis, J.},
title={Determining and applying sustainable supplier key performance indicators},
journal={Supply Chain Management},
year={2014},
volume={19},
number={3},
pages={275-291},
doi={10.1108/SCM-12-2013-0441},
note={cited By 166},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919474556&doi=10.1108%2fSCM-12-2013-0441&partnerID=40&md5=4756c8553e88d937818bc0d8629edb97},
affiliation={School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, China; WPI School of Business, Worcester Polytechnic Institute, Worcester, MA, United States},
abstract={Purpose: The purpose of this paper is to introduce a methodology to identify sustainable supply chain key performance indicators (KPI) that can then be used for sustainability performance evaluation for suppliers. Design/methodology/approach: Initially the complexity of sustainable supply chain performance measurement is discussed. Then, a two-stage method utilizing neighborhood rough set theory to identify KPI and data envelopment analysis (DEA) to benchmark and evaluate relative performance using the KPI is completed. Additional analysis is performed to determine the sensitivity of the KPI set formation and performance results. Findings: The results show that KPI can be determined using neighborhood rough set, and DEA performance results provide insight into relative performance of suppliers. The supply chain sustainability performance results from both the neighborhood rough set and DEA can be quite sensitive parameters selected and sustainability KPI sets that were determined. Research limitations/implications: The data utilized in this study are illustrative and simulated. Only one model for the neighborhood rough set and DEA was utilized. Additional investigations using a variation of rough set and DEA models can be completed. Practical implications: This tool set is valuable for managers to help identify sustainable supply chain KPI (from among hundreds of potential measures) and evaluate sustainability performance of various units within supply chains, including supply chain partners, departments, projects and programs. Social implications: Sustainability incorporates many business, economic and social implications. The methods introduced in this paper can help organizations and their supply chains become more strategically and operationally sustainable. Originality/value: Few tools and techniques exist in the sustainable supply chain literature to help develop KPIs and evaluate sustainability performance of suppliers and the supply chain. This paper is one of the first that integrates neighborhood rough set and DEA to address this important sustainable supply chain performance measurement issue. © Emerald Group Publishing Limited.},
author_keywords={Data envelopment analysis;  Green supply chains;  Key performance indicators;  Performance measurement;  Rough set theory;  Sustainable supply chains},
references={Antai, I., Supply chain vs supply chain competition: A niche-based approach (2011) Management Research Review, 34 (10), pp. 1107-1124; Antai, I., Olson, H., Interaction: A new focus for supply chain vs. supply chain competition (2013) International Journal of Physical Distribution and Logistics Management, 43 (7), pp. 511-528; Ashby, A., Leat, M., Hudson-Smith, M., Making connections: A review of supply chain management and sustainability literature (2012) Supply Chain Management: An International Journal, 17 (5), pp. 497-516; Audia, P.G., Brion, S., Reluctant to change: Self-enhancing responses to diverging performance measures (2007) Organizational Behavior and Human Decision Processes, 102 (2), pp. 255-269; Azadeh, A., Saberi, M., Moghaddam, R.T., Javanmardi, L., An integrated data envelopment analysis-artificial neural network-rough set algorithm for assessment of personnel efficiency (2011) Expert Systems with Applications, 38 (3), pp. 1364-1373; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Bai, C., Sarkis, J., Evaluating supplier development programs with a grey based rough set methodology (2011) Expert Systems with Applications, 38 (11), pp. 13505-13517; Bai, C., Sarkis, J., Supply-chain performance-measurement system management using neighbourhood rough sets (2012) International Journal of Production Research, 50 (9), pp. 2484-2500; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Banker, R.D., Estimating most productive scale size using data envelopment analysis (1984) European Journal of Operational Research, 17 (1), pp. 35-44; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bentes, A.V., Carneiro, J., da Silva, J.F., Kimura, H., Multidimensional assessment of organizational performance: Integrating BSC and AHP (2012) Journal of Business Research, 65 (12), pp. 1790-1799; Beynon, M., Curry, B., Morgan, P., Classification and rule induction using rough set theory (2000) Expert Systems, 17 (3), pp. 136-148; Cabral, I., Grilo, A., Cruz-Machado, V., A decision-making model for lean, agile, resilient and green supply chain management (2012) International Journal of Production Research, 50 (17), pp. 4830-4845; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Charnes, A., (1994) Data Envelopment Analysis: Theory, Methodology and Applications, , Springer, Netherlands; Charnes, A., Cooper, W.W., Rhodes, E., Measurement in the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Chen, M.C., Yang, T., Yen, C.T., Investigating the value of information sharing in multi-echelon supply chains (2007) Quality and Quantity, 41 (3), pp. 497-511; Colicchia, C., Melacini, M., Perotti, S., Benchmarking supply chain sustainability: Insights from a field study (2011) Benchmarking: An International Journal, 18 (5), pp. 705-732; Cooper, W.W., Seiford, L.M., Tone, K., (2007) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, , 2nd ed., Springer, Heidelberg; Daniel, D.R., Management information crisis (1961) Harvard Business Review, 39 (5), pp. 111-121; Dinter, B., Success factors for information logistics strategy-An empirical investigation (2013) Decision Support Systems, 54 (3), pp. 1207-1218; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M., Design and optimization of integrated E-supply chain for agile and environmentally conscious manufacturing (2006) IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 36 (1), pp. 62-75; Epstein, M.J., Wisner, P.S., Using a balanced scorecard to implement sustainability (2001) Environmental Quality Management, 11 (2), pp. 1-10; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard-linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11 (5), pp. 269-284; Genovese, A., Lenny Koh, S.C., Kumar, N., Tripathi, P.K., Exploring the challenges in implementing supplier environmental performance measurement models: A case study (2013) Production Planning and Control, pp. 1-14; Gimenez, C., Tachizawa, E.M., Extending sustainability to suppliers: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (5), pp. 531-543; Grimm, J.H., Hofstetter, J.S., Sarkis, J., Critical factors for sub-supplier management: A sustainable food supply chains perspective (2013) International Journal of Production Economics, 152, pp. 159-173; Gulledge, T., Chavusholu, T., Automating the construction of supply chain key performance indicators (2008) Industrial Management and Data Systems, 108 (6), pp. 750-774; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hu, Q., Yu, D., Liu, J., Wu, C., Neighborhood rough set based heterogeneous feature subset selection (2008) Information Sciences, 178 (18), pp. 3577-3594; Hu, Q., Yu, D., Xie, Z., Neighborhood classifiers (2008) Expert Systems with Applications, 34 (2), pp. 866-876; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Kannan, G., Rajendran, S., Sarkis, J., Murugesan, P., Multi criteria decision making approaches for green supplier evaluation and selection: A literature review (2013) Journal of Cleaner Production, , available online 13 July 2013; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston, MA; Kleijnen, J.P., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Lee, K.H., Saen, R.F., Measuring corporate sustainability management: A data envelopment analysis approach (2012) International Journal of Production Economics, 140 (1), pp. 219-226; Liang, J.Y., Shi, Z.Z., The information entropy, rough entropy and knowledge granulation in rough set theory (2004) International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12 (1), pp. 37-46; Liu, J., Ding, F.Y., Lall, V., Using data envelopment analysis to compare suppliers for supplier selection and performance improvement (2000) Supply Chain Management: An International Journal, 5 (4), pp. 143-150; Manning, L., Corporate and consumer social responsibility in the food supply chain (2013) British Food Journal, 115 (1), pp. 9-29; Mirhedayatian, S.M., Azadi, M., Saen, R.F., A novel network data envelopment analysis model for evaluating green supply chain management (2014) International Journal of Production Economics, 147, pp. 544-554; Möller, A., Schaltegger, S., The sustainability balanced scorecard as a framework for eco-efficiency analysis (2005) Journal of Industrial Ecology, 9 (4), pp. 73-83; Nataraja, N., Johnson, A.L., Guidelines for using variable selection techniques in data envelopment analysis (2011) European Journal of Operational Research, 215 (3), pp. 662-669; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Ong, C.S., Huang, J.J., Tzeng, G.H., Building credit scoring models using genetic programming (2005) Expert Systems with Applications, 29 (1), pp. 41-47; Pawlak, Z., Rough sets (1982) International Journal of Computer and Information Sciences, 11 (5), pp. 341-356; Qian, Y., Liang, J., Dang, C., Incomplete multigranulation rough set (2010) IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 40 (2), pp. 420-431; Richard, P.J., Devinney, T.M., Yip, G.S., Johnson, G., Measuring organizational performance: Towards methodological best practice (2009) Journal of Management, 35 (3), pp. 718-804; La Rovere, E.L., Soares, J.B., Oliveira, L.B., Lauria, T., Sustainable expansion of electricity sector: Sustainability indicators as an instrument to support decision-making (2010) Renewable and Sustainable Energy Reviews, 14 (1), pp. 422-429; Sá, J.A., Some empirical evidence on a contingency theory of success factors (1988) European Management Journal, 6 (3), pp. 236-249; Sadoyan, H., Zakarian, A., Mohanty, P., Data mining algorithm for manufacturing process control (2006) The International Journal of Advanced Manufacturing Technology, 28 (3), pp. 342-350; Sarkis, J., A comparative analysis of DEA as a discrete alternative multiple criteria decision tool (2000) European Journal of Operational Research, 123 (3), pp. 543-557; Sarkis, J., (2006) Greening the Supply Chain, , Springer, Berlin; Sarkis, J., Preparing your data for DEA (2007) Modelling Data Irregulaties Structural Complexities in Data Development Analysis, pp. 305-320. , in Zhu, J. and ve Cook, W.D. (Eds), Springer, New York, NY; Sarkis, J., Benchmarking the greening of business (2010) Benchmarking: An International Journal, 17 (3), pp. 317-319; Sarkis, J., Cordeiro, J.J., Ecological modernization in the electrical utility industry: An application of a bads-goods DEA model of ecological and technical efficiency (2012) European Journal of Operational Research, 219 (2), pp. 386-395; Sarkis, J., Talluri, S., A model for strategic supplier selection (2002) Journal of Supply Chain Management, 38 (1), pp. 18-28; Sarkis, J., Talluri, S., Ecoefficiency measurement using data envelopment analysis: Research and practitioner issues (2004) Journal of Environmental Assessment Policy and Management, 6 (1), pp. 91-123; Seuring, S., A review of modeling approaches for sustainable supply chain management (2013) Decision Support Systems, 54 (4), pp. 1513-1520; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shyng, J.Y., Wang, F.K., Tzeng, G.H., Wu, K.S., Rough set theory in analyzing the attributes of combination values for the insurance market (2007) Expert Systems with Applications, 32 (1), pp. 56-64; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40 (16), pp. 4257-4269; Tone, K., A slacks-based measure of super-efficiency in data envelopment analysis (2002) European Journal of Operational Research, 143 (1), pp. 32-41; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; Van Passel, S., Van Huylenbroeck, G., Lauwers, L., Mathijs, E., Sustainable value assessment of farms using frontier efficiency benchmarks (2009) Journal of Environmental Management, 90 (10), pp. 3057-3069; Vasconcellos e Sá, J., The impact of key success factors on company performance (1988) Long Range Planning, 21 (6), pp. 56-64; Wilson, D.R., Martinez, T.R., Improved heterogeneous distance functions (1997) Journal of Artificial Intelligence Research, 6 (1), 34p; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51; Xu, J., Li, B., Wu, D., Rough data envelopment analysis and its application to supply chain performance evaluation (2009) International Journal of Production Economics, 122 (2), pp. 628-638; Yakovleva, N., Sarkis, J., Sloan, T., Sustainable benchmarking of supply chains: The case of the food industry (2012) International Journal of Production Research, 50 (5), pp. 1297-1317; Yeh, C.C., Chi, D.J., Hsu, M.F., A hybrid approach of DEA, rough set and support vector machines for business failure prediction (2010) Expert Systems with Applications, 37 (2), pp. 1535-1541; Zhu, Q., Dou, Y., Sarkis, J., A portfolio-based analysis for green supplier management using the analytical network process (2010) Supply Chain Management: An International Journal, 15 (4), pp. 306-319},
correspondence_address1={Sarkis, J.; WPI School of Business, United States; email: jsarkis@wpi.edu},
publisher={Emerald Group Holdings Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Schaltegger2014232,
author={Schaltegger, S. and Burritt, R.},
title={Measuring and managing sustainability performance of supply chains: Review and sustainability supply chain management framework},
journal={Supply Chain Management},
year={2014},
volume={19},
number={3},
pages={232-241},
doi={10.1108/SCM-02-2014-0061},
note={cited By 209},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84907979289&doi=10.1108%2fSCM-02-2014-0061&partnerID=40&md5=229d9db569ab278a917fd93279d875e1},
affiliation={Centre for Sustainability Management (CSM), Leuphana University Lüneburg, Lüneburg, Germany; Department of Accounting and Corporate Governance, Macquarie University, Sydney, NSW, Australia},
abstract={Purpose – The purpose of this paper is to discuss what a fully sustainable supply chain might look like and the consequences that can be drawn from this view. Also, it develops a practical approach towards sustainability supply chain performance measurement and management. The paper develops an analytical framework for the assessment of approaches for the measurement and management of sustainability performance of supply chains (SPSCs). Little research has been conducted on the issues, existing methods and possible approaches to measure and manage SPSCs. Design/methodology/approach – Literature review and conceptual development of framework. Findings – The paper develops an analytical framework for the assessment of approaches for the measurement and management of SPSCs. Existing approaches discussed in the vein of improving supply chains and increasing recycling are examined in the light of our framework, and areas to extend the research on sustainability performance measurement and management are identified. Research limitations/implications – Opportunities for extension of research on sustainability performance measurement and management of supply chains are identified. Practical implications – The framework offers help to managers in their choice of sustainability performance measurement and management approaches. Originality/value – The paper provides a structured overview of sustainability performance measurement and management literature and approaches to supply chain management. The framework proposed provides a foundation for further research. © Emerald Group Publishing Limited.},
author_keywords={Performance management;  Performance measurement;  Sustainable supply chains},
references={Acquaye, A., Genovese, A., Barrett, J., Koh, S.C.L., Benchmarking carbon emissions performance in supply chains (2014) Supply Chain Management. An International Journal, 19 (3), pp. 307-323; Amann, M., Roehrich, J., Essig, M., Harland, C., Driving sustainable supply chain management in the public sector. The importance of public procurement in the European Union (2014) Supply Chain Management. An International Journal, 19 (3), pp. 353-368; Bai, C., Sarkis, J., Green supplier development: Analytical evaluation using rough set theory (2010) Journal of Cleaner Production, 18 (12), pp. 1200-1210; Bai, C., Sarkis, J., Determining and applying sustainable supplier key performance indicators (2014) Supply Chain Management. An International Journal, 19 (3), pp. 276-292; Beske, P., Seuring, S., Putting sustainability into supply chain management (2014) Supply Chain Management. An International Journal, 19 (3), pp. 324-333; Beske, P., Koplin, J., Seuring, S., The use of environmental and social standards by German first-tier suppliers of the Volkswagen AG (2008) Corporate Social Responsibility and Environmental Management, 15 (2), pp. 63-75; Blattel-Mink, B., Kastenholz, H., Transdisciplinarity in sustainability research: Diffusion conditions of an institutional innovation (2005) International Journal of Sustainability and World Ecology, 12 (1), pp. 1-12; Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S., Quantitative models for sustainable supply chain management: Developments and directions (2014) European Journal of Operational Research, 233 (2), pp. 299-312; Braungart, M., McDonough, W., Bollinger, A., Cradle-to-cradle design: Creating healthy emissions-a strategy for eco-effective product and system design (2007) Journal of Cleaner Production, 15 (13), pp. 1337-1348; Braziotis, C., Bourlakis, M., Rogers, H., Tannock, J., Supply chains and supply networks: Distinctions and overlaps (2013) Supply Chain Management: An International Journal, 18 (6), pp. 644-652; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-94; Burritt, R.L., Schaltegger, S., Sustainability accounting and reporting: Fad or trend? (2010) Accounting, Auditing and Accountability Journal, 23 (7), pp. 829-846; Burritt, R.L., Tingey-Holyoak, J., Forging cleaner production: The importance of academic-practitioner links for successful sustainability embedded carbon accounting (2012) Journal of Cleaner Production, 36, pp. 39-47; Carter, C.R., Jennings, M.M., The role of purchasing in corporate social responsibility: A structural equation analysis (2004) Journal of Business Logistics, 25 (1), pp. 145-186; Chambers, R.J., (1966) Accounting Evaluation and Economic Behavior, , Scholars Book Co, Houston, Texas; Cheung, D.K.K., Welford, R.J., Hills, P.R., CSR and the environment: Business supply chain partnerships in Hong Kong and PRDR China (2009) Corporate Social Responsibility and Environmental Management, 16 (5), pp. 250-263; Devisscher, T., Mont, O., An analysis of a product service system in Bolivia: Coffee in Yungas (2008) International Journal of Innovation and Sustainable Development, 3 (3-4), pp. 262-284; Firnkorn, J., Müller, M., What will be the environmental effects of new free-floating car-sharing systems?The case of Car2Go in Ulm (2011) Ecological Economics, 70 (8), pp. 1519-1528; Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., Van Der Laan, E., Van Nunen, J.A., Van Wassenhove, L.N., Quantitative models for reverse logistics: A review (1997) European Journal of Operational Research, 103 (1), pp. 1-17; Geffen, C.A., Rothenberg, S., Suppliers and environmental innovation: The automotive paint process (2000) International Journal of Operations and Production Management, 20 (2), pp. 166-186; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter-organizational resources: A literature review (2010) Corporate Social Responsibility and Environmental Management, 17 (4), pp. 230-245; Gold, S., Hahn, R., Seuring, S., Sustainable supply chain management in base of the pyramid food projects. A path to triple bottom line approaches for multinationals? (2013) International Business Review, 22 (5), pp. 784-799; Grosvold, J., Hoejmose, S.U., Roehrich, J., Squaring the circle, management, measurement and performance of sustainability in supply chains (2014) Supply Chain Management. An International Journal, 19 (3), pp. 293-306; Gualandris, J., Golini, R., Kalchschmidt, M., Do supply management and global sourcing matter for firm sustainability performance? An international study (2014) Supply Chain Management. An International Journal, 19 (3), pp. 258-275; Guoyou, Q., Saixing, Z., Chiming, T., Haitao, Y., Hailiang, Z., Stakeholders' influences on corporate green innovation strategy: A case study of manufacturing firms in China (2013) Corporate Social Responsibility and Environmental Management, 20 (1), pp. 1-4; Halldórsson, Á., Kotzab, H., Skjoett-Larsen, T., Supply chain management on the crossroad to sustainability: A blessing or a curse? (2009) International Journal of Physical Distribution and Logistics Management, 1 (2), pp. 83-94; Hansen, E.G., Große-Dunker, F., Reichwald, R., Sustainability innovation cube: A framework to evaluate sustainability-oriented innovations (2009) International Journal of Innovation Management, 13 (4), pp. 683-713; Hansen, E.G., Gomm, M.L., Bullinger, A.C., Möslein, K.M., A community-based toolkit for designing ride-sharing services: The case of a virtual network of ride access points in Germany (2010) International Journal of Innovation and Sustainable Development, 5 (1), pp. 80-99; Harms, D., Hansen, E., Schaltegger, S., Strategies in aligning supply chains for sustainability, an empirical investigation of large german companies (2013) Corporate Social Responsibility and Environmental Management, 20 (4), pp. 205-218; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Katzev, R., Car sharing: A new approach to urban transportation problems (2003) Analyses of Social Issues and Public Policy, 3 (1), pp. 65-86; Koplin, J., Seuring, S., Mesterharm, M., Incorporating sustainability into supply management in the automotive industry: The case of the Volkswagen AG (2007) Journal of Cleaner Production, 15 (11), pp. 1053-1062; Kramer, S., Hoffmann, C., Kuttler, T., Hendzlik, M., Electric car sharing as an integrated part of public transport: Customers' needs and experience (2014) Evolutionary Paths Towards the Mobility Patterns of the Future, pp. 101-112. , in Hülsmann, M. and Fornahl, D. (Eds), Springer, Heidelberg, Berlin; Leire, C., Mont, O., The implementation of socially responsible purchasing (2010) Corporate Social Responsibility and Environmental Management, 17 (1), pp. 27-39; Locke, R., The promise and perils of globalization: The case of Nike (2003) Management: Inventing and Delivering its Future, pp. 39-70. , in Kochan, T. and Schmalensee, R. (Eds), Cambridge, MA; Mont, O., Institutionalisation of sustainable consumption patterns based on shared use (2004) Ecological Economics, 50 (1-2), pp. 135-153; Pagell, M., Shevchenko, A., Why research in sustainable supply chain management should have no future (2013) Journal of Supply Chain Management, 50 (1), pp. 1-32; Pagell, M., Wu, Z., Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars (2009) Journal of Supply Chain Management, 45 (2), pp. 37-56; Pannell, D.J., Roberts, A.M., Park, G., Alexander, J., Designing a practical and rigorous framework for comprehensive evaluation and prioritisation of environmental projects (2013) Wildlife Research, 40 (2), pp. 126-133; Pokharel, S., Mutha, A., Perspectives in reverse logistics: A review (2009) Resources, Conservation and Recycling, 53 (4), pp. 175-182; Prettenthaler, F., Steiniger, K., From ownership to service use lifestyle: The potential of car sharing (1999) Ecological Economics, 28 (3), pp. 443-453; Reuter, C., Foerstl, K., Hartmann, E., Blome, C., Sustainable global supplier management: The role of dynamic capabilities in achieving competitive advantage (2010) Journal of Supply Chain Management, 46 (2-3), pp. 45-63; Sahamie, R., Stindt, D., Nuss, C., Transdisciplinary research in sustainable operations-an application to closed-loop supply chains (2013) Business Strategy and the Environment, 22 (4), pp. 245-268; Schaltegger, S., Accounting for eco-efficiency (1998) Green Accounting, pp. 287-302. , in Nath, B., Hens, L., Compton, P. and Devuyst, D. (Eds), Environmental Management in Practice, London, pp. 272-287, Reprinted in: Bartelmus, P. and Seifert, E. (Eds), (2002): Ashgate Publishing, Aldershot; Schaltegger, S., Sustainability as a driver for corporate economic success: Consequences for the development of sustainability management control (2011) Society and Economy, 33 (1), pp. 15-28; Schaltegger, S., Wagner, M., Sustainable entrepreneurship and sustainability innovation: Categories and interactions (2011) Business Strategy and the Environment, 20 (4), pp. 222-237; Schaltegger, S., Lüdeke-Freund, F., Hansen, E., Business cases for sustainability: The role of business model innovation for corporate sustainability (2012) International Journal of Innovation and Sustainable Development, 6 (2), pp. 95-119; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Seuring, S., Müller, M., Core issues in sustainable supply chain management: A Delphi study (2008) Business Strategy and the Environment, 17 (8), pp. 455-466; Stindt, D., Sahamie, R., Review of research on closed loop supply chain management in the process industry (2014) Flexible Services and Manufacturing Journal, 26 (1-2), pp. 268-293; Varsei, M., Soosay, C., Fahimnia, B., Sarkis, J., Framing sustainability performance of supply chains with multidimensional indicators (2014) Supply Chain Management. An International Journal, 19 (3), pp. 242-257; von Weizsäcker, E.U., Hargroves, K., Smith, M., Desha, C., Stasinopoulos, P., (2009) Factor five: Transforming the global economy through 80% improvements in resource productivity, , Earthscan, London; Wittstruck, D., Teuteberg, F., Understanding the success factors of sustainable supply chain management: Empirical evidence from the electrics and electronics industry (2011) Corporate Social Responsibility and Environmental Management, 19 (3), pp. 141-158; Bowen, F.E., Cousins, P.D., Lamming, R.C., Faruk, A.C., The role of supply management capabilities in green supply (2001) Production and Operations Management, 10 (2), pp. 174-189; Carter, C.R., Dresner, M., Purchasing's role in environmental management: Cross-functional development of grounded theory (2001) The Journal of Supply Chain Management, 37 (3), pp. 12-27; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) International Journal of Physical Distribution and Logistics Management, 41 (1), pp. 46-62; Cousins, P.D., Lamming, R.C., Bowen, F., The role of risk in environment-related supplier initiatives (2004) International Journal of Operations and Production Management, 24 (6), pp. 554-565; Delmas, M.A., Montiel, I., Greening the supply chain: When is customer pressure effective? (2009) Journal of Economics and Management Strategy, 18 (1), pp. 171-201; Dyer, J.H., Nobeoka, K., Creating and managing a high performance knowledge-sharing network: The toyota case (2000) Strategic Management Journal, 21 (3), pp. 345-367; Graafland, J.J., Sourcing ethics in the textile sector: The case of C&A (2002) Business Ethics, 11 (3), pp. 282-294; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice Hall, Upper Saddle River, NJ; Holt, D., Managing the interface between suppliers and organizations for environmental responsibility: An exploration of current practices in the UK (2004) Corporate Social Responsibility and Environmental Management, 11 (2), pp. 71-84; Klassen, R.D., Vachon, S., Collaboration and evaluation in the supply chain: The impact on plant-level environmental investment (2003) Production and Operations Management, 12 (3), pp. 336-352; Kolk, A., Van Tulder, R., Welters, C., International codes of conduct and corporate social responsibility: Can transnational corporations regulate themselves? (1999) Transnational Corporations, 8 (1), pp. 143-181; Kumar, S., Malegeant, P., Strategic alliance in a closed-loop supply chain: A case of manufacturer and eco-nonprofit organization (2006) Technovation, 26 (10), pp. 1127-1135; Lamming, R., Hampson, J., The environment as a supply chain management issues (1996) British Journal of Management, 7, pp. 45-62; Mamic, I., Managing global supply chain: The sports footwear, apparel and retail sectors (2005) Journal of Business Ethics, 59 (1-2), pp. 81-100; Matos, S., Hall, J., Integrating sustainable development in the supply chain: The case of life cycle assessment in oil and gas and agricultural biotechnology (2007) Journal of Operations Management, 2 (6), pp. 1083-1102; Min, H., Galle, W.P., Green purchasing practices of US firms (2001) International Journal of Operations and Production Management, 21 (9), pp. 1222-1238; Neilson, J., Pritchard, B., Green coffee? the contradictions of global sustainability initiatives from an Indian perspective (2007) Development Policy Review, 25 (3), pp. 311-331; Ortas, E., Moneva, J.M., Álvarez, I., Sustainable supply chain and company performance: A global examination (2014) Supply Chain Management: An International Journal, 19 (3); Ozgen, E., Baron, R.A., Social sources of information opportunity recognition: Effects of mentors, industry networks and professional forums (2007) Journal of Business Venturing, 22, pp. 174-192; Pedersen, E.R., Andersen, M., Safeguarding corporate social responsibility (CSR) in global supply chains: How codes of conduct are managed in buyer-supplier relationships (2006) Journal of Public Affairs, 6 (3-4), pp. 228-240; Peters, N., (2010) Inter-Organisational Design of Voluntary Sustainability Initiatives: Increasing the Legitimacy of Sustainability Strategies for Supply Chains, , Springer Gabler, Wiesbaden; Porter, M., (1999) Competitive Advantage, , Campus, Frankfurt; Porter, M.E., van der Linde, C., Toward a new conception of the environment-competitiveness relationship (1995) Journal of Economic Perspectives, 9 (4), pp. 97-118; Preuss, L., In dirty chains? purchasing and greener manufacturing (2001) Journal of Business Ethics, 34 (3-4), pp. 345-359; Preuss, L., Contribution of purchasing and supply management to ecological innovation (2007) International Journal of Innovation Management, 11 (4), pp. 515-537; Sarkis, J., Zhu, Q., Lai, K., An organizational theoretic review of green supply chain management literature (2011) International Journal of Production Economics, 130 (1), pp. 1-15; Schaltegger, S., Burritt, R., Corporate Sustainability (2005) The International Yearbook of Environmental and Resource Economics 2005/2006: A Survey of Current Issues, pp. 185-222. , Folmer, H. and Tietenberg, T. (Eds), Cheltenham; Schumpeter, J.A., (2007) The Theory of Economic Development. An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle, , 13th ed., Transaction Publishers, New Brunswick, Cambridge, MA, 1934; Seuring, S., Goldbach, M., Koplin, J., Managing time and complexity in supply chains: Two cases from the textile industry (2004) International Journal of Integrated Supply Management, 1 (2), pp. 180-198; Shane, S., Prior knowledge and the discovery of entrepreneurial opportunities (2000) Organization Science, 11 (4), pp. 448-469; Sharma, S., Vredenburg, H., Proactive corporate environmental strategy and the development of competitively valuable organizational capabilities (1998) Strategic Management Journal, 19 (8), pp. 729-754; Simon, H., You don't have to be German to be a hidden champion (1996) Business Strategy Review, 7 (2), pp. 1-13; Teuscher, P., Grüninger, B., Ferdinand, N., Risk management in sustainable supply chain management (SSCM): Lessons learnt from the case of GMO-free soybeans (2006) Corporate Social Responsibility and Environmental Management, 13 (1), pp. 1-10; The Chartered Institute of Purchasing and Supply, (2009) Win/Win: Achieving Sustainable Procurement with the Developing World, , CIPS: Stamford, London; Vachon, S., Green supply chain practices and the selection of environmental technologies (2007) International Journal of Production Research, 45 (18-19), pp. 4357-4379; Vermeir, I., Verbeke, W., Sustainable food consumption: Exploring the consumer attitude-behavioral intention gap (2006) Journal of Agricultural and Environmental Ethics, 19 (2), pp. 169-194; Walker, H., Di Sisto, L., McBain, D., Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sector (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 69-85; Walton, S.V., Handfield, R.B., Melnyk, S.A., The green supply chain: Integrating suppliers into environmental management processes (1998) International Journal of Purchasing and Materials Management, 34 (2), pp. 2-11; Welford, R., Frost, S., Corporate social responsibility in Asian supply chains (2006) Corporate Social Responsibility and Environmental Management, 13 (3), pp. 166-176; Wolf, J., Sustainable supply chain management integration: A qualitative analysis of the German manufacturing industry (2011) Journal of Business Ethics, 102 (2), pp. 221-235; Wycherley, I., Greening supply chains: The case of body shop international (1999) Business Strategy and the Environment, 8 (2), pp. 120-127; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: Drivers and practice (2006) Journal of Cleaner Production, 14 (5), pp. 471-486; Zhu, Q., Sarkis, J., Lai, K.-H., Geng, Y., The role of organizational size in the adoption of green supply chain management practices in China (2008) Corporate Social Responsibility and Environmental Management, 15 (6), pp. 322-337},
correspondence_address1={Schaltegger, S.; Centre for Sustainability Management (CSM), Germany; email: schaltegger@uni.leuphana.de},
publisher={Emerald Group Holdings Ltd.},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@BOOK{Avelar-Sosa2014517,
author={Avelar-Sosa, L. and García-Alcaraz, J.L. and Cedillo-Campos, M.G.},
title={Techniques and attributes used in the supply chain performance measurement: Tendencies},
journal={Lean Manufacturing in the Developing World: Methodology, Case Studies and Trends from Latin America},
year={2014},
volume={9783319049519},
pages={517-541},
doi={10.1007/978-3-319-04951-9_25},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930736277&doi=10.1007%2f978-3-319-04951-9_25&partnerID=40&md5=24846d373f15f1f71c6363e683602605},
affiliation={Institute of Engineering and Technology, Autonomous University of Ciudad Juarez, Av. del Charro 450 Norte. Col. Partido Romero, Ciudad Juárez, Chihuahua, 32315, Mexico; Department of Logistics and Supply Chain Management, Autonomous University of Nuevo Leon, Av. Universidad s/n. Ciudad Universitaria, San-Nicolás-de-los-Garza, Nuevo-León, 6645, Mexico},
abstract={The evaluation and performance measurement in supply chain (SC) is a key element in modern business competitiveness and it is an activity extremely important for improve its process, practices and common goals. The purpose of this chapter is to present a statistical analysis based in a literature review related to supply chain tendencies that includes 95 chapters published from January 2000 to June 2012. This research identifies the techniques and methodologies used in the supply chain performance evaluation process, and attributes most commonly evaluated as well as the industrial sector and regions where the solutions were applied, and finally the journals where they were published. The results show that multivariate analysis is the most important and frequent methodology for study supply chain performance, looking to find variables relations and association among them. Also, the finds indicate that most evaluated attributes in supply chain performance are the delivery, information flow and processes activities. The most important research groups that are investigating this area are located in Unites States of America, Taiwan and United Kingdom. © Springer International Publishing Switzerland 2014. All rights are reserved.},
author_keywords={Attributes in supply chain;  Supply chain management (SCM);  Supply chain performance},
keywords={Competition;  Industrial research;  Supply chain management, Industrial sector;  Information flows;  Literature reviews;  Multi variate analysis;  Performance measurements;  Supply chain managements (SCM);  Supply chain performance;  Supply chain performance measurements, Multivariant analysis},
references={Abu-Suleiman, A., Boardman, B., Priest, J., A framework for an integrated Supply Chain Performance Management System (2004) Industrial Engineering Research Conference, , University of Texas Arlington, Arlington, TX; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173, pp. 211-225; Akkermans, H.A., Bogerd, P., Yücesan, E., Van Wassenhove, L.N., The impact of ERP on supply chain management: Exploratory findings from a European Delphi study (2003) European Journal of Operational Research, 146, pp. 284-301; Arlbjørn, J.S., Lüthje, T., Global operations and their interaction with supply chain performance (2012) Industrial Management & Data Systems, 112 (7), pp. 1044-1064; Arun Kanda, A., Deshmukh, S.G., Supply chain coordination: Perspectives, empirical studies and research directions (2008) International Journal of Productions Economics, 115, pp. 316-335; Askarany, D., Yazdifar, H., Askary, S., Supply chain management, activity-based costing and organizational factors (2010) International Journal of Production Economics, 127, pp. 238-248; Autry, C.W., Grawe, S.J., Daugherty, P.J., Richey, R.G., The effects of technological turbulence and breath on supply chain technology acceptance and adoption (2010) Journal of Operations Management, 28, pp. 522-536; Babak, J.N., Saeid, I., Analyzing effective elements in agile supply chain (2012) Management Science Letters, 24, pp. 369-378; Bhatnagar, R., Sohal, A.S., Supply chain competitiveness: Measuring the impact of location factors, uncertainty and manufacturing practices (2005) Technovation, 25 (5), pp. 443-456; Blome, C., Schoenherr, T., Supply chain risk management in financial crises: A multiple case-study approach (2011) International Journal of Productions Economics, 134 (1), pp. 43-57; Boddy, D., Cahill, C., Charles, M., Fraser-Kraus, H., MacBeth, D., Success and failure in implementing supply chain partnering an empirical study (1998) European Journal of Purchasing and Supply Management, 4 (1), pp. 143-151; Büyüközkan, G., Vardaloglu, Z., Analyzing CPFR success factors using fuzzy cognitive maps in retail industry (2012) Expert Systems with Applications, 39 (12), pp. 10438-10455; Cambra-Fierro, J.J., Polo-Redondo, Y., Post-satisfaction factors effecting the long-term orientation of supply relationships (2011) Journal of Business and Industrial Marketing, 26 (6), pp. 395-406; Cannella, S., Ciancimino, E., Framian, J.M., Disney, S.M., The four arquetypes in supply chain (2010) Universia Business Review, Second Quarter, 26, pp. 134-149. , Universia Portal, Spain (In Spanish); Caniato, F., Golini, R., Kalchschmidt, M., The effect of global supply chain configuration on the relationship between supply chain improvement programs and performance (2013) International Journal of Production Economics, 143 (2), pp. 285-293; Cedillo, M.G., Sánchez, J., Sánchez, C., The new relational schemas of inter-firms cooperation: The case of the Coahuila automobile cluster in Mexico (2006) International Journal Automotive Technology and Management, 6 (4), pp. 406-418; Cedillo, M.G., Sánchez, C., (2008) Dynamic Analysis of Industrial Systems, , Mexico: In Spanish. Trillas Publishers; Cedillo, M.G., Pérez, A., Hybrid supply chains in emerging markets the case of the Mexican auto industry (2010) South African Journal of Industrial Engineering, 21 (1), pp. 193-206; Chan, F.T.S., Performance measurement in a Supply Chain (2003) International Journal Advanced Manufacturing of Technology, 21, pp. 534-548; Chen, D.C., Rajkumar, T.M., Tomochko, N.A., The antecedent factors on trust and commitment in supply chain relationships (2011) Computer Standards & Interfaces, 33, pp. 262-270; Chen, T., Gong, X., Performance evaluation of a supply chain network (2013) Procedia Computer Science, 17, pp. 1003-1009; Chen, J.V., Yen, D.C., Tomochko, N.A., The antecedent factors on trust and commitment in supply chain relationships (2011) Computers Standards and Interfaces, 33, pp. 262-270; Chen, C., Yan, H., Network DEA model for supply chain performance evaluation (2011) European Journal of Operational Research, 213 (1), pp. 147-155; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2012) Computers and Industrial Engineering, 62 (3), pp. 801-818; Choi, S., (2010) Key Success Factor of Supply Chain Relationships: Multiple Case Studies in China from Buyer's and Supplier's Perspective, , Master Thesis, University of Gävle, Sweden; Chong, A.Y., Ooi, K., Sohal, A., The relationship between supply chain factors and adoption of e-collaboration tools: An empirical examination (2009) International Journal of Production Economics, 122 (1), pp. 150-160; Cirtita, H., Glaser-Segura, D.A., Measuring downstream supply chain performance (2012) Journal of Manufacturing Technology Management, 23 (3), pp. 299-314; Cruz, J.M., The impact of corporate social responsibility in supply chain management: Multicriteria decisión-making approach (2009) Decision Support Systems, 48 (1), pp. 224-236; Cruz, J.M., Modeling the relationship of globalized supply chains and corporate social responsibility (2013) Journal of Cleaner Production, 56, pp. 73-85. , October); Cunha, V., Zwicker, R., The forerunners of relationship and performance in supply chain companies: Structuring and applying structural equations (2009) RAE: Revista de Adminis-tração de Empresas, 49 (2), pp. 147-161. , (in Portuguese); Daesoo, K., Process chain: A new paradigm of collaborative commerce and synchronized supply chain (2006) Kelly School of Business, 49 (5), pp. 359-367; De Giovanni, P., Esposito Vinzi, V., Covariance versus component-based estimations of performance in green supply chain management (2012) International Journal of Production Economics, 135 (2), pp. 907-916; Díaz, M.A., Pérez, C.C., (2002) Logistics Practices in Venezuela An Exploratory Study, , Working paper, Enterprise Institute, Madrid; Dorling, K., Scott, J., Deakins, E., Determinants of successful vendor managed inventory relationships in oligopoly industries (2006) International Journal of Physical Distribution and Logistics Management, 36 (3), pp. 176-191; Droge, C., Vickery, S.K., Jacobs, M., Does supply chain integration mediate the relationships between product/process strategy and service performance? An empirical study (2012) International Journal of Production Economics, 137 (2), pp. 250-262; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes' performance to a company's financial strategic objectives (2012) European Journal of Operational Research, 223 (1), pp. 276-289; Falk, H., Hogström, L., (2000) Key Success Factors for A Functioning Supply Chain in E-commerce B2B, , Master Thesis, Göteborg University, Sweden; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers, and bridges to effective supply chain management (2006) Supply Chain Management: An International Journal, 13 (1), pp. 35-48; Feng, Y., System dynamics modeling for supply chain information sharing (2012) Physics Procedia, 25, pp. 1463-1469; Forrester, J.W., (1961) Industrial Dynamics, , Portland (Or): Productivity Press; Forslund, H., Jonsson, P., The impact of forecast information quality on supply chain performance (2007) International Journal of Operations and Production Management, 27 (1), pp. 90-107; Gallear, D., Ghobadian, A., Chen, W., Corporate responsibility, supply chain partnership and performance: An empirical examination (2012) International Journal of Production Economics, 140 (1), pp. 83-91; Green K.W. J.R.., Inman, R.A., Using a just in time selling strategy to strengthen supply chain linkages (2005) International Journal of Production Research, 43 (16), pp. 3437-3453; Green, K.W., Jr., Whitten, D., Inman, R.A., Aligning marketing strategies throughout the supply chain to enhance performance (2012) Industrial Marketing Management, 41 (6), pp. 1008-1018; Guiffrida, A.L., Nagi, R., Cost characterizations of supply chain delivery performance (2006) International Journal of Production Economics, 102 (1), pp. 22-36; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGauhhey, R.E., A framework for supply chain performance measurement (2004) International Journal of Productions Economics, 87 (3), pp. 333-347; Gunasekaran, A., Ngai, E.W.T., Virtual supply chain management (2004) Production Planning and Control, 15 (6), pp. 584-595; Gunasekaran, A., Ngai, E.W.T., Modeling and analysis of built-to-order supply chains (2009) European Journal of Operational Research, 195 (2), pp. 319-334; Haytko, D.L., Kent, J.L., Mexican maquiladoras: Helping or hurting the US/Mexico cross-border supply chain? (2007) The International Journal of Logistics Management, 18 (3), pp. 347-363; Huang, M., Yang, M., Zhang, Y., Liu, B., System dynamics modeling-based study of contingent sourcing under supply discruptions (2012) Systems Engineering Procedia, 4, pp. 290-297; Hishamuddin, H., Sarker, R.A., Essam, D., Recovery model for a two-echelon serial supply chain with consideration of transportation disruption (2013) Computers and Industrial Engineering, 64 (2), pp. 552-561; Hui, L., Nuo, L., System dynamics modeling and simulation of multi-stage supply chain: The value of information sharing (2011) Energy Procedia, 13, pp. 4861-4867; Ip, W.H., Chan, S.L., Lam, C.Y., Modeling supply chain performance and stability (2011) Industrial Management and Data Systems, 111 (8), pp. 1332-1354; Janvier-James, A.M., A new introduction to supply chains and supply chain management: Definitions and theories perspective (2012) International Business Research, 5 (1), pp. 194-207; Jeong, J.S., Hong, P., Customer orientation and performance outcomes in supply chain management (2007) Journal of Enterprise Information Management, 20 (5), pp. 578-594; Jiménez, J.E., (2004) Critical Success Factors in Supply Chain (Technique Publication No. 237), , Mexican Institute of Transport, Sanfandila, Querétaro, Mexico; Jiménez, J.E., Hernández, S., (2002) Conceptual Framework of the Supply Chain: A New Logistics Focus (Technique Publication No. 215), , Mexican Institute of Transport, Sanfandila, Querétaro, Mexico; Kannan, V.R., Tan, K.C., Supply chain integration: Cluster analysis of the impact of span of integration (2010) Supply Chain Management: An International Journal, 15 (3), pp. 207-215; Khaji, M., Shafaei, R., A system dynamics approach for strategic partnering in supply networks (2011) International Journal of Computer Integrated Manufacturing, 24 (2), pp. 106-125; Kim, D., Cavusgil, S.T., Cavusgil, E., Does IT alignment between supply chain partners enhance customer value creation? An empirical investigation (2013) Industrial Marketing Management, 42 (6), pp. 880-889; Kisperska-Morón, D., Virtual logistics as a support for the decomposition process of supply chain (conceptual reflections) (2011) Scientific Journal of Logistics, 7 (5), pp. 49-60; Kroes, J.R., (2007) Outsourcing of Supply Chain Processes: Evaluating the Impact of Congruence between Outsourcing Drivers and Competitive Priorities on Performance, , PhD. Thesis, Georgia Institute of Technology, United States of America; Kumar, S., McCreary, M.L., Nottestad, D.A., Quantifying supply chain trade-offs using six sigma, simulation, and designed experiments to develop a flexible distribution network (2011) Quality Engineering, 23 (2), pp. 180-203; Laosirihongthog, T., Punnakitikashem, P., Adebanjo, D., Improving supply chain operations by adopting RFID technology: Evaluation and comparison of enabling factors (2011) Production Planning and Control, 1, pp. 1-20; Lassar, W., Haar, J., Montalvo, R., Hulser, L., Determinants of strategic risk management in emerging markets supply chain: Case of Mexico (2010) Journal of Economics, Finance and Administrative Science, 15 (28), pp. 25-140; Le Dain, M., Calvi, R., Cheriti, S., Measuring supplier performance in collaborative design: Proposition of a framework (2010) R&D Management, 41 (1), pp. 61-79; Lee, M.S., Lee, S., Success factors of open-source enterprise information systems development (2012) Industrial Management & Data Systems, 112 (7), pp. 1065-1084; Lin, C., Wing, S., Madu, C.N., Kuei, C., Yu, P., A structural equation model of supply chain quality management and organizational performance (2005) International Journal of Productions Economics, 96 (3), pp. 355-365; Lin, R., Chen, R., Nguyen, T., Green supply chain management performance in automobile manufacturing industry under uncertainty (2011) Procedia-Social and Behavioral Sciences, 25, pp. 233-245; Lin, Y., Wang, Y., Yu, C., Investigating the drivers of the innovation in cannel integration and supply chain performance: A strategy orientated perspective (2010) International Journal of Productions Economics, 127 (2), pp. 320-332; Lockamy, A.I.I.I., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218; Lu, X., Huang, L., Heng, M.S.H., Critical success factors of inter-organizational information systems: A case study of Cisco and Xiao Tong in China (2006) Information and Management, 43 (3), pp. 395-408; Lu, C., Lai, K., Chen, T.C.E., Application of structural equation modeling to evaluate the intention of shippers to use internet services in linear shipping (2007) European Journal of Operational Research, 180 (2), pp. 845-867; McCormack, K., Bronzo, M.L., Valadares, M.P., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Meixell, M.J., Gargeya, V.B., Global supply chain design: A literature review and critique (2005) Transportation Research Part e, 41 (6), pp. 531-550; Mendoza, E., (2007) Uncertainty, Integration and Supply Flexibility, , PhD. Thesis, Universitat Pompeu Fabra, Department of Economics and Business, Barcelona, Catalonia, Spain; Merschmann, U., Thonemann, U.W., Supply chain flexibility, uncertainty and form performance: An empirical analysis of German manufacturing firms (2011) International Journal of Productions Economics, 130 (1), pp. 43-53; Nath, T., Standing, C., Drivers of information technology use in the supply chain (2010) Journal of Systems and Information Technology, 12 (1), pp. 70-84; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Özdemir, A.I., Aslan, E., Supply chain integration, competition capability and business performance: A study on Turkish SMEs (2011) Asian Journal of Business Management, 3 (4), pp. 325-332; Papageorgiou, L.G., Supply chain optimization for the process industries: Advances and opportunities (2009) Computers and Chemical Engineering, 33 (12), pp. 1931-1938; Perotti, S., Zorzini, M., Cagno, E., Micheli, G.J.L., Green supply chain practices and company performance: The case of 3PLs in Italy (2012) International Journal of Physical Distribution and Logistics Management, 42 (7), pp. 640-672; Persson, F., SCOR template: A simulation based dynamic supply chain analysis tool (2011) International Journal of Production Economics, 131 (1), pp. 288-294; Power, D., Supply chain management integration and implementation: A literature review (2005) Supply Chain Management: An International Journal, 10 (4), pp. 252-263; Quesada, H., Gazo, R., Methodology for determining key internal business processes based on critical success factors (2007) Business Process Management Journal, 13 (1), pp. 5-20; Ramanathan, U., Performance of supply chain collaboration - A simulation study (2014) Expert Systems with Applications, 41 (1), pp. 210-220; Ramanathan, U., Gunasekaran, A., Supply chain collaboration: Impact of success in long-term partnerships (2014) International Journal of Production Economics, 147, pp. 252-259; Ranganathan, C., Teo, T.S.H., Dhaliwal, J., Web-enabled supply chain management: Key antecedents and performance impacts (2011) International Journal of Information Management, 31 (6), pp. 533-545; Revilla, E., Sáenz, M.J., Supply chain disruption management: Global convergence versus national specificity (2013) Journal of Business Research, , press), Corrected Proof; Ribas, I., Companys, R., Estado del arte de la planificación colaborativa en la cadena de suministro: contexto determinista e incierto (2007) Intangible Capital, 3 (3). , Universitat Politecnica de Catalunya; Ryu, I., So, S., Koo, C., The role of partnership in supply chain performance (2009) Industrial Management and Data Systems, 109 (4), pp. 496-514; Sánchez, C., Cedillo, M.G., Piña, M.R., Model for dynamic analysis of industrial clusters: The case of supply chain in the automotive clusters in the southeast region of Coahuila (2007) International Conference on Industrial Engineering. Proceedings of the 13th Annual; Sánchez, C., Cedillo, M.G., Pérez, V.P., Sensitivity analysis of the impact of inventory and cycle time on performance of the automotive supply chain (2008) International Conference on Industrial Engineering. Proceedings of the 13th Annual; Santos, J.C., (2010), http://ciclog.blogspot.mx/2010/11/decisiones-el-flujo-escondido-en-la.html, Accessed 15 May 2012; Sarimveis, H., Patrinos, P., Tarantilis, C.D., Kiranoudis, C.T., Dynamic modeling and control of supply chain systems: A review (2008) Computers and Operations Research, 35 (11), pp. 3530-3561; (2010) SCOR Model, , http://www.SupplyChain.org, SCC Accessed 20 June 2012; Schliephake, K., Stevens, G., Clay, S., Making resources work more efficiently - The importance of supply chain partnerships (2009) Journal of Cleaner Production, 17 (14), pp. 1257-1263; Schmitt, A., Singh, M., A quantitative analysis of disruption risk in a multi-echelon supply chain (2012) International Journal of Production Economics, 139 (1), pp. 22-32; Schotanus, F., Telgen, J., De Boer, L., Critical success factors for managing purchasing groups (2010) Journal of Purchasing and Supply Management, 16 (1), pp. 51-60; Singh, R., Singh, S.H., Metri, B.A., Kaur, R., Organizational performance and retail challenges: A structural equation approach (2011) Scientific Research, 3, pp. 159-168; Soin, S.S., (2004) Critical Success Factors in Supply Chain Management at High Technology Companies, , PhD. Thesis, University of Southern Queensland, Australia; Su, Y., Yang, C., A structural equation model for analyzing the impact of ERP on SCM (2010) Expert Systems with Applications, 37 (1), pp. 456-469; Su, Y., Yang, C., Why are enterprise resource planning systems indispensable to supply chain management? (2010) European Journal of Operational Research, 203 (1), pp. 81-94; Surana, A., Kumara, S., Greaves, M., Raghavan, N., Supply chain networks: A complex adaptive system perspective (2005) International Journal of Production Research, 43 (20), pp. 4235-4265; Swafford, P.M., Ghosh, S., Murthy, N., The antecedents of supply chain agility of a firm: Scale development and model testing (2006) Journal of Operations Management, 24 (2), pp. 170-188; Swierczek, A., The impact of supply chain integration on the "snowball effect" in the transmission of disruptions: An empirical evaluation of the model (2013) International Journal of Production Economics, , press), Corrected Proof; Tang, O., Musa, N., Identifying risk and research advancements in supply chain risk management (2011) International Journal of Production Economics, 133 (1), pp. 25-34; Teller, C., Kotzab, H., Grant, D.B., Improving the execution of supply chain management in organizations (2012) International Journal of Production Economics, 140 (2), pp. 713-720; Theeranuphattana, A., Tang, J.C.S., A conceptual model of performance measurement for supply chains (2008) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Thomassey, S., Sales forecasts in clothing industry: The key success factor of the supply chain management (2010) International Journal of Productions Economics, 128 (2), pp. 470-483; Vilko, J.P.P., Hallikas, J.M., Risk assessment in multimodal supply chains (2012) International Journal of Production Economics, 140 (2), pp. 586-595; Vlachos, I.P., A hierarchical model of the impact of RFID practices on retail supply chain performance (2014) Expert Systems with Applications, 41 (1), pp. 5-15; Whitten, G.D., Green, Jr.K.W., Zelbst, P.J., Triple-A supply chain performance (2012) International Journal of Operations and Production Management, 32 (1), pp. 28-48; Wiengarten, F., Pagell, M., Fynes, B., Supply chain environmental investments in dynamic industries: Comparing investment and performance differences with static industries (2012) International Journal of Production Economics, 135 (2), pp. 541-551; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381; Wu, T., Blackhurst, J., Chidambaram, V., A model for inbound supply risk analysis (2006) Computers in Industry, 57 (4), pp. 350-365; Youn, S., Yang, M.G., Hong, P., Integrative leadership for effective supply chain implementation: An empirical study of Korean Firms (2012) International Journal of Production Economics, 139 (1), pp. 237-246; Young, R.R., Esqueda, P., Supply chain vulnerability: Considerations of the case of latin America (2005) Revista Latinoamerican of Administration Journal, 34, pp. 63-77; Yu, W., Jacobs, M., Salisbury, W., Enns, H., The effects of supply chain integration on customer satisfaction and financial performance: An organizational learning perspective (2013) International Journal of Production Economics, 146 (1), pp. 346-358; Zhang, C., Dhaliwal, J., An investigation of resource-based and institutional theoretic factors in technology adoption for operations and supply chain management (2009) International Journal of Productions Economics, 120 (1), pp. 252-269; Zhou, H., (2003) The Role of Supply Chain Processes and Information Sharing in Supply Chain Management, , B.S., M.S., M.A. PhD. Thesis. The Ohio State University, United States of America},
correspondence_address1={Avelar-Sosa, L.; Institute of Engineering and Technology, Av. del Charro 450 Norte. Col. Partido Romero, Mexico},
publisher={Springer International Publishing},
isbn={9783319049519; 331904950X; 9783319049502},
language={English},
abbrev_source_title={Lean Mfg. in the Dev. World: Methodology, Case Stud. and Trends from Lat. Amer.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Rivera2014445,
author={Rivera, L. and Manotas, D.F.},
title={Performance measurement in lean manufacturing environments},
journal={Lean Manufacturing in the Developing World: Methodology, Case Studies and Trends from Latin America},
year={2014},
volume={9783319049519},
pages={445-460},
doi={10.1007/978-3-319-04951-9_21},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84930689447&doi=10.1007%2f978-3-319-04951-9_21&partnerID=40&md5=c2bf585188b08fe317f3896c2160ffe7},
affiliation={School of Industrial Engineering, Universidad Del Valle, Ciudad Universitaria Meléndez, Calle 13 No 100-00, Cali, Colombia},
abstract={This chapter presents the main metrics suggested for a lean manufacturing environment, as well as their use and application. The selection of metrics arises from a process that starts with the examination of a lean implementation, followed by the dimensions of improvement that will be examined. Then we suggest a set of metrics for each of the improvement dimensions, and we characterize the a priori impact that tools considered typical of lean manufacturing might have on the metrics. Based on this characterization we perform two types of analyses: A horizontal analysis to uncover the most critical performance indicators and a vertical analysis to present the lean tools that are most influential on the manufacturing system. We then propose different ways to use the indicators, based on their focus (results-oriented or process-oriented) and based on their scope (organizational scope and time scope). Finally, we propose future work to evaluate the impact and cost of implementing and operating a performance measurement system based on the metrics and their application we propose. © Springer International Publishing Switzerland 2014. All rights are reserved.},
author_keywords={Lean manufacturing;  Measurement cost;  Performance measurement},
keywords={Agile manufacturing systems;  Manufacture;  Supply chain management, Lean implementation;  Manufacturing environments;  Measurement costs;  Organizational scopes;  Performance indicators;  Performance measurement system;  Performance measurements;  Process-oriented, Lean production},
references={Aragón, A., Bueno, M.A., (2010) Desarrollo e Implementación Del Sistema de Gestión Diaria Operativa (GEDO), , Master's Thesis, Universidad Icesi, Cali, Colombia; Bayou, M.E., De Korvin, A., Measuring the leanness of manufacturing systems-A case study of Ford motor company and general motors (2008) Journal of Engineering and Technology Management, 25 (4), pp. 287-304; Ben Naylor, J., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 107-118; Comm, C.L., Mathaisel, D.F.X., A paradigm for benchmarking lean initiatives for quality improvement (2000) Benchmarking: An International Journal, 7 (2), pp. 118-127; Coriat, B., Pensar al revés: Trabajo y organización en la empresa japonesa (2000) Siglo XXI de España (Editores), , México; Doolen, T.L., Hacker, M.E., A review of lean assessment in organizations: An exploratory study of lean practices by electronics manufacturers (2005) Journal of Manufacturing Systems, 24 (1), pp. 55-67; Feld, W.M., (2001) Lean Manufacturing: Tools, Techniques and How to Use Them, , Florida: St. Lucie Press; Forza, C., Work organization in lean production and traditional plants: What are the differences? (1996) International Journal of Operations & Production Management, 16 (2), pp. 42-62; Hirano, H., (2009) JIT Implementation Manual: The Complete Guide to Just-In-Time Manufacturing, , New York: CRC Press; Karlsson, C., Ahlstrom, P., Assessing changes towards lean production (1996) International Journal of Operations & Production Management, 16 (2), pp. 24-41; Khadem, M.A., Sk Ahad, A., Hamid, S., Efficacy of lean metrics in evaluating the performance of manufacturing systems (2008) International Journal of Industrial Engineering, 15 (2), pp. 176-184; Kumar, S., Meade, D., (2007) Financial Models and Tools for Managing Lean Manufacturing, , Florida: Auerbach Publications; Liker, J., Convis, G., (2011) The Toyota Way to Lean Leadership: Achieving and Sustaining Excellence Through Leadership Development, , New York: McGraw-Hill; Martinez, A., Perez, M., Lean indicators and manufacturing strategies (2001) International Journal of Operations & Production Management, 21 (11), pp. 1433-1451; Mason-Jones, R., Naylor, B., Towill, D.R., Lean, agile or leagile? Matching your supply chain to the marketplace (2000) International Journal of Production Research, 38 (17), pp. 4061-4070; Maskell, B., Baggaley, B., (2004) Practical Lean Manufacturing: A Proven System for Measuring and Managing the Lean Entreprise, , New York: Productivity Press; Nicholas, J., (2010) Production for Competitive Advantage: A Comprehensive Guide to Lean Methodologies and Management Practices, , New York: Productivity Press; Niepce, W., Molleman, E., A case study: Characteristics of work organization in lean production and socio-technical systems (1996) International Journal of Operations & Production Management, 16 (2), pp. 77-90; Paez, O., Dewees, J., Genaidy, A., Tuncel, S., Karwowski, W., Zurada, J., The lean manufacturing enterprise: An emerging sociotechnological system integration (2004) Human Factors and Ergonomics in Manufacturing & Service Industries, 14, pp. 285-306; Rivera, L., Justificación conceptual de un modelo de implementación de lean manufacturing (2008) Heurística, Revista de Ingeniería Industrial de la Universidad Del Valle, 15, pp. 91-106; Rivera, L., Frank Chen, F., Measuring the impact of lean tools on the cost-time investment of a product using cost-time profiles (2007) Robotics and Computer-Integrated Manufacturing, 23 (6), pp. 684-689; Rivera, L., Manotas, D.F., Lean manufacturing measurement: The relationship between lean activities and lean metrics (2007) Estudios Gerenciales, 23 (105), pp. 69-83; Rother, M., Shook, J., Womack, J., Jones, D., (2009) Learning to See: Value Stream Mapping to Add Value and Eliminate MUDA (Version 1.4), , Cambridge, MA: Lean Enterprise Institute; Shah, R., Ward, P.T., Lean manufacturing: Context, practice bundles, and performance (2003) Journal of Operations Management, 21 (2), pp. 129-149; Wan, H.-D., Chen, F.F., A leanness measure of manufacturing systems for quantifying impacts of lean initiatives (2008) International Journal of Production Research, 46 (23), pp. 6567-6584; Womack, J.P., Jones, D., (1996) Lean Thinking: Banish Waste and Create Wealth in Your Corporation, , New York: Simon & Schuster; Womack, J., Lean Thinking: Where Have We Been and Where Are We Going? Forming & Fabricating, September 2002 (2002) Lean Manufacturing Special Insert, p. L2},
correspondence_address1={Rivera, L.; School of Industrial Engineering, Calle 13 No 100-00, Colombia},
publisher={Springer International Publishing},
isbn={9783319049519; 331904950X; 9783319049502},
language={English},
abbrev_source_title={Lean Mfg. in the Dev. World: Methodology, Case Stud. and Trends from Lat. Amer.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Chan201464,
author={Chan, F.T.S. and Nayak, A. and Raj, R. and Chong, A.Y.-L. and Manoj, T.},
title={An innovative supply chain performance measurement system incorporating Research and Development (R&D) and marketing policy},
journal={Computers and Industrial Engineering},
year={2014},
volume={69},
number={1},
pages={64-70},
doi={10.1016/j.cie.2013.12.015},
note={cited By 29},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893188677&doi=10.1016%2fj.cie.2013.12.015&partnerID=40&md5=7520452f7d089ea4dddb99fef0eb614d},
affiliation={Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong; Department of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur, India},
abstract={Various performance measurement techniques have been developed and applied in their respective fields, but the existing performance measurement methods have failed to provide significant assistance in the context of marketing strategies and innovation levels of a firm. In this paper, we have considered an important aspect of marketing policy involving examining the decision of a firm to distribute products and services to its consumer. The model developed in this paper is an extension to the Bass diffusion model which is generalized to incorporate the effects of marketing policy of the firm. In order to examine our model, computer simulation is conducted in order to measure the effect of innovation level and distribution of products and services on the change in the sales of a firm from its previous products and supply chain system. The performance measurement was developed by examining firm's level of innovation achieved by their Research and Development (R&D) performances, and sales of the products and services. © 2014 Elsevier B.V. All rights reserved.},
author_keywords={Bass diffusion model;  Marketing policy;  Performance measurement system;  R&D;  Supply chain management},
keywords={Bass Diffusion Model;  Level of innovation;  Performance measurement system;  Performance measurements;  Products and services;  Research and development;  Supply chain performance measurements;  Supply chain systems, Computer simulation;  Industry;  Marketing;  Supply chain management, Commerce},
references={Ahmed, A.M., Abdalla, H.S., An intelligent system for performance measurement selection (2002) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 216 (4), pp. 591-606. , DOI 10.1243/0954405021520085; Bass, F.M., Gordon, K., Ferguson, T.L., Githens, M.L., DIRECTV: Forecasting diffusion of a new technology prior to a product launch (2001) Interfaces, 31 (2), pp. 82-93; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , PII S0925527398000796; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Brookes, N.J., Backhouse, C.J., Measuring the performance of product introduction (1998) Proceedings of IMechE, Part B: Journal of Engineering Manufacture, 212 (1), pp. 1-11; Cassiman, B., Veugeleres, R., In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition (2006) Management Science, 52 (1), pp. 68-82; Chen, C.C., An objective-oriented and product-line-based manufacturing performance measurement (2008) Journal of Production Economics, 112 (1), pp. 380-390; Cohen, W.M., Klepper, S., Firm size and nature of innovation within industries: The case of process and product R&D (1996) The Review of Economics and Statistics, 78 (2), pp. 232-243; Cohen, M.W., Levinthal, D.A., Innovation and learning: The two faces of R&D (1989) The Economic Journal, 99, pp. 569-596; Elena, H., Lourdes, M., Does history matter for the relationship between R&D, innovation, and productivity? (2011) Industrial and Corporate Change, 20 (5), pp. 1335-1368; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA London; Gassmann, O., Enkel, E., Chesbrough, H., (2010) R&D Management, 40 (3), pp. 213-221; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Hvolby, H.-H., Thorstenson, A., Indicators for performance measurement in small and medium-sized enterprises (2001) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 215 (8), pp. 1143-1146; Kueng, P., (2001) Performance Measurement Systems in the Service Sector - The Potential of IT Is Not Yet Utilized, , Internal working paper no. 01-05 Department of Informatics, University of Fribourg, Rue Faucingy 2. 1700 Fribourg, Switzerland; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-19; Longo, F., Mirabelli, G., An advanced supply chain management tool based on modeling and simulation (2008) Computers and Industrial Engineering, 54 (3), pp. 570-588. , DOI 10.1016/j.cie.2007.09.008, PII S0360835207002100; Marchand, M., Raymond, L., Researching performance measurement systems - An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Marr, B., Neely, A., (2002) Balanced Scorecard Software Report, A Business Review Publication from Cranfield School of Management with Contributions by Gartner, Inc, , Connecticut, USA; Min, H., Zhou, G., Supply chain modeling: Past, present and future (2002) Computers and Industrial Engineering, 43 (1-2), pp. 231-249. , DOI 10.1016/S0360-8352(02)00066-9, PII S0360835202000669; Munson, C.L., Rosenblatt, M.J., Coordinating a three-level supply chain with quantity discounts (2000) IIE Transactions, 33 (5), pp. 371-384; Nag, R., Hambrick, D.C., Chen, M.-J., What is strategic management, really? Inductive derivation of a consensus definition of the field (2007) Strategic Management Journal, 28 (9), pp. 935-955. , DOI 10.1002/smj.615; Narasimhan, R., Jayaram, J., Causal linkages in supply chain management: An exploratory study of North American manufacturing firms (1998) Decision Sciences, 29 (6), pp. 579-605; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Neely . A, D., Performance measurement system design - Third phase (1994) Draft of the Fourth Section of the Performance Measurement System Design Workbook; Nordin, F., Linkages between service sourcing decisions and competitive advantage: A review, propositions, and illustrating cases" (2008) International Journal of Production Economics, 114 (1), pp. 40-55; Nudurupati, S.S., Bititci, U.S., Review of performance management information systems (2000) Centre for Strategic Manufacturing, , PerforMIS), internal report DMEM, University of Strathclyde, UK; Nudurupati, S.S., Bititci, U.S., Implementation and impact of IT-supported performance measurement systems (2005) Production Planning and Control, 16 (2), pp. 152-162. , DOI 10.1080/09537280512331333057, PII P728168303; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Schumann, P.A., Ransley, D.L., Prestwood, D.C.L., Measuring R&D performance (1995) Research Technology Management, 38 (3), pp. 45-54; Slone, R.E., Leading a supply chain turnaround (2004) Harvard Business Review, 82 (10), pp. 114-121; Taylor, T.A., Supply chain coordination under channel rebates with sales effort effects (2002) Management Science, 48 (8), pp. 992-1007; Thomas, D.J., Griffin, P.M., Co-ordinated supply chain management (1996) European Journal of Operational Research, 94 (3), pp. 1-15; Ukko, J., Tenhunen, J., Rantanen, H., Performance measurement impacts on management and leadership: Perspectives of management and employees (2007) International Journal of Production Economics, 110 (1-2), pp. 39-51. , DOI 10.1016/j.ijpe.2007.02.008, PII S0925527307000801, Recent Developments in Operations Strategy, Supply Chain Design and Production Systems; Van Hoek, R.I., "Measuring the immeasurable" - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192. , DOI 10.1108/13598549810244232},
correspondence_address1={Chan, F.T.S.; Department of Industrial and Systems Engineering, , Hung Hom, Hong Kong; email: f.chan@polyu.edu.hk},
issn={03608352},
coden={CINDD},
language={English},
abbrev_source_title={Comput Ind Eng},
document_type={Article},
source={Scopus},
}

@ARTICLE{Garengo2014220,
author={Garengo, P. and Sharma, M.K.},
title={Performance measurement system contingency factors: A cross analysis of Italian and Indian SMEs},
journal={Production Planning and Control},
year={2014},
volume={25},
number={3},
pages={220-240},
doi={10.1080/09537287.2012.663104},
note={cited By 34},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890399973&doi=10.1080%2f09537287.2012.663104&partnerID=40&md5=0b7f03de5101297241d0055f0eb7f5a9},
affiliation={Department of Innovation in Mechanics and Management, University of Padua, Via Venezia, Padua, Italy; Department of Production and Industrial Engineering, MBM Engineering College, JNV University, Jodhpur, Rajasthan, India},
abstract={Recent studies have highlighted the presence of a strong relationship between performance measurement systems (PMSs) and corporate governance structures in European firms. Taking into account the growing economic relevance of developing countries, we investigated the role of corporate governance structure as a PMS contingency factor in Italian and Indian SMEs using a multiple case-study approach. The results show that the first development of a PMS was determined by two different factors; in Italian SMEs, the changes in the corporate governance structure promoted the implementation of an advanced PMS, while in Indian SMEs, environmental changes pushed the development of the PMS without affecting any modification in the corporate governance structure. Moreover, after the adoption of an advanced (or almost advanced) performance management system, relevant environmental changes or the positive impacts of the PMS use, were enough to promote further development of the system in both Italian and Indian SMEs, without any change in the corporate governance structure. © 2012 © 2012 Taylor & Francis.},
author_keywords={competitive environment;  corporate governance structure;  management system;  managerial practices;  performance measurement;  small and medium enterprises},
keywords={Competitive environment;  Corporate governance;  Management systems;  Managerial practices;  Performance measurements;  Small and medium enterprise, Developing countries;  Environmental management;  Management;  Supply chain management, Industry},
references={Antonioli, D., Mazzanti, M., Pini, P., Productivity, innovation strategies and industrial relations in SMEs. Empirical evidence for a local production system in Northern Italy (2010) International Review of Applied Economics, 24 (4), pp. 453-482; Bala Subrahmanya, M.H., Small-scale industries in India in the globalisation era: Performance and prospects (2005) International Journal of Management and Enterprise Development, 2 (1), pp. 122-139; Bammens, Y., Voordeckers, W., Van Gils, A., Boards of directors in family firms: A generational perspective (2008) Small Business Economics, 31 (2), pp. 163-180; Barach, J.A., Is there a cure for the paralyzed family board? (1984) Sloan Management Review, 26 (1), pp. 3-13; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17, pp. 99-120; Baxi, C.V., The current context of corporate governance in India (2005) Global Business Review, 6 (2), pp. 303-314; Bititci, U., Managerial processes: Business process that sustain performance (2011) International Journal of Operation and Production Management, 31 (8), pp. 851-887; Bititci, U., Performance measurement: Where next? (2011) International Journal of Management Reviews, 13 (4); Boone, C., De Branbander, B., Van Witteloostuijn, A., CEO locus of control and small firm performance: An integrative framework and empirical test (1996) Journal of Management Studies, 33 (5), pp. 667-699; Bourne, M., Researching performance measurement system implementation: The dynamics of success and failure (2005) Production Planning & Control, 16 (2), pp. 101-113; Bourne, M., The success and failure of performance measurement initiatives (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Bowonder, B., Industrialization and economic growth of India: Interaction of indigenous and foreign technology (1998) International Journal of Technology Management, 15 (67), pp. 622-645; Cagliano, R., Blackmon, K., Voss, C., Small firms under microscope: International differences in production/operations management practices and performance (2001) Integrated Manufacturing Systems, 12 (7), pp. 469-482; Carney, M., Corporate governance and competitive advantage in family-controlled firms (2005) Entrepreneurship Theory and Practice, 29, pp. 249-266; Chakrabarti, R., Megginson, W.L., Yadav, P.K., Corporate governance in India (2008) Journal of Applied Corporate Finance, 20, pp. 59-72; Compagno, C., Corporate governance in Italian family - Owned small firms (2000) Small Business, 3, pp. 105-117; Cook, T., Campbell, D.T., (1979) Quasi Experimentation: Design and Analysis Issue for Field Settings, , Chicago: Rand McNally; Corbetta, G., Montemerlo, D., Ownership, governance, and management issues in small and medium-size family businesses (1999) Family Business Review, 12 (4), pp. 361-374; Corbetta, G., Salvato, C., The board of directors in family firms: One size fits all? (2004) Family Business Review, 17, pp. 119-134; Corbetta, G., Tomaselli, S., Boards of Directors in Italian family businesses (1996) Family Business Review, 9, pp. 403-421; Corbin, J.M., Strauss, A.C., (2008) Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory, , California: Sage; Cowling, M., Productivity and corporate governance in smaller firms (2003) Small Business Economics, 20, pp. 335-344; Daily, C.M., Dalton, D.R., Cannella, A.A., Corporate governance: Decades of dialogue and data (2003) Academy of Management Review, 28, pp. 371-382; Dalton, D.R., Meta analytic reviews of board composition, leadership structure and financial performance (1998) Strategic Management Journal, 9, pp. 269-290; Dangayach, G.S., Deshmukh, S.G., Manufacturing strategy: Experiences from select Indian organisations (2000) Journal of Manufacturing Systems, 19 (2), pp. 134-148; Dangayach, G.S., Deshmukh, S.G., Evidence of manufacturing strategies in Indian industry: A survey (2003) International Journal of Production Economics, 83, pp. 279-298; De Waal, A., (2007) Strategic Performance Management. A Managerial and Behavioural Approach, , New York: Pagrave Macmillan; Deakins, D., Oneill, E., Mileham, P., The role and influence of external directors in small, entrepreneurial companies: Some evidence on VC and non-VC appointed external directors (2000) Venture Capital, 2, pp. 111-127; Denzin, N.K., (1978) The Research Act, , New York: McGraw-Hill; Donckels, R., Fröhlich, E., Are family businesses really different? European experiences from STRATOS (1991) Family Business Review, 7, pp. 149-160; Dwivedi, N., Jain, A.K., Corporate governance and performance of Indian firms: The effect of board size and ownership (2005) Employee Responsibilities and Rights Journal, 17 (3), pp. 161-172; Eisenhardt, K., Building theories from case-study research (1989) Academy of Management Review, 14 (4), pp. 116-121; Fama, E.F., Jensen, M.C., Separation of ownership and control (1983) Journal of Law and Economics, 26, pp. 301-325; Fielding, N.G., Fielding, J.L., (1986) Linking Data, , Newbury Park, CA: Sage; Finkelstein, S., Hambrick, D.C., (1996) Strategic Leadership: Top Executives and Their Effects on Organisations, , Minneapolis/St Paul: West Publishing Company; Fitzgerald, L., (1991) Performance Measurement in Service Businesses, , London: CIMA; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Forbes, D., Milliken, F., Cognition and corporate governance: Understanding Boards of Directors as strategic decision making groups (1999) Academy of Management Review, 24, pp. 489-505; Gabrielsson, J., Huse, M., Outside directors in SME boards: A call for theoretical reflections (2005) Corporate Board: Role, Duties & Composition, 1 (1), pp. 28-38; Gabrielsson, J., Winlund, H., Boards of Directors in small and medium-sized industrial firms: Examining the effects of the board's working style on board task performance (2000) Entrepreneurship & Regional Development, 12 (4), pp. 311-330; Garengo, P., Performance measurement system in SMEs taking part to quality award programs (2009) Total Quality Management and Business Excellence, 20 (3), pp. 91-105; Garengo, P., Bernardi, G., Organisational capability in SMEs: Performance measurement as a key system in supporting company development (2007) International Journal of Productivity & Performance Management, 56 (56), pp. 518-532; Garengo, P., Biazzo, S., Bititci, U., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Garengo, P., Bititci, U., Towards a contingency approach to performance measurement: An empirical study in Scottish SMEs (2007) International Journal of Operations and Production Management, 27 (8), pp. 802-825; George, G., Wood Jr., D.R., Khan, R., Networking strategy of boards: Implications for small and medium-sized enterprises (2001) Entrepreneurship and Regional Development, 13 (3), pp. 269-285; Ghose, A.K., SMEs and environment protection (2001) Productivity, 42 (2), pp. 210-216; Gibson, C., Nolan, R.L., Managing the four stages of EDP growth (1974) Harvard Business Review, 52 (1), pp. 76-88; Gnan, L., Montemerlo, D., Structure and dynamics of ownership, governance and strategy: Role of family and impact on performance in Italian SMEs (2001) The Role of Family in Family Business, , edited by G. Corbetta and D. Montemerlo. Milano, Italy: EGEA; Grundei, J., Talaulicar, T., Company law and corporate governance of start-ups in Germany: Legal stipulations, managerial requirements, and modification strategies (2002) Journal of Management and Governance, 6, pp. 1-27; Hall, B.H., Lotti, F., Mairesse, J., Innovation and productivity in SMEs: Empiricl evidence for Italy (2009) Small Business Economics, 33 (1), pp. 13-33; Hambrick, D.C., Mason, P.A., Upper echelons: The organisation as a reflection of its top managers (1984) Academy of Management Review, 9, pp. 193-206; Handler, W.C., Handler, methodological issues and considerations in studying family business (1989) Family Business Review, 1 (4), pp. 257-276; Harvey, S.J.J., What can the family contribute to business? Examining contractual relationships (1999) Family Business Review, 12 (1), pp. 61-71; Hillman, A.J., Dalziel, T., Boards of Directors and firm performance: Integrating agency and resource dependence perspectives (2003) Academy of Management Review, 28, pp. 383-396; Huberman, M., Miles, M.B., (2002) The Qualitative Researcher's Companion, , Thousand Oaks: SAGE Publications, Inc; Hudson, M., Smart, P.A., Bourne, M., Theory and practice in SME performance measurement systems (2001) International Journal of Operations and Production Management, 21 (8), pp. 1096-1115; Hudson Smith, M., Smith, D., Implementing strategically aligned performance measurement in small firms (2007) International Journal of Production Economics, 106 (2), pp. 393-408; Huse, M., Corporate governance: Understanding important contingencies (2005) Corporate Ownership & Control, 2, pp. 41-50; Jackling, B., Johl, S., Board structure and firm performance: Evidence from India's top companies (2009) Corporate Governance: An International Review, 17 (4), pp. 492-509; Jain, S.K., Gumpert, D.E., Look to outsiders to strengthen small business boards (1980) Harvard Business Review, 58, pp. 162-170; Johannisson, B., Huse, M., Recruiting outside board members in the small family business: An ideological challenge (2000) Entrepreneurship & Regional Development, 12, pp. 353-378; Johnson, J., Dayly, C.M., Ellstrand, A.E., Board of directors: A review and research agenda (1996) Journal of Management, 22 (3), pp. 409-438; Kathuria, R., Competitive priorities and strategic consensus in emerging economies: Evidence from India (2010) International Journal of Operations and Production Management, 30 (8), pp. 879-896; Kidder, L.H., Judd, C.M., (1986) Research Methods in Social Relations, , New York: CBS College Publishing; Kumar, R., Sethi, K.A., (2005) Doing Business in India, , USA: Palgrave MacMillan; Lange, H., Sahu, C., (2008) Board Structure and Size: The Impact of Changes to Clause 49 in India. U21 Global Working Paper Series, (4), pp. 1-16; La Porta, R., Investor protection and corporate governance (2000) Journal of Financial Economics, 58, pp. 3-27; Mancinelli, S., Mazzanti, M., Innovation, networking and complementarity: Evidence on SME performances for a local economic system in North-Eastern Italy (2009) Annals of Regional Science, 43, pp. 567-597; Markman, G.D., Balkin, D.B., Schjoedt, L., Governing the innovation process in entrepreneurial firms (2001) Journal of High Technology Management Research, 12, pp. 273-293; Merton, R.K., (1957) Social Theory and Social Structure, , New York: Free Press; Miles, R.E., Huberman, A.M., (1994) Quantitative Data Analysis - A Source of New Methods, , London: Sage Publication; Miller, D., Toulouse, J.M., Chief executive personality and corporate strategy and structure in small firm (1996) Management Science, 32 (11), pp. 1389-1409; Mustakallio, M., Autio, E., Zahra, S.A., Relational and contractual governance in family firms: Effects on strategic decision making (2002) Family Business Review, 15, pp. 205-222; Nankervis, A.R., Chatterjee, S.R., The resurgence of China and India: Collaboration or competition? (2011) Human Systems Management, 30 (12), pp. 97-113; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15, pp. 80-116; Neely, A., (2002) Getting the Measure of Your Business, , UK: Cambridge University Press; Neubauer, F., Lank, A.G., (1998) The Family Business: Its Governance for Sustainability, , London: McMillan Press; Nierdekohr, K., Ikeda, J., (2005) Credit Union Governance. White Paper, , World Council of Credit Union SNC; Nordqvist, M., Melin, L., The dynamics of family firms: An institutional perspective on corporate governance and strategic change (2002) Understanding the Small, Family Firm, , edited by D. Fletcher. London: Routledge; (1999) OECD Principles of Corporate Governance, , Organisation for Economic Cooperation and Development [OECD]. Paris: OECD Publication Service; Rajagopalan, N., Zhang, Y., Corporate governance reforms in China and India: Challenges and opportunities (2008) Business Horizons, 51, pp. 55-64; Rajgopal, J., Challenges: Then and now (1999) The Hindu Survey of Indian Industry, pp. 39-41; Ramaswamy, K., Li, M., Veliyath, R., Variations in ownership behaviour and propensity to diversify: A study of the Indian corporate context (2002) Strategic Management Journal, 23, pp. 345-358; Sarkar, J., Sarkar, S., Multiple board appointments and firm performance in emerging economies: Evidence from India (2009) Pacific-Basin Finance Journal, 17 (2), pp. 271-293; Schulze, W.S., Lubatkin, M.H., Dino, R.N., Altruism, agency and the competitiveness of family firms (2002) Managerial and Decision Economics, 23, pp. 247-259; Schulze, W.S., Agency relationships in family firms: Theory and evidence (2001) Organisation Science, 12 (2), pp. 99-116; Schwartz, M.A., Barnes, L.B., Outside boards and family businesses: Another look (1991) Family Business Review, 4 (3), pp. 269-285; Sharma, M.K., Bhagwat, R., Performance measurement system: Case studies from SMEs in India (2007) International Journal of Productivity and Quality Management, 2 (4), pp. 475-509; Sharma, M.K., Bhagwat, R., Dangayach, G.S., Practice of performance measurement: Experience from Indian SMEs (2005) International Journal of Globalisation and Small Business, 1 (2), pp. 183-213; Speckbacher, G., Bischof, J., Pfeiffer, T., A descriptive analysis on the implementation of balanced scorecards in German-speaking countries (2003) Management Accounting Research, 14 (4), pp. 361-387; Tuomela, T.S., The interplay of different levers of control: A case study of introducing a new performance measurement system (2005) Management Accounting Research, 16 (3), pp. 293-320; Tuteja, S.K., Partnership for SME development: Ancillarisation/subcontracting (2001) Productivity, 42 (2), pp. 247-255; Urlacher, P.V., (2008) New Issues in Corporate Governance, , New York, NY: Nova Science Publishers; Van Den Heuvel, J., Van Gils, A., Voordeckers, W., Board roles in small and medium-sized family businesses: Performance and importance (2006) Corporate Governance: An International Review, 14 (5), pp. 467-485; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operational management (2002) International Journal of Operations and Production Management, 22 (2), pp. 195-219; Voss, C.A., Made in Europe: Small companies (1998) Business Strategy Review, 9 (4), pp. 1-19; Wacker, J., A definition of theory: Research guidelines for different theory-building research methods in operations management (1998) Journal of Operations Management, 16 (4), pp. 361-386; Wettstein, T., Kueng, P.A., A maturity model for performance measure systems (2002) Management Information Systems, , edited by C. Brebbia and P. Pascola. Southampton: WIT Press; Yin, R.K., (1994) Case Study Research, Design and Methods, , 2nd ed. Thousand Oaks, CA: Sage Publications; Zahra, S.A., Neubaum, D.O., Huse, M., Entrepreneurship in medium sized firm: Exploring the effects of ownership and governance systems (2000) Journal of Management, 26 (5), pp. 947-976; Zahra, S.A., Pearce, J.A., Board of Directors and corporate financial performance: A review and integrative model (1989) Journal of Management, 15 (2), pp. 291-334},
correspondence_address1={Sharma, M.K.; Department of Production and Industrial Engineering, , Jodhpur, Rajasthan, India; email: milindksharma@rediffmail.com},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{Janeš2014203,
author={Janeš, A.},
title={Empirical verification of the balanced scorecard},
journal={Industrial Management and Data Systems},
year={2014},
volume={114},
number={2},
pages={203-219},
doi={10.1108/IMDS-04-2013-0195},
art_number={17104115},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893598736&doi=10.1108%2fIMDS-04-2013-0195&partnerID=40&md5=4daaedde2cd5e31f135e2206307f821f},
affiliation={Faculty of Management, University of Primorska, Koper, Slovenia},
abstract={Purpose - The development and empirical verification of the balanced scorecard (BSC) model are key parts of the research of a case study on the performance measurement system (PMS) of the Ydria Motors LL Company (YM). The paper aims to discuss these issues. Design/methodology/approach - The research was performed as a single case study of modelling the BSC for the manufacturing company and founded on complementary use of qualitative and quantitative methods. The central part of the case study is an empirically evaluated layout of the BSC with the Engle-Granger two-step method. Findings - The results and findings from empirical analysis showed that the methods used are appropriate for inclusion in the methodological approach as they are complementary. Therefore, it can be asserted that the introduction of quantitative methods of continuous data analysis for the implementation of the BSC improved the established approach. In this research, an approach that represents the basis for further work in the field of research in PMSs of companies, with the use of econometric tools, was empirically tested and developed. Research limitations/implications - The generalization of research findings is limited to only one manufacturing company. With the continuation of the research on other case studies, the preliminary lessons learned can be expanded to other organizations. Originality/value - Following the research findings, it can be established that the methodology used provides support to organization's decision-making process in real-time and can be used with different strategies scenarios and forecast simulations and thus supports the prioritization of strategic initiatives. In addition, the developed model allows the integration and testing of various performance indicators and the identification and selection of the most appropriate KPIs. Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.},
author_keywords={Balanced scorecard;  Causality;  Error correction mechanism;  Performance measurement system;  Slovenia;  Strategic map},
keywords={Balanced scorecards;  Causality;  Correction mechanism;  Performance measurement system;  Slovenia;  Strategic map, Error correction;  Industry;  Manufacture;  Supply chain management, Industrial research},
references={Angrosino, M.V., Mays De Pérez, K.A., Rethinking observation (2000) Handbook of Qualitative Research, , Denzin, N.K. and Lincoln, Y.S. (Eds), Sage, Thousand Oaks, CA; Banerjee, A., Dolado, J.J., Hendry, D.F., Smith, G.W., Exploring equilibrium relationships in econometrics through static models: Some Monte Carlo evidence (1986) Oxford Bulletin of Economics and Statistics, 48 (3), pp. 253-277; Banker, R.D., Potter, G., Srinivasan, D., An empirical investigation of an incentive plan that includes nonfinancial performance measures (2000) The Accounting Review, 75 (1), pp. 65-92; Barnabè, F., A system dynamics-based balanced scorecard to support strategic decision making insights from a case study (2011) International Journal of Productivity and Performance Management, 60 (5), pp. 446-473; Barnabè, F., Busco, C., The causal relationships between performance drivers and outcomes (2012) Journal of Accounting & Organizational Change, 8 (4), pp. 528-538; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations & Production Management, 26 (12), pp. 1325-1350; Brock, W.A., Durlauf, S.N., Growth empirics and reality (2001) The World Bank Economic Review, 15 (2), pp. 229-272; Bukh, P.N., Malmi, T., Re-examining the cause-and-effect principle of the balanced scorecard (2005) Accounting in Scandinavia: The Northern Lights, , Jönsson, S. and Mouritsen, J. (Eds), Liber, Malmö; Chareonsuk, C., Chansa-Ngavej, C., Intangible asset management framework: An empirical evidence (2010) Industrial Management & Data Systems, 110 (7), pp. 1094-1112; Cobbold, I., Lawrie, G., Issa, K., Designing a strategic management system using the third-generation balanced scorecard: A case study (2004) International Journal of Productivity and Performance Management, 53 (7), pp. 624-633; Engle, R.F., Granger, C.W.J., Co-integration and error correction: Representation, estimation, and testing (1987) Econometrica, 55 (2), pp. 251-276; Faganel, A., Trnavčevič, A., Sustainable natural and cultural heritage tourism in protected areas: Case study (2012) Annales: Series Historia et Sociologia, 22 (2), pp. 589-600; Gomes, C.F., Yasin, M.M., Lisboa, J.V., Performance measurement practices in manufacturing firms revisited (2011) International Journal of Operations & Production Management, 31 (1), pp. 5-30; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437; Granger, C.W.J., Time series analysis, cointegration, and applications (2004) The American Economic Review, 94 (3), pp. 421-425; Gujarati, D.N., (1995) Basic Econometrics, , McGraw-Hill, New York, NY; Gummesson, E., (2000) Qualitative Methods in Management Research, , Sage, Thousand Oaks, CA; (2010) Balanced Scorecard Report the Strategy Execution Source Article Index 1999-2010, , www.findbooksfree.combscol-pdf.aspx, HBSPC, Harvard Business School Publishing Corporation, (accessed 27 July 2012); Hoque, Z., Chia, M., Competitive forces and the levers of control framework in a manufacturing setting (2012) Qualitative Research in Accounting & Management, 9 (2), pp. 123-145; Hylleberg, S., Mizon, G.E., Cointegration and error correction mechanisms (1989) The Economic Journal, 99 (395), pp. 113-125; Ittner, C.D., Larcker, D.F., Are nonfinancial measures leading indicators of financial performance? An analysis of customer satisfaction (1998) Journal of Accounting Research, 36, pp. 1-35; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the weighting of performance measures: Evidence from a balanced scorecard (2003) The Accounting Review, 78 (3), pp. 725-758; Janeš, A., Instruments and methods for the integration of company's strategic goals and key performance indicators (2013) Kybernetes, 42 (6), pp. 928-942; Jerman, M., Kavčič, S., Kavčič, B., The significance of intangibles: A comparative analysis between Croatia, Slovenia, Czech Republic, Germany and the USA (2010) Ekonomska Istraživanja, 23 (2), pp. 60-69; Kang, B., Kim, D., Kang, S., Periodic performance prediction for real-time business process monitoring (2012) Industrial Management & Data Systems, 112 (1), pp. 4-23; Kaplan, R.S., The balanced scorecard: Comments on balanced scorecard commentaries (2012) Journal of Accounting & Organizational Change, 8 (4), pp. 539-545; Kaplan, R.S., Norton, D., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D., Having trouble with your strategy? Then map it (2000) Harvard Business Review, 78 (5), pp. 167-176; Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, , Harvard Business School Publishing, Boston, MA; Kaplan, R.S., Norton, D.P., Conceptual foundations of the balanced scorecard (2009) Handbook of Management Accounting Research, , Chapman, C., Hopwood, A. and Shields, M. (Eds), Elsevier, Oxford; Kasperskaya, Y., Tayles, M., The role of causal links in performance measurement models (2013) Managerial Auditing Journal, 28 (5), pp. 426-443; Laitinen, E.K., Towards a microeconomic approach of the balanced scorecard (2004) Managerial Finance, 30 (6), pp. 1-27; León-Soriano, R., Muñoz-Torres, M.J., Chalmeta-Rosaleñ, R., Methodology for sustainability, strategic planning and management (2010) Industrial Management & Data Systems, 110 (2), pp. 249-268; Morard, B., Stancu, A., Jeannette, C., Time evolution analysis and forecast of key performance indicators in a balanced scorecard (2013) Global Journal of Business Research, 7 (2), pp. 9-27; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Nørreklit, H., The balance on the balanced scorecard a critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Norton, D.P., Russell, R.H., The office of strategy management - The state of the art, 2011 (2011) Balanced Scorecard Report, 13 (1), pp. 1-6; Phillips, P.C.B., Granger, C., The et interview: Professor Clive Granger (1997) Econometric Theory, 13 (2), pp. 253-303; Pindyck, R.S., Rubinfeld, D.L., (1998) Econometric Models and Economic Forecasts, , McGraw-Hill, Singapore; Stubelj, I., Dolenc, P., Fiscal sustainability of EU member states in the context of current financial crisis (2010) Ekonomska Istraživanja, 23 (4), pp. 37-62; Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R., Development of a balanced scorecard: An integrated approach of ISM and ANP (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 25-59},
correspondence_address1={Janeš, A.; Faculty of Management, , Koper, Slovenia; email: aleksander.janes@gmail.com},
issn={02635577},
coden={IMDSD},
language={English},
abbrev_source_title={Ind Manage Data Sys},
document_type={Article},
source={Scopus},
}

@BOOK{Lui2014187,
author={Lui, A. and Lo, C.K.Y.},
title={Measuring the impact of radio frequency identification (RFID) technologies in improving the efficiency of the textile supply chain},
journal={Fashion Supply Chain Management Using Radio Frequency Identification (RFID) Technologies},
year={2014},
pages={187-202},
doi={10.1533/9780857098115.187},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942803548&doi=10.1533%2f9780857098115.187&partnerID=40&md5=19bc3434beb12d7d070936befdd4bf48},
affiliation={The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong},
abstract={The value of radio frequency identification (RFID) technology is critical for the clothing and textiles supply chain, because the fashion business is characterized by a wide assortment of seasonal items with short life-cycles, high levels of impulse-purchasing and complicated distribution and logistics operations. This study aims to test whether the adoption of the RFID system can improve supply chain performance (measured as inventory days, accounts receivable days and operating cycle). Based on the 31 clothing and textiles companies that publicly announced their RFID adoption, 18 of them were matched with comparable control firms for the testing of abnormal supply chain performance. The results show that clothing and textiles RFID adopters' inventory days drop by about 12.89. days over the 5-year period, while other RFID adopters (other manufacturing sectors) only drop by about 2.47. days. © 2014 Woodhead Publishing Limited. All rights reserved.},
author_keywords={Event study;  RFID;  Supply chain efficiency},
keywords={Drops;  Efficiency;  Radio waves;  Supply chains;  Textiles, Accounts receivables;  Event studies;  Logistics operations;  Manufacturing sector;  Radio frequency identification technology;  Supply chain efficiency;  Supply chain performance;  Textile supply chain, Radio frequency identification (RFID)},
references={(2010) Research.com RFID Market to Reach $5.35Billion this Year, 2011. , ABI Research, New York; Barber, B.M., Lyon, J.D., Detecting abnormal operating performance: The empirical power and specification of test statistics (1996) Journal of Financial Economics, 41 (3), pp. 359-399; Barratt, M., Choi, T., Mandated RFID and institutional responses: Cases of decentralized business units (2007) Production and Operation Management, 16 (5), pp. 569-585; Chang, Y., Does RFID improve firms' financial performance? An empirical analysis (2011) Information Technology and Management, 12 (3), pp. 273-285; Corbett, C.J., Montes-Sancho, M.J., Kirsch, D.A., The financial impact of ISO 9000 certification: An empirical analysis (2005) Management Science, 51 (7), pp. 1046-1059; Das, R., Harrop, P., (2009) RFID Forecasts, Players and Opportunities 2009-2019, 2011. , IDTechEX; DiMaggio, P.J., Powell, W.W., The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (1983) American Sociological Review, 48 (2), pp. 147-160; Hendricks, K.B., Singhal, V.R., The effect of product introduction delays on operating performance (2008) Management Science, 54 (5), pp. 878-892; Lai, K., Wong, C.W.Y., Cheng, T.C.E., Institutional isomorphism and the adoption of information technology for supply chain management (2006) Computers in Industry, 57 (1), pp. 93-98; Landt, J., (2001) Shrouds of Time: The History of RFID, , An AIM Publication; Moon, K.L., Ngai, W.T., The adoption of RFID in fashion retailing: A business value-added framework (2008) Industrial Management & Data Systems, 108 (5), pp. 596-612; Whang, S., Timing of RFID adoption in a supply chain (2010) Management Science, 56 (2), pp. 343-355; Yang, X., Kishore, R., Liu, Z., (2008) The Institutional Facets of Innovation Diffusion Initiating: The Case of Wal-Mart's RFID Campaign},
correspondence_address1={Lo, C.K.Y.; The Hong Kong Polytechnic UniversityHong Kong},
publisher={Elsevier Ltd},
isbn={9780857098115; 9780857098054},
language={English},
abbrev_source_title={Fash. Supply Chain Manag. Using Radio Freq. Identif. (RFID) Technol.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Sari2014590,
author={Sari, E. and Shaharoun, A.B.M. and Ma'aram, A.B.},
title={Preliminary framework of sustainable maintenance performance measurement systems for automotive companies},
journal={Advanced Materials Research},
year={2014},
volume={845},
pages={590-595},
doi={10.4028/www.scientific.net/AMR.845.590},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891550726&doi=10.4028%2fwww.scientific.net%2fAMR.845.590&partnerID=40&md5=2420e7faf7bbdef92590b50124fa8d4e},
affiliation={Department of Industrial Engineering, Trisakti University, Kyai Tapa No 1, G, rogol, 11440 Jakarta Barat, Indonesia; Department of Materials, Manufacturing and Industrial Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia},
abstract={Sustainability has significantly emerged as a critical issue in automotive companies as they have been compelled to be more environmentally benign. Moreover, the role of maintenance management has also been increased as a crucial support function to become a successful and sustainable automotive company. Consequently, it is essential to incorporate sustainability thinking into maintenance management. This paper presents a preliminary framework for sustainable maintenance performance measurement system (SMPMS). The SMPMS is addressed to ensure maintenance management objectives are in-line with overall company objectives in terms of sustainability that balances economic, environmental and social factors. © (2014) Trans Tech Publications, Switzerland.},
author_keywords={Automotive company;  Maintenance;  Performance measures;  Sustainability},
keywords={Automotive companies;  Company objectives;  Critical issues;  Environmentally benign;  Maintenance management;  Performance measure;  Support functions;  Sustainable maintenance, Industrial engineering;  Industrial research;  Industry;  Maintenance;  Supply chain management, Sustainable development},
references={Aoudia, M., Belmokhtar, O., Zwingelstein, G., Economic impact of maintenance management ineffectiveness of an oil gas company (2008) Journal of Quality In Maintenance Engineering, 14 (3), pp. 237-261; Liyanage, J.P., Kumar, U., Towards a value-based view on operations and maintenance performance management (2003) Journal of Quality In Maintenance Engineering, 9 (4), pp. 333-350; Simoes, J.M., Gomes, C.F., Yasin, M.M., A literature review of maintenance performance measurement: A conceptual framework and directions for future research (2011) Journal of Quality In Maintenance Engineering, 17 (2), pp. 116-137; Parida, A., (2006) Development of a Multi-criteria Hierarchical Framework For Maintenance Performance Measurement: Concepts, Issues and Challenges, , Doctor Philosophy, Lulea University of Technology, Lulea; Kutucuoglu, K.Y., Hamali, J., Irani, Z., Sharp, J.M., A framework for managing maintenance using performance measurement systems (2001) International Journal of Operations & Production Management, 21 (2), pp. 173-194; Mather, D., (2005) The Maintenance Scorecard: Creating Strategic Advantages, , Industrial Press, New York; Tsang, A.H.C., Jardine, A.K.S., Kolodny, H., Measuring maintenance performance: A holistic approach (1999) International Journal of Operations & Production Management, 19 (7), pp. 691-715; Alsyouf, I., Measuring maintenance performance using a balanced scorecard approach (2006) Journal of Quality In Maintenance Engineering, 12 (2), pp. 133-149; Parida, A., Asset performance assessment (2012) Asset Management: The State of the Art In Europe From a Life Cycle Perspective, pp. 101-113. , in: T.V.D. Lei, P. Herder, and Y. Wijnia (Eds.), Springer, London; Liyanage, J.P., Badurdeen, F., Ratnayake, R.M.C., Industrial asset maintenance and sustainability performance: Economical, environmental, and societal implications (2009) Handbook of Maintenance Managementand Engineering, pp. 665-693. , M. Ben-Daya, S.O. Duffuaa, A. Raouf, J. Knezevic, and D. Ait-Kadi (Eds.), Springer, London; Keijzers, G., The transition to the sustainable enterprise (2002) Journal of Cleaner Production, 10, pp. 349-359; Liyanage, J.P., Operations and maintenance performance in production and manufacturing assets: The sustainability perspective (2007) Journal of Manufacturing Technology Management, 18 (3), pp. 304-314; Nunes, B., Bennett, D., Green operations initiatives in the automotive industry: An environmental reports analysis and benchmarking study (2010) Benchmarking: An International Journal, 17 (3), pp. 396-420; Sari, E., Shaharoun, A.B.M., A proposed maintenance performance measures for manufacturing companies (2012) APIEMS Conference, , Phuket; Kodali, R., Mishra, R.P., Anand, G., Justification of world-class maintenance systems using analytic hierarchy constant sum method (2009) Journal of Quality In Maintenance Engineering, 15 (1), pp. 47-77; Raouf, A., Maintenance quality and environmental performance improvement: An integrated approach (2009) Handbook of Maintenance Managementand Engineering, pp. 649-664. , in: M. Ben-Daya, S.O. Duffuaa, A. Raouf, J. Knezevic, and D. Ait-Kadi (Eds.), Springer, London; Tseng, M.L., Divinagracia, L., Divinagracia, R., Evaluating firm's sustainable production indicators in uncertainty (2009) Computers and Industrial Engineering, 57, pp. 1393-1403; Gupta, A., Jayal, A.D., Chimienti, M., Jawahir, I.S., A total life-cycle approach towards developing product metrics for sustainable manufacturing (2011) 18th CIRP International Conference On Life Cycle Engineering, , Braunschweig; Joung, C.B., Carrell, J., Sarkar, P., Feng, S.C., Categorization of indicators for sustainable manufacturing (2012) Ecological Indicators, 24, pp. 148-157; Ajukumar, V.N., Gandhi, O.P., Evaluation on green maintenance initiatives in design and development of mechanical systems using an integrated approach (2013) Journal of Cleaner Production},
correspondence_address1={Shaharoun, A. B. M.; Department of Materials, Manufacturing and Industrial Engineering, , 81310 UTM Johor Bahru, Johor, Malaysia; email: awaludin@ic.utm.my},
issn={10226680},
isbn={9783037859360},
language={English},
abbrev_source_title={Adv. Mater. Res.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{NoAuthor2014,
title={1st International Materials, Industrial, and Manufacturing Engineering Conference, MIMEC 2013},
journal={Advanced Materials Research},
year={2014},
volume={845},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891542932&partnerID=40&md5=5f2ef1381e471f403c6f3667df194f57},
abstract={The proceedings contain 187 papers. The special focus in this conference is on Materials, Industrial, and Manufacturing Engineering. The topics include: Liquid phase epitaxy of Si-doped AIN at 1300 °C in Ga-Al melt; influence of heat treatment cooling mediums on the degradation property of biodegradable Zn-3Mg alloy; mineral matter study in Adaro coal-EFB blends to mitigate ash slagging problem in the gasification process; effect of rare earth addition on microstructure and mechanical properties of Al-Si alloys; simulation of laminar mixing in fractal perforated plate static mixers; effects of various seeding methods on multi-layered diamond coatings deposited via hot filament chemical vapor deposition; time-frequency signal analysis in machinery fault diagnosis; analysis of axial vibration in the laminated rubber-metal spring; effect of variation in grain size on high temperature creep test of Fe-Ni-Cr alloy; performance and emission of a diesel engine fuelled with preheated palm oil biodiesel under high load conditions; spray characteristic of diesel-water injector for burner system; wear properties of oil palm cellulose fibre reinforced polymer composites; static analysis of a laminated rubber-metal spring using finite element method; characterizations of zeolite, polyaniline and zeolite/polyaniline as antifouling materials for marine applications; inclined injection of supersonic steam into subcooled water; analysis of residual wavelet scalogram for machinery fault diagnosis; effect of elements cerium and lanthanum on eutectic solidification of Al-Si-Cu near eutectic cast alloy; blade faults classification and detection methods; vibration analysis of multi stages rotor for blade faults diagnosis; the effectiveness of bioremediation treatment for diesel-soil contamination; determining the S-N fatigue curve for lava zirconium dioxide; development of microbial metabolic processes to repair concrete joint leakage; simulation of drill pipe lateral vibration due to riser's oscillation; corrosion behavior of low carbon steel welded joint in NaCl solution; three-dimensional modelling and finite element analysis of an ankle external fixator; influence of polyethylene thickness on axis pin in linked elbow implant; strength prediction of notched woven composite plates using a cohesive zone approach; synthesis and characterization of novel hybrid ladderlike polysilsesquioxanes containing polypropylene moieties; a well-condition asymptotic waveform evaluation method for heat conduction problems; the effect of cannulated screw placement angle in the management of femoral neck fracture; investigation of intermetallic phase formation and structural analysis in annealed Al/Cu bilayer thin films; improvement of cylinder buckling knockdown factor through imperfection sensitivity; XRD investigations on film thickness and substrate temperature effects of DC magnetron sputtered ZnO films; the effects of solution baths to the microstructure and thickness of Ni-Ti plating by high speed plating; characterization of Nigerian clay as porous ceramic material; characterizations of physical properties of Sn-Bi solder alloy; finite element analysis of fatigue in pipelines due to slug flow; partial discharge detection using acoustic and optical methods in high voltage power equipments; tensile properties and fatigue strength in high humidity in extruded 7075 Al alloys with different aging conditions; experimental investigation on free vibration of foam-core sandwich plate with and without circular polymer columns; quasi-static flexural and tensile behavior of glass fiber reinforced polymer; enhanced functional properties of natural fiber-reinforced composites; study of wear prediction on total ankle replacement; lubricant potentiality of degraded palm oil and effect of free fatty acid addition; linear elastic analysis of bovine cortical bone under compression loading; study the effect of fluid medium toward synthetic cancellous bone structure; piping vibration due to pressure pulsations; effect of alloying element on Al-Si alloys; mechanical behavior of functionally graded sandwich hollow cylinders under internal pressure; composition modification of electroplated nickel interlayer on tungsten carbide substrate by thermal carburizing; modelling the elastic constants of cubic zirconia using molecular dynamics simulations; influence of graphite reinforcement on mechanical properties of aluminum-boron carbide composites; failure analysis of cementless hip joint prosthesis; influence of fillers surface characteristics on bound rubber properties of filled natural rubber compounds; visible-light photodegradation of diisopropanolamine using bimetallic Cu-Fe/TiO2 photocatalyst; tensile behaviour of Al5052 alloy sheets annealed at different temperatures; influence of bias voltage on corrosion resistance of TiN coated on biomedical TiZrNb alloy; analysis of contamination soil with cu from road side by using laser ablation technique; preparation of Ni loaded on zeolite and its application for conversion of glycerol to hydrogen; influence of carbon concentrations in reducing Co and Cr ions release in cobalt based implant; analysis of building materials for indoor thermal performance and thermal comfort; short-term breakdown in silicone rubber based nanocomposites caused by electrical treeing; recycling of aluminium - developing design criteria for aluminium cans compressor; knowledge spillover absorptive ability and performance of firms in high-tech clusters; the need of end-of-life vehicles management system in Malaysia; performance measurement system for sustainable supply chain management; probabilistic formulation for emergency facility location in a divided area; multi-floor facility layout improvement using systematic layout planning; technology management; a hybrid genetic algorithm for solving job shop scheduling problems; solving an industrial shop scheduling problem using genetic algorithm; recycler selection using fuzzy AHP by considering sustainability; pattern recognition using Mahalanobis-Taguchi system on connecting rod through remanufacturing process; modeling and simulation of a heat recovery steam generator using partially known design point data; considered values for a supplier selection case at an automotive company; review of the relationship between reverse logistics and competitiveness; a review of inventory management for remanufacturing environment; competencies of work in agricultural research units; size optimization using normalized radial basis function and bat algorithm; semi-quantitative risk assessment matrix for rotating equipment; assessment of human factor performance using Bayesian inference and inherent safety; researches on mitigation of risks of logistics finance caused by information not fully shared; preliminary work towards development of a dynamic job shop scheduling model; conceptual optimization model of inventory/distribution network with multi role nodes; a conceptual methodology for recognition of constrained control chart patterns; effect of cutting parameters on surface roughness in turning of bone; review on methodology for life cycle costing of membrane system for wastewater filtration; determination of parameters for sustainability assessment of hollow fiber membrane module life cycle; effects of on-time on microstructure and corrosion resistance of pulse-micro-arc oxidation coatings formed on titanium alloy; texture feature analysis of milled components using vision system; autonomous mobile robot platform for on-the-road navigation; effect of buffer size variation on remanufacturing environment; tool-life of wiper and standard cutting tool in finish turning of SAE 4140; effect of butt joint on mechanical properties of welded low carbon steel; power demand calculations in turning of aluminum alloy; parametric control of oxyacetylene flame profile; synthesis and characterization of polyaniline-polypyrrole composite; the effect of cutting parameters on power consumption during turning nickel based alloy; sustainability assessment methodology for concrete manufacturing process; glycerol as plasticizer for waste polystyrene based metal injection molding (MIM) binder; development of multi-criteria decision analysis methodology to determine product end-of-life treatment options; investigation of recast layer of non-conductive ceramic due to micro-EDM; manufacturing of MWNT filled carbon fiber reinforced polypropylene with gelatin; effects of friction modifier additives on HDD substrate defects and surface topography during CMP; effect of manufacturing process on free vibration of foam-core sandwich plate; high-speed photographic study of chip formation during end milling of GFRP composites; design and fabrication of customised scaffold for femur bone using 3D printing; cutting force and temperature variation in bone drilling - a review; investigation of process performances and cut surface characteristics in the wire- EDMing of silicon; oil spills hazard and sustainable mitigation approach; effect of drill point angle on surface integrity when drilling titanium alloy; manufacturing process of blended delta-shaped wing model; TCP/IP-based control and monitoring of manufacturing system; micro-electrode fabrication process using EDM and Effect of electrospinning parameters setting towards fiber diameter.},
issn={10226680},
isbn={9783037859360},
language={English},
abbrev_source_title={Adv. Mater. Res.},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Torabizadeh2014516,
author={Torabizadeh, M.E. and Noordin, M.Y. and Awaluddin, M.S.},
title={Performance measurement system for sustainable supply chain management},
journal={Advanced Materials Research},
year={2014},
volume={845},
pages={516-520},
doi={10.4028/www.scientific.net/AMR.845.516},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891523856&doi=10.4028%2fwww.scientific.net%2fAMR.845.516&partnerID=40&md5=688837dc0424cb4752af3924c3f98b18},
affiliation={Universiti Teknologi Malaysia, Skudai, Malaysia},
abstract={In recent years the topic of sustainable supply chain has received dramatic attention and become an important research area. These days there are some new challenges which companies should consider them such as climate changes, the negative effect of downturn, face the growing attention of people to ecology. In order to evaluate the sustainability performance measurement system becomes an important tool to assess the sustainability on environmental, social and economy aspect of supply chain management. For making the business case for sustainability issues in supply chain, it is critical to call for effective performance measurement system and metrics development. © (2014) Trans Tech Publications, Switzerland.},
author_keywords={Performance measurement system;  Sustainable supply chain management;  Triple bottom line},
keywords={Business case;  Effective performance;  Performance measurement system;  Sustainability issues;  Sustainability performance;  Sustainable supply chains;  Triple Bottom Line, Climate change;  Industrial engineering;  Industrial research;  Sustainable development, Supply chain management},
references={Gunasekaran, A., Patel, C., Tirturoglu, E., Performance measures and metrics in a supply chain environment (2000) Int J Operation Prod Manag, 21 (1-2), pp. 71-87; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain, Supply Chain Management (2006) An International Journal, 11 (4), pp. 317-327; Govil, M., Proth, J.-M., (2002) Supply Chain Design and Managemnet, Strategic and Tactical Perspective, , First ed, Academic press,An Elsevier science imprint, Chapter 2,7-16; Mentzer, J., Witt, W.D., Keebler, J., Min, S., Nix, N., Smith, D., Zacharia, Z., Defining Supply Chain (SC) management (2001) Journal of Business Logistics, 22 (2); (1987) Our Common Future, , World commission on environment and development, Oxford, UK, Oxford University Press; Elkington, J., Cannibals with Forks: The Triple Bottom Line of 21st Century Business. Reprint (originally published 1997) (2002) Environmental Quality Management, pp. 37-51; Sikdar, S.K., Sustainable development and sustainability metrics (2003) AICHE Journal, 49 (8), pp. 1928-1932; Teuteberg, F., Wittstruck, D., (2010) A Systematic Review of Sustainable Supply Chain Management Research, Accounting and Information Systems, pp. 1001-1015. , University of Osnabruck; Robert, S., Kaplan, Norton, D.P., The balanced scorecard measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25, pp. 1264-1277; Cuthbertson, R., Piotrowicz, W., Supply chain best practices - identification and categorization of measures and benefits (2008) International Journal of Productivity and Performance Management, 57, pp. 389-404; Sloan, T.W., Measuring the Sustainability of Global Supply Chains: Current Practices and Future Directions (2010) Journal of Global Business Management, 6, pp. 1-16; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard, linking sustainability management to business strategy (2002) Business Strategy and The Environment, 11, pp. 269-284; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) Int. J. Production Economics, 140},
correspondence_address1={Universiti Teknologi Malaysia, Skudai, Malaysia},
issn={10226680},
isbn={9783037859360},
language={English},
abbrev_source_title={Adv. Mater. Res.},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Kurien2014259,
author={Kurien, G.P. and Qureshi, M.N.},
title={Measurement of flexibility and its benchmarking using data envelopment analysis in supply chains},
journal={Organisational Flexibility and Competitiveness},
year={2014},
pages={259-272},
doi={10.1007/978-81-322-1668-1_18},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028525342&doi=10.1007%2f978-81-322-1668-1_18&partnerID=40&md5=6f081850e80d38db06bbbe6926c83ec8},
affiliation={Department of Mechanical Engineering, Babaria Institute of Technology, Varnama, Vadodara, Gujarat  391240, India; College of Engineering, Department of Industrial Engineering, King Khalid University, Abha, Saudi Arabia; Department of Mechanical Engineering, Faculty of Technology and Engineering, M.S. University of Baroda, Vadodara, Gujarat  390001, India},
abstract={Supply Chain Flexibility is becoming a key strategic issue for Supply Chain (SC) performance. Performance Measurement Systems (PMS) for Flexibility is critical for its monitoring, control and improvement. A comparative analysis of some widely cited PMS for SC flexibility have been undertaken and it indicates that the flexibility measurement methodology proposed by Beamon, International Journal of Operations & Production Management, 19(3), 275-292, 1999 is a suitable framework for SC Flexibility measurement. However there are limitations in this framework when they are used for benchmarking similar SC. Data Envelopment Analysis (DEA) helps in finding relative efficiencies of similar SCs, bench marking and evaluate areas of possible improvements. This paper demonstrates use of DEA with SC Flexibility measurement to facilitate effective measurement and benchmarking of SC flexibility. © Springer India 2014.},
author_keywords={Data envelopment analysis (DEA);  Flexibility in supply chain;  Flexibility measurement;  Performance measurement system (PMS);  Supply chain management (SCM)},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems:A development guide (1997) International Journal of Operations & Production Management, 17 (5-6), pp. 522-534; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European journal of operational research, 2 (6), pp. 429-444; George, S.A., Rangaraj, N., A performance benchmarking study of Indian railway zones (2008) Benchmarking: An International Journal, 15 (5), pp. 599-617; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 126-147; Li, X., Goldsby, T.J., Holsapple, C.W., Supply chain agility: Scale development (2009) The International Journal of Logistics Management, 20 (3), pp. 408-424; Madu, C.N., Kuei, C.H., (1998) International Journal of Quality Science, , 3(4), 320-327; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Schmenner, R.W., Tatikonda, M.V., Manufacturing process flexibility revisited (2005) International Journal of Operations & Production Management, 25 (12), pp. 1183-1189; Sethi, A.K., Sethi, S.P., Flexibility in manufacturing: A survey (1990) International Journal of Flexible Manufacturing Systems, 2 (4), pp. 289-328; Slack, N., Flexibility as a manufacturing objective (1983) International Journal of Operations & Production Management, 3 (3), pp. 4-13; Talluri, S., Data envelopment analysis: Models and extensions (2000) Decision Line, 31 (3), pp. 8-11; Tangen, T., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Tangen, T., Insights from research: Improving the performance of a performance measure (2005) Measuring Business Excellence, 9 (2), pp. 4-11; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; William, J.C., Germain, R.N., Birou, L., Variance vs average: Supply chain lead-time as a predictor of financial performance (2007) Supply Chain Management: An International Journal, 12 (5), pp. 349-357},
correspondence_address1={Kurien, G.P.; Department of Mechanical Engineering, Varnama, India; email: gpkurien@sify.com},
publisher={Springer India},
isbn={9788132216681; 9788132216674},
language={English},
abbrev_source_title={Organisational Flex. and Competitiveness},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Sari2014161,
author={Sari, I.U. and Ugurlu, S. and Kahraman, C.},
title={Prioritization of Supply Chain Performance Measurement Factors by a Fuzzy Multi-criteria Approach},
journal={Springer Series in Advanced Manufacturing},
year={2014},
pages={161-180},
doi={10.1007/978-1-4471-5295-8_8},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963737617&doi=10.1007%2f978-1-4471-5295-8_8&partnerID=40&md5=e435163bfc1093c8dfb3dd9136d83f00},
affiliation={Department of Industrial Engineering, Istanbul Technical University, İstanbul, 34367, Turkey},
abstract={Measurement of supply chain performance is an important issue to identify success, to understand processes, to figure out problems and where improvements are possible as well as provide facts for decision-making. Using classical performance measurement techniques, it may not be possible to incorporate judgments of decision makers comprehensively. Hence, we propose a fuzzy multi-criteria evaluation method for this purpose in the framework of supply chain performance measurement. Fuzzy DEMATEL is used to prioritize the performance measurement criteria of supply chain. We also present a sensitivity analysis using different linguistic scales. © 2014, Springer-Verlag London.},
author_keywords={DEMATEL method;  Fuzzy sets;  Linguistic scale;  Performance measurement;  Supply chain},
references={Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Dec Supp Syst, 42 (1), pp. 283-301; Bai, C., Sarkis, J., Supply–chain performance–measurement system management using neighbourhood rough sets (2012) Int J Prod Res, 50 (9), pp. 2484-2500; Beamon, B.M., Measuring supply chain performance (1999) Int J Oper Prod Mgmt, 19 (3), pp. 275-292; Berrah, L., Cliville, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Comput Ind, 58 (7), pp. 709-719; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Prod Plan Con, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput Ind Eng, 53 (1), pp. 43-62; Chang, B., Chang, C.W., Wu, C.H., Fuzzy DEMATEL method for developing supplier selection criteria (2011) Expert Syst Appl, 38 (3), pp. 1850-1858; Chang, Y.H., Yeh, C.H., Cheng, J.H., Decision support for bus operations under uncertainty: A fuzzy expert system approach (1998) Omega, 26 (3), pp. 367-380; Chen, L.H., Chiou, T.W., A fuzzy credit–rating approach for commercial loans: A Taiwan case (1999) Omega, 27 (4), pp. 407-419; Chen, S.J., Hwang, C.L., Hwang, F.P., (1992) Fuzzy Multiple Attribute Decision Making: Methods and Applications. Lecture Notes in Economics and Mathematical Systems, , Springer–Verlag, Berlin; Chibba, A., (2007) Measuring Supply Chain Performance Measures Prioritizing Performance Measures, , Licentiate thesis, Sweden; Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London; Christopher, M., Towill, D., Supply chain migration from lean and functional to agile and customized (2000) Supply Chain Mgmt: Int J, 5 (4), pp. 206-213; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Bus Rev March– April Reprint Number: 97205, pp. 105-116; Gabus A, Fontela E (1972) World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Centre, Switzerland; Gabus, A., Fontela, E., (1973) Perceptions of the World Problematic: Communication Procedure, Communicating with Those Bearing Collective Responsibility (DEMATEL Report No, p. 1. , Battelle Geneva Research Centre, Switzerland; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature for research and applications (2007) Int J Prod Res, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tittiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int J Oper Prod Mgmt, 2 (1-2), pp. 71-87; Hausman, W.H., (2004) The Practice of Supply Chain Management: Where Theory and Application Converge, pp. 61-73. , Supply chain performance metrics. In: Harrison TP, Lee HL and Neale JJ, Springer Science and Business, New York; Hess, P., Siciliano, J., (1996) Management: Responsibility for Performance, , McGraw–Hill, New York; Hoehn, W., Managing organizational performance: linking the balanced scored to a process improvement technique (2003) Proceedings of the 4Th Annual International Symposium in Industrial Engineering on the performance–based Management, pp. 1-12. , Bangkok; Huang, S.H., Uppal, M., A product driven approach to manufacturing supply chain selection. Supply Chain Mgmt (2002) Int J, 7 (4), pp. 189-199; Jeng, D.J.F., Tzeng, G.H., Social influence on the use of clinical decision support systems: Revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique (2012) Comput Ind Eng, 62 (3), pp. 819-828; Kaplan, R.S., Norton, D., The balanced scorecard –measures that drive performance (1992) Harvard Bus Rev, 70, pp. 71-79; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int J Logist Mgmt, 12 (1), pp. 1-19; Li, R.J., Fuzzy method in group decision making (1999) Comput Math Appl, 38 (1), pp. 91-101; Lin, C.J., Wu, W.W., A causal analytical method for group decision–making under fuzzy environment (2008) Expert Syst Appl, 34 (1), pp. 205-213; Liou, J.J.H., Yen, L., Tzeng, G.H., Building an effective safety management system for airlines (2008) J Air Transp Mgmt, 14 (1), pp. 20-26; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) Int J Prod Eco, 62 (1-2), pp. 107-118; Opricovic, S., Tzeng, G.H., Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS (2004) Euro J Oper Res, 156 (2), pp. 445-455; Otto, A., Kotzab, A., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) Euro J Oper Res, 144 (2), pp. 306-320; Parker, C., Performance measurement (2000) Work Study, 49, pp. 63-66; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Proceedings of the 2Nd International Conference on Emerging Trends in Engineering and Technology, pp. ICETET-09; Ross, T.J., (1995) Fuzzy Logic with Engineering Applications, , McGraw-Hill, New York; Selldin, E., Olhager, J., Linking products with supply chains testing Fischer’s model (2007) Supply Chain Mgmt: Int J, 12 (1), pp. 42-51; Sink, D., Tuttle, T., (1989) Planning and Measurement in Your Organization of the Future, , IE Press, Norcross; Slack, N., (1991) The Manufacturing Advantage, , Mercury Books, London; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logist Inf Mgmt, 8 (2), pp. 38-44; Talluri, S., Narasimhan, R., Nair, A., Vendor performance with supply risk: A chance– constrained DEA approach (2006) Int J Prod Eco, 100 (2), pp. 212-222; Tseng, M.L., A causal and effect decision making model of service quality expectation using grey–fuzzy DEMATEL approach (2009) Expert Syst Appl, 36 (4), pp. 7738-7748; Turkett, R.L., (2001) Lean Manufacturing implementation–lean Supply Chain, , www.engin.umich.edu/class, Notes for the course of ‘‘IOE, Manufacturing Strategies’’; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Ind Mgmt Data Syst, 107 (3), pp. 361-381; Wu, W.W., Lee, Y.T., Developing global managers’ competencies using the fuzzy DEMATEL method (2007) Expert Syst Appl, 32 (2), pp. 499-507; Zadeh, L.A., (1965) Fuzzy Sets Inf Con, 8, pp. 338-353; Zadeh, L.A., (1975) The Concept of a Linguistic Variable and Its Application to Approximate Reasoning – I. Inf Sci, 8, pp. 199-249; Zhou, Q., Huang, W., Zhang, Y., Identifying critical success factors in emergency management using a fuzzy DEMATEL method (2011) Safety Sci, 49 (2), pp. 243-252; Zimmermann, H.J., (1991) Fuzzy Set Theory and Its Application, , 2nd edn. Kluwer, Dordrecht},
correspondence_address1={Kahraman, C.; Department of Industrial Engineering, Turkey; email: kahramanc@itu.edu.tr},
publisher={Springer Nature},
issn={18605168},
language={English},
abbrev_source_title={Springer Ser. Adv. Manuf.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Espinoza20141119,
author={Espinoza, O. and Buehlmann, U. and Bond, B.},
title={Quality measurement in the supply chain},
journal={FAIM 2014 - Proceedings of the 24th International Conference on Flexible Automation and Intelligent Manufacturing: Capturing Competitive Advantage via Advanced Manufacturing and Enterprise Transformation},
year={2014},
pages={1119-1126},
doi={10.14809/faim.2014.1119},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84960917280&doi=10.14809%2ffaim.2014.1119&partnerID=40&md5=9bb0b4c1ee1288ddfb908930d1233fd2},
affiliation={Department of Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN  55108, United States; Department of Sustainable Biomaterials, Virginia Polytechnic Institute and State University, Blacksburg, VA  24061, United States},
abstract={Supply Chain Management is the integration of business processes along the value chain, from suppliers to end customer. Performance measurement of these processes has so far received little attention from researchers and practitioners. This paper reports the results from a study of a discrete products supply chain and measures its performance. In particular, a case study was conducted on a wood product supply chain, with a focus on quality performance measurements. The research question was whether current practices were consistent with end customer satisfaction and if improvements were possible. Interviews with quality management personnel and onsite evaluations were conducted at all stages of the supply chain, and a new visualization tool was developed. Quantitative and qualitative analysis tools were used to evaluate current practices. Opportunities for improvement were found in external integration and information sharing between supply chain partners. Also, a need for true measures of supply chain performance measures was identified. A 5-step process to develop such metrics is proposed, and an application example is provided. The proposed supply chain performance measurement system uses Six Sigma measures and facilitates collaboration between supply chain partners and provides information that allows focusing on improvement projects more efficiently. © Copyright 2014 by DEStech Publications, Inc. All rights reserved.},
keywords={Competition;  Customer satisfaction;  Human resource management;  Process engineering;  Quality control;  Quality management, Application examples;  Performance measurements;  Product supply chains;  Quality measurements;  Quantitative and qualitative analysis;  Supply chain partners;  Supply chain performance;  Supply chain performance measurements, Supply chain management},
references={Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Li, S.H., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Fynes, B., Voss, C., Burca, S.D., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), p. 339; Rosenzweig, E.D., Roth, A.V., Dean, J.W., The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21 (4), pp. 437-456; Espinoza, O., Bond, B., Kline, E., Quality measurement in the wood products supply chain (2010) Forest Products Journal, 60 (3), pp. 249-257; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Cumbo, D., Kline, D.E., Bumgardner, M., Benchmarking performance measurement and lean manufacturing in the rough mill (2006) Forest Products Journal, 56 (6), pp. 25-30; Anonymous, Six sigma, lean come together (2008) Industry Week, 257 (3), p. 23; Raisinghani, M.S., Six Sigma: Concepts, tools, and applications (2005) Industrial Management & Data Systems, 105 (3-4), pp. 491-505; Goldsby, T.J., (2005) Lean Six Sigma Logistics : Strategic Development to Operational Success, 17, p. 282. , ed. R. Martichenko, Boca Raton, Fl. :: J. Ross Pub; George, M.L., (2002) Lean Six Sigma : Combining Six Sigma Quality with Lean Speed, 14, p. 322. , New York : London McGraw-Hill; Blanchard, D., What's working for U.S. Manufacturers (2006) Industry Week, 255 (10), pp. 49-51; Press, P., (2006) Lean Supply Chain : Collected Practices and Cases, 8, p. 136. , New York Productivity Press; Bolstorff, P., Putting it all together (2003) Logistics Today, p. 28; Wang, F.-K., Du, T.C., Li, E.Y., Applying six-sigma to supplier development (2004) Total Quality Management & Business Excellence, 15 (9-10), p. 1217; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management & Business Excellence, 14 (3), pp. 355-366; Theeranuphattana, A., Tang, J.C.S., A conceptual model of performance measurement for supply chains (2008) Journal of Manufacturing Technology Management, 19 (1), p. 125; Garg, D., Narahari, Y., Viswanadham, N., (2003) Design of Six Sigma Supply Chains, , Taipei, Taiwan: Institute of Electrical and Electronics Engineers Inc; Yu, D., Luh, P.B., (2004) Achieving Reliable Delivery in Supply Chains: The Control of Uncertainties, , New Orleans, LA, United States: Institute of Electrical and Electronics Engineers Inc., Piscataway, United States; Nukala, S., Gupta, S.M., (2006) A Six Sigma Tolerancing Approach for the Design of An Efficient Closed-loop Supply Chain Network, , Boston, MA, United States: International Society for Optical Engineering, Bellingham WA, WA 98227-0010, United States; Christopher, M., Towill, D.R., Supply chain migration from lean and functional to agile and customised (2000) Supply Chain Management: An International Journal, 5 (4), pp. 206-213; Myerson, P., (2012) Lean Supply Chain and Logistics Management., , Mcgraw-hill; Crespell, P., Knowles, C., Hansen, E., Innovativeness in the North American softwood sawmilling industry3 (2006) Forest Science, 52 (5), pp. 568-578; Jones, D., Womack, J., (2002) Seeing the Whole - Mapping the Extended Value Stream, pp. 3-5. , T.L.E.I. Inc., Editor, The Lean Enterprise Inst.: Brookline, MA; Lambert, D.M., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Womack, J.P., Value stream mapping (2006) Manufacturing Engineering, 136 (5), pp. 145-156; Barki, H., Pinsonneault, A., A model of organizational integration, implementation effort, and performance (2005) Organization Science, 16 (2), p. 165; Germain, R., Iyer, K.N.S., The interaction of internal and downstream integration and its association with performance (2006) Journal of Business Logistics, 27 (2), p. 29; Closs, D.J., Savitskie, K., Internal and external logistics information technology integration (2003) International Journal of Logistics Management, 14 (1), p. 63; Lee, C.W., Kwon, I.-W.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier (2007) Internal Integration, and Customer. Supply Chain Management, 12 (6), p. 444; Rodrigues, A.M., Stank, T.P., Lynch, D.F., Linking strategy, structure process and performance in integrated logistics (2004) Journal of Business Logistics, 25 (2), pp. 65-94; Sanders, N.R., Premus, R., Modeling the relationship betwee firm IT capability, collaboration, and performance (2005) Journal of Business Logistics, 26 (1), p. 1; Stank, T.P., Keller, S.B., Daugherty, P.J., Supply chain collaboration and logistical service performance (2001) Journal of Business Logistics, 22 (1), pp. 29-48; Aryee, G., Naim, M.M., Lalwani, C., Supply chain integration using a maturity scale (2008) Journal of Manufacturing Technology Management, 19 (5), pp. 559-575; Levy, P., Developing integration through total quality supply chain management (1995) Integrated Manufacturing Systems, 6 (3), p. 4; Vickery, S.K., The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships (2003) Journal of Operations Management, 21 (5), pp. 523-539; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Carr, A.S., Pearson, J.N., Strategically managed buyer-supplier reIationships and performance outcomes (1999) Journal of Operations Management, 17 (5), p. 497; Chen, C.-C., Yeh, T.-M., Yang, C.-C., Customer-focused rating system of supplier quality performance (2004) Journal of Manufacturing Technology Management, 15 (7), p. 599; Fram, E.H., Purchasing partnerships: The buyer's view (1995) Marketing Management, 4 (1), p. 49; Muralidharan, C., Anantharaman, N., Deshmukh, S.G., A multi-criteria group decisionmaking model for supplier rating (2002) Journal of Supply Chain Management, 38 (4), pp. 22-33; Hahn, C.K., Watts, C.A., Kim, K.Y., The supplier development program: A conceptual model (1990) Journal of Purchasing and Materials Management, 26 (2), p. 2; Rogers, K.W., A supplier development program: Rational process or institutional image construction? (2007) Journal of Operations Management, 25 (2), pp. 556-572; Lo, V.H.Y., Yeung, A., Managing quality effectively in supply chain: A preliminary study (2006) Supply Chain Management, 11 (3), p. 208; Chan, F.T.S., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), p. 635; Van-Aken, E.M., Coleman, G.D., Building better measurement (2002) Industrial Management, 44 (4), pp. 28-33; Espinoza, O., Bond, B., Kline, E., Supply chain measures of performance for wood products manufacturing (2010) Forest Products Journal, 60 (7-8), pp. 700-708; Graves, S., Six sigma rolled throughput yield (2001) Quality Engineering, 14 (2), p. 257},
correspondence_address1={Espinoza, O.; Department of Bioproducts and Biosystems Engineering, United States; email: espinoza@umn.edu},
editor={Chen F.F.},
publisher={DEStech Publications Inc.},
isbn={9781605951737},
language={English},
abbrev_source_title={FAIM - Proc. Int. Conf. Flex. Autom. Intell. Manuf.: Capturing Compet. Advant. via Adv. Manuf. Enterp. Transform.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Tundys2014504,
author={Tundys, B. and Rzeczycki, A. and Zioło, M. and Jankowski, J.},
title={Models of an integrated performance measurement system of intelligent and sustainable supply chains},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2014},
volume={8398 LNAI},
number={PART 2},
pages={504-514},
doi={10.1007/978-3-319-05458-2_52},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958534404&doi=10.1007%2f978-3-319-05458-2_52&partnerID=40&md5=56097047d028feacc48fb73d2d8ef1c6},
affiliation={Faculty of Management and Economics of Services, University of Szczecin, Szczecin, Poland; Faculty of Computer Science, West Pomeranian University of Technology, Szczecin, Poland},
abstract={Changes in the nature of supply chains require the dedication of the individual measures and their alignment with the latest trends in supply chain management. The concept presented in the article is to identify a dedicated and integrated performance measurement system for intelligent and sustainable supply chains. The economic conditions and trends, both in transport, shipping, logistics, as well as structural and organizational changes within the supply chain, need to be revised and adapted to a standard measurement in order to evaluate their performance. © 2014 Springer International Publishing Switzerland.},
author_keywords={diffusion of information;  performance measurement;  sustainable supply chains},
keywords={Database systems, Economic condition;  Organizational change;  Performance measurement system;  Performance measurements;  Standard measurements;  Sustainable supply chains, Supply chain management},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Beamon, B.M., Sustainability andthe future of supply chain management (2008) Operations and Supply Chain Management: An International Journal, 1 (1), pp. 4-18; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution & Logistics Management, 24 (1), pp. 17-28; Erkan, T.E., Baç, U., Supply chain performance measurement: A case study about applicability of scor model in a manufacturing industry firm (2011) International Journal of Business and Management Studies, 3 (1), pp. 381-390; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Production Economics, 87, pp. 333-347; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Hwang, H.J., Seruga, J., An intelligent supply chain management system to enhance collaboration in textile industry (2011) International Journal of u-and e-Service, Science and Technology, 4 (4), pp. 1-16; Khare, A., Saxena, A., Tewari, P., Supply chain performance measures for gaining competitive advantage: A review (2012) Journal of Management and Strategy, 3 (2), pp. 25-32; Kuik, S.S., Nagalingam, S.V., Amer, Y., Challenges in implementing sustainable supply chain within a collaborative manufacturing network (2010) 8th International Conference on Supply Chain Management and Information Systems, Hong Kong, pp. 662-669; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; (2006) Wydawnictwo Akademii Ekonomicznej w Katowicach, Katowice, , Pomiar funkcjonowania lańcuchów dostaw, red. D. Kisperska-Moroń; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16, pp. 1699-1710; Twaróg, J., (2003) Mierniki i Wskaźniki Logistyczne, , Instytut Logistyki i Magazynowania, Poznań; (1987) Our Common Future, , World Commission on Environment and Development. Oxford University Press, Oxford},
publisher={Springer Verlag},
issn={03029743},
isbn={9783319054575},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Öztayşi2014199,
author={Öztayşi, B. and Sürer, O.},
title={Supply chain performance measurement using a SCOR based fuzzy VIKOR approach},
journal={Studies in Fuzziness and Soft Computing},
year={2014},
volume={313},
pages={199-224},
doi={10.1007/978-3-642-53939-8_9},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958530228&doi=10.1007%2f978-3-642-53939-8_9&partnerID=40&md5=e58240d3a8df074ce4f5c17726e06b2d},
affiliation={Department of Industrial Engineering, Istanbul Technical University, 34367 Macka Istanbul, Turkey},
abstract={Supply Chain performance measurement is a vital issue for supply chain management. Both from the academia and professional life, various models are proposed for this subject. In this chapter, the literature is investigated for current performance measurement models and a multi-criteria decision making approach is proposed for supply chain performance measurement. In this study, SCOR model is used for structuring the problem, Fuzzy Analytic Hierarchy Process (AHP) is used to determine the importance weights of the criteria and finally Fuzzy VIKOR is used to rank the alternatives based on expert evaluations.© Springer-Verlag Berlin Heidelberg 2014.},
author_keywords={Fuzzy AHP;  Fuzzy VIKOR;  Performance measurement;  Supply chain},
references={Baykasoglu, A., Kaplanoglu, V., Application of activity-based costing to a land transportation company: A case study (2008) Int. J. Prod. Econ., 116 (2), pp. 308-324; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) Int. J. Prod. Econ., 55, pp. 281-294; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind. Eng., 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Prod. Plann. Control, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Prod. Plann. Control, 20 (8), pp. 678-690; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logistics, 21 (1), pp. 75-94; Buckley, J.J., Fuzzy hierarchical analysis (1985) Fuzzy Sets and Syst., 17, pp. 233-247; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) Int. J. Prod. Perform. Manage., 59 (2), pp. 163-185; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol., 21 (7), pp. 534-548; Chan, F.T.S., Qi, H., An innovative performance measurement method for supply chain management. Supply Chain Manag (2003) Int. J., 8 (3), pp. 209-223; Chang, D.Y., Applications of the extent analysis method on fuzzy AHP European (1996) J.Oper. Res., 95, pp. 649-655; Chen, L.Y., Wang, T.C., Optimizing partners? Choice in IS/IT outsourcing projects: The strategic decision of fuzzy VIKOR (2009) Int. J. Prod. Econ., 120 (1), pp. 233-242; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) Int. J. Logistics Manage., 8 (1), pp. 1-14; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Syst. Appl, 38 (6), pp. 6681-6688; Elgazzar, S.H., Tipi, N.S., Hubbard, N.J., Leach, D.Z., Linking supply chain processes performance to a company?s financial strategic objectives (2012) Eur. J. Oper. Res., 223 (1), pp. 276-289; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ., 142 (2), pp. 247-258; Felix, T.S., Chan, H.Q., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integr. Manuf. Syst., 14 (3), pp. 179-190; Folan, P., Browne, J., Jagdev, H., Performance: Its meaning and content for today?s business research (2007) Comput. Ind., 58, pp. 605-620; Forme, F.-A.G.L., Genoulaz, V.B., Campagne, J.-P., A framework to analyse collaborative performance (2007) Comput. Ind., 58 (7), pp. 687-697; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int. J. Prod. Econ., 134 (1), pp. 177-187; Gilmour, P., A strategic audit framework to improve supply chain performance (1999) J. Bus. Ind. Mark., 14 (5), pp. 355-366; Gou, J., Shen, G., Chai, R., Model of service-oriented catering supply chain performance evaluation (2013) J. Ind. Eng. Manage., 6 (1), pp. 215-226; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Harris, J.K., Swatman, P.M., Efficient consumer response (ECR): A survey of the Australian grocery industry. Supply Chain Manage (1999) Int. J., 4 (1), pp. 35-42; Kaplan, R.S., Norton, D.P., (1992) The Balanced Scorecard-Measures That Drive Performance, pp. 71-79. , Harvard Business Review, January-February; Kulak, O., Kahraman, C., Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process (2005) Inf. Sci., 170, pp. 191-210; Kurnia, S., Johnston, R.B., Adoption of efficient consumer response: The issue of mutuality (2001) Supply Chain Manage Int. J., 6 (5), pp. 230-241; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int. J. Logistics Manage., 12 (1), pp. 1-19; Lebas, M., Performance measurement and performance management (1995) Int. J. Prod. Econ., 41, pp. 23-25; Li, S., Rao, S.S., Ragu-Nathan, T., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) J. Opertions Manage., 23 (6), pp. 618-641; Lin, R.-J., Using fuzzy DEMATEL to evaluate the green supply chain management practices (2013) J. Cleaner Prod., 40, pp. 32-39; Lohtia, R., Tian, F., Xie Subramaniam, R., Efficient consumer response in Japan: Industry concerns, current status, benefits, and barriers to implementation (2004) J. Bus. Res., 57 (3), pp. 306-311; McCormac, K., Wilkerson, T., Marrow, D., Davey, M., Shah, M., Yee, D., (2008) Managing Risk in Your Organization with the SCOR Methodology, , http://supply-chain.org/f/Supply%20Chain%20Risk%20Project%20Report.pdf; Meyer, M.W., (2002) Rethinking Performance Measurement: Beyond the Balanced Scorecard, , Cambridge University Press, New York; Moussa, A.M., Kamdem, J.S., Terraza, M., Fuzzy risk adjusted performance measures: Application to Hedge funds (2012) Insurance: Mathematics and Economics, Article, , in press; Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resour. Conserv. Recycl., 55 (6), pp. 593-603; Najmi, A., Makui, A., Providing hierarchical approach for measuring supply chain performance using AHP and dematel methodologies (2010) Int. J. Ind. Eng. Comput., 1, pp. 199-212; (2013) Odette, , www.odette.org/html/home.htm, Accessed:24/03/2013; Opricovic, S., Tzeng, G.H., Compromise solution by MCDM methods: A comparative analysis of vikor and topsis (2004) Eur. J. Oper. Res., 156, pp. 445-455; Pan, N.F., Lin, T.C., Pan, N.H., Estimating bridge performance based on a matrix-driven fuzzy linear regression model (2009) Autom. Constr., 18 (5), pp. 578-586; Qian, L., Ben-Arieh, D., Parametric cost estimation based on activity-based costing: A case study for design and development of rotational parts (2008) Int. J. Prod. Econ., 113 (2), pp. 805-818; Ravi, V., Shankar, R., Tiwari, M.K., Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach (2005) Comput. Ind. Eng., 48 (2), pp. 327-356; Saaty, T.L., (1980) The Analytic Hierarchy Process, , McGraw Hill. RWS Publications, New York; Sanayei, A., Mousavi, S.F., Yazdankhah, A., Group decision making process for supplier selection with VIKOR under fuzzy environment (2010) Expert Syst. Appl., 37 (1), pp. 24-30; (2010) Supply Chain Council SCOR Model Version 10.00-Appendix A, , http://supply-chain.org/f/SCOR%208.0%20Metrics%20tables.pdf, SCC; Schulze, M., Seuring, S., Ewering, C., Applying activity-based costing in a supply chain environment (2012) Int. J. Prod. Econ., 135 (2), pp. 716-725; Shemshadi, A., Shirazi, H., Toreihi, M., Tarokh, M.J., A fuzzy vikor method for supplier selection based on entropy measure for objective weighting (2011) Expert Syst. Appl., 38 (10), pp. 12160-12167; Stapleton, D., Hanna, J.B., Yagla, S., Johnson Markussen, D., Measuring logistics performance using the strategic profit model (2002) Int. J. Logistics Manage., 13 (1), pp. 89-107; Theeranuphattana, A., Tang, J.C., Khang, D.B., An integrated approach to measuring supply chain performance (2012) Ind. Eng. Manage. Syst., 11 (1), pp. 54-69; Tsai, W.-H., Hung, S.-J., A fuzzy goal programming approach for green supply chain optimisation under activity-based costing and performance evaluation with a value-chain structure (2009) Int. J. Prod. Res., 47 (18), pp. 4991-5017; Van Laarhoven, P.J.M., Pedrycz, W., A fuzzy extension of Saaty?s priority theory (1983) Fuzzy Sets Syst., 11, pp. 229-241; Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteria decision-making methodology (2004) Int. J. Prod. Econ., 91 (1), pp. 1-15; Yalcin, N., Bayrakdaroglu, A., Kahraman, C., Application of fuzzy multi-criteria decision making methods for financial performance evaluation of Turkish manufacturing industries (2012) Expert Syst. Appl., 39 (1), pp. 350-364; Yu, P.L., A class of solutions for group decision problems (1973) Manage. Sci., 19, pp. 936-946; Yu, X., Guo, S., Guo, J., Huang, X., Rank B2C e-commerce websites in e-alliance based on AHP and fuzzy topsis (2011) Expert Syst. Appl., 38 (4), pp. 3550-3557; Zadeh, L., Fuzzy sets (1965) Inf. Control, 8, pp. 338-353; Zeleny, M., (1982) Multiple Criteria Decision Making., , McGraw-Hill,New York; Zhihong, W., Yan, W., He, W., Performance evaluation indicator system and model construction of the green supply chain (2013) Intelligent System Design and Engineering Applications, pp. 1042-1044; Zhou, Z., Zhao, L., Lui, S., Ma, C., A generalized fuzzy DEA/AR performance assessment model (2012) Math. Comput. Modell., 55 (11-12), pp. 2117-2128},
correspondence_address1={Öztayşi, B.; Department of Industrial Engineering, , 34367 Macka Istanbul, Turkey; email: oztaysib@itu.edu.tr},
publisher={Springer Verlag},
issn={14349922},
isbn={9783642539381},
language={English},
abbrev_source_title={Stud. Fuzziness Soft Comput.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Senvar2014143,
author={Senvar, O. and Tuzkaya, U.R. and Kahraman, C.},
title={Supply chain performance measurement: An integrated dematel and fuzzy-ANP approach},
journal={Studies in Fuzziness and Soft Computing},
year={2014},
volume={313},
pages={143-165},
doi={10.1007/978-3-642-53939-8_7},
note={cited By 17},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958528389&doi=10.1007%2f978-3-642-53939-8_7&partnerID=40&md5=5ccb80449bc3f53654a8fb2d2899f3fe},
affiliation={Institut Charles Delaunay, Laboratoire ĎOptimisation des Systèmes Industriels (LOSI), Université de Technologie de Troyes 12, rue Marie Curie-BP 2060, 10010 Troyes cedex, France; Department of Industrial Engineering, Yildiz Technical University, 34349 Besiktas, Istanbul, Turkey},
abstract={Supply chain performance measurement is vital for the continuous improvement of supply chain management. Effective supply chain performance measurement is one of the most important aspects for supply chain management in which decision makers can analyze the historical performance and current status, and set future performance targets. This chapter provides a conceptual point of view to supply chain performance measurement. Inevitably, quantification of the values with precision in a complex supply chain performance measurement system is difficult. The supply chain performance measurement under fuzziness can consider the uncertainty and ambiguity surrounding the supply chain performance measurement. The aim of this chapter is to present a fuzzy decision making approach to deal with the performance measurement in supply chain systems. In this chapter, DEMATEL method is adapted to model complex interdependent relationships and construct a relation structure using measurement criteria for evaluation. F-ANP is performed to overcome the problem of dependence and feedback among each measurement criteria. The integrated DEMATEL and F-ANP approach provides an effective decision tool for the supply chain performance measurement.© Springer-Verlag Berlin Heidelberg 2014.},
author_keywords={ANP;  DEMATEL;  Fuzzy logic;  Multi criteria decision making;  Performance management;  Suppy chain management},
references={Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logistics, 21 (1), pp. 75-93; Buyukozkan, G., Ciftci, G., A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers (2012) Expert Syst. Appl., 39, pp. 3000-3011; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol., 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management. Supply Chain Manag (2003) Int. J., 8 (3), pp. 209-223; Chang, S.-L., Wang, R.-C., Wang, S.-Y., Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle (2006) Int. J. Prod. Econ., 100 (2), pp. 348-359; Chen, S.J., Hwang, C.L., Hwang, F.P., Fuzzy multiple attribute decision making: Methods and applications. Lect. Notes Econ (1992) Math. Syst., 375, pp. 289-486; Chen, Y.-C., An application of fuzzy set theory to the external performance evaluation of distribution centers in logistics (2002) Soft Comput., 6 (1), pp. 64-70; Chung, S.H., Lee, A.H., Pearn, W.L., Product mix optimization for semiconductor manufacturing based on AHP and ANP analysis (2005) Int. J. Adv. Manuf. Technol., 25, pp. 1144-1156; Cooper, M., Lambert, D., Issues in supply chain management (2000) Ind. Mark. Manage., 29 (1), pp. 65-83; El-Baaz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Syst. Appl., 38, pp. 6681-6688; Estampe, D., Lamouri, S., Paris, J.-L., Djelloul, S.B., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ., 142 (2), pp. 247-258; Forker, L.B., Mendez, D., Hershauer, J.C., Total quality management in the supply chain: What is its impact on performance? (1997) Int. J. Prod. Res., 35 (6), pp. 1681-1701; Estampe, D., Lamouri, S., Paris, J.-L., Djelloul, S.B., A framework for analysing supply chain performance evaluation models (2013) Int. J. Prod. Econ., 142 (2), pp. 247-258; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) Int. J. Prod. Econ., 134 (1), pp. 177-187; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manage., 21 (1-2), pp. 71-87; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) Int. J. Prod. Res., 45 (12), pp. 2819-2840; Herat, A.T., Noorossama, R., Parsa, S., Serkani, E.S., Using DEMATEL-analytic network process (ANP) hybrid algorithm approach for selecting improvement projects of Iranian excellence model in healthcare sector (2012) Afr. J. Bus. Manage., 6 (2), pp. 627-645; Hung, S.J., Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach (2011) Expert Syst. Appl., 38 (8), pp. 9053-9062; Kahraman, C., Ates, N.Y., Cevik, S., Gulbay, M., Erdogan, S.A., Hierarchical fuzzy TOPSIS model for selection among logistics information technologies (2007) J. Enterp. Inf. Manage., 20 (2), pp. 143-168; Kaplan, R.S., Norton, D.P., (1992) The Balanced Scorecard-measures That Drive Performance, p. 71. , Harvard business review January-February; Lapide, L., True measures of supply chain performance (2000) Supply Chain Manage. Rev., 4 (3), pp. 25-28; Lau, H.C.W., Pang, W.K., Wong, C.W.Y., Methodology for monitoring supply chain performance: A fuzzy logic approach (2002) Logistics Inf. Manag., 15 (4), pp. 271-280; Mehrjerdi, Y.Z., Excellent supply chain management (2009) Assem Autom., 29 (1), pp. 52-60; Mohanty, R.P., Agarwal, R., Choudhury, A.K., Tiwari, M.K., A fuzzy ANP-based approach to R&D project selection: A case study (2005) Int. J. Prod. Res., 43 (24), pp. 5199-5216; Onut, S., Tuzkaya, U.R., Torun, E., Selecting container port via a fuzzy ANP-based approach: A case study in the marmara region (2011) Turk. Transp. Policy, 18 (1), pp. 182-193; Ramik, J., A decision system using ANP and fuzzy inputs (2006) 12th International Conference on the Foundadtions and Applications of Utility Risk and Decision Theory, , Roma; Saaty, T.L., (1996) Decision Making with Dependence and Feedback: The Analytic Network Process, , RWS Publications, Pittsburgh; Saaty, T.L., Vargas, L.G., Diagnosis with dependent symptoms: Bayes theorem and the analytic network process (1998) Oper. Res., 46 (4), pp. 491-502; Saaty, R.W., (2003) Decision Making in Complex Environment: The Analytic Hierarchy Process (AHP) for Decision Making and the Analytic Network Process (ANP) for Decision Making with Dependence and Feedback; Saaty, T.L., The analytic network process: Dependence and feedback in decision making (Part 1) theory and validation examples (2004) MCDM, 2004, pp. 6-11. , Whistler, B.C., Canada, August; Seyedhosseini, S.M., Taleghani, A.E., Bakhsha, A., Partovi, S., Extracting leanness criteria by employing the concept of balanced scorecard (2011) Expert Syst. Appl., 38, pp. 10454-10461; Stefanovic, N., Stefanovic, D., Supply chain performance measurement system based on scorecards and web portals (2011) ComSIS, 8 (1), pp. 167-192. , doi:10.2298/CSIS090608018S; Tuzkaya, U.R., Önüt, S., A fuzzy analytic network process based approach to transportationmode selection between Turkey and Germany: A case study (2008) Inf. Sci., 178 (15), pp. 3133-3146; Tuzkaya, U.R., Yilmazer, K.B., Tuzkaya, G., Location selection of emergency logistics centers using an integrated dematel-ANP approach (2012) The 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making Istanbul, , Turkey; Tzeng, G.H., Huang, J.J., (2011) Multiple Attribute Decision Making, , NewYork, CRC Press, 97-81-4398-6157-8; Waters, C.D., Waters, D., (2007) Global Logistics: New Directions in Supply Chain Management, , Kogan Page, London; Wu, H.H., Shieh, J.I., Li, Y., Chen, H.K., A combination of ahp and dematel in evaluating the criteria of employment service outreach program personnel (2010) Info. Tech. J., 9 (3), pp. 569-575; Yamin, S., Gunasekaran, A., Mavondo, F.T., Relationship between generic strategies, competitive advantage and organizational performance: An empirical analysis (1999) Technovation, 19 (8), pp. 507-551; Yang, J.L., Tzeng, G.-H., An integrated mcdm technique combined with dematel for a novel cluster-weighted with ANP method (2011) Expert Syst. Appl., 38, pp. 1417-1424; Yu, R., Tzeng, G.H., A soft computing method for multi-criteria decision making with dependence and feedback (2006) Appl. Math. Comput., 180, pp. 63-75; Zadeh, L., Fuzzy sets (1965) Inf. Control, 8, pp. 338-358; Zadeh, L., A fuzzy-algorithmic approach to the definition of complex or imprecise concepts (1976) Int. J. Man Mach. Stud., 8, pp. 249-291},
correspondence_address1={Senvar, O.; Institut Charles Delaunay, rue Marie Curie-BP 2060, 10010 Troyes cedex, France; email: ozlem.senvar@utt.fr},
publisher={Springer Verlag},
issn={14349922},
isbn={9783642539381},
language={English},
abbrev_source_title={Stud. Fuzziness Soft Comput.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Frederico201424,
author={Frederico, G.F. and Martins, R.A.},
title={Performance measurement systems for supply chain management: How to manage its maturity},
journal={International Journal of Supply Chain Management},
year={2014},
volume={3},
number={2},
pages={24-30},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949793499&partnerID=40&md5=37b393df0827ba588d92f33f896fa850},
affiliation={Business Administration Department, Federal University of Paraná -UFPR, Lothario Meissner Avenue, 632, 2nd floor, Curitiba/PR, 80210-170, Brazil; Industrial Engineering Department, Federal University of São Carlos -UFSCar, Washington Luís Highway, Km 235, São Carlos/SP, 13565-905, Brazil},
abstract={Supply chains have gained important focus after 2000's due to not only the opportunities that it can provide but also the complexity involved on its management. Into this context, the implementation of different management practices can help companies to achieve better performance in their supply chains. As part of these practices it can be considered the Performance Measurement Systems (PMSs). The theory for PMSs has been concentrated on only organization perspective. At the same time the theory about PMSs for SCM is focused more on scope of measurement leaving a lack with regards other dimensions that must be considered on the PMS maturity development. Therefore, this paper aims to present the findings from the literature review about PMS for SCM and maturity of PMS. As contribution an theoretical model is proposed to help practitioners and researchers on the maturity management of PMS for SCM. © ExcelingTech Pub, UK.},
author_keywords={Maturity;  Performance measurement systems;  Supply chain management},
references={Christopher, M., Logistics and Supply Chain Management (2005), 3rd ed. London: Pearson Education; Lambert, D., Cooper, M., Issues in Supply Chain Management (2000) Industrial Marketing Management, 29, pp. 65-83; Pires, S., Gestao da Cadeia de Suprimentos: Conceitos, Estrategias, Praticas e Casos (2009), 2nd. Sao Paulo: Atlas; Chan, F., Qi, H., Chan, H., Lau, H., Ip, R., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., Introduction to supply chain management (2003) Designing and Managing the Supply Chain: concepts, strategies and case studies, , In: Simchi-Levi, D.; Kaminsky, P.; Simchi-Levi, E. 2nd ed., Boston: McGraw-Hill; Ayers, J., Malmberg, D., Supply Chain Systems: Are You Ready? (2002), Information Strategy: The Executive's Journal; Lambert, D., Pohlen, T., Supply Chain Metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; (2011) Oxford Dictionaries, , Oxford University Press; Attadia, L., Martins, R., A. Medicao de desempenho como base para evolucao da melhoria continua (2003) Revista Producao, 13 (2), pp. 33-41; Kwak, Y., Ibbs, W., Project Management Process Maturity Model (2002) Journal of Management In Engineering, 18 (3), pp. 150-155; Venkatraman, N., Henderson, J., Real Strategies for Virtual Organizing (1998) Sloan Management Review, 40 (1), pp. 33-48. , Fall; Harter, D., Krishnan, M., Slaughter, S., Effects of Process Maturity on Quality, Cycle time, and Effort in Software Product Development (2000) Management Science, 46 (4), pp. 451-466; Stevens, G., Integrating the supply chain (1989) International Journal of Physical Distribution & Logistics Management, 19 (8), pp. 3-8; Competitive Advantage of Best in Class Supply Chains. Pennsylvania (2007), http://www.pmgbenchmarking.com/public/survey/dvantagesofbic.pdf, Available; Lockamy, A., McCormak, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278; Daozhi, Z., Liang, Z., Xin, L., Jianyong, S., A New Supply Chain Maturity Model With 3-Dimension Perspective (2006) ITIC - Information Technology and Innovation Conference, , Hangzhou; Oliveira De, M., Modelo de Maturidade de Processos em Cadeias de Suprimentos: Precedencias e os Pontos- Chave de Transicao (2009), Tese (Doutorado em Administração) Programa de Pos-Graduacao em Administracao, UFMG, Belo Horizonte; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Bititci, U., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Kennerley, M., Neely, A., Measuring performance in a changing business environment. (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Andersson, P., Aronsson, H., Storhagen, N., Measuring Logistics Performance (1989) Engineering Costs and Production Economics, 17, pp. 253-262; Van Hoek, R., Measuring the immeasurable-measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Beamon, B., Measuring Supply Chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Pires, S., Aravechia, C., Measuring Supply Chain Performance (2001) Annual Conference of the Production and Operations Management Society, , Orlando; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Brewer, P., Speh, T., Using the Balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Geary, S., Zonnenberg, J., What it means to be best in class (2000) Supply Chain Management Review, pp. 43-48. , Jan/Aug; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Supply-Chain Operations Reference-model: Version 9.0 - Overview (2008), http://www.supplychain.org/resources/scor, Available in; Wettstein, T., Kueng, P., A Maturity Model for Performance Measurement Systems (2002) Management Information Systems, pp. 113-122; Van Aken, E., Lettens, G., Coleman, G., Farris, J., Van Goubergen, D., Assessing maturity and effectiveness of enterprise performance measurement systems (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 400-418; Seuring, S., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Management: An International Journal, 13 (2), pp. 128-137; Seuring, S., Gold, S., Conducting content-analysis based literature reviews in supply chain management (2011) Supply Chain Management: An International Journal, 16 (5), pp. 544-555; Tranfield, D., Denyer, D., Palminder, S., Towards a Metodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review (2003) British Journal of Management, 14, pp. 207-222},
publisher={ExcelingTech Publishers},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Marchand2014254,
author={Marchand, M. and Raymond, L.},
title={Framing the IT artefact through Plato's ontology: A return to the user's perspective},
journal={Proceedings of the 7th IADIS International Conference Information Systems 2014, IS 2014},
year={2014},
pages={254-262},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84944067820&partnerID=40&md5=d32d9116b3c6c71d9a0460866d008f7d},
affiliation={Université Du Québec à Trois-Rivières, Canada},
abstract={Calls have been repeatedly made to characterize and theorize IT artefacts in IS studies. Based on ontological principles enunciated by Plato more than 2300 years ago, we propose to characterize the IT artefact with an approach that returns the user perspective to the forefront. As an illustration, performance measurement systems (PMS) are characterized from their users' perspective in terms of their specific attributes as systems dedicated to managing performance. The variety of contexts and configurations of these systems is of particular interest while looking for the principles that underlie their empirical manifestations. This study contributes to research by providing a framework of reference for the characterization, design and evaluation of IT artefacts. © 2014 IADIS.},
author_keywords={IT artefact;  IT artifact;  Ontology;  Performance measurement system;  Plato;  User},
keywords={Information systems;  Ontology, IT artefact;  IT artifact;  Performance measurement system;  Plato;  User, Supply chain management},
references={Alter, S., 18 reasons why IT-reliant work systems should replace 'the IT Artifact' as the core subject matter of the IS field (2003) Communications of the Association for Information Systems, 12 (23), pp. 366-395; Alter, S., Work systems and IT artifacts - Does the definition matter? (2006) Communications of the Association for Information Systems, 17 (14), pp. 299-313; Baker, L.R., The ontology of artifacts (2004) Philosophical Explorations, 7, pp. 99-112; Barley, S.R., What can we learn from the history of technology? (1998) Journal of Engineering and Technology Management, 15 (4), pp. 237-255; Benbasat, I., Zmud, R.W., The identity crisis within the IS discipline: Defining and communicating the discipline's core properties (2003) MIS Quarterly, 27 (2), pp. 183-194; Bergeron, F., Raymond, L., Evaluation of EIS from a managerial perspective (1992) Information Systems Journal, 2 (1), pp. 45-60; Brisson, L., Comment rendre compte de la participation du sensible à l'intelligible chez Platon? (2001) Les Formes Intelligibles, pp. 55-85. , J.-F. Pradeau (dir.), Platon :, Paris: Presses Universitaires de France; Cagliano, R., Blackmon, K., Voss, C., Small firms under microscope: International differences in production/operations management practices and performance (2001) Integrated Manufacturing Systems, 12 (7), pp. 469-482; Cherniss, H., The philosophical economy of the theory of ideas (1936) The American Journal of Philology, 57 (4), pp. 445-456; DeLone, W.H., McLean, E.R., Information systems success: The quest for the dependent variable (1992) Information Systems Research, 3 (1), pp. 60-95; DeLone, W.H., McLean, E.R., The delone and mclean model of information systems success: A ten-year update (2003) Journal of Management Information Systems, 19 (4), pp. 9-30; De Toni, A., Tonchia, S., Performance measurement systems - Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Fernandez, A., (2005) Les Nouveaux Tableaux de Bord des Managers : Le Projet Décisionnel dans Sa Totalit, , (3rd ed.). Paris: Éditions d'Organisation; Fine, G., (1993) On Ideas: Aristotle's Criticism of Plato's Theory of forms, , Oxford: Clarendon Press; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Gregor, S., The nature of theory in information systems (2006) MIS Quarterly, 30 (3), pp. 611-642; Hirschheim, R., Newman, M., Symbolism and information systems development: Myth, metaphor and magic (1991) Information Systems Research, 2, pp. 29-62; Hudson, M., Smart, A., Bourne, M., And practice in SME performance measurement systems (2001) International Journal of Operations & Production Management, 21 (8), pp. 1096-1115. , Theory; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard, , Boston: Harvard Business School Press; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Kling, R., Scacchi, W., The web of computing: Computer technology as social organization (1982) Advances in Computers, 21, pp. 1-90. , M.C. Yovits (ed); Kroes, P.A., Meijers, A.W.M., Introduction: The dual nature of technical artefacts (2006) Studies in History and Philosophy of Science, 37 (1), pp. 1-4. , Kroes, P.A. and Meijers, A.W.M. (eds); Kueng, P., Meier, A., Wettstein, T., Performance measurement systems must be engineered (2001) Communications of the Association for Information Systems, 7, pp. 1-27; Leonardi, P.M., Barley, S.R., Materiality and change: Challenges to building better theory about technology and organizing (2008) Information and Organization, 18 (3), pp. 159-176; Leroux, G., (2002) Platon, la République, Traduction Inédite, Introduction et Notes, , Paris: Flammarion; Madsen, K.H., A guide to metaphorical design (1994) Communications of the ACM, 37, pp. 57-62; Malone, T.W., Lai, K.Y., Fry, C., Experiments with Oval: A radically tailorable tool for cooperative work (1995) ACM Transactions on Information Systems, 13 (2), pp. 177-205; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Markus, M.L., Robey, D., Information technology and organizational change: Causal structure in theory and research (1988) Management Science, 34 (5), pp. 583-598; Matook, S., Brown, S.A., Conceptualizing the IT artifact for MIS research (2008) ICIS 2008 Proceedings, pp. 1-11. , Paris; Merrill, G.H., Ontology, ontologies, and science (2011) Topoi, 30 (1), pp. 71-83; Millikan, R.G., Historical kinds and the "special sciences (1999) Philosophical Studies, 95 (1-2), pp. 45-65; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , London: Financial Times Prentice Hall; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60, pp. 279-290; Oates, B.J., Fitzgerald, B., Multi-metaphor method: Organizational metaphors in information systems development (2007) Information Systems Journal, 17, pp. 421-449; Orlikowski, W.J., The sociomateriality of organisational life: Considering technology in management research (2010) Cambridge Journal of Economics, 34, pp. 125-141; Orlikowski, W.J., Iacono, C.S., Research commentary: Desperately seeking the "IT (2001) IT Research - A Call to Theorizing the IT Artefact. Information Systems Research, 12 (2), pp. 121-134; Pinsonneault, A., Rivard, S., Information technology and the nature of managerial work: From the productivity paradox to the Icarus paradox? (1998) MIS Quarterly, 22 (3), pp. 287-311; Petter, S., Straub, D.W., Rai, A., Specifying formative constructs in information systems research (2007) MIS Quarterly, 31 (4), pp. 623-656; (1991) The Republic, Translated, , Plato., with notes, an interpretive essay, and a new introduction by Allan Bloom, Second Edition, Basic Books; Pradeau J.-f., Les formes et les réalités intelligibles (2001) Platon : les Formes Intelligibles, pp. 17-54. , J.-F. Pradeau (dir.), . Paris : Presses Universitaires de France; Rai, A., Lang, S.S., Welker, R.B., Assessing the validity of the IS success model: An empirical test and theoretical analysis (2002) Information Systems Research, 13 (1), pp. 50-69; Sharif, A.M., Benchmarking performance management systems (2002) Benchmarking: An International Journal, 9 (1), pp. 62-85; Sinclair, D., Zairi, M., Performance measurement: A critical analysis of the literature with respect of total quality management (2000) International Journal of Management Reviews, 2 (2), pp. 145-168; St-Pierre, J., Delisle, S., An expert diagnosis system for the benchmarking of SMEs' performance (2006) Benchmarking: An International Journal, 13 (1-2), pp. 106-119; Strong, D.M., Volkoff, O., Understanding organization-enterprise system fit: A path for theorizing the information technology artifact (2010) MIS Quarterly, 34 (4), pp. 731-756; Thomasson, A.L., Artifacts in metaphysics (2009) Handbook of Philosophy of the Technological Sciences, pp. 191-212. , Meijers, A. (ed.), Elsevier Science; Weber, R., Still desperately seeking the IT artefact (2003) MIS Quarterly, 27 (2); Whinston, A.B., Geng, X., Operationalizing the essential role of the information technology artifact in information systems research: Gray area, pitfalls, and the importance of strategic ambiguity (2004) MIS Quarterly, 28 (2), pp. 149-159},
editor={Nunes M.B., Rodrigues L., Powell P., Isaias P.},
publisher={IADIS},
isbn={9789898704047},
language={English},
abbrev_source_title={Proc. IADIS Int. Conf. Inf. Syst., IS},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Liao2014,
author={Liao, H.-C. and Chen, M.-H. and Wang, Y.-H.},
title={The Study of the Optimal Parameter Settings in a Hospital Supply Chain System in Taiwan},
journal={Scientific World Journal},
year={2014},
volume={2014},
doi={10.1155/2014/967140},
art_number={967140},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84933059827&doi=10.1155%2f2014%2f967140&partnerID=40&md5=7fb51cf9bef73551a3eae5c9dc4e4723},
affiliation={Department of Health Services Administration, Chung Shan Medical University, No. 110, Jian-Koa N. Road, Taichung, 402, Taiwan; Department of Medical Management, Chung Shan Medical University Hospital, No. 110, Jian-Koa N. Road, Taichung, 402, Taiwan; Department of Applied Foreign Languages, Chung Shan Medical University, No. 110, Jian-Koa N. Road, Taichung, 402, Taiwan; Department of Medical Education, Chung Shan Medical University Hospital, No. 110, Jian-Koa N. Road, Taichung, 402, Taiwan},
abstract={This study proposed the optimal parameter settings for the hospital supply chain system (HSCS) when either the total system cost (TSC) or patient safety level (PSL) (or both simultaneously) was considered as the measure of the HSCS's performance. Four parameters were considered in the HSCS: safety stock, maximum inventory level, transportation capacity, and the reliability of the HSCS. A full-factor experimental design was used to simulate an HSCS for the purpose of collecting data. The response surface method (RSM) was used to construct the regression model, and a genetic algorithm (GA) was applied to obtain the optimal parameter settings for the HSCS. The results show that the best method of obtaining the optimal parameter settings for the HSCS is the simultaneous consideration of both the TSC and the PSL to measure performance. Also, the results of sensitivity analysis based on the optimal parameter settings were used to derive adjustable strategies for the decision-makers. © 2014 Hung-Chang Liao et al.},
keywords={Article;  experimental design;  genetic algorithm;  hospital planning;  hospital purchasing;  hospital supply chain system;  performance measurement system;  response surface method;  sensitivity analysis;  simulation;  Taiwan;  economics;  hospital cost;  hospital equipment;  human;  standards, Equipment and Supplies, Hospital;  Hospital Costs;  Humans;  Taiwan},
references={García-Dastugue, S., Lambert, D.M., Internet-enabled coordination in the supply chain (2003) Industrial Marketing Management, 32 (3), pp. 251-263; Wan, X., Pekny, J.F., Reklaitis, G.V., Simulation-based optimization with surrogate models: Application to supply chain management (2005) Computers and Chemical Engineering, 29 (6), pp. 1317-1328; Yao, Y., Evers, P.T., Dresner, M.E., Supply chain integration in vendor-managed inventory (2007) Decision Support Systems, 43 (2), pp. 663-674; Van Der Vlist, P., Kuik, R., Verheijen, B., Note on supply chain integration in vendor-managed inventory (2007) Decision Support Systems, 44 (1), pp. 360-365; Frota Neto, J.Q., Bloemhof-Ruwaard, J.M., Van Nunen, J.A.E.E., Van Heck, E., Designing and evaluating sustainable logistics networks (2008) International Journal of Production Economics, 111 (2), pp. 195-208; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) International Journal of Production Economics, 134 (1), pp. 177-187; Schut, F.T., Van Bergeijk, P.A.G., International price discrimination: The pharmaceutical industry (1986) World Development, 14 (9), pp. 1141-1150; Shah, N., Pharmaceutical supply chains: Key issues and strategies for optimisation (2004) Computers and Chemical Engineering, 28 (6-7), pp. 929-941; Lapierre, S.D., Ruiz, A.B., Scheduling logistic activities to improve hospital supply systems (2007) Computers & Operations Research, 34 (3), pp. 624-641; Ghandforoush, P., Sen, T.K., A DSS to manage platelet production supply chain for regional blood centers (2010) Decision Support Systems, 50 (1), pp. 32-42; Holweg, M., Disney, S.M., Hines, P., Naim, M.M., Towards responsive vehicle supply: A simulation-based investigation into automotive scheduling systems (2005) Journal of Operations Management, 23 (5), pp. 507-530; Delavar, M.R., Hajiaghaei-Keshteli, M., Molla-Alizadeh-Zavardehi, S., Genetic algorithms for coordinated scheduling of production and air transportation (2010) Expert Systems with Applications, 37 (12), pp. 8255-8266; Tiwari, M.K., Raghavendra, N., Agrawal, S., Goyal, S.K., A Hybrid Taguchi-Immune approach to optimize an integrated supply chain design problem with multiple shipping (2010) European Journal of Operational Research, 203 (1), pp. 95-106; Yang, T., Wen, Y., Wang, F., Evaluation of robustness of supply chain information-sharing strategies using a hybrid Taguchi and multiple criteria decision-making method (2011) International Journal of Production Economics, 134 (2), pp. 458-466; Schmidt, C.E., Bottoni, T., Improving medication safety and patient care in the emergency department (2003) Journal of Emergency Nursing, 29 (1), pp. 12-16; Dimick, J.B., Birkmeyer, J.D., Upchurch, G.R., Jr., Measuring surgical quality: What's the role of provider volume? (2005) World Journal of Surgery, 29 (10), pp. 1217-1221; Menachemi, N., Brooks, R.G., Reviewing the benefits and costs of electronic health records and associated patient safety technologies (2006) Journal of Medical Systems, 30 (3), pp. 159-168; Rogers, A., Jones, E., Oleynikov, D., Radio frequency identification (RFID) applied to surgical sponges (2007) Surgical Endoscopy, 21 (7), pp. 1235-1237; Liao, H., Using GA to dispatch the obtaining quantity for minimizing the total cost based on consideration of patient safety in HSCM (2009) Expert Systems with Applications, 36 (8), pp. 11358-11362; Wiendahl, H.-P., Von Cieminski, G., Begemann, C., A systematic approach for ensuring the logistic process reliability of supply chains (2003) CIRP Annals-manufacturing Technology, 52 (1), pp. 375-380; Koçoʇlu, I., Imamoʇlu, S.Z., Ince, H., Keskin, H., The effect of supply chain integration on information sharing: Enhancing the supply chain performance (2011) Proceedings of the 7th International Strategic Management Conference, 24, pp. 1630-1649. , July; Box, G.E.P., Wilson, K.B., On the experimental attainment of optimum conditions (1951) Journal of the Royal Statistical Society B, 13 (1), pp. 1-45; Gen, M., Cheng, R., (1997) Genetic Algorithms and Engineering Design, , John Wiley & Sons, New York, NY, USA; Khan, Z., Prasad, B., Singh, T., Machining condition optimization by genetic algorithms and simulated annealing (1997) Computers and Operations Research, 24 (7), pp. 647-657},
correspondence_address1={Wang, Y.-H.; Department of Applied Foreign Languages, Chung Shan Medical University, No. 110, Jian-Koa N. Road, Taiwan},
publisher={Hindawi Publishing Corporation},
issn={23566140},
pubmed_id={25250397},
language={English},
abbrev_source_title={Sci. World J.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Foulloy20148036,
author={Foulloy, L. and Clivillé, V. and Berrah, L.},
title={Fuzzy interpretation of performance scorecards for decision-making in the industrial context},
journal={IFAC Proceedings Volumes (IFAC-PapersOnline)},
year={2014},
volume={19},
pages={8036-8041},
doi={10.3182/20140824-6-za-1003.02794},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929791735&doi=10.3182%2f20140824-6-za-1003.02794&partnerID=40&md5=322e513668663a021e4cb6c4bb5dfe55},
affiliation={LISTIC Polytech Annecy Chambéry, France},
abstract={This article deals with the use, for industrial decision-making, of performance expressions that are provided by performance indicators and performance measurement systems. Two kinds of expression are distinguished in this sense: the performance measure on the one hand, and the performance evaluation on the other hand. While the performance measure identifies the achievement degree of the assigned objective, the performance evaluation handles the judgement of the decision-maker with regards to the obtained result. We focus in this study on the definition of a particular performance evaluation mechanism, which consists on a fuzzy symbolic handling of performance measures. Structural objective break-down that allows decision-makers to define the links between strategic, tactical and operational levels is the considered framework. We assume that performance measures are expressed at the operational level, for local control purposes, and look for translating them into performance evaluations, making them useful for the tactical decision-making. Performance measures are defined under numerical values. Fuzzification and defuzzification approaches are used in order to describe these values in linguistic terms, such as bad, medium or good. Symbolic items are also proposed. Some illustrations are presented, extracted from a case study which has been carried out in the Bosch Rexroth S.A. Company. © IFAC.},
author_keywords={Decision-making;  Fuzzy symbolic handling;  Industrial objectives;  Performance expression;  Structural breakdown},
keywords={Automation;  Linguistics;  Supply chain management, Fuzzy symbolic handling;  Industrial objectives;  Performance evaluations;  Performance expression;  Performance indicators;  Performance measurement system;  Structural breakdown;  Tactical decision makings, Decision making},
references={Belton, V., Stewart, T.J., (2002) Multiple Criteria Decision Analysis - An Integrated Approach, , Kluwer Academic Publishers; Berrah, L., Mauris, G., Vernadat, F., Information aggregation in industrial performance measurement: Rationales, issues and definitions (2004) International Journal of Production Research, 42 (20), pp. 4271-4293; Berrah, L., Mauris, G., Foulloy, L., Haurat, A., Fuzzy performance indicators for manufacturing processes (1998) Fuzzy System Design: Social and Engineering Applications, 17, pp. 225-248. , L. Reznik, V. Dimitrov and J. Kaprzyk, eds Physica-Verlag; Berrah, L., Mauris, G., Haurat, A., Foulloy, L., Global vision and performance indicators for an industrial improvement approach (2000) Computers in Industry, 43 (3), pp. 211-225; Bourguignon, A., Malleret, V., Nørreklit, H., The American balanced scorecard versus the french tableau de bord: The ideological dimension (2004) Management Accounting Research, 15 (2), pp. 107-134; Clivillé, V., Berrah, L., Mauris, G., Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method (2007) International Journal of Production Economics, 105 (1), pp. 171-189; Epstein, M., Mansoni, J., Implementing corporate strategy: From tableaux de bord to balanced scorecards (1998) European Management Journal, 16 (1), pp. 190-203; Fortuin, L., Performance indicators, why, where and how? (1988) European Journal of Operational Research, 34, pp. 1-9; Foulloy, L., Galichet, S., Typology of fuzzy controllers (1995) Theoretical Aspects of Fuzzy Control, pp. 65-90. , H. Nguyen, M. Sugeno, R. Tong and R. Yager, eds John Wiley & Sons; Gomez, T., Gonzalez, M., Luque, M., Multiple objectives decomposition-coordination methods for hierarchical organizations (2001) European Journal of Operational Research, 133, pp. 323-341; Kaplan, R., Norton, D., (1992) The Balanced Scorecard: Measures that Drive Performances, , Harvard Business Review; Kaplan, R., Norton, D., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston; Lebas, M., Performance measurement and performance management (1995) International Journal of Production Economics, 1-3, pp. 23-35; Nair, A., Meta-analysis of the relationship between quality management practices and firm performance-implications for quality management theory development (2006) Journal of Operations Management, 24 (6), pp. 948-975; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 48 (4), pp. 80-116; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Ruspini, E., A new approach to clustering (1969) Information and Control, 15 (1), pp. 22-32; Simon, H., (1977) The New Science of Management Decision, , Prentice-Hall; Vallespir, B., Merle, C., Doumeingts, G., Gim: A technico-economic methodology to design manufacturing systems (1993) Control Engineering Practice, 1 (6), pp. 1031-1038; Zadeh, L., Fuzzy sets (1965) Information and Control, 8, pp. 38-353; Zadeh, L., Quantitative fuzzy semantics (1971) Information Sciences, 3, pp. 159-176},
editor={Boje E., Xia X.},
publisher={IFAC Secretariat},
issn={14746670},
isbn={9783902823625},
language={English},
abbrev_source_title={IFAC Proc. Vol. (IFAC-PapersOnline)},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Wen2014737,
author={Wen, H.},
title={Process-based supply chain performance measurement},
journal={Advances in Intelligent Systems and Computing},
year={2014},
volume={280},
pages={737-747},
doi={10.1007/978-3-642-55182-6_64},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927943301&doi=10.1007%2f978-3-642-55182-6_64&partnerID=40&md5=e7458377ad9725c815dbc3ce54435d5f},
affiliation={Department of Business Administration, Chengdu University, Chengdu, 610000, China},
abstract={Performance measurement is important to improve organizational competitiveness. Organizations and researchers have developed and investigated various performance-measurement systems to manage and improve internal and external operations for logistics and supply chain (SC). Supply chain management (SCM) refers to all of the processes, technologies, and strategies, and the many-factored elements interact each other, so the performance measurement approach of supply chain is a difficult proposition. In traditional SC performance assessment approach, the SC metrics are difficult to define and to measure, and this kind of assessment focuses more on financial data and outcomes. Process-basedSCperformance assessment supports a process-oriented view, and gives early warning signals. This paper describes the common SC performance measurement approaches and the process-based SC performance measurement approaches in detail, and aims at revealing different kind of approaches of performance measurement in the new supply chain era. © Springer-Verlag Berlin Heidelberg 2014.},
author_keywords={Performance measurement;  Process-based measurement;  Supply chainmanagement},
keywords={Intelligent systems;  Management science;  Supply chain management, Financial data;  Performance assessment;  Performance measurement system;  Performance measurements;  Process-based;  Process-oriented;  Supply chain managements (SCM);  Supply chain performance measurements, Chains},
references={Ainapur, B., Singh, R., Vittal, P., Strategic study on enhancement of supply chain performance (2011) Int J Bus Insights Transform, 5 (1), pp. 98-108; Akyuz, G., Erkan, T., Supply chain performance measurement: A literature review (2010) Int J Prod Res, 48 (17), pp. 5137-5155; Beamon, B., Measuring supply chain performance (1999) Int J Oper Prod Manage, 19 (3), pp. 274-292; Childerhouse, P., Towill, D., Arcs of supply chain integration (2011) Int J Prod Res, 49 (24), pp. 7441-7468; Cohen, S., Roussel, J., (2007) Strategic Supply Chain Management, , McGraw Hill, New York; Cox, A., (2001) Supply Chains, Markets and Power: Mapping Buyer and Supplier Power Regimes, , Routledge, London; Fabbe-Costes, N., Jahre, M., Supply chain integration and performance: A review of the evidence (2008) Int J Logistics Manage, 19 (2), pp. 130-154; Flynn, B., Huo, B., Zhao, X., The impact of supply chain integration on performance: A contingency and configuration approach (2010) J Oper Manage, 28 (1), pp. 58-71; Frohlich, M., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2001) J Oper Manage, 19 (5), pp. 185-200; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature for research and applications (2007) Int J Prod Res, 45 (12), pp. 2819-2840; Holmberg, S., A system perspective on supply chain measurement (2000) Int J Phy Distrib Logistics, 30, pp. 847-868; Holmberg, S., A systems perspective on supply chain measurements (2000) Int J Phys Distrib Logistics Manage, 30 (10), pp. 847-868; Jayant, A., Evaluation of 3pl service provider in supply chain management: An analytic network process approach (2013) Int J Bus Insights Transf, 6 (2), pp. 78-83; Kueng, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Qual Manage, 11 (1), pp. 67-85; Lee, H., Creating value through supply chain integration (2000) Supply Chain Manage Rev, 4, pp. 30-36; Lockamy, A., McCormack, K., Linking the scor planning practices to supply chain performance (2004) Int J Oper Prod Manage, 24 (11-12), pp. 1192-1218; Martin, P., Patterson, J., On measuring company performance within a supply chain (2009) Int J Prod Res, 47 (9), pp. 2449-2460; Meng, X., SunM, JonesM(2011) Maturity model for supply chain relationship in construction J Manage Eng, 4, pp. 97-106; Smith, D., Smith, C., What’s wrong with supply chain metrics? (2013) Strateg Finance, 10, pp. 28-35; Thakkar, J., Scm based performance measurement system: A preliminary conceptualization (2012) Dicision, 39 (3), pp. 05-44; Der Vaart, T.V., Donk, D.V., Buyer-focussed operations as a supply chain strategy: Identifying the influence of business characteristics (2006) Int J Oper Prod Manage, 26 (1), pp. 8-23; Yao, K., Liu, C., An integrated approach for measuring supply chain performance (2006) J Mod Acc Auditing, 2 (10), pp. 17-37},
correspondence_address1={Wen, H.; Department of Business Administration, China; email: 846937934@qq.com},
editor={Xu J., Cruz-Machado V.A., Nickel S., Lev B.},
publisher={Springer Verlag},
issn={21945357},
isbn={9783642551819},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Duarte2014645,
author={Duarte, S. and Cruz-Machado, V.A.},
title={Lean and green supply chain performance: A balanced scorecard perspective},
journal={Advances in Intelligent Systems and Computing},
year={2014},
volume={280},
pages={645-654},
doi={10.1007/978-3-642-55182-6_56},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84927915215&doi=10.1007%2f978-3-642-55182-6_56&partnerID=40&md5=b48158fce11b08b1d6e89eb932e98c25},
affiliation={UNIDEMI, Departamento de Engenharia Mecânica e Industrial, Universidade Nova de Lisboa, Caparica, 2829-516, Portugal},
abstract={The performance evaluation has become an important subject to get competitive advantage on supply chain. New business paradigms as lean and green are able to develop improvements on the supply chain performance. To get a balanced performance measurement system, a balanced scorecard (BSC) may be developed. This study is motivated by the lack of evidence on the lean and green supply chain performance through a BSC framework. The purpose of this study is to explore how to reach benefits on supply chain performance, considering the lean and green performance measures through the traditional BSC perspectives. To attain the paper objective in a first stage a literature review was carried out, to provide a comprehensive understanding of how the BSC can incorporate the lean and green supply chain performance measures. Next, a conceptual framework was developed to investigate how lean and green initiatives can affect the supply chain performance, using a cause-and-effect BSC approach. It is possible to recognize that lean and green initiatives influence the linkages between performance measures. These linkages in the scorecard are an example of how to evaluate the organization’s supply chain. © Springer-Verlag Berlin Heidelberg 2014.},
author_keywords={Balanced scorecard;  Green supply chain;  Lean supply chain;  Performance measurement},
keywords={Competition;  Intelligent systems;  Management science;  Supply chains, Balanced scorecards;  Competitive advantage;  Conceptual frameworks;  Green supply chain;  Lean supply chains;  Performance measurement system;  Performance measurements;  Supply chain performance, Chains},
references={Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput Ind Eng, 53 (1), pp. 43-62; Bhasin, S., Lean and performance measurement (2008) Jmanuf Technol Manage, 19 (5), pp. 670-684; Broek, F., (2010) Green Supply Chain Management, Marketing Tool Or Revolution, , NHTV Breda University of Applied Sciences; Cetinkaya, B., (2011) Developing a Sustainable Supply Chain Strategy. In: Sustainable Supply Chain Management, pp. 17-55. , Springer, Berlin; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: Int J, 16 (5), pp. 605-620; Gordon, P., (2001) Lean and Green: Profit for Yourworkplace and Environmental. Berrett-Koehler Publications, , USA; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int J Oper Prod Manage, 21 (1-2), pp. 71-87; Gurumurthy, A., Kodali, R., A multi-criteria decision-making model for the justification of lean manufacturing systems (2008) Int J Manage Sci Eng Manage, 3 (2), pp. 100-118; Gurumurthy, A., Kodali, R., Application of benchmarking for assessing the lean manufacturing implementation (2009) Benchmarking: Int J, 16 (2), pp. 274-308; Hervani, A., Helms, M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: Int J, 12 (4), pp. 330-353; Hsu, Y., Liu, C., Environmental performance evaluation and strategy management using balanced scorecard (2010) Environ Monit Assess, 170 (14), pp. 599-607; Hubbard, G., Measuring organizational performance: Beyond the triple bottom line (2009) Bus Strategy Environ, 18 (3), pp. 177-191; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) Int J Prod Econ, 101 (1), pp. 99-108; Kaplan, R.S., Norton, D., (1996) The Balanced Scorecard: Translating Strategy into Action. Harvard Business Press, , Boston; Lapide, L., What about measuring supply chain performance? (2000) Achieving Supply Chain Excellence Technol, 2, pp. 287-297; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decis Support Syst, 46 (2), pp. 512-521; Mollenkopf, D., Stolze, H., Green, lean, and global supply chains (2010) Int J Phys Distrib Logistics Manage, 40 (1-2), pp. 14-41; Morgan, C., Supply network performance measurement: Future challenges? (2007) Int J Logistics Manage, 18 (2), pp. 255-273; Nawrocka, D., Brorson, T., Lindhqvist, T., Iso 14001 in environmental supply chain practices (2009) J Cleaner Prod, 17 (16), pp. 1435-1443; Pettersen, J., Defining lean production: Some conceptual and practical issues (2009) TQM J, 21 (2), pp. 127-142; Shepherd, C., Gunter, H., ; Shingo, P., (2012) The Shingo Prize for Operational Excellence Model &Amp; Application Guidelines. Jon M Hustsman School of Business, , Utah State University, Logan, Utah; Sidiropoulos, M., Mouzakitis, Y., Applying sustainable indicators to corporate strategy: The eco-balanced scorecard (2004) Environ Res Eng Manage, 1 (27), pp. 28-33; Simpson, D., Power, D., Use the supply relationship to develop lean and green suppliers (2005) Supply Chain Manage: Int J, 10 (1), pp. 60-68; Stenzel, J., (2008) Lean Accounting: Best Practices for Sustainable Integration, , Wiley, New York; http://www.epa.gov/lean/; Zhu, Q., Sarkis, J., Lai, K., Green supply chain management: Pressures, practices and performance within the chinese automobile industry (2007) J Cleaner Prod, 15 (11), pp. 1041-1052; Zhu, Q., Sarkis, J., Lai, K., Confirmation of a measurement model for green supply chain management practices implementation (2008) Int J Prod Econ, 111 (2), pp. 261-273},
correspondence_address1={Duarte, S.; UNIDEMI, Portugal; email: scd@campus.fct.unl.pt},
editor={Xu J., Cruz-Machado V.A., Nickel S., Lev B.},
publisher={Springer Verlag},
issn={21945357},
isbn={9783642551819},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{DeAlmeidaCoelho2014,
author={De Almeida Coelho, R. and Van Aken, E. and Gregory, R.P. and Dell'Antonio, C.J.},
title={High-tech start-up engineering firms: How to generate value through performance management and reward systems},
journal={2014 International Annual Conference of the American Society for Engineering Management - Entrepreneurship Engineering: Harnessing Innovation, ASEM 2014},
year={2014},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925679458&partnerID=40&md5=60c7a987e0f01b860f0be66f0157b930},
affiliation={Universidade do Estado de Santa Catarina, Av. Madre Benvenuta, 2007, Florianopolis, SC  88.035-001, Brazil; Virginia Polytechnic Institute and State University, 250 Durham Hall BlacksburgVA  24061, United States; Universidade Federal de Santa Catarina, Centro Tecnologico, Campus Universitario, Florianopolis, SC  88040-900, Brazil},
abstract={Technology-based companies, particularly those invested by venture capital and private equity funds search to maximize their value as a major goal. Therefore, it makes sense to develop performance management systems aligned with the company's value drivers. Hence, one has to identify which are the company's key value drivers and which are the respective performance indicators linked to them. However, since companies are ultimately made of people and they are the ones who will accomplish the expected performance for the company, their reward or compensation systems also need to be aligned with the targets set for each performance indicator. Thus, being able to align performance indicators, reward systems and drivers of value becomes a task of high importance. The methodology applied in this paper involves carrying initially an assessment of the main types of performance management systems, reward systems, and valuation techniques and parameters. Next, a proposed integrated model using a performance measurement system, reward systemand value management strategies in high-tech engineering companies will be evaluated. Finally, we evaluate the use of this approach in three high-tech engineering based companies active in the market, which have been invested by venturecapital.Copyright, American Society for Engineering Management, 2014.},
author_keywords={High-tech start-up;  Performance management;  Reward and compensation systems;  Valuation;  Venture capital},
keywords={Benchmarking;  Investments;  Resource valuation;  Waste disposal, Compensation systems;  Engineering companies;  High tech;  Performance indicators;  Performance management;  Performance management systems;  Performance measurement system;  Venture Capital, Supply chain management},
references={Aggarwal, R.K., Samwick, A.A., Empire-builders and shirkers: Investment, firm performance, and managerial incentives (2006) Journal of Corporate Finance, 12 (3), pp. 489-515; Argüden, Y., Right reward for right performance: Aligning executive compensation with good governance (2013) International Journal of Disclosure and Governance, 10 (2), pp. 122-134; Arokiasamy, A.R.A., Tat, H.H., Bin Abdullah, A.G.K., The effects of reward system and motivation on job satisfaction: Evidence from the education industry in Malaysia (2013) World Applied Sciences Journal, 24 (12), pp. 1597-1604; Backes-Gellner, U., Pull, K., Tournament compensation systems, employee heterogeneity, and firm performance (2013) Human Resource Management, 52 (3), pp. 375-398; Baker, G.P., Hall, B.J., CEO incentives and firm size (2004) Journal of Labor Economics, 22 (4), pp. 767-798; Bartlett, J.W., Equity-Flavored executive compensation: A key element of US growth, but in need of a doable fix (2008) The Journal of Private Equity, 11 (3), pp. 41-49; Boyd, B.K., Salamin, A., (2001) Strategic Reward Systems: A C; Berk, J.B., DeMarzo, P.M., (2013) Corporate Finance, , Pearson Education; Contingency model of pay system design Strategic Management Journal, 22 (8), pp. 777-792; Carhart, M.M., On persistence in mutual fund performance (1997) The Journal of Finance, 52 (1), pp. 57-82; Carnahan, S., Agarwal, R., Campbell, B., The effect of relative compensation dispersion of firms on the mobility and entrepreneurship of extreme performers (2011) Academy of Management 2011 Annual Meeting - West Meets East: Enlightening, , Balancing. Transcending, AOM 20116; Cooke, F.L., Huang, K., Postacquisition evolution of the appraisal and reward systems: A study of Chinese IT firms acquired by US firms (2011) Human Resource Management, 50 (6), pp. 839-858; Coombs, J.E., Gilley, K.M., Stakeholder management as a predictor of CEO compensation: Main effects and interactions with financial performance (2005) Strategic Management Journal, 26 (9), pp. 827-840; Copeland, T.E., Koller, T., Murrin, J., (1994) Valuation: Measuring and Managing the Value of Companies; Cuevas-Rodríguez, G., Gomez Mejia, L.R., Wiseman, R.M., Has agency theory run its course?: Making the theory more flexible to inform the management of reward systems (2012) Corporate Governance: An International Review, 20 (6), pp. 526-546; Damodaran, A., (2006) Damodaran on Valuation: Security Analysis for Investment and Corporate Finance-2/E; Dittmann, I., Maug, E., Spalt, O., Sticks or carrots? Optimal CEO compensation when managers are loss-averse (2010) Journal of Finance, 65, pp. 2015-2050; Du, Y., Deloof, M., Jorissen, A., Headquarters- subsidiary interdependencies and the design of performance evaluation and reward systems in multinational enterprises (2013) European Accounting Review, 22 (2), pp. 391-424; Fama, E.F., French, K.R., Common risk factors in the returns on stocks and bonds (1993) Journal of Financial Economics, 33 (1), pp. 3-56; Gores, J., How effective is your incentive compensation system? (2000) Journal of Management in Engineering, 16 (1), pp. 29-33; Gupta, N., Shaw, J.D., Employee compensation: The neglected area of HRM research (2014) Human Resource Management Review, 24 (1), pp. 1-4; Hoffmann, F., Pfeil, S., Reward for luck in a dynamic agency model (2010) Review of Financial Studies, 23 (9), pp. 3329-3345; Holmstrom, B., Kaplan, S.N., The state of US corporate governance: What's right and what's wrong? (2003) Journal of Applied Corporate Finance, 15 (3), pp. 8-20; Jensen, M.C., Murphy, K.J., Performance pay and top-management incentives (1990) Journal of Political Economy, pp. 225-264; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, pp. 71-79. , Jan-Feb; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (5), pp. 134-140; Kuhn, K.M., Compensation as a signal of organizational culture: The effects of advertising individual or collective incentives (2009) The International Journal of Human Resource Management, 20 (7), pp. 1634-1648; Lintner, J., The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets (1965) The Review of Economics and Statistics, pp. 13-37; Morck, R., Shleifer, A., Vishny, R.W., Management ownership and market valuation: An empirical analysis (1988) Journal of Financial Economics, 20, pp. 293-315; Nazir, T., Khan, S.U.R., Shah, S.F.H., Zaman, K., Impact of rewards and compensation on job satisfaction: Public and private universities of UK (2013) Middle East Journal of Scientific Research, 14 (3); Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Oyer, P., Why do firms use incentives that have no incentive effects? (2004) The Journal of Finance, 59 (4), pp. 1619-1650; Penman, S.H., (2007) Financial Statement Analysis and Security Valuation; Selvam, A., Pay for value. Survey shows executive compensation at hospitals, systems continues shift to rewards for outcomes-financial and clinical (2013) Modern Healthcare, 43 (32), pp. 26-28; Sharpe, W.F., Capital asset prices: A theory of market equilibrium under conditions of risk (1964) The Journal of Finance, 19 (3), pp. 425-442; Wright, A.M., Culture and compensation: Unpicking the intricate relationship between reward and organizational culture (2010) Thunderbird International Business Review, 52 (3), pp. 189-202},
publisher={American Society for Engineering Management},
language={English},
abbrev_source_title={Int. Annu. Conf. Am. Soc. Eng. Manag. - Entrep. Eng.: Harnessing Innov., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Keathley2014,
author={Keathley, H. and Van Aken, E. and Letens, G.},
title={Performance measurement system implementation: Systematic review of success factors},
journal={2014 International Annual Conference of the American Society for Engineering Management - Entrepreneurship Engineering: Harnessing Innovation, ASEM 2014},
year={2014},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925597107&partnerID=40&md5=baf74b74a32e2983d2d25d724336839c},
affiliation={Grado Department of Industrial and Systems Engineering, Virginia Tech, 250 Durham Hall, Blacksburg, VA  24061, United States; Department of Economics, Management and Leadership, Royal Military Academy, Renaissancelaan 30, Brussels, 1000, Belgium},
abstract={Introducing a new or redesigned Performance Measurement system is known to be challenging, but a well-designed system can provide significant benefits to an organization if effectively established. Each phase of this process, i.e., design, implementation, use, and review, presents unique obstacles but emphasis has been placed on the implementation phase as an area that is particularly problematic in practical applications. Many researchers focus on identifying enablers and barriers to success as part of the study on effective implementation. A previous systematic literature review was conducted which found that this research area is in a moderately early stage of development with many inconsistencies in terminology and operationalization of variables. In order to gain a more comprehensive understanding of this area, an extensive systematic literature review was designed which features a sensitive search strategy designed to capture a wider range of papers. This paper presents the partial results of this ongoing study and provides an example of an advanced search strategy. The results show that the advanced strategy provides a more complete review of the literature and increases the accuracy of the analyses. Characterizing the implementation success factors will allow resources to be targeted in such a way as to mitigate these factors more effectively. By managing the effects of the critical factors, PM system implementation can be improved which will support overall effectiveness of these systems.},
author_keywords={Implementation;  Performance measurement systems;  Success factors;  Systematic literature review},
keywords={Implementation;  Implementation success;  Overall effectiveness;  Performance measurement system;  Search strategies;  Success factors;  Systematic literature review;  Systematic Review, Supply chain management},
references={Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; (2011) Cochrane Handbook for Systematic Reviews of Interventions, , http://handbook.cochrane.org/, Retrieved from the Cochrane Collaboration; (2013) Campbell Resource Center Online Resource, , http://www.campbellcollaboration.org/, Retrieved from the Campbell Collaboration; De Waal, A.A., Behavioral factors important for the successful implementation and use of performance management systems (2003) Management Decision, 41 (8), pp. 688-697; De Waal, A.A., Counet, H., Lessons learned from performance management systems implementations (2009) International Journal of Productivity and Performance Management, 58 (4), pp. 367-390; De Waal, A., Kourtit, K., Performance measurement and management in practice: Advantages, disadvantages and reasons for use (2013) International Journal of Productivity and Performance Management, 62 (5), pp. 446-473; Keathley, H., Van Aken, E., Systematic literature review on the factors that affect performance measurement system implementation (2013) Proceedings from the 2013 Institute of Industrial Engineers Annual Conference, Engineering Management Track, , San Juan, Puerto Rico, May 18-23, CD-ROM; Keathley, H., Gonzalez Aleu, F., Cardenas Orlandini, P.F., Van Aken, E., Deschamps, F., Rosa Leite, L., Maturity assessment of performance measurement implementation success factor literature (2013) Prodeedings from the 2013 American Society for Engineering Management Annual Conference, , Minnianapolis, Minnisota October; Keathley, H., Van Aken, E., Letens, G., Review of factors affecting performance measurement system implementation (2014) Proceedings of the 2014 Performance Measurement Association Conference, , Aarhus, Denmark, June 24-27, CD-ROM; Neely, A., Bourne, M., Why measurement initiatives fail (2000) Measuring Business Excellence, 4 (4), pp. 3-7; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Theurer, J., Seven pitfalls to avoid when establishing performance measures (1998) Public Management, 80, pp. 21-24; Tranfield, D., Denyer, D., Palminder, S., Toward a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review (2003) British Journal of Management, 14, pp. 207-222; Wagner, M., The role of policy analyst in implementing performance measurement methods (1986) Review of Policy Research, 6 (1), pp. 121-136},
publisher={American Society for Engineering Management},
language={English},
abbrev_source_title={Int. Annu. Conf. Am. Soc. Eng. Manag. - Entrep. Eng.: Harnessing Innov., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Jothimani2014944,
author={Jothimani, D. and Sarmah, S.P.},
title={Supply chain performance measurement for third party logistics},
journal={Benchmarking},
year={2014},
volume={21},
number={6},
pages={944-963},
doi={10.1108/BIJ-09-2012-0064},
note={cited By 50},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924492169&doi=10.1108%2fBIJ-09-2012-0064&partnerID=40&md5=2ae60d53b5dd0782a66d3ab5e0a2e456},
affiliation={Department of Management Studies, IIT Delhi, Delhi, India; Department of Industrial Engineering and Management, IIT Kharagpur, Kharagpur, India},
abstract={Purpose – The purpose of the paper is to explore the applicability of the Supply Chain Operations Reference (SCOR) model and to identify the key performance indicators (KPIs) for the service-oriented sector – namely a third-party logistics (3PL) service provider. Design/methodology/approach – The performance attributes of SCOR model (reliability, responsiveness, flexibility, cost measures and asset management efficiency) are used as the basis for defining the KPIs. A questionnaire was sent to relevant decision makers. Findings – This paper illustrates the use of the integrated approach of SCOR, fuzzy analytic hierarchy process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for measuring the supply chain performance (SCP) in the light of a real life case study company. Research limitations/implications – This method forms the basis for performance measurement using the SCOR model to evaluate strategy. In this work, the performance of the company has been compared with its own previous performance. The work can be extended to external benchmarking and also to other sectors. Practical implications – The paper attempts to overcome the conflict between the top-down strategy and bottom-up implementation process. The paper links the strategic objective with operations which would aid managers at different levels of an organization with decision making. The KPIs, when implemented in a business intelligence (BI) tool, would result in real time performance measurement. Originality/value – The paper focusses on 3PLs. It provides a base for measuring the SCP using SCOR model. The paper also identified KPIs for three domains of 3PL, namely freight forwarding, customs and warehousing. © Emerald Group Publishing Limited.},
author_keywords={Benchmarking;  Fuzzy ahp;  Logistics;  Supply chain management;  Supply chain performance;  Third party logistics;  Topsis},
references={Buckley, J.J., Eslami, E., (2002) An Introduction to Fuzzy Logic and Fuzzy Set, , Physica-Verl, Heidelberg, New York, NY; Chang, D.-Y., Extent analysis and synthetic decision (1992) International Conference on Optimization: Techniques and Applications, pp. 352-368; Chen, S.J., Hwang, C.L., (1992) Fuzzy Multiple Attribute Decision Making: Methods and Applications, , Springer-Verlag, Berlin; Ertuğrul, I., Karakasoğlu, N., Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods (2009) Expert Systems with Applications, 36 (1), pp. 702-715; Gumus, A.T., Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology (2009) Expert Systems with Applications, 36 (2), pp. 4067-4074; Hokey, M., Seong, J.J., Benchmarking the operational efficiency of third party logistics providers using data envelopment analysis (2006) Supply Chain Management: An International Journal, 11 (3), pp. 259-265; Huan, S.H., Sunil, K., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Joshi, R., Banwet, B.K., Shankar, R., A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain (2011) Expert Systems with Applications, 38 (8), pp. 10170-10182; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston, MA; Kocaoğlu, B., Gülsün, B., Tanyas, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2011) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Lai, K.-H., Ngai, E., Cheng, T., An empirical study of supply chain performance in transport logistics (2004) Int. J. Production Economics, 87 (3), pp. 321-331; Mahmoodzadeh, S., Shahrabi, J., Pariazar, M., Zaeri, M.S., Project selection by using fuzzy AHP and TOPSIS technique (2007) International Journal of Human and Social Sciences, 2 (7), pp. 397-402; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Realising strategy through measurement (1994) International Journal of Operations &Amp; Production Management, 14 (3), pp. 140-152; Neely, A., Marr, B., Roos, G., Pike, S., Gupta, O., Towards the third generation of performance measurement (2003) Controlling, 15 (3-4), pp. 129-136; Oslon, D.L., (2004) Comparison of weights in TOPSIS models, Mathematical and Computer Modelling, 40 (7-8), pp. 721-727; Qureshi, M.N., Kumar, D., Kumar, P., An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers (2008) Asia Pacific Journal of Marketing and Logistics, 20 (2), pp. 227-249; Saaty, T.L., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; Selviaridis, K., Spring, M., Third party logistics: A literature review and research agenda (2007) International Journal of Logistics Management, 18 (1), pp. 125-150; Stephens, S., Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; Hum, S.H., A Hayes-Wheelwright framework approach for strategic management of third party logistics services (2000) Integrated Manufacturing Systems, 11 (2), pp. 132-137; Sahay, B.S., Jayanthi, R., Real time business intelligence in supply chain analytics (2008) Information Management &Amp; Computer Security, 16 (1), pp. 28-48},
correspondence_address1={Sarmah, S.P.; Department of Industrial Engineering and Management, IIT KharagpurIndia},
publisher={Emerald Group Publishing Ltd.},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{DeFelice2014398,
author={De Felice, F. and Petrillo, A.},
title={Critical success factors for e-healthcare: Integrated Set Of Performance Indicators System (ISPIS)},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2014},
volume={8868},
pages={398-401},
doi={10.1007/978-3-319-13105-4_58},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921728001&doi=10.1007%2f978-3-319-13105-4_58&partnerID=40&md5=ba0f3a6cb09ed734b737fff0b2c78278},
affiliation={University of Cassino and Southern Lazio, Cassino, Italy; University of Naples Parthenope, Napoli, Italy},
abstract={Healthcare information technology (health IT) is a critical factor of healthcare system worldwide. The aim of this paper is to propose an integrated performance measurement system to evaluate access to healthcare and healthcare cost reduction is proposed. This paper presents the use of the Analytic Hierarchy Process (AHP) to prioritize all of the measures and strategies in a Balanced Scorecard framework (BSC). Results show that the methodological approach is very flexible and useful in several scenarios. © Springer International Publishing Switzerland 2014.},
author_keywords={AHP;  BSC;  e-healthcare;  Performance;  Service innovation;  Telemedicine},
keywords={Analytic hierarchy process;  Assisted living;  Hierarchical systems;  Telemedicine;  Cost reduction;  Supply chain management, Analytic hierarchy process (ahp);  Critical success factor;  E-healthcare;  Healthcare information technology;  Methodological approach;  Performance;  Performance measurement system;  Service innovation;  AHP;  BSC, Cost reduction;  Health care},
references={Kaye, R., Kokia, E., Shalev, V., Idar, D., Chinitz, D., Barriers and success factors in health information technology: A practitioner’s perspective (2010) J. of Management & Marketing in Healthcare, 3, pp. 163-175; De Felice, F., Petrillo, A., Key success factors for organizational innovation in the fashion industry (2013) International J. of Engineering Business Management, 5, pp. 47-57; Poveda-Bautista, R., Baptista, D.C., García-Melón, M., Setting competitiveness indicators using BSC and ANP (2012) International J. of Production Research, 50, pp. 4738-4752; De Felice, F., Editorial Research and applications of AHP/ANP and MCDA for decision making in manufacturing (2012) International J. of Production Research, 50, pp. 4735-4737; Kaplan, R.S., Norton, D.P., The balance scorecard. Measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Atkinson, M., Measuring the Performance of the IT Function in the UK Health Service Using a Balanced Scorecard Approach (2004) Electronic J. of Information Systems Evaluation, 7, pp. 1-10; Kloot, L., Martin, J., Strategic performance management: A balanced approach to performance management issues in local government (2000) Management Accounting Research, 11, pp. 231-251; Saaty, T.L., (1980) The Analytic Hierarchy Process, , McGraw-Hill, New York; Pecchia, L., Martin, J.L., Ragozzino, A., Vanzanella, C., Scognamiglio, A., Mirarchi, L., Morgan, S.P., User needs elicitation via analytic hierarchy process (AHP). A case study on a Computed Tomography (CT) scanner (2013) BMC Medical Informatics and Decision Making, 13, pp. 1-11; Saaty, T.L., (1994) Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, , RWS Publications, Pittsburgh},
editor={Pecchia L., Pecchia L., Chen L.L., Nugent C., Bravo J.},
publisher={Springer Verlag},
issn={03029743},
isbn={9783319131047},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Callado2014135,
author={Callado, A.A.C. and Callado, A.L.C.},
title={Relations between supply chain performance indicators usage patterns and strategic goals typologies: Evidence from Brazilian agribusiness companies},
journal={Research in Accounting in Emerging Economies},
year={2014},
volume={14},
pages={135-149},
doi={10.1108/S1479-356320140000014004},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84912567911&doi=10.1108%2fS1479-356320140000014004&partnerID=40&md5=74059c615cd3c434b74929d7b9b3183c},
abstract={Findings-The groups of eligible performance indicators relating to Balanced Scorecard frameworks for specific strategic goals changes its composition significantly if upper quartiles or estimated percentages are applied. The results also suggest that the intensity of use of performance indicators from the learning and growth perspective changes significantly among groups of individual companies as they aim different strategic goals. Research limitations/implications-The research carried out in this paper is based on responses provided by senior managers regarding managerial practices, but it does present a new perspective about relations between strategic goals and usage patterns of supply chain performance indicators from the perspectives of the Balanced Scorecard. Practical implications-The managerial implications arising from the findings suggest that supply chain performance metrics selection criteria should consider the characteristics of strategic goal.
Methodology/approach-A survey among 121 individual Brazilian agribusiness companies was undertaken. Each of them was asked to declare their respective strategic goal as well as point out which performance indicators they used from a list composed by 49 performance indicators divided into the four traditional perspectives of the Balanced Scorecard. Percentages were used to identify the use extent of these performance indicators and upper quartiles and estimated percentages were used to identify the groups of performance indicators for all strategic goal considered.
Originality/value of paper-There is little existing literature that addresses attention to the relations between strategic goals and the structure of Balanced Scorecard framework toward supply chain performance measurement and this paper aims to fill this gap.
Purpose-The underlying question to the investigation is "are supply chain performance indicators usage patterns related to specific strategic goals?" The paper aims to analyze whether specific supply chain performance metrics from the Balanced Scorecard are related to specific strategic goals among individual agribusiness companies. © 2014 by Emerald Group Publishing Limited All rights of reproduction in any form reserved.},
author_keywords={Agribusiness;  Strategic goals;  Supply chain performance measurement},
references={Araujo, M.J., (2013) Fundamentos de Agronegocios (4th Ed.), , Sao Paulo: Atlas; Beamon, B., Supply chain and analysis models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Berry, A.J., Coad, A.F., Harris, E.P., Otley, D.T., Stringer, C., Emerging themes in management control: A review of recent literature (2009) The British Accounting Review, 41 (1), pp. 2-20; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Butler, A., Letza, S.R., Neale, B., Linking the balanced scorecard to strategy (1997) Long Range Planning, 30 (2), pp. 242-253; Callado, A.A.C., Mendes, E., Callado, A.L.C., Um estudo empirico da significan-cia das relacoes entre a elaboracao de metas estrategicas e o uso de indicadores de desempenho (2013) Revista Iberoamericana de Contabilidade de Gestion, 11 (21), pp. 1-15; Chen, S., Wang, H., Yang, K., Establishment and application of performance measure indicators for universities (2009) The Total Quality Management Journal, 21 (3), pp. 220-235; Chia, A., Goh, M., Hum, S.-H., Performance measurement in supply chain entities: Balanced scorecard perspective. Benchmarking (2009) An International Journal, 16 (5), pp. 605-620; Chizzotti, A., (1991) A Pesquisa em Ciencias Humanas e Sociais, , Sao Paulo: Cortez; Ferreira, A., Otley, D., The design and use of performance management systems: An extended framework for analysis (2009) Management Accounting Research, 20 (4), pp. 263-282; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Gil, A.C., (2002) Como Elaborar Projetos de Pesquisa (4th Ed.), , Sao Paulo: Atlas; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Ittner, C.D., Larcker, D.F., Quality strategy, strategic control systems and organizational performance (1997) Accounting, Organizations and Society, 22 (3-4), pp. 293-314; Ittner, C.D., Larcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting, Organizations and Society, 28 (7-8), pp. 715-741; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Langfield-Smith, K., Management control systems and strategy: A critical review (1997) Accounting, Organizations and Society, 22 (2), pp. 207-232; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Lucena, M.D., Da, S., (2004) Planejamento Estrategico e Gestão Do Desempenho Para Resultados, , Sao Paulo: Atlas; Marconi, M., Lakatos, E.M., (1999) Tecnicas Depesquisa (4th Ed.), , Sao Paulo: Atlas; Melnik, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-218; Miles, R.W., Snow, C.C., (1978) Organizational Strategy, Structure, and Process, , New York, NY: McGraw-Hill; Miller, D., Friesen, P.H., Innovation in conservative and entrepreneurial firms: Two models of strategic momentum (1982) Strategic Management Journal, 3 (1), pp. 1-25; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Norreklit, H., The balance on the balanced scorecard-A critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Pereira, E., Contabilidade e controladoria em agribusine (1996) Controladoria, Gestão Empresarial e Indicador de Eficiencia em Agribusiness, , J. C. Marion (Ed.) Sao Paulo: Atlas; Porter, M.E., (1980) Competitive Strategy, , New York, NY: Free Press; Porter, M.E., (1985) Competitive Advantage, , New York, NY: Free Press; Prieto, V.C., Pereira, F.L.A., De Carvalho, M.M., Laurindo, F.J.B., Fatores criticos na implementacao do balanced scorecard (2006) Gestão e Produção, 13 (1), pp. 81-92; Punniyamoorthy, M., Murali, R., Balanced score for the balanced scorecard: A benchmarking tool (2008) Benchamarking: An International Journal, 15 (4), pp. 420-443; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Silver, M., (2000) Estatísticapara Administração, , Sao Paulo: Atlas; Simons, R., (1995) Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal, , Boston, MA: Harvard Business School Press},
publisher={Emerald Group Publishing Ltd.},
issn={14793563},
language={English},
abbrev_source_title={Res. Account. Emerg. Econ.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Leite20142918,
author={Leite, L.R. and Yagasaki, C.A. and Van Aken, E. and Martins, R.A.},
title={Sustainability as trigger to change performance measurement system},
journal={IIE Annual Conference and Expo 2014},
year={2014},
pages={2918-2926},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910093702&partnerID=40&md5=c88edf2ecbb0c61177363f71ab2a511f},
affiliation={Department of Industrial Engineering, Federal University of Sao Carlos, Sao Carlos, SP, Brazil; Departament of Industrial and Systems Engineering, Virginia Polytechnic Institute, State University, Blacksburg, VA, United States},
abstract={The triple bottom line is on the agenda of many companies around the world, which have been increasingly implementing sustainable practices to both comply with government regulations and attend to the demands of customers and non-governmental organizations. Indeed, the concept of sustainability has been seen as a new opportunity for improving business performance and creating competitive advantage. Therefore, the adoption of sustainability practices can change in different ways how companies measure their performance. This paper aims to investigate how these practices affect performance measurement systems. Two cases studies were conducted in companies that are ranked in sustainability within Brazil. Although the performance measurement system in one company seems to be more affected than the other, case study empirical findings demonstrate changes to the performance measurement system in both companies. Specifically, these changes have occurred in three performance measurement system elements: performance indicators, how the companies communicate their results, and the process of information management.},
author_keywords={Performance indicators;  Performance measurement system;  Sustainability;  Triple bottom line},
keywords={Benchmarking;  Competition;  Laws and legislation;  Supply chain management;  Sustainable development, Competitive advantage;  Government regulation;  Nongovernmental organizations;  Performance indicators;  Performance measurement system;  Sustainability practices;  Sustainable practices;  Triple Bottom Line, Information management},
references={Hubbard, G., Measuring organizational performance: Beyond the triple bottom line (2009) Business Strategy and the Environment., 19, pp. 177-191; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International. Journal of Management Reviews, 14 (3), pp. 305-327; Searcy, C., Updating corporate sustainability performance measurement systems (2011) Measuring Business Excellence, 15 (2), pp. 44-56; Leite, L.R., Yagasaki, C., Martins, R.A., Van Aken, E., Bibliometric analysis of literature on performance measurement systems and sustainability (2012) Proc. of 2012 Industrial and Systems Engineering Research Conference, pp. 1-10. , May 19-23, Orlando, Florida; Leite, L.R., Systematic literature review on performance measurement and sustainability (2012) Proc. of the American Society for Engineering Management 2012 International Annual Conference, pp. 1-10. , October 17-20, Virginia Beach, Virginia; Ghalayini, A., Noble, J., The changing basis of performance measurement (1996) International Journal of Operations and Production Management, 16 (8), pp. 63-80; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2), pp. 213-229; Leite, R.L., Van Aken, E., Martins, R.A., Case study on the effect of sustainability on performance measurement systems (2013) Proc. of 2013 Industrial and Systems Engineering Research Conference, pp. 1-8. , May 18-22, San Juan, Puerto Rico; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International. Journal of Business Performance Management, 5 (1), pp. 1-24; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Business Performance Management, 27 (8), pp. 784-801; Yin, R.K., (2009) Case Study Research: Design and Methods, , 4th Edition, SAGE Publications, Trousand Oaks, California; Eisenhardt, K.M., Building theories from case studies (1989) The Academy of Management Review, 14 (4), pp. 532-550},
correspondence_address1={Leite, L.R.; Department of Industrial Engineering, Federal University of Sao CarlosBrazil},
publisher={Institute of Industrial Engineers},
isbn={9780983762430},
language={English},
abbrev_source_title={IIE Annual Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Keathley20142491,
author={Keathley, H. and Van Aken, E.},
title={Performance measurement system implementation: Meta-evaluation of literature on success factors},
journal={IIE Annual Conference and Expo 2014},
year={2014},
pages={2491-2500},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910090267&partnerID=40&md5=b720056de205b46d44183e09f13a0c4e},
affiliation={Grado Department of Industrial and Systems Engineering, Virginia Tech., Blacksburg, VA, United States},
abstract={Performance measurement systems have become widely known for both the potential to make a significant impact on organizational outcomes and the challenge of actually attaining those results. While some organizations are able to effectively design, implement and use these systems, many others face challenges that lead to reduced system benefits and, in some cases, system failure. Researchers have identified factors that affect the success of performance measurement systems with particular emphasis on the implementation phase as a possible source of this problem. A systematic literature review was conducted in previous research that identified a range of factors presented in the literature. This paper continues the previous investigation by conducting an exploratory study to evaluate the use of a meta-evaluation methodology to synthesize the final paper set. This method consists of a meta-analysis of the quantitative studies and a qualitative meta-synthesis of the entire paper set. The results show that there is a dearth of quantitative studies that meet the requirements of meta-analysis which increases the importance of including the qualitative synthesis for this research area.},
author_keywords={Implementation;  Meta-Analysis;  Meta-Synthesis;  Performance Measurement System;  Success Factors},
keywords={Systems engineering, Implementation;  Meta-analysis;  Meta-synthesis;  Performance measurement system;  Success factors, Supply chain management},
references={De Waal, A.A., Counet, H., Lessons learned from performance management systems implementations (2008) International Journal of Productivity and Performance Management, 58 (4), pp. 367-390; De Waal, A.A., Goedegebuure, R., Geradts, P., The impact of performance management on the results of a non-profit organization (2011) International Journal of Productivity and Performance Management, 60 (8), pp. 778-796; Ittner, C., Larcker, D.F., Coming up short on nonfinancial performance measures (2003) Harvard Business Review, pp. 1-9. , November; De Waal, A.A., Kourtit, K., Performance measurement and management in practice: Advantages, disadvantages and reasons for use (2013) International Journal of Productivity and Performance Management, 62 (5), pp. 446-473; Bourne, M., Neely, A., Why some performance measurement initiatives fail: Lessons from the change management literature (2003) Int. J. Business Performance Management, 5 (2-3), pp. 245-269; Bourne, M., Neely, A., Implementing performance measurement systems: A literature review (2003) Int. J. Business Performance Management, 5 (1), pp. 1-24; Keathley, H., Van Aken, E., Systematic literature review on the factors that affect performance measurement system implementation (2013) Institute of Industrial Engineers Annual Conference Proceedings, , San Juan, Puerto Rico; Cochrane handbook for systematic reviews of interventions (2011) Cochrane Handbook for Systematic Reviews of Interventions. The Cochrane Collaboration, , http://handbook.cochrane.org/, Cochrane Mar. 2011. Web. 19 Aug. 2013; Lipsey, M.W., Wilson, D.B., (2001) Practical Meta-analysis, , Sage Publications, Thousand Oaks, CA; Cooper, H.M., Hedges, L.V., Valentine, J.C., (2009) The Handbook of Research Synthesis and Meta-analysis, , New York: Russell Sage Foundation, Print; Dixon-Woods, M., Agarwal, S., Jones, D., Young, B., Sutton, A., Synthesising qualitative and quantitative evidence: A review of possible methods (2005) Journal of Health Services Research & Policy, 10 (1), pp. 45-53B; Walsh, D., Downe, S., Meta-synthesis method for qualitative research: A literature review (2005) Journal of Advanced Nursing, 50 (2), pp. 204-211; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14 (3), pp. 207-222; Cochrane handbook for systematic reviews of interventions (2011) Cochrane Handbook for Systematic Reviews of Interventions. The Cochrane Collaboration, , http://handbook.cochrane.org/, Cochrane Mar. 2011. Web. 19 Aug. 2013; The cochrane collaboration open learning material (2002) The Cochrane Collaboration Open Learning Material, , Cochrane The Cochrane Collaboration, Web. 19 Aug. 2013; (2013) Campbell Resource Center, , http://www.campbellcollaboration.org/, The Campbell Collaboration. N.p., n.d. Web. 19 Aug; Finlayson, K.W., Dixon, A., Qualitative meta-synthesis: A guide for the novice (2008) Nurse Researcher, 15 (2), p. 59; Zimmer, L., Qualitative meta-synthesis: A question of dialoguing with texts (2006) Journal of Advanced Nursing, 53 (3), pp. 311-318; Aloe, A.M., Becker, B.J., An effect size for regression predictors in meta-analysis (2012) Journal of Educational and Behavioral Statistics, 37 (2), pp. 278-297; Kim, R.S., (2011) Standardized Regression Coefficients as Indices of Effect Sizes in Meta-analysis., , Diss. Florida State University, Print; Paul, P.A., Lipps, P.E., Madden, L.V., Meta-analysis of regression coefficients for the relationship between fusarium head blight and deoxynivalenol content of wheat (2006) Phytopathology, 96 (9), pp. 951-961; Becker, B.J., Wu, M.J., The synthesis of regression slopes in meta-analysis (2007) Statistical Science, 22 (3), pp. 414-429; Evanschitzky, H., Eisend, M., Calantone, R.J., Jiang, Y., Success factors of product innovation: An updated meta-analysis (2012) Journal of Product Innovation Management, 29 (5), pp. 21-37; Song, M., Podoynitsyna, H., Der Bij, H., Halman, J.I.M., Success factors in new ventures: A meta-analysis∗ (2008) Journal of Product Innovation Management, 25 (1), pp. 7-27; Park, Y.A., Gretzel, U., Success factors for destination marketing web sites: A qualitative meta-analysis (2007) Journal of Travel Research, 46 (1), pp. 46-63; Barnett-Page, E., Thomas, J., Methods for the synthesis of qualitative research: A critical review (2009) BMC Medical Research Methodology, 9 (1), p. 59; Paterson, B.L., Canam, C., (2001) Meta-study of Qualitative Health Research: A Practical Guide to Meta-analysis and Meta-synthesis, 3. , Sage; Field, A.P., Gillett, R., How to do a meta-analysis (2010) British Journal of Mathematical and Statistical Psychology, 63 (3), pp. 665-694; Braun, V., Clarke, V., Using thematic analysis in psychology (2006) Qualitative Research in Psychology, 3 (2), pp. 77-101; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) International Journal of Operations & Production Management, 31 (12), pp. 1287-1310},
correspondence_address1={Keathley, H.; Grado Department of Industrial and Systems Engineering, Virginia Tech.United States},
publisher={Institute of Industrial Engineers},
isbn={9780983762430},
language={English},
abbrev_source_title={IIE Annual Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Brown20143022,
author={Brown, A. and Badurdeen, F.},
title={Supply Chain Disruption Management: Review of issues and research directions},
journal={IIE Annual Conference and Expo 2014},
year={2014},
pages={3022-3029},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910050296&partnerID=40&md5=851fe502d8d8183077ff4cdcb7779e72},
affiliation={Department of Mechanical Engineering, University of Kentucky, Lexington, KY  40506, United States},
abstract={Supply Chain Risk Management (SCRM) is an increasingly popular subject of research which emphasizes the goals of achieving improved supply chain robustness through development of design and operational strategies. Disruption management is one aspect of SCRM which examines the ability of the supply chain to maintain a high level of performance under the effects of major disruptions. Specifically, disruptions refer to events characterized by a low likelihood of occurrence and a large impact. Because of their limited rate of occurrence, disruptions are associated with a high uncertainty with respect to their expected impact. Improved modeling of the disruption impact is one key issue in this field. Other issues include the design of methods for supply chain performance measurement, disruption monitoring and detection, evaluation of recovery strategies, and methods of optimal supply chain design. Design features to be considered include flexibility, redundancy, and operating efficiency. The relevant literature is presented in the context of these major issues and future directions suggested by researchers in the field are discussed.},
author_keywords={Disruptions;  Gray Swan;  Risk Management;  Supply Chain},
keywords={Chains;  Design;  Research and development management;  Risk management;  Supply chains, Disruption management;  Disruptions;  Gray Swan;  Operational strategies;  Optimal supply chain;  Supply chain performance measurements;  Supply chain risk management (SCRM);  Supply-chain disruptions, Supply chain management},
references={Taleb, N.N., (2010) The Black Swan: The Impact of the Highly Improbable, , New York: Random House, Inc; Tang, C.S., Perspectives in supply chain risk management (2006) International Journal of Production Economics, 103 (2), p. 451; Brown, A.O., Tang, C.S., The impact of alternative performance measures on single-period inventory policy (2006) Journal of Industrial and Management Optimization, 2 (3), pp. 297-318; Christopher, M., Peck, H., Building the resilient supply chain (2004) International Journal of Logistics Management, 15 (2), pp. 1-13; Klibi, W., Martel, A., Guitouni, A., The design of robust value-creating supply chain networks: A critical review (2010) European Journal of Operational Research, 203 (2), pp. 283-293; Abyaneh, M., Hassanzadeh, S., Raddum, H., Tutorial paper on quantitative risk assessment (2011) Norsk in Formatikko Conferanse, , Universitet i Tromso; Garrick, J.B., (2008) Quantifying and Controlling Catastrophic Risks, , Elsevier; Kleindorfer, P.R., Saad, G.H., Managing disruption risks in supply chains (2005) Production and Operations Management, 14 (1), pp. 53-68; Xia, Y., Ramachandran, K., Gurnani, H., Sharing demand and supply risk in a supply chain (2011) IIE Transactions, 43 (6), pp. 451-469; Raheja, A.S., Subramaniam, V., Reactive recovery of job shop schedules - A review (2002) The International Journal of Advanced Manufacturing Technology, 19 (10), pp. 756-763; Brown, A., Badurdeen, F., Optimization of queuing theory wait time through multi-skilled worker assignments (2011) Industrial Engineering and Engineering Management (IEEM), , 2011 IEEE International Conference on; Brown, A.J., Badurdeen, F., A queuing model for systems with rework and process downtime (2013) Automation Science and Engineering (CASE), , 2013 IEEE International Conference on; Blackhurst, J., Craighead, C.W., Elkins, D., Handfield, R.B., An empirically derived agenda of critical research issues for managing supply-chain disruptions (2005) International Journal of Production Research, 43 (19), pp. 4067-4081; Guojun, J., Caihong, Z., (2008) Study on Supply Chain Disruption Risk Management Strategies and Model, , Melbourne, Australia: Inst. of Elec. and Elec. Eng. Computer Society; Jianxin, X., (2008) Managing the Risk of Supply Chain Disruption: Towards a Resilient Approach of Supply Chain Management, , Guangzhou, China: Inst. of Elec. and Elec. Eng. Computer Society; Xanthopoulos, A., Vlachos, D., Iakovou, E., Optimal sourcing decisions for unreliable reverse supply chains (2011) Asia-Pacific Journal of Operational Research, 28 (1), pp. 125-146; Tomlin, B., On the value of mitigation and contingency strategies for managing supply chain disruption risks (2006) Management Science, 52 (5), pp. 639-657; Reese, A.K., Disaster-proofing the supply chain (2007) Supply & Demand Chain Executive, 8 (3), pp. 42-46; Wakolbinger, T., Cruz, J.M., Supply chain disruption risk management through strategic information acquisition and sharing and risk-sharing contracts (2011) International Journal of Production Research, 49 (13), pp. 4063-4084; Azad, N., Saharidis, G.K.D., Davoudpour, H., Malekly, H., Yektamaram, S.A., Strategies for protecting supply chain networks against facility and transportation disruptions: An improved benders decomposition approach (2013) Annals of Operations Research, 210 (1), pp. 125-163; Shukla, A., Lalit, V.A., Venkatasubramanian, V., Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions (2011) International Journal of Physical Distribution & Logistics Management, 41 (6), pp. 623-647; Hendricks, K.B., Singhal, V.R., Zhang, R., The effect of operational slack, diversification, and vertical relatedness on the stock market reaction to supply chain disruptions (2009) Journal of Operations Management, 27 (3), p. 233; Norrman, A., Jansson, U., Ericsson's proactive supply chain risk management approach after a serious sub-supplier accident (2004) International Journal of Physical Distribution & Logistics Management, 34 (5), pp. 434-456; Carvalho, H., Machado, V.C., Lean, agile, resilient and green supply chain: A review (2009) Third International Conference on Management Science and Engineering Management, , Bangkok, Thailand: World Academic Press, World Academic Union; Zsidisin, G.A., Ellram, L.M., Carter, J.R., Cavinato, J.L., An analysis of supply risk assessment techniques (2004) International Journal of Physical Distribution & Logistics Management, 34 (5), pp. 397-413; Akkermans, H.A., Van Wassenhove, L.N., Searching for the grey swans: The next 50 years of production research (2013) International Journal of Production Research, 51 (23-24), pp. 6746-6755},
correspondence_address1={Brown, A.; Department of Mechanical Engineering, University of KentuckyUnited States},
publisher={Institute of Industrial Engineers},
isbn={9780983762430},
language={English},
abbrev_source_title={IIE Annual Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Mello20141837,
author={Mello, R. and Leite, L.R. and Martins, R.A.},
title={Is big data the next big thing in performance measurement systems?},
journal={IIE Annual Conference and Expo 2014},
year={2014},
pages={1837-1846},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910049262&partnerID=40&md5=bbfe52152dd2f9086d72adb735e201db},
affiliation={Department of Industrial Engineering, Federal University of Sao Carlos, Sao Carlos, Sao Paulo, Brazil},
abstract={This paper explores the relationship between big data and performance measurement systems. Generally, big data is associated to data that are produced in big velocity, variety, and volume. Companies nowadays are searching for ways to extract useful information from large amount of data. Nevertheless, big data requires advanced computational resources, statistical techniques, and data visualization techniques in order to store, process, and, in special, analyze all different kinds of data. The capability to create value from such high quantity of data is critical to get competitive advantages from the big data. In fact, big data is probably a new revolution in information management that can influence the way in which companies do their businesses. Consequently, the performance measurement system can also be affected. These systems are composed of a multidimensional set of performance measures used for planning, controlling, and improving business performance. Therefore, big data could contribute to different ways of using the performance measurement systems. Finally, this paper provides some insights into how big data influences the changes in performance measurement systems such as new kind of analytics, strategy challenge, and support to continuous improvement.},
author_keywords={Big Data;  Big Data Analytics;  Performance Measurement System},
keywords={Competition;  Data visualization;  Information management;  Supply chain management, Business performance;  Competitive advantage;  Computational resources;  Continuous improvements;  Data analytics;  Performance measurement system;  Statistical techniques;  Visualization technique, Big data},
references={Tien, J.M., Big data: Unleashing information (2013) Journal of Systems Science and Systems Engineering, 22 (2), pp. 127-151; Waller, M.A., Fawcett, S.E., Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management (2013) Journal of Business Logistics, 34 (2), pp. 77-84; McAfee, A., Brynjolfsson, E., Big data: The management revolution (2012) Harvard Business Review, 90 (10), pp. 60-66; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) International Journal of Operations & Production Management, 31 (12), pp. 1287-1310; Neely, A.D., (1998) Measuring Business Performance: Why, What, How, , Economist Books, London; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Leite, L.R., Aken, E.V., Martins, R.A., Case study on the effect of sustainability on performance measurement systems (2013) Proceedings of Industrial & Systems Engineering Research Conference 2013, 1, pp. 1-8. , San Juan; Barton, D., Court, D., Making advanced analytics work for you (2012) Harvard Business Review, 90 (10), pp. 78-83; Russom, P., Big data analytics (2011) TDWI Research, TDWI Best Practices Report, , ftp://129.35.224.12/software/tw/Defining_Big_Data_through_3V_v.pdf, Renton, WA, 98057-2996. Available from; Stapleton, L.K., Taming big data (2011) IBM Data Magazine, , http://www.ibm.com/developerworks/data/library/dmmag/DMMag_2011_Issue2/BigData/, Available from; Courtney, M., Puzzling out big data (2012) Engineering & Technology, 7 (12), pp. 56-60; Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H., Big data: The next frontier for innovation, competition, and productivity (2011) Technical Report, McKinsey Global Institute; Chen, H., Chiang, R.H.L., Storey, V.C., Business intelligence and analytics: From big data to big impact (2012) MIS Quarterly, 36 (4), pp. 1165-1188; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14 (3), pp. 207-222; Leite, L.R., Systematic literature review on performance measurement and sustainability (2012) American Society for Engineering Management 2012 International Annual Conference, , Virginia Beach, VA: ASEM; Demirkan, H., Delen, D., Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud (2013) Decision Support Systems, 55 (1), pp. 412-421; Ueda, N., Communication science for the big data era (2012) NTT Technical Review, , https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr201211fa1.html, Available from; Davenport, T.H., Barth, P., Bean, R., How 'Big data' is different (2012) MIT Sloan Management Review, 54 (1), pp. 22-24; White, M., Digital workplaces: Vision and reality (2012) Business Information Review, 29 (4), pp. 205-214; Brown, B., Chui, M., Manyika, J., Are you ready for the era of 'big data'? (2011) McKinsey Quarterly, 4, pp. 24-35; Jelinek, M., Bergey, P., Innovation as the strategic driver of sustainability: Big data knowledge for profit and survival (2013) IEEE Engineering Management Review, 41 (2), pp. 14-22; Davenport, T.H., Patil, D.J., Data scientist: The sexiest job of the 21st century (2012) Harvard Business Review, 90 (10), pp. 70-77; Tien, J.M., The next industrial revolution: Integrated services and goods (2012) Journal of Systems Science and Systems Engineering, 21 (3), pp. 257-296},
correspondence_address1={Mello, R.; Department of Industrial Engineering, Federal University of Sao CarlosBrazil},
publisher={Institute of Industrial Engineers},
isbn={9780983762430},
language={English},
abbrev_source_title={IIE Annual Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Alexis2014188,
author={Alexis, N. and Caroline, V.E. and Ndiaye, B.A.},
title={Managing hospital efficiency and effectiveness: Designing a hospital performance measurement system},
journal={Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014},
year={2014},
pages={188-200},
doi={10.1109/GOL.2014.6887440},
art_number={6887440},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908616109&doi=10.1109%2fGOL.2014.6887440&partnerID=40&md5=971782772a53ddca989a33dbdf70a28c},
affiliation={Université Libre Bruxelles, Brussels School of Engineering, Qalinca Labs, Belgium},
abstract={Challenges that hospitals are currently facing justify the focus on this research subject. The sector has to balance the seemingly incompatible demand of cost reduction and quality improvement. Despite hospital supply chains having a great impact on hospital budgets, still opportunities for optimization exist. The prerequisite for hospital supply chain improvement is measurement. Therefore the purpose of this research is to identify performance indicators that allow the design of a hospital performance measurement system (HPMS) for the downstream pharmaceutical supply chain. This HPMS should answer the following question: to which degree the pharmaceutical supply chain in the hospital is efficient and effective and how to improve them? This paper is organized as follow: first, reviewing the literature on the hospital supply chain and the performance measurement. Second, we describe pharmaceutical Supply Chain within the hospital in order to gain profound understanding in its processes. Finally, designing a performance measurement system. Throughout the research, SCOR reference model is used for the selection of indicators for the HPMS. Furthermore, the field research has been conducted based on semi-structured interviews with hospital staff from Belgian hospitals. © 2014 IEEE.},
author_keywords={Hospital supply chain;  performance measurement;  pharmaceutical supply chain;  SCOR},
keywords={Budget control;  Chains;  Cost reduction;  Supply chain management;  Supply chains, Hospital performance;  Performance indicators;  Performance measurement system;  Performance measurements;  Pharmaceutical supply chains;  Quality improvement;  SCOR;  Semi structured interviews, Hospitals},
references={Measuring Hospital Performance to Improve the Quality of Care in Europe: A Need for Clarifying the Concepts and Defining the Main Dimensions, , WHO regional office for Europe 2003, Barcelona: Report on a WHO Workshop; (2010) ANAP, , Ie repertoire d'indicateurs: piloiter I'activite et analyser I'efficience, Paris: s.n; Augusto, V., Xie, X., Redesigning pharmacy delivery processes of a health care complex (2009) Health Care Management Science, 12, pp. 166-178; Baboli, A., Guinet, A., (2009) Logistique Hospitaliere Besoins de Gestion des Flux Pharmaceutiques, 5, p. 430. , Dossier techniques de I'ingenieur, Reference: AG, 10 April; Beamon, B., Measuring supply chain performanc (1999) International Journal of Operations &Production Management, 19 (3), pp. 275-292; Beaulieu, M., Landry, S., Comment gerer la logistique hospitaliere? Deux pays, deux realites (2002) Gestion, 222, pp. 91-98; Bon Voisin, F., (2011) Evaluation de la Performance des Blocs Operatoires, , du modele aux indicateurs, s.l.: s.n; Bonnabry, P., Flux du Medicament i'Hopital, , Cours de pharmacie hospitaliere, 3eme annee, Geneve: s.n; Cattan, M., Idrissi, N., Knockaert, P., (1998) Maitriser les Processus de i'Entreprise, , Paris: s.n; Costin, M., Chitou, I., (2012) Demarche Logistique-Une Performance Organisationnelle Pour Une Meilleure Satisfaction des Patients, , Montreal, s.n; De, M.I., (2011) De Ziekenhuisapotheek Doorgelicht Vanuit Een Logistiek Perspectief, , automatisering van 24-uurs distributie s.1.: s.n; De Toni, A., Montagner, M., (2009) A PMS for Facility Services in Supply Chain: A Case in the Healthcare Sector, , Orlando, s.n; De Vries, I., Huusman, R., Supply chain management in health services: An Overview (2011) Supply Chain Management An International Journal, 16 (3), pp. 159-165; Martinelly, D.C., (2008) Proposition of A Framework to Reengineer and Evaluate the Hospital Supply Chain, , s.l.: s.n; Martinelly, C.D.I., Guinet, A., Riane, F., Chaine Logistique en Milieu Hospitalier : Modelisation des (2005) Processus de Distribution de la Pharmacie, , Besan;on-France, s.n; Martinell, Y.C.D.I., Ai, E., (2007) Etude de la Mise en Place d'Armoires Automatiques de Distribution des Medicaments dans un Service d'Urgence, , Quebec, s.n; Di Marti Nelly, C., Riane, F., Guinet, A., (2006) Approches de Modelisation Pour la Logistique Pharmaceutique, , s.l.: s.n; Di Martinelly, C., Riane, F., Guinet, A., A porter-SCOR modeling approach for the hospital supply chain (2009) International Journal of Logistics Systems and Management, 5 (3-4), pp. 436-456; Martinelly, C.D.I., Riane, F., Guinet, A., A porter-SCOR modeling approach for the hospital supply chain (2009) International Journal of Logistics Systems and Management, 5 (3-4), pp. 436-456; Ebel, T., (2012) Strength in Unity: The Promise of Global Standards in Health Care, , s.l.: s.n; Frontini, R., (2012) Drug Distribution in Hospitals and Barcoding, , Lisbon, s.n; Gerkens, S., Merkur, S., Belgium: Health system review (2010) Health Systems in Transition, 12 (5), pp. 1-266; Hammami, S., (2006) Aide A la Decision dans Ie Pilotage des Flux Materiels et Patients d'Un Plateau Medico-technique, , s.l s.n; Harmon, P., An introduction to the supply chain council's scor methodology, sl (2003) Business Process Trends; Hassan, T., (2006) Logistique Hospitaliere Organisation de la Chaine Logistique Pharmaceutique Aval et Optimisation des Flux de Consommables et des Materiels A Usage Unique, , s.l s.n; (2012) Engaging 2012-Healthcare Supply Chain Top Trends. Healthcare Purchasing News, 36 (3), p. 61. , Health sector supply chain research consortium; Hsiang-Jen, C.S., Whittemore, G., (2008) An Engineering Approach to Improving Hospital Supply Chains, , s.l s.n; Huan, S., Seoran, S., Wang, G., A review an analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management An International Journal, 9 (1), pp. 23-29; Ibn Farouk, E.L.I., Ai, E., (2012) Gestion des Approvisionnements des Produits Pharmaceutiques A i'Hopital Quels Indicateurs Pour Piloter la Performance?, , Montreal, s.n; Jlassi, J., (2009) Amelioration de la Performance Par la Modelisation des Flux Patients dans un Service d'Urgence Hospitalier, , s.l s.n; Landry, S., Beaulieu, M., La logistique hospitaliere : un remMe aux maux du secteur de la sante (2001) Gestion, 26 (4), pp. 34-41; Lauras, M., (2004) Methodes de Diagnostic et d'Evaluation de Performance Pour la Gestion de Chaines Logistiques: Application A la Cooperation Maison-mere-Filiales Internationales dans un Groupe Pharmaceutique et Cosmetique, , s.l s.n; Lepor, E., (2012) Apports et Limites du Modele SCOR Pour i'Evaluation de la Performance en Supply Chain Management: Application A un Entre Pot Logistique, , s.l.: s.n; Lillrank, P., Groop, J., Venesmaa, J., Processes, episodes and events in health service supply chains (2011) Supply Chain Management An International Journal, 16 (3), pp. 194-201; Magnussen, J., Efficiency measurement and the operationalization of hospital production (1996) HSR: Health Services Research, 31 (1), pp. 21-37; Marty, C., Merlin, H., (2007) Mesurer la Performance des Hopitaux Publics en France Observatoire du Management Alternative, , Paris: s.n; Mebrek, F., (2012) Outils d'Aide A la Decision Bases sur la Simulation Pour la Logistique Hospitaliere, , application a un nouvel hopital, s.l.: s.n; Meijboom, B., Schmidt-Bakx, S., Westert, G., Supply chain management practices for improving patient-oriented care (2011) Supply Chain Management An International Journal, 16 (3), pp. 166-175; Ndia, Y.E.A., (2012) Logistics, Quality and Supply Chain Management Course, , Brussels: s.n; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations &Production Management, 15 (4), pp. 80-116; Perras, C., Ai, E., (2009) Technologies to Reduce Errors in Dispensing and Administration of Medication in Hospitals: Clinical and Economic Analyses, S.l, , s.n; Rivard-Royer, H., Beaulieu, M., Friel, T., (2003) Healthcare Ecosystem: Linking Logistical Flows and Clinical Flows, , Cahiers de recherche du Groupe de recherche; Romeyer, C., Ai, E., Modelisation par les processus: une methode prealable indispensable a la mise en oevre d'un systeme d'information communicant centre sur Ie patient (2003) Gestions Hospitaliere, 447, pp. 451-459; Schmitt, E., (2000) Unit-dose Drug Distribution Systems: Old-fashioned or Safer Ways for Pharmaceutical Care?, p. 7. , s.l., s.n; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Staccini, P., Ai, E., Mapping care processes within a hospital: From theory to a web-based proposal merging enterprise modelling and ISO normative principles (2005) International Journal of Medical Informatics, p. 33; Staccini, P., Ai, E., Modelling health care processes for eliciting user requirements: A way to link a quality paradigm and clinical information system design (2001) International Journal of Medical Informatics, 64, pp. 129-142; (2012) Supply Chain Operations Reference Model, , Supply chain council, Version 11. s.l.:s.n; Su, S.G.B., Yang, S., Logistics innovation process revisited: Insights from a hospital case study (2011) International Journal of Physical Distribution &Logistics Management, 41 (6), pp. 577-600; (2010) De Geautomatiseerde Apotheek, , SWISSLOG, s.l.: s.n; Trilling, L., Besombes, B., Chaabane, S., Guinet, A., (2004) Modelisation des Pratiques: Investigation et Comparaison des Methodes et Outils d'Analyse Pour i'Etude des Systemes Hospitaliers, , s.l s.n; (2010) The Hospital Supply Chain-A Healthy State of Affairs, , vlerick business school s.1.: s.n; (2007) Handboek voor Intercollegiale Toetsing voor Ziekenhuisapothekers, , vza &pfizer. s.I.:VZA-UZ Gent},
correspondence_address1={Alexis, N.; Université Libre Bruxelles, Brussels School of Engineering, Qalinca LabsBelgium},
editor={Benadada Y.},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={9781479946501},
language={English},
abbrev_source_title={Proc. IEEE Int. Conf. Logist. Oper. Manag., GOL},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Galankashi2014131,
author={Galankashi, M.R. and Memari, A. and Anjomshoae, A. and Ma'aram, A. and Helmi, S.A.},
title={Selection of supply chain performance measurement frameworks in electrical supply chains},
journal={International Journal of Industrial Engineering and Management},
year={2014},
volume={5},
number={3},
pages={131-137},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908567040&partnerID=40&md5=b343c08d4d6677e34d516107c2445533},
affiliation={Department of Material, Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia},
abstract={Performance measurement plays an important role in today's competitive market due to its direct effect on supply chain's profitability. Many approaches have been applied for evaluating performance of firms, including financial, operational and balanced frameworks. Companies peruse to find the best fit performance measurement framework due to a variety of existing approaches. The main significance of performance framework selection is its important role in strategic decisions where many factors related to the companies' performance may be neglected. Due to the nature of electrical companies such as high uncertainty in demand, short life cycle of products and competitive market, they have to frequently assess their performance. A fit framework should be determined based on the characteristics of this competitive market. In this study, a multi criteria decision making (MCDM) approach is used to find a proper framework for performance evaluation in electrical companies. Analytical hierarchical process (AHP) has been applied to select the best performance measurement framework based on supply chain macro processes. Finally, performance metrics for the selected framework are determined.},
author_keywords={AHP;  MCDM;  Performance measurement frameworks;  Performance measurement metrics;  Supply chain performance measurement},
references={Sink, S.P.E., Performance and productivity measurement: the art of developing creative score boards (1986) Industrial Engineer, 1, pp. 86-90; Brady, M.K., Cronin Jr., J.J., Brand, R.R., Performance-only measurement of service quality: a replication and extension (2002) Journal of business research, 55 (1), pp. 17-31; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International journal of operations & production management, 15 (4), pp. 80-116; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) International Journal of Logistics Management, The, 6 (1), pp. 61-74; Jammernegg, W., Reiner, G., Customer satisfactionoriented evaluation of quality (process) improvements (1997) Total Quality Management, 8 (2-3), pp. 191-194; Cheung, K.L., Hausman, W.H., An exact performance evaluation for the supplier in a two-echelon inventory system (2000) Operations Research, 48 (4), pp. 646-653; Ittner, C.D., Larcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting, Organizations and Society, 28 (7), pp. 715-741; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: a framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Morgan, C., Supply network performance measurement: future challenges? (2007) International Journal of Logistics Management, The, 18 (2), pp. 255-273; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) Int. J. Bus. Manage. Soc. Sci, 2 (4), pp. 19-34; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard business review, 74 (1), pp. 75-85; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California management review, 39 (1); Chen, S., Dodd, J.L., Economic value added (EVA™): An empirical examination of a new corporate performance measure (1997) Journal of Managerial Issues, pp. 318-333; Garvin, D.A., (1988) Managing quality: The strategic and competitive edge, , Simon and Schuster; Council, S.C., Supply Chain Operations Reference Model: Overview of SCOR Version 7.0 (2005), www.supply-chain.org, Available online at (accessed 25 June 2006); Yeniyurt, S., A literature review and integrative performance measurement framework for multinational companies (2003) Marketing Intelligence & Planning, 21 (3), pp. 134-142; Bukowitz, W.R., Petrash, G.P., Visualizing, measuring and managing knowledge (1997) Research-Technology Management, 40 (4), pp. 24-31; Bora, A., Krairit, S.C.D., Developing Key performance indicators for performance controlling of a supply chain (2004) Proceedings of the Fifth Asia Pacific Industrial Engineering and Management Systems Conference; Lee-Mortimer, A., Six Sigma: a vital improvement approach when applied to the right problems, in the right environment (2006) Assembly Automation, 26 (1), pp. 10-17; George, M.L., George, M., Lean six sigma for service (2003), pp. 117-130. , New York; Geary, S., Childerhouse, P., Towill, D.R., Uncertainty and the seamless supply chain (2002) SUPPLY CHAIN MANAGEMENT REVIEW, 6 (4), pp. 52-61. , (JULY/AUG. 2002) ILL; Memari, A., Anjomshoae, A., Galankashi, M.R., Bin Abdul Rahim, A.R., Scenario-based simulation in production-distribution network under demand uncertainty using ARENA (2012) Computing and Convergence Technology (ICCCT), pp. 1443-1448. , (2012, December). 7th International Conference on. IEEE; McGuffog, T., Wadsley, N., The general principles of value chain management (1999) Supply Chain Management: An International Journal, 4 (5), pp. 218-225; Knowles, G., Whicker, L., Femat, J.H., Canales, F.D.C., A conceptual model for the application of Six Sigma methodologies to supply chain improvement (2005) International Journal of Logistics: Research and Applications, 8 (1), pp. 51-65; Saaty, T.L., (1988) What is the analytic hierarchy process?, pp. 109-121. , Springer Berlin Heidelberg; Chopra, S., Meindl, P., Supply chain management (2007) Strategy, planning & operation, pp. 265-275. , Gabler; Taylor III, B.W., (2005) Introduction to Management Science, , 8th Edition.New Jersey: Prentice Hall; Saaty, T.L., A scaling method for priorities in hierarchical structures (1977) Journal of mathematical psychology, 15 (3), pp. 234-281; Ishizaka, A., Labib, A., Analytic hierarchy process and expert choice: Benefits and limitations (2009) OR Insight, 22 (4), pp. 201-220; Galankashi, M.R., Moazzami, A., Madadi, N., Roudsari, A.H., Helmi, S.A., (2013) Supplier Selection for Electrical Manufacturing Companies Based on Different Supply Chain Strategies, , Electrical Engineering; Dargi, A., Anjomshoae, A., Galankashi, M.R., Memari, A., Tap, M.B.M., Supplier Selection: A Fuzzy-ANP Approach (2014) Procedia Computer Science, 31, pp. 691-700},
publisher={University of Novi Sad},
issn={22172661},
language={English},
abbrev_source_title={Int. J. Ind. Eng. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Stefanovic2014,
author={Stefanovic, N.},
title={Proactive supply chain performance management with predictive analytics},
journal={Scientific World Journal},
year={2014},
volume={2014},
doi={10.1155/2014/528917},
art_number={528917},
note={cited By 33},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908317616&doi=10.1155%2f2014%2f528917&partnerID=40&md5=f7610d4bbf4233e564fea2f54d8613fc},
affiliation={Institute of Mathematics and Informatics, Faculty of Science, University of Kragujevac, Radoja Domanovica 12, Kragujevac, 34000, Serbia},
abstract={Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. © 2014 Nenad Stefanovic.},
keywords={Article;  commercial phenomena;  data base;  data mining;  decision making;  key performance indicator;  online analytical processing;  performance measurement system;  predictive supply chain performance management;  predictive validity;  data mining;  human;  theoretical model, Commerce;  Data Mining;  Humans;  Models, Theoretical},
references={Handfield, B.R., Nichols, L.E., (2002) Supply Chain Redesign: Transforming Supply Chains into Integrated Value Systems, , New York, NY, USA Financial Times Prentice Hall; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131. , 2-s2.0-33646173373; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87. , 2-s2.0-0035619784; Vesset, D., (2007) Worldwide Business Analytics Software 2007-2011 Forecast: The Growth Cycle Continues, , Framingham, Mass, USA IDC; Lapide, L., What about measuring supply chain performance? (1999) Achieving Supply Chain Excellence Through Technology, 2, pp. 287-297; Microsoft, (2008) Predictive Analysis with SQL Server, , http://download.microsoft.com/download/6/9/D/69D1FEA7-5B42-437A-B3BA-A4AD13E34EF6/SQL2008PredictAnalysis.docx; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , 2-s2.0-33645668377; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258. , 2-s2.0-84874550245; Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155. , 2-s2.0-77954454289; Gopal, P.R.C., Thakkar, J., A review on supply chain performance measures and metrics: 2000-2011 (2012) International Journal of Productivity and Performance Management, 61 (5), pp. 518-547. , 2-s2.0-84863465145; Lappide, L., Predictive metrics (2010) The Journal of Business Forecasting, 29 (2), pp. 1-23; Bauer, K., Predictiveanalytics: The Next Wave in KPIs, , http://www.information-management.com/issues/20051101/1040476-1.html, Information Management Magazine, 2005; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , 2-s2.0-3543100440; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521. , 2-s2.0-57749086941; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347. , 2-s2.0-0346339454; Kaplan, R., Norton, D., (2006) Strategy-Focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment, , Boston, Mass, USA Harvard Business School Press; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , 2-s2.0-34447249471; Stefanovic, N., Stefanovic, D., Bramer, M., Supply chain business intelligence: Technologies, issues and trends (2009) Artificial Intelligence: An International Perspective, 5640, pp. 217-245. , New York, NY, USA Springer LNAI; McDonald, K., Wilmsmeier, A., Dixon, C.D., Inmon, W.H., (2006) Mastering the SAP Business Information Warehouse, , Indianapolis, Indiana, USA John Wiley & Sons; https://supply-chain.org/f/SCOR11QRG.pdf, COR Quick Reference Guide, Version 11.0. Supply Chain Council, 2014; Kocaoʇlu, B., Gülsün, B., Tanyaş, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24 (1), pp. 113-132; Shapiro, F.J., (2001) Modeling the Supply Chain, , Pacific Grove, Calif, USA Duxbury; Chae, B.K., Olson, D.L., Business analytics for supply chain: A dynamic-capabilities framework (2013) International Journal of Information Technology & Decision Making, 12 (1), pp. 9-26. , 2-s2.0-84874805547; Raisinghani, S.M., Singh, K.M., Wang, J., Data mining for supply chain management in complex networks (2008) Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, pp. 2468-2475. , Hershey, Pa, USA IGI Global; Dignan, L., Data Depot, , http://www.baselinemag.com/c/a/Projects-Supply-Chain/Data-Depot, 2003; Hofmann, M., (2004) Best Practices: VW Revs Its B2B Engine, , http://www.informationweek.com/best-practices-vw-revs-its-b2b-engine/18201367; Dowse, S., http://www.predictiveanalyticsworld.com/patimes/why-supply-chains-need-business-intelligence, Why supply chains need business intelligence, 2013; Mathieu, R., Levary, R.R., Wang, J., Data warehousing and mining in supply Chains (2009) Encyclopedia of Data Warehousing and Mining, pp. 596-591. , 2nd Hershey, Pa, USA IGI Global; Predictive Inventory Management: Keeping Your Supply Chain in Balance, , ftp://ftp.boulder.ibm.com/common/ssi/ecm/en/ytw03260usen/YTW03260USEN.PDF, IBM, 2012; Dhond, A., Gupta, A., Vadhavkar, V., Data mining techniques for optimizing inventories for electronic commerce Proceedings of the SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 480-486. , August 2000 2-s2.0-0034592766; Symeonidis, A.L., Nikolaidou, V., Mitkas, P.A., Exploiting data mining techniques for improving the efficiency of a supply chain management agent Proceedings of the International Conference on Web Intelligence and Intelligent Agent Technology, pp. 23-26. , December 2006 Hong Kong IEEE 2-s2.0-34250692021; Stefanovic, N., Stefanovic, D., Radenkovic, B., Ellis, R., Allen, T., Petridis, M., Application of data mining for supply chain inventory forecasting (2008) Applications and Innovations in Intelligent Systems XV, pp. 175-188. , Berlin, Germany Springer; Gheorghe, C., (2006) Predictive Analytics: BPM Drives the Dynamic Organization, , http://businessfinancemag.com/business-performance-management/predictive-analytics-bpm-drives-dynamic-organization; Eckerson, W.W., (2006) Performance Dashboards Measuring, Monitoring, and Managing Your Business, , New York, NY, USA John Wiley & Sons; Seifert, M., Eschenbaecher, J., Luis, C.M., Predictive performance measurement in virtual organisations (2005) Emerging Solutions for Future Manufacturing Systems, pp. 299-306. , Boston, Mass, USA Springer; Derrouiche, R., Holimchayachotikul, P., Leksakul, K., Predictive performance model in collaborative supply chain using decision tree and clustering technique Proceedings of the 4th International Conference on Logistics (LOGISTIQUA '11), pp. 412-417. , June 2011 Chicago, Ill, USA IEEE Computer Society Press 2-s2.0-79960920320; Maleki, M., Cruz-Machado, V., Supply chain performance monitoring using Bayesian network (2013) International Journal of Business Performance and Supply Chain Modeling, 5 (2), pp. 177-197; Stefanovic, N., Stefanovic, D., Radenkovic, B., Mohhebi, S., Mahdavi, I., Cho, N., Integrated supply chain intelligence through collaborative planning, analytics and monitoring (2011) Electronic Supply Network Coordination in Intelligent and Dynamic Environment: Modeling and Implementation, pp. 43-92. , Hershey, Pa, USA IGI Global; Stefanovic, D., Stefanovic, N., Radenkovic, B., Supply network modelling and simulation methodology (2009) Simulation Modelling Practice and Theory, 17 (4), pp. 743-766. , 2-s2.0-62249215703; Abukhousa, E., Al-Jaroodi, J., Lazarova-Molnar, S., Mohamed, N., Simulation and modeling efforts to support decision making in healthcare supply chain management (2014) The Scientific World Journal, 2014, p. 16. , 354246 2-s2.0-84896339223; Stefanovic, N., Stefanovic, D., Integrated and interactive software solution for knowledge-based supply network design (2013) Computer Systems Science and Engineering, 28 (1), pp. 5-23. , 2-s2.0-84878941030; Stefanovic, D., Stefanovic, N., Methodology for modeling and analysis of supply networks (2008) Journal of Intelligent Manufacturing, 19 (4), pp. 485-503. , 2-s2.0-46249107123; Stefanović, N., Stefanović, D., Supply chain performance measurement system based on scorecards and web portals (2011) Computer Science & Information Systems, 8 (1), pp. 167-192},
correspondence_address1={Stefanovic, N.; Institute of Mathematics and Informatics, Faculty of Science, University of Kragujevac, Radoja Domanovica 12, Serbia},
publisher={Hindawi Publishing Corporation},
issn={23566140},
pubmed_id={25386605},
language={English},
abbrev_source_title={Sci. World J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Carosi2014691,
author={Carosi, A. and Heydari, M. and Zanetti, C. and Taisch, M. and Ducq, Y.},
title={Service Performance Assessment: A PI Toolset Methodology for VEs},
journal={IFIP Advances in Information and Communication Technology},
year={2014},
volume={439},
number={PART 2},
pages={691-698},
doi={10.1007/978-3-662-44736-9_84},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906928368&doi=10.1007%2f978-3-662-44736-9_84&partnerID=40&md5=28a863356a55b5d607c3e58886b5dfcd},
affiliation={Politecnico di Milano, Piazza Leonardo Da Vinci 32, 20133, Milan, Italy; Université Bordeaux, IMS, UMR 5218, F-334400, Talence, France},
abstract={Nowadays service sector is becoming more and more relevant in building successful collaborative economies. In this environment Virtual Enterprises (VEs) are forcing a change in the way traditional manufacturing systems are managed. Therefore measuring service performances plays an important role in turning company strategic goals into reality. Performance Indicators (PIs) consist in a supporting tool to assess service efficiency and effectiveness. Consequently, determining the most significant activities which need to be controlled and measured through proper PIs becomes essential. Within this paper, a PI Toolset is going to be presented and tested through industrial use case. The PI Toolset has been developed to support VEs in selecting significant activities, to manage governance processes and to support the design and implementation of specific PIs related to the precise use case objectives. Finally, a lesson learnt approach has been adopted so to stress strengths and weaknesses of both proposed methodology and tools. © IFIP International Federation for Information Processing 2014.},
author_keywords={Performance measurement systems and performance indicators;  PI toolset;  Service governance support toolset;  Virtual enterprises},
keywords={Benchmarking;  Knowledge based systems;  Manufacture;  Virtual corporation, Design and implementations;  Industrial use case;  Performance indicators;  Service performance;  Service sectors;  Toolsets;  Traditional manufacturing;  Virtual enterprise, Supply chain management},
references={Heydari, M., Taisch, C., Carosi, A., Service performance monitoring and control Toolset (2014) The 6th CIRP Conference On Industrial Product-Service Systems, , Windsor, Canada; Camarinha-Matos, Collaborative networked organizations, Concepts and practice in manufacturing enterprises (2008) Computers Industrial Engineeringn, 57, p. 2009; Afsarmanesh, H., Camarinha-Matos, L., (2005) A Framework For Management of VO Breeding Environment In Collaborative Networks and Their Breeding Environments, , Springer; (2007), European Collaborative Networked Organizations Leadership IST IP506958 project; Ducq, Y., Vallespir, B., Definition and aggregation of a performance measurement system in three Aeronautical workshops using the ECOGRAI Method (2005) Production Planning and Control, 16 (2), pp. 163-177; Martinez, M.T., Fouletier, P., Park, K.H., Favrel, J., Virtual enterprise organisation, evolution and control (2001) Int. J. Production Economics, 74, pp. 225-238; Thoben, K.D., Extended Products: Evolving Traditional Product concepts (2001) Engineering the Knowledge Economy Through Co-operation, pp. 429-439. , Bremen; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard For Measuring and Managing Business Success, , Pearson Education, Harlow; Dignum, V., Dignum, F., Towards an agent-based infrastructure to support VOs (2002) Collaborative Business Ecosystems and VEs, , Kluwer Academic Publishers, Boston; Camarinha-Matos, L., Afsarmanesh, H., Elements of a base VE infrastructure {double pipe} (2003) Computers In Industry, 5, pp. 139-163; Sveiby, K., (2007) Methods For Measuring Intangible Assets, , Sveiby Knowledge Associates; Ducq, Y., Chen, D., Doumeingts, G., A contribution of system theory to sustainable enterprise interoperability science base (2012) Computers In Industry, 63, pp. 844-857},
publisher={Springer New York LLC},
issn={18684238},
isbn={9783662447352},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{NoAuthor2014,
title={IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2014},
journal={IFIP Advances in Information and Communication Technology},
year={2014},
volume={440},
number={PART 3},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906923146&partnerID=40&md5=a30b249b025cc0946a332534ceab1c31},
abstract={The proceedings contain 72 papers. The special focus in this conference is on Advances in Production Management Systems. The topics include: Community of practice theory and process modelling; lean production and just in time; evaluating human work in the digital factory - a new German guideline; alignment of the purchasing strategy to the business strategy; fuzzy symbolic handling of industrial instantaneous and trend performance expressions; a framework for production system design; improvement of human-plant interactivity via industrial cloud-based supervisory control and data acquisition system; focusing early phases in production system design; global and regional production networks; a regulation mechanism based on work activity to improve lean approach; management of a production cell lubrication system with model predictive control; product upgradability; a hybrid method for solving buffer sizing and inspection stations allocation; negative side effects of lean management; supply uncertainty in food processing supply chain; framework for resilient production systems; TCO evaluation in physical asset management; flexibility in lean mixed model assembly lines; a method to create a process reference model adapted to heterogeneous organizations; implementation of lean project planning; use of MCDM and AI techniques for mechanization of in-service inspection planning process; flow disturbance analysis in workshops with high reworks rate; a strategic approach for automation technology initiatives selection; capability maturity model integrated for ship design and construction; managing uncertainty in innovative design; metrics for assessing product variety utilization; decentralized approach for efficient simulation of Devs models; a production system reconfiguration model based on repair approach; reconfigurable manufacturing system design; layout design by integration of multi-agent based simulation and optimization; requirements for a value stream mapping in make-to-order environments; exploring the impact of national culture on performance measurement; simulation analysis of sweet pepper harvesting operations; the effect of the existence of defective items in assembly operations; designing an engineer-to-order performance measurement system; the importance of emerging markets for petroleum technology companies in Norway; supply network complexity; development of the auto parts industry in Brazil; a macro sectorial study of semiconductor production; CART for supply chain simulation models reduction; life cycle evaluation of factories; integrating aspects of supply chain design into the global sourcing process - insights from the automotive industry; the supply chain design of biomass energy plants and taxonomy of engineer-to-order companies.},
publisher={Springer New York LLC},
issn={18684238},
isbn={9783662447321},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Review},
source={Scopus},
}

@CONFERENCE{Chabchoub2014344,
author={Chabchoub, S. and Hachicha, W.},
title={Associating risk management with a performance measurement system: Case of academic libraries},
journal={2014 International Conference on Advanced Logistics and Transport, ICALT 2014},
year={2014},
pages={344-349},
doi={10.1109/ICAdLT.2014.6866336},
art_number={6866336},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906895278&doi=10.1109%2fICAdLT.2014.6866336&partnerID=40&md5=d063b2e0f1c8343450ba873dba57f738},
affiliation={Unit of Logistic, Industrial and Quality Management (LOGIQ), Higher Institute of Industrial Management, Sfax, Tunisia; Laboratory of Mechanic, Modeling and Production (U2MP) Engineering School of Sfax, Sfax, Tunisia},
abstract={Risk management and performance measurement should be linked together to enable, to each supply chain partner, to define and guide its overall risk profile, as well as to shape its strategic direction. In this frame, the aim of this paper is to propose and to validate an integrated approach for relating risk management into supply chain performance measurement system. The proposed approach is carried out in two sequential phases. After identifying flow, missions and goals of an organization, the first phase focuses on the application of the Supply chain operations reference (SCOR) model and the Activity Breakdown Structure (ABS) to identify supply chain processes and activities. After that, Risk Breakdown Structure (RBS) and Risk Breakdown Matrix (RBM) are applied to identify critical risks and to give a set of performance metrics that is based on those risks. Finally, the second phase completes this set by using the Balanced Scorecard (BSC) framework. For a validation purpose, the proposed approach is applied to the Higher Institute of Industrial Management (ISGI, Sfax-Tunisia) library. A comparative analysis between the identified metrics and those required by the standard ISO 11620 (version 2008) shows the significance and the importance of the proposed approach. © 2014 IEEE.},
author_keywords={Academic library;  Balanced Scorecard;  performance measurement system;  SCOR model;  supply chain risk mangement},
keywords={Libraries;  Risk management;  Supply chains, Academic libraries;  Balanced scorecards;  Performance measurement system;  SCOR models;  Supply chain risk, Supply chain management},
references={Kaplan, R., Norton, D., The balanced scorecard -measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74, pp. 75-85; Kaplan, R., Norton, D., Linking the balanced scorecard to strategy (1996) California Management Review, 39, pp. 53-79; Kaplan, R., Norton, D., The strategy-focused organisation (2000) Strategy and Leadership, pp. 41-43; Kaplan, R., Norton, D., Measuring the strategic readiness of intangible assets (2004) Harvard Business Review, 82, pp. 52-63; Rasid, S.Z.A., Golshan, N.M., Ismail ., W.K.W., Ahmad, F.S., Risk management Performance measurement and organisztional performance a conceptual framework (2012) International Journal of Production Research; Liebenberg, A.P., Hoyt, R.E., The determinants of enterprise risk management: Evidence from the appoitnment of chief risk officers (2003) Risk Management and Insurance Review, 6, pp. 37-52; Committee of sponsoring organizations of the treadway commission (2004) Enterprise Risk Management-Integrated Framework; Bohn, C., Kemp, B., Enterprise risk management quantification-An opportunity (2006) AON, pp. 1-12; Blome, C., Schoenherr, T., Supply chain risk management in financial crises a multiple case-study approach (2011) International Journal of Production Economics, 134, pp. 43-57; Norrman, A., Linroth, R., Supply chain risk management: Purchaser's vs planner's views on sharing capacity investment risks in the Telecom Industry (2002) Proceedings of the 11th International Annual IPSERA Conference, pp. 577-595; Hachicha, W., Elmsalmi, M., An integrated approach basedstructural modeling for risk prioritization in supply network management Journal of Risk Research, 17, p. 2014. , In press; Artebrand, A., Jonsson, E., Nordhemmer, M., Risks and risk management in the supply chain flow-A case study based on some of marsh's clients (2004) Master Thesis, Lund Institute of Technology, Division of Engineering Logistics; Elmsalmi, M., Hachicha, W., Risks prioritization in global supply networks using micmac method: A real case study (2013) International Conference on Advanced Logistics and Transport, pp. 394-399; (2000) SCC Supply Chain Operations Reference Model, p. 203. , Cypress, Texas, Etats-Unis: Supply Chain Council; Paul, J., Laville, J.J., Le modèle SCOR : vecteur d'excellence de la Supply Chain (2007) Supply Chain Magazine; Persson, F., Araldi, M., The development of a dynamic supply chain analysis tool-Integration of SCOR and discrete event simulation (2009) International Journal of Production Economics, 121, pp. 574-583; Thomas, R., Gable, M., Dickinson, R., (1999) An Application of the Balanced Scorecard in Retailing the International Review of Retail, Distribution and Consumer Research, 9, pp. 41-67; Norreklit, H., The balance on the balanced scorecard -A critical analysis of some of its assumptions (2000) Management Accounting Research, 11, pp. 65-88; Woods, M., Linking risk management to strategic controls: A case study of Tesco plc (2008) International Journal of Risk Assessment and Management, 7, pp. 1074-1088; Tommaso, P., Integrating risk and performance in management reporting (2011) London School of Economics and Political Science, Chartered Institute of Management Accountants, 8; Moullin, M., Using the public sector scorecard in health and social care (2004) Proceedings of the PMA Conference, Edinburgh; Beasley, M., Chen, A., Nunez, K., Wright, L., Working hand in hand: Balanced scorecards and enterprise risk management (2006) Strategic Finance, 87, pp. 49-55; Nagumo, T., Aligning enterprise risk management with strategy through the BSC: The Bank of Tokyo-Mitsubishi approach (2005) Balanced Scorecard Report, pp. 3-6; Calandro Jr., H.J., Lane, S., An introduction to the enterprise risk scorecard (2006) Measuring Business Excellence, 10, pp. 31-40; Cagliano, A.C., Marco, A.A.D., Grimaldi, S., Rafele, C., An integrated approach to supply chain risk analysis (2012) Journal of Risk Research, 15, pp. 817-840; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers &Industrial Engineering, 53, pp. 43-62; Babalhavaeji, F., Isfandyari-Moghaddam, A., Quality assessment of academic libraries' performance with a special reference to information technology-based services (2010) The Electronic Library, 28, pp. 592-621; (2008) Information and Documentation-Library Performance Indicators, , ISO 11620, International Organization for Standardization, Geneva},
publisher={IEEE Computer Society},
isbn={9781479948390},
language={English},
abbrev_source_title={Int. Conf. Adv. Logist. Transp., ICALT},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Vlachos201449,
author={Vlachos, I.P.},
title={Reverse food logistics during the product life cycle},
journal={International Journal of Integrated Supply Management},
year={2014},
volume={9},
number={1-2},
pages={49-73},
doi={10.1504/IJISM.2014.064356},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906655327&doi=10.1504%2fIJISM.2014.064356&partnerID=40&md5=585c7caae2be82a91ef5e25c0c230480},
affiliation={Newcastle Business School, Northumbria University, City Campus East 1, New-castle Upon Tyne, NE1 8ST, United Kingdom},
abstract={This study aimed to examine reverse logistics across the food product life cycle. The literature review of reverse logistics factors in food industry produced a set of supply chain performance measures. Using a survey, questionnaires were sent to 200 practitioners with experience in food supply chain operations. In total, 48 usable questionnaires were returned, resulting in 24% response rate. The contribution of this study lies in extending the body of knowledge of reverse food logistics during product life cycle. It offers practical advice to manage reverse food logistics. Results indicate that customer expectations are more significant during the introduction and growth stages. The effectiveness of the return process in the introduction stage will determine the future of the product and companies need to manage quality problems effectively. The paper discusses managerial implications and offers recommendations for future research. Copyright © 2014 Inderscience Enterprises Ltd.},
author_keywords={Food industry;  Product life cycle;  Reverse logistics},
references={Aramyan, L., Lansink, A., Vorst, J., Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Aramyan, L., Ondersteijn, C., Van Kooten, O., Lansink, A., Performance indicators in agri-food production chains (2006) Quantifying the Agri-food Supply Chain, pp. 47-64. , Ondersteijn, C.J., Wijnands, J.H.M. and Huirne, R.B.M. (Eds.), Springer, Dordrecht; Armstrong, J.S., Overton, T.S., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Bai, C., Sarkis, J., Flexibility in reverse logistics: A framework and evaluation approach (2013) Journal of Cleaner Production, 47, pp. 306-318; Bernon, M., Cullen, J., An integrated approach to managing reverse logistics (2007) International Journal of Logistics: Research and Applications, 10 (1), pp. 41-56; Bernon, M., Rossi, S., Cullen, J., Retail reverse logistics: A call and grounding framework for research (2010) International Journal of Physical Distribution & Logistics Management, 41 (5), pp. 484-510; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Blackburn, J.D., Guide, V.D., Souza, G., Wassenhove, L.V., Reverse supply chains for commercial returns (2004) California Management Review, 46 (2), pp. 6-22; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Cardona, M.I.G., (2012) Performance Objectives Vs Product Lifecycle: An Assessment Applied to Identify the Relevant Reverse Logistics Variables and Trade-Offs in the Food Industry, , Master of Science in Operations and Supply Chain Management, The University of Liverpool; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; (2011) Food Drink Europe Competitiveness Report, , http://www.fooddrinkeurope.eu/uploads/press-releases_documents/ FoodDrinkEurope_Competitiveness_Report_2011.pdf, CIAA (Accessed 28 January, 2013); Coelho, T.M., Castro, R., Gobbo, J.A., PET containers in Brazil: Opportunities and challenges of a logistics model for post-consumer waste recycling (2011) Resources, Conservation and Recycling, 55 (3), pp. 291-299; De Koster, R., De Brito, M., Van De Vendel, M., Return handling: An exploratory study with nine retailer warehouses (2002) International Journal of Retail & Distribution Management, 30 (8), pp. 407-421; Doughton, M., Incorporate reverse logistics into your sustainability goals (2008) Food Logistics, p. 20; Estampe, D., Lamouri, S., Paris, J.L., Djelloul, S.B., A framework for analysing supply chain performance evaluation models (2013) International Journal of Production Economics, 142 (2), pp. 247-258; Fotopoulos, C., Vlachos, I.P., Maglaras, G., The process and critical success factors of evolving from product excellence to market excellence: The case of Mastiha in Chios, Greece (2010) Market Orientation (Food and Agricultural Marketing), pp. 307-324. , Lindgreen, A., Hingley, M., Harness, D. and Custance, P. (Eds.), Gower, UK; Georgiadis, P., Vlachos, D., Iakovou, E., A system dynamics modeling framework for the strategic supply chain management of food chains (2005) Journal of Food Engineering, 70 (3), pp. 351-364; González-Benito, J., Supply strategy and business performance: An analysis based on the relative importance assigned to generic competitive objectives (2010) International Journal of Operations & Production Management, 30 (8), pp. 774-797; Guide, V.D., Souza, G., Wassenhove, L.V., Blackburn, J.D., Time value of commercial product returns (2006) Management Science, 52 (8), pp. 1200-1214; Gustavsson, J., Otterdijk, R., Global food losses and food waste: Extent, causes and prevention (2011) International Congress Save Food!, pp. 1-29. , 12-18 May, Food and Agriculture Organization of the United Nations, Düseldorf, Germany, Rome; Hobbs, J.E., Liability and traceability in agri-food supply chains (2006) Quantifying the Agri-food Supply Chain, pp. 87-102. , Ondersteijn, C.J. Wijnands, J.H.M. and Huirne, R.B.M. (Eds.), Springer, Dordrecht; Hogg, R.V., Tanis, E., (2013) Probability and Statistical Inference, , 8th ed., Pearson, London; Hsiao, H.I., Kemp, R.G.M., A classification of logistic outsourcing levels and their impact on service performance: Evidence from the food processing industry (2010) International Journal of Production Economics, 124 (1), pp. 75-86; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48 (2), pp. 377-394; Karalis, V., Vlachos, I.P., Supplier-retailer collaboration in food networks: A typology and examination of moderating factors (2004) Dynamics in Chains and Networks, pp. 280-287. , Bremmers, H.J., Omta, S.W.F., Trienekens, J.H. and Wubben, E.F.M. (Eds.), Wageningen Academic Publishers, The Netherlands; Karim, M.A., Smith, A.J.R., Halgamuge, S., Empirical relationships between some manufacturing practices and performance (2008) International Journal of Production Research, 46 (13), pp. 3583-3613; Karolefski, J., Cracking down on unsealables (2007) Food Logistics, , www.foodlogistics.com/article/10316057/cracking-down-on-unsealables, 12 February, 2007 (Accessed 25 January, 2013); Kumar, S., Nigmatullin, A., A system dynamics analysis of food supply chains - Case study with non-perishable products (2011) Simulation Modeling Practice and Theory, 19 (10), pp. 2151-2168; Kumar, S., Putnam, V., Cradle to cradle: Reverse logistics strategies and opportunities across three industry sectors (2008) International Journal of Production Economics, 115 (1), pp. 305-315; Madaan, J., Wadhwa, S., Flexible process planning for sustainable decisions in reverse logistics system (2007) Global Journal of Flexible Systems Management, 8 (4), pp. 1-8; Marquis, K.H., Marquis, S., Polich, J.M., Response bias and reliability in sensitive topic surveys (1986) Journal of the American Statistical Association, 81, pp. 381-389; Mollenkopf, D., Russo, I., Frankel, R., The returns management process in supply chain strategy (2007) International Journal of Physical Distribution & Logistics Management, 37 (7), pp. 568-592; (2011) Waste and Recovery of By-Products, , http://www.nestle.com/CSV/WaterAndEnvironmentalSustainability/ WasteAndRecovery/Pages/WasteAndRecovery.aspx, (Accessed 28 March, 2013); Nikolaou, I.E., Evangelinos, K., Allan, S., A reverse logistics social responsibility evaluation framework based on the triple bottom line approach (2011) Journal of Cleaner Production, pp. 1-30; O'Hora, A., (2013) Horse Meat Discovery Knocks £300m off the Value of Tesco Shares, , http://www.independent.ie/sport/other-sports/horse-meat-discovery-knocks- 300m-off-the-value-of-tesco-shares-28959295.html, 16 January, 2013, (Accessed 25 July, 2013); Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Petersen, J.A., Kumar, V., Are product returns a necessary evil? Antecedents and consequences (2009) Journal of Marketing, 73 (3), pp. 35-51; Pokharel, S., Mutha, A., Perspectives in reverse logistics: A review (2009) Resources, Conservation and Recycling, 53 (4), pp. 175-182; Richey, R.G., Daugherty, P., Genchev, S., Autry, C., Reverse logistics: The impact of timing and resources (2004) Journal of Business Logistics, 25 (2), pp. 229-250; Sarkis, J., Helms, M., Hervani, A., Reverse logistics and social sustainability (2010) Corporate Social Responsibility and Environmental Management, 17 (6), pp. 337-354; Slack, N., Chambers, S., Johnston, B., (2001) Operations Management, Financial Times, , Prentice Hall, Harlow; Srivastava, S., Srivastava, R., Managing product returns for reverse logistics (2006) International Journal of Physical Distribution & Logistics Management, 36 (7), pp. 524-546; Stock, J., Mulki, J.P., Product returns processing: An examination of practices of manufacturers, wholesalers/distributors, and retailers (2009) Journal of Business Logistics, 30 (1), pp. 33-62; Terreri, A., Reverse logistics moves forward (2010) Food Logistics, , http://www.foodlogistics.com/print/Food-Logistics/Reverse-Logistics- Moves-Forward/1$3458, January-February [Online] (Accessed 25 January, 2013); Tibben-Lembke, R., Life after death: Reverse logistics and the product life cycle (2002) International Journal of Physical Distribution & Logistics Management, 32 (3), pp. 223-244; Vaidyanathan, J., Yadong, L., Creating competitive advantages through new value creation: A reverse logistics perspective (2007) Academy of Management Perspectives, 21 (2), pp. 56-73; Van Der Vorst, J., Tromp, S.O., Van Der Zee, D.J., Simulation modeling for food supply chain redesing, integrated decisión making on product quality, sustainability and logistics (2009) International Journal of Production Research, 47 (23), pp. 6611-6631; Vlachos, I., Adoption of electronic data interchange by agribusiness organizations (2003) Journal of International Food and Agribusiness Marketing, 15 (3), pp. 19-42; Vlachos, I.P., Business-to-business e-commerce: An innovative tool for food chain management (2002) 1st Pan-Hellenic Conference of Hellenic Association of Information and Communication Technology in Agriculture, Food and Environment, pp. 37-44; Wadhwa, S., Madaan, J., Verma, M., Flexibility focused decision and information sharing model for product recovery system (2009) Global Journal of Flexible Systems Management, 10 (3), pp. 15-22; (2011) Consumer Insight: Date Labels and Storage Guidance, , http://www.wrap.org.uk/downloads/Technical_report_dates_final.fa35a387. 11175.pdf, (Accessed 27 February, 2013); Yakovleva, N., Sarkis, J., Sloan, T., (2010) Sustainable Benchmarking of Supply Chains: The Case Study of the Food Industry, , http://faculty.uml.edu/tsloan/papers/YakovlevaSarkisSloan-IJPR- Benchmarking-2010.pdf, [Online] (Accessed 27 February, 2013)},
correspondence_address1={Vlachos, I.P.; Newcastle Business School, City Campus East 1, New-castle Upon Tyne, NE1 8ST, United Kingdom; email: Ilias.vlachos@northumbria.ac.uk},
publisher={Inderscience Publishers},
issn={14775360},
language={English},
abbrev_source_title={Int. J. Integr. Supply Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Jakhar2014235,
author={Jakhar, S.K.},
title={Designing the green supply chain performance optimisation model},
journal={Global Journal of Flexible Systems Management},
year={2014},
volume={15},
number={3},
pages={235-259},
doi={10.1007/s40171-014-0069-6},
note={cited By 25},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906567533&doi=10.1007%2fs40171-014-0069-6&partnerID=40&md5=18b8800b36c0a47bca522c451796d2ae},
affiliation={Indian Institute of Management Rohtak, Rohtak, India},
abstract={There is a growing concern that business enterprises and their supply chains focus on economic activities while ignoring their impact on the environment. An attempt is made here to develop a green supply chain to address this concern. With the objectives of achieving economic growth and environmental protection, the paper develops a green supply chain performance measures and proposes a partner selection and flow allocation decision making model. An integrated methodology of structural equation modeling (SEM), analytical hierarchy process (AHP), and multiobjective linear programming (MOLP), has been applied to the proposed model. To assess green performance of a supply chain, a scale is developed that includes qualitative as well as quantitative measures. Using the scale and the survey data of 278 business organizations from Indian apparel industry supply chain network, the SEM model is developed to estimate the weights of the green constructs and their measures. Based on the measures, relative scores of different supply chain alternatives (i.e. suppliers, manufacturing plants, distribution and logistics) are calculated by the AHP. The weights estimated by the SEM approach are combined with AHP relative scores to obtain weighted relative scores of different available alternatives. These weighted relative scores are then used in MOLP problem formulation for partner selection and flow allocation decision making. To illustrate the use of the optimization model, real-time data from an apparel manufacturer are presented. © 2014 Global Institute of Flexible Systems Management.},
author_keywords={Analytical hierarchy process;  Flow optimization;  Green supply chain;  Multi-objective linear programming;  Performance evaluation;  Structural equation modeling},
references={Albino, V., Dangelico, R.M., Pontrandolfo, P., Do inter-organizational collaborations enhance a firm's environmental performance? A study of the largest US companies (2012) Journal of Cleaner Production, 37, pp. 304-315; Alexopoulos, I., Kounetas, K., Tzelepis, D., Environmental performance and technical efficiency, is there a link?: The case of Greek listed firms (2011) International Journal of Productivity and Performance Management, 61 (1), pp. 6-23; Aras, G., Aybars, A., Kutlu, O., Managing corporate performance: Investigating the relationship between corporate social responsibility and financial performance in emerging markets (2010) International Journal of Productivity and Performance Management, 59 (3), pp. 229-254; Azevedo, S.G., Carvalho, H., Machado, V.C., The influence of green practices on supply chain performance: A case study approach (2011) Transportation Research Part e, 47 (6), pp. 850-871; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Baskaran, V., Nachiappan, S., Rahman, S., Indian textile suppliers' sustainability evaluation using the grey approach (2012) International Journal of Production Economics, 135 (2), pp. 647-658; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Björklund, M., Forslund, H., The purpose and focus of environmental performance measurement systems in logistics (2013) International Journal of Productivity and Performance Management, 62 (3), pp. 230-249; Brito, M.P., Carbone, V., Blanquart, C.M., Towards a sustainable fashion retail supply chain in Europe: Organisation and performance (2008) International Journal of Production Economics, 114 (2), pp. 534-553; Burgess, T.F., Heap, J., Creating a sustainable national index for social, environmental and economic productivity (2012) International Journal of Productivity and Performance Management, 61 (4), pp. 334-358; Caniato, F., Caridi, M., Crippa, L., Moretto, A., Environmental sustainability in fashion supply chains: An exploratory case based research (2012) International Journal of Production Economics, 135 (2), pp. 659-670; Carter, C.R., Purchasing and social responsibility: A replication and extension (2004) Journal of Supply Chain Management, 40 (4), pp. 4-16; Chaabane, A., Ramudhin, A., Paquet, M., Designing supply chains with sustainability considerations (2011) Production Planning and Control: The Management of Operations, 22 (8), pp. 727-741; Chaabane, A., Ramudhin, A., Paquet, M., Design of sustainable supply chains under the emission trading scheme (2012) International Journal of Production Economics, 135 (1), pp. 37-49; Chan, T.-Y., Wong, C.W.Y., The consumption side of sustainable fashion supply chain: Understanding fashion consumer eco-fashion consumption decision (2012) Journal of Fashion Marketing and Management, 16 (2), pp. 193-215; Chen, Y.S., Lai, S.B., Wen, C.T., The influence of green innovation performance on corporate advantage in Taiwan (2006) Journal of Business Ethics, 67 (4), pp. 331-339; Chiou, T.Y., Chan, H.K., Lettice, F., Chung, S.H., The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan (2011) Transportation Research Part e, 47 (6), pp. 822-836; Cho, D.W., Lee, Y.H., Ahn, S.H., Hwang, M.K., A framework for measuring the performance of service supply chain management (2011) Computers & Industrial Engineering, 62 (3), pp. 801-818; Christmann, P., Effect of 'best practices' of environmental management on cost advantage: The role of complementary assets (2000) Academy of Management Journal, 43 (4), pp. 663-680; Daft, R.L., (1983) Organization Theory and Design, , Cengage Learning New York; Diabat, A., Govindan, K., An analysis of the drivers affecting the implementation of green supply chain management (2011) Resources, Conservation and Recycling, 55 (6), pp. 659-667; Ding, G.K., Developing a multicriteria approach for the measurement of sustainable performance (2005) Building Research & Information, 33 (1), pp. 3-16; Djassemi, M., A computer-aided approach to material selection and environmental auditing (2012) Journal of Manufacturing Technology Management, 23 (6), pp. 704-716; Dyer, J.H., Singh, H., The relational view: Cooperative strategy and sources of inter organizational competitive advantage (1998) Academy of Management Review, 23 (4), pp. 660-679; Elhedhli, S., Merrick, R., Green supply chain network design to reduce carbon emissions (2012) Transportation Research Part D, 17 (5), pp. 370-379; El Saadany, A.M.A., Jaber, M.Y., Bonney, M., Environmental performance measures for supply chains (2011) Management Research Review, 34 (11), pp. 1202-1221; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70 (6), pp. 1088-1100; Fawcett, S.E., Cooper, M.B., Logistics performance measurement and customer success (1998) Industrial Marketing Management, 27 (4), pp. 341-357; Fayet, L., Vermeulen, W.J.V., Supporting smallholders to access sustainable supply chains: Lessons form the Indian cotton supply chain (2013) Sustainable Development, pp. 1-22; Fornell, C., Larcker, D., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Förstl, K., Reuter, C., Hartmann, E., Blome, C., Managing supplier sustainability risks in a dynamically changing environment-Sustainable supplier management in the chemical industry (2010) Journal of Purchasing and Supply Management, 16 (2), pp. 118-130; Garcés-Ayerbe, C., Rivera-Torres, P., Murillo-Luna, J.L., Stakeholder pressure and environmental proactivity: Moderating effect of competitive advantage expectations (2012) Management Decision, 50 (2), pp. 189-206; Ghoshal, S., Bad management theories are destroying good management practices (2005) Academy of Management Learning & Education, 4 (1), pp. 75-91; Gimenez, C., Sierra, V., Rodon, J., Sustainable operations: Their impact on the triple bottom line (2012) International Journal of Production Economics, 140 (1), pp. 149-159; Gold, S., Seuring, S., Beske, P., Sustainable supply chain management and inter- organizational resources: A literature review (2010) Corporate Social Responsibility and Environmental Management, 17 (4), pp. 230-245; Golicic, S.L., Smith, C.D., A meta-analysis of environmentally sustainable supply chain management practices and firm performance (2013) Journal of Supply Chain Management, 49 (2), pp. 78-95; Gopalakrishnan, K., Yusuf, Y.Y., Musa, A., Abubakar, T., Ambursa, H.M., Sustainable supply chain management: A case study of British Aerospace (BAe) Systems (2012) International Journal of Production Economics, 140 (1), pp. 193-203; Govindan, K., Khodaverdi, R., Jafarian, A., A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach (2012) Journal of Cleaner Production, , 10.1016/j.jclepro.2012.04.014; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , Prentice Hall Upper Saddle River, NJ; Hamdouch, A., Zuindeau, B., Sustainable development, 20 years on: Methodological innovations, practices and open issues (2010) Journal of Environmental Planning and Management, 53 (4), pp. 427-438; Harris, I., Naim, M., Palmer, A., Potter, A., Mumford, C., Assessing the impact of cost optimization based on infrastructure modeling on CO2 emissions (2011) International Journal of Production Economics, 131 (1), pp. 313-321; Hart, S.L., A natural-resource-based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1014; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Herron, C., Braiden, P.M., A methodology for developing sustainable quantifiable productivity improvement in manufacturing companies (2006) International Journal of Production Economics, 104 (1), pp. 143-153; Ho, H.P.Y., Choi, T.M., A Five-R analysis for sustainable fashion supply chain management in Hong Kong: A case analysis (2012) Journal of Fashion Marketing and Management, 16 (2), pp. 161-175; Hugo, A., Pistikopoulos, E.N., Environmentally conscious long-range planning and design of supply chain networks (2005) Journal of Cleaner Production, 13 (15), pp. 1471-1491; Jakhar, S.K., Barua, M.K., An integrated model of supply chain performance evaluation and decision-making using structural equation modelling and fuzzy AHP (2013) Production Planning & Control, pp. 1-20; Jin, B., Achieving an optimal global versus domestic sourcing balance under demand uncertainty (2004) International Journal of Operations & Production Management, 24 (12), pp. 1292-1305; Kejun, J., Cosbey, A., Murphy, D., Embodied carbon in traded goods (2008) Trade and Climate Change Seminar, , http://www.iisd.org/pdf/2008/cph_trade_climate_carbon.pdf, June 18-20, Copenhagen, Denmark. Retrieved, November 10, 2011; Kenneth, W.G.J., Zelbst, P.J., Meacham, J., Bhadauria, V.S., Green supply chain management practices: Impact on performance (2012) Supply Chain Management: An International Journal, 17 (3), pp. 290-305; Klassen, R.D., Vachon, S., Evaluation and collaboration in the supply chain: Their impact on plant-level environmental investments (2003) Production and Operations Management, 12 (3), pp. 336-352; Klassen, R.D., Vereecke, A., Social issues in supply chains: Capabilities link responsibility, risk (opportunity), and performance (2012) International Journal of Production Economics, 140 (1), pp. 103-115; Klassen, R.D., Whybark, D.C., The impact of environmental technologies on manufacturing performance (1999) Academy of Management Journal, 42 (6), pp. 599-615; Krajnc, D., Glavic, P., How to compare on relevant dimensions of sustainability (2005) Ecological Economics, 55 (4), pp. 551-563; Kumar, S., Teichman, S., Timpernagel, T., A green supply chain is a requirement for profitability (2012) International Journal of Production Research, 50 (5), pp. 1278-1296; Lai, K.H., Cheng, T.C.E., Tang, A.K.Y., Green retailing: Factors for success (2010) California Management Review, 52 (2), pp. 6-31; Lee, H.L., Creating value through supply chain integration (2000) Supply Chain Management Review, 4 (4), pp. 30-36; Lee, K.-H., Integrating carbon footprint into supply chain management: The case of Hyundai Motor Company (HMC) in the automobile industry (2011) Journal of Cleaner Production, 19, pp. 1216-1223; Lee, K.-H., Carbon accounting for supply chain management in the automobile industry (2012) Journal of Cleaner Production, 36, pp. 83-93; Lee, K.-H., Cheong, I.-M., Measuring a carbon footprint and environmental practice: The case of Hyundai Motors Co. (HMC) (2011) Industrial Management & Data Systems, 111 (6), pp. 961-978; Lee, K.H., Kim, J.W., Current status of CSR in the realm of supply management: The case of the Korean electronics industry (2009) Supply Chain Management: An International Journal, 14 (2), pp. 138-148; Lee, S.M., Kim, S.T., Choi, D., Green supply chain management and organizational performance (2012) Industrial Management & Data Systems, 112 (8), pp. 1148-1180; Lee, T.-R.J.-S., Le, T.P.N., Genovese, A., Koh, L.S.C., Using FAHP to determine the criteria for partner's selection within a green supply chain: The case of hand tool industry in Taiwan (2012) Journal of Manufacturing Technology Management, 23 (1), pp. 25-55; Li, X., Gu, X.J., Liu, G., A strategic performance measurement system for firms across supply and demand chains on the analogy of ecological succession (2009) Ecological Economics, 68 (12), pp. 2918-2929; Liang, L., Feng, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; López-Gamero, M.D., Molina-Azorín, J.F., Claver-Cortés, E., The potential of environmental regulation to change managerial perception, environmental management, competitiveness and financial performance (2010) Journal of Cleaner Production, 18 (10), pp. 963-974; Moneva, J.M., Ortas, E., Corporate environmental and financial performance: A multivariate approach (2010) Industrial Management & Data Systems, 110 (2), pp. 193-210; Montabon, F., Melnyk, S.A., Sroufe, R., Calantone, R.J., ISO 14000: Assessing its perceived impact on corporate performance (2000) Journal of Supply Chain Management, 36 (2), pp. 4-16; Montabon, F., Sroufe, R.P., Narasimhan, R., An examination of corporate reporting, environmental management practices and firm performance (2007) Journal of Operations Management, 25 (5), pp. 998-1014; Nagurney, A., Supply chain network design under profit maximization and oligopolistic competition (2010) Transportation Research Part e, 46 (3), pp. 281-294; Nagurney, A., Yu, M., Fashion supply chain management through cost and time minimization from a network perspective (2011) Fashion Supply Chain Management: Industry and Business Analysis, , T.-M. Choi (Ed.) Hershey, Pennsylvania: IGI Global; Nagurney, A., Yu, M., Sustainable fashion supply chain management under oligopolistic competition and brand differentiation (2012) International Journal of Production Economics, 135 (2), pp. 532-540; Naini, S.G.J., Aliahmadi, A.R., Jafari-Eskandari, M., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 593-603; Neto, J.Q.F., Bloemhof-Ruwaard, J.M.B., Van Nunen, J., Van Heck, E., Designing and evaluating sustainable logistics networks (2008) International Journal of Production Economics, 111 (2), pp. 195-208; Neto, J.Q.F., Walther, G., Bloemhof, J., Vannunen, J., Spengler, T.S., A methodology for assessing eco-efficiency in logistics networks (2009) European Journal of Operational Research, 193 (3), pp. 670-682; Nusair, K., Hua, N., Comparative assessment of structural equation modeling and multiple regression research methodologies: E-commerce context (2010) Tourism Management, 31 (3), pp. 314-324; Olugu, E.U., Wong, K.Y., An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry (2012) Expert Systems with Applications, 39 (1), pp. 375-384; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Paksoy, T., Bektas, T., Özceylan, E., Operational and environmental performance measures in a multi-product closed-loop supply chain (2011) Transportation Research Part e, 47 (4), pp. 532-546; Peng, Y.S., Lin, S.S., Local responsiveness pressure, subsidiary resources, green management adoption and subsidiary's performance: Evidence from Taiwanese manufactures (2008) Journal of Business Ethics, 79 (1-2), pp. 199-212; Perotti, S., Zorzini, M., Cagno, E., Micheli, G.J.L., Green supply chain practices and company performance: The case of 3PLs in Italy (2012) International Journal of Physical Distribution & Logistics Management, 42 (7), pp. 640-672; Pullman, M.E., Maloni, M.J., Carter, C.R., Food for thought: Social versus environmental sustainability practices and performance outcomes (2009) Journal of Supply Chain Management, 45 (4), pp. 38-54; Quariguasi, F.N.J., Walther, G., Bloemhof, J., Van Nunen, J., Spengler, T., A methodology for assessing eco-efficiency in logistic networks (2009) European Journal of Operational Research, 193 (3), pp. 647-914; Ramanathan, R., Black, A., Nath, P., Muyldermans, L., Impact of environmental regulations on innovation and performance in the UK industrial sector (2010) Management Decision, 48 (10), pp. 1493-1513; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations & Production Management, 25 (9), pp. 898-916; Raykov, T., Marcoulides, G.A., (2006) A First Course in Structural Equation Modelling, , Lawrence Erlbaum associates, Mahwah, publisher New Jersey; Reddy, B.S., Ray, B.K., Decomposition of energy consumption and energy intensity in Indian manufacturing industries (2010) Energy for Sustainable Development, 14 (1), pp. 35-47; Ren, X., Development of environmental performance indicators for textile process and product (2000) Journal of Cleaner Production, 8 (6), pp. 473-481; Riccaboni, A., Leone, E.L., Implementing strategies through management control systems: The case of sustainability (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 130-144; Ross, A.D., Parker, H., Benavides-Espinosa, M.D.M., Droge, C., Sustainability and supply chain infrastructure development (2012) Management Decision, 50 (10), pp. 1891-1910; Russo, M.V., Fouts, P.A., A resource-based perspective on corporate environmental performance and profitability (1997) Academy of Management Journal, 40 (3), pp. 534-559; Saraph, J.V., Benson, P.G., Schroeder, R.G., An instrument for measuring the critical factors of quality management (1989) Decision Sciences, 20 (4), pp. 810-829; Sarkis, J., Zhu, Q., Lai, K., An organizational theoretic review of green supply chain management literature Review Article (2011) International Journal of Production Economics, 130 (1), pp. 1-15; Saaty, T.L., (1980) Multi-criteria Decision Making: The Analytic Hierarchy Process, , New York: McGraw Hill; Schoenherr, T., The role of environmental management in sustainable business development: A multi-country investigation (2012) International Journal of Production Economics, 140 (1), pp. 116-128; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Shah, R., Goldstein, S.M., Use of structural equation modeling in operations management research: Looking back and forward (2006) Journal of Operations Management, 24 (2), pp. 148-169; Shaw, K., Shankar, R., Yadav, S.S., Thakur, L.S., Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain (2012) Expert Systems with Applications, 39 (9), pp. 8182-8192; Shen, L.Y., Lu, W.S., Yao, H., Wu, D.H., A computer-based scoring method for measuring the environmental performance of construction activities (2005) Automation in Construction, 14 (3), pp. 297-309; Sheu, J.-B., Green supply chain management, reverse logistics and nuclear power generation (2008) Transportation Research Part e, 44 (1), pp. 19-46; Sheu, J.-B., Chou, Y.-H., Hu, C.-C., An integrated logistics operational model for green-supply chain management (2005) Transportation Research Part e, 41 (4), pp. 287-313; Sheu, H.-J., Lo, S.-F., A new conceptual framework integrating environment into corporate performance evaluation (2005) Sustainable Development, 13 (2), pp. 79-90; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., Development of composite sustainability performance index for steel industry (2007) Ecological Indicators, 7 (3), pp. 565-588; Sroufe, R., Effects of environmental management systems on environmental management practices and operations (2003) Production and Operations Management, 12 (3), pp. 416-431; Sundarakani, B., Souza, R., Goh, M., Wagner, S.M., Manikandan, S., Modeling carbon footprints across the supply chain (2010) International Journal of Production Economics, 128 (1), pp. 43-50; Tsai, W.-H., Hung, S.-J., A fuzzy goal programming approach for green supply chain optimization under activity-based costing and performance evaluation with a value-chain structure (2009) International Journal of Production Research, 47 (18), pp. 4991-5017; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; Vachon, S., Klassen, R.D., Green project partnership in the supply chain: The case of the package printing industry (2006) Journal of Cleaner Production, 14 (6-7), pp. 661-671; Vachon, S., Mao, Z., Linking supply chain strength to sustainable development: A country-level analysis (2008) Journal of Cleaner Production, 16 (15), pp. 1552-1560; Vaidya, O.S., Kumar, S., Analytic hierarchy process: An overview of applications (2006) European Journal of Operational Research, 169 (1), pp. 1-29; Veleva, V., Ellenbecker, M., Indicators of sustainable production: Framework and methodology (2001) Journal of Cleaner Production, 9 (6), pp. 519-549; Venus, L.Y.H., Green management practices and firm performance: A case of container terminal operations (2011) Conservation & Recycling, 55 (6), pp. 559-566; Wagner, M., Schaltegger, S., The effect of corporate environmental strategy choice and environmental performance on competitiveness and economic performance: An empirical study of EU manufacturing (2004) European Management Journal, 22 (5), pp. 557-572; Wang, X., Chan, H.K., Yee, R.W.Y., Diaz-Rainey, I., A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain (2012) International Journal of Production Research, 135 (2), pp. 595-606; Wernerfelt, B., A resource-based view of the firm (1984) Strategic Management Journal, 5 (2), pp. 171-180; Winebrake, J., Corbett, J., Falzarano, A., Hawker, J., Kormacher, K., Ketha, S., Zilora, S., Assessing energy, environmental, and economic tradeoffs in intermodal freight transportation (2008) Journal of Air and Waste Management Association, 58 (8), pp. 1004-1013; Wu, G.-C., Ding, J.-H., Chen, P.-S., The effects of GSCM drivers and institutional pressures on GSCM practices in Taiwan's textile and apparel industry (2012) International Journal of Production Research, 135 (2), pp. 618-636; Wu, S.J., Melnyk, S.A., Calantone, R.J., Assessing the core resources in the environmental management system from the resource perspective and the contingency perspective (2008) IEEE Transactions on Engineering Management, 55 (2), pp. 304-315; Zailani, S., Jeyaraman, K., Vengadasan, G., Premkumar, R., Sustainable supply chain management (SSCM) in Malaysia: A survey (2012) International Journal of Production Economics, 140 (1), pp. 330-340; Zeydan, M., Çolpan, C., Çobanoǧlu, C., A combined methodology for supplier selection and performance evaluation (2011) Expert Systems with Applications, 38 (3), pp. 2741-2751; Zhu, Q., Sarkis, J., Cordeiro, J.J., Lai, K.-H., Firm-level correlates of emergent green supply chain management practices in the Chinese context (2008) Omega, 36 (4), pp. 577-591},
correspondence_address1={Jakhar, S.K.; Indian Institute of Management Rohtak, Rohtak, India; email: suresh.jakhar@iimrohtak.ac.in},
publisher={Global Institute of Flexible Systems Management},
issn={09722696},
language={English},
abbrev_source_title={Global J. Flexible Syst. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Rivera2014,
author={Rivera, L. and DeStefano, L.},
title={Integrating performance measurement and program evaluation to promote understanding},
journal={ACM International Conference Proceeding Series},
year={2014},
doi={10.1145/2616498.2616577},
art_number={72},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905447639&doi=10.1145%2f2616498.2616577&partnerID=40&md5=a419f7b783d0aa62eddf15756f035019},
affiliation={University of Illinois at Urbana-Champaign, 704 S. Sixth Street, Champaign, IL 61820 +1 217 333 9625, United States},
abstract={In this extended abstract, we describe the trend in reporting performance assessments of publicly funded social programs as a result of public demands for accountability and its effect on evaluation. XSEDE's Training, Education, and Outreach Services (TEOS) external evaluation team outlines the differences between performance measurement and program evaluation, makes a case for their integration, and describes their effort to design and implement such an evaluation within XSEDE. Plans for integrating the developing performance measurement system and program evaluation within TEOS are discussed as well as initial steps toward integration to date. Copyright 2014 ACM.},
author_keywords={Evaluation methods;  Evaluators;  Performance indicators;  Performance measurement;  Program evaluation;  Program improvement},
keywords={Computer applications;  Computer programming, Evaluation methods;  Evaluators;  Performance indicators;  Performance measurements;  Program evaluation;  Program improvement, Supply chain management},
references={Public statement on cultural competence in evaluation (2011) American Evaluation Association; (2010) GPRA Modernization Act of 2010. U.S.C. H.R, 2142; Mark, F.M., Rog, D., Thomas, V., Frierson, H., Hood, S., Hughes, G., The 2010 user-friendly handbook for project evaluation (2010) National Science Foundation; Greene, J.C., The inequality of performance measurements (1999) Evaluation, 5 (2), pp. 160-172. , Apr; Newcomer, K.E., Using performance measurement to improve programs (1997) New Directions for Evaluation, 1997 (75), pp. 5-14; Barrados, M., Blain, J.S., Improving program results through the use of predictive operational performance indicators: A canadian case study (2013) American Journal of Evaluation, 34 (1), pp. 45-56. , Feb; Blank, R.K., Developing a system of education indicators: Selecting, implementing, and reporting indicators (1993) Educational Evaluation and Policy Analysis, 15 (1), pp. 65-80. , Jan., svol},
publisher={Association for Computing Machinery},
isbn={9781450328937},
language={English},
abbrev_source_title={ACM Int. Conf. Proc. Ser.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Charkha201440,
author={Charkha, P.G. and Jaju, S.B.},
title={Supply chain performance measurement system: An overview},
journal={International Journal of Business Performance and Supply Chain Modelling},
year={2014},
volume={6},
number={1},
pages={40-60},
doi={10.1504/IJBPSCM.2014.058892},
note={cited By 16},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905116435&doi=10.1504%2fIJBPSCM.2014.058892&partnerID=40&md5=dae2bba7f695d605f3d15a2965bffbfc},
affiliation={Department of Mechanical Engineering, G.H. Raisoni College of Engineering, CRPF Gate No. 3, Digdoh Hills, Hingna Road, Nagpur, Maharashtra, India},
abstract={There has been exponential increase in the research of supply chain performance measurement system (SCPMS). Successful supply chains use measurement systems as a driver to achieve their organisational goals and process effectiveness. Various frameworks designed for SCPMS have been studied to study the research gaps and limitations and need arise for development of proactive and integrated PMS; also an approach is needed to focus the SCM with different perspective. Selecting appropriate supply chain performance measures is difficult due to complexity of these systems. The measures for SCPMS should be functions based. The objective of this paper is to suggest an integrated SCPMS for the production process activities, useful for internal supply chain management. The development of SCPMS is criteria dependent, hence proposed use of analytical hierarchy process (AHP) for deciding the ranking prioritising the criteria, and to integrate the improvement process Six Sigma metrics is used. The selection of performance measures/metrics group also kept unique and precisely related with actual production process. Metric groups are inventory scheduling, production operation scheduling and human resource scheduling. Copyright © 2014 Inderscience Enterprises Ltd.},
author_keywords={AHP;  Analytical hierarchy process;  Performance measurement system;  Performance measures;  SCM;  Six Sigma metrics;  Supply chain management},
references={Andersen, B., Fagerhaug, T., (2007) Performance Measurement of Logistic Processes, , http://www.prestasjonsledelse.net/publikasjoner/Performance%20measurement%20of0logistics%20processes.pdf, accessed 16 April 2007; Anvari, A., Hossein, H.S.M., Yusof, I., Mohd, Y.R., Norzima, Z., Majid, M., Performance measurement system through supply chain management to lean manufacturing (2011) WAS Jrnl., 14 (2), pp. 285-292; Bac, U., Erkan, T.E., A model to evaluate supply chain performance & flexibility (2011) African Journal of Business Management, 5 (11), pp. 4263-4271. , 4 June; Beamon, B.M., Measuring supply chain performance (1999) Int. Jrnl. of Operation and Production Management, 19 (3), pp. 275-292; Behrouzi, F., Wong, K.Y., An investigation and identification of lean supply chain performance measures in the automotive SMEs (2011) Scientific Research & Essays, 6 (24), pp. 5239-5252. , 23 October; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management (2007) Elsevier's International Journal of Computer & Industrial Engineering, 53, pp. 43-62; Browne, J., Devlin, J., Rolstadas, A., Andersen, B., (1997) Performance Measurement: The ENAPS Approach, , http://www.amazon.com/gp/reader/0915299992/ref=sib_dp_ptu/179-0349845-7321608#reader-link; Cai, J., Liu, X., Xiao, Z., Jin, L., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Elsevier Decision Support System, 46 (2), pp. 512-521; Chaowarut, W., Wanitwattanakosol, J., Sopadang, A., A framework for performance measurement of supply chains in frozen food industries (2009) Thailand VCML, 4 (1), pp. 98-108; Chen, H., Amodeo, L., Chu, F., Labadi, K., Modeling & performance evaluation of supply chains using batch deterministic & stochastic petri nets (2005) IEEE Transaction on Automation Science & Engineering, 2 (2), pp. 132-144. , April; Chen, S.-J., Huang, E., A systematic approach for supply chain improvement using design structure matrix (2007) Springer's Jrnl. Intellectual Manufacturing, 18 (2), pp. 285-299; Dalgedo, S., Caganova, D., Cambal, M., Supply chain performance measurement: Proposal of integral indicator with multi criteria approach for supporting decision making (2011) WASET 59; Damese, P., Romano, P., Supply chain integration & efficiency performance: A study on the interactions between customer and supplier integration (2011) Supply Chain Management: An International Journal, 16 (4), pp. 220-230; Daniels, R.C., Burns, N.D., A framework for proactive performance measurement system introduction (1997) International Journal of Operation & Production Management, 17 (1), pp. 100-116; Della, B., Ensslin, L., Ensslin, S.R., Supply chain performance evaluation: A case study in a company of equipment for refrigeration (2011) IEEE International Conference on Technology Management, pp. 969-978. , San Jose, CA, 27-30 June; Eyaa, S., Ntyagi, J.M., Supply chain performance of SMEs in Kampala (2010) Asian Journal of Business Management, 2 (4), pp. 82-88; Ghaderi, H., Darestani, S.A., Zulkiflle, L., Ismail, M.Y., Development of a soft network model to improve supply chain management performance of SMEs (2010) Academic Journal's Scientific Research & Essays, 5 (13), pp. 1759-1764. , 4 July; Gunasekaran, A., Williams James, H., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Elsevier Technovation, 25 (5), pp. 523-533; Gunasekaran, A., Kobu, B., Performance measures & metrics in logistics & SCM: A review of recent literature (1995-2004) for research & implication (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Intl. Jrnl. of Prod. Eco., 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in supply chain management (2001) Emerald's International Journal of Operation & Production Management, 21 (1-2), pp. 71-87; Hall, D.C., Saygin, C., Impact of information sharing on supply chain performance (2012) Springer's Int. Jrnl. of Adv. Mfg. Technol., 58 (1-4), pp. 397-409. , January; Hill, C.A., Scudder, G.D., Supply chain coordination using EDI with performance implications (2010) Int. J. of Manufacturing Technology and Management, 19 (1-2), pp. 6-26; Kamalabadi, N., Bayat, A., Ahmadi, P., Ebrahimi, A., Safari, K.M., Presentation a new algorithm for performance measurement of supply chain by using FMADM approach (2008) World Applied Science Journal, 5 (5), pp. 582-589; Koprolu, A., Supply chain management in textile industry: A supplier selection model with the AHP (2007) ISAHP, , Vina Del Mar, Chile, 3-6 August; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management & Social Sciences, 2 (4), pp. 19-34; Lin, L.-C., Li, T.-S., An integrated framework of supply chain performance measurement using Six Sigma metrics (2010) Springer's Software Quality Journal, 18 (3), pp. 387-406; Melnyk, S.A., Steward, D.M., Swink, M., Metrics and performance measurement in operation management dealing with metric maze (2004) Elsevier's Journal of Operation Management, 22 (3), pp. 209-217; Molnar, A., Gellynck, X., Kuhne, B., Conceptual framework for measuring supply chain performance: An innovative approach (2007) 1st International European Forum on Innovation & System Dynamics in Food Networks, pp. 261-272. , 15-17 February; Najmi, A., Makui, A., Providing hierarchical approach for measuring supply chain performance using AHP & DEMATEL methodologies (2010) International Journal of Industrial Engineering & Computation, 1 (2), pp. 199-212; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing & testing a process-based approach (2000) Int. Jrnl of Operation & Production Management, 20 (10), pp. 1119-1145; Olugu, E.O., Wong, K.Y., Shahaaoun, A.M., A comprehensive approach in assessing the performance of an automobile closed-loop supply chain (2010) Journal on Sustainability, 2 (4), pp. 871-899; Ragu-Nathan, S., Li, B., Ragu-Nathan, T.S., Subba Rao, S., The impact of supply chain management practices on competitive advantage and organizational performances (2006) Omega, 34 (2), pp. 107-124; Tangen, T., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Tanyas, M., Candan, C., Cesur, K., Supply chain management in textile & supplier performance evaluation (2010) 10the Global Conference on Business & Economics, , Rome, Italy, 15-16 October; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small & medium scale enterprises (2009) Emerald's Benchmarking: An International Journal, 16 (5), pp. 702-723; Thoo, A.-C., Huam, H.-T., Md Yusoff, R., Rasli, A., A proposed conceptual model of determinants of supply chain performance in the Malaysian electronics manufacturing services (EMS) industry (2010) International Journal of Business and Social Science, 1 (1), pp. 137-144; Thoo, A.C., Hamid, A.B.A., Huam, H.-T., Baharun, R., Md Yusoff, R., Rasli, A., The proposed conceptual model for investigating moderating effects of contextual factors on supply chain management practice-performance link in Malaysian small and medium enterprises (2011) Int. J. of Business and Management, 6 (12), pp. 135-145; Van Der Vorst, J.G.A.J., Performance measurement in agri food supply chain networks (2006) Springer's Quantifying the Agri Food Supply Chain, pp. 13-24. , Netherlands; Varma, S., Wadhwa, S., Deshmukh, S.G., Evaluating petroleum supply chain performance: Application of analytical hierarchy process to balanced scorecard (2008) Emerald's Asia Pacific Journal of Marketing & Logistics, 20 (3), pp. 343-356},
correspondence_address1={Charkha, P.G.; Department of Mechanical Engineering, CRPF Gate No. 3, Digdoh Hills, Hingna Road, India; email: pgcharkha@gmail.com},
publisher={Inderscience Publishers},
issn={17589401},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Supply Chain Model.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dewangan2014536,
author={Dewangan, V. and Godse, M.},
title={Towards a holistic enterprise innovation performance measurement system},
journal={Technovation},
year={2014},
volume={34},
number={9},
pages={536-545},
doi={10.1016/j.technovation.2014.04.002},
note={cited By 99},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904758605&doi=10.1016%2fj.technovation.2014.04.002&partnerID=40&md5=af29a00a845be1491aabd66933e81b54},
affiliation={Infosys Limited, Rajiv Gandhi InfoTech Park, Phase I Hinjawadi, Taluka Mulshi, Pune 411057, India},
abstract={This paper identifies the gaps in previously proposed enterprise innovation performance measurement schemes vis-à-vis todays enterprise needs and proffers a process based innovation performance measurement scheme, which improves upon existing schemes by significantly broadening the measurement scope. A number of industry surveys bear out the discontent of enterprises with existing innovation performance measurement schemes. In order to address pertinent enterprise needs, a literature review of innovation performance measurement schemes is presented, followed by a set of guiding principles for developing a more robust scheme that emphasizes the distinction between the measurement of invention and exploitation and can be used as a taxonomy for innovation key performance indicators. © 2013 Elsevier Ltd.},
author_keywords={Innovation management;  Innovation performance measurement;  Performance measurement systems;  Performance metrics},
keywords={Benchmarking, Guiding principles;  Innovation management;  Innovation performance;  Key performance indicators;  Literature reviews;  Performance measurement system;  Performance metrics;  Process-based innovations, Supply chain management},
references={Adams, R., Bessant, J., Phelps, R., Innovation management measurement: A review (2006) International Journal of Management Reviews, 8 (1), pp. 21-47. , DOI 10.1111/j.1468-2370.2006.00119.x; Andrew, J.P., Manget, J., Michael, D.C., Taylor, A., Zablit, H., Innovation 2010: A Return to Prominence - And the Emergence of a New World Order (2010) The Boston Consulting Group, pp. 1-29. , http://www.bcg.com/documents/file42620.pdf; Bange, C., Marr, B., Bange, A., Performance Management - Current Challenges and Future Directions (2009) Business Application Research Center, pp. 1-24. , http://www.ap-institute.com/downloads/ 090820%20Survey_Performance%20Management%20Processes_barc_api.pdf; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , DOI 10.1108/01443570010330739; Bourne, M., Franco, M., Wilkes, J., Corporate performance management (2003) Measuring Bus. Excellence, 7 (3), pp. 15-21; Branson, R., (1998) An Audience with Innovation: Innovation in Management, , Department of Trade and Industry London United Kingdom; Bremser, W.G., Barsky, N.P., Utilizing the balanced scorecard for R&D performance measurement (2004) R&D Manage., 34 (3), pp. 229-234; Brown, G.M., Svenson, R.A., Measuring R&D productivity (1988) Res. Technol. Manage., 31 (4), pp. 11-15; Bukh, P.N., Malmi, T., Re-examining the Cause-and-effect Principle of the Balanced Scorecard (2005) Aarhus School of Business, pp. 1-27. , http://www.pnbmap.com/files/pdf_artikler/BukhMalmi.pdf; Chan, V., Musso, C., Shankar, V., (2008) McKinsey Global Survey Results: Assessing Innovation Metrics, pp. 1-11. , https://www.mckinseyquarterly.com/ghost.aspx?ID=/ McKinsey_Global_Survey_Results_Assessing_innovation_metrics_2243, McKinsey & Company; Chiesa, V., Frattini, F., An exploratory study on R&D performance measurement practices - A survey of Italian R&D-intensive firms (2009) Int. J. Innovation Manage., 13 (1), pp. 65-104; Clarkson, M.B.E., A stakeholder framework for analyzing and evaluating corporate social performance (1995) Acad. Manage. Rev., 20 (1), pp. 92-117; Collins, J., Smith, D., Innovation Metrics: A Framework to Accelerate Growth (1999) Prism-Cambridge Massachusetts, 3, pp. 33-47; Cooper, R.G., (2001) Winning at New Products: Accelerating the Process from Idea to Launch, , third ed. Basic Books; Cooper, R.G., Kleinschmidt, E.J., Benchmarking the firm's critical success factors in new product development (1995) J. Prod. Innov. Manage., 12 (5), pp. 374-391; Cruz-Cázaresa, C., Bayona-Sáezb, C., García-Marcob, T., You can't manage right what you can't measure well: Technological innovation efficiency (2013) Res. Policy, 42, pp. 1239-1250; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA Publishing London; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) Int. J. Oper. Prod. Manage., 27 (8), pp. 784-801; Gama, N., Da Silva, M.M., Ataíde, J., Innovation scorecard: A balanced scorecard for measuring the value added by innovation (2007) Digital Enterprise Technology, pp. 417-424. , P.F. Cunha, P.G. Maropoulos, Springer US; Globerson Shlomo, Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Godener, A., Soderquist, K., Use and impact of performance measurement results in R&D and NPD: An exploratory study (2004) R&D Manage., 34 (2), pp. 191-219; Godin, B., (1996) The Rise of Innovation Surveys: Measuring A Fuzzy Concept (Working Paper), pp. 1-26. , http://www.csiic.ca/PDF/Godin_16.pdf, The Center for Innovation Studies Edmonton, Canada; Griffin, A., Page, A.L., PDMA success measurement project: Recommended measures for product development success and failure (1996) Journal of Product Innovation Management, 13 (6), pp. 478-496. , DOI 10.1016/S0737-6782(96)00052-5, PII S0737678296000525; Gurhan Gunday, G., Ulusoy, G., Kilic, K., Alpkan, L., Effects of innovation types on firm performance (2011) Int. J. Prod. Econ., 133, pp. 662-676; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Bus. Rev., 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Bus. Rev., 74 (1), pp. 75-85; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part i (2001) Acc. Horiz., pp. 87-104; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Manage. Acc., 70 (12), pp. 45-50; Kennerley, M., Neely, A., (2002) Performance Measurement Frameworks - A Review, pp. 1-8. , http://www.som.cranfield.ac.uk/som/cbp/pm2000%20paper%20-%20kennerley.pdf, Centre for Business Performance, Cranfield School of Management UK; Kerssens-van Drongelen, I.C., Cook, A., Design principles for the development of measurement systems for research and development processes (1997) R and D Management, 27 (4), pp. 345-357; Kirchhoff, B.A., Linton, J.D., Walsh, S.T., Neo-Marshellian Equilibrium versus Schumpeterian Creative Destruction: Its Impact on Business Research and Economic Policy (2013) J. Small Bus. Manage., 51 (2), pp. 159-166; Kuczmarski, T.D., Measuring your return on innovation (2000) Marketing Manage., 9 (1), pp. 25-32; Kueng, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Q. Manage., 11 (1), pp. 67-85; Lawson, B., Samson, D., Developments in managing innovation (2003) Knowledge and E-Business in Knowledge and Information Technology Management: Human and Social Perspectives, pp. 1-13. , A. Gunasekaran, A. Khalil, O. Rahman, S.M Editors Idea Group Inc; Lazzarotti, V., Manzini, R., Mari, L., A model for R&D performance measurement (2011) Int. J. Prod. Econ., 134, pp. 212-223; Lynch, R.L., Cross, K.F., (1991) Measure Up - The Essential Guide to Measuring Business Performance, , Mandarin London; Milbergs, E., Vonortas, N., (2005) Innovation Metrics: Measurement to Insight. White Paper for the National Innovation Initiative 21st Century Innovation Working Group, pp. 1-7. , http://www.innovationtools.com/pdf/Innovation-Metrics-NII.pdf; Muller, A., Valikangas, L., Merlyn, P., Metrics for innovation: Guidelines for developing a customized suite of innovation metrics (2005) Strategy and Leadership, 33 (1), pp. 37-45. , DOI 10.1108/10878570510572590; Neely, A., (1998) Performance Measurement: Why, What and How, pp. 5-6. , Economist Books London; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , Financial Times Prentice Hall; Ojanen, V., Vuola, O., Coping with the multiple dimensions of R&D performance analysis (2006) International Journal of Technology Management, 33 (2-3), pp. 279-290; Porter, M.E., (1998) Competitive Advantage: Creating and Sustaining Superior Performance, , Free Press; Ravichandran, T., Redefining organizational innovation: Towards theoretical advancements (1999) Journal of High Technology Management Research, 10 (2), pp. 243-274; Roberts, E.B., Managing invention and innovation (1998) Res. Technol. Manage., 31 (1), pp. 11-27; Sandt, J., Schaeffer, U., Weber, J., Balanced Performance Measurement Systems and Manager Satisfaction - Empirical Evidence from a German Study (2001) WHU-Otto Beisheim Graduate School of Management, pp. 1-17. , http://cosmic.rrz.uni-hamburg.de/webcat/hwwa/edok01/whu/FPCCM02.pdf; Suomala, P., The life cycle dimension of new product development performance measurement (2004) Int. J. Innov. Manage., 8 (2), pp. 193-221; Verhaeghe, A., Kfir, R., Managing innovation in a knowledge intensive technology organisation (2002) R&D Manage., 32 (5), pp. 409-417; Wong, A., The Metrics of Innovation (2001) Virtual Strategist, 1, pp. 1-5. , http://www.virtualstrategist.net/pdf/1-4-1.pdf},
publisher={Elsevier Ltd},
issn={01664972},
coden={TNVTD},
language={English},
abbrev_source_title={Technovation},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Schuh2014266,
author={Schuh, G. and Rudolf, S. and Vogels, T.},
title={Performance measurement of modular product platforms},
journal={Procedia CIRP},
year={2014},
volume={17},
pages={266-271},
doi={10.1016/j.procir.2014.02.028},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904499501&doi=10.1016%2fj.procir.2014.02.028&partnerID=40&md5=bea7cb46eef81347e033066f95c12d20},
affiliation={Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University, Steinbachstraße 19, 52074 Aachen, Germany},
abstract={It is a vital issue for producing companies in high wage countries to maximize economies of scale and economies of scope simultaneously. Many companies nowadays use a modular product platform approach to solve this dilemma. The preservation of the modular product platform over the lifecycle is one of the central success factors of this strategy. Therefore, a performance measurement system for modular product platforms is described in this paper. The approach is based on three steps to evaluate the performance of a modular product platform. In the first step the company-specific objectives for the modular product platform are defined and weighted. Afterwards relevant performance figures are identified and integrated into a balanced scorecard model, which is developed for the performance measurement of modular product platforms. At last an evaluation is done, which performance indicators can be obtained automatically by using data from different IT-systems. © 2014 Published by Elsevier B.V.},
author_keywords={Modular product platform;  Performance measurement;  Standardization},
keywords={Economics;  Manufacture;  Standardization;  Supply chain management, Balanced scorecards;  Economies of scale;  Economies of scope;  Modular products;  Performance indicators;  Performance measurement system;  Performance measurements;  Success factors, Life cycle},
references={Elmaraghy, W.H., Urbanic, R.J., Modelling of manufacturing systems complexity (2003) Annals of the CIRP, 52 (1), pp. 363-366; Arnoscht, S., Nubaum, B., Integrative asssessment and configuration of production systems (2011) Cirp Annals - Manufacturing Technology, 60, pp. 457-460; Elmaraghy, H., Schuh, G., Elmaraghy, W., Piller, F.D., Schönsleben, P., Tseng, M., Bernard, A., Product variety management (2013) Cirp Annals, 2, pp. 629-652; Martin, M.V., Design for variety (1999) A Methodology for Developing Product Platform Architectures, , Dissertation Stanford University; Schuh, G., Arnoscht, J., Rudolf, S., (2010) Integrated Development of Modular Product Platforms, PICMET 2010; Ericsson, A., Erixon, G., (1999) Controlling Design Variants - Modular Product Platforms, SME, pp. 18-42; Simpson, T.W., Maier, J.R.A., Mistree, F., (2001) Product Platform Design: Method and Application, , Springer; Reichmann, (2006) Controlling Mit Kennzahlen und Management-Tools (Controlling with Key Figures and Management Tools), p. 12; Gleich, R., Quitt, A., Görner, A., (2011) Performance Measurement - Konzepte, Fallstudien und Grundschema für Die Praxis (Performance Measurement - Concepts, Case Studies and Base Structure for Industrial Use), pp. 9-128. , Franz Vahlen, München; Junge, M., Controlling modularer Produktfamilien in der Automobilindustrie (Controlling of modular product families in the automotive sector) (2004) Gabler, pp. 95-97; Rennekamp, M., (2013) Methode Zur Bewertung des Komplexitätsgrades von Unternehmen (Methodology to Evaluate the Complexity of Companies), , Dissertation RWTH Aachen; Nußbaum, C.L., (2011) Modell Zur Bewertung des Wirkungsgrads von Produktkomplexität (Model to Evaluate the Efficiency of Product Complexity), , Dissertation RWTH Aachen; Kaplan, R.S., Norton, B.P., (1992) The Balanced Scorecard - Measures That Drive Performance, Harvard Business Review, pp. 71-96; Pfeifer, T., Schmitt, R., (2010) Qualitätsmanagement: Strategien, Methoden, Techniken (Quality Management, Strategies, Methods and Methods), pp. 12-16. , Hanser; Schuh, G., (2005) Produktkomplexität Managen (Managing Product Complexity), , Springer; Malik, F., (2000) Systemisches Management (Systematic Management), , 2nd ed., Haupt, Bern; Kaiser, A., (1995) Integriertes Variantenmanagement Mit Hilfe der Prozesskostenrechnung (Integrated Variant Management by Use of Complexity Costs), , Dissertation Universität St. Gallen; Wiendahl, H.-P., Scholtissek, P., Management and control of complexity in manufacturing (1994) Annals of the CIRP, 43 (2), pp. 533-540; Eigner, S., Product Lifecycle Management, pp. 43-44. , Springer; Blecker, T., Abdelkafi, N., Kaluza, B., Friedrich, G., Controlling varietyinduced complexity in mass customisation: A key metrics-based approach (2006) Int. J. Mass Customisation, 1 (2-3); Webb, G.S., (2011) Product Variety: An Investigation into Its Revenue, Cost, and Profit, , Michigan State University: United States - Michigan; Caesar, C., (1991) Kostenorientierte Gestaltungsmethodik für Variantenreiche Serienprodukte (Cost Oriented Methodology for Series Products with Many Variants), , Dissertation RWTH Aachen; Suh, E.S., De Weck, O.L., Flexible product platforms: Framework and case study (2007) Research in Engineering Design, 18 (2), pp. 67-89; Lee, L., Tang, C., Modelling the costs and benefits of delayed product differentiation (1997) Management Science, 43 (1), pp. 40-53; Seidenschwarz, W., (1993) Target Costing: Marktorientiertes Zielkostenmanagement (Target Costing: Market Oriented Cost Management), , München},
correspondence_address1={Vogels, T.; Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University, Steinbachstraße 19, 52074 Aachen, Germany; email: t.vogels@wzl.rwth-aachen.de},
publisher={Elsevier B.V.},
issn={22128271},
language={English},
abbrev_source_title={Procedia CIRP},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kusrini20141,
author={Kusrini, E. and Subagyo and Masruroh, N.A.},
title={Good criteria for supply chain performance measurement},
journal={International Journal of Engineering Business Management},
year={2014},
volume={6},
number={1},
pages={1-7},
doi={10.5772/58435},
note={cited By 19},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903435840&doi=10.5772%2f58435&partnerID=40&md5=9610d20da84543c3b665ad6e194ba7f7},
affiliation={Department of Mechanical and Industrial Engineering, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia; Department of Industrial Engineering, Faculty of Engineering, Indonesian Islamic University, Yogyakarta, Indonesia},
abstract={This paper discusses good criteria for a supply chain performance measurement model and their level of importance. The criteria were obtained from results of previous research findings and recommendations from several researchers. The criteria are divided into two categories, namely efficient and effective. To examine which criteria can be used to assess a supply chain performance measurement model, a survey was conducted by distributing questionnaires to supply chain actors from different industry fields (both manufacturing and services) in Indonesia using random sampling techniques. Data analysis using pairwise comparisons with an analytical Hierarchy Process model showed that efficient criteria were more important than effective criteria. Efficient criteria consisted of three sub-criteria: the amount of data is not too much and low cost and output is presented in a simple form. Effective criteria consisted of the criteria in the order of importance as follows: clarity, validity, comprehensiveness, responsiveness and dynamics. Further research is needed for developing a model that meets these criteria. © 2014 The Author(s).},
author_keywords={Good criteria;  Measurement;  Pairwise comparison;  Performance;  Supply chain management},
references={Agami, N., Saleh, M., Rasmy, M., Supply Chain Performance Measurement Approaches: Review and Classification (2012) IBIMA Publishing Journal of Organizational Management Studies, 2012, pp. 1-20; Blekinsop, S., Davis, L., The Road to Continuous Improvement (1991) Insight, 4 (3), pp. 23-26; Chan, F.T.S., Performance Measurement in a Supply Chain (2003) International Journal of Advance Manufacturing Technology, 21, pp. 534-548; Crawford, K.M., Cox, J.F., Designing Performance Measurement Systems for Just In Time Operations (1990) International Journal of Production Research, 28 (11), pp. 2025-2036; Cuthbertson, R., Piotrowicz, W., Performance Measurement Systems in Supply Chains: A Framework for Contextual Analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Dixon, J.R., Nanni, A.J., Vollman, T.E., The New Performance Challenge Measuring Operations for World-class Competition (1990), Irwin, Homewood, IL; Fry, T.D., Cox, J.F., Manufacturing Performance: Local Versus Global Measures (1989) Production and Inventory Management Journal, 30 (2), pp. 52-56; Kaplan, R., Norton, D., Using the Balanced Scorecard as a Strategic Management System (1996) Harvard Business Review, 74 (1), pp. 75-85; Kaplan, R., Norton, D., Measuring the Strategic Readiness of Intangible Assets (2004) Harvard Business Review, 82 (1), pp. 52-63; Luthra, S., Garg, D., Haleem, A., Identifying and ranking of strategies to implement green supply chain management in Indian manufacturing industry using Analytical Hierarchy Process (2013) Journal of Industrial Engineering and Management, 6 (4), pp. 930-962; Maheshwarkar, M., Sohani, N., Combined AHP-TOPSIS Based Approach for the Evaluation of Knowledge Sharing Capabilities of Supply Chain Partners (2013) Management Science and Engineering, 7 (3), pp. 27-32. , 2013; Mann, I.J.S., Murphy, S.A., Kumar, V., Unit of Analysis: A Case for Performance Measurement in Supply Chain Management (2009) Journal of Supply Chain Management, 6 (3), pp. 41-56; Maskel, B., Performance Measures of World Class Manufacturing (1989) Management Accounting, pp. 32-33. , May; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (1995) International Journal of Operation and Production Management, 15, pp. 80-116; Neely, A., Mills, J., Gregory, M., Richards, H., Platts, K., Bourne, M., Getting the Measure of Your Business (1996), Findlay Publications, Horton Kirby; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: a structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1153; Neely, A., Bourne, M., Kennerley, M., Performance Measurement System Design: Developing and testing a process based approach (2000) International Journal of Operation and Production Management, 20 (10), pp. 1119-1145; Neely, A., Adams, C., Crowe, P., The Performance Prism in Practice (2001) Measuring Business Excellence, 5 (2), pp. 6-11; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design: A Literature Review and Research Agenda (2005) International Journal of Operation and Production Management, 25, pp. 1228-1263; Rolstands, A., Performance Measurement: A Business Process Benchmarking Approach (1995), Chapman and Hall, New York, USA; Sardana, G.D., Exploring the Performance of a Responsive Supply Chain (2009) International Journal of Supply Chain Forum, 10 (2), pp. 38-51; Saaty, T.L., The Analytic Hierarchy Process (1980), McGraw-Hill International, New York, NY, U.S.A},
correspondence_address1={Kusrini, E.; Department of Mechanical and Industrial Engineering, Faculty of Engineering, Gadjah Mada University, Yogyakarta, Indonesia; email: elisa_kusrini@yahoo.com},
publisher={InTech Europe},
issn={18479790},
language={English},
abbrev_source_title={Intern. J. Eng. Bus. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Choong20144174,
author={Choong, K.K.},
title={Has this large number of performance measurement publications contributed to its better understanding? A systematic review for research and applications},
journal={International Journal of Production Research},
year={2014},
volume={52},
number={14},
pages={4174-4197},
doi={10.1080/00207543.2013.866285},
note={cited By 55},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902839316&doi=10.1080%2f00207543.2013.866285&partnerID=40&md5=01f796821b9bc980b6c2faa517bf8a21},
affiliation={Department of Accounting and Information Management, University of Macau, Taipa, Macau},
abstract={This paper uses a systematic review to answer two research questions: (1) has this large number of performance measurement systems (PMS) publications contributed to a better understanding of performance measurement? and (2) what would a better understanding of PMS lead us to? This paper found that the field has not evolved substantially for the past 30 years. While various PMS are discussed and proposed, there are many instances of a common ground on what were discussed but in a few instances, there is little to no agreement concerning the activities and characteristics used to describe them, and there is also a great deal of overlap in the meaning of definitions and terms. Nevertheless, the synthesis review revealed that many of the discussions of the fundamental characteristics useful and in certain cases, interesting and insightful. Research gaps were identified and narrowed by synthesising instances of a common ground, and if a common ground is not forthcoming, references to non-PMS articles were made in order to ascertain the 'true' meaning of terms, purposes and applications of PMS. This improves our understanding of PMS, and thus we can use the result of the review to conceptualise and present coherent arguments in the pursuance of a good PMS. This research has important implications for both practitioners and academics in the area of performance measurement within an organisation, and shall provide a basis on which ongoing and future research efforts on this field can be built upon. © 2012 Taylor & Francis.},
author_keywords={Organisation;  Performance measurement;  Performance measurement system},
keywords={Supply chain management, Fundamental characteristics;  Organisation;  Performance measurement system;  Performance measurements;  Research and application;  Research efforts;  Research questions;  Systematic Review, Research},
references={Atkinson, A.A., Strategic performance measurement and incentive compensation (1998) European Management Journal, 16 (5), pp. 552-561; Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A stakeholder approach to strategic performance measurement (1997) Sloan Management Review, 38 (3), pp. 25-37; Beach, M., Price, E., Gary, T.L., Robinson, K., Gozu, A., Palacio, A., Smarth, C., Cultural competence (2005) Medical Care, 43 (4), pp. 356-373; Behn, R., Why measure performance? Different purposes require different measures (2003) Public Administration Review, 63 (5), pp. 586-606; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Bohrnstedt, G.W., (2010) An Overview of Measurement in the Social Sciences, , Washington, DC: American Institutes for Research; Bourne, M.C., Performance measurement: Learning from the past and projecting the future (2008) Measuring Business Excellence, 12 (4), pp. 67-72; Bourne, M.C., Bourne, P., (2011) Handbook of Corporate Performance Management, , Chichester: Wiley; Bourne, M.C., Mills, J.F., Wilcox, M., Neely, A.D., Platts, K.W., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bourne, M.C., Neely, A.D., Mills, J.F., Platts, K.W., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Cartwright, N., Bradburn, N., (2010) A Theory of Measurement, Working Paper, , London School of Economics/UK and University of Chicago, US respectively; Caws, P., Definitions and measurement in physics (1959) Measurement, Definitions, and Thesis, pp. 32-34. , edited by Charles West Churchman and Philburn Ratoosh, New York, NY:John Wiley and Sons; Chandler, A.D., (1977) The Visible Hand: Managerial Revolution in American Business, , Boston, MA: Harvard University Press; Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., Morton, S., Shekelle, P., Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care (2006) Annals of Internal Medicine, 144 (10), pp. 742-773; Choong, K.K., Are PMS meeting the measurement needs of BPM? A literature review (2013) Business Process Management Journal, 19 (3), pp. 535-574; Choong, K.K., Understanding the features of performance measurement system: A literature review (2013) Measuring Business Excellence, 17 (4), pp. 102-121; Cooper, H., (1989) Synthesizing Research - A Guide for Literature Reviews, , 3rd ed. Thousand Oaks, CA: Sage; Eccles, R.G., Pyburn, P.J., Creating a comprehensive system to measure performance (1992) Management Accounting (USA), 74 (4), pp. 41-45; Fenton, N.E., Pfleeger, S.L., (1997) Software Metrics-A Rigorous and Practical Approach. Pacific Grove, , CA: PWS Publishing; Fitz-Gibbon, C., (1990) Performance Indicators, , Clevedon: Bera Dialogues; Forza, C., Salvador, F., Assessing some distinctive dimensions of performance feedback information in high performing plants (2000) International Journal of Operations & Production Management, 20 (3), pp. 359-385; Franco-Santos, M., Bourne, M., An examination of the literature relating to issues affecting how companies manage through measures (2005) Production Planning & Control, 16 (2), pp. 114-124; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Gagliardi, J.-P., Renaud, J., Ruiz, A., Models for automated storage and retrieval systems: A literature review (2012) International Journal of Production Research, 50 (24), pp. 7110-7125; Gates, S., (1999) Aligning Strategic Performance Measures and Results, , New York: The Conference Board; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Gill, J., Johnson, P., (2002) Research Methods for Managers, , London: Paul Chapman Publishers; Glass, G., Primary, secondary, and meta-analysis of research (1976) Educational Researcher, 5 (10), pp. 3-8; González, L.S., Rubio, F.G., González, F.R., Piattini Velthuis, M., Measurement in business processes: A systematic review (2010) Business Process Management Journal, 16 (1), pp. 114-134; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Harbour, J.L., (1997) The Basics of Performance Measurement, , 2nd ed. Broken Sound Parkway, NW: CRC Press; Hemingway, P., Brereton, N., What is a systematic review? (2009) Evidence-Based Medicine, , www.whatisseries.co.uk; Hubbard, D., (2009) The Failure of Risk Management: Why It's Broken and How to Fix It, , New Jersey, NJ: John Wiley & Sons; Ittner, C., Larcker, D., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting, Organizations and Society, 28 (7-8), pp. 715-741; Jiang, B., Frazier, G.V., Heiser, D., China-related pom research: A literature review and suggestions for future research (2007) International Journal of Operations and Production Management, 27 (7), pp. 662-684; Johnson, H.T., Towards an understanding of nineteenth century cost accounting (1981) The Accounting Review, 56 (3), pp. 510-518; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost - The Rise and Fall of Management Accounting, , Boston, MA: Harvard Business School Press; Julnes, P., (2007) Performance Measurement. Encyclopedia of Public Administration and Public Policy, , Broken Sound Parkway, NW: CRC Press; Kanji, G.K., Performance measurement system (2002) Total Quality Management, 13 (5), pp. 715-728; Kanji, G.K., Performance measurement: A system approach for excellence (2007) 51st European Organization for Quality Congress, Prague, Czechoslovakia, pp. 22-23. , May; Kaplan, R.S., Norton, P.N., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Karliner, L.S., Jacobs, E.A., Chen, A.H., Mutha, S., Do professional interpreters improve clinical care for patients with limited english proficiency? A systematic review of the literature (2007) Health Services Research, 42 (2), pp. 727-754; Kerssens-Van Drongelen, I.C., Fisscher, O.A.M., Ethical dilemmas in performance measurement (2003) Journal of Business Ethics, 45 (1-2), pp. 51-63; Kitchenham, B., Stuart, C., (2007) Guidelines for Performing Systematic Literature Reviews in Software Engineering, , Technical Report. Keele: Keele University and University of Durham; Kueng, P., Andres, M., Wettstein, T., Performance measurement systems must be engineered (2001) Communications of the Association for Information Systems, 7 (3), pp. 1-27; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Leseure, M., Bauer, J., Birdi, K., Neely, A., Denyer, D., Adoption of promising practices: A systematic review of the evidence (2004) International Journal of Management Reviews, 5-6 (3-4), pp. 169-190; Levey, A.S., Measurement of renal function in chronic renal disease (1990) Kidney International, 38 (1), pp. 167-184; Locatelli, F., Covic, A., Macdougall, I., Wiecek, A., ORAMA: A study to investigate ebpg impact on renal anaemia - Design and baseline data, european best practice guidelines for peritoneal dialysis (2005) Nephrology Dialysis Transplantation, 20 (SUPPL. 9), pp. 3-24; Luce, R.D., Krantz, D.M., Suppes, P., Tversk, A., (1971) Foundations of Measurement (Vol. 1): Additive and Polynomial Representations, , New York, NY: Academic Press; Lynch, R.L., Cross, K.F., (1991) Measure Up - The Essential Guide to Measuring Business Performance, , London: Mandarin; Maisel, L.S., (2001) Performance Measurement Practices Survey Results, , New Jersey, NJ: AICPA; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Marshall, M., Wray, L., Epstein, P., Grifel, S., 21st century community focus: Better results by linking citizens, government, and performance measurement (1999) Public Management, 81 (10), pp. 12-18; Maskell, B.H., (1991) Performance Measurement for World-Class Manufacturing, , Cambridge, MA: Productivity Press; Matthews, R.L., Marzec, P.E., Social capital, a theory for operations management: A systematic review of the evidence (2012) International Journal of Production Research, 50 (24), pp. 7081-7099; McGee, J.V., (1992) What Is Strategic Performance Measurement?, , Boston, MA: Ernst and Young Center for Business Innovation; (2012), http://www.merriam-webster.com/, Merriam-Webster Online English dictionary. 2012. Accessed January 7; Nadzam, D., Nelson, M., The benefits of continuous performance measurement (1997) Nursing Clinics of North America, 32 (3), pp. 543-559; Narens, L., Luce, R., Measurement: The theory of numerical assignments (1986) Psychological Bulletin, 99 (2), pp. 166-180; Neely, A.D., (1998) Measuring Business Performance: Why, What and How, , London: The Economist and Profile Books Ltd; Neely, A.D., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Neely, A.D., (2002) Business Performance Measurement, , Cambridge: Cambridge University Press; Neely, A.D., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A.D., Adams, C., Kennerley, M.P., (2002) The Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationship, , London: Prentice Hall; Neely, A.D., Gregory, M.J., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Neely, A.D., Mills, J.F., Platts, K., Richards, H., Gregory, M.J., Bourne, M.C., Kennerley, M.P., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Okoli, C., Schabram, K., A guide to conducting a systematic literature review of information systems research (2010) Sprouts: Working Papers on Information Systems, 10 (26). , http://sprouts.aisnet.org/10-26; Otley, D.T., Performance management: A framework for management control systems research (1999) Management Accounting Research, 10 (4), pp. 363-382; http://oxforddictionaries.com/?attempted=true, Oxford Dictionaries Online. 2012. Accessed October 7, 2010; Petersen, J.A., McAlister, L., Reibstein, D.J., Winer, R.S., Kumar, V., Atkinson, G., Atkinson, G., Choosing the right metrics to maximize profitability and shareholder value (2009) Journal of Retailing, 85 (1), pp. 95-111; Pfleeger, S., Atlee, J., (2010) Software Engineering, Theory and Practice, , 4th ed. Upper Saddle River, NJ: Prentice Hall; Pino, F.J., Garcia, F., Piattini, M., Software process improvement in small and medium software enterprises: A systematic review (2008) Software Quality Journal, 16 (2), pp. 337-340; Rogers, S., (1990) Performance Management in Local Government, , London: Longman; Rosenthal, R., Dimatteo, M.R., Meta-analysis: Recent developments in quantitative methods for literature reviews (2001) Annual Review of Psychology, 52 (1), pp. 59-82; Scudder, G.D., Hill, C.A., A review and classification of empirical research in operations management (1998) Journal of Operations Management, 16 (1), pp. 91-101; Serrat, O., (2010) The Perils of PM Knowledge Solutions, , Manila: Asian Development Bank, May; Sharma, M., Bhagwat, R., Dangayach, G., Practice of performance measurement: Experience from indian smes (2005) International Journal of Globalisation and Small Business, 1 (2), pp. 183-213; Smith, P.C., Goddard, M., Performance management and operational research: A marriage made in heaven? (2002) Journal of the Operational Research Society, 53 (3), pp. 247-255; Sole, F., Schiuma, G., Using performance measures in public organisations: Challenges of italian public administrations (2010) Measuring Business Excellence, 14 (3), pp. 70-84; (2011) Encyclopedia of Philosophy Necessary and Sufficient Conditions, , http://plato.stanford.edu/entries/necessary-sufficient/, Stanford University Accessed March, 12; Stevens, S.S., On the theory of scales of measurement (1946) Science, 103 (2684), pp. 677-680; Stevens, S.S., Mathematics, measurement and psychophysics (1951) Handbook of Experimental Psychology, pp. 1-49. , edited by S. S.Stevens, New York, NY: John Wiley; Tangen, S., (2002) A Theoretical Foundation for Productivity Measurement and Improvement of Automatic Assembly Systems, , Licentiate thesis, The Royal Institute of Technology, Stockholm; Tangen, S., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Production Management, 53 (8), pp. 726-737; Taticchi, P., (2010) Measurement and Management: New Contexts, Themes and Challenges, , New York: Springer-Verlag; Paolo, T., Balachandran, K., Tonelli, F., Performance measurement and management systems: State of the art, guidelines for design and challenges (2012) Measuring Business Excellence, 16 (2), pp. 1-19; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Thürer, M., Stevenson, M., Silva, C., Three decades of workload control research: A systematic review of the literature (2011) International Journal of Production Research, 49 (23), pp. 6905-6935; De Toni, A., Tonchia, S., Performance measurement systems - Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-71; Trochim, W., An introduction to concept mapping for planning and evaluation (1989) Evaluation and Program Planning, 12 (1), pp. 1-16; Trochim, W., Concept mapping (1989) Evaluation and Program Planning, 12 (1), pp. 87-110; Trochim, W., (2006) The Research Methods Knowledge Base, , http://www.socialresearchmethods.net/kb/scallik.php, Accessed April 30, 2009; Tseng, F.-M., Chiu, Y.-J., Chen, J.-S., Measuring business performance in the high-tech manufacturing industry: A case study of taiwan's large-sized tft-lcd panel companies (2009) Omega, 37 (3), pp. 686-697; Tung, A., Baird, K., Schoch, H., Factors influencing the effectiveness of performance measurement systems (2011) International Journal of Operations & Production Management, 31 (12), pp. 1287-1310; (2000) Good Practice in Performance Reporting in Executive Agencies and Non-Departmental Public Bodies, , UK Comptroller and Auditor General London: HM Treasury, HC 272; (2012) Performance-Based Management Special Interest Group (PBM SIG), Performance-Based Management Handbook of Techniques and Tools - How to Measure Performance (Revised), , http://www.orau.gov/pbm, US DOE (US Department of Energy); (2012) Performance Measurement and Evaluation: Definitions and Relationships (Revised), , http://www.gao.gov/special.pubs/gg98026.pdf, US GAO (US General Accounting Office) GAO-11-646SP; (2012) Baldrige Performance Excellence Program, , http://www.nist.gov/index.html, US National Institute of Standards and Technology Washington, DC: US Department of Commerce; Von Bertalanffy, L., (1968) General System Theory: Foundations, Development, Applications, , New York, NY: George Braziller; Wang, J., Li, J., Arinez, J., Biller, S., Indicators for quality improvability and bottleneck sequence in flexible manufacturing systems with batch production (2012) International Journal of Production Research, 50 (22), pp. 6388-6402; White, L., The use of performance measures and their outcomes (2008) Journal of American Academy of Business, 13 (1), pp. 133-137},
correspondence_address1={Choong, K.K.; Department of Accounting and Information Management, University of Macau, Taipa, Macau; email: keongchoong@umac.mo},
publisher={Taylor and Francis Ltd.},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Review},
source={Scopus},
}

@ARTICLE{Arif-Uz-Zaman2014588,
author={Arif-Uz-Zaman, K. and Ahsan, A.M.M.N.},
title={Lean supply chain performance measurement},
journal={International Journal of Productivity and Performance Management},
year={2014},
volume={63},
number={5},
pages={588-612},
doi={10.1108/IJPPM-05-2013-0092},
note={cited By 92},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902469167&doi=10.1108%2fIJPPM-05-2013-0092&partnerID=40&md5=1d0f5411101307c81da548bc51a3c2b0},
affiliation={Department of Industrial Engineering and Management, Khulna University of Engineering and Technology, Khulna, Bangladesh},
abstract={Purpose: The purpose of this paper is to present supply chain metrics and to propose a fuzzy-based performance evaluation method for lean supply chain. Design/methodology/approach: To understand the overall performance of cost competitive supply chain the paper investigates the alignment of market strategy and position of the supply chain. Since lean is applicable in many supply chains, the authors propose a set of metrics to evaluate supply chain performance. Moreover, the paper uses a fuzzy model to evaluate the performance of cost competitive supply chains. Fuzzy is an appropriate model method when uncertainty is present. It also allows modelling of a significant number of performance metrics across multiple supply chain elements and processes. Competitive strategy can be achieved by using a different weight calculation for different supply chain situations. Findings: Research provides optimal metrics for lean supply chains. The proposed method can measure the performance of lean supply chains using a fuzzy approach and competitive strategies. Research limitations/implications: The metrics which have been selected to measure the performance of lean supply chains is particularly applicable for high volume, low-price products. Practical implications: By identifying optimal performance metrics and applying performance evaluation methods, managers can predict the overall supply chain performance under lean strategy. By identifying performance for each metric they can also categorize the existing performance and optimise them accordingly. Originality/value: This study provides a performance evaluation method for supply chain managers to assess the effects of lean tools and competitive strategies. © Emerald Group Publishing Limited.},
author_keywords={Fuzzy-based evaluation method;  Lean supply chain;  Supply chain metrics},
references={Abdulmaleka, F.A., Rajgopal, J., Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study (2007) International Journal of Production Economics, 107 (1), pp. 223-236; Adel, E.-B., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38 (6), pp. 6681-6688; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Amid, A., Ghodsypour, S.H., O'brien, C., A weighted max-min model for fuzzy multi-objective supplier selection in a supply chain (2011) International Journal of Production Economics, 131 (1), pp. 139-145; Arshinder, K., Arun, K., Deshmukh, S.G., Supply chain coordination: Perspectives, empirical studies and research directions (2008) International Journal of Production Economics, 115 (2), pp. 316-335; Awasthi, A., Chauhan, S., Goyal, S.K., A fuzzy multi criteria approach for evaluating environmental performance of suppliers (2010) International Journal of Production Economics, 126 (2), pp. 370-378; Bayou, M.E., Measuring the leanness of manufacturing systems - a case study of ford motor company and general motors (2008) Journal of Engineering and Technology Management, 25 (4), pp. 287-304. , de Korvin, A; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Behrouzi, F., Wong, K.Y., Lean performance evaluation of manufacturing systems: A dynamic and innovative approach (2011) Procedia Computer Science, 3, pp. 388-395; Behrouzi, F., Wong, K.Y., Kuah, C.T., A fuzzy-based model to measure supplier performance with lean attributes (2010) Fourth Asia International Conference on Mathematical/Analytical Modelling and Computer Simulation, pp. 372-377; Chen, C.T., Lin, C.T., Huang, S.F., A fuzzy approach for supplier evaluation and selection in supply chain management (2006) International Journal of Production Economics, 102 (2), pp. 289-301; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains - a framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Díaz, M.S., Gil, M.J.A., Machuca, J.A.D., Performance measurement systems, competitive priorities, and advanced manufacturing technology (2005) International Journal of Operations & Production Management, 25 (8), pp. 781-799; Florent, T.M., Zhen, H., Study on the supplier evaluation index system of lean supply chain (2010), 2010 International Conference on e-Education, e-Business, e-Management and e-Learning, Sanya; Fullerton, R.R., Wempe, W.F., Lean manufacturing, non-financial performance measures, and financial performance (2009) International Journal of Operations and Production Management, 29 (3), pp. 214-240; Ganga, G.M.D., Carpinetti, L.C.R., A fuzzy logic approach to supply chain performance management (2011) International Journal of Production Economics, 134 (1), pp. 177-187; Gomes, C.F., Yasin, M.M., Lisboa, J.V., Performance measurement practices in manufacturing firms revisited (2011) International Journal of Operations and Production Management, 31 (1), pp. 5-30; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGayghey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hanson, J.D., Melnyk, S.A., Calantonr, R.A., Defining and measuring alignment in performance management (2011) International Journal of Operations & Production Management, 31 (10), pp. 1089-1114; Ho, W., Xu, X., Dey, P.K., Multi-criteria decision making approaches for supplier evaluation and selection: A literature review (2010) European Journal of Operational Research, 202, pp. 16-24; Jagdev, H.S., Browne, J., The extended enterprise - a context for manufacturing (1998) Production Planning & Control, 9 (3), pp. 216-229; Kuhlang, P., Edtmayr, T., Sihn, W., Methodical approach to increase productivity and reduce lead time in assembly and production-logistic processes (2011) CIRP Journal of Manufacturing Science and Technology, 4 (1), pp. 24-32; Kwong, C.K., Ip, W.H., Chan, J.W.K., Combining scoring method and fuzzy expert systems approach to supplier assessment: A case study (2002) Integrated Manufacturing Systems, 13 (7), pp. 512-519; Lamming, R., Squaring lean supply with supply chain management (1996) International Journal of Operations & Production Management, 16 (2), pp. 183-196; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Mamdani, E.H., Applications of fuzzy algorithms for control of simple dynamic plant (1974) IEEE Proceedings, 121 (12), pp. 1585-1588; Morgan, C., Supply network performance measurement: Future challenges? (2007) The International Journal of Logistic Management, 18 (2), pp. 255-273; Naim, M., Gosling, J., On leanness, agility and leagile supply chains (2011) Int. J. Production Economics, 131 (1), pp. 342-354; Najmi, A., Makui, A., Providing hierarchical approach for measuring supply chain performance using AHP and DEMATEL methodologies (2010) International Journal of Industrial Engineering Computations, 1 (2), pp. 199-212; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Persson, F., Olhager, J., Performance simulation of supply chain design (2002) International Journal of Production Economics, 77 (3), pp. 231-245; Ramasamy, K., A comparative analysis of management accounting systems on lean implementation (2005), MSc dissertation, The University of Tennessee, Knoxville, TN; Rother, M., Shook, J., (1999) Learning to See - Value Stream Mapping to Create Value and Eliminate Muda Version 1.2, , www.lean.org, available at, The Lean Enterprise Institute, Brookline, MA; Sahay, B.S., Gupta, J.N.D., Mohan, R., Managing supply chains for competitiveness (2006) Supply Chain Management: An International Journal, 11 (1), pp. 15-24; Sarkar, A., Mohapatra, P.K.J., Evaluation of supplier capability and performance: A method for supply base reduction (2006) Journal of Purchasing & Supply Management, 12 (3), pp. 148-163; Schonberger, R.J., Japanese production management: An evolution with mixed success (2007) Journal of Operations Management, 25 (2), pp. 403-419; Shah, R., Ward, P.T., Lean manufacturing: Context, practice bundles, and performance (2003) Journal of Operations Management, 21 (2), pp. 129-149; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Soni, G., Kodali, R., The strategic fit between 'competitive strategy' and 'supply chain strategy' in Indian manufacturing industry: An empirical approach (2011) Measuring Business Excellence, 15 (2), pp. 70-89; Wan, H.-D., Measuring leanness of manufacturing systems and identifying leanness target by considering agility (2006), pp. 1-186. , Doctor of Philosophy thesis, Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VI, pp; Wang, S.Y., Chang, S.L., Wang, R.C., Assessment of supplier performance based on product-development strategy by applying multi-granularity linguistic term sets (2009) Omega, 37 (1), pp. 215-226; Wang, X., Chan, H.K., Yee, R.W.Y., Rainey, I.D., A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain (2011) International Journal of Production Economics, 135 (2), pp. 595-606; Womack, J.P., Jones, D.T., Ross, D., (1991) The Machine that Changed the World, , A Division of Harper Collins Publishers, New York, NY; Wong, B.K., Lai, V.S., A survey of the application of fuzzy set theory in production and operations management: 1998-2009 (2011) International Journal of Production Economics, 129 (1), pp. 157-168; Zadeh, L.A., Fuzzy set (1965) Information and Control, 8 (3), pp. 338-353; Zarei, M., Fakhrzad, M.B., Paghaleh, M.J., Food supply chain leanness using a developed QFD model (2011) Journal of Food Engineering, 102, pp. 25-33; Zimmermann, H.J., (1991) Fuzzy Set Theory and Its Applications, , 2nd ed., Kluwer Academic Publishers, London; Eroglu, C., Hofer, C., Lean, leaner, too lean? The inventory-performance link revisited (2011) Journal of Operations Management, 29 (4), pp. 356-369; Kojima, S., Kaplinsky, R., The use of a lean production index in explaining the transition to global competitiveness: The auto components sector in South Africa (2004) Technovation, 24 (3), pp. 199-206; Ondersteijn, C., Kooten, O.V., Aramyan, L., Lansink, A.O., Performance indicators in agri-food production chains (2006) Quantifying the Agri-Food Supply Chain, pp. 47-64},
correspondence_address1={Arif-Uz-Zaman, K.; Department of Industrial Engineering and Management, Khulna University of Engineering and Technology, Khulna, Bangladesh; email: zaman735@yahoo.com},
publisher={Emerald Group Publishing Ltd.},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Alomar201446,
author={Alomar, M. and Pasek, Z.J.},
title={A supply chain strategy management model for small and medium sized enterprises},
journal={ICORES 2014 - Proceedings of the 3rd International Conference on Operations Research and Enterprise Systems},
year={2014},
pages={46-56},
doi={10.5220/0004813700460056},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902335893&doi=10.5220%2f0004813700460056&partnerID=40&md5=d6839012ff7cfe63ee6d89449867f765},
affiliation={Industrial and Manufacturing Systems Engineering, University of Windsor, Windsor, Canada},
abstract={This paper proposes a model that will assist companies, particularly the small and medium-sized enterprises, assess their performance by prioritizing performance measures and selecting an adequate operations strategy under various market scenarios. The outlined model utilizes and integrates the Supply Chain Operations Reference framework and the Analytical Hierarchy Process approach to construct, link, and assess a four level hierarchal structure. The model also helps small and medium-sized enterprises put more emphasis on supply chain operations and management. The use and benefits of the proposed model are illustrated on a case of a family owned, medium-sized manufacturing company. Copyright © 2014 SCITEPRESS.},
author_keywords={AHP;  Dynamic strategy;  Performance measurement system;  SCOR model;  Small and medium-sized enterprises;  Supply chain strategy},
keywords={Enterprise resource planning;  Operations research;  Supply chains, Dynamic strategies;  Performance measurement system;  SCOR models;  Small and medium sized enterprise;  Supply chain strategy, Supply chain management},
references={Arend, R., Wisner, J., Small business and supply chain management: Is there a fit? (2005) Journal of Business Venture, 20, pp. 403-436; Bititci, U., Modeling of performance measurement systems in manufacturing enterprises (1995) Journal of Production Economics, 42 (2), pp. 137-147; Branch, S., (2012) Key Small Business Statistics - SME Research and Statistics, , http://ic.gc.ca/eic/site/061.nsf/eng/h_02689.html, [online] Accessed: 25 Sep 2013; Chen, K., Huang, M., Chang, P., Performance evaluation on manufacturing times (2006) The International Journal of Advanced Manufacturing Technology, 31 (3-4), pp. 335-341; Cheng, E., Li, H., Analytic hierarchy process: An approach to determine measures for business performance (2001) Measuring Business Excellence, 5 (3), pp. 30-37; Cheng, E., Li, H., Ho, D., Analytic hierarchy process: A defective tool when used improperly (2002) Measuring Business Excellence, 6 (4), pp. 33-37; Chennell, A., Dransfield, S., Field, J., Fisher, N., Saunders, I., Shaw, D., OPM: A system for organizational performance measurement (2005) Performance Measurement in Past, pp. 96-103. , paper presented at, Present and Future, Cranfield: Cranfield University, Cranfield; Chopra, S., Meindl, P., (2004) Supply Chain Management, , Upper Saddle River, NJ: Pearson Prentice-Hall; Cocca, A., Alberti, M., SME's three step pyramid: A new performance measurement framework for SMEs (2009) 16th International Annual EUROMA Conference: Implementation-realizing Operations Management Knowledge, , paper presented at, Göteborg; Dixon, J., Nanni, A., Vollmann, T., The new performance challenge: Measuring operations for worldclass competition (1990) Business One, Homewood, , paper presented at, IL, Dow Jones-Irwin; (2013) Collaboration and Decision Support Software for Groups & Organizations, , http://expertchoice.com, [online] Accessed: 7 Oct 2013; Fisher, M., What is the right supply chain for your product? (1997) Harvard Business Review, 75, pp. 105-117; Garengo, P., Biazzo, S., Bititci, U., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437; Haq, A., Kannan, G., Fuzzy analytical hierarchy process for evaluating and selecting a vendor in a supply chain model (2006) The International Journal of Advanced Manufacturing Technology, 29 (7-8), pp. 826-835; Holban, I., Strategic performance measurement system and SMEs competitive advantage (2009) International Conference on Economics and Administration, , paper presented at, University of Bucharest, Romania, Bucharest: Faculty of administration and businesses; Hudson, M., Lean, J., Smart, P., Improving control through effective performance measurement in SMEs (2001) Production Planning \& Control, 12 (8), pp. 804-813; Hvolby, H., Thorstenson, A., Indicators for performance measurement in small and medium-sized enterprises (2001) The Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, pp. 1143-1146. , paper presented at; Melnyk, S., Stewart, D., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-218; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Rangone, A., An analytical hierarchy process framework for comparing the overall performance of manufacturing departments (1996) International Journal of Operations Production Management, 16 (8), pp. 104-119; Saaty, T., Decision making with the analytic hierarchy process (2008) International Journal of Services Sciences, 1 (1), pp. 83-98; (2013) SCOR | Supply Chain Council, , http://supply-chain.org/scor, [online] Accessed: 25 Sep 2013; Tangen, S., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Tenhunen, J., Rantanen, H., Ukko, J., SME oriented implementation of performance measurement system (2001) Working Paper, Lappeenranta University of Technology, , Lahti, Finland: Department of Indutrial engineering and management; Wang, G., Huang, S., Dismukes, J., Manufacturing supply chain design and evaluation (2005) The International Journal of Advanced Manufacturing Technology, 25 (1-2), pp. 93-100},
publisher={SciTePress},
isbn={9789897580178},
language={English},
abbrev_source_title={ICORES - Proc. Int. Conf. Oper. Res. Enterp. Syst.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Agami2014336,
author={Agami, N. and Saleh, M. and Rasmy, M.},
title={An innovative fuzzy logic based approach for supply chain performance management},
journal={IEEE Systems Journal},
year={2014},
volume={8},
number={2},
pages={336-342},
doi={10.1109/JSYST.2012.2219913},
art_number={6355602},
note={cited By 17},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902287076&doi=10.1109%2fJSYST.2012.2219913&partnerID=40&md5=6782d6329ba220af4d7fd49a60882513},
affiliation={Department of Operations Research and Decision Support, Faculty of Computers and Information, Cairo University, Giza 12613, Egypt},
abstract={In the last decade, the topic of supply chain performance measurement has attracted the attention of many researchers and practitioners worldwide. A series of significant changes and advancements in the theory and applications has been noticed. Nevertheless, gaps still exist. Current supply chain performance measurement systems still suffer from being too inward looking, ignoring external environmental factors that might affect the overall supply chain performance in the future when setting new targets. In this paper, we introduce an innovative approach to supply chain performance management. The proposed idea integrates fuzzy logic with trend impact analysis and provides a scenarios-based method that simulates and quantifies the possible effect of external factors on the supply chain performance, and thus enables setting realistic achievable targets. The idea is novel and beyond the state of the art of supply chain performance measurement. © 2012 IEEE.},
author_keywords={Fuzzy logic;  performance management;  supply chain;  trend impact analysis},
keywords={Electroplating shops;  Fuzzy logic;  Management, Environmental factors;  External factors;  Fuzzy logic based approach;  Impact analysis;  Innovative approaches;  Performance management;  Supply chain performance;  Supply chain performance measurements, Supply chains},
references={Agami, N., Saleh, M., Rasmy, M., Supply chain performance measurement approaches: Review and classification (2012) Org. Mang. Stud. J., IBIMA, 18. , to be published [Accepted on September; Agami, N., Saleh, M., Rasmy, M., A hybrid dynamic framework for supply chain performance improvement (2012) IEEE Syst. J., 6 (3), pp. 469-478. , Sep; Gordon, T., Trend impact analysis (2003) Futures Research Methodology V2, CD ROM, the Mill, , Proj. American Council for the United Nations University; Agami, N., Saleh, M., Omran, A., El-Shishiny, H., An enhanced approach for trend impact analysis (2008) Technol. Forecast. Soc. Change J., 75 (9), pp. 1439-1450; Agami, N., Saleh, M., Atiya, A., El-Shishiny, H., A neural network based dynamic forecasting model for trend impact analysis (2009) Tech. Forecast. Soc. Change J., 76 (7), pp. 952-962; Agami, N., Saleh, M., El-Shishiny, H., A fuzzy logic based tend impact analysis method (2010) Tech. Forecast. Soc. Change J., 77 (7), pp. 1051-1060; Baldwin, J., Fuzzy logic and fuzzy reasoning (1981) Fuzzy Reasoning and Its Applications, , E. H. Mamdani and B. R. Gaines, Eds. London, U.K.: Academic Press; Vadiee, N., Jamshidi, M., The promising future of fuzzy logic (1994) IEEE Expert, 9 (4), pp. 36-38; Kruse, R., Gebhardt, J., Klawon, F., (1994) Foundations of Fuzzy Systems, , Chichester U.K.: Wiley; Zadeh, L., The calculus of fuzzy if-then rules (1992) AI Exp., 7 (3), pp. 22-27; Lapide, L., What about measuring supply chain performance (2000) White Paper, 2 (15), pp. 287-297; Hughes, B., Hillebrand, E., (2006) Exploring Shaping International Futures, , Boulder CO: Paradigm},
publisher={Institute of Electrical and Electronics Engineers Inc.},
issn={19328184},
language={English},
abbrev_source_title={IEEE Syst. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bulsara2014231,
author={Bulsara, H.P. and Qureshi, M.N. and Patel, H.},
title={Supply chain performance measurement - An exploratory study},
journal={International Journal of Logistics Systems and Management},
year={2014},
volume={18},
number={2},
pages={231-249},
doi={10.1504/IJLSM.2014.062328},
note={cited By 24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902283295&doi=10.1504%2fIJLSM.2014.062328&partnerID=40&md5=981799257576ffae140b4235791a80a2},
affiliation={Management Section, Applied Mathematics and Humanities Department, SV National Institute of Technology, Ichchhanath, Surat - 395007, Gujarat, India; Mechanical Engineering Department, M.S. University of Baroda, Vadodara - 390002, Gujarat, India; Management Department, SR Luthra Institute of Management, Adarsh Society, Athwalines, Surat - 395001, Gujarat, India},
abstract={To stay in ever growing global market, it is an essential prerequisite to understand and practice principles of supply chain (SC). Performance of a SC is characterised by its ability to remain responsive without losing the integration through its chain. Measuring supply chain performance can facilitate a greater understanding of the supply chain, positively influence supply chain players' behaviour, operations of supply chain and improve its overall performance. This papers aims at reviewing literature related to supply chain performance measurement. It refers 89 papers from several distinguished science cited journals. It is found that several researchers have considered performance measures parameters for the supply chain measurement, however, they have not included measurement of parameters of performance for agile supply chain. Copyright © 2014 Inderscience Enterprises Ltd.},
author_keywords={Empirical study;  Performance measurement methods;  SCPM;  Supply chain metrics;  Supply chain performance measurement;  Supply chain performance measurement taxonomy},
references={Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable supply management: An empirical study (2011) International Journal of Production Economies, 140 (1), pp. 168-182; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Angeles, R., Anticipated IT infrastructure and supply chain integration capabilities for RFID and their associated deployment outcomes (2009) International Journal of Information Management, 29 (3), pp. 219-231; Awasthi, A., Chauhan, S.S., Goyal, S.K., A fuzzy multi-criteria approach for evaluating environmental performance of suppliers (2010) International Journal of Production Economics, 126 (2), pp. 370-378; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Baozhu, L., Supply chain management performance evaluation based on fuzzy analytical network process (2009) International Conference on Management and Service Science, MASS '09, 1, pp. 1-5; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Bititici, U.S., Nudurupati, S.S., Using performance measurement to derive continuous improvement (2002) Manufacturing Engineer, 81 (5), pp. 230-235; Boonitt, S., Pongpanarat, C., Measuring service supply chain management processes: The application of the Q-sort technique (2011) International Journal of Innovation, Management and Technology, 2 (3), pp. 217-221; Brimson, J.A., (1991) Activity Accounting, , Editions: John Wiley and Sons, New York; Bruce, M., Daly, L., Towers, N., Lean or agile: A solution for supply chain management in the textiles and clothing industry? (2004) International Journal of Operations and Production Management, 24 (1-2), pp. 151-170; Byrd, T.A., Turner, D.E., Measuring the flexibility of information technology infrastructure: Exploratory analysis of a construct (2000) Journal of Management Information Systems, 17 (1), pp. 167-208; Cai, J.A., Liu, X.B., Xiao, Z.B., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2008) Decision Support Systems, 46 (2), pp. 512-521; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131. , DOI 10.1504/IJBPM.2006.009032; Charan, P., Shankar, R., Baisya, R.K., Modelling the barriers of supply chain performance measurement system implementation in the Indian automobile supply chain (2009) International Journal of Logistics Systems and Management, 5 (6), pp. 614-630; Charkaoui, A., Aitouahman, A., Bouayad, B., Application of ECOGRAI/BSC method for controlling logistic performance: Case of a Moroccan clothing company (2012) International Journal of Business, Humanities and Technology, 2 (2), pp. 26-35; Che, Z.H., Wang, H.S., Sha, D.Y., A multi-criterion interaction-oriented model with proportional rule for designing supply chain networks (2007) Expert Systems with Applications, 33 (4), pp. 1042-1053. , DOI 10.1016/j.eswa.2006.08.015, PII S0957417406002430; Chen, C.C., Cheng, W.Y., Customer-focused and product-line-based manufacturing performance measurement (2007) International Journal of Advanced Manufacturing Technology, 34 (11-12), pp. 1236-1245; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-146; Christopher, M., Towill, D.R., An integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution and Logistics Management, 31 (4), pp. 235-246; Conboy, K., Agility from first principles: Reconstructing the concept of agility in information systems development (2009) Information Systems Research, 20 (3), pp. 329-354; Costantino, F., Gravio, G., Shaban, A., Tronci, M., Information sharing policies based on tokens to improve supply chain performances (2013) International Journal of Logistics Systems and Management, 14 (2), pp. 133-160; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1989) National Production Review, 8 (1), pp. 23-33; Decker, H.C., Van Goor, A.R., Supply chain management and management accounting: A case study of activity-based costing (2000) International Journal of Logistics: Research and Applications, 3 (1), pp. 41-52; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge - Measuring Operations for World-Class Competition, , Dow Jones-Irwin, Homewood, IL; Dotoli, M., Fanti, M.P., Meloni, C., Zhou, M.C., Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing (2006) IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 36 (1), pp. 62-75. , DOI 10.1109/TSMCA.2005.859189; Doumeingts, G., Clave, F., Ducq, Y., ECOGRAI: A method to design and to implement performance indicators systems using GRAI approach (1994) Proceedings International Dedicated Conference on Lean/Agile Manufacturing in the Automotive Industries, Aachen, pp. 389-396; Ducq, Y., Vallespir, B., Doumeingts, G., Coherence analysis methods for production systems by performance aggregation (2001) International Journal of Production Economics, 69 (1), pp. 239-252; Fiala, P., Information sharing in supply chains (2005) Omega, 33 (5), pp. 419-423; Fink, L., Neumann, S., Exploring the perceived business value of the flexibility enabled by information technology infrastructure (2009) Information and Management, 46 (2), pp. 90-99; Forrester, J.W., (1961) Industrial Dynamics, , MIT Press Cambridge, MA; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Gosling, J., Purvisa, L., Naima, M.M., Supply chain flexibility as a determinant of supplier selection (2010) International Journal of Production Economics, 128 (1), pp. 11-21; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., McNeil, R., Singh, D., Activity-based management in a small company: A case study (2000) Production Planning and Control, 11 (4), pp. 391-399. , DOI 10.1080/095372800232126; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Research, 87 (3), pp. 333-347; Hausman, W.H., Supply chain performance metrics', a chapter (2002) The Practice of Supply Chain Management, , Kluwer Academic Publishers, Dordrecht, Netherlands; Ho, C., Measuring system performance of an ERP-based supply chain (2007) International Journal of Production Research, 45 (6), pp. 1255-1277; Hoekstra, S., Romme, J., (1992) Integral Logistics Structures: Developing Customer Oriented Goods Flow, , McGraw-Hill, London; Hofmann, P., Reiner, G., Drivers for improving supply chain performance: An empirical study (2006) International Journal of Integrated Supply Management, 2 (3), pp. 214-230. , DOI 10.1504/IJISM.2006.008594; Hughes, A., ABC/ABM - Activity-based costing and activity-based management: A profitability model for SMEs manufacturing clothing and textiles in the UK (2005) Journal of Fashion Marketing and Management, 9 (1), pp. 8-19; Hwang, Y., Lin, Y., Lyu Jr., J., The performance evaluation of SCOR sourcing process - The case study of Taiwans TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Kaplan, R., Norton, D., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-99; Katayama, H., Bennett, D., Agility, adaptability and leanness: A comparison of concepts and a study of practice (1999) International Journal of Production Economics, 60-61 (1), pp. 43-51; Khazanchi, S., Lewis, M.W., Boyer, K.K., Innovation-supportive culture: The impact of organizational values on process innovation (2007) Journal of Operations Management, 25 (4), pp. 871-884. , DOI 10.1016/j.jom.2006.08.003, PII S0272696306000982; Knapp, K.J., Marshall, T.E., Rainer Jr., R.K., Ford, F.N., Information security: Management's effect on culture and policy (2006) Information Management and Computer Security, 14 (1), pp. 24-36. , DOI 10.1108/09685220610648355; Kueng, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Quality Management, 11 (1), pp. 67-85; Kueng, P., Krahn, A.J.W., Building a Process Performance Measurement System: Some Early Experiences (1999) Journal of Scientific and Industrial Research, 58 (3), pp. 149-159; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Kurien, G.P., Qureshi, M.N., Performance measurement systems for green supply chains using modified balanced score card and analytical hierarchical process (2012) Scientific Research and Essays, 7 (36), pp. 3149-3161; Lampel, J., Mintzberg, H., Customizing customization (1996) Sloan Management Review, 38 (1), pp. 21-30; Liang, T.F., Cheng, H.W., Application of fuzzy sets to manufacturing/distribution planning decisions with multi-roduct and multi-time period in supply chains (2009) Expert Systems with Applications, 36, pp. 3367-3377. , Nos. 2/2; Lin, C.-T., Chiu, H., Chu, P.-Y., Agility index in the supply chain (2006) International Journal of Production Economics, 100 (2), pp. 285-299. , DOI 10.1016/j.ijpe.2004.11.013, PII S0925527304004451; Lin, X., Zhang, Y., Bank ownership reform and bank performance in China (2009) Journal of Banking & Finance, 33 (1), pp. 20-29; Martin, P.R., Patterson, J.W., On measuring company performance within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; Mason-Jones, R., Towill, D.R., Total cycle time compression and the agile supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 61-73; Mason-Jones, R., Naylor, B., Towill, D.R., Lean, agile or leagile? Matching your supply chain to the marketplace (2000) International Journal of Production Research, 38 (17), pp. 4061-4070. , DOI 10.1080/00207540050204920; McCormack, K., Ladeira, M.B., Oliviera, M.P., Supply chain maturity and performance in Brazil', Supply Chain Management (2008) An International Journal, 13 (4), pp. 272-282; McGrath, R.G., Exploratory learning, innovative capacity, and managerial oversight (2001) Academy of Management Journal, 44 (1), pp. 118-131; Moberg, C.R., Cutler, B.D., Gross, A., Speh, T.W., Identifying antecedents of information exchange within supply chains (2002) International Journal of Physical Distribution & Logistics Management, 32 (9), pp. 755-770; Mondragon, A.E.C., Lyons, A.C., Kehoe, D.F., Assessing the value of information systems in supporting agility in high-tech manufacturing enterprises (2004) International Journal of Operations & Production Management, 24 (12), pp. 1219-1246; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1), pp. 107-118; Neelay, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Nelson, K.M., Nelson, H.J., Ghods, M., Technology flexibility: Conceptualization, validation, and measurement (1997) Proceedings of the Thirtieth Annual Hawaii International Conference on System Sciences; Olhager, J., Strategic positioning of the order penetration point (2003) International Journal of Production Economics, 85 (3), pp. 319-329; Overby, E., Bharadwaj, A., Sambamurthy, V., Enterprise agility and the enabling role of information technology (2006) European Journal of Information Systems, 15 (2), pp. 120-131. , DOI 10.1057/palgrave.ejis.3000600, PII 3000600; Pearcy, D.H., Giunipero, L.C., Using e-rocurement applications to achieve integration: What role does firm size play? (2008) Supply Chain Management: An International Journal, 13 (1), pp. 26-34; Peng, D.X., Schroeder, R.G., Shah, R., Linking routines to operations capabilities: A new perspective (2008) Journal of Operations Management, 26 (6), pp. 730-748; Perea, E., Grossmann, I., Ydstie, E., Tahmassebi, T., Dynamic modeling and classical control theory for supply chain management (2000) Computers and Chemical Engineering, 24 (2-7), pp. 1143-1149. , DOI 10.1016/S0098-1354(00)00495-6, PII S0098135400004956; Pettersson, A.I., Segerstedt, A., Performance measurements in supply chains within Swedish industry (2011) International Journal of Logistics Systems and Management, 9 (1), pp. 69-88; Power, D.J., Sohal, A., Rahman, S.U., Critical success factors in agile supply chain management: An empirical study (2001) International Journal of Physical Distribution and Logistics Management, 31 (4), pp. 247-265; Puigjaner, L., Lainez, J.M., Capturing dynamics in integrated supply chain management (2008) Computers and Chemical Engineering, 32 (11), pp. 2582-2605; Pun, K.F., White, A.S., A performance measurement paradigm for integrating strategy formulation: A review of systems and frameworks (2005) International Journal of Management Reviews, 7 (1), pp. 49-71. , DOI 10.1111/j.1468-2370.2005.00106.x; Saaty, T.L., (2001) Analytic Hierarchy Process - Decision Making for Leaders, 2, p. 315. , AHP Series, RWS Publication (New Edition), Pittsburg, PA 15213, USA; Sambamurthy, V., Bharadwaj, A., Grover, V., Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms (2003) MIS Quarterly, 27 (2), pp. 237-263; Schoeneborn, F., Linking balanced scorecard to system dynamics (2003) Proceeding 21st International Conference of the System Dynamics Society, , NY, USA; Sharma, M.K., Bhagwat, R., Dangayach, G.S., Practice of performance measurement: Experience from Indian SMEs (2005) International Journal of Globalisation and Small Business, 1 (2), pp. 38-44; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Managing the Supply Chain: The Definitive Guide for the Business Professional, , McGraw-Hill, New York; Slack, N., Flexibility as a manufacturing objective (1983) International Journal of Operations & Production Management, 3 (3), pp. 4-13; Subramanian, N., Ramanathan, R., A review of applications of analytic hierarchy process in operations management (2012) International Journal of Production Economics, 138 (2), pp. 215-241; Sueyoshi, T., Shang, J., Chiang, W.C., A decision support framework for internal audit prioritization in a rental car company: A combined use between DEA and AHP (2009) European Journal of Operational Research, 199 (1), pp. 219-231; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64 (1), pp. 231-241. , DOI 10.1016/S0925-5273(99)00061-4; Swafford, P.M., Ghosh, S., Murthy, N.N., A framework for assessing value chain agility (2006) International Journal of Operations and Production Management, 26 (2), pp. 118-140. , DOI 10.1108/01443570610641639; Tippins, M.J., Sohp, R.S., IT competency and firm performance: IS organizational learning a missing link? (2003) Strategic Management Journal, 24 (8), pp. 745-761; Tseng, F.M., Chiu, Y.J., Chen, J.S., Measuring business performance in the high-tech manufacturing industry: A case study of Taiwan's large-sized TFT-LCD panel companies (2009) Omega, 37 (3), pp. 686-697; Vitale, M., Mavrinac, S.C., Hauser, M., New process/financial scorecard: A strategic performance measurement system (1994) Planning Review, 22 (4), pp. 12-16; Yang, B., Burns, N., Implications of postponement for the supply chain (2003) International Journal of Production Research, 41 (9), pp. 2075-2090; Zhou, M.C., Zurawski, R., Introduction to petri nets in flexible and agile automation (1995) Petri Nets in Flexible and Agile Automation, pp. 1-42. , Zhou, M.C. (Ed.): Kluwer, Norwell, MA},
correspondence_address1={Bulsara, H.P.; Management Section, , Ichchhanath, Surat - 395007, Gujarat, India; email: hbulsara@ashd.svnit.ac.in},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Charkha2014216,
author={Charkha, P.G. and Jaju, S.B.},
title={Designing innovative framework for supply chain performance measurement in textile industry},
journal={International Journal of Logistics Systems and Management},
year={2014},
volume={18},
number={2},
pages={216-230},
doi={10.1504/IJLSM.2014.062327},
note={cited By 20},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902247379&doi=10.1504%2fIJLSM.2014.062327&partnerID=40&md5=197cf0c864d768c2cb5d3986796cc0a7},
affiliation={Department of Mechanical Engineering, G.H. Raisoni College of Engineering, CRPF Gate No. 3, Digdoh Hills, Hingna Road, Nagpur-16, Maharashtra, India},
abstract={Supply chain management (SCM) has become very important technology management concept to attain the quality consciousness. The attainment of quality in supply chains (SCs) will only be achieved with the help of good supply chain performance evaluation system. Also, the textile industries in India are the fastest growing and prime contributor to Indian economic development. As we all know, designing performance measurement system is dependent on various attributes, hence a multi criteria decision making tool is required, and similarly, as supply chain management is a process oriented approach, hence the same process improvement concept is needed. Therefore, the main aim of the paper is to review the existence of supply chain management and supply chain performance measurement system (SCPMS) in the Indian textile industry. Literature reviewed reveals that there is a need of proactive and integrated performance measurement framework for the said industry, also the selection of performance measures/metrics should be unique. Hence, the study here suggests the SCPMS for the core textile industry with the use of three performance metrics; inventory scheduling, production operation and scheduling and human resource scheduling. The analytical techniques used for developing framework are also kept unique and they are analytical hierarchy process (AHP) and Six Sigma. Copyright © 2014 Inderscience Enterprises Ltd.},
author_keywords={AHP;  Analytical hierarchy process;  Logistics management;  SCM;  SCPMS;  Six Sigma;  Supply chain management;  Supply chain performance measurement system},
references={Basu, B., Sharma, A., Supply chain management in textile (2011) The Indian Textile Journal, 1 (3), pp. 1-15. , August; Cao, N., Zhang, Z., To, C., Ng, R., (2010) Supply Chain Management and Competitive Advantage in Textile and Apparel Industries: An Empirical Investigation, , Fire 2 Fashion, Institute of Textile & Clothing, The Hongkong Polytechnic University; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Cooper, D.W., Textile and apparel supply chain management technology adoption: The burlington industries case and beyond (2006) Journal of Textile and Apparel, Technology and Management, 5 (2), pp. 1-22; Costantino, F., Gravio, G., Shaban, A., Tronci, M., Information sharing policies based on tokens to improve supply chain performance (2013) Intl. Jrnl. of Logistics, Systems & Management, 14 (2), pp. 133-160; Dangi, H., Bardhan, A., Narag, A., (2012) Humanitarian Relief Logistics: An Exploratory Study for Need and Importance of Performance Measurement System, 13 (1), pp. 1-16; De Marcella, M., Marasco, A., Approaches to supply chain logistics integration in textile/clothing sector: An exploratory study in the region of Campania (2007) European Transport, 35 (12), pp. 99-111; Eckes, G., (2001) The Six Sigma Revolution: How GE and Others Turned Process into Profits, , Wiley New York; Hall, D.C., Saygin, C., Impact of information sharing on supply chain performance Springer's (2011) Int. Jrnl. of Adv. Mfg. Technology, , 16 May; Jakhar, S.K., Barua, M.K., Performance measures for the textile-apparel supply chain (2012) Emerging Challenges for Sustainable Business 2012, , ISBN- 978-93-81583-46-53; Jitesh, T., Arun, K., Deshmukh, S.G., Supply chain performance measurement framework for small & medium scale enterprises (2009) EME EMERALD's Benchmarking: An International Journal, 16 (5), pp. 702-723; Koprulu, A., Albayrakoglu, M.M., Supply chain management in the textile industry: A supplier selection model with analytical hierarchy process (2007) ISAHP 2007, pp. 3-6. , Vina Del Mar, Chile, August; Kritchanchai, D., Wasuri, T., Implementing supply chain management in Thailand textile industry (2007) International Journal of Information System for Logistics and Management, 2 (2), pp. 107-116; Kumar, D., Singh, O., Singh, J., Integrated supply chain: A survey of Indian textile organization (2011) International Journal of Services and Operation Management, 9 (4), pp. 411-428; Lin, L.-C., Li, T.-S., An integrated framework of supply chain performance measurement using Six Sigma metrics Springer's (2010) Software Quality Journal, 18 (3), pp. 387-406; Mohammad, A., Habib, M., Supply chain management of textile industry: A case study of Bangladesh (2012) September, 1 (2), pp. 35-40; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) Int. J. Operation Production Management, 25 (12), pp. 1264-1277; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145. , DOI 10.1108/01443570010343708; Pettersson, A., Segertedt, A., (2011) Performance Measurements in Supply Chains Within Swedish Industry, 9 (1), pp. 69-88; Phong-Arjarn, E., Jeenanunta, C., Watcharapanyawong, K., Suanmali, S., Key performance indicators (KPIs) for measuring supply chain performance in Thai apparel industry (2009) The International Conference on Logistics and Transportation (ICLT2009), , Chiang Mai, Thailand; Ramaa, A., Rangaswamy, T., Subramanya, K.N., AHP based performance measurement system of supply chain (2010) Global Jrnl. of Mgmt. & Buss. Research, 10 (5), pp. 9-16. , July, (Ver 10); Samvedi, A., Jain, V., Effect of sharing forecast information on the performance of supply chain experiencing disruptions (2012) Intl. Jrnl. of Logistics, System & Management, 13 (4), pp. 509-524; Siddarth, V., Subhash, W., Deshmukh, S.G., Evaluating petroleum supply chain performance: Application of analytical hierarchy process to balanced scorecard (2008) Emerald's Asia Pacific Journal of Marketing & Logistics, 20 (3), pp. 343-356; Sillanpaa, I., Kess, P., Supply chain performance measurement framework for manufacturing industries - A theoretical approach (2011) 12th Management Intl. Conference, pp. 801-823. , 23-26 November, Portoroz, Slovenia; Sundarakini, B., Tan, A., Over, D., (2012) Enhancing the Supply Chain Management Performance Using Information Technology: Some Evidences from UAE Companies, 11 (3), pp. 306-324; Tanyas, M., Candan, C., Cesur, K., Supply chain management and supplier performance evaluation (2010) 10th Global Conference on Business & Economics, pp. 15-16. , October, Rome, Italy; Verdecho, M.J., Alfaro Saiz, J.J., Rodriguez-Rodriguez, R., A performance measurement framework for supply chain sustainability (2012) 6th Intl. Conf. on Ind. Engg. & Ind. Mgmt. Vigo, pp. 18-20. , July; Verma, S., (2002) Export Competitiveness of Indian Textile Industry, , November, Indian Council for Research on International Economic Relation, Paper No. 94},
correspondence_address1={Charkha, P.G.; Department of Mechanical Engineering, CRPF Gate No. 3, Digdoh Hills, Hingna Road, Nagpur-16, Maharashtra, India; email: pgcharkha@gmail.com},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Pillutla2014207,
author={Pillutla, S. and Yao, D.-Q. and Li, X.},
title={Data mining the adoption intention of e-procurement system for Chinese companies},
journal={International Journal of Management and Decision Making},
year={2014},
volume={13},
number={2},
pages={207-219},
doi={10.1504/IJMDM.2014.060872},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899885008&doi=10.1504%2fIJMDM.2014.060872&partnerID=40&md5=99f9e8814684bee1628b873850753dba},
affiliation={Department of E-Business and Technology Management, College of Business and Economics, Towson University, MD 21252, United States},
abstract={We conduct a structured survey among Chinese firms in the City of Suzhou on the adoption of e-procurement systems. We analytically mine the data to explore the impact of operations as well as supply chain performance measures on organisational behavioural intention to adopt e-procurement systems. The results demonstrate that a series of operations and supply chain performance measures, such as expected improved supply chain efficiency, improved operational efficiency, and reduced inventory level, are critical drivers for the adoption of e-procurement systems among Chinese companies. The findings highlight the importance of operations and supply chain performance benefits of an e-procurement systems as well as the importance of strategic marketing by e-procurement systems vendors to promote those benefits. Copyright © 2014 Inderscience Enterprises Ltd. Copyright © 2014 Inderscience Enterprises Ltd.},
author_keywords={China;  E-procurement;  Operations and supply chain performance;  Strategic marketing;  Technology adoption},
references={Abu-El Samen, A., Chakraborty, G., Warren, D., A process-based analysis of e-procurement adoption (2010) Journal of Internet Commerce, 9 (3-4), pp. 243-259; Ahmed, T., An empirical-based model for examining e-purchasing intention in electronic commerce at developing countries (2011) International Journal of Academic Research, 3 (3), pp. 683-688; Arbin, K., The structure of determinants of individual adoption and use of e-ordering systems (2008) Human Systems Management, 27 (2), pp. 143-159; Chang, H.H., Wong, K.H., Adoption of e-procurement and participation of e-marketplace on firm performance: Trust as a moderator (2010) Information and Management, 47 (5-6), pp. 262-270; Gunasekaran, A., Ngai, E.W.T., Adoption of e-procurement in Hong Kong: An empirical research (2008) International Journal of Production Economics, 113 (1), pp. 159-175; Kusuma, H., Pramunita, R., The effect of risk and trust on the behavioural intention of using e-procurement system (2011) European Journal of Economics, Finance and Administrative Sciences, 5 (40), pp. 138-145; McCue, C., Roman, A.V., E-procurement: Myth or reality? (2012) Journal of Public Procurement, 12 (2), pp. 221-248; Moon, M., E-procurement management in state governments: Diffusion of e-procurement practices and its determinants (2005) Journal of Public Procurement, 5 (1), pp. 54-72; Nucciarelli, A., Gastaldi, M., Information technology and collaboration tools within e-supply chain management of the aviation industry (2008) Technology Analysis and Strategic Management, 20 (2), pp. 169-184; Padhi, S.S., Mohapatra, P., Adoption of e-procurement in the government departments (2010) Electronic Government, an International Journal, 7 (1), pp. 41-59; Peleg, B., Lee, H.L., Hausman, W.H., Short-term e-procurement strategies versus long-term contracts (2002) Production and Operations Management, 11 (4), pp. 458-479; Richard, P., Devinney, T., Modular strategies: B2B technology and architectural knowledge (2005) California Management Review, 47 (4), pp. 86-113; Shakir, M., Smith, G.R., Gulec, E., E-procurement: Reaching out to small and medium businesses (2007) MIS Quarterly Executive, 6 (4), pp. 225-238; Shmueli, G., Patel, N., Bruce, P., (2007) Data Mining for Business Intelligence, , John Wiley & Sons, Inc., Hoboken, New Jersey, USA; Tai, Y., Ho, C., Wu, W., The performance impact of implementing web-based e-procurement systems (2010) International Journal of Production Research, 48 (18), pp. 5397-5414; Vaidyanathan, G., Devaraj, S., D'Arcy, J., Does security impact e-procurement performance? Testing a model of direct and moderated effects (2012) Decision Sciences Journal, 43 (3), pp. 437-458; Wu, F., Mahajan, V., Balasubramanian, S., An analysis of e-business adoption and its impact on business performance (2003) Journal of the Academy of Marketing Science, 31 (4), pp. 425-447},
correspondence_address1={Yao, D.-Q.; Department of E-Business and Technology Management, , MD 21252, United States; email: dyao@towson.edu},
publisher={Inderscience Publishers},
issn={14624621},
language={English},
abbrev_source_title={Int. J. Manage. Decis. Mak.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chelariu2014332,
author={Chelariu, C. and Asare, A.K. and Brashear-Alejandro, T.},
title={"A ROSE, by any other name"...: Relationship typology and performance measurement in supply chains},
journal={Journal of Business and Industrial Marketing},
year={2014},
volume={29},
number={4},
pages={332-343},
doi={10.1108/JBIM-08-2013-0178},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899000139&doi=10.1108%2fJBIM-08-2013-0178&partnerID=40&md5=e3a53f93df0796b225c4bd81e504f83a},
affiliation={Marketing Department, Sawyer Business School, Suffolk University, Boston, MA, United States; Department of Marketing, School of Business, Quinnipiac University, Hamden, CT, United States; Eugene M. Isenberg School of Management, University of Massachusetts, Amherst, MA, United States},
abstract={Purpose: The aim of this paper is to develop a comprehensive framework of supply chain performance that includes relationship, operational, strategic and economic performance measures. Design/methodology/approach: The literature regarding inter-organizational performance including: supply chain management, logistics and marketing performance measures is reviewed. A synthesis of the review provides the foundation for developing a comprehensive model of supply chain management performance. Findings: The review and synthesis finds that supply chain performance focuses primarily on operational and economic performance measures while paying less attention to relational and strategic performance measures. The comprehensive framework identifies four major categories of supply chain performance measures: relational; operational; strategic; and economic - hence the name ROSE. Originality/value: This comprehensive framework identifies four types of supply chain measures that can be used as a guiding framework by both academics and practitioners. The paper also offers directions for future work in the form of propositions. © Emerald Group Publishing Limited.},
author_keywords={Governance;  Performance measurement;  Relationship marketing;  Supply chain management},
references={Achrol, R.S., Etzel, J.M., The structure of reseller goals and performance in marketing channels (2003) Journal of the Academy of Marketing Science, 31 (2), pp. 146-164; Al-Zubaidi, H., Tyler, D., A simulation model of quick response replenishment of seasonal clothing (2004) International Journal of Retail & Distribution Management, 32 (6), pp. 320-327; Anand, P., Stern, L.W., A sociopsychological explanation for why marketing channel members relinquish control (1985) Journal of Marketing Research, 22 (4), pp. 365-377; Anderson, E., Weitz, B., Determinants of continuity in conventional industrial channel dyads (1989) Marketing Science, 8 (4), p. 310; Aulakh, P.S., Kotabe, M., Antecedents and performance implications of channel integration in foreign markets (1997) Journal of International Business Studies, 28 (1), pp. 145-175; Autry, C.W., Stanley, E.G., Supply chain capital: The impact of structural and relational linkages on firm execution and innovation (2008) Journal of Business Logistics, 29 (1), pp. 157-168; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-121; Barthon, P., Jepsen, B., How time affects transaction costs and relational governance in the distribution channel: A review and research propositions (1997) Management Research News, 20 (6), pp. 14-30; Bechtel, C., Jayaram, J., Supply chain management: A strategic perspective (1997) International Journal of Logistics Management, 8 (1), pp. 15-34; Bello, D.C., Gilliland, D.I., The effect of output controls, process controls, and flexibility on export channel performance (1997) Journal of Marketing, 61 (1), pp. 22-38; Benton, W.C., Maloni, M., The influence of power driven buyer/seller relationships on supply chain satisfaction (2005) Journal of Operations Management, 23 (1), pp. 1-22; Brewer, P.C., Speh, T.H., Using the balance scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Chakravarthy, B.S., Measuring strategic performance (1986) Strategic Management Journal, 7 (5), pp. 437-458; Charvet, F.F., Cooper, M.C., Gardner, J.T., The intellectual structure of supply chain management: A bibliometric approach (2008) Journal of Business Logistics, 29 (1), pp. 47-58; Chen, H., Daugherty, P.J., Landry, T.D., Supply chain process integration: A theoretical framework (2009) Journal of Business Logistics, 30 (2), pp. 27-46; Choi, T.Y., Hong, Y., Unveiling the structure of supply networks: Case studies in Honda Acura, and Daimler Chrysler (2002) Journal of Operations Management, 20 (5), pp. 469-493; Claro, D.P., Hagelaar, G., Omta, O., The determinants of relational governance and performance: How to manage business relationships? (2003) Industrial Marketing Management, 32 (8), pp. 703-713; Deepen, J.A., Goldsby, T., Knemeyer, M.A., Wallenburg, C.M., Beyond expectations: An examination of logistics outsourcing goal achievement and goal exceedance (2008) Journal of Business Logistics, 29 (2), pp. 75-104; Duarte, M., Davies, G., Testing the conflict-performance assumption in business-to-business relationships (2003) Industrial Marketing Management, 32 (2), pp. 91-99; Duffy, R., Fearne, A., The impact of supply chain partnerships on supplier performance (2004) International Journal of Logistics Management, 15 (1), pp. 57-72; Dwyer, F.R., Schurr, P.H., Oh, S., Developing buyer-seller relationships (1987) Journal of Marketing, 51 (2), pp. 11-27; Esper, T., Fugate, B.S., Sramek, B.D., Logistics learning capabilities: Sustaining the competitive advantage gained through logistics leverage (2007) Journal of Business Logistics, 28 (2), pp. 57-78; Fawcett, S.E., Magnan, G.M., A three stages implementation for supply chain collaboration (2008) Journal of Business Logistics, (1), pp. 93-128; Fiksel, J., Designing resilient, sustainable systems (2003) Environmental Science and Technology, 37, pp. 5330-5339; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, pp. 105-116; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production Planning and Control, 16 (6), pp. 531-544; Frankel, R., Bolumole, Y.A., Eltantawy, R.A., Paulraj, A., Gundlach, T.G., The domain and scope of SCM's foundational disciplines-insights and issues to advance research (2008) Journal of Business Logistics, 29 (1); Frazier, G.L., On the measurement of interfirm power in channels of distribution (1983) Journal of Marketing Research, 20 (2), pp. 158-167; Fugate, B.S., Mentzer, J.T., Stank, T.P., Logistics performance: Efficiency, effectiveness and differentiation (2010) Journal of Business Logistics, 31 (1), pp. 43-62; Fynes, B., Búrca, D.S., Marshall, D., Environmental uncertainty, supply chain relationship quality and performance (2004) Journal of Purchasing and Supply Management, 10 (4), pp. 179-190; Fynes, B., Voss, C., Búrca, D.S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354; Ganesan, S., Determinants of long-term orientation in buyer-seller relationships (1994) Journal of Marketing, 58 (2), pp. 1-16; Gaski, J.F., Nevin, J.R., The differential effects of exercised and unexercised power sources in a marketing channel (1985) Journal of Marketing Research, 22 (2), pp. 130-142; Goldberg, V.P., Regulation and administered contracts (1976) Bell Journal of Economics, 7 (2), pp. 426-448; Griffis, S.E., Cooper, M., Goldsby, J.T., Closs, D.J., Performance measurement: Measure selection based upon firm goals and information reporting needs (2004) Journal of Business Logistics, 25 (2), pp. 95-129; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-86; Gundlach, G.T., Cadotte, E.R., Exchange interdependence and interfirm interaction: Research in a simulated channel setting (1994) Journal of Marketing Research, 31 (4), pp. 516-532; Gundlach, G.T., Murphy, P.E., Ethical and legal foundations of relational marketing exchanges (1993) Journal of Marketing, 57 (4), pp. 35-50; Heide, J.B., Interorganizational governance in marketing channels (1994) Journal of Marketing, 58 (1), pp. 71-85; Heide, J.B., John, G., The role of dependence balancing in safeguarding transaction-specific assets in conventional channels (1988) Journal of Marketing, 52 (1), pp. 20-35; Hofer, A.R., Knemeyer, M.A., Dressner, M.E., Antecedents and dimensions of customer partnership behavior in logistics outsourcing relationships (2009) Journal of Business Logistics, 30 (2), pp. 141-163; Jap, S.D., Pie-expansion efforts: Collaboration processes in buyer-supplier relationships (1999) Journal of Marketing Research, 36 (4), pp. 461-475; Johnson, J.L., Sohi, R.S., Grewal, R., The role of relational knowledge stores in interfirm partnering (2004) Journal of Marketing, 68 (3), pp. 21-36; Kalwani, M.U., Narayandas, N., Long-term manufacturer-supplier relationships: Do they pay off for supplier firms? (1995) Journal of Marketing, 59 (1), pp. 1-16; Kaufmann, P.J., Stern, L.W., Relational exchange norms, perceptions of unfairness, and retained hostility in commercial litigation (1988) Journal of Conflict Resolution, 32 (3), pp. 534-552; Keysuk, K., Frazier, G.L., A typology of distribution channel systems: A contextual approach (1996) International Marketing Review, 13 (1), pp. 19-33; Kumar, N., Stern, L., Achrol, R.S., Assessing reseller performance from the perspective of the supplier (1992) Journal of Marketing, 29 (2), pp. 238-253; Lambert, D.M., Garcia-Dastugue, S.J., Croxton, K.L., The role of logistics managers in the cross-functional implementation of supply chain management (2008) Journal of Business Logistics, 29 (1), pp. 113-132; Lee, H.L., Aligning supply chain strategies with product uncertainties (2002) California Management Review, 44 (3), pp. 105-119; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lehmann, D.R., Reibstein, D.J., (2006) Marketing Metrics and Financial Performance, , Marketing Science Institute, Cambridge, MA; Li, Z.G., Dant, R.P., An exploratory study of exclusive dealing in channel relationships (1997) Journal of the Academy of Marketing Science, 25 (3), pp. 201-213; Liker, J.K., Wu, Y.C., Japanese automakers, US suppliers, and supply-chain superiority (2000) Sloan Management Review, 42 (1), pp. 81-100; Lusch, R.F., Brown, J.R., Interdependency, contracting, and relational behavior in marketing channels (1996) Journal of Marketing, 60 (4), pp. 19-38; Macneil, I.R., Economic analysis of contractual relations: Its shortfalls and the need for a rich classificatory apparatus (1981) Northwestern University Law Review, 75 (6), pp. 1018-1063; Maloni, M., Benton, W.C., Power influences in the supply chain (2000) Journal of Business Logistics, 21 (1), pp. 49-61; Mehta, R., Dubinsky, A.J., Anderson, R.E., Leadership style, motivation and performance in international marketing channels: An empirical investigation of the USA, Finland and Poland (2003) European Journal of Marketing, 37 (1), pp. 50-85; Mentzer, J.T., Stank, T.P., Esper, T.L., Supply chain management and its relationship to logistics, marketing, production, and operations management (2008) Journal of Business Logistics, 29 (1), pp. 31-43; Meyer, A.D., What is strategy's distinctive competence? (1991) Journal of Management, 17 (4), pp. 821-835; Min, S., Kim, S.K., Chen, H., Developing social identity and social capital for supply chain management (2008) Journal of Business Logistics, 29 (1), pp. 283-304; Mohr, J., Nevin, J.R., Communication strategies in marketing channels: A theoretical perspective (1990) Journal of Marketing, 54 (4), pp. 36-51; Morgan, R.M., Hunt, S.D., The commitment-trust theory of relationship marketing (1994) Journal of Marketing, 58 (3), pp. 20-38; Noordewier, T.G., John, G., Nevin, J.R., Performance outcomes of purchasing arrangements in industrial buyer-vendor relationships (1990) Journal of Marketing, 54 (4), pp. 80-93; Osborn, R.N., Baughn, C.C., Forms of interorganizational governance for multinational alliances (1990) Academy of Management Journal, 33 (3), pp. 503-513; Ouchi, W.G., A conceptual framework for the design of organizational control mechanisms (1979) Management Science, 25 (9), pp. 833-848; Parsons, T., (1970) The Social System, , Routledge & Kegan Paul, London; Parsons, T., Shils, E., Naegle, K., Pitts, J., (1961) Theories of Society, , The Free Press, New York, NY; Pettitt, T.J., Fiksel, J., Crowston, K.L., Ensuring supply chain resilience: Development of a conceptual framework (2010) Journal of Business Research, 31 (1), pp. 1-21; Quinn, R.E., Rohrbaugh, J., A spatial model of effectiveness criteria: Towards a competing values approach to organizational analysis (1983) Management Science, 29 (3), pp. 363-377; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Richey Jr., G.R., Roath, A.S., Whipple, J.M., Fawcett, S.E., Exploring a governance theory of supply chain management: Barriers and facilitators to integration (2010) Journal of Business Logistics, 31 (1), pp. 237-255; Ring, P.S., Van de Ven, A.H., Developmental processes of cooperative interorganizational relationships (1994) Academy of Management Review, 19 (1), pp. 90-118; Robicheaux, R.A., Coleman, J.E., The structure of marketing channel relationships (1994) Journal of the Academy of Marketing Science, 22, pp. 38-51; Ruamsock, K., Russell, D., Thomchick, E., US sourcing form low-cost countries: A comparative analysis of supplier performance (2007) Journal of Supply Chain Management, (Fall), pp. 16-30; Rust, R.T., Moorman, C., Dickson, P.R., Getting return on quality: Revenue expansion, cost reduction, or both (2002) Journal of Marketing, 66 (4), pp. 7-24; Scheer, L.K., Stern, L.W., The effect of influence type and performance outcomes on attitude toward the influencer (1992) Journal of Marketing Research, 29 (1), pp. 128-142; Spekman, R.E., Spear, J., Kamauff, J., Supply chain competency: Learning as a key component (2002) Supply Chain Management: An International Journal, 7 (1), pp. 41-45; Spriggs, M.T., A framework for more valid measures of channel member performance (1994) Journal of Retailing, 70 (4), pp. 327-343; Tangpong, C., Kuo-Ting, H., Ro, Y.K., The interaction effect of relational norms and agent cooperativeness on opportunism in buyer-supplier relationships (2010) Journal of Operations Management, 28 (5), pp. 398-414; Trent, R.J., Creating the ideal supplier scorecard (2010) Supply Chain Management Review, pp. 24-29; Wernerfelt, B., Montgomery, C.A., What is an attractive industry? (1986) Management Science, 32 (10), pp. 1223-1230; Williamson, O.E., Comparative economic organization: The analysis of discrete structural alternatives (1991) Administrative Science Quarterly, 36 (2), pp. 269-296; Wu, Y.C., Lean manufacturing: A perspective of lean suppliers (2003) International Journal of Operations & Production Management, 23 (11), pp. 1349-1376; Yilmaz, C., Sezen, B., Ozdemir, O., Joint and interactive effects of trust and (inter) dependence on relational behaviors in long-term channel dyads (2005) Industrial Marketing Management, 34 (3), pp. 235-248; Zablah, A.R., Johnston, W.J., Bellenger, D.N., Transforming partner relationships through technological innovation (2005) Journal of Business & Industrial Marketing, 20 (7), pp. 355-363; Zhao, X., Huo, B., Flynn, B.B., Hoi, J., Yeung, Y., The impact of power and relationship commitment on the integration between manufacturers and customers in a supply chain (2008) Journal of Operations Management, 26 (3), pp. 368-388; Ford, J.D., Schellenberg, D.A., Conceptual issues of the linkage in the assessment of organizational performance (1982) The Academy of Management Review, 7 (1), pp. 49-58; Janda, S., Seshadri, S., The influence of purchasing strategies on performance (2001) The Journal of Business & Industrial Marketing, 16 (4), pp. 294-306; Oliver, K.R., Webber, M.D., Supply chain management: Logistics catches up with strategy, outlook (1982) Logistics, The Strategic Issues, , Christopher, M. (Ed.), Chapman & Hall, London; Skaggs, B.C., Snow, C.S., The strategic signaling of capabilities by service firms in different information asymmetry environments (2004) Strategic Organization, 2 (3), pp. 27-29; Webb, K., Hogan, J.E., Hybrid channel conflict: Causes and effects on channel performance (2002) The Journal of Business & Industrial Marketing, 17 (5), pp. 338-356; Zacharia, Z.G., Nix, N.W., Lusch, R.F., An analysis of supply chain collaborations and their effect on performance outcomes (2009) Journal of Business Logistics, 30 (2), pp. 101-124},
correspondence_address1={Chelariu, C.; Marketing Department, Sawyer Business School, Suffolk University, Boston, MA, United States; email: cchelariu@suffolk.edu},
publisher={Emerald Group Publishing Ltd.},
issn={08858624},
language={English},
abbrev_source_title={J. Bus. Ind. Mark.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Amirjabbari201442,
author={Amirjabbari, B. and Bhuiyan, N.},
title={Determining supply chain safety stock level and location},
journal={Journal of Industrial Engineering and Management},
year={2014},
volume={7},
number={1},
pages={42-71},
doi={10.3926/jiem.543},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898467542&doi=10.3926%2fjiem.543&partnerID=40&md5=7afcb7d0c8be18cef75c97cdfebef601},
affiliation={Concordia University, Canada},
abstract={Purpose: The lean methodology and its principles have widely been applied in supply chain management in recent decades. Manufacturers are one of the most important contributors in a supply chain and inventory plays a paramount role for them to become lean. Therefore, there should be appropriate management of inventory and all of its drivers in accordance with a lean strategy. Safety stock is one of the main drivers of inventory; it protects against increasing the stretch in the breaking points of the supply chain, which in turn can result in possible reduction of inventory. In this paper an optimization model and a simulation model are developed and applied in a real case to optimize the safety stock level with the objective of logistics cost minimization. Design/methodology/approach: In order to optimize the safety stock level while minimizing logistics costs, a nonlinear cost minimization safety stock model is developed in this paper and then it is applied in a real world manufacturing case company. A safety stock simulation model based on appropriate metrics in the case company's supply chain performance is also provided. Findings: These models result in not only the optimum levels but also locations of safety stock within the supply chain. Originality/ value: In this research, two models of cost minimization and simulation have been developed and also applied in a real case company to result in not only optimized levels but also optimized locations of safety stock across the whole supply chain. In addition, the appropriate supply chain performance measurement metrics have been introduced in this paper and the simulation model is developed based on those.},
author_keywords={Cost;  Lean;  Optimization;  Safety stock;  Supply chain},
keywords={Computer simulation;  Industry;  Manufacture;  Optimization;  Supply chain management;  Supply chains, Appropriate metrics;  Design/methodology/approach;  Lean;  Optimization modeling;  Safety stock;  Safety stock levels;  Supply chain performance;  Supply chain performance measurements, Costs},
references={Adamides, E.D., Karacapilidis, N., Pylarinou, H., Koumanakos, D., Supporting collaboration in the development and management of lean supply networks (2008) Production Planning & Control, 19 (1), pp. 35-52. , http://dx.doi.org/10.1080/09537280701773955; Aleotti Maia, L.O., Qassim, R.Y., Minimum cost safety stocks for frequently delivery manufacturing (1998) International Journal of Production Economics, pp. 233-236; Cagliano, R., Caniato, F., Spina, G., Lean, Agile and traditional supply: How do they impact manufacturing performance? (2004) Journal of Purchasing & Supply Management, 10, pp. 151-164. , http://dx.doi.org/10.1016/j.pursup.2004.11.001; Chen, I.J., Paulraj, A., Understanding supply cahin management: Critical research and theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-163. , http://dx.doi.org/10.1080/00207540310001602865; Chun Wu, Y., Lean manufacturing: A perspective of lean suppliers (2003) International Journal of Operations & Production Management, 23 (11), pp. 1349-1376. , http://dx.doi.org/10.1108/01443570310501880; Coyle, J.J., Bardi, E.J., Langley, C.J., (2009) Supply Chain Management: A Logistics Perspective; Crino, S.T., McCarthy, D.J., Carier, J.D., Lean Six Sigma for supply chain management as applied to the Army Rapid Fielding Initiative (2007) In 1st Annual IEEE Systems Conference; Desmet, B., Aghezzaf, E.H., Vanmaele, H., A normal approximation for safety stock optimization in a two-echelon distribution system (2010) Journal of the Operational Research Society, 61 (1), pp. 156-163. , http://dx.doi.org/10.1057/jors.2008.150; de Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21, pp. 46-70. , http://dx.doi.org/10.1108/01443570110358459; Graves, S.C., Rinnooy Kan, A.H.G., Logistics of production and inventory (1993) Handbooks In Operations Research and Management Science; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347. , http://dx.doi.org/10.1016/j.ijpe.2003.08.003; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87. , http://dx.doi.org/10.1108/01443570110358468; Inderfurth, K., Safety stock optimization in multi-stage inventory system (1991) International Journal of Production Economics, 24 (1-2), pp. 103-113. , http://dx.doi.org/10.1016/0925-5273(91)90157-O; Jung, J.Y., Blau, G., Pekny, J.F., Reklaitis, G.V., Eversdyk, D., Integrated safety stock management for multi-stage supply chains under production capacity constraints (2008) Computers and Chemical Engineering, 32, pp. 2570-2581. , http://dx.doi.org/10.1016/j.compchemeng.2008.04.003; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) International Journal of Production Economics, 101, pp. 99-108. , http://dx.doi.org/10.1016/j.ijpe.2005.05.010; Lamming, R., Squaring lean supply with supply chain management (1996) International Journal of Operations & Production Management, 16 (2), pp. 183-196. , http://dx.doi.org/10.1108/01443579610109910; Li, Q.Y., Li, S.J., A dynamic model of the safety stock under VMI (2009) Proceedings of the Eighth International Conference On Machine Learning and Cybernetics, pp. 1304-1308; Long, Q., Liu, L.F., Meng, L.Q., Chen, W., Logistics cost optimized election in the manufacturing process based on ELECTURE-II Algorithm (2009) In Proceedings of the IEEE International Conference On Automation and Logistics; Maloni, M.J., Benton, W.C., Supply chain partnerships: Opportunities for operations research (1997) European Journal of Operational Research, 101 (3), pp. 419-429. , http://dx.doi.org/10.1016/S0377-2217(97)00118-5; Mason-Jones, R., Nalor, B., Towill, D.R., Lean, agile or leagile? Matching your supply chain to the marketplace (2000) International Journal of Production Research, 38 (17), pp. 4061-4070. , http://dx.doi.org/10.1080/00207540050204920; McCullen, P., Towill, D., Achieving lean supply through agile manufacturing (2001) Integrated Manufacturing Systems, pp. 524-533. , http://dx.doi.org/10.1108/EUM0000000006232; Natarajan, R., Goyal, S., Safety stocks in JIT environments (1994) International Journal of Operations & Production Management, 14 (10), pp. 64-71. , http://dx.doi.org/10.1108/01443579410067261; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62, pp. 107-118. , http://dx.doi.org/10.1016/S0925-5273(98)00223-0; Qi, F., Xuejun, X., Zhiyong, G., Research on Lean, Agile and Leagile Supply Chain (2007) National Natural Foundation of China Grand and IOM European, pp. 4902-4905; Silver, E.A., Pyke, D.F., Peterson, R., (1998) Inventory Management and Production Planning and Scheduling. 3rd Ed; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , http://dx.doi.org/10.1108/17410400610653219; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , Pitman Publishing, London; Taylor, D., Parallel Incremental Transformation Strategy: An Approach to the Development of Lean Supply Chains (1999) International Journal of Logistics Research and Applications, 2 (3), pp. 305-323. , http://dx.doi.org/10.1080/13675569908901587; Tempelmeier, H., (2006) Inventory Management In Supply Networks, , Problems, Models, Solutions; Thomas, J., (1999) Why Your Supply Chain Doesn't Work, , Logistics Management and Distribution Report; Wu, H., The Lean manufacture research in environment of the supply chain of modern industry engineering (2009) 16th Conference On Industrial Engineering and Engineering Management, pp. 297-300; Wu, S., Wee, H.M., How Lean supply chain effects product cost and quality- A case study of the Ford Motor Company (2009) In 6th International Conference On Service Systems and Service Management; Xia, L.X.X., Ma, B., Lim, R., AHP Based Supply Chain Performance Measurement System (2007) In Emerging Technologies and Factory Automation; Zhao, X., Lai, F., Lee, T.S., Evaluation of safety stock methods in multilevel material requirements planning (MRP) systems (2001) Production Planning & Control, 12 (8), pp. 794-803. , http://dx.doi.org/10.1080/095372800110052511},
publisher={Universitat Politecnica de Catalunya},
issn={20138423},
language={English},
abbrev_source_title={J. Ind. Eng. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sahu2014237,
author={Sahu, A.K. and Datta, S. and Mahapatra, S.S.},
title={Use of IVFNs and MULTIMOORA method for supply chain performance measurement, benchmarking and decision-making: An empirical study},
journal={International Journal of Business Excellence},
year={2014},
volume={7},
number={2},
pages={237-280},
doi={10.1504/IJBEX.2014.059572},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897712441&doi=10.1504%2fIJBEX.2014.059572&partnerID=40&md5=fca8c253a3ea38f5755a4e71b5ce663e},
affiliation={Department of Mechanical Engineering, National Institute of Technology, Rourkela-769008, India},
abstract={In today's competitive global marketplace, supply chain management (SCM) has become a key strategic consideration for achieving the organisational goals. The performance measurement of the entire supply chain is an important issue because it allows for tracking of efficacy and efficiency failures and leads to more informed decision-making with regard to chain design. Performance measurement is important as it affects behaviour that impacts supply chain performance. As such, performance measurement provides the means by which a company can assess whether its supply chain has improved or degraded. To this end, the present research aims to develop an efficient evaluation framework for selecting the best supply chain performance alternative from available alternatives. In this study, a fuzzy multi-criteria decision-making (FMADM) combined with multi-objective optimisation by ratio analysis (MULTIMOORA) has been explored towards appraisement and selection of the best supply chain in accordance with performance. © 2014 Inderscience Enterprises Ltd.},
author_keywords={FMADM;  Fuzzy multi-criteria decision-making;  Multi-objective optimisation by ratio analysis;  MULTIMOORA;  SCM;  Supply chain management},
references={Agarwal, A., Shankar, R., Modeling supply chain performance variables (2005) Journal of Asian Academy Management, 10 (2), pp. 47-68; Ba, U., Erkan, T.E., A model to evaluate supply chain performance and flexibility (2011) African Journal of Business Management, 5 (11), pp. 4263-4271; Baleentis, T., Zeng, S., Group multi-criteria decision making based upon intervalvalued fuzzy numbers: An extension of the MULTIMOORA method (2013) Expert Systems with Applications, 40 (2), pp. 543-550; Brauers, W.K.M., Zavadskas, E.K., The MOORA method and its application to privatization in a transition economy (2006) Control and Cybernetics, 35 (2), pp. 445-469; Brauers, W.K.M., Zavadskas, E.K., Robustness of the multiobjective MOORA method with a test for the facilities sector (2009) Technological and Economic Development of Economy: Baltic J on Sustainability, 15 (2), pp. 352-375; Brauers, W.K.M., Zavadskas, E.K., Project management by MULTIMOORA as an instrument for transition economies (2010) Technological and Economic Development of Economy, 16 (1), pp. 5-24; Brauers, W.K.M., Zavadskas, E.K., MULTIMOORA optimization used to decide on a bank loan to buy property (2011) Technologic Al and Economic Development of Economy, 17 (1), pp. 174-188; Chakraborty, S., Applications of the MOORA method for decision making in manufacturing environment (2011) Int. J. Adv. Manuf. Technol., 54 (9-12), pp. 1155-1166; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol., 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Chan, F.T., Qi, H.J., Chan, H.K., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) Management Decision -london Then Bradford, 41 (7), pp. 635-642. , DOI 10.1108/00251740310495568; Chen, C.T., Extensions of the TOPSIS for group decision-making under fuzzy environment (2000) Fuzzy Sets and Systems, 114 (1), pp. 1-9; Chen, S.J., Chen, S.M., Fuzzy risk analysis based on similarity measures of generalized fuzzy numbers (2003) IEEE Transactions on Fuzzy Systems, 11 (1), pp. 45-46; Chen, S.M., Sanguansat, K., Analyzing fuzzy ris k based on similarity measures between interval-valued fuzzy numbers (2011) Expert Systems with Applications, 38, pp. 8612-8621; Eccles, R.G., Pyburn, P.J., Creating a comprehensive system to measure p erformance (1992) Management Accounting [US], 27 (4), pp. 41-44. , October; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hardy, T.L., (1995) Multi-Objective Decision-Making under Uncertainty: Fuzzy Logic Methods, , 28-30 March, Lewis Research Center, Cleveland, Ohio, Prepared for the Computing in Aerospace 10 Meeting sponsored by the American Institute of Aeronautics and Astronautics, San Antonio, Texas; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353. , DOI 10.1108/14635770510609015; Ho, G.T.S., Lau, H.C.W., Chung, S.H., Fung, R.Y.K., Chan, T.M., Lee, C.K.M., A fuzzy rule sets for enhancing performance in a supply chain network (2008) Industrial Management & Data Systems, 108 (7), pp. 947-972; Holmberg, S., A system perspective in supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Hongxia, J., Zhipeng, C., Performance appraisal on supply chain b ased on value engineering and multistage fuzzy comprehensive evaluation (2007) International Conference on Wireless Communications, Networking and Mobile Computing, 2007, pp. 4722-4725. , (WiCo 2007), Date of Conference: 21-25 September; Kamalabadi, N., Bayat, A., Ahmadi, P., Ebrahimi, A., Kahreh, M.S., Presenting a new algorithm for performance measurement of supply chain by using (FMADM) approach (2008) Journal of World Applied Sciences, 5 (5), pp. 582-589; Kaplan, R.S., Norton, D., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79. , January-February; Klir, G.J., Yuan, B., (1995) Fuzzy Sets and Fuzzy Logic, , Prentice-Hall New Jersey; Lau, H.W.C., Pang, W.K., Wong, C.W.Y., Methodology measure supply chain performance: Fuzzy logic approach (2002) Logistic Information Management, 15 (4), pp. 271-280; Liu, P.D., A weighted aggregation operators multi-attribute group decision making method based on interval-valued trapezoidal fuzzy numbers (2011) Expert Systems with Applications, 38 (1), pp. 1053-1060; Liu, P.D., An extended TOPSIS method for multiple attribute group decision making based on generalized interval-valued trapezoidal fuzzy numbers (2011) Informatica, 35 (2), pp. 185-196; Liu, P.D., Jin, F., A multi-attribute group decision-making method based on weighted geometric operators of interval-valued trapezoidal fuzzy numbers (2012) Applied Mathematical Modelling, 36 (6), pp. 2498-2509; Liu, P.D., Jin, F., A multi-attribute group decision-making method based on weighted geometric operators of interval-valued trapezoidal fuzzy numbers (2012) Applied Mathematical Modelling, 36 (6), pp. 2498-2509; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Peronaa, M., Miragliottab, G., Complexity management and supply chain performance assessment. A field study and a conceptual framework (2004) Int. J. Production Economics, 90 (1), pp. 103-115; Prasad, C.H.V.V.S.N.V., Multiple performance dimensions to measure supply chain performance (2012) International Journal of Business and General Management (IJBGM), 1 (1), pp. 40-55; Ramaa, A., Rangaswamy, T.M., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Second International Conference on Emerging Trends in Engineering and Technology, , ICETET-09; Sarode, A.D., Sunnapwar, V.K., Khodke, P.M., A literature review for identification of performance measures for establishing framework for performance measurement in supply chains (2005) The International Journal of Applied Mana Gement and Technology, 6 (3), pp. 241-273; Schermerhorn, J.R., Chappell, S., (2000) Introducing Management, , John Wiley & Sons, Inc., New York; Shukla, R.K., Garg, D., Agarwal, A., Understanding of supply chain: A literature review (2011) International Journal of Engineering Science and Technology (IJEST), 3 (3), pp. 2059-2072. , March; Trkman, P., McCormack, K., Oliveira, M.P.V., Ladeira, M.B., The impact of business analytics on supply chain performance (2010) Decision Support Systems, 49 (3), pp. 318-327; Wang, L., (1997) A Course in Fuzzy Systems and Control, , Prentice-Hall New Jersey; Wei, S.H., Chen, S.M., Fuzzy risk analysis based on interval-valued fuzzy numbers (2009) Expert Systems with Applications, 36 (2), pp. 2285-2299; Yager, R.R., On ordered weighted averaging aggregation operators in multicriteria decision making (1988) IEEE Transactions on Systems, Man and Cybernetics, 18 (1), pp. 183-190},
correspondence_address1={Datta, S.; Department of Mechanical Engineering, , Rourkela-769008, India; email: sdattaju@gmail.com},
publisher={Inderscience Publishers},
issn={17560047},
language={English},
abbrev_source_title={Int. J. Bus. Excellence},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dubey2014367,
author={Dubey, R. and Ali, S.S. and Aital, P. and Venkatesh, V.G.},
title={Mechanics of humanitarian supply chain agility and resilience and its empirical validation},
journal={International Journal of Services and Operations Management},
year={2014},
volume={17},
number={4},
pages={367-384},
doi={10.1504/IJSOM.2014.059999},
note={cited By 51},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896907784&doi=10.1504%2fIJSOM.2014.059999&partnerID=40&md5=82f30951fd5dcc89ca165d27855a1a8b},
affiliation={Symbiosis Institute of Operations Management, Symbiosis International University, Plot No. A-23, Shravan Sector, New Nashik-422008, Maharashtra, India; Fortune Institute of International Business, Rao Tula Ram Marg, Vasant Vihar, New Delhi, 110057, India},
abstract={The purpose of this paper is to establish the relationship between chain agility, supply chain resilience and humanitarian supply chain performance. In this paper, researchers have attempted to explain supply chain agility and supply chain resilience using oscillation physics theory and further attempted to test the theoretical concept empirically using psychometric tool. The researchers have collected data which represent 20% response rate is subjected to exploratory factor analysis (EFA) using principal component analysis (PCA). The EFA output is further tested using ordinary least-square regression to check the fitness of constructs as a CFA test. The confirmatory factor analysis (CFA) test output suggests that factors obtained using EFA represent the constructs of the model. The multivariate analysis shows that supply chain agility, supply chain resilience are two important determinants of the pre-disaster and post-disaster supply chain performance measures. Copyright © 2014 Inderscience Enterprises Ltd.},
author_keywords={Agility;  Factor analysis;  Humanitarian supply chain;  Resilience},
references={Aitken, J., Martin, C., Towill, D., Understanding, implementing and exploiting agility and leanness (2002) International Journal of Logistics: Research and Applications, 5 (1), pp. 59-74; Akhtar, P., Marr, N.E., Garnevska, E.V., Coordination in humanitarian relief chains: Chain coordinators (2012) Journal of Humanitarian Logistics and Supply Chain Management, 2 (1), pp. 85-103; Altay, N., Green, W.G., OR/MS research in disaster operations management (2006) European Journal of Operational Research, 175 (1), pp. 475-493; Altay, N., Prasad, S., Sounderpandian, J., Strategic planning for disaster relief logistics: Lessons from supply chain management (2009) International Journal of Services Sciences, 2 (2), pp. 142-161; Balcik, B., Beamon, B.M., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Baldini, G., Securing disaster supply chains with cryptography enhanced RFID (2012) Disaster Prevention and Management, 21 (1), pp. 51-70; Blecken, A., Supply chain process modeling for humanitarian organizations (2010) International Journal of Physical Distribution & Logistics Management, 40 (8-9), pp. 675-692; Boin, A., Kelle, P., Whybark, D.C., Resilient supply chain for extreme situations: Outlining a new field of study (2010) International Journal of Production Economics, 126 (1), pp. 1-6; Booth, R., More agile than lean (1995) Proceedings of the British Production and Inventory Control Society Conference, pp. 191-207; Bullinger, H.J., Turbulent time requires creative thinking: New European concepts in production management (1999) International Journal of Production Economics, 60 (61), pp. 9-27; Cadden, T., Marshall, D., Cao, G., Opposites attract: Organizational culture and supply chain performance (2013) Supply Chain Management : An International Journal, 18 (1), pp. 86-103; Cassidy, W.B., (2003) A Logistics Lifeline, p. 1. , Traffic World, 27 October; Charles, A., Lauras, M., Wassenhove, L.V., A model to define and assess the agility of supply chains: Building on humanitarian experience (2010) International Journal of Physical Distribution & Logistics Management, 40 (8), pp. 722-741; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: The constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Cho, H., Jung, M., Kim, M., Enabling technologies of agile manufacturing and its related activities in Korea (1996) Computers & Industrial Engineering, 30 (3), pp. 323-334; Christopher, M., The agile supply chain (2000) Industrial Marketing Management, 29 (1), pp. 37-44; Christopher, M., Tatham, P., Introduction (2011) Humanitarian Logistics. Meeting the Challenge of Preparing for and Responding to Disasters, pp. 1-14. , in Christopher, M. and Tatham, P. (Eds.) , Kogan Page, London; Conboy, K., Fitzgerald, B., Toward a conceptual framework of agile methods (2004) Proceedings of the 2004 ACM Workshop on Interdisciplinary Software Engineering Research, Newport Beach, CA, pp. 37-44; Cozzolino, A., Rossi, S., Conforti, A., Agile and lean principles in the humanitarian supply chain. The case of the United Nations world food programme (2012) Journal of Humanitarian Logistics and Supply Chain Management, 2 (1), pp. 16-33; Devor, R., Graves, R., Mills, J.J., Agile manufacturing research: Accomplishments and opportunities (1997) IIE Transactions, 29 (10), pp. 813-823; Ertem, A.M., Buyurgan, N., Rossetti, M.D., Multiple-buyer procurement auctions framework for humanitarian supply chain management (2010) International Journal of Physical Distribution & Logistics Management, 40 (3), pp. 202-227; Faisal, M.N., Banwet, D.K., Shankar, R., Mapping supply chain on risk and customer sensitivity dimensions (2006) Industrial Management and Data Systems, 106 (6), pp. 878-895; Faisal, M.N., Banwet, D.K., Shankar, R., An approach to measure supply chain agility (2007) International Journal of Industrial and Systems Engineering, 2 (1), pp. 79-98; Gehani, R.R., Time-based management of technology: A taxonomic integration of tactical strategic roles (1995) International Journal of Operations & Production Management, 15 (2), pp. 19-35; Gligor, D.M., Holcomb, M.C., Understanding the role of logistics capabilities in achieving supply chain agility: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), pp. 438-453; Goldman, S., Preiss, K., (1991) 21st Century Manufacturing Enterprise Strategy: An Industry Led View, 1. , Iacocca Institute, Bethlehem, PA; Goldman, S.L., Nagel, R.N., Preiss, K., (1995) Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer, , Van Nostrand Reinhold, New York, NY; Gunasekaran, A., Agile manufacturing: Enablers and an implementation framework (1998) International Journal of Production Research, 26 (5), pp. 1223-1247; Gunasekaran, A., Patel, C., McGaughey, E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gupta, U.G., Mittal, R.O., Quality, time, and innovation based performance measurement system for agile manufacturing (1996) Proceedings-Annual Meeting of the Decision Sciences Institute, 3, pp. 1511-1513; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, Prentice-Hall International, , Inc., Upper Saddle River, New Jersey; Holguin-Veras, J., Jaller, M., Wassenhove, L.V., Perez, N., Watchtendorf, T., On the unique features of post-disaster humanitarian logistics (2012) Journal of Operations Management, 30 (6), pp. 494-506; Hoyt, J., Faizul, H., From arms-length to collaborative relationships in the supply chain (2000) International Journal of Physical Distribution & Logistics Management, 20 (9), pp. 750-765; Ismail, H.S., Sharifi, H., A balanced approach to building agile supply chains (2006) International Journal of Physical Distribution & Logistics Management, 36 (6), pp. 431-444; Ivanov, D., Sokolov, B., Kaschel, J., Adaptation-based supply chain resilence (2013) Supply Chain Safety Management, pp. 267-287. , Essig, M. et al. (Eds.) , Springer-Verlag, Berlin, Hiedelberg; Jain, V., Benyoucef, L., Deshmukh, S.G., What's the buzz about moving from 'lean' to 'agile' integrated supply chains? A fuzzy intelligent agent-based approach (2008) International Journal of Production Research, 46 (23), pp. 6649-6677; Khan, O., Christopher, M., Creazza, A., Aligning product design with the supply chain: A case study (2012) Supply Chain Management: An International Journal, 17 (3), pp. 323-336; Kidd, P.T., (1994) Agile Manufacturing: Forging New Frontiers, , Addison-Wesley, Reading, MA; Kovacs, G., Spens, K.M., Humanitarian logistics in disaster relief operations (2007) International Journal of Physical Distribution and Logistics Management, 37 (2), pp. 99-114; Kovacs, G., Spens, K.M., Identifying challenges in humanitarian logistics (2009) International Journal of Physical Distribution & Logistics Management, 39 (6), pp. 506-528; Kovacs, G., Spens, K.M., Trends and developments in humanitarian logistics-A gap analysis (2011) International Journal of Physical Distribution and Logistics Management, 41 (1), pp. 32-43; Kovacs, G., Tatham, P.H., Responding to disruptions in the supply network-from dormant to action (2009) Journal of Business Logistics, 30 (2), pp. 215-229; Krafcik, J., Triumph of the lean production system (1988) Sloan Manage. Rev., 30 (1), pp. 41-52; Kumar, G.A., Devadasan, S.R., Shalij, P.R., Vinodh, S., Rajanayagam, D., Edinbarough, I., Implementation of agile supply chain model in an electronic switches manufacturing company (2010) International Journal of Services and Operations Management, 6 (4), pp. 452-469; Kumar, P., Deshmukh, S., A model for flexible supply chain through flexible manufacturing (2006) Global Journal of Flexible Systems Management, 7 (3-4), pp. 17-24; Lee, H.L., Aligning supply chain strategies with product uncertainties (2002) California Management Review, 44 (3), pp. 105-119; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Li, X., Chung, C., Goldsby, T.J., Holsapple, C.W., A unified model of supply chain agility: The work-design perspective (2008) International Journal of Logistics Management, 19 (3), pp. 408-435; Long, D.C., Wood, D.F., The logistics of famine relief (1995) Journal of Business Logistics, 16 (1), pp. 213-229; McCann, J., Selsky, J., Lee, J., Building agility, resilience and performance in turbulent environments (2009) People & Strategy, 32 (3), pp. 45-51; Murray, S., (2005) How to Deliver on the Promises: Supply Chain Logistics: Humanitarian Agencies Are Learning Lessons from Business in Bringing Essential Supplies to Regions Hit by the Tsunami, p. 9. , Financial Times, 6-7 January; Nagel, R., Dove, R., (1991) 21st Century Manufacturing Enterprise Strategy-An Industry Led Review, 1-2. , Iacocca Institute, Lehigh University, Bethlehem, PA; Pettit, S.J., Beresford, A.K.C., Emergency relief logistics: An evaluation of military, non-military, and composite response models (2006) International Journal of Logistics: Research and Applications, 8 (4), pp. 313-331; Power, D.J., Sohal, A.S., Rahman, S.U., Critical success factors in agile supply chain management-an empirical study (2001) International Journal of Physical Distribution & Logistics Management, 31 (4), pp. 247-265; Quinn, R.D., Causey, G.C., Merat, F.L., Sargent, D.M., Barendt, N.A., Newman, W.S., Velasco, V.B., Kim, Y., An agile manufacturing workcell design (1997) IIE Transactions, 29 (10), pp. 901-909; Richey, R.G., The supply chain crisis and disaster pyramid: A theoretical framework for understanding preparedness and recovery (2009) International Journal of Physical Distribution and Logistics Management, 39 (7), pp. 619-628; Sanchez, L.M., Nahi, R., A review of agile manufacturing systems (2001) International Journal of Production Research, 39 (16), pp. 3561-3600; Sarkis, J., Benchmarking for agility (2001) Benchmarking: An International Journal, 8 (2), pp. 88-107; Scholten, K., Scott, P.S., Fynes, B., (Le) agility in humanitarian aid (NGO) supply chains (2010) International Journal of Physical Distribution & Logistics Management, 40 (8-9), pp. 623-635; Sharp, J.M., Irani, Z., Desai, S., Working towards agile manufacturing in the UK industry (1999) International Journal of Production Economics, 62 (1-2), pp. 155-169; Sheffi, Y., (2005) The Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage, , MIT Press, Cambridge, MA; Swafford, P.M., Ghosh, S., Murthy, N., The antecedents of supply chain agility of a firm: Scale development and model testing (2006) Journal of Operations Management, 24 (2), pp. 170-188; Tatham, P., Petitt, S.J., Transforming humanitarian logistics: The journey to supply network management (2010) International Journal of Physical Distribution and Logistics Management, 40 (8-9), pp. 609-622; Tatham, P.H., Kovacs, G., The humanitarian supply network in rapid onset disasters (2007) Proceedings of NOFOMA, , Rejavik, 7/8 June; Tatham, P.H., Spens, K.M., Towards a humanitarian logistic knowledge management system (2011) Disaster Prevention and Management, 20 (1), pp. 6-26; Thomas, A., Kopczak, L., (2005) From Logistics to Supply Chain Management. The Path Forward in the Humanitarian Sector, , http://www.fritzinstitute.org/PDFs/WhitePaper/FromLogisticsto.pdf, Fritz Institute, [online] (accessed 17 March 2013); Trunick, P.A., Special report: Delivering relief to tsunami victims (2005) Logistics Today, 46 (2), pp. 1-3; Wassenhove, L.V., Humanitarian aid logistics: Supply chain management in high gear (2006) Journal of the Operational Research Society, 57 (5), pp. 475-489; Wieland, A., Wallenburg, C.M., Dealing with supply chain risks: Linking risk management practices and strategies to performance (2012) International Journal of Physical Distribution & Logistics Management, 42 (10), pp. 887-905; Womack, J.P., Jones, D.T., Roos, D., (1991) The Machine That Changed the World: The Story of Lean Production, , Harper Perenial, NY; Yusuf, Y.Y., Sarhadi, M., Gunasekaran, A., Agile manufacturing: The drivers, concepts and attributes (1999) International Journal of Production Economics, 62 (1-2), pp. 33-43; Zhang, Z., Sharifi, H., A methodology for achieving agility in manufacturing organisations (2000) International Journal of Operations & Production Management, 20 (4), pp. 496-513; Zhao, M., Droge, C., Stank, T.P., The effects of logistics capabilities on firm performance: Customer focused versus information-focused capabilities (2001) Journal of Business Logistics, 22 (2), pp. 91-107},
correspondence_address1={Dubey, R.; Symbiosis Institute of Operations Management, Plot No. A-23, Shravan Sector, New Nashik-422008, Maharashtra, India; email: rameshwardubey@gmail.com},
publisher={Inderscience Publishers},
issn={17442370},
language={English},
abbrev_source_title={Int. J. Serv. Oper. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Li2013556,
author={Li, H. and Yun, H. and Shi, J.},
title={Critical criteria when implementing electronic chain traceability in a fish supply chain},
journal={Journal of Chemical and Pharmaceutical Research},
year={2013},
volume={5},
number={11},
pages={556-561},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890943120&partnerID=40&md5=e3a934196b2e96f800f90f7dd107de66},
affiliation={Department of Computer Engineering, Huai Hai Institute of Technology, Lianyungang, China; School of Information and Electrical Engineering, China University of Mining and Technology, China; Department of Computer Science and Technology, Nanjing University, Nanjing, China},
abstract={In order to enhance product quality management in fresh fish supply chains through improved logistics management and ensured traceability effectiveness, we investigate and discuss the underlying nature of the requirements traceability problem. We introduce the distinction between pre-requirements specification (pre-RS) traceability and post-requirements specification (post-RS) traceability, to demonstrate why an all-encompassing solution to the problem is unlikely, and to provide a framework through which to understand its multifaceted nature.},
author_keywords={Criteria;  Fresh;  Supply chain;  Traceability},
keywords={Criteria;  Electronic chain;  Fresh;  Fresh fish;  Logistics management;  Requirements traceability;  Traceability, Fish;  Quality management;  Specifications, Supply chains, article;  catering service;  commercial phenomena;  cost minimization analysis;  decision making;  fish;  fishery management;  food safety;  legal liability;  materials management;  performance measurement system;  process optimization;  radiofrequency identification;  total quality management},
references={Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, , Planning and Operation, 3st edition, Pearson Education; Bhattacharjee, D., Das, S., (2013) J. Chem. Pharm. Res, 5 (4), pp. 90-96; Bigliardi, B., Bottani, E., (2010) Emerald Group Publishing Limited, 28 (5-6), pp. 249-260; Kavitha, C., Malarvizhi, A., Kumaran, S.S., Ramesh, M., (2010) Food Chem. Toxicol, 48 (10), pp. 2848-2854; Brewer, P.C., Speh, T.W., (2000) J. of Business logistics, 21 (1), pp. 75-93; Cai, J., Liu, X., Xiao, Z., Liu, J., (2009) Decision Support Systems, 46, pp. 512-521; Aramyan, L.H., Lansink, A.G., Vorst, J.G., Kooten, O., (2007) Int J Environ Res, 2 (4), pp. 304-315; Najmi, A., Makui, A., (2010) Int J of Industrial Eng. Computations, 1 (2), pp. 199-212; Duarte, S., Cabrita, R., Machado, V.C., Proceedings of the 2011 (2011) Int. Conference on Ind. Eng. and Operations Management, pp. 520-525; Kamalabadi, N., Bayat, A., Ahmadi, P., Ebrahimi, A., Kahreh, M.S., (2008) World Applied Sci. J, 5 (5), pp. 582-589; Fauske, H., Kollberg, M., Dreyer, H.C., Bolseth, S., (2006) 14th Int. Annual EurOMA conf. in Ankara Turkey, pp. 1-10; Jamil, C.M., Mohamed, R., (2011) World J. of Social Scie, 1 (3), pp. 200-212; Talavera, M.G., (2010) Philippine Management Review, 17, pp. 52-65; Lockamy, A., (2004) Int. J of Operations & Production Management, 24 (12), pp. 1192-1218; Putra, A.F., (2009) J. Chem. Pharm. Res, 2 (2), pp. 1-8; Saleem, H., Khan, M.Z., Afzal, S., (2012) Int. J of Computer Sceience Issues, 9 (5), pp. 120-135; Gorin, V., Kerov, S., Vershik, A., (2012) Representation Theory, 22 (2), pp. 85-100; Wang, L.P., (2011) Cryptography and Communications, 3 (1), pp. 29-42; Shen, Q., (2011) Systems, Man and Cybernetics, 41 (1), pp. 85-96; Gottlob, G., (2011) Lecture Notes in Computer Science, 6867 (2), pp. 325-339; Davis, C.G., Vick, C.R., (1997) IEEE Transactions on Software Engineering, 3 (1), pp. 69-84; Duarte, J.P., Beirao, J., (2010) Enviroment and Planning, 38 (5), pp. 879-902; Lee, D.D., Seung, H.S., (1999) Nature, 401 (6775), pp. 788-791},
correspondence_address1={Li, H.; Department of Computer Engineering, , Lianyungang, China},
issn={09757384},
language={English},
abbrev_source_title={J. Chem. Pharm. Res.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Schödwell20131003,
author={Schödwell, B. and Erek, K. and Zarnekow, R.},
title={Data center green performance measurement: State of the art and open research challenges},
journal={19th Americas Conference on Information Systems, AMCIS 2013 - Hyperconnected World: Anything, Anywhere, Anytime},
year={2013},
volume={2},
pages={1003-1017},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893209920&partnerID=40&md5=6c1a3dd8425154150df3d941eba1c56c},
affiliation={Technische Universität Berlin, Germany},
abstract={Data centers (DC/DCs) are indispensable elements of information systems. The increase in information technology service demand drives their worldwide grow in number, size and energy consumption. In the light of depleting raw natural resources and climate change induced by greenhouse gases (GHG) the environmental impacts of DCs have received particular attention. This paper reviews literature to highlight major issues that contribute to DCs ecologic sustainability, and explores the state of the art of green performance indicators (GPIs) to assess DCs environmental performance, in particular the energy, GHG and resource efficiency. Afterwards, the identified GPIs are classified and clustered to construct a green performance measurement system. Furthermore, the paper generates insights in relation to the recognition and application of proposed GPIs in practice through 13 questionnaires and two expert interviews. Thus, the paper provides academics and practitioners with the body of knowledge on DC green performance measurement, and moreover formulates open research challenges. © (2013) by the AIS/ICIS Administrative Office All rights reserved.},
author_keywords={Data center;  Green IT;  Green performance indicators;  Metrics;  Performance measurement;  Sustainability},
keywords={Data centers;  Green IT;  Green Performance Indicators;  Metrics;  Performance measurements, Benchmarking;  Climate change;  Digital storage;  Energy utilization;  Environmental management;  Greenhouse gases;  Information systems;  Supply chain management;  Surveys;  Sustainable development, Environmental impact},
references={Aebischer, B., Eubank, H., Tschudi, W., Energy Efficiency Indicators for Data Centers (2004) Proceedings of the International Conference On Improving Energy Efficiency In Commercial Buildings (IEECB'04), , April 21-22, Frankfurt, Germany, ECEEE; Anderson, D., Cader, T., Darby, T., Gruendler, N., Hariharan, R., Holler, A., Lindberg, C., Wallerich, J., (2008) A Framework For Data Center Energy Productivity, , www.thegreengrid.org//media/whitepapers/whitepaper13frameworkfordatacenterenergyproductivity5908.pdf, White Paper #13, The Green Grid, Beaverton, OR, USA, last accessed 13.05.2013; Energy Efficiency for Telecommunication Equipment: Methodology for Measurement and Reporting for Router and Ethernet Switch Products (2013) ANSI Standard, Alliance For Telecommunications Industry Solutions, , http://webstore.ansi.org/RecordDetail.aspx?sku=ATIS-0600015.06.2011, ATIS, last accessed 13.05.2013; Azevedo, D., Rawson, A., Measuring Data Center Productivity, The Green Grid (2008) Presentation Held On 1st Annual Green Grid Technical Forum and Members Meeting 2008, , http://www.thegreengrid.org//media/TechForumPresentations2008/Measuring_Data_Center_Productivity.pdf, February 5-6, Beaverton, OR, USA, last access: 13.05.2013; Barroso, L.A., Hölzle, U., The Case for Energy-Proportional Computing (2007) Computer, 40 (12), pp. 33-37; Belady, C.L., Malone, C.G., Metrics and an Infrastructure Model to Evaluate Data Center Efficiency (2007) Proceedings of the 2007 ASME InterPack Conference (IPACK'07), pp. 751-755. , July 8-12, Vancouver, BC, Canada, ASME; Belady, C., Patterson, M., (2008) The Green Grid Productivity Indicator, , http://www.thegreengrid.org//media/whitepapers/white_paper_15_-_tgg_productivity_indicator_063008.pdf, White Paper #15, The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; Belady, C., Rawson, A., Pfleuger, J., Cader, T., (2008) The Green Grid Data Center Power Efficiency Metrics: PUE and DCIE, , http://www.thegreengrid.org//media/WhitePapers/White_Paper_6_-_PUE_and_DCiE_Eff_Metrics_30_December_2008.pdf, White Paper #6, The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; Belady, C., Azevedo, D., Patterson, M., Pouchet, J., Tipley, R., (2010) Carbon Usage Effectiveness (CUE): A Green Grid Data Center Sustainability Metric, , http://www.thegreengrid.org//media/WhitePapers/CarbonUsageEffectivenessWhitePaper20101202.ashx, White Paper #32, The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; Blackburn, M., Azevedo, D., Hawkins, A., Ortiz, Z., Tipley, R., Van Den Berghe, S., (2010) The Green Grid Data Center Compute Efficiency Metric: DCcE, , http://www.thegreengrid.org//media/WhitePapers/DCcE_White_Paper_Final.pdf, White Paper #34, The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; Brill, K.G., (2007) Data Center Energy Efficiency and Productivity, , http://www.uptimeinstitute.org/symp_pdf/%28TUI3004C%29DataCenterEnergyEfficiency.pdf, White Paper,. The Uptime Institue, Santa Fe, NM, USA, last access: 13.05.2013; Brown, E., Banks, M., Benjamin, E., Calderwood, T., Llera, R.G., Pflueger, J., Schroeder, G., Watson, M., (2012) Electronics Disposal Efficiency: An it Recycling Metric For Enterprises and Data Centers, , http://www.thegreengrid.org//media/whitepapers/wp53-electronicsdisposalefficiencyanitrecyclingmetricforenterprisesanddatacenters.pdf, White Paper #53, The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; Butler, T., Institutional Imperatives on Environmental Sustainability: A Sense Making Perspective on Green IS (2011) Strategic Information Systems, 20 (1), pp. 6-26; (2013), http://www.green500.org, Compu Green, The Green500 List, last access: 13.05.2013; Costa, G., Hlavacs, H., Methodology of Measurement for Energy Consumption of Applications (2010) In Proceedings of the 11th IEEE/ACM International Conference On Grid Computing, pp. 290-297. , October 25-28, Brussel, Belgium, IEEE; Daim, T., Justice, J., Krampits, M., Letts, M., Subramanian, G., Thirumalai, M., Data Center Metrics: An Energy Efficiency Model for Information Technology Managers (2009) Management of Environmental Quality: An International Journal, 20 (6), pp. 712-731; (2013) The Energy Consumption Rating (ECR) Initiative, , http://www.ecrinitiative.org, ECR, last access: 13.05.2013; Elkington, J., (1997) Cannibals With Forks: The Triple Bottom Line of 21st Century Business, , Capstone, Oxford, UK; Elliot, S., Binney, D., Environmentally Sustainable ICT: Developing Corporate Capabilities and an Industry- Relevant IS Research Agenda (2008) Proceedings of the 12th Pacific Asia Conference On Information Systems (PACIS'08), , July 3-7, Suzhou, China; (2007) Report to Congress On Server and Data Center Energy Efficiency, , http://www.energystar.gov/ia/partners/prod_development/downloads/EPA_Datacenter_Report_Congress_Final1.pdf, EPA, U.S. Environmental Protection Agency, last access: 13.05.2013; Garnier, C., Aggar, M., Banks, M., Dietrich, J., Shatten, B., Stutz, M., Tong-Viet, E., (2012) Data Centre Life Cycle Assessment Guidelines, , http://www.thegreengrid.org//media/WhitePapers/WP45v2DataCentreLifeCycleAssessmentGuidelines.pdf, White Paper #45, The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; (2011) Proposal of OGE, On-site Generation Efficiency Metric, and ECI, Energy Carbon Intensity Metric, , http://home.jeita.or.jp/greenitpc/topics/release/pdf/dppe_e_20110222.pdf, GITPC, White Paper, Green IT Promotion Council, Tokyo, Japan, last access: 13.05.2013; (2012) DPPE: Holistic Framework For Data Center Energy Efficiency, , http://home.jeita.or.jp/greenit-pc/topics/release/pdf/dppe_e_20120824.pdf, GITPC, White Paper, Green IT Promotion Council, Tokyo, Japan, last access: 13.05.2013; Greenberg, S., Mills, E., Tschudi, B., Rumsey, P., Myatt, B., Best Practices for Data Centers: Lessons Learned from Benchmarking 22 Data Centers (2006) Proceedings of the 2006 ACEEE Summer Study On Energy Efficiency In Buildings, pp. 77-87. , August 13-18, Pacific Grove, CA, USA, ACEEE; Greenberg, S., Tschudi, W., Weale, J., (2006) Self Benchmarking Guide For Data Center Energy Performance Version 1.0, , http://hightech.lbl.gov/documents/data_centers/self_benchmarking_guide-2.pdf, White Paper, Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, USA, last access 13.05.2013; Greenhill, D., (2005) SWaP - Space, Watts and Power, Presentation Held On the EPA Power Efficiency Forum, , http://www.energystar.gov/ia/products/downloads/Greenhill_Pres.pdf, last access: 13.05.2013; Hannemann, C.R., Carey, V.P., Shah, A.J., Patel, C., Lifetime Exergy Consumption of an Enterprise Server (2008) In Proceedings of the 2008 IEEE International Symposium On Electronics and The Environment (ISEE '08), pp. 1-5. , Washington, DC, USA, IEEE; Herrlin, M.K., Rack Cooling Effectiveness in Data Centers and Telecom Central Offices: The Rack Cooling Index (RCI) (2005) ASHRAE Transactions, 111 (2), pp. 725-731; Hevner, A.R., March, S.T., Park, J., Ram, S., Design Science in Information Systems Research (2004) MIS Quarterly, 28 (1), pp. 75-105; Jenkin, T., Webster, J., McShane, L., An Agenda for 'Green' Information Technology and Systems Research (2010) Information and Organization, 21 (1), pp. 17-40; Kansal, A., Zhao, F., Fine-grained Energy Profiling for Power-aware Application Design (2008) SIGMETRICS Performance Evaluation Review, 36 (2), pp. 26-31; Kant, K., Data Center Evolution: A Tutorial on State of the Art, Issues, and Challenges (2009) Computer Networks, 53 (17), pp. 2939-2965; Kaplan, J.M., Forrest, W., Kindler, N., (2008) Revolutionizing Data Center Energy Efficiency, , http://www.ecobaun.com/images/Revolutionizing_Data_Center_Efficiency.pdf, White Paper, McKinsey & Co, last access: 13.05.2013; Kipp, A., Jiang, T., Fugini, M., Salomie, I., Layered Green Performance Indicators (2012) Future Generation Computer Systems, 28 (2), pp. 478-489; Koomey, J., (2011) Growth In Data Center Electricity Use 2005 to 2010, , http://www.analyticspress.com/datacenters.html, Analytics Press, Oakland, CA, last access: 13.05.2013; Loos, P., Nebel, W., Gómez, J.M., Hasan, H., Watson, R.T., Brocke, J.V., Green IT: A Matter of Business and Information Systems Engineering? (2011) Business & Information Systems Engineering, 3 (4), pp. 245-252; Mathew, P., Greenberg, S., Sartor, D., Bruschi, J., Chu, L., (2010) Self-benchmarking Guide For Data Center Infrastructure, , http://www1.eere.energy.gov/femp/pdfs/dc_benchmarking_infrastructure.pdf, White Paper, Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA, USA, last access: 13.05.2013; Meadows, D.H., Meadows, D.L., Randers, J., Behrens, W.W., (1972) The Limits to Growth: A Report For the Club of Rome's Project On the Predicament of Mankind, , 2nd Edition, Universe Books, New York, NY, USA; Meza, J., Shih, R., Shah, A., Ranganathan, P., Chang, J., Bash, C., Lifecycle-based Data Center Design (2010) Proceedings of the ASME 2010 International Mechanical Engineering Congress & Exposition (IMECE'10), November, pp. 14-17. , Vancouver, BC, Canada, ASME; Naumann, S., Dick, M., Kern, E., Johann, T., The GREENSOFT Model: A reference model for green and sustainable software and its engineering (2011) Sustainable Computing: Informatics and Systems, 1 (1), pp. 294-304; Patterson, M., Tschudi, B., Vangeet, O., Cooley, J., Azevedo, D., (2010) ERE: A Metric For Measuring the Benefit of Reuse Energy From a Data Center, , http://www.thegreengrid.org//media/WhitePapers/ERE_WP_101510_v2.pdf, White Paper #29, The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; Patterson, M., Azevedo, D., Belady, C., Pouchet, J., (2011) Water Usage Effectiveness (WUE) - a Green Grid Data Center Sustainability Metric, , http://www.thegreengrid.org//media/WhitePapers/WUE_v1.pdf, White Paper #29, The Green Grid, Beaverton, O, USA, last access: 13.05.2013; Rivoire, S., Shah, M.A., Ranganathan, P., Kozyrakis, C., JouleSort: A balanced energy-efficiency benchmark (2007) Proceedings of the ACM 2007 International Conference On Management of Data (SIGMOD'07), pp. 365-376. , June 11 - 14, Bejing, China, ACM; Schmidt, N., Erek, K., Kolbe, L., Zarnekow, R., Sustainable Information Systems Management (2009) Business & Information Systems Engineering, 1 (5), pp. 400-402; Schödwell, B., Wilkens, M., Erek, K., Zarnekow, R., Towards a holistic multi-level green performance indicator framework (GPIF) to improve the Energy Efficiency of data center operation - A resource usage-based approach (2012) Proceedings of the Electronics Goes Green (EGG'12), September, 9-12, pp. 1-6. , Berlin, Germany, IEEE; Singh, H., Azevedo, D., Ibarra, D., Newark, R., O'Donell, S., Ortiz, Z., Pfleuger, J., Smith, V., (2011) Data Center Maturity Model, , http://www.thegreengrid.org//media/WhitePapers/Data%20Center%20Maturity%20Model%20White%20Paper_final_v2.pdf, White Paper #36 The Green Grid, Beaverton, OR, USA, last access: 13.05.2013; (2013), http://snia.org/emerald, SNIA, Storage Networking Industry Association Emerald, last access: 13.05.2013; (2013), http://www.storageperformance.org/specs, SPC, Storage Performance Council, last access: 13.05.2013; (2013), http://www.spec.org/power_ssj2008, SPEC, Standard Performance Evaluation Corporation, last access: 13.05.2013; Stanley, J.R., Brill, K.G., Koomey, J., (2007) Four Metrics Define Data Center Greenness, , http://www.dcxdc.ru/files%5C4ede4eff-13b0-49d9-b4da-b0406bfc190e.pdf, White Paper, The Uptime Institue, Santa Fe, NM, last access: 13.05.2013; (2013), http://www.tpc.org/tpc_energy, TPC, Transaction Processing Performance Council, last access: 13.05.2013; Brocke, J.V., Seidel, S., Recker, J., (2012) Green Business Process Management: Towards the Sustainable Enterprise, , Springer, Berlin; Wang, L., Khan, S.U., Review of performance metrics for green data centers: A taxonomy study (2011) The Journal of Supercomputing, 58 (1), pp. 1-18; Watson, R.T., Boudreau, M.-C., (2011) Energy Informatics, , Green ePress, Athens, GA; Watson, R., Boudreau, M.-C., Chen, A., Information systems and environmentally sustainable development (2010) MIS Quarterly, 34 (1), pp. 23-38; West, J., (2009), http://insidehpc.com/2009/04/21/sicortex-revises-greenperformance-index-intros-tool, SiCortex revises green performance index, last access: 13.05.2013},
correspondence_address1={Technische Universität BerlinGermany},
isbn={9781629933948},
language={English},
abbrev_source_title={19th Amer. Conf. Info. Syst., - Hyperconnected World: Anything, Anywhere, Anytime},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{PinheiroDeLima2013524,
author={Pinheiro De Lima, E. and Gouvea Da Costa, S.E. and Angelis, J.J. and Munik, J.},
title={Performance measurement systems: A consensual analysis of their roles},
journal={International Journal of Production Economics},
year={2013},
volume={146},
number={2},
pages={524-542},
doi={10.1016/j.ijpe.2012.05.007},
note={cited By 60},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886716787&doi=10.1016%2fj.ijpe.2012.05.007&partnerID=40&md5=6e459230d1ed84234ea5f1806b411c61},
affiliation={Warwick Business School, Operations Management Group, CV4 7AL Coventry, United Kingdom; Pontifical Catholic University of Paraná, Industrial and Systems Engineering Graduate Program, Rua Imaculada Conceição 1155, Curitiba, PR 80215-901, Brazil; Federal University of Technology, Parana, Curitiba 80230-901, Brazil; Department of Industrial Management, Royal Institute of Technology, 100 44 Stockholm, Sweden},
abstract={Competitive pressures and advances in product and process technologies challenge performance management systems in terms of their design and their strategic and operational use. Understanding the roles of such systems is a first step in developing and employing the appropriate system capabilities and functions. This paper first identifies the roles a performance measurement system can perform. A synthesis is then used to capture expert views through interviews and a Delphi exercise. Twenty international operations management experts participated in the study - eleven academics and nine industry professionals. The study resulted in a refined list of proposed roles of performance measurement systems from the existing operations literature. The findings from the study show that continuous improvement, organisational learning and change management are new elements that characterise the refined measurement system roles, and that the appropriate PMS roles are contingent on design recommendations. The study provides insights for the design, management and use of PMS in organisations. © 2012 Elsevier B.V.},
author_keywords={Delphi method;  Operations strategy;  Performance measurement;  Strategic management},
keywords={Delphi method;  International operations management;  Operations strategies;  Organisational learning;  Performance management systems;  Performance measurement system;  Performance measurements;  Strategic management, Management;  Product design, Supply chain management},
references={Acur, N., Bititci, U., A balanced approach to strategy process (2004) International Journal of Operations and Production Management, 24 (4), pp. 388-408; Akkermans, H.A., Bogerd, P., Yücesan, E., Van Wassenhove, L.N., The impact of ERP on supply chain management: Exploratory findings from a European Delphi study (2003) European Journal of Operational Research, 146 (2), pp. 284-301; Alegre, J., Chiva, R., Assessing the impact of organizational learning capability on product innovation performance: An empirical test (2008) Technovation, 28 (6), pp. 315-326; Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), pp. 217-223; Bhimani, A., Performance measures in UK manufacturing companies: The state of play (1993) Management Accounting, 71 (11), pp. 20-22; Binder, M., Clegg, B., Enterprise management: A new frontier for organizations (2007) International Journal of Production Economics, 106 (2), pp. 409-430; Blenkinsop, S., Davis, L., The road to continuous improvement (1991) Insight, 4 (3), pp. 23-26; Bourne, M.C.S., Researching performance measurement system implementation: The dynamics of success and failure (2005) Production Planning and Control, 16 (2), pp. 101-113; Bourne, M.C.S., Kennerley, M., Franco-Santos, M., Managing through measures: A study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), pp. 373-395; Bourne, M.C.S., Mills, J.F., Wilcox, M., Neely, A.D., Platts, K.W., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Bourne, M.C.S., Mills, J.F., Bicheno, J., Hamblin, D.J., Wilcox, M., Neely, A.D., Platts, K.W., Performance measurement system design: Testing a process approach in manufacturing companies (1999) International Journal of Business Performance Measurement, 1 (2), pp. 154-170; Bradley, L., Stewart, K., A Delphi study of Internet banking (2003) Marketing Intelligence & Planning, 21 (3), pp. 272-281; Brown, S., Fai, F., Strategic resonance between technological and organisational capabilities in the innovation process within firms (2006) Technovation, 26 (1), pp. 60-75; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting, Organisations and Society, 30 (5), pp. 395-422; Chiesa, V., Frattini, F., Lazzarotti, V., Manzini, R., Designing a performance measurement system for the research activities: A reference framework and an empirical study (2008) Journal of Engineering and Technology Management, 25 (3), pp. 213-226; Clarke, P., Non-financial measures of performance in management (1995) Accountancy Ireland, 27 (2), pp. 22-24; Colledani, M., Tolio, T., Performance evaluation of production systems monitored by statistical process control and off-line inspections (2009) International Journal of Production Economics, 120 (2), pp. 348-367; Cossette, P., Analysing the thinking of F.W. Taylor using cognitive mapping (2002) Management Decision, 40 (20), pp. 168-182; Devaraj, S., Hollingworth, D.G., Schroeder, R.G., Generic manufacturing strategies and plant performance (2004) Journal of Operations Management, 22 (3), pp. 313-333; Drucker, P.E., The emerging theory of manufacturing (1990) Harvard Business Review, 68 (3), pp. 94-102; Ensslin, L., Montibeller Neto, G., Noronha, S.M., (2001) Decision Support: Methodologies for Structuring Problems and Multi-Criteria Alternatives Assessment, , Insular Florianopolis, Brazil; Fernandes, K.J., Raja, V., Whalley, A., Lessons from implementing the balanced scorecard in a small and medium size manufacturing organization (2006) Technovation, 26 (5-6), pp. 623-634; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA London; Flynn, B.B., Flynn, E.J., An exploratory study of the nature of cumulative capabilities (2004) Journal of Operations Management, 22 (5), pp. 439-457; Flynn, B.B., Schroeder, R.G., Flynn, E.J., World class manufacturing: An investigation of Hayes and Wheelwright's foundation (1999) Journal of Operations Management, 17 (3), pp. 249-269; Flynn, B.B., Sakakibara, S., Schroeder, R.G., Bates, K.A., Flynn, E.J., Empirical research methods in operations management (1990) Journal of Operations Management, 9 (2), pp. 250-284; Folan, P., Browne, J., Jagdev, H., Performance: Its meaning and content for today's business research (2007) Computers in Industry, 58 (7), pp. 605-620; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Franco-Santos, M., Kennerley, M.P., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A.D., Towards a definition of a business performance measurement system (2007) International Journal of Operations and Production Management, 27 (8), pp. 784-801; Franco-Santos, M., Bourne, M.C.S., Factors that play a role in managing through measures (2003) Management Decision, 41 (8), pp. 698-710; Franco-Santos, M., Bourne, M.C.S., An examination of the literature relating to issues affecting how companies manage through measures (2005) Production Planning and Control, 16 (2), pp. 114-124; Gargeya, V.B., Plant level performance measurement: An exploratory case study of a pharmaceutical encapsulation company (2005) Technovation, 25 (12), pp. 1457-1467; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations and Production Management, 16 (8), pp. 63-80; Globerson, S., Issues in developing a performance criteria system for an organisation (1985) International Journal of Production Research, 23 (4), pp. 639-646; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: A framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Grady, M.W., Performance measurement: Implementing strategy (1991) Management Accounting, 72 (12), pp. 49-53; Gray, J.V., Roth, A.V., Leiblein, M.J., Quality risk in offshore manufacturing: Evidence from the pharmaceutical industry (2011) Journal of Operations Management, 29 (78), pp. 737-752; Gupta, U.G., Clarke, R.E., Theory and applications of the Delphi technique: A bibliography (1975-1994) (1996) Technological Forecasting and Social Change, 53 (2), pp. 185-211; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., (2009) Multivariate Data Analysis, , 7th edition Pearson Education Upper Saddle River; Hasson, F., Keeney, S., Enhancing rigour in the Delphi technique research (2011) Technological Forecasting and Social Change, 78 (9), pp. 1695-1704; Henchion, M., McIntyre, B., Market access and competitiveness issues for food SMEs in Europe's lagging rural regions (LRRs) (2005) British Food Journal, 107 (6), pp. 404-422; Henry, J.F., Management control systems and strategy: A resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558; Herzog, N.V., Tonchia, S., Polajnar, A., Linkages between manufacturing strategy, benchmarking, performance measurement and business process reengineering (2009) Computers and Industrial Engineering, 57 (3), pp. 963-975; Hinkin, T.R., A review of scale development practices in the study of organizations (1995) Journal of Management, 21 (5), pp. 967-988; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Harvard Business School Press Boston; Johnston, R., Brignall, S., Fitzgerald, L., Good enough performance measurement: A trade-off between activity and action (2002) Journal of the Operational Research Society, 53 (3), pp. 256-262; Jonker, M., Romijn, H., Szirmai, A., Technological effort, technological capabilities and economic performance: A case study of the paper manufacturing sector in West Java (2006) Technovation, 26 (1), pp. 121-134; Joshi, M.P., Kathuria, R., Porth, S.J., Alignment of strategic priorities and performance: An integration of operations and strategic management perspectives (2003) Journal of Operations Management, 21 (3), pp. 353-369; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., (2001) The Strategy Focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment, , Harvard Business School Press Boston; Kathuria, R., Joshi, M.P., Porth, S.J., Organizational alignment and performance: Past, present and future (2007) Management Decision, 45 (3), pp. 503-517; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, 70 (2), pp. 45-50; Kennerley, M.P., Neely, A.D., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2), pp. 213-229; Kennerley, M.P., Neely, A.D., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Ketokivi, M., Schroeder, R.G., Manufacturing practices, strategic fit and performance: A routine-based view (2004) International Journal of Operations and Production Management, 24 (2), pp. 171-191; Kiewiet, D.J., Jorna, R.J., Von Wezel, W., Planners and their cognitive maps: An analysis of domain representations using multi dimensional scaling (2005) Applied Ergonomics, 36 (6), pp. 695-708; Kling, R., In search of efficiency: Concurrent concept elaboration and improvement (2006) Technovation, 26 (7), pp. 753-760; Kolkman, M.J., Kok, M., Van Der Veen, A., Mental model mapping as a new tool to analyse the use of information in decision-making in integrated water management (2005) Physics and Chemistry of the Earth, Parts A/B/C, 30 (5-6), pp. 317-332; Kumar, V., De Grosbois, D., Choisne, F., Kuma, U., Performance measurement by TQM adopters (2008) The TQM Journal, 20 (3), pp. 209-222; Kwak, Y.H., Anbari, F.T., Benefits, obstacles, and future of six sigma approach (2006) Technovation, 26 (5-6), pp. 708-715; Kwong, E., Lee, W.B., Knowledge elicitation in reliability management in the airline industry (2009) Journal of Knowledge Management, 13 (20), pp. 35-48; Laakso, K., Rubin, A., Linturi, H., The role of regulation in the mobile operator business in Finland (2012) Foresight, 14 (2), pp. 1-30; Lee, S., Courtney, J.F., O'Keefe, R.M., A system for organizational learning using cognitive maps (1992) OMEGA International Journal of Management Science, 20 (1), pp. 23-36; Leong, G.K., Snyder, D.L., Ward, P.T., A system for organisational learning using cognitive maps (1990) OMEGA International Journal of Management Science, 18 (2), pp. 109-122; Li, P., Tang, G., Performance measurement design within its organisational context: Evidence from China (2009) Management Accounting Research, 20 (3), pp. 193-207; Linstone, H.A., Turoff, M., (2002) The Delphi Method: Techniques and Applications, , NJIT Newark; Lynch, R.L., Cross, K.F., (1991) Measure Up: The Essential Guide to Measuring Business Performance, , Mandarin London; Maccarthy, B.L., Atthirawong, W., Factors affecting location decisions in international operations - A Delphi study (2003) International Journal of Operations and Production Management, 23 (7), pp. 794-818; Mäkitalo, M., Hilmola, O.-P., Analysing the future of railway freight competition: A Delphi study in Finland. (2010) Foresight, 12 (6), pp. 20-37; Manoochehri, G., The road to manufacturing excellence: Using performance measures to become world-class (1999) Industrial Management, 41 (2), pp. 7-13; Marr, B., Schiuma, G., Business performance measurement - Past, present and future (2003) Management Decision, 41 (8), pp. 680-687; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing: A Model for American Companies, , Productivity Press Cambridge; Maslen, R., Platts, K.W., Building manufacturing capabilities (2000) International Journal of Manufacturing Technology and Management, 1 (4-5), pp. 349-365; McNair, C.J., Mosconi, W., Measuring performance in an advanced manufacturing environment (1987) Management Accounting, 69 (1), pp. 28-31; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533; Melnyk, S.A., Lummus, R., Vokurka, R.J., Sandor, J., (2006) Strategic Supply Chain Management: 2010 and Beyond. Research Report, , Michigan State University and APICS E&R Foundation Lansing; Molina-Castillo, F.J., Munuera-Alemán, J.L., New product performance indicators: Time horizon and importance attributed by managers (2009) Technovation, 29 (10), pp. 714-724; Neely, A.D., Al Najjar, M., Management learning not management control: The true role of performance measurement (2006) California Management Review, 48 (3), pp. 100-114; Neely, A.D., Gregory, M.J., Platts, K.W., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Neely, A.D., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Neely, A.D., Adams, C., Kennerley, M.P., (2002) Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationships, , Financial Times/Prentice Hall London; Neely, A.D., Mills, J.F., Platts, K.W., Richards, H., Gregory, M.J., Bourne, M.C.S., Kennerley, M.P., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Nenadal, J., Process performance measurement in manufacturing organizations (2008) International Journal of Productivity and Performance Management, 57 (6), pp. 460-467; Nevo, D., Chan, Y.E., A Delphi study of knowledge management systems: Scope and requirements (2007) Information and Management, 44 (6), pp. 583-597; Nilsson, F., Olve, R.-G., Control systems in multibusiness companies: From performance management to strategic management (2001) European Management Journal, 19 (4), pp. 344-358; Noci, G., Accounting and non-accounting measures of quality-based performances in small firms (1995) International Journal of Operations and Production Management, 15 (7), pp. 78-105; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Olsen, E.O., Zhou, H., Lee, D.M.S., Ng, Y., Chong, C.C., Padunchwit, P., Performance measurement system and relationships with performance results: A case analysis of a continuous improvement approach to PMS design (2007) International Journal of Productivity and Performance Management, 56 (7), pp. 559-582; Pettigrew, A., Whipp, R., Rosenïeld, R., Competitiveness and the management of strategic change processes (1989) The Competitiveness of European Industry: Country Policies and Company Strategies, , A. Francis, P.K.M. Tharakan, Routledge London; Phusavat, K., Anussornnitisarn, P., Helo, P., Dwight, R., Performance measurement: Roles and challenges (2009) Industrial Management & Data Systems, 109 (5), pp. 646-664; Pilkington, A., Fitzgerald, R., Operations management themes, concepts and relationships: A forward retrospective of IJOPM (2006) International Journal of Operations and Production Management, 26 (11), pp. 1255-1275; Pinheiro De Lima, E., Gouvea Da Costa, S.E., Angelis, J.J., The strategic management of operations system performance (2008) International Journal of Business Performance Management, 10 (1), pp. 108-132; Pinheiro De Lima, E., Gouvea Da Costa, S.E., Reis De Faria, A., Taking operations strategy into practice: Developing a process for defining priorities and performance measures (2009) International Journal of Production Economics, 122 (1), pp. 403-418; Platts, K.W., Integrated manufacturing: A strategic approach (1995) Integrated Manufacturing Systems, 6 (3), pp. 18-23; Popova, V., Sharpanskykh, A., Modelling organizational performance indicators (2010) Information Systems, 35 (4), pp. 505-527; Quezada, L.E., Cordova, F.M., Palominos, P., Godoy, K., Ross, J., Method for identifying strategic objectives in strategy maps (2009) International Journal of Production Economics, 122 (1), pp. 492-500; Rajesh, R., Pugazhendhi, S., Ganesh, K., Ducq, Y., Lenny Koh, S.C., Generic balanced scorecard framework for third party logistics service providerInternational Journal of Production Economics, , in press, Corrected Proof, Available online 15 February 2012; Santori, P.R., Anderson, A.D., Manufacturing performance in the 1990s: Measuring for excellence (1987) Journal of Accountancy, 164 (5), pp. 141-147; Scavarda, A.J., Bouzdine-Chameeva, T., Goldstein, S.M., Hays, J.M., Hill, A.V., A methodology for constructing collective causal maps (2006) Decision Sciences, 37 (2), pp. 263-283; Simons, R., Strategic orientation and top management attention to control systems (1991) Strategic Management Journal, 12 (1), pp. 49-62; Slack, N., Lewis, M., Bates, H., The two worlds of operations management research and practice: Can they meet, should they meet? (2004) International Journal of Operations and Production Management, 24 (4), pp. 372-387; Slack, N., The flexibility of manufacturing systems (1987) International Journal of Operations and Production Management, 7 (4), pp. 35-45; Tan, K.H., Platts, K.W., Linking operations objectives to actions: A plug and play approach (2009) International Journal of Production Economics, 121 (2), pp. 610-619; Taticchi, P., Balachandran, K.R., Forward performance measurement and management integrated frameworks (2008) International Journal of Accounting and Information Management, 16 (2), pp. 140-154; Teece, D., Pisano, G., Shuen, A., Dynamic capabilities and strategic management (1997) Strategic Management Journal, 18 (7), pp. 509-533; Tidd, J., Bessant, J., Pavitt, K., (2001) Managing Innovation: Integrating Technological, Marketing and Organizational Change, , 2nd edition John Wiley & Sons Chichester; Tsang, A.H.C., Jardine, A.K.S., Kolodny, H., Measuring maintenance performance: A holistic approach (1999) International Journal of Operations and Production Management, 19 (7), pp. 691-715; Van Veen-Dirks, P., Management control and the production environment: A review (2005) International Journal of Production Economics, 93-94, pp. 263-272; Verbeeten, F.H.M., Boons, A.N.A.M., Strategic priorities, performance measures and performance: An empirical analysis in Dutch firms (2009) European Management Journal, 27 (2), pp. 113-128; Vergidis, K., Turner, C.J., Tiwari, A., Business process perspectives: Theoretical developments vs. Real-world practice (2008) International Journal of Production Economics, 114 (1), pp. 91-104; Wibe, S., Efficiency: A dynamic approach (2008) International Journal of Production Economics, 115 (1), pp. 86-91; Wu, C.W., Chen, C.L., An integrated structural model toward successful continuous improvement activity (2006) Technovation, 26 (5-6), pp. 697-707; Yusuf, Y., Gunasekaran, A., Wu, C., Implementation of enterprise resource planning in China (2006) Technovation, 26 (12), pp. 1324-1336; Zu, X., Robbins, T.L., Fredendall, L.D., Mapping the critical links between organizational culture and TQM/six sigma practices (2010) International Journal of Production Economics, 123 (1), pp. 86-106},
correspondence_address1={Pinheiro De Lima, E.; Pontifical Catholic University of Paraná, Rua Imaculada Conceição 1155, Curitiba, PR 80215-901, Brazil; email: e.pinheiro@pucpr.br},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Article},
source={Scopus},
}

@ARTICLE{NoAuthor2013,
title={IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2012},
journal={IFIP Advances in Information and Communication Technology},
year={2013},
volume={398},
number={PART 2},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887856920&partnerID=40&md5=9065866b135ee2ace4b7122466101a62},
abstract={The proceedings contain 90 papers. The special focus in this conference is on Production Management Systems. The topics include: Analysis of manufacturing process sequences, using machine learning on intermediate product states (as process proxy data); improving tree-based classification rules using a particle swarm optimization; profiling context awareness in mobile and cloud based engineering asset management; seamless access to sensor networks for enhanced manufacturing processes; wireless sensor network technologies for condition monitoring of industrial assets; a critical evaluation of RFID in manufacturing; semantic data model for operation and maintenance of the engineering asset; integrated model-based manufacturing for rapid product and process development; real-time production monitoring in large heterogeneous environments; formal specification of batch scheduling problems; introducing "2.0" functionalities in an ERP; designing and implementing a web platform to support SMEs in collaborative product development; MES support for lean production; handling unexpected events in production activity control systems; analyzing IT supported production control by relating Petri nets and UML static structure diagrams; enabling information sharing in a port; designing a lifecycle integrated data network for remanufacturing using RFID technology; implementing sustainable supply chain in PLM; full exploitation of product lifecycle management by integrating static and dynamic viewpoints; enterprise information systems' interoperability; closed-loop life cycle management concept for lightweight; towards an harmonious and integrated management approach for lifecycle planning; an MDA approach for PLM system design; dynamic alarm management in next generation process control systems; equipment's prognostics using logical analysis of data; designing an optimal shape warehouse; a fourth party energy provider for the construction value chain; performance measurement systems for craft-oriented small enterprises; reference model concept for structuring and representing performance indicators in manufacturing; productivity measurement and improvements; manufacturing service innovation ecosystem; improvement method of service productivity for taxi company; a choice experiment for air travel services; product-service systems modelling and simulation as a strategic diagnosis tool; improving customer's subjective waiting time introducing digital signage; framework for lean management in industrial services; demand control loops for a global spare parts management; the value and management practices of installed base information in product-service systems; reference decision models in the medico-social service sector; service model for the service configuration; dynamic manufacturing networks monitoring and governance; the insignificant role of national culture in global lean programmes; idiosyncratic behavior of globally distributed manufacturing; proposing an environmental excellence self-assessment model; method for quality appraisal in supply networks; supplier selection criteria in fractal supply network; a dyadic study of control in buyer-supplier relationships; a basic study on highly distributed production scheduling; improving port terminal operations through information sharing; perishable inventory challenges; a concept for project manufacturing planning and control for engineer-to-order companies and practical considerations about error analysis for discrete event simulations model.},
issn={18684238},
isbn={9783642403606},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Taticchi2013782,
author={Taticchi, P. and Tonelli, F. and Pasqualino, R.},
title={Performance measurement of sustainable supply chains: A literature review and a research agenda},
journal={International Journal of Productivity and Performance Management},
year={2013},
volume={62},
number={8},
pages={782-804},
doi={10.1108/IJPPM-03-2013-0037},
note={cited By 173},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885064283&doi=10.1108%2fIJPPM-03-2013-0037&partnerID=40&md5=175f32e68f692fb162e88f8fc5ac32af},
affiliation={Royal Docks Business School, University of East London, London, United Kingdom; Department of Production Engineering, Thermo-energetic and Mathematical Models, University of Genoa, Genoa, Italy},
abstract={– The purpose of this paper is to develop the body of knowledge in the area of sustainable supply chains by providing a critical literature review in the field of sustainable supply chain performance measurement (SSCPM) and suggesting paths for future research. – At a macro level, the evolution of the research field is analysed with the use of citation and co-citation analysis techniques. Further, the evolution of research on performance measurement (PM), supply chain PM, sustainability measurement and SSCPM is presented so as to highlight the criticalities of the research field. – The paper highlights a research field that is immature, but is growing very fast. Moreover, key issues are highlighted in order to help scholars in planning future research. – The research presented in this paper is mainly limited to work that is referred directly with PM, operations management and supply chain management. Related research, such as literature on strategy or accounting has only been partially considered, even if future researches could include it. – Research in this area is receiving large attention from both academic and practitioners due to the significant relevance of the sustainability debate and the key role of supply chains in this discussion. The body of knowledge is yet immature, and the paper provides an updated state of the art, a critical analysis of the available literature and guidelines for future research. © 2013, Emerald Group Publishing Limited},
author_keywords={Literature review;  Performance measurement;  Supply chain management;  Sustainable supply chains},
references={Ackali, E., Karakayali, I., Emir-Farinas, H., An analysis of decentralized collection and processing of end-of-life products (2007) Journal of Operations Management, 25 (6), pp. 1161-1183; Ayres, R.U., Kneese, A.V., Production, consumption, and externalities (1969) The American Economic Review, 59 (3), pp. 282-297; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Bai, C., Sarkis, J., Wei, X., Koh, L., Evaluating ecological sustainable performance measures for supply chain management (2012) Supply Chain Management: An International Journal, 17 (1), pp. 78-92; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Bebbington, J., Brown, J., Frame, B., Accounting technologies and sustainability assessment models (2007) Ecological Economics, 61 (2-3), pp. 224-236; Beloff, B., Tanzil, D., Lines, M., Sustainable development performance assessment (2004) Environmental Progress, 23 (4), pp. 271-276; Berrah, L., Clivillé, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Computers in Industry, 58 (7), pp. 709-719; Bianchi, N.P., Evans, S., Revetria, R., Tonelli, F., Influencing factors of successful transitions towards product-service systems: a simulation approach (2009) International Journal of Mathematics and Computers in Simulation, 3 (1), pp. 30-43; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Bjorklund, M., Martinsen, U., Abrahamsson, M., Performance measurements in the greening of supply chains (2012) Supply Chain Management: An International Journal, 17 (1), pp. 29-39; Bonacchi, M., Rinaldi, L., DartBoards and clovers as new tools in sustainability planning and control (2007) Business Strategy and the Environment, 16 (7), pp. 461-473; Borgatti, S.P., Everett, M.G., Freeman, L.C., (2002) Ucinet for Windows: Software for Social Network Analysis, , Analytic Technologies, Harvard, MA; Bruzzone, A.A.G., Anghinolfi, D., Paolucci, M., Tonelli, F., Energy-aware scheduling for improving manufacturing process sustainability: a mathematical model for flexible flow shops (2012) CIRP Annals – Manufacturing Technology, 61 (1), pp. 459-462; Burgess, T., Ong, T.S., Shaw, N., Traditional or contemporary? The prevalence of performance measurement system types (2007) International Journal of Productivity and Performance Management, 56 (7), pp. 583-602; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2009) The International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Cannon, M., Kouvaritakis, B., Huang, G., Modelling and optimisation for sustainable development policy assessment (2005) European Journal of Operational Research, 164 (2), pp. 475-490; Carbone, V., Moatti, V., Esposito Vinzi, V., Mapping corporate responsibility and sustainable supply chains: an exploratory perspective (2012) Business Strategy and the Environment, 21 (7), pp. 475-494; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Carter, C.R., Kale, R., Grimm, C.M., Environmental purchasing and firm performance: an empirical investigation (2000) Transportation Research Part E: Logistics and Transportation Review, 36 (3), pp. 219-228; (2013) CDP Climate Change Reporting Guidance, , www.cdproject.net/en-US/Pages/guidance.aspx, available at: (accessed 23 February 2013); Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Che, Z.H., Using fuzzy analytic hierarchy process and particle swarm optimisation for balanced and defective supply chain problems considering WEEE/RoHS directives (2010) International Journal of Production Research, 48 (11), pp. 3355-3381; Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, Planning, and Operation”, , Prentice-Hall, NJ; Corbett, C.J., Kleindorfer, P.R., Environmental management and operations management: introduction to the third special issue (2003) Production and Operations Management, 12 (3), pp. 287-289; Cruz, J.M., Matsypura, D., Supply chain networks with corporate social responsibility through integrated environmental decision-making (2009) International Journal of Production Research, 47 (3), pp. 621-648; Culnan, M., The intellectual development of management information systems (1986) Management Science, 32 (2), pp. 156-172; Dou, Y., Sarkis, J., A joint location and outsourcing sustainability analysis for a strategic offshoring decision (2010) International Journal of Production Research, 48 (2), pp. 567-592; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Eccles, R.G., Krzus, M.P., (2010) One Report: Integrated Reporting for a Sustainable Strategy, , John Wiley & Sons Inc, Hoboken, NJ; Elkington, J., (1997) Cannibals with forks: the triple bottom line of 21st century business, , Capstone, Oxford; Epstein, M.J., (2008) Making Sustainability Work, , Green leaf Publishing Limited, Sheffield; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70 (6), pp. 1088-1100; Directive 2002/96/EC of the European Parliament and of the Council of 27 January 2003 on waste electrical and electronic equipment (WEEE) (2003) Official Journal L 037, pp. 24-39. , European Union, Brussels, 13 February; Fleischmann, M., Runwaard, J.B.M., Dekker, R., Laan, E., Nnunen, J.A.E.E., Van Wassenhove, L., Quantitative models for reverse logistics: a review (1997) European Journal of Operational Research, 103 (1), pp. 1-17; Foran, B., Lenzen, M., Dey, C., Bilek, M., Integrating supply chain management with triple bottom line accounting (2005) Ecological economics, 52 (2), pp. 143-157; Frota Neto, J.Q., Walther, G., Bloemhof, J., Van Nunen, J., Spengler, T., From closed-loop to sustainable supply chains: the WEEE case (2010) International Journal of Production Research, 48 (15), pp. 4463-4481; Gonzalez-Benito, J., Gonzalez-Benito, Ó., The role of stakeholder pressure and managerial values in the implementation of environmental logistics practices (2006) International Journal of Production Research, 44 (7), pp. 1353-1373; (2013) Reporting Framework Overview, , www.globalreporting.org/reporting/reporting-framework-overview/Pages/default.aspx, available at: (accessed 23 February 2013); Guide, V.D.R., Jr., Van Wassenhove, L.N., The reverse supply chain (2002) Harvard Business Review, 80 (2), pp. 25-26; Guide, V.D.R., Jr., Jayaraman, V., Srivastava, R., Production planning and control for remanufacturing: a state-of-the-art-survey (1999) Robotics and Computer-Integrated Manufacturing, 15 (3), pp. 221-230; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Parel, C., Mc Gaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hassini, E., Surti, C., Searcy, C., A literature review and a case study of sustainable supply chains with a focus on metrics (2012) International Journal of Production Economics, 140 (1), pp. 69-82; Hu, G., Bidanda, B., Modeling sustainable product lifecycle decision support systems (2009) International Journal of Production Economics, 122 (1), pp. 366-375; (2013) IFAC Sustainability Framework 2.0, , www.ifac.org/publications-resources/ifac-sustainability-framework-20, available at: (accessed 23 February 2013); Ji, G., Performance evaluation of complaint management and virtual enterprise in closed-loop supply chains by using exergoeconomics and extenics (2008) International Journal of Services and Operations Management, 4 (3), pp. 368-397; Kaplan, R., Norton, D., The balanced scorecard: measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, January-February, pp. 75-85; Kempener, R., Beck, J., Petrie, J., Design and analysis of bioenergy networks. A complex adaptive systems approach (2009) Journal of Industrial Ecology, 13 (2), pp. 284-305; Kleindorfer, P.R., Singhal, K., Van Wassenhove, L.N., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Kocabasoglu, C., Prahinski, C., Klassen, R., Linking forward and reverse supply chain investments: the role of business uncertainty (2007) Journal of Operations Management, 25 (6), pp. 1141-1160; Kovacs, G., Framing a demand network for sustainability (2004) Progress in Industrial Ecology: an International Journal, 1 (4), pp. 397-410; Lehtinen, J., Ahola, T., Is performance measurement suitable for an extended enterprise? (2010) International Journal of Operations & Production Management, 30 (2), pp. 181-204; Lejeune, M.A., A variable neighborhood decomposition search method for supply chain management planning problems (2006) European Journal of Operational Research, 175 (2), pp. 959-976; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: an introduction (2007) Journal of Operations Management, 25 (6), pp. 1075-1082; Matos, S., Hall, J., Integrating sustainable development in the supply chain: the case of life cycle assessment in oil & gas and agricultural biotechnology (2007) Journal of Operations Management, 25 (6), pp. 1083-1102; Mazhar, M.I., Kara, S., Kaebernick, H., Remaining life estimation of used components in consumer products: life cycle data analysis by Weibull and artificial neural networks (2007) Journal of Operations Management, 25 (6), pp. 1184-1193; Milne, M.J., On sustainability; the environment and management accounting (1996) Management Accounting Research, 7 (1), pp. 135-161; Min, H., Galle, W.P., Green purchasing strategies: trends and implications (1997) The Journal of Supply Chain Management, 33 (3), pp. 10-17; Murphy, P.R., Poist, R.F., Green logistics strategies: an analysis of usage patterns (2000) Transportation Journal, 40 (2), pp. 5-16; Neely, A., The evolution of performance measurement research: development in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (2), pp. 1264-1277; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: the Scorecard for Measuring and Managing Stakeholder Relationship, , Prentice-Hall, London; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Perrini, F., Tencati, A., Sustainability and stakeholder management: the need for new corporate performance evaluation and reporting systems (2006) Business Strategy and the Environment, 15 (5), pp. 296-308; Petrini, M., Pozzebon, M., Managing sustainability with the support of business intelligence: integrating socio-environmental indicators and organisational context (2009) Journal of Strategic Information Systems, 18 (4), pp. 78-191; Pilkington, A., Liston-Heyes, C., Is production and operations management a discipline? A citation/co-citation study (1999) International Journal of Operations & Production Management, 19 (1), pp. 7-20; Sarkis, J., Helms, M.M., Hervani, A.A., Reverse logistics and social sustainability (2010) Corporate Social Responsibility and Environmental Management, 17 (6), pp. 337-354; Schildt, H.A., (2002) SITKIS: Software for Bibliometric Data Management and Analysis, , http://users.tkk.fi/hschildt/sitkis, Helsinki Institute of Strategy and International Business, available at: /; Schildt, H.A., Zahra, S.A., Sillanpää, A., Scholarly communities in entrepreneurship research: a co-citation analysis (2006) Entrepreneurship Theory and Practice, 30 (3), pp. 399-416; Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Sharma, M.K., Bhagwat, R., Dangayach, G.S., Practice of performance measurement: experience from Indian SMEs (2005) International Journal of Globalisation and Small Business, 1 (2), pp. 183-213; Sharplin, A., Mabry, R., The relative importance of journals used in management research: an alternative ranking (1985) Human Relations, 38 (2), pp. 139-149; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2005) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., Development of composite sustainability performance index for steel industry (2007) Ecological Indicators, 7 (3), pp. 565-588; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., Integrated environment management in steel industries (2008) International Journal of Management and Decision Making, 9 (1), pp. 103-128; Sivadasan, S., Efstathiou, J., Calinescu, A., Huaccho Huatuco, L., Supply chain complexity (2004) Understanding Supply Chains, pp. 133-163. , in New, S., and Westbrook, R. (Eds.), Oxford University Press, Oxford; Srivastava, S.K., Network design for reverse logistics (2008) Omega, 36 (4), pp. 535-548; (2013) Supply-chain operations reference model, , www.supply-chain.org, Version 10.0, available at: (accessed 23 February 2013); Svensson, G., Aspects of sustainable supply chain management (SSCM): conceptual framework and empirical example (2007) Supply Chain Management, 28 (3), pp. 262-266; Taticchi, P., Balachandran, K.R., Forward performance measurement and management integrated frameworks (2008) International Journal of Accounting and Information Management, 16 (2), pp. 140-154; Taticchi, P., Tonelli, F., Cagnazzo, L., A decomposition and hierarchical approach for business performance measurement and management (2009) Measuring Business Excellence, 13 (4), pp. 47-57; Taticchi, P., Cagnazzo, L., Tonelli, F., Performance measurement and management: a literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Taticchi, P., Balachandran, K., Tonelli, F., Performance measurement and management systems: state of the art, guidelines for design and challenges (2012) Measuring Business Excellence, 16 (2), pp. 41-54. , a; Taticchi, P., Cagnazzo, L., Barber, K., Beach, R., A management framework for organisational networks: a case study (2012) Journal of Manufacturing Technology Management, 23 (5), pp. 593-614. , b; Tonelli, F., Evans, S., Taticchi, P., Industrial sustainability: challenges, perspectives, actions (2013) International Journal of Business Innovation Research, 7 (2), pp. 143-163; Tsai, W.-H., Hung, S.-J., A fuzzy goal programming approach for green supply chain optimisation under activity-based costing and performance evaluation with a value-chain structure (2009) International Journal of Production Research, 47 (18), pp. 4991-5017; Van Der Vorst, J.G.A.J., Tromp, S.O., Van Der Zee, D.J., Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics (2009) International Journal of Production Research, 47 (23), pp. 6611-6631; Van der Woerd, F., Van den Brink, T., Feasibility of a responsive business scorecard – a pilot study (2004) Journal of Business Ethics, 55 (2), pp. 173-186; Vlachos, D., Georgiadis, P., Iakovou, E., A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains (2007) Computers and Operations Research, 34 (2), pp. 367-394; Wang, H.F., Hsu, H.W., A closed-loop logistic model with a spanning-tree based genetic algorithm (2010) Computers and Operations Research, 37 (2), pp. 376-389; Wasserman, S., Faust, K., (1994) Social Network Analysis, , Cambridge University Press, Cambridge; Webster, S., Mitra, S., Competitive strategy and the impact of take-back laws (2007) Journal of Operations Management, 25 (6), pp. 1123-1140; Wiedmann, T.O., Lenzen, M., Barrett, J.R., Companies on the scale: comparing and benchmarking the sustainability performance of businesses (2009) Journal of Industrial Ecology, 13 (3), pp. 361-383; Young, A., Kielkiewicz-Young, A., Sustainable supply network management (2001) Corporate Environmental Strategy, 8 (3), pp. 260-268; Zsidisin, G.A., Siferd, S.P., Environmental purchasing: a framework for theory development (2001) European Journal of Purchasing & Supply Management, 7 (1), pp. 61-73; Gungor, A., Gupta, S., Issues in environmentally conscious manufacturing and product recovery: a survey (1999) Computers and Industrial Engineering, 36 (4), pp. 811-853; Gunther, E., Scheibe, L., The hurdle analysis. A self-evaluation tool for municipalities to identify, analyse and overcome hurdles to green procurement (2006) Corporate Social Responsibility and Environmental Management, 13 (2), pp. 61-77; Kim, H., Kim, S., Dale, B.E., Biofuels, land use change, and greenhouse gas emissions: some unexplored variables (2009) Environmental Science and Technology, 43 (3), pp. 961-967; Piplani, R., Pujawan, N., Ray, S., Foreword: sustainable supply chain management (2008) International Journal of Production Economics, 111 (2), pp. 193-194; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations & Production Management, 25 (9), pp. 898-916; Simpson, D., Power, D., Samson, D., Greening the automotive supply chain: a relationship perspective (2007) International Journal of Operations & Production Management, 27 (1), pp. 28-48; Vachon, S., Klassen, R.D., Extending green practices across the supply chain: the impact of upstream and downstream integration (2006) International Journal of Operations & Production Management, 26 (7), pp. 795-821; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: pressures, practices and performance (2005) International Journal of Operations & Production Management, 25 (5), pp. 449-468; Zhu, Q., Sarkis, J., Lai, K., Green supply chain management: pressures, practices and performance within the Chinese automobile industry (2007) Journal of Cleaner Production, 15 (11-12), pp. 1041-1052},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Lobna2013382,
author={Lobna, K. and Mounir, B. and Hichem, K.},
title={Using the ECOGRAI method for performance evaluation in maintenance process},
journal={2013 International Conference on Advanced Logistics and Transport, ICALT 2013},
year={2013},
pages={382-387},
doi={10.1109/ICAdLT.2013.6568489},
art_number={6568489},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883425559&doi=10.1109%2fICAdLT.2013.6568489&partnerID=40&md5=f8c5be6aa175b769b0363ed76ef55312},
affiliation={FSEG- University of Sfax, LOGIQ, Sfax, Tunisia; University of Sfax, OASIS Laboratory, ENIT, Tunis, Tunisia; FSEGS-University of Sfax, MODELIS, Sfax, Tunisia},
abstract={Continuously improving performance is a needed strategic service in any production context. It can be used to improve supply chain effectiveness, efficiency, reactiveness and flexibility. Moreover, some companies have used different performance, management tools to support their supply chain strategies. Therefore evaluation, monitoring and improvement of performance of a supply chain have become an increasingly important task. In this paper we present an ECOGRAI method of performance evaluation applied in a maintenance process at the end we implement a system performance indicators. © 2013 IEEE.},
author_keywords={ECOGRAI;  performance evaluation;  Supply chain performance measurement},
keywords={ECOGRAI;  Improving performance;  Maintenance process;  performance evaluation;  Supply chain performance measurements;  Supply chain strategy;  Supply-chain effectiveness;  System performance indicators, Maintenance, Supply chains},
references={Zeydan, A., Colpan, C., Cemal, C., A combined methodology for supplier selection and performance evaluation (2011) Expert Systes with Application, 38, pp. 2741-2751; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; (1999) Product and Supply Chain Focused Policies and Tools for Sustainable Development, , Five Winds International Prepared for Environment Canada Ottawa; Christopher, M.G., (1998) Logistics and Supply Chain Management; Strategies for Reducing Costs and Improving Services, , London: Pitman Publishing; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply Chain Management: More Than a New Name for Logistics (1997) International journal of logistics management, 8 (1), pp. 1-14; Mentzer, J.T., De Witt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Christopher, M., Ryals, L., Supply chain strategy: Its impact on shareholder value (1999) The International Journal of Logistics Management, 10, p. 1; Cooper, M., Lambert, D., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Chi, D.W., Ahn, S.H., Hwang, M.K., Aframework for measuring the performance of service supply chain management (2012) Computer and Industriel Engineering, 62, pp. 801-818; Hon, K.K.B., Performance and Evaluation of Manufacturing Systems, , Department of Engineering University of Liverpool, Liverpool, UK; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521; Clivillé, V., (2004) Systemic Approach and Multicriteria Method for the Definition of A System of Performance Indicators, , thesis of doctorat Universityu of Savoie; Lauras, M., Diagnosis and evaluation of performance management chains logisyiques: Application to co-parent international subsidiaries in a pharmaceutical and cosmetic thesis of doctorat (2004) National Polytechnic Institute of Toulouse; Stella, M., Contribution to the definition of a generic framework for the definition, implementation and operation of the performance: Application to the method ECOGRAI thesis of doctorat (2009) University of Bordeaux, 1; Humez, V., Proposal of a tool for decision support order management in case of shortage: An approach based on performance thesis of doctorat (2008) National Polytechnic Institute of Toulouse; Blanc, S., Ducq, Y., Vallespir, B., Evolution management towards interoperable supply chains using performance measurement (2007) Computers in Industry, 58 (7), pp. 720-732. , DOI 10.1016/j.compind.2007.05.011, PII S0166361507000760; Charkaoui, A., Aitouahman, A., Bouayad, B., Application of ECOGRAI/BSC method for controlling logistic performance: Case of a moroccan clothing company (2012) International Journal of Business, Humanities and Technology, 2. , Merch},
correspondence_address1={FSEG- University of Sfax, , Sfax, Tunisia},
isbn={9781479903122},
language={English},
abbrev_source_title={Int. Conf. Adv. Logist. Transp., ICALT},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Elrod201339,
author={Elrod, C. and Murray, S. and Bande, S.},
title={A review of performance metrics for supply chain management},
journal={EMJ - Engineering Management Journal},
year={2013},
volume={25},
number={3},
pages={39-50},
doi={10.1080/10429247.2013.11431981},
note={cited By 40},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894299882&doi=10.1080%2f10429247.2013.11431981&partnerID=40&md5=7a56fe1c12ead67095f30c5552fae5c1},
affiliation={Missouri University of Science and Technology, Department of Business and Information Technology, United States},
abstract={There are hundreds of metrics that can be used to measure supply chain performance. Supply chain managers often find it difficult to select appropriate measures for their particular business or product. It is important for managers to have meaningful performance information to avoid becoming lost in a sea of data. As businesses expand into different regions and partnerships become a necessity for survival, successful managers must measure the performance of the supply chain, as well as understand the advantages, disadvantages, and limitations of the use of the supply chain. This study compiles and synthesizes important elements related to supply chain performance measurement. The results of this study can provide both managers and practitioners guidance for evaluating and measuring their supply chain effectively and, more importantly, efficiently. © 2013 by the American Society for Engineering Management.},
author_keywords={Performance Metrics;  Supply Chain Management;  Supply Chain Metrics},
keywords={Managers, Performance metrics;  Supply chain performance;  Supply chain performance measurements, Supply chain management},
references={Akintoye, A., McIntosh, G., Fitzgerald, E., A Survey of Supply Chain Collaboration and Management in the UK Construction Industry (2000) European Journal of Purchasing and Supply Management, 6 (3-4), pp. 159-168; Ayers, J.B., Odegaard, M.A., (2008) Retail Supply Chain Management, , Auerbach Publications, Taylor and Francis Group; Beamon, B.M., Measuring Supply Chain Performance, International (1999) Journal of Operations and Production Management, 19 (3), pp. 275-292; Beamon, B.M., Supply Chain Design and Analysis: Models and Methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Burns, L.D., Hall, R.W., Blumenfeld, D.E., Daganzo, C.F., Distribution Strategies that Minimize Transportation and Inventory Costs (1985) Operations Research, 33 (3), pp. 469-490. , May/Jun; Camp Robert, C., (1989) Benchmarking: The Search for Industry Best Practices that Lead to Superior Performance, , ASQS Quality Press; Capkun, V., Hameri, A.-P., Weiss, L.A., On the Relationship Between Inventory and Financial Performance in Manufacturing Companies (2009) International Journal of Operations and Production Management, 29 (8), pp. 789-806; Chan, F.T.S., Performance Measurement in a Supply Chain (2003) The International Journal of Advanced Manufacturing Technology, 231 (7), pp. 534-548; Christopher, M., The Agile Supply Chain, Competing in Volitle Markets (2000) Industrial Marketing Management, 29 (1), pp. 37-44; Cooper, M.C., Ellram, L.M., Gardner, J.T., Hanks, A.M., Meshing Multiple Alliances (1997) Journal of Business Logistics, 18 (1), pp. 67-89; Cooper, R.G., Stage-Gate Systems: A New Tool for Managing New Products (1990) Business Horizons, 33 (3), pp. 44-54; Doolen, T., Traxler, M., McBride, K., Using Scorecards for Supplier Performance Improvement: Case Application in a Lean Manufacturing Organization (2006) Engineering Management Journal, 18 (2), pp. 26-34; Emerson, D., Piramuthu, S., Agent-Based Framework for Dynamic Supply Chain Configuration (2004) System Sciences, Proceedings of the 37Th Annual Hawaii International Conference, p. 9; Fine, C.H., Simchi-Levi, D., Your Next Supply Chain (2010) MIT Sloan Management Review, 51 (2). , Winter; Frankel, R., The (2006) Business Horizons, 49 (4), pp. 275-286. , July-August; Galbraith, J.R., Organization Design: An Information Processing View (1974) Interfaces, 4 (3), pp. 28-36; Gianpaolo, C., Montgros, X.D., Slagmulder, R., Wassenhove, L.N.V., Wright, L., Inventory-Driven Costs (2005) Harvard Business Review, 83 (3), pp. 135-141; Gunasekaran, A., Ngai, E.W.T., Build-to-Order Supply Chain Management: A Literature Review and Framework for Development (2005) Journal of Operations Management, 23 (5), pp. 423-451; Gunasekaran, A., Patel, C., McGaughey, R.E., A Framework for Supply Chain Performance Measurement (2004) International Journal of Production Economics, 88 (3), pp. 333-347; Hausman, W.H., (2003) Supply Chain Performance Metrics - the Practice of Supply Chain Management, , (C. Billington, T. Harrison, H. Lee, and J. Neale Eds.), Kluwer; Hervani, A.A., Helms, M.M., Sarkis, J., Performance Measurement for Green Supply Chain Management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Huehn-Brown, J.W., Murray, S.L., Are Companies Continuously Improving Their Supply Chain? (2010) Engineering Management Journal, 22 (4), pp. 3-10; Jahera, S.J., Jr., Lloyd, W.P., Additional Evidence on the Validity of ROI as a Measure of Business Performance (1992) Mid-Atlantic Journal of Business, 28 (2), pp. 105-113; Jenkins, G.D., Mitra, A., Gupta, N., Shaw, J.D., Are Financial Incentives Related to Performance? A Meta-Analytic Review of Empirical Research (1998) Journal of Applied Psychology, 83 (5), pp. 777-787; Jensen, M.C., Murphy, K.J., Performance Pay and Top-Management Incentives (1990) Journal of Political Economy, 98 (2), pp. 225-264; Kaplan, R.S., Norton, D.P., The Balanced Scorecard: Measures That Drive Performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Katz, N.R., Incentives and Performance Management in the Public Sector Public Sector Performance Management Executive Session: An Initiative of the Visions of Governance in the Twenty-First Century Project, , Kennedy School of Government: Harvard University; Kepczyk, R.H., The Dell Way,” Infotech Update (2001) American Institute of Certified Public Accountants; Kleijnen, J.P.C., Martin, T., Smits, “Performance Metrics in Supply Chain Management,” (2003) Journal of the Operational Research Society, 54, pp. 507-514; La, L., Bernard, J., Masters, J.M., Emerging Logistics Strategies: Blueprints for the Next Century (1994) International Journal of Physical Distribution and Logistics Management, 24 (7), pp. 35-47; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Leavy, B., Supply Chain Effectiveness: Strategy and Integration (2006) Handbook of Business Strategy7, (1), pp. 331-336; Lee, H.L., Billington, C., Managing Supply Chain Inventory: Pitfalls and Opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lummus, R.R., Vokurka, R.J., Defining Supply Chain Management: A Historical Perspective and Practical Guidelines (1999) Industrial Management and Data Systems, 99 (1), pp. 11-17; Mentzer, J., Dewitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining Supply Chain Management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Patel, C., Gunasekaran, A., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Ren, C., Dong, J., Ding, H., Wang, W., (2006) A SCOR-Based Framework for Supply Chain Performance Management,” Service Operations and Logistics, and Informatics, pp. 1130-1135; Ritchie, B., Brindley, C., “Supply Chain Risk Management and Performance: A Guiding Framework for Future Development (2007) ” International Journal of Operations and Production Management, 27 (3), pp. 303-322; Sabri, E.H., Beamon, B.M., A Multi-Objective Approach to Simultaneous Strategic and Operational Planning in Supply Chain Design (2000) OMEGA, 28 (5), pp. 581-598; Sharif, A.M., Irani, Z., Lloyd, D., Information Technology and Performance Management for Built-to- Order Supply Chains (2007) International Journal of Operations and Production Management, 27 (11), pp. 1235-1253; Simchi-Levi, D., Fine, C.H., Your Next Supply Chain (2010) MIT Sloan Management Review, 51 (2), pp. 17-24. , (Winter; Slack, N., The Flexibility of Manufacturing Systems (2005) International Journal of Operations and Production Management, 25 (12), pp. 1190-1200; Stewart, G., Supply Chain Performance Benchmarking Study Reveals Keys to Supply Chain Excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Tian, C., Chai, Y., Yi, L., Ren, S., Performance Management in Supply Chai (2003) Systems, Man and Cybernetics, IEEE International Conference, pp. 4934-4939; Tobin, J.J., A General Equilibrium Approach To Monetary Theory (1969) Journal of Money, Credit and Banking, 1 (1), pp. 15-29; Vickery, S., Calantone, R., Dröge, C., Supply Chain Flexibility: An Empirical Study (1999) Journal of Supply Chain Management, 35 (3), pp. 16-24; Xia, L.X.X., Lee, W., Sing, C.L., Li, Z., “Performance Metrics Design Framework for Software Focused Supply Chain (2005) Industrial Informatics, 3Rd IEEE International Conference, pp. 176-180; Youngblood, A., Collins, T.R., Addressing Balanced Scorecard Trade-Off Issues Between Metrics Using Multi-Attribute Utility Theory (2003) Engineering Management Journal, 15 (1), pp. 11-17; Zhang, M., Wang, J., Zhang, J., Research on Construction Supply Chain Management in E-Business Environment (2009) Industrial Engineering and Engineering Management, 16Th International Conference, pp. 1565-1568},
correspondence_address1={Elrod, C.; Missouri University of Science and Technology, 301 W. 14th Street, 107E Fulton, Rolla, MO, 65401, United States; email: cassa@mst.edu},
issn={10429247},
language={English},
abbrev_source_title={EMJ Eng Manage J},
document_type={Article},
source={Scopus},
}

@ARTICLE{Taylor20135485,
author={Taylor, A. and Taylor, M.},
title={Antecedents of effective performance measurement system implementation: An empirical study of UK manufacturing firms},
journal={International Journal of Production Research},
year={2013},
volume={51},
number={18},
pages={5485-5498},
doi={10.1080/00207543.2013.784412},
note={cited By 29},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884587420&doi=10.1080%2f00207543.2013.784412&partnerID=40&md5=bc0e750352d57e9a7852d18af0ebd470},
affiliation={School of Management, University of Bradford, Bradford, United Kingdom},
abstract={This paper examines the antecedents to effective performance measurement system (PMS) implementation. There have been only a few studies which have investigated this topic, and to-date there is no consensus about what factors are significant. Using empirical data collected from 349 UK manufacturing firms, we identify six potential factors from an item pool derived from an extensive literature review. Subsequent regression analysis reveals that all six factors have a significant influence on PMS implementation effectiveness. We characterise these factors into two sub-sets, with the technical sub-set accounting for most of the variance in the dependent variable. We also demonstrate that softer social factors additionally have a significant, albeit comparatively small, effect. These, we argue, are essential in order to bind an organisations approach to performance measurement into one cohesive whole. Our findings provide managers with a clearer view of the factors which may influence the effectiveness of their PMS implementation. Moreover, we anticipate that the findings should provide empirical stimulation of an important debate about how to ensure that organisations can implement PMSs that are adequate for their purposes. As such, while the findings have current utility, their additional value is in providing a platform for subsequent research. © 2013 Taylor and Francis Group, LLC.},
author_keywords={effectiveness of implementation;  empirical study;  performance measurement systems;  UK manufacturing},
keywords={Dependent variables;  Effective performance;  effectiveness of implementation;  Empirical studies;  Literature reviews;  Manufacturing firms;  Performance measurement system;  Performance measurements, Industry;  Regression analysis;  Supply chain management, Manufacture},
references={Ablanedo-Rosas, J.H., Alidaee, B., Moreno, J.C., Urbina, J., Quality improvement supported by the 5s, an empirical case study of mexican organisations (2010) International Journal of Production Research, 48 (23), pp. 7063-7087; Albadvi, A., Keramati, A., Razmi, J., Assessing the impact of information technology on firm performance considering the role of intervening variables: Organizational infrastructures and business processes reengineering (2007) International Journal of Production Research, 45 (12), pp. 2697-2734; Aranda, C., Arellano, J., Strategic performance measurement systems and managers' understanding of the strategy: A field research in a financial institution (2010) Journal of Accounting & Organizational Change 6, 3, pp. 330-358; Argyris, C., Schön, D., (1974) Theory in Practice: Increasing Professional Effectiveness, , San Francisco, CA Jossey-Bass; Argyris, C., Schön, D., (1978) Organizational Learning: A Theory of Action Perspective, , Reading, MA Addison Wesley; Berrah, L., Mauris, G., Vernadat, F., Information aggregation in industrial performance measurement: Rationales, issues and Definitions (2004) International Journal of Production Research, 42 (20), pp. 4271-4293; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Bititci, U.S., Mendibil, K., Nudurupati, S., Turner, T., Garengo, P., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations & Production Management, 26, pp. 1325-1350; Bititci, U.S., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Blackstone, J.H., Cox, J.F., Schleier, J.G., A tutorial on project management from a theory of constraints perspective (2011) International Journal of Production Research, 47 (24), pp. 7029-7046; Bou-Llusar, J.C., Escrig-Tena, A.B., Roca-Puig, V., Beltrán- Martín, I., An empirical assessment of the efqm excellence model: Evaluation as a tqm framework relative to the MBNQA model (2009) Journal of Operations Management, 27 (1), pp. 1-22; Bourne, M., (2001) The Handbook of Performance Measurement, , London: Gee Publishing; Bourne, M., Performance measurement: Learning from the past and projecting the future (2008) Measuring Business Excellence, 12 (4), pp. 67-72; Bourne, M.C.S., Mills, J.F., Bicheno, J., Hamblin, D.J., Wilcox, M., Neely, A.D., Platts, K.W., Performance measurement system design: Testing a process approach in manufacturing companies (1999) International Journal of Business Performance Management, 1 (2), pp. 154-170; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Boyd, L., Gupta, M., Constraints management: What is the theory? (2004) International Journal of Operations & Production Management, 24 (4), pp. 350-371; Braunscheidel, M.J., Suresh, N.C., The organizational antecedents of a firm's supply chain agility for risk mitigation and response (2009) Journal of Operations Management, 27, pp. 119-140; Brown, S., Squire, B., Lewis, M., The impact of inclusive and fragmented operations strategy processes on operational performance (2010) International Journal of Production Research, 48 (14), pp. 4179-4198; Burton, R.M., Lauridsen, J., Obel, B., The impact of organizational climate and strategic fit on firm performance (2004) Human Resource Management, 43 (1), pp. 67-82; Bushardt, S.C., Glascoff, D.W., Doty, D.H., Organizational culture, formal reward structure, and effective strategy implementation: A conceptual model (2011) Journal of Organizational Culture, Communications and Conflict, 15 (2), pp. 57-70; Calantone, R.J., Cavusgil, S.T., Zhao, Y., Learning orientation, firm innovation capability, and firm performance (2002) Industrial Marketing Management, 31, pp. 515-524; Cangemi, J., Miller, R., Breaking-out-of-the-box in organizations: Structuring a positive climate for the development of creativity in the workplace (2007) Journal of Management Development, 26 (5), pp. 401-410; Carr, A.S., Kaynak, H., Hartley, J.L., Ross, A., Supplier dependence: Impact on supplier's participation and performance (2008) International Journal of Operations & Production Management 28, 9, pp. 899-916; Cater, T., Pucko, D., Factors of effective strategy implementation: Empirical evidence from slovenian business practice (2010) Journal for East European Management Studies, 15 (3), pp. 207-236; Challis, D., Samson, D., Lawson, B., Impact of technological, organizational and human resource investments on employee and manufacturing performance: Australian and new zealand evidence (2005) International Journal of Production Research, 43 (1), pp. 81-107; Chang, S.-J., Van Witteloostuijn, A., Eden, L., From the editors: Common method variance in international business research (2010) Journal of International Business Studies, 41, pp. 178-184; Chenhall, R.H., Management control systems design within its organizational context: Findings from contingency-based research and directions for the future (2003) Accounting, Organizations and Society, 28, pp. 127-168; Chennell, A., Dransfield, S., Field, J., Fisher, N., Saunders, I., Shaw, D., OPM: A system for organisational performance measurement (2000) Proceedings of the Performance Measurement-Past, Present and Future Conference, pp. 19-21. , Cambridge July; Chian-Son, Y., Causes influencing the effectiveness of the post-implementation erp system (2005) Industrial Management & Data Systems, 105 (1), pp. 115-132; Churchill, G.A., A paradigm for developing better measures of marketing constructs (1979) Journal of Marketing Research, 16 (1), pp. 64-73; Cowan-Sahadath, K., Business transformation: Leadership, integration and innovation- A case study (2010) International Journal of Project Management, 28 (4), pp. 395-404; Da Silveira, G., Our own translation box: Exploring proximity antecedents and performance implications of customer co-design in manufacturing (2011) International Journal of Production Research, 49 (13), pp. 3833-3854; Da Silveira, G., Sousa, R.S., Paradigms of choice in manufacturing strategy: Exploring performance relationships of fit, best practices, and capability-based approaches (2010) International Journal of Operations & Production Management, 30 (12), pp. 1219-1245; Dankbaar, B., Lean production: Denial, confirmation or extension of sociotechnical systems Design? (1997) Human Relations, 50 (5), pp. 567-583; Das, A., Jayaram, J., Socio-technical perspective on manufacturing system synergies (2007) International Journal of Production Research, 45 (1), pp. 169-205; Dassisti, M., HY-CHANGE: A hybrid methodology for continuous performance improvement of manufacturing processes (2010) International Journal of Production Research, 48 (15), pp. 4397-4422; De Leeuw, S., Van Den Berg, J.P., Improving operational performance by influencing shopfloor behavior via performance management practices (2011) Journal of Operations Management, 29, pp. 224-235; Del Brío, J.A., Junquera, B., Ordiz, M., Human resources in advanced environmental approaches- A case analysis (2008) International Journal of Production Research, 46 (21), pp. 6029-6053; Ferreira, A., Otley, D., The design and use of performance management systems: An extended framework for analysis (2009) Management Accounting Research, 20, pp. 263-282; Forza, C., Survey research in operations management: A process-based perspective (2002) International Journal of Operations & Production Management, 22 (2), pp. 152-194; Franco, M., Bourne, M., Factors that play a role in managing through measures (2003) Management Decision, 41 (8), pp. 698-710; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Gadenne, D., Sharma, B., An investigation of the hard and soft quality management factors of australian SMEs and their association with firm performance (2009) International Journal of Quality & Reliability Management 26, 9, pp. 865-880; Gareis, R., Changes of organizations by projects International Journal of Project Management, 28 (4), pp. 314-327; Garengo, P., Performance measurement system in smes taking part to quality award programs (2009) Total Quality Management and Business Excellence, 20, pp. 91-105; Garengo, P., Bititci, U.S., Towards a contingency approach to performance measurement: An empirical study in scottish SMEs (2007) International Journal of Operations & Production Management, 27, pp. 802-825; Garengo, P., Sharma, M.K., Performance measurement system contingency factors: A cross analysis of italian and indian smes (2012) Production Planning & Control, , DOI:10.1080/09537287.2012.663104; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in smes: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47; Giles, W.D., Making strategy work (1991) Long Range Planning, 24 (5), pp. 75-91; Gimbert, X., Bisbe, J., Mendoza, X., The role of performance measurement systems in strategy formulation processes (2010) Long Range Planning, 43 (4), pp. 477-497; Gray, C., Mabey, C., Management development: Key differences between small and large businesses in europe (2005) International Small Business Journal, 23 (5), pp. 467-485; Gurowitz, E.M., (2007) The Challenge of Strategy Implementation, , www.gurowitz.com/articles/strategy.pdf, Accessed 26 October 2007; Gutiérrez, L.J.G., Bustinza, O.F., Molina, V.B., Six sigma, absorptive capacity and organisational learning orientation (2012) International Journal of Production Research, 50 (3), pp. 661-675; Hahn, W., Strategic plan quality, implementation capability, and firm performance (2010) Academy of Strategic Management Journal, 9 (1), pp. 63-81; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , 5th ed. Upper Saddle River, NJ: Prentice Hall; Hakala, H., Strategic orientations in management literature: Three approaches to understanding the interaction between market, technology, entrepreneurial and learning orientations (2011) International Journal of Management Reviews, 13, pp. 199-217; Handfield, R., Petersen, K., Cousins, P., Lawson, B., An organizational entrepreneurship model of supply management integration and performance outcomes (2009) International Journal of Operations & Production Management 29, 2, pp. 100-126; Hlupic, V., Pouloudi, A., Rzevski, G., Towards an integrated approach to knowledge management: 'hard', 'soft' and 'abstract' issues (2002) Knowledge and Process Management, 9 (2), pp. 90-102; Hrebiniak, L.G., (2005) Making Strategy Work: Leading Effective Execution and Change, , Upper Saddle River, NJ: Pearson Education; Hsieh, Y.H., Chen, H.M., Strategic fit among business competitive strategy, human resource strategy, and reward system (2011) Academy of Strategic Management Journal, 10 (2), pp. 11-32; Hudson, M., Stuart, P.A., Bourne, M.C.S., Theory and practice in sme performance measurement systems (2001) International Journal of Operations and Production Management, 21 (8), pp. 1095-1115; Hult, G.T.M., Ketchen, D.J., Nichols, E.L., Organizational learning as a strategic resource in supply management (2003) Journal of Operations Management, 21, pp. 541-556; Kahyaoglu, Y., Kayaligil, S., Conceptualizing manufacturing flexibility: An operational approach and a comparative evaluation (2002) International Journal of Production Research, 40 (10), pp. 2187-2206; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, pp. 75-85. , January/February; Kaur, H., Soft ems, hard ems, and environmental performance relationships: A review of the literature (2011) Journal of Environmental Management & Tourism, 2 (1), pp. 34-41; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Khalil, F., Kin, D., Shin, D., Optimal task design: To integrate or separate planning and implementation? (2006) Journal of Economics & Management Strategy, 15 (2), pp. 457-478; Kim, J., Mueller, C.W., (1978) Factor Analysis: Statistical Methods and Practical Issues, , Beverly Hills, CA Sage; Kim, I., Seo, H.L., Depreciation and transfer of knowledge: An empirical exploration of a shipbuilding process (2009) International Journal of Production Research, 47 (7), pp. 1857-1876; Kirkman, B.L., Shapiro, D.L., The impact of cultural values on job satisfaction and organizational commitment in self-managing work teams: The mediating role of employee resistance (2001) Academy of Management Journal, 44 (3), pp. 557-569; Kuei, C.-H., Madu, C.N., Lin, C., Developing global supply chain quality management systems (2011) International Journal of Production Research, 49 (15), pp. 4457-4481; Lander, E., Liker, J.K., The toyota production system and art: Making highly customized and creative products the toyota way (2007) International Journal of Production Research, 45 (16), pp. 3681-3698; Lee, A.H.I., Chen, H.H., Tong, Y., Developing new products in a network with efficiency and innovation (2008) International Journal of Production Research, 46 (17), pp. 4687-4707; Li, Y., Wang, L., Liuab, Y., Organisational learning, product quality and performance: The moderating effect of social ties in chinese cross-border outsourcing (2011) International Journal of Production Research, 49 (1), pp. 159-182; Lockamy III, A., Cox, F., An empirical study of division and plant performance measurement systems in selected world class manufacturing firms: Linkages for competitive advantage (1995) International Journal of Production Research, 33 (1), pp. 221-236; Lucey, J.J., The concept of a lean sustainability zone (2009) Management Services, 53 (3), pp. 8-13; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; McAdam, R., Bannister, A., Business performance measurement and change management within a tqm framework (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 88-108; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-218; Micheli, P., Manzoni, J.-F., Strategic performance measurement: Benefits, limitations and paradoxes (2010) Long Range Planning, 43 (4), pp. 465-476; Mintzberg, H., Generic strategies: Toward a comprehensive framework (1988) Advances in Strategic Management, 5, pp. 1-67; Neely, A.D., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationships, , London Prentice Hall; Nudurupati, S.S., Bititci, U.S., Implementation and impact of it-supported performance measurement systems (2005) Production Planning & Control, 16 (2), pp. 152-162; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60, pp. 279-290; Podsakoff, P.M., Organ, D.W., Self-reports in Organizational Research: Problems and Prospects (1986) Journal of Management, 12 (4), pp. 531-544; Podsakoff, P.M., Mackenzie, S.B., Lee, J.Y., Podsakoff, N.P., Common method biases in behavioral research: A critical review of the literature and recommended remedies (2003) Journal of Applied Psychology, 88 (5), pp. 879-903; Porter, M.E., Competitive strategy (1980) Techniques for Analyzing Industries and Competitors, , New York, NY: The Free Press; Prajogo, D.I., Sohal, A.S., The integration of TQM and technology/r&d management in determining quality and innovation performance (2006) Omega, 34, pp. 296-312; Pucko, D., Cater, T., What effective strategy implementation depends on: The experience of slovenian companies (2008) An Enterprise Odyssey: Tourism-Governance and Entrepreneurship: 4th International Conference, pp. 1220-1234. , Zagreb: Graduate School of Economics and Business; Rai, A., Borah, S., Ramaprasad, A., Critical success factors for strategic alliances in the information technology industry: An empirical study (1996) Decision Sciences, 27 (1), pp. 141-155; Rosas, J., Camarinha-Matos, L.M., An approach to assess collaboration readiness (2009) International Journal of Production Research, 47 (17), pp. 4711-4735; Rungtusanatham, M., Forza, C., Filippini, R., Anderson, J.C., A replication study of a theory of quality management underlying the deming management method: Insights from an italian context (1998) Journal of Operations Management, 17 (1), pp. 77-95; Saeed, K.A., Abdinnour-Helm, S., Examining the effects of information system characteristics and perceived usefulness on post adoption usage of information systems (2008) Information & Management, 45 (6), pp. 376-386; Samson, D., Terziovski, M., The relationship between total quality management practices and operational performance (1999) Journal of Operations Management, 17, pp. 393-409; Schein, E.H., Culture: The missing concept in organization studies (1996) Administrative Science Quarterly, 41 (2), pp. 229-240; Senge, P.M., (1990) The Fifth Discipline: The Art and Practice of the Learning Organization, , New York, NY: Doubleday; Sinkula, J., Baker, W., Noordewier, T., A framework for market-based organizational learning: Linking values, knowledge, and behavior (1997) Journal of the Academy of Marketing Science, 25, pp. 305-318; Sousa, R., Voss, C.A., Contingency research in operations management practices (2008) Journal of Operations Management, 26, pp. 697-713; Tamimi, N., An empirical investigation of critical tqm factors using exploratory factor analysis (1995) International Journal of Production Research, 33 (11), pp. 3041-3051; Tan, K.C., Kannan, V.R., Jayaram, J., Narasimhan, R., Acquisition of operations capability: A model and test across us and european firms (2004) International Journal of Production Research, 42 (4), pp. 833-851; Tung, A., Baird, K., Schoch, H.P., Factors influencing the effectiveness of performance measurement systems (2011) International Journal of Operations & Production Management, 31 (12), pp. 1287-1310; Vargo, J., Seville, E., Crisis strategic planning for smes: Finding the silver lining (2011) International Journal of Production Research, 49 (18), pp. 5619-5635; Venkateswarlu, P., Nilakant, V., Adoption and persistence of tqm programmes-case studies of five new zealand organizations (2005) Total Quality Management & Business Excellence, 16 (7), pp. 807-825; Vickery, S., Droge, C., Setia, P., Sambamurthy, V., Supply chain information technologies and organisational initiatives: Complementary versus independent effects on agility and firm performance (2010) International Journal of Production Research, 48 (23), pp. 7025-7042; Voss, C.A., Blackmon, K.L., Cagliano, R., Hanson, P., Wilson, F., Made in europe: Small companies (1998) Business Strategy Review, 9 (4), pp. 1-19; Walker, G.H., Stanton, N.A., Salmon, P.M., Jenkins, D.P., A review of sociotechnical systems theory: A classic concept for new command and control paradigms (2008) Theoretical Issues in Ergonomics Science, 9 (6), pp. 479-499; Wang, R.Y., Strong, D.M., Beyond accuracy: What data quality means to data consumers (1996) Journal of Management Information Systems, 14 (4), pp. 5-34; Yaprak, A., Xu, S., Cavusgil, E., Effective global strategy implementation (2011) Management International Review, 51, pp. 179-192; Yeung, A.C.L., Lai, K.-H., Lee, R.W.Y., Organizational learning, innovativeness, and organizational performance: A qualitative investigation (2007) International Journal of Production Research, 45 (11), pp. 2459-2477},
correspondence_address1={Taylor, A.; School of Management, , Bradford, United Kingdom; email: w.a.taylor@bradford.ac.uk},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Review},
source={Scopus},
}

@ARTICLE{Dey2013702,
author={Dey, P.K. and Cheffi, W.},
title={Green supply chain performance measurement using the analytic hierarchy process: A comparative analysis of manufacturing organisations},
journal={Production Planning and Control},
year={2013},
volume={24},
number={8-9},
pages={702-720},
doi={10.1080/09537287.2012.666859},
note={cited By 134},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883549319&doi=10.1080%2f09537287.2012.666859&partnerID=40&md5=88d9e3b67d770d66e6d43bdadd876bd7},
affiliation={Aston Business School, Aston University, Birmingham B4 7ET, United Kingdom; Rouen Business School, Rue du Maréchal Juin-BP 215, 76825 Mont Saint Aignan, France},
abstract={The main purpose of this research is to develop and deploy an analytical framework for measuring the environmental performance of manufacturing supply chains. This work's theoretical bases combine and reconcile three major areas: supply chain management, environmental management and performance measurement. Researchers have suggested many empirical criteria for green supply chain (GSC) performance measurement and proposed both qualitative and quantitative frameworks. However, these are mainly operational in nature and specific to the focal company. This research develops an innovative GSC performance measurement framework by integrating supply chain processes (supplier relationship management, internal supply chain management and customer relationship management) with organisational decision levels (both strategic and operational). Environmental planning, environmental auditing, management commitment, environmental performance, economic performance and operational performance are the key level constructs. The proposed framework is then applied to three selected manufacturing organisations in the UK. Their GSC performance is measured and benchmarked by using the analytic hierarchy process (AHP), a multiple-attribute decision-making technique. The AHP-based framework offers an effective way to measure and benchmark organisations GSC performance. This study has both theoretical and practical implications. Theoretically it contributes holistic constructs for designing a GSC and managing it for sustainability; and practically it helps industry practitioners to measure and improve the environmental performance of their supply chain. © 2013 Copyright Taylor and Francis Group, LLC.},
author_keywords={analytical hierarchy process;  green supply chains;  manufacturing industry;  performance measurement;  strategic and operational performance constructs},
keywords={Analytical Hierarchy Process;  Green supply chain;  Manufacturing industries;  Operational performance;  Performance measurements, Analytic hierarchy process;  Benchmarking;  Distribution of goods;  Hierarchical systems;  Industrial research;  Manufacture;  Societies and institutions;  Supply chain management, Environmental management},
references={Agndal, H., Nilsson, U., Different open book accounting practices for different purchasing strategies (2010) Management Accounting Research, 21, pp. 147-166; Angell, L.-C., Klassen, R.D., Integrating environmental issues into the mainstream: An agenda for research in operations management (1999) Journal of Operations Management, 17 (5), pp. 575-598; Beamon, B.-M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Beamon, B.-M., Environmental and sustainability ethics in supply chain management (2005) Science and Engineering Ethics, 11, pp. 221-234; Beamon, B.-M., Sustainability and the future of supply chain management (2008) Operations and Supply Chain Management, 1 (1), pp. 4-18; Belton, V., Gear, T., On shortcomings of Saatys method of analytical hierarchies (1983) Omega, 11, pp. 227-230; Bhagwat, M., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53, pp. 43-62; Bond, T.C., The role of performance measurement in continuous improvement (1999) International Journal of Operations and Production Management, 19 (12), pp. 1318-1334; Brown, D., It is good to be green-Environmentally friendly credentials are influencing business outsourcing decisions (2008) Strategic Outsourcing: An International Journal, 1 (1), pp. 87-95; Butner, K., Geuder, D., Hittner, J., (2008) Mastering Carbon Management: Balancing Trade-offs to Optimize Supply Chain Efficiencies, , Somers, NY: IBM Global Business Services; Chan, F.-T.-S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chan, F.-T.-S., Qi, H.-J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (4), pp. 209-223; Child, J., Tsai, T., The dynamic between firms environmental strategies and institutional constraints in emerging economies: Evidence from China and Taiwan (2004) Journal of Management Studies, 42 (1), pp. 95-125; Corbett, C.J., Klassen, R.D., Extending the horizons: Environmental excellence as key to improving operations (2006) Manufacturing and Service Operations Management, 8 (1), pp. 5-22; Dyer, R.F., Forman, E.H., Group decision support with the analytic hierarchy process (1992) Decision Support Systems, 8, pp. 99-124; (2000) The Lean and Green Supply Chain: A Practical Guide for Materials Managers and Supply Chain Managers to Reduce Costs and Improve Environmental Performance, , EPA. Washington, DC: United States Environmental Protection Agency; (2009) European Union, Eco-Management and Audit Scheme, , Regulation (EC) No 1221/2009 repealing Regulation (EC) No 761/2001 and Commission Decisions 2001/681/EC and 2006/193/EC; Finan, J.-S., Hurley, W.-J., The analytic hierarchy process: Can wash criteria be ignored? (2002) Computers and Operations Research, 29 (8), pp. 1025-1030; Geffen, C.-A., Rothenberg, S., Suppliers and environmental innovation: The automotive paint process (2000) International Journal of Operations & Production Management, 20 (2), pp. 166-186; Gilbert, S., (2001) Greening Supply Chain: Enhancing Competitiveness Through Green Productivity, , Tapei, Taiwan: Asian Productivity Association; Golden, B.-L., Wasli, E.-A., Harker, P.-T., (1989) The Analytic Hierarchy Process: Applications and Studies, , New York: Springer-Verlag; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain (2001) International Journal of Operations and Production Management, 21 (12), pp. 71-87; Gupta, M., Sharma, K., Environmental operations management: An opportunity for improvement (1996) Production and Inventory Management Journal, 37 (3), pp. 40-46; Hall, J., Environmental supply chain dynamics (2000) Journal of Cleaner Production, 8, pp. 455-471; Handfield, R., Walton, S., Sroufe, R., Applying environmental criteria to supplier assessment: A study of the application of the analytical hierarchy process (2002) European Journal of Operational Research, 141, pp. 70-87; Haskins, C., Multidisciplinary investigation of eco-industrial parks (2006) Systems Engineering, 9 (4), pp. 313-330; Hervani, A.-A., Helms, M.-M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Ilgin, M.-A., Gupta, M., Environmental conscious manufacturing and product recovery (ECMPRO): A review of the state of the art (2010) Journal of Environmental Management, 91 (3), pp. 563-591; Iraldo, F., Testa, F., Frey, M., Is an environmental management system able to influence environmental and competitive performance? The case of the eco-management and audit scheme (EMAS) in the European Union (2009) Journal of Cleaner Production, 17 (16), pp. 1444-1452; (2002) ISO 14031, , International Organisation for Standardisation (2002). International Standard ISO 14031:2002. Environmental management-environmental performance evaluation-guidelines. Geneva, Switzerland; Ittner, C.-D., Supplier selection, monitoring practices, and firm performance (1999) Journal of Accounting and Public Policy, 18, pp. 253-281; Kainumaa, Y., Tawarab, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) International Journal of Production Economics, 101, pp. 99-108; Kleindorfer, P.-R., Singhal, K., Wassenhove, L.-N.-V., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Liang, L., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145, pp. 35-49; Linton, J.-D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) Journal of Operations Management, 25, pp. 1075-1082; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: A survey in the mines (2006) Management Accounting Research, 17, pp. 315-339; Mentzer, J.-T., Myers, M.-B., Stank, T.-P., (2007) Handbook of Global Supply Chain Management, , California: Sage; Mildenberger, U., Khare, A., Planning for an environment-friendly car (2000) Technovation, 20, pp. 205-214; Nawrocka, D., Brorson, T., Lindhqvist, T., ISO 14001 in environmental supply chain practices (2009) Journal of Cleaner Production, 17 (16), pp. 1435-1443; Olugu, E.-U., Wong, K.-Y., Shaharoun, A.-M., Development of key performance measures for the automobile green supply chain (2010) Resources Conservation and Recycling, 65 (6), pp. 567-579; Orsato, R.-J., Competitive Environmental strategies: When does it pay to be green? (2006) California Management Review, 48 (2), pp. 127-143; Porter, M.-E., Van Der Linde, C., Green and competitive (1995) Harvard Business Review, 73 (5), pp. 120-134; Presley, A., Meade, L., Sarkis, J., A strategic sustainability justification methodology for organizational decisions: A reverse logistics illustration (2007) International Journal of Production Research, 45 (1819), pp. 4595-4620; Rao, P., Greening the supply chain: A new initiative in South East Asia (2002) International Journal of Operations and Production Management, 22 (6), pp. 632-655; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916; Reinhardt, F.L., (2000) Down to Earth, , Boston: Harvard Business School Press; Rothenberg, S., Schenck, B., Maxwell, J., Lessons from benchmarking environmental performance at automobile assembly plants (2005) Benchmarking: An International Journal, 12 (1), pp. 5-15; Saaty, T.-L., (1980) The Analytic Hierarchy Process, , USA: McGraw-Hill; Saaty, T.-L., (1982) Decision Making for Leaders, , New York: Lifetime Learning; Saaty, T.-L., Priority setting in complex problems (1983) IEEE Transactions on Engineering Management, pp. 140-155. , EM-30, August; Salam, M.-A., Corporate social responsibility in purchasing and supply chain (2009) Journal of Business Ethics, 85 (2), pp. 355-370; Sarkis, J., A strategic decision framework for green supply chain management (2003) Journal of Cleaner Production, 11 (4), pp. 397-409; Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16, pp. 1699-1710; Smith, M., Crotty, J., Environmental regulation and innovation driving ecological design in the UK automotive industry (2008) Business Strategy and Environment, 17, pp. 341-349; Sundarakani, B., Modeling carbon footprints across the supply chain. International (2010) Journal of Production Economics, 128, pp. 43-50; Tsoulfas, G., Pappis, C., Environmental principles applicable to supply chains design and operation (2006) Journal of Cleaner Production, 14 (18), pp. 1593-1602; Tsoulfas, G., Pappis, C., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16, pp. 1647-1657; Vachon, S., Klassen, R., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Van Den Abbeele, A., Roodhoft, F., Warlop, L., The effect of cost information on buyer-supplier negotiations in different power settings (2009) Accounting, Organizations and Society, 34, pp. 245-266; Van Hoek, R.I., Case studies of greening the automotive supply chain through technology and operations (2002) International Journal of Technology Management, 23 (3-12), pp. 89-112; Vargas, L.G., An overview of the analytic hierarchy process and its applications (1990) European Journal of Operation Research, 48 (1), pp. 2-8; Veleva, V., Indicators of sustainable production (2001) Journal of Cleaner Production, 9, pp. 447-452; Wong, W.-P., Wong, K.-Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51; Yang, C.L., Mediated effect of environmental management on manufacturing competitiveness: An empirical study (2010) International Journal of Production Economics, 123, pp. 210-220; Zhu, Q., Sarkis, J., Lai, K.-H., Green supply chain management: Pressures, practices and performance within the Chinese automobile industry (2007) Journal of Cleaner Production, 15 (1112), pp. 1041-1052; Zhu, Q., Sarkis, J., Lai, K.-H., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) Journal of Environmental Management, 85 (1), pp. 179-189; Zhu, Q., Sarkis, J., Lai, K.-H., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111, pp. 261-273; Zhu, Q., Sarkis, J., Lai, K.-H., Green supply chain management implications for "closing the loop (2008) Transportation Research Part e, 44, pp. 1-18},
correspondence_address1={Cheffi, W.; Rouen Business School, , 76825 Mont Saint Aignan, France; email: walid.cheffi@rouenbs.fr},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{Vidalakis2013469,
author={Vidalakis, C. and Sommerville, J.},
title={Transportation responsiveness and efficiency within the building supply chain},
journal={Building Research and Information},
year={2013},
volume={41},
number={4},
pages={469-481},
doi={10.1080/09613218.2012.715824},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878618501&doi=10.1080%2f09613218.2012.715824&partnerID=40&md5=109cc13a6813dd40c47740713b262aa6},
affiliation={Department of Construction and Property, University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol BS16 1Q, United Kingdom; School of the Built and Natural Environment, Glasgow Caledonian University, Glasgow G4 0BA, United Kingdom},
abstract={Following the example of the manufacturing industry, the construction industry has utilized supply chain management to improve its performance and competitiveness. However, although performance measurement has attracted particular attention in the sector since the 1990s, supply chain performance measurement has been largely overlooked. Therefore, given that buildings are outputs of supply chain systems, there is a need to develop a better understanding of performance measurement pertaining to the interactions and interdependencies between the constituent parts of these systems. Based on primary data collected through a field study, the performance of two material distribution companies is examined in terms of their capacity to provide a time-responsive and cost-effective service to the building industry. Descriptive and inferential statistics are used to assess performance according to a range of performance indicators and to explore the relationship between transportation responsiveness and cost efficiency. The results show that vehicle-loading efficiency levels are particularly lower than those estimated in other sectors and reveal the prevalence of empty vehicle runs during backhauling. Significant variations in performance between the two companies are observed to be mainly attributable to the nature of business itself, as well as to adopted distribution strategies. © 2013 Taylor and Francis Group, LLC.},
author_keywords={construction industry;  logistics;  responsiveness;  supply chain;  total acquisition cost;  transport;  transportation efficiency},
keywords={Acquisition costs;  Distribution strategies;  Manufacturing industries;  Performance measurements;  responsiveness;  Supply chain performance measurements;  transport;  Transportation efficiency, Benchmarking;  Competition;  Cost effectiveness;  Logistics;  Supply chain management;  Supply chains, Construction industry},
references={Agapiou, A., Flanagan, R., Norman, G., Notman, D., The changing role of builders merchants in the construction supply chain (1998) Construction Management and Economics, 16 (3), pp. 351-361; Ailawadi, S., Rakesh, S., (2005) Logistics Management, , New Delhi: Prentice-Hall; Ala-Risku, T., Karkkainen, M., Material delivery problems in construction projects: A possible solution (2006) International Journal of Production Economics, 104 (1), pp. 19-29. , DOI 10.1016/j.ijpe.2004.12.027, PII S0925527305000393, Strategic Issues and Innovation in Production Economics; Assaf, S.A., Al-Hejji, S., Causes of delay in large construction projects (2006) International Journal of Project Management, 24 (4), pp. 349-357. , (doi:10.1016/j.ijproman.2005.11.010); Bassioni, H.A., Price, A.D.F., Hassan, T.M., Performance measurement in construction firms (2004) Journal of Management in Engineering, 20 (2), pp. 42-50. , (doi:10.1061/(ASCE)0742-597X(2004)20:2(42)); Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 247-292. , (doi:10.1108/01443579910249714); Bjork, B., A formalized model of the information and materials handling activities in the construction process (2002) Construction Innovation: Information, Process, Management, 2 (3), pp. 133-149; Bowersox, D.J., Closs, D.J., Cooper, M.B., (2007) Supply Chain Logistics Management, 2. , New York, NY: McGraw-Hill; (2003) Construction Site Transport: The Next Big Thing, , Building Research Establishment (BRE). Watford: BRE; Burns, R.B., Burns, A.R., (2008) Business Research Methods and Statistics Using SPSS, , Los Angeles, CA: Sage; Chan, T.K., Measuring performance of the Malaysian construction industry (2009) Construction Management and Economics, 27 (12), pp. 1231-1244. , (doi:10.1080/01446190903233137); Chen, R.R., Roundy, R.O., Zhang, R.Q., Janakiraman, G., Efficient auction mechanisms for supply chain procurement (2005) Management Science, 51 (3), pp. 467-482. , DOI 10.1287/mnsc.1040.0329, Electronic Markets; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply Chain Management: More Than a New Name for Logistics (1997) International journal of logistics management, 8 (1), pp. 1-14; (2010) Council of Supply Chain Management Professionals' Supply Chain Management Definitions, , http://cscmp.org/aboutcscmp/definitions.asp, Council of Supply Chain Management Professionals (CSCMP) (accessed on 5 February 2010); (2007) Construction Products Industry Key Performance Indicators, , Department for Business, Enterprise and Regulatory Reform (BERR). London: Construction Products Association; (2006) Key Performance Indicators for the Builders' Merchants Sector, , Department for Transport (DfT). London: HMSO; (1995) Efficiency and Value in Purchasing and Supply, , Department of Trade and Industry (DTI). London: HMSO; Duthie, J., Ferguson, E., Unnikrishnan, A., Waller, S.T., (2009) Multimodal Network Models for Robust Transportation Systems, , Research Report No. SWUTC/09/167867-1, Center for Transportation Research, University of Texas, Austin, TX; Egan, J., (1998) Rethinking Construction: The Report of the Construction Task Force to the Deputy Prime Minister, , London: Department of the Environment, Transport and the Regions (DETR); Errasti, A., Beach, R., Oduoza, C., Apaolaza, U., Close coupling value chain functions to improve subcontractor manufacturing performance (2009) International Journal of Project Management, 27 (3), pp. 261-269. , (doi:10.1016/j.ijproman.2008.01.010); Fearne, A., Fowler, N., Efficiency versus effectiveness in construction supply chains: The dangers of "lean" thinking in isolation (2006) Supply Chain Management, 11 (4), pp. 283-287. , DOI 10.1108/13598540610671725; Field, A.P., (2005) Discovering Statistics Using SPSS, , London: Sage; Frazelle, E.H., (2002) Supply Chain Strategy: The Logistics of Supply Chain Management, , New York, NY: McGraw-Hill; Geunes, J., Taaffe, K., Transportation systems overview (2008) Logistics Engineering Handbook, , Edited by: Taylor, D. Boca Raton, FL: CRC Press, Taylor & Francis Group; Ghiani, G., Laporte, G., Musmanno, R., (2004) Introduction to Logistics Systems Planning and Control, , Chichester: Wiley; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Production and Operations Management, 21 (12), pp. 71-87. , (doi:10.1108/01443570110358468); Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353. , DOI 10.1108/14635770510609015; Hill, N., Wagner, A., Graham, G., Li, Y., (2008) Transport Carbon Emissions in the Highlands and Islands: Final Report, , Report to the Highlands and Islands Enterprise, AEA Energy and Environment, Didcot; Holweg, M., Miemczyk, J., Logistics in the three-day car age: Assessing the responsiveness of vehicle distribution logistics in the UK (2002) International Journal of Physical Distribution and Logistics Management, 32 (10), pp. 829-850. , (doi:10.1108/09600030210455438); Houlihan, J.B., International supply chain management (1985) International Journal of Physical Distribution and Materials Management, 15 (1), pp. 22-38. , (doi:10.1108/eb014601); Hugos, M., (2006) Essentials of the Supply Chain Management, 2. , Hoboken, NJ: Wiley; Jones, M., Supply chain management in construction (2005) Construction Project Management: An Integrated Approach, , Edited by: Fewings, P. London: Taylor & Francis; Josephson, P.-E., Larsson, B., Li, H., Illustrative benchmarking rework and rework costs in Swedish construction industry (2002) Journal of Management in Engineering, 18 (2), pp. 76-83. , DOI 10.1061/(ASCE)0742-597X(2002)18:2(76); Kagioglou, M., Cooper, R., Aouad, G., Performance management in construction: A conceptual framework (2001) Construction Management and Economics, 19 (1), pp. 85-95. , (doi:10.1080/01446190010003425); Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83. , (doi:10.1016/S0019-8501(99)00113-3); Lazzarini, S., Chaddad, F.R., Cook, M.L., Integrating supply chain and network analysis: The study of netchains (2000) Journal on Chain and Network Science, 1, pp. 7-22. , (doi:10.3920/JCNS2001.x002); Leonardi, J., Baumgartner, M., CO2 efficiency in road freight transportation: Status quo, measures and potential (2004) Transportation Research Part D: Transport and Environment, 9 (6), pp. 451-464. , DOI 10.1016/j.trd.2004.08.004, PII S1361920904000471; McKinnon, A., Outsourcing the logistics function (2003) Global Logistics and Distribution Planning: Strategies for Management, 4. , Edited by: Waters, D. London: Kogan Page; Mentzer, J.T., Dewitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25. , (doi:10.1002/j.2158-1592.2001.tb00001.x); Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228. , DOI 10.1108/01443579910247437; Nozick, L.K., Turnquist, M.A., Inventory, transportation, service quality and the location of distribution centers (2001) European Journal of Operational Research, 129 (2), pp. 362-371. , DOI 10.1016/S0377-2217(00)00234-4; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320. , DOI 10.1016/S0377-2217(02)00396-X, PII S037722170200396X; Polat, G., Arditi, D., Mungen, U., Simulation-based decision support system for economical supply chain management of rebar (2007) Journal of Construction Engineering and Management, 133 (1), pp. 29-39. , DOI 10.1061/(ASCE)0733-9364(2007)133:1(29); Saad, M., Jones, M., James, P., A review of the progress towards the adoption of supply chain management (SCM) relationships in construction (2002) European Journal of Purchasing and Supply Management, 8 (3), pp. 173-183. , (doi:10.1016/S0969-7012(02)00007-2); Shakantu, W.M., Muya, M., Tookey, J.E., Bowen, P.A., Flow modelling of construction site materials and waste logistics: A case study from Cape Town, South Africa (2008) Engineering, Construction and Architectural Management, 15 (5), pp. 423-439. , (doi:10.1108/09699980810902721); Shakantu, W.M., Tookey, J.E., Bowen, P.A., The hidden cost of transportation of construction materials: An overview (2003) Journal of Engineering, Design and Technology, 1 (1), pp. 103-118. , (doi:10.1108/eb060892); Shutt, R.C., (1995) Economics for the Construction Industry, 3. , Harlow: Longman; Sobotka, A., Czarnigowska, A., Analysis of supply system models for planning construction project logistics (2005) Journal of Civil Engineering and Management, 11 (1), pp. 73-82; Soderman, S., (1985) Om Byggmaterialdistribution [On the Distribution of Building Materials], , Project No. 791344-9, Byggfiorskninsradet, Sweden; Stadtler, H., Supply-chain management - An overview (2008) Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies, 4, pp. 9-35. , Edited by: Stadtler, H. and Kilger, C. Berlin: Springer; (2005) Improving Construction Logistics: Report of the Strategic Forum for Construction Logistics Group, , Strategic Forum for Construction (SFC)/Construction Products Association (CPA). London: SFC; Takim, R., Akintoye, A., Kelly, J., Performance measurement systems in construction (2003) Proceedings of the 19th Annual ARCOM Conference, , UK: Brighton; Tan, C.T., Lyman, S.B., Wisner, J.D., Supply chain management: A strategic perspective (2002) International Journal of Operations and Production Management, 22 (6), pp. 614-631. , (doi:10.1108/01443570210427659); (1999) Transport and the Economy, , The Standing Advisory Committee on Trunk Road Appraisal (SACTRA). London: Department of the Environment, Transport and the Regions (DETR); Vanteddu, G., Chinnam, R.B., Gushikin, O., Supply chain focus dependent supplier selection problem (2011) International Journal of Production Economics, 129 (1), pp. 204-216. , (doi:10.1016/j.ijpe.2010.10.003); Vidalakis, C., Tookey, J.E., Sommerville, J., The logistics of construction supply chains: The builders' merchant perspective (2011) Engineering, Construction and Architectural Management, 10 (1), pp. 66-80. , (doi:10.1108/09699981111098694); Von Lanzenauer, C.H., Pilz-Glombik, K., Coordinating supply chain decisions: An optimization model (2002) OR Spectrum, 24, pp. 59-78. , (doi:10.1007/s291-002-8200-3); Vrijhoef, R., Koskela, L., The four roles of supply chain management in construction (2000) European Journal of Purchasing and Supply Management, 6 (34), pp. 169-178. , (doi:10.1016/S0969-7012(00)00013-7); Waters, D., Inventory management (2001) Handbook of Logistics and Supply Chain Management, pp. 195-212. , Edited by: Brewer, A. M. Button, K. J. and Hensher, D. A. Amsterdam: Elsevier Science; Wegelius-Lehtonen, T., Measuring and re-engineering logistics chain in the construction industry (1995) Re-engineering the Enterprise, pp. 209-219. , Edited by: Browne, J. and O'Sullivan, D. London: Chapman & Hall; Yu, I., Kim, K., Jung, Y., Chin, S., Comparable performance measurement system for construction companies (2007) Journal of Management in Engineering, 23 (3), pp. 131-139. , DOI 10.1061/(ASCE)0742-597X(2007)23:3(131)},
correspondence_address1={Department of Construction and Property, Coldharbour Lane, Bristol BS16 1Q, United Kingdom},
issn={09613218},
coden={BREIE},
language={English},
abbrev_source_title={Build Res Inf},
document_type={Article},
source={Scopus},
}

@BOOK{Lam2013571,
author={Lam, H.L. and Ng, W.P.Q.},
title={A Process Integration Approach for Supply Chain Development},
journal={Handbook of Process Integration (PI): Minimisation of Energy and Water Use, Waste and Emissions},
year={2013},
pages={571-593},
doi={10.1533/9780857097255.4.571},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84935038334&doi=10.1533%2f9780857097255.4.571&partnerID=40&md5=1536d7d432d8fcf8deaf640ee5baf9a4},
affiliation={Department of Chemical and Environmental Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih, Selangor Darul Ehsan, 43500, Malaysia; The University of Nottingham, Malaysia, Selangor, Malaysia},
abstract={This chapter extends the application of the Process Integration (PI) approach to supply chains, especially developing further the analogy between Pinch Analysis and the field of operational research. The first part of this chapter introduces supply chains and supply chain performance measurement. In the following parts, the chapter provides clear insight into how the PI concept brings a significant contribution to the development of supply chain design. This novel approach, Pinch Analysis for the supply chain, will be presented. After an overview of the approach, case studies are outlined for purposes of demonstration. © 2013 Woodhead Publishing Limited All rights reserved..},
author_keywords={Pinch Analysis;  Process Integration;  Supply chain},
keywords={Energy management;  Energy resources, Case-studies;  Operational research;  Pinch analysis;  Process integration;  Supply chain design;  Supply chain performance measurements, Supply chains},
references={Beamon, B.M., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Clark, A.J., Scarf, H., Optimal policies for a multi-echelon inventory problem (1960) Management Science, 6 (4), pp. 475-490; Cox, J.F., Blackstone, J.H., (1998) APICS Dictionary, p. 93. , APICS. 9th; Annual Energy Review 2010: Report No (2011) DOE/EIA-0384(2010), , Energy Information Administration, Washington, USA; Foo, D.C.Y., Ooi, M.B.L., Tan, R.R., Tan, J.S., A heuristic-based algebraic targeting technique for aggregate planning in supply chains (2008) Computers & Chemical Engineering, 32, pp. 2217-2232; Krajewski, L.J., Ritzman, L.P., Malhotra, M.K., (2010) Operations Management: Processes and Supply Chains, , Pearson Education, New Jersey, USA, 9th ed; Lam, H.L., Varbanov, P.S., Klemeš, J.J., Optimisation of regional energy supply chains utilising renewables: P-graph approach (2010) Computers & Chemical Engineering, 34, pp. 782-792; Lam, H.L., Varbanov, P.S., Klemeš, J.J., Regional renewable energy and resource planning (2011) Applied Energy, 88, pp. 545-550; Linnhoff, B., Ahmad, S., Cost optimum heat exchanger networks - I (1990) Minimum energy and capital using simple models for capital cost. Computers & Chemical Engineering, 14 (7), pp. 729-750; Linnhoff, B., Hindmarsh, E., The pinch design method for heat exchanger networks (1983) Chemical Engineering Science, 38 (5), pp. 745-763; Shapiro, J.F., (2001) Modeling the Supply Chain, , Duxbury Press, California, USA; Singhvi, A., Madhavan, K.P., Shenoy, U.V., Pinch analysis for aggregate production planning in supply chains (2004) Computers & Chemical Engineering, 28, pp. 993-999; Singhvi, A., Shenoy, U.V., Aggregate planning in supply chains by pinch analysis (2002) Trans IChem, 80, pp. 597-605. , Part A; Townsend, D.W., Linnhoff, B., Heat and power networks in process design (1983) Part II: design procedure for equipment selection and process matching. AIChE Journal, 29 (5), pp. 748-771; Wang, Y.P., Smith, R., Wastewater minimisation (1994) Chemical Engineering Science, 49, pp. 981-1006},
correspondence_address1={Lam, H.L.; Department of Chemical and Environmental Engineering, Jalan Broga, Malaysia},
publisher={Elsevier Inc.},
isbn={9780857097255; 9780857095930},
language={English},
abbrev_source_title={Handb. of Process Integr. (PI): Minimisation of Energy and Water Use, Waste and Emiss.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Opoku-Anokye201345,
author={Opoku-Anokye, S. and Tang, Y.},
title={The design of a semantics-oriented organisational performance measurement system},
journal={ICISO 2013 - Proceedings of the 14th International Conference on Informatics and Semiotics in Organisations, IFIP WG8.1 Working Conference},
year={2013},
pages={45-49},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880317601&partnerID=40&md5=bb704a2adc0d79fa6df064d465f56ef1},
affiliation={Informatics Research Centre, Henley Business School, University of Reading, Reading, RG6 6UD, United Kingdom},
abstract={Current methods and techniques used in designing organisational performance measurement systems do not consider the multiple aspects of business processes or the semantics of data generated during the lifecycle of a product. In this paper, we propose an organisational performance measurement systems design model that is based on the semantics of an organisation, business process and products lifecycle. Organisational performance measurement is examined from academic and practice disciplines. The multi-discipline approach is used as a research tool to explore the weaknesses of current models that are used to design organisational performance measurement systems. This helped in identifying the gaps in research and practice concerning the issues and challenges in designing information systems for measuring the performance of an organisation. The knowledge sources investigated include on-going and completed research project reports; scientific and management literature; and practitioners' magazines.},
author_keywords={Business process performance measurement;  Organisational performance measurement;  Product-based performance measurement;  Semantic-oriented performance measurement;  Systems design},
keywords={Business Process;  Issues and challenges;  Knowledge sources;  Multi disciplines;  Organisational performance;  Performance measurements;  Project reports;  Techniques used, Information science;  Life cycle;  Research;  Semantics;  Supply chain management;  Systems analysis, Product design},
references={Beynon-Davies, P., (2002) Information Systems: An Introduction to Informatics in Organisations, , Palgrave; Chandler, D., (2007) Semiotics: The Basics, , Abingdon, UK, Routledge; (2005) CIMA Official Terminology, , Cima CIMA Publishing; Clearinghouse, I., (2005) Interoperability Clearinghouse Glossary of Terms, , Retrieved January, 29, 2006; Commerce, O.O.G., (2005) Managing Successful Projects with PRINCE2, , Norwich, UK, The Stationery Office; Daft, R.L., (2009) Organization Theory and Design, , South-Western Pub; Davenport, T.H., Short, J.E., Information technology and business process redesign (2003) Operations Management: Critical Perspectives on Business and Management, 1, pp. 1-27; Deangelo, L.E., Managerial competition, information costs, and corporate governance * 1:: The use of accounting performance measures in proxy contests (1988) Journal of Accounting and Economics, 10, pp. 3-36; Demski, J.S., Performance measure manipulation* (1998) Contemporary Accounting Research, 15, pp. 261-285; Fisher, C., Downes, B., Performance measurement and metric manipulation in the public sector (2008) Business Ethics: A European Review, 17, pp. 245-258; Gazedam, H.W.M., (2004) Organizational Semiotics: A State of the Art Report, , Semiotix 1. Retrieved January, 29; Hopper, T., Scapens, R.W., Northcott, D., (2007) Issues in Management Accounting, , Pearson Education; Kahn, B.K., Strong, D.M., Wang, R.Y., Information quality benchmarks: Product and service performance (2002) Communications of the ACM, 45, pp. 184-192; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Laudon, K.C., Laudon, J.P., Prentice-Hall, I., (2011) Essentials of Management Information Systems, , Prentice Hall; Liu, K., (2000) Semiotics in Information Systems Engineering, , Cambridge Univ Pr; Liu, K., (2002) Coordination and Communication Using Signs, , Kluwer Academic Publishers; Lord, R.G., Maher, K.J., (1994) Leadership and Information Processing: Linking Perceptions and Performance, , Psychology Press; Parmenter, D., (2009) Key Performance Indicators: Developing, Implementing, and Using Winning KPIs, , Wiley; Performance, T.C.F.B., (2009) Literature Review on Performance Measurement and Management, , Cranfield School of Management; Smith, H., (2005) From CIO to CPO Via BPM: The Next Generation of Enterprise, , Retrieved January, 29; Stamper, R.K., (1973) Information in Business and Administrative System, , New York, USA, John Wiley and Sons},
correspondence_address1={Informatics Research Centre, , Reading, RG6 6UD, United Kingdom},
isbn={9789898565518},
language={English},
abbrev_source_title={ICISO - Proc. Int. Conf. Informatics Semiotics in Organ., IFIP WG Work. Conf.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{NoAuthor2013,
title={ICISO 2013 - Proceedings of the 14th International Conference on Informatics and Semiotics in Organisations, IFIP WG8.1 Working Conference},
journal={ICISO 2013 - Proceedings of the 14th International Conference on Informatics and Semiotics in Organisations, IFIP WG8.1 Working Conference},
year={2013},
page_count={279},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880299919&partnerID=40&md5=a2c442e9c6d70d61f5b729b3925cd779},
abstract={The proceedings contain 31 papers. The topics discussed include: communicative spheres - towards a localized theory of communicative action; semiotic engineering, modeling and the use of multi-actor systems (MAS) for studying social sustainability theoretical backgrounds and specifications; an organizational semiotics inspired information architecture: pervasive healthcare as a case study; the design of a semantics-oriented organizational performance measurement system; beyond flatland? what does semiotics add to information sciences?; semiotic approach to a practice-oriented knowledge transfer; enterprise architecture (EA) benefits: the divergence between desirability and realizability in EA implementation; mutual dependency grid for stakeholder mapping a component-based approach to supply chain participant analysis; and user interaction based framework for protecting user privacy in online social networks.},
isbn={9789898565518},
language={English},
abbrev_source_title={ICISO - Proc. Int. Conf. Informatics Semiotics in Organ., IFIP WG Work. Conf.},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{Björklund2013230,
author={Björklund, M. and Forslund, H.},
title={The purpose and focus of environmental performance measurement systems in logistics},
journal={International Journal of Productivity and Performance Management},
year={2013},
volume={62},
number={3},
pages={230-249},
doi={10.1108/17410401311309168},
note={cited By 21},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874793845&doi=10.1108%2f17410401311309168&partnerID=40&md5=0558ef695aec9131d3a02917272c1ea1},
affiliation={Department of Management and Engineering, Linköping University, Linköping, Sweden; School of Business and Economics, Linnaeus University, Växjö, Sweden},
abstract={Environmental performance measurement systems (EPMS) in supply chains are increasingly important. The aim of this paper is to investigate the purposes of having an EPMS in logistics and in what ways the purpose of an EPMS can influence its focus in the supply chain. The study is based on a survey of shippers and logistics service providers in Sweden. Exploratory factor analysis grouped the EPMS purposes. Regression analysis investigated the relation between EPMS purpose and supply chain focus. Those companies that had an EPMS had several purposes. The most common EPMS purpose was target setting. The seven purposes could be grouped into one external demand and one internal management factor. The most common focus was company-internal, and the least common was to have a downstream focus. Significant relationships were verified between EPMS purpose and supply chain focus; companies seem to design their EPMS mainly out of internal management purposes. The study contributed to theory on performance measurement in logistics by expanding it to include environmental performance. The richest theoretical contribution is related to the descriptive first research question, which provided theoretical structures and empirical knowledge on EPMS, which to the best of our knowledge has not been published before. Logical, valid and reliable factors or scales for measuring the purpose of EPMS were provided. The conceptualized relation between EPMS purpose and focus is an additional theoretical contribution. Managers are provided with descriptions, structure and input to EPMS design. This paper has increased the understanding on EPMS design, related both to the own company's and other supply chain actors' EPMS. It indicates the complex practices of having many purposes for the EPMS and offers explanation to why companies may be reluctant to expand EPMS beyond their company boundaries. Despite the increasing importance of environmental performance, few studies on EPMS are conducted. This study expands performance measurement knowledge by providing empirical evidence in a Swedish context. © 2013, Emerald Group Publishing Limited},
author_keywords={Environmental performance;  Logistics;  Measurement system;  Performance management;  Purpose;  Supply chain focus;  Supply chain management;  Sweden},
references={Alexopoulos, I., Kounetas, K., Tzelepis, D., Environmental performance and technical efficiency, is there a link? The case of Greek listed firms (2012) International Journal of Productivity & Performance Management, 61 (1), pp. 6-23; Arimura, T.H., Darnall, N., Katayama, H., Is ISO14001 a gateway to more advanced voluntary action? The case of green supply chain management (2011) Journal of Environmental Economics and Management, 61 (2), pp. 170-182; Björklund, M., Influences from the business environment on environmental purchasing – drivers and hinders of purchasing green transportation services (2010) Journal of Purchasing and Supply Management, 17 (1), pp. 11-22; Bourne, M., Researching performance measurement system implementation: the dynamics of success and failure (2005) Production Planning & Control, 16 (2), pp. 101-113; Carter, C.R., Carter, J.R., Interorganizational determinants of environmental purchasing: initial evidence from the consumer products industries (1998) Decision Sciences, 29 (3), pp. 659-684; Cho, C.H., Patten, D.M., Roberts, R.W., Corporate political strategy: an examination of the relation between political expenditures, environmental performance and environmental disclosure (2006) Journal of Business Ethics, 67 (2), pp. 139-154; Colicchia, C., Melacini, M., Perotti, S., Benchmarking supply chain sustainability: insights from a field study (2011) Benchmarking: An International Journal, 18 (5), pp. 705-732; Cuthbertson, R., Piotrowicz, W., Supply chain best practices – identification and categorisation of measures and benefits (2008) International Journal of Productivity & Performance Management, 57 (5), pp. 389-404; Darnall, N., Jolley, G.J., Handfield, R., Environmental management systems and green supply chain management: complements for sustainability? (2008) Business Strategy and the Environment, 11 (1), pp. 30-45; Fabbe-Costes, N., Jahre, M., Supply chain integration and performance: a review of the evidence (2008) International Journal of Logistics Management, 58 (1), pp. 130-154; Facanha, C., Horvath, O., Environmental assessment of logistics outsourcing (2005) Journal of Management in Engineering, 21 (1), pp. 27-38; Flynn, B.B., Sakakibara, S., Schroeder, R.G., Bates, K.A., Flynn, E.J., Empirical research methods in operations management (1990) Journal of Operations Management, 9 (2), pp. 250-284; Folan, P., Browne, J., A review of performance measurement towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Forslund, H., Deciding the size of a logistics performance measurement system (2011) Facilities Journal, 29 (3-4), pp. 133-148; Forslund, H., Performance management in supply chains: logistics service providers' perspective (2012) International Journal of Physical Distribution & Logistics Management, 42 (3), pp. 296-311; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hahn, T., Figge, F., Liesen, A., Barkemeyer, R., Opportunity cost based analysis of corporate eco-efficiency: a methodology and its application to the CO2-efficiency of German companies (2010) Journal of Environmental Management, 91 (10), pp. 1997-2007; Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L., (2006) Multivariate Data Analysis, , Pearson Prentice Hall, Upper Saddle River, NJ; Harrison, A., New, C., The role of coherent supply chain strategy and performance management in achieving competitive advantage: an international survey (2002) Journal of Operational Research Society, 53 (3), pp. 263-271; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Holmberg, S., (2000) Supply chain integration through performance measurement, , doctoral thesis, Department of Design Sciences and Logistics, Lund University, Lund; Holt, D., Ghobadian, A., An empirical study of green supply chain management practices amongst UK manufacturers (2009) Journal of Manufacturing Technology Management, 20 (7), pp. 933-956; (2004) Environmental Management Systems – Requirements With Guidance for Use (ISO 14001:2004), , www.iso.org/iso/iso_14000_essentials, ISO, available at: (accessed 14 November, 2011); Kadlecová, T., Dvoráková, L., Srchová, M., New trends in environmental cost reduction in industrial engineering in the Czech Republic (2009) International Journal of Sustainable Engineering, 2 (2), pp. 134-142; Kleindorfer, P., Singhal, K., van Wassenhove, L., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Kohn, C., Huge Brodin, M., Centralised distribution systems and the environment: how increased transport work can decrease the environmental impact of logistics (2008) International Journal of Logistics: Research and Applications, 11 (3), pp. 229-245; Lambert, D.M., Harrington, T.C., Measuring non-response bias in customer service mail surveys (1990) Journal of Business Logistics, 11 (2), pp. 5-26; Langley, C.J., (2009) The state of logistics outsourcing; 2009 third-party logistics, , www.scl.gatech.edu 100126, available at:; Lieb, K.J., Lieb, R.C., Environmental sustainability in the third-party logistics (3PL) industry (2010) International Journal of Physical Distribution & Logistics Management, 40 (7), pp. 524-533; Lu, L.Y.Y., Wu, C.H., Kuo, T.C., Environmental principles applicable to green supplier evaluation by using multi-objective decision analysis (2007) International Journal of Production Research, 45 (18-19), pp. 4317-4331; McKinnon, A., Environmental sustainability: a new priority for logistics managers (2010) Green Logistics Improving the Environmental Sustainability of Logistics, pp. 3-30. , a in McKinnon, A. and Browne, M. (Eds), Kogan Page, London; McKinnon, A., Product-level carbon auditing of supply chains, environmental imperative or wasteful distraction? (2010) International Journal of Physical Distribution & Logistics Management, 40 (1-2), pp. 42-60. , b; Markley, M.J., Davis, L., Exploring future competitive advantage through sustainable supply chains (2007) International Journal of Physical Distribution & Logistics Management, 39 (9), pp. 763-774; Martinsen, U., Björklund, M., Matches and gaps in the green logistics market (2011) International Journal of Physical Distribution & Logistics Management, 42 (6), pp. 562-583; Melacini, M., Salgaro, A., Brognoli, D., A model for the management of WEEE reverse logistics (2010) International Journal of Logistics Systems and Management, 7 (1), pp. 1-18; Melnyk, S.A., Sroufe, R.P., Calantone, R., Assessing the impact of environmental management systems on corporate and environmental performance (2003) Journal of Operations Management, 21 (2), pp. 329-351; Min, H., Galle, W.P., Green purchasing practices of US firms (2001) International Journal of Operations & Production Management, 21 (9), pp. 1222-1238; Mollenkopf, D., Stolze, H., Tate, W.L., Ueltschy, M., Green, lean, and global supply chains (2010) International Journal of Physical Distribution & Logistics Management, 40 (1-2), pp. 14-41; Neely, A., Gregory, M., Platts, K., Performance measurement system design (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Öberg, C., Huge-Brodin, M., Björklund, M., Applying a network level in environmental impact assessments (2012) Journal of Business Research, 65 (2), pp. 247-255; Pullman, M.E., Maloni, M.J., Carter, C.R., Food for thought: social versus environmental sustainability practices and performance outcomes (2009) Journal of Supply Chain Management, 45 (4), pp. 38-54; Rogers, M.M., Weber, W.L., Evaluating CO2 emissions and fatalities tradeoffs in truck transports (2011) International Journal of Physical Distribution & Logistics Management, 41 (8), pp. 750-767; Schmitz, J., Platts, K.W., Supplier logistics performance measurement – indications from a study in the automotive industry (2004) International Journal of Production Economics, 89, pp. 231-243; Searcy, C., Updating corporate sustainability performance measurement systems (2011) Measuring Business Excellence, 15 (2), pp. 44-56; Simatupang, T., Sridharan, R., Wright, A., Applying the theory of constraints to supply chain collaboration (2004) Supply Chain Management: An International Journal, 9 (1), pp. 57-70; Simons, R., (1999) Performance Measurement and Control Systems for Implementing Strategy – Text and Cases, , Prentice Hall, Upper Saddle River, NJ; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; Tuttle, T., Heap, J., Green productivity: moving the agenda (2008) International Journal of Productivity & Performance Management, 57 (1), pp. 93-106; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain (2006) Supply Chain Management: An International Journal, 11 (4), pp. 317-327; Veleva, V., Hart, M., Greiner, T., Crumbley, C., Indicators for measuring environmental sustainability (2003) Benchmarking: An International Journal, 10 (2), pp. 107-119; Waage, S., Geiser, K., Irwin, F., Fitting together the building blocks for sustainability: a revised model for integrating ecological, social, and financial factors into business decision-making (2005) Journal of Cleaner Production, 13 (12), pp. 1145-1163; Walker, H., Sisto, L.D., McBain, D., Drivers and barriers to environmental supply chain management practices: lessons from the public and private sectors (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 69-85; (2009) Supply Chain Decarbonisation: The Role of Logistics and Transport in Reducing Supply Chain Carbon Emissions, , World Economic Forum and Accenture, Geneva; Wu, H.J., Dunn, S.C., Environmentally responsible logistics systems (1995) International Journal of Physical Distribution & Logistics Management, 25 (2), pp. 20-39. , www.iso.org/iso/iso_14001_essentials, available at: (accessed 14 November 2011); Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: pressures, practices and performance (2005) International Journal of Operations & Production Management, 25 (5-6), pp. 449-468; Zhu, Q., Sarkis, J., Lai, K., Green supply chain management implications for ‘closing the loop’ (2008) Transportation Research Part E: Logistics and Transportation Review, 44 (1), pp. 1-18},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zhou20132132,
author={Zhou, S.-P. and Liu, G.-S.},
title={The application of contingency theory in the every development stage of cluster supply-chain},
journal={Applied Mechanics and Materials},
year={2013},
volume={278-280},
pages={2132-2136},
doi={10.4028/www.scientific.net/AMM.278-280.2132},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873120951&doi=10.4028%2fwww.scientific.net%2fAMM.278-280.2132&partnerID=40&md5=68d86c50d0ce33da8c765bc8a7505150},
affiliation={Guangzhou Railway Occupational Technology Institute, Guangzhou 510430, China; School of Business Administration, South China University of Technology, Guangzhou, 510640, China},
abstract={The global economic integration, promoting industrial clusters and supply chains to a more perfect direction, during their development, in order to achieve greater advantage, Industrial clusters and supply chains automatically couple to form cluster supply chain. In this paper, we have made an analysis of the performance measurement system based on the cluster supply chain's total benefit and the development of the cluster supply chain, and then we have study the focus of every development stage according to contingency theory. © (2013) Trans Tech Publications, Switzerland.},
author_keywords={Cluster supply chain;  Contingency theory;  Evaluation metrics;  Total benefit},
keywords={Cluster supply chain;  Contingency theory;  Development stages;  Evaluation metrics;  Form clusters;  Global economics;  Industrial cluster;  Performance measurement system;  Total benefits, Mechanics, Supply chains},
references={Li, J., Cluster supply chain management [M] (2006) Beijing: Chain's Economy Press, p. 9; Li, J., Chunling, L., Analysis of the Competitive Power of Cluster Supply Chain and Its Cultivation [J] (2006) Economic Survey, (6), pp. 104-107; Liping, W., Management Principles [M] (2005) Beijing: Renmin University of China Press, p. 8; The application of contingency theory in the modern enterprise[J] (2006) Northern Economy and Trade, (11), pp. 104-105; Li, J., Chunling, L., Li, B., Modeling of the across chain inventory coordination in cluster supply chains[J] (2007) Systems Engineering and Electronic, (9), pp. 1479-1483; Li, J., Chunling, L., Decision-Making of cluster supply chains strategic cooperation based on dynamic game[J] (2006) Journal of Industrial Engineering/Engineering Management, (4), pp. 20-24; Li, B., Across-chian Inventory Management in Cluster Supply Chains Based on System Dynamics[J] (2007) Systems Engineering, (7), pp. 25-32; Xiaoli, X., He, J., Research on Cluster SC Based on the Theory of Self- organized Innovation Network[J] (2006) Logistics Technology, (9), pp. 59-62},
correspondence_address1={Zhou, S.-P.; Guangzhou Railway Occupational Technology Institute, Guangzhou 510430, China; email: zsp00111@126.com},
issn={16609336},
isbn={9783037855959},
language={English},
abbrev_source_title={Appl. Mech. Mater.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Georgise2013477,
author={Georgise, F.B. and Thoben, K.-D. and Seifert, M.},
title={A Framework of the Forces Influencing the Adaptation of the SCOR Model to the Situation of the Manufacturing Industry in Developing Countries},
journal={Lecture Notes in Logistics},
year={2013},
pages={477-487},
doi={10.1007/978-3-642-35966-8_40},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101328034&doi=10.1007%2f978-3-642-35966-8_40&partnerID=40&md5=84969243fe7343d09fcb059b6651568a},
affiliation={International Graduate School for Dynamics in Logistics (IGS), University of Bremen, Bremen, Germany; Bremen Institute for Production and Logistics-BIBA GmbH, Hochschulring 20, Bremen, 28359, Germany},
abstract={In the current dynamic competitive market, it is supply chains rather than companies that compete. Models such as the SCOR model enable the supply chain wide modeling, evaluation and improvement of processes. However, this approach is tailored to the needs of western, developed industries. Especially, the early processes’ industry products (as a source raw of material and simple products) come from developing countries; those industries have totally different conditions and constraints. In consequence, the early processes’ industry in the supply chain can often not be considered in a model. Many cross-cultural studies provide evidence that, because of cultural and operating environment differences, successful models, approaches and practices in one country need to be adapted for effective use in another country. Based on our literature analysis, the forces that influence the DCs’ supply can be classified into four principal capability’s areas. These are: (1) technical & IT, e g. outdated technology & lack integrated computerized systems; (2) supply chain relationship, e.g. reluctant to adopt partnership & alliance based relationship; (3) national infrastructure, e.g. inadequate physical infrastructure like road, rail and (4) organizational & managerial, e.g. obsolete functional based model and working culture difference. The paper presents a framework of the forces that influencing the adaptation of SCOR model to suit to developing country’s operating situations. © 2013, Springer-Verlag Berlin Heidelberg.},
author_keywords={Balance Scorecard;  Business Process;  Performance Measurement System;  Supply Chain;  Supply Chain Performance},
references={Abdelsalam, H.M., Fahmy, G.A., Major variables affecting the performance of the textile and clothing supply chain operations in Egypt (2009) Int J Logistics Res Appl, 12 (3), pp. 147-163; Md, A.R., Jaafar, H.S., Mohamad, S., Logistics and supply chain in Malaysia: Issues and challenges (2008) EASTS International Symposium on Sustainable Transportation Incorporating Malaysian Universities Transport Research Forum Conference (MUTRFC08), , Skudai, Johor Bahru; Babbar, S., Organizational factors affecting supply chains in developing countries (2008) Int J Commer Manage, 18 (3), pp. 234-251; Becker, J., Knackstedt, R., Stein, A., Extending the supply chain operations reference model: Potentials and their tool support (2007) Proceedings of the European Conference on Information Systems (ECIS), pp. 1827-1838. , St. Gallen S. pp; Bolstorff, P., (2003) Measuring the Impact of Supply Chain Performance, a Step by Step Guide of Using the Supply Chain Council’s SCOR Model. Chief Logistic Officer, pp. 6-11. , , pp; Borade, A.B., Bansod, S.V., Study of vendor-managed inventory practices in Indian industries (2010) J Manuf Technol Manage, 21 (8), pp. 1013-1038; Darroch, M.A.G., Challenges facing agribusiness supply Chains in South Africa Beyond 2000. Agrekon: Agric Econ Res Policy Pract South (2001) Africa, 40, pp. 505-521; Davidson, N.E., Performance measures can transform export-led initiatives into competitive strength: A wake-up call for the developing countries of Africa (2007) Newcastle Bus Sch Res Working Papers, 2, pp. 48-74; Dimaggio, P.J., Powell, W.W., The iron cage revisited: Institutional isomorphism andcollective rationality in organizational fields (1983) Am Sociol Rev, 48 (2), pp. 147-160; Doss, A.N., Bekki, J.M., Henderson, M., Akhavan, O., Tsang, J., Modeling supply chains for new ventures in developing countries (2010) National Collegiate Inventors & Innovators Alliance (NCIIA), pp. 25-27. , 14th annual conference, California, pp; Easton, R.J., Zhang, T.B., (2002) Supply Chain in Asia: Challenges and Opportunities, , http://www.accenture.com/kren/Documents/PDF/asia_supply.pdf; Falah, K.A., Zairi, M., Ahmed, A.M., The role of supply-chain management in world class manufacturing’’ An empirical study in the Saudi context (2003) Int J Phys Distrib Logistics Manage, 33 (5), pp. 396-407; Flores, M., Cherian, M., Canetta, L., (2008) Scenarios to Collaborate with Emerging Markets: Initial Focus India, rin; Galazzo, N., (2006) Using SCOR Model in a Real Collaborative Sourcing Scenario, p. 2006. , VIB ABCAL_PICS, Supply Chain Award; Gargeya, V.B., Birdwell, J., Martin, R., Challenges in managing the global supply chain (2001) Forum Empresarial, 6 (2), pp. 22-45; Hamisi, S., Challenges and opportunities of Tanzanian SMEs in adapting supply chain management (2011) Afr J Bus Manage, 5 (4), pp. 1266-1276; Han, D., Kwon, I.G., Bae, M., Sung, H., Supply chain integration in developing countries for foreign retailers in Korea: Walmart experience (2002) Comput Industrial Eng, 43, pp. 111-121; Hieber, R., (2002) Supply Chain Management a Collaborative Performance Measurement Approach. Zürich, Vdf Hochschulverlag AG an Der ETH Zürich, p. 154; Hernandez, R., Reardon, T., Berdegue, J., Supermarkets, wholesalers, and tomatogrowers in guatemala (2007) Agr Econ, 36, pp. 281-290; Holmes, J.S., Pineres, S.A.G., Kiel, L.D., Reforming government agencies internationally: Is there a role for the balanced scorecard? (2006) Int J Publ Adm, 29 (12); Huang, S.H., Sheoran, S.K., Keskar, H., Computer assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Comput Ind Eng, 48 (2), pp. 377-394; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations (SCOR) model (2004) Supply Chain Manage: Int J, 9 (1), pp. 23-29; Irfan, D., Xiaofei, X., Chun, D.S., A SCOR reference model of the supply chain management system in an enterprise (2008) Int Arab J Inf Technol, 5 (3); Kauffman, R.G., Crimi, T.A., (2005) A Best-Practice Approach for Developing Global Supply Chains. In: Institute of Supply Management’s (ISM’s) 90Th Annual International Supply Management Conference & Educational Exhibit, pp. 8-11. , San Antonio, TX, pp; Khalifa, N., White, A., Eisayed, A., Supply chain challenges in developing countries: Cross industry case studies (2008) IEEE SMC UK & RI 7Th Conference on Cybernetic Intelligent Systems, , London; Kureshi, N., (2010) Supply Chain Integration in SMEs—A Model for Developing Economies, , http://pk.linkedin.com/pub/nadeem-kureshi/13/934/170, Proceeding of the internationalization of SMEs conference, Pau, France; Kurnia, S., Peng, F., Electronic commerce readiness in developing countries: The case of the Chinese grocery industry. In: Proceedings of the 41st annual Hawaii international conference on system sciences (2008) Hilton Waikoloa Village Resort Waikoloa, , Big Island, Hawaii; Lapide, L., MIT’s SC2020 project: The essence of excellence (2006) SC Manag’t Review; Luder, K.G., A contingency model of governmental accounting innovations in thepoliticaladministrative environment (1992) Res Gov Non-Profit Account, 7, pp. 99-127; Meixell, M.J., Gargeya, V.B., Global supply chain design: A literature review andcritique (2005) Transport Res E, 41 (6), pp. 531-550; Msimangira, K.A.B., Tesha, C.P., International supply chain practices in developing countries: A study in Tanzania (2009) In: Conference Proceedings of the 20Th Annual Production and Operations Management Society Conference Held in Orlando, , Florida, USA; Msimangira, K.A.B., Purchasing and supply chain management practices in Botswana (2003) Supply Chain Manage Int J, 8 (1), pp. 7-11; Munir, R., Perera, S., Baird, K., (2009) Changes in Performance Measurement Systems in Banking Sector: An Analytical Framework. In: Performance Measurement Association Conference, , Dunedin, New Zealand; Naude, M.J., Badenhorst-Weiss, J.A., Supply chain management problems at South African automotive component manufacturers (2011) South Afr Bus Rev, 15 (1), pp. 70-99; Ohemeng, F., The dangers of internationalization and ‘‘one-size-fits-all’’ in public sector management: Lessons from performance management policies in Ontario and Ghana (2010) Int J Publ Sect Manage, 23 (5), pp. 456-478; Ong, T.S., The, B.H., Factors influencing the design and use of performance measurement systems in the Malaysian electrical and electronics industry (2008) Int J Eco Man’t, 2, pp. 437-457; Razzaque, M.A., Challenges to logistics development: The case of a third world country Bangladesh (1997) Int J Phys Distrib Logistics Manage, 27 (1), pp. 18-38; Md Riyadh Al, N., Akter, S., Islam, N., The adoption of E-banking in developing countries: A theoretical model for SMEs (2009) Int Rev Bus Res Papers, 5 (6), pp. 70-80; Ruteri, J.M., Xu, Q., Supply chain management and challenges facing the food industry sector in Tanzania (2009) Int J Bus Manage, 4 (12), pp. 70-80; Rutkowski, K., Best practices in logistics and supply chain management (2010) The Case of Central and Eastern Europe, Global Logistics: New Directions in Supply Chain Management, , Donald W, 6th edn. Kogan Page Limited, London; (2010) Supply Chain Operations Reference Model (SCOR), Version, 10, p. 0; Sinkovic, G., Currie, D.M., Bevanda, V., From IT to BSC: The importance of culture in implementing a performance measurement system in Croatia (2011) Indian J Eco Bus, 10, pp. 39-55; Stephens, S., Supply chain reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Inf Syst Front, 3 (4), pp. 471-476; Stewart, R., Mohamed, S., Barriers to implementation information technology indeveloping countries (2002) Proceedings of the Council for Research and Innovation in Building Andconstruction-Working Group 107 1St International Conference Creating a Sustainableconstruction Industry in Developing Countries, pp. 11-13. , Spier, Stellenbosch, South Africa, pp; Swaminathan, J.M., Managing supply chain operations in India: Pitfalls and opportunities (2007) Int Series Oper Res Manage Sci, 98, pp. 137-154; Tippayawong, K.Y., Patitad, P., Sopadang, A., Enkawa, T., Factors affecting efficient supply chain operational performance of high and low technology companies in Thailand (2010) Manage Sci Eng, 4 (3), pp. 24-33; Upping, P., Oliver, J., Accounting change model for the public sector: Adapting Luder’s model for developing countries (2011) Int Rev Bus Res Papers, 7 (1); Voordijk, H., Obstacles and preconditions for logistics and manufacturing improvements in Africa—a case study (1999) Int J Oper Prod Manage, 19 (3); de Waal, A.A., Is performance management applicable in developing countries? The case of a Tanzanian college (2007) Int J Emerging Markets, 2 (1), pp. 69-83; Waggoner, D., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems (1999) An Interdisciplinary Review. Int J Prod Econ, 60-61, pp. 53-60; Walsham, G., Sahay, S., GIS for district-level administration in india: Problems and opportunities (1999) MIS Q, 23 (1), pp. 39-66},
correspondence_address1={Georgise, F.B.; International Graduate School for Dynamics in Logistics (IGS), Germany; email: geo@biba.uni-bremen.de},
publisher={Springer Science and Business Media B.V.},
issn={21948917},
language={English},
abbrev_source_title={Lect. Notes Logist.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Ogunyemi2013,
author={Ogunyemi, T. and Aktas, E.},
title={The impact of green information systems on sustainable supply chain and organisational performance},
journal={Proceedings of the European, Mediterranean and Middle Eastern Conference on Information Systems, EMCIS 2013},
year={2013},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084018822&partnerID=40&md5=f5b84d9d03a7acf63a153c597ef8308e},
affiliation={Brunel Business School, Brunel University, Uxbridge Middlesex, United Kingdom},
abstract={This paper reports about a research in progress focusing on the impact of green information systems on sustainable supply chain performance. Green information systems, supply chains and their relation to sustainability and performance measurement are explained. The preliminary literature review resulted in a draft conceptual framework where sustainable supply chain measures focusing on economic, environmental and social aspects are combined with traditional supply chain performance measures of cost, quality, dependability, flexibility and speed. The measures for the triple bottom line of sustainability are listed as internal and external indicators. The research challenges addressed in this preliminary work comprise of integration of sustainability performance with traditional performance objectives in the supply chain, measurement of the social performance of supply chain and establishment of the relationship amongst the sustainable supply chain and performance indicators using a green information system infrastructure. © 2013 European and Mediterranean Conference on Information Systems. All rights reserved.},
author_keywords={Green information systems;  Performance measurement;  Sustainable supply chain},
keywords={Information systems;  Social aspects;  Supply chains;  Sustainable development, Green information systems;  Organisational performance;  Performance indicators;  Performance measurements;  Performance objective;  Supply chain performance;  Sustainability performance;  Sustainable supply chains, Information use},
references={Azevedo, S.G., Carvalho, H., Duarte, S., Cruz-Machado, V., Influence of green and lean upstream supply chain management practices on business sustainability (2012) Engineering Management, IEEE Tr6Ansactions on, 59 (4), pp. 753-765; Bititci, U., Garengo, P., Dorfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow* (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Boudreau, M.-C., Chen, A., Huber, M., Green is: Building sustainable business practices (2008) Information Systems: A Global Text, , http://www.ceport.com/files/file/Green%20IS%20CePORT%20Article.pdf, Accessed on 30 July 2013; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 46-62; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Chandra, C., Grabis, J., Tumanyan, A., Problem taxonomy: A step towards effective information sharing in supply chain management (2007) International Journal of Production Research, 45 (11), pp. 2507-2544; Chopra, S., Meindl, P., (2007) Supply Chain Management. Strategy, Planning & Operation, , Springer; Dedrick, J., Green is: Concepts and issues for information systems research (2010) Communications of the Association for Information Systems, 27 (1), p. 11; Holger, S., Klaus, T., SCOR in the cloud – potential of cloud computing for the optimization of supply chain management systems (2011) European and Mediterranean Conference on Information Systems 2011, , Athens: 30-31 May; Howard, G.R., Lubbe, S., Synthesis of green is frameworks for achieving strong environmental sustainability in organisations (2012) Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference, pp. 306-315. , ACM; Jenkin, T., Webster, J., McShane, L., An agenda for ‘green’ information technology and systems research (2011) Journal of Information and Organization, 21 (1), pp. 17-40; Lai, R.S., Hsu, L.-L., Chen, J.C., Green supply chain management systems: A case study in the textile industry (2012) Human Systems Management, 31 (2), pp. 111-121; Meacham, J., Toms, L., Green, K.W., Jr., Bhadauria, V.S., Impact of information sharing and green information systems (2013) Management Research Review, 36 (5), pp. 478-494; Melville, N.P., Information systems innovation for environmental sustainability (2010) MIS Quarterly, 34 (1), pp. 1-21; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Piotrowicz, W., Cuthbertson, R., Sustainability–a new dimension in information systems evaluation (2008) European and Mediterranean Conference on Information Systems 2008, , Dubai: 25-26 May; Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Shields, P., Rangarajan, N., (2013) A Playbook for Research Methods: Integrating Conceptual Frameworks and Project Management Skills, , New Forums Press; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Wee, H., Lee, M., Yu, J., Wang, C., Optimal replenishment policy for a deteriorating green product: Life cycle costing analysis (2011) International Journal of Production Economics, 133 (2), pp. 608-611; Wong, C., Skipworth, H., Godsell, J., Achimugu, N., Towards a theory of supply chain alignment enablers: A systematic literature review (2012) Supply Chain Management: An International Journal, 17 (4), p. 419437. , 7; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in chinese manufacturing enterprises (2004) Journal of Operations Management, 22 (3), pp. 265-289. , 8},
editor={Ghoneim A., AlShawi S., Ali M.},
publisher={European and Mediterranean Conference on Information Systems},
isbn={9781908549112},
language={English},
abbrev_source_title={Proc. Eur., Mediterr. Middle East. Conf. Inf. Syst., EMCIS},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Froufe2013,
author={Froufe, S. and Fredouet, C.-H.},
title={Interactions between Inter-Organizational Information Systems and Supply Chain Risk management [Interactions entre les systèmes d'information inter-organisationnels et la gestion des risques de la chaîne logistique]},
journal={18th Symposium of the Association Information and Management 2013, AIM 2013},
year={2013},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84996478433&partnerID=40&md5=e55a852aca815050eabb6ebc49e426f8},
affiliation={ISEL, Le Havre University, Le-Havre, France; ISEL, Université du Havre, Quai Frissard, BP 1137, Le Havre Cedex, 76063, France},
abstract={It is well known that supply chain performance, measured by its ability to satisfy customers, is strongly impacted by the quality of inter-organizational information systems (IOIS). The past ten years, marked by the development of terrorism and major natural disasters, have specifically put forward this dependence, through the pressure and variations these events have brought to the activity of logistics organizations. This makes us wondering about the role played by IOIS in logistics risk management, and particularly in cases of supply chain disruption: how can IOIS help an organization to better deal with such risks? Conversely, how can they, at the same time, be a source of risks? To answer this double question, we follow a hybrid research methodology structured according to the traditional 3 steps of risk management (identification, evaluation and treatment). It is a mixed qualitative and quantitative one, through semi-directive interviews and a self-administered survey on the Internet. Considering the specific interviews, the first results confirm the relationship between supply chain performance and IOIS identified by the literature in other contexts: on one hand, the improvement brought to an organization's performance by integrated IOIS, and on the other hand, the danger represented by the operational dependence of an organization on IOIS, all the higher as the level of integration of those systems is increasing. More specifically, it appears that the sensitivity of interviewees to the positive or negative contribution of IOIS to supply chain risk management partially depends upon their perception of the characteristics of those risks and of the level of sophistication of their risk management processes. Those two dimensions are to be explored through the quantitative survey.},
author_keywords={Business intelligence systems;  Inter-organizational information systems;  Supply chain risk management;  Supply chain security},
keywords={Customer satisfaction;  Disasters;  Information management;  Information retrieval systems;  Information systems;  Risk perception;  Supply chain management;  Supply chains;  Surveys, Business intelligence systems;  Inter-organizational information system;  Major natural disasters;  Risk management process;  Supply chain performance;  Supply chain risk management;  Supply chain security;  Supply-chain disruptions, Risk management},
references={Adams, J., (1995) Risk, , London: Press UCL; Chopra, S., Sodhi, M.S., Managing Risk to Avoid Supply-Chain Breakdown (2004) MIT Sloan Management Review, 46 (1), pp. 53-61; Christopher, M., Peck, H., Building the Resilient Supply Chain (2004) International Journal of Logistics Management, 15 (2), pp. 1-13; Clemons, E.K., Row, M.C., Sustaining IT Advantage: The Role of Structural Differences (1991) MIS Quarterly, 15 (3), pp. 275-292; Galbraith, J.R., (1973) Designing Complex Organizations, , Boston, MA: Addison Wesley; Gaonkar, R.S., Viswanadham, N., Analytical framework for the management of risk in supply chains (2007) IEEE Transactions on Automation Science and Engineering, (4), pp. 265-273; Harland, C., Brenchley, R., Walker, H., Risk in supply networks (2003) Journal of Purchasing and Supply Management, 9 (2), pp. 51-62; Jüttner, U., Supply chain risk management - Understanding the business requirements from a practitioner perspective (2005) The International Journal of Logistics Management, 16 (1), pp. 120-141; Kleindorfer, P.R., Saad, G.H., Managing Disruption Risks in Supply Chains (2005) Production and Operations Mannagement, 14 (1), pp. 53-68; Larousse, (2006) Le Petit Larousse Illustré, , [2007]. Paris; Lee, H.L., Aligning Supply Chain Strategies with Product Uncertainties (2002) California Management Review, 44 (3), pp. 105-119; Ritchie, B., Brindley, C., Supply chain risk management and performance: A guiding framework for future development (2007) International Journal of Operations and Production Management, 27 (3), pp. 303-322; Ritchie, R.L., Marshall, D.V., (1993) Business Risk Management, , London: Chapman Hall; Romelaer, P., L'entretien de recherche (2005) Management des Erssources Humaines - Méthodes de Recherches en Sciences Humaines et Sociales, pp. 101-138. , P. Roussel, & F. Wacheux, Bruxelles: de boeck; Sitkin, S.B., Pablo, A.L., Reconceptualizing the determinants of risk behaviour (1992) Academy of Management Review, 17 (1), pp. 9-38; Smallman, C., Risk and organisational behaviour: A research model (1996) Disaster Prevention and Management, 5 (2), pp. 12-26; Usunier, J.-C., Easterby-Smith, M., Thorpe, R., (2000) Introduction À la Recherche en Gestion, , (2e édition ed.). Paris: Economica; Van Campenhoudt, L., Quivy, R., (2011) Manuel de Recherches en Sciences Sociales, , (4e édition ed.). Paris: Dunod},
publisher={Association Information and Management},
language={French},
abbrev_source_title={AIM - Colloq. l'AIM - Symp. Assoc. Inf. Manag., AIM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Cagliano2013212,
author={Cagliano, A.C. and Mangano, G. and Mustafa, M.S. and Rafele, C.},
title={A performance dashboard for a logistics service company in the fast fashion industry},
journal={Proceedings of the Summer School Francesco Turco},
year={2013},
volume={11-13-September-2013},
pages={212-217},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982871963&partnerID=40&md5=53e448c2373e385efd9f989cc8db575f},
affiliation={Department of Management and Production Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy},
abstract={Purpose-This paper proposes a structured performance measurement system for the logistics activities carried out by a company operating in the fast fashion industry Design/methodology/approach-After a review of the logistics measurements models existing in literature, the Logistiqual has been selected because it considers both the inventory control and the transport service. Then the logistic process of the company is mapped and crucial Key Performance Indicators (KPIs) are defined according each area of Logistiqual. The system has been validated by applying the indicators on past data. Originality/value-The effectiveness of Logistiqual to monitor the logistics process and the importance of measuring performances are reaffirmed. The proposed approach proves to be suitable for the updating of the measurement system, so that it can reflect the current needs of an organization.},
author_keywords={Fast fashion industry;  Key performance indicators;  Logistics;  Supply chain management},
references={Brewer, P.C., Aligning supply chain incentives using the balanced scorecard (2002) Supply Chain Forum: An International Journal, 3 (1), pp. 12-19; Cagliano, A.C., De Marco, A., Rafele, C., Volpe, S., Using system dynamics in warehouse management: A fast-fashion case study (2011) Journal of Manufacturing Technology Management, 22 (2), pp. 171-188; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; Grimaldi, S., Rafele, C., Current applications of a reference framework for the supply chain performance measurement (2007) International Journal of Business Performance Measurement, 9 (2), pp. 206-225; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston (MA); Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , Financial Times Prentice Hall, London (UK); Parasuraman, A., Zeithaml, V.A., Berry, L.L., Servqual: A multiple-Item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Supply Chain Council, (2010) SCOR Overview Version 10.0, , www.supply-chain.org},
publisher={AIDI - Italian Association of Industrial Operations Professors},
issn={22838996},
language={English},
abbrev_source_title={Proc. Summer Sch. Francesco Turco},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Maleki2013177,
author={Maleki, M. and Cruz-Machado, V.},
title={Supply chain performance monitoring using Bayesian network},
journal={International Journal of Business Performance and Supply Chain Modelling},
year={2013},
volume={5},
number={2},
pages={177-197},
doi={10.1504/IJBPSCM.2013.053492},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84965080284&doi=10.1504%2fIJBPSCM.2013.053492&partnerID=40&md5=10ced858900a93dd3a7f03e0bde8eb16},
affiliation={UNIDEMI, Department of Mechanical and Industrial Engineering, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Campus Universitário, Caparica, 2829-516, Portugal},
abstract={Performance measures of supply chain have been investigated through recent scholar works and different tools have been proposed to follow, monitor, and analyse them. This research addresses the embedded uncertainty as well as mutual dependency among supply chain performance measures and proposes employing Bayesian network (BN) to monitor them. BN has been successfully used in related areas, however, in this particular context it is lacking in published literature. In addition, a framework is proposed to provide researchers and practitioners with step by step procedure of developing BN to monitor the performance measures of their supply chain. Finally, a case study is presented, which takes advantage of proposed methodology and follows the framework. Copyright © 2013 Inderscience Enterprises Ltd.},
author_keywords={Bayesian network;  Data modelling;  Performance measure;  Supply chain},
references={Abad-Grau, M.M., Arias-Aranda, D., Operations strategy and flexibility: Modeling with Bayesian classifiers (2006) Industrial Management & Data Systems, 106 (4), pp. 460-484; Abd El-Aal, M.A.M., El-Sharief, M.A., Ezz El-Deen, A., Nassr, A.-B., Supply chain performance evaluation: A comprehensive evaluation system (2011) International Journal of Business Performance and Supply Chain Modelling, 3 (2), pp. 141-166; Algarni, A., Jamal, A., Ahmad, A., Algarni, A.M., Tozan, M., Neural network-based failure rate prediction for de Havilland Dash-8 tires (2006) Engineering Applications of Artificial Intelligence, 19 (6), pp. 681-691; Angerhofer, B., Angelides, M., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 115-128; Azevedo, S.G., Carvalho, H., Cruz-Machado, V., A proposal of LARG supply chain management practices and a performance measurement system (2011) International Journal of E-Education, E-Business, E-Management and E-Learning, 1 (1), pp. 7-14; Baesens, B., Verstraeten, G., Den, V.P.D., Egmont-Petersen, M., Kenhove, P.V., Vanthienen, J., Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers (2004) European Journal of Operational Research, 156 (2), pp. 508-523; Baldi, P., Long, A.D., A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inferences of gene changes (2001) Bioinformatics, 17 (6), pp. 509-519; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Biegler, L., Biros, G., Ghattas, O., Keyes, D., Mallick, B., Tenorio, L., Waanders, B.B., Marzouk, Y., (2011) Large-Scale Inverse Problems and Quantification of Uncertainty, , Wiley, West Sussex, UK; Blessing, L.T.M., Chakrabarti, A., (2009) DRM, A Design Research Methodology, , Springer, London, UK; Boudali, H., Dugan, J., A discrete-time Bayesian network reliability modeling and analysis framework (2005) Reliability Engineering & System Safety, 87 (3), pp. 337-349; Bozarth, C., Warsing, D., Flynn, B., Flynn, E., The impact of supply chain complexity on manufacturing plant performance (2009) Journal of Operations Management, 27 (1), pp. 78-93; Bullinger, H., Kühner, M., Van Hoof, H., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Cai, Z., Sun, S., Si, S., Yannou, B., Identifying product failure rate based on a conditional Bayesian network classifier (2011) Expert Systems with Applications, 38 (5), pp. 5036-5043; Carvalho, H., Azevedo, S.G., Cruz-Machado, V., Supply chain performance management: Lean and green paradigms (2010) International Journal of Business Performance and Supply Chain Modelling, 2 (3-4), pp. 304-333; Chen, M., Xia, Y., Wang, X., Managing supply uncertainties through Bayesian information update (2009) IEEE Transactions on Automation Science and Engineering, 7 (1), pp. 24-36; Chen, W., Tseng, S., Wang, C., A novel manufacturing defect detection method using association rule mining techniques (2005) Expert Systems with Applications, 29 (4), pp. 807-815; Chin, K.-S., Tang, D.-W., Yang, J.-B., Wong, S.Y., Wang, H., Assessing new product development project risk by Bayesian network with a systematic probability generation methodology (2009) Expert Systems with Applications, 36 (6), pp. 9879-9890; Christopher, M., Peck, H., Building the resilient supply chain (2004) The International Journal of Logistics Management, 15 (2), pp. 1-14; Cinar, D., Kayakutlu, G., Scenario analysis using Bayesian networks: A case study in energy sector (2010) Knowledge-Based Systems, 23 (3), pp. 267-276; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) International Journal of Logistics Management, 8 (1), pp. 1-14; Dada, M., Petruzzi, N.C., Schwarz, L.B., (2003) A Newsvendor Model with Unreliable Suppliers, , University of Illinois at Urbana-Champaign, Champaign, Illinois, USA; Gambelli, D., Bruschi, V., A Bayesian network to predict the probability of organic farms' exit from the sector: A case study from Marche, Italy (2010) Computers and Electronics in Agriculture, 71 (1), pp. 22-31; Goldwater, S., Griffiths, T.L., Johnson, M., A Bayesian framework for word segmentation: Exploring the effects of context (2009) Cognition, 112 (1), pp. 21-54; Goncharuk, A.G., Enterprise performance management: A three-level approach (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 162-186; Gopnik, A., Tenenbaum, J.B., Bayesian networks, Bayesian learning and cognitive development (2007) Developmental Science, 10 (3), pp. 281-287; Gunasekaran, A., Patel, C., McGauRoland, E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (2), pp. 71-87; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Upper Saddle River, New Jersey; Hoole, R., Five ways to simplify your supply chain (2005) Supply Chain Management: An International Journal, 10 (1), pp. 3-6; Hult, G.T.M., Ketchen, D.J., Jr., Adams, G.L., Mena, J.A., Supply chain orientation and balanced scorecard performance (2008) Journal of Managerial Issues, 20 (4), pp. 526-544; Kiekintveld, C., Miller, J., Jordan, P.R., Callender, L.F., Wellman, M.P., Forecasting market prices in a supply chain game (2009) Electronic Commerce Research and Applications, 8 (2), pp. 63-77; Kim, B., Oh, H., The impact of decision-making sharing between supplier and manufacturer on their collaboration performance (2005) Supply Chain Management: An International Journal, 10 (3), pp. 223-236; Kumar, D., Singh, J., Singh, O.P., A decision support system for supplier selection for Indian textile industry using analytic hierarchy process based on fuzzy simulation (2011) International Journal of Business Performance and Supply Chain Modelling, 3 (4), pp. 364-382; Lambert, D.M., (2008) Supply Chain Management: Processes, Partnerships, Performance, , 3srd ed., Supply Chain Management Institute, Sarasota, Florida, USA; Langseth, H., Bayesian networks in reliability analysis (2007) 5th International Conference on Mathematical Methods in Reliability, , July, Glasgow, Scotland; Leung, K.N.F., An integrated production-inventory system with lot streaming and complete backordering in a three-stage multi-firm supply chain solved algebraically (2012) International Journal of Business Performance and Supply Chain Modelling, 4 (1), pp. 1-15; Li, J., Gao, Y., Base on Bayesian network of supply chain enterprises collaborative sensitivity analysis (2010) 2010 International Conference on Logistics Systems and Intelligent Management, , 9-10 January, Harbin, China; Li, M., Wang, L., Feature fatigue analysis in product development using Bayesian networks (2011) Expert Systems with Applications, 38 (8), pp. 10631-11063; Mahadevan, S., Zhang, R., Smith, N., Bayesian networks for system reliability reassessment (2001) Structural Safety, 23 (3), pp. 231-251; Maleki, M., Grilo, A., Cruz-Machado, V., Obstacles of stochastic modeling in supply chain (2011) ENEGI 2011 Conference, , 27-28 May, Guimaraes, Portugal; Meena, P.L., Sarmah, S.P., Sinha, S., Measuring satisfaction in buyer-supplier relationship from suppliers perspective (2012) International Journal of Business Performance and Supply Chain Modelling, 4 (1), pp. 60-74; Muller, A., Suhner, M., Iung, B., Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system (2008) Reliability Engineering & System Safety, 93 (2), pp. 234-253; Muogboh, O.S., Assessing the relationship between logistics skills and performance of firms in Nigeria (2010) International Journal of Business Performance and Supply Chain Modelling, 2 (1), pp. 25-44; Nadkarni, S., Shenoy, P.P., A Bayesian network approach to making inferences in causal maps (2001) European Journal of Operational Research, 128 (3), pp. 479-498; Neapolitan, R.E., (2003) Learning Bayesian Networks, , Prentice Hall, New Jersey, USA; Oliver, R.K., Webber, M.D., (1982) Supply-Chain Management: Logistics Catches Up with Strategy, , Outlook, Booz, Allen and Hamilton Inc; Panchal, G., Jain, V., A review on select models for supply chain formation (2011) International Journal of Business Performance and Supply Chain Modelling, 3 (2), pp. 113-123; Panicker, V.V., Sridharan, R., Modelling supply chain decision problem with fixed charge-a review (2011) International Journal of Business Performance and Supply Chain Modelling, 3 (3), pp. 195-207; Pearl, J., Russell, S., (1998) Bayesian Networks, , Computer Science Dept., University of California; Pochampally, K.K., Gupta, S.M., Govindan, K., Metrics for performance measurement of a reverse/closed-loop supply chain (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (1), pp. 8-32; Radhakrishnan, P., Prasad, V.M., Gopalan, M.R., Genetic algorithm based inventory optimization analysis in supply chain management (2009) IEEE International Advance Computing Conference, , 6-7 March, Patiala, India; Sengupta, K., Heiser, D.R., Cook, L.S., Manufacturing and service supply chain performance: A comparative analysis (2006) The Journal of Supply Chain Management, 42 (4), pp. 4-15; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shevtshenko, E., Wang, Y., Decision support under uncertainties based on robust Bayesian networks in reverse logistics management (2009) International Journal of Computer Applications in Technology, 36 (3-4), pp. 247-258; Tomlin, B., Impact of supply learning when suppliers are unreliable (2008) Manufacturing & Service Operations Management, 11 (2), pp. 192-209; Towill, D.R., Childerhouse, P., Disney, S.M., Integrating the automotive supply chain: Where are we now? (2002) International Journal of Physical Distribution & Logistics Management, 32 (2), pp. 79-95; Wang, Y., Imprecise probabilities based on generalized intervals for system reliability assessment (2010) International Journal of Reliability & Safety, 4 (4), pp. 319-342; Weber, P., Jouffe, L., Complex system reliability modelling with Dynamic Object Oriented Bayesian Networks (DOOBN) (2006) Reliability Engineering & System Safety, 91 (2), pp. 149-162; Whicker, L., Bernon, M., Templar, S., Mena, C., Understanding the relationships between time and cost to improve supply chain performance (2009) International Journal of Production Economics, 121 (2), pp. 641-650; Witten, I.H., Frank, E., (2005) Data Mining: Practical Machine Learning Tools and Techniques, , 2nd ed., Morgan Kaufmann, San Francisco, California, USA; Wong, W.P., Performance evaluation of supply chain in stochastic environment: Using a simulation based DEA framework (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 203-228; Wu, J., Liu, F., Jiao, L.C., Wang, X., Compressive sensing SAR image reconstruction based on Bayesian framework and evolutionary computation (2011) IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society; Xing, B., Gao, W.J., Battle, K., Marwala, T., Nelwamondo, F.V., Artificial intelligence in reverse supply chain management: The state of the art (2010) Paper Presented in the 21th Annual Symposium of the Pattern Recognition Association of South Africa, , 22-23 November, Stellenbosch, South Africa; Yuan, W.-F., Liu, S.-F., Fang, Z.-G., Chen, H.-Z., Comprehensive evaluation on suppliers of Aircraft based on grey Bayesian network model (2009) IEEE International Conference on Grey Systems and Intelligent Services (GSIS 2009), , 10-12 November, Nanjing, China; Zhang, L., Tong, M.H., Marks, T.K., Cottrell, G.W., SUN: A Bayesian framework for saliency using natural statistics (2008) Journal of Vision, 8 (7), pp. 1-20},
correspondence_address1={Maleki, M.; UNIDEMI, Portugal; email: maleki@fct.unl.pt},
publisher={Inderscience Publishers},
issn={17589401},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Supply Chain Model.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Sellitto2013,
author={Sellitto, M.A. and Pereira, G.M. and Borchardt, M. and Silva, R.I.},
title={Model for supply chain performance measurement},
journal={22nd International Conference on Production Research, ICPR 2013},
year={2013},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929448200&partnerID=40&md5=e9734152be9fc60f6edd181fc16f8a60},
affiliation={Production and Systems Engineering Graduate Program, UNISINOS, São Leopoldo, RS, Brazil},
abstract={This article presents a model for performance measurement in supply chains. The methodology had two-steps: a qualitative part to identify constructs and indicators and a quantitative one to measure them. The model has two dimensions: SCOR model process: source, make, deliver, and return; and priorities: cost, quality, delivery, flexibility. A 4 x 4 matrix resulted, weighted by AHP. In the cells, indicators, with ranges and five categories [very good, good, neutral, bad, very bad] were allocated to appraise the priority in the process. The ranges were set in the planning process of SCOR. An application was done in the hard core of a supply chain of the footwear industry, including 72 indicators in four suppliers; the focal company; three distribution channels; and a return channel. The SC overall performance reached near 80% of the maximum possible value. Worst performance was flexibility in deliver and delivery in make.},
author_keywords={Complexity;  Performance measurement;  SCOR model;  Supply chain performance;  Supply chain strategy},
keywords={Chains;  Shoe manufacture, Complexity;  Performance measurements;  SCOR models;  Supply chain performance;  Supply chain strategy, Supply chains},
references={Vijayvargiya, A., Dey, A., An analytical approach for selection of a logistics provider (2010) Management Decision, 48, pp. 403-418; Arshinder, A., Deshmukn, S., Supply chain coordination: Perspectives, empirical studies and research direction (2008) International Journal of Production Economics, 15, pp. 316-335; Nyaga, G., Whipple, J., Lynch, D., Examining supply chain relationships: Do buyer and supplier perspectives on collaborative relationships differ? (2009) Journal of Operations Management, 28, pp. 101-114; Holweg, M., Pil, F., Theoretical perspectives on the coordination of supply chains (2008) Journal of Operations Management, 26, pp. 389-406; Zelbst, P., Green, K., Jr., Sower, V., Reyes, P., Impact of supply chain linkages on supply chain performance (2009) Industrial Management & Data Systems, 109, pp. 665-682; Handfield, R., Nichols, E., (1999) Introduction to Supply Chain Management, , New Jersey: Prentice-Hall; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Chan, F., Qi, H., Chan, H., Lau, H., Ip, R., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41, pp. 635-642; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21, pp. 46-70; Mentzer, J., De Witt, W., Keebler, J., Min, S., Nix, N., Smith, C., Zacharia, Z., Defining supply chain management (2001) Journal of Business Logistics, 22, pp. 1-25; Lee, H., Creating value through supply chain integration (2000) Supply Chain Management Review, 4, pp. 30-36; Lai, K., Ngai, E., Cheng, T., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part e, 38, pp. 439-456; Pramatari, K., Collaborative supply chain practices and evolving technological approaches, Supply Chain Management (2007) An International Journal, 12, pp. 210-220; Lockamy, A., III, McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24, pp. 1192-1218; Wadhwa, S., Mishra, M., Chan, F., Ducq, Y., Effects of information transparency and cooperation on supply chain performance: A simulation study (2010) International Journal of Production Research, 48, pp. 145-166; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Akyuz, G., Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48, pp. 5137-5155; Van Hoek, R., Measuring the unmeasurable-Measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3, pp. 187-192; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 275-292; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30, pp. 847-868; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Lambert, D., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-15; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Chan, F., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (4), pp. 534-548; Agarwal, A., Shankar, R., Tiwari, M., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173, pp. 211-225; Aramyan, L., Lansink, A., Vorst, J., Kooten, O., Performance measurement in agri-food supply chains: A case study, Supply Chain Management (2007) An International Journal, 12 (4), pp. 304-315; Berrah, L., Clivillé, V., Towards a quantitative performance measurement model in a buyer-supplier relationship context (2008) Supply Chain, Theory and Applications, Vienna: ITech Education and Publishing, , KORDIC,V (org.); Fu, H., Chu, K., Lin, S., Chen, C., A study on factors for retailers implementing CPFR-A fuzzy AHP analysis (2010) Journal of Systems Science and Systems Engineering, 19 (2), pp. 192-209; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management & Data Systems, 110 (9), pp. 1297-1318; Elgazzar, S., Tipi, N., Hubbard, N., Leach, D., Linking supply chain processes' performance to a company's financial strategic objectives (2012) European Journal of Operational Research, 223, pp. 276-289; SCC-Supply Chain Council, What is SCOR? Available in, , http://supply-chain.org/scor, Last access in jan/2013; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15, pp. 80-116; Bourne, M., Neely, A., Mills, J., Platts, K., Wilcox, M., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20, pp. 754-771; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) International Journal of Business Performance and Supply Chain Modelling, 1, pp. 115-128; Neely, A., The evolution of performance measurement research (2005) International Journal of Operations and Production Management, 25, pp. 1264-1277; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement of the after-sales service network: Evidence from the automotive industry (2007) Computers in Industry, 58, pp. 698-708; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59, pp. 163-185; Yang, C., Su, Y., The relationship between benefits of ERP systems implementation and its impacts on firm performance of SCM (2009) Journal of Enterprise Information Management, 22, pp. 722-752; Ketchen, D., Jr., Hult, G., Bridging organization theory and supply chain management: The case of best value supply chains (2007) Journal of Operations Management, 25, pp. 573-580; Fawcett, S., Magnan, G., McCarter, M., Benefits, barriers, and bridges to effective supply chain management (2008) Supply Chain Management: An International Journal, 13, pp. 35-48; Law, K., Helo, P., Kanchana, R., Phusavat, K., Managing supply chains: Lessons learned and future challenges (2009) Industrial Management & Data Systems, 109, pp. 1137-1152; Phusavat, K., Anussornnitisarn, P., Helo, P., Dwight, R., Performance measurement: Roles and challenges (2009) Industrial Management & Data Systems, 109, pp. 646-664; Lamsali, H., Performance measurement of green supply chain management (PM/GSCM) (2006) Complexity Management in Supply Chains-Concepts, Tools and Methods, pp. 303-311. , Berlin: Erich Schmidt Verlag; Bullinger, H., Kuhner, M., Van Hoff, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40, pp. 3533-3543; Zhu, J., (2003) Quantitative Models for Performance Evaluation and Benchmarking, , Dordrecht: Kluwer Academic Publisher; Simon, H., The architecture of complexity (1999) Facets of Systems Sciences, , G. Klin (org.) New York: Plenum Press; Aravechia, C., Pires, S., Supply chain performance evaluation: A case study (2002) Proc. of the XIII Meeting of the POMS, , San Francisco; Pires, S., Aravechia, C., Measuring supply chain performance (2001) Proc. of the XII Meeting of the POMS; Bechtel, C., Jayaram, J., Supply chain management: A strategic perspective (1997) International Journal of Logistics Management, 8, pp. 15-34; Chan, F., Qi, H., An innovative performance measurement method for supply chain management (2003) International Journal of Operations & Production Management, 8, pp. 209-223; Hausman, W., Supply chain performance metrics (2003) The Practice of Supply Chain Management: Where Theory and Application Converge, , Billington, C., Harrison, T., Lee, H., Neale, J. (orgs.) Amsterdam: Kluwer Academic Publisher; Azevedo, S., Carvalho, H., Cruz-Machado, V., A proposal of LARG supply chain management practices and a performance measurement system (2011) International Journal of E-Education, E-Business, E-Management and E-Learning, 1, pp. 7-14; Kleijnen, J., Smits, M., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54, pp. 507-514; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21, pp. 75-93; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Sellitto, M., Mendes, L., Avaliação comparativa do desempenho de três cadeias de suprimentos em manufatura (2005) Produção, 16, pp. 552-568. , in Portuguese; Bai, C., Sarkis, J., Supply-chain performancemeasurement system management using neighbourhood rough sets (2012) International Journal of Production Research, 50, pp. 2484-2500; Pavan, F., Pires, S., Medição de desempenho na gestão da cadeia de suprimentos: uma proposta de indicadores de desempenho baseados no modelo SCORE e nas dimensões competitivas da manufatura (2004) Proc. of the XXIV ENEGEP. Florianópolis, , in Portuguese; Ward, P., McCreery, J., Ritzman, L., Sharma, D., Competitive priorities in operations management (1998) Decision Sciences, 29, pp. 1035-1046; Saarijärvi, H., Kuusela, H., Spence, M., Using the pairwise comparison method to assess competitive priorities within a supply chain (2012) Industrial Marketing Management, 41, pp. 631-638; Jabbour, C., Silva, E., Paiva, E., Santos, F., Environmental management in Brazil: Is it a completely competitive priority? (2012) Journal of Cleaner Production, 21, pp. 11-22; Sellitto, M., Borchardt, M., Pereira, G., Gomes, L., Environmental performance assessment of a provider of logistical services in an industrial supply chain (2012) Theoretical Foundations of Chemical Engineering, 46, pp. 691-703; Kocaoglu, B., Gülsün, B., Tanyas, M., A SCOR based approach for measuring a benchmarkable supply chain performance (2013) Journal of Intelligent Manufacturing, 24, pp. 113-132; Clivillé, V., Berrah, L., Overall performance measurement in a supply chain: Towards a supplierprime manufacturer based model (2012) Journal of Intelligent Manufacturing, 23, pp. 2459-2469; Goldsby, T., Griffis, S., Roath, A., Modeling lean, agile, and leagile supply chain strategies (2006) Journal of Business Logistics, 27 (1), pp. 57-80; Kleinsorge, I., Schary, P., Tanner, R., The shippercarrier partnership: A new tool for performance evaluation (1991) Journal of Business Logistics, 12, pp. 35-57; Cigolini, R., Cozzi, M., Perona, M., A new framework for supply chain management: Conceptual model and empirical test (2004) International Journal of Operations & Production Management, 24, pp. 7-41; Beamon, B., Designing the green supply chain (1999) Logistics Information Management, 12, pp. 332-342; Christiansen, T.W., Berry, W., Bruun, P., Ward, P.A., Mapping of competitive priorities, manufacturing practices, and operational performance in groups of Danish manufacturing companies (2003) International Journal of Operations and Production Management, 23, pp. 1163-1183; Hugo, A., Pistikopoulos, E., Environmentally conscious long-range planning and design of supply chain networks (2005) Journal of Cleaner Production, 13, pp. 1471-1491; Van Hoek, R., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21, pp. 15-29; Hervani, A., Helms, M., Sarkis, J., Performance measurement for green supply chain management, Benchmarking (2005) An International Journal, 12, pp. 330-353; Paulraj, A., Environmental motivations: A classification scheme and its impact on environmental strategies and practices (2009) Business Strategy and the Environment, 18 (453-468), p. 2009; Mondragon, A., Lalwani, C., Mondragon, C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Management: An International Journal, 16, pp. 43-56; Boyer, K., Lewis, M., Competitive priorities: Investigating the need for trade-offs in operations strategy (2002) Production and Operations Management, 11, pp. 9-20; Olhager, J., Strategic positioning of the order penetration point (2003) International Journal of Production Economics, 85, pp. 319-329; Prater, E., Biehl, M., Smith, M., International supply chain agility-Tradeoffs between flexibility and uncertainty (2001) International Journal of Operations and Production Management, 21, pp. 823-839; Martín-Peña, M., Díaz-Garrido, E., A taxonomy of manufacturing strategies in Spanish companies (2008) International Journal of Operations & Production Management, 28, pp. 455-477; Saaty, T., (1980) The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation, , New York: McGraw-Hill; Slack, N., The importance-performance matrix as a determinant of improvement priority (1994) International Journal of Operations and Production Management, 14, pp. 59-75; Vanany, I., Suwignjo, P., Yulianto, D., Design of supply chain performance measurement system for lamp industry (2005) Proc. of the 1st International Conference on Operations and Supply Chain Management, , Bali},
publisher={International Foundation for Production Research (IFPR)},
language={English},
abbrev_source_title={Int. Conf. Prod. Res., ICPR},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Hundzinski2013,
author={Hundzinski, L.N. and De Lima, E.P. and Gouvea Da Costa, S.E. and Machado, C.G. and Cestari, J.M.A.P. and Kluska, R.A.},
title={Sustainability standards and guidelines requirements for integrated management},
journal={22nd International Conference on Production Research, ICPR 2013},
year={2013},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929440054&partnerID=40&md5=f78b96dba02d231e0b10108c1e08e2be},
affiliation={Industrial and Systems Engineering Graduate Program, Pontifical Catholic Univeristy of Paraná - PUCPR, R. Imaculada Conceição, 1155, Curitiba, Paraná, Brazil},
abstract={Organizations are influenced by external demands which can be represented by changes on the markets organizations are part of. Companies are operating on a new context, where the importance of sustainability has been highlighted by events such as environmental problems, social responsibility issues and quality assurance of products and services. Those aspects are represented by the areas of the Triple Bottom-Line (3BL), which treat organizations operational performance on economic, environmental and social aspects. However, organizations are still trying to develop new performance measurement systems, which relate to the 3BL and that can be used for sustainable performance assessment and benchmarking. In order to address this need, this research aims to develop a process that enables standards and guidelines to be used as tools for the establishment of a performance measurement system which can address the needs of integrated management on behalf of sustainability aspects. The method used on the development of this article is based on concepts of the Process Approach methodology from Cambridge University, as well as Business Process Management Notation to represent processes for performance assessment. Those processes were built with a high emphasis on Performance Assessment frameworks and Key Performance Indicators (KPIs) theories. The processes generated have been applied in a project of innovation in sustainable management, in which its application enabled to work with requirements from various standards and guidelines in a practical context, through the usage of indicators and performance measurement. Exemplifying the application of this process, the article will aim to clarify the usage of indicators based on standards and guidelines as a tool for sustainability assessment, through an integrated management of an organization set of standards and guidelines.},
author_keywords={Process approach;  Standards;  Sustainable operations;  Sustainable performance measurement system},
keywords={Administrative data processing;  Benchmarking;  Economic and social effects;  Enterprise resource management;  Quality assurance;  Social aspects;  Societies and institutions;  Standards;  Supply chain management, Business process management notation;  Key performance indicators;  Performance measurement system;  Performance measurements;  Process approach;  Sustainability assessment;  Sustainable operations;  Sustainable performance, Sustainable development},
references={Danvers, M., Building blocks - ISO standards as powerful tools for taking action (2012) International Organization for Standardization, , http://www.iso.org/iso/home/news_index/news_archive/news.htm?Refid=Ref1522; Rosing, M.V., Schell, H.V., (2010) Creating A Better Future with the Right Business Sustainability Management Framework, , Business Management Team; Neely, A., Bourne, M., Mills, J., Platts, K., Richards, H., (2002) Getting the Measure of Your Business, , Cambridge: Cambridge University Press; Parmenter, D., (2007) Key Performance Indicators, , Indianapolis: John Wiley & Sons; Hubbard, G., Measuring organizational performance: Beyond the triple bottom line (2009) Business Strategy and the Environment, 19, pp. 177-191; Fiksel, J., Macdaniel, J., Mendenhall, C., Measuring progress towards sustainability principles, process, and best practices (1999) Greening of Industry Network Conference - Best Practice Proceeding; Schwarz, J., Beloff, B., Beaver, E., Use sustainability metrics to guide decision making (2002) Chemical Engineering Progress, 98, pp. 58-63; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) Int. J. Production Economics, 46-47, pp. 423-431. , 1996; Baker, K., (1999) The Development of Organization and Program Performance Indicators, , Pacific Northwest Laboratory; Hacking, T., Guthrie, P., A framework for clarifying the meaning of triple bottom-line, integrated, and sustainability assessment (2008) Environmental Impact Assessment Review, 28, pp. 73-89; Baldam, R., Da Silva, H.P., Valle, R., (2007) Gerenciamento de Processos de Negócios, , BPM - Business Process Management. São Paulo: Érica; Bonitasoft, S.A., (2012) Bonita Studio - Community Edition Version 5.7.2, , http://www.bonitasoft.com; De Leite, L.O., Rezende, D.A., (2007) Gestão Corporativa Por Processos Na Administração Pública Municipal: Estudo de Caso da Implantação Do Business Process Management (BPM) No Instituto Curitiba de Informática, , EnADI, Florianópolis (SC); Westbrook, R., Action research: A new paradigma for research in production and management (1995) International Journal of Operations and Production Management, 15, pp. 6-20; Reason, P., Bradbury, H., (2001) Handbook of Action Research: Participative Inquiry and Practice, , London: Sage Publications; Labuschagne, C., Brent, A.C., Erck, R.P.G., Assessing the sustainability performances of industry (2005) Jornal of Cleaner Production, 13, pp. 373-385; Searcy, C., The role of sustainable development indicators in corporate decisionmaking (2009) IISD, pp. 1-23; ISO, (2012) Rio+20 - Forging Action from Agreement, , International Organization for Standardization; Sustainability Compendium, Standards and Certifications, Sustainability Compendium - Social and Environmental Responsability Management Tools, , http://www.institutoatkwhh.org.br/compendio/pdf/novo/en/compendium_part_03.pdf; The Global Compact, (2011) Corporate Sustainability in the World Economy, , United Nations Global Compact; Naveh, E., Marcus, A., Achieving competitive advantage through implementing a replicable management standard: Installing and using ISO 9000 (2005) Journal of Operations Management (JOM), 24, pp. 1-26; Developing Value, The Business Care for Sustainability in Emerging Markets, , http://www.greenbiz.com/sites/default/files/document/O16F30165.pdf, SustainAbility, International Finance Corporation, Ethos Institute - Business and Social Reponsibility; ISO, (2004) ABNT NBR ISO 14031: Gestão Ambiental - Avaliação de Desempenho Ambiental - Diretrizes, , Rio de Janeiro: Associação Brasileira de Normas Técnicas; ISO, (2010) ISO 26000: Guidance on Social Responsibility, , International Organization for Standardization; ISO, (2011) ISO 50001: Energy Management Systems - Requirements with Guidance for Use, , International Organization for Standardization; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) IJOPM, 23, pp. 213-229; ISO, (2004) ISO 14001: Environmental Management Systems - Requirements with Guidance for Use, , International Organization for Standardization; Balanced Scorecard Institute, How A Balanced Scorecard Can Help Your Organization, , http://www.balancedscorecard.org/Portals/0/BalancedScorecardInstitute-BSC.pdf; Mosse, R., Sontheimer, L.E., Performance monitoring indicators handbook (1996) World Bank Technical Paper No 334; Whitelaw, K., (2004) ISO 14001 Environmental Systems Handbook, , Second Edition Elsevier Ltd; Machado, C.G., Pinheiro De Lima, E., Gouvea Da Costa, S.E., Cestari, J.M.A.P., Kluska, R.A., Hundzinski, L.N., Indicators formulation process for sustainable operations management (2012) ICPR Americas, , Santiago, Chile},
publisher={International Foundation for Production Research (IFPR)},
language={English},
abbrev_source_title={Int. Conf. Prod. Res., ICPR},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ferreira2013,
author={Ferreira, P. and Pinheiro, E. and Gouvea, S.},
title={Distributed team's performance measurement capabilities: A model to evaluate systematic literature review result},
journal={22nd International Conference on Production Research, ICPR 2013},
year={2013},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929376531&partnerID=40&md5=34aac00d8f359d15a400a1874debcc4a},
affiliation={Pontifical University of Parana, Graduate Program in Production and Systems Engineering, R. Imaculada Conceicao, 1155, Curitiba, Parana, Brazil},
abstract={Companies are more commonly adopting the distributed teams' model, in order to increase their knowledge and skill set. In doing so the company will obtain several benefits, but it will lose its personal touch in managing their employees. Aiming to verify if this tradeoff is beneficial for the company, Performance Measurement Systems are more and more being applied to distributed teams and to follow this growth researchers are consistently focusing on this area. Due to this focus the count of articles reporting developments in the required capabilities to form and manage distributed teams performance measurement system increases as well. Along the way some researchers are developing/applying different types of literature review on the subject in order to identify its growth and the main areas the topic is growing. Following this trend the author performed a systematic review on the theme "Which organizational capabilities/competences a company must have in order to manage/develop distributed teams performance?". This paper aims to present a model to evaluate such literature review in order to quantify and qualify the results to make it even more clearly to other researchers. Based on the results of the research, others will be able to compare the research areas and contribute with its development.},
author_keywords={Capabilities;  Develop;  Distributed teams;  Performance measurement system},
keywords={Capabilities;  Develop;  Distributed teams;  Literature reviews;  Organizational capabilities;  Performance measurement system;  Performance measurements;  Systematic literature review, Supply chain management},
references={Neely, A.D., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Thomson, A., Stone, A., Ion, W., Distributed team design in smalland mediumsized enterprises: How to get it right (2007) AI EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 21, p. 203218; Bell, B.S., Kozlowski, S.W.J., A typology of virtual teams. Implications for effective leadership (2002) Group and Organization Management, 27 (1), pp. 14-49; Bradner, E., Mark, G., Why distance matters: Effects on cooperation, persuasion and deception (2002) CSCW 2002, , November 16-20. New Orleans, LA: ACM Press; Carmel, E., Agarwal, R., Tactical approaches for alleviating distance in global software development (2001) IEEE Software, pp. 22-29; Carmel, E., Tjia, P., (2005) Offshoring Information Technology: Sourcing and Outsourcing to A Global Workforce, , Cambridge Univ. Press; Cascio, W.F., The virtual workplace: A reality now (1998) Society for Industrial & Organizational Psychology, 35, p. 4; De Lima, E.P., Sergio Da Costa, G.E., Angelis, J.J., Strategic performance measurement systems: A discussion about their roles (2009) Measuring Business Excellence, 13 (3), pp. 39-48; Furst, S., Blackburn, R., Rosen, B., Virtual team effectiveness: A proposed research agenda (2001) Information Systems Journal, 9 (4), pp. 249-269; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: A framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Hinds Pamela, J., Bailey Diane, E., Out of sight, out of sync: Understanding conflict in distributed teams (2003) Organization Science, 14 (6). , November-December; Fransoo, J.C., Wouters, M.J.F., Measuring the bullwhip effect in a supply chain (2000) Supply Chain Management, 5 (2), pp. 78-89; Jarvenpaa, S.L., Leidner, D.E., Communication and trust in global virtual teams (1999) Organization Science, 10 (6), pp. 791-815; Jarvenpaa, S.L., Leidner, D.E., Communication and trust in global virtual teams (1998) Journal of Computer Mediated Communication, 3, p. 4; Klassen, T.P., Jadad, A.R., Moher, D., Guides for reading and interpreting systematic reviews. 1. Getting started (1998) Arch. Pediatr. Adolesc. Med, 152, pp. 700-704; Fortuin, L., Performance indicators-Why where and how? (1988) European Journal of Operational Research, 34, pp. 1-9; Lipnack, J., Stamps, J., (2000) Virtual Teams-People Working Across Boundaries with Technology, , nNew York: Wiley; Majchrzak, A., Rice, R.E., Malhotra, A., King, N., Ba, S., Technology adaptation: The case of a computer-supported inter-organizational virtual team (2000) MIS Quarterly, 24 (4), pp. 569-600; Ong, T.S., (2005) Performance Measurement Systems and Their Influences in High Technology Manufacturing Firms of A Developing Country, , Leeds University Business School, Leeds University, Leeds, Vol. PhD; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Engineering Costs and Production Economics, 17, pp. 253-262; Pawson, R., (2006) Evidence-Based Policy: A Realist Perspective', , London: SAGE Publications; Petrie, C., Madefast Home Page, , http://www.madefast.stanford.edu; Platts, K.W., Integrated manufacturing: A strategic approach (1995) Integrated Manufacturing Systems, 6 (3), pp. 18-23; Powell, A., Piccoli, G., Ives, B., Virtual teams: A review of current literature and directions for future research (2004) The DATABASE for Advances in Information Systems, 35, pp. 6-36; Prikladnicki, R., Audy, J.L.N., Comparing offshore outsourcing and the internal offshoring of software development: A qualitative study (2009) Proceedings of the Americas Conference on Information Systems, , Paper 680; PRISMA, http://www.prisma-statement.org; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review 69, pp. 131-137. , January-February; Flapper, S.D., Fortuin, L., Stoop, P.P.M., Towards consistent performance measurement systems (1996) International Journal of Operations & Production Management, 16 (7), pp. 27-37; Slack, N., Flexibility, trade-offs and learning in manufacturing system design (2000) International Journal of Manufacturing Technology and Management, 1 (4-5), pp. 331-348; Tarr, J.D., Performance measurements for a continuous improvement strategy (1996) Hospital Material Management, 18, pp. 77-85; Gear, T., Ford motor company case study (1996) Computer Weekly March 7th; Carmel, E., Agarwal, R., Tactical approaches for alleviating distance in global software development (2001) IEEE Software, pp. 22-29; Carmel, E., Tjia, P., (2005) Offshoring Information Technology: Sourcing and Outsourcing to A Global Workforce, , Cambridge Univ. Press; Powell, A., Piccoli, G., Ives, B., Virtual teams: A review of current literature and directions for future research (2004) The DATABASE for Advances in Information Systems, 35, pp. 6-36; Prikladnicki, R., Audy, J.L.N., Evaristo, R., Global software development in practice lessons learned (2003) Software Process Improvement and Practice, 8, pp. 267-279; Kaplan, R., Measuring manufacturing performance: A new challenge for managerial accounting research (1983) The Accounting Review, 58 (4), pp. 686-705; Eccles, R., The performance measurement manifesto (1991) Harvard Business Review, pp. 131-137. , Jan-Feb; Ghalayini, A., Noble, J., The changing basis of performance measurement (1996) International Journal of Operations and Production Management, 16 (8), pp. 63-80; Kaplan, R., Norton, D., The balanced scorecard: The measures that drive performance (1992) Harvard Business Review, pp. 71-79. , Jan-Feb; Lynch, R., Cross, K., (1991) Measure Up! Yardsticks for Continuous Improvement, , Blackwell, Oxford; Neely, A., Mills, J., Gregory, M., Richards, H., Platts, K., Bourne, M., Getting the measure of your business (1996) Works Management, , Cambridge; Ghalayini, A., Noble, J., Crowe, T., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Cramton, C.D., The mutual knowledge problem and its consequences for dispersed collaboration (2001) Organ. Sci, 12 (3), pp. 346-371; Martins, L.L., Gilson, L.L., Maynard, M.T., Virtual teams: What do we know and where do we go from here? (2004) Journal of Management, 30 (6), pp. 805-835; Maznevski, M.L., Chudoba, K.M., Bridging space over time: Global virtual team dynamics and effectiveness (2000) Organization Science, 11 (5), pp. 473-492; Powell, A., Piccoli, G., Ives, B., Virtual teams: A review of current literature and directions for future research (2004) The DATABASE for Advances in Information Systems, 35 (1), pp. 6-36; Kaplan, R., Norton, D., Putting the balanced scorecard to work (1993) Harvard Business Review, pp. 34-147. , September-October; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228},
publisher={International Foundation for Production Research (IFPR)},
language={English},
abbrev_source_title={Int. Conf. Prod. Res., ICPR},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Nuttasophon2013,
author={Nuttasophon, P. and Rotaru, K. and Schulz, A.},
title={Risk framing in performance measurement systems: The effect on risky decision-making},
journal={Proceedings - Pacific Asia Conference on Information Systems, PACIS 2013},
year={2013},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84928471403&partnerID=40&md5=aaf50e0a6024d53c3138c8890c208a54},
affiliation={Department of Accounting, and Finance, University of Melbourne, Australia},
abstract={To address the limitations of the traditional performance measurement systems (PMSs) in visualizing risk and preventing excessive managerial risk-taking, a number of research studies proposed to extend the functionality of PMSs by incorporating risk measures and goals and thereby enabling a better view on organizational risk exposures. While researchers still continue to provide claims regarding the benefits of a balanced approach to combined risk and performance representation in PMSs, the literature still lacks a uniform vision about the design of such risk-aware PMS and about how effective it is in the context of making risky decisions. In this study, a laboratory experiment was conducted to investigate how framing risk through strategic goals and key indicators incorporated in a PMS affects risky decision-making. The findings demonstrated a significant direct effect between PMS problem frame and risky decision-making. Specifically, the choice of a PMS frame with performance-driven strategic goal (emphasizing organizational focus on increasing gain/performance) and no risk measure was proved to result in a higher level of managerial risk taking compared to the choice of a risk-aware PMS frame which adopted a risk-driven strategic goal (emphasizing organizational focus on minimizing potential loss/risk), a risk measure or both. The study demonstrated that the inclusion of risk measure(s) and/or risk-driven strategic goal(s) in PMSs allows for risk information to be incorporated in managerial decision-making and thereby results in lower risk-taking.},
author_keywords={Decision support;  Laboratory experiment;  Performance measurement system;  Risky decision-making},
keywords={Decision making;  Decision support systems;  Health hazards;  Managers;  Risk management;  Supply chain management, Decision supports;  Laboratory experiments;  Managerial decision making;  Managerial risks;  Performance measurement system;  Performance-driven;  Research studies;  Risk information, Risk assessment},
references={AS/NZS ISO 31000, Risk management-Principles and guidelines on implementation (2009) International Organization for Standardization, , http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=43170, (accessed 15 July 2012); Arena, M., Arnaboldi, M., Azzone, G., Is enterprise risk management real? (2011) Journal of Risk Research, 14 (7), pp. 779-797; Bai, X., Nunez, M., Kalagnanam, J.R., Managing data quality risk in accounting information systems (2012) Information Systems Research, 23 (2), pp. 453-473; Banker, R.D., Chang, H., Pizzini, M.J., The balanced scorecard: Judgmental effects of performance measures linked to strategy (2004) Accounting Review, 79 (1), pp. 1-23; Beasley, M.S., Chen, A., Nunez, K., Wright, L., Working hand in hand: Balanced scorecards and enterprise risk management (2006) Strategic Finance, 87 (9), pp. 49-55; Biner, P.M., Hua, D.M., Determinants of the magnitude of goal valence: The interactive effects of need, instrumentality, and the difficulty of goal attainment (1995) Basic & Applied Social Psychology, 16 (1-2), pp. 53-74; Bonner, S.E., (2007) Judgment and Decision Making in Accounting, , (1 ed.). Upper Saddle River, N.J.: Pearson/ Prentice Hall; Breden, D., Adding value to your organisation through operational risk management (2009) Journal of Securities Operations & Custody, 2 (2), pp. 120-127; Brownell, P., (1995) Research Methods in Management Accounting, , Melbourne, Vic: Coopers and Lybrand and Accounting Association of Australia and New Zealand; Calendro, J., Jr., Lane, S., An introduction to the enterprise risk scorecard (2006) Measuring Business Excellence, 10 (3), pp. 31-40; Carmines, E.G., Zeller, R.A., (1979) Reliability and Validity Assessment, , Beverly Hills, Calif.: SAGE publications; Committee of Sponsoring Organizations of the Treadway Commission (COSO), (2004) Enterprise Risk Management-Integrated Framework, Executive Summary and Framework, , New York, NY: American Institute of Certified Public Accountants (AICPA); Chen, C.C., Jones, K.T., Are companies really ready for stretch targets? (2005) Management Accounting Quarterly, 6 (4), pp. 10-18; Chenhall, R.H., Management control systems design within its organizational context: Findings from contingency-based research and directions for the future (2003) Accounting, Organizations & Society, 28 (2-3), pp. 127-168; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting, Organizations & Society, 30 (5), pp. 395-422; Chow, C.W., Kohlmeyer, J.M., Wu, A., Performance standards and managers' adoption of risky projects (2007) Advances in Management Accounting, 16, pp. 63-105; Cook, T.D., Campbell, D.T., (1979) Quasi-experimentation: Design & Analysis Issues for Field Settings, , Boston: Houghton Mifflin; Davies, J., Finlay, M., Legnaghen, T., Wilson, D., Key risk indicators-their role in operational risk management and measurement (2006) The Advanced Measurement Approach to Operational Risk, pp. 215-246. , E. Davis (Ed.), London: Risk Books; Dikstein, D.I., Flast, R.H., (2009) Excuses: A Business Process Approach to Managing Operational Risk, , NY: Wiley, Hoboken; Fischhoff, B., Lichtenstein, S., Slovic, P., Derby, S.L., Keeney, R., (1981) Acceptable Risk, , New York: Cambridge University Press; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Guo, L., Libby, T., W-O-W, B., Yang, D., (2011) Limiting the Effect of Attribution and Construal Errors on Strategy Evaluation Using A Strategic Performance Measurement System., , Working paper; Hecht, G., Tafkov, I., Towry, K.L., Performance Spillover in a multitask environment (2012) Contemporary Accounting Research, 29 (2), pp. 563-589; Highhouse, S., Yúce, P., Perspectives, perceptions, and risk-taking behavior (1996) Organizational Behavior & Human Decision Processes, 65 (2), pp. 159-167; Huelsbeck, D.P., Merchant, K.A., Sandino, T., On testing business models (2011) Accounting Review, 86 (5), pp. 1631-1654; Ittner, C.D., Larcker, D.F., Innovations in performance measurement: Trends and research implications (1998) Journal of Management Accounting Research, 10, pp. 205-238; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the weighting of performance measures: Evidence from a balanced scorecard (2003) Accounting Review, 78 (3), pp. 725-758; Kahneman, D., Tversky, A., Prospect theory: An analysis of decision under risk (1979) Econometrica, 47 (2), pp. 263-291; Kaplan, R.S., Risk management and the strategy execution system (2009) Balanced Scorecard Report, 11 (6); Kaplan, R.S., Mikes, A., Managing the multiple dimensions of risk: Part i of a two-part series (2011) Balanced Scorecard Report, 13 (4); Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston: Harvard Business School Press; Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangibles Assets into Tangibles Outcomes, , Boston Massachusetts: Harvard Business School Press; Keh, H.T., Foo, M.D., Lim, B.C., Opportunity evaluation under risky conditions: The cognitive processes of entrepreneurs (2002) Entrepreneurship: Theory & Practice, 27 (2), p. 125; Kline, R.B., (2005) Principles and Practice of Structural Equation Modeling, , (2 ed.). New York, NY: Guildford Press; Kober, R., Ng, J., Paul, B.J., The interrelationship between management control mechanisms and strategy (2007) Management Accounting Research, 18 (4), pp. 425-452; Levin, I.P., Schneider, S.L., Gaeth, G.J., All frames are not created equal: A typology and critical analysis of framing effects (1998) Organizational Behavior & Human Decision Processes, 76 (2), pp. 149-188; Libby, T., Salterio, S.E., Webb, A., The balanced scorecard: The effects of assurance and process accountability on managerial judgment (2004) Accounting Review, 79 (4), pp. 1075-1094; Lipe, M.G., Salterio, S.E., The balanced scorecard: Judgmental effects of common and unique performance measures (2000) Accounting Review, 75 (3), p. 283; Luft, J., Bonus and penalty incentives: Contract choice by employees (1994) Journal of Accounting & Economics, 18 (2), pp. 181-206; Maccrimmon, K.R., Wehrung, D.A., A portfolio of risk measures (1985) Theory and Decision, 19 (1), pp. 1-29; March, J.G., Shapira, Z., Managerial perspectives on risk and risk taking (1987) Management Science, 33 (11), pp. 1404-1418; Mikes, A., Risk management and calculative cultures (2009) Management Accounting Research, 20 (1), pp. 18-40; Mikes, A., From counting risk to making risk count: Boundary-work in risk management (2011) Accounting, Organizations & Society, 36 (4-5), pp. 226-245; Power, M., The risk management of nothing (2009) Accounting, Organizations & Society, 34 (6-7), pp. 849-855; Scandizzo, S., Risk mapping and key risk indicators in operational risk management (2005) Economic Notes, 34 (2), pp. 231-256; Scholey, C., Risk and the balanced scorecard (2006) CMA Management, 80 (4), pp. 32-35; Shields, J.F., Shields, M.D., Antecedents of participative budgeting (1998) Accounting, Organizations & Society, 23 (1), pp. 49-76; Sitkin, S.B., Pablo, A.L., Reconceptualizing the determinants of risk behavior (1992) Academy of Management Review, 17 (1), pp. 9-38; Sitkin, S.B., Weingart, L.R., Determinants of risky decision-making behavior: A test of the mediating role of risk perceptions and propensity (1995) Academy of Management Journal, 38 (6), pp. 1573-1592; Tversky, A., Kahneman, D., The framing of decisions and the psychology of choice (1981) Science, 211 (4481), pp. 453-458; Valacich, J.S., Sarker Pratt S, J., Groomer, M., Understanding risk-taking behavior of groups: A "decision analysis" perspective (2009) Decision Support Systems, 46 (4), pp. 902-912; Weber, E.U., Milliman, R.A., Perceived risk attitudes: Relating risk perception to risky choice (1997) Management Science, 43 (2), p. 123; Williams, S., Zainuba, M., Jackson, R., Determinants of managerial risk perceptions and intentions (2008) Journal of Management Research, 8 (2), pp. 59-75; Wong, K.F.E., The role of risk in making decisions under escalation situations (2005) Applied Psychology: An International Review, 54 (4), pp. 584-607; Young, S.M., Participative budgeting: The effects of risk aversion and asymmetric information on budgetary slack (1985) Journal of Accounting Research, 23 (2), pp. 829-842},
publisher={Pacific Asia Conference on Information Systems},
language={English},
abbrev_source_title={Proc. - Pac. Asia Conf. Inf. Syst., PACIS},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Teimoury201325,
author={Teimoury, E. and Fathian, M. and Chambar, I.},
title={Automation of the supply chain performance measurement based on multi-agent system},
journal={International Journal of Agile Systems and Management},
year={2013},
volume={6},
number={1},
pages={25-42},
doi={10.1504/IJASM.2013.052225},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84926122402&doi=10.1504%2fIJASM.2013.052225&partnerID=40&md5=4a7ba92b66546b9cd43d9bcb1e1a4e24},
affiliation={Industrial Engineering Department, Iran University of Science and Technology (IUST), Narmak street, Tehran, Iran},
abstract={Several studies have been done on challenges of performance measurement. One of these challenges is the delay in the performance measurement processes. The scope and extend of the supply chain and tendency towards managing its entities independently increase this delay. Some scholars have suggested that automating the performance measurement processes solve this problem, but less attention has been paid to implementation of the framework suggested by an appropriate information system for automated collecting and assessing of the measures values. Considering the capabilities of the multi-agent systems against performance measurement challenges in a supply chain scale through the automated execution of business rules, the authors have identified, classified and then converted the performance measurement rules to an applicable form in multi-agent systems. Here we have employed some innovative methodologies of multi-agent systems development to create an automated supply chain performance measurement multi-agent system. Copyright © 2013 Inderscience Enterprises Ltd.},
author_keywords={Business rules;  MAS;  Multi-agent system;  Performance measurement system;  PMS;  SC;  Supply chain},
references={Adair, C.E., Simpson, E., Casebeer, A.L., Performance measurement in healthcare: Part II - State of the science findings by stage of the performance measurement process (2006) Healthcare Policy, 2 (1), pp. 56-78; Alameh, R., Zazworka, N., Hollingsworth, J.K., Performance measurement of novice HPC programmers code (2007) Proceedings of Third International Workshop on Software Engineering for High Performance Computing Applications, pp. 3-8. , Minneapolis, USA; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Busi, M., Monitoring supply chain flows through improved performance measurement of extended processes (2007) Supply Chain Management: Issues in the New Era of Collaboration and Competition, pp. 313-354. , Wang, W., Heng, M. and Chau, P. (Eds.): Idea Group Publishing, London; Chiu, H.Y., Sheng, C.C., Chen, A.P., Modeling agent-based performance evaluation for e-learning systems (2008) The Electronic Library, 26 (4), pp. 345-362; El Fallah Seghrouchni, A., Olaru, A., Nguyen, N., Salomone, D., Ao Dai: Agent oriented design for ambient intelligence (2011) Principles and Practice of Multi-Agent Systems, pp. 259-269. , Desai, N., Liu, A. and Winikoff, M. (Eds.): 7057 of Lecture Notes of Computer Sciences, Springer, Berlin; Eriksson, H.E., Penker, M., (2000) Business Modeling with UML, , Wiley, New York; Farshchi, M., Jusoh, Y.Y., Murad, M.A.A., Agent-based distributed performance measurement system for ITSP projects (2010) Proceedings of International Conference on Electronics and Information Engineering (ICEIE), pp. 51476-51481. , Kyoto, Japan; Graser, F., Westphal, I., Eschenbaecher, J., (2005) Roadmap on VOPM Challenges on Operational and Strategic Level, ECOLEAD Project Deliverable Reports, D31 (1). , http://www.ve-forum.org/projects/284/Deliverables/d31_1_vopm.pdf, (accessed 4 December 2012); Isik, Z., (2009) A Conceptual Performance Measurement Framework for Construction Industry, , PhD thesis, Middle East Technical University (METU), Ankara, Turkey; Kueng, P., Meier, A., Wettstein, T., Computer-based performance measurement in SMEs: Is there any option? (2000) Proceedings of 1st International Conference on Systems Thinking in Management, pp. 318-323. , Geelong, Australia; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Myles, J.F., (2008) Discovering Critical Success Factors for Implementing An Automated Performance Measurement System: A Case Study Approach, , PhD thesis, Edith Cowan University, Perth, Australia; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Paranjape, B., Rossiter, M., Pantano, V., Insights from the balanced scorecard performance measurement systems: Successes, failures and future - A review (2006) Measuring Business Excellence, 10 (3), pp. 4-14; Pavlov, A., Bourne, M., Responding to contemporary challenges in performance management: Measuring organisational routines (2007) Proceedings of the 14th International Annual EurOMA Conference, , Ankara, Turkey; Ramaa, A., Rangaswamy, T., Subramanya, K.N., A review of literature on performance measurement of supply chain network (2009) Proceedings of the Second International Conference on Emerging Trends in Engineering and Technology (ICETET-09), pp. 802-807. , Nagpur, India; Saiz, J.J.A., Bas, A.O., Rodríguez, R.R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; Spreeuwenberg, S., Gerrits, R., Business rules in the semantic web, are there any or are they different? (2006) Reasoning Web 2006, pp. 152-163. , Barahona et al. (Eds.): 4126 of Lecture Notes in Computer Science, Springer, Berlin; Taveter, K., (2004) A Multi-Perspective Methodology for Agent-oriented Business Modelling and Simulation, , PhD thesis, Tallinn University of Technology, Tallinn, Estonia; Taveter, K., Wagner, G., Towards radical agent-oriented software engineering processes based on AOR modeling (2005) Agent-Oriented Methodologies, pp. 277-316. , Henderson-Sellers, B. and Giorgini, P. (Eds.): Idea Group Publishing, Hershey, PA; Teixeira, N.S., Component-oriented software development with UML (2011) Proceedings of The, Sixth International Conference on Software Engineering Advances (ICSEA), pp. 269-274. , Barcelona, Spain; Van Der Vorst, J., Performance measurement in agri-food supply chain networks. An overview (2005) Quantifying the Agri-food Supply Chain, pp. 13-24. , Ondersteijn, C.J., Wijnands, J.H., Huirne, R.B. and van Kooten, O. (Eds.): Springer, Dordrecht; Yeniyurt, S., A literature review and integrative performance measurement framework for multinational companies (2003) Marketing Intelligence & Planning, 21 (3), pp. 134-142; Zheng, P., Lai, K.K., A rough set approach on supply chain dynamic performance measurement (2008) KES-AMSTA 2008, pp. 312-322. , Nguyen, N.T. et al. (Eds.): 4953 of Lecture Notes in Computer Science, Springer, Berlin},
correspondence_address1={Teimoury, E.; Industrial Engineering Department, Narmak street, Iran; email: Teimoury@iust.ac.ir},
publisher={Inderscience Publishers},
issn={17419174},
language={English},
abbrev_source_title={Int. J. Agil. Syst. Manag.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Kashiwagi20131237,
author={Kashiwagi, D. and Gajjar, D. and Smithwick, J. and Kashiwagi, J.},
title={Implementation of a real-time information system for a roofing Manufacturer},
journal={ISEC 2013 - 7th International Structural Engineering and Construction Conference: New Developments in Structural Engineering and Construction},
year={2013},
pages={1237-1242},
doi={10.3850/978-981-07-5354-2-C-40-319},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923206436&doi=10.3850%2f978-981-07-5354-2-C-40-319&partnerID=40&md5=f377448f1fe00a35caa3a4c7d90f7141},
affiliation={Performance Based Studies Research Group, Arizona State University, Tempe, United States},
abstract={In 1996, a coating and water proofing systems Manufacturer approached the Performance Based Studies Research Group (PBSRG) at Arizona State University (ASU) and requested assistance to identify the differential of their product as compared to others in the industry, create a performance measurement system to identify the high quality applicators, and minimize the risk of non-performance of low quality applicators. Collectively, these goals form the foundation of the Manufacturer's risk management structure. In the 16 years since inception, the system has gone through several iterations that correlate to the Manufacturer personnel's increased understanding. The latest iteration of the structure utilizes web-based technology, which has resulted in increased capability of the Manufacturer and its applicators to resolve issues. The objectives of this paper are to describe each of the system's major iterations, describe the manufacturer's current real-time best value information system, and document the impact of the system. Copyright © 2013 by Research Publishing Services.},
author_keywords={Best value;  Information technology;  Performance measurement;  Risk management},
keywords={Applicators;  Information systems;  Information technology;  Risk assessment;  Risk management;  Social networking (online);  Structural design;  Supply chains, Arizona state university;  Best value;  Management structure;  Performance based;  Performance measurement system;  Performance measurements;  Real-time information systems;  Web-based technologies, Manufacture},
references={Agrawal, J., Richardson, P.S., Grimm, P.E., The relationship between warranty and product reliability (1996) Journal of Consumer Affairs, 30 (2), pp. 421-443; Christozov, D., Chukova, S., Mateev, P., On two types of warranties: Warranty of malfunctioning and warranty of misinforming (2009) Asia-Pacific Journal of Operational Research, 26 (3), pp. 399-420; Gajjar, D., Kashiwagi, D., Kashiwagi, J., Manufacturer's end user to minimize risk (2012) COBRA 2012, , Kashiwagi Dean, Sullivan Kenneth (eds.), (1859-1868), Las Vegas, NV: The Royal Institution of Chartered Surveyors'; Kashiwagi, D.T., Pandey, M.K., Impact resistance of polyurethane foam roofs against hail (1997) Journal of Thermal Insulation and Building Envelopes, 21, pp. 137-152. , October; Kashiwagi, D.T., Savicky, J.A., (2003) Hail Resistance of the Alpha Sprayed Polyurethane Foam Roof System, , Arizona State University, Tempe, AZ; Kashiwagi, D., (2012) The Best Value Standard, , Performance Based Studies Research Group, Tempe, AZ, Publisher KSM Inc; Lollar, J.G., Beheshti, H.M., Wade, E.W., A comparative study of web-based technology applications in corporations (2010) Journal of Promotion Management, 16 (3), pp. 316-330; Murthy, D.N.P., Djamaludin, I., New product warranty: A literature review (2002) International Journal of Production Economics, 79 (3), pp. 231-260},
editor={Yazdani S., Singh A.},
publisher={Research Publishing Services},
isbn={9810753551; 9789810753559},
language={English},
abbrev_source_title={ISEC - Int. Struct. Eng. Constr. Conf.: New Dev. Struct. Eng. Constr.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Chalyvidis201352,
author={Chalyvidis, C.E. and Ogden, J.A. and Johnson, A.W.},
title={Using supply chain interoperability as a measure of supply chain performance},
journal={Supply Chain Forum},
year={2013},
volume={14},
number={3},
pages={52-73},
doi={10.1080/16258312.2013.11517321},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921860924&doi=10.1080%2f16258312.2013.11517321&partnerID=40&md5=5d4e0effaea09fa155403d490cc93c09},
affiliation={Hellenic Air Force Academy, Dekelia Air Force Base, Athens, Greece; Air Force Institute of Technology, Wright-Patterson Air Force Base, Ohio, United States},
abstract={Inadequate supply chain performance measurement systems are still one of the major barriers to successful supply chain collaboration. System architecture concepts provide a new perspective to study supply chain performance measurement. Various supply chain characteristics such as complexity, differences of business cultures and firm’s orientation, emergence vs. control of processes and the great number of interfaces between firms can, and we propose should, be viewed through the wider measure of supply chain interoperability. © Copyright KEDGE BS},
author_keywords={Interoperability;  References;  Supply chain performance measurement;  System of systems},
references={Ackere, A., Larsen, E.R., Morecroft, J.D.W., System thinking and business process redesign (1993) European Management Journal, 11 (4), pp. 412-424; Angerhofer, B.J., Angelides, M.C., (2000) System Dynamics Modeling in Supply Chain Management: Research Review. Proceedings of the 32Nd Conference on Winter Simulation, pp. 342-351. , Orlando FL. Society for Computer Simulation International; Ashby, W.R., (1957) Introduction to Cybernetics, , London: Chapman & Hall; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Barratt, M., Understanding the meaning of collaboration in the supply chain (2004) Supply Chain Management, 9 (1), pp. 30-42; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bhagwat, R., Sharma, M., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning and Control, 20 (8), pp. 678-690; Bititci, U., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Blanc, S., Ducq, Y., Vallespir, B., Evolution management towards interoperable supply chains using performance measurement (2007) Computers in Industry, 58, pp. 720-732; Boardman, J., Sauser, B., (2006) System of Systems: The Meaning of Of, pp. 118-123. , Los Angeles: International Conference on System of Systems Engineering (IEEE); Brewer, P., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bryceson, K.P., Slaughter, G., Alignment of performance metrics in a multi enterprise agribusiness: Achieving integrated autonomy? (2010) The International Journal of Productivity and Performance Management, 59 (4), pp. 325-350; Buchanan, J.M., Game theory, mathematics, and economics (2001) Journal of Economic Methodology, 8 (1), pp. 27-32; Bullinger, H., Kühner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Burgess, K., Singh, P.J., Koroglu, R., Supply chain management: A structured literature review and implications for future research (2006) International Journal of Operations & Production Management, 26 (7), pp. 703-729; Busi, M., Bititci, U., Collaborative performance management: Present gaps and future research (2006) International Journal of Productivity and Performance Management, 55 (1), pp. 7-25; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Chan, F., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F., Qi, H., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F., Qi, H., Chan, H., Lau, H., Ralph, W., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Charan, P., Shankar, R., Baisya, R., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management, 14 (4), pp. 512-529; Chen, D., Doumeingts, G., European initiatives to develop interoperability of enterprise applications-basic concepts, framework and roadmap (2003) Annual Reviews in Control, 27, pp. 153-162; Chen, D., Doumeingts, G., Vernadat, F.B., Architectures for enterprise integration and interoperability: Past, present and future (2008) Computers in Industry, 59 (7), pp. 647-659; Chen, D., Vallespir, B., Daclin, N., (2008) An Approach for Enterprise Interoperability Measurement. CEUR Workshop Proceedings (1613-0073), 341, pp. 1-12; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: The constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Choi, T.Y., Dooley, K.J., Rungtusanatham, M., Supply networks and complex adaptive systems: Control versus emergence (2001) Journal of Operations Management, 19 (3), pp. 351-366; Cooper, M., Lambert, D., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-14; Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D., Rogers, D.S., The supply chain management processes (2001) The International Journal of Logistics Management, 12 (2), pp. 13-36; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-71; Defee, C.C., Williams, B., Randal, T., Thomas, R., An inventory of theory in logistics and SCM research (2010) The International Journal of Logistics Management, 21 (3), pp. 404-489; Disney, S.M., Towill, D.R., The effect of vendor managed inventory (VMI) dynamics on the bullwhip effect in supply chains (2003) International Journal of Production Economics, 85 (2), pp. 199-215; Dumond, E.J., Making best use of performance measures and information (1994) International Journal of Operations & Production Management, 14 (9), pp. 16-31; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Fawcett, S.E., Magnan, G.M., The rhetoric and reality of supply chain integration (2002) International Journal of Physical Distribution & Logistics Management, 32 (5), pp. 339-361; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers, and bridges to effective supply chain management (2008) Supply Chain Management: An International Journal, 13 (1), pp. 35-48; Fawcett, S.E., Waller, M.A., Making sense out of chaos: Why theory is relevant to supply chain research (2011) Journal of Business Logistics, 32 (1), pp. 1-5; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production Planning and Control, 16 (6), pp. 531-544; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Ford, C.T., Colombi, J.M., Jacques, D.R., Graham, S.R., A general method of measuring interoperability and describing its impact on operational effectiveness (2009) Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 6 (1), pp. 17-32; Ford, C.T., Ogden, J.A., Johnson, A.W., A method for measuring supply chain interoperability (2010) Proceedings of the 2010 Industrial Engineering Research Conference, pp. 1-6. , Abstract ID 881; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Frankel, R., Bolumole, Y.A., Eltantawy, R.A., Paulraj, A., Gundlach, G.T., The domain and scope of SCM's foundational disciplines: Insights and issues to advance research (2008) Journal of Business Logistics, 29 (1), pp. 1-31; Giannakis, M., Croom, S., Toward the development of a supply chain management paradigm: A conceptual framework (2004) Journal of Supply Chain Management, 40 (2), pp. 27-37; Gorod, A., Sauser, B., Boardman, J., System-of-systems engineering management: A review of modern history and a path forward (2008) Systems Journal IEEE, 2 (4), pp. 484-499; Griffis, S., Cooper, M., Goldsby, T.J., Closs, D.J., Performance measurement: Measure selection based upon firm goals and information reporting needs (2004) Journal of Business Logistics, 25 (2), pp. 95-118; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglou, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Williams, H.J., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Hofman, D., The hierarchy of supply chain metrics (2004) Supply Chain Management Review, 8 (6), pp. 28-37; Holland, J., (1998) Emergence: From Chaos to Order, , Boston: Addison-Wesley Longman; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Kaplan, R., Norton, D., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kasunic, M., Anderson, W., (2004) Measuring Systems Interoperability: Challenges and Opportunities, , CMU/SEI-2004-TN-003. Pittsburgh, PA: Carnegie-Mellon University; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Lambert, D., Cooper, M., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Lambert, D., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12, pp. 1-19; Larson, P., Poist, R., Halldósson, Á., Perspectives on logistics vs. Supply chain management: A survey on SCM professionals (2007) Journal of Business Logistics, 28, pp. 1-24; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Mahoney, J.T., Pandian, J.T., The resource based view within the conversation of strategic management (1992) Strategic Management Journal, 13 (5), pp. 363-380; Maier, M., Architecting principles for systems of systems (1998) Systems Engineering, 1 (4), pp. 267-284; Maier, M., Rechtin, E., (2000) The Art of Systems Architecting, , Boca Raton FL., CRC Press; Mentzer, J.T., Dewitt, W., Keebler, J.S., Min, S., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Mentzer, J.T., Min, S., Zacharia, Z., The nature of interfirm partnering in supply chain management (2000) Journal of Retailing, 76 (4), pp. 549-568; Min, S., Roath, A.S., Daugherty, P.J., Genchev, S.E., Chen, H., Arndt, A., Richey, A.R., Supply chain collaboration: What's happening (2005) The International Journal of Logistics Management, 16 (2), pp. 237-256; Naudet, Y., Guedria, W., Chen, D., (2009) Systems Science for Enterprise Interoperability. International Conference on Interoperability for Enterprise Software and Application, pp. 107-113. , China; Nechansky, H., Elements of a cybernetic epistemology: Design rules for complex goal-orientated systems (2009) Systems Research and Behavioral Science, 26 (4), pp. 83-93; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structural approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Oliver, R.K., Webber, M.D., (1982) Supply Chain Management: Logistics Catches up with Strategy, 5 (1), pp. 42-47. , Outlook; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management and Data Systems, 110 (9), pp. 1297-1318; Pathak, S.D., Day, J.M., Nair, A., Sawaya, W.J., Kristal, M.M., Complexity and adaptivity in supply networks: Building supply network theory using a complex adaptive systems perspective (2007) Decision Sciences, 38 (4), pp. 547-580; Pavlov, A., Bourne, M., Explaining the effects of performance measurement on performance (2011) International Journal of Operations & Production Management, 31 (1), pp. 101-122; Ralyté, J., Jeusfeld, M., Backlund, P., Kühn, H., Arni-Bloch, N., A knowledge-based approach to manage information systems interoperability (2008) Information Systems, 33 (7-8), pp. 754-784; Saiz, J., Rodriguez, R., Bas, A., A performance measurement system for virtual and extended enterprises (2005) International Federation of Information Processing, 186, pp. 285-292; Sanders, N.R., Wagner, S.M., Multidisciplinary and multimethod research for addressing contemporary supply chain challenges (2011) Journal of Business Logistics, 32 (4), pp. 317-323; Simatupang, T.M., Sridharan, R., The collaboration index: A measure for supply chain collaboration (2005) International Journal of Physical Distribution & Logistics Management, 35 (1), pp. 44-62; Skjoett-Larsen, T., Supply chain management: A new challenge for researchers and managers in logistics (1999) The International Journal of Logistics Management, 10 (2), pp. 41-54; Stephens, S., Supply chain operation reference model 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; Stock, J.R., Applying theories from other disciplines to logistics (1997) International Journal of Physical Distribution & Logistics Management, 27 (9-10), pp. 515-539; Suwignjo, P., Bititci, U., Carrie, A., Qualitative models for performance measurement system (2000) International Journal of Production Economics, 64, pp. 231-241; Tangen, S., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; Tangen, S., Improving the performance of a performance measure (2005) Measuring Business Excellence, 9 (2), pp. 4-11; Vernadat, F.B., Interoperable enterprise systems: Principles, concepts, and methods (2007) Annual Reviews in Control, 31 (1), pp. 137-145; Vernadat, F.B., Technical, semantic and organizational issues of enterprise interoperability and networking (2010) Annual Reviews in Control, 34 (1), pp. 139-144; Whitman, L.E., Panetto, H., The missing link: Culture and language barriers to interoperability (2006) Annual Reviews in Control, 30 (2), pp. 233-241; Wilding, R., The supply chain complexity triangle: Uncertainty generation in the supply chain (1998) International Journal of Physical Distribution & Logistics Management, 28 (8), pp. 599-616; Williamson, O.E., Outsourcing: Transaction cost economics and supply chain management (2008) Journal of Supply Chain Management, 44 (2), pp. 5-16; Wognum, P.M., Faber, E.C.C., Infrastructures for collaboration in virtual organisations (2002) International Journal Networking and Virtual Organisations, 1 (1), pp. 1-23; Wong, W., Wong, K., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381; Wycisk, C., McKelvey, B., Hülsmann, M., Smart parts supply networks as complex adaptive systems: Analysis and implications (2008) International Journal of Physical Distribution & Logistics Management, 38 (2), pp. 108-125; Ye, Y., Yang, D., Jiang, Z., Tong, L., Ontology-based semantic models for supply chain management (2008) International Journal of Advanced Manufacturing Technology, 37, pp. 1250-1260; Zhao, F., (2002) Performance Measures for Inter-Organisational Partnerships. 7Th International Conference on ISO9000 & TQM, pp. 1-6. , Melbourn; IDEAS (Interoperability Development for Enterprise Application and Software) Roadmap for E-Business Interoperability, , ftp://ftp.cordis.europa.eu/pub/ist/docs/so/business-govt/zwegers.pdf; IEEE Standards Glossary, , http://www.ieee.org/education_careers/education/standards/standards_glossary.html; INTEROP NoE (2003) Interoperability Research for Networked Enterprises Applications and Software Network of Excellence, , http://www.cimosa.de/Modelling/INTEROP.html},
correspondence_address1={Chalyvidis, C.E.; Hellenic Air Force Academy, Greece; email: chrischalyvi@gmail.com},
publisher={Institute for Supply Chain Excellence of BEM - Bordeaux},
issn={16258312},
language={English},
abbrev_source_title={Supply Chain Forum},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tewari201328,
author={Tewari, S.K. and Misra, M.},
title={Developing supply chain evaluation framework through performance assessment approach},
journal={International Journal of Business Performance and Supply Chain Modelling},
year={2013},
volume={5},
number={1},
pages={28-45},
doi={10.1504/IJBPSCM.2013.051646},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908244284&doi=10.1504%2fIJBPSCM.2013.051646&partnerID=40&md5=3091da29b3e2675f6a79759378a67f6a},
affiliation={RSA-401, Indian Institute Information Technology, Deoghat, Jhalwa, Allahabad (UP), 211012, India},
abstract={In this paper, we have developed the supply chain evaluation framework by assessing the performance of supply chain in its operational environment. This is a conceptual paper, which includes extensive literature review, environmental analysis and brainstorming with experts of this field. It gives an insight into various aspects of supply chain and the issues associated with it which include: supply chain performance and its benefits, need of a good evaluation system for supply chain, existing frameworks/models and framework for the evaluation of supply chain performance measurement systems. We propose the framework for supply chain evaluation in dynamic business settings. Our work is confined to the development of framework for making a system to evaluate the supply chain. Development and execution of this system and its success will decide the adaptability of the framework proposed to meet the needs of the industry. The proposed framework is a noble approach to the development of supply chain evaluation system which considers supply chain functional issues, issues associated with its partners and environmental issues in which it operates. Copyright © 2013 Inderscience Enterprises Ltd.},
author_keywords={Framework;  Performance management;  Supply chain evaluation},
references={Abdel-Maksoud, A., Dugdaleb, D., Luther, R., Non-financial performance measurement in manufacturing companies (2005) The British Accounting Review, 37 (3), pp. 261-297; Biswas, S., (2000) Supply Chain Performance Measures, , http://lcm.csa.iisc.ernet.in/scm/coimbatore/node11.html, accessed on 7 August 2011; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131; Christopher, M., (1998) Logistics and Supply Chain Management-Strategies for Reducing Cost and Improving Service, , Financial Times, prentice-hall, Englewood Cliffs, NJ; Christopher, M., Towill, An integrated model for design of agile supply chain (2001) International Journal of Physical Distribution & Logistics Management, 31 (1), pp. 235-246; Clemens, L., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Cohen, S., Roussel, J., (2005) Strategic Supply Chain Management: The Five Disciplines for Top Performance, , McGraw-Hill, New York, NY; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-14; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Davis, K., (1975) The Challenge of Business, p. 43. , McGraw-Hill, New York; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge Measuring Operations for World-class Competition, , Business One Irwin, Homewood, IL; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert System with Application, 38 (6), pp. 6681-6688; Gammelgaard, B., Larson, P.D., Logistics skills and competencies for supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 27-50; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Harland, C.M., Lamming, R.C., Cousins, P.D., Developing the concept of supply strategy (1999) International Journal of Operations and Production Management, 19 (7), pp. 650-673; Hausman, W.H., Supply chain performance metrics (2002) The Practice of Supply Chain Management, , in Billington, C., Harrison, T., Lee, H. and Neale, J. (Eds.), Kluwer, Boston; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference model (2004) Supply Chain Management: An International Journal, 19 (1), pp. 23-29; Iyer, A.V., Seshadri, S., Vasher, R., (2009) Toyota Supply Chain Management: A Strategic Approach to the Principles of Toyota's Renowned System, , McGraw-Hill, New York; Jessuo, L., Valacich, J., (2007) Information System Today: Why is Matters, , 2nd ed., Prentice-Hall, New-York, NY; Kalpan, R.S., Norton, D.P., The balance score card-measures that derive performance (1992) Harvard Business Review, pp. 71-79. , January-February; Kurien, G.P., Qureshi, M.N., Study of performance measurement practices in supply chain management (2011) International Journal of Business Management and Social Sciences, 2 (4), pp. 19-34; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lamming, R., (1993) Beyond Partnership: Strategies for Innovation and Lean Supply, , Prentice-Hall, New-York, NY; Lapid, L., What about measuring supply chain performance (1999) Achieving Supply Chain Excellence Through Technology, pp. 80-116. , in Anderson, D. L. (Ed.), Montgomery Research, San Francisco, CA; Morgan, C., Supply network performance measurement: Future challenges (2007) The International Journal of Logistics Management, 18 (2), pp. 255-273; Nag, B., Banerjee, S., Chatterjee, R., Changing features of the automobile industry in Asia: Comparison of production, trade and market structure in selected countries (2007) Asia-Pacific Research and Training Network on Trade Working Paper Series No. 37, , ESCAP, Bangkok; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-12; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , Pearson Education, Great Britain; New, S., Ramsay, J., A critical appraisal of aspects of the lean approach (1997) European Journal of Purchasing & Supply Management, 3 (2), pp. 93-102; Panchal, G., Jain, V., A review on select models for supply chain formation (2011) International Journal of Business Performance and Supply Chain Modelling, 3 (2), pp. 113-123; Pettigrew, A., (1985) The Awakening Giant, , Continuity and Change in ICI, Blackwell, Oxford; Ross, D.F., Supply chain performance measurement (2010) Presentation of Advance Productivity, Innovation and Competitive Success (APICS): The Association for Operation Management, , http://www.apics-chicago.org/downloads/Perform_Measure_Nov_2010.pdf, accessed on 18 October 2010; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automobile sector (2006) Benchmarking: An International Journal, 13 (1), pp. 36-53; Saad, M., Jones, M., Jones, P., A review of processes towards the adoption of supply chain management (SCM) relationships in construction (2002) European Journal of Purchasing & Supply Management, 8, pp. 173-183; Slack, N., Chambers, S., Johnston, R., (2001) Operations Management, , 3rd ed., Financial Times, Prentice-Hall, Eaglewood cliff, NJ; Stockdale, R., Standing, C., An interpretive approach to evaluating information system: A content, context, process framework (2006) European Journal of Operational Research, 173 (3), pp. 1090-1102; Symons, V.J., A review of information systems evaluation: Content, context and process (1991) Journal of the Operational Research Society, 1 (3), pp. 205-212; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Vollman, T., Cordon, C., Raabe, H., (1997) Supply Chain Management in Mastering Management, pp. 316-322. , FT Pitman, London; Zeng, G., Frazier, G., Assessing supply chain risk and performance simultaneously via multiple tier DEA efficiency (2010) 2010 Southwest Decision Sciences Institute Conference, , http://www.swdsi.org, in Posey, G. (Ed.), Dallas, TX (accessed on 7 December 2011)},
correspondence_address1={Tewari, S.K.; RSA-401, India; email: tewari.sumant@gmail.com},
publisher={Inderscience Publishers},
issn={17589401},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Supply Chain Model.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ramaa2013343,
author={Ramaa, A. and Subramanya, K.N. and Rangaswamy, T.M.},
title={Performance measurement system of supply chain - An empirical study},
journal={International Journal of Business Performance and Supply Chain Modelling},
year={2013},
volume={5},
number={4},
pages={343-360},
doi={10.1504/IJBPSCM.2013.058202},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908234181&doi=10.1504%2fIJBPSCM.2013.058202&partnerID=40&md5=088f4830071966e54561137a3f334567},
affiliation={Department of Industrial Engineering and Management, RV College of Engineering, RV Vidyaniketan Post, Mysore Road, Bangalore, 560059, India},
abstract={Performance measurement (PM) of supply chain is gaining more interest in researchers and practitioners in this globalised era. It is a powerful tool which helps organisations to effectively and efficiently manage their resource utilisation and achieve their goals and objectives. However, literature reveals that medium scale organisations have paid little attention to performance measurement system (PMS) design and implementation. This paper is an attempt to explore and investigate issues concerning the design and implementation of PMS in Indian manufacturing industry. An empirical survey was conducted in Indian medium scale industries to explore the dimensions and characteristics of PMSs. The study highlighted that medium scale enterprises in India pay less attention to integration of supply chain and use of technology in their business processes. It revealed the continuing importance given to financial measures by these industries despite the increasing emphasis put on non-financial measures in the literature. © Copyright 2013 Inderscience Enterprises Ltd.},
author_keywords={Manufacturing;  Medium scale industries;  Performance measurement},
references={Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Productions Planning & Control, 18 (8), pp. 666-680; Fowler, F.J., Design and evaluation of survey questions (1998) Handbook of Applied Social Research Methods, pp. 343-374. , Bickman, L. and Rog, D. J. (Eds.), Sage, Newbury Park, CA; Garg, D., Narahari, Y., Viswanadham, N., A new approach to achieving sharp and timely deliveries in supply chain networks (2003) Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pp. 2315-2320. , October; Gunasekaran, A., Patel, A., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347. , http://planningcommission.nic.in/reports/sereport/ser/ser_ssi, accessed June 2010; Lin, L., Li, T., An integrated framework for supply chain performance measurement using six-sigma metrics (2010) Software Quality Journal, 18 (3), pp. 387-406; O'Regan, N., Ghobadian, A., Sims, M., Fast tracking innovation in manufacturing SMEs (2006) Technovation, 26 (2), pp. 251-261; Quayle, M., A study of supply chain management practices in UK industrial SMEs (2003) Supply Chain Management: An International Journal, 8 (1), pp. 79-86; Sahay, B.S., Mohan, R., Supply chain management practices in Indian industry (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 582-606; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Singh, S., SMEs in competitive market (2007) Productivity, 48 (3), pp. 250-266; Waller, D., (2003) Operations Management: A Supply Chain Approach, , 2nd ed., Thomson Learning, London; Westerlund, M., Rajala, R., Leminen, S., SME business models in global competition: A network perspective (2008) International Journal of Globalization and Small Business, 2 (3), pp. 38-55},
correspondence_address1={Ramaa, A.; Department of Industrial Engineering and Management, RV Vidyaniketan Post, Mysore Road, India; email: ramaa@rvce.edu.in},
publisher={Inderscience Publishers},
issn={17589401},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Supply Chain Model.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Ferreira2013827,
author={Ferreira, P.G.S. and Pinheiro De Lima, E. and Gouvea, S.},
title={Distributed teams performance measurement capabilities: A systematic literature review},
journal={IIE Annual Conference and Expo 2013},
year={2013},
pages={827-836},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900338207&partnerID=40&md5=2cd121daf15737add0119e847c0fff35},
affiliation={Pontifical University of Parana, The Woodlands, TX, United States; Pontifical University of Parana, Curitiba - Parana, Brazil},
abstract={Performance Measurement Systems are consistently being used in daily activities in every company, regardless of its size. Also, in the past recent years companies' globalization process is forcing them to adopt distributed teams. In order to be successful, these companies are required to manage teams remotely and make decisions based on performance indicators of their remote teams. However, it is an easy task to find in the performance measurement literature articles that report research on the required capabilities to form and manage distributed teams performance measurement systems. To identify possible research opportunities in performance measurement area, it was conducted a systematic literature review using 2 major databases: ISI web of knowledge and SCOPUS, which initially resulted approximately 65.000 articles related to the theme: "Which organizational capabilities/competences a company must have in order to manage/develop distributed teams performance?". This paper aims to show that despite the scientific evolution of Performance Measurement studies, the field lacks of dedicated research concerning the capabilities and competences for managing distributed teams performance and suggests new research topics willing to take a step forward into the development of such area.},
author_keywords={Capabilities;  Competences;  Distributed Teams;  Manage;  Performance measurement;  Systematic literature review;  Virtual teams},
keywords={Exhibitions;  Industry;  Supply chain management, Capabilities;  Competences;  Distributed teams;  Manage;  Performance measurements;  Systematic literature review;  Virtual team, Research},
references={Neely, A.D., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Thomson, A., Stone, A., Ion, W., Distributed team design in smalland mediumsized enterprises: How to get it right (2007) AI EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 21, p. 203218; Bell, B.S., Kozlowski, S.W.J., A typology of virtual teams. Implications for effective leadership (2002) Group and Organization Management, 27 (1), pp. 14-49; Bradner, E., Mark, G., Why distance matters: Effects on cooperation, persuasion and deception (2002) CSCW 2002, pp. 16-20. , November New Orleans, LA ACM Press; Carmel, E., Agarwal, R., Tactical approaches for alleviating distance in global software development (2001) IEEE Software, pp. 22-29; Carmel, E., Tjia, P., (2005) Offshoring Information Technology: Sourcing and Outsourcing to A Global Workforce, , Cambridge Univ. Press; Cascio, W.F., The virtual workplace: A reality now (1998) Society for Industrial & Organizational Psychology, 35 (4); De Lima, E.P., Da Costa, S.E.G., Angelis, J.J., Strategic performance measurement systems: A discussion about their roles (2009) Measuring Business Excellence, 13 (3), pp. 39-48; Furst, S., Blackburn, R., Rosen, B., Virtual team effectiveness: A proposed research agenda (2001) Information Systems Journal., 9 (4), pp. 249-269; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: A framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Hinds Pamela, J., Bailey Diane, E., Out of sight, out of sync: Understanding conflict in distributed teams (2003) Organization Science, 14 (6). , November-December; Fransoo, J.C., Wouters, M.J.F., Measuring the bullwhip effect in a supply chain (2000) Supply Chain Management, 5 (2), pp. 78-89; Jarvenpaa, S.L., Leidner, D.E., Communication and trust in global virtual teams (1999) Organization Science, 10 (6), pp. 791-815; Jarvenpaa, S.L., Leidner, D.E., Communication and trust in global virtual teams (1998) Journal of Computer Mediated Communication, 3 (4); Klassen, T.P., Jadad, A.R., Moher, D., Guides for reading and interpreting systematic reviews. 1. Getting started (1998) Arch. Pediatr. Adolesc. Med., 152, pp. 700-704; Fortuin, L., Performance indicators-why where and how? (1988) European Journal of Operational Research, 34, pp. 1-9; Lipnack, J., Stamps, J., (2000) Virtual Teams-People Working Across Boundaries with Technology, , New York Wiley; Majchrzak, A., Rice, R.E., Malhotra, A., King, N., Ba, S., Technology adaptation: The case of a computer-supported inter-organizational virtual team (2000) MIS Quarterly, 24 (4), pp. 569-600; Ong, T.S., (2005) Performance Measurement Systems and Their Influences in High Technology Manufacturing Firms of A Developing Country, , Leeds University Business School, Leeds University, Leeds, Vol. PhD; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Engineering Costs and Production Economics, 17, pp. 253-262; Pawson, R., (2006) Evidence-Based Policy: A Realist Perspective, , London SAGE Publications; Petrie, C., Madefast Home Page, , http://www.madefast.stanford.edu; Platts, K.W., Integrated manufacturing: A strategic approach (1995) Integrated Manufacturing Systems, 6 (3), pp. 18-23; Powell, A., Piccoli, G., Ives, B., Virtual teams: A review of current literature and directions for future research (2004) The DATABASE for Advances in Information Systems, 35, pp. 6-36; Prikladnicki, R., Audy, J.L.N., Comparing offshore outsourcing and the internal offshoring of software development: A qualitative study (2009) Proceedings of the Americas Conference on Information Systems, p. 680. , Paper; PRISMA, , http://www.prisma-statement.org/; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69, pp. 131-137. , January-February; Flapper, S.D., Fortuin, L., Stoop, P.P.M., Towards consistent performance measurement systems (1996) International Journal of Operations & Production Management, 16 (7), pp. 27-37; Slack, N., Flexibility, trade-offs and learning in manufacturing system design (2000) International Journal of Manufacturing Technology and Management, 1 (4-5), pp. 331-348; Tarr, J.D., Performance measurements for a continuous improvement strategy (1996) Hospital Material Management, 18, pp. 77-85; Ford motor company case study (1996) Computer Weekly March 7th, , Top Gear},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Leite2013774,
author={Leite, L.R. and Van Aken, E. and Martins, R.A.},
title={Case study on the effect of sustainability on performance measurement systems},
journal={IIE Annual Conference and Expo 2013},
year={2013},
pages={774-780},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900317398&partnerID=40&md5=64b40ddb4452f970c8c7bd303de90bb8},
affiliation={Departament of Industrial Engineering, University of Sao Carlos, Sao Carlos, SP, 13565-905, Brazil; Departament of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, United States},
abstract={Sustainability has increasingly been the subject of research in the engineering management community, as well as in the performance measurement literature. Sustainability has been presented as a new opportunity for improving performance and, particularly, for gaining competitive advantage. The implementation of sustainability practices is related to changes in a company's strategy. Therefore, pursuing this strategy may influence how firms should measure performance. This paper aims to investigate the effect of sustainability practices on performance measurement systems. A case study was conducted in a beverage company that has implemented sustainability practices since 2005. The empirical findings point to changes in characteristics of the company's performance measurement system. Hence, the implementation of sustainability practices acted as the external and internal trigger to change the performance measurement system.},
author_keywords={Performance measurement system;  PMS;  Sustainability;  Sustainable company},
keywords={Competition;  Exhibitions;  Industry;  Research;  Supply chain management, Company's strategy;  Competitive advantage;  Engineering management;  Improving performance;  Performance measurement system;  Performance measurements;  PMS;  Sustainability practices, Sustainable development},
references={Bititci, U., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2012) International Journal of Management Reviews, 14 (3), pp. 305-327; Hubbard, G., Measuring organizational performance : Beyond the triple bottom line (2009) Business Strategy and the Environment, 18, pp. 177-191; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Leite, L.R., Yagasaki, C., Martins, R.A., Van Aken, E., Bibliometric analysis of literature on performance measurement systems and sustainability (2012) Proc. of IIE Annual IE Conference and Expo, pp. 19-23. , May Orlando, Florida; Leite, L.R., Systematic literature review on performance measurement and sustainability (2012) Proc. of American Society for Engineering Management 2012 International Annual Conference, pp. 17-20. , Out. Virginia Beach, Virginia; Melnyk, S., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-218; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing , implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Chearskul, P., (2010) An Empirical Investigation of Performance Measurement System Use and Organizational Performance, , Ph.D. dissertation Virginia Polytechnic Institute and State University; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Yin, R.K., (2009) Case Study Research: Design and Methods, , 4th ed. Trousand Oaks, California SAGE Publications, Inc; Eisenhardt, K.M., Building theories from case (1989) The Academy of Management Review, 14 (4), pp. 532-550},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Wen2013140,
author={Wen, L. and Shi, Y.},
title={Performance evaluation of supply chain ecological sustainable development based on trapezoidal fuzzy numbers similarity},
journal={International Journal of Applied Mathematics and Statistics},
year={2013},
volume={51},
number={21},
pages={140-147},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84896373745&partnerID=40&md5=23b2b583a60624666d6fbfd2ad0d25c4},
affiliation={Department of Economics and Management, North China Electric Power University, Baoding 071003, China},
abstract={The supply chain ecological sustainable performance evaluation can help managers from more substantial angle of the mastery of sustainable supply chain operation, and have a definite object in view of the ecological sustainable supply chain process improvement, enhancement, in order to improve the whole supply chain integrated competition ability. The purpose of this paper is to introduce a method to help evaluate, select, and monitor sustainable supply chain performance measurement that can be integrated into a performance management system. Trapezoidal fuzzy numbers is used to help arrive at a core set of important business and environmental performance measures for sustainable supply chains. The supply chain operations reference (SCOR) model is used to develop both business and environmental measures for supply chain sourcing. The methodology can be valuable for companies that are trying to identify key environmental and business performance measures for their supply chains. It helps save resources by not requiring the management of a burdensome and complex set of performance measurement. © 2013 by CESER Publications.},
author_keywords={Ecological sustainable development;  Performance evaluation;  Supply chain management;  Trapezoidal fuzzy numbers similarity},
keywords={Ecology;  Environmental management;  Fuzzy rules;  Fuzzy systems;  Planning;  Supply chain management, Business performance measure;  Environmental performance;  Performance evaluation;  Performance management systems;  Performance measurements;  Supply chain operations reference models;  Sustainable supply chains;  Trapezoidal fuzzy numbers, Sustainable development},
references={Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Keating, B., Quazi, A., Kriz, A., Coltman, T., In pursuit of a sustainable supply chain: insights from Westpac Banking Corporation (2008) Supply Chain Management: An International Journal, 13 (3), pp. 175-179; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Chen, B., Developing key performance indicators for supply chain: an industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 442-448; Chen, J.H., A new method for ranking problems (2006), pp. 1196-1199. , In Proceedings of the 9th joint conference on information sciences; Schmucker, K.J., (1984) Fuzzy sets, natural language computations and risk, , analysis M: Computer Science press; Xu, Z., A method for fuzzy risk analysis based on the new similarity of trapezoidal fuzzy numbers Expert Systems with Applications (2010) Management Science, 37 (1), pp. 20-27; Shen, X., An Application of Bayesian Nash Equilibrium Model to Service Quality Surveillance (2011) Advances in Information Sciences and Service Sciences, 3 (10), pp. 90-91},
publisher={Centre for Environment Social and Economic Research},
issn={09731377},
language={English},
abbrev_source_title={Int. J. Appl. Math. Stat.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Mahajan2013167,
author={Mahajan, R. and Garg, S. and Sharma, P.B.},
title={Frozen corn manufacturing and its supply chain: Case study using SAP-LAP approach},
journal={Global Journal of Flexible Systems Management},
year={2013},
volume={14},
number={3},
pages={167-177},
doi={10.1007/s40171-013-0040-y},
note={cited By 16},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893132830&doi=10.1007%2fs40171-013-0040-y&partnerID=40&md5=afeed1ba8a654bdd09ae91729e4c4da8},
affiliation={DTU, New Delhi, India},
abstract={Supply chain performance concerns with overall coordination and management of all the business associates involved in the chain. All the associates share a common platform for achieving smooth business practices. The temperature control supply chain becomesmore andmore crucial for fresh and processed food. The major reason is that different products require different type of supply chain management (SCM). The task is to manage SCM of shorter shelf life and different type of temperature control requirement. Fresh fruits and vegetables require plus 8° temperature and frozen fruits and vegetables require minus 12-18° temperature. The purpose of this research paper is to analyze a case study of manufacturing process and supply chain of frozen corn for A1company (Name of company is disguised) and to look into its status of supply chain performance. A1 is a leading processed foodmanufacturer andmarketer in India.A case study method is selected to study processed food sector's supply chain performance in terms of frozen corn manufacturing and its supply chain. A situation-actor-process (SAP), learning-action-performance (LAP) model has been applied to analyze the case study on A1. The situation is showing a present scenario of the organization. Actors are the SCM participants. They are influencing the situation to evolve different business processes. The various learning issues have been analyzed. The suitable actions are followed by the impact of SAP on the performance of A1's SCM. The individual quick freezing is a unique manufacturing technique. It is used to convert fresh vegetable and fruit into frozen. It is helpingA1 to maintain consistent quality of agri product like corn in its frozen form. It is crucial to maintain cold chain during manufacturing, at the storage time, transporting to distributors and at retail. It is crucial to make consumers aware about storage of frozen vegetables in deep freezer of their refrigerator. The SAP-LAPmodel is used to describe the soft and hard issues of frozen cornmanufacturing and its SCMcoordination in a managerial context with reference to a dynamic SAP- LAPmodel as applied to theA1.TheSAP-LAPmodel presents the situation of frozen corn manufacturing and cold supply chain of frozen corn. The exercise is to identify the gaps in cold supply chainmanagement. The synthesis of SAP leads to LAP. It bridges the gap of flexibility in suggesting improvement actions or the learning from the present situation, actors and processes. This is a unique approach to analyze quantitative and qualitative issues of supply chain performance initiatives in a single model and its impact on the performance of the supply chain. © Global Institute of Flexible Systems Management 2013.},
author_keywords={Cold chain;  Frozen corn supply chain;  SAP-LAP analysis;  Supply chain performance measurement},
references={Ahuja, V., Yang, J., Shankar, R., IT-enhanced communication protocols for building project Management (2010) Engineering, Construction and Architectural Management, 17 (2), pp. 159-179; Amador, C., Emond, J.P., Evaluation of sensor readability and thermal relevance for RFID temperature tracking (2010) Computers and Electronics in Agriculture, 73, pp. 84-90; Arshinder Kanda, A., Deshmukh, S.G., Supply chain coordination issues: SAP-LAP framework (2007) Asia Pacific Journal of Marketing and Logistics, 19 (3), pp. 240-264; Berger, H., Dealing with dynamics engineering solutions to the dairy dilemma (2007) Trends in Food Science and Technology, 18 (1), pp. 123-134; Charan, P., Supply chain performance issues in an automobile company: A SAP-LAP analysis (2012) Measuring Business Excellence, 16 (1), pp. 67-86; Chen, K.Y., Shaw, Y.C., Applying back propagation network to cold chain temperature monitoring (2011) Advanced Engineering Informatics, 25, pp. 11-22; (2012) Euro Monitor Report, , http://www.euromonitor.com/, Accessed 27 Jan 2013; Garg, A., Deshmukh, S.G., Engineering support issues for flexibility in maintenance: An SAP-LAP framework (2010) Asia Pacific Journal of Marketing and Logistics, 22 (2), pp. 227-270; Goel, S., Dwivedi, R., Sherry, A.M., Critical factors for successful mplementation of e-governance programs: A case study of huda (2012) Global Journal of Flexible Systems Management, 13 (4), pp. 233-244; Hahn, K.H., Hwant, H., Shinn, S.W., A returns policy for distribution channel coordination of perishable items (2004) European Journal of Operational Research, 152, pp. 770-780; Jacxsens, L., Devlieghere, F., Debevere, J., Predictive modelling for packaging design: Equilibrium modified atmosphere packages of fresh-cut vegetables subjected to a simulated distribution chain (2002) International Journal of Food Microbiology, 73, pp. 331-341; Mahajan, R., Garg, S., Sharma, P.B., Indian frozen peas market: A case study on FPIL (2011) International Journal of Globalisation and Small Business, 4 (2), pp. 154-169; Mangina, E., Vlachos, I.P., The changing role of information technology in food and beverage logistics management: Beverage network optimization, using intelligent agent technology (2005) Journal of Food Engineering, 70, pp. 403-420; Omta, S.W.F., Trienekens, J.H., Beers, G., Chain and network science: A research framework (2001) Journal on Chain and Network Science, 1 (1), pp. 1-6; Ovca, A., Jevsnik, M., Maintaining a cold chain from purchase to the home and at home: Consumer opinions (2008) Food Control, 20 (2), pp. 167-172; Palanisamy, R., Building information systems flexibility in SAP-LAP framework: A case study evidence from SME sector (2012) Global Journal of Flexible Systems Management, 13 (1), pp. 57-74; Pramod, V.R., Banwet, D.K., System modelling of telecom service sector supply chain: A SAP-LAP analysis (2010) International Journal of Business Excellence, 3, pp. 38-64; Sahoo, T., Banwet, D.K., Momaya, K., Strategic technology management in the auto component industry in India: A case study of select organizations (2011) Journal of Advances in Management Research, 8 (1), pp. 9-29; Salin, V., Nayga, R.M., A cold chain network for food exports to developing countries (2003) International Journal of Physical Distribution and Logistics Management, 33 (10), pp. 918-933; Shukla, R.K., Garg, D., Agarwal, A., Study of select issues related to supply chain coordination: Using SAP-LAP analysis framework (2011) Global Journal of Enterprise Information System, 3 (2), pp. 56-69; Siddiqui, F., Haleem, A., Sharma, C., Strategic planning of service supply chain using dynamic SAP-LAP model: A case study of a leading gas organisation in India International Journal of Services (2012) Economics and Management, 4 (3), pp. 169-185; Suri, P.K., Towards a strategy for implementing e-governance applications: A case study of integrated fertilizers management information system based on SAP-LAP framework (2008) Electronic Government, An International Journal, 5 (4), pp. 420-444; Sushil, Flexible systems management: An evolving paradigm (1997) Systems Research and Behavioural Science, 14 (4), pp. 259-275; Sushil, SAP-LAP models of inquiry (2000) Management Decision, 38 (5), pp. 347-353; Valeeva, N.I., Huirne, R.B.M., Meuwissen, M.P.M., Lansink, A.O., Modeling farm-level strategies for improving food safety in the dairy chain (2006) Food Control, 17, pp. 108-113; Van Der Vorst, J.G.A.J., Tromp, S.O., Van Der Zee, D.J., Simulation modelling for food supply chain redesign; Integrated decision making on product quality, sustainability and logistics (2009) International Journal of Production Research, 47 (23), pp. 6611-6631},
correspondence_address1={Mahajan, R.; DTU, New Delhi, India; email: mahajanrajneesh.phd@gmail.com},
publisher={Global Institute of Flexible Systems Management},
issn={09722696},
language={English},
abbrev_source_title={Global J. Flexible Syst. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Salman20131045,
author={Salman, M. and Iqbal, S.A. and Khalid, R.},
title={Implementing supply chain operation reference (SCOR) model in manufacturing firm of a developing country},
journal={International Asia Conference on Industrial Engineering and Management Innovation: Core Areas of Industrial Engineering, IEMI 2012 - Proceedings},
year={2013},
pages={1045-1055},
doi={10.1007/978-3-642-38445-5_108},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84891761376&doi=10.1007%2f978-3-642-38445-5_108&partnerID=40&md5=e6b07e023156d72f939c6efcafd44236},
affiliation={Department of Industrial and Manufacturing Engineering, NED University of Engineering and Technology, University Road, Karachi, Pakistan},
abstract={In modern era where lean manufacturing has reached considerable level of perfection rather started following law of diminishing returns, supply chain performance becomes one of the key business success factors. Due to this fact, lot of work has been done on the development of supply chain performance measurement models and their implementation to improve overall supply chain performance. These models are balanced combination of business process re-engineering and efficient usage of information technology. There are several practical examples around the globe where firms got significant benefits by implementing these models but such models are not yet common in developing countries, instead several considerations are important if they are to be implemented there. This paper discusses the implementation of SCOR in a manufacturing firm in Pakistan along with the challenges faced during its implementation. The objective is to keep SCOR's efficiency as good as in the developed countries. © Springer-Verlag Berlin Heidelberg 2013.},
author_keywords={Aggregate planning;  Business process reengineering (BPR);  SCOR framework;  SCOR implementation;  Supply chain performance management},
keywords={Developing countries;  Manufacture;  Supply chains, Aggregate planning;  Business process re-engineering;  SCOR framework;  SCOR implementation;  Supply chain performance, Reengineering},
references={Chandran, P.M., (2003) Wal Mart's Supply Chain, , vols OPER-020; Chopra, S., Meindl, P., (2010) Supply Chain Management: Strategy, , planning and operation. Pearson; Gulledge, T., Cavusoglu, T., Kessler, T., (2001) Aligning the Supply Chain Operations Reference (SCOR) Model with Enterprise Applications: Real-time Value Chain Intelligence; Heizer, J., Render, B., Rajashekhar, J., (2008) Operations Management, , Pearson, India; Kaminsky, P., Simchi-Levi, D., Simchi-Levi, E., Shankar, R., (2010) Designing and Managing Supply Chain: Concept, Strategies and Case-studies, , special Indian edition. Tata McGraw-Hill, New Delhi; Kirchmer, M., E-business process networks-successful value chains through standards (2004) J Enterp Inf Manage, 17 (1), pp. 20-30; Kirchmer, M., Emerald article: E-business process networks-successful value chains through standards.J Enterp (2004) Manage, 17 (1), pp. 2-30; Laudon, J.P., Laudon, K.C., (2006) Management Information System: Managing the Digital Firm, , Pearson-Prentice Hall, New Jersey; Lee, H.L., Managing supply chain strategies: Inventory pitfalls and opportunities (1992) Sloan Manage Rev, 33 (3), pp. 65-73; Lee, H.L., Aligning supply chain strategies with product uncertainties (2002) Calif Manage Rev, 44 (3), pp. 105-120; Lee, H.L., (2009) Tripple A Supply Chain, , Harvard Business Review; Ralf, M., Michels, W., Hughes, J., (1998) Transform Your Supply Chain: Releasing Value in Business, , smart strategies series. Thomson, London; Ren, C., Dong, J., Ding, H., Wang, W., (2006) A SCOR-based Framework for Supply Chain Performance Management, , vol RC23989; Rosenbaum, R., Bolstorff, P., (2003) Supply Chain Excellence: Handbook for Dramatic Improvement Using SCOR Model, , AMACOM, New York; Council, S.C., (2010) Supply Chain Operations Reference (SCOR, , model overview-Version 10.0; Schultz, G.J., (2003) Keeping SCOR on Your Supply Chain: Basic Operations Reference Model Updates with the Times. Inf Strategy, pp. 12-20; Sheoran, K.S., Wang, G., Huan, S.H., A review and analysis of supply chain operations reference (SCOR) model (2003) Supply Chain Manage Int J., 9 (1), pp. 23-29; Stewart, G., (1997) Emerald Article: Supply-chain Operations Reference Model (SCOR): The First Crossindustry Framework for Integrated Supply-chain Management, 10 (2). , Emerald Publishing; Chain Council, S., (2006) Supply Chain Operations Reference Model SCOR- Version 8.0; Swartwood, D., (2003) Using Lean, Six Sigma, and SCOR to Improve Competitiveness; Taha, H.A., (2008) Operations Research: An Introduction, 8/E. Pearson Education, India; Tersine, R.J., (1994) Principles of Inventory and Materials Management, , Prentice Hall International Inc, New Jersy},
correspondence_address1={Salman, M.; Department of Industrial and Manufacturing Engineering, University Road, Karachi, Pakistan; email: muhammad.salman@hotmail.com},
publisher={Springer Science and Business Media Deutschland GmbH},
isbn={9783642384448},
language={English},
abbrev_source_title={Int. Asia Conf. Ind. Eng. Manage. Innov.: Core Areas Ind. Eng., IEMI - Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Gamme2013265,
author={Gamme, I. and Seim, E.A. and Lodgaard, E.},
title={Performance measurement systems for craft-oriented small enterprises},
journal={IFIP Advances in Information and Communication Technology},
year={2013},
volume={398},
number={PART 2},
pages={265-272},
doi={10.1007/978-3-642-40361-3_34},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887916236&doi=10.1007%2f978-3-642-40361-3_34&partnerID=40&md5=96c19e2ae12b9fc43eeeb1444e329990},
affiliation={Gjøvik University College, Gjøvik, Norway; Norwegian University of Science and Technology, Trondheim, Norway; SINTEF Raufoss Manufacturing, Raufoss, Norway},
abstract={Many leisure boat manufacturers have thrived on designing and building highly customized boats based on longstanding traditions of craftsmanship. The industry is to some extent moving towards the paradigm of "Mass Customization" in which the low cost of mass production is combined with customization. Existing research on performance measurement systems (PMS), covers in little extent the implementation process and use of PMS in SMEs (small and medium sized enterprises). In this article, important elements for implementing a PMS and how it relates to creating a learning environment have been identified. © IFIP International Federation for Information Processing 2013.},
author_keywords={Continuous improvement;  Crafts man;  Performance measurement system;  SMEs},
keywords={Boats;  Computer aided instruction;  Manufacture, Continuous improvements;  Crafts man;  Implementation process;  Learning environments;  Mass customization;  Performance measurement system;  Small and medium sized enterprise;  SMEs, Supply chain management},
references={Buschfeld, D., Dilger, B., Hess, L., Schmid, K., Voss, E., (2011) Identification of Future Skills Needs in Micro and Craft (-type) Enterprises up to 2020, , Final report, Cologne, Hamburg, Vienna (January; de Vries, H., Margaret, J., The Development of a Model to Assess the Strategic Management Capability of Small-and Medium-Size Businesses (2003) Journal of American Academy of Business, 3, p. 85; O'Sullivan, D., Rolstadås, A., Filos, E., Global Education in Manufacturing Strategy (2011) Journal of Intelligent Manufacturing, 22, pp. 663-674; Økland, A., Lillebo, B., Amdahl, E., Seim, A., A Framework for Transparency (2010) POMS 21st Annual Conference, , In:, Vancouver, Canada; Bititci, U., Performance Measurement: Challenges for Tomorrow (2011) International Journal of Management Reviews; Garengo, P., Biazzo, S., Bititci, U.S., Performance Measurement Systems in SMEs: A review for a Research Agenda (2005) International Journal of Management Reviews, 7, pp. 25-47; Svensson, C., Barfod, A., Limits and Opportunities in Mass Customization for "Build to Order" SMEs (2002) Computers in Industry, 49, pp. 77-89; Shrader, C.B., Mulford, C.L., Blackburn, V.L., Strategic and Operational Planning, Uncertainty, and Performance in Small Firms (1989) Journal of Small Business Management, 27, p. 45; Lovett, P.J., Ingram, A., Bancroft, C.N., Knowledge-Based Engineering for SMEs-a Methodology (2000) Journal of Materials Processing Technology, 107, pp. 384-389; Nonaka, I., Takeuchi, H., (1995) The Knowledge-Creating Company, , Oxford University Press, New York; Vinten, G., Corporate Communications In Small-and Medium-Sized Enterprises (1999) Industrial and Commercial Training, 31, pp. 112-119; Parry, G.C., Turner, C.E., Application of Lean Visual Process Management Tools (2006) Production Planning & Control, 17, pp. 77-86; Womack, J.P., Jones, D.T., (1996) Lean Thinking: Banish Waste and Create Wealth for Your Corporation, , Simon & Schuster, New York; Glouberman, S., Mintzberg, H., Managing the Care of Health and the Cure of Disease-Part II: Integration (2001) Health Care Management Review, 26, pp. 56-69; Hudson, M., Lean, J., Smart, P.A., Improving Control through Effective Performance Measurement in SMEs (2001) Production Planning & Control, 12, pp. 804-813; Khan, K., Shah, A., Understanding Performance Measurement Through the Literature (2011) African Journal of Business Management, 5, pp. 13410-13418; Andersen, B., Fagerhaug, T., (2001) Performance Measurement Explained-Designing and Implementing Your State-of-the-Art System, , American Society for Quality, Milwaukee; Van Dooren, W., What Makes Organisations Measure? Hypotheses on the Causes and Conditions for Performance Measurement (2005) Financial Accountability & Management, 21, pp. 363-383; Andersen, B., Henriksen, B., Aarseth, W., Holistic Performance Management: An Integrated Framework (2006) International Journal of Productivity and Performance Management, 55, pp. 61-78; Mike Bourne, J., Designing, Implementing and Updating Performance Measurement systems (2000) Emerald Group Publishing, 20, pp. 754-771; Karlsson, C., (2009) Researching Operations Management, , Routledge, New York},
publisher={Springer New York LLC},
issn={18684238},
isbn={9783642403606},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Friemann2013584,
author={Friemann, F. and Wandfluh, M. and Schönsleben, P. and Alard, R.},
title={Improving the application of financial measures in supply chain management},
journal={IFIP Advances in Information and Communication Technology},
year={2013},
volume={398},
number={PART 2},
pages={584-591},
doi={10.1007/978-3-642-40361-3_74},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887876439&doi=10.1007%2f978-3-642-40361-3_74&partnerID=40&md5=ee17595b81bf0297b26b69a71f65e11d},
affiliation={ETH Zürich, BWI Center for Industrial Management, WEINBERGSTRASSE 56, 8092 ZÜRICH, Switzerland; University of Applied Sciences, Promenade 26, 5200 Brugg, Switzerland},
abstract={Many companies (especially SMEs) still feel poorly prepared and notice a deficiency of financial know-how when facing situations such as limitations in working capital. To reduce the amount of capital employed, close linkage of financial measures to the daily operations within the companies is required. Yet, supply chain performance measures are often not directly linked to overall financial targets (e.g. cost of capital vs. service level). This paper proposes taking financial parameters into consideration when making supply chain management decisions. It outlines supply chain finance (SCF) solutions available to bigger corporates, analyses current financial metrics for supply chain management and proposes concepts for a greater linkage between finance and supply chain performance measures. Finally, this paper will also reveal gaps where current concepts and metrics have limitations and future research is needed. © IFIP International Federation for Information Processing 2013.},
author_keywords={Financial measures;  Metrics;  Supply chain management (SCM)},
keywords={Finance;  Technology transfer, Financial measures;  Financial metrics;  Financial parameters;  Metrics;  Supply chain finances;  Supply chain managements (SCM);  Supply chain performance;  Working capitals, Supply chain management},
references={(2011) Studie" Stärken und Schwächen mittlerer und grosser Unternehmen im Vergleich, , Institut für Demoskopie Allensbach; Lamoureux, J.F., Evans, T., (2011) Supply Chain Finance: A New Means to Support the Competitiveness and Resilience of Global Value Chains; (2012) Trade and Supply Chain Finance, , Global Business Intelligence, 3rd edn; Supply chain finance: The next generation (2011) Treasury Today, 1, pp. 28-32. , Treasury Today; Wagner, S.M., Locker, A., Working Capital reduzieren (2008) Beschaffungsmanagement-Revue de l'acheteur, 9, pp. 6-8; Schneider, O., (2009) Adding enterprise value; Farris II, M.T., Hutchison, P.D., Cash-to-cash: The new supply chain management metric (2002) International Journal of Physical Distribution & Logistics Management, 32, pp. 288-298; Farris, T., Staberhofer, F., Losbichler, H., (2010) Managing the Supply Chain Using the Cash-to-Cash Metric, , RIRL 2010; Losbichler, H., Rothböck, M., Der Cash-to-cash Cycle als Werttreiber im SCM-Ergebnisse einer europäischen Studie (2008) Controlling & Management, 52, pp. 47-57; Seifert, R.W., Seifert, D., Financing the Chain (2011) International Commerce Review, 10, pp. 32-44; (2012) World's best supply chain finance providers, , Global Finance magazine; Yunqi, W., (2011) Online Supply Chain Finance: Profound Changes in Financing of SMEs; Davies, J., (2010) Show me the money, pp. 10-11. , supplychainstandard. com; Alferink, H., (2010) Buyer initiated non-recourse factoring of confirmed payables: A major global corporation case study, , Master Thesis; Buzacott, J.A., Zhang, R.Q., Inventory management with asset-based financing (2004) Management Science, pp. 1274-1292; Protopappa, M., (2009) Interrelating operational and financial performance measure-ments in inventory theory, , EPFL; (2010) Supply Chain Operations Reference Model 10.0, , The Supply Chain Council Inc},
correspondence_address1={Friemann, F.; ETH Zürich, WEINBERGSTRASSE 56, 8092 ZÜRICH, Switzerland; email: ffriemann@ethz.ch},
publisher={Springer New York LLC},
issn={18684238},
isbn={9783642403606},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Lueg201386,
author={Lueg, R. and E'Silva, A.L.C.},
title={When one size does not fit all: A literature review on the modifications of the balanced scorecard},
journal={Problems and Perspectives in Management},
year={2013},
volume={11},
number={3},
pages={86-94},
note={cited By 23},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887126435&partnerID=40&md5=3dfa41b5475d8eea7f1d0eafd4c48dce},
affiliation={Aarhus University, School of Business and Social Sciences, Department of Economics and Business, Denmark},
abstract={Robert Kaplan and David Norton emphasize that the four perspectives of their standard balanced scorecard (BSC) need to be adapted to the organizational context. Yet, we lack a coherent body of knowledge on these adaptations. 20 years after the implementation of the BSC, a literature review is warranted to investigate if and how the original BSC has been modified in practice. The authors conduct a systematic literature review of leading academic journals from 1992 to 2012 to identify and analyze the extant empirical evidence on the BSC. The authors find 117 empirical studies on the BSC, of which 27 deal with BSC-modifications. First, the authors conclude that the BSC-perspectives have been modified to match different industries, organizational levels, or functions (e.g., the public sector, information systems, supply chain management, corporate social responsibility, or incentive systems). Second, the authors use an established BSC classification, and argue that the BSC has also been modified in terms of its sophistication. This paper can only identify few examples where the BSC has become an incentive-relevant management system as imagined by Kaplan and Norton; many organizations stop at the level of a measurement system. On the one hand, this article demonstrates the versatility of the BSC and its perspectives as claimed by Kaplan and Norton. On the other hand, the lack of sophisticated implementations shed a critical light on the relevance of the BSC in practice. © Rainer Lueg, Ana Luisa Carvalho e Silva, 2013.},
author_keywords={Balanced scorecard;  Corporate social responsibility;  Environment;  Implementation;  Incentives;  Information systems;  Literature review;  Organizational design;  Performance evaluation;  Performance measurement system;  Public sector;  Supply chain management},
references={Akkermans, H.A., Van Oorschot, K.E., Relevance assumed: A case study of balanced scorecard development using system dynamics (2005) Journal of the Operational Research Society, 56 (8), pp. 931-941. , DOI 10.1057/palgrave.jors.2601923; Al-Ashaab, A., Flores, M., Doultsinou, A., Magyar, A., A balanced scorecard for measuring the impact of industry-university collaboration (2011) Production Planning & Control, 22, pp. 554-570; Atkinson, A.A., Kaplan, R.S., Matsumura, E.M., Young, S.M., (2011) Management Accounting, International, , Edition 6th ed. Pearson, New Jersey, NJ; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Burkert, M., Lueg, R., Differences in the sophistication of value-based management - The role of top executives (2013) Management Accounting Research, 24, pp. 3-22; Chalmeta, R., Palomero, S., Methodological proposal for business sustainability management by means of the balanced scorecard (2010) Journal of the Operational Research Society, 62, pp. 1344-1356; Chang, L.-C., The NHS performance assessment framework as a balanced scorecard approach: Limitations and implications (2007) International Journal of Public Sector Management, 20 (2), pp. 101-117. , DOI 10.1108/09513550710731472; Chenhall, R.H., Management control systems design within its organizational context: Findings from contingency-based research and directions for the future (2003) Accounting, Organizations and Society, 28, pp. 127-168; Creamer, G., Freund, Y., Learning a board balanced scorecard to improve corporate performance (2010) Decision Support Systems, 49, pp. 365-385; Decoene, V., Bruggeman, W., Strategic alignment and middle-level managers' motivation in a balanced scorecard setting (2006) International Journal of Operations and Production Management, 26 (4), pp. 429-448. , DOI 10.1108/01443570610650576; Dias-Sardinha, I., Reijnders, L., Evaluating environmental and social performance of large Portuguese companies: A balanced scorecard approach (2005) Business Strategy and the Environment, 14 (2), pp. 73-91. , DOI 10.1002/bse.421; Greatbanks, R., Tapp, D., The impact of balanced scorecards in a public sector environment: Empirical evidence from dunedin city council, new zealand (2007) International Journal of Operations & Production Management, 27, pp. 846-873; Griffith, R., Neely, A., Performance pay and managerial experience in multitask teams: Evidence from within a firm (2009) Journal of Labor Economics, 27, pp. 49-82; Gumbus, A., Lussier, R.N., Entrepreneurs use a balanced scorecard to translate strategy into performance measures (2006) Journal of Small Business Management, 44 (3), pp. 407-425. , DOI 10.1111/j.1540-627X.2006.00179.x; Gumbus, A., Lyons, B., The balanced scorecard at philips electronics (2002) Strategic Finance, 84, pp. 45-49; Hansen, E.G., Sextl, M., Reichwald, R., Managing strategic alliances through a community-enabled balanced scorecard: The case of merck ltd, thailand (2010) Business Strategy and the Environment, 19, pp. 387-399; Harvey, C., Kelly, A., Morris, H., Rowlinson, M., (2010) Academic Journal Quality Guide, the Association of Business Schools, , London; Huang, C.D., Hu, Q., Achieving it-business strategic alignment via enterprise-wide implementation of balanced scorecards (2007) Information Systems Management, 24, pp. 173-184; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the weighting of performance measures: Evidence from a balanced scorecard (2003) Accounting Review, 78 (3), pp. 725-758; Kaplan, R.S., (1999) The Balanced Scorecard for Public Sector Organisations, , Harvard Business School Press, Cambridge, MA; Kaplan, R.S., The balanced scorecard: Comments on balanced scorecard commentaries (2012) Journal of Accounting & Organizational Change, 8, pp. 539-545; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kaplan, R.S., Norton, D.P., Implementing the balanced scorecard at fmc corporation: An interview with larry D. Brady (1993) Harvard Business Review, 71, pp. 143-147; Kaplan, R.S., Norton, D.P., (1993) Putting the Balanced Scorecard to Work, the Performance Measurement, pp. 66-79. , Management and Appraisal Sourcebook; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Norton, D.P., (2001) The Strategy-Focussed Organization: How Balanced Scorecard Companies Thrive in the New Business Environment, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, , Harvard Business Press, Boston, MA; Kaplan, R.S., Norton, D.P., (2006) Alignment: Using the Balanced Scorecard to Create Corporate Synergies, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Norton, D.P., (2008) The Execution Premium: Linking Strategy to Operations for Competitive Advantage, , Harvard Business Press, Boston, MA; Katsikeas, C.S., Leonidou, L.C., Morgan, N.A., Firm-level export performance assessment: Review, evaluation, and development (2000) Journal of the Academy of Marketing Science, 28, pp. 493-511; Kim, J., Suh, E., Hwang, H., A model for evaluating the effectiveness of crm using the balanced scorecard (2003) Journal of Interactive Marketing, 17 (2), pp. 5-19. , DOI 10.1002/dir.10051; Kraus, K., Lind, J., The impact of the corporate balanced scorecard on corporate control - A research note (2010) Management Accounting Research, 21, pp. 265-277; Lueg, R., (2008) Value-based Management: Empirical Evidence on Its Determinants and Performance Effects, , WHU Otto Beisheim School of Management, Vallendar; Lueg, R., Shareholder Value und Value Based Management - Wie steuern die HDAX-Konzerne? (2010) Zeitschrift für Controlling, 22, pp. 337-344; Lueg, R., Clemmensen, S.N., Pedersen, M.M., (2013) The Role of Corporate Sustainability in A Low-cost Business Model - A Case Study in the Scandinavian Fashion Industry, , Business Strategy and the Environment (forthcoming); Lueg, R., Lu, S., Improving efficiency in budgeting - An interventionist approach to spreadsheet accuracy testing (2012) Problems and Perspectives in Management, 10, pp. 32-41; Lueg, R., Lu, S., How to improve efficiency in budgeting - The case of business intelligence in smes (2013) European Journal of Management, , (forthcoming); Lueg, R., Nedergaard, L., Svendgaard, S., The use of intellectual capital as a competitive tool: A danish case study (2013) International Journal of Management, 30 (2), pp. 217-231; Lueg, R., Nørreklit, H., Performance measurement systems - Beyond generic strategic actions (2012) The Routledge Companion to Cost Management, pp. 342-359. , Mitchell, F., Nørreklit, H., Jakobsen, M. (Eds.), Routledge, New York, NY; Lueg, R., Schäffer, U., Assessing empirical research on value-based management: Guidelines for improved hypothesis testing (2010) Journal für Betriebswirtschaft, 60, pp. 1-47; Malina, M.A., Selto, F.H., Communicating and controlling strategy: An empirical study of the effectiveness of the balanced scorecard (2001) Journal of Management Accounting Research, 13, pp. 47-90; Malmi, T., Balanced scorecards in Finnish companies: A research note (2001) Management Accounting Research, 12 (2), pp. 207-220. , DOI 10.1006/mare.2000.0154, PII S1044500500901549; Martinsons, M., Davison, R., Tse, D., The balanced scorecard: A foundation for the strategic management of information systems (1999) Decision Support Systems, 25, pp. 71-88; McAdam, R., Walker, T., An inquiry into balanced scorecards within best value implementation in UK local government (2003) Public Administration, 81 (4), pp. 873-892. , DOI 10.1111/j.0033-3298.2003.00375.x; Nørreklit, H., The balance on the balanced scorecard: A critical analysis of some of its assumptions (2000) Management Accounting Research, 11, pp. 65-88; Norreklit, H., The Balanced Scorecard: What is the score? A rhetorical analysis of the Balanced Scorecard (2003) Accounting, Organizations and Society, 28 (6), pp. 591-619. , DOI 10.1016/S0361-3682(02)00097-1, PII S0361368202000971; Nørreklit, H., Nørreklit, L., Mitchell, F., Bjørnenak, T., The rise of the balanced scorecard - Relevance regained? (2012) Journal of Accounting and Organizational Change, 8, pp. 490-510; Rampersad, H.K., The way to a highly engaged and happy workforce based on the personal balanced scorecard (2008) Total Quality Management, 19, pp. 11-27; Sandstrom, J., Toivanen, J., The problem of managing product development engineers: Can the balanced scorecard be an answer? (2002) International Journal of Production Economics, 78 (1), pp. 79-90. , DOI 10.1016/S0925-5273(01)00199-2, PII S0925527301001992; Songini, L., Pistoni, A., Accounting, auditing and control for sustainability (2012) Management Accounting Research, 23, pp. 202-204; Speckbacher, G., Bischof, J., Pfeiffer, T., A descriptive analysis on the implementation of balanced scorecards in german-speaking countries (2003) Management Accounting Research, 14, pp. 361-388; Tsai, W., Chou, W., Hsu, W., The sustainability balanced scorecard as a framework for selecting socially responsible investment: An effective mcdm model (2008) Journal of the Operational Research Society, 60, pp. 1396-1410; Van Der Zee, J.T.M., De Jong, B., Alignment is not enough: Integrating business and information technology management with the balanced business scorecard (1999) Journal of Management Information Systems, 16 (2), pp. 137-156; Wiersma, E., For which purposes do managers use balanced scorecards? An empirical study (2009) Management Accounting Research, 20, pp. 239-251; Wu, I.-L., Chang, C.-H., (2012) Using the Balanced Scorecard in Assessing the Performance of E-SCM Diffusion: A Multi-stage Perspective, , Decision Support Systems; Zimmerman, J.L., Conjectures regarding empirical managerial accounting research (2001) Journal of Accounting and Economics, 32 (1-3), pp. 411-427. , DOI 10.1016/S0165-4101(01)00023-4, PII S0165410101000234; Zimmermann, K., Seuring, S., Two case studies on developing, implementing and evaluating a balanced scorecard in distribution channel dyads (2009) International Journal of Logistics: Research and Applications, 12, pp. 63-81},
publisher={LLC CPC Business Perspectives},
issn={17277051},
language={English},
abbrev_source_title={Probl. Perspect. Manage.},
document_type={Review},
source={Scopus},
}

@CONFERENCE{Tracht2013324,
author={Tracht, K. and Niestegge, A. and Schuh, P.},
title={Demand planning based on performance measurement systems in closed loop supply chains},
journal={Procedia CIRP},
year={2013},
volume={12},
pages={324-329},
doi={10.1016/j.procir.2013.09.056},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886800002&doi=10.1016%2fj.procir.2013.09.056&partnerID=40&md5=9cdbbe20080c7e246d5d59e22a40b1d9},
affiliation={Bremen Institute for Mechanical Engineering, University of Bremen, Badgasteiner Str. 1, Bremen, 28359, Germany; Bundesvereinigung Logistik (BVL), E.V., Schlachte 31, 28195 Bremen, Germany},
abstract={The operational readiness of an aircraft fleet is based on a reliable maintenance, repair and overhaul (MRO). In order to reduce the downtimes of aircrafts, damaged components are replaced on site. The need for immediate replacements associated with long lead times require spare parts stocking. To operate on a competitive basis, the optimization of the spare parts inventory level has to take place under consideration of financial and non financial aspects. In order to be able to evaluate and improve the spare parts supply process in the civil aviation industry, the formulation of a performance measurement system is subject matter of this paper. The performance measurement system presented in this paper is based on a balanced scorecard. Herewith, the complex coherences deriving from multiple participants in the closed loop supply chain can be considered by means of cause and effect relationships between the parameters used. The holistic approach leads to a performance measurement system that enables planning divisions to meet the requirements of complex closed loop supply chains and improve operating efficiency.},
author_keywords={Logistics;  Maintenance;  Performance measurement},
keywords={Aircraft manufacture;  Civil aviation;  Logistics;  Maintenance;  Manufacture;  Repair;  Supply chain management;  Supply chains, Cause-and-effect relationships;  Closed-loop supply chain;  Maintenance , repair and overhauls;  Operating efficiency;  Operational readiness;  Performance measurement system;  Performance measurements;  Spare parts inventories, Fighter aircraft},
references={Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, p. 534. , 2003; Reichmann, T., (2001) Controlling Mit Kennzahlen Und Managementberichten: Grundlagen Einer Systemgestützten Controlling-Konzeption, 6. , Überarb. und erw. Aufl, München, Vahlen; Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), p. 75; Hon, K.K.B., Perfomance and evaluation of manufacturingsystems (2005) Annals of the CIRP, 54 (2), p. 675; Czenskowsky, T., Piontek, J., (2007) Logistikcontrolling: Marktorientiertes Controlling Der Logistik Und Der Supply Chain, , Gernsbach, Dt. Betriebswirte-Verl; Broeke, A.M., Meulen, M.V.D., (1989) Performance Indicators in Logistics: Approach and Coherence, , Kempston, Bedford: IFS Publ. [u.a]; Gaismayer, J., (2004) Performance Measurement Für Logistik- Dienstleister: Instrumente - Erfolgsfaktoren - Zukunft, Düsseldorf, , VDM, Verl. Dr. Müller; Lynch, R.L., Cross, K.F., (1998) Measure Up! Yardsticks for Continuous Improvement, , Cambridge, Blackwell; Hornung, V., Luczak, H., Oster, M., Virtual benchmarking in logistics: A concept for the determination of benchmarks (1998) Advances in Production Management Systems, p. 136. , Okino N. Tamura H. Fujii S editors. Springer; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, p. 71. , January-February; Tracht, K., Schneider, D., Schuh, P., A feedback control system for spare parts logistic networks in the field of civil aviation (2010) 7th CIRP International Conference on Intelligent Computation in Manufacturing Engineering CIRP, , ICME, Capri; Kilpi, J., Vepsäläinen, A., Pooling of spare components between airlines (2004) Journal of Air Transport Management, (10), p. 137. , 2004; Tracht, K., Mederer, M., Schneider, D., Queuing of seasonally demanded spare parts in a repair shop of a closed- loop supply chain (2010) 8th Global Conference on Sustainable Manufacturing CIRP, , GSCM, Abu Dhabi},
correspondence_address1={Schuh, P.; Bremen Institute for Mechanical Engineering, Badgasteiner Str. 1, Bremen, 28359, Germany; email: schuh@bime.de},
publisher={Elsevier B.V.},
issn={22128271},
language={English},
abbrev_source_title={Procedia CIRP},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Kafa201371,
author={Kafa, N. and Hani, Y. and El Mhamedi, A.},
title={Sustainability performance measurement for green supply chain management},
journal={IFAC Proceedings Volumes (IFAC-PapersOnline)},
year={2013},
volume={6},
number={PART 1},
pages={71-78},
doi={10.3182/20130911-3-BR-3021.00050},
note={cited By 30},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885796233&doi=10.3182%2f20130911-3-BR-3021.00050&partnerID=40&md5=3955f77f9bd722c53e5a298ff5524cbd},
affiliation={Equipe MGSI/ LISMMA, Université de Paris8, 140, rue de la nouvelle France, 93100 Montreuil, France},
abstract={This article focuses on sustainability performance in supply chain management (SCM) and adding the Green concept to it so as to emphasize the importance of environmental issues and the need of eco-friendly systems. The purpose of this study is to propose a model for evaluating the sustainability performance of green supply chain management (GSCM). This article seeks to develop key performance measures for GSCM incorporating considerations of sustainable development (SD).We present a literature review on green supply chain management and sustainability performance measurement. Key indicators for the green supply chain performance measurement under the context of sustainable development are derived from a literature. We propose an analytical model to explore the influence of green practices on the sustainability performance in supply chain. This is a first step towards providing a comprehensive overview that explains the relationship between GSCM and sustainability performance. We conclude by suggesting perspectives for future research. © IFAC.},
author_keywords={Green practices;  Green supply chain management;  Performance assessment.;  Sustainability performance measures},
keywords={Environmental protection;  Planning;  Sustainable development, Environmental issues;  Green practices;  Green supply chain management;  Literature reviews;  Performance assessment;  Performance measure;  Supply chain managements (SCM);  Sustainability performance, Supply chain management},
references={Ageron, B., Gunasekaran, A., Spalanzani, A., Sustainable supply management: An empirical study (2012) Int. J. Production Economics, 140 (1), pp. 168-182; Azevedo, S., Carvalho, H., Cruz Machado, V., The influence of green practices on supply chain performance: A case study (2011) Transportation Research Part e, 47 (6), pp. 850-871; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B.M., Environmental and sustainability ethics in supply chain management (2005) Science and Engineering Ethics, 11, pp. 221-234; Bititci, U., Turner, T., Begemann, C., (2000) Dynamics of Performance Measurement Systems. International Journal of Operations & Production Management, 20 (6), pp. 692-704; Charan, P., Shankar, R., Baisya, R., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Chardine-Baumann, E., Modèles d'évaluation des performances économique, environnementale et sociale dans les chaines logistiques: INSA de Lyon. Deif, A. M., 2011 (2011) A System Model for Green Manufacturing. Journal of Cleaner Production, 19 (14), pp. 1553-1559; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Harrington, H., (1991) Business Process Improvement: The Breakthrough Strategy for Total Quality, Productivity, and Competitiveness, , New York: McGraw-Hill; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management. Benchmarking (2005) An International Journa, 12 (4), pp. 330-353; Klassen, R., Whybark, D., The impact of environmental technologies on manufacturing performance (1999) Academy of Management Journal, 42 (6), pp. 615-599; Linton, J., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) Journal of Operations Management, 25 (1), pp. 1075-1082; Markley, M., Davis, L., Exploring future competitive advantage through sustainable supply chains (2007) International Journal of Physical Distribution & Logistics Management, 39 (9), pp. 763-774; Min, H., Galle, W., Green purchasing strategies: Trends and implications (1997) International Journal of Purchasing and Materials Management, pp. 10-17. , Volume module4; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Olugu, E., Wong, K., Shaharoun, A., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 657-579; Pal, U., Identifying the path to successful green manufacturing (2002) Journal of Minerals, Metals and Materials Society, 54 (5), pp. 25-25; Pochampally, K., Gupta, S., Govindan, K., Metrics for performance measurement of a reverse/closed-loop supply chain (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (1), pp. 8-32; Quazi, H., Sustainable development: Integrating environmental issues into strategic planning (2001) Industrial Management & Data Systems, 101 (2), pp. 64-70; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28 (5), pp. 2581-2598; Salam, M., Corporate social responsibility in purchasing and supply chain (2009) Journal of Business Ethics, 85 (2), pp. 335-370; Srivastava, K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Zhu, Q., Sarkis, J., Cordeiro, J., Lai, K., Firm-level correlates of emergent green supply chain management practices in the Chinese context (2008) Omega, 36 (4), pp. 577-591; Zhu, Q., Sarkis, J., Lai, K., Green supply chain management: Pressures, practices and performance within the Chinese automobile industry (2007) Journal of Cleaner Production, 15 (11-12), pp. 1041-1052},
publisher={IFAC Secretariat},
issn={14746670},
language={English},
abbrev_source_title={IFAC Proc. Vol. (IFAC-PapersOnline)},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Hofmann2013286,
author={Hofmann, E. and Wessely, P.},
title={Structuring and quantifying the value contribution of supply chain initiatives},
journal={International Journal of Management and Decision Making},
year={2013},
volume={12},
number={3},
pages={286-311},
doi={10.1504/IJMDM.2013.056454},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884789631&doi=10.1504%2fIJMDM.2013.056454&partnerID=40&md5=3176e4f8ecff4b3d053518f381180713},
affiliation={Department of Logistics Management, University of St. Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland; A.T. Kearney (International) AG, Weberstrasse 4, 8004 Zurich, Switzerland},
abstract={Since the implementation of supply chain initiatives (SCI) requires specific investments in technology, material, organisation and staff, an assessment of the economic viability of the inter-organisational cooperation is necessary in advance. The paper addresses the financial interface by providing a structured approach for the determination of an SCI's value contribution measured by the EVA. The paper is based upon explorative research. Thanks to an integrated extrapolation method, the approach is transferable analogously to all supply chain players affected by the SCI. A numerical example from the food and beverage industry illustrates our findings. The setting of the quantification approach extrapolates the calculation value of each supply chain partner separately since an SCI can be rolled out to a specific group of suppliers and customers individually. The quantification approach allows the determination of the value generated by an SCI under consideration of the individual integration of several supply chain partners within the network of suppliers and customers. It supports management's decision-making in the forefront of a possible implementation. Copyright © 2013 Inderscience Enterprises Ltd.},
author_keywords={Economic value added;  Logistics;  SCM;  Supply chain management;  Supply chain performance measurement;  Value creation},
references={Abate, J.A., Grant, J.L., Stewart III, G.B., The EVA style of investing (2004) Journal of Portfolio Management, 30 (4), pp. 61-72; Anderson, E.W., Fornell, C., Lehmann, D.R., Customer satisfaction, market share and profitability: Findings from Sweden (1994) Journal of Marketing, 58 (3), pp. 53-66; Ashayeri, J., Lemmes, L., Economic value added of supply chain demand planning: A system dynamics simulation (2006) Robotics Computer-integrated Manufacturing, 22 (5-6), pp. 550-556; Bernabucci, R.J., Supply chain gains from integration (2008) Financial Executive, 24 (3), pp. 46-48; Bititci, U.S., Martinez, V., Albores, P., Parung, J., Creating and managing value in collaborative networks (2004) International Journal of Physical Distribution & Logistics Management, 34 (3), pp. 251-268; Bookbinder, J.H., Lynch, M.E., Customer service in physical distribution: A utility-function approach (1997) International Journal of Physical Distribution & Logistics Management, 27 (9-10), pp. 540-559; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Buzzell, R.D., Ortmeyer, G., Channel partnerships streamline distribution (1995) Sloan Management Review, 36 (3), pp. 85-96; Cachon, G., Lariviere, M., Contracting to assure supply: How to share demand forecasts in a supply chain (2001) Management Science, 47 (5), pp. 629-646; Cannella, S., Barbosa-Povoa, A.P., Framinan, J.M., Relvas, S., Metrics for bullwhip effect analysis (2013) Journal of the Operational Research Society, 64 (1), pp. 1-16; Cannella, S., Ciancimino, E., Framinan, J.M., Inventory policies and information sharing in multi-echelon supply chains (2011) Production Planning & Control, 22 (7), pp. 649-659; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Ciancimino, E., Cannella, S., Bruccoleri, M., Framinan, J.M., On the bullwhip avoidance phase: The synchronised supply (2012) European Journal of Operational Research, 221 (1), pp. 49-63; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) International Journal of Logistics Management, 8 (1), pp. 1-14; D'Avanzo, R., Von Lewinski, H., Van Wassenhove, L., The link between supply chain and financial performance (2003) Supply Chain Management Review, 7 (11-12), pp. 40-47; Dekker, H., Value chain analysis in interfirm relationships: A field study (2003) Management Accounting Research, 14 (1), pp. 1-23; Dresner, M., Xu, K., Customer service, customer satisfaction, and corporate performance in the service sector (1995) Journal of Business Logistics, 16 (1), pp. 23-40; Ellram, L., Liu, B., The financial impact of supply chain management (2002) Supply Chain Management Review, 6 (11-12), pp. 30-37; Everaert, P., Bruggeman, W., Sarens, G., Anderson, S., Levant, Y., Cost modeling in logistics using time-driven ABC (2008) International Journal of Physical Distribution & Logistics Management, 38 (3), pp. 172-191; Fawcett, S.E., Magnan, G.M., The rhetoric and reality of supply chain integration (2002) International Journal of Physical Distribution & Logistics Management', 32 (5), pp. 339-361; Ferguson, B.R., Implementing supply chain management (2000) Production and Inventory Management Journal, 41 (2), pp. 64-67; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Ganeshan, R., Boone, T., Stenger, A., The impact on inventory and flow planning parameters on supply chain performance: An exploratory study (2001) International Journal of Production Economics, 71 (1-3), pp. 111-118; Grant, D.B., Lambert, D.M., Stock, J.R., Ellram, L.M., (2006) Fundamentals of Logistics Management, , European edition, McGraw-Hill, Berkshire; Gunasekaran, A., Lai, K., Cheng, T.C.E., Responsive supply chain: A competitive strategy in a networked economy (2008) Omega, 36 (4), pp. 549-564; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: A case study from the packaging industry (2009) Production Planning & Control, 20 (1), pp. 68-81; Holweg, M., Bicheno, J., Supply chain simulation - A tool for education, enhancement and endeavor (2002) International Journal of Production Economics, 78 (2), pp. 163-175; Innis, D., La Londe, B., Customer service: The key to customer satisfaction, customer loyalty and market share (1994) Journal of Business Logistics, 15 (1), pp. 1-27; Kaplan, R., Innovation action research: Creating new management theory and practice (1998) Journal of Management Accounting Research, 10 (1), pp. 89-118; Kasanen, E., Lukka, K., Siitonen, A., The constructive approach in management accounting research (1993) Journal of Management Accounting Research, 5 (1), pp. 243-264; Kim, B., Kim, J., Values of the balanced decision making between supply chain partners (2008) International Transactions in Operational Research, 15 (5), pp. 623-649; Kleijnen, J., Smits, M., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Koltai, T., Lozano, S., Guerrero, E., Onieva, L., A flexible costing system for flexible manufacturing systems using activity based costing (2000) International Journal of Production Research, 38 (7), pp. 1615-1630; La Londe, B.J., Zinszer, P.H., (1976) Customer Service. Meaning and Measurement, the National Council of Physical Distribution Management, , Chicago, IL; Lambert, D.M., Burduroglu, R., Measuring and selling the value of logistics (2000) International Journal of Logistics Management, 11 (1), pp. 1-17; Lambert, D.M., Cooper, M., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lambert, D.M., Stock, J.R., (2001) Strategic Logistics Management, , 4th ed., McGraw-Hill Higher Education, Boston; Lau, R.S.M., Xie, J., Zhao, X., Effects of inventory policy on supply chain performance: A simulation study of critical decision parameters (2008) Computers & Industrial Engineering, 55 (3), pp. 620-633; Mangan, J., Lalwani, C., Gardner, B., Combining quantitative and qualitative methodologies in logistics research (2004) International Journal of Physical Distribution & Logistics Management, 34 (7), pp. 565-578; Meredith, J., Theory building through conceptual methods (1993) International Journal of Operations and Production Management, 13 (5), pp. 3-11; Meredith, J., Raturi, A., Amoako-Gyampah, K., Kaplan, B., Alternative research paradigms in operations (1989) Journal of Operations Management, 8 (4), pp. 297-326; Neely, A., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Ojala, M., Hallikas, J., Investment decision-making in supplier networks: Management of risk (2006) International Journal of Production Economics, 104 (1), pp. 201-213; Ozment, J., Chard, N., Effects of customer service on sales: An analysis of historical data (1986) International Journal of Physical Distribution & Logistics Management, 16 (3), pp. 14-28; Panayides, P.M., Lun, Y.H.V., The impact of trust on innovativeness and supply chain performance (2009) International Journal of Production Economics, 122 (1), pp. 35-46; Persson, F., Olhager, J., Performance simulation of supply chain designs (2002) International Journal of Production Economics, 77 (3), pp. 231-245; Pohlen, T.L., Coleman, B.J., Evaluating internal operations and supply chain performance using EVA and ABC (2005) SAM Advanced Management Journal, 70 (2), pp. 45-58; Presutti Jr., W.D., Mawhinney, J.R., The supply chain-finance link (2007) Supply Chain Management Review, 11 (6), pp. 32-38; Reason, P., Choice and quality in action research practice (2006) Journal of Management Inquiry, 15 (2), pp. 187-203; Riddalls, C.E., Bennett, S., Tipi, N.S., Modeling the dynamics of supply chains (2000) International Journal of Systems Science, 31 (8), pp. 969-976; Ross, S.A., Westerfield, R.W., Jordan, B.D., (2003) Fundamentals of Corporate Finance, , 6th ed., McGraw-Hill Higher Education, Boston; Roztocki, N., Needy, K.L., Integrating activity-based costing and economic value added in manufacturing (1999) Engineering Management Journal, 11 (2), pp. 17-22; Stern, J.M., Stewart III, G.B., Chew, D.H., The EVA financial management system (1995) Journal of Applied Corporate Finance, 8 (2), pp. 32-46; (2006) Supply-chain Operations Reference-model: Version 8.0 SCOR Overview, , http://www.supply-chain.org, Supply Chain Council SCC, online, accessed 22 November 2008; Terzi, S., Cavalieri, S., Simulation in the supply chain context: A survey (2004) Computers in Industry, 53 (1), pp. 3-16; Timme, S., Williams-Timme, C., The financial-SCM connection (2000) Supply Chain Management Review, 4 (5-6), pp. 32-43; Tracey, M., The importance of logistics efficiency to customer service and firm performance (1998) International Journal of Logistics Management, 9 (2), pp. 65-81; Van Hoek, R., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management - An International Journal, 3 (4), pp. 187-192; Walters, D., The implications of shareholder value planning and management for logistics decision making (1999) International Journal of Physical Distribution & Logistics Management, 29 (4), pp. 240-258; Webster, M., Supply system structure, management and performance: S conceptual model (2002) International Journal of Management Reviews, 4 (4), pp. 353-369; Wikner, J., Towill, D., Naim, M., Smoothing supply chain dynamics (1991) International Journal of Production Economics, 22 (3), pp. 231-248; Wouters, M., Implementation costs and redistribution mechanisms in the economic evaluation of supply chain management initiatives (2006) Supply Chain Management: An International Journal, 11 (6), pp. 510-521; Yeung, A.C., Strategic supply management, quality initiatives and organizational performance (2008) Journal of Operations Management, 26 (4), pp. 490-502; Young, S.D., O'Byrne, S., (2001) EVA and Value-based Management: A Practical Guide to Implementation, , McGraw-Hill Higher Education, New York; Zhao, X., Huo, B., Flynn, B.B., Yeung, J.H.Y., The impact of power and relationship commitment on the integration between manufacturers and customers in a supply chain (2008) Journal of Operations Management, 26 (3), pp. 368-388},
correspondence_address1={Hofmann, E.; Department of Logistics Management, Dufourstrasse 40a, 9000 St. Gallen, Switzerland; email: erik.hofmann@unisg.ch},
publisher={Inderscience Publishers},
issn={14624621},
language={English},
abbrev_source_title={Int. J. Manage. Decis. Mak.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Janeš2013928,
author={Janeš, A. and Faganel, A.},
title={Instruments and methods for the integration of company's strategic goals and key performance indicators},
journal={Kybernetes},
year={2013},
volume={42},
number={6},
pages={928-942},
doi={10.1108/K-08-2012-0022},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884528916&doi=10.1108%2fK-08-2012-0022&partnerID=40&md5=be5e6b8081e6471951f19c48c6790b1e},
affiliation={University of Primorska, Koper, Slovenia},
abstract={Purpose: The purpose of this paper is to explore and clarify the cause and effect relations between key performance indicators (KPIs) which significantly contribute to the benefits of the business processes exploitation in the Luka Koper, d.d. Company. Design/methodology/approach: The paper developed a single equation microeconomic error correction model (ECM) with the Engle and Granger two-step method. With the ECM approach, the paper performed application on the KPIs and estimated short- and long-term effects between them. Findings: From the final ECM model, it can be recognized that the total turnover has been increased, by increased maritime throughput. Increasing the maritime throughput means a reduction in electricity consumption per tonne reloaded and increasing consumption of fossil fuels and water. Revenue per unit of maritime throughput has a negative regression coefficient, which may lead to an increase in income or increased amount of maritime throughput and simultaneously reducing the cost per tonne reloaded. Results are reflecting the impact of sharp declining in maritime throughput with the greatest added value in the years 2007 and 2008. All these results and observations suggest that an error correction mechanism exists and that the paper set up a stable model, which describes the dynamics of short-term determinants of the long-term service performance. Research limitations/implications: The following limitations exist to this study: sample size and quality of the data that were available and the quantitative analysis in the four perspectives of the Kaplan and Norton's balanced scorecard (BSC). Since this is the case study that investigates the impact of the KPIs' on the results and causalities between them, the paper also encountered the data, which are treated as a business secret. Further research into the impact of introducing the four perspectives of the BSC to monitor the implementation of strategies and strategic projects is recommended. Practical implications: The presented quantitative approach is useful in combination with a qualitative approach, which is a common practice in determining the causal relations resulting in the strategic map of BSC. Simulations of the developed model are possible on all levels of management, by combining the KPIs, and consecutively acquire new knowledge about their relations. Developed quantitative approach supports improving the monitoring of operational efficiency of an organization, improving business processes, project efficiency and achievement of the strategic goals. Originality/value: Developed approach supports identification and classification of strategic goals and their KPIs that are best suited for inclusion in the BSC strategic map, improvements to the monitoring of implemented strategic initiatives and achievement of strategic goals. © Emerald Group Publishing Limited.},
author_keywords={Balanced scorecard;  Business processes;  Cybernetics;  Diagnostics;  Key performance indicators;  Performance measurement systems;  Strategic goals},
keywords={Cybernetics;  Efficiency;  Error correction;  Fossil fuels;  Planning;  Plasma diagnostics;  Strategic planning;  Supply chain management;  Throughput, Balanced scorecards;  Business Process;  Key performance indicators;  Performance measurement system;  Strategic goals, Benchmarking},
references={Abernethy, M.A., Horne, M., Lillis, A.M., Malina, M.A., Selto, F.H., A multi-method approach to building causal performance maps from expert knowledge (2005) Management Accounting Research, 16 (2), pp. 135-155; Achterberg, J., Beeres, R., Vriens, D., Does the balanced scorecard support organizational viability? (2003) Kybernetes, 32 (9-10), pp. 1387-1404; Alkhathlan, K.A., Foreign direct investment and export growth in Saudi Arabia: A cointegration analysis (2011) China-USA Business Review, 10 (2), pp. 137-149; Assiri, A., Zairi, M., Eid, R., How to profit from the balanced scorecard: An implementation roadmap (2006) Industrial Management & Data Systems, 106 (7), pp. 937-952; Banerjee, A., Dolado, J.J., Hendry, D.F., Smith, G.W., Exploring equilibrium relationships in econometrics through static models: Some Monte Carlo evidence (1986) Oxford Bulletin of Economics and Statistics, 48 (3), pp. 253-277; Biloslavo, R., Grebenc, A., Integrating group Delphi, analytic hierarchy process and dynamic fuzzy cognitive maps for a climate warning scenario (2012) Kybernetes, 41 (3-4), pp. 414-428; Bititci, U.S., Measuring your way to profit (1994) Management Decision, 32 (6), pp. 16-24; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations & Production Management, 26 (12), pp. 1325-1350; Brock, W.A., Durlauf, S.N., Growth empirics and reality (2001) The World Bank Economic Review, 15 (2), pp. 229-272; Bryde, D.J., Project management concepts, methods and application (2003) International Journal of Operations & Production Management, 23 (7), pp. 775-793; Bukh, P.N., Malmi, T., Re-examining the cause-and-effect principle of the balanced scorecard (2005) Accounting in Scandinavia: The Northern Lights, , Jönsson, S., Mouritsen, J. (Eds.), Liber, Malmö; Cobbold, I., Lawrie, G., Issa, K., Designing a strategic management system using the third-generation balanced scorecard: A case study (2004) International Journal of Productivity and Performance Management, 53 (7), pp. 624-633; Denbaly, M., Vroomen, H., Dynamic fertilizer nutrient demands for corn: A cointegrated and error-correcting system (1993) American Journal of Agricultural Economics, 75 (1), pp. 203-209; Engle, R.F., Granger, C.W.J., Co-integration and error correction: Representation, estimation, and testing (1987) Econometrica, 55 (2), pp. 251-276; Fritz, R.G., Fritz, J.M., Linguistic structure and economic method (1985) Journal of Economic Issues, 19 (1), pp. 75-101; Granger, C.W.J., (1983) Co-integrated variables and error-correcting models, , Working Paper 83-13, University of California, San Diego, CA; Granger, C.W.J., On modelling the long run in applied economics (1997) The Economic Journal, 107 (440), pp. 169-177; Granger, C.W.J., Time series analysis, cointegration, and applications (2004) The American Economic Review, 94 (3), pp. 421-425; Gujarati, D.N., (1995) Basic Econometrics, , McGraw-Hill, New York, NY; Hall, S.G., An application of the Granger and Engle two-step estimation procedure to United Kingdom aggregate wage data (1986) Oxford Bulletin of Economics and Statistics, 48 (3), pp. 229-239; Hylleberg, S., Mizon, G.E., Cointegration and error correction mechanisms (1989) The Economic Journal, 99 (395), pp. 113-125; Ittner, C.D., Larcker, D.F., Indicators of financial performance? An analysis of customer satisfaction (1998) Journal of Accounting Research, 36, pp. 1-35; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the weighting of performance measures: Evidence from a balanced scorecard (2003) The Accounting Review, 78 (3), pp. 725-758; Janeš, A., (2012) Development and empirical verification of the balanced scorecard model, , doctoral dissertation, Faculty of Management, University of Primorska, Koper; Janeš, A., Managing sustainability with the error correction model (2013) International Journal of Sustainable Economy, 5 (2), pp. 125-139; Janeš, A., Dolinšek, S., Do we need a new compass for the journey through the global crisis? (2010) Journal of Industrial Engineering and Management, 3 (2), pp. 255-293; Janeš, A., Dolinšek, S., Can we use the statistical causality with the sufficient reliability? (2011) Conference Proceedings Knowledge and Learning International Conference 2011, Celje, June, pp. 259-266; Jiha, J., Orphee, G., A note on inflation in Haiti: Evidence from cointegration analysis (1995) Social and Economic Studies, 44 (1), pp. 95-115; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Harvard Business School Press, Boston, MA; Jones, P., (2009) Avoiding problems and predicting recovery during the economic crisis, , www.excitant.co.uk, available at:, (accessed 20 April 2012); Kaplan, R., Norton, D., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R., Norton, D., Having trouble with your strategy? Then map it (2000) Harvard Business Review, 78 (5), pp. 167-176; Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, , Harvard Business School Publishing, Boston, MA; Kaplan, R.S., Norton, D.P., (2006) Alignment: Using the Balanced Scorecard to Create Corporate Synergies, , Harvard Business School Publishing, Boston, MA; Kavčič, K., Bertoncelj, A., Strategic orientation of organizations: Risk management perspective (2010) Kybernetes, 39 (5), pp. 735-749; Laitinen, E.K., Towards a microeconomic approach of the balanced scorecard (2004) Managerial Finance, 30 (6), pp. 1-27; Macunovich, D.J., Easterlin, R.A., Application of Granger-Sims causality tests to monthly fertility data, 1958-1984 (1988) Journal of Population Economics, 1 (1), pp. 71-88; Miller, S.M., Monetary dynamics: An application of cointegration and error-correction modeling (1991) Journal of Money, Credit and Banking, 23 (2), pp. 139-154; Modell, S., Bundling management control innovations (2009) Accounting, Auditing & Accountability Journal, 22 (1), pp. 59-90; Monge, P.R., Theoretical and analytical issues in studying organizational processes (1990) Organization Science, 1 (4), pp. 406-430; Nörreklit, H., The balance on the balanced scorecard a critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Olve, N., Roy, J., Wetter, M., (1999) Performance Drivers: A Practical Guide to Using the Balanced Scorecard, , Wiley, Chichester; Phillips, P.C.B., Granger, C., The ET interview: Professor Clive Granger (1997) Econometric Theory, 13 (2), pp. 253-303; Poister, T.H., Developing performance indicators for the Pennsylvania Department of Transportation (1982) Public Productivity Review, 6 (1-2), pp. 51-77; (2013) Annual Report 2012, , Port of Koper, Port of Koper; Robson, I., From process measurement to performance improvement (2004) Business Process Management Journal, 10 (5), pp. 510-521; Smith, P.K., Welfare as a cause of poverty: A time series analysis (1993) Public Choice, 75 (2), pp. 157-170; Stock, J.H., Watson, M.W., Variable trends in economic time series (1988) The Journal of Economic Perspectives, 2 (3), pp. 147-174; Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R., Development of a balanced scorecard: An integrated approach of interpretive structural modeling (ISM) and analytic network process (ANP) (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 25-59; Vanita, A., Yang, J., Skitmore, M., Shankar, R., An empirical test of causal relationships of factors affecting ICT adoption for building project management: An Indian SME case study (2010) Construction Innovation: Information, Process, Management, 10 (2), pp. 164-180; Wang, W., An evaluation of the balanced scorecard in equity valuation: The case of exchange ratio in the M&As of Taiwan's financial industry (2005) Journal of Intellectual Capital, 6 (2), pp. 206-221; Wisniewski, M., Dickson, A., Measuring performance in Dumfries and Galloway constabulary with the balanced scorecard (2001) The Journal of the Operational Research Society, 52 (10), pp. 1057-1066; Yin, R.K., (1994) Case Study Research: Design and Methods, , Sage, Thousand Oaks, CA; Aaltonen, P., Ikavalko, H., Implementing strategies successfully (2002) Integrated Manufacturing Systems, 13 (6), pp. 415-418; Abernathy, W., Balanced scorecards make teamwork a reality (1997) Journal for Quality and Participation, 20 (5), pp. 58-59; Agostino, D., Arnaboldi, M., How the BSC implementation process shapes its outcome (2011) International Journal of Productivity and Performance Management, 60 (2), pp. 99-114; Atkinson, H., Strategy implementation: A role for the balanced scorecard? (2006) Management Decision, 44 (10), pp. 1441-1460; Basu, R., Little, C., Millard, C., Case study: A fresh approach of the balanced scorecard in the Heathrow Terminal 5 project (2009) Measuring Business Excellence, 13 (4), pp. 22-33; Camacho, M., Perez-Quiros, G., This is what the leading indicators lead (2002) Journal of Applied Econometrics, 17 (1), pp. 61-80; Caporale, G.M., Katsimi, M., Pittis, N., Causality links between consumer and producer prices: Some empirical evidence (2002) Southern Economic Journal, 68 (3), pp. 703-711; Geweke, J., Meese, R., Dent, W., Comparing alternative tests of causality in temporal systems (1983) Journal of Econometrics, 21, pp. 161-194; Goold, M., Strategic control in the decentralised firm (1991) Sloan Management Review, 32 (2), pp. 69-81; Granger, C., Statistics and causal inference: Comment source (1986) Journal of the American Statistical Association, 81 (396), pp. 967-968; Guilkey, D.K., Salemi, M.K., Small sample properties of three tests for Granger-causal ordering in a bivariate stochastic system (1982) The Review of Economics and Statistics, 64 (4), pp. 668-680; Holland, P.W., Statistics and causal inference (1986) Journal of the American Statistical Association, 81 (396), pp. 945-960; Hylleberg, S., Engle, R.F., Granger, C.W.J., Yoo, B.S., Seasonal integration and cointegration (1990) Journal of Econometrics, 44, pp. 215-238; Murray, M.P., A drunk and her dog: An illustration of cointegration and error correction (1994) The American Statistician, 48 (1), pp. 37-39; Neely, A., (2008) Does the balanced scorecard work: An empirical investigation, , RP 1/08; Neely, A., Bourne, M., Mills, J., Platts, K., Richards, H., (2002) Strategy and Performance: Getting the Measure of Your Business, , Cambridge University; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Young, J.Z., (1978) Programs of the Brain, , Oxford University Press},
correspondence_address1={Janeš, A.; University of Primorska, Koper, Slovenia; email: aleksander.janes@gmail.com},
publisher={Emerald Group Publishing Ltd.},
issn={0368492X},
language={English},
abbrev_source_title={Kybernetes},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cannella20131,
author={Cannella, S. and Barbosa-Póvoa, A.P. and Framinan, J.M. and Relvas, S.},
title={Metrics for bullwhip effect analysis},
journal={Journal of the Operational Research Society},
year={2013},
volume={64},
number={1},
pages={1-16},
doi={10.1057/jors.2011.139},
note={cited By 68},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870688224&doi=10.1057%2fjors.2011.139&partnerID=40&md5=5a1d369b99a643f1348d30380f873b58},
affiliation={Centre for Management Studies, Instituto Superior Técnico, Technical University of Lisbon, Av. Prof Cavaco Silva, Porto Salvo 2780-990, Portugal; University of Seville, Seville, Spain},
abstract={A bullwhip measurement system based on a two-criterion assessment-internal process efficiency and customer service level-is developed in this paper. The framework is designed to assess both individual (single member) and systemic (whole supply chain) performances. Data collection and calculation methods, update and monitoring mechanisms, as well as related procedures for each metric used, are detailed. A comparative analysis with a recent work by Barlas and Gunduz is performed, showing that the adoption of the proposed performance measurement system can help academics and practitioners to better understand, study and avoid the bullwhip effect. Such analysis also provides evidence on the relevance of considering when analysing the bullwhip effect in supply chains, the customer importance aspect that is often forgotten in the published literature. © 2013 Operational Research Society Ltd. All rights reserved.},
author_keywords={demand amplification;  inventory management;  performance measure;  supply chain costs;  system dynamics},
keywords={Inventory control, Demand amplification;  Inventory management;  Performance measure;  Supply chain costs;  System Dynamics, Supply chains},
references={Arzu Akyuz, G., Erman Erkan, T., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management, 16 (1), pp. 20-31; Barlas, Y., Gunduz, B., Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains (2011) Journal of the Operational Research Society, 62 (3), pp. 458-473; Bayraktar, E., Lenny Koh, S.C., Gunasekaran, A., Sari, K., Tatoglu, E., The role of forecasting on bullwhip effect for E-SCM applications (2008) International Journal of Production Economics, 113 (1), pp. 193-204; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Brun, A., Fahmy Salama, K., Gerosa, M., Selecting performance measurement systems: Matching a supply chain's requirements (2009) European Journal of Industrial Engineering, 3 (3), pp. 336-362; Butner, K., The smarter supply chain of the future (2010) Strategy and Leadership, 38 (1), pp. 22-31; Cachon, G., Managing supply chain demand variability with scheduled ordering policies (1999) Management Science, 458 (6), pp. 843-856; Cachon, G.P., Olivares, M., Drivers of finished-goods inventory in the US automobile industry (2010) Management Science, 56 (1), pp. 202-216; Cachon, G.P., Randall, T., Schmidt, G.M., In search of the bullwhip effect (2007) Manufacturing and Service Operations Management, 9 (4), pp. 457-479; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Cannella, S., Ciancimino, E., On the bullwhip avoidance phase: Supply chain collaboration and order smoothing (2010) International Journal of Production Research, 48 (22), pp. 6739-6776; Cannella, S., Ciancimino, E., Marquez, A.C., Capacity constrained supply chains: A simulation study (2008) International Journal of Simulation and Process Modelling, 4 (2), pp. 139-147; Cannella, S., Ciancimino, E., Framinan, J.M., Disney, S.M., The four supply chain archetypes (2010) Universia Business Review, (26), pp. 134-149; Cannella, S., Ciancimino, E., Framinan, J.M., Inventory policies and information sharing in multi-echelon supply chains (2011) Production Planning & Control, 22 (7), pp. 649-659; Chandler, A.D., (1990) Scale and Scope, , Belknap Press: Cambridge, MA; Chen, F., Drezner, Z., Ryan, J.K., Simchi-Levi, D., Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information (2000) Management Science, 46 (3), pp. 436-443; Christopher, M., Holweg, M., Supply chain 2.0': Managing supply chains in the era of turbulence (2011) International Journal of Physical Distribution and Logistics Management, 41 (1), pp. 63-82; Coppini, M., Rossignoli, C., Rossi, T., Strozzi, F., Bullwhip effect and inventory oscillations analysis using the beer game model (2010) International Journal of Production Research, 48 (13), pp. 3943-3956; Dejonckheere, J., Disney, S.M., Lambrecht, M.R., Towill, D.R., Measuring and avoiding the bullwhip effect: A control theoretic approach (2003) European Journal of Operational Research, 147 (3), pp. 567-590; Dejonckheere, J., Disney, S.M., Lambrecht, M.R., Towill, D.R., The impact of information enrichment on the Bullwhip effect in supply chains: A control engineering perspective (2004) European Journal of Operational Research, 153 (3), pp. 727-750; De Kok, T., Janssen, F., Van Doremalen, J., Vanwachem, E., Clerkx, M., Peeters, W., Philips Electronics synchronizes its supply chain to end the bullwhip effect (2005) Interfaces, 35 (1), pp. 37-48; Disney, S.M., Lambrecht, M.R., On replenishment rules, forecasting, and the bullwhip effect in supply chains (2008) Foundations and Trends in Technology, Information and Operations Management, 2 (1), pp. 1-80; Disney, S.M., Towill, D.R., A discrete transfer function model to determine the dynamic stability of a vendor managed inventory supply chain (2002) International Journal of Production Research, 40 (1), pp. 179-204; Disney, S.M., Towill, D.R., On the bullwhip and inventory variance produced by an ordering policy (2003) Omega, 31 (3), pp. 157-167; Disney, S.M., Towill, D.R., A methodology for benchmarking replenishment-induced bullwhip (2006) Supply Chain Management, 11 (2), pp. 160-168; Disney, S.M., Naim, M.M., Potter, A., Assessing the impact of e-business on supply chain dynamics (2004) International Journal of Production Economics, 89 (2), pp. 109-118; El-Beheiry, M., Wong, C.Y., El-Kharbotly, A., Empirical quantication of the bullwhip effect (2004) Proceedings of the 13th Working Seminar on Production Economics, p. 3. , 16-20 February, Igls/Innsbruck, Austria 259-274; Estampe, D., Lamouri, S., Paris, J.-L., Brahim-Djelloul, S., A framework for analysing supply chain performance evaluation models (2010) International Journal of Production Economics, , doi:10.1016/j.ijpe.2010.11.024; Fioriolli, J.C., Fogliatto, F.S., A model to quantify the bullwhip effect in systems with stochastic demand and lead time (2008) IEEE International Conference on Industrial Engineering and Engineering Management, , doi: 10.1109/IEEM.2008.4738040; Forrester, J., (1961) Industrial Dynamics, , MIT Press: Cambridge, MA; Forslund, H., Jonsson, P., Obstacles to supply chain integration of the performance management process in buyersupplier dyads: The buyers' perspective (2009) International Journal of Operations and Production Management, 29 (1), pp. 77-95; Fransoo, J.C., Wouters, M.J.F., Measuring the bullwhip effect in the supply chain (2000) Supply Chain Management, 5 (2), pp. 78-89; Geary, S., Disney, S.M., Towill, D.R., On bullwhip in supply chains-Historical review, present practice and expected future impact (2006) International Journal of Production Economics, 101 (1), pp. 2-18. , Special Issue; Graves, S.C., Willems, S.P., Supply chain design: Safety stock placement and supply chain configuration (2003) Handbooks in or & MS, 11. , de Kok AG and Graves SC (eds Elsevier: Amsterdam, The Netherlands; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature 1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Hammond, J.H., (1994) Barilla SpA (A, pp. 694-046. , Harvard Business School Case; Haughton, M.A., Distortional bullwhip effects on carriers (2009) Transportation Research Part E: Logistics and Transportation Review, 45 (1), pp. 172-185; Hausman, W.H., Supply chain performance metrics (2003) The Practice of Supply Chain Management: Where Theory and Practice Converge, pp. 61-73. , Harrison TP, Lee HL and Neale JJ (eds Kluwer Academic Publishing: Boston, MA; Holweg, M., Disney, S.M., The evolving frontiers of the bullwhip problem (2005) EUROMA Conference Proceedings, pp. 707-716. , Budapest, Hungary, 19-22 June; Holweg, M., Disney, S.M., Holmstro, J., Smaros, J., Supply chain collaboration: Making sense of the strategy continuum (2005) European Management Journal, 23 (2), pp. 170-181; Hosoda, T., Disney, S.M., The governing dynamics of supply chains: The impact of altruistic behaviour (2006) Automatica, 42 (8), pp. 1301-1309; Keebler, J.S., Plank, R.E., Logistics performance measurement in the supply chain: A benchmark (2009) Benchmarking, 16 (6), pp. 785-798; Kleijnen, J.P.C., Supply chain simulation tools and techniques: A survey (2005) International Journal of Simulation and Process Modelling, 1 (1-2), pp. 82-89; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Lee, H.L., Taming the bullwhip (2010) Journal of Supply Chain Management, 46 (1), p. 7; Lee, H.L., Billington, C., Managing supply chain inventory pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lee, H.L., Padmanabhan, V., Whang, S., Information distortion in a supply chain: The bullwhip effect (1997) Management Science, 43 (4), pp. 546-558; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect: Reflections (2006) The Bullwhip Effect in Supply Chains: A Review of Methods, Components and Cases, pp. 1-14. , Carranza Torres OA and Villegas Moran FA (eds Palgrave Macmillan: New York; Lehtinen, J., Ahola, T., Is performance measurement suitable for an extended enterprise? (2010) International Journal of Operations & Production Management, 30 (2), pp. 181-204; Magee, J.F., (1958) Production Planning and Inventory Control, , McGraw-Hill: New York; Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Winkler, R., The accuracy of extrapolation (time series) methods: Results of a forecasting competition (1982) Journal of Forecasting, 1 (2), pp. 111-153; Miragliotta, G., Layers and mechanisms: A new taxonomy for the bullwhip effect (2006) International Journal of Production Economics, 104 (2), pp. 365-381; Mitchell, T., Competitive illusion as a cause of business cycles (1924) Quarterly Journal of Economics, 38 (4), pp. 631-652; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management and Data Systems, 110 (9), pp. 1297-1318; Potter, A., Lalwani, C., Investigating the impact of demand amplification on freight transport (2008) Transportation Research Part E: Logistics and Transportation Review, 44 (5), pp. 835-846; Reichhart, A., Framinan, J.M., Holweg, M., On the link between inventory and responsiveness in multi-roduct supply chains (2008) International Journal of Systems Science, 39 (7), pp. 677-688; Riddalls, C.E., Bennett, S., The optimal control of batched production and its effect on demand amplification (2001) International Journal of Production Economics, 72 (2), pp. 159-168; Riddalls, C.E., Bennett, S., Tipi, N.S., Modelling the dynamics of supply chains (2000) International Journal of Systems Science, 31 (8), pp. 969-976; Sajadi, S.M., Seyed Esfahani, M.M., Sorensen, K., Production control in a failure-rone manufacturing network using discrete event simulation and automated response surface methodology (2011) International Journal of Advanced Manufacturing Technology, 53 (1-4), pp. 35-46; Schmenner, R.W., Looking ahead by looking back: Swift, even flow in the history of manufacturing (2001) Production and Operations Management, 10 (1), pp. 87-96; Sharma, S., Policies concerning decisions related to quality level (2010) International Journal of Production Economics, 125 (1), pp. 146-152; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Sterman, J., Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment (1989) Management Science, 35 (3), pp. 321-339; Strozzi, F., Bosch, J., Zaldar, J.M., Beer game order policy optimization under changing customer demand (2007) Decision Support Systems, 42 (4), pp. 2153-2163; Syntetos, A.A., Babai, M.Z., Dallery, Y., Teunter, R., Periodic control of intermittent demand items: Theory and empirical analysis (2009) Journal of the Operational Research Society, 60 (5), pp. 611-618; Syntetos, A.A., Boylan, J.E., Disney, S.M., Forecasting for inventory planning: A 50-year review (2009) Journal of the Operational Research Society, 60 (5), pp. 149-160; Syntetos, A.A., Georgantzas, N.C., Boylan, J.E., Dangerfield, B.C., Judgement and supply chain dynamics (2011) Journal of the Operational Research Society, 62 (6), pp. 1138-1158; Teunter, R.H., Duncan, L., Forecasting intermittent demand: A comparative study (2009) Journal of the Operational Research Society, 60 (3), pp. 321-329; Towill, D.R., Zhou, L., Disney, S.M., Reducing the bullwhip effect: Looking through the appropriate lens (2007) International Journal of Production Economics, 108 (1-2), pp. 444-453; Warburton, R.D.H., An analytical investigation of the Bullwhip Effect (2004) Production and Operations Management, 13 (2), pp. 150-160; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking, 15 (1), pp. 25-51; Wouters, M., A developmental approach to performance measures-Results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78; Zhou, L., Disney, S.M., Bullwhip and inventory variance in a closed loop supply chain (2006) OR Spectrum, 28 (1), pp. 127-149; Zhou, L., Disney, S., Towill, D.R., A pragmatic approach to the design of bullwhip controllers (2010) International Journal of Production Economics, 128 (2), pp. 556-568},
correspondence_address1={Cannella, S.; Centre for Management Studies, Av. Prof Cavaco Silva, Porto Salvo 2780-990, Portugal; email: salvatore.cannella@ist.utl.pt},
publisher={Palgrave Macmillan Ltd.},
issn={01605682},
coden={JORSD},
language={English},
abbrev_source_title={J.Oper.Res.Soc.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wu2013761,
author={Wu, Y. and Jiang, Z.},
title={A performance measurement system for supply chain based on AI and J2EE},
journal={Advances in Intelligent Systems and Computing},
year={2013},
volume={181 AISC},
pages={761-767},
doi={10.1007/978-3-642-31698-2_107},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868323059&doi=10.1007%2f978-3-642-31698-2_107&partnerID=40&md5=9e1587ecf08782100b204f294f422332},
affiliation={School of Business, Shandong Jianzhu University, Jinan, China},
abstract={Performance measurement systems have become very popular and largely applied in organizations, as they provide not only a tool for measuring and controlling performance activities, but also a powerful management tool at the most strategic business level. The necessity for researching on a performance measurement system for supply chain based on AI and J2EE is discussed. Furthermore an integrated performance measurement system for supply chain based on AI and J2EE is proposed. Finally, the plan to design and implement the system is expounded. © 2013 Springer-Verlag.},
author_keywords={AI;  J2EE;  Performance measurement system;  Supply chain},
keywords={Artificial intelligence;  Communication;  Supply chains, J2EE;  Management tool;  Performance measurement system;  Strategic business, Supply chain management},
references={Wu, Y., Han, N., Wang, H., (2008) Implementation of the Computer Aided Business Process Reengineering System Based on UML and J2EE Technology, pp. 173-176. , Computer Society Press January; Wu, Y., Jiang, Z., Han, N., (2010) The Design and Implementation of the Integrated Supply Chain Management System Based on UML and J2EE Technology, pp. 1361-1365. , Harbin Institute of Technology Press March; Xu, Y., Chen, J.-L., Li, Z.-B., Wang, Z.-X., Study and Realization on Framework of Universal Performance Evaluation System for Supply Chain (2004) Application Research of Computers, pp. 152-154; Wang, M., Wu, Z., Research and Design of Multi-tiered Fuzzy Comprehensive Performance Evaluation System for Suppliers Based on J2EE Technique (2008) Computer Application and Software, 25, pp. 124-126; Zhao, X.-Y., Huang, X.-Y., Sun, F.-Q., An Important Supplier and Customer Oriented Approach to Supply Chain Design (2005) System Engineering, 23, pp. 34-38; Du, Y.-L., Zhu, X.-W., Supplier Evaluation and Selection in Supply Chain Design (2007) Journal of Tianjin University (Social Sciences), 9, pp. 312-317},
correspondence_address1={Wu, Y.; School of Business, , Jinan, China; email: clarence-wyc@126.com},
publisher={Springer Verlag},
issn={21945357},
isbn={9783642316975},
language={English},
abbrev_source_title={Adv. Intell. Sys. Comput.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Kazi201219,
author={Kazi, Lj. and Radulovic, B. and Kazi, Z.},
title={Performance indicators in software project monitoring: Balanced scorecard approach},
journal={2012 IEEE 10th Jubilee International Symposium on Intelligent Systems and Informatics, SISY 2012},
year={2012},
pages={19-25},
doi={10.1109/SISY.2012.6339539},
art_number={6339539},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870708429&doi=10.1109%2fSISY.2012.6339539&partnerID=40&md5=78b4010306d5af73a0e566666c421635},
affiliation={University of Novi Sad, Technical Faculty Mihajlo Pupin, Zrenjanin, Serbia},
abstract={Balanced scorecard is one of most used methodologies for performance measurement in enterprises and non-profit organizations. It presents a general framework that has been applied more precisely by many scientists and practitioners. It has been adapted to particular performance measurement systems. These monitoring systems are based on collecting data and analysis of these data. This paper presents overview of implementing balanced scorecard in IT project management. Framework for software project success monitoring, based on PRINCE2 methodology and balanced scorecard is proposed. Key performance indicators as measures to be performed upon data collected during software project implementation are defined. This way, analytics to be performed upon data according to specified key performance indicators, needed for decision support is enabled. © 2012 IEEE.},
keywords={Balanced scorecard approach;  Balanced scorecards;  Decision supports;  Key performance indicators;  Monitoring system;  Non profit organizations;  Performance indicators;  Performance measurement system;  Performance measurements;  PRINCE2;  Software project, Decision support systems;  Information science;  Intelligent systems;  Project management;  Supply chain management, Benchmarking},
references={http://agilemanifesto.org/; Bocci, F., Defining performance measurement - A comment (2004) Perspectives on Performance Journal, 3 (1-2). , Sept; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, pp. 71-79. , January-February; Performance Measurement Management Framework, , http://www.businessballs.com/dtiresources/ performance_measurement_management.pdf; Krolick, M., Ariyachandra, T., Business performance management: One truth (2006) Information System Management Journal, , Winter; http://www.bpmstandardsgroup.org/; Ciric, B., (2006) Business Intelligence, , Data Status, Belgrade; Pesalj, B., (2006) Enterprise Performance Measurement, , University of Belgrade, School of Economics; About Balanced Scorecard, , http://www.balancedscorecard.org/BSCResources/AbouttheBalancedScorecard/ tabid/55/Default.aspx; James, M., Summary of Kaplan's Balanced Scorecard, , http://maaw.info/ArticleSummaries/ArtSumKaplanNorton92.htm; (2005) A Guide to Project Management Body of Knowledge, , Project management institute; Yeong, A., (2007) The Marriage Proposal of PRINCE2 and PMBOK; Siegelaub, J.M., (2004) How PRINCE2 Can Complement PMBOK and Your PMP, , PMI/Westchester chapter; P. Jovanovic: Project management [Upravljanje projektom], Faculty of organizational sciences, University of Belgrade, 2006; Dobrovic, Z., Tomicic, M., Vrcek, N., Towards an effective e-government: Implementation of a balanced scorecard in the public sector (2008) Intellectual Economics Journal, 3 (1), pp. 7-17; Virtanen, T., Guidelines for implementing Balanced Scorecard (2009) QPR Software White Paper; Alleman, G.B., Using Balanced Scorecard to Build a Project Focused IT Organization Balanced Scorecard Conference Proceedings, San Francisco, Oct 28-30, 2003; Brock, S., Hendricks, D., Linnell, S., Smith, D., A balanced approach to IT project management (2003) SAICSIT '03 Proceedings of the 2003 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists on Enablement Through Technology, pp. 2-10. , Publisher South African Institute for Computer Scientists and Information Technologists, Republic of South Africa; Asosheh, A., Nalchigar, S., Jamporazmey, M., Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach (2010) Expert Systems with Applications: An International Journal, 37 (8), pp. 5931-5938. , August, Pergamon Press, Inc. Tarrytown, NY, USA ISSN: 0957-4174; (2007) CobiT 4.1., Excerpt Executive Summary Framework, , IT governance Institute; Measuring the Effectiveness of An Agile Software Development Team, , http://agile101.net/2009/07/18/agile-balanced-scorecard-measuring-the- effectiveness-of-an-agile-software-development-team/; Goethert, W., Fisher, M., (2003) Deriving Enterprise-Based Measures Using the Balanced Scorecard and Goal-Driven Measurement Techniques, , Technical Note, Software Engineering Institute, Carnegie Mellon University, October; http://www.berr.gov.uk/aboutus/corporate/projectcentre/pm-templates/ page12526.html, Department for Business innovation and skills: PRINCE 2 templates; Kazi, Lj., Radulovic, B., Information system based on Balanced Scorecard for student teamwork software project management International Symposium MIPRO 2011, Opatija, Croatia, 2011; Kazi, Lj., Radosav, D., Nikolic, M., Chotaliya, N., Balanced Scorecard Framework in Software Project Monitoring (2011) Journal of Engineering Management and Competitiveness, 1 (1-2). , Technical faculty "Mihajlo Pupin" Zrenjanin, Online ISSN: 2217-8147},
correspondence_address1={Kazi, Lj.; University of Novi Sad, , Zrenjanin, Serbia; email: leremic@tfzr.uns.ac.rs},
isbn={9781467347518},
language={English},
abbrev_source_title={IEEE Jubilee Int. Symp. Intelligent Syst. Informatics, SISY},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Moskovitz201210,
author={Moskovitz, E. and Even, A.},
title={The impact of BI-supported performance measurement system on a public police force},
journal={18th Americas Conference on Information Systems 2012, AMCIS 2012},
year={2012},
volume={1},
pages={10-17},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877889177&partnerID=40&md5=d3c5cc4e3e09a6acdfa6e10bfe2f0a78},
affiliation={Dept. of Industrial Engineering and Management, Ben-Gurion University of the Negev, Israel},
abstract={Performance Measurement Systems (PMS) may assist implementing organizational strategy and act as an effective tool for control and surveillance. The PMS explored in this study was implemented by the public police forces, using advanced Business Intelligence (BI) technologies. Using that system, police commanders can analyze performance scores of their own units and get feedback on their success. The study examines the system's impact, through analysis of the metric results over a 5-year time period. The results indeed show a positive impact, as with the vast majority of the metrics examined, the performance indeed improved. Further, the results confirmed the preliminary assumptions that the relative weight of each metric will moderate the improvement, and that metrics that reflect activity will behave differently than those that reflect outcomes. The study discusses the implications of these results, and proposed directions for future research. © (2012) by the AIS/ICIS Administrative Office All rights reserved.},
author_keywords={Business intelligence;  Is success;  Performance measurement system},
keywords={Effective tool;  IS success;  Organizational strategy;  Performance measurement system;  Police forces;  Relative weights;  Time-periods, Competitive intelligence;  Information systems;  Law enforcement, Supply chain management},
references={Austin, R., Gittell, J.H., When It Should not Work but Does: Anomalies of High Performance (2002) Business Performance Measurement: Theory and Practice, pp. 80-106. , A. Neely (editor), Boston, MA: Cambridge Univ. Press; Behn, R.D., Why Measure Performance? Different Purposes Require Different Measures (2003) Public Administration Review, 63 (5), pp. 586-606; Bourne, M., Performance Measurement: Learning from the Past and Projecting the Future (2008) Measuring Business Excellence, 12 (4), pp. 67-72; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, Implementing and Updating Performance Measurement Systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Cohen, J., A Power Primer (1992) Psychological Bulletin, 112, pp. 155-159; Davis, S., Albright, T., An Investigation of the Effect of Balanced Scorecard Implementation on Financial Performance (2004) Management Accounting Research, 15 (2), pp. 135-153; Franco, M., Bourne, M.C., (2004) Are Strategic Performance Measurement Systems Really Effective: A Closer Look At the Evidence, In the Proceedings of the EurOMA Conference, pp. 163-174. , INSEAD, Paris, France; Ghosh, D., Lusch, R.F., Outcome Effect, Controllability and Performance Evaluation of Managers: Some Field Evidence from Multi-Outlet Businesses (2000) Accounting, Organizations and Society, 25 (4-5), pp. 411-425; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the Weighting of Performance Measures: Evidence from a Balanced Scorecard (2003) The Accounting Review, 78 (3), pp. 725-758; Kaplan, R.S., Norton, D.P., (2001) Strategy Focused Organization: How Balanced Scorecard Companies Thrive In the New Business Environment, , Boston, MA: Harvard Business School Press; Kennerley, M., Neely, A., A Framework of the Factors Affecting the Evolution of Performance Measurement Systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Mahama, H., Management Control Systems, Cooperation and Performance in Strategic Supply Relationships: A Survey in the Mines (2006) Management Accounting Research, 17 (3), pp. 315-339; Murphy, K., Oyer, P., (2001) Discretion In Executive Incentive Contracts: Theory and Evidence (A Working Paper), , University of Southern California and Stanford University; Neely, A., Bourne, M., Why Measurement Initiatives Fail (2000) Measuring Business Excellence, 4 (4), pp. 3-7; Neely, A., Gregory, M., Platts, K., Performance Measurement System Design (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Wood, S.N., Modeling and Smoothing Parameter Estimation with Multiple Quadratic Penalties (2000) Journal of the Royal Statistical Society: Series B - Statistical Methodology, 62 (2), pp. 413-428},
correspondence_address1={Dept. of Industrial Engineering and Management, Israel},
isbn={9781622768271},
language={English},
abbrev_source_title={18th Amer. Conf. Inf. Sys. 2012, AMCIS 2012},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Springinklee2012309,
author={Springinklee, M. and Wallenburg, C.M.},
title={Improving distribution service performance through effective production and logistics integration},
journal={Journal of Business Logistics},
year={2012},
volume={33},
number={4},
pages={309-323},
doi={10.1111/jbl.12004},
note={cited By 24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84876245338&doi=10.1111%2fjbl.12004&partnerID=40&md5=4ed9fe7d77ebb8570267daa7446ae015},
affiliation={Bayer Business Consulting, Germany; Kühne-Foundation Department of Logistics and Services Management, WHU, Otto Beisheim School of Management, Burgplatz 2, Vallendar 56179, Germany},
abstract={Current empirical research on logistics' relationships with other departments has mostly neglected the interface with production. On the basis of data from 387 manufacturing companies, we answer a recent call for research and extend the literature by examining effective working relationships and achieved integration between the production and logistics functions regarding their performance impacts and their antecedents. With respect to the former, we take a resource-based perspective, showing that integration achieved between the production and logistics functions drives distribution service performance by serving as a pivotal capability element in the resource-capability-value chain. Within this chain, effective working relationships constitute the underlying resources and prove to be a strong driver of achieved integration. With respect to antecedents, we employ the theory of cooperation and competition and identify positive goal interdependence to substantially improve working relationships between production and logistics. Furthermore, we find this effect to be amplified via formalized performance measurement systems. © Council of Supply Chain Management Professionals.},
author_keywords={Achieved integration;  Goal interdependence;  Performance measurement;  Production;  Working relationship effectiveness},
references={Amit, R., Schoemaker, P., Strategic assets and organizational rent (1993) Strategic Management Journal, 14 (1), pp. 33-46; Armstrong, S., Overton, T., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 14 (3), pp. 396-402; Arshinder, A., Deshmukh, S., Supply chain coordination: Perspectives, empirical studies and research directions (2008) International Journal of Production Economics, 115 (2), pp. 316-335; Bagozzi, R., Yi, Y., On the evaluation of structural equation models (1988) Journal of the Academy of Marketing Science, 16 (1), pp. 74-94; Baker, G., Gibbons, R., Murphy, K., Subjective performance measures in optimal incentive contracts (1994) The Quarterly Journal of Economics, 109 (4), pp. 1125-1156; Ballou, R., (1985) Business Logistics Management: Planning and Control, , Englewood Cliffs, NJ: Prentice Hall, Inc; Bandura, A., (1986) Social Foundations of Thought and Action: A Social Cognitive Theory, , Englewood Cliffs, NJ: Prentice Hall, Inc; Barki, H., Pinsonneault, A., A model of organizational integration, implementation effort, and performance (2005) Organization Science, 16 (2), pp. 165-179; Barney, J., Firm resources and sustainable competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Baron, R., Kenny, D., The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations (1986) Journal of Personality and Social Psychology, 51 (6), pp. 1173-1182; Beersma, B., Hollenbeck, J., Humphrey, S., Moon, H., Conlon, D., Ilgen, D., Cooperation, competition, and team performance: Toward a contingency approach (2003) The Academy of Management Journal, 46 (5), pp. 572-590; Bonner, S., Sprinkle, G., The effects of monetary incentives on effort and task performance: Theories, evidence, and a framework for research (2002) Accounting, Organizations and Society, 27 (4-5), pp. 303-345; Bouwens, J., Van Lent, L., Assessing the performance of business unit managers (2007) Journal of Accounting Research, 45 (4), pp. 667-697; Burns, T., Stalker, G., (1961) The Management of Innovation, , London: Tavistock; Chen, G., Tjosvold, D., Organizational values and procedures as antecedents for goal interdependence and collaborative effectiveness (2008) Asia Pacific Journal of Management, 25 (1), pp. 93-112; Chen, H., Daugherty, P., Roath, A., Defining and operationalizing supply chain process integration (2009) Journal of Business Logistics, 30 (1), pp. 63-84; Chen, I., Paulraj, A., Towards a theory of supply chain management: The constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Cheng, J., Organizational coordination, uncertainty, and performance: An integrative study (1984) Human Relations, 37 (10), pp. 829-851; Cronbach, L., Coefficient alpha and the internal structure of tests (1951) Psychometrika, 16 (3), pp. 297-334; Das, A., Narasimhan, R., Talluri, S., Supplier integration - Finding an optimal configuration (2006) Journal of Operations Management, 24 (1), pp. 563-582; Daugherty, P., Chen, H., Mattioda, D., Grawe, S., Marketing/logistics relationships: Influence on capabilities and performance (2009) Journal of Business Logistics, 30 (1), pp. 1-18; Davis-Sramek, B., Mentzer, J., Stank, T., Creating consumer durable retailer customer loyalty through order fulfillment service operations (2008) Journal of Operations Management, 26 (6), pp. 781-797; Deutsch, M., A theory of cooperation and competition (1949) Human Relations, 2 (2), pp. 129-152; Dillman, D., (2000) Mail and Internet Surveys, , New York: Wiley; Donaldson, L., (2001) The Contingency Theory of Organizations, , Thousand Oaks: Sage Publications; Drake, A., Haka, S., Ravenscroft, S., Cost system and incentive structure effects on innovation, efficiency and profitability in teams (1999) Accounting Review, 74 (3), pp. 323-345; Droge, C., Jayaram, J., Vickery, S., The effects of internal versus external integration practices on time-based performance and overall firm performance (2004) Journal of Operations Management, 22 (6), pp. 557-573; Ellinger, A., Improving marketing/logistics cross-functional collaboration in the supply chain (2000) Industrial Marketing Management, 29 (1), pp. 85-96; Ellinger, A., Daugherty, P., Keller, S., The relationship between marketing/logistics interdepartmental integration and performance in U.S. Manufacturing firms: An empirical study (2000) Journal of Business Logistics, 21 (1), pp. 1-22; Ellinger, A., Keller, S., Hansen, J., Bridging the divide between logistics and marketing: Facilitating collaborative behavior (2006) Journal of Business Logistics, 27 (2), pp. 1-26; Fawcett, S., Ogden, J., Magnan, G., Cooper, B., Organizational commitment and governance for supply chain success (2006) International Journal of Physical Distribution & Logistics Management, 36 (1), pp. 22-35; Fawcett, S.E., Fawcett, S.A., The firm as a value-added system (1995) International Journal of Physical Distribution & Logistics Management, 25 (5), pp. 24-42; Fornell, C., Larcker, D., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Fullerton, R., McWatters, C., The role of performance measures and incentive systems in relation to the degree of JIT implementation (2002) Accounting, Organizations and Society, 27 (8), pp. 711-735; Galbraith, J., (1973) Designing Complex Organizations, , Reading: Addison-Wesley; Galbraith, J., (1977) Organizational Design, , Reading: Addison-Wesley; Garver, M., Mentzer, J., Logistics research methods: Employing structural equation modeling to test for construct validity (1999) Journal of Business Logistics, 20 (1), pp. 33-58; Georgopoulos, B., Mann, F., (1962) The Community General Hospital, , New York: MacMillan; Gerbing, D., Anderson, J., An updated paradigm for scale development incorporating unidimensionality and its assessment (1988) Journal of Marketing Research, 25 (2), pp. 186-192; Gerdin, J., The impact of departmental interdependencies and management accounting system use on subunit performance (2005) European Accounting Review, 14 (2), pp. 297-327; Gimenez, C., Ventura, E., Logistics-production, logistics-marketing and external integration: Their impact on performance (2005) International Journal of Operations & Production Management, 25 (1), pp. 20-38; Hansen, A., Nonfinancial performance measures, externalities and target setting: A comparative case study of resolutions through planning (2010) Management Accounting Research, 21 (1), pp. 17-39; Hausman, W., Montgomery, D., Roth, A., Why should marketing and manufacturing work together? Some exploratory empirical results (2002) Journal of Operations Management, 20 (3), pp. 241-257; Hayes, R., Wheelwright, S., (1984) Restoring our Competitive Edge: Competing Through Manufacturing, , New York: Wiley; Van Hoek, R., Ellinger, A., Johnson, M., Great divides: Internal alignment between logistics and peer functions (2008) International Journal of Logistics Management, 19 (2), pp. 110-129; Hutt, M., Cross-functional working relationships in marketing (1995) Journal of the Academy of Marketing Science, 23 (4), pp. 351-357; Jensen, M., Meckling, W., Specific and general knowledge, and organizational structure (1995) Journal of Applied Corporate Finance, 8 (2), pp. 4-18; Johnson, D., Social interdependence: Interrelationships among theory, research, and practice (2003) American Psychologist, 58 (11), pp. 934-945; Kahn, K., Reizenstein, R., Rentz, J., Sales-distribution interfunctional climate and relationship effectiveness (2004) Journal of Business Research, 57 (10), pp. 1085-1091; Kerr, S., On the folly of rewarding A, while hoping for B (1975) Academy of Management Journal, 18 (4), pp. 769-783; Kline, R., (2004) Principles and Practice of Structural Equation Modeling, , New York: The Guilford Press; Konijnendijk, P., Dependence and conflict between production and sales (1993) Industrial Marketing Management, 22 (3), pp. 161-167; Koufteros, X., Testing a model of pull production: A paradigm for manufacturing research using structural equation modeling (1999) Journal of Operations Management, 17 (4), pp. 467-488; Koufteros, X., Vonderembse, M., Doll, W., Examining the competitive capabilities of manufacturing firms (2002) Structural Equation Modeling, 9 (2), pp. 256-282; Larson, P., A note on mail surveys and response rates in logistics research (2005) Journal of Business Logistics, 26 (2), pp. 211-222; Lawrence, P., Lorsch, J., (1967) Organization and Environment: Managing Differentiation and Integration, , Homewood, IL: Irwin; Lawrence, P., Lorsch, J., Differentiation and integration in complex organizations (1967) Administrative Science Quarterly, 12 (1), pp. 1-47; Lessard, D., Zaheer, S., Breaking the silos: Distributed knowledge and strategic responses to volatile exchange rates (1998) Strategic Management Journal, 17 (7), pp. 513-533; Lindell, M., Whitney, D., Accounting for common method variance in cross-sectional research designs (2001) Journal of Applied Psychology, 86 (1), pp. 114-121; Locke, E., Latham, G., (1984) Goal Setting: A Motivational Technique that Works!, , Englewood Cliffs, NJ: Prentice Hall, Inc; Locke, E., Latham, G., Smith, K., (1990) A Theory of Goal Setting & Task Performance, , Englewood Cliffs, NJ: Prentice Hall, Inc; Macintosh, R.L., Management control systems and departmental interdependencies: An empirical study (1987) Accounting, Organizations and Society, 12 (1), pp. 49-61; Mentzer, J., Min, S., Bobbitt, L., Toward a unified theory of logistics (2004) International Journal of Physical Distribution and Logistics Management, 34 (7-8), pp. 606-627; Mentzer, J., Stank, T., Esper, T., Supply chain management and its relationship to logistics, marketing, production and operations management (2008) Journal of Business Logistics, 29 (1), pp. 31-46; Merchant, K., Van Der Stede, W., (2003) Management Control Systems: Performance Measurement, Evaluation and Incentives, , Englewood Cliffs, NJ: Prentice Hall, Inc; Mollenkopf, D., Gibson, A., Ozanne, L., The integration of marketing and logistics functions: An empirical examination of New Zealand firms (2000) Journal of Business Logistics, 21 (2), pp. 89-112; Nunnally, J., Bernstein, I., (1978) Psychometric Theory, , New York: McGraw-Hill; Olavarrieta, S., Ellinger, A.E., Resource-based theory and strategic logistics research (1997) International Journal of Physical Distribution and Logistics Management, 27 (9-10), pp. 559-587; Pagell, M., Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics (2004) Journal of Operations Management, 22 (1), pp. 459-487; Phillips, L., Assessing measurement error in key informant reports: A methodological note on organizational analysis in marketing (1981) Journal of Marketing Research, 18 (4), pp. 395-415; Phillips, L., Bagozzi, R., On measuring organizational properties of distribution channels: Methodological issues in the use of key informants (1986) Research in Marketing, 8 (1), pp. 313-369; Pinto, M., Pinto, J., Prescott, J., Antecedents and consequences of project team cross-functional cooperation (1993) Management Science, 39 (10), pp. 1281-1297; Podsakoff, P., MacKenzie, S., Lee, J.-Y., Podsakoff, N., Common method biases in behavioral research: A critical review of the literature and recommended remedies (2003) Journal of Applied Psychology, 88 (5), pp. 879-903; Poirier, C., Swink, M., Quinn, F., Still chasing the leaders. Sixth annual global survey of supply chain progress (2008) Supply Chain Management Review October, 2008, pp. 26-32; Pyke, D., Cohen, M., Performance characteristics of stochastic integrated production- distribution systems (1993) European Journal of Operational Research, 68 (1), pp. 23-48; Rhea, M., Shrock, D., Measuring the effectiveness of physical distribution customer service programs (1987) Journal of Business Logistics, 8 (1), pp. 31-45; Ridgway, V., Dysfunctional consequences of performance measurements (1956) Administrative Science Quarterly, 1 (2), pp. 240-247; Rodrigues, A., Stank, T., Lynch, D., Linking strategy, structure, process, and performance in integrated logistics (2004) Journal of Business Logistics, 25 (2), pp. 65-94; Rosenzweig, E., Roth, A., Dean, J., The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21 (4), pp. 437-456; Ruekert, R., Walker Jr., O., Marketing's interaction with other functional units: A conceptual framework and empirical evidence (1987) The Journal of Marketing, 51 (1), pp. 1-19; Sawhney, R., Piper, C., Value creation through enriched marketing-operations interfaces: An empirical study in the printed circuit board industry (2002) Journal of Operations Management, 20 (1), pp. 259-272; Shaw, J., Gupta, N., Delery, J., Congruence between technology and compensation systems: Implications for strategy implementation (2001) Strategic Management Journal, 22 (1), pp. 379-386; Sherif, M., (1962) Intergroup Relations and Leadership, , New York: John Wiley; Sousa, R., Voss, C., Contingency research in operations management practices (2008) Journal of Operations Management, 26 (1), pp. 697-713; Stank, T., Goldsby, T., Vickery, S., Savitskie, K., Logistics service performance: Estimating its influence on market share (2003) Journal of Business Logistics, 24 (1), pp. 27-56; Stank, T., Keller, S., Closs, D., Performance benefits of supply chain logistical integration (2001) Transportation Journal, 41 (2-3), pp. 32-46; Stank, T., Keller, S., Daugherty, P., Supply chain collaboration and logistical service performance (2001) Journal of Business Logistics, 22 (1), pp. 29-48; Thompson, J., (1967) Organizations in Action, , New York: McGraw-Hill; Thun, J.-H., Angles of integration: An empirical analysis of the alignment of internet-based information technology and global supply chain management (2010) Journal of Supply Chain Management, 46 (2), pp. 30-44; Tjosvold, D., Cooperation theory and organizations (1984) Human Relations, 37 (9), pp. 743-767; Tjosvold, D., Cooperative and competitive interdependence: Collaboration between departments to serve customers (1988) Group & Organization Studies, 13 (3), pp. 274-289; Tjosvold, D., Dann, V., Wong, C., Managing conflict between departments to serve customers (1992) Human Relations, 45 (10), pp. 1035-1054; Tjosvold, D., Yu, Z.-Y., Hui, C., Team learning from mistakes: The contribution of cooperative goals and problem-solving (2004) Journal of Management Studies, 41 (7), pp. 1223-1245; Tomer, J., (1987) Organizational Capital, , New York: Praeger Publishing; Trautmann, G., Turkulainen, V., Hartmann, E., Bals, L., Integration in the global sourcing organization - An information processing perspective (2009) Journal of Supply Chain Management, 45 (2), pp. 57-74; Tsui, A., Ashford, S., Adaptive self-regulation: A process view of managerial effectiveness (1994) Journal of Management, 20 (1), pp. 93-121; Turkulainen, V., (2008) Managing Cross-Functional Interdependencies, , The Contingent Value of Integration. Espoo: Helsinki University of Technology; Tushman, M., Nadler, D., Information processing as an integrating concept in organizational design (1978) The Academy of Management Review, 3 (1), pp. 613-624; Van Veen-Dirks, P., Different uses of performance measures: The evaluation versus reward of production managers (2010) Accounting, Organizations and Society, 35 (2), pp. 141-164; Van De Ven, A., On the nature, formation, and maintenance of relations among organizations (1976) The Academy of Management Review, 1 (4), pp. 24-36; Van De Ven, A., Ferry, D., (1980) Measuring and Assessing Organizations, , New York: John Wiley & Sons; Wageman, R., Interdependence and group effectiveness (1995) Administrative Science Quarterly, 40 (1), pp. 145-180; Wageman, R., Baker, G., Incentives and cooperation: The joint effects of task and reward interdependence on group performance (1997) Journal of Organizational Behavior, 18 (2), pp. 139-158; Wagner, S.M., Kemmerling, R., Handling nonresponse in logistics research (2010) Journal of Business Logistics, 31 (2), pp. 357-381; Zacharia, Z., Nix, N., Lusch, R., An analysis of supply chain collaborations and their effect on performance outcomes (2009) Journal of Business Logistics, 30 (2), pp. 101-123},
correspondence_address1={Wallenburg, C.M.; Kühne-Foundation Department of Logistics and Services Management, Burgplatz 2, Vallendar 56179, Germany; email: wallenburg@whu.edu},
issn={07353766},
language={English},
abbrev_source_title={J. Bus. Logist.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{VanAnholt2012,
author={Van Anholt, R.G. and Vis, I.F.A.},
title={Real-time performance measurement system for automated teller machines},
journal={Proceedings - Winter Simulation Conference},
year={2012},
doi={10.1109/WSC.2012.6465152},
art_number={6465152},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874736339&doi=10.1109%2fWSC.2012.6465152&partnerID=40&md5=8a726cebd6018f646c1fa004373c49fe},
affiliation={VU University Amsterdam, Faculty of Economics and Business Administration, Boelelaan 1105, Amsterdam NL-1081HV, Netherlands; University of Groningen, Faculty of Economics and Business, Nettelbosje 2, Groningen, NL-9747AE, Netherlands},
abstract={Performance measurement systems have proven to facilitate process improvement in the past decades in various markets and environments. The objective of this paper is to design a performance measurement system to actively control, monitor and improve performance of automated teller machines. In our realtime performance measurement system we apply different weights for different types of potential lost sales. We implement the measurement system in an ATM inventory management context and conduct discrete event simulation experiments to demonstrate its added value in terms of cost and service. We use data from Dutch commercial banks in our performance and sensitivity analyses. Exhaustive numerical validation demonstrates that the implementation of our performance measurement system leads to a higher fill rate - 99% instead of 98% - with equal expenses, or a 3.7% cost reduction while maintaining an equal fill rate. © 2012 IEEE.},
keywords={Added values;  Automated teller machines;  Commercial bank;  Fill rate;  Improve performance;  Inventory management;  Lost sale;  Measurement system;  Numerical validations;  Performance measurement system;  Process Improvement;  Real time performance, Discrete event simulation;  Inventory control, Supply chain management},
references={Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; (2011) World Payments Report 2011, 7, pp. 1-60. , Capgemini RBS and EFMA; (2011) Cash Report 2011, pp. 1-48. , G4S Cash Solutions; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Harvey, J., Service quality: A tutorial (1998) Journal of Operations Management, 16 (5), pp. 583-597. , PII S0272696397000260; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54, pp. 507-514; Law, A.M., Kelton, W.D., (2000) Simulation Modeling and Analysis, , Boston: McGraw-Hill; Sargent, R.G., Verification and vaildation of simulation models (2005) Proceedings Winter Simulation Conference, 37, pp. 130-143. , ed. M. E. Kuhl, N. M. Steiger, F. B. Armstrong, and J. A. Joines, eds; Silver, E.A., Meal, H.C., A heuristic for selecting lot size quantities for the case of a deterministic time-varying demand rate and discrete opportunities for replenishment (1973) Production and Inventory Management Journal, 14, pp. 64-74; Suri, P.K., Singh, D., Chander, R., A simulator for forecasting the location of atms in banking industry (2007) Challenges & Opportunities in Information Technology, , Paper presented at the, Punjab, India; Van Anholt, R.G., Vis, I.F.A., An integrative online atm forecasting and replenishment model with a target fill rate (2010) Proceedings of the International Conferene on Logistics and Maritime Systems, pp. 1-10; Van Anholt, R.G., Vis, I.F.A., Key determinants of atm performance: Survey results from businesses and users (2012) Working Paper VU University Amsterdam; Wagner, M., Analyzing inventory routing costs (2010) International Conference on Enterpreise Systems, Accounting and Logistics, , Paper presented at the, Rhodes Island, Greece},
correspondence_address1={Van Anholt, R.G.; VU University Amsterdam, , Amsterdam NL-1081HV, Netherlands; email: r.g.van.anholt@vu.nl},
issn={08917736},
isbn={9781467347792},
coden={WSCPD},
language={English},
abbrev_source_title={Proc. Winter Simul. Conf.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Kaare2012324,
author={Kaare, K.K. and Kuhi, K. and Koppel, O.},
title={Tire and pavement wear interaction monitoring for road performance indicators [Rehvide ja teekatte kulumise jälgimine teehoiu tulemusnäitajate süsteemis]},
journal={Estonian Journal of Engineering},
year={2012},
volume={18},
number={4},
pages={324-335},
doi={10.3176/eng.2012.4.04},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869831991&doi=10.3176%2feng.2012.4.04&partnerID=40&md5=49fd4339231e854c0e381cc5096f23b1},
affiliation={Department of Logistics and Transport, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia; Ericsson Eesti, Ltd, Järvevana tee 9, 11314 Tallinn, Estonia},
abstract={The design, building and maintenance of national roads constitute a substantial part of the government budget. Taking into account the magnitude and importance of these investments, the expedience, efficiency and sustainability of roads are of great public interest and their performance should be measured. For performance assessment of roads or road sections it is important to gather data about different key performance indicators. Flexible pavements deteriorate under traffic loads and climate effects. This effect depends on the technology and materials of the road, but the greatest effects depend on traffic loads and volumes. Systems can be developed to give information about tire wearing and its impact on the pavement wearing. To ensure traffic safety, especially in countries with cold climate, it is very important to gather continuously data about tire tread wear. New technologies have made it possible to integrate the measuring of tire tread wear and tire type on roads continuously and present new opportunities to monitor and analyse conditions of tires. This paper is part of a road performance measurement system presenting a concept for input data collecting.},
author_keywords={Pavement wear;  Road performance measurement;  Sensors;  Tire wear},
keywords={Climate effects;  Cold climate;  Flexible pavements;  Input datas;  Interaction monitoring;  Key performance indicators;  National roads;  Performance assessment;  Public interest;  Road performance;  Road section;  Tire tread;  Tire wear;  Traffic loads;  Traffic safety, Benchmarking;  Pavements;  Sensors;  Supply chain management, Tires},
references={(1997) The Damaging Effects of Overloaded Heavy Vehicles On Roads, , 4th ed. Department of Transport, Pretoria; Hjort, M., Haraldsson, M., Jansen, J.M., (2008) Road Wear From Heavy Vehicles - An Overview, , NVF committee Vehicles and Transports, Borlänge; Thiriez, K., Subramanian, R., (2001) Tire Pressure Special Study: Tread Depth Analysis, pp. 1-10. , National Highway Traffic Safety Administration Research Note, October; Collop, A., Cebon, D., Modelling whole-life pavement performance (1995) Road Transport Technol, 4, pp. 201-212; Arnold, G., Steven, B., Alabaster, D., Fussell, A., Effect on pavement wear of increased mass limits for heavy vehicles - Stage 3 (2005) Land Transport New Zealand Research Report, 279, pp. 1-118; Haider, S., Harichandran, R., Dwaikat, M., Impact of systematic axle load measurement error on pavement design using mechanistic-empirical pavement design guide (2012) J. Transp. Eng, 138, pp. 381-386; Swedberg, C., Michelin uses RFID to track tire pressure and tread for London bus company (2012) RFID J. Online, , http://www.rfidjournal.com/article/print/9697, July, accessed 01.08.2012; Holmes, J., British police may introduce tire tread camera (2011) Automobile Mag. Online, , http://rumors.automobilemag.com/british-police-may-introduce-tire-tread- cameras-79083.html#ixzz22grtodf2, October, accessed 30.06.2012; Einsiedler, I., (2012) ThinkIO In Transportation, , Kontron, Munich; Brey, T.A., (2005) Tire Tread Wear Sensor System, , U.S. Patent No. 7180409. Filed: March 11; Manas, D., Manas, M., Stanek, M., Pata, V., Wear of tyre treads (2009) J. Achievements Mater. Manufact. Eng, 3, pp. 538-543; Zhang, W., Suo, C., Wang, Q., A Novel sensor system for measuring wheel loads of vehicles on highways (2008) Sensors, 8, pp. 7671-7689; Tewolde, G.S., Sensor and network technology for intelligent transportation systems (2012) IEEE International Conference On Electro/Information Technology (EIT) 2012, pp. 1-7. , Indianapolis; Wu, E., Diao, Y., Rizvi, S., High-performance complex event processing over streams (2006) Proc. 2006 ACM SIGMOD International Conference On Management of Data SIGMOD 2006, pp. 407-418. , Chicago},
correspondence_address1={Kaare, K. K.; Department of Logistics and Transport, Ehitajate tee 5, 19086 Tallinn, Estonia; email: kati.korbe@ttu.ee},
issn={17366038},
language={English; Estonian},
abbrev_source_title={Est. J. Eng.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ozbek2012995,
author={Ozbek, M.E. and de la Garza, J.M. and Triantis, K.},
title={Efficiency measurement of the maintenance of paved lanes using data envelopment analysis},
journal={Construction Management and Economics},
year={2012},
volume={30},
number={11},
pages={995-1009},
doi={10.1080/01446193.2012.725939},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868292638&doi=10.1080%2f01446193.2012.725939&partnerID=40&md5=520070591d869e4b59d3e7066db88805},
affiliation={Department of Construction Management, Colorado State University, Guggenheim Hall, Fort Collins, 80523-1584, United States; Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University, United States; Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, United States},
abstract={While the road maintenance performance measurement systems that are currently in use investigate maintenance level of service (i.e. effectiveness of the road maintenance), the fundamental relationship between the maintenance level of service and the budget requirements (i.e. the efficiency of road maintenance) has not received as much attention. Given that not knowing how 'efficient' transportation agencies are in being 'effective' can lead to excessive and unrealistic maintenance budget expectations, there is a need for a performance measurement system that can assess the overall efficiency of road maintenance operations. To address this need, a framework was developed and implemented to assess the relative cost efficiency of eight counties in Virginia, USA in maintaining the paved lanes of the Interstate roads. Such framework is developed using a methodology known as data envelopment analysis which is based on the production theory and principles of linear programming. The results indicate that the Rockbridge County is the most efficient and the Roanoke County is the least efficient in maintaining the paved lanes. The findings of this research are intended to be used as guides for benchmarking as well as managerial actions and policy making for decision making with respect to road maintenance. © 2012 Copyright Taylor and Francis Group, LLC.},
author_keywords={Asset management;  benchmarking;  decision making;  efficiency;  measurement},
keywords={Cost efficiency;  Efficiency measurement;  Maintenance budgets;  Maintenance levels;  Managerial actions;  Overall efficiency;  Performance measurement system;  Policy making;  Production theory;  Road maintenance;  Transportation agencies;  Virginia, Asset management;  Benchmarking;  Budget control;  Data envelopment analysis;  Decision making;  Efficiency;  Measurements;  Roads and streets;  Supply chain management;  Transportation, Maintenance, benchmarking;  data envelopment analysis;  decision making;  efficiency measurement;  maintenance;  policy making;  road, Roanoke County;  Rockbridge County;  United States;  Virginia},
references={Banker, R.D., Morey, R.C., Efficiency analysis for exogenously fixed inputs and outputs (1986) Operations Research, 34 (4), pp. 513-521; Boussofiane, A., Dyson, R.G., Thanassoulis, E., Applied data envelopment analysis (1991) European Journal of Operational Research, 52 (1), pp. 1-15; Charnes, A., Cooper, W.W., Data envelopment analysis (1990) Twelfth IFORS International Conference on Operational Research, pp. 641-646. , in Bradley, H.E. (ed.), Athens, Greece, 25-29 June, Pergamon Press, Athens; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Charnes, A., Cooper, W.W., Lewin, A.Y., Seiford, L.M., (1994) Data Envelopment Analysis: Theory, Methodology, and Applications, , Boston: Kluwer Academic; Cook, W.D., Roll, Y., Kazakov, A., A DEA model for measuring the relative efficiency of highway maintenance patrols (1990) INFOR, 28 (2), pp. 113-123; Cook, W.D., Kazakov, A., Roll, Y., On the measurement of relative efficiency of highway maintenance patrols (1994) Data Envelopment Analysis: Theory, Methodology, and Applications, pp. 195-210. , In: Charnes A., Cooper W.W., Lewin A.Y., Seiford L.M., editors Boston: Kluwer Academic; Cooper, W.W., Seiford, L.M., Tone, K., (1999) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, and DEA-Solver Software, , Kluwer Academic, Norwell, MA; Craig, C.E., Harris, R.C., Total productivity measurement at the firm level (1973) Sloan Management Review, 14 (3), pp. 13-28; Dadson, D.K., (2001) Impact of environmental classification on steel girder bridge elements using bridge inspection data, , PhD dissertation, Civil and Environmental Engineering, Virginia Polytechnic Institute and State University; Dadson, D.K., de la Garza, J.M., Weyers, R.E., Service life and impact of Virginia environmental exposure condition on paint on steel girder bridges (2002) Journal of Infrastructure Systems, 8 (4), pp. 149-159; de la Garza, J.M., Triantis, K., Fallah-Fini, S., Efficiency measurement of highway maintenance strategies using data envelopment analysis (2009) In Proceedings of 2009 NSF Engineering Research and Innovation Conference, Honolulu, HI, 22-25 June; Fallah-Fini, S., (2010) Measuring the efficiency of highway maintenance operations: Environmental and dynamic considerations, , PhD dissertation, Industrial and Systems Engineering, Virginia Polytechnic Institute and State University; (2006) Price Trends for Federal-aid Highway Construction, , Federal Highway Administration, report number FHWA-IF-06-048, Federal Highway Administration, Washington, DC; Golany, B., Roll, Y., An application procedure for DEA (1989) OMEGA International Journal of Management Science, 17 (3), pp. 237-250; Muniz, M., Paradi, J., Ruggiero, J., Yang, Z., Evaluating alternative DEA models used to control for non-discretionary inputs (2006) Computers & Operations Research, 33 (5), pp. 1173-1183; (2003) CEO Leadership Forum for State Departments of Transportation: A Summary Report, , National Cooperative Highway Research Program, University of Minnesota Center for Transportation Studies; Ozbek, M.E., (2007) Development of a comprehensive framework for the efficiency measurement of road maintenance strategies using data envelopment analysis, , PhD dissertation, Civil and Environmental Engineering, Virginia Polytechnic Institute and State University; Ozbek, M.E., de la Garza, J.M., Triantis, K., Data envelopment analysis as a decision making tool for the transportation professionals (2009) ASCE Journal of Transportation Engineering, 135 (11), pp. 822-831; Ozbek, M.E., de la Garza, J.M., Triantis, K., Data and modeling issues faced during the efficiency measurement of road maintenance using data envelopment analysis (2010) ASCE Journal of Infrastructure Systems, 16 (1), pp. 21-30; Ozbek, M.E., de la Garza, J.M., Triantis, K., Efficiency measurement of bridge maintenance using data envelopment analysis (2010) ASCE Journal of Infrastructure Systems, 16 (1), pp. 31-39; Pastor, J., Chapter 3 translation invariance in data envelopment analysis: a generalization (1996) Annals of Operations Research, 66 (2), pp. 91-102; Ramanathan, R., (2003) An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement, , Thousand Oaks, CA: Sage; Rouse, P., (1997) A methodology of performance measurement with applications using data envelopment analysis, , PhD dissertation, The University of Auckland; Rouse, P., Putterill, M., Ryan, D., Towards a general managerial framework for performance measurement: a comprehensive highway maintenance application (1997) Journal of Productivity Analysis, 8, pp. 127-149; Sengupta, J., A model of dynamic efficiency measurement (1994) Applied Economics Letters, 1, pp. 119-121; Sexton, T.R., The methodology of data envelopment analysis (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, pp. 1-6. , in Silkman, R.H. (ed.), Jossey-Bass, San Francisco; Silkman, R.H., Editor's notes (1986) Measuring Efficiency: An Assessment of Data Envelopment Analysis, pp. 7-29. , in Silkman, R.H. (ed.), Jossey-Bass, San Francisco; Sowlati, T., Use of nonparametric statistical tests in defining the number of periods to include in an intertemporal DEA analysis (2007) International Transactions in Operational Research, 14 (3), pp. 203-215; Thanassoulis, E., (2001) Introduction to the Theory and Application of Data Envelopment Analysis, , Norwell, MA: Kluwer Academic; (2006) Maintenance and Operations of Transportation Facilities 2005 Strategic Vision, , Transportation Research Board, report number E-C092 Transportation Research Board, Washington, DC; (2002) An Action Plan for Distributed Authority to Promote Effective Administration and Management of VDOT's Contract with VMS, Inc., , Virginia Department of Transportation, Virginia Department of Transportation, Richmond, VA; Wakchaure, S.S., Jha, K.N., Prioritization of bridges for maintenance planning using data envelopment analysis (2011) Construction Management and Economics, 29 (9), pp. 957-968},
correspondence_address1={Ozbek, M. E.; Department of Construction Management, Guggenheim Hall, Fort Collins, 80523-1584, United States; email: mehmet.ozbek@colostate.edu},
issn={01446193},
coden={CMECF},
language={English},
abbrev_source_title={Constr. Manage. Econ.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Ferreira2012,
author={Ferreira, A. and Azevedo, S. and Fazendeiro, P.},
title={A linguistic approach to supply chain performance assessment},
journal={IEEE International Conference on Fuzzy Systems},
year={2012},
doi={10.1109/FUZZ-IEEE.2012.6251327},
art_number={6251327},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867587889&doi=10.1109%2fFUZZ-IEEE.2012.6251327&partnerID=40&md5=23408c2afc4751d9efa5a236f19eb12b},
affiliation={Department of Informatics, University of Beira Interior, Covilhã, Portugal; Management and Economics Department, University of Beira Interior, Covilhã, Portugal; Instituto de Telecomunicações - IT, Portuguese Telecommunications Institute, Coimbra, Portugal},
abstract={This paper proposes a fuzzy LARG (Lean, Agile, Resilient, Green) index model for supply chain (SC) performance assessment. Through its performance evaluation, SCs are able to measure their level of efficiency and its ability to react, efficiently, to changes in a competitive environment. The case study presented shows that due to the uncertainties surrounding the SC's environment and to the qualitative description of the SC's practices, fuzzy logic can provide an effective assessment tool able to quickly incorporate changes in the SC's business policy. © 2012 IEEE.},
author_keywords={Fuzzy Supply Chain Performance Measures;  Interpretability;  Numeric-Linguistic Interfaces},
keywords={Assessment tool;  Business policies;  Competitive environment;  Index models;  Interpretability;  Linguistic approach;  Performance assessment;  Performance evaluation;  Supply chain performance, Fuzzy logic;  Fuzzy systems;  Linguistics, Supply chains},
references={Gunasekaran, P., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Anand, G., Kodali, R., Development of a framework for implementation of lean manufacturing systems (2010) International Journal of Management Practice, 4 (1), pp. 95-116; Hong, P., Kwon, H., Roh, J., Implementation of strategic green orientation in supply chain: An empirical study of manufacturing firm (2009) European Journal of Innovation Management, 12 (4), pp. 512-532; Naylor, B., Naim, M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (10), pp. 107-118; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) International Journal of Production Economics, 101 (1), pp. 99-108. , DOI 10.1016/j.ijpe.2005.05.010, PII S0925527305001362; Christopher, M., Rutherford, C., (2004) Creating Supply Chain Resilience Through Agile Six Sigma, pp. 24-28. , Critical Eye, (June-August); Rosic, H., Bauer, G., Jammernegg, W., A framework for economic and environmental sustainability and resilience of supply chains (2009) Rapid Modelling for Increasing Competitiveness, pp. 91-104. , Reiner, G., Springer, New York; Womack, P., Jones, D., Roos, D., (1991) The Machine that Changed the World: The Story of Lean Production, , Harper Perennial; Baramichai, M., Zimmers, J., Marangos, A., Agile supply chain transformation matrix: An integrated tool for creating an agile enterprise (2007) Supply Chain Management: An International Journal, 12 (5), pp. 334-348; Azevedo, S., Machado, V., Barroso, A., Machado, V., Supply chain vulnerability: Environment changes and dependencies (2008) International Journal of Logistics and Transport, 2 (1), pp. 41-55; Srivastava, S., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Azevedo, S., Carvalho, H., Machado, V.C., The influence of green practices on supply chain performance: A case study approach (2011) Transportation Research Part E: Logistics and Transportation Review; Olugu, E., Wong, Y., Supply chain performance evaluation: Trends and challenges (2009) American J. of Engineering and Applied Sciences, 2 (1), pp. 202-211; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Chen, T., Sun, F., Lu, Y., Research on TPL-oriented fuzzy integrated evaluation method for supply chain (2007) The Sixth Wuhan International Conference on E-business; Chen, C.-T., Lin, C.-T., Huang, S.-F., A fuzzy approach for supplier evaluation and selection in supply chain management (2006) Int. J. Production Economics, 102, pp. 289-301; Pilevari, N., SeyedHosseini, S., Jassbi, J., Fuzzy logic supply chain agility assessment methodology (2008) IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1113-1117. , 8-11; Seyedhoseini, S., Jassbi, J., Pilevari, N., Application of adaptive neuro fuzzy inference system in measurement of supply chain agility: Real case study of a manufacturing company (2010) African Journal of Business Management, 4 (1), pp. 083-096; Erol, I., Sencer, S., Sari, R., A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain (2011) Ecological Economics, 70, pp. 1088-1100; Zadeh, L., Fuzzy sets (1965) Inform. Contr., 8, pp. 338-353; Ruspini, E., Bonissone, P., Pedrycz, W., (1998) Handbook of Fuzzy Computation, , Institute of Physics publishing, Bristol and Philadelphia; De Oliveira, J., Fazendeiro, P., On the achievement of both accurate and interpretable fuzzy systems using data-driven design processes (2003) Accuracy Improvements in Linguistic Fuzzy Modeling, , J. Casillas et al (Eds.) Studies in Fuzziness and Soft Computing, Physica-Verlag; Dubois, D., Prade, H., The mean value of a fuzzy number (1987) Fuzzy Sets and Systems, 24, pp. 279-300; Dubois, D., Prade, H., Ughetto, L., Fuzzy logic, control engineering and artificial intelligence (1999) Fuzzy Algorithms for Control, pp. 17-57. , H. B. Verbruggen, H.-J. Zimmermann, and R. Babuska, editors Kluwer Academic Publishers; Zhou, S.-M., Gan, J., Low-level interpretability and high-level interpretability: A unified view of data-driven interpretable fuzzy system modelling (2008) Fuzzy Sets and Systems, 159, pp. 3091-3131},
correspondence_address1={Ferreira, A.; Department of Informatics, , Covilhã, Portugal; email: andrefilipe90_9@hotmail.com},
issn={10987584},
isbn={9781467315067},
coden={PIFSF},
language={English},
abbrev_source_title={IEEE Int Conf Fuzzy Syst},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Aziz2012570,
author={Aziz, N. and Hui, W.S. and Othman, R.},
title={The use of Performance Measurement System (PMS) in transforming public sector organization},
journal={ICIMTR 2012 - 2012 International Conference on Innovation, Management and Technology Research},
year={2012},
pages={570-573},
doi={10.1109/ICIMTR.2012.6236460},
art_number={6236460},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864807024&doi=10.1109%2fICIMTR.2012.6236460&partnerID=40&md5=ba7ae97b1fe35e5acd99ded0525c8e3b},
affiliation={Faculty of Accountancy, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia; School of Accountancy, College of Business, Massey University, New Zealand},
abstract={The purpose of this paper is to propose how Performance Measurement System (PMS) can be used in enhancing readiness among managers towards the successful implementation of transformation in Malaysian public sector organisations. The proposed research agenda is based on the review of mainstream literature of PMS and its use in public sector change. Despite the paucity of literature on the role played by PMS in transforming public sector entities, this paper highlights the gap between the use of PMS and readiness for transformation (RFT). © 2012 IEEE.},
author_keywords={Performance Measurement System (PMS);  Public Sector Change;  Readiness for Transformation},
keywords={Malaysians;  Performance measurement system;  Public sector;  Public sector organization;  Readiness for Transformation;  Research agenda, Supply chain management},
references={Akbar, R., Performance measurement in the Indonesian local government: To perform or to simply conform? IIUM International Accounting Conference V, Pan Pacific KLIA Hotel, Malaysia, 2011, , presented at the; Armenakis, A.A., Harris, S.G., Crafting a change message to create transformational readiness (2002) Journal of Organisational Change, 15 (2), pp. 169-183; (2007) Auditor General Report; Azhar, R., (2009) Adoption of Performance Measurement in Malaysian Public Sector Organisations, , Masters's dissertation, University of Malaya; Barbosa, D.H., Musetti, M.A., The use of performance measurement system in logistics change process: Proposal of a guide (2011) International Journal of Productivity and Performance Management, 60 (4), pp. 339-359; Bertok, J., Hall, J., Kraan, D.J., Malinska, J., Manning, N., Mathhews, E., Issues in outcome measurement for (Government at a glance) (2006) OECD Project on Management in Government Technical Paper 3, , Paris, OECD; Burnes, B., Kurt Lewin and complexity theories: Back to the future? (2004) Journal of Change Management, 4 (4), pp. 309-325; Burnes, B., Kurt Lewin and Planned Approach to Change: A Re-appraisal (2004) Journal of Management Studies, 41 (6), pp. 977-1325; Cavaluzzo, K.S., Ittner, C.D., Implementing performance measurement innovations: Evidence from government (2004) Accounting, Organisation and Society, 29, pp. 243-267; Choi, M., Ruona, W.E.A., Individual readiness for organisational change and its implications for Human Resource and Organisational development (2011) Human Resource Development Review, 10 (46), pp. 45-73; Cinite, I., Duxbury, L.E., Higgins, C., Measurement of perceived organisational readiness for change in the public sector (2009) British Journal of Mangement, 20, pp. 26-277; Daszko, M., Sheinberg, S., Survival is optional: Only leaders with new knowledge can lead the transformation (2005) Theory of Transformation, pp. 1-12; Dixon, J., Kouzmin, A., Korac-Kakabadse, N., Managerialism - Something old, something borrowed, little new Economic prescription versus effective organisational change in public agencies (1998) The International Journal of Public Sector Management, 11, p. 164; Elias, S.M., Employee commitment in times of change: Assessing the importance of attitudes toward organisational change (2009) Journal of Management, 35 (37), pp. 37-54; (2010) Roadmap to Economic Transformation Program, , www.pemandu.gov.my; Goddard, A., Contemporary public sector accounting research: An international comparison of journal papers (2010) The British Accounting Review, 42, pp. 75-87; Goyal, K.A., Impact of Globalization on Developing Countries: With Special Reference to India (2006) International Research Journal of Finance and Economics, 5, pp. 166-171; (2010) Roadmap to Government Transformation Plan, , www.pemandu.gov.my; Hall, M., The effect of comprehensive performance measurment systems on role clarity, psychological empowerment and managerial performance (2008) Accounting, Organisation, and Society, 33, pp. 141-163; Hall, M., Do comprehensive performance measurement systems help or hinder managers' mental model development? (2010) Management Accounting Research, , doi:10.1016/j.mar.2010.10. 002; Hoque, Z., Measuring and reporting public sector outcomes: Exploratory evidence from Australia (2008) International Journal of Public Sector Management, 21 (5), pp. 468-493; Jarrar, Y., Schiuma, G., Measuring performance in the public sector: Challenges and trends (2007) Measuring Business Excellence, 11 (4), pp. 4-8; Lehman, W.E.K., Greener, J.M., Simpson, D.D., Assessing organisational readiness for change (2002) Journal of Substance Abuse Treatment, 22, pp. 197-209; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Norhayati, M.A., Siti-Nabiha, A.K., A case study of the performance management system in a Malaysian government linked companies (2009) Journal of Accounting and Organisational Change, 5 (2), pp. 243-276; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60, pp. 279-290; Ogden, S.G., Anderson, F., The role of accounting in organisational change: Promoting performance improvement in the privatised UK industry (1999) Critical Perspective on Accounting, 10, pp. 91-124; Rafferty, A.E., Simons, R.H., An examination of the antecendents of readiness for fine-tuning and corporate transformation changes (2006) Journal of Business and Psychology, 20 (3), pp. 325-350; Seijts, G.H., Roberts, M., The impact of employee perceptions on change in amunicipal government (2011) Leadership and Organisation Development Journal, 32 (2), pp. 190-213; Self, D.R., Schraeder, M., Enhancing the success of organisational change: Matching readiness strategies with sources of resistance (2009) Leadership and Organisation Development Journal, 30 (2), pp. 167-182; Sezen, S., The Impact of Globalization on the Organisation of Public Administration: Turkish Case (2002) Turkish Public Administration Annual, 27-28, pp. 3-26; Sholihin, M., Pike, R., Mangena, M., Reliance on multiple performance measures and manager performance (2010) Journal of Applied Accounting Research, 11 (1), pp. 24-42; Siddiquee, N.A., Managing for results: Lessons from public management reform in Malaysia (2010) International Journal of Public Sector Management, 23 (1), pp. 38-53; Siddique, N.A., Public management reform in Malaysia. Recent initiatives and experiences (2006) International Journal of Public Sector Management, 19 (4), pp. 339-358; Tsamenyi, M., Onumahb, J., Tetteh-Kumah, E., Post-privatization performance and organisational changes: Case studies from Ghana (2010) Critical Perspectives of Accounting, 21, pp. 428-442; Verbeeten, F.H.M., Performance management practices in public sector organisations: Impact on performance (2008) Accounting, Auditing, and Accountability Journal, 21 (3), pp. 427-454; Wouters, M., Wilderom, C., Developing performance measurement systems as enabling formalization: A longitudinal field study of logistics department (2008) Accounting, Organisations and Society, 33, pp. 488-516},
correspondence_address1={Aziz, N.; Faculty of Accountancy, , Shah Alam, Selangor, Malaysia; email: nazirah_00@yahoo.com},
isbn={9781467306553},
language={English},
abbrev_source_title={ICIMTR - Int. Conf. Innov., Manage. Technol. Res.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Gorane2012146,
author={Gorane, S.J. and Kant, R. and Singh, M.D.},
title={Functional benchmarking of organisations on the basis of supply chain performance measures},
journal={2012 IEEE 6th International Conference on Management of Innovation and Technology, ICMIT 2012},
year={2012},
pages={146-151},
doi={10.1109/ICMIT.2012.6225795},
art_number={6225795},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864195641&doi=10.1109%2fICMIT.2012.6225795&partnerID=40&md5=e0fa9dd34d952120a9bc2bc743030e4e},
affiliation={Departments of Mechanical Engineering, S. V. National Institute of Technology, Surat, India; Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, India},
abstract={The process of choosing appropriate supply chain (SC) performance measures is difficult due to the complexity of these systems. Most of the organisations measure their internal performance but they are unable to benchmark it. The performance of the organisation can be only improved if the SC performance of the one organisation is compared with the supply chain performance of the other organisations. The paper presents a new method for benchmarking the performance evaluation of three project type of the organisations. The performance of the all these organisations are evaluated by comparing the SC performance measures. © 2012 IEEE.},
author_keywords={Benchmarking;  Performance measurement;  SC;  supply chain management},
keywords={Functional benchmarking;  Internal performance;  Performance evaluation;  Performance measure;  Performance measurements;  Supply chain performance, Engineering research;  Innovation;  Scandium;  Societies and institutions;  Supply chain management, Benchmarking},
references={Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, Planning, and Operation, pp. 3-25. , Pearsoned Prentice Hall; Chin, K., Tummala, V.M., Raoand Leung, V.M., Jendy, P.F., A study on supply chain management practices the Hong Kong manufacturing perspective (2004) International Journal of Physical Distribution & Logistics Management, 34 (6), pp. 505-524; Jain, R., Yadav, O.P., Rathore, A.P.S., The propagation of benchmarking concepts in Indian manufacturing industry (2008) Benchmarking: An International Journal, 15 (1), pp. 101-117; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journa, 15 (1), pp. 25-51; Gunasekaran, L., Forker, L., Kobu, B., Improving operations performance in a small company: A case study (2000) International Journal of Operations & Production Management, 20 (3), pp. 316-335; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Beamon, B.M., Measuring supply chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Amaratunga, D., Baldry, D., Sarshar, M., Process improvement through performance measurement: The balanced scorecard methodology (2001) Work Study, 50 (5), pp. 179-188; Dey, P.K., Hariharan, S., Clegg, B.T., Measuring the operational performance of intensive care units using the analytic hierarchy process approach (2006) International Journal of Operations & Production Management, 26 (8), pp. 849-865; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities: Balanced scorecard perspective, Benchmarking (2009) An International Journal, 16 (5), pp. 605-620; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Cheng, M., Dainty, A., Moore, D., Implementing a new performance management system within a project-based organization A case study (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 60-75; Adebanjo, D., Abbas, A., Mann, R., An investigation of the adoption and implementation of benchmarking (2010) International Journal of Operations & Production Management, 30 (11), pp. 1140-1169; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Danese, P., Romano, P., Supply chain integration and efficiency performance: A study on the interactions between customer and supplier integration (2011) Supply Chain Management: An International Journal, 16 (4), pp. 220-230; Ting, S., Cho, D.I., An integrated approach for supplier selection and purchasing decisions (2008) Supply Chain Management: An International Journal, 13 (2), pp. 116-212; Lin, R., Chen, R., Chiu, K.K., Customer relationship management and innovation capability: An empirical study (2010) Industrial Management & Data Systems, 110 (1), pp. 111-133; Sezgin, B.O.S., Ozok, A.F., A measurement tool for customer relationship management processes (2011) Industrial Management & Data Systems, 111 (6), pp. 943-960; Spring, M., Dalrymple, J.F., Product customisation and manufacturing strategy (2010) International Journal of Operations & Production Management, 20 (4), pp. 441-467; Cagliano, R., Spina, G., How improvement programmes of manufacturing are selected the role of strategic priorities and past experience (2000) International Journal of Operations & Production Management, 20 (7), pp. 772-791; Amin, M.R., Banerjee, S., Benchmarking environmental performance: Five leading steel mills in India (2010) Benchmarking: An International Journal, 17 (3), pp. 378-395; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339},
correspondence_address1={Gorane, S.J.; Departments of Mechanical Engineering, , Surat, India; email: shrikantgorane@gmail.com},
isbn={9781467301107},
language={English},
abbrev_source_title={IEEE Int. Conf. Manage. Innov. Technol., ICMIT},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhang2012377,
author={Zhang, J.J. and Joglekar, N. and Verma, R.},
title={Pushing the frontier of sustainable service operations management: Evidence from US hospitality industry},
journal={Journal of Service Management},
year={2012},
volume={23},
number={3},
pages={377-399},
doi={10.1108/09564231211248462},
note={cited By 38},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863631801&doi=10.1108%2f09564231211248462&partnerID=40&md5=e1de736be8d47765bee16ee9b350526e},
affiliation={School of Business Administration, University of Vermont, Burlington, VT, United States; Department of Operations and Technology Management, Boston University, Boston, MA, United States; School of Hotel Administration, Cornell University, Ithaca, NY, United States},
abstract={Purpose: The purpose of this study is to develop a performance measurement system of environmental sustainability in service settings and to empirically examine the relationship between the measured environmental sustainability and operating performance. Design/methodology/approach: This study applies exploratory factor analysis (EFA) to a six-year panel dataset of 984 US hotels to construct a two-factor standardized measure of environmental sustainability. The authors then conduct a stochastic frontier analysis (SFA) to investigate the relationship between the measured environmental sustainability and the operating performance frontier, considering the impact of operating structure. Findings: Customer behavior and operational decisions are two key drivers of environmental sustainability. There is a positive link between environmental sustainability and operating performance. Operating structure has a significant impact on the operating performance. The performance frontier varies across market segment and location characteristics such as degree of urbanization and climate condition. Practical implications: The findings indicate that service providers should actively involve customers, and manage both front-office and back-office operations in environmental sustainability initiatives. Operating structures that favor the alignment of multiple service supply chain partners' interests contribute positively to performance. The managers should be mindful of varying best-in-class performance due to operating unit characteristics such as market segment, and location characteristics. Originality/value: This study is among the first attempts to develop a performance measurement system of environmental sustainability. The resulted standardized measure of environmental sustainability considers both the revenue and cost impacts in service operations. This research is among the first generation of papers that bring the unique characteristics of service operations, particularly service co-production, into sustainability research. © Emerald Group Publishing Limited.},
author_keywords={Consumer behaviour;  Environmental sustainability;  Performance measurement system;  Performance measures;  Resource efficiency;  Service co-production;  United States of America},
references={Adams, W.M., The future of sustainability: Re-thinking environment and development in the twenty-first century (2006), paper presented at the IUCN Renowned Thinkers Meeting, 29-31 January, Hotel Uto Kulm, Zurich, Switzerland; Best practices (2011), www.ahla.com/content.aspx?id=3616, available at, (accessed May 26), AHandL A; Aigner, D., Lovell, C.A.K., Schmidt, P., Formulation and estimation of stochastic frontier production models (1977) Journal of Econometrics, 6 (1), pp. 21-37; Ambec, S., Lanoie, P., Does it pay to be green? A systematic overview (2008) The Academy of Management Perspectives, 22 (4), pp. 45-62; Angell, L.C., Klassen, R.D., Integrating environmental issues into the mainstream: An agenda for research in operations management (1999) Journal of Operations Management, 17 (5), pp. 575-598; Battese, G.E., Coelli, T.J., A model for technical inefficiency effects in a stochastic frontier production function for panel data (1995) Empirical Economics, 20 (2), pp. 325-332; Bettencourt, L.A., Ostrom, A.L., Brown, S.W., Roundtree, R.I., Client co-production in knowledge-intensive business services (2002) California Management Review, 44 (4), pp. 100-128; Bitner, M.J., Faranda, W.T., Hubbert, A.R., Zeithaml, V.A., Customer contributions and roles in service delivery (1997) International Journal of Service Industry Management, 8 (3-4), pp. 193-205; Brown Jr., G.M., Field, B.C., Implications of alternative measures of natural resource scarcity (1978) The Journal of Political Economy, 86 (2), pp. 229-243; Camagni, R., Gibelli, M.C., Rigamonti, P., Urban mobility and urban form: The social and environmental costs of different patterns of urban expansion (2002) Ecological Economics, 40 (2), pp. 199-216; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Choi, J.G., Olsen, M.D., Kwansa, F.A., Tse, E.C.Y., Forecasting industry turning points: The US hotel industry cycle model (1999) International Journal of Hospitality Management, 18 (2), pp. 159-170; (2009) The Center for Hospitality Research - 2009 Sustainability Roundtable, , www.hotelschool.cornell.edu/research/chr/events/roundtables/documents/SustainabilityRoundtableprogramtoprint.pdf, available at, (accessed January 22, 2012), CHR; Christensen, L.R., Greene, W.H., Economies of scale in US electric power generation (1976) The Journal of Political Economy, 84 (4), pp. 655-676; Cobb, C.W., Douglas, P.H., A theory of production (1928) The American Economic Review, 18 (1), pp. 139-165; Comrey, A.L., Lee, H.B., (1992) A First Course in Factor Analysis, , Lawrence Erlbaum, Mahwah, NJ; Corbett, C.J., Montes-Sancho, M.J., Kirsch, D.A., The financial impact of ISO 9000 certification in the United States: An empirical analysis (2005) Management Science, 51 (7), pp. 1046-1059; Cox, N.J., (1998) WINSOR: Stata Module to Winsorize a Variable, , Boston College Department of Economics, Chestnut Hill, MA; Daly, H.E., Toward some operational principles of sustainable development (1990) Ecological Economics, 2 (1), pp. 1-6; Hospitality 2015: Game changers or spectators? (2010), www.deloitte.com/view/en_GB/uk/industries/thl/2efeace372df8210VgnVCM100000ba42f00aRCRD.htm, available at, (accessed August 10, 2011), Deloitte; Dowell, G., Hart, S.L., Yeung, B., Do corporate global environmental standards create or destroy market value? (2000) Management Science, 46 (8), pp. 1059-1074; Enz, C.A., Siguaw, J.A., Best hotel environmental practices (1999) Cornell Hotel & Restaurant Administration Quarterly, 40 (5), pp. 72-77; Fisher, A.C., Rothkopf, M.H., Market failure and energy policy: A rationale for selective conservation (1989) Energy Policy, 17 (4), pp. 397-406; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, , CIMA, London; Flynn, B.B., Sakakibara, S., Relationship between JIT and TQM: Practices and performance (1995) Academy of Management Journal, 38 (5), pp. 1325-1360; GfK, (2011) 2011 Green Gauge US Report, , GfK Roper Consulting, New York, NY; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Greene, W.H., Distinguishing between heterogeneity and inefficiency: Stochastic frontier analysis of the World Health Organization's panel data on national health care systems (2004) Health Economics, 13 (10), pp. 959-980; Greene, W.H., (2007) LIMDEP Version 9.0 Econometric Modeling Guide, , Econometric Software, Plainview, NY; Grönroos, C., Ravald, A., Service as business logic: Implications for value creation and marketing (2011) Journal of Service Management, 22 (1), pp. 5-22; Guide, V.D.R., Jayaraman, V., Srivastava, R., Benton, W., Supply-chain management for recoverable manufacturing systems (2000) Interfaces, 30 (3), pp. 125-142; Habib, M.A., Ljungqvist, A., Firm value and managerial incentives: A stochastic frontier approach (2005) The Journal of Business, 78 (6), pp. 2053-2094; Hair, J.F.J., Anderson, R.E., Tatham, R.L., Black, W.C., (1995) Multivariate Data Analysis: With Readings, , Prentice-Hall, Upper Saddle River, NJ; Harrison, J.S., Freeman, R.E., Stakeholders, social responsibility, and performance: Empirical evidence and theoretical perspectives (1999) The Academy of Management Journal, 42 (5), pp. 479-485; Hilton, LightStay by Hilton (2011), www.hiltonworldwide.com/aboutus/aboutlightstay.htm, available at, (accessed May 26); MindClick SGM study: Green corporate business travel to make strong impact on hotel brand selection (2011), Hospitality Sustainable Purchasing Consortium, Los angeles, CA, HSPI; Jacobs, R., Alternative methods to examine hospital efficiency: Data envelopment analysis and stochastic frontier analysis (2001) Health Care Management Science, 4 (2), pp. 103-115; Jaffe, A.B., Stavins, R.N., The energy-efficiency gap (1994) Energy Policy, 22 (10), pp. 804-810; Jaffe, A.B., Newell, R.G., Stavins, R.N., A tale of two market failures: Technology and environmental policy (2005) Ecological Economics, 54 (2-3), p. 164; Johnston, R., Jones, P., Service productivity: Towards understanding the relationship between operational and customer productivity (2004) International Journal of Productivity and Performance Management, 53 (3), pp. 201-213; Kassinis, G.I., Soteriou, A.C., Greening the service profit chain: The impact of environmental management practices (2003) Production and Operations Management, 12 (3), pp. 386-403; Kim, J.O., Mueller, C.W., (1978) Factor Analysis: Statistical Methods and Practical Issues, , Sage, Thousand Oaks, CA; King, A.A., Lenox, M.J., Does it really pay to be green? An empirical study of firm environmental and financial performance (2001) Journal of Industrial Ecology, 5 (1), pp. 105-116; King, A.A., Lenox, M.J., Lean and green? An empirical examination of the relationship between lean production and environmental performance (2001) Production and Operations Management, 10 (3), pp. 244-256; King, A.A., Lenox, M.J., Exploring the locus of profitable pollution reduction (2002) Management Science, 48 (2), pp. 289-299; Klassen, R.D., McLaughlin, C.P., The impact of environmental management on firm performance (1996) Management Science, 42 (8), pp. 1199-1214; Lamminmaki, D., Why do hotels outsource? An investigation using asset specificity (2005) International Journal of Contemporary Hospitality Management, 17 (6), pp. 516-528; Lapre, M.A., Scudder, G.D., Performance improvement paths in the US airline industry: Linking trade-offs to asset frontiers (2004) Production and Operations Management, 13 (2), pp. 123-134; Lieberman, M.B., Dhawan, R., Assessing the resource base of Japanese and US auto producers: A stochastic frontier production function approach (2005) Management Science, 51 (7), pp. 1060-1075; Lovins, L.H., Lovins, A.B., Natural capitalism: Path to sustainability? (2001) Corporate Environmental Strategy, 8 (2), pp. 99-108; Lyon, T.P., Maxwell, J.W., Greenwash: Corporate environmental disclosure under threat of audit (2011) Journal of Economics & Management Strategy, 20 (1), pp. 3-41; Mandelbaum, R., Hotel utility costs surge protection is needed (2004), www.hotel-online.com/News/PR2004_1st/Mar04_HotelUtilityCosts.html, available at, (accessed March 17, 2010); Meeusen, W., van den Broeck, J.D., Efficiency estimation from Cobb-Douglas production functions with composed error (1977) International Economic Review, 18 (2), pp. 435-444; Melnyk, S.A., Sroufe, R.P., Calantone, R., Assessing the impact of environmental management systems on corporate and environmental performance (2003) Journal of Operations Management, 21 (3), pp. 329-351; Metters, R., Marucheck, A., Service management - academic issues and scholarly reflections from operations management researchers (2007) Decision Sciences, 38 (2), pp. 195-214; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Parasuraman, A., Zeithaml, V.A., Berry, L.L., Reassessment of expectations as a comparison standard in measuring service quality - implications for further research (1994) Journal of Marketing, 58 (1), pp. 111-124; Porter, M.E., van der Linde, C., Green competitiveness (1991) Scientific American, 67 (5), p. 168; Porter, M.E., van der Linde, C., Toward a new conception of the environment-competitiveness relationship (1995) The Journal of Economic Perspectives, 9 (4), pp. 97-118; Rosenblum, J., Horvath, A., Hendrickson, C., Environmental implications of service industries (2000) Environmental Science & Technology, 34 (22), pp. 4669-4676; Roth, A.V., Menor, L.J., Insights into service operations management: A research agenda (2003) Production and Operations Management, 12 (2), p. 145; Sampson, S.E., Customer-supplier duality and bidirectional supply chains in service organizations (2000) International Journal of Service Industry Management, 11 (4), pp. 348-364; Sampson, S.E., Froehle, C.M., Foundations and implications of a proposed unified services theory (2006) Production and Operations Management, 15 (2), pp. 329-343; Schleich, J., Gruber, E., Beyond case studies: Barriers to energy efficiency in commerce and the services sector (2008) Energy Economics, 30 (2), pp. 449-464; Schmenner, R.W., Swink, M.L., On theory in operations management (1998) Journal of Operations Management, 17 (1), pp. 97-113; Shepherd, B., (2009) Dealing with Endogeneity, , www.unescap.org/tid/artnet/mtg/gravity09_tues3.pdf, available at, (accessed May 26, 2011); Silvestro, R., Fitzgerald, L., Johnston, R., Voss, C., Towards a classification of service processes (1992) International Journal of Service Industry Management, 3 (3), pp. 62-75; Stipanuk, D.M., Energy management in 2001 and beyond: Operational options that reduce use and cost (2001) Cornell Hotel & Restaurant Administration Quarterly, 42 (3), p. 57; STR global - glossary (2011), www.strglobal.com/Resources/Glossary.aspx, available at, (accessed June 10), STR; Susskind, A.M., Verma, R., Hotel guests' reactions to guest room sustainability initiatives (2011), Cornell Hospitality Report, Withiam, G. (Ed.), The Center for Hospitality Research at Cornell University, Ithaca, NY; Swink, M., Talluri, S., Pandejpong, T., Faster, better, cheaper: A study of NPD project efficiency and performance tradeoffs (2006) Journal of Operations Management, 24 (5), p. 542; Theokary, C., Ren, Z.J., An empirical study of the relations between hospital volume, teaching status, and service quality (2011) Production and Operations Management, 20 (3), pp. 303-318; Walley, N., Whitehead, B., It's not easy being green (1994) Harvard Business Review, 72 (3), pp. 46-52; Williamson, O.E., (1975) Markets and Hierarchies: Analysis and Antitrust Implications: A Study in the Economics of Internal Organization, , The Free Press, New York, NY},
correspondence_address1={Zhang, J. J.; School of Business Administration, University of Vermont, Burlington, VT, United States; email: jie.zhang@uvm.edu},
issn={17575818},
language={English},
abbrev_source_title={J. Serv. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wong2012419,
author={Wong, C. and Skipworth, H. and Godsell, J. and Achimugu, N.},
title={Towards a theory of supply chain alignment enablers: A systematic literature review},
journal={Supply Chain Management},
year={2012},
volume={17},
number={4},
pages={419-437},
doi={10.1108/13598541211246567},
note={cited By 126},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863496233&doi=10.1108%2f13598541211246567&partnerID=40&md5=426f50165ed03b1a75227c21eed3ef0c},
affiliation={Business School and Logistics Institute, University of Hull, Hull, United Kingdom; Cranfield School of Management, Cranfield University, Cranfield, United Kingdom; Authentic Sourcing Knowledge Ltd, London, United Kingdom},
abstract={Purpose: The importance of supply chain alignment has been discussed since the birth of supply chain management (SCM). Yet it remains a major challenge for supply chains. This paper aims to systematically review the cross disciplinary literature on supply chain alignment in order to identify, and develop constructs for enablers to alignment, and an associated set of hypotheses. Design/methodology/approach: A systematic approach has been taken to the literature review, which ensures it is auditable and repeatable. The selection criteria are clearly aligned with the review question ensuring all literature pertinent to the question is identified and reviewed. Relevant information is extracted from the selected papers and synthesised into a set of hypotheses. Findings: Six main constructs for the enablers of alignment are identified and defined: organisational structure, internal relational behaviour, customer relational behaviour, top management support, information sharing and business performance measurement system. While the literature is disparate, across different disciplines there is good support for these enablers. The relationships between supply chain alignment and shareholder and customer value are also argued with the support of the literature. Although each of the enablers is argued to positively affect shareholder and customer value, their interactions with one another are not well supported in the literature, either theoretically or empirically, and therefore this could be an area for further research. Research limitations/implications: While the hypotheses remain theoretical, it is now possible to test them and understand the relative significance of the various enablers to alignment. Practical implications: The significance of shareholder and customer alignment on the delivery of shareholder and customer value can be examined, thus moving towards a theory of supply chain alignment. This is needed since in practice companies are struggling with supply chain alignment. Originality/value: The existing literature on supply chain alignment is disparate and multi-disciplinary as this descriptive analysis shows, with 72 papers published in 43 different journals. Moreover, most of the papers focus on particular enablers, while this paper brings together six key enablers from the literature to produce a set of hypotheses. © Emerald Group Publishing Limited.},
author_keywords={Alignment;  Customer value;  Enablers;  Inhibitors;  Shareholder value;  Shareholder value analysis;  Supply chain;  Supply chain management},
references={Akkermans, H., Bogerd, P., Vos, B., Virtuous and vicious cycles on the road towards international supply chain management (1999) International Journal of Operations & Production Management, 19 (5-6), pp. 565-582; Aldrich, H., Herker, D., Boundary spanning roles and organization structure (1977) The Academy of Management Review, 2 (2), pp. 217-230; Anderson, J.C., Narus, J.A., A model of distribution firm and manufacturer firm working partnerships (1990) Journal of Marketing, 54 (1), pp. 42-58; Auramo, J., Tanskanen, K., Smaros, J., Increasing operational efficiency through improved customer service: Process maintenance case (2004) International Journal of Logistics: Research and Applications, 7 (3), pp. 167-180; Bagchi, P.K., Skjoett-Larsen, T., Integration of information technology and organizations in a supply chain (2003) International Journal of Logistics Management, 14 (1), pp. 89-108; Baier, C., Hartman, E., Moser, R., Strategic alignment and purchasing efficacy: An exploratory analysis of their impact on financial performance (2008) Journal of Supply Chain Management, 44 (4), pp. 36-52; Barratt, M., Positioning the role of collaborative planning in grocery supply chains (2003) International Journal of Logistics Management, 14 (2), pp. 53-66; Barratt, M., Oliveira, A., Exploring the experiences of collaborative planning initiatives (2001) International Journal of Physical Distribution & Logistics Management, 31 (4), pp. 266-289; Bessant, J., Caffyn, S., Gilbert, J., Harding, R., Webb, S., Rediscovering continuous improvement (1994) Technovation, 14 (1), pp. 17-29; Beth, S., Burt, D.N., Capacino, W., Gopal, C., Lee, H.L., Lynch, R.P., Morris, S., Supply chain challenges: Building relationships (2003) Harvard Business Review, 81 (7), pp. 64-73; Brown, C.V., Magill, S.L., Alignment of the IS functions with the enterprise: Toward a model of antecedents (1994) MIS Quarterly, 18 (4), pp. 371-403; Brown, S., Squire, B., Blackmon, K., The contribution of manufacturing strategy involvement and alignment to world class manufacturing performance (2007) International Journal of Operations Management & Production Management, 27 (3), pp. 282-302; Bourguignon, A., Management accounting and value creation: The profit and loss of reification (2005) Critical Perspectives on Accounting, 16 (4), pp. 353-389; Bourland, K.E., Powell, S.G., Pyke, D.F., Exploiting timely demand information to reduce inventories (1996) European Journal of Operational Research, 92 (2), pp. 239-253; Bowersox, D.J., Daugherty, P.J., Logistics paradigms: The impact of information technology (1995) Journal of Business Logistics, 16 (1), pp. 65-80; Buhner, R., Increasing shareholder value through human asset management (1997) Long Range Planning, 30 (5), p. 710; Burgelman, R.A., Doz, Y.L., The power of strategic integration (2001) MIT Sloan Management Review, 42 (3), pp. 28-38; Buyukozkan, G., An organizational information network for corporate responsiveness and enhanced performance (2004) Journal of Manufacturing Technology Management, 15 (1), p. 57; Cachon, G.P., Lariviere, M.A., Supply chain coordination with revenue-sharing contracts, strengths and limitations (2005) Management Science, 51 (1), pp. 30-44; Campbell, A.J., Cooperation in international value chains: Comparing an exporter's supplier versus customer relationships (1998) The Journal of Business & Industrial Marketing, 13 (1), p. 22; Campbell, J., Sankaran, J., An inductive framework for enhancing supply chain integration (2005) International Journal of Production Research, 43 (16), p. 3321; Carrillat, F., Jaramillo, F., Locander, W., Market-driving organisations: A framework (2004) Academy of Marketing Science Review, 39 (5), pp. 1-14; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), p. 635; Chen, I.J., Paulraj, A., Lado, A.A., Strategic purchasing, supply management and firm performance (2004) Journal of Operations Management, 22 (5), pp. 505-523; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting, Organizations and Society, 30 (5), p. 395; Childerhouse, P., Aitken, J., Towill, D.R., Analysis and design of focused demand chains (2002) Journal of Operations Management, 20 (6), p. 675; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , Pearson, Harlow, 2nd ed; Christopher, M., Gattorna, J., Supply chain cost management and value-based pricing (2005) Industrial Marketing Management, 34 (2), p. 115; Christopher, M., Ryals, L., Supply chain strategy: Its impact on shareholder value (1999) International Journal of Logistics Management, 10 (1), pp. 1-10; Christopher, M., Lowson, R., Peck, H., Creating agile supply chains in the fashion industry (2004) International Journal of Retail & Distribution Management, 31 (4), p. 235; Chung, W.W.C., Leung, S.W.F., Collaborative planning, forecasting and replenishment: A case study in copper clad laminate industry (2005) Production Planning & Control, 16 (6), pp. 563-574; Clark, T.H., Croson, D.C., Schiano, W.T., A hierarchical model of supply-chain integration: Information sharing and operational interdependence in the US grocery channel (2001) Information Technology and Management, 2 (3), pp. 261-288; Cleland, A.S., Bruno, A.V., Building customer and shareholder value (1997) Strategy & Leadership, 25 (3), pp. 22-28; Clemons, E.K., Weber, B.W., Segmentation, differentiation, and flexible pricing: Experiences with information technology and segment-tailored strategies (1994) Journal of Management Information Systems, 11 (2), pp. 9-36; Copulsky, W., Balancing the needs of customers and shareholders (1991) Journal of Business Strategy, 12 (6), pp. 44-45; Cornelius, I., Davies, M., (1997) Shareholder Value, , FT Financial Publishing, London; Danese, P., Romano, P., Improving inter-functional coordination to face high product variety and frequent modifications (2004) International Journal of Operations Management, 24 (9-10), pp. 863-885; Davenport, T.H., (1993) Process Innovation: Reengineering Work Through Information Technology, , Harvard Business School Press, Boston, MA; Day, G.S., The capabilities of market-driven organizations (1994) Journal of Marketing, 58 (4), pp. 37-52; Day, G.S., Fahey, L., Putting strategy into shareholder value analysis (1990) Harvard Business Review, 68 (2), p. 156; de Toni, A., Tonchia, S., Lean organization, management by process and performance measurement (1996) International Journal of Operations & Production Management, 16 (2), pp. 221-236; Denyer, D., Tranfield, T., Producing a systematic review (2009) The Sage Handbook of Organizational Research Methods, , Buchanan, D.A., Bryman, A. (Eds.), Sage, London; Doyle, P., (1994) Marketing Management and Strategy, , Prentice Hall, Hemel Hempstead; Drucker, P.F., What is our business? (2001) Executive Excellence, 18 (6), p. 3; Earl, M., The new and the old of business process redesign (2002) The Journal of Strategic Information Systems, 3 (1), pp. 5-22; Ellinger, E., Improving marketing/logistics cross-functional collaborations in the supply chain (2000) Industrial Marketing Management, 29 (1), p. 85; Eng, T.-Y., An investigation into the mediating role of cross-functional coordination on the linkage between organizational norms and SCM performance (2006) Industrial Marketing Management, 35 (6), pp. 762-773; Esper, T.L., Ellinger, A.E., Stank, T.P., Flint, D.J., Moon, M., Demand and supply integration: A conceptual framework of value creation through knowledge management (2010) Academy of Marketing Science Journal, 38 (1), pp. 5-18; Ettlie, J.E., Reza, E.M., Organizational integration and process innovation (1992) Academy of Management Journal, 35 (4), pp. 795-827; Fassoula, E.D., Transforming the supply chain (2006) Journal of Manufacturing Technology Management, 17 (6), pp. 848-860; Fawcett, S.E., Cooper, M.B., Process integration for competitive success: Benchmarking barriers and bridges (2001) Benchmarking, 8 (5), p. 396; Fawcett, S.E., Magnan, G.M., The rhetoric and reality of supply chain integration (2002) International Journal of Physical Distribution & Logistics Management, 32 (5), pp. 339-361; Fawcett, S.E., Odgen, J.A., Magnan, G.M., Cooper, M.B., Organizational commitment and governance for supply chain success (2006) International Journal of Physical Distribution & Logistics Management, 36 (1), pp. 22-35; Feurer, R., Chaharbaghi, K., Defining competitiveness: A holistic approach (1994) Management Decision, 32 (2), pp. 49-58; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: A contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Franco-Santos, M., Marr, B., Martinez, V., Gray, D., Adams, C., Micheli, P., Bourne, M., Neely, A., Towards a definition of a business performance measurement system (2004), paper presented at PMA 2004 Conference "Performance Measurement and Management Public and Private", Edinburgh International Conference Centre, Edinburgh, 27-30 July; Frohlich, M.T., E-integration in the supply chain: Barriers and performance (2002) Decision Sciences, 33 (4), pp. 537-556; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2002) Journal of Operations Management, 19 (2), pp. 185-200; Gattorna, J.L., (1998) Strategic Supply Chain Alignment, , Gower, Aldershot; Gattorna, J.L., (2009) Dynamic Supply Chain Alignment, , Gower, Aldershot; Gattorna, J.L., Walters, D.W., (1996) Managing the Supply Chain: A Strategic Perspective, , Palgrave, Basingstoke; Gattorna, J.L., Chorn, N.H., Day, A., Pathways to customers: Reducing complexity in the logistics pipeline (1991) International Journal of Physical Distribution & Logistics Management, 21 (8), pp. 5-11; George, M., Freeling, A., Court, D., Reinventing the marketing organization (1994) The McKinsey Quarterly, (4), pp. 43-62; Gerbing, D.W., Hamilton, J.G., Freeman, E.B., A large-scale second-order structural equation model of the influence of management participation on organizational planning benefits (1994) Journal of Management, 20 (4), pp. 859-885; Godsell, J., Juttner, J., Christopher, M., Demand chain management: The missing link (2005), paper presented at the 10th International Symposium on Logistics, Lisbon; Godsell, J., Harrison, A., Emberson, C., Storey, J., Customer responsive supply chain strategy: An unnatural act? (2006) International Journal of Logistics: Research and Applications, 9 (1), pp. 47-56; Groth, J.C., Managing action inhibitors to increase sales (1994) Marketing Intelligence & Planning, 12 (4), pp. 4-10; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Halldórsson, A., Larson, P.D., Poist, R.F., Supply chain management: A comparison of Scandinavian and American perspectives (2008) International Journal of Physical Distribution & Logistics Management, 38 (2), pp. 126-142; Hamel, G., Prahalad, C., Strategic intent: To revitalise corporate performance we need a whole new model of strategy (1989) Harvard Business Review, 67 (3), p. 63; Hammer, M., Champy, J., (1993) Reengineering the Corporation: A Manifesto for Business Revolution, , Collins Business Essentials, New York, NY; Harrison, A., Investigating business processes: Does process simplification always work? (1998) Business Process Management Journal, 4 (2), pp. 137-153; Hartley, J.L., Collaborative value analysis: Experiences from the automotive industry (2000) Journal of Supply Chain Management, 36 (4), pp. 27-33; Heskett, J.L., Jones, T.O., Loveman, G.W., Sasser Jr., W.E., Schlesinger, L.A., Putting the service-profit chain to work (1994) Harvard Business Review, 72 (2), pp. 164-170; Hill, C.A., Scudder, G.D., The use of electronic data interchange for supply chain coordination in the food industry (2002) Journal of Operations Management, 20 (4), pp. 375-397; Hoffman, N.P., An examination of the 'sustainable competitive advantage' concept: Past, present, and future (2000) Academy of Marketing Science Review, (4), p. 1. , www.amsreview.org/articles/hoffman04-2000.pdf; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Holweg, M., An investigation into supplier responsiveness: Empirical evidence from the automotive industry (2005) International Journal of Logistics Management, 16 (1), pp. 96-119; Houlihan, J.B., International supply chain management (1985) International Journal of Physical Distribution and Materials Management, 15 (1), pp. 22-38; Huang, G.Q., Lau, J.S.K., Mak, K.L., The impacts of sharing production information on supply chain dynamics: A review of the literature (2003) International Journal of Production Research, 41 (7), pp. 1483-1517; Jaworski, B.J., Kohli, A.K., Market orientation: Antecedents and consequences (1993) Journal of Marketing, 57 (3), pp. 53-70; Jeong, J.S., Hong, P., Customer orientation and performance outcomes in supply chain management (2007) Journal of Enterprise Information Management, 20 (5), pp. 578-594; Johnson, G., Scholes, K., (1999) Exploring Corporate Strategy: Text and Cases, , Prentice Hall, Hemel Hempstead, 5th ed; Jones, T.C., Riley, D.W., Using inventory for competitive advantage through supply chain management (1985) International Journal of Physical Distribution & Materials Management, 15 (5), pp. 16-26; Kahn, K.B., Mentzer, J.T., Logistics and interdepartment integration (1996) International Journal of Physical Distribution & Logistics Management, 26 (9), pp. 6-14; Kalchschmidt, M., Zotteri, G., Verganti, R., Inventory management in a multi-echelon spare parts supply chain (2003) International Journal of Production Economics, 81-82, pp. 397-413; Kannan, V.R., Tan, K.C., Supply chain integration: Cluster analysis of the impact of span of integration (2010) Supply Chain Management: An International Journal, 15 (3), pp. 207-215; Kaplan, R.S., Norton, D.P., The strategy map: Guide to aligning intangible assets (2004) Strategy & Leadership, 32 (5), pp. 10-17; Karapetrovic, S., Willborn, W., Self-audit of process performance (2002) The International Journal of Quality & Reliability Management, 19 (1), pp. 24-45; Kathuria, R., Joshi, M.P., Porth, S.J., Organizational alignment and performance: Past, present and future (2007) Management Decision, 45 (3), pp. 503-517; Kauremaa, J., Kärkkäinen, M., Ala-Risku, T., Customer initiated interorganizational information systems: The operational impacts and obstacles for small and medium sized suppliers (2009) International Journal of Production Economics, 119 (2), pp. 228-239; Kelley, H.H., Thibaut, J.W., Experimental studies of group problem solving and process (1954) Handbook of Social Psychology, 2. , Lindzey, G. (Ed.), Addison-Wesley, Cambridge, MA; Kim, S.W., The effect of supply chain integration on the alignment between corporate competitive capability and supply chain operational capability (2006) International Journal of Operations Management, 26 (10), pp. 1084-1107; Laitamaki, J., Kordupleski, R., Building and deploying profitable growth strategies based on the waterfall of customer value added (1997) European Management Journal, 15 (2), pp. 158-166; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) International Journal of Logistics Management, 9 (2), pp. 1-13; Lambert, D.M., García-Dastugue, S.J., Croxton, K.L., An evaluation of process-oriented supply chain management frameworks (2005) Journal of Business Logistics, 26 (1), pp. 25-51; Lawrence, P., Lorsch, J., (1967) Organization and Environment: Managing Differentiation and Integration, , Irwin, Homewood, IL; Lee, H., The Triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Lee, H., Whang, S., Information sharing in supply chain (2000) International Journal of Technology Management, 20 (3-4), pp. 373-387; Lee, H.L., Padmanabham, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lehtonen, J., Småros, J., Holmström, J., The effect of demand visibility in product introductions (2005) International Journal of Physical Distribution & Logistics Management, 35 (2), pp. 101-115; Lewis, M., Slack, N., (2003) Operations Management: Critical Perspectives on Business Management, , Routledge, London; Li, S., Lin, B., Accessing information sharing and information quality in supply chain management (2006) Decision Support Systems, 42 (3), pp. 1641-1656; Luftman, J., Enablers and inhibitors (1998) Information Week, pp. 283-286. , 14 September; Luftman, J., Brier, T., Achieving and sustaining business-IT alignment (1999) California Management Review, 42 (1), pp. 109-122; Lummus, R.R., Vokurka, R.J., Managing the demand chain through managing the information flow: Capturing 'moments of information' (1999) Production and Inventory Management Journal, 40 (1), pp. 16-20; McAdam, R., Brown, L., Strategic alignment and the supply chain for the steel stockholder sector: An exploratory case study analysis (2001) Supply Chain Management: An International Journal, 6 (2), pp. 83-94; Maku, T.C., Collins, T.R., Beruvides, M.G., The impact of human interaction on supply chain management practices (2005) Performance Improvement, 44 (7), pp. 26-33; Marquez, A.C., Blanchar, C., A decision support system for evaluating operations investments in high-technology business (2006) Decision Support Systems, 41 (2), pp. 472-487; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Cambridge, MA; Matthyssens, P., Vandenbempt, K., Moving from basic offerings to value-added solutions: Strategies, barriers and alignment (2008) Industrial Marketing Management, 37 (3), pp. 316-328; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-218; Miles, M.B., Huberman, M.A., (1994) Qualitative Data Analysis, , Sage, Thousand Oaks, CA; Monczka, R.M., Handfield, R.B., Giuipero, L.C., Pattersson, J.L., (2009) Purchasing and Supply Chain Management, , South Western, Cincinnati, OH; Morash, E.A., Clinton, S.R., Supply chain integration: Customer value through collaborate closeness versus operational excellence (1998) Journal of Marketing Theory & Practice, 6 (4), pp. 104-120; Morash, E.A., Droge, C.L.M., Vickery, S.K., Boundary spanning interfaces between logistics, production, marketing and new product development (1996) International Journal of Physical Distribution & Logistic Management, 26 (8), pp. 43-62; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Muratoglu, K., Performance measurement of supply chain via balanced scorecard: The case of a brewing group (2008) The Business Review, 10 (1), pp. 330-337; Nadler, D., Tushman, M., (1988) Strategic Organization Design: Concepts, Tools and Processes, , Scott, Foresman, Glenview, IL; (2002) Business Performance Measurement: Theory and Practice, , Neely, A. (Ed.), Cambridge University Press; Nichol, R.L., Get middle managers involved in the planning process (1992) The Journal of Business Strategy, 13 (3), pp. 26-32; O'Leary-Kelly, S.W.O., Flores, B.E., The integration of manufacturing and marketing/sales decisions: Impact on organizational performance (2002) Journal of Operations Management, 20 (3), pp. 221-240; O'Mara, C.E., Hyland, P.W., Chapman, R.L., Performance measurement and strategic change (1998) Managing Service Quality, 8 (3), pp. 178-182; Osterlund, J., Loven, E., Information versus inertia: A model for product change with low inertia (2005) Systems Research and Behavioral Science, 22 (6), pp. 547-560; Othman, R., Ghani, R.A., Supply chain management and suppliers' HRM practice (2008) Supply Chain Management: An International Journal, 13 (4), pp. 259-262; Pagell, M., Understanding the factors that enable and inhibit the integration of operations and logistics (2004) Journal of Operations Management, 22 (5), p. 459; Petersen, K.J., Handfield, R.B., Ragatz, G.L., Supplier integration into new product development: Coordinating product, process and supply chain design (2005) Journal of Operations Management, 23 (3-4), pp. 371-388; Pollalis, Y.A., Patterns of co-alignment in information-intensive organizations: Business performance through integration strategies (2003) International Journal of Information Management, 23 (6), pp. 469-492; Porter, M.E., (1985) Competitive Advantage: Creating and Sustaining Superior Performance, , Free Press, a division of Macmillan Inc., New York, NY; Powell, T.C., Organizational alignment as competitive advantage (1992) Strategic Management Journal, 13 (2), pp. 119-134; Powers, V., Benchmarking study illustrates how best-in-class achieve alignment, communicate change (1996) Communication World, 14 (1), p. 30; Rappaport, A., Linking competitive strategy and shareholder value analysis (1987) Journal of Business Strategy, 7 (4), pp. 58-67; Reichhart, A., Holweg, M., Creating the customer-responsive supply chain: A reconciliation of concepts (2007) International Journal of Operations & Production Management, 27 (11), pp. 1144-1172; Rich, N., Hines, P., Supply-chain management and time-based competition: The role of the supplier association (1997) International Journal of Physical Distribution & Logistics Management, 27 (3-4), p. 210; Robertson, T.S., Barich, H., A successful approach to segmenting industrial markets (1992) Planning Review, 20 (6), pp. 4-11; Robson, I., From process measurement to performance improvement (2004) Business Process Management Journal, 10 (5), pp. 510-521; Rodrigues, A.M., Stank, T.P., Lynch, D.F., Linking strategy, structure, process, and performance in integrated logistics (2004) Journal of Business Logistics, 25 (2), pp. 65-94; Sabath, R., Whipple, J.M., Using the customer/product action matrix to enhance internal collaboration (2004) Journal of Business Logistics, 25 (2), pp. 1-19; Sahay, B.S., Understanding trust in supply chain relationships (2003) Industrial Management & Data Systems, 103 (8-9), pp. 553-563; Sahin, F., Robinson, E.P., Flow coordination and information sharing in supply chains: Review, implications, and directions for future research (2002) Decision Sciences, 33 (4), pp. 505-536; Sandberg, E., Logistics collaboration in supply chains: Practice vs theory (2007) International Journal of Logistics Management, 18 (2), pp. 274-293; Schmenner, R.W., Vollmann, T.E., Performance measures: Gaps, false alarms and the 'usual suspects' (1994) International Journal of Operations & Production Management, 14 (12), pp. 58-69; Scott, C., Westbrook, R., New strategic tools for supply chain management (1991) International Journal of Physical Distribution & Logistics Management, 21 (1), pp. 23-33; Sha, D.Y., Chen, P.K., Chen, Y.H., The strategic fit of supply chain integration in the TFT-LCD industry (2008) Supply Chain Management: An International Journal, 13 (5), pp. 339-342; Simatupang, T.M., Sridharan, R., Benchmarking supply chain collaboration: An empirical study (2004) Benchmarking, 11 (5), pp. 484-503; Skinner, W., Manufacturing: Missing link in corporate strategy (1969) Harvard Business Review, 47, pp. 136-145. , May-June; Slater, S.F., Narver, J.C., Market orientation, customer value, and superior performance (1994) Business Horizons, 37 (2), pp. 22-28; Soosay, C.A., Hyland, P.W., Ferrer, M., Supply chain collaboration: Capabilities for continuous innovation (2008) Supply Chain Management: An International Journal, 13 (2), pp. 160-169; Srivastava, R.K., Shervani, T.A., Fahey, L., Marketing, business processes, and shareholder value: An organizationally embedded view of marketing activities and the discipline of marketing (1999) Journal of Marketing, 63 (SPEC. ISSUE), pp. 168-179. , October; Starbuck, W.H., (2006) The Production of Knowledge, , Oxford University Press; Storbacka, K., Nenonen, S., Customer relationships and the heterogeneity of firm performance (2009) The Journal of Business & Industrial Marketing, 24 (5-6), pp. 360-372; Storey, J., Emberson, C., Reade, D., The barriers to customer responsive supply chain management (2005) International Journal of Operations & Production Management, 25 (3-4), p. 242; Susman, G.I., Dean Jr., J.W., Development of a model for predicting design for manufacturability effectiveness (1992) Integrating Design and Manufacturing for Competitive Advantage, pp. 207-227. , Susman, G.I. (Ed.), Oxford University Press, New York, NY; Tamas, M., Mismatched strategies: The weak link in the supply chain? (2000) Supply Chain Management: An International Journal, 5 (4), pp. 171-175; Tarn, J.M., Yen, D.C., Beaumont, M., Exploring the rationales for ERP and SCM integration (2002) Industrial Management & Data Systems, 102 (1-2), pp. 26-34; Thompson, J., (1967) Organizations in Action, , McGraw-Hill, New York, NY; Tracey, M., Lim, J., Vonderembse, M.A., The impact of supply-chain management capabilities on business performance (2005) Supply Chain Management, 10 (3-4), pp. 179-191; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14 (3), pp. 207-222; Tsinopoulos, C., Bell, K., Supply chain integration systems by small engineering to order companies: The challenge of implementation (2010) Journal of Manufacturing Technology Management, 21 (1), pp. 50-62; van Hoek, R.I., Mitchell, A.J., The challenge of internal misalignment (2006) International Journal of Logistics: Research and Applications, 9 (3), pp. 269-281; Venkatraman, N., The concept of fit in strategy research: Toward verbal and statistical correspondence (1989) The Academy of Management Review, 14 (3), pp. 423-444; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60; Wheelwright, S.C., Manufacturing strategy: Defining the missing link (1984) Strategy Management Journal, 5 (1), pp. 77-91; Wheelwright, S.C., Clark, K.B., (1992) Revolutionising Product Development, , Free Press, New York, NY; Wilcox, M., Bourne, M., Predicting performance (2003) Management Decision, 41 (8), pp. 806-816; Woodruff, R.B., Customer value: The next source for competitive advantage (1997) Academy of Marketing Science Journal, 25 (2), pp. 139-153; Yasin, M.M., Bayes, P.E., Czuchry, A.J., The changing role of accounting in supporting the quality and customer goals of organizations: An open system perspective (2005) International Journal of Management, 22 (3), pp. 323-332; Quinn, J., (1993) The Intelligent Enterprise, , The Free Press, New York, NY},
correspondence_address1={Skipworth, H.; Cranfield School of Management, , Cranfield, United Kingdom; email: heather.skipworth@cranfield.ac.uk},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Gopal2012518,
author={Gopal, P.R.C. and Thakkar, J.},
title={A review on supply chain performance measures and metrics: 2000-2011},
journal={International Journal of Productivity and Performance Management},
year={2012},
volume={61},
number={5},
pages={518-547},
doi={10.1108/17410401211232957},
note={cited By 128},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863465145&doi=10.1108%2f17410401211232957&partnerID=40&md5=ace74f09623d896e0fde68c4601d5b6c},
affiliation={Department of Industrial Engineering and Management, Indian Institute of Technology Kharagpur, Kharagpur, India},
abstract={Purpose: The paper reports a comprehensive review of supply chain performance measurement systems and raises a set of research issues leading to selected research questions. Design/methodology/approach: The articles which have been published in selected peer-reviewed international journals in the last ten years were collected by using databases Scopus and ISI Web of Knowledge. The articles were scrutinized based on authors' perspective analysis (using supply chain measures and metrics, and enhancing supply chain performance as the key perspectives). The articles contributing significantly in the domain of supply chain measures and metrics were selected for final review and various issues were identified. Findings: The paper argues that, despite considerable evidence from the literature in supply chain performance measurement in recent years, there is a large scope for research to address the issues in supply chain performance measurement, including: characteristics of measures and metrics, benchmarking of measures, use of management practices, integration and partnership and socio-environmental relevance. Research implications/limitations: The insights deduced in the paper are primarily based on 28 articles selected for critical review and analysis in the domain of supply chain measures and metrics and hence should be interpreted only as key concerns in the area. Originality/value: The paper reports an evaluation of 28 key articles reported in the domain of supply chain performance measurement and indicates the research opportunities in the area. © Emerald Group Publishing Limited.},
author_keywords={Distribution and inventory management;  Performance measurement;  Supply chain management;  Supply chain metrics},
references={Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173 (1), pp. 211-225; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3-4), pp. 275-292; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) The International Journal of Public Sector Management, 21 (1), pp. 4-25; Bénassy-Quéré, A., Decreux, Y., Fontagné, L., Khoudour-Casteras, D., (2009) Economic crisis and global supply chains, , www.cepii.fr/anglaisgraph/workpap/summaries/2009/wp2009-15.htm, CEPII, working paper No. 2009-15, available at; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using analytical hierarchy process (2007) Production Planning and Control, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning and Control, 20 (8), pp. 678-690; Bititci, U., Cavalieri, S., von Cieminski, G., Implementation of performance measurement systems: Private and public sectors (2005) Production Planning and Control, 16 (2), pp. 99-100; Bititci, U., Garengo, P., Dörfler, V., Nudurpati, S., (2008) Performance measurement: Questions for tomorrow, , www.strath.ac.uk/siom/research/researchpapers, SIOM Research Paper Series, 005, 27 October, available at; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Bourne, M., Mils, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56; Bullinger, H.R., Kuehner, M., Hoof, A.V., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) International Journal of Logistics Management, 5 (2), pp. 11-28; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (1), pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3-4), pp. 209-223; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Supply Chain Management: An International Journal, 16 (5), pp. 605-620; Childerhouse, P., Deakins, E., Bøhme, T., Towill, D.R., Disney, S.M., Banomyong, R., Supply chain integration: An international comparison of maturity (2011) Asia Pacific Journal of Marketing and Logistics, 23 (4), pp. 531-552; Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution & Logistics Management, 24 (1), pp. 17-28; Clift, R., Metrics for supply chain sustainability (2003) Clean Technologies and Environmental Policy, 5 (3-4), pp. 240-247; Cuthbertson, R., Piotrowicz, W., Supply chain best practices - identification and categorization of measures and benefits (2008) International Journal of Productivity and Performance Management, 57 (5), pp. 389-404; Cuthbertson, R., Piotrowicz, W., Performance measurement systems in supply chains: A framework for contextual analysis (2011) International Journal of Productivity and Performance Management, 60 (6), pp. 583-602; Dasguptha, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management & Business Excellence, 14 (3), pp. 355-366; de Toni, A., Tonchia, S., Performance measurement systems (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge - Measuring Operations for World-Class Competition, , Dow Jones-Irwin, Homewood, IL; El Saadany, A.M.A., Jaber, M.Y., Bonney, M., Environmental performance measures for supply chains (2011) Management Research Review, 34 (11), pp. 1202-1221; Epstein, M.J., Westbrook, R.A., Linking actions to profits in strategic decision making (2001) MIT Sloan Management Review, pp. 39-49. , Spring; Fabbe-Costes, N., Jahre, N., Supply chain integration and performance: A review of the evidence (2008) International Journal of Logistics Management, 19 (2), pp. 130-154; Fabbe-Costes, N., Roussat, C., Colin, J., Future sustainable supply chains: What should companies scan? (2011) International Journal of Physical Distribution & Logistics Management, 41 (3), pp. 228-252; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA, London; Flynn, B.B., Huo, B., Zhao, X., The impact of supply chain integration on performance: A contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Fynes, B., Voss, C., de Búrca, S., The impact of supply chain relationship dynamics on manufacturing performance (2005) International Journal of Operations & Production Management, 25 (1), pp. 6-19; Gulledge, T., Chavusholu, T., Automating the construction of supply chain key performance indicators (2008) Industrial Management & Data Systems, 108 (6), pp. 750-774; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Heskett, J.L., Jones, T.O., Loveman, G.W., Sasser Jr., W.E., Schlesinger, L.A., Putting the service profit chain to work (1994) Harvard Business Review, pp. 105-111. , March-April; Hieber, R., (2002) Supply Chain Management: A Collaborative Performance Measurement Approach, , Verlag der Fachvereine Hochschulverlag AG an der ETH Zurich; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Jalalvand, F., Teimoury, E., Makui, A., Aryanezhad, M.B., Jolai, F., A method to compare supply chains of an industry (2011) Supply Chain Management: An International Journal, 16 (2), pp. 82-97; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost - The Rise and Fall of Management Accounting, , Harvard University Press, Boston, MA; Kanji, G.K., Measurement of business excellence (1998) Total Quality Management, 9 (7), pp. 633-643; Kannan, V.R., Tan, K.C., Just in time, total quality management, and supply chain management: Understanding their linkages and impact on business performance (2005) OMEGA: Int. J. Mgmt. Sci., 33, pp. 153-162; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, pp. 71-79. , January-February; Keebler, J.S., Plank, R.E., Logistics performance measurement in the supply chain: A benchmark (2009) Benchmarking: An International Journal, 16 (6), pp. 785-798; Keegan, D.P., Eiler, R.G., Jones, C., Are your performance measures obsolete? (1989) Management Accounting, 70 (12), pp. 45-50; Kennerley, M.P., Neely, A.D., Performance measurement frameworks - a review (2000) Proceedings of the 2nd International Conference on Performance Measurement, pp. 291-298; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Kim, S.W., Organizational structures and the performance of supply chain management (2007) Production Economics, 106 (2), pp. 323-345; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lapide, L., True measures of supply chain performance (2000) Supply Chain Management Review, pp. 25-28. , July/August; Lee, K-H., Cheong, I-M., Measuring a carbon footprint and environmental practice: The case of Hyundai Motors Co. (HMC) (2011) Industrial Management & Data Systems, 111 (6), pp. 961-978; Lin, L.C., Li, T.S., An integrated framework for supply chain performance management using the using six-sigma metrics (2010) Software Quality Journal, 18 (3), pp. 387-406; Martin, P.R., Patterson, J.W., On measuring company performance within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; Meade, L., Sarkis, J., Analyzing organizational project alternatives for agile manufacturing process: An analytical network approach (1999) International Journal of Production Research, 37 (2), pp. 241-261; Meade, L.M., Liles, D.H., Sarkis, J., Justifying strategic alliances and partnering: A prerequisite for virtual enterprising (1997) Omega, 25 (1), pp. 29-42; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations & Production Management, 20 (5), pp. 520-533; Meixell, M., Gargeya, V.B., Global supply chain design: A literature review and critique (2005) Transportaion Research Part E, 41 (6), pp. 531-550; Micheli, P., Neely, A., Performance measurement in the UK's public sector: Searching for the golden thread (2010) Public Administration Review, 70 (4), pp. 591-600; Mondragon, A.E.C., Lalwani, C., Mondragon, C.E.C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Management: An International Journal, 16 (1), pp. 43-56; Neely, A., The performance measurement revolution: Why now and what Next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-12; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (9-10), pp. 1119-1145; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A.D., Mills, J., Gregory, M., Richards, H., Platts, K., Bourne, M., (1996) Getting the Measure of Your Business, , Findlay Publications, Horton Kirby; Nudurupati, S.S., Bititci, U.S., Implementation and impact of IT-supported performance measurement systems (2005) Production Planning & Control, 16 (2), pp. 152-162; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resources, Conservation and Recycling, 55 (6), pp. 567-579; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Panayides, P., Lun, Y.H.V., The impact of trust on innovativeness and supply chain performance (2009) International Journal of Production Economics, 122 (1), pp. 35-46; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management & Data Systems, 110 (9), pp. 1297-1318; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14 (4), pp. 335-346; Puigjaner, L., Lainez, J.M., Capturing dynamics in integrated supply chain management (2008) Computers and Chemical Engineering, 32 (11), pp. 2582-2605; Ramanathan, U., Gunasekaran, A., Subramanian, N., Supply chain collaboration performance metrics: A conceptual framework (2011) Benchmarking: An International Journal, 18 (6), pp. 856-872; Ratnatunga, J., Gray, N., Balachandran, K.R., CEVITA: The valuation and reporting of strategic capabilities (2004) Management Accounting Research, 15 (1), pp. 77-105; Ryu, I., So, S., Koo, C., The role of partnership in supply chain performance (2009) Industrial Management & Data Systems, 109 (4), pp. 496-514; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Schönsleben, P., (2004) Integral Logistics Management: Planning and Control of Comprehensive Supply Chains, , Auerbach Publications, Boca Raton, FL; Shah, J., Singh, N., Benchmarking internal supply chain performance: Development of a framework (2000) Journal of Supply Chain Management, 37 (1), pp. 37-47; Shaw, S., Grant, D.B., Mangan, J., Developing environmental supply chain performance measures (2010) Benchmarking: An International Journal, 17 (3), pp. 320-339; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Simatupang, T.M., Sridharan, R., Supply chain discontent (2005) Business Process Management Journal, 11 (4), pp. 349-369; Slone, R.E., Mentzer, J.T., Dittmann, J.P., Are you the weakest link in your company's supply chain? (2007) Harvard Business Review, , September; Solvang, W.D., Roman, E., Deng, Z., Solvang, B., A framework for holistic greening of value chains: Knowledge enterprise: Intelligent strategies in product design, manufacturing, and management (2006) International Federation for Information Processing (IFIP), 207, pp. 350-355; Srinivasan, M., Mukharjee, D., Gaur, A., Buyer-supplier partnership quality and supply chain performance: Moderating role of risks, and environmental uncertainty (2011) European Management Journal, 29 (4), pp. 260-271; Sundram, V.P.K., Ibrahim, A.R., Chandran Govindaraju, V.G.R., Supply chain management practices in the electronics industry in Malaysia: Consequences for supply chain performance (2011) Benchmarking: An International Journal, 18 (6), pp. 834-855; Tate, W.L., Ellram, L.M., Kirchoff, J.F., Corporate social responsibility reports: A thematic analysis related to supply chain management (2010) Journal of Supply Chain Management, 46 (1), pp. 19-44; Taticchi, P., Tonelli, F., Cagnazzo, L., Performance measurement and management: A literature review and a research agenda (2010) Measuring Business Excellence, 14 (1), pp. 4-18; Thakkar, J., Kanda, A., Deshmukh, S.G., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), pp. 702-723; Thonemann, U.W., Bradley, J.R., The effect of product variety on supply-chain performance (2002) European Journal of Operational Research, (143), pp. 548-569; van Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 126-147; Venkatraman, N., Ramanujam, V., Measurement of business performance in strategy research: A comparison of approaches (1986) Academy of Management Review, 11 (4), pp. 801-814; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51; Wu, F., Yeniyurt, S., Kim, D., Cayusgil, T., The impact of information technology on supply chain capabilities and firm performance: A resource-based view (2006) Industrial Marketing Management, 35 (4), pp. 493-504; Yao, K., Liu, C., An integrated approach for measuring supply chain performance (2006) Journal of Modern Accounting and Auditing, 2 (10). , (Serial No. 17); Zhang, L., Wang, S., Lic, F., Wange, H., Wange, L., Tanf, W., A few measures for ensuring supply chain quality (2011) International Journal of Production Research, 49 (1), pp. 87-97},
correspondence_address1={Gopal, P. R. C.; Department of Industrial Engineering and Management, , Kharagpur, India; email: prcgopal@gmail.com},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Bai20122484,
author={Bai, C. and Sarkis, J.},
title={Supply-chain performance-measurement system management using neighbourhood rough sets},
journal={International Journal of Production Research},
year={2012},
volume={50},
number={9},
pages={2484-2500},
doi={10.1080/00207543.2011.581010},
note={cited By 34},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864410087&doi=10.1080%2f00207543.2011.581010&partnerID=40&md5=fc00162d0ce1c9663c53f19b3deb5bb6},
affiliation={School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, China; Graduate School of Management, Clark University, Worcester, United States},
abstract={Performance measurements are central to improving organisational competitiveness. Organisations and researchers have developed and investigated various performance-measurement systems to manage and improve internal and external operations for logistics and supply chains. Yet, formal modelling tools and approaches for organisations to help evaluate the relationships between the performance measures and the desired competitive outcomes are limited, especially in logistics and supply-chain management functions. To help address this gap, we introduce a novel application of neighbourhood rough-set theory for the identification and selection of performance measures related to externally derived desired outcomes on the sourcing function. Our contribution includes introducing an innovative neighbourhood rough-set approach using elements of the Supply-Chain Operations Reference model. The model may aid in determining a core set of external logistics and supply-chain performance measures to internal performance expectations and outcomes. Implications and results interpretation of the methodology, sensitivity analysis, limitations, and future research directions conclude the paper. © 2012 Taylor & Francis.},
author_keywords={Neighbourhood rough set;  Performance-measurement system;  Sourcing;  Supply-chain operations reference (SCOR)},
keywords={Core set;  Formal modelling;  Future research directions;  Internal performance;  Neighbourhood;  Novel applications;  Performance measure;  Performance measurements;  Performance-measurement system;  Reference models;  Rough set;  Sourcing;  Supply-chain operations reference (SCOR);  System management, Competition;  Measurements;  Rough set theory;  Supply chains, Supply chain management},
references={Adams, S., Sarkis, J., Liles, D.H., The development of strategic performance metrics (1995) Engineering Management Journal, 7 (1), pp. 24-32; Alfaro, J., Ortiz, A., Poler, R., Performance measurement system for business processes (2007) Production Planning and Control, 18 (8), pp. 641-654. , DOI 10.1080/09537280701599772, PII 785680113; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301. , DOI 10.1016/j.dss.2004.12.005, PII S0167923604003070; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Bierbusse, P., Siesfeld, T., Measures that matter (1997) Journal of Strategic Performance Measurement, 1 (2), pp. 6-11; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , DOI 10.1108/01443570010330739; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24. , DOI 10.1504/IJBPM.2003.002097; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), p. 2003; Coskun, A., Bayyurt, N., Measurement frequency of performance indicators and satisfaction on corporate performance: A survey on manufacturing companies (2008) European Journal of Economics, Finance and Administrative Sciences, (13), pp. 79-87; Cross, K.F., Lynch, R.L., The 'SMART' way to define and sustain success (1988) National Productivity Review, 8 (1), pp. 23-33; Fink, A., Marr, B., Siebe, A., Kuhle, J.-P., The future scorecard: Combining external and internal scenarios to create strategic foresight (2005) Management Decision, 43 (3), pp. 360-381. , DOI 10.1108/00251740510589751; Florence, M., A performance technologist's approach to process performance improvement (2008) Performance Improvement, 47 (2), pp. 30-40; Franceschini, F., Galetto, M., Turina, E., Service quality monitoring by performance indicators: A proposal for a structured methodology (2009) International Journal of Services and Operations Management, 5 (2), pp. 251-273; Fullerton, R.R., Wempe, W.F., Lean manufacturing, non-financial performance measures, and financial performance (2009) International Journal of Operations and Production Management, 29 (3), pp. 214-240; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47. , DOI 10.1111/j.1468-2370.2005.00105.x; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hu, Q.-H., Yu, D.-R., Xie, Z.-X., Numerical attribute reduction based on neighborhood granulation and rough approximation (2008) Ruan Jian Xue Bao/Journal of Software, 19 (3), pp. 640-649. , DOI 10.3724/SP.J.1001.2008.00640; Hu, Q., Neighborhood rough set based heterogeneous feature subset selection (2008) Information Sciences, 178 (18), pp. 3577-3594; Hu, Q., Yu, D., Xie, Z., Neighborhood classifiers (2008) Expert Systems with Applications, 34 (2), pp. 866-876. , DOI 10.1016/j.eswa.2006.10.043, PII S0957417406003484; Jagdev, H., Bradley, P., Molloy, O., A QFD based performance measurement tool (1997) Computers in Industry, 33 (2-3), pp. 357-366. , PII S0166361597000419; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston, MA: Harvard Business School Press; Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, , Boston, MA: Harvard Business School Press; Li, P., Tang, G., Performance measurement design within its organisational context-Evidence from China (2009) Management Accounting Research, 20 (3), pp. 193-207; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533. , DOI 10.1108/01443570010318896; Miller, G.A., The magical number seven, plus or minus two: Some limits on our capacity for processing information (1956) Psychological Review, 63 (2), pp. 81-97; Neely, A., (1998) Measuring Business Performance, , London: The Economist and Profile Books Ltd; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228. , DOI 10.1108/01443579910247437; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Olsen, E.O., Performance measurement system and relationships with performance results: A case analysis of a continuous improvement approach to PMS design (2007) International Journal of Productivity and Performance Management, 56 (7), pp. 559-582; Pawlak, Z., Rough sets (1982) International Journal of Computer and Information Sciences, 11 (5), pp. 341-356; Pinheiro De Lima, E., Eduardo Gouvêa Da Costa, S., Reis De Faria, A., Taking operations strategy into practice: Developing a process for defining priorities and performance measures (2009) International Journal of Production Economics, 122 (1), pp. 403-418; Rodriguez-Rodriguez, R., Building internal business scenarios based on real data from a performance measurement system (2009) Technological Forecasting and Social Change, , Forthcoming; Rodriguez, R.R., Saiz, J.J.A., Bas, A.O., Quantitative relationships between key performance indicators for supporting decision-making processes (2009) Computers in Industry, 60 (2), pp. 104-113; Rosegrant, S., Shapiro, R., Watkins, M., (1997) A Measure of Delight: The Pursuit of Quality at ATandT Universal Card Services (Vol. Case 9-694-047), , Cambridge, MA: Harvard Business School; Sarkis, J., Quantitative models for performance measurement systems-alternate considerations (2003) International Journal of Production Economics, 86 (1), pp. 81-90; Sarkis, J., Talluri, S., A synergistic framework for evaluating business process improvements (2002) International Journal of Flexible Manufacturing Systems, 14 (1), pp. 53-71. , DOI 10.1023/A:1013827026600; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , DOI 10.1108/17410400610653219; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64 (1), pp. 231-241. , DOI 10.1016/S0925-5273(99)00061-4; Wilson, D.R., Martinez, T.R., Improved heterogeneous distance functions (1997) Journal of Artificial Intelligence Research, 6, pp. 1-34; Wouters, M., A developmental approach to performance measures-Results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78; Ziarko Wojciech, Variable precision rough set model (1993) Journal of Computer and System Sciences, 46 (1), pp. 39-59. , DOI 10.1016/0022-0000(93)90048-2},
correspondence_address1={Sarkis, J.; Graduate School of Management, , Worcester, United States; email: jsarkis@clarku.edu},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Danese20122002,
author={Danese, P. and Romano, P.},
title={Relationship between downstream integration, performance measurement systems and supply network efficiency},
journal={International Journal of Production Research},
year={2012},
volume={50},
number={7},
pages={2002-2013},
doi={10.1080/00207543.2011.575894},
note={cited By 26},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861359854&doi=10.1080%2f00207543.2011.575894&partnerID=40&md5=611643fe15aa13edec79071e850d724e},
affiliation={Department of Management and Engineering, University of Padova, Vicenza, Italy; Department of Electrical, Managerial and Mechanical Engineering, University of Udine, Udine, Italy},
abstract={Downstream integration is a key managerial area to improve performance in supply networks. Though most studies agree that downstream integration positively influences performances, the literature also reports cases of failures in achieving significant improvements. This evidence suggests that some factors may act as moderators on the downstream integration-performance relationship. This paper analyses the impact of downstream integration on supply network efficiency and the moderating effect on this relationship of supply network performance measurement systems. Data from a sample of 200 manufacturing plants settled in several countries around the world demonstrate that this moderating effect exists. Therefore, in order to strengthen the impact of downstream integration on supply network efficiency, managers should combine interventions on downstream integration and supply network performance measurement systems, rather than investing and focusing on downstream integration only. © 2012 Copyright Taylor and Francis Group, LLC.},
author_keywords={downstream integration;  performance measurement systems;  supply chain management;  supply network efficiency;  survey},
keywords={Manufacturing plant;  Moderating effect;  Performance measurement system;  Supply networks, Efficiency;  Managers;  Network performance;  Supply chain management;  Surveys, Integration},
references={Bayraktar, E., A causal analysis of the impact of information systems and supply chain management practices onoperational performance: Evidence from manufacturing smes in turkey (2009) International Journal of Production Economics, 122 (1), pp. 133-149; Bechtel, C., Jayaram, J., Supply chain management: A strategic perspective (1997) The International Journal of Logistics Management, 8 (1), pp. 15-34; Bozarth, C.C., The impact of supply chain complexity on manufacturing plant performance (2009) Journal of Operations Management, 27 (1), pp. 78-93; Brambor, T., Clark, W.R., Golder, M., Understanding interaction models: Improving empirical analyses (2006) Political Analysis, 14 (1), pp. 63-82; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, March/April (, 2, pp. 48-56; Chen, F., Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times and information (2000) Management Science, 46 (3), pp. 436-443; Cohen, J., Cohen, P., (1983) Applied Multiple Regression for the Behavioral Sciences, , Hillsdale: Lawrence Erlbaum; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-13; Coppini, M., Bullwhip effect and inventory oscillations analysis using the beer game model (2010) International Journal of Production Research, 48 (13), pp. 3943-3956; Cousins, P.D., Menguc, B., The implications of socialisation and integration in supply chain management (2006) Journal of Operations Management, 24 (5), pp. 604-620; Cousins, P.D., Lawson, B., Squire, B., Performance measurement in strategic buyer-supplier relationships: The mediating role of socialisation mechanisms (2008) International Journal of Operations & Production Management, 28 (3), pp. 238-258; Danese, P., The extended vmi for coordinating the whole supply network (2006) Journal of Manufacturing Technology Management, 17 (7), pp. 888-907; Danese, P., Towards a contingency theory of collaborative planning initiatives (2011) International Journal of Production Research, 49 (4), pp. 1081-1103; Danese, P., Romano, P., Vinelli, A., Sequences of improvement in supply networks: Case studies from the pharmaceutical industry (2006) International Journal of Operations & Production Management, 26 (11), pp. 1199-1222; Danese, P., Romano, P., Supply chain integration and efficiency performance: A study on the interactions between customer and supplier integration (2011) Supply Chain Management: An International Journal, , in press; Das, A., Narasimhan, R., Talluri, S., Supplier integration - Finding an optimal configuration (2006) Journal of Operations Management, 24 (5), pp. 563-582; De Treville, S., Shapiro, R.D., Hameri, A.P., From supply chain to demand chain: The role of lead time reduction in improving demand chain performance (2004) Journal of Operations Management, 21 (6), pp. 613-627; Disney, S.M., Towill, D.R., A procedure for the optimisation of the dynamic response of a vendor managed inventory system (2002) Computers & Industrial Engineering, 43 (1-2), pp. 27-58; Disney, S.M., Naim, M.M., Potter, A., Assessing the impact of e-business on supply chain dynamics (2004) International Journal of Production Economics, 89 (2), pp. 109-118; Flynn, B.B., Flynn, J., Information-processing alternatives for coping with manufacturing environment complexity (1999) Decision Sciences, 17 (3), pp. 249-269; Forslund, H., Jonsson, P., Obstacles to supply chain integration of the performance management process in buyersupplier dyads (2009) International Journal of Operations & Production Management, 29 (1), pp. 77-95; Forslund, H., The impact of performance management on customers' expected logistics performance (2007) International Journal of Operations & Production Management, 36 (8), pp. 580-595; Frohlich, M., Westbrook, R., Arcs of integration: An international study of supply chain strategies (2001) Journal of Operations Management, 19 (2), pp. 185-200; Fynes, B., Voss, C., De Búrca, S., The impact of supply chain relationships dynamics on manufacturing performance (2005) International Journal of Operations & Production Management, 25 (1), pp. 6-19; Gavirneni, S., Kapusincski, R., Tayur, S., Value of information in capacitated supply chains (1999) Management Science, 45 (1), pp. 16-24; Gimenez, C., Ventura, E., Logistics-production, logistics-marketing and external integration: Their impact on performance (2005) International Journal of Operations & Production Management, 25 (1), pp. 20-38; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hair, J.F., (2006) Multivariate Data Analysis, , 6th ed. Upper Saddle River: Pearson Prentice Hall; Handfield, R., An organisational entrepreneurship model of supply management integration and performance outcomes (2009) International Journal of Operations & Production Management, 29 (2), pp. 100-126; Jaccard, J., Turrisi, R., (2003) Interaction Effects in Multiple Regression, , 2nd ed Thousand Oaks: Sage Publications; Jain, V., Wadhwa, S., Deshmukh, S.G., Select supplier-related issues in modelling a dynamic supply chain: Potential, challenges and direction for future research (2009) International Journal of Production Research, 47 (11), pp. 3013-3039; Jayaram, J., Tan, K., Nachiappan, S.P., Examining the interrelationships between supply chain integration scope and supply chain management efforts (2010) International Journal of Production Research, 48 (22), pp. 6837-6857; Kannan, V.R., Tan, K.C., Supply chain integration: Cluster analysis of the impact of span of integration (2010) Supply Chain Management: An International Journal, 15 (3), pp. 207-215; Kim, D., Kumar, V., Kumar, U., Performance assessment framework for supply chain partnership (2010) Supply Chain Management: An International Journal, 15 (3), pp. 187-195; Kim, S.W., Effects of supply chain management practices, integration and competition capability on performance (2006) Supply Chain Management: An International Journal, 11 (3), pp. 241-248; Lawson, B., Strategic purchasing, supply management practices and buyer performance improvement: An empirical study of uk manufacturing organisations (2009) International Journal of Production Research, 47 (10), pp. 2649-2667; Lee, C.W., Kwon, I.G., Severance, D., Relationships between supply chain performance and degree of linkage among supplier, internal integration and customer (2007) Supply Chain Management: An International Journal, 12 (6), pp. 444-452; Lee, H.L., So, K.C., Tang, C.S., The value of information sharing in two level supply chain (2000) Management Science, 46 (5), pp. 626-643; Li, G., The impact of it implementation on supply chain integration and performance (2009) International Journal of Production Economics, 120 (1), pp. 125-138; Marchand, M., Raymond, L., Researching performance measurement systems: An information systems perspective (2008) International Journal of Operations & Production Management, 28 (7), pp. 663-686; Ou, C.S., A structural model of supply chain management on firm performance (2010) International Journal of Operations & Production Management, 30 (5), pp. 526-545; Romano, P., Co-ordination and integration mechanisms to manage logistics processes across supply networks (2003) Journal of Purchasing and Supply Management, 9 (3), pp. 119-134; Romano, P., How can fluid dynamics help supply chain management? (2009) International Journal of Production Economics, 118 (2), pp. 463-472; Ryu, S., Tsukishima, T., Onari, H., A study on evaluation of demand information-sharing methods in supply chain (2009) International Journal of Production Economics, 120 (1), pp. 162-175; Sahin, F., Robinson, E.P., Information sharing and coordination in make-to-order supply chains (2005) Journal of Operations Management, 23 (6), pp. 579-598; Sezen, B., Relative effects of design, integration and information sharing (2008) Supply Chain Management: An International Journal, 13 (3), pp. 233-240; Schroeder, R., Flynn, B., (2001) High performance manufacturing: Global perspectives, , New York: John Wiley & Sons, Inc; Siggelkow, N., Change in the presence of fit: The rise, the fall, and the renaissance of liz claiborne (2001) Academy of Management Journal, 44 (4), pp. 838-857; Singh, P.J., Power, D., The nature and effectiveness of collaboration between firms, their customers and suppliers: A supply chain perspective (2009) Supply Chain Management: An International Journal, 14 (3), pp. 189-200; Stock, G.N., Greis, N.P., Kasarda, J.D., Enterprise logistics and supply chain structure: The role of fit (2000) Journal of Operations Management, 18 (5), pp. 531-547; Swink, M., Narasimhan, R., Wang, C., Managing beyond the factory walls: Effects of four types of strategic integration on manufacturing plant performance (2007) Journal of Operations Management, 25 (1), pp. 148-164; Terwiesch, C., An empirical analysis of forecast sharing in the semiconductor equipment supply chain (2005) Management Science, 51 (2), pp. 208-220; Vachon, S., Halley, A., Beaulieu, M., Aligning competitive priorities in the supply chain: The role of interactions with suppliers (2009) International Journal of Operations & Production Management, 29 (4), pp. 322-334; Van Der Vaart, T., Van Donk, D.P., A critical review of survey-based research in supply chain integration (2008) International Journal of Production Economics, 111 (1), pp. 42-55; Vickery, S., The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationship (2003) Journal of Operations Management, 21 (5), pp. 523-539; Wagner, S., Krause, D.R., Supplier development: Communication approaches, activities and goals (2009) International Journal of Production Research, 47 (12), pp. 3161-3177; Wangphanich, P., Kara, S., Kayis, B., Analysis of the bullwhip effect in multi-product, multi-stage supply chain systems - A simulation approach (2010) International Journal of Production Research, 48 (15), pp. 4501-4517; Wiengarten, F., Collaborative supply chain practices and performance: Exploring the key role of information quality (2010) Supply Chain Management: An International Journal, 15 (6), pp. 463-473; Zhao, X., Xie, J., Zhang, W.J., The impact of information sharing and ordering co-ordination on supply chain performance (2002) Supply Chain Management: An International Journal, 7 (1), pp. 24-40},
correspondence_address1={Danese, P.; Department of Management and Engineering, , Vicenza, Italy; email: pamela.danese@unipd.it},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cho2012801,
author={Cho, D.W. and Lee, Y.H. and Ahn, S.H. and Hwang, M.K.},
title={A framework for measuring the performance of service supply chain management},
journal={Computers and Industrial Engineering},
year={2012},
volume={62},
number={3},
pages={801-818},
doi={10.1016/j.cie.2011.11.014},
note={cited By 229},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858080790&doi=10.1016%2fj.cie.2011.11.014&partnerID=40&md5=a6e54f9f6301f5c5c863b058ecf6f882},
affiliation={Department of Industrial and Management Engineering, Hanyang University, South Korea},
abstract={Despite the increasing attention to the service supply chain management by both practitioners and academics, the performance measurement of service supply chains still remains unexplored. Most service firms realize that, in order to evolve an efficient and effective service supply chain, service supply chain management needs to be assessed for its performance. A literature review was conducted on performance measurement issues of service supply chains. This paper develops a framework of service supply chain performance measurement. Based on the strategic, tactical and operational level performance in a service supply chain, measures and metrics are discussed. The emphasis is on performance measures dealing with service supply chain processes such as demand management, customer relationship management, supplier relationship management, capacity and resource management, service performance, information and technology management and service supply chain finance. And to prioritize service supply chain performance measurement indicators to improve service supply chain performance, a methodology based on the extent fuzzy analytic hierarchy process is stressed. The developed framework of service supply chain performance measurement is applied to the hotel supply chain. The results of this study are useful both to practitioners in the service supply chain and to researchers carrying out further studies in the field. © 2011 Elsevier Ltd. All rights reserved.},
author_keywords={Fuzzy-AHP;  Performance measurement;  Service supply chain},
keywords={Customer relationship management;  Fuzzy AHP;  Fuzzy analytic hierarchy process;  Literature reviews;  Operational level;  Performance measure;  Performance measurements;  Resource management;  Service firms;  Service performance;  Service supply chain managements;  Service supply chains;  Supplier relationships;  Technology managements, Information management;  Supply chain management, Mobile telecommunication systems},
references={Arlbjørn, J.S., Freytag, P.V., De Haas, H., Service supply chain management: A survey of lean application in the municipal sector (2011) International Journal of Physical Distribution & Logistics Management, 41 (3), pp. 277-295; Autry, C.W., Golicic, S.L., Evaluating buyer-supplier relationship- Performance spirals: A longitudinal study (2010) Journal of Operations Management, 28 (2), pp. 87-100; Ballou, R.H., (2004) Business Logistics/supply Chain Management, , 5th ed. Pearson-Prentice Hall Upper Saddle River, New Jersey; Baltacioglu, T., Ada, E., Kaplan, M.D., Yurt, O., Kaplan, Y.C., A new framework for service supply chains (2007) Service Industries Journal, 27 (2), pp. 105-124. , DOI 10.1080/02642060601122629, PII 772888003; Banomyong, R., Supatn, N., Developing a supply chain performance tool for SMEs in Thailand (2011) Supply Chain Management: An International Journal, 16 (1), pp. 20-31; Boonitt, S., Pongpanarat, C., Measuring service supply chain management processes: The application of the Q-sort technique (2011) International Journal of Innovation, Management and Technology, 2 (3), pp. 217-221; Brignall, S., Ballantine, J., Performance measurement in service businesses revisited (1996) International Journal of Service Industry Management, 7 (1), pp. 6-31; Bruhn, M., Georgi, D., (2006) Services Marketing: Managing the Service Value Chain, , Prentice Hall, Harlow England; Buyukozkan, G., Cifci, G., Guleryuz, S., Strategic analysis of healthcare service quality using fuzzy AHP methodology (2011) Expert Systems with Applications, 38 (8), pp. 9407-9424; (2003) Managing Your Services Spend in Today's Services Economy, , CAPS Research Purchasing Performance Benchmarking Study CAPS Research, Tempe, AZ; Chang, D.Y., Applications of the extent analysis method on fuzzy AHP (1996) European Journal of Operational Research, 95 (3), pp. 649-655; Chopra, S., Meindl, P., (2004) Supply Chain Management: Strategy, Planning, and Operation, , 3rd ed. Pearson-Prentice Hall Upper Saddle River, New Jersey; Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman Publishing London; Kilger, C., The selection process (2002) Supply Chain Management and Advanced Planning, pp. 261-273. , H. Stadtler, C. Kilger, Springer Berlin; Doney, P.M., Barry, J.M., Abratt, R., Trust determinants and outcomes in global B2B services (2007) European Journal of Marketing, 41 (9-10), pp. 1096-1116; Doran, D., Thomas, P., Caldwell, N., Examining buyer-supplier relationships within a service sector context (2005) Supply Chain Management, 10 (4), pp. 272-277. , DOI 10.1108/13598540510612749; Duran, O., Aguilo, J., Computer-aided machine-tool selection based on a Fuzzy-AHP approach (2008) Expert Systems with Applications, 34 (3), pp. 1787-1794. , DOI 10.1016/j.eswa.2007.01.046, PII S0957417407000553; Ellram, L., Tate, W., Billington, C., Understanding and managing the services supply chain (2004) Journal of Supply Chain Management, 40 (4), pp. 17-32; Ellram, L., Tate, W., Billington, C., Services supply management: The next frontier for improved organisational performance (2007) California Management Review, 49 (4), pp. 44-66; Feng, B., Fan, Z.P., Li, Y., A decision method for supplier selection in multi-service outsourcing (2011) International Journal of Production Economics, 132 (2), pp. 240-250; Fitzgerald, L., Johnston, R., Brignall, T.J., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, , CIMA London; Foggin, J., Mentzer, J., Monroe, C., A supply chain diagnostic tool (2004) International Journal of Physical Distribution & Logistics Management, 34 (10), pp. 827-855; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement systems in the after sales service: An integrated framework (2006) International Journal of Business Performance Measurement, 9 (2), pp. 147-171; Giannakis, M., Management of service supply chains with a service oriented reference model: The case of management consulting source (2011) Supply Chain Management: An International Journal, 16 (5). , on-line print; Gomm, M.L., Supply chain finance: Applying finance theory to supply chain management to enhance finance in supply chains (2010) International Journal of Logistics: Research and Applications, 13 (2), pp. 133-142; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Haksever, C., Render, B., Russell, R.S., Murdick, R.G., (2000) Service Management and Operations, , 2nd ed. Prentice Hall Upper Saddle River, New Jersey; Hansen, J.M., The evolution of buyer-supplier relationships: An historical industry approach (2009) Journal of Business & Industrial Marketing, 24 (3-4), pp. 227-236; Hill, T., (1985) Manufacturing Strategy- The Strategic Management of the Manufacturing Function, , Macmillan; Isaai, M.T., Kanani, A., Tootoonchi, M., Afzali, H.R., Intelligent timetable evaluation using fuzzy AHP (2011) Expert Systems with Applications, 38 (4), pp. 3718-3723; Johnston, R., Glark, G., (2008) Service Operations and Management, , 3rd ed. Prentice Hall Harlow; Kahraman, C., Cebeci, U., Ruan, D., Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey (2004) International Journal of Production Economics, 87 (2), pp. 171-184; Kaplan, R.S., Norton, P.D., The balanced scoreboard-measures that drives performance (1992) Harvard Business Review, , January-February, 71-79; Kathawala, Y., Abdou, K., Supply chain evaluation in the service industry: A framework development compared to manufacturing (2003) Managerial Auditing Journal, 18 (2), pp. 140-149; Kerr, J., Streamlining the cash flow (2006) Supply Chain Management Review, 10 (7), pp. 25-31; Kim, S.H., Ellegaard, C., Supplier evaluation processes: The shaping and reshaping of supplier performance (2011) International Journal of Operations & Production Management, 31 (8), pp. 888-910; Korhonen, P., Huttunen, K., Eloranta, E., Demand chain management in a global enterprise - Information management view (1998) Production Planning and Control, 9 (6), pp. 526-531; Kothari, T., Hu, C., Roehl, W.S., E-Procurement: An emerging tool for the hotel supply chain management (2005) International Journal of Hospitality Management, 24 (3), pp. 369-389. , DOI 10.1016/j.ijhm.2004.09.004, PII S027843190400088X; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lee, A.H.I., Chang, H.J., Lin, C.Y., An evaluation model of buyer-supplier relationships in high-tech industry- the case of an electronic components manufacturer in Taiwan (2009) Computers & Industrial Engineering, 57 (4), pp. 1417-1430; Mettler, T., Rohner, P., Supplier relationship management: A vase study in the context of health care (2009) Journal of Theoretical and Applied Electronic Commerce Research, 4 (3), pp. 58-71; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , FT Prentice-Hall London; Ordoobadi, S.M., Wang, S., A multiple perspectives approach to supplier selection (2011) Industrial Management & Data Systems, 111 (4), pp. 629-648; Parasuraman, A., Zeithaml, V., Berry, L.L., SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 13-40; Saaty, T.L., (1980) The Analytic Hierarchy Process: Planning, Priority Setting, , McGraw-Hill International Book New York; Sampson, S.E., Customer-supplier duality and bidirectional supply chains in service organizations (2000) International Journal of Service Industry Management, 11 (4), pp. 348-364; Sengupta, K., Heiser, D., Koll, L., Manufacturing and service supply chain performance. A comparative analysis (2006) Journal of Supply Chain Management, 42 (4), pp. 4-15; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2008) Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies, , 3rd ed. McGraw-Hill NY; Silvestro, R., Cross, S., Applying the service profit chain in a retail environment: Challenging the satisfaction mirror (2000) International Journal of Service Industry Management, 11 (3), pp. 244-268; Smith, J.S., Karwan, K.R., Markland, R.E., A note on the growth of research in service operations management (2007) Production and Operations Management, 16 (6), pp. 780-790; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Srie, C., Wagner, M., Supply chain analysis (2002) Supply Chain Management and Advanced Planning, pp. 29-43. , H. Stadtler, C. Kilger, 2nd ed. Springer Berlin; Thakkar, J., Kanda, A., Deshmukh, S.G., Evaluation of buyer-supplier relationships using an integrated mathematical approach of interpretive structural modeling (ISM) and graph theoretic matrix: The case study of Indian automotive SMEs (2007) Journal of Manufacturing Technology Management, 19 (1), pp. 92-124; Toni, A.D., Nissimbeni, G., Tonchia, S., New trends in supply environment (1994) Logistics Information Management, 7 (4), pp. 41-50; Van Ark, B., Mahony, M., Timmer, M.P., The productivity gap between Europe and the United States: Trends and causes (2008) Journal of Economic Perspectives, 22 (1), pp. 25-44; Van Der Valk, W., Wynstra, F., Axelsson, B., Effective buyer-supplier interaction patterns in ongoing service exchange (2009) International Journal of Operations & Production Management, 29 (8), pp. 807-833; Yasin, M.M., Gomes, C.F., Performance management in service operational settings: A selective literature examination (2010) Benchmarking: An International Journal, 17 (2), pp. 214-231},
correspondence_address1={Lee, Y.H.; Department of Industrial and Management Engineering, South Korea; email: yhlee@hanyang.ac.kr},
issn={03608352},
coden={CINDD},
language={English},
abbrev_source_title={Comput Ind Eng},
document_type={Article},
source={Scopus},
}

@ARTICLE{Charan201267,
author={Charan, P.},
title={Supply chain performance issues in an automobile company: A SAP-LAP analysis},
journal={Measuring Business Excellence},
year={2012},
volume={16},
number={1},
pages={67-86},
doi={10.1108/13683041211204680},
note={cited By 41},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858387224&doi=10.1108%2f13683041211204680&partnerID=40&md5=0234a8952a873ea9255aa1482284fce8},
affiliation={Indian Institute of Management Rohtak, Rohtak, India},
abstract={Purpose: Supply chain performance is concerned with managing dependencies between various supply chain members and the joint efforts of all supply chain members to achieve mutually defined goals. There are various performance initiatives reported in the literature but these are often discussed in isolation. Information sharing, information technology, collaborative partnerships, and vendor management that help in improving the supply chain performance are some of the noteworthy initiatives. But there is a need to consider the supply chain in a holistic sense. The purpose of this paper is to analyze a case study of A1, a leading original equipment manufacturer in India, to throw light on the status of supply chain performance initiatives taken in its supply chain. Design/methodology/approach: A situation-actor-process (SAP)-learning-action-performance (LAP) model has been applied for the case study of A1. The situation represents the present scenario of the organization. Actors are the participants, influencing the situation to evolve different business processes. Based on SAP, various learning issues have been analyzed which lead to suitable action followed by impact on the performance of the supply chain of the organization. Resource-based view (RBV) of the firm has been suggested to understand the tangible and intangibles associated with the implementation of a supply chain performance measurement system. Findings: The case study organization has taken various initiatives for improving supply chain performance, such as vendor managed inventory and dealer management system. However, the issues that need attention include measurement of the vendor's performance on criteria which have supply chain orientation, as well as connecting dealers' point of sale information to vendors; This way connects the supply chain as a whole. Emphasis should be placed on second and third tier suppliers, as most of the quality-related problem is due to them. In the twenty-first century only those organizations which pay attention to agility, alignment and adaptability for responding to the challenges can gain competitive advantage. SCPMS as a resource can provide a sustained competitive advantage for a firm. Research limitations/implications: The SAP-LAP analysis is used to explain soft and hard issues of supply chain performance in a managerial context as applied to the case of A1. The value of SCPMS can be augmented only when it is embedded in an organization through resource complementarity and co-specialization. Practical implications: The SAP-LAP model presents the situation of supply chain performance to participants who may initiate the processes needed to make the supply chain effective and efficient. The synthesis of SAP leads to LAP, which bridges the gap by suggesting improvement actions based on the learning from the present situation, actors and processes. Originality/value: This is a novel approach to analyze quantitative and qualitative issues of supply chain performance initiatives in a single model and its impact on performance of the supply chain. © Emerald Group Publishing Limited.},
author_keywords={Automotive industry;  Automotive organization;  Competitive advantage;  India;  Performance measures;  Resource-based view;  SAP-LAP analysis;  Supply chain management;  Supply chain performance measurement systems},
references={Amit, R., Schoemaker, P.J.H., Strategic assets and organizational rent (1993) Strategic Management Journal, 14 (1), pp. 33-46; Aramyan, L., Ondersteijn, C.J.M., van Kooten, O., Oude Lansink, A., Performance indicators in agri-food production chains (2006) Quantifying the Agri-Food Supply Chain, 5, pp. 47-64. , Ondersteijn, C.J.M., Wijnands, R.B.M., Huirne, M., van Kooten, O. (Eds.), Frontis, Wageningen; Arshinder, K.A., Deshmukh, S.G., Supply chain coordination issues: An SAP-LAP framework (2007) Asia Pacific Journal of Marketing and Logistics, 19 (3), pp. 240-264; Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A stakeholder approach to strategic performance management (1997) Sloan Management Review, 19 (2), pp. 25-37; Austin, J.E., (1990) Managing in Developing Countries, , Free Press, New York, NY; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Beamon, B., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Charan, P., Shankar, R., Baisya, R.K., Analysis of interactions among the variables of supply chain performance measurement system implementation (2008) Business Process Management Journal, 14 (4), pp. 512-529; Christopher, M.G., (1998) Logistics and Supply Chain Management: Strategies for Reducing Costs and Improving Services, , Pitman, London; Dangayach, G.S., (2001) Some issues in manufacturing strategy: A select study of Indian manufacturing companies, , unpublished PhD thesis, Indian Institute of Technology Delhi, New Delhi; Day, D.L., Raising radicals: Different processes for championing innovative corporate ventures (1994) Organization Science, 5 (2), pp. 148-172; Defee, C.C., Stank, T.P., Applying the strategy-structure performance paradigm to the supply chain environment (2005) The International Journal of Logistics Management, 16 (1), pp. 28-50; Dierickx, I., Cool, K., Asset stock accumulation and sustainability of competitive advantage (1989) Management Science, 35 (11), pp. 1504-1511; Ebinger, F., Goldbach, M., Schneidewind, U., Greening supply chains: A competence-based perspective (2006) Greening the Supply Chain, pp. 189-204. , Sarkis, J. (Ed.), Springer, Berlin; Eisenhardt, K.M., Building theory from case study research (1989) Academy of Management Review, 14 (4), pp. 532-550; Eisenhardt, K.M., Martin, J.A., Dynamic capabilities: What are they? (2000) Strategic Management Journal, 21 (10-11), pp. 1105-1121; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Fynes, B., Voss, C., Burca, D.S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354; Glaser, B.G., Strauss, A.L., (1967) The Discovery of Grounded Theory: Strategies for Qualitative Research, , Aldine, Chicago, IL; Grant, R.M., The resource-based theory of competitive advantage: Implications for strategy formulation (1991) California Management Review, 33 (3), pp. 114-135; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Ngai, E.W.T., Build-to-order supply chain management: A literature review and framework for development (2005) Journal of Operations Management, 23 (5), pp. 423-451; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Husain, Z., Sushil, Pathak, R.D., A technology management perspective on collaborations in Indian automobile industry: A case study (2002) Journal of Engineering Technology Management, 19 (2), pp. 167-201; Itami, H., (1987) Mobilizing Invisible Assets, , Harvard University Press, Cambridge, MA; Ittner, C.D., Larker, D.F., Coming up short on non-financial performance measurement (2003) Harvard Business Review, 81 (11), pp. 88-96; Kadambi, B., (2000) IT-enabled supply chain management-a preliminary study of few manufacturing companies in India, , www.xlri.ac.in/cltm/LogistiX-2-2000.htm, available at; Kak, A., Strategic management, core competence and flexibility: Learning issues for select pharmaceutical organizations (2004) Global Journal of Flexible Systems Management, 5 (4), pp. 1-16; Kanungo, S., Sharma, S., Bhatia, K., Baby, S., Towards a model for relating supply chain management and use of IT: An empirical study (1998) Supply Chain Management for Global Competitiveness, pp. 849-866. , Sahay, B.S. (Ed.), Macmillan Indian Limited, New Delhi; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kapoor, V., Ellinger, A.E., Transforming supply chain operations in response to economic reform: The case of a motorcycle manufacturer in India (2004) Supply Chain Management: An International Journal, 9 (1), pp. 16-22; Ketchen Jr., D.J., Hult, G.T.M., Bridging organization theory and supply chain management: The case of best value supply chains (2007) Journal of Operations Management, 25 (2), pp. 573-580; Leonard-Barton, D., A dual methodology for case studies: Synergistic use of a longitudinal single site with replicated multiple sites (1990) Organisation Science, 1 (1), pp. 248-266; Lummus, R.R., Vokurka, K.L., Managing the demand chain through managing the information flow: Capturing moments of information (1999) Production and Inventory Management Journal, 40 (1), pp. 15-16; Majumdar, S.K., Gupta, M.P., E-business strategy of car industry: SAP-LAP analysis of select case studies (2001) Global Journal of Flexible Systems Management, 2 (3), pp. 13-28; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Portland, OR; Okada, A., Skills development and interfirm learning linkages under globalization: Lessons from the Indian automobile industry (2004) World Development, 32 (7), pp. 1265-1288; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14 (4), pp. 335-346; Penrose, E.T., (1959) The Theory of the Growth of the Firm, , Wiley, New York, NY; Porter, M.E., (1985) Competitive Advantage: Creating and Sustaining Superior Performance, , Free Press, New York, NY; Prahalad, C.K., Hamel, G., The core competence of the corporation (1990) Harvard Business Review, 68 (3), pp. 79-91; Prajogo, D.I., McDermott, P., Goh, M., Impact of value chain activities on quality and innovation (2008) International Journal of Operations & Production Management, 28 (7), pp. 615-635; Quinn, F.J., What is the buzz? (1997) Logistics Management, 32 (2), pp. 43-47; Rawani, A.M., Gupta, M.P., Flexible framework for strategic information systems planning: A case study from banking sector (2001) Global Journal of Flexible Systems Management, 2 (2), pp. 37-54; Rungtusanatham, M., Salvador, F., Forza, C., Choi, T.Y., Supply-chain linkages and operational performance: A resource-based-view perspective (2003) International Journal of Operations & Production Management, 23 (9), pp. 1084-1099; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Sahay, B.S., Understanding trust in supply chain relationships (2003) Industrial Management & Data Systems, 103 (8), pp. 553-563; Sahay, B.S., Cavale, V., Mohan, R., The Indian supply chain architecture (2003) Supply Chain Management: An International Journal, 8 (2), pp. 93-106; Saiz, J.J.A., Bas, A.O., Rodriguez, R.R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; Sanchez, R., Heene, A., Thomas, H., (1996) Dynamics of Competence-Based Competition, , Elsevier, Amsterdam; Shapiro, J.F., On the connections among activity-based costing, mathematical programming models for analyzing strategic decisions, and the resource-based view of the firm (1999) European Journal of Operational Research, 118 (2), pp. 295-314; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., Shankar, R., (2008) Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies, , Tata McGraw-Hill, New Delhi, 3rd ed; Snow, C.C., Hrebiniak, L.G., Strategy, distinctive competence and organizational performance (1980) Administrative Science Quarterly, 25 (2), pp. 317-336; Strauss, A., Corbin, J., (1990) Basics of Qualitative Research: Grounded Theory Procedures and Techniques, , Sage Publications, London; Sushil, SAP-LAP models of inquiry (2000) Management Decision385347353, 38 (5), pp. 347-353; Sushil, SAP-LAP models (2001) Global Journal of Flexible Systems Management, 2 (2), pp. 55-61; Tarafdar, M., Gordon, S.R., Understanding the influence of information systems competencies on process innovation: A resource-based view (2007) Journal of Strategic Information Systems, 16 (4), pp. 353-392; Teece, D.J., Pisano, G., Shuen, A., Dynamic capabilities and strategic management (1997) Strategic Management Journal, 18 (7), pp. 509-533; Theeranuphattana, A., Tang, J.C.S., A conceptual model of performance measurement for supply chains alternative considerations (2008) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; van Hoek, R.I., Measuring the unmeasurable: Measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Vivek, S.D., Banwet, D.K., Shankar, R., Analysis of interactions among core, transaction and relationship-specific investments: The case of offshoring (2008) Journal of Operations Management, 26 (2), pp. 180-197; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations & Production Management, 22 (2), pp. 195-219; Vrat, P., Supply chain management in India: Problems and challenges (1998) Supply Chain Management for Global Competitiveness, pp. 10-24. , Sahay, B.S. (Ed.), MacMillan, New Delhi; Wernerfelt, B., A resource-based view of the firm (1984) Strategic Management Journal, 5 (2), pp. 171-180; Womack, J.P., Jones, D.T., (2005) Lean Solutions: How Companies and Customers Can Create Wealth Together, , Simon & Schuster, New York, NY; Zhu, Q., Sarkis, J., Cordeiro, J.J., Lai, K.-H., Firm-level correlates of emergent green supply chain management practices in the Chinese context (2008) Omega, 36 (4), pp. 577-591},
correspondence_address1={Charan, P.; Indian Institute of Management Rohtak, Rohtak, India; email: parikshit.charan@iimrohtak.ac.in},
issn={13683047},
language={English},
abbrev_source_title={Measuring Bus. Excellence},
document_type={Article},
source={Scopus},
}

@ARTICLE{Okongwu2012581,
author={Okongwu, U. and Lauras, M. and Dupont, L. and Humez, V.},
title={A decision support system for optimising the order fulfilment process},
journal={Production Planning and Control},
year={2012},
volume={23},
number={8},
pages={581-598},
doi={10.1080/09537287.2011.566230},
note={cited By 16},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84862568502&doi=10.1080%2f09537287.2011.566230&partnerID=40&md5=47c83c5967dd2c82515a38d7b4dac545},
affiliation={Department of Industrial Organisation, Logistics and Technology, Université de Toulouse, Toulouse Business School, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France; Department of Industrial Engineering, Université de Toulouse, Mines Albi, Route de Teillet, 81013 Albi Cedex 9, France},
abstract={Many authors have highlighted gaps at the interfaces between supply chains (SCs) and demand chains. Generally, the latter tends primarily to be 'agile' by maximising effectiveness and responsiveness while the former tends to be lean by maximising efficiency. When, in the SC, disruptions (that lead to stock-out situations) occur after customer orders have been accepted, managers are faced with the problem of maximising customer satisfaction while taking into consideration the conflicting objectives of the supply and demand sides of the order fulfilment process. This article proposes a cross-functional multi-criteria decision-making (advanced available-to-promise) tool that provides different strategic options from which a solution can be chosen. It also proposes a performance measurement system to support the decision-making and improvement process. The results of some experimental tests show that the model enables to make strategic decisions on the degree of flexibility required to achieve the desired level of customer service. © 2012 Taylor & Francis.},
author_keywords={advanced available-to-promise;  agility;  demand chain;  lean;  order fulfilment process;  supply chain},
keywords={agility;  Available-to-promise;  Demand chains;  lean;  order fulfilment process, Artificial intelligence;  Decision making;  Decision support systems;  Economics;  Supply chains, Customer satisfaction},
references={Anderson, S.W., Dekker, H.C., Strategic cost management in supply chains, Part 1: Structural cost management (2009) Accounting Horizons, 23 (2), pp. 201-220; Anderson, S.W., Dekker, H.C., Strategic cost management in supply chains, Part 2: Executional cost management (2009) Accounting Horizons, 23 (3), pp. 289-305; (2005) American Production and Inventory Control Society Dictionary, , APICS 11th ed. Alex and ria: APICS; Bescos, P.L., Dobler, P., (1995) Contrôle de Gestion et Management, , Paris: Montchrestien; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International journal of business performance management, 5 (1), pp. 1-24. , DOI 10.1504/IJBPM.2003.002097; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131. , DOI 10.1504/IJBPM.2006.009032; Chen, J.H., Lin, J.T., Wu, Y.S., Order promising rolling planning with ATP/CTP reallocation mechanism (2008) Iesm, 7 (1), pp. 57-65; Chen, C.-Y., Zhao, Z., Ball, M.O., A model for batch advanced available-to-promise (2002) Production and Operations Management, 11 (4), pp. 424-440; Childerhouse, P., Towill, D.R., Engineering supply chains to match customer requirements (2000) Logistics Information Management, 13 (6), pp. 337-345; Christopher, M., (1992) Logistics and Supply Chain Management, , London: Pitman; Christopher, M., Towill, D.R., Supply chain migration from lean and functional to agile and customised (2000) Supply Chain Management, 5 (4), pp. 206-213. , DOI 10.1108/13598540010347334; Cokins, G., (1989) Activity-based Cost Management: An Executive's Guide, Edition Hardcover, , New York, USA: Cross and Lynch; Croxton, K.L., The order fulfilment process (2003) The International Journal of Logistics Management, 14 (1), pp. 19-32; Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D.M., The supply chain management processes (2001) The International Journal of Logistics Management, 2 (12), pp. 13-36; Ducq, Y., Vallespir, B., Definition and aggregation of a performance measurement system in three aeronautical workshops using the ECOGRAI method (2005) Production Planning and Control, 16 (2), pp. 163-177. , DOI 10.1080/09537280512331333066, PII U53495118015; Halley, A., Guilhon, A., Logistics behaviour of small enterprises: Performance, strategy and definition (1997) International Journal of Physical Distribution and Logistics Management, 16 (8), pp. 475-495; Herbert, M., Customer segmentation, allocation planning and order promising in make-to-stock production (2009) OR Spectrum, 31 (1), pp. 229-256; Holweg, M., An investigation into supplier responsiveness: Empirical evidence from the automotive industry (2005) International journal of logistics management, 16 (1), pp. 96-119; Hoover, W., (2001) Managing the Dem And-supply Chain, , New York: Wiley; Jeong, B., Sim, S.-B., Jeong, H.-S., Kim, S.-W., An available-to-promise system for TFT LCD manufacturing in supply chain (2002) Computers and Industrial Engineering, 43 (1-2), pp. 191-212. , DOI 10.1016/S0360-8352(02)00064-5, PII S0360835202000645; Johansson, H.J., (1993) Business Process Reengineering: Breaking Strategies for Market Dominance, , Chichester: John Wiley & Sons; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston: Harvard Business School Press; Kilger, C., Schneeweiss, L., Dem and fulfilment and ATP (2000) Supply Chain Management and Advanced Planning: Concepts, Models, Software and Case Studies, pp. 135-148. , H. Stadtler and C. Kilger, eds Berlin: Springer; Kirche, E.T., Kadipasaoglu, S.N., Khumawala, B.M., Maximizing supply chain profits with effective order management: Integration of Activity-Based Costing and Theory of Constraints with mixed-integer modelling (2005) International Journal of Production Research, 43 (7), pp. 1297-1311. , DOI 10.1080/00207540412331299648; Lee, Y.H., Jung, J.W., Eum, S.C., Park, S.M., Nam, H.K., Production quantity allocation for order fulfilment in the supply chain: A neural network based approach (2006) Production Planning and Control, 17 (4), pp. 378-389. , DOI 10.1080/09537280600621909, PII M467132674886; Lin, J.T., Chen, J.-H., Enhance order promising with ATP allocation planning considering material and capacity constraints (2005) Journal of the Chinese Institute of Industrial Engineers, 22 (4), pp. 282-292; Lin, F.R., Shaw, M.J., Reengineering the order fulfilment process in supply chain networks (1998) The International Journal of Flexible Manufacturing System, 10, pp. 197-229; Logue, D.E., Market-making and the assessment of market efficiency (1975) The Journal of Finance, 30 (1), pp. 115-123; Mas-Colell, A., Whinston, M.D., Green, J.R., (1995) Microeconomic Theory, p. 150. , New York: Oxford University Press; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Interfacing the lean and agile manufacturing paradigm in the total supply chain (1999) International Journal of Production Economics, 62, pp. 107-118; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , New Jersey: Prentice Hall; Pibernik, R., Advanced available to promise: Classification, selected methods and requirements for operations and inventory management (2005) International Journal of Production Economics, 93-94, pp. 239-252; Pibernik, R., Managing stock-outs effectively with order fulfilment systems (2006) Journal of Manufacturing Technology Management, 17 (6), pp. 721-736. , DOI 10.1108/17410380610678765, e and hyphen;Technology and manufacturing enterprise competitiveness; Rainbird, M., Dem and and Supply Chains: The value catalyst (2004) International Journal of Physical Distribution and Logistics Management, 34 (3-4), pp. 230-250; Reichhart, A., Holweg, M., Creating the customer-responsive supply chain: A reconciliation of concepts (2007) International Journal of Operations and Production Management, 27 (11), pp. 1145-1172; Siala, M., Campagne, J.P., Ghedira, K., (2006) Proposition d'Une Nouvelle Approche Pour la Gestion du Disponible dans les Cha?̂ne Logistiques., , MOSIM'06, Rabat, Maroc, Avril; Slack, N., Lewis, M., (2008) Operations Strategy, , 2nd ed. Engl and : Pearson Education Limited; Stevenson, M., Spring, M., Flexibility from the supply chain perspective: Definition and review (2007) International Journal of Operations and Production Management, 27 (7), pp. 685-713; Chain Council, S., (2008) Supply Chain Operations Reference Model-SCOR, , Version 9. 0. USA and Europe: Supply Chain Council; Walters, D., Effectiveness and efficiency: The role of supply chains management (2006) The International Journal of Logistics Management, 17 (1), pp. 75-94; Walters, D., Demand chain effectiveness - Supply chain efficiencies: A role for enterprise information management (2006) Journal of Enterprise Information Management, 19 (3), pp. 246-261. , DOI 10.1108/17410390610658441, Supply chains and the enterprise; Walters, D., Rainbird, M., The demand chain as an integral component of the value chain (2004) Journal of Consumer Marketing, 21 (7), pp. 465-475. , DOI 10.1108/07363760410568680; Xiong, M., A web-enhanced dynamic BOM based available to promise system (2003) International Journal of Production Economics, 84, pp. 133-147; Xiong, M.H., Tor, S.B., Bhatnagar, R., Khoo, L.P., Venkat, S., A DSS approach to managing customer enquiries for SMEs at the customer enquiry stage (2006) International Journal of Production Economics, 103 (1), pp. 332-346. , DOI 10.1016/j.ijpe.2005.08.008, PII S0925527306000090; Zokaei, K., Hines, P., Achieving consumer focus in supply chains (2007) International Journal of Physical Distribution and Logistics Management, 37 (3), pp. 223-247; Zschorn, L., An extended model of ATP to increase flexibility of delivery (2006) International Journal of Computer Integrated Manufacturing, 19 (5), pp. 434-442. , DOI 10.1080/09511920500399037, PII U76H4W1W41355631},
correspondence_address1={Lauras, M.; Department of Industrial Organisation, Logistics and Technology, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France; email: matthieu.lauras@mines-albi.fr},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{RezaNasiri2012159,
author={Reza Nasiri, G. and Davoudpour, H.},
title={Coordinated location, distribution and inventory decisions in supply chain network design: A multi-objective approach},
journal={South African Journal of Industrial Engineering},
year={2012},
volume={23},
number={2},
pages={159-175},
doi={10.7166/23-2-338},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051715366&doi=10.7166%2f23-2-338&partnerID=40&md5=f3797a58946c0fb7aaebf6c51570fa35},
affiliation={Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran},
abstract={This research presents an integrated multi-objective distribution model for use in simultaneous strategic and operational food supply chain (SC) planning. The proposed method is adopted to allow use of a performance measurement system that includes conflicting objectives such as distribution costs, customer service level (safety stock holding), resource utilisation, and the total delivery time, with reference to multiple warehouse capacities and uncertain forecast demands. To deal with these objectives and enable the decision makers (DMs) to evaluate a greater number of alternative solutions, three different approaches are implemented in the proposed solution procedure. A detailed case study derived from food industrial data is used to illustrate the preference of the proposed approach. The proposed method yields an efficient solution and an overall degree of DMs’ satisfaction with the determined objective values. © 2012, South African Institute of Industrial Engineering. All rights reserved.},
keywords={Decision making;  Food supply, Alternative solutions;  Conflicting objectives;  Customer service levels;  Distribution models;  Inventory decisions;  Performance measurement system;  Resource utilisation;  Supply chain network design, Supply chains},
references={Liang, T.F., Distribution planning decisions using interactive fuzzy multi-objective linear programming (2006) Fuzzy Sets and Systems, 46, pp. 1303-1316; Torabi, S.A., Hassini, E., An interactive possibilistic programming approach for multiple objective supply chain master planning (2008) Fuzzy Sets and Systems, 159, pp. 193-214; Snyder, L.V., Facility location under uncertainty: A review (2006) IIE Transactions, 38, pp. 537-554; Farahani, R.Z., Elahipanah, M., A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain (2008) International Journal of Production Economics, 111, pp. 229-243; Miranda, P.A., Garrido, R.A., Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand (2004) Transportation Research Part E, 40, pp. 183-207; Miranda, P.A., Garrido, R.A., Valid inequalities for Lagrangian relaxation in an inventory location problem with stochastic capacity (2008) Transportation Research Part E, 44, pp. 47-65; Daskin, M.S., Coullard, C.R., Shen, Z-J.M., An inventory-location model: Formulation, solution algorithm and computational results (2002) Annals of Operations Research, 110, pp. 83-106; Shen, Z.-J.M., Coullard, C., Daskin, M.S., A joint location–inventory model (2003) Transportation Science, 37 (1), pp. 40-55; Erlebacher, S.J., Meller, R.D., The interaction of location and inventory in designing distribution systems (2000) IIE Transactions, 32, pp. 155-166; Selim, H., Ozkarahan, I., A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach (2008) International Journal of Advanced Manufacturing Technology, 36 (3-4); Gourdin, E., Labbe, M., Laporte, G., The uncapacitated facility location problem with client matching (2000) Operations Research, 48, pp. 671-685; Jayaraman, V., An efficient heuristic procedure for practical sized capacitated warehouse design and management (1998) Decision Sciences, 29L, pp. 729-745; Pirkul, H., Jayaraman, V., A multi-commodity, multi-plant, capacitated facility location problem: Formulation and efficient heuristic solution (1998) Computers & Operations Research, 25 (10), pp. 869-878; Tragantalerngsak, S., Holt, J., Ronnqvist, M., An exact method for the two-echelon, single-source, capacitated facility location problem (2000) European Journal of Operational Research, 123, pp. 473-489; Amiri, A., Designing a distribution network in a supply chain system: Formulation and efficient solution procedure (2006) European Journal of Operational Research, 171, pp. 567-576; Hunt, I., Wall, B., Jadgev, H., Applying the concepts of extended products and extended enterprises to support the activities of dynamic supply networks in the agri-food industry (2005) Journal of Food Engineering, 70, pp. 393-402},
correspondence_address1={Reza Nasiri, G.; Department of Industrial Engineering, Iran; email: reza_nasiri@aut.ac.ir},
publisher={South African Institute of Industrial Engineering},
issn={1012277X},
language={English},
abbrev_source_title={South Afr. J. Ind. Eng.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Khalili-Damghani2012206,
author={Khalili-Damghani, K. and Taghavifard, M. and Olfat, L. and Feizi, K.},
title={Measuring agility performance in fresh food supply chains: An ordinal two-stage data envelopment analysis},
journal={International Journal of Business Performance and Supply Chain Modelling},
year={2012},
volume={4},
number={3-4},
pages={206-231},
doi={10.1504/IJBPSCM.2012.050390},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015275607&doi=10.1504%2fIJBPSCM.2012.050390&partnerID=40&md5=89de60ab5ff16828753891450ede2c54},
affiliation={Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, P.O. Box 1584743311, E.A., K.A.Ave., Maz. St., Em.-Ho. Sq., Tehran, Iran; Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, P.O. Box 6479-14155, Haft-Pe. Al., N.G.Ave., Tav. St., Tehran, Iran},
abstract={In this paper, a process-based model including several measures for providers of agility, capabilities of agility, and main goals of supply chain is proposed. Then, an ordinal two-stage data envelopment analysis (OTSDEA) model has been developed using qualitative measures. The proposed OTSDEA can measure the relative efficiency of the aforementioned process and sub-processes in supply chain. As the overall efficiency of supply chain decomposed among aforementioned sub-processes so, decision maker (DM) can gain a proper perspective of each sub-process as well as the inefficient segments. Moreover, if a given DMU is proved to be inefficient, the source of inefficiency can be found from among its sub-DMUs. This can supplies more managerial insights for modification of an inefficient sub-process in a DMU more efficiently. Real case study of a dairy supply chain is supplied to address the ability of proposed procedure to measure efficiency of two-stage process in real environment. Copyright © 2012 Inderscience Enterprises Ltd.},
author_keywords={Agile supply chain;  Asc;  Ordinal data;  Supply chain performance measurement;  Two-stage dea},
references={Abd El-Aal, M.A.M., El-Sharief, M.A., El-Deen, A.E., Nassr, A.-B., Supply chain performance evaluation: A comprehensive evaluation system (2011) Int. J. of Business Performance and Supply Chain Modelling, 3 (2), pp. 141-166; Abtahi, A.-R., Khalili-Damghani, K., Fuzzy data envelopment analysis for measuring agility performance of supply chains (2011) Int. J. Modelling in Operations Management, 1 (3), pp. 263-288; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale efficiencies in data envelopment analysis (1984) Management Science, 30 (9), pp. 1078-1092; Bhatti, R.S., Kumar, P., Kumar, D., Integrated model for selection of third party service providers by global lead logistics providers (2009) Int. J. of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 187-202; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision-making units (1978) European Journal Operational Research, 2 (6), pp. 429-444; Chen, Y., Zhu, J., Measuring information technology's indirect impact on firm performance (2004) Information Technology & Management Journal, 5 (1-2), pp. 9-22; Chen, Y., Cook, W.D., Li, N., Zhu, J., Additive efficiency decomposition in two-stage DEA (2009) European Journal of Operational Research, 196 (3), pp. 1170-1176; Chen, Y., Liang, L., Zhu, J., Equivalence in two-stage DEA approaches (2009) European Journal of Operational Research, 193 (2), pp. 600-604; Chen, Y., Liang, L., Yang, F., Zhu, J., Evaluation of information technology investment: A data envelopment analysis approach (2006) Computers & Operations Research, 33 (5), pp. 1368-1379; Christopher, M., The agile supply chain: Competing in volatile markets (2000) Industrial Marketing Management, 29 (1), pp. 37-44; Christopher, M., Towill, D., Supply chain migration from lean and functional to agile and customized (2001) Supply Chain Management, 5 (4), pp. 206-213; Cook, W.D., Seiford, L.M., Data envelopment analysis (DEA) - Thirty years on (2009) European Journal of Operational Research, 192 (1), pp. 1-17; Cook, W.D., Zhu, J., Rank order data in DEA: A general framework (2006) European Journal of Operational Research, 174 (2), pp. 1021-1038; Cook, W.D., Kress, M., Seiford, L.M., On the use of ordinal data in data envelopment analysis (1993) Journal of the Operational Research Society, 44 (2), pp. 133-140; Cook, W.D., Kress, M., Seiford, L.M., Data envelopment analysis in the presence of both quantitative and qualitative factors (1996) Journal of the Operational Research Society, 47 (7), pp. 945-953; Cook, W.D., Liang, L., Zhu, J., Measuring performance of two-stage network structures by DEA: A review and future perspective (2010) Omega, 38 (5), pp. 423-430; Cooper, W.W., Park, K.S., Yu, G., IDEA and AR-IDEA: Models for dealing with imprecise data in DEA (1999) Management Science, 45 (4), pp. 597-607; Cooper, W.W., Seiford, L.M., Tone, K., (2006) Introduction to Data Envelopment Analysis and its Uses with DEA Solver Software and References, pp. 10-45. , Springer Science and Business Media, New York; Crama, Y., Pochet, Y., Wera, Y., (2001) A Discussion of Production Planning Approaches in the Process Industry, , http://citeseer.nj.nec.com/crama01discussion.html, accessed on 24 March 2003; Goldman, S.L., Nagel, R.N., Preiss, K., (1995) Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer, , Van Nostrand Reinhold, New York; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Günther, H.-O., Van Beek, P., Advanced planning and scheduling in process industry (2003) Advanced Planning and Scheduling Solutions in Process Industry, pp. 1-7. , Günther, H-O. and Van Beek, P. Eds.:, Springer, Berlin; Ismail, H.S., Sharifi, H., A balanced approach to building agile supply chain (2005) International Journal of Physical Distribution & Logistics Management, 36 (6), pp. 431-444; Kao, C., Hwang, S.N., Efficiency decomposition in two-stage data envelopment analysis: An application on life insurance companies in Taiwan (2008) European Journal of Operational Research, 185 (1), pp. 418-429; Khalili-Damghani, K., Taghavifard, M., Olfat, L., Feizi, K., A hybrid approach based on fuzzy DEA and simulation to measure the efficiency of agility in supply chain: Real case of dairy industry (2011) International Journal of Management Science and Engineering Management, 6 (3), pp. 163-172; Kidd, P.T., (1994) Agile Manufacturing: Forging New Frontiers, , Addison-Wesley, London; Lewis, H.F., Sexton, T.R., Network DEA: Efficiency analysis of organizations with complex internal structure (2004) Computers & Operations Research, 31 (2), pp. 1365-1410; Liang, L., Cook, W.D., Zhu, J., DEA models for two-stage processes: Game approach and efficiency decomposition (2008) Naval Research Logistics, 55 (7), pp. 643-653; Lin, C.-T., Chiu, H., Chu, P.-Y., Agility index in supply chain (2006) International Journal of Production Economics, 100 (2), pp. 285-299; Naylor, J., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 107-118; Oveby, E., Bharadwaj, A., Sambamurthy, V., Enterprise agility and the enabling role of information technology in organizations (2000) Organization Science, 11 (4), pp. 404-428; Ramanathan, R., Estimating relative attractiveness of locations using data envelopment analysis (2009) Int. J. of Business Performance and Supply Chain Modelling, 1 (1), pp. 99-113; Sambamurthy, V., Baharadwaj, A., Grover, V., Shaping agility through digital options (2003) MIS Quarterly, 27 (2), pp. 237-263; Seiford, L.M., Zhu, J., Profitability and market ability of the top 55 US commercial banks (1999) Management Science, 45 (9), pp. 1270-1288; Sharifi, H., Zhang, Z., A methodology for achieving agility in manufacturing organization: An introduction (1999) International Journal of Production Economics, 62 (1), pp. 7-22; Sharp, J.M., Irani, Z., Desai, S., Working towards agile manufacturing in the UK industry (1999) International Journal of Production Economics, 62 (1), pp. 155-169; Swafford, P.M., Ghosh, S., Murthy, N., A framework for assessing value chain agility (2006) International Journal of Operation & Production Management, 26 (2), pp. 118-140; Van Hoak, R., Mitigating the minefield of pitfalls in creating agile supply chain (2005) Proceedings of the International Conference on Agility - ICAM 2005, , Andersin, H. E., Niemi, E. and Hirvonen, V. Eds.:, Helsinki University of Technology, Otaniemi, Finland; Wang, Y.-M., Chin, K.-S., Some alternative DEA models for two-stage process (2010) Expert Systems with Applications, 37 (12), pp. 8799-8808; White, A., Daniel, E.M., Mohdzain, M., The role of emergent information technologies and systems in enabling supply chain agility (2005) Information Management, 25 (5), pp. 394-410; Wong, W.P., Performance evaluation of supply chain in stochastic environment: Using a simulation based DEA framework (2009) Int. J. of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 203-228; Yusuf, Y., Gunasekaran, A., Agile manufacturing: The driver concepts and attributes (1999) International Journal of Production Economics, 62 (1-2), pp. 34-43; Zhu, J., Multi-factor performance measure model with an application to Fortune 500 companies (2000) European Journal of Operational Research, 123 (1), pp. 105-124},
correspondence_address1={Khalili-Damghani, K.; Department of Industrial Engineering, P.O. Box 1584743311, E.A., K.A.Ave., Maz. St., Em.-Ho. Sq., Iran; email: kaveh.khalili@gmail.com},
publisher={Inderscience Publishers},
issn={17589401},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Supply Chain Model.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Widyaningrum201220,
author={Widyaningrum, D. and Masruroh, N.A.},
title={Development of the sea fishery supply chain performance measurement system: A case study},
journal={International Journal of Supply Chain Management},
year={2012},
volume={1},
number={3},
pages={20-32},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949811788&partnerID=40&md5=80a83e322d8ccf66714e5df47d7dd6f5},
affiliation={Mechanical and Industrial Engineering Department, Gadjah Mada University, Jalan Grafika No. 2 Kampus UGM, Yogyakarta, 55281, Indonesia},
abstract={Good performance measurement system is required to assess the success of supply chain. However, choosing the most appropriate indicator is not easy as it depends on the system's characteristics. Sea fishery industry characterized as perishable products, seasonal in production, and highly dependence on nature (uncontrollable). Motivated by the uniqueness of the sea fishery industry, this work proposed a performance measurement system for the sea fishery supply chain. The performance of the proposed model is evaluated using Indonesian sea fishery supply chain case, mostly consists of small and medium enterprises (SME). This instrument has six dimensions; efficiency, flexibility, responsiveness, product quality, process quality, facilities, and government involvement. Beside having specific indicators of sea fishery industry, the proposed instrument also considers the involvement of government. Further, this instrument can be used not only for monitoring but also suggesting directions for improvement. © ExcelingTech Pub, UK.},
author_keywords={Performance measurement system;  Sea fishery;  Supply chain management},
references={Agarwal, A., Shankar, R., Modeling Supply Chain Performance Variables (2005) Asian Academy of Management Journal, 10 (2), pp. 47-68; Aramyan, L.H., Lansink, A.G.J.M.O., Vorst, J.G.A.J., Kooten, O., Performance Measurement in Agri-food Supply Chain: A Case Research (2007) Supply Chain Manangement: An International Journal, 12 (4), pp. 304-315; Beamon, B.M., 'Measuring Supply Chain Performance" (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bigliardi, B., Bottani, E., "Performance Measurement in the Food Supply Chain: A Balanced Scorecard Approach" (2010) Emerald Group Publishing Limited, 28 (5-6), pp. 249-260; Bolstorff, P., "Measuring the Impact of Supply Chain Performance: A Step-by-step Guide to Using the Supply Chain Council's SCOR Model" www.logisticstoday.com, Last access (02-02-2012]; Brewer, P.C., Speh, T.W., Using the Balanced Scorecard to Measure Supply Chain Performance (2000) Journal of Business logistics, 21 (1), pp. 75-93; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving Supply Chain Performance Management: A Systematic Approach to Analyzing Iterative KPI Accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, Planning and Operation, , 3rd ed., Pearson Education; Duarte, S., Cabrita, R., Machado, V.C., "Exploring Lean and Green Supply Chain Performance Using Balanced Scorecard Perspective" (2011) Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, pp. 520-525. , IEOM, Malaysia; Fauske, H., Kollberg, M., Dreyer, H.C., Bolseth, S., "Criteria for Supply Chain Performance Measurement Systems" (2006) 14th International Annual EurOMA conference, pp. 1-10. , Ankara, Turkey; Gunasekaran, A., Patel, C., McGaughey, R.E., A Framework for Supply Chain Performance Measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Jamil, C.M., Mohamed, R., Performance Measurement System (PMS) In Small Medium Enterprises (SMEs): A Practical Modified Framework (2011) World Journal of Social Sciences, 1 (3), pp. 200-212; Kamalabadi, N., Bayat, A., Ahmadi, P., Ebrahimi, A., Kahreh, M.S., Presentation a New Algorithm for Performance Measurement of Supply Chain by Using FMADM Approach (2008) World Applied Sciences Journal, 5 (5), pp. 582-589; Kaplan, R.S., Norton, D.P., (2000) Balanced Scorecard: Menerapkan Strategi Menjadi Aksi, , Erlangga; Kurien, G.P., Qureshi, M.N., Study of Performance Measurement Practices in Supply Chain Management (2011) International Journal of Business, Management and Social Sciences, 2 (4), pp. 19-34; Lockamy, A., III, Linking SCOR Planning Practices to Supply Chain Performance: An Exploratory Study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Najmi, A., Makui, A., Providing Hierarchical Approach for Measuring Supply Chain Performance Using AHP and DEMATEL Methodologies (2010) International Journal of Industrial Engineering Computations, 1 (2), pp. 199-212; Neely, A., The Evolution of Performance Measurement Research: Developments in the Last Decade and a Research Agenda for the Next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Putra, A.F.P., "Formulasi Model SCM UMKM dalam Konteks Daya Saing Industri dan Local Wisdom" (2005), pp. 1-8. , Seminar Nasional IEC, JTI-FTIUPN 'Veteran' Yogyakarta, Indonesia; www.supplychain.org, Last accessed (10-02-2012); Talavera, M.G.V., "Measuring Supply Chain Performance in Selected Philippine Manufacturing Companies" (2010) Philippine Management Review, 17, pp. 52-65; Tarokh, M.J., Shooshtari, D.F., "Supply Chain Strategic Management Using Transformed Balanced ScoreCard" (2005) IEEE, 2, pp. 868-874},
publisher={ExcelingTech Publishers},
issn={20513771},
language={English},
abbrev_source_title={Int. J. Supply Chain Manag.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Garg201259,
author={Garg, A. and Deshmukh, S.G.},
title={Designing balanced scorecard for multi echelon repair inventory systems},
journal={Journal of Modelling in Management},
year={2012},
volume={7},
number={1},
pages={59-96},
doi={10.1108/17465661211208811},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943231996&doi=10.1108%2f17465661211208811&partnerID=40&md5=4968cd14f24d550b8f14cc0ed83e26ab},
affiliation={Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi, India},
abstract={Purpose–A number of approaches have been suggested in the literature for maintenance performance measurement, including use of the balanced scorecard (BSC). However, its application towards assessing maintenance contribution to business objectives is limited. Currently, a framework integrating maintenance activities in a supply chain environment is lacking in the literature. The purpose of this paper is to develop a maintenance performance measurement framework using BSC for multi echelon repair inventory systems (MERIS) comprising of modular electronic equipment. The paper also suggests a large number of performance measures/indicators (PMs/PIs) pertaining to both maintenance and supply chain components including a few BSC implementation issues for such types of supply chains. Design/methodology/approach–The paper analyses a base model of a large maintenance organization and after carrying out SWOT analysis, identifies strategic themes/objectives for performance measurement. Performance measures for various objectives are identified for MERIS and implementation strategy including cascading of BSC is suggested. The developed performance measurement model is also validated using an action research (AR) based approach. Findings–A performance measurement system (PMS) for MERIS using BSC approach has been developed and an implementation strategy suggested. AR methodology is used for developing BSC for an organization using the selected performance measures. The importance of cause-effect relationships between various performance objectives and measures is also established. Research limitations/implications–Quantification of each of the developed PMs/PIs and establishing an effective information system for MERIS are the limitations of the present work and may be taken up for more research. Practical implicationsThe paper will be useful for practitioners in the field of MERIS of electronic repairables wherein relevant PMs/PIs can be selected for implementation as per the requirement of the application. Originality/value–The paper is of value by being original, as no work towards performance measurement of MERIS using BSC concept (as applicable for non-profit organizations like military, government, NGOs etc) is reported in literature so far. The paper is relevant in this context and its contribution lies in the area of performance measurement. © 2012, © Emerald Group Publishing Limited.},
author_keywords={Balanced scorecard;  Cause-and-effect relationship;  Maintenance;  Multi echelon repair inventory;  Performance measures},
references={Alsyouf, I., Measuring maintenance performance using a BSC approach (2006) Journal of Quality in Maintenance Engineering, 12 (2), pp. 133-149. , ()“”-; Atkinson, H., Strategy implementation: a role for the balanced scorecard (2006) Management Decision, 44 (10), pp. 1441-1460. , ()“”-; Babu, S., (1980) Optimal policies for spares in multi-echelon repair-inventory systems, , ()“”unpublished PhD thesisIndian Institute of TechnologyDelhi:; Baiman, S., Fischer, E.P., Rajan, V.M., Performance measurement and design in supply chains (2001) Management Science, 47 (1), pp. 173-188. , ()“”-; Beamon, B.M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218. , ()“”-; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62. , ()“”-; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18 (8), pp. 666-680. , ()“”-; Bhagwat, R., Sharma, M.K., An application of the integrated AHP-PGP model for performance measurement of supply chain management (2009) Production Planning & Control, 20 (8), pp. 678-690. , ()“”-; Bhagwat, R., Chan, F.T.S., Sharma, M.K., Performance measurement model for supply chain management in SMEs (2008) International Journal of Globalisation and Small Business, 2 (4), pp. 428-445. , ()“”-; Bititci, U.S., Nudurupati, S.S., Turner, T.J., Creighton, S., Web enabled performance measurement systems: management implications (2002) International Journal of Operations & Production Management, 22 (11), pp. 1273-1287. , ()“”-; Brewer, C.P., Speh, W.T., Using the BSC to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93. , ()“”-; Brewer, C.P., Speh, W.T., Adapting the BSC to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56. , ()“”-; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521. , ()“”-; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2), pp. 110-131. , ()“”-; Chavan, M., The balanced scorecard: a new challenge (2009) Journal of Management Development, 28 (5), pp. 393-406. , ()“”-; French, S., Action research for practicing managers (2009) Journal of Management Development, 28 (3), pp. 187-204. , ()“”-; Garg, A., Deshmukh, S.G., Maintenance management: literature review and directions (2006) Journal of Quality in Maintenance Engineering, 12 (3), pp. 480-492. , ()“”-; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: a framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530. , ()“”-; Groote, P.D., Maintenance performance analysis: a practical approach (1995) Journal of Quality in Maintenance Engineering, 1 (2), pp. 4-24. , ()“”-; Guide, V.D.R.J., Srivastava, R., Repairable inventory theory: models and applications (1997) European Journal of Operational Research, 102 (1), pp. 1-20. , ()“”-; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (2), pp. 71-87. , ()“”-; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353. , ()“”-; Kaplan, S.R., Norton, P.D., (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, , ()Harvard Business SchoolBostonMA:; Neely, A., Gazing into the crystal ball: the future of performance measurement (2003) Perspectives on Performance, 2 (2), pp. 12-13. , ()“”-; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263. , ()“”-; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennedy, M., Performance measurement system design: developing and testing a process based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145. , ()“”-; Niven, P.R., (2003) BSC for Government and Non-profit Organizations, , ()WileyHobokenNJ:; Othman, R., Balanced scorecard and causal model development: preliminary findings (2006) Management Decision, 44 (5), pp. 690-702. , ()“”-; Paranjape, B., Rossiter, M., Pantano, V., Insights from the balanced scorecard performance measurement system: successes, failures and future – a review (2006) Measuring Business Excellence, 10 (3), pp. 4-14. , ()“”-; Parida, A., Study and analysis of maintenance performance indicators (MPIs) for LKAB: a case study (2007) Journal of Quality in Maintenance Engineering, 13 (4), pp. 325-337. , ()“”-; Parida, A., Maintenance performance assessment (MPA) framework for engineering asset (2008) International Engineering and Engineering Management 2008 Proceedings, 8-11 December, pp. 1351-1354. , ()“”-; Parida, A., Chattopadhyay, G., Development of a multi-criteria hierarchical framework for maintenance performance measurement (MPM) (2007) Journal of Quality in Maintenance Engineering, 13 (3), pp. 241-258. , ()“”-; Parida, A., Chattopadhyay, G., Kumar, U., Multi criteria maintenance performance measurement: a conceptual model (2005) Proceedings of the 18th International Congress of COMADEM, Cranefield, 31 August-2 September, pp. 349-356. , ()“”-; Sale, R.S., Sale, M.L., Lending validity and consistency to performance measurement (2005) Managerial Auditing Journal, 20 (9), pp. 915-927. , ()“”-; Sarin, P., (2000) Military Logistics: The Third Dimension, , ()ManasNew Delhi:; Sarkis, J., Quantitative models for performance measurement systems – alternate considerations (2003) International Journal of Production Economics, 86, pp. 81-90. , ()“”-; Self, J., Metrics and management: applying the results of the balanced scorecard (2004) Performance Measurement & Metrics, 5 (3), pp. 101-105. , ()“”-; Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68. , ()“”-; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (4), pp. 242-258. , ()“”-; Tangen, S., Performance measurement: from philosophy to practice (2004) International Journal of Performance Management, 53 (8), pp. 726-737. , ()“”-; Tsang, A.H.C., Jarcline, A.K.S., Kennedy, M., Measuring maintenance performance - holistic approch (1999) International Journal of Operations & Production Management, 19 (7), pp. 691-715. , ()“”-; Felix, T.S., Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (3), pp. 110-131. , ()“”-; Garg, A., Deshmukh, S.G., Designing performance measures for repair inventory systems using balanced scorecard approach (2008) Sixth AIMS International Conference on Management Proceedings, Indian Business Academy, 28-31 December, pp. 665-673. , ()“”-},
correspondence_address1={Deshmukh, S.G.; Mechanical Engineering Department, India; email: deshmukh@mech.iitd.ac.in},
publisher={Emerald Group Publishing Ltd.},
issn={17465664},
language={English},
abbrev_source_title={J. Model. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Frederico2012405,
author={Frederico, G.F. and Martins, R.A.},
title={The relationship between maturity of performance measurement system and maturity of supply chain management},
journal={62nd IIE Annual Conference and Expo 2012},
year={2012},
pages={405-414},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900333048&partnerID=40&md5=6bb95ff12b62fa1557fbf4cbc622c415},
affiliation={Department of Industrial Engineering, Federal University of Sao Carlos, Sao Carlos, SP, 13565-905, Brazil},
abstract={Supply chains play a very important role to help companies to survive in today's competitive market. The implementation of different management practices lead companies to different levels of maturity in supply chain management. The same applies to performance measurement systems, i.e., different practices lead to different levels of maturity. This paper aims to present the findings of two case studies on relationship between the maturity of performance measurement systems and the maturity of supply chain management. From a literature review, a model for understanding the relationship between the maturity of supply chain management practices and the maturity of performance measurement systems is proposed. The rationale behind the framework is the appropriate match between the dimensions of maturity of supply chain management and performance measurement systems. The preliminary findings show the existence of relationship between maturity of supply chain management and maturity of performance measurement systems although the match between the dimensions is not exact as the model predicts. Nonetheless, this does not invalidate the model since the maturity dimensions do not change at the same pace. Indeed, this means that the performance measurement of studied company could not provide relevant information to supply chain decision makers.},
author_keywords={Maturity;  Performance measurement systems;  Supply chain management},
keywords={Exhibitions;  Industry, Competitive markets;  Decision makers;  Literature reviews;  Management practices;  Maturity;  Performance measurement system;  Performance measurements;  Supply chain management practices, Supply chain management},
references={Christopher, M., (2005) Logistics and Supply Chain Management, , 3rd ed. London: Pearson Education; Lambert, D., Cooper, M., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Pires, S., (2009) Gestao da Cadeia de Suprimentos: Conceitos, Estrategias, Praticas E Casos, , 2nd. Sao Paulo: Atlas; Chan, F., Qi, H., Chan, H., Lau, H., Ip, R., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., Introduction to supply chain management (2003) Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies, , In: Simchi-Levi, D. Kaminsky, P. Simchi-Levi, E. 2nd ed. Boston: McGraw-Hill; Ayers, J., Malmberg, D., Supply chain systems: Are you ready? (2002) Information Strategy: The Executive's Journal; Lambert, D., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; (2011) Oxford Dictionaries, , Oxford., Oxford University Press; Attadia, L., Martins, R., A. Medicao de desempenho como base para evolucao da melhoria continua (2003) Revista Producao, 13 (2), pp. 33-41; Kwak, Y., Ibbs, W., Project management process maturity model (2002) Journal of Management in Engineering, 18 (3), pp. 150-155; Venkatraman, N., Henderson, J., Real strategies for virtual organizing (1998) Sloan Management Review, 40 (1), pp. 33-48. , Fall; Harter, D., Krishnan, M., Slaughter, S., Effects of process maturity on quality, cycle time, and effort in software product development (2000) Management Science, 46 (4), pp. 451-466; Stevens, G., Integrating the supply chain (1989) International Journal of Physical Distribution & Logistics Management, 19 (8), pp. 3-8; (2007) Competitive Advantage of Best in Class Supply Chains. Pennsylvania, , http://www.pmgbenchmarking.com/public/survey/dvantagesofbic.pdf, Available in; Lockamy, A., McCormak, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278; Daozhi, Z., Liang, Z., Xin, L., Jianyong, S., A new supply chain maturity model with 3-dimension perspective (2006) ITIC - Information Technology and Innovation Conference, , Hangzhou; Oliveira De, M., (2009) Modelo de Maturidade de Processos Em Cadeias de Suprimentos: Precedencias E Os Pontos-Chave de Transicao, , Tese (Doutorado em Administração) Programa de Pos-Graduacao em Administracao, UFMG, Belo Horizonte; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Bititci, U., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Andersson, P., Aronsson, H., Storhagen, N., Measuring logistics performance (1989) Engineering Costs and Production Economics, 17, pp. 253-262; Van Hoek, R., Measuring the immeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Pires, S., Aravechia, C., Measuring supply chain performance (2001) Annual Conference of the Production and Operations Management Society, , Orlando; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Geary, S., Zonnenberg, J., What it means to be best in class (2000) Supply Chain Management Review, pp. 43-48. , Jan/Aug; Bhagwat, R., Sharma, M., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; (2008) Supply-Chain Operations Reference-model: Version 9.0 - Overview, , http://www.supply-chain.org/resources/scor, Available; Wettstein, T., Kueng, P., A maturity model for performance measurement systems (2002) Management Information Systems, pp. 113-122; Van Aken, E., Lettens, G., Coleman, G., Farris, J., Van Goubergen, D., Assessing maturity and effectiveness of enterprise performance measurement systems (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 400-418; Seuring, S., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Management: An International Journal, 13 (2), pp. 128-137; Yin, R., (1994) Case Study Research: Design and Methods, , Sage Publications, Newbury Park},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Leite2012504,
author={Leite, L.R. and Yagasaki, C.A. and Van Aken, E. and Martins, R.A.},
title={Bibliometric analysis of literature on performance measurement systems and sustainability},
journal={62nd IIE Annual Conference and Expo 2012},
year={2012},
pages={504-513},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900327512&partnerID=40&md5=2fbf8422aef03a8defbfbaeaa8d8296b},
affiliation={Department of Industrial Engineering, Federal University of Sao Carlos, Sao Carlos, SP, 13565-905, Brazil; Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States},
abstract={This paper presents findings from a bibliometric study on performance measurement systems and sustainability with focus on Industrial and System Engineering literature. The papers from the literature which were reviewed for this work were selected from the Web of Science using the following keywords: performance metrics, performance measures, performance measurement, PMS, key performance indicator, sustainability, triple bottom line and corporate responsibility. Filters were applied to select papers related to industrial engineering, business, and operations research management fields, resulting in 202 papers. The findings show a gap between the performance measurement and sustainability fields. The keyword analysis shows a strong relationship between corporate social responsibility and financial performance. Otherwise, the keywords sustainability and sustainable development do not show a strong relationship. The top eleven cited references, including the top three (McGuire et al. 1988, Freeman, 1984, and Waddock and Graves, 1997), are related to the relationship between competitiveness and sustainability. The authors of the most frequently-cited papers do not related sustainability with performance measurement systems.},
author_keywords={Bibliometric study;  Co-citation analysis;  Keyword networks;  Performance measurement system;  Sustainability},
keywords={Benchmarking;  Exhibitions;  Operations research;  Social aspects;  Supply chain management, Bibliometric;  Co-Citation Analysis;  Corporate responsibility;  Corporate social responsibilities (CSR);  Key performance indicators;  Keyword networks;  Performance measurement system;  Performance measurements, Sustainable development},
references={Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of art literature review on performance measurement (2011) Computers & Industrial Engineering, 60, pp. 279-290; Bititci, U.S., Garengo, P., Dörfler, V., Nudurupati, S., Performance measurement: Challenges for tomorrow (2011) International Journal of Management Reviews, , http://onlinelibrary.wiley.com/doi/10.1111/j.1468-2370.2011.00318.x/pdf, Available at, accessed 5 January 2012; Searcy, C., Updating corporate sustainability performance measurement systems (2011) Measuring Business Excellence, 15 (2), pp. 44-56; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard - linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11, pp. 269-284; Hubbard, G., Measuring organizational performance: Beyond the triple bottom line (2009) Business Strategy and the Environment, 19, pp. 177-191; Robert, K., Tools and concepts for sustainable development: How they relate to a general framework for sustainable development, and to each other (2000) Journal of Cleaner Production, 8 (3), pp. 243-254; Searcy, C., Mccartney, D., Karapetrovic, S., Sustainable development indicators for the transmission system of an electric utility (2007) Corporate Social Responsibility and Environmental Management, 14 (3), pp. 135-151; Okubo, Y., Bibliometric indicators and analysis of research systems: Methods and examples (1997) OECD Science, Technology and Industry Working Papers, 1997/01, , http://dx.doi.org/10.1787/208277770603, OECD Publishing. Available at, (accessed 5 January 2012); Schildt, H.A., (2002) Software for Bibliometric Data Management and Analysis, , Helsinki Institute of Strategy and International Business; Borgatti, S.P., Everett, M.G., Freeman, L.C., (1999) UCINET 6.0 Version 1.00, Analytic Technologies, , Natick; Neely, A., The evolution of performance measurement research (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Griffin, J.J., Mahon, J.F., The corporate social performance and corporate financial performance debate: Twenty-five years of incomparable research (1997) Business Society, 36 (4), pp. 5-31; Friedman, M., The social responsibility of business is to increase its profits (1970) New York Times Magazine, pp. 122-126. , reprint from 1962, September 13; McGuire, J.B., Sundgren, A., Schneeweis, T., Corporate social responsibility and firm financial performance (1988) Academy of Management Journal, 31 (4), pp. 854-872; Carroll, A.B., A three-dimensional conceptual model of corporate performance (1979) Academy of Management Review, 4 (4), pp. 497-505; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 70-79; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard - Translating Strategy into Action, , Boston: Harvard Business Review Press; Spicer, B.H., Investors, corporate social performance and information disclosure: An empirical study (1978) Accounting Review, 53 (1), pp. 94-111; Bowman, E.H., Haire, M., A strategic posture toward corporate social responsibility (1975) California Management Review, 18, pp. 49-58; Donaldson, T., Preston, L., The stakeholder theory of the modern corporation: Concepts, evidence and implications (1995) Academy of Management Review, 20 (1), pp. 65-91; Sternberg, W., Cooked books: Shoddy and in some cases sharp practices by the big six accounting firms are the hidden element linking the financial scandals of the 1980s and 1990s (1992) The Atlantic Monthly, 269 (1), p. 20; Waddock, S.A., Graves, S.B., The corporate social performance-financial performance link (1997) Strategic Management Journal, 18 (4), pp. 303-319; Alexander, G.J., Buchholz, R.A., Research notes: Corporate social responsibility and stock market performance (1978) Academy of Management Journal, 21 (3), pp. 479-486; Wartick, S.L., Cochran, P.L., The evolution of the corporate social performance model (1985) Academy of Management Review, 10 (4), pp. 756-769; Cochran, P.L., Wood, R.A., Corporate social responsibility and financial performance (1984) Academy of Management Journal, 27 (1), pp. 42-56; Freeman, R.E., (1984) Strategic Management: A Stakeholder Approach, , Boston: Pitman Publishing; Wood, D.J., Social performance revisited (1991) Academy of Management Review, 16 (4), pp. 691-718; Ullmann, A., Data in search of theory: A critical examination of the relationships among social performance, social disclosure, and economic performance of u.s. Firms (1985) Academy of Management Review, 10 (3), pp. 540-557; Aupperle, K.E., Carroll, A.B., Hatfiel, J.D., An empirical examination of the relationship between corporate social responsibility and profitability (1985) Academy of Management Journal, 28 (2), pp. 446-463; Hopkins, M.S., 8 Reasons sustainability will change management (that you never thought of) (2009) Sloan Management Review, 51, pp. 27-30; Lubin, D.A., Esty, D.C., The sustainability imperative (2010) Harvard Business Review, 88, pp. 42-50; Nidumolu, R., Prahalad, C.K., Rangaswami, M.R., Why sustainability is now the key driver of innovation (2009) Harvard Business Review, 87, pp. 57-64; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Boston: Harvard Business School Press},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Neto2012733,
author={Neto, M.S. and Pires, S.R.I.},
title={Performance measurement in supply chains: A study in the automotive industry [Medição de desempenho em cadeias de suprimentos: Um estudo na indústria automobilística]},
journal={Gestao e Producao},
year={2012},
volume={19},
number={4},
pages={733-746},
doi={10.1590/s0104-530x2012000400006},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873339045&doi=10.1590%2fs0104-530x2012000400006&partnerID=40&md5=82297e02717e6180d2aa146149a4b5fd},
affiliation={Programa de Pós-graduação Em Administraçã o - PPGA, Faculdade de Gestão E Negócios, Universidade Metodista de Piracicaba - UNIMEP, Rod. do Açúcar, Piracicaba, SP, Brazil},
abstract={Much of the literature on performance measurement in industrial companies is based on an internal perspective, i.e., companies are investigated individually. With the emergence of the supply chain management concept, the demand for a review of knowledge and performance measurement systems has increased considering the supply chain as a whole. Thus, the introduction of new productive configurations, such as the industrial condominium, has stimulated the development of new standards of relationship in supply chains introducing new ways to conduct performance management. Therefore, the main objective of the present study was to identify the occurrence and to analyze the key characteristics of performance measurement in a representative supply chain of the Brazilian automotive industry that has an industrial condominium configuration. This study was exploratory in nature, and the data were collected through interviews conducted with executives involved with this topic in these companies using a semi-structured questionnaire. As main result, the occurrence of a single supply chain performance measurement system was not identified in the supply chain studied. In fact, the automaker internal performance measurement system unfolds upstream and downstream affecting the performance measurement systems of the four suppliers and the car dealer studied. Analyzing the performance indicators of these companies, it can be said that the upstream measures are connected to the operational aspects related to quality, production, and delivery performance, while the downstream measures are more strategic and related to marketing. Furthermore, it was also found that the supply chain performance measurement tends to be more intense in collaborative relationships.},
author_keywords={Automotive industry;  Performance measurement;  Supply chain management},
references={Aláez-aller, R., Longás-garcía, J.C., Dynamic supplier management in the automotive industry (2010) International Journal of Operations & Production Management, 30 (3), pp. 312-335. , http://dx.doi.org/10.1108/01443571011024647; Alves Filho, A.G., Pressupostos da gestão da cadeia de suprimentos: Evidências de estudos sobre a indústria automobilística (2004) Gestão & Produção, 11 (3), pp. 275-288. , http://dx.doi.org/10.1590/S0104-530X2004000300003; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , http://dx.doi.org/10.1016/S0925-5273(98)00079-6; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292. , http://dx.doi.org/10.1108/01443579910249714; Brandão, H.P., Guimarães, T.A., Gestão de competências gestão do desempenho: Tecnologias distintas ou instrumentos de um mesmo constructo? (2001) Revista de Administração de Empresas, 41 (1), pp. 8-15. , http://dx.doi.org/10.1590/S0034-75902001000100002; Carvalho, R.Q., Globalização e reestruturação da cadeia produtiva na indústria automotiva: Qual é o papel do Mercosul? (2000) Convênio IPEA-DPCT/IG/UNICAMP, p. 252. , FUNCAMP, Relatório Final; Chia, A., Goh, M., Hum, S.H., Performance measurement in supply chain entities: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), pp. 605-620; Cox, J.F., Blackstone, J.H., Spencer, M.S., (1995) APICS Dictionary, , 8th. ed. Falls Church: American Production and Inventory Control Society; Cunha, V., Zwicker, R., Antecedentes do relacionamento e da performance em empresas da cadeia de suprimentos: Estruturação e aplicação de modelos de equações estruturais (2009) Revista de Administração de Empresas, 49 (2), pp. 147-161. , http://dx.doi.org/10.1590/S0034-75902009000200003; Croxton, K.L., The supply Chain management processes (2001) International Journal of Logistic Management, 12 (2), pp. 13-36. , http://dx.doi.org/10.1108/09574090110806271; Daugherty, P.J., Review of logistics and supply chain relationship literature and suggested research agenda (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 16-31. , http://dx.doi.org/10.1108/09600031111101402; Eccles, R.G., Manifesto da mensuração do desempenho (2000) Harvard Business Review, , Medindo o Desempenho empresarial. Rio de Janeiro: Editora Campus; Fabbe-costes, N., Jahre, M., Supply chain integration and performance: A review of the evidence (2008) International Journal of Logistics Management, 19 (2), pp. 130-154. , http://dx.doi.org/10.1108/09574090810895933; Fleury, A., The changing pattern of operations management in developing countries: The case of Brazil (1999) International Journal of Operations & Production Management, 19 (5-6), pp. 552-564. , http://dx.doi.org/10.1108/01443579910260874; Gereffi, G., International trade and industrial upgrading in the apparel commodity chain (1999) Journal of International Economics, 48 (1), pp. 37-70. , http://dx.doi.org/10.1016/S0022-1996(98)00075-0; Green Junior, K.W., Whitten, D., Inman, R.A., The impact of logistics performance on organizational performance in a supply chain context (2008) Supply Chain Management: An International Journal, 13-14, pp. 317-327; Gulledge, T., Chavusholu, T., Automating the construction of supply chain key performance indicators (2008) Industrial Management & Data Systems, 108 (6), pp. 750-774. , http://dx.doi.org/10.1108/02635570810883996, Enterprise Integration, Inc. Alexandria, Virginia, USA and George Mason University, Fairfax, Virginia, USA; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International. Journal of Operations & Production Management, 21 (1-2), pp. 71-87. , http://dx.doi.org/10.1108/01443570110358468; Helper, S., How much has really changed between U.S. Automakers and their suppliers? (1991) Sloan Management Review, 32 (4), pp. 15-28; Hilsdorf, W.C., Rotondaro, R.G., Pires, S.R.I., Integração de processos na cadeia de suprimentos e desempenho do serviço ao cliente: Um estudo na indústria calçadista de Franca (2009) Gestão & Produção, 16 (2), pp. 232-244. , http://dx.doi.org/10.1590/S0104-530X2009000200007; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868. , http://dx.doi.org/10.1108/09600030010351246; Hronec, S.M., (1994) Sinais Vitais: Usando Medidas de Desempenho da Qualidade, Tempo, E Custos Para Traçar a Rota do Futuro da Sua Empresa, , São Paulo: Makron Books; Kaydos, W., (1991) Measuring, Managing and Maximizing Performance, , Portland: Productivity Press; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply Chain management: Implementation Issues and research opportunities (1998) International Journal of Logistics Management, 9 (2), pp. 1-19. , http://dx.doi.org/10.1108/09574099810805807; Lambert, D., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19. , http://dx.doi.org/10.1108/09574090110806190; Lamming, R.C., (1993) Beyond Partnership - Strategies for Innovation and Lean Supply, , London: Prentice-Hall; McCormack, K., Ladeira, M.B., Oliveira, M.P.V., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282. , http://dx.doi.org/10.1108/13598540810882161; Motta, F.P., Vasconcelos, I.F.G., (2005) Teoria Geral da Administração, , Bookman: Porto Alegre; Nauri, M.H.C., (1998) As Medidas de Desempenho Como Base Para a Melhoria Continua Dos Processos: O Caso da Fundação de Amparo à Pesquisa E Extensão Universitária (FAPEU), , Dissertação (Mestrado em Engenharia de Produçã o)-Universidade de Santa Catarina, Florianópolis, 1998; Neely, A., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145. , http://dx.doi.org/10.1108/01443570010343708; Oliveira, G.T., Martins, R.A., Efeitos da adoção do modelo do prêmio nacional da qualidade na medição de desempenho: Estudos de caso em empresas ganhadoras do prêmio (2008) Gestão & Produção, 15, pp. 167-179. , http://dx.doi.org/10.1590/S0104-530X2008000200004; Ou, C.S., A structural model of supply chain management on firm performance (2010) International Journal of Operations & Production Management, 30 (5), pp. 526-545. , http://dx.doi.org/10.1108/01443571011039614; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management & Data Systems, 110 (9), pp. 1297-1318. , http://dx.doi.org/10.1108/02635571011087400; Pires, S.R.I., Sacomano Neto, M., New configurations in supply Chains: The case of a condominium in Brazil's automotive industry (2008) Supply Chain Management: An International Journal, 13, pp. 37-48. , http://dx.doi.org/10.1108/13598540810882215; Pires, S.R.I., (2004) Gestão da Cadeia de Suprimentos: Conceitos, Estratégias, Práticas E Casos, , São Paulo: Atlas; Richey Junior, R.G., The moderating role of barriers on the relationship between drivers to supply chain integration and firm performance (2009) International Journal of Physical Distribution & Logistics Management, 39 (10), pp. 826-840. , http://dx.doi.org/10.1108/09600030911011432; Ritchie, B., Brindley, C., Supply chain risk management and performance A guiding framework for future development (2007) International Journal of Operations & Production Management, 27 (3), pp. 303-322. , http://dx.doi.org/10.1108/01443570710725563; Rodrigues, D.M., Sellitto, M., A. Análise do desempenho de fornecedores de uma empresa de manufatura apoiada em análise de aglomerados (2009) Produção, 19 (1), pp. 55-69. , http://dx.doi.org/10.1590/S0103-65132009000100005; Slack, N., (1993) Vantagem Competitiva Em Manufatura, , 3. ed. São Paulo: Atlas; Sellitto, M.A., Mendes, L.W., Avaliação comparativa do desempenho de três cadeias de suprimentos em manufatura (2006) Produção, 16 (3), pp. 552-568. , http://dx.doi.org/10.1590/S0103-65132006000300015; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directionsInternational (2006) Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , http://dx.doi.org/10.1108/17410400610653219; Soni, G., Kodali, R., Internal benchmarking assessment of supply chain performance (2010) Benchmarking: An International Journal, 17 (1), pp. 44-76; Storper, M., Harrison, B., Flexibility, hierarchy and regional developments: The changing structure of industrial production systems and their forms of governance in the 1990s (1991) Research Policy, 20 (5), pp. 407-422. , http://dx.doi.org/10.1016/0048-7333(91)90066-Y; Suzigan, W., Garcia, R., Furtado, J., Governança de Sistemas de MPMES em clusters Industriais (2002) SemináRio Internacional PolíTicas Para Sistemas Produtivos Locais de MPMES, , Rio de Janeiro. Anais. Rio de Janeiro, 2002; Tan, K.C., Supply chain information and relational alignments: Mediators of EDI on firm performance (2010) Distribution & Logistics Management, 40 (5), pp. 377-394. , http://dx.doi.org/10.1108/09600031011052831; Waggoner, D.B., Nelly, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60 (61), pp. 53-60. , http://dx.doi.org/10.1016/S0925-5273(98)00201-1},
correspondence_address1={Neto, M.S.; Programa de Pós-graduação Em Administração - PPGA, Rod. do Açúcar, Piracicaba, SP, Brazil; email: msacomano@unimep.br},
publisher={Brazilian Institute for Information in Science and Technology},
issn={0104530X},
language={Portuguese},
abbrev_source_title={Gestao Prod.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Frederico2012857,
author={Frederico, G.F. and Martins, R.A.},
title={Model for alignment between performance measurement systems and maturity of supply chain management [Modelo para alinhamento entre a maturidade dos sistemas de medição de desempenho e a maturidade da gestão da cadeia de suprimentos]},
journal={Gestao e Producao},
year={2012},
volume={19},
number={4},
pages={857-871},
doi={10.1590/s0104-530x2012000400014},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873338657&doi=10.1590%2fs0104-530x2012000400014&partnerID=40&md5=273a818d84d090bbf57411e4d2b9807c},
affiliation={Departamento de Engenharia de Produção, Universidade Federal de São Carlos - UFSCar, São Carlos, SP, Brazil},
abstract={Supply chains have become increasingly important for companies to survive in today's competitive market. Theories and methods that contribute to the improvement of their management are important elements of research and development in the field of Industrial Engineering. Considering performance measurement systems as one of these elements, this paper aims to propose a model for alignment between performance measurement systems and its maturity to the maturity levels of supply chain management due to the lack of studies that address the relationship between these two elements. Therefore, performance measurement systems and their maturity, as well as maturity models for supply chain management were studied enabling the creation of a theoretical model of relationship between these two elements. The multiple case study method was applied to three different units of analysis to verify the suitability of the theoretical model proposed. In most of the cases, as the main results of this study, it was identified the existence of a relationship between the maturity of performance measurement system and the maturity of supply chain management although the dimensions of maturity of these two elements are not always at the same development level. The contribution of this model is to present a new approach to the theory of the performance measurement systems in the supply chain management, as well as to provide practitioners with the correct alignment between the maturity of supply chain management and maturity of performance measurement systems.},
author_keywords={Maturity of performance measurement systems;  Maturity of supply chain management;  Performance measurement systems;  Supply chain management},
references={Amaratunga, D., Quantitative and qualitative research in the built environment: Application of mixed research approach (2002) Work Study, 51 (1), pp. 17-31. , http://dx.doi.org/10.1108/00438020210415488; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Engineering Costs and Production Economics, 17, pp. 253-262. , http://dx.doi.org/10.1016/0167-188X(89)90074-8; Attadia, L.C.L., Martins, R.A., Medição de desempenho como base para evolução da melhoria contínua (2003) Revista Produção, 13 (2), pp. 33-41; Ayers, J.B., Malmberg, D.M., (2002) Supply Chain Systems: Are you Ready? Information Strategy: The Executive's Journal, pp. 18-27. , http://ayers-consulting.com/download/SC%20Systems%20- %20Are%20You%20Ready.pdf, Disponível em, Acesso em: 01 mar. 2008; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292. , http://dx.doi.org/10.1108/01443579910249714; Bessant, J., Caffyn, S., Gallagher, M., An evolutionay model of continuous improvement behavior (2001) Technovation, 21 (2), pp. 67-77. , http://dx.doi.org/10.1016/S0166-4972(00)00023-7; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704. , http://dx.doi.org/10.1108/01443570010321676; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62. , http://dx.doi.org/10.1016/j.cie.2007.04.001; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply Chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93. , v., n; Bryman, A., (1989) Research Methods and Organization Studies, , http://dx.doi.org/10.4324/9780203359648, London: Unwin Hyman; Bryman, A., Bell, E., (2007) Business Research Methods, , 2nd ed. Oxford: Oxford University Express; Chan, F.T.S., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642. , http://dx.doi.org/10.1108/00251740310495568; Christopher, M., (2005) Logistics and Supply Chain Management, , 3rd ed. London: Pearson Education; Daozhi, Z., A new supply chain maturity model with 3-Dimension perspective (2006) Information Technology and Inovation Conference - ITIC, 2006, , Hangzhou. Proceedings. Hangzhou: ITIC; Geary, S., Zonnenberg, J.P., What it means to be best in class (2000) Supply Chain Management Review, 4 (3), pp. 43-48; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87. , http://dx.doi.org/10.1108/01443570110358468; Hammer, M., The process audit (2007) Harvard Business Review, 85 (4), pp. 111-123. , Apr. PMid:17432158; Harter, D.E., Krishnan, M.S., Slaughter, S.A., Effects of process maturity on quality, cycle time, and effort in software product development (2000) Management Science, 46 (4), pp. 451-466. , http://dx.doi.org/10.1287/mnsc.46.4.451.12056; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868. , http://dx.doi.org/10.1108/09600030010351246; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229. , http://dx.doi.org/10.1108/01443570310458465; Kwak, Y.H., Ibbs, W.C., Project management process maturity model (2002) Journal of Management in Engineering, 18 (3), pp. 150-155. , http://dx.doi.org/10.1061/(ASCE)0742-597X(2002)18:3(150; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83. , http://dx.doi.org/10.1016/S0019-8501(99)00113-3; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19. , http://dx.doi.org/10.1108/09574090110806190; Lockamy Iii, A., McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278. , http://dx.doi.org/10.1108/13598540410550019; Martins, R.A., Quality management as trigger to performance measurement systems evolution (2008) Industrial Engineering Research Conference IIE - Annual Conference, , Hangzhou. Proceedings. Vancouver: Industrial Engineering Research Conference, 2008; Miguel, P.A.C., Estudo de caso na engenharia de produção: Estruturação e recomendações para sua condução (2007) Revista Produção, 17 (1), pp. 216-229. , http://dx.doi.org/10.1590/S0103-65132007000100015; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536. , http://dx.doi.org/10.1108/14637150410559207; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116. , http://dx.doi.org/10.1108/01443579510083622; Neely, A., (1998) Measuring Business Performance, p. 208. , London: The Economist Books Ltd; Oliveira, G.T., Martins, R.A., Efeitos da adoção do modelo do Prêmio Nacional da Qualidade na medição de desempenho: Estudos de caso em empresas ganhadoras do prêmio (2008) Revista Gestão E Produção, 15 (2), pp. 247-259. , http://dx.doi.org/10.1590/S0104-530X2008000200004; Oliveira, M.P.V., (2009) Modelo de Maturidade de Processos Em Cadeias de Suprimentos: Precedências E Os Pontos-Chave de Transição, , Tese (Doutorado em Administração)-Universidade Federal de Minas Gerais, Belo Horizonte, 2009; (2007) Competitive Advantage of Best in Class Supply Chains, , http://www.pmgbenchmarking.com/public/survey/advantagesofbic.pdf, Pennsylvania: PMG, Disponível em, Acesso em: 17 nov. 2008; Pires, S.R.I., Aravechia, C.H.M., Measuring supply chain performance (2001) Annual Conference of the Production and Operations Management Society, 12. , Orlando. Proceedings. Orlando: POMS, 2001; Pires, S.R.I., (2009) Gestão da Cadeia de Suprimentos: Conceitos, Estratégias, Práticas E Casos, , 2. ed. São Paulo: Atlas; Seuring, S.A., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Management: An International Journal, 13 (2), pp. 128-137. , http://dx.doi.org/10.1108/13598540810860967; Simchi-levi, D., Kaminsky, P., Simchi-levi, E., Introduction to supply chain management (2003) Designing and Managing the Supply Chain: Concepts, Strategies and Case Studies, , SIMCHI-LEVI, D. KAMINSKY, P. SIMCHI-LEVI, E. 2nd ed. Boston: McGraw-Hill; Stevens, G., Integrating the supply chain (1989) International Journal of Physical Distribution & Logistics Management, 19 (8), pp. 3-8. , http://dx.doi.org/10.1108/EUM0000000000329; (2008) Supply-Chain Operations Reference-model, , http://www.supply-chain.org/resources/scor, version 9.0 - Overview. Supply-Chain Council, Disponível em, Acesso em: 5 nov. 2008; Teixeira, R., Lacerda, D.P., Gestão da cadeia de suprimentos: Análise dos artigos publicados em alguns periódicos acadêmicos entre os anos de 2004 e 2006 (2010) Gestão & Produção, 17 (1), pp. 207-227. , http://dx.doi.org/10.1590/S0104-530X2010000100016; Tezza, R., Bornia, A.C., Vey, I.H., Sistemas de medição de desempenho: Uma revisão e classificação da literatura (2010) Gestão & Produção, 17 (1), pp. 75-93. , http://dx.doi.org/10.1590/S0104-530X2010000100007; Van Hoek, R.I., Measuring the unmeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192. , http://dx.doi.org/10.1108/13598549810244232; Van Aken, E.M., Assessing maturity and effectiveness of enterprise performance measurement systems (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 400-418. , http://dx.doi.org/10.1108/17410400510604557; Venkatraman, N., Henderson, J.C., Real strategies for virtual organizing (1998) Sloan Management Review, 40 (1), pp. 33-48; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations & Production Management, 22 (2), pp. 195-219. , http://dx.doi.org/10.1108/01443570210414329; Wettstein, T., Kueng, P., A maturity model for performance measurement systems (2002) Management Information Systems, pp. 113-122; Yin, R.K., (2005) Estudo de Casos, , 3. ed. Porto Alegre: Bookman},
correspondence_address1={Frederico, G.F.; Departamento de Engenharia de Produção, , São Carlos, SP, Brazil; email: fredericogf@bol.com.br},
publisher={Brazilian Institute for Information in Science and Technology},
issn={0104530X},
language={Portuguese},
abbrev_source_title={Gestao Prod.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Guido2012198,
author={Guido, A.L. and Mainetti, L. and Patrono, L.},
title={Evaluating potential benefits of the use of RFID, EPCglobal, and ebXML in the pharmaceutical supply chain},
journal={International Journal of Healthcare Technology and Management},
year={2012},
volume={13},
number={4},
pages={198-222},
doi={10.1504/IJHTM.2012.050625},
note={cited By 20},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870653702&doi=10.1504%2fIJHTM.2012.050625&partnerID=40&md5=2103524f4318ae73ccbeee41d461855d},
affiliation={Department of Innovation Engineering, University of Salento, Via Monteroni, Lecce 73100, Italy},
abstract={This paper attempts to evaluate the impact of radio frequency identification (RFID) technology, EPCglobal and ebXML on some business processes that are critical to the success of the pharmaceutical supply chain. Particular attention is focused on the wholesaler because it represents a particular stress point in terms of constraints and product flow. A quantitative and qualitative analysis of some current (AS IS) supply chain processes was performed on a big pharmaceutical wholesaler. It allowed us to identify some critical factors in the main business processes. In order to guarantee an efficient item-level tracing system in the whole supply chain, a re-engineering procedure has been defined exploiting an approach based on the combined use of RFID, EPCglobal, and ebXML. The key performance indicator (KPI) method has been chosen to carry out the analysis of the potential benefits by using an empirical method based on the living laboratory approach. This rigorous experimental approach, performed in a controlled test environment, allowed us to derive a realistic estimate of the selected performance indicators for the reengineered (TO BE) model. Copyright © 2012 Inderscience Enterprises Ltd.},
author_keywords={EbXML;  EPCglobal;  Healthcare;  Item-level tracing;  Key performance indicator;  KPI;  Living laboratory approach;  Pharmaceutical supply chain management;  Radio frequency identification;  RFID},
keywords={article;  commercial phenomena;  electronic business using extensible markup language;  EPCglobal;  health care quality;  human;  information processing;  information system;  laboratory;  performance measurement system;  pharmaceutics;  qualitative analysis;  quantitative analysis;  radiofrequency identification;  standardization},
references={Ahson, S., Ilyas, M., (2008) RFID Handbook: Applications, Technology, Security, and Privacy, , CRC Press, Taylor & Francis, ISBN 978-1-4200-5499-6; Barchetti, U., Bucciero, A., De Blasi, M., Mainetti, L., Patrono, L., Implementation and testing of an EPC global-aware discovery service for item-level traceability (2009) Proceedings of IEEE International Conference on Modern Ultra Telecommunications, pp. 1-8. , 12-14 October 2009, St. Petersburg, Russia; Bendavid, Y., Bourgault, M., A living laboratory for managing the front-end phase of innovation adoption: The case of RFID implementation (2010) International Journal of Organisation and Management, 2 (1), pp. 84-108; Bendavid, Y., Lefebvre, E., Lefebvre, L.A., Wamba, S.F., Key performance indicators for the evaluation of RFID-enabled B-to-B e-commerce applications: The case of a five-layer supply chain (2009) Information Systems and E-Business Management, 7 (1), pp. 1-20; Bottani, E., Reengineering, simulation and data analysis of an RFID system (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 13-29; Bottani, E., On the impact of RFID and EPC network on traceability management: A mathematical model (2009) International Journal of RF Technologies: Research and Applications, 1 (2), pp. 95-113; Bottani, E., Ferretti, G., Montanari, R., Rizzi, A., The impact of RFID technology on logistics processes of the fashion industry supply chain (2009) International Journal of RF Technologies: Research and Applications, 1 (4), pp. 225-252; Catarinucci, L., Colella, R., De Blasi, M., Patrono, L., Tarricone, L., Improving item-level tracing systems through ad hoc UHF RFID tags (2010) Proceeding of IEEE Radio and Wireless Symposium, pp. 160-163. , 10-14 January 2010, New Orleans, LA, USA; De Blasi, M., Mighali, V., Patrono, L., Stefanizzi, M.L., Performance evaluation of UHF RFID tags in the pharmaceutical supply chain (2010) ITWDC 2010: Proceedings of the 21st Tyrrhenian Workshop on Digital Communications: Trustworthy Internet - The Internet of Things, pp. 283-292. , 6-8 September 2010, Pula, Italy, Springer, New York, NY, USA; (2004) CPG. Sec. 400.210, Radiofrequency Identification Feasibility Studies and Pilot Programs for Drugs, , FDA, US FDA; Finkenzeller, K., (2003) RFID Handbook, Fundamentals and Applications in Contact-less Smart Cards and Identification, , John Wiley & Sons, The Atrium, Southern Gate, Chichester, England, UK, ISBN 978-0-470-84402-1; Franceschini, F., Galetto, M., Maisano, D., (2007) Management by Measurement: Designing Key Indicators and Performance Measurement Systems, , Springer, Berlin Heidelberg; (2011) FreebXML, , http://www.freebxml.org/, Center for E-Commerce Infrastructure Development, Department of Computer Science, University of Hong Kong, 2002, available at (accessed on 20 April 2011); Harrison, J.R., Lin, Z., Carroll, G.R., Carley, K.M., Simulation modeling in organizational and management research (2007) Academy of Management Review, 32 (4), pp. 1229-1245; Law, A.M., How to build valid and credible simulation models (2006) Proceedings of the 2006 IEEE Winter Simulation Conference, pp. 58-66; Mirowski, L.T., Hartnett, J.S., Williams, R.N., An RFID attacker behavior taxonomy (2009) IEEE Pervasive Computing Magazine, 8 (4), pp. 79-84; Muguira, L., Ruiz-De-Garibay, J., Vazquez, J.I., Interacting with the digital world through RFID-powered gadgets (2009) Proceedings of the 17th International Conference on Software, Telecommunications & Computer Networks, , 24-26 September 2009, Hvar, Croatia; Niktin, P.V., Rao, K.V., Performance limitations of passive UHF RFID systems (2006) Proceedings of the 2006 IEEE Antennas and Propagation Society International Symposium, , 9-14 July 2006, Albuquerque, NM; (2011) Business Process Modeling Notation Specification, , http://www.omg.org/spec/BPMN/1.2/, OMG, Object Management Group; 1997-2002, available at (accessed on 15 March 2011); Peris-Lopez, P., Orfila, A., Mitrokotsa, A., Van Der Lubbe, J.C.A., A comprehensive RFID solution to enhance inpatient medication safety (2011) International Journal of Medical Informatics, 80 (1), pp. 13-24; Rida, A., Yang, L., Tentzeris, M.M., (2010) RFID-Enabled Sensor Design and Applications, , Artech House, Norwood, MA, ISBN 978-1-60783-981-1; Rockart, J., Chief executives define their own data needs (1979) Harvard Business Review, 57 (2), pp. 238-241; Srivastava, B., Radio frequency ID technology: The next devolution in SCM (2004) Business Horizons, 47 (6), pp. 60-68; Thiesse, F., Floerkemeier, C., Harrison, M., Michahelles, F., Roduner, C., Technology, standards, and real-world deployments of the EPC network (2009) IEEE Internet Computing, 13 (2), pp. 36-43; Uysal, D.D., Emond, J.P., Engels, D.W., Evaluation of RFID performance for a pharmaceutical distribution chain: HF vs. UHF (2008) Proceedings of IEEE International Conference on RFID, , 16-17 April 2008, Las Vegas, Nevada, USA; Wamba, S.F., Achieving supply chain integration using RFID technology: The case of emerging intelligent B-to-B e-commerce processes in a living laboratory (2011) Business Process Management Journal, 18 (1), pp. 58-81},
correspondence_address1={Guido, A.L.; Department of Innovation Engineering, Via Monteroni, Lecce 73100, Italy; email: annalisa.guido@unisalento.it},
publisher={Inderscience Publishers},
issn={13682156},
coden={IJHMF},
language={English},
abbrev_source_title={Int. J. Healthc. Technol. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ullrich2012323,
author={Ullrich, C.A.},
title={Supply chain scheduling: Makespan reduction potential},
journal={International Journal of Logistics Research and Applications},
year={2012},
volume={15},
number={5},
pages={323-336},
doi={10.1080/13675567.2012.742045},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870540923&doi=10.1080%2f13675567.2012.742045&partnerID=40&md5=13c3b69929ad5f72c510f10a983e3819},
affiliation={Department of Business Administration and Economics, Bielefeld University, Germany},
abstract={Supply chain scheduling addresses the coordination of machine and delivery scheduling of two or more supply chain stages. The aim is to improve supply chain performance measured in total logistics costs or the supply chain makespan, for example. However, the potential for improvement has never before been quantified, so this relatively new and ambitious field of study has not yet been properly justified. If performance improvements would barely compensate for the cost of coordination, supply chain scheduling would be expendable. This paper deals with a supply chain consisting of four stages. Focusing on the supply chain makespan, eight scheduling scenarios are compared by means of a numerical study. The simplest scenario is characterised by separate scheduling of all stages. The most promising scenario is a joint scheduling approach that treats the supply chain as a flow shop. In the other six scenarios, different subsets of stages coordinate their schedules. Joint supply chain scheduling of all stages significantly outperforms the other seven scenarios. This result holds for the case with a single machine as well as for two identical parallel machines at each stage. Owing to the complexity of some of the scheduling problems to be tackled in the experiments, the numerical study is based on small-size instances with only five jobs so that it can be solved using a commercial optimisation software. Hence, a simpler two-stage supply chain with a single machine at each stage is investigated to obtain results for large-size instances. Since the shortest processing time priority applied at the first stage leads to permutation schedules which on average result in near-optimum makespans, a joint supply chain coordination approach based on Johnson's algorithm turns out to be unreasonable especially when considering the cost of coordination. © 2012 Copyright Taylor and Francis Group, LLC.},
author_keywords={hybrid flow shop;  makespan;  numerical study;  supply chain scheduling},
references={Agnetis, A., Hall, N.G., Pacciarelli, D., Supply chain scheduling: sequence coordination (2006) Discrete Applied Mathematics, 154 (15), pp. 2044-2063. , doi:10.1016/j.dam.2005.04.019; Baker, K.R., (1997) Elements of sequencing and scheduling, , Hanover, Hanover,: Dartmouth College; Barnes, D., Competing supply chains are the future (2006) Financial Times, , (November 8); Biskup, D., Herrmann, J., Gupta, J.N.D., Scheduling identical parallel machines to minimize total tardiness (2008) International Journal of Production Economics, 115 (1), pp. 134-142. , doi:10.1016/j.ijpe.2008.04.011; Bray, R.L., Mendelson, H., Information transmission and the bullwhip effect: an empirical investigation (2012) Management Science, 58 (5), pp. 860-875. , doi:10.1287/mnsc.1110.1467; Bruce, M., Daly, L., Adding value: challenges for UK apparel supply chain management - A review (2011) Production Planning & Control, 22 (3), pp. 210-220. , doi:10.1080/09537287.2010.498574; Bruce, M., Daly, L., Towers, N., Lean or agile - A solution for supply chain management in the textiles and clothing industry? (2004) International Journal of Operations & Production Management, 24 (2), pp. 151-170. , doi:10.1108/01443570410514867; Burgess, K., Singh, P.J., Koroglu, R., Supply chain management: a structured literature review and implications for future research (2006) International Journal of Operations & Production Management, 26 (7), pp. 703-729. , doi:10.1108/01443570610672202; Cachon, G.P., Swinney, R., The value of fast fashion: quick response, enhanced design, and strategic consumer behaviour (2011) Management Science, 57 (4), pp. 778-795. , doi:10.1287/mnsc.1100.1303; Cachon, G.P., Randall, T., Schmidt, G.M., In search of the bullwhip effect (2007) Manufacturing & Service Operations Management, 9 (4), pp. 457-479. , doi:10.1287/msom.1060.0149; Caro, F., Gallien, J., Inventory management of a fast-fashion retail network (2010) Operations Research, 58 (2), pp. 257-273. , doi:10.1287/opre.1090.0698; Caro, F., Zara uses operations research to reengineer its global distribution process (2010) Interfaces, 40 (1), pp. 78-84. , doi:10.1287/inte.1090.0472; Chen, Z.-L., Integrated production and distribution operations - Taxonomy, models, and review (2004) Handbook of quantitative supply chain analysis, pp. 711-740. , In: Simchi-Levi D., Wu S. D., Shen Z.-J., editors Boston, MA, Boston, MA,: Kluwer Academic; Chen, Z.-L., Integrated production and outbound distribution scheduling: review and extension (2010) Operations Research, 58 (1), pp. 130-148. , doi:10.1287/opre.1080.0688; Chopra, S., Meindl, P., (2012) Supply chain management - strategy, planning, and operation, , 5, London, London,: Pearson; Christopher, M., (1992) Logistics and supply chain management, , 1, London, London,: Financial Times/Pitman; Conway, R., Johnson, B., Maxwell, W., An experimental investigation of priority dispatching (1960) Journal of Industrial Engineering, 11 (3), pp. 221-229; Conway, R.W., Maxwell, W.L., Miller, L.W., (1967) Theory of scheduling, , Reading, MA, Reading, MA,: Addison-Wesley; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: more than a new name for logistics (1997) International Journal of Logistics Management, 8, pp. 1-14. , doi:10.1108/09574099710805556; Cournot, A.A., (1897) Research into the mathematical principles of the theory of wealth, , New York, New York,: MacMillian; Forrester, J.W., Industrial dynamics: a major breakthrough for decision makers (1958) Harvard Business Review, 36 (4), pp. 37-66; Framinan, J.M., Gupta, J.N.D., Leisten, R., A review and classification of heuristics for permutation flow-shop scheduling with makespan objective (2004) Journal of the Operational Research Society, 55 (12), pp. 1243-1255. , doi:10.1057/palgrave.jors.2601784; French, S., (1982) Sequencing and scheduling: An introduction to the mathematics of job-shop, , New York, New York,: Wiley; Garey, M.R., Johnson, D.S., (1979) Computers and intractability, , New York, New York,: W. H. Freemann; Garey, M.R., Johnson, D.S., Sethi, R., The complexity of flowshop and jobshop scheduling (1976) Mathematics of Operations Research, 1 (2), pp. 117-129. , doi:10.1287/moor.1.2.117; Gavirneni, S., Kapuscinski, R., Tayur, S., Value of information in capacitated supply chains (1999) Management Science, 45 (1), pp. 16-24. , doi:10.1287/mnsc.45.1.16; Graham, Optimization and approximation in deterministic sequencing and scheduling: a survey (1979) Annals of Discrete Mathematics, 5 (1), pp. 287-326. , doi:10.1016/S0167-5060(08)70356-X; Hall, N.G., Potts, C.N., Supply chain scheduling: batching and delivery (2003) Operations Research, 51, pp. 566-584. , doi:10.1287/opre.51.4.566.16106; Handfield, R.B., Nichols, E.L., (1998) Introduction to supply chain management, , Englewood Cliffs, NJ, Englewood Cliffs, NJ,: Prentice-Hall; Hejazi, S.R., Saghafian, S., Flowshop-scheduling problems with makespan criterion: a review (2005) International Journal of Production Research, 43 (14), pp. 2895-2929. , doi:10.1080/0020754050056417; Herrmann, J., Ullrich, C.A., (2011) Supply chain scheduling: Cost-savings potential, , May working paper (ssrn.com/abstract=1829138); Johnson, S.M., Optimal two- and three-stage production schedules with setup times included (1954) Naval Research Logistics, 1 (1), pp. 61-68. , doi:10.1002/nav.3800010110; Kreipl, S., Pinedo, M., Planning and scheduling in supply chains: an overview of issues in practice (2004) Production and Operations Management, 13 (1), pp. 77-92. , doi:10.1111/j.1937-5956.2004.tb00146.x; Lee, H.L., Padmanabhan, V., Whang, S., Information distortion in a supply chain: the bullwhip effect (1997) Management Science, 43 (4), pp. 546-558. , doi:10.1287/mnsc.43.4.546; Lee, H.L., Padmanabhan, V., Whang, S., the bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lenstra, J.K., Rinnoy Kan, A.H.G., Brucker, P., Complexity of machine scheduling problems (1977) Annals of Discrete Mathematics, 1, pp. 343-362. , doi:10.1016/S0167-5060(08)70743-X; Linn, R., Zhang, W., Hybrid flow shop scheduling: a survey (1999) Computers & Industrial Engineering, 37, pp. 57-61. , doi:10.1016/S0360-8352(99)00023-6; Machlup, F., Taber, M., Bilateral monopoly, successive monopoly, and vertical integration (1960) Economica, 27 (106), pp. 101-119. , doi:10.2307/2550895; Mentzer, J.T., Stank, T.P., Myers, M.B., Why global supply chain management? (2007) Handbook of global supply chain management, pp. 1-18. , In: Mentzer J. T., Myers M. B., Stank T. P., editors London, London,: Sage; Mihm, B., Fast fashion in a flat world: global sourcing strategies (2010) International Business & Economics Research Journal, 9 (6), pp. 55-64; Moyaux, T., Chaib-Draa, B., D'Amours, S., Information sharing as a coordination mechanism for reducing the bullwhip effect in a supply chain (2007) IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and reviews, 37 (3), pp. 396-409. , doi:10.1109/TSMCC.2006.887014; Oliver, R.K., Webber, M.D., (1982) Supply chain management: Logistics catches up with strategy, , London, London,: Booz, Allen and Hamilton; Oliver, R.K., Webber, M.D., Supply-chain management: logistics catches up with strategy (reprint from Booz, Allen and Hamilton 1982) (1992) Logistics - the strategic issues, pp. 63-75. , In: Christopher M., editors London, London,: Chapman Hall; Potts, C., Strusevich, V., Fifty years of scheduling: a survey of milestones (2009) Journal of the Operational Research Society, 60 (1), pp. 41-68. , doi:10.1057/jors.2009.2; Prahalad, C.K., Hamel, G., The core competence of the corporation (1990) Harvard Business Review, 68 (3), pp. 79-92; Ribas, I., Leisten, R., Framiñan, J.M., Review and classification of hybrid flow shop scheduling problems from a production system and a solution procedure perspective (2010) Computers & Operations Research, 37 (8), pp. 1439-1454. , doi:10.1016/j.cor.2009.11.001; Ruiz, R., Vázquez-Rodriguez, J.A., The hybrid flow shop scheduling problem (2010) European Journal of Operational Research, 205 (1), pp. 1-18. , doi:10.1016/j.ejor.2009.09.024; Simon, H.A., On the application of servomechanism theory to the study of production control (1952) Econometrica, 20 (2), pp. 247-268. , doi:10.2307/1907849; Spengler, J.J., Vertical restraints and antitrust policy (1950) Journal of Political Economy, 58 (4), pp. 347-352. , doi:10.1086/256964; Stadtler, H., Supply chain management and advanced planning - basics, overview and challenges (2005) European Journal of Operational Research, 163, pp. 575-588. , doi:10.1016/j.ejor.2004.03.001; Stadtler, H., Supply chain management - an overview (2008) Supply chain management and advanced planning, 4, pp. 7-37. , In: Stadtler H., Kilger C., editors Berlin, Berlin,: Springer; Tadei, Minimising makespan in two-machine flow-shop with release times (1998) Journal of Operational Research Society, 49 (1), pp. 77-85; Taylor, T.A., Xiao, W., Does a manufacturer benefit from selling to a better-forecasting retailer? (2010) Management Science, 56 (9), pp. 1584-1598. , doi:10.1287/mnsc.1100.1204; Tirole, J., (1988) The theory of industrial organization, , 2, Boston, Boston,: MIT Press; Tokatli, N., Global sourcing: insights from the global clothing industry - the case of Zara, a fast fashion retailer (2008) Journal of Economic Geography, 8 (1), pp. 21-38. , doi:10.1093/jeg/lbm035; Tokatli, N., Wrigley, N., Kizilgün, O., Shifting global supply networks and fast fashion: made in Turkey for Marks & Spencer (2008) Global Networks, 8 (3), pp. 261-280. , doi:10.1111/j.1471-0374.2008.00195.x; Vandaele, How to govern business services exchanges: contractual and relational issues (2007) International Journal of Management Reviews, 9 (3), pp. 237-258. , doi:10.1111/j.1468-2370.2007.00212.x},
correspondence_address1={Ullrich, C. A.; Department of Business Administration and Economics, Germany; email: cullrich@wiwi.uni-bielefeld.de},
publisher={Taylor and Francis Ltd.},
issn={13675567},
language={English},
abbrev_source_title={Int. J. Logist. Res. Applic.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Khalili-Damghani2012293,
author={Khalili-Damghani, K. and Taghavi-Fard, M. and Abtahi, A.-R.},
title={A fuzzy two-stage DEA approach for performance measurement: Real case of agility performance in dairy supply chains},
journal={International Journal of Applied Decision Sciences},
year={2012},
volume={5},
number={4},
pages={293-317},
doi={10.1504/IJADS.2012.050019},
note={cited By 38},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868247141&doi=10.1504%2fIJADS.2012.050019&partnerID=40&md5=557c757543146effe8542d70745541fd},
affiliation={Department of Industrial Engineering, South-Tehran Branch, Islamic Azad University, Kafayee Amani Ave., Mazandaran St., Emam-Hossein Sq., Tehran, Iran; Faculty of Management and Accounting, Allameh Tabataba'i University, Haft-Peikar Alley, NezamiGanjavi Ave., Tavanir St., Tehran, Iran; Department of Knowledge Engineering and Decision Sciences, University of Economic Sciences, Somayeh Ave. Villa St., Tehran, Iran},
abstract={In this paper, a fuzzy two-stage data envelopment analysis (FTSDEA) has been proposed. Each decision making unit (DMU) has been supposed to make up of two serially connected sub-DMUs. The vagueness of measures has been represented using linguistic terms parameterised through fuzzy sets. Mathematical modelling of proposed FTSDEA results in great reduction of computational efforts. The proposed FTSDEA associates an interval efficiency score to each DMU and sub-DMU. A Monte-Carlo simulation procedure has been supplied to rank and discriminate the interval efficiency scores of efficient DMUs and sub-DMU. A conceptual model including providers of agility, capabilities of agility, and goals of supply chain has been proposed. The model has been associated to a two-stage DMU with fuzzy inputs and outputs. Providers of agility are changed into capabilities of agility in first stage. Then, capabilities of agility are changed into goals of supply chain in second stage. The proposed approach has been applied in real case study of dairy supply chains. Copyright © 2012 Inderscience Enterprises Ltd.},
author_keywords={Agile supply chain;  ASC;  FTSDEA;  Fuzzy two-stage data envelopment analysis;  Supply chain performance measurement},
keywords={Data envelopment analysis;  Efficiency;  Intelligent systems;  Linguistics;  Monte Carlo methods;  Supply chains, Agile supply chains;  Computational effort;  Decision making unit;  FTSDEA;  Fuzzy two-stage data envelopment analysis;  Interval efficiency;  Performance measurements;  Supply chain performance measurements, Decision making},
references={Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444. , DOI 10.1016/0377-2217(78)90138-8; Chen, Y., Zhu, J., Measuring information technology's indirect impact on firm performance (2004) Inform Technol. Manag. J., 5 (1-2), pp. 9-22; Chen, Y., Cook, W.D., Li, N., Zhu, J., Additive efficiency decomposition in two-stage dea (2009) Eur. J. Oper. Res., 196 (1), pp. 1170-1176; Chen, Y., Liang, L., Yang, F., Zhu, J., Evaluation of information technology investment: A data envelopment analysis approach (2006) Computers and Operations Research, 33 (5), pp. 1368-1379. , DOI 10.1016/j.cor.2004.09.021, PII S0305054804002436; Christopher, M., The agile supply chain: Competing in volatile markets (2000) Ind. Market. Manag., 29 (1), pp. 37-44; Cook, W.D., Seiford, L.M., Data envelopment analysis (dea) - Thirty years on (2009) Eur. J. Oper. Res., 192 (1), pp. 1-17; Cook, W.D., Liang, L., Zhu, J., Measuring performance of two-stage network structures by dea: A review and future perspective (2010) Omega, 38 (6), pp. 423-430; Despotis, D.K., Smirlis, Y.G., Data envelopment analysis with imprecise data (2002) European Journal of Operational Research, 140 (1), pp. 24-36. , DOI 10.1016/S0377-2217(01)00200-4, PII S0377221701002004; FarzipoorSaen, R., A decision model for selecting the best entry modes via data envelopment analysis', int (2011) J. of Applied Decision Sciences, 4 (3), pp. 213-229; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Man., 21 (1-2), pp. 71-87; Günther, H.-O., Van Beek, P., (2003) Advanced Planning and Scheduling in Process Industry, pp. 1-7. , Springer, Berlin; Hatami-Marbini, A., Emrouznejad, A., Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011) Eur. J. Oper. Res., 214 (3), pp. 457-472; Ismail, H.S., Sharifi, H., A balanced approach to building agile supply chain (2005) Int. J. Physic. Dist. Log. Manag., 36 (6), pp. 431-444; Jahanshahloo, G.R., HosseinzadehLotfi, F., RezaiBalf, F., ZhianiRezai, H., Using monte carlo method for ranking interval data (2008) Appl. Math Comput., 201 (1-2), pp. 613-620; Kao, C., Hwang, S.-N., Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan (2008) European Journal of Operational Research, 185 (1), pp. 418-429. , DOI 10.1016/j.ejor.2006.11.041, PII S0377221707000112; Lewis, H.F., Sexton, T.R., Network DEA: Efficiency analysis of organizations with complex internal structure (2004) Computers and Operations Research, 31 (9), pp. 1365-1410. , DOI 10.1016/S0305-0548(03)00095-9, PII S0305054803000959; Liang, L., Cook, W.D., Zhu, J., Dea models for two-stage processes: Game approach and efficiency decomposition (2008) Nav. Res. Log., 55 (7), pp. 643-653; Lin, C.-T., Chiu, H., Chu, P.-Y., Agility index in supply chain (2006) Int. J. Prod. Econ., 100 (2), pp. 285-299; Meenakumari, R., Kamaraj, N., Thakur, T., Measurement of relative operational efficiency of soeus in india using data envelopment analysis', int (2009) J. of Applied Decision Sciences, 2 (1), pp. 87-104; Parthiban, P., Punniyamoorthy, M., Ganesh, K., RangaJanardhana, G., A hybrid model for sourcing selection with order quantity allocation with multiple objectives under fuzzy environment', int (2009) J. of Applied Decision Sciences, 2 (3), pp. 275-298; Sharifi, H., Zhang, Z., A methodology for achieving agility in manufacturing organization: An introduction (1999) Int. J. Prod. Econ., 62 (1), pp. 7-22; Storbeck, J.E., A postmodern turn to estimating performance frontiers', int (2008) J. of Applied Decision Sciences, 1 (1), pp. 107-124; Sufian, F., The evolution of malaysian banking sector's efficiency during financial duress: Consequences, concerns, and policy implications', int (2010) J. of Applied Decision Sciences, 3 (4), pp. 366-389; Swafford, P.M., Ghosh, S., Murthy, N.N., A framework for assessing value chain agility (2006) International Journal of Operations and Production Management, 26 (2), pp. 118-140. , DOI 10.1108/01443570610641639; Wang, Y.-M., Chin, K.-S., Some alternative dea models for two-stage process (2010) Expert Syst. Appl., 37 (12), pp. 8799-8808; Zadeh, L.A., Fuzzy sets as a basis for a theory of possibility (1978) Fuzzy Set Syst., 1 (1), pp. 3-28; Zimmermann, H.-J., (1996) Fuzzy Set Theory and its Applications, pp. 11-38. , 3rd ed. Kluwer Academic Publishers, Boston, Dordrecht, London},
correspondence_address1={Khalili-Damghani, K.; Department of Industrial Engineering, Kafayee Amani Ave., Mazandaran St., Emam-Hossein Sq., Tehran, Iran; email: kaveh.khalili@gmail.com},
publisher={Inderscience Publishers},
issn={17558077},
language={English},
abbrev_source_title={Int. J. Appl. Decis. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Khalili-Damghani2012147,
author={Khalili-Damghani, K. and Taghavifard, M.},
title={A three-stage fuzzy DEA approach to measure performance of a serial process including JIT practices, agility indices, and goals in supply chains},
journal={International Journal of Services and Operations Management},
year={2012},
volume={13},
number={2},
pages={147-188},
doi={10.1504/IJSOM.2012.048828},
note={cited By 39},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866042399&doi=10.1504%2fIJSOM.2012.048828&partnerID=40&md5=9395d14976a50e56f72f87126486146d},
affiliation={Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran; Department of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Haft-Peikar Alley, Nezami Ganjavi Ave., Tavanir St., Tehran, Iran},
abstract={In this paper, a generic process in which just-in-time (JIT) practices are changed into agility indices, and agility indices are converted into performance measurement in supply chain has been supposed in form of a conceptual model. Then, a three-stage data envelopment analysis (TSDEA) model has been proposed to measure the relative efficiency of aforementioned process and sub-processes. The uncertainties of criteria in the processes were modelled through linguistic terms parameterised using fuzzy sets. Afterwards, the TSDEA had been developed in fuzzy environment. In proposed fuzzy TSDEA, each decision making unit (DMU) has been supposed to make up of three serially connected sub-DMUs. Mathematical modelling of proposed fuzzy TSDEA resulted in great reduction of computational efforts. Moreover, fuzzy TSDEA associated an interval efficiency score to each DMU and sub-DMU. DMU and sub-DMU were classified based on interval efficiency scores. Solving, the proposed fuzzy TSDEA resulted in establishing managerial view into the modules and sub-modules which required improvement in a multi-stage process. The proposed approach has been applied in real case study of dairy supply chains. Copyright © 2012 Inderscience Enterprises Ltd.},
author_keywords={Agile supply chain;  ASC;  Fuzzy three-stage DEA;  JIT practices;  Supply chain performance measurement},
references={Abtahi, A.-R., Khalili-Damghani, K., Fuzzy data envelopment analysis for measuring agility performance of supply chains (2011) Int. J. Modelling in Operations Management, 1 (3), pp. 263-288; Aksezer, C.S., Benneyan, J.C., Assessing the efficiency of hospitals operating under a unique owner: A DEA application in the presence of missing data (2010) Int. J. of Services and Operations Management, 7 (1), pp. 53-75; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444. , DOI 10.1016/0377-2217(78)90138-8; Chen, Y., Zhu, J., Measuring information technology's indirect impact on firm performance (2004) Information Technology Management Journal, 5 (1-2), pp. 9-22; Chen, Y., Cook, W.D., Li, N., Zhu, J., Additive efficiency decomposition in two-stage DEA (2009) European J. of Operational Research, 196 (1), pp. 1170-1176; Chen, Y., Liang, L., Yang, F., Zhu, J., Evaluation of information technology investment: A data envelopment analysis approach (2006) Computers and Operations Research, 33 (5), pp. 1368-1379. , DOI 10.1016/j.cor.2004.09.021, PII S0305054804002436; Christopher, M., The agile supply chain: Competing in volatile markets (2000) Industrial Marketing Management, 29 (1), pp. 37-44; Cook, W.D., Seiford, L.M., Data envelopment analysis (DEA) - Thirty years on (2009) European J. of Operational Research, 192 (1), pp. 1-17; Cook, W.D., Liang, L., Zhu, J., Measuring performance of two-stage network structures by DEA: A review and future perspective (2010) Omega, 38 (6), pp. 423-430; Dal Pont, G., Furlan, A., Vinelli, A., Interrelationships among lean bundles and their effects on operational performance (2009) Operations Management Research, 1 (2), pp. 150-158; Despotis, D.K., Smirlis, Y.G., Data envelopment analysis with imprecise data (2002) European Journal of Operational Research, 140 (1), pp. 24-36. , DOI 10.1016/S0377-2217(01)00200-4, PII S0377221701002004; Dharmapala, P.S., Exploring the impact of 'intrinsic assurance regions' in DEA on adding value in supply chains using 'uniquely inefficient' decision-making units (2010) Int. J. of Services and Operations Management, 6 (1), pp. 1-16; Entani, T., Maeda, Y., Tanaka, H., Dual models of interval DEA and its extension to interval data (2002) European Journal of Operational Research, 136 (1), pp. 32-45. , DOI 10.1016/S0377-2217(01)00055-8, PII S0377221701000558; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operation and Production Management, 21 (1-2), pp. 71-87; Günther, H.-O., Van Beek, P., (2003) Advanced Planning and Scheduling Solutions in Process Industry, pp. 1-7. , Springer, Berlin; Guo, P., Tanaka, H., Fuzzy DEA: A perceptual evaluation method (2001) Fuzzy Sets and Systems, 119 (1), pp. 149-160. , DOI 10.1016/S0165-0114(99)00106-2; Hatami-Marbini, A., Emrouznejad, A., Tavana, M., A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011) European J. of Operational Research, 214 (3), pp. 457-472; Hougaard, J.L., Fuzzy scores of technical efficiency (1999) European Journal Operational Research, 115 (3), pp. 529-541; Inman, R.A., Sale, R.S., Green Jr., K.W., Whitten, D., Agile manufacturing: Relation to JIT, operational performance and firm performance (2011) Journal of Operations Management, 29 (4), pp. 343-355; Ismail, H.S., Sharifi, H., A balanced approach to building agile supply chain (2005) International Journal of Physical Distribution and Logistics Management, 36 (6), pp. 431-444; Jahanshahloo, G.R., Soleimani-Damaneh, M., Nasrabadi, E., Measure of efficiency in DEA with fuzzy input-output levels: A methodology for assessing, ranking and imposing of weights restrictions (2004) Applied Mathematics and Computations, 156 (1), pp. 175-187; Joo, S.-J., Messer Jr., G.H., Bradshaw, R., The performance evaluation of existing suppliers using data envelopment analysis (2009) Int. J. of Services and Operations Management, 5 (4), pp. 429-443; Kao, C., Hwang, S.-N., Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan (2008) European Journal of Operational Research, 185 (1), pp. 418-429. , DOI 10.1016/j.ejor.2006.11.041, PII S0377221707000112; Kao, C., Liu, S.T., Fuzzy efficiency measures in data envelopment analysis (2000) Fuzzy Sets and Systems, 113 (3), pp. 427-437; Kao, C., Liu, S.T., A mathematical programming approach to fuzzy efficiency ranking (2003) International Journal of Production Economics, 86 (2), pp. 145-154; Karsak, E.E., Using data envelopment analysis for evaluating flexible manufacturing systems in the presence of imprecise data (2008) International Journal of Advanced Manufacturing Technology, 35 (9), pp. 867-874; Khakbaz, M.H., Ghapanchi, A.H., Tavana, M., A multicriteria decision model for supplier selection in portfolios with interactions (2010) Int. J. of Services and Operations Management, 7 (3), pp. 351-377; Khalili-Damghani, K., Abtahi, A.-R., Measuring efficiency of just in time implementation using a fuzzy data envelopment analysis approach: Real case of Iranian dairy industries (2011) Int. J. Advanced Operations Management, , accepted for publication in Corrected Proof; Khalili-Damghani, K., Taghavifard, M., Olfat, L., Feizi, K., A hybrid approach based on fuzzy DEA and simulation to measure the efficiency of agility in supply chain: Real case of dairy industry (2011) International Journal of Management Science and Engineering Management, 6 (3), pp. 163-172; Kumar, G.A., Devadasan, S.R., Shalij, P.R., Vinodh, S., Rajanayagam, D., Edinbarough, I., Implementation of agile supply chain model in an electronic switches manufacturing company (2010) Int. J. of Services and Operations Management, 6 (4), pp. 452-469; León, T., Liern, V., Ruiz, J.L., Sirvent, I., A fuzzy mathematical programming approach to the assessment of efficiency with DEA models (2003) Fuzzy Sets and Systems, 139 (2), pp. 407-419; Lertworasirikul, S., Fang, S.C., Joines, J.A., Nuttle, H.L.W., Fuzzy data envelopment analysis (DEA): A possibility approach (2003) Fuzzy Sets and Systems, 139 (2), pp. 379-394; Lewis, H.F., Sexton, T.R., Network DEA: Efficiency analysis of organizations with complex internal structure (2004) Computers and Operations Research, 31 (9), pp. 1365-1410. , DOI 10.1016/S0305-0548(03)00095-9, PII S0305054803000959; Liang, L., Cook, W.D., Zhu, J., DEA models for two-stage processes: Game approach and efficiency decomposition (2008) Naval Research Logistics, 55 (7), pp. 643-653; Lin, C.-T., Chiu, H., Chu, P.-Y., Agility index in supply chain (2006) International Journal of Production Economics, 100 (2), pp. 285-299; MacKelprang, A.W., Nair, A., Relationship between just-in-time manufacturing practices and performance: A meta-analytic investigation (2010) Journal of Operations Management, 28 (4), pp. 283-302; Min, H., Min, H., Joo, S.-J., Kim, J., A data envelopment analysis for establishing the financial benchmark of Korean hotels (2008) Int. J. of Services and Operations Management, 4 (2), pp. 201-217; Revilla, E., Sarkis, J., Modrego, A., Exploring public and private R&D partnership performance: A knowledge-based view of inter-organisational alliances (2007) Int. J. of Services and Operations Management, 3 (4), pp. 371-393; Sandanayake, Y.G., Oduoza, C.F., Proverbs, D.G., A systematic modelling and simulation approach for JIT performance optimization (2008) Robotics and Computer-integrated Manufacturing, 24 (6), pp. 735-743; Sarmiento, R., Byrne, M., Beale, J., Knowles, G., An exploratory study on contextual variables and manufacturing efficiency (2009) Int. J. of Services and Operations Management, 5 (3), pp. 275-298; Sharifi, H., Zhang, Z., A methodology for achieving agility in manufacturing organization: An introduction (1999) International Journal of Production Economics, 62 (1), pp. 7-22; Sreenivasa, C.G., Devadasan, S.R., Identification of arenas unconquered by agile manufacturing researches and practices through a literature survey (2011) Int. J. of Services and Operations Management, 9 (2), pp. 227-244; Swafford, P.M., Ghosh, S., Murthy, N.N., A framework for assessing value chain agility (2006) International Journal of Operations and Production Management, 26 (2), pp. 118-140. , DOI 10.1108/01443570610641639; Tajima, M., Adaptability and achieving supply chain agility (2005) Int. J. of Services and Operations Management, 1 (2), pp. 134-146; Vinodh, S., Sundararaj, G., Devadasan, S.R., Rajanayagam, D., Murugesh, R., Infusing agility, innovation and quality in products: An integrated effort through the agile ITQFD technique (2008) Int. J. of Services and Operations Management, 4 (2), pp. 243-263; Wang, Y.-M., Chin, K.-S., Some alternative DEA models for two-stage process (2010) Expert Systems with Applications, 37 (12), pp. 8799-8808; Zadeh, L.A., Fuzzy sets as a basis for a theory of possibility (1978) Fuzzy Sets and System, 1 (1), pp. 3-28; Zerafat Angiz, L., Emrouznejad, A., Mustafa, A., Fuzzy assessment of performance of a decision making units using DEA: A non-radial approach (2010) Expert Systems with Applications, 37 (7), pp. 5153-5157; Zhu, J., Imprecise data envelopment analysis (IDEA): A review and improvement with an application (2003) European Journal of Operational Research, 144 (3), pp. 513-529. , DOI 10.1016/S0377-2217(01)00392-7, PII S0377221701003927; Zimmermann, H.-J., (1996) Fuzzy Set Theory and Its Applications, 3rd Ed, pp. 11-38. , Kluwer Academic Publishers, Boston, Dordrecht, London},
correspondence_address1={Khalili-Damghani, K.; Department of Industrial Engineering, , Tehran, Iran; email: kaveh.khalili@gmail.com},
publisher={Inderscience Publishers},
issn={17442370},
language={English},
abbrev_source_title={Int. J. Serv. Oper. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Dangi20121,
author={Dangi, H. and Bardhan, A.K. and Narag, A.S.},
title={Humanitarian relief logistics: An exploratory study for need and importance of performance measurement system},
journal={International Journal of Logistics Systems and Management},
year={2012},
volume={13},
number={1},
pages={1-16},
doi={10.1504/IJLSM.2012.048630},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865800045&doi=10.1504%2fIJLSM.2012.048630&partnerID=40&md5=087f25258885fef157b3b5ee7309bea6},
affiliation={Faculty of Management Studies, University of Delhi, Delhi 110007, India},
abstract={Data from clearly defined and relevant metrics on performance management of relief supply chains can make the success of disaster relief efforts more likely. Studies on relief logistics are far and few, unlike those under commercial supply chain management. Moreover, not much seems to have been done to design metrics that are unique to developing countries in Asia and Africa. The objective of this paper is to provide motivation for development of performance management systems on humanitarian relief logistics and is based on review of the existing literature and extensive interviews with experts, and has a special focus on India. Copyright © 2012 Inderscience Enterprises Ltd.},
author_keywords={Distribution management system;  Humanitarian relief logistics;  Performance measurement system},
references={(2000), http://www.adpc.net/casita/course-materials/Mod-4-Disaster-Mgmt.pdf, ADPC Community Based Disaster Management (CBDM): Trainer's Guide' Module 4, Disaster Management, Asian Disaster Preparedness Center, Bangkok, viewed on 4 November 2011; Alexander, D., The study of natural disasters, 1977-1997: Some reflections on a changing field of knowledge (1997) Disasters, 21 (4), pp. 284-304; Anderskov, C., (2004) Anthropology and Disaster: An Analysis of Current Trends Within Anthropological Disaster Research, and An Attempt to Construct an Approach that Facilitates Theory Building and Applied Practices: Analyzed with Vantage Point in a Case Study from the Flood-rone Mutarara District in Mozambique, , http://www.anthrobase.com/Txt/A/Anderskov_C_03.htm, viewed 2 November 2011; Asghar, S., Damminda, A., Churilov, L., A dynamic integrated model for disaster management decision support systems (2005) International Journal of Simulation Systems Science Technology, 6 (10-11), pp. 95-114; Asghar, S., Alahakoon, D., Churilov, L., A Comprehensive conceptual model for disaster management (2006) Journal of Humanitarian Assistance, , Department of Peace Studies, University of Bradford, UK; Asghar, S., Alahakoon, D., Churilov, L., A modular subroutine selection process in disaster management based on a needs classification scheme (2008) International Journal of Internet and Enterprise Management, 5 (3), pp. 212-236; Beamon, B., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Blaikie, P., Cannon, T., Davis, I., Wisner, B., (1994) At Risk: Natural Hazards, People's Vulnerability and Disasters, , Routledge, London; Cahlink, G., Just in time war (2004) Government Executive, 36 (3), p. 30; Davidson, A.L., (2006) Key Performance Indicator in Humanitarian Logistics, , Master of Engineering in Logistic Thesis, Massachusetts Institute of Technology, Cambridge, MA; (2010), http://www.emdat.be/publications, EM-DAT, Disaster Statistics 1990-May 2010, Centre for Research on the Epidemiology of Disasters (CRED), Université Catholique de Louvain, Louvain-La-Neuve, viewed on 2 November 2011; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement systems (2007) International Journal of Operations and Production Management, 21 (8), pp. 1096-1115; Kaplan, R.S., Strategic performance measurement and management in nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 353-370; Kelly, C., Simplifying disasters: Developing a model for complex non-linear events (1998) Proceedings of International Conference on Disaster Management: Crisis and Opportunity: Hazard Management and Disaster Preparedness in Australasia and the Pacific Region, Cairns, Queensland, Australia, pp. 25-28; Kimberly, A., Disaster preparedness in virginia hospital center-Arlington after Sept 11, 2001 (2003) Disaster Management and Response, 1 (3), pp. 80-86; (2000), http://www.adpc.net/casita/course-materials/Mod-4-Disaster-Mgmt.pdf, ADPC, Community Based Disaster Management (CBDM): Trainer's Guide' Module 4, Disaster Management, Asian Disaster Preparedness Center, Bangkok, viewed on 4 November 2011; Alexander, D., The study of natural disasters, 1977-1997: Some reflections on a changing field of knowledge (1997) Disasters, 21 (4), pp. 284-304; Anderskov, C., (2004) Anthropology and Disaster: An Analysis of Current Trends Within Anthropological Disaster Research, and An Attempt to Construct an Approach that Facilitates Theory Building and Applied Practices: Analyzed with Vantage Point in a Case Study from the Flood-rone Mutarara District in Mozambique, , http://www.anthrobase.com/Txt/A/Anderskov_C_03.htm, viewed 2 November 2011; Asghar, S., Damminda, A., Churilov, L., A dynamic integrated model for disaster management decision support systems (2005) International Journal of Simulation Systems Science Technology, 6 (10-11), pp. 95-114; Asghar, S., Alahakoon, D., Churilov, L., A Comprehensive conceptual model for disaster management (2006) Journal of Humanitarian Assistance, , Department of Peace Studies, University of Bradford, UK; Asghar, S., Alahakoon, D., Churilov, L., A modular subroutine selection process in disaster management based on a needs classification scheme (2008) International Journal of Internet and Enterprise Management, 5 (3), pp. 212-236; Beamon, B., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Blaikie, P., Cannon, T., Davis, I., Wisner, B., (1994) At Risk: Natural Hazards People's Vulnerability And Disasters, , Routledge, London; Cahlink, G., Just in time war (2004) Government Executive, 36 (3), p. 30; Davidson, A.L., (2006) Key Performance Indicator in Humanitarian Logistics, , Master of Engineering in Logistic Thesis, Massachusetts Institute of Technology, Cambridge, MA; (2010), http://www.emdat.be/publications, EM-DAT, Disaster Statistics 1990-May 2010, Centre for Research on the Epidemiology of Disasters (CRED), Université Catholique de Louvain, Louvain-La-Neuve, viewed on 2 November 2011; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement systems (2007) International Journal of Operations and Production Management, 21 (8), pp. 1096-1115; Kaplan, R.S., Strategic performance measurement and management in nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 353-370; Kelly, C., Simplifying disasters: Developing a model for complex non-linear events (1998) Proceedings of International Conference on Disaster Management: Crisis and Opportunity: Hazard Management and Disaster Preparedness in Australasia and the Pacific Region, pp. 25-28. , Cairns, Queensland, Australia; Kimberly, A., Disaster preparedness in virginia hospital center-Arlington after Sept 11, 2001 (2003) Disaster Management and Response, 1 (3), pp. 80-86},
correspondence_address1={Dangi, H.; Faculty of Management Studies, , Delhi 110007, India; email: hkdangi@fms.edu},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Agami2012469,
author={Agami, N. and Saleh, M. and Rasmy, M.},
title={A hybrid dynamic framework for supply chain performance improvement},
journal={IEEE Systems Journal},
year={2012},
volume={6},
number={3},
pages={469-478},
doi={10.1109/JSYST.2011.2177109},
art_number={6117061},
note={cited By 25},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865518248&doi=10.1109%2fJSYST.2011.2177109&partnerID=40&md5=32ac45a25a1865070fd031db22300d9f},
affiliation={Department of Operations Research and Decision Support, Faculty of Computers and Information, Cairo University, Giza 12613, Egypt; System Dynamics Group, University of Bergen, Bergen 5096, Norway},
abstract={Improving supply chain performance has become a critical issue for gaining a competitive edge for companies. Many critical drawbacks prevent the existing performance measurement systems from making a significant contribution to the development and improvement of supply chain management and thus, several research is still needed in this area. In an attempt to fill this gap, an enhanced process-based approach for measuring, managing, and hence improving supply chain performance is presented. The proposed framework is dynamic, continuous, and hybrid. It integrates various sciences, methodologies, and tools, namely systems thinking, strategic planning, optimization, balanced scorecards, supply chain operations reference model, and theory of constraints thinking processes into a cohesive performance measurement system. In this paper, a comparison between the proposed approach and currently existing systems is provided highlighting how each methodology contributed in the enhancement. © 2007-2012 IEEE.},
author_keywords={Optimization;  performance improvement;  supply chain;  systems thinking;  theory of constraints thinking processes (TOCTP)},
keywords={Balanced scorecards;  Competitive edges;  Critical issues;  Existing systems;  Hybrid dynamics;  Performance improvements;  Performance measurement system;  Process-based approach;  Supply chain operations reference models;  Supply chain performance;  Systems thinking;  Theory of constraint;  Thinking process, Optimization;  Supply chains;  System theory, Supply chain management},
references={Udoncy, E., Wong, K., Supply chain performance evaluation: Trends and challenges (2009) Am. J. Eng. Appl. Sci., 2 (1), pp. 202-211; Ramaa, A., Rangaswamy, T., Subramanya, K., A review of literature on performance measurement of supply chain network (2009) Proc. 2nd Int. Conf. Emerging Trends Eng. Technol., pp. 802-807; Akyuz, G., Erkan, T., Supply chain performance measurement: A literature review (2010) Int. J. Prod. Res., 48 (17), pp. 5137-5155; Agami, N., Saleh, M., Rasmy, M., Taxonomy of literature on supply chain performance measurement IEEE Syst. J., , submitted for publication under review; Kaplan, R., Bruns, W., (1987) Accounting and Management: A Field Study Perspective, , Boston, MA: Harvard Business School Press; Stern, J., Stewart, B., Chew, D., The EVA financial management system (1995) J. Appl. Corp. Fin., 8 (2), pp. 32-46; Kurien, G., Qureshi, M., Study of performance measurement practices in supply chain management (2011) Int. J. Bus. Manage. Soc. Sci., 2 (4), pp. 19-34; Christopher, M., (2005) Logistics and Supply Chain Management: Creating Value-Adding Networks, , London, U. K.: FT Prentice-Hall; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Gunasekaran, A., Patel, A., Mcgaughey, R., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ., 87 (3), pp. 333-347; Lambert, D., Pohlen, T., Supply chain metrics (2001) Int. J. Logis. Manage., 12 (1), pp. 1-19; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320. , DOI 10.1016/S0377-2217(02)00396-X, PII S037722170200396X; Chan, F., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Int. J. Manuf. Tech. Manage., 14 (3), pp. 179-190; Choy, K., Chow, H., Lee, W., Chan, F., Development of a performance measurement system in managing supplier relationship for maintenance logistics providers (2007) Int. J. Bench., 14 (3), pp. 352-368; Bhagwat, R., Chan, F., Sharma, M., Performance measurement model for supply chain management in SMEs (2008) Int. J. Glob. Small Bus., 2 (4), pp. 428-445; Chan, F., Performance measurement in a supply chain (2003) Int. J. Adv. Man. Tech., 21 (7), pp. 534-548; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131. , DOI 10.1504/IJBPM.2006.009032; Charan, P., Shankar, R., Baisya, R., Selection of supply chain performance measurement system using AHP approach (2007) Proc. 18th POMS Ann., pp. 1-27; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381. , DOI 10.1108/02635570710734271; Liang, L., Yang, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49. , DOI 10.1007/s10479-006-0026-7, Performance Evaluation and Beyond: Data Envelopment Analysis Research Frontiers in Honor of William W. Cooper; Chen, Y., Liang, L., Yang, F., A DEA game model approach to supply chain efficiency (2006) Annals of Operations Research, 145 (1), pp. 5-13. , DOI 10.1007/s10479-006-0022-y, Performance Evaluation and Beyond: Data Envelopment Analysis Research Frontiers in Honor of William W. Cooper; Chan, F.T.S., Qi, H.J., A fuzzy basis channel-spanning performance measurement method for supply chain management (2002) Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 216 (8), pp. 1155-1167. , DOI 10.1243/095440502760272421; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Berrah, L., Cliville, V., Towards an aggregation performance measurement system model in a supply chain context (2007) Computers in Industry, 58 (7), pp. 709-719. , DOI 10.1016/j.compind.2007.05.012, PII S0166361507000759; Stephens, S., Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476. , DOI 10.1023/A:1012881006783; (2008) SCOR Overview: Version 9.0. Supply-Chain Operations Reference-Model, , http://www.supply-chain.org, Online; Kaplan, R., Norton, D., The balanced scorecard: Measures that drive performance (1992) Harv. Bus. Rev., 70, pp. 71-79. , Jan.-Feb; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) J. Meas. Bus. Exc., 5 (2), pp. 6-13; Tangen, S., Performance measurement: From philosophy to practice (2004) Int. J. Prod. Perf. Manage., 53 (8), pp. 726-737; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) Int. J. Oper. Prod. Manage., 20 (10), pp. 1119-1145; Amer, Y., Luong, L., Lee, S., Wang, Y., A systems approach to order fulfillment using design for six sigma methodology (2007) Int. J. Bus. Syst. Res., 1 (3), pp. 302-316; Amer, Y., Luong, L., Lee, S., Optimizing order fulfillment using design for six sigma and fuzzy logic (2008) Int. J. Manage. Sci. Eng. Manage., 3 (2), pp. 83-99; Gunasekaran, A., Patel, C., Tittiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Manage., 2 (1-2), pp. 71-87; Holmberg, S., A system perspective in supply chain measurement (2000) Int. J. Phys. Dist. Logis. Manage., 30 (10), pp. 847-868; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , DOI 10.1108/01443570010330739; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Neely, A., (1998) Measuring Business Performance, , London, U. K.: Economist Books; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277. , DOI 10.1108/01443570510633648; Chan, F.T., Qi, H.J., Chan, H.K., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) Manage. Dec. J., 41 (7), pp. 635-642. , DOI 10.1108/00251740310495568; Vanteddu, G., Chinnam, R.B., Yang, K., A performance comparison tool for supply chain management (2006) International Journal of Logistics Systems and Management, 2 (4), pp. 342-356; Terwilliger, J., Infrastructure is in (2002) Frontline Sol. Mag., 3 (8), pp. 52-53; Cottril, K., Reforging the supply chain (1997) Bus. Strat. J., 18 (6), pp. 35-39; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Brewer, C., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Int. J. Bus. Logist., 21 (1), pp. 75-93; Abu-Suleiman, A., Boardman, B., Priest, J., A framework for an integrated supply chain performance management system (2005) Int. J. Prod. Res., 43 (15), pp. 3287-3296; Goldratt, E., (1990) What Is This Thing Called Theory of Constraints and How Should It Be Implemented?, , New York: North River Press; Dettmer, H.W., (1997) Goldratt's Theory of Constraints: A System Approach to Continuous Improvement, , Milwaukee, WI: ASQC Quality Press; Rahman, S., Theory of constraints: A review of the philosophy and its application (1998) Int. J. Oper. Prod. Manage., 18 (4), pp. 336-355; Rahman, S., The theory of constraints' thinking processes approach to developing growth strategies in supply chain (2002) Working Paper, ITS-WP-02-09; Scheinkopf, L., (1999) Thinking for Change: Putting the TOC Thinking Processes to use, , St. Lucie Press/APICS Series on Constraints Management. Boca Raton, FL: St. Lucie Press/APICS; Lapide, L., (2000) What About Measuring Supply Chain Performance?" AMR Res., ASCET White Paper, 2 (15), pp. 287-297; Wong, W., Wong, K., A review on benchmarking of supply chain performance measures (2008) Benchmarking Int. J., 15 (1), pp. 2-51; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPIs accomplishment (2009) Dec. Suppl. Syst. J., 46 (2), pp. 512-521},
correspondence_address1={Agami, N.; Department of Operations Research and Decision Support, , Giza 12613, Egypt; email: n.agami@fci-cu.edu.eg},
issn={19328184},
language={English},
abbrev_source_title={IEEE Syst. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Venkatesan201219,
author={Venkatesan, S.P. and Kumanan, S.},
title={Supply chain risk prioritisation using a hybrid AHP and PROMETHEE approach},
journal={International Journal of Services and Operations Management},
year={2012},
volume={13},
number={1},
pages={19-41},
doi={10.1504/IJSOM.2012.048274},
note={cited By 47},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864652179&doi=10.1504%2fIJSOM.2012.048274&partnerID=40&md5=5e3b2ecfa8078815806f99c683e65963},
affiliation={Department of Production Engineering, National Institute of Technology, Tiruchirappalli - 620015, India},
abstract={Supply chain is a network of facilities designed to procure, produce, and distribute goods to customers at right quantities, to the right locations and at the right time. Supply chain studies are focused for improving the operational efficiency through cost reduction. In recent years, supply chains have become increasingly vulnerable to risks resulting in poor financial performance and customer service. Managing the supply chain risks is a challenging task. Resilience and responsiveness-based strategies are being attempted to build a robust supply chain. To select and evaluate the appropriate mitigation strategies, supply chain risks are to be prioritised. Supply chain risk prioritisation is a multi criteria decision making (MCDM) problem. Developing the MCDM models for supply chain risk prioritisation need attention. In this research a hybrid analytic hierarch process (AHP) and preference ranking organisation method for enrichment evaluation (PROMETHEE) approach is proposed for supply chain risk prioritisation. A case example of a typical plastic industry is presented to illustrate the performance of the proposed approach. Copyright © 2012 Inderscience Enterprises Ltd.},
author_keywords={AHP;  Hybrid analytic hierarch process;  MCDM;  Multi criteria decision making;  Preference ranking organisation method for enrichment evaluation;  PROMETHEE;  Supply chain performance measures;  Supply chain risk management;  Supply chain risk prioritisation},
references={Basu, G., Ben-Hamida, M., Butner, K., Cope, E., Dao, H., Deleris, L., Dong, J., Torpy, J., (2008) Supply Chain Risk Management: A Delicate Balancing Act, , http://www.ftp.software.ibm.com/common/ssi/sa/wh/n/gbw03015usen/ GBW03015USEN.PDF, (accessed on 16 March 2010); Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Behzadian, M., Samizadeh, R., Nazemi, J., Decision making in house of quality: A hybrid AHP-PROMETHEE approach (2010) Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), pp. 930-934. , Macau, China; Behzadian, M., Kazemzadeh, R.B., Albadvi, A., Aghdasi, M., PROMETHEE: A comprehensive literature review on methodologies and applications (2010) European Journal of Operational Research, 200 (1), pp. 198-215; Blackhurst, J., Craighead, C.W., Elkins, D., Handfield, R.B., An empirically derived agenda of critical research issues for managing supply-chain disruptions (2005) International Journal of Production Research, 43 (19), pp. 4067-4081. , DOI 10.1080/00207540500151549; Brans, J.P., Mareschal, B., (2005) PROMETHEE Methods, , http://www.info.wlu.ca/~wwwmath/courses/graduatecourses/ma536/promethee. pdf, (accessed on 27 September 2010); (2009) Supply Chain Resilience - Survey Report, , http://www.thebci.org/supplychainsummary.pdf, Business Continuity Institute (BCI), (accessed 25 on September 2010); Chopra, S., Sodhi, M.S., Managing risk to avoid supply-chain breakdown (2004) MIT Sloan Management Review, 46 (1), pp. 52-61; Chun-Ping, Z., Cui, L., Choosing the supplier for emergency supplies based on AHP/linear programming (2011) Proceedings of the International Conference on Management Science and Industrial Engineering (MSIE), pp. 386-390. , Harbin, China; Cucchiella, F., Gastaldi, M., Risk management in supply chain: A real option approach (2006) Journal of Manufacturing Technology Management, 17 (6), pp. 700-720. , DOI 10.1108/17410380610678756, e and hyphen;Technology and manufacturing enterprise competitiveness; Dagdeviren, M., Decision making in equipment selection: An integrated approach with AHP and PROMETHEE (2008) Journal of Intelligent Manufacturing, 19 (4), pp. 397-406; Davood, A., On inconsistency of a pairwise comparison matrix (2009) International Journal of Industrial Mathematics, 1 (4), pp. 343-350; Faisal, M.N., Benchmarking supply chains on risk dimensions (2009) International Journal of Services and Operations Management, 5 (3), pp. 402-427; Falasca, M., Zobel, C.W., Cook, D., A decision support framework to assess supply chain resilience (2008) Proceedings of the 5th International Conference on Information Systems for Crisis Response and Management ISCRAM, pp. 596-605. , Fiedrich, F. and Van de Walle, B. (Eds.), Washington, DC, USA; Gaudenzi, B., Borghesi, A., Managing risks in the supply chain using the AHP method (2006) The International Journal of Logistics Management, 17 (1), pp. 114-136; Gnanasekaran, S., Velappan, S., Ayappan, S., An integrated model for supplier selection: An automobile industry case study (2010) International Journal of Services and Operations Management, 6 (1), pp. 89-105; Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.-M., Tuominen, M., Risk management processes in supplier network (2004) International Journal of Production Economics, 90 (1), pp. 47-58; Harrington, K., O'Connor, J., (2009) How Cisco Succeeds at Global Risk Management, , http://www.scmr.com, (accessed on 25 September 2010); Hendricks, K.B., Singhal, V.R., Association between supply chain glitches and operating performance (2005) Management Science, 51 (5), pp. 695-711. , DOI 10.1287/mnsc.1040.0353; Hofman, D., (2004) The Hierarchy of Supply Chain Metrics: Diagnosing Your Supply Chain Health, , http://www.concisemanagement.ch/medialibrary/site1/ AMR_Research_REPORT_16962_The_Hierarchy_of_Supply_Chain_Metrics.pdf, (accessed 26 on September 2010); Juttner, U., Supply chain risk management: Understanding the business requirements from a practitioner perspective (2005) International Journal of Logistics Management, 16 (1), pp. 120-141; Juttner, U., Peck, H., Christopher, M., Supply Chain Risk Management: Outlining an Agenda for Future Research (2003) International Journal of Logistics, 6 (4), pp. 197-210. , DOI 10.1080/13675560310001627016; Khan, O., Burnes, B., Risk and supply chain management: Creating a research agenda (2007) The International Journal of Logistics Management, 18 (2), pp. 197-216; Klibi, W., Martel, A., Guitouni, A., The design of robust value-creating supply chain networks: A critical review (2009) European Journal of Operational Research, 203 (2), pp. 283-293; Koh, S.C.L., Gunasekaran, A., Performance prediction using supply chain uncertainty modelling (2006) International Journal of Services and Operations Management, 2 (3), pp. 279-293. , DOI 10.1504/IJSOM.2006.009861; Kull, T.J., Talluri, S., A supply risk reduction model using integrated multi criteria decision making (2008) IEEE Transactions on Engineering Management, 55 (3), pp. 409-419; Lavastre, O., Gunasekaran, A., Spalanzani, A., Supply chain risk management in French companies (2011) Decision Support Systems, , print, doi: 10.1016/j.dss.2011.11.017; Levary, R.R., Using the analytic hierarchy process to rank foreign suppliers based on supply risks (2008) Computers and Industrial Engineering, 55 (2), pp. 535-542; Lin, Z.-K., Wang, J.-J., Qin, Y.-Y., A decision model for selecting an offshore outsourcing location: Using a multi criteria method (2007) Proceedings of IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2007, pp. 1-5. , Philadelphia, PA, USA; Longo, F., Oren, T., (2008) Supply Chain Vulnerability and Resilience: A State of the Art Overview, , http://www.site.uottawa.ca/~oren/pubs-pres/2008/pub-0806-supplyChain- Italy.pdf, (accessed on 20 August 2009); MacHaris, C., Springael, J., Brucker, K.D., Verbeke, A., PROMETHEE and AHP: The design of operational synergies in multi criteria analysis. Strengthening PROMETHEE with the ideas of AHP (2004) European Journal of Operational Research, 153 (2), pp. 307-317; Meena, P.L., Sarmah, S.P., Sarkar, A., Sourcing decisions under risks of catastrophic event disruptions (2011) Transportation Research Part e, 47 (6), pp. 1058-1074; Min, H., Kim, L., Measuring the risk of sourcing from low cost countries: A macro perspective (2011) International Journal of Services and Operations Management, 9 (3), pp. 308-329; Norrman, A., Jansson, U., Ericsson's proactive supply chain risk management approach after a serious sub-supplier accident (2004) International Journal of Physical Distribution and Logistics Management, 34 (5), pp. 434-456; Paulsson, U., (2007) On managing disruption risks in the supply chain - The DRISC model, , http://www.biblioteket.ehl.lu.se/faculty/publ/00002155.pdf, (accessed on 11 February 2010); Pavlou, S., Manthou, V., Identifying and evaluating unexpected events as sources of supply chain risk (2008) International Journal of Services and Operations Management, 4 (5), pp. 604-617; Pfohl, H.-C., Kohler, H., Thomas, D., State of the art in supply chain risk management research: Empirical and conceptual findings and a roadmap for the implementation in practice (2010) Logistics Research, 2 (1), pp. 33-44; Pinto, R., (2007) A general note about supply chain risk management, , http://www.riskcentral.org, (accessed on 1 May 2009); Radjou, N., (2002) Adapting to Supply Network Change, , http://www.fipa.org/docs/input/f-in-00060/f-in-00060.pdf, (accessed 5 on March 2010); Rao, S., Goldsby, T.J., Supply chain risks: A review and typology (2009) The International Journal of Logistics Management, 29 (1), pp. 97-123; Ravindran, R.A., Bilsel, U.R., Wadhwa, V., Yang, T., Risk adjusted multi criteria supplier selection models with applications (2010) International Journal of Production Research, 48 (2), pp. 405-424; Rice, J.B., Caniato, F., (2003) Building A Secure and Resilient Supply Network, , http://www.web.mit.edu/scresponse/repository/Rice_SCResp_Article_SCMR.pdf, (accessed on 1 May 2010); Ritchie, B., Brindley, C., Supply chain risk management and performance a guiding frame work for future development (2007) International Journal of Operations and Production Management, 27 (3), pp. 303-322; Satty, T.L., How to make a decision: The analytic hierarchy process (1990) European Journal of Operational Research, 48 (1), pp. 9-26; Schoenherr, T., Raotummala, V.M., Harrison, T.P., Assessing supply chain risks with the analytic hierarchy process: Providing decision support for the off shoring decision by a US manufacturing company (2008) Journal of Purchasing and Supply Management, 14 (2), pp. 100-111; Sheffi, Y., Supply chain management under the threat of international terrorism (2001) The International Journal of Logistics Management, 12 (2), pp. 1-11; Sheffi, Y., Building a resilient organization (2007) The Bridge, 37 (1), pp. 30-36; Shi, D., A review of enterprise supply chain risk management (2004) Journal of Systems Science and Systems Engineering, 13 (2), pp. 219-244; Tang, C.S., Robust strategies for mitigating supply chain disruptions (2006) International Journal of Logistics: Research and Applications, 9 (1), pp. 33-45; Tang, C.S., Perspectives in supply chain risk management (2006) International Journal of Production Economics, 103 (2), pp. 451-488. , DOI 10.1016/j.ijpe.2005.12.006, PII S0925527306000405; Tang, O., Musa, S.N., Identifying risk issues and research advancements in supply chain risk management (2011) International Journal of Production Economics, 133 (1), pp. 25-34; Thun, J.H., Hoenig, D., An empirical analysis of supply chain risk management in the German automotive industry (2011) International Journal of Production Economics, 131 (1), pp. 242-249; Tuncel, G., Alpan, G., Risk assessment and management for supply chain networks: A case study (2010) Computers in Industry, 61 (3), pp. 250-259; Vaidya, O.S., Kumar, S., Analytic hierarchy process: An overview of applications (2006) European Journal of Operational Research, 169 (1), pp. 1-29. , DOI 10.1016/j.ejor.2004.04.028, PII S0377221704003054; Vanany, I., Zailani, S., Pujawan, N., Supply chain risk management: Literature review and future research (2009) International Journal of Information Systems and Supply Chain Management, 2 (1), pp. 16-33; Van Landeghem, H., Vanmaele, H., Robust planning: A new paradigm for demand chain planning (2002) Journal of Operations Management, 20 (6), pp. 769-783. , DOI 10.1016/S0272-6963(02)00039-6, PII S0272696302000396; Wagner, S.M., Bode, C., An empirical investigation into supply chain vulnerability (2006) Journal of Purchasing and Supply Management, 12 (6), pp. 301-312; Wang, J.-J., Yang, D.-L., Using a hybrid multi-criteria decision aid method for information systems outsourcing (2007) Computers and Operations Research, 34 (12), pp. 3691-3700. , DOI 10.1016/j.cor.2006.01.017, PII S030505480600027X; Wang, J.-J., Gu, R., Diao, X.-J., Using a hybrid multi-criteria decision aid method for outsourcing vendor selection (2008) Proceedings of 4th IEEE International Conference on Wireless Communications, pp. 1-4. , Networking and Mobile Computing, WiCOM '08, Dalian; Wu, T., Blackhurst, J., Chidambaram, V., A model for inbound supply risk analysis (2006) Computers in Industry, 57 (4), pp. 350-365; Yuen, K.K.F., Analytic hierarchy prioritization process in the AHP application development: A prioritization operator selection approach (2010) Applied Soft Computing, 10 (4), pp. 975-989; Zeng, A.A., Berger, P.D., Gerstenfeld, A., (2005) Managing the Supply Side-risks in Supply Chains: Taxonomies, Processes and Examples of Decision Making Modeling, , http://www.springerlink.com/content/l17616801g138415/fulltext.pdf, (accessed on 9 April 2010); Zhaoxu, S., Min, H., Multi-criteria decision making based on PROMETHEE method (2010) Proceedings of IEEE International Conference on Computing, Control and Industrial Engineering, pp. 416-418. , Wuhan; Zsidisin, G.A., Ellram, L.M., Carter, J.R., Cavinator, J.L., An analysis of supply risk assessment techniques (2004) International Journal of Physical Distribution and Logistics Management, 34 (5), pp. 397-413},
correspondence_address1={Venkatesan, S.P.; Department of Production Engineering, , Tiruchirappalli - 620015, India; email: prasanna@nitt.edu},
publisher={Inderscience Publishers},
issn={17442370},
language={English},
abbrev_source_title={Int. J. Serv. Oper. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Fernandez201243,
author={Fernandez, N.S. and Scavarda, L.F. and Leiras, A. and Hamacher, S.},
title={Design of supplier performance measurement systems: Experiences from an action research [Diseño de sistemas de medición de desempeño de proveedores: Experiencias de un caso de estudio]},
journal={Producao},
year={2012},
volume={22},
number={1},
pages={43-57},
doi={10.1590/S0103-65132011005000043},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861791074&doi=10.1590%2fS0103-65132011005000043&partnerID=40&md5=af1d08632e499cd5e4d7078961fc9641},
affiliation={PUC-Rio, Brazil},
abstract={The evaluation of suppliers' performance is important for organizations because of the imperative role they play in the supply chains and the importance that purchasing functions have due to the outsourcing of activities which are not part of the company's know-how. There is a need to work on the identification of practical problems concerning the design and implementation of supplier performance measurement systems (SPMS). This paper presents the most important factors in designing and implementing such systems through an action research in a multinational media enterprise. In addition to the details of the method and results of its application, the paper presents an analysis of processes and attributes that define the design of the proposed system. The authors aim to contribute to the academic literature in providing practical issues regarding SPMS.},
author_keywords={Performance measurement systems;  Supplier performance evaluation;  Suppliers;  Supply chain},
references={Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , http://dx.doi.org/10.1016/S0925-5273(98)00079-6; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292. , http://dx.doi.org/10.1108/01443579910249714; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Production Management, 20, pp. 692-704. , http://dx.doi.org/10.1108/01443570010321676; Bourne, M., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , http://dx.doi.org/10.1108/01443570010330739; Cebi, F., Bayraktar, D., An integrated approach for supplier selection (2003) Logistics Information Management, 16 (6), pp. 395-400. , http://dx.doi.org/10.1108/09576050310503376; Collins, R., Bechler, K., Pires, S., Outsourcing in the automotive industry: From JIT to Modular Consortia (1997) European Management Journal, 15 (5), pp. 498-508. , http://dx.doi.org/10.1016/S0263-2373(97)00030-3; Coyle, J.J., Bardi, E.J., Langley, C.J.R.S., (2002) The Management of Business Logistics - A Supply Chain Perspective, , Canada: Thomson Learning; Do Lago Lago Attadia, L.C., Martins, R.A., Medição de desempenho como base para evolução da melhoria contínua (2003) Revista Produção, 13 (2), pp. 33-41; Fortuin, L., Performance indicators-Why, where and how? (1988) European Journal of Operational Research, 34 (1), pp. 1-9. , http://dx.doi.org/10.1016/0377-2217(88)90449-3; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47. , http://dx.doi.org/10.1111/j.1468-2370.2005.00105.x; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80. , http://dx.doi.org/10.1108/01443579610125787; Globerson, S., Issues in developing a performance criteria system for an organisation (1985) International Journal of Production Research, 23 (4), pp. 639-646. , http://dx.doi.org/10.1080/00207548508904734; Gunasekaran, A., Patel, C., Mcgaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347. , http://dx.doi.org/10.1016/j.ijpe.2003.08.003; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87. , http://dx.doi.org/10.1108/01443570110358468; Harrison, T.P., Lee, H.L., Neale, J.J., The Practice of Supply Chain Management: Where Theory and Application Converge (2003) (International Series in Operations Research & Management Science); Hoek, R.I., van. Measuring the unmeasurable: measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement systems (2000) International Journal of Operations and Production Management, 21 (8), pp. 1096-1115. , http://dx.doi.org/10.1108/EUM0000000005587; Jusoh, R., Ibrahim, D.N., Zainuddin, Y., The performance consequence of multiple performance measures usage: Evidence from the Malaysian manufacturers (2008) International Journal of Productivity and Performance Management, 57 (2), pp. 119-136. , http://dx.doi.org/10.1108/17410400810847393; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard - Translating Strategy into Action, , Boston: Harvard Business Press; Kocabasoglu, C., Suresh, N.C., Strategic Sourcing: An Empirical investigation of the concept and its practices in US manufacturing firms (2006) The Journal of Supply Chain Management, 42 (2), pp. 4-15. , http://dx.doi.org/10.1111/j.1745-493X.2006.00008.x; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19. , http://dx.doi.org/10.1108/09574090110806190; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286. , http://dx.doi.org/10.1016/S0377-2217(02)00918-9; Neely, A., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431. , http://dx.doi.org/10.1016/S0925-5273(96)00080-1; Neely, A., Designing performance measures: a structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152. , http://dx.doi.org/10.1108/01443579710177888; Neely, A.D., (2002) Getting the measure of your Business, , Cambridge: Cambridge University Press; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116. , http://dx.doi.org/10.1108/01443579510083622; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , http://dx.doi.org/10.1108/17410400610653219; Spekman, R.E., Kamauff, J.W., Salmond, D.J., At last purchasing is becoming strategic (1994) Long Range Planning, 27 (2), pp. 76-84. , http://dx.doi.org/10.1016/0024-6301(94)90211-9; Tate, W.L., van der Valk, W., Managing the performance of outsourced customer contact centers (2008) Journal of Purchasing and Supply Management, 14 (3), pp. 160-169. , http://dx.doi.org/10.1016/j.pursup.2008.04.002; Tracey, M., Tan, C.L., Empirical analysis of supplier selection and involvement, customer satisfaction and firm performance (2001) Supply Chain Management: An International Journal, 6 (4), pp. 174-188. , http://dx.doi.org/10.1108/EUM0000000005709; Wagner, S.M., Johnson, J.L., Configuring and managing strategic supplier portfolios (2004) Industrial Marketing Management, 33 (8), pp. 717-730. , http://dx.doi.org/10.1016/j.indmarman.2004.01.005; Wisner, J.D., Fawcett, S.E., Link firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, n. Terceiro Trimestre, pp. 5-11; Wisner, J.D., Stanley, L.L., (2007) Process Management: Creating Value Along the Supply Chain; Texts & Cases, , Thomson, South-Western},
correspondence_address1={Fernandez, N.S.; PUC-Rio, Rio de Janeiro, RJ, Brazil; email: nicolesf@aluno.puc-rio.br},
publisher={Associacao Brasileira de Engenharia de Producao},
issn={01036513},
language={Spanish},
abbrev_source_title={Producao},
document_type={Article},
source={Scopus},
}

@ARTICLE{Franceschini201263,
author={Franceschini, F. and Turina, E.},
title={Proposal for a Performance Dashboard for the Monitoring of Water and Sewage Service Companies (WaSCs)},
journal={Water Resources Management},
year={2012},
volume={26},
number={1},
pages={63-80},
doi={10.1007/s11269-011-9901-3},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860391264&doi=10.1007%2fs11269-011-9901-3&partnerID=40&md5=48e6c2f59bc5ebe0f8868623e3b3a74b},
affiliation={Politecnico di Torino, DISPEA, Corso Duca degli Abruzzi 24, 10129 Torino, Italy},
abstract={The water and sewage industry provides an essential service to the community, but it is characterized by natural monopoly tendencies of service suppliers. In this framework, it is very important to assist regulators with a small set of critical indicators (performance dashboard) for the evaluation and monitoring of the service provided by Water and Sewage Companies (WaSCs). The paper originates from the analysis of situation of Piemonte (Italy), where each regional and local body adopts a proprietary Performance Measurement System (PMS). In order to improve the coordination of information flow and to support the definition of common service standards, a methodology to merge existing PMSs and define a unique shared reference system is proposed. The Kaplan and Norton's Balanced Scorecard (BSC) is adopted as the reference model of this approach. BSC is widely recognized to be an exhaustive and balanced framework in describing the performances of an organization and ensures that all the operational aspects of WaSCs are adequately monitored. The output of the proposed procedure is a general performance dashboard for the monitoring of WaSCs. The dashboard is shown and some remarks about indicators properties are developed. In particular, this analysis highlights some common pitfalls originated by a 'rushed' aggregation of several performance indicators. Description is supported by several examples. © 2011 Springer Science+Business Media B.V.},
author_keywords={Balanced scorecard;  Key performance indicators;  Performance dashboard;  Performance measurement;  Performance Measurement Systems;  Water and sewage service},
keywords={Balanced scorecards;  Key performance indicators;  Performance dashboard;  Performance measurement system;  Performance measurements, Benchmarking;  Industry;  Sewage;  Supply chain management, Monitoring, methodology;  performance assessment;  sewage disposal;  water management, Italy;  Piedmont [Italy]},
references={Akao, Y., (1990) Quality Function Deployment, , Cambridge: Productivity; Alegre, H., Baptista, J.M., Cabrera Jr., E., Cubillo, F., Duarte, P., Hirner, W., Merkel, W., Parena, R., (2006) Performance Indicators for Water Supply Services, , 2nd edn., London: IWA Publishing; Anderberg, M.R., (1973) Cluster Analysis for Applications, , New York: Academic; Biellese, Vercellese, Casalese (2011) Convenzione regolante i rapporti tra l'Autorità d'Ambito, il gestore del servizio idrico integrato ed il coordinatore Atodueacque s. c. a. r. l., , http://www.ato2piemonte.it/documenti/DOCUMENTI/Convenzione_12-06-07.pdf, Autorità d'Ambito n. 2, Available at, Accessed 10 June; (2009) Veolia Water uses Balanced Scorecard to Drive North American Strategy, , http://www.balancedscorecard.org/LinkClick.aspx?fileticket=8Zup08adIQg%3d&tabid=36, Balanced Scorecard Institute, Strategy Management Group, Available at, Accessed 1 March 2011; Barraqué, B., (1995) Les Politiques De L'eau En Europe, , Paris: La découverte; Barrett, G., Wallace, M., An institutional economics perspective: the impact of water provider privatisation on water conservation in England and Australia (2011) Water Resources Management, , doi: 10. 1007/s11269-010-9747-0; Bebb, P., (2002) Draw of the Scorecard, , http://www.performance-measurement.net/news-detail.asp?nID=33, Available at, Accessed 1 March 2011; Bianchi, C., Montemaggiore, G.B., Enhancing strategy design and planning in public utilities through "dynamic" balanced scorecards: insights from a project in a city water company (2008) System Dynamics Review, 24 (2), pp. 175-213; Cabrera Jr., E., (2001) Diseño de un sistema para la evaluación de la gestión de abastecimientos urbanos, , http://www.ita.upv.es/formacion/fichatrabajo-en.php?id=125, PhD Thesis, Universidad Politecnica de Valencia. Available at, Accessed 10 June 2011; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; Carta del servizio idrico Integrato, , http://www.ato2piemonte.it/documenti/DOCUMENTI/Carta_servizio_2006.pdf, Available at, Accessed 10 June 2011; (2004) Continual Improvement of Utility Management: A Framework of Integration, , http://www.charlestonwater.com/documents/continual_improvement_in_untility_management.pdf, EPA (United States Environmental Protection Agency), WEF (Water Environment Federation), Available at, Accessed 1 March 2011; (2009) Relatório Anual do Sector de Águas e Resíduos em Portugal (RASARP), , http://www.ersar.pt/website/ViewContent.aspx?SubFolderPath=&Section=MenuPrincipal&FinalPath=RASARP&FolderPath=%5cRoot%5cContents%5cSitio%5cRASARP, ERSAR (Entidade Reguladora dos Serviços do Águas e Resíduos), Available at, Accessed 10 June 2011; Franceschini, F., (2002) Advanced Quality Function Deployment, , Boca Raton: St. Lucie Press; Franceschini, F., Galetto, M., Maisano, D., (2007) Management by Measurement: Designing Key Indicators and Performance Measurements, , Berlin: Springer; Franceschini, F., Galetto, M., Turina, E., Service Quality monitoring by performance indicators: a proposal for a structured methodology (2009) International Journal of Services and Operations Management, 5 (2), pp. 251-273; Franceschini, F., Galetto, M., Turina, E., Water and sewage service quality: a proposal of a new multi-questionnaire monitoring tool (2010) Water Resources Management, 24 (12), pp. 3033-3050; Franco, M., Bourne, M., (2003) Business performance measurement systems: A systematic review, , POMS/EurOMA Conference, Lake Como, June 2003; Gialis, S.E., Loukas, A., Laspidou, C.S., Theoretical perspectives and empirical facts on water sector privatization: the Greek case against European and global trends (2011) Water Resources Management, , doi: 10. 1007/s11269-010-9769-7; Activities relating to drinking water and wastewater services - Guidelines for the assessment and for the improvement of the service to users, , ISO 2007: 24510; Activities relating to drinking water and wastewater services - Guidelines for the management of wastewater utilities and for the assessment of wastewater services, , ISO 2007: 24511; Activities relating to drinking water and wastewater services - Guidelines for the management of drinking water utilities and for the assessment of drinking water services, , ISO 2007: 24512; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, (70), pp. 71-79; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Cambridge: Harvard Business School Press; Kaplan, R.S., Norton, D.P., Marked impact (the balanced scorecard) (2001) People Management, 2 (21), pp. 52-55; Kaplan, R.S., Norton, D.P., (2001) The Strategy Focused Organisation: How Balanced Scorecard Companies Thrive in the New Business Environment, , Boston: Harvard Business School Press; Lepage, H., (1997) Le choix régie publique contre gestion déléguée, , http://ami.du.laissez-faire.eu/_lepage2.php5, Available at, Accessed 26 July 2011; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: a case study (2004) European Journal of Operational Research, 156, pp. 267-286; Matos, R., Cardoso, A., Ashley, R., Duarte, P., Molinari, A., Schulz, A., (2003) Performance Indicators for Wastewater Services, , London: IWA Publishing; Neto, F., Water privatization and regulation in England and France: a tale of two models (1998) Natural Resources Forum, 22 (2), pp. 107-117; (2008) Level of service for the water industry in England and Wales. 2006-2007 Report, , http://www.ofwat.gov.uk/regulating/reporting/rpt_los_0607.pdf, Ofwat, Available at, Accessed 1 March 2011; (2009) Creation of an observatory on the performance of public water and water-treatment services, , http://www.onema.fr/IMG/EV/EV/plus/Fiche-Observatoire.pdf, ONEMA (Office National de l'Eau et des Milieux Aquatiques), Available at, Accessed 10 June 2011; (2001) The Performance-Based Management Handbook, vol 1, Establishing and Maintaining A Performance-Based Management Program, , Performance-Based Management Special Interest Group (PBM SIG), Oak Ridge Institute for Science and Education, (ORISE)-U. S. Department of Energy; (2007) Piano di Tutela delle Acque (PTA). Sezione C-Norme di Piano, , http://www.regione.piemonte.it/acqua/pianoditutela/norme/dwd/norme.pdf, Regione Piemonte, Available at, Accessed 10 June 2011; (1997) Delimitazione degli ambiti territoriali ottimali per l'organizzazione del servizio idrico integrato e disciplina delle forme e dei modi di cooperazione tra gli Enti Locali ai sensi della Legge 5 gennaio 1994, n. 36 e successive modifiche e integrazioni. Indirizzo e coordinamento dei soggetti istituzionali in materia di risorse idriche, , http://arianna.consiglioregionale.piemonte.it/ariaint/TESTO?LAYOUT=PRESENTAZIONE&TIPODOC=LEGGI&LEGGE=013&LEGGEANNO=1997, Regione Piemonte, legge 13/1997, Available at, Accessed 1 March 2011; (1994) Disposizioni in materia di risorse idriche, , http://www.regione.piemonte.it/bdgiur/download?nomefile=05_01_1994_36.htm, Repubblica Italiana, legge 36/1994, Available at, Accessed 1 March 2011; (2001) Attuazione della direttiva 98/83/CE relativa alla qualità delle acque destinate al consumo umano, , http://www.camera.it/parlam/leggi/deleghe/testi/01031dl.htm, Repubblica Italiana, Decreto Legislativo n. 31/2001, Available at, Accessed 1 March 2011; (2006) Norme in materia ambientale, , http://www.camera.it/parlam/leggi/deleghe/06152dl.htm, Repubblica Italiana, Decreto Legislativo 3 aprile, n. 152, Available at, Accessed 10 June 2011; Rocha Fernandes, B.H., Mills, J.F., Fleury, M.T.L., Resources that drive performance: an empirical investigation (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 340-354; Smith, A., Connolly, S., (2008) Implementing lean process management in Severn Trent Water, , http://www.iwo.org.uk/images//eventImages/AndySmithSeanConnolly65.ppt, Institute of Water. Available at, Accessed 1 March 2011; Tebbutt, R.J., Cochin, R.J., Lester, J.N., Balanced scorecard investment appraisal in the water industry of England and Wales (2003) Environmental Technology, 24 (7), pp. 845-854; Annual Business Plan Fiscal Year 2005, , http://www.valleywater.org/media/pdf/FY05%20WUE%20ABP%20Final.pdf, Water Utility Enterprise, Available at, Accessed 1 March 2011; (2008) Sustainability indicators 2008, , http://www.wessexwater.co.uk/WorkArea/showcontent.aspx?id=2684, Wessex Water, Available at, Accessed 1 March 2011},
correspondence_address1={Franceschini, F.; Politecnico di Torino, DISPEA, Corso Duca degli Abruzzi 24, 10129 Torino, Italy; email: fiorenzo.franceschini@polito.it},
issn={09204741},
language={English},
abbrev_source_title={Water Resour. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bai201278,
author={Bai, C. and Sarkis, J. and Wei, X. and Koh, L.},
title={Evaluating ecological sustainable performance measures for supply chain management},
journal={Supply Chain Management},
year={2012},
volume={17},
number={1},
pages={78-92},
doi={10.1108/13598541211212221},
note={cited By 171},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856766970&doi=10.1108%2f13598541211212221&partnerID=40&md5=01f1b0b0e888986aa17208b4928a4d44},
affiliation={School of Management Science and Engineering, Dongbei University of Finance and Economics, Dalian, China; Graduate School of Management, Clark University, Worcester, MA, United States; School of Management, Dalian University of Technology, Dalian, China; School of Management, University of Sheffield, Sheffield, United Kingdom},
abstract={Purpose: The purpose of this paper is to introduce a methodology to help evaluate, select, and monitor sustainable supply chain performance measurement that can be integrated into a performance management system (PMS). Design/methodology/approach: Grey-based neighborhood rough set theory is used to help arrive at a core set of important business and environmental performance measures for sustainable supply chains. The supply chain operations reference (SCOR) model is used to develop both business and environmental measures for supply chain sourcing. Findings: A case illustration shows the applicability of the methodology. A sensitivity analysis shows that variations in outcome considerations may greatly influence the set of key performance measures for a sustainable supply chain PMS. Research limitations/implications: The methodology and presentation is conceptual, yet the tool can provide very useful interpretations for both researchers and practitioners. Practical implications: The tool can be valuable for companies that are trying to identify key environmental and business performance measures for their supply chains. It helps save resources by not requiring the management of a burdensome and complex set of performance measures. Originality/value: This is one of the few approaches that helps to clearly identify and narrow the set of performance measures for sustainable supply chains. It attempts to do so with minimal information loss. It is also the first time that grey techniques have been integrated with neighborhood rough set methodology. © Emerald Group Publishing Limited.},
author_keywords={Ecological sustainable performance measures;  Supply chain management;  Supply chain operations reference model; Neighborhood rough set;  Sustainable development},
references={An, S., Yan, J., Yu, X., Grey-system studies on agricultural ecoengineering in the Taihu Lake area, Jiangsu, China (1996) Ecological Engineering, 7 (3), pp. 235-245; Bai, C., Sarkis, J., Green supplier development: Analytical evaluation using rough set theory (2010) Journal of Cleaner Production, 18 (12), pp. 1200-1210; Bai, C., Sarkis, J., Integrating sustainability into supplier selection with grey system and rough set methodologies (2010) International Journal of Production Economics, 124 (1), pp. 252-264; Beynon, M., Curry, B., Morgan, P., Classification and rule induction using rough set theory (2000) Expert Systems, 17 (3), pp. 136-148; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), pp. 422-428; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chang, T.C., Lin, S.J., Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan (1999) Journal of Environmental Management, 56 (4), pp. 247-257; Chen, C.-N., Ting, S.-C., A study using the grey system theory to evaluate the importance of various service quality factors (2002) International Journal of Quality & Reliability Management, 19 (7), pp. 838-861; Deng, J.-L., Introduction to grey system theory (1989) Journal of Grey Systems, 1 (1), pp. 1-24; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45, pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Hsu, L.-C., Applying the Grey prediction model to the global integrated circuit industry (2003) Technological Forecasting and Social Change, 70 (6), pp. 563-574; Hu, Q., Numerical attribute reduction based on neighborhood granulation and rough approximation (2008) Journal of Software, 19 (3), pp. 640-649; Hu, Q., Yu, D., Xie, Z., Neighborhood classifiers (2008) Expert Systems with Applications, 34 (2), pp. 866-876; Hu, Q., Yu, D., Liu, J., Wu, C., Neighborhood rough set based heterogeneous feature subset selection (2008) Information Sciences, 178 (18), pp. 3577-3594; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston, MA; Keating, B., Quazi, A., Kriz, A., Coltman, T., In pursuit of a sustainable supply chain: Insights from Westpac Banking Corporation (2008) Supply Chain Management: An International Journal, 13 (3), pp. 175-179; Li, G.D., Yamaguchi, D., Nagai, M., A grey-based rough decision-making approach to supplier selection (2008) The International Journal of Advanced Manufacturing Technology, 36 (9-10), pp. 1032-1040; Li, P., Tan, T.C., Lee, J.Y., Grey relational analysis of amine inhibition of mild steel corrosion in acids (1997) Corrosion, 53 (3), pp. 186-194; Lin, S.J., Lu, I.J., Lewis, C., Grey relation performance correlations among economics, energy use and carbon dioxide emission in Taiwan (2007) Energy Policy, 35 (3), pp. 1948-1955; Liu, S., Lin, Y., (2006) Grey Information: Theory and Practical Applications, , Physica-Verlag Boulder, Heidelberg; Liu, S., Forrest, J., Vallee, R., Emergence and development of grey systems theory (2009) Kybernetes, 38 (7-8), pp. 1246-1256; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental performance indicators for integrated supply chains: The case of Xerox Ltd (1998) Supply Chain Management: An International Journal, 3 (3), pp. 149-156; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-13; Ong, C.-S., Huang, J.-J., Tzeng, G.-H., Building credit scoring models using genetic programming (2005) Expert Systems with Applications, 29 (1), pp. 41-47; Parmar, D., Wu, T., Callarman, T., Fowler, J., Wolfe, P., A clustering algorithm for supplier base management (2010) International Journal of Production Research, 14 (13), pp. 3803-3821; Pawlak, Z., Rough sets (1982) International Journal of Computer and Information Sciences, 11 (5), pp. 341-356; Sadoyan, H., Zakarian, A., Mohanty, P., Data mining algorithm for manufacturing process control (2006) The International Journal of Advanced Manufacturing Technology, 28 (3), pp. 342-350; Sarkis, J., Talluri, S., A synergistic framework for evaluating business process improvements (2002) International Journal of Flexible Manufacturing Systems, 14 (1), pp. 53-71; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Shyng, J.-Y., Wang, F.-K., Tzeng, G.-H., Wu, K.-S., Rough set theory in analyzing the attributes of combination values for the insurance market (2007) Expert Systems with Applications, 32 (1), pp. 56-64; Song, S.Y., The application of grey system theory to earthquake prediction in Jiangsu area (1992) Journal of Grey Systems, 4 (4), pp. 359-367; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40 (16), pp. 4257-4269; Tseng, T.L., Huang, C.C., Jiang, F., Ho, J.C., Applying a hybrid data-mining approach to prediction problems: A case of preferred suppliers prediction (2006) International Journal of Production Research, 44 (14), pp. 2935-2954; Vasileiou, K., Morris, J., The sustainability of the supply chain for fresh potatoes in Britain (2006) Supply Chain Management: An International Journal, 11 (4), pp. 317-327; Wickramatillake, C.D., Koh, S.C.L., Gunasekaran, A., Arunachalam, S., Measuring performance within the supply chain of a large scale project (2007) Supply Chain Management: An International Journal, 12 (1), pp. 52-59; Wilson, D.R., Martinez, T.R., Improved heterogeneous distance functions (1997) Journal of Artificial Intelligence Research, 6 (1), pp. 1-34; Yang, C.-C., Chen, B.-S., Supplier selection using combined analytical hierarchy process and grey relational analysis (2006) Journal of Manufacturing Technology Management, 17 (7), pp. 926-941; Zhu, Q., Dou, Y.J., Sarkis, J., A portfolio-based analysis for green supplier management using the analytical network process (2010) Supply Chain Management: An International Journal, 15 (4), pp. 306-319; Ziarko, W., Variable precision rough sets model (1993) Journal of Computer and System Sciences, 46 (1), pp. 39-59},
correspondence_address1={Bai, C.; School of Management Science and Engineering, , Dalian, China; email: Chunguang.bai@gmail.com},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Olugu2012375,
author={Olugu, E.U. and Wong, K.Y.},
title={An expert fuzzy rule-based system for closed-loop supply chain performance assessment in the automotive industry},
journal={Expert Systems with Applications},
year={2012},
volume={39},
number={1},
pages={375-384},
doi={10.1016/j.eswa.2011.07.026},
note={cited By 129},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-81855169991&doi=10.1016%2fj.eswa.2011.07.026&partnerID=40&md5=6b077a9da198afc9de2aa48364935ce1},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia},
abstract={Closed-loop supply chain management has been identified as an efficient, effective and economical strategy towards environmental sustainable practices in manufacturing companies. Without a formidable closed-loop supply chain to complement green supply chain management, most of the goals will not be achieved. A performance evaluation system is crucial for achieving a successful closed-loop supply chain in the automotive industry. Hence, a suitable expert fuzzy rule-based system for evaluation was developed in this study using Visual Basic.Net. The fuzzy rules and arithmetic used were described. The resulting performance measurement system was evaluated using a case study company from the automotive industry. The study culminated with recommendations and proposal of directions for future studies. © 2011 Elsevier Ltd. All rights reserved.},
author_keywords={Closed-loop supply chain;  Fuzzy logic;  Green supply chain;  Performance measurement;  Rule-based system},
keywords={Closed-loop supply chain;  Closed-loop supply chain management;  Economical strategy;  Fuzzy rule-based systems;  Green supply chain;  Green supply chain management;  Manufacturing companies;  Performance evaluation system;  Performance measurement system;  Performance measurements;  Rule based system;  Sustainable practices, Automotive industry;  Fuzzy logic;  Fuzzy rules;  Sustainable development, Supply chain management},
references={Alex, R., Fuzzy point estimation and its application on fuzzy supply chain analysis (2007) Fuzzy Sets and Systems, 158 (14), pp. 1571-1587; Amaro, A.C.S., Barbosa-Póvoa, A.P.D.F., Optimal planning of closed loop supply chain: A discrete versus a continuous time formulation (2007) Computer Aided Chemical Engineering, 24, pp. 673-678; Amaro, A.C.S., Barbosa-Póvoa, A.P.F.D., The effect of uncertainty on the optimal closed-loop supply chain planning under different partnership structure (2009) Computer in Chemical Engineering, 33 (12), pp. 2144-2158; Amelia, L., Wahab, D.A., Che Haron, C.H., Muhamad, H., Azhari, C.H., Initiating automotive component reuse in Malaysia (2009) Journal of Cleaner Production, 17 (17), pp. 1572-1579; Autry, C.W., Formalization of reverse logistics programs: A strategy for managing liberalized returns (2005) Industrial Marketing Management, 34 (7), pp. 749-757; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Beamon, B.M., Environmental and sustainability ethics in supply chain management (2005) Science and Engineering Ethics, 11 (2), pp. 221-234; Beamon, B.M., Sustainability and the future of supply chain management (2008) Operations and Supply Chain Management, 1 (1), pp. 4-18; Chung, S.L., Wee, H.M., Yang, P.C., Optimal policy for a closed-loop supply chain inventory system with remanufacturing (2008) Mathematics and Computer Modeling, 48 (5-6), pp. 867-881; Cox, E., (1994) The Fuzzy Systems Handbook: A Practitioner's Guide to Building, Using and Maintaining Fuzzy Systems, , AP Professional Boston; Dweiri, F.T., Kablan, M.M., Using fuzzy decision making for the evaluation of project management internal efficiency (2006) Decision Support Systems, 42 (2), pp. 712-726; Dyckhoff, H., Souren, R., Keilen, J., The expansion of supply chains to close loop systems: A conceptual framework and the automotive industry's point of view (2004) Supply Chain Management and Reverse Logistics, pp. 13-34. , H. Dyckhoff, R. Lackes, J. Reese, Springer-Verlag Berlin; Directive 2000/53/EC of the European parliament and of the council of 18 September 2000 on end-of-life vehicles (2000) Official Journal of the European Communities, 269 L, pp. 34-42. , 21 OCTOBER; Efendigil, T., Onut, S., Kongar, E., A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness (2008) Computers & Industrial Engineering, 54 (2), pp. 269-287; Fleischmann, M., Dekker, R., Van Der Laan, E., Van Numen, J., Van Wassenhove, L., Ruwaard, J., Quantitative models for reverse logistics: A review (1997) European Journal of Operational Research, 103 (1), pp. 1-17; Fox, J., Towards a reconciliation of fuzzy logic and standard logic (1981) International Journal of Man-Machine Studies, 15 (2), pp. 213-220; French, M.L., Laforge, R.L., Closed-loop supply chains in process industries: An empirical study of producer re-use issues (2006) Journal of Operations Management, 24 (3), pp. 271-286; Ganesh, M., (2006) Introduction to Fuzzy Sets and Fuzzy Logic, , Prentice-Hall New Delhi; Guide Jr., V.D.R., Van Wassenhove, L.N., Closed-loop supply chains (2000) Working Paper 2000/75/TM INSEAD, Fontainebleau, France; Guide, Jr.V.D.R., Jayaraman, V., Linton, J.D., Building contingency planning for closed-loop supply chains with product recovery (2003) Journal of Operations Management, 21 (3), pp. 259-279; Guide, Jr.V.D.R., Van Wassenhove, L.N., Closed-loop supply chains (2002) Quantitative Approaches to Distribution Logistics and Supply Chain Management, pp. 47-60. , A. Klose, M.G. Speranza, L. van Wassenhove, Springer-Verlag Berlin; Hammond, D., Beullens, P., Closed-loop supply chain network equilibrium under legislation (2007) European Journal of Operational Research, 183 (2), pp. 895-908; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; (2009) Position on R1 and D10 Criteria in the EU Waste Framework Directive, , http://www.iswa.org, International Solid Waste Association Accessed November 2009; Johnson, P.F., Managing value in reverse logistics systems (1998) Transportation Research. Part E: Logistics and Transportation Review, 34 (3), pp. 217-227; Kannan, G., Sasikumar, P., Devika, K., A genetic algorithm approach for solving a closed loop supply chain model: A case of battery recycling (2010) Applied Mathematical Modelling, 34 (3), pp. 655-670; Kaufmann, M., Tobias, S., Schulin, R., Quality evaluation of restored soils with a fuzzy logic expert system (2009) Geoderma, 151 (3-4), pp. 290-302; Kosko, B., (1997) Fuzzy Engineering, , Prentice-Hall New Jersey; Kumar, S., Malegeant, P., Strategic alliance in a closed-loop supply chain, a case of manufacturer and eco-non-profit organization (2006) Technovation, 26 (10), pp. 1127-1135; Lettice, F., Wyatt, C., Evans, S., Buyer-supplier partnerships during product design and development in the global automotive sector: Who invests, in what and when? (2010) International Journal of Production Economics, 127 (2), pp. 309-319; Lin, C., Chiu, T.H., Tseng, Y.H., Agility evaluation using fuzzy logic (2006) International Journal of Production Economics, 101 (2), pp. 353-368; MacDonell, S.G., Software source code sizing using fuzzy logic modelling (2003) Information and Software Technology, 45 (7), pp. 389-404; Nunes, K.R.A., Mahler, C.F., Valle, R.A., Reverse logistics in the Brazilian construction industry (2009) Journal of Environmental Management, 90 (1), pp. 3717-3720; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., A comprehensive approach in assessing the performance of an automobile closed-loop supply chain (2010) Sustainability, 2 (4), pp. 871-889; Ordoobadi, S.M., Fuzzy logic and evaluation of advanced technologies (2008) Industrial Management and Data Systems, 108 (7), pp. 928-946; Östlin, J., Sundin, E., Björkman, M., Importance of closed-loop supply chain relationships for product remanufacturing (2008) International Journal of Production Economics, 115 (2), pp. 336-348; Pun, K.F., Determinants of environmentally responsible operations: A review (2006) International Journal of Quality and Reliability Management, 23 (3), pp. 279-297; Rao, P., Greening of the supply chain: A new initiative in South East Asia (2002) International Journal of Operations and Production Management, 22 (6), pp. 632-655; Schultmann, F., Zumkeller, M., Rentz, O., Modeling reverse logistic tasks within closed-loop supply chains: An example from the automotive industry (2006) European Journal of Operational Research, 171 (3), pp. 1033-1050; Shen, L., Song, X., Murai, Y., Manabu, I., Yamamoto, F., Velocity and size measurement of falling particles with fuzzy PTV (2001) Flow Measurement and Instrumentation, 12 (3), pp. 191-199; Solvang, W.D., Roman, E., Deng, Z., Solvang, B., A framework for holistic greening of value chains (2006) International Federation for Information Processing (IFIP), Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing and Management, 207, pp. 350-355; Spengler, T., Stolting, W., Ploog, M., Recovery planning in closed loop supply chain: An activity analysis based approach (2004) Supply Chain Management and Reverse Logistics, pp. 61-89. , H. Dyckhoff, R. Lackes, J. Reese, Springer-Verlag Berlin; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; Turnbull, P., Oliver, N., Wilkinson, B., Buyer-supplier relations in the UK automotive industry: Strategic implications of the Japanese manufacturing model (1992) Strategic Management Journal, 13 (2), pp. 159-168; Van Hoek, R.I., From reversed logistics to green supply chains (1999) Supply Chain Management: An International Journal, 4 (3), pp. 129-134; Vlachos, D., Georgiadis, P., Iakovou, E., A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains (2007) Computers & Operations Research, 34 (2), pp. 367-394; Von Ahsen, A., Environmental management in automotive supply chains: An empirical analysis (2006) Greening the Supply Chain, pp. 293-306. , J. Sarkis, Springer-Verlag London; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, H.P., Designing supply chains: Towards theory development (2006) International Journal of Production Economics, 100 (2), pp. 223-238; Walker, H., Di Sisto, L., McBain, D., Drivers and barriers to environmental supply chain management practices: Lessons from the public and private sectors (2008) Journal of Purchasing and Supply Management, 14 (1), pp. 69-85; Wells, P., Seitz, M., Business models and closed-loop supply chains: A typology (2006) Supply Chain Management: An International Journal, 10 (4), pp. 249-251; Xu, Z., Khoshgoftaarb, T.M., Allen, E.B., Application of fuzzy expert systems in assessing operational risk of software (2003) Information and Software Technology, 45 (7), pp. 373-388; Yang, G., Wang, Z., Li, X., The optimization of the closed-loop supply chain network (2009) Transportation Research. Part E: Logistics and Transportation Review, 45 (1), pp. 16-28; Zadeh, L.A., Fuzzy set as a basis for theory of possibility (1978) Fuzzy Sets and Systems, 1 (1), pp. 3-28; Zhu, Q., Sarkis, J., Lai, K., Green supply chain management implications for closing the loop (2008) Transportation Research. Part E: Logistics and Transportation Review, 44 (1), pp. 1-18},
correspondence_address1={Wong, K.Y.; Department of Manufacturing and Industrial Engineering, , 81310 UTM Skudai, Malaysia; email: wongky@fkm.utm.my},
issn={09574174},
coden={ESAPE},
language={English},
abbrev_source_title={Expert Sys Appl},
document_type={Article},
source={Scopus},
}

@ARTICLE{Garcia2012284,
author={Garcia, F.A. and Marchetta, M.G. and Camargo, M. and Morel, L. and Forradellas, R.Q.},
title={A framework for measuring logistics performance in the wine industry},
journal={International Journal of Production Economics},
year={2012},
volume={135},
number={1},
pages={284-298},
doi={10.1016/j.ijpe.2011.08.003},
note={cited By 79},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80055061021&doi=10.1016%2fj.ijpe.2011.08.003&partnerID=40&md5=3df9479e1c0cac507897171db85c9c32},
affiliation={School of Engineering, National University of Cuyo, Centro Universitario, CC405 (M5500AAT) Mendoza, Argentina; Research Team in Innovative Processes (EA N3767 ERPI), ENSGSI, Institut National Polytechnique de Lorraine, 8 rue Bastien Lepage, 54010 Nancy Cedex, France},
abstract={Nowadays, in order for wine companies to reach a world-class standard, it is necessary to implement the industry best practices and continuously adapt their logistics processes. Through benchmarking, these enterprises can find opportunities for improvement. So far, little research in benchmarking and performance measurement has been developed for the wine industry. In this paper a logistics benchmarking framework for the wine industry is proposed. A benchmarking study considering several wineries from Mendoza (Argentina) is presented as a case study, in order to demonstrate the validity of the developed framework. © 2010 Elsevier B.V. All rights reserved.},
author_keywords={Logistics benchmarking;  Performance measurement systems;  Wine industry;  Wine supply chain},
keywords={Argentina;  Little research;  Logistics process;  Performance measurement system;  Performance measurements;  Wine industry, Benchmarking;  Industry;  Supply chains, Wine},
references={Adamo, C., (2004) A Global Perspective of the Wine Supply Chain - The Case of Argentinean Wineries and the U.S. Market, , M.Sc. Thesis. Sloan School of Management, Massachusetts Institute of Technology; Alturria, L., Solsona, J.E., Antoniolli, E.R., Winter, P., Ceresa, A.M., Wine making: Defects in the process that originate costs of nonquality (2008) Rev. FCA UNCuyo, (1), pp. 1-16; Andersen, B., Fagerhaug, T., Randmñl, S., Schuldmaier, J., Prenninger, J., Benchmarking supply chain management: Finding best practices (1999) Journal of Business & Industrial Marketing, 14 (5-6), pp. 378-389; Awasthi, A., Chauhan, S.S., Goyal, S.K., A fuzzy multicriteria approach for evaluating environmental performance of suppliers (2010) International Journal of Production Economics, 126, pp. 370-378; Bailey, R., Measuring for success in the wine industry (2003) The Australian & New Zealand Wine Industry Journal, 18, p. 3; Banks, G., Overton, J., Old world, new world, third world: Reconceptualising the worlds of wine (2010) Journal of Wine Research, 21 (1), pp. 57-75; Bayraktar, E., Demirbag, M., Lenny Koh, S.C., Tatoglu, E., Zaim, H., A causal analysis of the impact of information systems and supply chain management practices on operational performance: Evidence from manufacturing SMEs in Turkey (2009) International Journal of Production Economics, 122, pp. 133-149; Beitz, A., Wieczorek, I., Applying benchmarking to learn from best practices (2000) Proceedings of the Second International Conference on Product Focused Software Process Improvement, 1840, p. 5972. , Lecture Notes in Computer Science; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Bichou, K., Review of port performance approaches and a supply chain framework to port performance benchmarking (2007) Researching Transportation Economics, 17, pp. 567-598; (2006) Transport Company Benchmarking Study, Joint Initiative Of: BizSys Partner Reference Group, Transport Distribution and Logistics Industry Round Table, , BizSys The Victorian Governments Department of Innovation, Industry and Regional Development, Victorian Transport Association, bRex Pty Ltd; Bustinza, O.F., Arias-Aranda, D., Gutierrez-Gutierrez, L., Outsourcing, competitive capabilities and performance: An empirical study in service firms (2010) International Journal of Production Economics, 126, pp. 276-288; Camp, R., (1989) Benchmarking: The Search for Industry Best Practices That Lead to Superior Performance, , 1st ed. Productivity Press; Choy, K.L., An intelligent supplier management tool for benchmarking suppliers in outsource manufacturing (2002) Expert Systems with Applications, 22, pp. 213-224; Cohen, M., Zheng, Y.-S., Agrawal, V., Service parts logistics: A benchmark analysis (1997) IIE Transactions, 29 (8), pp. 627-639; Collins, R., Cordon, C., Julien, D., Lessons from the made in Switzerland study: What makes a world-class manufacturer? (1996) European Management Journal, 14 (6), pp. 576-589; Colman, T., Päster, P., Red, white and green: The cost of carbon in the global wine trade (2007) The American Association of Wine Economists, , Working Paper No 9; Cooper, R., Edgett, S., (2003) Benchmarking Best Practices: Performance Results and the Role of Senior Management, , Product Development Institute Inc., White Paper No 32; Cusmano, L., Morrison, A., Rabellotti, R., Catching up trajectories in the wine sector: A comparative study of Chile, Italy, and South Africa (2010) World Development, 38 (11), pp. 1588-1602. , 10.1016/j.worlddev.2010.05.002; Danese, P., Kalchschmidt, M., The impact of forecasting on companies' performance: Analysis in a multivariate setting (2010) International Journal of Production Economics, , doi:10.1016/j.ijpe.2010.04.016, in press; (2008) New Zealand Wine Industry Benchmarking Survey, Vintage 2007. A Joint Publication from Deloitte and New Zealand Winegrowers, , Deloitte and New Zealand Winegrowers; Dollet, J.N., Diaz, A., Network orchestration in response to customized mass markets of premium wines and spirits (2010) Supply Chain Forum: An International Journal, 11, p. 1; Dunstall, S., Higgins, A., Sciberras, P., Innovation opportunities in bulk wine transport (2008) CSIROSinclair-Knight-Mertz Report.; Duraj, V., Miles, J.A., Meyers, J.M., Faucett, J.A., Janowitz, I.L., Tarter, M.E., Tejeda, D.G., Weber, E.A., (2000) Harvesting Aids for Reducing Ergonomics Risk Factors in Wine Grape Hand Harvesting, , University of California Agricultural Ergonomics Research Center; Ferrer, J.C., Mac Cawley, A., Maturana, S., Toloza, S., Vera, J., An optimization approach for scheduling wine grape harvest operations (2008) International Journal of Production Economics, 112, pp. 985-999; Frazelle, E.H., (2002) Supply Chain Strategy the Logistics of Supply Chain Management, , Mc Graw-Hill; Gabzdylova, B., Raffensperger, J.F., Castka, P., Sustainability in the New Zealand wine industry: Drivers, stakeholders and practices (2009) Journal of Cleaner Production, 17, pp. 992-998; Galitsky, C., Radspieler, A., Healy, P., Zechiel, S., Benchmarking and self-assessment in the wine industry (2005) Proceedings of the 2005 ACEEE Summer Study on Energy Efficiency in Industry; Garcia, F.A., (2009) Modeling and Measuring Logistics Performance in the Wine Supply Chain: The Case of the Argentine Wine Industry, , M.Sc. Thesis. School of Engineering, National University of Cuyo, Argentina; Garcia, A.F., Camargo, M., Marchetta, M.G., Forradellas, R., A framework for logistics benchmarking in the wine industry (2009) Proceedings of the 18th International Conference on Management of Technology (IAMOT); Geary, S., Zonnenberg, J.P., What it means to be best in class (2000) Supply Chain Management Review, 4 (3), pp. 42-48; Gigler, J.K., Hendrix, E.M.T., Heesen, R.A., Van Den Hazelkamp, V.G.W., Meerdink, G., On optimisation of agri chains by dynamic programming (2002) European Journal of Operational Research, 139, pp. 613-625; (2005) EAN-UCC Standards App Guideline, , GS1 Wine Supply Chain Traceability; Hackman, S., Frazelle, E., Griffin, P., Griffin, S., Vlasta, D., Benchmarking warehousing and distribution operations: An inputoutput approach (2001) Journal of Productivity Analysis, 16, pp. 79-100; Hamilton, J., Benchmarking on-line services industries (2006) Journal of Systems Science and Systems Engineering, 15 (1), pp. 48-69; Hines, P., Benchmarking toyotas supply chain: Japan vs. U.K (1998) Long Range Planning, 31 (6), pp. 911-918; Hsiao, H.I., Kemp, R.G.M., Van Der Vorst, J.G.A.J., Omta, S.W.F.O., A classification of logistic outsourcing levels and their impact on service performance: Evidence from the food processing industry (2010) International Journal of Production Economics, 124, pp. 75-86; (2000) Tercer Estudio de Benchmarking en Indicadores Logísticos y Scordcard en ECR y EHCR, IAC Colombia, , IAC; (2010) The International Wine & Spirit Record Forecast Report 20112015, , http://www.iwsr.co.uk/, IWSR November 2010; Jolly-Desodt, A.-M., Rabenasolol, B., Lo, J.L.W., Benchmarking of the textile garment Supply Chain using the SCOR model (2006) International Conference on Service Systems and Service Management, 2, pp. 1427-1432; Kojima, M., Nakashima, K., Ohno, K., Performance evaluation of SCM in JIT environment (2008) International Journal of Production Economics, 115, pp. 439-443; Krakovics, F., Leal, J.E., Mendes Jr., P., Santos, R.L., Defining and calibrating performance indicators of a 4PL in the chemical industry in Brazil (2008) International Journal of Production Economics, 115, pp. 502-514; Kulmala, H.I., Ahoniemi, L., Nissinen, V., Performance through measuring leaders profiles: An empirical study (2009) International Journal of Production Economics, 122, pp. 385-394; Lai, K.-H., Ngai, E.W.T., Cheng, T.C.E., An empirical study of supply chain performance in transport logistics (2004) International Journal of Production Economics, 87, pp. 321-331; Lambert, D., Garcia-Dastugue, S., Croxton, K., Knemeyer, A.M., Rogers, D., Goldsby, T.J., Gardner, J.T., Leuschner, R., (2008) Supply Chain Management: Processes, Partnerships, Performance, , 3rd edition Supply Chain Management Institute; Li, G., Yang, H., Sun, L., Sohal, A.S., The impact of IT implementation on supply chain integration and performance (2009) International Journal of Production Economics, 120, pp. 125-138; Llera, J., Martinengo, N., Flow diagrams for quality control system design white wine processing (2004) Rev FCA UNCuyo, 36 (1), pp. 101-110; Lo, J.L.W., Camargo, M., Rabenasolo, B., Jolly-Desodt, A.M., Benchmarking supply chain performance in the textile and clothing industry (2005) Textile Asia, 36, pp. 58-62; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156, pp. 267-286; Martinsons, M., Davison, R., Tse, D., The balanced scorecard: A foundation for the strategic management of information systems (1999) Decision Support Systems, 25, pp. 71-88; Muchiri, P., Pintelon, L., Gelders, L., Martin, H., Development of maintenance function performance measurement framework and indicators (2010) International Journal of Production Economics, , 10.1016/j.ijpe.2010.04.039; Musee, N., Lorenzen, L., Aldrich, C., Cellar waste minimization in the wine industry: A systems approach (2007) Journal of Cleaner Production, 15, pp. 417-431; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 4647, pp. 423-431; Pestana, B.C., Peypoch, N., An evaluation of European airlines operational performance (2009) International Journal of Production Economics, 122, pp. 525-533; Pinheiro De Lima, E., Gouvea Da Costa, S.E., Reis De Faria, A., Taking operations strategy into practice: Developing a process for defining priorities and performance measures (2009) International Journal of Production Economics, 122, pp. 403-418; Roy, R.N., Cordery, N., Comparing and improving value chain-supply chain in the three most important wine regions of New Zealand (2010) International Journal of Value Chain Management, 4 (3), pp. 288-303; Schmidberger, S., Bals, L., Hartmann, E., Jahns, C., Ground handling services at European hub airports: Development of a performance measurement system for benchmarking (2009) International Journal of Production Economics, 117 (1), pp. 104-116; Sheu, J.B., Chou, Y.-H., Hu, C.-C., An integrated logistics operational model for green-supply chain management (2005) Transportation Research, Part e, 41, pp. 287-313; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; (2010) Supply Chain Operation Reference Model (SCOR Model), , http://supply-chain.org, Supply Chain Council; Voss, C.A., Åhlström, P., Blackmon, K., Benchmarking and operational performance: Some empirical results (1997) International Journal of Operations & Production Management, 17 (10), pp. 1046-1058; Voss, C., Blackmon, K., Hanson, P., Oak, B., The competitiveness of European manufacturing - A four country study (1995) Business Strategy Review, 6 (1), pp. 1-25; Wen, H., Tong, J., Yao, S., The demand chain management for new world winery: Key features of Chinese wine market (2010) ICLEM 2010: Logistics for Sustained Economic Development - Infrastructure, Information, Integration, Proceedings of the 2010 International Conference of Logistics Engineering and Management, 387, p. 43854391; (2004) Annual Financial Benchmarking Survey for Australian Wine Industry, 2004 Regional Report, , Deloitte, Winemakers Federation of Australia A joint project of Deloitte and the Winemakers' Federation of Australia; Wong, C.Y., Karia, N., Explaining the competitive advantage of logistics service providers: A resource-based view approach (2009) International Journal of Production Economics, , 10.1016/j.ijpe.2009.08.026; Xu, J., Li, B., Wu, D., Rough data envelopment analysis and its application to supply chain performance evaluation (2009) International Journal of Production Economics, 122, pp. 628-638},
correspondence_address1={Garcia, F.A.; School of Engineering, , CC405 (M5500AAT) Mendoza, Argentina; email: fgarcia@fing.uncu.edu.ar},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Nacereddine2011390,
author={Nacereddine, B. and Mohammed, K. and Moufida, Y.},
title={An integrated approach of analysing a production system's PMS: A case study},
journal={ISBEIA 2011 - 2011 IEEE Symposium on Business, Engineering and Industrial Applications},
year={2011},
pages={390-394},
doi={10.1109/ISBEIA.2011.6088843},
art_number={6088843},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84255161843&doi=10.1109%2fISBEIA.2011.6088843&partnerID=40&md5=a81d57d19aba302bff4cc32a90e69573},
affiliation={Department of Management, University of Biskra, Algeria; Scott Sutherland School, Robert Gordon University, Aberdeen AB10 7QB, United Kingdom},
abstract={This study is based on in-depth empirical data taken, as an outside-observer, from a medium-size enterprise. It provides hard evidence of the new non-financial Keys Performance Indicators (KPIs)' efficiency to evaluate a production system's (PS) performance. It has been proven that the use of only the internal performance measurement system (PMS) is not enough as a significant measurement. The need of an external performance measurement (PM) is very important. As will be demonstrated the combination between the new non-financial PMSs and the systemic approach; a vital tool to support the continuous improvement of a PS's performance. © 2011 IEEE.},
author_keywords={KPIs;  Operation Management;  Performance Management;  Performance Measurement;  Production System},
keywords={KPIs;  Operation management;  Performance Management;  Performance measurements;  Production System, Benchmarking;  Industrial applications;  Supply chain management, Production engineering},
references={Berrah, L., Cliville, V., Farat, C., Haurat, A., L'emploi d'indicateurs pour l'amélioration de la performance: Le cas de la société Fournier (2003) Evaluation des Performances des Systèmes de Production, pp. 177-191. , C. Tahon, ed. Paris: Hermès; Bititci, U.S., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Production Management, 20 (6), pp. 692-704. , DOI 10.1108/01443570010321676; Courtois, A., Pillet, M., Martin-Bonnefous, C., (2007) Gestion de Production, , 4th ed. Paris: Editions d'Organisation; Dawes, J., The relationship between subjective and objective company performance measures in market orientation research: Further empirical evidence (1999) Marketing Bulletin, 10, pp. 65-75; Eboli, L., Mazzulla, G., A methodology for evaluating transit service quality based on subjective and objective measures from the passenger's point of view (2010) Transport Policy, 18 (1), pp. 172-181; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47. , DOI 10.1111/j.1468-2370.2005.00105.x; Gonzalez-Benito, O., Gonzalez-Benito, J., Cultural vs. operational market orientation and objective vs. subjective performance: Perspective of production and operations (2005) Industrial Marketing Management, 34 (8), pp. 797-829. , DOI 10.1016/j.indmarman.2005.01.002, PII S0019850105000052; Islam, A.E., Proposing a performance and change management framework (2011) Annual Conference on Innovations in Business & Management, , London, UK; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Sinclair, D., Zairi, M., Performance measurement: A critical analysis of the literature with respect to total quality management (2000) International Journal of Management, 2 (2), pp. 145-168; Tenhunen, J., Ukko, J., Markus, T., Rantanen, H., Designing a performance measurement system: A case study in the telecom business (2002) Frontiers of E-business Research, pp. 487-497},
correspondence_address1={Nacereddine, B.; Department of Management, Algeria; email: n.bouriche@yahoo.co.uk},
isbn={9781457715495},
language={English},
abbrev_source_title={ISBEIA - IEEE Symp. Bus., Eng. Ind. Appl.},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Shepherd2011105,
author={Shepherd, C. and Günter, H.},
title={Measuring supply chain performance: Current research and future directions},
journal={Behavioral Operations in Planning and Scheduling},
year={2011},
pages={105-121},
doi={10.1007/978-3-642-13382-4_6},
note={cited By 63},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885767254&doi=10.1007%2f978-3-642-13382-4_6&partnerID=40&md5=408f5013e9a57f1a2b3fbf45d10ab9ce},
affiliation={Nottingham University Business School, Nottingham, United Kingdom; Department of Organization and Strategy, Maastricht University, Maastricht, Netherlands},
abstract={This chapter aims to go some way towards addressing the dearth of research into performance measurement systems and metrics of supply chains by critically reviewing the contemporary literature and suggesting possible avenues for future research. The article provides a taxonomy of performance measures followed by a critical evaluation of measurement systems designed to evaluate the performance of supply chains. The chapter argues that despite considerable advances in the literature in recent years, a number of important problems have not yet received adequate attention, including: the factors influencing the successful implementation of performance measurement systems for supply chains; the forces shaping their evolution over time; and, the problem of their ongoing maintenance. The chapter provides both a taxonomy of measures and outlines specific implications for future research. © 2011 Springer-Verlag GmbH Berlin Heidelberg.},
references={Artz, K.W., Buyer-supplier performance: The role of asset specificity reciprocal investments and relational exchange (1999) British Journal of Management, 10, pp. 113-126; Baiman, S., Fischer, P.E., Rajan, M.V., Performance measurement and design in supply chains (2001) Management Science, 47 (1), pp. 173-188; Balasubramanian, P., Tewary, A.K., Design of supply chains: Unrealistic expectations on collaboration (2005) Sadhana-Academy Proceedings in Engineering Sciences, 30 (2-3), pp. 463-473; Basnet, C., Corner, J., Wisner, J., Tan, K.-C., Benchmarking supply chain management practice in New Zealand (2003) Supply Chain Management, 8 (1), pp. 57-64. , DOI 10.1108/13598540310463369; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , PII S0925527398000796; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Beamon, B.M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218. , DOI 10.1080/00207540110053156; Billig, M., Arguing and thinking: A rhetorical approach to social psychology (1996) Cambridge: Cambridge University Press; Bititci, U., Cavalieri, S., Von Cieminski, G., Implementation of performance measurement systems: Private and public sectors (2005) Production Planning and Control, 16 (2), pp. 99-100. , DOI 10.1080/09537280512331333002, PII J11062750822; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , DOI 10.1108/01443570010330739; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives- Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Bradford, K.D., Stringfellow, A., Weitz, B.A., Managing conflict to improve the effectiveness of retail networks (2004) Journal of Retailing, 80 (3), pp. 181-195. , DOI 10.1016/j.jretai.2003.12.002, PII S0022435904000375; Cachon, G.P., Lariviere, M.A., Capacity choice and allocation: Strategic behavior and supply chain performance (1999) Management Science, 45 (8), pp. 1091-1108; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Chen, H.X., Amodeo, L., Chu, F., Labadi, K., Modelling the performance evaluation of supply chains using batch deterministic and stochastic petri nets (2005) IEEE Transactions on Automated Science and Engineering, 2 (2), pp. 132-144; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Chiang, W.Y.K., Monahan, G.E., Managing inventories in a two-echelon dual-channel supply chain (2005) European Journal of Operational Research, 162, pp. 325-341; Choy, K.L., Lee, W.B., An intelligent supplier relationship management system for selecting and benchmarking suppliers (2003) International Journal of Technology Management, 26 (7), pp. 717-742; Clegg, C.W., Wall, T.D., Pepper, K., Stride, C., Woods, D., Morrison, D., Cordery, J., Kogi, K., An international survey of the use and effectiveness of modern manufacturing practices (2002) Human Factors and Ergonomics In Manufacturing, 12 (2), pp. 171-191. , DOI 10.1002/hfm.10006; Cox, A., Benchmarking: A dead end for supply-chain management? (2000) Sloan Management Review, 41 (4), p. 5; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management and Business Excellence, 14 (3), pp. 355-366. , DOI 10.1080/1478336032000046652; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge-measuring Operations for World Class Competition, , Homewood, IL: Dow Jones-Irwin; Dyapur, K.R., Patnaik, K.K., Transaction oriented computing (Hive Computing) using GRAM-soft (2005) Lecture Notes in Computer Science, 3516, pp. 879-882. , Computational Science-ICCS 2005: 5th International Conference, Proceedings; Ellinger, A.E., Improving marketing/logistics cross functional collaboration in the supply chain (2000) Industrial Marketing Management, 29, pp. 85-96; Fergueson, B.R., Implementing supply chain management (2000) Production and Inventory Management Journal, pp. 64-67. , March; Flynn, B.B., Flynn, E.J., Synergies between supply chain management and quality management: Emerging implications (2005) International Journal of Production Research, 43 (16), pp. 3421-3436. , DOI 10.1080/00207540500118076; Fynes, B., De Burca, S., Voss, C., Supply chain relationship quality, the competitive environment and performance (2005) International Journal of Production Research, 43 (16), pp. 3303-3320. , DOI 10.1080/00207540500095894; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) International Journal of Purchasing & Materials Management, 30 (4), pp. 13-18; Green Jr., K.W., Inman, R.A., Using a just-in-time selling strategy to strengthen supply chain linkages (2005) International Journal of Production Research, 43 (16), pp. 3437-3453. , DOI 10.1080/00207540500118035; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Williams, H.J., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533. , DOI 10.1016/j.technovation.2003.09.010, PII S0166497203001767; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , New Jersey: Prentice Hall; Harrison, A., New, C., The role of coherent supply chain strategy and performance management in achieving competitive advantage: An international survey (2002) Journal of the Operational Research Society, 53 (3), pp. 263-271. , DOI 10.1057/palgrave/jors/2601193, Performance management; Hieber, R., (2002) Supply Chain Management: A Collaborative Performance Measurement Approach, , Zurich: VDF; Holmberg, S., A system perspective in supply chain measurement (2000) International Journal of Physical Distribution and Logistics, 30 (10), pp. 847-868; Huan, S.H., Sheoran, S.K., Wan, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management, 9 (1), pp. 23-29. , DOI 10.1108/13598540410517557; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers and Industrial Engineering, 48 (2), pp. 377-394. , DOI 10.1016/j.cie.2005.01.001, PII S0360835205000021; Hwarng, H.B., Chong, C.S.P., Xie, N., Burgess, T.F., Modelling a complex supply chain: Understanding the effect of simplified assumptions (2005) International Journal of Production Research, 43 (13), pp. 2829-2872. , DOI 10.1080/00207540500066812; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, , June, 134-147; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2), pp. 213-229. , DOI 10.1108/01443570310458465; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Krajewski, L., Wei, J.C., Tang, L.-L., Responding to schedule changes in build-to-order supply chains (2005) Journal of Operations Management, 23 (5), pp. 452-469. , DOI 10.1016/j.jom.2004.10.006, PII S0272696304001196, The Build to Order Chain (BOSC) A Competitive Strategy for the 21st Century; Lai, K.-H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-456. , DOI 10.1016/S1366-5545(02)00019-4, PII S1366554502000194; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-113; Li, G., Yan, H., Wang, S., Xia, Y., Comparative analysis on value of information sharing in supply chains (2005) Supply Chain Management, 10 (1), pp. 34-46. , DOI 10.1108/13598540510578360; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641. , DOI 10.1016/j.jom.2005.01.002, PII S0272696305000501; Lin, F.R., Sung, Y.W., Lo, Y.P., Effects of trust mechanisms on supply-chain performance: A multi-agent simulation study (2005) International Journal of Electronic Commerce, 9 (4), pp. 91-112; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (11-12), pp. 1192-1218; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Lowe, A., Jones, A., Emergent strategy and the measurement of performance: The formulation of performance indicators at the microlevel (2004) Organization Studies, 25 (8), pp. 1313-1337. , DOI 10.1177/0170840604046344; Lummus, R.R., Duclos, L.K., Vokurka, R.J., The impact of marketing initiatives on the supply chain (2003) Supply Chain Management, 8 (4), pp. 317-323. , DOI 10.1108/13598540310490071; Maloni, M.J., Benton, W.C., Supply chain partnerships: Opportunities for operations research (1997) European Journal of Operational Research, 101 (3), pp. 419-429. , PII S0377221797001185; Melnyk, S., Stewart, D.M., Swink, M., Metrics and performance measures in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22, pp. 209-217; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145. , DOI 10.1108/01443570010343708; Nudurupati, S.S., Bititci, U.S., Implementation and impact of IT-supported performance measurement systems (2005) Production Planning and Control, 16 (2), pp. 152-162. , DOI 10.1080/09537280512331333057, PII P728168303; Parker, S.K., Axtell, C.M., Seeing another viewpoint: Antecedents and outcomes of employee perspective taking (2001) Academy of Management Journal, 44 (6), pp. 1085-1100; Ramdas, K., Spekman, R.E., Chain or shackles: Understanding what drives supply-chain performance (2000) Interfaces, 30 (4), pp. 3-21; Reiner, G., Customer-oriented improvement and evaluation of supply chain processes supported by simulation models (2005) International Journal of Production Economics, 96 (3), pp. 381-395. , DOI 10.1016/j.ijpe.2004.07.004, PII S0925527304002981, Quality in Supply Chain Management and Logistics; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89 (2), pp. 231-243; Schönsieben, P., (2004) Integral Logistics Management: Planning and Control of Comprehensive Supply Chains, , Boca Ratön, FL: St Lucie Press; Smith, M.F., Lancioni, R.A., Oliva, T.A., The effects of management inertia on the supply chain performance of produce-to-stock firms (2005) Industrial Marketing Management, 34 (6 SPEC. ISSUE), pp. 614-628. , DOI 10.1016/j.indmarman.2004.11.003, PII S0019850105000283; Soltani, E., Van Meer, R.D., Williams, T.M., A contrast of HRM and TQM approaches to performance management: Some evidence (2005) British Journal of Management, 16 (3), pp. 211-230. , DOI 10.1111/j.1467-8551.2005.00452.x; Sperka, M., Zur Entwicklung eines Fragebogens der Kommunikation in Organisationen (1997) Zeitschrift fur Arbeits-und Organisationspsychologie, 41 (4), pp. 182-190; Stephens, S., Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476. , DOI 10.1023/A:1012881006783; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40 (16), pp. 4257-4269; Thomas, J., Why your supply chain doesn't work (1999) Logistics Management and Distribution Report, 38 (6), pp. 42-44; Tranfield, D., Denyer, D., Smart, P., Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review (2003) British Journal of Management, 14 (3), pp. 207-222; Ulusoy, G., An assessment of supply chain and innovation management practices in the manufacturing industries in Turkey (2003) International Journal of Production Economics, 86 (3), pp. 251-270; Van Der Vorst, J., Beulens, A., Identifying sources of uncertainty to generate supply chain redesign strategies (2001) International Journal of Physical Distribution and Logistics, 32 (6), pp. 409-430; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 15-29; Van Landeghem, R., Persoons, K., Benchmarking of logistical operations based on a causal model (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 254-266; Van Veen-Dirks, D., Management control and production environment (2005) International Journal of Production Economics, 93 (4), pp. 263-272; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60; Wang, F.-K., Du, T.C., Li, E.Y., Applying Six-Sigma to supplier development (2004) Total Quality Management and Business Excellence, 15 (9-10), pp. 1217-1229. , DOI 10.1080/1478336042000255596; Wang, G., Huang, S.H., Dismukes, J.P., Manufacturing supply chain design and evaluation (2005) International Journal of Advanced Manufacturing Technology, 25 (1-2), pp. 93-100. , DOI 10.1007/s00170-003-1791-y; Webster, M., Supply system structure, management and performance: A conceptual model (2002) International Journal of Management Reviews, 4 (4), pp. 353-369; Windischer, A., Kooperatives planen (2003) Dissertation, University of Zurich, Zurich; Windischer, A., Grote, G., Success factors for collaborative planning (2003) Strategy and Organization in Supply Chain, pp. 131-146. , S. Seuring, M. Muller, & M. Goldbach Eds., Heidelberg: Physica; Wood, S.J., Stride, C., Wall, T.D., Clegg, C.W., Revisiting the use and effectiveness of modern manufacturing practices (2004) Human Factors & Ergonomics in Manufacturing, 14 (4), pp. 415-432; Wu, J.H., Quantity flexibility contracts under Bayesian updating (2005) Computers & Operations Research, 32 (5), pp. 1267-1288},
correspondence_address1={Shepherd, C.; Nottingham University Business School, Nottingham, United Kingdom; email: craig.shepherd@nottingham.ac.uk},
publisher={Springer Berlin Heidelberg},
isbn={9783642133817},
language={English},
abbrev_source_title={Behav. Oper. in Plann. and Scheduling},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Pidun201199,
author={Pidun, T. and Felden, C.},
title={Limitations of performance measurement systems based on key performance indicators},
journal={17th Americas Conference on Information Systems 2011, AMCIS 2011},
year={2011},
volume={1},
pages={99-106},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870312297&partnerID=40&md5=c81ff55eb38ee9c2734f3c0897747a01},
affiliation={Technische Universität Bergakademie Freiberg, Information Science, Germany},
abstract={Measuring performance is a key to optimize business processes. However, many of them cannot be easily measured due to their non-deterministic or qualitative nature. So, to fit in common performance measurement systems (PMS) that normally use numeric parameters (KPIs), artificial and simplifying measures have to be used that are complicated and costly to create and evaluate. Using a literature review, this paper documents the dominance of PMS that rely on KPIs and a lack of those that also incorporate non-numeric, generic indicators that better address qualitative problems. Combined with the discussion of different viewpoints to PMS properties as well as the demand for better transparency and comparability of business processes, the necessity of deployment for a refined PMS using additional indirect indicators is derived. It would be able to assess hidden performance problems and hence to reveal additional business process improvement possibilities.},
author_keywords={Business process management;  Indicator;  KPI;  Performance measurement system;  Process performance management},
keywords={Business Process;  Business process improvement;  Business process management;  Key performance indicators;  KPI;  Literature reviews;  Measuring performance;  Paper documents;  Performance measurement system;  Performance problems;  Process performance, Benchmarking;  Enterprise resource management;  Indicators (instruments);  Information systems, Supply chain management},
references={(2010) Glossary, , http://www.actano.com/20911_EN-What%B4s_new-Glossary.htm, last Paccess 4 November 2011; Anderson, E., Fornell, C., Lehmann, D., Customer satisfaction, market share and profitability: Findings from Sweden (1994) Journal of Marketing, 58, pp. 53-66; Beatham, S., Anumba, C., Thorpe, T., Hedges, I., KPIs: A critical appraisal of their use in construction (2004) Benchmarking: An International Journal, 1, pp. 93-117; Bierbusse, P., Siesfeld, T., Measures that matter (1997) Journal of Strategic Performance Measurement, 2, pp. 6-11; Bititci, U., Carrie, A., McDevitt, L., Integrated performance measurement systems: A development guide (1997) IJOPM, 17, pp. 522-534; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5, pp. 1-24; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) IJOPM, 7, pp. 754-771; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) IJOPM, 11, pp. 1288-1310; Bradley, P., (1996) A performance measurement approach to the reengineering of manufacturing enterprises Ph.D. Thesis, , NUI Galway; Brown, M., (1996) Keeping Score: Using the Right Metrics to Drive World-class Performance Quality Resources, , New York; Bucher, T., Winter, R., Realisierungsformen des Geschäftsprozessmanagements-Eine explorative Klassifikationsanalyse (2007) Organisation: Service, Prozess-, Market-Engineering Iss, 1, pp. 695-713. , In: Oberweis, A. (ed.); Chennell, A., Dransfield, S., Field, J., Fisher, N., Saunders, I., Shaw, D., OPM: A system for organisational performance measurement. Proceedings of the Performance Measurement-Past (2000) Present and Future Conference Cambridge; Craciun, L., Criveanu, R., The performance measurement system and key performance indicators (2005) Annals of the University of Petrosani Economics, 5, pp. 63-72; de Toni, A., Tonchia, S., Performance measurement systems: Models characteristics and measures (2001) IJOPM, 21 (1-2), pp. 46-70; Drucker, P., (1966) The Effective executive Harper & Row, , New York, 1966; Denyer, D., Tranfield, R., Producing a Systematic Review (2009) The Sage Handbook of Organizational Research Methods Sage Publications, pp. 671-689. , in D.A. Buchanan and A. Bryman (Eds.), London; Dixon Nanni A., J., Vollmann, T., (1990) The New Performance Challenge: Measuring operations for World-class Competition Business One Irwin, , Homewood; Edvinsson, L., Developing intellectual capital at Skandia (1997) Long Range Planning, pp. 366-373. , 1997; Fitzgerald, L., Moon, P., (1996) Performance measurement in Service Industries: Making it work The Chartered Institute of Management Accountants, , London; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56, pp. 663-680; Franco-Santos, M., Bourne, M., An Examination of the Literature Relating to Issues Affecting How Companies Manage Through Measures (2005) Production Planning and Control, 2, pp. 114-124; Franco-Santos, M., Kennerly, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) IJOPM, 8, pp. 784-801; Garengo, P., Biazzo, S., Bititci, U., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 1, pp. 25-47; Ghalayini, A., Noble, J., Crowe, T., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production economics, 48, pp. 207-225; Ittner, D., Larcker, D., (2003) Coming Up Short on Nonfinancial Performance Measurement, pp. 88-95. , Harvard Business Review HBS Publishing, Boston; Jeanes, C., Customer satisfaction and business results: Is there a link? (1996) Customer Service Management March, pp. 46-48; Kaplan, R., Norton, D., Using the Balanced Scorecard as Strategic Management System (1996) Harvard Business Review January-February, pp. 75-85. , 1996; Keegan, D., Eiler, R., Jones, C., Are your performance measures obsolete? (1989) Management Accounting ABI/INFORM, June 1989, 12, pp. 45-50; Kneuper, R., (2003) CMMI: Verbesserung von Softwareprozessen mit Capability Maturity Model Integration 1. Edition, , d. punkt, Heidelberg; Kueng, P., Krahn, A., Building a Process Performance Meaasurement System: Some early experiences (1999) Journal of Scientific and Industrial Research, 58, pp. 149-159; Latintnen, E., Framework for small business performance measurement (1996) Proceedings of the University of Vaasa Research Papers Business Administration, (77). , University of Vaasa; Lin, F., Yang, M., Pai, Y., A generic structure for business process modeling (2002) BPMJ, 1, pp. 19-41; Lönnqvist, A., (2004) Measurement of Intangible Success Factors: Case Studies on the Design, , Implementation and Use of Measures Dissertation, Tampere University of Technology, Publication 485; Lynch, R., Cross, K., (1991) Measure Up: The Essential Guide to Measuring Business Performance Mandarin, , London; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) IJOPM, 5, pp. 520-533; Miles, J., (2008) Discovering critical success factors for implementing an automated performance measurement system a case study, , Dissertation, Edith Cowan University; McHand, M., Raymond, L., Researching performance measurement systems-an information systems perspective (2008) IJOPM, 7, pp. 663-686; Moll, A., Rundum erneuert-EFQM-Modell und Radar-Logik in revidierter Version 2010 (2009) Qualität und Zuverlässigkeit (QZ), pp. 13-15; Moxham, C., Performance measurement-Examining the applicability of the existing body of knowledge to nonprofit organisations (2009) IJOPM, 7, pp. 740-763; Neely, A., Gregory, M., Platts, K., Performance Measurement system design: A literature review and research agenda (1995) IJOPM, 4, pp. 80-116; Neely, A., Adams, C., Kennerly, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success FT Prentice Hall, , London; Pidun, T., Felden, C., A Reference Model Catalog of Models for Business Process Analysis (2010) Proceedings of the Sixteenth Americas Conference on Information Systems, , http://aisel.aisnet.org/amcis2010/169; Pun, K., White, A., A performance measurement paradigm for integrating strategy formulation: A review of systems and frameworks (2005) International Journal of Management Reviews, 1, pp. 49-71; Raschke, R., Ingraham, L., Business Process Maturity's Effect on Performance (2010) Proceedings of the Sixteenth Americas Conference on Information Systems, , http://aisel.aisnet.org/amcis2010/402, last download 27 August 2010; Rehage, J., (2009) Zwischen Realität und Wunschdenken, pp. 36-39. , IT&Produktion 12/09, Special publication ERP/CRM Wissen kompakt, TeDo Verlag, Marburg; Rouse, P., Putterill, M., An integral framework for performance measurement (2003) Management Decision, 41 (8), pp. 791-805; Schneiderman, A., Why balanced scorecards fail (1999) Journal of Strategic Performance Measurement special edition, 6, pp. 6-11; Sheu, C., Wacker, J., Effectiveness of planning and control systems (2001) International Journal of Production Research, 5, pp. 887-905; Sink, D., Tuttle, T., Planning and Measurement in your organisation of the Future Chapter 5 (1989) Industrial Engineering and Management, pp. 170-184. , Press, Norcross; Smullen, J., (1997) Balance Scorecards and Activity Based Management: The Practical Application FT Pitman Publishing, , London; Stalk, G., Hout, T., How time-based management measures performance (1993) Strategy & Leadership, 6, pp. 26-29; Tangen, S., Performance measurement: From philosophy to practice (2004) IJPPM, 8, pp. 726-737; Wisner, J., Fawcett, S., Link firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal Third Quarter, pp. 5-11. , 1991; Zigon, J., (2007) Measuring the hard stuff, , http://www.zigonperf.com/hardstuff.html},
correspondence_address1={Pidun, T.; Technische Universität Bergakademie Freiberg, Germany; email: tim.pidun@bwl.tu-freiberg.de},
isbn={9781618390981},
language={English},
abbrev_source_title={Amer. Conf. Inf. Sys., AMCIS},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pidun20111732,
author={Pidun, T. and Felden, C.},
title={A Performance Assessment System incorporating indirect indicators and semantics},
journal={17th Americas Conference on Information Systems 2011, AMCIS 2011},
year={2011},
volume={3},
pages={1732-1740},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870174167&partnerID=40&md5=3d87e124e43ff4945b9ba1350e2d7b78},
affiliation={Technische Universität Bergakademie Freiberg, Chair of Information Science, Germany},
abstract={Measuring performance is key to reengineering and optimization of business processes. Although many of them cannot easily be measured due to their quantitative or non-deterministic nature, most performance measurement systems rely on the usage of numeric parameters (Key Performance Indicators, KPIs). So, performance problems stay invisible that could be assessed by other indirect indicators like goals, complexity, maturity, relations or dependencies. In this paper, a Four-Box-Model is presented that also includes internal process views, descriptive approaches and semantics in addition to KPIs. It offers a broad range of possibilities to better identify performance problems and hence, to increase process performance.},
author_keywords={Goals;  Indicator;  Indirect;  KPI;  Metrics;  Non-numeric;  Ontologies;  Performance Assessment System;  Performance Measurement System;  Process Performance Management;  Semantics},
keywords={Goals;  Indirect;  KPI;  Metrics;  Non-numeric;  Performance assessment;  Performance measurement system;  Process performance, Benchmarking;  Indicators (instruments);  Information systems;  Ontology;  Supply chain management, Semantics},
references={Aburub, F., Odeh, M., Beeson, I., Modelling non-functional requirements of business processes (2007) Information and Software Technology, 49, pp. 1162-1171; Almeida, J.P.A., GuizzarDi, G., Eliciting Goals for Business Process Models with Non-Functional Requirements Catalogues: ECS Cardoso, RSS 2009-Springer, T. Halpin (Eds.): BPMDS 2009 and EMMSAD 2009 (2009) LNBIP, 29, pp. 33-45; BarouDi, R., (2011) KPI Mega Library: 17,000 Key Performance Indicators, , CreateSpace, Scotts Valley; Becker, J., Breuker, D., Pfeiffer, J., Räckers, M., Constructing Comparable Business Process Models with Domain Specific Languages-An Empirical Evaluation (2009) Proceedings of the 17th European Conference on Information Systems, pp. 1-13. , in; Bierbusse, P., Siesfeld, T., Measures that matter (1997) Journal of Strategic Performance Measurement, 1 (2), pp. 6-11; Born, M., Hoffmann, J., Kaczmarek, T., Kowalkiewicz, M., Markovic, I., Scicluna, J., Semantic Annotation and Composition of Business Processes with Maestro (2008) 5th European Semantic Web Conference, , ESWC, 2008; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5, p. 1. , Inderscience, Geneve, 1-24; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Brown, M., (1996) Keeping Score: Using the Right Metrics to Drive World-class Performance, , Quality Resources, New York; Bruce, A., Kutnick, D., (2002) Building Operational Excellence: IT People and Process Best Practices, , Pearson Educational; Cardoso, J., Mendling, J., Neumann, G., Reijers, H.A., A Discourse on Complexity of Process Models (2006) BPM 2006 Workshops, pp. 115-126. , in Eder, J. and Dustdar, S. (Eds.); Dehning, B., Richardson, V.J., Returns on Investments in Information Technology: A Research Synthesis (2002) Journal of Information Systems, 16 (1), pp. 7-30; Denyer, D., Tranfield, R., (2009) Producing a Systematic Review, , in Buchanan, D.A. and Bryman, A. (Eds.), The Sage; DeToni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) IJOPM, 21 (1-2), pp. 46-70; Dimitrov, M., Simov, A., Stein, S., Konstantinov, M., A BPMO Based Semantic Business Process Modelling Environment (2007) Proceedings of the Workshop SBPM 2007, , in Hepp, M., Hinkelmann, K., Karagiannis, D., Klein, R. and Stijanovic, N. (Eds.): Semantic Business Process and Product Lifecycle Management. at CEUR-WS. org/Vol-251/, last access 18.2.2011; Franco-Santos, M., Bourne, M., An Examination of the Literature Relating to Issues Affecting How Companies Manage Through Measures (2005) Production Planning and Control, 16 (2), pp. 114-124. , Taylor & Francis, London; Genrich, M., Kokkonen, A., Moormann, J., Zur Muehlen, M., Tregear, R., Mendling, J., Weber, B., Challenges for Business Process Intelligence: Discussions at the BPI Workshop (2007) Business Process Management Workshops, , in, (A. Ter Hofstede, B. and Benatallah, and H.-Y. Paik, Eds); Gruber, T., A Translation Approach to Portable Ontology Specifications (1993) Knowledge Acquisition, pp. 199-220. , in, Band 5 (2), Stanford University Academic Press; Heinrich, R., Paech, B., Defining the Quality of Business Processes (2010) Modellierung, 2010, pp. 133-148. , In: Engels, G., Karagiannis, D., Mayr, H. (Eds.); Ittner, D., Larcker, D., Coming Up Short on Nonfinancial Performance Measurement (2003) Harvard Business Review, pp. 88-95. , HBS Publishing, Boston; Juan, Y., Ou-Yang, C., A Process Logic Comparison Approach to Support Business Process Benchmarking, in (2005) IJAMT, 26 (1-2), pp. 191-210; Kaplan, R., Norton, D., Using the Balanced Scorecard as strategic management system (1996) Harvard Business Review, pp. 75-85. , in, January-February 1996, Boston: Harvard Business Publishing; Kueng, P., Krahn, A., Building a Process Performance Measurement System: Some Early experiences (1999) Journal of Scientific and Industrial Research, National Institute of Science Communication And Information Resources, New Delhi, 58 (3-4), pp. 149-159; Letier, E., van Lamsweerde, A., Reasoning about partial goal satisfaction for requirements and design engineering (2004) Proceedings of 12th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 53-62. , in; List, B., Machaczek, K., Towards a Corporate Performance Measurement System (2004) Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 1344-1350. , in; Lönnqvist, A., (2004) Measurement of Intangible Success Factors: Case Studies on the Design, , Implementation and Use of Measures, Dissertation, Tampere University of Technology, Publication 485; Melville, N., Kraemer, K., Gurbaxani, V., Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value (2004) MIS Quarterly, 28 (2), pp. 283-322; Merdan, M., Zoitl, A., Koppensteiner, G., Demmelmayr, F., Semantische Technologien-Stand der Technik (2010) Elektrotechnik und Informationstechnik 127/10, pp. 291-299. , Springer, Berlin; Moll, A., Rundum erneuert-EFQM-Modell und Radar-Logik in revidierter Version 2010 (2009) Qualität und Zuverlässigkeit, pp. 13-15. , in, (QZ), 11/2009; Moxham, C., Performance measurement-Examining the applicability of the existing body of knowledge to nonprofit organisations (2009) IJOPM, 29 (7), pp. 740-763; Naumann, F., Rolker, C., Assessment methods for Information Quality Criteria (2000) Proceedings of the Fifth Conference on Information Quality, pp. 148-162. , in; Neely, A., Adams, C., Kennerly, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , FT Prentice Hall, London; Parmenter, D., (2010) Key performance indicators. Developing, implementing, and using winning KPIs, , 2nd. Hoboken, N. J: John Wiley & Sons; Pavlovski, C.J., Zou, J., Non-functional requirements in business process modeling (2008) Proceedings of the Fifth on Asia-Pacific Conference on Conceptual Modelling, 79. , in; Pidun, T., Felden, C., (2010) A Reference Model Catalog of Models for Business Process Analysis, , Proceedings of the Sixteenth Americas Conference on Information Systems; Pidun, T., (2011) Limitations of Performance Measurement Systems based on Key Performance Indicators, , Proceedings of the Seventeenth Americas Conference on Information Systems; Popova, V., Sharpanskykh, A., Modeling organizational performance indicators (2010) Information Systems, 35, pp. 505-527; Raschke, R., Ingraham, L., Business Process Maturity's Effect on Performance (2010) Proceedings of the Sixteenth Americas Conference on Information Systems, , http://aisel.aisnet.org/amcis2010/402, last access 27 August 2010., at; Rehage, J., Zwischen Realität und Wunschdenken (2009) IT&Produktion, pp. 36-39. , 12/09, Special publication ERP/CRM Wissen kompakt, TeDo Verlag, Marburg; Remus, U., (2002) Prozessorientiertes Wissensmanagement-Konzepte und Modellierung, , Dissertation, University Regensburg; Samiresh, J., Momotko, M., Ruggaber, R., (2006) Manifesto for the proposal of Nessi Working Group on Business Process Management, , http://www.nessieurope.eu/Nessi/LinkClick.aspx?fileticket=3zqrf6byVko=&tabid=257&mid=767, last access 2 May 2009., at; Sheu, C., Wacker, J., Effectiveness of planning and control systems (2001) International Journal of Production Research, 39 (5), pp. 887-905. , Taylor & Francis, London; Stein, S., (2009) Modelling Method Extension for Service-Oriented Business Process Management, , Dissertation, Christian-Albrechts-Universität, Kiel; Wettstein, T., Kueng, P., (2002) A Maturity Model for Performance Measurement Systems, pp. 113-122. , in Brebbia, C., Pascolo, P. (Ed.): Management information systems 2002, Incorporating GIS and remote sensing; van der Aalst, W.M.P., Reijers, H.A., Weijters, A.J.M.M., van Dongen, B.F., de Medeiros, A.K.A., Song, M., Verbeek, H.M.W., Business Process Mining: An Industrial Application (2007) Information Systems, 32 (5), pp. 713-732; Vanderfeesten, I., Cardoso, J., Mendling, J., Reijers, H., van der Aalst, W., Quality Metrics for Business Process Models (2007) BPM & Workflow Handbook, pp. 179-190. , In: Fischer, L. (Ed.), Workflow Management Coalition},
correspondence_address1={Pidun, T.; Technische Universität Bergakademie Freiberg, Germany; email: tim.pidun@bwl.tu-freiberg.de},
isbn={9781618390981},
language={English},
abbrev_source_title={Amer. Conf. Inf. Sys., AMCIS},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Anvari2011285,
author={Anvari, A. and Hojjati, S.M.H. and Ismail, Y. and Mohd Yusuff, R. and Zulkifli, N. and Mojahed, M.},
title={Performance measurement system through supply chain management to lean manufacturing},
journal={World Applied Sciences Journal},
year={2011},
volume={14},
number={2},
pages={285-292},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864928656&partnerID=40&md5=5aba3a7dd50834dd3d776a253d4626e9},
affiliation={Department of Industrial Engineering and Management, IAU-Gachsaran Branch, Iran; Department of Industrial Engineering, IAU-Shiraz Branch, Iran; Department of Manufacturing Engineering, UMP, Malaysia; Department of Mechanical and Manufacturing Engineering, UPM, Malaysia},
abstract={This paper discusses the relationship between Performance Measurement Systems in Supply Chain Management and Lean Manufacturing. Nowadays, companies regard Lean Manufacturing as a role model for optimizing production and survival in a globally competitive market. Supply Chain Management is a key element of Lean Manufacturing that covers strategic, tactical and functional levels in companies. Planning, human resources, product realization and customer delivery are different processes of a supply chain. A Performance Measurement System is very important for analyzing improvement and achieving goals. Creating a Performance Measurement System in Supply Chain Management for measuring Supply Chain performance in Lean Manufacturing is a success factor for managers and stakeholders. The results of this study show that if the performance of Supply Chain Management can be measured and be exactly based on pre-determined goals to create a Lean Supply Chain, then it can direct the system toward Leanness and business excellence.},
author_keywords={Lean manufacturing;  Performance measurement system;  Supply chain management},
references={Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33, pp. 65-73; Quayle, M., Supplier development and supply chain management in small and medium size enterprises (2002) International J. Technol. Management, 23, pp. 172-188; Davis, T., Effective Supply Chain Management (1993) Sloan Management Review, pp. 34-35; McGuffog, T., (1996) Managing the Supply Chain With Speed and Certainty, , Article Number Association (UK); Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International J. Production Economics, 87, pp. 333-347; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International J. Production Economics, 89, pp. 231-243; Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution and Logistics Management, 24, pp. 17-28; Maisel, L.S., Performance measurement: The balanced scorecard approach (1992) J. Cost Management, 6, pp. 47-52; Ben Naylor, J., Nairn, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International J. Production Economics, 62, pp. 107-118; Christopher, M., Peck, H., (2003) Marketing Logistics, , Butterworth-Heinemann; Feizabadi, J., Introduction to Suplly Chain (2003) Tadbir, 131, p. 49; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International J. Operation and Production Management, 20 (111), pp. 9-1145; Liu, J., Ding, F.Y., Lall, V., Using data envelopment analysis to compare suppliers for supplier selection and performance improvement (2000) Supply Chain Management: An International J, 5, pp. 143-150; Vokurka, R.J., Choobineh, J., Vadi, L., A prototype expert system for the evaluation and selection of potential suppliers (1996) International J. Operation and Production Management, 16, pp. 106-127; Garvin, D.A., Manufacturing Strategic Planning (1993) California Management Review, 35, p. 85; Kaplan, R.S., Norton, D.P., The Balanced Scorecard: Measures that Drive Performance (2005) Harvard Business Review, 83, p. 172; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69, pp. 131-137; Fisher, J., Use of non-financial performance measures (1992) J. Cost Management, 6, pp. 31-38; Kaplan, R.S., (1984) Yesterday's Accounting Undermines Production, , Graduate School of Business Administration, Harvard University; Beamon, B.M., Measuring supply chain performance (1999) International J. Operation and Production Management, 19, pp. 275-292; van der Vaart, T., van Donk, D.P., A critical review of survey-based research in supply chain integration (2008) International J. Production Economics, 111, pp. 42-55; Holmberg, S., A systems perspective on supply chain measurements (2000) International J. Physical Distribution and Logistics Management, 30, pp. 847-868; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) J. Operations Management, 23, pp. 618-641; de Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International J. Operations and Production Management, 21, pp. 46-70; Fynes, B., Voss, C., De, S., The impact of supply chain relationship dynamics on manufacturing performance (2005) International J. Operations and Production Management, 25, pp. 6-19; Fiennes, B., Voss, C., De, S., The impact of supply chain relationship quality on quality performance (2005) International J. Production Economics, 96, pp. 339-354; Bhatnagar, R., Sohal, A.S., Supply chain competitiveness: Measuring the impact of location factors, uncertainty and manufacturing practices (2005) Technovation, 25, pp. 443-456; Chan, F.T.S., Performance Measurement in a Supply Chain (2003) The International J. Advanced Manufacturing Technol, 21, pp. 534-548; Beamon, B.M., Supply Chain design and Analysis: Models and Methods (1998) International J. Production Economics, 55, pp. 281-294; Ragu-Nathan, S., Li, B., Ragu-Nathan, T.S., Subba Rao, S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) Omega, 34, pp. 107-124; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International J. Operation and Production Management, 21, pp. 71-87; Mohammad, M., Osman, M.R., Yusuff, R.M., Ismail, N., (2005) Strategies and Critical Success Factors For Integrated Management System Implementation, , Proceedings of the 35 th International Conference on Computers and Industrial Engineering, June 19-22, 2005, Istanbul, Turkey; Lucansky, P., Ducharme, L., Burke, R., (2002) Performance Measurement System. the Lean Perspective, , http://www.vipgroup.us/articles.shtml:http://www.vipgroup.us/Perf_Meas_Article2.pdf; Neely, A., Gregory, M., Platts, K., Performance measurement system design A literature review and research agenda (1995) International J. Operations and Production Management, 15, pp. 80-116; Zutshi, A., Sohal, A.S., Integrated management system (2005) Management, 16, pp. 211-232; Manavizade, N., (2005) Performance Measurement of Supply Chain Management For Implementing Lean Production, , M.S. thesis, Tehran University, Tehran, Iran; Berrah, L., Clivillq, V., Towards an Aggregation Performance Measurement System Model in a Supply Chain Context (2007) Computers In Industry, 58, pp. 709-719; Kleynen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) J. the Operational Research Society, 54, pp. 507-514; Camerinelli, E., (2005) Performance Metrics For Supply Chain Visibility, , Microsoft, Suooly Chain Council, Inc; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) J. Operations Management, 22, pp. 209-218; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers In Industry, 56, pp. 663-680; Simpson, D.F., Power, D.J., Use the supply relationship to develop lean and green suppliers (2005) Supply Chain Management: An International J, 10, pp. 60-68; Lantelme, E., Formoso, C.T., (1999) Improving Performance Through Measurement: The Application of Lean Production and Organizational Learning Principles, , 8 th Conference of International Group for Lean Construction, Sussex University, Brighton; Womack, J.P., Jones, D.T., Lean Consumption (2005) Harvard Business Review, 83, pp. 58-68; Lamming, R., Squaring lean supply with supply chain management (1996) International J. Operation and Production Management, 16, pp. 183-196; Razmi, J., Akbarijokar, M.R., Karbasian, S., A model of decision supporting regarding to planning, assessing and supplier selection (2004) Business Researching, 30, pp. 119-142; Shah, R., Ward, P.T., Lean manufacturing: Context, practice bundles and performance (2003) J. Operations Management, 21, pp. 129-149; Womack, J.P., Jones, D.T., (2003) Lean Thinking: Banish Waste and Create Wealth In Your Corporation, , Free Press},
correspondence_address1={Anvari, A.; Islamic Azad University, Iran},
issn={18184952},
language={English},
abbrev_source_title={World Appl. Sci. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Verdecho2011151,
author={Verdecho, M.-J. and Rodríguez-Rodríguez, R. and Alfaro-Saiz, J.-J.},
title={A structured methodology to implement performance measurement systems in collaborative networks},
journal={IFIP Advances in Information and Communication Technology},
year={2011},
volume={362 AICT},
pages={151-158},
doi={10.1007/978-3-642-23330-2_17},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864886161&doi=10.1007%2f978-3-642-23330-2_17&partnerID=40&md5=1cc0d21889cccaa3b8191fdc270fb528},
affiliation={Department of Business Organization, CIGIP (Research Centre on Production Management and Engineering), Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Spain},
abstract={Collaboration is one of the most commonly used strategies in the business environment. Enterprises that collaborate need reliable and efficient performance information. However, many collaborative enterprises fail to implement a common performance measurement system in an adequate manner due to the lack of sound mechanisms that connect all the elements within a performance measurement system. The purpose of this paper is to provide a methodology based on a multi-criteria decision method that aids to implement the different elements that compose a performance measurement system for collaborative networks. With this methodology enterprises have a tool to define all the aspects involved in the performance measurement system and to implement it. This approach has been applied to a collaborative enterprise network belonging to the automotive sector. © 2011 IFIP International Federation for Information Processing.},
author_keywords={Collaborative networks;  Performance management;  Performance measurement systems},
keywords={Automotive sector;  Business environments;  Collaborative enterprise;  Collaborative network;  Multicriteria decision;  Performance management;  Performance measurement system, Automotive industry;  Virtual corporation, Supply chain management},
references={Anthony, T., Supply Chain Collaboration: Success in the New Internet Economy (Online) di ASCET, , http://anthony.ASCET.com; Bowersox, D.J., Closs, D.J., Stank, T.P., How to master cross-enterprise collaboration (2003) Supply Chain Management Review, 7 (4), pp. 18-27; Brewer, P.C., Speh, T.W., Using the Balanced ScoreCard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises. Intern (2005) J. of Operations & Production Management, 25 (4), pp. 333-353; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production Planning and Control, 16 (6), pp. 531-544; Alfaro, J., Ortiz, A., Rodríguez, R., Performance measurement system for Enterprise Networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; Romero, D., Galeano, N., Molina, A., A conceptual model for virtual breeding environments value system (2007) Establishing the Foundation of Collaborative Networks., , Camarinha-Matos, L., Afsarmanesh, H., Novais, P., Analide, C. (eds.). Springer, Heidelberg; Msanjila, S.S., Afsarmanesh, H., Trust analysis and assessment in virtual organization breeding environments (2008) International Journal of Production Research, 46 (5), pp. 1253-1295; Westphal, I., Thoben, K.D., Seifert, M., Measuring collaboration performance in virtual organizations (2007) Establishing the Foundation of Collaborative Networks., , Camarihna-Matos, L., Afsarmanesh, H., Novais, P., Analide, C. (eds.). Springer, Boston; Verdecho, M.J., Alfaro, J.J., Rodriguez-Rodriguez, R., Foundations for collaborative performance measurement (2009) Production Planning and Control, 20 (3), pp. 193-205; Verdecho, M.J., Alfaro, J.J., Rodriguez-Rodriguez, R., COL-PMS: A collaborative performance measurement system (2010) BASYS 2010. IFIP AICT, 322, pp. 56-67. , Ortiz, Á., Franco, R.D., Gasquet, P.G. (eds.). Springer, Heidelberg; Yüksel, I., Dagdeviren, M., Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm (2010) Expert Systems with Applications, 37, pp. 1270-1278; Barba-Romero, S., Pomerol, J., (1997) Decisiones Multicriterio: Fundamentos Teóricos y Utilización Práctica, , Editorial: Universidad de Alcalá de Henares. Alcalá de Henares; Fishburn, P., Independence in utility theory with whole product sets (1965) Operations Research, 13, pp. 28-45; Roy, B., Classement et choix en présence de points de vue multiples: la méthode ELECTRE (1968) Revue Française D'Informatique et de Reserche Opé rationnelle, 8, pp. 57-75; Brans, J., Vincke, P., Mareschal, B., How to select and how to rank projects: The PROMETHEE method (1986) European Journal of Operational Research, 24, pp. 228-238; Saaty, T.L., (1980) The Analytic Hierarchy Process, , McGraw-Hill, New York; Mohr, J., Spekman, R., Characteristics of partnership success: Partnership attributes, communication (1994) Strategic Management Journal, 15 (2), pp. 135-152; Verdecho, M.J., Alfaro, J.J., Rodriguez-Rodriguez, R., A multi-criteria approach to select suppliers based on performance (2010) BASYS 2010. IFIP AICT, 322, pp. 47-47. , Ortiz, Á., Franco, R.D., Gasquet, P.G. (eds.). Springer, Heidelberg; Masella, C., Rangone, A., A contigent approach to the design of vendor selection systems for different types of co-operative customer/supplier relationships (2000) International Journal of Operations & Production Management, 20 (1), pp. 70-84; Ghodsypour, S.H., O'brien, C., A decision support system for supplier selection using an integrated analytic hierarchy process and linear programming (1998) International Journal of Production Economics, 56-57, pp. 199-212; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18 (8), pp. 666-680; Lee, A.H.I., Chen, W.C., Chang, C., A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan (2008) Expert Systems with Applications, 34 (1), pp. 96-107; Cebeci, U., Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard (2009) Expert Systems with Applications, 36, pp. 8900-8909},
correspondence_address1={Verdecho, M.-J.; Department of Business Organization, Camino de Vera, s/n, 46022, Valencia, Spain; email: mverdecho@cigip.upv.es},
issn={18684238},
isbn={9783642233296},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Behrouzi2011237,
author={Behrouzi, F. and Wong, K.Y. and Behrouzi, F.},
title={A study on lean supply chain performance measures of SMEs in the automotive industry},
journal={IEEE International Conference on Industrial Engineering and Engineering Management},
year={2011},
pages={237-241},
doi={10.1109/IEEM.2011.6117914},
art_number={6117914},
note={cited By 17},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856538264&doi=10.1109%2fIEEM.2011.6117914&partnerID=40&md5=6d782b90cb4a4c3dd3c48e88296387fc},
affiliation={Department of Manufacturing and Industrial Engineering, Universiti Teknologi Malaysia, Malaysia},
abstract={This paper aims to investigate the concept of lean supply chain, and develop a list of performance measures for manufacturing small and medium size enterprises (SMEs) in the automotive industry. A total number of 28 performance measures were identified to represent the leanness of supply chain. Data were collected from 133 supply chain practitioners working at manufacturing SMEs in Iran's automotive industry. Lean supply chain performance was divided into four components (attributes), i.e. waste elimination, continuous improvement, just-in-time (JIT), and flexibility. Delivery & reliability (D&R) was selected as a representative for JIT. Flexibility was directly connected to the measures without any division. This study gives a real insight into the lean supply chain performance measures, and contributes to expand the knowledge in this area. The lean performance attributes and related measures can also be used as inputs for a lean supply chain performance measurement system. © 2011 IEEE.},
author_keywords={Automotive industry;  lean supply chain;  performance measures;  SMEs},
keywords={Continuous improvements;  Just-in-time;  Lean supply;  Performance attributes;  Performance measure;  Small and medium-size enterprise;  SMEs, Industrial engineering;  Just in time production;  Manufacture;  Supply chains, Automotive industry},
references={Gunasekaran, A., Patel, C., McGaughey, R., Performance measures and metrics in supply chain environment (2001) International Journal of Operations & Production Management, 21, pp. 71-87; Chan, F.T., Qi, H.J., Chan, H.K., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) MANAGEMENT DECISION -LONDON THEN BRADFORD, 41 (7), pp. 635-642. , DOI 10.1108/00251740310495568; Neely, A., Bourne, M., Kennerly, M., Performance measurement system design (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Womack, J.P., Jones, D.T., (1990) The Machine That Changed the World, , Rawson Associates, NY; Oliver, R.K., Webber, M.D., Supply Chain Management: Logistics catches up with strategy (1992) Logistics: The Strategic Issues; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance improvement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Keepler, J.S., Plank, R.E., Logistics performance measurement in the supply chain: A benchmark (2009) International Journal of Benchmarking, 16 (6), pp. 785-798; Beamon, B.M., Performance measures in supply chain management (1996) Proceedings of the Agile and Intelligent Manufacturing Symposium, , Rensselaer Polytechnic Institute; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) The Journal of the Operational Research Society, 54 (5), pp. 507-514; Maskell, B., Performance measure for world class manufacturing (1989) Management Accounting, pp. 32-33. , May; Doolen, T.L., Hacker, M.E., A review of lean assessment in organizations: An exploratory study of lean practices by electronics manufacturers (2005) Journal of Manufacturing Systems, 24 (1), pp. 55-67. , PII S027861250580007X; Kojima, S., Kaplinsky, R., The use of a lean production index in explaining the transition to global competitiveness: The auto components sector in South Africa (2004) Technovation, 24, pp. 199-206; Wan, H., (2006) Measuring Leanness of Manufacturing Systems and Identifying Leanness Target by Considering Agility, , Ph.D. Dissertation, Virginia Polytechnic Institute and State University; Bayou, M.E., De Korvin, A., Measuring the leanness of manufacturing systems: A case study of Ford Motor Company and General Motors (2008) Journal of Engineering Technology Management, 25, pp. 287-304; Gurumurthy, A., Kodali, R., Application of benchmarking for assessing the lean manufacturing implementation (2009) International Journal of Benchmarking, 25 (2), pp. 274-308; Momeni, M., Monavarian, A., Shaabani, E., Ghasemi, R., A Conceptual Model for Knowledge Management Process Capabilities and Core Competencies by SEM the Case of Iranian Automotive Industry (2011) European Journal of Social Sciences, 25 (4), pp. 473-489; Samani, M.B., Attafar, A., Khouzani, N.K., A Conceptual Model for IRAN's Car Industry Customers'Loyalty (2011) Proc. 2rd Int. Conf. Business and Economic Research, Langkawi Kedah, Malaysia, pp. 790-797},
correspondence_address1={Behrouzi, F.; Department of Manufacturing and Industrial Engineering, Malaysia; email: Farzad_behrouzi@yahoo.com},
issn={21573611},
isbn={9781457707391},
language={English},
abbrev_source_title={IEEE Int. Conf. Ind. Eng. Eng. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Jiang2011375,
author={Jiang, Y. and Zhou, L.-J.},
title={The quantitative research on the index system of supply chain performance measurement based on SCOR},
journal={Advances in Intelligent and Soft Computing},
year={2011},
volume={112},
pages={375-382},
doi={10.1007/978-3-642-25194-8_46},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855222083&doi=10.1007%2f978-3-642-25194-8_46&partnerID=40&md5=905648cf0a34dbe9bfe4aabec7f4dd7d},
affiliation={Department of Foreign Languages, Nanjing Institute of Industry Technology, Nanjing, China; School of Economy and Management, Nanjing Institute of Industry Technology, Nanjing, China},
abstract={Supply chain performance measurement is the key part of supply chain management. Its systematic and profound research is helpful to develop supply chain management and its application. Based on SCOR analysis and the definition of the contents of supply chain performance measurement, the design principle and characteristic of supply chain performance measurement index system can be summarized and supply chain performance measurement index system based on the overall performance of supply chain. Along with fuzzy comprehensive measurement method, the specific quantitative method and process of these indexes can be defined so as to improve performance measurement system systematically and practically. At last, the paper puts forward how to further study it. © Springer-Verlag Berlin Heidelberg 2011.},
author_keywords={Fuzzy comprehensive measurement method;  Performance measurement;  SCOR;  Supply chain},
keywords={Comprehensive measurement;  Design Principles;  Index systems;  Key parts;  Performance measurement system;  Performance measurements;  Quantitative method;  Quantitative research;  SCOR;  Supply chain performance, Measurements;  Supply chains, Supply chain management},
references={Liu, C., Application of fuzzy comprehensive measurement in logistics system measurement (2004) China Construction Machinery, (7); Schultz, G.J., Keeping SCOR on your supply chain. Basic operations refenrence model updates with the times (2003) Information Strategy, 19 (4), pp. 12-20; Zhang, Y., Core Competitive Power, , China Materials Press, Beijing; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, (55), pp. 281-294; Zuo, Y., Application of fuzzy comprehensive measurement in distribution center logistics performance measurement (2006) Market Modernization, (7), pp. 94-96},
correspondence_address1={Jiang, Y.; Department of Foreign Languages, , Nanjing, China; email: jiangy@niit.edu.cn},
editor={Jiang L.},
issn={18675662},
isbn={9783642251931},
language={English},
abbrev_source_title={Adv. Intell. Soft Comput.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lei2011684,
author={Lei, X. and Qiu, G. and Liu, Y.},
title={Study on supply chain performance measure system},
journal={Proceedings of 2011 IEEE International Conference on Grey Systems and Intelligent Services, GSIS'11 - Joint with the 15th WOSC International Congress on Cybernetics and Systems},
year={2011},
pages={684-688},
doi={10.1109/GSIS.2011.6044100},
art_number={6044100},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80155132478&doi=10.1109%2fGSIS.2011.6044100&partnerID=40&md5=1e0644db0aab6cc4a00a93287f4fbeef},
affiliation={Department of Business Administration, Tongling University, Tongling 244000, China; College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China},
abstract={In this paper domestic and foreign performance measure theory about supply chain (SC) is reviewed comprehensively, as well as the problems and disadvantages in performance measure system is discussed in details. Based on these, SC performance measure index system is constructed from the perspectives of customers, to address benefit-relative, SC stability, finance, life cycle and sustainable development. Finally, the future research is discussed. © 2011 IEEE.},
author_keywords={Measure system;  performance measure;  SC performance;  supply chain (SC)},
keywords={Index systems;  Measure system;  Performance measure;  Supply chain performance, Cybernetics;  Supply chains;  System theory, Supply chain management},
references={Huo, J.-Z., Sui, M.-G., Liu, Z.-Y., Whole performance measure system construction of integration supply chain (2000) Journal of Tongji University (Natural Science), 30 (4), pp. 495-499. , in Chinese; Liu, Y.-S., Ma, Y., Ecology supply chain performance measure system (2003) Enterprise Economy, (6), pp. 45-48. , in Chinese; Huo, J.-Z., (2001) Enterprise Measure Innovation - Integration Supply Chain Measure and Innovation (in Chinese), , People press in He nan; Ellram, L.M., A managerial guideline for the development and implementation of purchasing partnerships (1991) Journai of Purchasing and Material Management, pp. 2-12. , August; Kaplan, R.S., Norton, D.P., The Balanced Scorecard-Measures That Drive Performance (1992) Harvard Business Review, pp. 71-79. , Jan-Feb; SCOR (Supply Chain Operation Reference Model), , PRTM; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, pp. 281-294; Supply chain management: Theory and applications (2004) European Journal of Operation Research, (159), pp. 265-268. , Editorial; Neely, A., MiIs, J., Platts, K., Performance measurement system design: Developing and testing approcess-based approach (2000) Iternational Jurnal of Oeration & Poduction Mnagement, (10), pp. 1119-1145; Gunaasekaranan, A., Patel, C., Gaughey, R.E.M., A framework for Supply chain performance management (2004) International Journal of Production Economics, 87, pp. 333-347; Krause, D.R., Pagell, M., Curkovic, S., Toward a measure of competitive priorities for purchasing (2001) Journal of Operation Management, 19, pp. 497-512; Dong, Y., Carter, C.R., Dresner, M.E., JIT purchasing and performance: An exploratory analysis of buyer and supplier perspective (2001) Journal of Operation Management, 19, pp. 471-483},
correspondence_address1={Lei, X.; Department of Business Administration, , Tongling 244000, China; email: leixunping@126.com},
isbn={9781612844886},
language={English},
abbrev_source_title={Proc. IEEE Int. Conf. Grey Syst. Intelligent Serv., GSIS - Jt. WOSC Int. Congr. Cybern. Syst.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Turhan2011421,
author={Turhan, Z.D. and Vayvay, O. and Birgun, S.},
title={Supply chain reengineering in a paint company using axiomatic design},
journal={International Journal of Advanced Manufacturing Technology},
year={2011},
volume={57},
number={5-8},
pages={421-435},
doi={10.1007/s00170-011-3296-4},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-83555174655&doi=10.1007%2fs00170-011-3296-4&partnerID=40&md5=c2b113d97c270230fee2c703e07b9994},
affiliation={Marmara University, Göztepe, Istanbul 34722, Turkey; Istanbul Commerce University, Kucukyali, Istanbul 34840, Turkey},
abstract={The purpose of this paper is to outline the importance and benefits an organization can achieve through using a decomposition-based decision-making tool incorporating supply chain-balanced scorecard performance measures for supply chain reengineering. An axiomatic design-based model built on the insights gained from supply chain performance measures and reengineering model is applied to a paint company. The model aids supply chain reengineering by identifying the scope of the reengineering design. The resultant reengineering design based on the needs and strategies of the firm in question is presented. A thorough simulation study follows to impart substantial improvements. The case displays how the model can help companies adopt a more structured approach to the complicated problem of supply chain reengineering. This paper focuses on supply chain reengineering models where no conclusive work exists and is expected to fill a gap in the area and be instrumental to both researchers and practitioners by providing a direction for coherent decision making on supply chain reengineering studies. © 2011 Springer-Verlag London Limited.},
author_keywords={Axiomatic design;  Case study;  Performance measurement;  Reengineering;  Supply chain management},
keywords={Axiomatic design;  Paint companies;  Performance measure;  Performance measurements;  Simulation studies;  Structured approach;  Supply chain performance, Decision making;  Design;  Industry;  Paint;  Supply chain management, Reengineering},
references={Rao Tummala, V.M., Phillips, C.L.M., Johnson, M., Assessing supply chain management success factors: A case study (2006) Supply Chain Management, 11 (2), pp. 179-192. , DOI 10.1108/13598540610652573; Ozcelik, Y., Do business process reengineering projects pay off? Evidence from the United States (2010) Int J Proj Manag, 28, pp. 7-13. , 10.1016/j.ijproman.2009.03.004; Burgess, R., Avoiding supply chain management failure: Lessons from business process re-engineering (1998) Int J Logist Manag, 9 (1), pp. 15-23. , 10.1108/09574099810805717; Van Hoek, R.I., 'Measuring the immeasurable' - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192. , DOI 10.1108/13598549810244232; Evans, G.N., Towill, D.R., Naim, M.M., Business process reengineering the supply chain (1995) Prod Plan Control, 6 (3), pp. 38-42; Nissen, M.E., Intelligent tool for process redesign: Manufacturing supply-chain applications (2000) International Journal of Flexible Manufacturing Systems, 12 (4), pp. 321-339. , DOI 10.1023/A:1008178217369; Segars, A.H., Harkness, W.J., Kettinger, W.J., Process management and supply-chain integration at the Bose Corporation (2001) Interfaces, 31 (3), pp. 102-114; Trkman, P., Stemberger, M.I., Jaklic, J., Groznik, A., Process approach to supply chain integration (2007) Supply Chain Manag, 12 (2), pp. 116-128. , 10.1108/13598540710737307; Mohanty, R.P., Deshmukh, S.G., Reengineering of a supply chain management system: A case study (2000) Production Planning and Control, 11 (1), pp. 90-104. , DOI 10.1080/095372800232522; Kumar, A., Ozdamar, L., Zhang, C.N., Supply chain redesign in the healthcare industry of Singapore (2008) Supply Chain Manag, 13 (2), pp. 95-103. , 10.1108/13598540810860930; McLaughlin, J., Motwani, J., Madan, M., Gunasekaran, A., Using information technology to improve downstream supply chain operations: A case study (2003) Bus Process Manag J, 9 (1), pp. 69-80. , 10.1108/14637150310461413; Sehgal, S., Sahay, B.S., Goyal, S.K., Reengineering the supply chain in a paint company (2006) International Journal of Productivity and Performance Management, 55 (8), pp. 655-670. , DOI 10.1108/17410400610710198; Varma, S., Wadhwa, S., Deshmukh, S.G., Implementing supply chain management in a firm: Issues and remedies (2006) Asia Pac J Market Logist, 18 (3), pp. 223-243. , 10.1108/13555850610675670; Gorla, N., Chinta, R., Chu, T.W., An enhanced business process re-engineering model for supply chain management and a case study (2007) J Inf Tech Case App Res, 9 (2), pp. 5-27; Zhang, L.L., Jaio, R.J., Ma, Q., Accountability-based order fulfillment process reengineering towards supply chain management: A case study at a semiconductor equipment manufacturer (2010) J Manuf Tech Manag, 21 (2), pp. 287-305. , 10.1108/17410381011014413; Fowler, A., Operations management and systemic modelling as frameworks for BPR (1998) International Journal of Operations and Production Management, 18 (9-10), pp. 1028-1056. , DOI 10.1108/01443579810225603; Trienekens, J.H., Hvolby, H.-H., Models for supply chain reengineering (2001) Production Planning and Control, 12 (SPEC. 3), pp. 254-264. , DOI 10.1080/095372801300107761; Cheng, L.C., Grimm, C.M., The application of empirical strategic management research to supply chain management (2006) J Bus Logist, 27 (1), pp. 1-55. , 10.1002/j.2158-1592.2006.tb00240.x; Rodriguez-Diaz, M., Espino-Rodriguez, T.F., Redesigning the supply chain: Reengineering, outsourcing, and relational capabilities (2006) Business Process Management Journal, 12 (4), pp. 483-502. , DOI 10.1108/14637150610678087; Chen, S.-J., Huang, E., A systematic approach for supply chain improvement using design structure matrix (2007) Journal of Intelligent Manufacturing, 18 (2), pp. 285-299. , DOI 10.1007/s10845-007-0022-z; Simatupang, T.M., Sridharan, R., Supply chain discontent (2005) Business Process Management Journal, 11 (4), pp. 349-369. , DOI 10.1108/14637150510609390; Suh, N.P., (1990) The Principles of Design, , Oxford University Press New York; Cochran, D.S., Arinez, J.F., Duda, J.W., Linck, J., A decomposition approach for manufacturing system design (2002) J Manuf Syst, 20 (6), pp. 371-389. , 10.1016/S0278-6125(01)80058-3; Schnetzler, M.J., Sennheiser, A., Schonsleben, P., A decomposition-based approach for the development of a supply chain strategy (2007) International Journal of Production Economics, 105 (1), pp. 21-42. , DOI 10.1016/j.ijpe.2006.02.004, PII S0925527306000600; Kulak, O., Cebi, S., Kahraman, C., Applications of axiomatic design principles: A literature review (2010) Expert Syst Appl, 37, pp. 6705-6717. , 10.1016/j.eswa.2010.03.061; Brown, C.A., Teaching axiomatic design to engineers-theory, applications, and software (2005) J Manuf Syst, 24 (3), pp. 186-195. , 10.1016/S0278-6125(06)80007-5; Houshmand, M., Jamshidnezhad, B., An extended model of design process of lean production systems by means of process variables (2006) Robotics and Computer-Integrated Manufacturing, 22 (1), pp. 1-16. , DOI 10.1016/j.rcim.2005.01.004, PII S0736584505000165; Kulak, O., Durmusoglu, M.B., Tufekci, S.A., Complete cellular manufacturing system design methodology based ob axiomatic design principles (2004) Comp Ind Eng, 48, pp. 765-787. , 10.1016/j.cie.2004.12.006; Kulak, O., Kahraman, C., Fuzzy multi-attribute selection among transportation companies using axiomatic design and analytic hierarchy process (2005) Information Sciences, 170 (2-4), pp. 191-210. , DOI 10.1016/j.ins.2004.02.021, PII S0020025504000477; Turhan, Z.D., Vayvay, O., A performance-based decision-making tool for supply chain reengineering (2011) Int J Bus Excel, 4 (3), pp. 298-320. , 10.1504/IJBEX.2011.040107; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int J Prod Eco, 87, pp. 333-347. , 10.1016/j.ijpe.2003.08.003; Slone, R.E., Leading a supply chain turnaround (2004) Harv Bus Rev, 82, pp. 114-121; Wang, G., Huang, S.H., Dismukes, J.P., Manufacturing supply chain design and evaluation (2005) International Journal of Advanced Manufacturing Technology, 25 (1-2), pp. 93-100. , DOI 10.1007/s00170-003-1791-y; Butner, K., The smarter supply chain of the future (2010) Strat Lead, 38 (1), pp. 22-31. , 10.1108/10878571011009859},
correspondence_address1={Vayvay, O.; Marmara University, Göztepe, Istanbul 34722, Turkey; email: ozalp@marmara.edu.tr},
issn={02683768},
coden={IJATE},
language={English},
abbrev_source_title={Int J Adv Manuf Technol},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cha20111319,
author={Cha, H.S. and Kim, C.K.},
title={Quantitative approach for project performance measurement on building construction in South Korea},
journal={KSCE Journal of Civil Engineering},
year={2011},
volume={15},
number={8},
pages={1319-1328},
doi={10.1007/s12205-011-1323-5},
note={cited By 35},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80355144849&doi=10.1007%2fs12205-011-1323-5&partnerID=40&md5=7e970e40250bf7eb95ea5873303927ef},
affiliation={Dept. of Architectural Engineering, Ajou University, Suwon 443-749, South Korea; Dept. of Architectural Engineering, Sunmoon University, Asan 336-708, South Korea},
abstract={In general, the performance of a particular construction project can be evaluated by how many of the project goals are achieved in terms of cost, time, quality, safety, environment, and so forth. However, in the construction industry, which is characterized as project specific, a clear delineation of scope of performance is absent, and the standard procedure and operations are not fully developed. Thus, it is very difficult to assess an overall performance level of a constructed project in a reasonable manner. The objective of this research is to define a quantitative performance measurement system and establish the evaluation criteria by identifying 18 key performance indicators, focusing on residential building projects. Based on the performance assessment methodology, 22 project cases have been collected for comparative analysis and have been evaluated to determine whether the system is applicable to the potential projects. The set of identified performance indicators is also useful in developing a customized performance measurement system for a particular type of project. The research findings are expected to encourage the project stakeholders to develop a performance-based project control system, thereby enhancing their level of satisfaction. © 2011 Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg.},
author_keywords={building construction;  key performance indicator;  metrics;  quantitative analysis},
keywords={Building construction;  Comparative analysis;  Construction projects;  Evaluation criteria;  Key performance indicators;  Level of satisfaction;  metrics;  Performance assessment methodology;  Performance indicators;  Performance level;  Performance measurement system;  Project control systems;  Project goals;  Project performance;  Project stakeholders;  Quantitative approach;  Residential building;  South Korea;  Standard procedures, Construction industry;  Measurements;  Project management;  Rating;  Supply chain management;  Waste disposal, Benchmarking},
references={Alarcon, L.F., Ashley, D.B., Modeling project performance for decision making (1996) ASCE J. Constr. Eng. Manage., 122 (3), pp. 265-273; Cha, H., Kim, T., Developing measurement system for key performance indicators on building construction projects (2008) KICEM J. Of Constr. Mgmt., 9 (4), pp. 120-130; Cha, H., O'Connor, J.T., Characteristics for leveraging value management processes on capital facility projects (2006) ASCE J. Of Mgmt Engr., 22 (3), pp. 135-147; Chan, A.P.C., Scott, D., Chan, A.P.L., Factors affecting the success of a construction project (2004) ASCE J. Constr. Eng. Manage., 130 (1), pp. 153-155; (2006) Industry Performance Report 2006, , Constructing Excellence, London, U.K.: CE; (2001) CII Benchmarking and Metrics Data Report 2001, , Construction Industry Institute, Austin, Tex: CII; (2008) Benchmarking & Metrics Project Level Survey Version 10, , Construction Industry Institute (CII), 3, Austin, Tex; Costa, D.B., Formoso, C.T., Kagioglou, M., Alarcon, L.F., Performance measurement system for benchmarking in the construction industry (2004) Proc. 12th IGLC Conf, pp. 451-463. , LO-School, Elsinore, Denmark; Cox, R.F., Issa, R.R.A., Ahrens, D., Management's perception of key performance indicators for construction (2003) ASCE J. Constr. Eng. Manage., 129, p. 2; Egan, J., (1997) Rethinking Construction, , London: Construction Task Force; Hwang, B., Thomas, S., Caldas, C., Performance metric development for pharmaceutical construction projects (2010) Int. J. Of Proj. Mgmt, 28 (3), pp. 265-274; Jung, S., Kim, Y., Jin, S., Yoon, S., A proposal on the assessment criteria and conceptual methodology of performance measurement based on efficiency through construction project life cycle (2005) J. Of Architectural Institut. Of Korea, 21 (7), pp. 139-148; Khaled, M.N., Hordur, G.G., Mohamed, Y.H., Using weibull analysis for evaluation of cost and schedule performance (2005) ASCE J. Constr. Engrg. And Mgmt., 131 (12), pp. 1257-1262; Kim, W., Koo, K., Hyun, C., A study on the selection of post-evaluation factors for the building construction (2003) J. Of Architectural Institut. Of Korea, 19 (12), pp. 171-178; O'Connor, J.T., Yang, L., Project performance vs. use of technologies at the project- and phase-levels (2004) ASCE J. Constr. Eng. Manage., 130 (3), pp. 322-329; Shin, Y., Kim, H., Performance measurement techniques and best practice in construction projects (2004) J. Of Architectural Institut. Of Korea, 20 (3), pp. 109-116; Song, S., Lee, H., Park, M., Quality performance management system for construction projects using quality performance indicators (2006) KICEM J. Of Const. Mgmt., 7 (3), pp. 76-85; Yu, I., Kim, K., Jung, Y., Chin, S., Comparable performance measurement system for construction companies (2007) ASCE J. Of Mgmt. Engr., 23 (3), pp. 131-139},
correspondence_address1={Cha, H. S.; Dept. of Architectural Engineering, , Suwon 443-749, South Korea; email: hscha@ajou.ac.kr},
issn={12267988},
language={English},
abbrev_source_title={KSCE J. Civ. Eng.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Li20115316,
author={Li, Y. and Li, C.},
title={Study on supply chain performance measurement system for agribusiness},
journal={2011 2nd International Conference on Mechanic Automation and Control Engineering, MACE 2011 - Proceedings},
year={2011},
pages={5316-5319},
doi={10.1109/MACE.2011.5988192},
art_number={5988192},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054821040&doi=10.1109%2fMACE.2011.5988192&partnerID=40&md5=aff6deb64f3207c5db0bab5f059c2a40},
affiliation={College of Economic and Management, Heilongjiang Bayi Agricultural University, Daqing, China},
abstract={Agribusiness, as a sub-system of China's market economy system, has an extremely important position. The improvement of agribusiness supply chain performance, is an important way to enhance the market competitiveness of agricultural enterprises in China. Based on BP Neural Network Model, the evaluation system of China's agricultural enterprise supply chain performance, which is on the basis of drawing on research findings on the supply chain at home and abroad, is constructed in this paper. The results show that the performance evaluation system constructed in this paper runs better and is. suitable for the performance evaluation on China's agribusiness supply chain. © 2011 IEEE.},
author_keywords={Agribusiness;  BP Neural Network Model;  Performance;  Supply chain},
keywords={Agribusiness;  Agricultural enterprise;  BP neural network model;  Evaluation system;  Market economies;  Performance;  Performance evaluation;  Performance evaluation system;  Sub-systems;  Supply chain performance, Competition;  Economics;  Mechanics;  Neural networks;  Supply chain management, Supply chains},
references={Robert, S., Kaplan, D.P., Norton, Thebalancedscorecard-measuresthat dirve pefrormance,. (1992) Harvard Business Review, pp. 7l-791; Bullinger, H., Kuhner, M., Hoof, A.V., Analyzing supply chain pefrormance using balanced measurement method (2002) Production Research, 3533, p. 3543; Lummus, R.R., Vokurka, R., Jand, A.K., Strategic, L., Supply chain planning (1998) Production and Inventory Managemen T Journal, 49, p. 58; Huo, J.-Z., Sui, M.-G., Liu, Z.-Y., Enterprise performance and supply chain performance evaluation research (2001) Journal of Tongji University,Natural Science Edition, 976, p. 979. , In chinese; Kang, S.-Y., (2005) Multi-level Dynamic Fuzzy Evaluation and the Combination of BP Neural Network Method, p. 22. , Chongqing Technology and Business University Press. (In chinese); Sun, C.-H., Price Value engineering (2004) Supply Chain Management Enterprise Performance Evaluation Index System, , In chinese},
correspondence_address1={Li, Y.; College of Economic and Management, , Daqing, China; email: lym1184@163.com},
isbn={9781424494392},
language={Chinese},
abbrev_source_title={Int. Conf. Mech. Autom. Control Eng., MACE - Proc.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pidun201196,
author={Pidun, T. and Felden, C.},
title={On the restriction to numeric indicators in Performance Measurement Systems},
journal={Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOC},
year={2011},
pages={96-102},
doi={10.1109/EDOCW.2011.16},
art_number={6037608},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054817792&doi=10.1109%2fEDOCW.2011.16&partnerID=40&md5=7925be943b8d5ac0cfce29dfacaf0daa},
affiliation={Information Science, TU Bergakademie Freiberg, Freiberg, Germany},
abstract={Many business processes cannot be easily measured due to their non-deterministic or qualitative nature. So, to fit in common performance measurement systems (PMS), artificial and simplifying measures are used that are complicated and costly to create and evaluate. Using a literature review, this paper documents the dominance of PMS that rely on KPIs and a lack of those that also incorporate non-numeric, generic indicators that better address qualitative problems. Considering the need for better transparency and comparability of business processes as well as visibility of process performance, the final necessity of deployment for a refined PMS using additional indirect indicators is derived. It would be able to assess hidden performance problems and to reveal additional improvement possibilities. © 2011 IEEE.},
author_keywords={BPM;  Indicator;  KPI;  Measurement paradigm;  Performance Measurement System;  Process Performance Management;  Visibiliy},
keywords={BPM;  KPI;  Performance measurement system;  Process performance;  Visibiliy, Measurements, Supply chain management},
references={Chennell, A., Dransfield, S., Field, J., Fisher, N., Saunders, I., Shaw, D., OPM: A system for organisational performance measurement (2000) Proceedings of the Performance Measurement -Past, Present and Future Conference, , Cambridge; Raschke, R., Ingraham, L., Business process maturity's effect on performance Proceedings of the Sixteenth Americas Conference on Information Systems, , http://aisel.aisnet.org/amcis2010/402, last access 27 August 2010; Pidun, T., Felden, C., A reference model catalog of models for business process analysis (2010) Proceedings of the Sixteenth Americas Conference on Information Systems, , http://aisel.aisnet.org/amcis2010/169; Franco-Santos, M., Bourne, M., An examination of the literature relating to issues affecting how companies manage through measures (2005) Production Planning and Control, 16 (2), pp. 114-124. , DOI 10.1080/09537280512331333020, PII N76175243811; Sheu, C., Wacker, J., Effectiveness of planning and control systems (2001) International Journal of Production Research, 39 (5), pp. 887-905. , Taylor & Francis, London; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) IJOPM, 22 (11), pp. 1288-1310. , Emerald, Bingley; Miles, J., (2008) Discovering Critical Success Factors for Implementing An Automated Performance Measurement System: A Case Study, , Dissertation, Edith Cowan University; Lönnqvist, A., (2004) Measurement of Intangible Success Factors: Case Studies on the Design, Implementation and Use of Measures, 485. , Dissertation, Tampere University of Technology, Publication; Detoni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) IJOPM, 21 (1-2), pp. 46-70. , MCB, University Press, Bingley; Pun, K.F., White, A.S., A performance measurement paradigm for integrating strategy formulation: A review of systems and frameworks (2005) International Journal of Management Reviews, 7 (1), pp. 49-71. , DOI 10.1111/j.1468-2370.2005.00106.x; Franco-Santos, M., Kennerly, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) IJOPM, 27 (8), pp. 784-801. , Emerald, Bingley; Drucker, P., (1966) The Effective Executive, , Harper & Row, New York; Actano Glossary, , http://www.actano.com/20911_ENWhat%B4s_new-Glossary.htm#P, last access 4 November 2010; Denyer, D., Tranfield, R., Producing a Systematic Review (2009) The Sage Handbook of Organizational Research Methods, pp. 671-689. , D.A. Buchanan and A. Bryman (Eds.), Sage Publications, London; Wisner, J., Fawcett, S., Link firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, pp. 5-11. , Third Quarter, APICS, Chicago; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533. , DOI 10.1108/01443570010318896; MacHland, M., Raymond, L., Researching performance measurement systems - An information systems perspective (2008) IJOPM, 28 (7), pp. 663-686. , Emerald, Bingley; Smullen, J., (1997) Balance Scorecards and Activity Based Management: The Practical Application, , FT Pitman Publishing, London; Kaplan, R., Norton, D., Using the Balanced Scorecard as Strategic Management System (1996) Harvard Business Review, pp. 75-85. , January-February, Harvard Business Publishing, Boston; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , DOI 10.1108/01443570010330739; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24. , DOI 10.1504/IJBPM.2003.002097; Kueng, P., Krahn, A.J.W., Building a Process Performance Measurement System: Some Early Experiences (1999) Journal of Scientific and Industrial Research, 58 (3), pp. 149-159; Ittner, D., Larcker, D., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, pp. 88-95. , HBS Publishing, Boston; Bierbusse, P., Siesfeld, T., Measures that matter (1997) Journal of Strategic Performance Measurement, 1 (2), pp. 6-11. , Emerald, Bingley; Schneiderman, A., Why balanced scorecards fail (1999) Journal of Strategic Performance Measurement, pp. 6-11. , special edition 6; Brown, M., (1996) Keeping Score: Using the Right Metrics to Drive Worldclass Performance, , Quality Resources, New York; Meekings, A., Unlocking the potential of performance Measurement (1995) Public Money and Management, pp. 1-8. , Oct-Dec, Routledge, Abingdon; Moxham, C., Performance measurement - Examining the applicability of the existing body of knowledge to nonprofit organisations (2009) IJOPM, 29 (7), pp. 740-763. , Emerald, Bingley; Rehage, J., Zwischen Realität und Wunschdenken (2009) IT&Produktion, 12 (9), pp. 36-39. , Special publication ERP/CRM Wissen kompakt, TeDo, Verlag, Marburg; Stalk, G., Hout, T., How time-based management measures performance (1993) Strategy & Leadership, 18 (6), pp. 26-29. , MCB, University Press, Bingley; Tangen, S., Performance measurement: From philosophy to practice (2004) IJPPM, 53 (8), pp. 726-737. , Emerald, Bingley; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Lin, F., Yang, M., Pai, Y., A generic structure for business process modeling (2002) BPMJ, 8 (1), pp. 19-41. , Emerald, Bingley; Beatham, S., Anumba, C., Thorpe, T., Hedges, I., KPIs: A critical appraisal of their use in construction (2004) Benchmarking, 11 (1), pp. 93-117. , DOI 10.1108/14635770410520320; Craciun, L., Criveanu, R., The performance measurement system and key performance indicators (2005) Annals of the University of Petrosani, Economics, 5, pp. 63-72. , University of Petrosani, Petrosany; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680. , DOI 10.1016/j.compind.2005.03.001, PII S0166361505000412; Garengo, P., Biazzo, S., Bititci, U.S., Performance measurement systems in SMEs: A review for a research agenda (2005) International Journal of Management Reviews, 7 (1), pp. 25-47. , DOI 10.1111/j.1468-2370.2005.00105.x; Neely, A., Adams, C., Kennerly, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , FT, Prentice Hall, London; Lynch, R., Cross, K., (1991) Measure Up: The Essential Guide to Measuring Business Performance, , Mandarin, London; Fitzgerald, L., Moon, P., (1996) Performance Measurement in Service Industries: Making It Work, , The Chartered Institute of Management Accountants, London; Dixon, J., Nanni, A., Vollmann, T., (1990) The New Performance Challenge: Measuring Operations for World-class Competition, , Business One, Irwin, Homewood; Keegan, D., Eiler, R., Jones, C., Are your performance measures obsolete? (1989) Management Accounting, 70 (12), pp. 45-50. , ABI/INFORM, June, ABI/Inform Global, Ann Arbor; Sink, D., Tuttle, T., (1989) Planning and Measurement in Your Organisation of the Future, pp. 170-184. , Chapter 5, Industrial Engineering and Management Press, Norcross; Moll, A., Rundum erneuert - EFQM-Modell und Radar-Logik in revidierter Version 2010 (2009) Qualität und Zuverlässigkeit (QZ), 11 (2009), pp. 13-15; Senapati, N.R., Six Sigma: myths and realities (2004) International Journal of Quality and Reliability Management, 21 (6), pp. 683-690. , DOI 10.1108/02656710410542070; Anderson, E., Fornell, C., Lehmann, D., Customer satisfaction, market share and profitability: Findings from Sweden (1994) Journal of Marketing, 58, pp. 53-66; Jeanes, C., Customer satisfaction and business results: Is there a link? (1996) Customer Service Management, pp. 46-48. , March; Bucher, T., Winter, R., Realisierungsformen des Geschäftsprozessmanagements - Eine explorative Klassifikationsanalyse (2007) Organisation: Service, Prozess-, Market-Engineering, (1), pp. 695-713. , A. Oberweis (ed.), Universitätsverlag Karlsruhe, Karlsruhe; Pidun, T., Buder, J., Felden, C., Optimizing process performance visibility through additional descriptive features in performance measurement (2011) 4th International Workshop on Evolutionary Business Processes (EVL-BP 2011), , in Press; Power, M., (1997) The Audit Society, , Rituals of Verification, Oxford, University Press; Zerfass, A., Rituale der Verifikation? Grundlagen und Grenzen des Kommunikations- Controlling (2005) Distinktion und Deutungsmacht Studien zu Theorie und Pragmatik der Public Relations, pp. 183-222. , L. Rademacher (Ed.). VS Verlag für Sozialwissenschaften, Wiesbaden; Broadbent, J., If you can't measure it, how can you manage it? (2007) Management and Governance in Higher Educational Institutions, Public Money and Management, 27 (3), pp. 193-198; Dette, D., Abele, A., Renner, O., Zur Definition und Messung von Berufserfolg (2004) Zeitschrift für Personalpsychologie, 3 (4), pp. 170-183},
correspondence_address1={Pidun, T.; Information Science, , Freiberg, Germany; email: tim.pidun@bwl.tu-freiberg.de},
issn={15417719},
isbn={9780769544267},
language={English},
abbrev_source_title={Proc IEE Int. Enterprise Distrib. Obj. Comput. Workshop EDOC},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Behrouzi20115239,
author={Behrouzi, F. and Wong, K.Y.},
title={An investigation and identification of lean supply chain performance measures in the automotive SMES},
journal={Scientific Research and Essays},
year={2011},
volume={6},
number={24},
pages={5239-5252},
note={cited By 34},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80055003772&partnerID=40&md5=080f496f4de03ff68b745970b5258f31},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia},
abstract={The basic goal of this paper is to identify the underlying lean supply chain performance components and related measures with a special focus on small and medium enterprises in Iran's automotive industry. An initial list of supply chain performance measures was compiled and then modified by six experts in order to extract the appropriate lean supply chain performance measures. Following this, a questionnaire was designed and sent to 580 supply chain practitioners working at small and medium enterprises in Iran's automotive industry in order to score the measures. Principal component analysis was applied to identify and group the lean supply chain performance components and related measures. From the initial list, a total of 28 performance measures were chosen by the experts and considered essential to monitor the leanness of a supply chain. Four underlying components were identified (quality, cost, flexibility, and delivery and reliability) and the 28 related measures were subsequently grouped under these performance components. By identifying and validating a multi-dimensional list of lean supply chain performance components and related measures for small and medium enterprises, the research can be useful for practitioners or academics that are going to evaluate the leanness of a supply chain, © 2011 Academic Journals.},
author_keywords={Automotive industry;  Iran;  Lean supply chain;  Performance measures;  Small and medium enterprises},
references={Ayers, J.B., (2001) Handbook of supply chain management, , The St. Lucie Press/APICS Series on Resource Management: New York; Bayou, M.E., de Korvin, A., Measuring the leanness of manufacturing systems: A case study of Ford Motor Company and General Motors (2008) J. Eng. Technol. Manage, 25, pp. 287-304; Beamon, B.M., In Proceedings of the Agile and Intelligent Manufacturing Systems (1996) Performance Measures In Supply Chain Management, , Troy, New York; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) Int. J. Prod. Econ, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage, 19 (3), pp. 275-292; Behrouzi, F., Wong, K.Y., Lean performance evaluation of manufacturing systems: A dynamic and innovative approach (2011) Procedia Computer Science, 3, pp. 388-395; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Comput. Ind Eng, 53 (1), pp. 43-62; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Logist, 21 (1), pp. 75-92; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Manage.: Int. J, 14 (6), pp. 422-428; Chan, F., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Manage.: Int. J, 8 (3), pp. 209-223; Chopra, S., Meindl, P., Supply chain management: Strategy (2007) Planning & Operations, , 3rd Ed., Prentice Hall, Pearson Education International: New Jersey; Cohen, S., Roussel, J., (2005) Strategic Supply Chain Management: The Five Disciplines For Top Performance, , McGraw-Hill: New York; Cousins, P.D., Lawson, B., Squire, B., Supply chain management: Theory and practice-the emergence of an academic discipline (2006) Int. J. Oper. Prod. Manage, 26 (7), pp. 697-702; Doolen, T.L., Hacker, M.E., A review of lean assessment in organizations: An exploratory study of lean practices by electronics manufacturers (2005) J. Manuf. Syst, 24 (1), pp. 55-67; Gillyard, A.E., (2003) The supply chain characteristics, logistics and manufacturing strategies, and performance, , Ph.D Thesis, Ohio State University, US; Gulledge, T., Chavusholu, T., Automating the construction of supply chain key performance indicators (2007) Ind. Manage. Data Syst, 108 (6), pp. 750-774; Gunasekaran, A., Patel, C., Tirtirogulu, E., Performance measures and metrics in supply chain environment (2001) Int. J. Oper. Prod. Manage, 21 (2), pp. 71-87; Gurumurthy, A., Kodali, R., Application of benchmarking for assessing the lean manufacturing implementation (2009) Benchmarking Int. J, 25 (2), pp. 274-308; Hoffman, D., The hierarchy of supply chain metrics: Diagnosing your supply chain health (2004) AMR Research, , Boston, Massachusetts; Husby, P.C., Swartwood, D., Fix your supply chain: How to create a sustainable lean improvement roadmap (2009) CRC Press, , Taylor & Francis Group: New York; Jeong, J.S., Hong, P., Customer orientation and performance outcomes in supply chain management (2007) J. Enterprise Inform. Manage, 20 (5), pp. 578-594; Jin, H., (2008) Collaborative decision making in modern supply chains, , Ph.D Thesis, College of Business and Economics, University of Kentucky, US; Johansson, H.J., McHugh, P., Pendlebury, A.J., Wheeler, W.A., Business process reengineering: Breakpoint strategies for market dominance (1993) Wiley, , Chichester, UK; Johnson, D.E., Applied multivariate methods for data analysis (1998) Duxbury Press, , Pacific Grove, California; Jordan, J., Michel, F., The lean company: Making the right choices (2001) Society of Manufacturing Engineering, , Dearborn, Michigan; Kaiser, H.F., The application of electronic computers to factor analysis (1960) Educ. Psychol Measurement, 20, pp. 141-151; Kaplan, R.S., Norton, D.P., The balanced score card: Measures that drive performance (1992) Harvard Bus Rev, 70 (1), pp. 71-79; Keebler, J.S., Manrodt, K.B., Durtsche, D.A., Ledyard, D.M., Keeping SCORE: Measuring the business value of logistics in the supply chain (1999) Council of Logistics Management, , Oak Brook, IL; Kojima, S., Kaplinsky, R., The use of a lean production index in explaining the transition to global competitiveness: The auto components sector in South Africa (2004) Technovation, 24, pp. 199-206; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int. J. Logist. Manage, 12 (1), pp. 1-17; Manrodt, K.B., Abott, J., Vitasek, K., (2005) Understanding the Lean Supply Chain: Beginning the Journey, , www.apics.org, Report on Lean Practices in the Supply Chain, APICS; Manrodt, K.B., Abott, J., Vitasek, K., (2008) Lean Practices In the Supply Chain, , www.apics.org, Report on Lean Practices in the Supply Chain, APICS; Maskell, B., Performance measure for world class manufacturing (1989) Manage. Account, 67 (5), pp. 32-33. , May; Maskell, B.H., Baggaley, B., Practical Lean Accounting: A Proven System For Measuring and Managing the Lean Enterprise (2004), Productivity Press: New York; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) Int. J. Prod. Econ, 62 (1), pp. 107-118; Neely, A., Gregory, M., Platts, K., Performance measurement system design (2005) Int. J. Oper. Prod. Manage, 25 (12), pp. 1228-1263; Nunnally, J.C., (1978) Psychometric Theory, , McGraw-Hill: New York; Oliver, R.K., Webber, M.D., (1982) Supply chain management: Logistics catches up with strategy, , Outlook, Booz, Allen and Hamilton: New York; Olugu, E.U., Wong, K.Y., Fuzzy logic evaluation of reverse logistics performance in the automotive industry (2011) Sci. Res Essays, 6 (7), pp. 1639-1649; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2011) Resour Conserv Recy, 55 (6), pp. 567-579; Phelps, T., Hoenes, T., Smith, M., (2003) Developing lean supply chains: A guidebook, , www.presstv.com/detail/151704.html.accessed.on.June.2011, Altarum Institute, The Boeing Company and Messier-Dowty Inc. Press TV, [accessed on June 2011]; Saad, M., Patel, B., An investigation of supply chain performance measurement in the A87 automotive sector (2006) Benchmarking Int. J, 13 (1-2), pp. 36-53; Sambasivan, M., Nandan, T., Mohamed, Z.A., Consolidation of performance measures in a supply chain environment (2008) J. Enterprise Inform. Manage, 22 (6), pp. 660-689; Srinivasan, M., Streamlined: 14 principles for building & managing the lean supply chain (2004) Thomson Business and Professional Publishing, , Mason, Ohio; Wan, H., (2006) Measuring leanness of manufacturing systems and identifying leanness target by considering agility, , Ph.D Thesis, Virginia Polytechnic Institute and State University, US; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Ind. Manage Data Syst, 107 (3), pp. 361-381},
correspondence_address1={Wong, K. Y.; Department of Manufacturing and Industrial Engineering, , 81310 UTM Skudai, Malaysia; email: wongky@mail.fkm.utm.my},
issn={19922248},
language={English},
abbrev_source_title={Sci. Res. Essays},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tan2011349,
author={Tan, W. and Chai, Y. and Liu, Y.},
title={Business excellence measurement of supply chains based on collaboration degree and service level},
journal={Advanced Materials Research},
year={2011},
volume={339},
number={1},
pages={349-352},
doi={10.4028/www.scientific.net/AMR.339.349},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053544300&doi=10.4028%2fwww.scientific.net%2fAMR.339.349&partnerID=40&md5=d3a312d3cc8beb243841ddddbd9941ec},
affiliation={National Computer Integrated Manufacturing System Engineering and Research Center, Department of Automation, Tsinghua University, Beijing 100084, China},
abstract={Supply chain refers to a network of business entities that work together in efforts to acquire materials, convert these materials into products, and deliver these products to customers. The current performance measurement systems concentrate on measuring operational processes in a supply chain and fail to provide evaluation of the integrated business excellence. This paper conducts the research on the business excellence measurement of supply chains, where "collaboration degree" (CD) and "service level" (SL) are introduced to represent the supply chain's immediate and long-term yields respectively. As a result, an integrated indicator named "business excellence indicator" (BEI) is obtained to quantitatively evaluate the supply chain's overall business advantage. The rationality of our work is analyzed empirically through an application case. © (2011) Trans Tech Publications, Switzerland.},
author_keywords={Business excellence;  Manufacturing enterprise;  Performance measurement;  Supply chain},
keywords={Business excellence;  Current performance;  Integrated business;  Long-term yields;  Manufacturing enterprise;  Operational process;  Performance measurements;  Service levels, Manufacture;  Supply chains, Supply chain management},
references={Oztemel, E., Tekez, E., Interactions of agents in performance based supply chain management (2009) Journal of Intelligent Manufacturing, 20 (2), pp. 159-167; Ren, C., Chai, Y., Liu, Y., Active performance management in supply chains (2004) IEEE International Conference on Systems, Man, and Cybernetics, , New York; http://supply-chain.org/resources/scor, Information on; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38 (6), pp. 6681-6688; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155; Papakiriakopoulos, D., Pramatari, K., Collaborative performance measurement in supply chain (2010) Industrial Management & Data Systems, 110 (8-9), pp. 1297-1318},
correspondence_address1={Tan, W.; National Computer Integrated Manufacturing System Engineering and Research Center, , Beijing 100084, China; email: tanwz04@mails.tsinghua.edu.cn},
issn={10226680},
isbn={9783037852491},
language={English},
abbrev_source_title={Adv. Mater. Res.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Braz2011751,
author={Braz, R.G.F. and Scavarda, L.F. and Martins, R.A.},
title={Reviewing and improving performance measurement systems: An action research},
journal={International Journal of Production Economics},
year={2011},
volume={133},
number={2},
pages={751-760},
doi={10.1016/j.ijpe.2011.06.003},
note={cited By 94},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80051549537&doi=10.1016%2fj.ijpe.2011.06.003&partnerID=40&md5=cb17042942351fe9e4cd2b091fd73b59},
affiliation={Industrial Engineering Department, Pontifícia Universidade Católica Do Rio de Janeiro, Rua Marquês de São Vicente, 22453-900, Gávea, Rio de Janeiro (RJ), Brazil; Center of Excellence in Optimization Solutions (NExO), Industrial Engineering Department, Pontifícia Universidade Católica Do Rio de Janeiro, Rio de Janeiro (RJ), Brazil; Industrial Engineering Department, Universidade Federal de São Carlos, Rod. Washington Luís, Km 235, 13565-905, São Carlos (SP), Brazil},
abstract={Reviewing and updating performance measurement systems (PMS) based on internal and external environmental changes are as important as developing and implementing them. The results of an action research study carried out to improve the PMS of an energy companys maritime transportation area are presented. The findings of this longitudinal study illustrate the difficulty and complexity of reviewing and updating an energy companys PMS for its maritime transportation area. This difficulty is due to the involvement of PMS users, the assessment of performance measures, the establishment of targets, and data availability. The complexity is related to the changes in information technology when implementing changes in procedures for computing performance measures. This article contributes to a better understanding of the process of reviewing and updating a companys existing PMS. © 2011 Elsevier B.V. All right reserved.},
author_keywords={Maritime transportation;  Performance measure;  Performance measurement system;  Review of performance measurement;  Updating the performance measurement system},
keywords={Supply chain management;  Waterway transportation, Computing performance;  Environmental change;  Improving performance;  Longitudinal study;  Maritime transportation;  Performance measure;  Performance measurement system;  Performance measurements, Graphical user interfaces},
references={Attadia, L.C.L., Martins, R.A., Medição de desempenho como base para a evolução da melhoria contínua (2003) Revista Produção, 13 (2), pp. 33-41. , (São Paulo); Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , PII S0925527398000796; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Bertrand, J.W.M., Fransoo, J.C., Modeling and simulation: Operations management research methodologies using quantitative modeling (2002) International Journal of Operations and Production Management, 22 (2), pp. 241-254; Bititci, U.S., Suwignjo, P., Carrie, A.S., Strategy management through quantitative modelling of performance measurement systems (2001) International Journal of Production Economics, 69 (1), pp. 15-22. , DOI 10.1016/S0925-5273(99)00113-9; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations and Production Management, 26 (12), pp. 1325-1350. , DOI 10.1108/01443570610710579; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771. , DOI 10.1108/01443570010330739; Chen, C.C., An objective-oriented and product-line-based manufacturing performance measurement (2008) International Journal of Production Economics, 112 (1), pp. 380-390; Coughlan, P., Coghlan, D., Action research for operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 220-240; Coyle, J.J., Bardi, E.J., Langley, C.J.R.S., (2002) The Management of Business Logistics - A Supply Chain Perspective, , seventh ed. Thomson Learning Canada 707 p; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Davies, G.B., Strategies for information requirements determination (1982) IBM Systems Journal, 21 (1), pp. 4-30; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (12), pp. 46-70; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, pp. 131-137; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA Londres; Fortuin Leonard, PERFORANCE INDICATORS - WHY, WHERE AND HOW? (1988) European Journal of Operational Research, 34 (1), pp. 1-9. , DOI 10.1016/0377-2217(88)90449-3; Forza, C., Survey research in operations management: A process-based perspective (2002) International Journal of Operations and Production Management, 22 (2), pp. 152-194; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations and Production Management, 27 (8), pp. 784-801; Garengo, P., Nudurupati, S., Bititci, U., Understanding the relationship between PMS and MIS in SMEs: An organizational life cycle perspective (2007) Computers in Industry, 58 (7), pp. 677-686. , DOI 10.1016/j.compind.2007.05.006, PII S0166361507000723; Globerson Shlomo, Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (12), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Goold, M., Quinn, J.J., The paradox of strategic controls (1990) Strategic Management Journal, 11 (1), pp. 43-57; Hausman, W.H., Supply chain performance metrics (2003) The Practice of Supply Chain Management: Where Theory and Application Coverage, pp. 62-73. , C. Billington, T. Harrison, H. Lee, J. Neale, Kluwer Academic Publisher; Huan, S.H., Sheoran, S.K., Wan, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management, 9 (1), pp. 23-29. , DOI 10.1108/13598540410517557; Kaplan, R.S., Measuring manufacturing performance: A new challenge for managerial accounting research (1983) The Accounting Review, 58 (4), pp. 686-705; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (5), pp. 134-147. , September/October; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard - Translating Strategy into Action, , Harvard Business Press Boston 322 p; Kaplan, R.S., Norton, D.P., (2001) The Strategy-Focused Organization: How Balanced Scorecard Companies Thrive, , Harvard Business Press Boston 400 p; Kaplan, R.S., Norton, D.P., (2006) Alignment: Using the Balanced Scorecard to Create Corporate Synergies, , Harvard Business Press Boston 302 p; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 11, pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2), pp. 213-229. , DOI 10.1108/01443570310458465; Krakovics, F., Leal, J.E., Mendes, J.R.P., Santos, R.L., Defining and calibrating performance indicators of a 4PL in the chemical industry in Brazil (2008) International Journal of Production Economics, 115 (2), pp. 502-514; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641. , DOI 10.1016/j.jom.2005.01.002, PII S0272696305000501; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system design: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Martins, R.A., Costa Neto, P.L., Indicadores de desempenho para a gestão pela qualidade total: Uma proposta de sistematização (1998) Revista Produção, 5 (3), pp. 298-311; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431. , DOI 10.1016/S0925-5273(96)00080-1, PII S0925527396000801; Neely, A.D., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations and Production Management, 17 (11), pp. 1131-1152; Neely, A., (1998) Measuring Business Performance, , The Economist and Profile Books London 224 p; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228. , DOI 10.1108/01443579910247437; Neely, A.D., Bourne, M., Mills, J., Platts, K., Richards, H., (2002) Getting the Measure of Your BUSINESS, , Cambridge University Press UK 143 p; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263. , DOI 10.1108/01443570510633639; Nudurupati, S.S., Bititci, U.S., Implementation and impact of IT-supported performance measurement systems (2005) Production Planning and Control, 16 (2), pp. 152-162. , DOI 10.1080/09537280512331333057, PII P728168303; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers and Industrial Engineering, 60 (2), pp. 279-290; Schmidberger, S., Bals, L., Hartmann, E., Jahns, C., Ground handling services at European hub airports: Development of a performance measurement system for benchmarking (2009) International Journal of Production Economics, 117 (1), pp. 104-116; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , DOI 10.1108/17410400610653219; Stephens, S., Supply Chain Operations Reference Model Version 5.0: A New Tool to Improve Supply Chain Efficiency and Achieve Best Practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476. , DOI 10.1023/A:1012881006783; Ukko, J., Tenhunen, J., Rantanen, H., Performance measurement impacts on management and leadership: Perspectives of management and employees (2007) International Journal of Production Economics, 110 (1-2), pp. 39-51. , DOI 10.1016/j.ijpe.2007.02.008, PII S0925527307000801, Recent Developments in Operations Strategy, Supply Chain Design and Production Systems; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 195-219; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60; Wisner, J.D., Fawcett, S.E., Link firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, 32 (3), pp. 5-11; Wouters, M., Sportel, M., The role of existing measures in developing and implementing performance measurement systems (2005) International Journal of Operations and Production Management, 25 (11), pp. 1062-1082. , DOI 10.1108/01443570510626899},
correspondence_address1={Braz, R.G.F.; Industrial Engineering Department, Rua Marquês de São Vicente, 22453-900, Gávea, Rio de Janeiro (RJ), Brazil; email: rfrutuoso@hotmail.com},
publisher={Elsevier B.V.},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Setamanit2011,
author={Setamanit, S.-O.},
title={Evaluation of diverse inventory policies using simulation and design of experiment},
journal={PICMET: Portland International Center for Management of Engineering and Technology, Proceedings},
year={2011},
art_number={6017865},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80053347987&partnerID=40&md5=8d286fbb5a5387b1687be72113fa1e4a},
affiliation={Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand},
abstract={The importance of inventory management has been evident for a long time since inventories are not only necessary for operations, but they also contribute to customer satisfaction. However, holding inventory comes with costs such as inventory carrying cost and opportunity cost. Therefore, it is vital for a firm to find way to manage its inventory to maximize customer service, minimize total investment, and maintaining operation efficiency. Unfortunately, these three objectives are frequently in conflict with each other. In this paper, the author presents a simulation-based approach to evaluate the impact of continuous and periodic review policies on supply chain performance measures including cost and service level. This simulation model is served as an experimental platform for manager to evaluate diverse inventory policies for different products under different circumstances. For the case study company, continuous and periodic review policies provide relatively the same costs given the same service level. Furthermore, this paper also applies Design of Experiment (DOE) to an inventory simulation model. DOE is used to investigate factors that affect performance including variation in demand, lead time, and review interval, and to analyze the interaction of these factors. All three factors affect total costs but in different magnitude. Interaction between lead time and review interval is also significant. The results of the experimentation enables managers to gain better understanding of different inventory policies and be able to select the configuration that best suit the company's objective. © 2011 IEEE.},
keywords={Customer services;  Experimental platform;  Inventory management;  Inventory policies;  Leadtime;  Operation efficiencies;  Opportunity costs;  Periodic review;  Service levels;  Simulation model;  Simulation-based;  Supply chain performance;  Total costs;  Total investment, Costs;  Customer satisfaction;  Design of experiments;  Experiments;  Industrial management;  Industry;  Inventory control;  Investments;  Managers;  Supply chains;  Technology, Computer simulation},
references={Al-Harkan, I., Hariga, M., A simulation optimization solution to the inventory continuous review problem with lot size dependent lead time (2007) The Arabian Journal for Science and Engineering, 32, pp. 329-337. , October; Cerda, C., Monteros, A., Evaluation of A (R, s, Q, c) multi-item inventory replenishment policy through simulation (1997) 1997 Winter Simulation Conference; Heizer, J., Render, B., (2009) Operations Management, , 9th ed.: Pearson Education; Larson, L., Investigation of inventory and production policies using simulation (1984) Simulation in Inventory and Production Control, pp. 37-39. , H. Bekiroglu, Ed.: Society for Computer Simulation Proceedings; Law, A.M., Kelton, W.D., (1991) Simulation Modeling and Analysis, , 2nd ed. New York: McGraw-Hill; Lipman, B., (1975) How to Control to Reduce Inventory, , Englewood Cliffs, NJ: Prentice-Hall; Merkuryeva, G., Vecherinska, O., Simulation-based approach for comparison of (s, Q) and (R, S) replenishment policies utilization efficiency in multi-echelon supply chains (2008) Tenth International Conference on Computer Modeling and Simulation, pp. 434-440; Morrice, D., Valdez, R., Chida, J.J., Eido, M., Discrete event simulation in supply chain planning and inventory control at freescale semiconductor, inc (2005) 2005 Winter Simulation Conference; Setamanit, S., Using simulation to explore the impact of inventory policies on supply chain performance (2010) Portland International Conference on Management of Engineering and Technology (PICMET), , Phuket, Thailand; Simchi-Levi, D., Kaminski, P., Simchi-Levi, E., (2008) Designing and Managing the Supply Chain: Concepts, Strategies, Case Studies, , McGraw-Hill; Wakeland, W.W., Martin, R.H., Raffo, D., Using Design of Experiments, sensitivity analysis, and hybrid simulation to evaluate changes to a software development process: A case study (2004) Software Process Improvement and Practice, 9 (2), pp. 107-119. , DOI 10.1002/spip.200; Xudong, X., Kumar, A., Kwan, W., Tan, A., A simulation-based approach for evaluating diverse inventory policies in a supply chain (2008) Int. J. Simulation and Process Modelling, 4, pp. 18-30},
correspondence_address1={Setamanit, S.-O.; Faculty of Commerce and Accountancy, , Bangkok, Thailand},
isbn={1890843245; 9781890843243},
language={English},
abbrev_source_title={PICMET: Portland Int. Cent. Manage. Eng. Technol., Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Hadiguna2011232,
author={Hadiguna, R.A. and Jaafar, H.S. and Mohamad, S.},
title={Performance measurement for sustainable supply chain in automotive industry: A conceptual framework},
journal={International Journal of Value Chain Management},
year={2011},
volume={5},
number={3-4},
pages={232-250},
doi={10.1504/IJVCM.2011.043228},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80054954507&doi=10.1504%2fIJVCM.2011.043228&partnerID=40&md5=54ccaeb5817b9d5f53e9c73c198c47e4},
affiliation={Malaysia Institute of Transport, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia; Faculty of Engineering, Andalas University, Padang 25163, Indonesia; Malaysia Logistic and Supply Chain Research Center, Malaysia Institute of Transport, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia},
abstract={Many automobile organisations need to establish suitable measures for effective green supply chain performance measurement because they have failed in supply chain management due to their inability to develop the performance measures and metrics required for complete supply chain integration and performance measurement. This paper discusses how these challenges may be addressed through the application of a systems approach to sustainable performance measurement of automotive supply chain management. This paper briefly reviews some of papers published to evaluate and develop performance measurement of automotive supply chain as a hybrid concept. Copyright © 2011 Inderscience Enterprises Ltd.},
author_keywords={Automotive industry;  Performance measurement;  Sustainable supply chain},
references={Ahumada, O., Villalobos, J.R., Application of planning models in the agri-food supply chain: A review (2009) European Journal of Operational Research, 195 (1), pp. 1-20; Aller, R.A., García, J.C.L., Dynamic supplier management in the automotive industry (2010) International Journal of Operations & Production Management, 30 (3), pp. 312-335; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Bigliardi, B., Bottani, E., Performance measurement in the food supply chain: A balanced scorecard approach (2010) Facilities, 28 (5-6), pp. 249-260; Bennett, D., O'Kane, J., Achieving business excellence through synchronous supply in the automotive sector (2006) Benchmarking, 13 (1-2), pp. 12-22. , DOI 10.1108/14635770610644547; Binder, M., Gust, P., Clegg, B., The importance of collaborative frontloading in automotive supply networks (2008) Journal of Manufacturing Technology Management, 19 (3), pp. 315-331. , DOI 10.1108/17410380810853759, Inter-company Collaboration; Blengini, G.A., Shields, D.J., Overview of the building products supply chain in Italy (2010) Management of Environmental Quality: An International Journal, 21 (4), pp. 477-493; Bloemhof, J., Sustainable supply chains for the future (2005) Medium Econometrische Toepassingen, 13 (1), pp. 12-15; Carter, C.R., Easton, P.L., Sustainable supply chain management: Evolution and future directions (2011) International Journal of Physical Distribution & Logistics Management, 41 (1), pp. 46-62; Carter, C., Rogers, D.S., A framework of sustainable supply chain management: Moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) International Journal Production Research, 42 (1), pp. 131-163; Chung, S.L., Wee, H.M., Sustainable supply chain coordination policy using price discounting (2006) Proceedings of the 7th Asia Pacific Industrial Engineering and Management Systems Conference 2006, pp. 1899-1911. , 17-20 December 2006, Bangkok, Thailand; Doran, D., Rethinking the supply chain: An automotive perspective (2004) Supply Chain Management, 9 (1), pp. 102-109. , DOI 10.1108/13598540410517610; El-Baz, M.A., Fuzzy performance measurement of a supply chain in manufacturing companies (2011) Expert Systems with Applications, 38 (6), pp. 6681-6688; Estampe, D., Lamouri, S., Paris, J.L., Djelloul, S.B., A framework for analysing supply chain performance evaluation models (2010) International Journal of Production Economic, 128 (1), pp. 77-95; Faisal, M.N., Sustainable supply chains: A study of interaction among the enablers (2010) Business Process Management Journal, 16 (3), pp. 508-529; Guide Jr., V.D.R., Van Wassenhove, L.N., The evolution of closed-loop supply chain research (2009) Operations Research, 57 (1), pp. 10-18; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Production and Operations Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., Ronald, E., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-348; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement of the after-sales service network-Evidence from the automotive industry (2007) Computers in Industry, 58 (7), pp. 698-708. , DOI 10.1016/j.compind.2007.05.008, PII S0166361507000747; Godlevskaja, O., Van Iwaarden, J., Van Der Wiele, T., Moving from product-based to service-based business strategies services categorisation schemes for the automotive industry (2011) International Journal of Quality & Reliability Management, 28 (1), pp. 62-94; Hwang, Y.D., Lin, Y.C., Lyu Jr., J., The performance evaluation of SCOR sourcing process - The case study of Taiwan's TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Iyer, A.V., Seshadri, S., Vasher, R., (2009) Toyota Supply Chain Management: A Strategic Approach to The Principles of Toyota's Renowned System, , McGraw Hill, USA; Irfan, D., Xiaofei, X., Chun, D.S., A SCOR reference model of the supply chain management system in an enterprise (2008) The International Arab Journal of Information Technology, 5 (3), pp. 288-295; Jouini, S.B.M., Lenfle, S., Platform re-use lessons from the automotive industry (2010) International Journal of Operations & Production Management, 30 (1), pp. 98-124; Kasi, V., Systemic assessment of SCOR for modelling supply chains (2005) Proceedings of the 38th Hawaii International Conference on System Sciences, pp. 1-10. , 3-6 January 2005, Big Island, HI, USA; Kamalabad, I.N., Bayat, A., Ahmadi, P., Ebrahimi, A., Kahreh, M.S., Presentation a new algorithm for performance measurement of supply chain by using FMADM approach (2008) World Applied Sciences Journal, 5 (5), pp. 582-589; Kamaruddin, N.K., Udin, Z.M., Supply chain technology adoption in Malaysian automotive suppliers (2009) Journal of Manufacturing Technology Management, 20 (3), pp. 385-403; Kushwaha, G.S., Sustainable development through strategic green supply chain management (2010) International Journal of Engineering and Management Science, 1 (1), pp. 7-11; Kim, K.T., Rhee, S.K., Oh, J., The strategic role evolution of foreign automotive parts subsidiaries in China: A case study from the perspective of capabilities evolution (2011) International Journal of Operations & Production Management, 31 (1), pp. 31-55; Lai, K.-H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-456. , DOI 10.1016/S1366-5545(02)00019-4, PII S1366554502000194; Lin, Y., Zhou, L., Shi, Y., Ma, S., 3C framework for modular supply networks in the Chinese automotive industry (2009) The International Journal of Logistics Management, 20 (3), pp. 322-341; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) Journal of Operations Management, 25 (6), pp. 1075-1082. , DOI 10.1016/j.jom.2007.01.012, PII S0272696307000149; Lockamy III, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; McLarney, C., A driving force: An analysis of strategic planning in the Canadian automotive industry (2003) Business Process Management Journal, 9 (4), pp. 421-439; Maia, J.L., Cerra, A.L., Filho, A.G.A., Exploring variables of transaction costs in Brazilian automotive supply chains (2010) Industrial Management & Data Systems, 110 (4), pp. 567-590; Miyake, D.I., Silveira, A., Junior, T., Favaro, C., Supply chain mapping initiatives in the Brazilian automotive industry: Challenges and opportunities (2010) Journal of Operations and Supply Chain Management, 3 (1), pp. 79-97; Mondragon, A.E.C., Lalwani, C., Measures for auditing performance and integration in closed-loop supply chains (2011) Supply Chain Management: An International Journal, 16 (1), pp. 43-56; Naini, S.G.J., Aliahmadi, A.R., Eskandari, M.J., Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto-industry supply chain (2010) Resources, Conservation and Recycling, , in press; Niebecker, K., Eager, D., Moulton, B., Collaborative and cross-company project management within the automotive industry using the balanced scorecard (2010) International Journal of Managing Projects in Business, 3 (2), pp. 328-337; Nunes, B., Bennett, D., Green operations initiatives in the automotive industry an environmental reports analysis and benchmarking study (2010) Benchmarking: An International Journal, 17 (3), pp. 396-420; Nudurupati, S.S., Bititci, U.S., Kumar, V., Chan, F.T.S., State of the art literature review on performance measurement (2011) Computers & Industrial Engineering, 60 (2), pp. 279-290; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., A comprehensive approach in assessing the performance of an automobile closed-loop supply chain (2010) Sustainability, 2 (4), pp. 871-889; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2010) Resources, Conservation and Recycling, 55 (6), pp. 659-667; (2002) Sustainable development: The contribution by the automotive industry, , http://oica.net/wp-content/uploads/.../pressrelease_worldsummit02.pdf, OICA, accessed on 17 February 2011; Papadopoulos, I., Karagouni, G., Trigkas, M., Platogianni, E., Green marketing: The case of Greece in certified and sustainably managed timber products (2010) EuroMed Journal of Business, 5 (2), pp. 166-190; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14 (4), pp. 335-346. , DOI 10.1057/palgrave.ejis.3000544, PII 3000544; Pokharel, S., Mutha, A., Perspectives in reverse logistics: A review resources (2009) Conservation and Recycling, 53 (4), pp. 175-182; Phongpetra, V., Johri, L.M., Impact of business strategies of automobile manufacturers in Thailand (2011) International Journal of Emerging Markets, 6 (1), pp. 17-37; Punnakitikashem, P., Laosirihongthong, T., Adebanjo, D., McLean, M.W., A study of quality management practices in TQM and non-TQM firms findings from the ASEAN automotive industry (2010) International Journal of Quality & Reliability Management, 27 (9), pp. 1021-1035; Ravia, V., Shankara, R., Tiwarib, M.K., Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach (2005) Computers & Industrial Engineering, 48 (2), pp. 327-356; Rostamzadeh, R., Sofian, S., Prioritizing effective 7Ms to improve production systems performance using fuzzy AHP and fuzzy TOPSIS (case study) (2011) Expert Systems with Applications, 38 (5), pp. 5166-5177; Sarkis, J., Zhu, Q., Lai, K.H., An organizational theoretic review of green supply chain management literature (2011) International Journal of Production Economics, 130 (2), pp. 1-15; Sabóia, E.M., Duclós, L.C., Quandt, C.O., Souza, A., (2006) Strategic Management Indicators for Internal Logistics: A Proposal based on the Balanced Scorecard for an Automotive Sector Company, , XII ICIEOM - Fortaleza, 9-11 October, CE, Brasil; Shafieezadeh, M., Hajfataliha, A., A conceptual framework for supply chain coordination in fuzzy environment (2009) Journal of Theoretical and Applied Information Technology, 8 (2), pp. 123-135; (2011) Supply-chain Operations Reference-model: SCOR overview: Version 8.0, , http://archive.supply-chain.org/galleries/default-file/ SCOR%2080%20Overview20Booklet2.pdf, SCOR, accessed on 24 April 2011; Searcy, C., Karapetrovic, S., McCartney, D., Application of a systems approach to sustainable development performance measurement (2008) International Journal of Productivity and Performance Management, 57 (2), pp. 182-197; Shang, K.C., Lu, C.S., Li, S., A taxonomy of green supply chain management capability among electronics-related manufacturing firms in Taiwan (2010) Journal of Environmental Management, 91 (5), pp. 1218-1226; Subramoniam, R., Huisingh, D., Chinnam, R.B., Remanufacturing for the automotive aftermarket-strategic factors: Literature review and future research needs (2009) Journal of Cleaner Production, 17 (13), pp. 1163-1174; Suthikarnnarunai, N., Automotive supply chain and logistics management (2008) Proceedings of the International Multi Conference of Engineers and Computer Scientists II (IMECS 2008), , 19-21 March, Hong Kong; Sikdar, S.K., Sustainable development and sustainability metrics (2003) AIChE Journal, 49 (8), pp. 1928-1932. , DOI 10.1002/aic.690490802; Svensson, G., Aspects of sustainable supply chain management (SSCM): Conceptual framework and empirical example (2007) Supply Chain Management: An International Journal, 12 (4), pp. 262-266; Solvang, W.D., Hakam, M.H., Sustainable logistics networks in sparsely populated areas (2010) Journal Service Science & Management, 3 (4), pp. 72-77; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Thun, J.H., Hoenig, D., An empirical analysis of supply chain risk management in the German automotive industry (2009) International Journal of Production Economics, 131 (1), pp. 242-249; Van Riel, A.C.R., Liljander, V., Semeijn, J., Polsa, P., EU deregulation and dealer-supplier relations in automotive distribution (2011) Journal of Business & Industrial Marketing, 26 (2), pp. 115-131; Widodo, K.H., Sustainable supply chain based scenarios for optimizing trade-off between Indonesian furniture and crude-palm-oil industries (2010) Operations and Supply Chain Management: An International Journal, 3 (3), pp. 176-185; Wei, C.M., Chen, C.Y., An empirical study of purchasing strategy in automotive industry (2008) Industrial Management & Data Systems, 108 (7), pp. 973-987; Xiao, R., Cai, Z., Zhang, X., An optimization approach to cycle quality network chain based on improved SCOR model (2009) Progress in Natural Science, 19 (7), pp. 881-890; Yang, J., Integrative performance evaluation for supply chain system based on logarithm triangular fuzzy number-AHP method (2009) Kybernetes, 38 (10), pp. 1760-1770},
correspondence_address1={Hadiguna, R.A.; Malaysia Institute of Transport, , Shah Alam 40450, Selangor, Malaysia; email: hadiguna05@yahoo.com},
issn={17415357},
language={English},
abbrev_source_title={Int. J. Value Chain Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Zang2011192,
author={Zang, P.},
title={The system design on performance of the employees in manufacture enterprise},
journal={Communications in Computer and Information Science},
year={2011},
volume={210 CCIS},
number={PART 3},
pages={192-197},
doi={10.1007/978-3-642-23065-3_29},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80051675191&doi=10.1007%2f978-3-642-23065-3_29&partnerID=40&md5=45b6666a4efeb7dc7f007bbfb6d5e842},
affiliation={Jilin Business and Technology College, Changchun, China},
abstract={With further development of the commodity economy, the competition of manufacture enterprise has become more and more intense in the market. One of the most important methods is to establish the performance measurement system in order to achieve those objectives.As one of the most useful technological tools in human resources management, performance measurement has become the key factor of the enterprise to compete successfully. This paper in written on the background of manufacture enterprise from the actual situation, has designed the key performance indicators (KPI) based on the performance measurement system and was in the guiding ideology of the Balanced Scorecard (BSC) from the system internal processes factors and etc. This paper has also established the performance measurement system for manufacture enterprise in order to enhance its human resources development and management and to improve its improve its market competitiveness. © 2011 Springer-Verlag Berlin Heidelberg.},
author_keywords={human resources management;  manufacture enterprise;  performance measurement},
keywords={Balanced scorecards;  Further development;  Human resources development;  Human resources management;  Key factors;  Key performance indicators;  Manufacture enterprise;  Performance measurement system;  Performance measurements;  Technological tools, Benchmarking;  Competition;  Economics;  Measurements;  Personnel;  Supply chain management;  Systems analysis, Manufacture},
references={Cai, Z., Chang, Z., Wang, N., Study on manufacturing enterprise solution system (2005) Computer Application Research, 2, pp. 22-26; Ma, L., Wang, Y., Xia, J., (2009) Analysis of Production Abnormity and Exploration of Corrective Ways in Workshop of Manufacturing Enterprise, 1, pp. 35-39. , Northeast Agricultural University · Social Science; Li, Y., Wang, G., Qiu, X., Performance evaluation of college teachers (2007) Higher Education Research (Wuchang), 7, pp. 82-86; Cai, Y., Lin, C., Teacher performance evaluation theory and practice (2005) Teacher Education Research, 1, pp. 36-39; Li, S., Analysis of human resource information management (2006) Economic Forum, 15, pp. 50-53},
correspondence_address1={Zang, P.; Jilin Business and Technology College, Changchun, China; email: wangkefei888@sina.com},
issn={18650929},
isbn={9783642230646},
language={English},
abbrev_source_title={Commun. Comput. Info. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Sacristán-Díaz201149,
author={Sacristán-Díaz, M. and De Haan, J.},
title={Which partner can impose a performance measurement system on the supply chain as a whole? [Qué miembro de la cadena de suministro puede imponer un sistema conjunto de medición del rendimiento?]},
journal={Revista Europea de Direccion y Economia de la Empresa},
year={2011},
volume={20},
number={3},
pages={49-62},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961240971&partnerID=40&md5=a8ab1761df7e0743419d3f8ab1deacae},
affiliation={Universidad de Sevilla, Spain; Tilburg University, Netherlands},
abstract={This paper analyses one of the key aspects in performance measurement of the supply chain: which partner has sufficient power to impose a performance measurement system on the chain as a whole? In order for this to be done, two exploratory cases from European agro-industry companies are set out, one Spanish and the other from the Netherlands, which allows an analysis of five different chains to be made. The diversity of cases de-rived from two companies with very different backgrounds and contingency factors increases the richness of data for comparison and strengthens the findings. Results reveal the absence of a PMS for the chain when there is no part-ner than can be considered to be the chain director. When a chain director exists, it can translate final customers' demands into performance criteria for the chain as a whole which its suppliers accept because of benign self inte-rest.},
author_keywords={Agro supply chains;  Chain directors;  Performance measurement},
references={Aramyan, L., Oude Lansink, A., Van Der Vorst, J., Van Kooten, A., Performance meas-urement in agri-food supply chains: A case study (2007) Supply Chain Management: An Internatio-nal Journal, 12 (4), pp. 304-315; Bailey, K., Francis, M., Managing information flows for improved value chain performance (2008) International Journal of Production Economics, 111 (1), pp. 2-12. , DOI 10.1016/j.ijpe.2006.11.017, PII S092552730700028X, Managing Supply Chain Metwork; Batt, P.P.J., Building trust between gro-wers and market agents (2003) Supply Chain Management: An International Journal, 8 (1), pp. 65-78; Benbasat, I., Goldstein, D.K., Mead, M., The case research strategy in studies of information systems (1987) MIS Quarterly, 11 (3), pp. 369-386; Chan, F.T.S., I, H.J.Q., Feasibility of perfor-mance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F., Performance measurement in a Supply Chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Ciliberti, F., De Groot, G., De Haan, J., Pon-Trandolfo, P., Codes to coordinate Supply Chains: SMEs' experiences with SA8000 (2009) Supply Chain Management: An International Journal, 14 (2), pp. 117-127; Claro, D.P., Hagelaar, G., Omta, O., The determinants of relational governance and performance: How to manage business relationships? (2003) Industrial Marketing Management, 32 (8), pp. 703-716. , DOI 10.1016/j.indmarman.2003.06.010, PII S0019850103001020; Cox, A., The art of the possible: Relationship management in power regimes and supply chains (2004) Supply Chain Management, 9 (5), pp. 346-356. , DOI 10.1108/13598540410560739; Cox, A., Watson, G., Lonsdale, C., Sanderson, J., Managing appropriately in power regimes: Relationship and performance management in 12 supply chain cases (2004) Supply Chain Management, 9 (5), pp. 357-371. , DOI 10.1108/13598540410560748; Dapiran, G.P., Hogarth-Scott, S., Are cooperation and trust being confused with power? an analysis of food retailing in Australia and the UK (2003) International Journal of Retail and Distribution Management, 32 (4-5), pp. 256-267; De Haan, J., De Groot, G., Loo, E., Ypenburg, M., Flows of goods or supply chains; Lessons from the natural rubber industry in Kerala, India (2003) International Journal of Production Economics, 81-82, pp. 185-194. , DOI 10.1016/S0925-5273(02)00362-6, PII S0925527302003626; Denzin, N.K., (1989) Strategies of Multiple Triangu-lation. The Research Act: A Theoretical Introduc-tion to Sociological Methods, , New York, NY: McGraw-Hill; Eisenhart, K., Building theories from case study research (1989) The Academy of Management Review, 14 (4), pp. 532-550; Eltantawy, R.A., Giunipero, L., Fox, G., A strategic skill based model of supplier integra-tion and its effect on supply management performance (2009) Industrial Marketing Management, 38 (8), pp. 925-936; George, A., Bennett, A., (2005) Case Studies and Theory Development in the Social Sciences, , Cam-bridge, MA: MIT Press; Gereffi, G., International trade and industrial upgrading in the apparel commodity chain (1999) Journal of International Economics, 48 (1), pp. 37-70. , DOI 10.1016/S0022-1996(98)00075-0, PII S0022199698000750; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply Chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Hamprecht, J., Corsten, D., Noll, M., Meier, E., Controlling the sustainability of food supply chains (2005) Supply Chain Management, 10 (1), pp. 7-10. , DOI 10.1108/13598540510578315; Handfield, R.B., Bechtel, C., The role of trust and relationship structure in improving supply Chain Responsiveness (2002) Industrial Marketing Management, 31 (4), pp. 367-382; Harrison, A., New, C., The role of coherent supply chain strategy and performance management in achieving competitive advantage: An international survey (2002) Journal of the Operational Research Society, 53 (3), pp. 263-271. , DOI 10.1057/palgrave/jors/2601193, Performance management; Hingley, M.K., Power to all our friends? Living with imbalance in supplier-retailer relationships (2005) Industrial Marketing Management, 34 (8), pp. 848-858. , DOI 10.1016/j.indmarman.2005.03.008, PII S0019850105000398; Hrebiniak, L.G., Joyce, W.F., (1984) Implementing Strategy, , New York NY: MacMillan; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50. , June; Kraljic, P.P., Purchasing must become supply management (1983) Harvard Business Review, pp. 109-117. , Sep-tember-October; Kwon, I.-W.G., Suh, T., Trust, commitment and relationships in supply chain management: A path analysis (2005) Supply Chain Management, 10 (1), pp. 26-33. , DOI 10.1108/13598540510578351; Lambert, D., Cooper, M., Issues in Supply Chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Leong, G.K., Ward, P.T., The six ps of manufacturing strategy (1995) International Journal OfOperations and Production Management, 15 (12), pp. 32-45; Meredith, J., Building operations management theory through case and field research (1998) Journal of Operations Management, 16 (4), pp. 441-454. , PII S0272696398000230; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) Interna-tional Journal of Operations and Production Management, 15 (4), pp. 80-116. , 4; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A litera-ture review and research agenda (2005) International Journal of Operations and Production Management, 25 (2), pp. 12-28; Sarkis, J., Talluri, S., Evaluating and selecting ecommerce software and communication systems for a Supply Chain (2004) European Journal of Operational Research, 159 (2), pp. 318-329; Seuring, S.A., Assessing the rigor of case study research in Supply Chain management (2008) Supply Chain Management: An International Journal, 13 (2), pp. 128-137; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , DOI 10.1108/17410400610653219; Skinner, W., Manufacturing - Missing link in corporate strategy (1969) Harvard Business Review, pp. 136-145. , May-June; Suh, T., Kwon, I.-W.G., Matter over mind: When specific asset investment affects calculative trust in supply chain partnership (2006) Industrial Marketing Management, 35 (2), pp. 191-201. , DOI 10.1016/j.indmarman.2005.02.001, PII S0019850105000386; Voss, C., Tslkriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 195-219; Yin, R.K., (1994) Case Study Research: Design and Methods, , Newbury Park, CA: Sage},
correspondence_address1={Sacristán-Díaz, M.; Universidad de SevillaSpain},
issn={10196838},
language={Spanish},
abbrev_source_title={Rev. Eur. Dir. Econ. Empresa},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cuthbertson2011583,
author={Cuthbertson, R. and Piotrowicz, W.},
title={Performance measurement systems in supply chains: A framework for contextual analysis},
journal={International Journal of Productivity and Performance Management},
year={2011},
volume={60},
number={6},
pages={583-602},
doi={10.1108/17410401111150760},
note={cited By 103},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961081141&doi=10.1108%2f17410401111150760&partnerID=40&md5=934c1fbbdbdec1044bc8e8f48945186f},
affiliation={Saïd Business School, University of Oxford, Oxford, United Kingdom},
abstract={Purpose: The purpose of this article is to propose a common framework for the empirical analysis of supply chain performance measurement systems used in different supply chain contexts. Design/methodology/approach: This is a conceptual paper, which includes an extensive literature review and an illustrative case study. The content, context, process framework is applied to structure the body of knowledge and the case study. Findings: Supply chain performance measurement is a context-dependent process, tailored to specific supply chain requirements. To understand how a performance measurement system in a supply chain has developed and is used there is a need to capture its context, process and content. Research limitations/implications: The framework is illustrated by a single case study. Further empirical research is required to fully appreciate the breadth of application of this framework. Practical implications: The proposed framework can help to develop performance measurement systems that are suitable for certain organisational and supply chain contexts in which a company operates, as well as to compare different systems used across different supply chains. Originality/value: The paper demonstrates an approach for analysing existing supply chain performance measurement systems that can be applied across different supply chains and sectors. This will create an opportunity to use a consistent data collection process across a variety of supply chain situations and thus generate data for further theory development. © Emerald Group Publishing Limited.},
author_keywords={Logistics;  Metrics;  Performance management systems;  Performance measurement (quality);  Supply chain management;  United Kingdom},
references={Akyuz, G.K., Erkan, T.E., Supply chain performance measurement: A literature review (2009) International Journal of Production Research, 11, pp. 1-19; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Aramyan, L.H., Lansink, A., van der Vorst, J., van Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-315; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3-4), pp. 275-292; Beamon, B.M., Balcik, B., Performance measurement in humanitarian relief chains (2008) International Journal of Public Sector Management, 21 (1), pp. 4-25; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Bititci, U., Mendibil, K., Martinez, V., Albores, P., Measuring and managing extended enterprises (2006) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56; Bullinger, H.-J., Kuhner, M., van Hoff, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Burgess, K., Singh, P.J., A proposed integrated framework for analysing supply chains (2006) Supply Chain Management: An International Journal, 11 (4), pp. 337-344; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46 (2), pp. 512-521; Chae, B., Developing key performance indicators for supply chain: An industry perspective (2009) Supply Chain Management: An International Journal, 14 (6), p. 422; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement systems for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Charan, P., Shankar, R., Baisya, R., Analysis of interactions among the variables of supply chain performance measurement systems (2008) Business Process Management Journal, 14 (4), pp. 512-529; Chia, A., Goh, M., Hum, S.-H., Performance measurement in supply chain: Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16 (5), p. 605; Clift, R., Metrics for supply chain sustainability (2003) Clean Technologies and Environmental Policy, 5 (3-4), pp. 240-247; Cooper, M., Lambert, D., Pagh, J., Supply chain management: More than new name for logistics (1997) International Journal of Logistics Management, 8 (1), pp. 1-14; Croxton, K.L., Garcia-Dastuge, S.J., Lambert, D.M., Rogers, D.S., The supply chain management process (2001) International Journal of Logistics Management, 1 (2), pp. 13-36; Cuthbertson, R., Piotrowicz, W., Supply chain best practices - identification and categorisation of measures and benefits (2008) International Journal of Productivity and Performance Management, 57 (5), pp. 389-404; Farris II, M.T., Hutchison, P.D., Cash-to-cash: The new supply chain management metric (2002) International Journal of Physical Distribution & Logistics Management, 32 (4), p. 2002; Friedlos, D., (2006) RFID tracks Jaguar car parts, , http://s0.2mdn.net/1829324/ctgwelcomepage.html?rfp=http://www.computing.co.uk/computing/news/2165657/rfid-tracks-jaguar-car-parts, available at:; Green, M., (2008) Unipart: Continuous improvement and Lean principles in logistics - the Unipart way, , Proceedings of the The bestLog Conference, 3 December, Prague; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Harrison, A., van Hoek, R.I., (2005) Logistics Management and Strategy, , FT Prentice Hall, Harlow; Hervani, A.A., Helms, M.M., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hofmann, E., Locker, A., Value-based performance measurement in supply chains: A case study from the packaging industry (2009) Production Planning & Control, 20 (1), pp. 68-81; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Hrycyk, M., What's behind a slick supply chain? (2006) Manufacturing Computer Solutions, (May), pp. 34-35; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of Operational Research Society, 54 (5), pp. 507-514; Lai, K.-H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research E: Logistics & Transportation Review, 38, pp. 439-456; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lambert, D., Cooper, M., Pagh, J., Supply chain management: Implementation issues and research opportunities (1998) International Journal of Logistics Management, 9 (2), pp. 1-19; Lockamy III, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (11-12), pp. 1192-1218; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; McNeill, W., Fontanella, J., Ruggles, K., (2007) Service parts planning and optimisation landscape: Saving millions through inventory reductions and increased sale, , AMR Service Parts Planning and Optimisation Landscape, November; Manrodt, K.B., Vitasek, K., Global process standardization: A case study (2004) Journal of Business Logistics, 25 (1), pp. 1-23; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Morgan, C., Supply network performance measurement: Future challenges? (2007) International Journal of Logistics Management, 18 (2), pp. 255-273; Muratoglu, K., Performance measurement of supply chain via balanced scorecard: The case of a brewing group (2008) The Business Review, 10 (1), pp. 330-337; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144, pp. 306-320; Park, J.H., Lee, J.K., Yoo, J.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14 (4), pp. 335-346; Pettigrew, A., (1985) The Awakening Giant, Continuity and Change in ICI, , Blackwell, Oxford; Powell, C., (2007) Supply Chain Excellence in the Global Automotive Market, , Sapphire, Vienna; Radnor, Z.J., Barnes, D., Historical analysis of performance measurement and management in operations management (2007) International Journal of Productivity and Performance Management, 56 (5-6), pp. 384-396; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Saiz, J.J.A., Bas, A.O., Rodriguez, R.R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; (2008) SAP Customer Success Story, Travel and Logistics Services, , www.sap.com/uk/about/success/casestudies/Unipart_Logistics_Success_Story(A4).pdf, SAP, available at:; Seuring, S., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Management: An International Journal, 13 (2), pp. 128-137; Sharif, A.M., Benchmarking performance management systems (2002) Benchmarking: An International Journal, 9 (1), pp. 62-85; Sharif, A.M., Irani, Z., Lloyd, D., Information technology and performance management for build-to-order supply chains (2007) International Journal of Operations & Production Management, 1235, p. 1253; Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68; Shepherd, C., Gunter, H., Measuring supply chain: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Spens, K., Bask, A., Developing a framework for supply chain management (2002) International Journal of Logistics Management, 13 (1), pp. 73-88; Stockdale, R., Standing, C., An interpretive approach to evaluating information systems: A content, context, process framework (2006) European Journal of Operational Research, 173 (3), pp. 1090-1102; Symons, V.J., A review of information systems evaluation: Content, context and process (1991) Journal of the Operational Research Society, 1 (3), pp. 205-212; Thakkar, J., Kanda, A., Deshemukh, S., Supply chain performance measurement framework for small and medium scale enterprises (2009) Benchmarking: An International Journal, 16 (5), p. 702; (2008) Corporate Responsibility, The Unipart Way - CSR Report, , www.unipart.co.uk/wps/wcm/connect/6cd40f004fb48387aa0bef93deecefb0/Unipart+CSR+Report+2008+v9c+Final+Version.pdf?MOD=AJPERES&CACHEID=6cd40f004fb48387aa0bef93deecefb0, Unipart Group, available at:; (2009) Conducting Business, , www.unipartrail.com/WebPDF/StockPDF/Conducting%20Business%20the%20Unipart%20Way.pdf, Unipart Group, The Unipart Way, available at:; van Hoek, R.I., Measuring the unmeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Varnom, M., (2004) Supplier performance management, , www.smmt.co.uk/businessimprovement/2004%20seminar/unipart.pdf, Unipart Logistics, available at:; Varnom, M., (2008) The Unipart Group Lean transformation, , http://video.wch.org.au/qi/intranet/Lean_Thinking_in_Manufacturing_and_Service_2008/Mike_Varnom2.pdf, Unipart Logistics, available at:; Vitasek, K., Next in outsourcing: Performance-based logistics (2008) Proceedings of the Material Handling & Logistics Conference, , Park City, UT; Wickramatillake, C.D., Koh, S.C.L., Gunasekaran, A., Measuring performance within the supply chain of a large-scale project (2007) Supply Chain Management: An International Journal, 12 (1), pp. 52-59; Yilmaz, Y., Bititci, U., Performance measurement in the value chain: Manufacturing v. tourism (2006) International Journal of Productivity and Performance Management, 55 (5), pp. 371-389},
correspondence_address1={Piotrowicz, W.; Saïd Business School, , Oxford, United Kingdom; email: wojciech.piotrowicz@sbs.ox.ac.uk},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Liao2011213,
author={Liao, S. and Hsieh, C. and Lin, Y.},
title={A multi-objective evolutionary optimization approach for an integrated location-inventory distribution network problem under vendor-managed inventory systems},
journal={Annals of Operations Research},
year={2011},
volume={186},
number={1},
pages={213-229},
doi={10.1007/s10479-010-0801-3},
note={cited By 32},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79958773695&doi=10.1007%2fs10479-010-0801-3&partnerID=40&md5=6793ad006596544d308c4e36766a1f13},
affiliation={Department of Management Sciences and Decision Making, Tamkang University, Taipei 251, Taiwan; Department of Industrial Management, National Taiwan University of Science and Technology (Taiwan Tech), Taipei 106, Taiwan},
abstract={In this paper, we propose an integrated model to incorporate inventory control decisions-such as economic order quantity, safety stock and inventory replenishment decisions-into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk poling phenomenon. Multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (responsive level). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single measure approaches. A multi-objective location-inventory model which permits a comprehensive trade-off evaluation for multi-objective optimization is initially presented. More specifically, a multiobjective evolutionary algorithm is developed to determine the optimal facility location portfolio and inventory control parameters in order to reach best compromise of these conflicting criteria. An experimental study using practical data was then illustrated for the possibility of the proposed approach. Computational results have presented promising solutions in solving a practical-size problem with 50 buyers and 15 potential DCs and proved to be an innovative and efficient approach for so called difficult-to-solve problems. © 2010 Springer Science+Business Media, LLC.},
author_keywords={Multi-objective evolutionary algorithm;  Multi-objective supply chain model;  Supply chain integration;  Vendor-managed inventory},
references={Achabal, D., McLntyre, S., Smith, S., Kalyanam, K., A decision support system for vendor managed inventory (2000) Journal of Retailing, 76 (4), pp. 430-454; Cetinkaya, S., Lee, C.Y., Stock replenishment and shipment scheduling for vendor-managed inventory systems (2000) Management Science, 46 (2), pp. 217-232; Coello Coello, C.A., Evolutionary multiobjective optimization: a historical view of the field (2006) IEEE Computational Intelligence Magazine, 1 (1), pp. 28-36; Daskin, M.S., Coullard, C.R., Shen, Z.M., An inventory-location model: formulation, solution algorithm and computational results (2002) Annals of Operations Research, 110 (1-4), pp. 83-106; Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multi-objective genetic algorithm: NSGAII (2002) IEEE Trans. On Evolutionary Computation, 6 (2), pp. 181-197; Disney, S.M., (2001) The production and inventory control problem in vendor managed inventory supply chains, , Ph. D. Thesis, Cardiff Business School, Cardiff University; Dong, Y., Xu, K., A supply chain model of vendor managed inventory (2002) Transportation Research. Part E: Logistics and Transportation Review, 38 (2), pp. 75-95; Eppen, G., Effects of centralization on expected costs in a multi-location newsboy problem (1979) Management Science, 25 (5), pp. 498-501; Erlebacher, S.J., Meller, R.D., The interaction of location and inventory in designing distribution systems (2000) IIE Transactions, 32 (2), pp. 155-166; Gaur, S., Ravindran, A.R., A bi-criteria model for the inventory aggregation problem under risk pooling (2006) Computers and Industrial Engineering, 51 (3), pp. 482-501; Gunasekaran, A., Lai, K., Cheng, T.C., Responsive supply chain: a competitive strategy in a networked economy (2008) Omega, 36 (4), pp. 549-564; Jayaraman, V., Transportation, facility location and inventory issues in distribution network design: an investigation (1998) International Journal of Operations and Production Management, 18 (5), pp. 471-494; Michalewicz, Z., (1996) Genetic Algorithms + Data Structures = Evolution Programs, , Berlin: Springer; Miranda, P.A., Garrido, R.A., Incorporating inventory control decisions into a strategic distribution network model with stochastic demand (2004) Transportation Research. Part E: Logistics and Transportation Review, 40 (3), pp. 183-207; Nozick, L.K., Turnquist, M.A., A two-echelon allocation and distribution center location analysis (2001) Transportation Research. Part E: Logistics and Transportation Review, 37 (6), pp. 425-441; Ozsen, L., (2004) Location-inventory planning models: Capacity issues and solution algorithms, , Ph. D. dissertation, Northwestern University; Romeijn, H.E., Shu, J., Teo, C.P., Designing two-echelon supply networks (2007) European Journal of Operational Research, 178 (2), pp. 449-462; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28 (5), pp. 581-598; Schaffer, J.D., Multiple objective optimization with vector evaluated genetic algorithms (1985) The 1st International Conference on Genetic Algorithms, pp. 93-100. , Hillsdale, NJ; Shen, Z.M., Daskin, M.S., Trade-offs between customer service and cost in integrated supply chain design (2005) Manufacturing and Service Operations Management, 7 (3), pp. 188-207; Shen, Z.J., Coullard, C.R., Daskin, M.S., A joint location-inventory model (2003) Transportation Science, 37 (1), pp. 40-55; Teo, C.P., Shu, J., Warehouse-Retailer Network Design Problem (2004) Operations Research, 52 (3), pp. 396-408; Waller, M., Johnson, M.E., Davis, T., Vendor-managed inventory in the retail supply chain (1999) Journal of Business Logistics, 20 (1), pp. 183-203; Yao, Y.L., Evers, P.T., Dresner, M.E., Supply chain integration in vendor-managed inventory (2007) Decision Support Systems, 43 (2), pp. 663-674},
correspondence_address1={Liao, S.; Department of Management Sciences and Decision Making, , Taipei 251, Taiwan; email: michael@mail.tku.edu.tw},
issn={02545330},
language={English},
abbrev_source_title={Ann. Oper. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Erol20111088,
author={Erol, I. and Sencer, S. and Sari, R.},
title={A new fuzzy multi-criteria framework for measuring sustainability performance of a supply chain},
journal={Ecological Economics},
year={2011},
volume={70},
number={6},
pages={1088-1100},
doi={10.1016/j.ecolecon.2011.01.001},
note={cited By 218},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79953733850&doi=10.1016%2fj.ecolecon.2011.01.001&partnerID=40&md5=779690ad1ab71c4ed3c64d8b954a4d73},
affiliation={Abant Izzet Baysal University, Department of Business Administration, 14280, Bolu, Turkey; Middle East Technical University, Department of Business Administration, 06531, Ankara, Turkey},
abstract={Sustainable supply chain performance measurement is aimed at addressing environmental, social and economic aspects of sustainable supply chain management. It can be argued that it is not easy to reduce all dimensions of sustainable supply chain to a single unit. Then, the issue is that all valuations should somehow be reducible to a single one-dimensional standard. Multi-criteria evaluation introduces a framework to remedy this issue. As a consequence, multi-criteria evaluation seems to supply a proper and adequate assessment framework for sustainable supply chain assessment. In this study, a multi-criteria framework based on fuzzy entropy and fuzzy multi-attribute utility (FMAUT) is proposed in order to evaluate and compare the company performances in terms of sustainable supply chain. However, note that reducing all aspects of sustainable supply chain to a single unit using a multi-criteria framework may not be sufficient to satisfy all the needs of decision makers although it is used to evaluate sustainability performance of supply chains with respect to three aspects. Therefore, in this research, an alert management system is also developed to satisfy further requirements of users. The proposed frameworks are tested using data obtained from one of the middle sized Turkish grocery retailers. © 2011 Elsevier B.V.},
author_keywords={Fuzzy arithmetic;  Multi-criteria decision making;  Performance analysis;  Sustainable supply chain},
keywords={data set;  decision making;  environmental economics;  food market;  fuzzy mathematics;  multicriteria analysis;  performance assessment;  retailing;  socioeconomic impact;  sustainability;  sustainable development;  valuation, Turkey},
references={Beloff, B., Tanzil, D., Lines, M., Sustainable development performance assessment (2004) Environmental Progress, 23 (4), pp. 271-276; Belton, V., Stewart, T.J., (2002) Multi-Criteria Decision Analysis: an Integrated Approach, , Kluwer Academic Publishers, USA; Carter, C.R., Rogers, D.S., A framework of sustainable supply chain management: moving toward new theory (2008) International Journal of Physical Distribution & Logistics Management, 38 (5), pp. 360-387; Chang, Y.H., Yeh, C.H., Evaluating airline competitiveness using multi-attribute decision making (2000) Omega, 29, pp. 405-415; Chen, S., Hwang, C., (1992) Multiple Attribute Decision Making: Method and Applications, , Springer, New York; Ding, G.K.C., Developing a multi-criteria approach for the measurement of sustainable performance (2007) Building Research and Information, 33, pp. 3-16; Erol, I., Çakar, N.D., Erel, D., Sari, R., Sustainability in Turkish retailing industry (2009) Sustainable Development, 17, pp. 49-67; Harris, J.M., Macroeconomic policy and sustainability (2007) Encyclopedia of Earth, , http://www.eoearth.org/article/Macroeconomic_policy_and_sustainability, accessed on 21 July 2009, C.J. Cleveland (Ed.); Hervani, A.A., Helms, M.M., Sariis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hwang, C., Yoon, K., (1981) Multiple Attribute Decision Making: Methods and Applications, , Springer Verlag, New York; Iles, A., Seeing sustainability in business operations: US and British food retailer experiments with accountability (2005) Business Strategy and the Environment, 16, pp. 290-301; Itterhus, B.E., Arnestad, P., Lothe, S., Environmental initiatives in the retailing sector: an analysis of supply chain pressures and partnerships (1999) Eco-Management and Auditing, 6, pp. 181-188; Jones, P., Comfort, D., Hillier, D., Eastwood, I., Retailers and sustainable development in the UK (2005) International Journal of Retail and Distribution Management, 33, pp. 207-214; Kaufmann, A., Gupta, M.M., (1988) Fuzzy Mathematical Models in Engineering and Management Science, , North-Holland Book Co., Amsterdam; Keating, B., Quazi, A., Kriz, A., Coltman, T., In pursuit of a sustainable supply chain: insights from Westpac Banking Corporation (2008) Supply Chain Management: An International Journal, pp. 175-179; Kleindorfer, P.R., Singhal, K., Van Wassenhove, L.N., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Krajnc, D., Glavic, P., How to compare on relevant dimensions of sustainability (2005) Ecological Economics, 55, pp. 551-563; Lozano, R., Collaboration as a pathway for sustainability (2007) Sustainable Development, 15, pp. 370-381; Munda, G., Social multi-criteria evaluation: methodological foundations and operational consequences (2004) European Journal of Operational Research, 158 (3), pp. 662-677; Munda, G., Multiple criteria decision analysis and sustainable development (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, 23, pp. 953-986. , J. Figueria, S. Greco, M. Ehrgott (Eds.); Ness, B., Urbel-Piirsalu, E., Anderberg, S., Olsson, L., Categorizing tools for sustainability assessment (2007) Ecological Economics, 60, pp. 498-508; Pohekar, S.D., Ramachandran, M., Multi-criteria evaluation of cooking devices with special reference to utility of parabolic solar cooker (PSC) in India (2006) Energy, 31, pp. 1215-1227; Roy, B., (1996) Multi-Criteria Methodology for Decision Analysis, , Kluwer Academic Publishers, Dordrecht; Seuring, S., Müller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16, pp. 1699-1710; Seuring, S., Müller, M., Core issues in sustainable supply chain management - a Delphi study (2008) Business. Strategy and Environment, 17, pp. 455-466; Singh, R.K., Murty, H.R., Gupta, S.K., Dikshit, A.K., Development of composite sustainability performance index for steel industry (2007) Ecological Indicators, 7, pp. 565-588; Sopadang, A., Cho, B.R., Leonard, M., Development of the hybrid weight assessment system for multiple quality attributes (2002) Quality Engineering, 15 (1), pp. 75-89; Svensson, G., Aspects of sustainable supply chain management: conceptual framework and empirical example (2007) Supply Chain Management: An International Journal, 12 (4), pp. 262-266; Weber, M., Borcherding, K., Behavioral influences on weight judgements in multi-attribute decision making (1993) European Journal of Operational Research, 67, pp. 1-12; Xu, S.Q., Hu, Z.G., Liu, Q., Multi-objective decision analysis of diversion standards based on entropy (2004) China Rural Water and Hydropower, 8, pp. 45-47; Yeh, C.H., Willis, R.J., Deng, H., Pan, H., Task-oriented weighting in multi-criteria analysis (1999) European Journal of Operational Research, 119 (1), pp. 130-146; Zeleny, M., (1982) Multiple Criteria Decision Making, , McGraw Hill, New York; Zimmermann, H.J., (2001) Fuzzy Sets Theory and Its Applications, , Kluwer, Boston, MA; Zou, Z., Yun, Y., Sun, J., Entropy method for determination of weight of evaluating indicators in fuzzy synthetic evaluation for water quality assessment (2006) Journal of Environmental Sciences, 18 (5), pp. 1020-1023},
correspondence_address1={Erol, I.; Abant Izzet Baysal University, , 14280, Bolu, Turkey; email: erol_i@ibu.edu.tr},
issn={09218009},
coden={ECECE},
language={English},
abbrev_source_title={Ecol. Econ.},
document_type={Article},
source={Scopus},
}

@ARTICLE{JalaliNaini2011593,
author={Jalali Naini, S.G. and Aliahmadi, A.R. and Jafari-Eskandari, M.},
title={Designing a mixed performance measurement system for environmental supply chain management using evolutionary game theory and balanced scorecard: A case study of an auto industry supply chain},
journal={Resources, Conservation and Recycling},
year={2011},
volume={55},
number={6},
pages={593-603},
doi={10.1016/j.resconrec.2010.10.008},
note={cited By 117},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956217243&doi=10.1016%2fj.resconrec.2010.10.008&partnerID=40&md5=8738026a4f73f6ea2a20ce40be1c5e05},
affiliation={Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran},
abstract={This paper proposes a mixed performance measurement system using a combination of evolutionary game theory and the balanced scorecard (BSC) in environmental supply chain management (ESCM) that measures and evaluates business operations using the four different perspectives of finance, customer, internal business process, and learning and growth. ESCM plays an important role in the supply chain which leads to the reduction, reuse and recycling of resources involved in both upstream and downstream activities. This paper presents guidance for practical managers in evaluating and measuring ESCM by developing a knowledge-based BSC and evolutionary game theory. The primary purpose of this paper is to apply the proposed method in a case study to one of Iran's biggest auto industry supply chain SAIPA Company. The results of this study indicate that the adoption of ESCM, in the absence of regulatory pressures and cost-saving measures is triggered by public pressures and its implementation is limited by organizational factors and strategic myopia. © 2010 Elsevier B.V. All rights reserved.},
author_keywords={Balanced scorecard;  Environmental supply chain management;  Evolutionary game theory;  Green Supply Chain;  Performance measurement system},
keywords={Balanced scorecards;  Environmental supply chain management;  Evolutionary game theory;  Green Supply Chain;  Performance measurement system, Automotive industry;  Environmental management;  Game theory;  Knowledge based systems;  Measurements;  Strategic planning, Supply chain management, automobile industry;  business development;  corporate strategy;  cost-benefit analysis;  economic activity;  evolutionary theory;  game theory;  implementation process;  industrial performance;  regulatory framework, article;  automobile industry;  balanced scorecard;  case study;  decision making;  economic development;  economic evaluation;  environmental management;  environmental protection;  environmental supply chain management;  evolutionary game theory;  game;  knowledge base;  organizational development;  performance measurement system;  recycling;  risk reduction;  social marketing;  strategic planning;  waste minimization, Iran},
references={Adachi, N., Matsuo, K., Ecological dynamics under different selection rules in distributed and iterated Prisoner's dilemma games, parallel problem solving from nature (1991) Lecture Notes in Computer Science, 496. , Berlin: Springer-Verlag; Akkermans, H.A., Oorschot, K.E., Relevance assumed: A case study of the balanced scorecard development using system dynamics (2002) Journal of the Operational Research Society, 56; Alexander, J., McKenzie, Evolutionary explanations of distributive justice (2000) Philosophy of Science, 67, pp. 490-516; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Bobillo, F., Delgado, M., Romero, J., Lopez, E., A semantic fuzzy system for a fuzzy balanced scorecard (2009) Expert Systems with Applications, 36, pp. 423-433; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2); Cai, G., Kock, N., An evolutionary game theoretic perspective on e-collaboration (2009) European Journal of Operational Research, 194, pp. 821-833; Cebeci, U., Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard (2009) Expert Systems with Applications, 36; Christopher, M.G., (1998) Logistics and Supply Chain Management; Strategies for Reducing Costs and Improving Services, , Pitman Publishing London; Dewhurst, F., Spring, M., Arkle, N., Environmental change and supply chain management: A multi-case study exploration of the impact of Y2000 (2000) Supply Chain Management: An International Journal, 5 (5); (1999) Product and Supply Chain Focused Policies and Tools for Sustainable Development, , Five Winds International Prepared for Environment Canada, Ottawa; Goldbach, M., Coordinating interactions in supply chains - The example of greening textile chains (2003) Strategy and Organizations in Supply Chains; Gumbus, A., Introducing the balanced scorecard: Creating metrics to measure performance (2005) Journal of Management Education, 29 (4); Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3); Hass, J.L., "Greening" the supply chain: A case study and the development of the conceptual model (1996) Industry and the Approaches in Business; Huang, H., Designing a knowledge-based system for strategic planning: A balanced scorecard perspective (2009) Expert Systems with Applications, 36; Kaplan, R.S., Norton, D.P., The balanced scorecards: Measures that drive performance (1992) Harvard Business Review, 70 (1); Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review (January-February), 74 (1); Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, , Harvard Business School Press Boston; Kaplan, R.S., Norton, D.P., (2006) Alignment: Using the Balanced Scorecard to Create Corporate Synergies, , Harvard Business School Press Boston, MA; Ko, T.G., Development of a tourism sustainability assessment procedure: A conceptual approach (2005) Tourism Management, 26 (3); Kogg, B., Power and incentives in environmental supply chain management (2003) Strategy and Organizations in Supply Chains; De Koning, G.M.J., Making the balanced scorecard work (part 1) (2004) Gallup Management Journal, , http://gmj.gallup.com/content/12208/making-balanced-scorecard-work-part. aspx]; Lee, A.H.I., Chen, W.C., Chang, C.J., A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan (2008) Expert Systems with Applications, 34; Leung, L.C., Lam, K.C., Cao, D., Implementing the balanced scorecard using the analytic hierarchy process & the analytic network process (2006) Journal of the Operational Research Society, 57; Li, X., Gu, X.J., Liu, Z.G., A strategic performance measurement system for firms across supply and demand chains on the analogy of ecological succession (2009) Ecological Economics, 68; Linnanen, L., Halme, M., Can sustainable industrial networks be created? Environmental value chain management in the paper-based packaging industry (1996) Industry and the Environment: Practical Applications of Environmental Management Approaches in Business; Maynard Smith, J., Price, G., The logic of animal conflict (1973) Nature, 246; Murphy, P.R., Poist, R.F., Green perspectives and practices: A "comparative logistics" study (2003) Supply Chain Management: An International Journal, 8 (2); Neely, A., (1998) Measuring Business Performance, , Profile Books Ltd. London; Pfeffer, J., Sutton, R.I., (2000) The Knowing-doing Gap: How Smart Companies Turn Knowledge into Action, , Harvard Business School Press Boston, MA; Richmond, B., A new language for leveraging scorecard-driven learning (2001) Reprint from Balanced Scorecard Report, 3 (1); Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence Journal, 11 (3); Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4); Stevels, A., Green supply chain management much more than questionnaires and ISO 14.001 (2001) 2002 IEEE International Symposium on Electronics and the Environment; (2002) Climate Change and Waste, , United States Environmental Protection Agency EPA 530-E-03-002. Washington; (2002) Waste Management and Greenhouse Gases: A Life Cycle Assessment of Emissions and Sinks, , United States Environmental Protection Agency EPA 530-R-02-006. Washington; Wang, S., Zheng, R., Liu, Z., Liu, G., Construction of dynamic green supply chain based on agent (2003) 2003 IEEE International Symposium on Electronics and the Environment; Yuksel, I., Dagdeviren, M., Using the fuzzy analytic network process (ANP) for Balanced Scorecard (BSC): A case study for a manufacturing firm (2010) Expert Systems with Applications, 37, pp. 1270-1278; Zhu, Q., Cote, R.P., Green supply chain management into an embryonic eco-industrial development: A case study of the Guitang Group (2004) Journal of Cleaner Production, 12 (810); Zsidisin, G.A., Siferd, S.P., Environmental purchasing: A framework for theory development (2001) European Journal of Purchasing & Supply Management, 7; Zsidisin, G.A., Siferd, S.P., Environmental purchasing: A framework for theory development (2001) European Journal of Purchasing and Supply Management, 7 (1)},
correspondence_address1={Jalali Naini, S. G.; Department of Industrial Engineering, , Narmak, Tehran, Iran; email: sgjalali@iust.ac.ir},
issn={09213449},
coden={RCREE},
language={English},
abbrev_source_title={Resour. Conserv. Recycl.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Olugu2011567,
author={Olugu, E.U. and Wong, K.Y. and Shaharoun, A.M.},
title={Development of key performance measures for the automobile green supply chain},
journal={Resources, Conservation and Recycling},
year={2011},
volume={55},
number={6},
pages={567-579},
doi={10.1016/j.resconrec.2010.06.003},
note={cited By 234},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956216392&doi=10.1016%2fj.resconrec.2010.06.003&partnerID=40&md5=30eb801dc0a385a94557ed4a72b9bad0},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia},
abstract={The main purpose of this study was to develop a set of measures for evaluating the performance of the automobile green supply chain. This study reviewed various literatures on green supply chain performance measurement, environmental management, traditional supply chain performance measurement, and automobile supply chain management. In order to comprehensively and effectively establish the relevant measures, a suitable framework which considered the automobile green supply chain as a two-in-one chain was adopted. This two-in-one chain comprised a forward and backward chain for the automobile industry. Consequently, 10 measures with 49 metrics and 6 measures with 23 metrics were identified and developed for the forward and backward chains, respectively. Sequel to the development of these measures, a survey was conducted using a four-page questionnaire distributed to experts (including academics and practitioners) to establish their importance and applicability. The findings of this study suggested that the importance and applicability of all the developed measures have been substantiated. For the forward chain, the most crucial measure was customer perspective while the most applicable one was traditional supply chain cost. The reverse chain measures were topped by management commitment in terms of both importance and applicability. This study contributed to the advancement of knowledge by pioneering the development of a set of holistic measures for evaluating the performance of the automobile green supply chain. The study was wrapped up with the proposition of directions for further studies. © 2010 Elsevier B.V. All rights reserved.},
author_keywords={Green supply chain;  Performance measurement;  Performance measures;  Reverse logistics},
keywords={Automobile industry;  Green supply chain;  Performance measure;  Performance measurements;  Performance measures;  Reverse logistics;  Supply chain costs;  Supply chain performance, Environmental management;  Logistics;  Surveys, Supply chain management, automobile industry;  environmental management;  logistics;  performance assessment, air pollution control;  article;  automobile green supply chain;  automobile industry;  business ethics;  certification;  conceptual framework;  downstream processing;  economic evaluation;  energy consumption;  environmental management;  evaluation research;  good manufacturing practice;  green chemistry;  industrial production;  organizational development;  organizational structure;  performance measurement system;  personnel management;  process optimization;  recycling;  social marketing;  waste disposal;  waste minimization},
references={Artz, K.W., Buyer-supplier performance: The role of asset specificity, reciprocal investments and relational exchange (1999) British Journal of Management, 10, pp. 113-126; Baumgarten, H., Burtz, C., Pietschmann, N., Reverse logistics (2007) Sustainability in Manufacturing: Recovery of Resources in Product and Material Cycles, pp. 130-141; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Beamon, B.M., Environmental and sustainability ethics in supply chain management (2005) Science and Engineering Ethics, 11 (2), pp. 221-234; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Bond, T.C., The role of performance measurement in continuous improvement (1999) International Journal of Operations and Production Management, 19 (12), pp. 1318-1334. , DOI 10.1108/01443579910294291; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3), pp. 209-223. , DOI 10.1108/13598540310484618; Chung, C., Wee, M., Green-component life-cycle value on design and reverse manufacturing in semi-closed supply chain (2008) International Journal of Production Economics, 113, pp. 528-545; Cruz-Rivera, R., Ertel, J., Reverse logistics network design for the collection of end-of-life vehicles in Mexico (2009) European Journal of Operational Research, 196, pp. 930-939; Dyckhoff, H., Souren, R., Keilen, J., The expansion of supply chains to close loop systems: A conceptual framework and the automotive industry's point of view (2004) Supply Chain Management and Reverse Logistics, pp. 13-34; Directive 2000/53/EC of the European Parliament and of the Council of 18 September 2000 on end-of-life vehicles (2000) Official Journal of the European Communities, 269 (OCTOBER), pp. 34-42; Elmer, C.F., Efficient economic organization (2007) Sustainability in Manufacturing: Recovery of Resources in Product and Material Cycles, pp. 104-125; Fleischer, G., Dose, J., Ackermann, R., Saving resources by reuse, recycling and recovery (2007) Sustainability in Manufacturing: Recovery of Resources in Product and Material Cycles, pp. 68-77; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) International Journal of Purchasing and Material Management, 30 (4), pp. 13-18; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McCaughey, R.E., Framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Hall, J., Environmental supply chain dynamics (2000) Journal of Cleaner Production, 8 (6), pp. 455-471; Hamner, B., Effects of green purchasing strategies on supplier behavior (2006) Greening the Supply Chain, pp. 25-37; Helms, M.M., Hervani, A.A., Reverse logistics for recycling: Challenges facing the carpet industry (2006) Greening the Supply Chain, pp. 117-135; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice-Hall Upper Saddle River, NJ; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353. , DOI 10.1108/14635770510609015; Hesselbach, J., Herrmann, C., Luger, T., Assessment of recyclability (2007) Sustainability in Manufacturing: Recovery of Resources in Product and Material Cycles, pp. 90-103; Hieber, R., (2002) Supply Chain Management: A Collaborative Performance Measurement Approach, , VDF Zurich; Inderfurth, K., Product recovery behavior in a closed loop supply chain (2004) Supply Chain Management and Reverse Logistics; Klassen, R., Whybark, D.C., The impact of environmental technologies on manufacturing performance (1999) Academy of Management Journal, 42 (6), pp. 599-615; Lai, K.-H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-456. , DOI 10.1016/S1366-5545(02)00019-4, PII S1366554502000194; Liang, L., Yang, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49. , DOI 10.1007/s10479-006-0026-7, Performance Evaluation and Beyond: Data Envelopment Analysis Research Frontiers in Honor of William W. Cooper; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) Journal of Operations Management, 25 (6), pp. 1075-1082. , DOI 10.1016/j.jom.2007.01.012, PII S0272696307000149; Loewenthal, K.M., (2001) An Introduction to Psychological Tests and Scales, , Psychology Press East Sussex; Malhotra, N.K., Birks, D.F., (2006) Marketing Research: An Applied Approach, , Prentice-Hall Harlow, England; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental performance indicators for integrated supply chains: The case of Xerox Ltd (1998) Supply Chain Management, 3 (3), pp. 149-156. , DOI 10.1108/13598549810230877; Mentzer, J.T., Myers, M.B., Stank, T.P., (2007) Handbook of Global Supply Chain Management, , Sage Publications California; Mezher, T., Ajam, M., Integrating quality, environmental and supply chain management systems into the learning organization (2006) Greening the Supply Chain; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263. , DOI 10.1108/01443570510633639; Olugu, E.U., Wong, K.Y., Supply chain performance evaluation: Trends and challenges (2009) American Journal of Engineering and Applied Sciences, 2 (1), pp. 202-211; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., A conceptual framework for green supply chain performance measurement in the automotive industry (2009) Proceedings of the 13th IBIMA Conference on Knowledge Management and Innovation in Advancing Economies, pp. 40-50. , Marrakech, Morocco; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Proposed performance measurement areas for an automobile green supply chain (2009) Proceedings of the 13th IBIMA Conference on Knowledge Management and Innovation in Advancing Economies, pp. 130-138. , Marrakech, Morocco; Preuss, L., In dirty chains? Purchasing and greener manufacturing (2001) Journal of Business Ethics, 34 (3-4), pp. 345-359. , DOI 10.1023/A:1012549318786; Pun, K.F., Determinants of environmentally responsible operations: A review (2006) International Journal of Quality and Reliability Management, 23 (3), pp. 279-297. , DOI 10.1108/02656710610648233; Rao, P., Greening the supply chain: A new initiative in South East Asia (2002) International Journal of Operations and Production Management, 22 (6), pp. 632-655; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916. , DOI 10.1108/01443570510613956; Roberts, S., Supply Chain Specific? Understanding the Patchy Success of Ethical Sourcing Initiatives (2003) Journal of Business Ethics, 44 (2-3), pp. 159-170; Sabri, E.H., Beamon, M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega: An International Journal of Management Science, 28, pp. 581-598; Salam, M.A., Corporate social responsibility in purchasing and supply chain (2009) Journal of Business Ethics, 85 (2), pp. 355-370; Schonsleben, P., (2004) Integral Logistics Management: Planning and Control of Comprehensive Supply Chains, , St Lucie Press Boca Raton, FL; Schultmann, F., Jochum, R., Rentz, O., A methodological approach for the economic assessment of Best Available Techniques: Demonstrated for a case study from the steel Industry (2001) International Journal of Life Cycle Assessment, 6 (1), pp. 19-27; Schultmann, F., Zumkeller, M., Rentz, O., Integrating spent products material into supply chains: The recycling of end-of-life vehicles as an example (2004) Supply Chain Management and Reverse Logistics; Solvang, W.D., Roman, E., Deng, Z., Solvang, B., A framework for holistic greening of value chains. Knowledge enterprise: Intelligent strategies in product design, manufacturing, and management (2006) International Federation for Information Processing (IFIP), 207, pp. 350-355; Spengler, T., Stolting, W., Ploog, M., Recovery planning in closed loop supply chain: An activity analysis based approach (2004) Supply Chain Management and Reverse Logistics; Sroufe, R.P., A framework for strategic environmental sourcing (2006) Greening the Supply Chain, pp. 3-23; Stephens, S., Supply Chain Operations Reference Model Version 5.0: A New Tool to Improve Supply Chain Efficiency and Achieve Best Practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476. , DOI 10.1023/A:1012881006783; Steven, M., Network in reversed logistics (2004) Supply Chain Management and Reverse Logistics; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Theyel, G., Customer and supplier relations for environmental performance (2006) Greening the Supply Chain, pp. 139-149; Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) Journal of Cleaner Production, 16 (15), pp. 1647-1657; (1999) Sector Based Public Policy in the Asia-Pacific Region, , US-AEP; Vachon, S., Klassen, R.D., Extending green practices across the supply chain: The impact of upstream and downstream integration (2006) International Journal of Operations and Production Management, 26 (7), pp. 795-821. , DOI 10.1108/01443570610672248; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315. , DOI 10.1016/j.ijpe.2006.11.030, PII S0925527307001892; Van Hoek, R.I., From reversed logistics to green supply chains (1999) Supply Chain Management, 4 (3), pp. 129-134. , DOI 10.1108/13598549910279576; Van Hoek, R.I., Harrison, A., Christopher, M., Measuring the agile capabilities in the supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 126-147; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, J.P., Designing supply chains: Towards theory development (2006) International Journal of Production Economics, 100 (2), pp. 223-238. , DOI 10.1016/j.ijpe.2004.11.014, PII S0925527305000241; Windischer, A., Grote, G., Success factors for collaborative planning (2003) Strategy and Organization in Supply Chain, pp. 131-146; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381. , DOI 10.1108/02635570710734271; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. 25-51; Zhu, Q., Sarkis, J., Lai, K.-h., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) Journal of Environmental Management, 85 (1), pp. 179-189. , DOI 10.1016/j.jenvman.2006.09.003, PII S0301479706002465; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: Drivers and practices (2006) Journal of Cleaner Production, 14 (5), pp. 472-486. , DOI 10.1016/j.jclepro.2005.01.003, PII S0959652605000065},
correspondence_address1={Wong, K. Y.; Department of Manufacturing and Industrial Engineering, , 81310 UTM Skudai, Malaysia; email: wongky@mail.fkm.utm.my},
issn={09213449},
coden={RCREE},
language={English},
abbrev_source_title={Resour. Conserv. Recycl.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Ilgin2011580,
author={Ilgin, M.A. and Gupta, S.M.},
title={Performance improvement potential of sensor embedded products in environmental supply chains},
journal={Resources, Conservation and Recycling},
year={2011},
volume={55},
number={6},
pages={580-592},
doi={10.1016/j.resconrec.2010.05.001},
note={cited By 58},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956213143&doi=10.1016%2fj.resconrec.2010.05.001&partnerID=40&md5=a1bd19085e066d4011d4c797e8bcde88},
affiliation={Department of Industrial Engineering, Dokuz Eylul University, Buca 35160, Izmir, Turkey; Laboratory of Responsible Manufacturing, 334 SN Department of MIE, Northeastern University, Boston, MA 02115, United States},
abstract={Environmental supply chain is a desirable alternative to traditional supply chain due to new and stricter environmental regulations by local governments and increasing consumer awareness towards environmental issues. The two most common types of environmental supply chains are reverse supply chains and closed-loop supply chains. In reverse or closed-loop supply chains, collected used products are subjected to one of four recovery options, viz., reuse, recycling, remanufacturing or disposal. Regardless of the chosen recovery option, all products are disassembled up to some level. However, there is a high level of uncertainty associated with the disassembly yield due to non-functional and/or missing components. Sensor embedded products (SEPs) is a viable approach to cope with this uncertainty since they involve sensors which can detect non-functional and/or missing components prior to disassembly. This study presents a quantitative assessment of the impact of SEPs on the various performance measures of a kanban-controlled disassembly line. First, separate design of experiments studies based on Orthogonal Arrays are carried out for conventional products and SEPs. Then, the results of paired t-tests comparing the two cases based on different performance measures are presented. © 2010 Elsevier B.V. All rights reserved.},
author_keywords={Closed-loop supply chain;  Disassembly line;  Multi-kanban system;  Orthogonal arrays;  Reverse supply chain;  Sensor embedded products},
keywords={Closed-loop supply chain;  Disassembly line;  Multi-kanban system;  Orthogonal array;  Reverse supply chains, Design of experiments;  Environmental regulations;  Linear matrix inequalities;  Sensors;  Supply chain management, Supply chains, environmental impact;  environmental issue;  environmental policy;  recycling;  waste disposal;  waste management, article;  computer analysis;  computer graphics;  computer memory;  computer program;  computer system;  controlled study;  economic evaluation;  electronic sensor;  environmental management;  environmental supply chain;  government regulation;  performance measurement system;  product recovery;  quantitative analysis;  recycling;  sensor embedded product;  waste disposal},
references={Aksoy, H.K., Gupta, S.M., Buffer allocation plan for a remanufacturing cell (2005) Computers and Industrial Engineering, 48 (3), pp. 657-677. , DOI 10.1016/j.cie.2003.03.007, PII S0360835204002086, GroupTechnology/Cellular Manufacturing; Altekin, F.T., Kandiller, L., Ozdemirel, N.E., Profit-oriented disassembly-line balancing (2008) International Journal of Production Research, 46 (10), pp. 2675-2693. , DOI 10.1080/00207540601137207, PII 772388101; Aras, N., Aksen, D., Locating collection centers for distance- and incentive-dependent returns (2008) International Journal of Production Economics, 111 (2), pp. 316-333. , DOI 10.1016/j.ijpe.2007.01.015, PII S0925527307001909; Barba-Gutierrez, Y., Adenso-Diaz, B., Gupta, S.M., Lot sizing in reverse MRP for scheduling disassembly (2008) International Journal of Production Economics, 111 (2), pp. 741-751. , DOI 10.1016/j.ijpe.2007.03.017, PII S0925527307001612; Bruce, J.W., Hathcock, L.A., Maintenance and monitoring object models for high-availability network appliances (2004) IEEE Transactions on Consumer Electronics, 50, pp. 472-477; Cheng, F.T., Huang, G.W., Chen, C.H., Hung, M.H., A generic embedded device for retrieving and transmitting information of various customized applications (2004) Proceedings of the 2004 IEEE International Conference on Robotics and Automation, pp. 978-983; Efendigil, T., Onut, S., Kongar, E., A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness (2008) Computers and Industrial Engineering, 54 (2), pp. 269-287. , DOI 10.1016/j.cie.2007.07.009, PII S0360835207001726; Guide, V.D.R., Teunter, R.H., Van Wassenhove, L.N., Matching demand and supply to maximize profits from remanufacturing (2003) Manufacturing & Service Operations Management, 5, pp. 303-316; Gungor, A., Gupta, S.M., A solution approach to the disassembly line balancing problem in the presence of task failures (2001) International Journal of Production Research, 39 (7), pp. 1427-1467. , DOI 10.1080/00207540110052157; Gungor, A., Gupta, S.M., Disassembly line in product recovery (2002) International Journal of Production Research, 40 (11), pp. 2569-2589. , DOI 10.1080/00207540210135622; Gupta, S.M., Al-Turki, Y.A.Y., An algorithm to dynamically adjust the number of Kanbans in stochastic processing times and variable demand environment (1997) Production Planning and Control, 8 (2), pp. 133-141; Gupta, S.M., Al-Turki, Y.A.Y., Adapting just-in-time manufacturing systems to preventive maintenance interruptions (1998) Production Planning and Control, 9 (4), pp. 349-359; Gupta, S.M., Al-Turki, Y.A.Y., The effect of sudden material handling system breakdown on the performance of a JIT system (1998) International Journal of Production Research, 36 (7), pp. 1935-1960; Gupta, S.M., Al-Turki, Y.A.Y., Perry, R.F., Flexible kanban system (1999) International Journal of Operations and Production Management, 19 (10), pp. 1065-1093. , DOI 10.1108/01443579910271700; Herzog, M.A., Marwala, T., Heyns, P.S., Machine and component residual life estimation through the application of neural networks (2009) Reliability Engineering & System Safety, 94, pp. 479-489; Iakovou, E., Moussiopoulos, N., Xanthopoulos, A., Achillas, C., Michailidis, N., Chatzipanagioti, M., A methodological framework for end-of-life management of electronic products (2009) Resources, Conservation and Recycling, 53, pp. 329-339; Ilgin, M.A., Gupta, S.M., Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art (2010) Journal of Environmental Management, 91, pp. 563-591; Kannan, G., Noorul Haq, A., Devika, M., Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation (2009) International Journal of Production Research, 47, pp. 1175-1200; Kannan, G., Pokharel, S., Sasi Kumar, P., A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider (2009) Resources, Conservation and Recycling, 54, pp. 28-36; Kara, S., Pornprasitpol, P., Kaebernick, H., Selective disassembly sequencing: A methodology for the disassembly of end-of-life products (2006) CIRP Annals - Manufacturing Technology, 55 (1), pp. 37-40; Karlsson, B., A distributed data processing system for industrial recycling (1997) Proceedings of 1997 IEEE Instrumentation and Measurement Technology Conference, pp. 197-200; Karlsson, B., Fuzzy handling of uncertainty in industrial recycling (1998) Proceedings of 1998 IEEE Instrumentation and Measurement Technology Conference, pp. 832-836; Kazmierczak, K., Neumann, W.P., Winkel, J., A case study of serial-flow car disassembly: Ergonomics, productivity and potential system performance (2007) Human Factors and Ergonomics In Manufacturing, 17 (4), pp. 331-351. , DOI 10.1002/hfm.20078; Kazmierczak, K., Mathiassen, S.E., Forsman, M., Winkel, J., An integrated analysis of ergonomics and time consumption in Swedish 'craft-type' car disassembly (2005) Applied Ergonomics, 36 (3), pp. 263-273. , DOI 10.1016/j.apergo.2005.01.010; Kelton, D.W., Sadowski, R.P., Sadowski, D.A., (2007) Simulation with Arena, , 4th ed. McGraw-Hill New York; Kizilkaya, E.A., Gupta, S.M., Modeling operational behavior of a disassembly line (2004) Proceedings of SPIE - The International Society for Optical Engineering, 5583, pp. 79-93. , DOI 10.1117/12.580419, 08, Environmentally Conscious Manufacturing IV; Kizilkaya, E.A., Gupta, S.M., Impact of different disassembly line balancing algorithms on the performance of dynamic kanban system for disassembly line (2005) Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing v, pp. 103-110; Kizilkaya, E.A., Gupta, S.M., Sudden material handling system breakdown in a disassembly line (2005) Proceedings of the 2005 Northeast Decision Sciences Institute Conference; Kizilkaya, E.A., Gupta, S.M., Dynamic kanban system for disassembly line applied to an industrial voice recognition client unit (2006) Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing VI, pp. 22-29; Klausner, M., Grimm, W.M., Hendrickson, C., Reuse of electric motors in consumer products (1998) Journal of Industrial Ecology, 2, pp. 89-102; Klausner, M., Grimm, W.M., Horvath, A., Integrating product takeback and technical service (1999) Proceedings of the 1999 IEEE International Symposium on Electronics and the Environment, pp. 48-53; Klausner, M., Grimm, W.M., Hendrickson, C., Horvath, A., Sensor-based data recording of use conditions for product takeback (1998) Proceedings of the 1998 IEEE International Symposium on Electronics and the Environment, pp. 138-143; Kondo, Y., Deguchi, K., Hayashi, Y.-I., Obata, F., Reversibility and disassembly time of part connection (2003) Resources, Conservation and Recycling, 38 (3), pp. 175-184. , DOI 10.1016/S0921-3449(02)00153-2, PII S0921344902001532; Lambert, A.J.D., Disassembly sequencing: A survey (2003) International Journal of Production Research, 41, pp. 3721-3759; Lambert, A.J.D., Gupta, S.M., (2005) Disassembly Modeling for Assembly, Maintenance, Reuse, and Recycling, , CRC Press Boca Raton, FL; Li, J., González, M., Zhu, Y., A hybrid simulation optimization method for production planning of dedicated remanufacturing (2009) International Journal of Production Economics, 117, pp. 286-301; Liu, C., Zha, X.F., Miao, Y., Lee, J., Internet server controller based intelligent maintenance system for information appliance products (2005) International Journal of Knowledge Based Intelligent Engineering Systems, 9 (2), pp. 137-148. , Integrated and hybrid intelligent systems in product design and development; Mazhar, M.I., Kara, S., Kaebernick, H., Reusability assessment of components in consumer products - A statistical and condition monitoring data analysis strategy (2005) Proceedings of the 4th Australian LCA Conference; Mazhar, M.I., Kara, S., Kaebernick, H., Remaining life estimation of used components in consumer products: Life cycle data analysis by Weibull and artificial neural networks (2007) Journal of Operations Management, 25 (6), pp. 1184-1193. , DOI 10.1016/j.jom.2007.01.021, PII S0272696307000204; McGovern, S.M., Gupta, S.M., Ant colony optimization for disassembly sequencing with multiple objectives (2006) International Journal of Advanced Manufacturing Technology, 30 (5-6), pp. 481-496. , DOI 10.1007/s00170-005-0037-6, Intelligent Disassembly in the Demanufacturing Process; McGovern, S.M., Gupta, S.M., Combinatorial optimization analysis of the unary NP-complete disassembly line balancing problem (2007) International Journal of Production Research, 45 (18-19), pp. 4485-4511. , DOI 10.1080/00207540701476281, PII 781767801, Sustainable Design and Manufacture; Moore, P.R., Pu, J., Xie, C., Simon, M., Bee, G., Life cycle data acquisition methods and devices (2000) Proceedings of the 7th Mechatronics Forum International Conference; Ni, J., Lee, J., Djurdjanovic, D., Watchdog: Information technology for proactive product maintenance and its implications to ecological product re-use (2003) Proceedings of the Symposium on Ecological Manufacturing, pp. 101-110; Petriu, E.M., Georganas, N.D., Petriu, D.C., Makrakis, D., Groza, V.Z., Sensor-based information appliances (2000) IEEE Instrumentation and Measurement Magazine, 3 (4), pp. 31-35. , DOI 10.1109/5289.887458; Phadke, M.S., (1989) Quality Engineering Using Robust Design, , Prentice Hall New Jersey; Pochampally, K.K., Nukala, S., Gupta, S.M., (2009) Strategic Planning Models for Reverse and Closed-loop Supply Chains, , CRC Press Boca Raton, Florida; Salema, M.I.G., Barbosa-Povoa, A.P., Novais, A.Q., Simultaneous design and planning of supply chains with reverse flows: A generic modelling framework (2010) European Journal of Operational Research, 203, pp. 336-349; Scheidt, L., Shuqiang, Z., An approach to achieve reusability of electronic modules (1994) Proceedings of the IEEE International Symposium on Electronics and the Environment, pp. 331-336; Seliger, G., Buchholz, A., Grudzein, W., Multiple usage phases by component adaptation (2002) Proceedings of the 9th CIRP International Seminar on Life Cycle Engineering, pp. 47-54; Simon, M., Pu, J., Moore, P.R., The whitebox-capturing and using product life cycle data (1998) Proceedings of the 5th CIRP Seminar on Life Cycle Design, pp. 161-170; Simon, M., Bee, G., Moore, P., Pu, J.S., Xie, C., Life cycle data acquisition unit - design, implementation, economics and environmental benefits (2000) IEEE International Symposium on Electronics and the Environment, pp. 284-289; Simon, M., Bee, G., Moore, P., Pu, J.-S., Xie, C., Modelling of the life cycle of products with data acquisition features (2001) Computers in Industry, 45 (2), pp. 111-122. , DOI 10.1016/S0166-3615(01)00088-4, PII S0166361501000884; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9, pp. 53-80; Takahashi, K., Nakamura, N., Reacting JIT ordering systems to the unstable changes in demand (1999) International Journal of Production Research, 37, pp. 2293-2313; Takahashi, K., Nakamura, N., Reactive logistics in a JIT environment (2000) Production Planning and Control, 11 (1), pp. 20-31. , DOI 10.1080/095372800232450; Takahashi, K., Nakamura, N., Decentralized reactive Kanban system (2002) European Journal of Operational Research, 139 (2), pp. 262-276. , DOI 10.1016/S0377-2217(01)00358-7, PII S0377221701003587; Takahashi, K., Morikawa, K., Nakamura, N., Reactive JIT ordering system for changes in the mean and variance of demand (2004) International Journal of Production Economics, 92, pp. 181-196; Tang, Y., Zhou, M.C., A systematic approach to design and operation of disassembly lines (2006) IEEE Transactions on Automation Science and Engineering, 3, pp. 324-329; Tardif, V., Maaseidvaag, L., An adaptive approach to controlling kanban systems (2001) European Journal of Operational Research, 132 (2), pp. 411-424. , DOI 10.1016/S0377-2217(00)00119-3, PII S0377221700001193; Torres, F., Gil, P., Puente, S.T., Pomares, J., Aracil, R., Automatic PC disassembly for component recovery (2004) The International Journal of Advanced Manufacturing Technology, 23, pp. 39-46; Tsai, C.F., Wu, H.C., MASSIHN: A multi-agent architecture for intelligent home network service (2002) IEEE Transactions on Consumer Electronics, 48, pp. 505-514; Udomsawat, G., Gupta, S.M., The effect of sudden server breakdown on the performance of a disassembly line (2005) Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing v, pp. 93-102; Udomsawat, G., Gupta, S.M., Multi-kanban mechanism for appliance disassembly (2005) Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing v, pp. 30-41; Udomsawat, G., Gupta, S.M., Multi-kanban in disassembly line with component-discriminating demand (2005) Proceedings of the 2005 Northeast Decision Sciences Institute Conference; Udomsawat, G., Gupta, S.M., Controlling disassembly line with multi-kanban system (2006) Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing VI, pp. 42-53; Udomsawat, G., Gupta, S.M., Multikanban system for disassembly line (2008) Environment Conscious Manufacturing, pp. 311-330; Udomsawat, G., Gupta, S.M., Kamarthi, S.V., A multi-kanban model for disassembly (2003) Proceedings of the 2003 Northeast Decision Sciences Institute Conference, pp. 169-171; Udomsawat, G., Gupta, S.M., Al-Turki, Y.A.Y., Multi-kanban model for disassembly line with demand fluctuation (2003) Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing III, pp. 85-93; Vadde, S., Kamarthi, S.V., Gupta, S.M., Optimal pricing of reusable and recyclable components under alternative product acquisition mechanisms (2007) International Journal of Production Research, 45 (18-19), pp. 4621-4652. , DOI 10.1080/00207540701449973, PII 781768847, Sustainable Design and Manufacture; Vadde, S., Kamarthi, S., Gupta, S.M., Zeid, I., Product life cycle monitoring via embedded sensors (2008) Environment Conscious Manufacturing, pp. 91-103; Wallaschek, J., Wedman, S., Wickord, W., Lifetime observer: An application of mechatronics in vehicle technology (2002) International Journal of Vehicle Design, 28 (1-3), pp. 121-130; Weber, P., Towards easy-to-communicate and transparent LCA data: Application of life cycle design structure matrix (2000) Proceedings of the 7th CIRP International Seminar on Life Cycle Engineering, pp. 52-59; Yalabik, B., Petruzzi, N.C., Chhajed, D., An integrated product returns model with logistics and marketing coordination (2005) European Journal of Operational Research, 161, pp. 162-182; Yang, X., Moore, P., Pu, J.-S., Wong, C.-B., A practical methodology for realizing product service systems for consumer products (2009) Computers & Industrial Engineering, 56, pp. 224-235},
correspondence_address1={Ilgin, M. A.; Department of Industrial Engineering, , Buca 35160, Izmir, Turkey; email: mehmetali.ilgin@deu.edu.tr},
issn={09213449},
coden={RCREE},
language={English},
abbrev_source_title={Resour. Conserv. Recycl.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zhu2011623,
author={Zhu, Q. and Sarkis, J. and Lai, K.-H.},
title={An institutional theoretic investigation on the links between internationalization of Chinese manufacturers and their environmental supply chain management},
journal={Resources, Conservation and Recycling},
year={2011},
volume={55},
number={6},
pages={623-630},
doi={10.1016/j.resconrec.2010.12.003},
note={cited By 58},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956192081&doi=10.1016%2fj.resconrec.2010.12.003&partnerID=40&md5=7aa2ba65876765835c6b89192391349f},
affiliation={Business School of Management, Dalian University of Technology, Dalian, Liaoning Province 116024, China; Graduate School of Management, Clark University, 950 Main Street, Worcester, MA 01610-1477, United States; Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong},
abstract={Increased market internationalization with more and stricter environmental regulations in different countries has caused manufacturers to extend their environmental management practices to cover their supply chain partners. Thus, environmental supply chain management (ESCM) has become an important competitive and environmental strategy for manufacturers to pursue. China, as a global manufacturer, is also facing this balance of economic development with environmental protection. The literature remains unclear on whether internationalization is beneficial or costly when it comes to environmental management practices such as ESCM within China. Further insight into this issue will be useful for organizations in developing and emergent economies to more effectively prepare for internationalizing their operations and markets. Using internationalization drivers that propel the implementation of environmental management practices in the Chinese manufacturing industry, we applied cluster analysis to group a sample of 377 Chinese manufacturers surveyed in 2007 into three clusters: mature internationalization, emergent internationalization, and domestic-focused. Institutional theory within an internationalization context sets the theoretical foundation for evidence that the international environmentally oriented institutional drivers encountered are positively associated with the adoption of ESCM practices by Chinese manufacturers. We also find significant differences in the environmental, economic and operational performance outcomes across the three manufacturer clusters. We conclude that manufacturing organizations experiencing internationalization with a greater extent of adopting ESCM practices tend to perform better. © 2010 Elsevier B.V. All rights reserved.},
author_keywords={China;  Environmental management;  Globalization;  Institutional theory;  Supply chain},
keywords={China;  Chinese manufacturers;  Economic development;  Environmental management practices;  Environmental strategy;  Environmental supply chain management;  Globalization;  Institutional theory;  Manufacturing industries;  Manufacturing organizations;  Operational performance;  Supply chain partners;  Theoretical foundations, Cluster analysis;  Economics;  Environmental management;  Environmental regulations;  Management;  Manufacture;  Societies and institutions, Supply chain management, cluster analysis;  economic development;  environmental management;  environmental protection;  globalization;  implementation process;  industrial practice;  institutional framework;  management practice;  manufacturing;  organizational framework, article;  Chinese;  cluster analysis;  commercial phenomena;  conceptual framework;  controlled study;  economic development;  economic evaluation;  environmental management;  environmental protection;  environmental supply chain management;  government regulation;  internationalization;  intitutional theory;  nonhuman;  organizational development;  performance measurement system;  questionnaire;  social marketing;  strategic planning;  theory, China},
references={Ambec, S., Lanoie, P., Does it pay to be green? A systematic overview (2008) Academy of Management Perspectives, 23 (4), pp. 45-62; Armstrong, J., Overton, T.S., Estimating nonresponse bias in mail surveys (1977) Journal of Marketing Research, 9 (4), pp. 69-75; Barba-Gutierrez, Y., Adenso-Diaz, B., Hopp, M., An analysis of some environmental consequences of European electrical and electronic waste regulation (2008) Resources, Conservation and Recycling, 52 (3), pp. 481-495. , DOI 10.1016/j.resconrec.2007.06.002, PII S0921344907001462; Budeanu, A., Environmental supply chain management in tourism: The case of large tour operators (2009) Journal of Cleaner Production, 17 (16), pp. 1385-1392; (2004) European Community Established Measures on Electronic Waste Management, , http://www.okxr.com/news/temp/20040816/20040816152152296.shtml; Child, J., Rodrigues, S.B., The internationalization of Chinese firms: A case for theoretical extension (2005) Management and Organization Review, 1 (3), pp. 381-410; Dimaggio, P.J., Powell, W.W., The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (1983) American Sociological Review, 48, pp. 147-160; Fang, Y., Cote, R.P., Qin, R., Industrial sustainability in China: Practice and prospects for eco-industrial development (2007) Journal of Environmental Management, 83 (3), pp. 315-328. , DOI 10.1016/j.jenvman.2006.03.007, PII S0301479706001216; Guthrie, D., Organizational learning and productivity: State structure and foreign investment in the rise of the Chinese corporation (2005) Management and Organization Review, 1 (2), pp. 165-195; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (2010) Multivariate Data Analysis, , 7th ed. Prentice-Hall New Jersey; King, A.A., Lenox, M.J., Terlaak, A., The strategic use of decentralized institutions: Exploring certification with the ISO 14001 management standard (2005) Academy of Management Journal, 48 (6), pp. 1091-1106; Lai, K.H., Bao, Y., Li, X., Channel relationship and business uncertainty: Evidence from the Hong Kong market (2008) Industrial Marketing Management, 37 (6), pp. 713-724; Lai, K.-H., Wong, C.W.Y., Cheng, T.C.E., Institutional isomorphism and the adoption of information technology for supply chain management (2006) Computers in Industry, 57 (1), pp. 93-98. , DOI 10.1016/j.compind.2005.05.002, PII S0166361505000862; Lai, K.H., Cheng, T.C.E., Tang, A.K.Y., Green retailing: Factors for success (2010) California Management Review, 52 (2), pp. 6-31; Liu, X., Buck, T., Innovation performance and channels for international technology spillovers: Evidence from Chinese high-tech industries (2007) Research Policy, 36 (3), pp. 355-366. , DOI 10.1016/j.respol.2006.12.003, PII S0048733307000029; Lo, C.K.Y., Yeung, A.C.L., Cheng, T.C.E., Meta-standards, financial performance and senior executive compensation in China: An institutional perspective (2010) International Journal of Production Economics; Nnorom, I.C., Osibanjo, O., Overview of electronic waste (e-waste) management practices and legislations, and their poor applications in the developing countries (2008) Resources, Conservation and Recycling, 52 (6), pp. 843-858. , DOI 10.1016/j.resconrec.2008.01.004, PII S0921344908000165; Qi, Y., Ma, L., Zhang, H., Li, H., Translating a global issue into local priority: China's local government response to climate change (2008) The Journal of Environment Development, 17 (4), pp. 379-400; Quer, D., Claver, E., Rienda, L., Business and management in China: A review of empirical research in leading international journals (2007) Asia Pacific Journal of Management, 24, pp. 359-384; Rivera, J., Institutional pressures and voluntary environmental behavior in developing countries: Evidence from the Costa Rican hotel industry (2004) Society and Natural Resources, 17 (9), pp. 779-797. , DOI 10.1080/08941920490493783; Roth, A., Tsay, A.A., Pullman, M.E., Gray, J.V., Unraveling the food supply chain: Strategic insights from China and the 2007 recalls (2008) The Journal of Supply Chain Management, 44 (1), pp. 22-39; Sarkis, J., Zhu, H., Information technology and systems in China's circular economy: Implications for sustainability (2008) Journal of Systems and Information Technology, 10 (3), pp. 202-279; Sarkis, J., Convincing industry that there is value in environmental supply chains (2009) Problemy Ekorozwoju, 4 (1), pp. 61-64; Saranen, J., Szekely, B., Hilmola, O.P., Toikka, T., Transportation strategy in international supply chains - The case of Russia (2010) International Journal of Shipping and Transport Logistics, 2 (2), pp. 168-186; Terlaak, A., Order without law? the role of certified management standards in shaping socially desired firm behaviors (2007) Academy of Management Review, 32, pp. 968-985; Walton, S.V., Handfield, R.B., Melnyk, S.T., The green supply chain: Integrating suppliers into environmental management process (1998) Journal Supply Chain Management, 34 (2), pp. 2-11; Wong, C.W.Y., Lai, K.H., Cheng, T.C.E., Complementarities and alignment of information systems management and supply chain management (2009) International Journal of Shipping and Transport Logistics, 1 (2), pp. 156-171; Wong, C.W.Y., Lai, K.H., Ngai, E.W.T., The role of supplier operational adaptation on the performance of IT-enabled logistics under environmental uncertainty (2009) International Journal of Production Economics, 122 (1), pp. 47-55; Wong, C.W.Y., Lai, K.H., Teo, T.S.H., Institutional pressures and mindful IT management: The case of a container terminal in China (2009) Information & Management, 46 (8), pp. 434-441; Yang, J., Wong, C.W.Y., Lai, K.H., Ntoko, A.N., The antecedents of dyadic quality performance and its effect on buyer-supplier relationship improvement (2009) International Journal of Production Economics, 120 (1), pp. 243-251; Yang, C.L., Lin, S.P., Chan, Y.H., Sheu, C., Mediated effect of environmental management on manufacturing competitiveness: An empirical study (2010) International Journal of Production Economics, 123 (1), pp. 210-220; Yang, S., Feng, N., A case study of industrial symbiosis: Nanning Sugar Co., Ltd. in China (2008) Resources, Conservation and Recycling, 52 (5), pp. 813-820. , DOI 10.1016/j.resconrec.2007.11.008, PII S0921344907002224; Yin, H., Ma, C., International integration: A hope for a greener China? (2009) International Marketing Review, 26 (3), pp. 348-367; Yuan, Z., Bi, J., Moriguchi, Y., The circular economy: A new development's strategy in China (2006) Journal of Industrial Ecology, 10, pp. 4-7; Yu, J., Hills, P., Welford, R., Extended producer responsibility and eco-design changes: Perspectives from China (2008) Corporate Social Responsibility and Environmental Management, 15 (2), pp. 111-124. , DOI 10.1002/csr.168; Zeng, K., Eastin, J., International economic integration and environmental protection: The case of China (2007) International Studies Quarterly, 51 (4), pp. 971-995. , DOI 10.1111/j.1468-2478.2007.00485.x; Zhang, B., Bi, J., Yuan, Z., Ge, J., Liu, B., Bu, M., Why do firms engage in environmental management? An empirical study in China (2008) Journal of Cleaner Production, 16 (10), pp. 1036-1045. , DOI 10.1016/j.jclepro.2007.06.016, PII S0959652607001576; Zhao, Y., Trade environment: Challenges after China's WTO accession (2007) Columbia Journal of Environmental Law, 32, pp. 41-97; Zhu, Q., Geng, Y., Lai, K.H., Circular economy practices among Chinese manufacturers varying in environmental-oriented supply chain cooperation and the performance implications (2010) Journal of Environmental Management, 91 (6), pp. 1324-1331; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among followers of green supply chain management practices in Chinese manufacturing enterprises (2004) Journal of Operations Management, 22, pp. 265-289; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: Drivers and practices (2006) Journal of Cleaner Production, 14 (5), pp. 472-486. , DOI 10.1016/j.jclepro.2005.01.003, PII S0959652605000065; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: Pressures, practices and performance (2005) International Journal of Operations and Production Management, 25 (5), pp. 449-468. , DOI 10.1108/01443570510593148; Zhu, Q., Sarkis, J., Lai, K.-H., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111 (2), pp. 261-273. , DOI 10.1016/j.ijpe.2006.11.029, PII S0925527307001855; Zhu, Q., Zhao, Y., Business sectors in China's social capacity for environmental management (2004) Proceedings for the Second Symposium on SCEM (Social Capacity Development on Environmental Management and International Cooperation in Developing Countries), pp. 44-53. , January 12-14, 2004 Hiroshima University Japan; Zsidisin, G.A., Hendrick, T.E., Purchasing's involvement in environmental issues: A multi-country perspective (1998) Industrial Management and Data Systems, 98 (7), pp. 313-320},
correspondence_address1={Zhu, Q.; Business School of Management, , Dalian, Liaoning Province 116024, China; email: zhuqh@dlut.edu.cn},
issn={09213449},
coden={RCREE},
language={English},
abbrev_source_title={Resour. Conserv. Recycl.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Aydogan20113992,
author={Aydogan, E.K.},
title={Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment},
journal={Expert Systems with Applications},
year={2011},
volume={38},
number={4},
pages={3992-3998},
doi={10.1016/j.eswa.2010.09.060},
note={cited By 126},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650695091&doi=10.1016%2fj.eswa.2010.09.060&partnerID=40&md5=46fde15ab7ba2efa55032a66714c782f},
affiliation={Department of Industrial Engineering, Faculty of Engineering, Erciyes University, Kayseri, Turkey},
abstract={In today's organizations, performance measurement comes more to the foreground with the advancement in the high technology. So as to manage this power, which is an important element of the organizations, it is needed to have a performance measurement system. Increased level of competition in the business environment and higher customer requirements forced industry to establish a new philosophy to measure its performance beyond the existing financial and non-financial based performance indicators. In this paper, a conceptual performance measurement framework that takes into account company-level factors is presented for a real world application problem. In order to use the conceptual framework for measuring performance, a methodology that takes into account both quantitative and qualitative factors and the interrelations between them should be utilized. For this reason, an integrated approach of analytic hierarchy process (AHP) improved by rough sets theory (Rough-AHP) and fuzzy TOPSIS method is proposed to obtain final ranking. © 2010 Elsevier Ltd. All rights reserved.},
author_keywords={AHP;  Decision making;  Fuzzy TOPSIS;  Performance measurement;  Rough set},
keywords={AHP;  Business environments;  Conceptual frameworks;  Customer requirements;  Fuzzy environments;  FUZZY TOPSIS;  High technology;  Integrated approach;  Measuring performance;  Performance indicators;  Performance measurement frameworks;  Performance measurement system;  Performance measurements;  Qualitative factors;  Real-world application;  Rough set;  Rough Sets Theory;  TOPSIS method;  Turkishs, Benchmarking;  Competition;  Decision making;  Decision theory;  Hierarchical systems;  Industry;  Rough set theory;  Supply chain management, Fuzzy set theory},
references={Albayrak, E., Erensal, Y.C., Using analytic hierarchy process (AHP) to improve human performance. An application of multiple criteria decision making problem (2004) Journal of Intelligent Manufacturing, 15, pp. 491-503; Amaratunga, D., Baldry, D., Performance evaluation in facilities management (2000) Proceedings of COBRA, , Greenwich, UK; Bassioni, H.A., Price, A.D.F., Hassan, T.M., Performance measurement in construction firms (2004) Journal of Management in Engineering, 20 (2), pp. 42-50; Bourne, M., Franco, M., Wilkes, J., Corporate performance management (2003) Measuring Business Excellence, 7, pp. 3-15; Buckley, J.J., Fuzzy hierarchical analysis (1985) Fuzzy Sets and Systems, 17, pp. 233-247; Chan, F.T.S., Kumar, N., Global supplier development considering risk factors using fuzzy extended AHP-based approach (2007) OMEGA, 35, pp. 417-431; Chen, C.T., Extensions of the TOPSIS for group decision-making under fuzzy environment (2000) Fuzzy Sets and Systems, 114, pp. 1-9; Chu, T.C., Selecting plant location via a fuzzy TOPSIS approach (2002) International Journal of Advanced Manufacturing Technology, 20, pp. 859-864; Chu, T.C., Lin, Y.C., Improved extensions of the TOPSIS for group decision making under fuzzy environment (2002) Journal of Information and Optimization Sciences, 23, pp. 273-286; Chu, T.C., Lin, Y.C.A., Fuzzy TOPSIS method for robot selection (2003) Advanced Manufacturing Technology, 21 (4), pp. 284-290; Dadeviren, M., Decision making in equipment selection: An integrated approach with AHP and PROMETHEE (2008) Journal of Intelligent Manufacturing, 19, pp. 397-406; Dadeviren, M., Yavuz, S., Kilin, N., Weapon selection using the AHP and TOPSIS methods under fuzzy environment (2008) Expert Systems with Applications, , 10.1016/j.eswa.2008.10.016; Deng, H., Yeh, C., Willis, R.J., Inter-company comparison using modified TOPSIS with objective weights (2000) Computers and Operations Research, 27 (10), pp. 963-973; Ertugrul, I., Karakasoglu, N., Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods (2007) Expert Systems with Applications, 36 (1), pp. 702-715; Evangelidizs, K., Performance measured is performance gained (1992) The Treasurer, pp. 45-47; Gamble, J., Strickland, A., Thompson, A., (2007) Crafting & Executing Strategy, , McGraw-Hill New York; Geerken, M., (2008) Performance Measurement for Justice Information System Projects, , Bureau of Justice Assistance, Texas State University; Gibney, R., Shang, J., Decision making in academia: A case of the dean selection process (2007) Mathematical and Computer Modelling, 46, pp. 1030-1040; Guoyin, W., (2001) Theory and Knowledge Acquisition of Rough Sets, , Xi'an Jiao Tong University Publication Xi'an; Hwang, C.L., Yoon, K., (1981) Multiple Attribute Decision Making, , Springer-Verlag Berlin; Jee, D., Kang, K., A method for optimal material selection aided with decision making theory (2000) Materials and Design, 21 (3), pp. 199-206; Kagioglu, M., Cooper, R., Aouad, G., Performance management in construction: A conceptual framework (2001) Construction Management and Economics, 19 (1), pp. 85-95; Kao, C., Hung, H.T., Management performance: An empirical study of the manufacturing companies in Taiwan (2007) Omega, 35 (2), pp. 152-160; Kaufmann, A., Gupta, M.M., (1985) Introduction to Fuzzy Arithmetic: Theory and Applications, , Von Nostrand Reinhold New York; Kingsbury, N.R., (2005) Performance Measurement and Evaluation, , US Government Accountability Office Glossary; Laitinen, E.A., Dynamic performance measurement system: Evidence from small Finnish technology companies (2002) Scandinavian Journal of Management, 18 (1), pp. 65-99; London, M., Beatty, R.W., 360-degree feedback as competitive advantage (2006) Human Resource Management, 32 (23), pp. 353-372; Meade, L.M., Sarkis, J., Analyzing organizational project alternatives for agile manufacturing processes: An analytical network approach (1999) International Journal of Production Research, 37, pp. 241-261; Önüt, S., Kara, S.S., Iik, E., Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company (2009) Expert Systems with Applications, 36, pp. 3887-3895; Önüt, S., Soner, S., Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment (2007) Waste Management, 1. , 10.1016/j.wasman.2007.05.019; Parkan, C., Wu, M., Decision-making and performance measurement models with applications to robot selection (1999) Computers and Industrial Engineering, 36 (3), pp. 503-523; Saatty, T.L., (1980) The Analytic Hierarchy Process, , McGraw-Hill New York; Wang, T.C., Chang, T.H., Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment (2007) Expert Systems with Applications, 33, pp. 870-880; Wang, J., Liu, S.Y., Zhang, J., An extension of TOPSIS for fuzzy MCDM based on vague set theory (2005) Journal of Systems Science and Systems Engineering, 14, pp. 73-84; Xia, W., Wu, Z., Supplier selection with multiple criteria in volume discount environments (2007) Omega, 35 (5), pp. 494-504. , DOI 10.1016/j.omega.2005.09.002, PII S0305048305001180; Yang, T., Hung, C.C., Multiple-attribute decision making methods for plant layout design problem (2007) Robotics and Computer-Integrated Manufacturing, 23, pp. 126-137; Yurdakul, M., AHP as strategic decision making tool to justify machine tool selection (2004) Journal of Materials Processing Technology, 146, pp. 365-376; Zadeh, L.A., Fuzzy sets (1965) Information and Control, 8, pp. 338-353; Zadeh, L.A., The concept of a linguistic variable and its application to approximate reasoning (1975) Information Sciences, 8; Zeydan, M., Olpan, C., A new decision support system for performance measurement using combined fuzzy TOPSIS/DEA approach (2009) International Journal of Production Research, 47 (15), pp. 4327-4349; Zimmermann, H.J., (1991) Fuzzy Set Theory and Its Applications, , 2nd ed. Kluwer Academic Publishers London},
correspondence_address1={Aydogan, E. K.; Department of Industrial Engineering, , Kayseri, Turkey; email: ekaydogan@erciyes.edu.tr},
issn={09574174},
coden={ESAPE},
language={English},
abbrev_source_title={Expert Sys Appl},
document_type={Article},
source={Scopus},
}

@ARTICLE{Forslund2011133,
author={Forslund, H.},
title={The size of a logistics performance measurement system},
journal={Facilities},
year={2011},
volume={29},
number={3},
pages={133-148},
doi={10.1108/02632771111109261},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79952288733&doi=10.1108%2f02632771111109261&partnerID=40&md5=aaeb6c08acb9758da954ab803f0ca7b1},
affiliation={School of Business and Economics, Linnaeus University, Växjö, Sweden},
abstract={Purpose: One practical challenge in managing logistics performance concerns the size, or the number of actors involved, in a performance measurement (PM) system. The first objective of this paper is to describe and compare the advantages and disadvantages of four logistics performance measurement system sizes. The second objective is to develop a model of the factors affecting logistics PM system size. Design/methodology/approach: This is a conceptual paper based on a literature review. Findings: Internal PM systems were related to advantages connected with convenience and the avoidance of implementation problems. Disadvantages of an applied system were severe, especially in a supply chain context. Supply chain PM systems contained attractive advantages that can be reached with an applied system. However, a number of disadvantages in the shape of implementation problems must be handled. A model of the factors affecting the decision on logistics PM system sizes was developed. Power, purpose and implementation were found to be important factors. Five propositions were formulated. Research limitations/implications: The contribution of this paper is mainly theoretical; the results remain unverified until empirical studies are conducted. Practical implications: The paper can be seen as a first step towards new knowledge on how logistics PM system size is decided. Originality/value: As little research exists in the area, this paper highlights the theoretical aspects of a practical challenge. © Emerald Group Publishing Limited.},
author_keywords={Distribution management;  Performance measurement (quality);  Supply chain management},
references={Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), pp. 217-223; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Basu, R., New criteria of performance management - a transition from enterprise to collaborate supply chain (2001) Measuring Business Excellence, 5 (4), pp. 7-12; Benton, W.C., Maloni, M., The influence of power driven buyer/seller relationships on supply chain satisfaction (2005) Journal of Operations Management, 23, pp. 1-22; Bourne, M., Researching performance measurement system implementation: The dynamics of success and failure (2005) Production Planning & Control, 16 (2), pp. 101-113; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Brewer, P.C., Speh, T.W., Adapting the Balanced Scorecard to supply chain management (2001) Supply Chain Management Review, March and April, pp. 48-56; Busi, M., Bititci, U., Collaborative performance management: Present gaps and future research (2006) International Journal of Productivity & Performance Management, 55 (1), pp. 7-25; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21 (7), pp. 534-548; Cox, A., Power, value and supply chain management (1999) Supply Chain Management: An International Journal, 4 (4), pp. 167-175; Cox, A., Watson, G., Lonsdale, C., Sanderson, J., Managing appropriately in power regimes: Relationship and performance in 12 supply chain cases (2004) Supply Chain Management: An International Journal, 9 (5), pp. 357-371; Daugherty, P.J., Richey, R.G., Roath, A.S., Min, S., Chen, H., Arndt, A.D., Genchev, S.E., Is collaboration paying off for firms? (2006) Business Horizons, 49, pp. 61-70; Farmer, D., Ploos van Amstel, R., (1991) Effective Pipeline Management, , Gower, Aldershot; Fawcett, S.E., Cooper, M.B., Logistics performance measurement and customer success (1998) Industrial Marketing Management, 27 (4), pp. 341-357; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers and bridges to effective supply chain management (2008) Supply Chain Management: An International Journal, 13 (1), pp. 35-48; Folan, P., Browne, J., A review of performance measurement towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Forslund, H., Performance gaps in the dyadic order fulfillment process (2006) International Journal of Physical Distribution & Logistics Management, 36 (8), pp. 580-595; Forslund, H., Jonsson, P., Dyadic integration of the performance management process: A delivery service case study (2007) International Journal of Physical Distribution & Logistics Management, 37 (7), pp. 546-567; Forslund, H., Jonsson, P., Obstacles to supply chain integration of the performance management process in customer-supplier dyads: The buyers' perspective (2009) Journal of Operations & Production Management, 29 (1), pp. 77-95; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Harland, C.M., Supply chain management: Relationships, chains and networks (1996) British Journal of Management, 7 (1), pp. 63-80; Harrison, A., New, C., The role of coherent supply chain strategy and performance management in achieving competitive advantage: An international survey (2002) Journal of Operational Research Society, 53, pp. 263-271; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Holmberg, S., Supply chain integration through performance measurement (2000), doctoral thesis, Department of Design Sciences, Logistics, Lund University, Lund; Holmberg, S., A systems perspective on supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Hsu, L.L., SCM system effects on performance for interaction between suppliers and buyers (2005) Industrial Management & Data Systems, 105 (7), pp. 857-875; Keebler, J., Durtsche, D., Logistics performance measurement and the 3PL value proposition (2001) Logistics Quarterly, 7 (2), pp. 10-11+22; Larson, P.D., Gammelgaard, B., The logistics triad: Survey and case study results (2001) Transportation Journal, 41 (2-3), pp. 71-83; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156, pp. 267-286; Morgan, C., Measuring performance: Supply chain challenge (2003) Journal of the Institute of Logistics & Transport, March, pp. 44-47; Neely, A., Gregory, M., Platts, K., Performance measurement system design (2005) International Journal of Operations & Production Management, 25 (12), pp. 1228-1263; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Phillips, J.M., Liu, B.S., Costello, T.G., A balance theory perspective of triadic supply chain relationships (1998) Journal of Marketing Theory & Practice, 6 (4), pp. 78-92; Pohlen, T.L., Coleman, B.J., Evaluating internal operations and supply chain performance using EVA and ABC (2005) SAM Advanced Management Journal, 70 (2), pp. 45-59; Schmitz, J., Platts, K.W., Supplier logistics performance measurement - indications from a study in the automotive industry (2004) International Journal of Production Economics, 89, pp. 231-243; Seuring, S., Supply chain controlling: Summarizing recent developments (2006) Supply Chain Management: An International Journal, 11 (1), pp. 10-14; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity & Performance Management, 55 (3-4), pp. 242-258; Simatupang, T., Sridharan, R., Wright, A., Applying the theory of constraints to supply chain collaboration (2004) Supply Chain Management: An International Journal, 9 (1), pp. 57-70; Simons, R., (1999) Performance Measurement and Control Systems for Implementing Strategy - Text and Cases, , Prentice-Hall, Upper Saddle River, NJ; Stock, J.R., Lambert, D.M., (2001) Strategic Logistics Management, , McGraw-Hill, New York, NY; Vollman, T.E., Berry, W.L., Whybark, D.C., Jacobs, F.R., (2005) Manufacturing Planning and Control for Supply Chain Management, , McGraw-Hill, New York, NY; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60; Wu, L., Song, Y., Measuring integrated supply chain performance (2005) Services Systems and Services Management - International Conference, 1, pp. 91-96; Wu, Z., Choi, T.Y., Supplier-supplier relationships in the buyer-supplier triad: Building theories from eight case studies (2005) Journal of Operations Management, 24, pp. 27-52},
correspondence_address1={Forslund, H.; School of Business and Economics, , Växjö, Sweden; email: helena.forslund@lnu.se},
issn={02632772},
language={English},
abbrev_source_title={Facilities},
document_type={Article},
source={Scopus},
}

@ARTICLE{Nabelsi2011137,
author={Nabelsi, V.},
title={Performance measurement in the healthcare supply chain},
journal={International Journal of Services, Economics and Management},
year={2011},
volume={3},
number={2},
pages={137-156},
doi={10.1504/IJSEM.2011.039430},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952972064&doi=10.1504%2fIJSEM.2011.039430&partnerID=40&md5=65079bd7f819a514c1583a1ff3a72606},
affiliation={Faculty of Management, Université du Québec en Outaouais, C.P. 1240, Succ. Hull, Gatineau, J8X 3X7, Canada},
abstract={The supply chain plays a decisive role to ensure proper operation of the healthcare facilities. To obtain a comprehensive view of their processes, healthcare institutions must develop an integrated performance measurement system. Key Performance Indicators (KPIs) can help the healthcare professionals to measure progress towards their strategic goals and objectives. The development and use of KPIs can improve internal decision-making processes for each and across all levels in the organisation. The aim of this paper is to (a) identify and validate a list of KPIs from different professional groups who were involved in restocking activities and (b) empirically assess the relative importance of KPIs. This paper analyses empirical evidence from a sample of 163 respondents among three non-profit hospitals and one purchasing organisation in Montreal, Canada. The results reveal that all groups share a small number of KPIs and they do not necessarily attribute the same relative importance to them. © 2011 Inderscience Enterprises Ltd.},
author_keywords={healthcare organisations;  healthcare personnel;  hospitals;  key performance indicators;  procurement department;  purchasing organisation;  restocking;  supply chain},
references={Bakar, A.H.A., Hakin, I.L., Chong, S.C., Lin, B., Measuring supply chain performance among public hospital laboratories (2010) International Journal of Productivity and Performance Management, 59 (1), pp. 75-97; Beaumon, B.N., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Burns, L.R., (2002) How the Healthcare Value Chain Operates, pp. 41-56. , Burns, L.R. (ed.): The Healthcare Value Chain: Producers, Purchasers and Providers Jossey-Bass, San Francisco; Cameron, K.S., Effectiveness as paradox: consensus and conflict in conceptions of organizational effectiveness (1986) Management Science, 35 (5), pp. 539-553; Caudle, A., Finding gold in the supply chain (2009) Healthcare Financial Management, 63 (12), pp. 38-41; Chow, G., Heaver, T.D., Logistics in the Canadian healthcare industry (1994) Canadian Logistics Journal, 1 (1), pp. 29-73; Danas, K., Roudsari, A., Ketikidis, P.H., The applicability of a multi-attribute classification framework in the healthcare industry (2006) Journal of Manufacturing Technology Management, 17 (6), pp. 772-785; DeJohn, P., Managing logistics costs pays off in lowering overall supply expense (2006) Hospital Materials Management, 31 (5), pp. 1-3; De Vries, G., Bertrand, J.W.M., Vissers, J.M.H., Design requirements for healthcare production control systems (1999) Production Planning and Control, 10 (6), pp. 559-569; Eisenhardt, K.M., Building theories from case study research (1989) Academy of Management, 14 (4), pp. 532-550; Essig, M., Purchasing consortia as symbiotic relationships: developing the concept of consortium sourcing (2000) European Journal of Purchasing and Supply Management, 6 (1), pp. 13-22; Evrard, Y., Pras, B., Roux, E., (1997) Market, études et recherches en marketing, connaitre etpratiquer la gestion, , Nathan, Paris; Fowler, F.J., (2002) Survey Research Methods, , Sage Publications, Thousand Oaks, CA; Glouberman, S., Mintzberg, H., Managing the care of health and the cure of disease – part I: differentiation (2001) Healthcare Management Review, 26 (1), pp. 56-69; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , Prentice Hall, Upper Saddle River, New Jersey; Johansen, M.S., Gjerberg, E., Unitary management, multiple practices? (2009) Journal of Health Organization and Management, 23 (4), pp. 396-410; Jobin, M.H., Beaulieu, M., Blouin, A., Gérer la performance de la logistique hospitalière (2004) Logistique et Management, numéro spécial, pp. 21-30; Kaplan, R.S., Norton, D.P., (2001) The Strategy-Focused Organization: How Balanced Scorecard Companies Thrive in the New Business Environment, , Harvard Business School Press, Boston, MA; Kirchoff, B.A., Organizational effectiveness measurement and policy research (1977) Academy of Management Review, 2 (3), pp. 347-355; Kumar, A., Ozdamar, L., Zhang, C.N., Supply chain redesign in the healthcare industry of Singapore (2008) International Journal of Supply Chain Management, 13 (2), pp. 95-103; Kumar, K., Subramanian, R., Strandholm, K., Market and efficiency-based strategic responses to environmental changes in the healthcare industry (2002) Healthcare Management Review, 27 (3), pp. 21-31; Kumar, K., Subramanian, R., Yauger, C., Examining the market orientationperformance relationship: a context-specific study (1998) Journal of Management, 24 (2), pp. 201-233; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research, 38 (6), pp. 439-456; Landry, S., Beaulieu, M., L’approvisionnement face aux bouleversements du secteur de la santé: le cas québécois (1999) Revue Internationale de l’achat, 19 (3-4), pp. 17-25; Landry, S., Beaulieu, M., Logistique hospitalières: un remède aux maux du secteur de la santé? (2001) Groupe Chaine, cahier de recherche, , No. 01-01; Lorino, P., (2001) Méthodes et pratiques de la performance: le pilotage par les processus et les competences, , Editions d’organisation; Matjucha, K.A., Chung, B., And never the twain shall meet? integrating revenue cycle and supply chain functions (2008) Healthcare Financial Management, 62 (9), pp. 92-95; Mintzberg, H., (1979) The Structuring of Organizations: A Synthesis of the Research, , Prentice-Hall, Englewood Cliffs, NJ; Morgan, J., How effective are your measurement systems? (2002) Purchasing, 129 (10), pp. 26-28; Nabelsi, V., (2007) Processus de réapprovisionnement dans le secteur de la santé: collaboration et performance, p. 397. , Doctoral Dissertation, Ecole Polytechnique de Montréal, Canada; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Newman, W.H., Wallender, H.W., Managing not-for-profit enterprises (1978) Academy of Management Review, 3 (1), pp. 24-31; Nollet, J., Beaulieu, M., The development of group purchasing: an empirical study in the healthcare sector (2003) Journal of Purchasing Supply Management, 9 (1), pp. 3-10; Nunnally, J.L., Bernstein, I., (1994) Psychometric Theory, , McGraw-Hill, New York; Pan, Z.X., Pokharel, S., Logistics in hospitals: a case study of some Singapore hospitals (2007) Leadership in Health Services, 20 (3), pp. 195-207; Pennings, J.M., Goodman, P.S., Toward a workable framework (1977) New Perspectives on Organizational Effectiveness, , Goodman, P.S. and Pennings, J.M. (Eds) Josey-Bass Publishers, San Francisco; Purbey, S., Mukherjee, K., Bhar, C., Performance measurement system for healthcare processes (2007) International Journal of Productivity and Performance Management, 56 (3), pp. 241-251; Quinn, R.E., Rohrbaugh, J., A spatial model of effectiveness criteria: towards a competing values approach to organizational analysis (1983) Management Science, 29 (3), pp. 363-377; Rivard-Royer, H., Beaulieu, M., L’évolution du commerce électronique et la performance de la chame d’approvisionnement du secteur de la santé: changement de paradigme (2003) Gestion, 27 (4), pp. 17-23; Rivard-Royer, H., Landry, S., Beaulieu, M., Hybrid stockless; a case study – lessons for healthcare supply chain integration (2002) International Journal of Operations and Production Management, 22 (4), pp. 412-424; Sampieri, N., (2000) Contribution àl’analyse de la logistique hospitalière: proposition d’une typologie des pratiques logistiques des hôpitaux publics français à partir d’une étude empirique, , Doctoral Dissertation, Université de la Méditerranée; Seidler, J., On using informants: a technique for collecting quantitative data and controlling measurement error in organization analysis (1974) American Sociological Review, 39 (6), pp. 816-831; Selltiz, C., Wrightsman, L.S., Cook, S.W., (1976) Research Methods in Social Relations, , Holt, Rinehart & Winston, New York; Seth, N., Deshmukh, S.G., Vrat, P., A conceptual model for quality of service in the supply chain (2006) International Journal of Physical Distribution & Logistics Management, 36 (7), pp. 547-575; Shortell, S.M., Morrison, E., Friedman, B., (1990) Strategic Choices for America’s Hospitals: Managing Change in Turbulent Times, , Jossey-Bass, San Francisco; Sinreich, D., Jabali, O., Staggered work shifts: a way to downsize and restructure an emergency department workforce yet maintain current operational performance (2007) Healthcare Management Science, 10 (3), pp. 293-308; Steers, R.M., (1977) Organizational Effectiveness: A Behavioral View, , Goodyear, Santa Monica, CA; Storey, J., Holti, R., Sense-making by clinical and non-clinical executive directors within new governance arrangements (2009) Journal of Health Organization and Management, 23 (2), pp. 149-169; Strauss, A., Corbin, J., (1990) Basics of Qualitative Research: Grounded Theory Procedures and Techniques, , Sage, Newbury Park, USA; Utley, M., Jit, M., Gallivan, S., Restructuring routine elective services to reduce overall capacity requirements within a local health economy (2008) Healthcare Management Science, 11 (3), pp. 240-247; Van de Ven, A.H., Ferry, D.L., (1980) Measuring and Assessing Organizations, , Wiley Interscience, New York; Venkatraman, N., Strategic orientation of business enterprises: the construct, dimensionality and measurement (1989) Management Science, 35 (8), pp. 942-962; Venkatraman, N., Ramanujam, V., Measurement of business performance in strategy research: a comparison of approaches (1986) Academy of Management Review, 11 (4), pp. 801-815; Walsh, J.P., Hinings, C.R., Greenwood, R., Ranson, S., (1981) Power and advantage in organizations, organization studies, 2 (2), pp. 131-152; Yin, R.K., (2003) Case Study Research: Design and Methods, , Sage Publications, Newbury Park, CA; Zell, D., Organizational change as a process of death, dying and rebirth (2003) The Journal of Applied Behavioral Science, 39 (1), pp. 73-96},
issn={17530822},
language={English},
abbrev_source_title={Int. J. Serv. Econ. Manag.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Gong2011,
author={Gong, J. and Ogasawara, T. and Suzuki, S.},
title={Supply chain operational perfprmance and its influential factors: Cross national comparison between Japan and China},
journal={21st International Conference on Production Research: Innovation in Product and Production, ICPR 2011 - Conference Proceedings},
year={2011},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923461292&partnerID=40&md5=7804b56213e1ad22629f8a1734744c16},
affiliation={Department of Industrial Engineering and Management, Tokyo Institute of Technology, 2-12-1, O-okayama, Meguro-ku, Tokyo, Japan},
abstract={This research paper is intended to investigate the supply chain operational performance and its potential factors that constitute an efficient supply chain operational performance in mainly two different countries, Japan and China, respectively. In order to perform this, the SCM logistics scorecard (LSC) has been utilized as a self-evaluation tool for participated companies in these countries. The LSC focused on four decisive areas, namely, company strategy, planning and execution capability, logistics performance, and IT method and implementation. The numbers of participated companies were approximately 1037 from Japan and 236 from China. The scores in each assessment area were compared between the two countries. Subsequently, factor analysis has been conducted in this research by using the result of LSC in order to identify the significant factors which established the operational performance of SCM in each country. The result of the factor analysis indicated that the structure in generating successful SCM of Japanese industry and the Chinese were considered similar in the aspect of SCM realization, SCM strategy and the utilization of IT along the chain. Finally, the correlation between extracted factors and financial bottom lines was conducted, which indicated that improving SCM performance brought positive impact on financial outcome, especially when IT utilization in cooperation with SCM organization strategy.},
author_keywords={Factor analysis;  SCM logistics scorecard (LSC);  Supply chain performance measurement},
keywords={Chains;  Multivariant analysis;  Supply chains, Influential factors;  IT utilizations;  Japanese industry;  Operational performance;  Planning and execution;  SCM logistics scorecard (LSC);  Self evaluation;  Supply chain performance measurements, Factor analysis},
references={Kuwaiti, M.E., Kay, J.M., The role of performance measurement in business process reengineering (2000) International Journal of Operations and Production Management, 20 (12), pp. 1411-1426; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) OMEGA: The International Journal of Management Science, 34, pp. 107-124; Arashida, K., Enkawa, T., Hamasaki, A., Suzuki, S., Developing the SCM logistics scorecard and analysing its relation to the managerial performance (2004) Journal of Japan Industrial Management Association, 55 (2), pp. 95-103; Yaibuathet, K., Enkawa, T., Suzuki, S., Influences of institutional environment toward the development of supply chain management (2008) International Journal of Production Economics, 115 (2), pp. 262-271; Suzuki, S., Kitamura, S., Enkawa, T., The influence of market uncertainty on SCM competencies and managerial performance (2009) Journal of Japan Industrial Management Association, 60 (2), pp. 69-76; Suzuki, S., Ohno, A., Enkawa, T., Quantifying inter-organizational recognition gaps which impede SCM efficiency (2007) Journal of Japan Logistics Society, 7 (2), pp. 11-19; Xie, J.L., Chen, Z., Roy, J.P., Cultural and personality determinants of leniency in self-rating among Chinese people (2006) Management and Organization Review, 2 (2), pp. 181-207; Takada, T., Self-enhancement and self-criticism in Japanese culture (2003) Journal of Cross-cultural Psychology, 34 (5), pp. 542-551; Yaibuathet, K., Enkawa, T., Suzuki, S., Supply chain operational performance and its influential factors: Cross national analysis (2007) Journal of Japan Industrial Management Association, 57 (6), pp. 473-482; Shin, N., The impact of information technology on financial performance: The importance of strategic choice (2001) European Journal of Information Science, 10 (4), pp. 227-236; Yaibuathet, K., Enkawa, T., Yoshika, T., Impact of information technology and SCM organizational strategy on corporate financial performance and its cross national comparison (2007) International Journal of Information Systems for Logistics and Management, 3 (1), pp. 13-24; Gu, F.F., Hung, K., Tse, D.K., When does guanxi matter ? Issues of capitalization and its dark sides (2008) Journal of Marketing, 72, pp. 12-28; Hofstede, G., Cultural dimensions in management and planning (1984) Asia Pacific Journal of Management, 1 (2), pp. 81-99; Enkawa, T., A cultural perspective on Japanese strengths and weaknesses in operation management (2010) Journal of Japan Industrial Management Association, 61 (3), pp. 131-138},
editor={Krause T., Spath D., Ilg R.},
publisher={Fraunhofer-Verlag},
isbn={9783839602935},
language={English},
abbrev_source_title={Int. Conf. Prod. Res.: Innov. Prod. Prod., ICPR - Conf. Proc.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Olugu2011347,
author={Olugu, E.U. and Wong, K.Y.},
title={Validation of green supply chain performance measures in the automotive industry},
journal={Innovation and Knowledge Management: A Global Competitive Advantage - Proceedings of the 16th International Business Information Management Association Conference, IBIMA 2011},
year={2011},
volume={1},
pages={347-357},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905102735&partnerID=40&md5=d34b12beb955997d7a8bb32875918d72},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia},
abstract={The objective of this study was to establish the validity of the measures and metrics for green supply chain performance measurement. The test was carried out with 16 measures and 72 corresponding metrics. An exploratory factor analysis was conducted in order to investigate the construct validity of the measures and their metrics. Sequel to this, a confirmatory factor analysis was conducted to test the model fitness. Finally, a multiple regression analysis was conducted to test the criterion validity of the measures. From the results obtained, the validity of the measures and their corresponding metrics has been established. The paper culminated with recommendations for further studies.},
author_keywords={Confirmatory factor analysis;  Exploratory factor analysis;  Green supply chain;  Performance measurement;  Validity},
keywords={Automotive industry;  Competition;  Knowledge management;  Regression analysis, Confirmatory factor analysis;  Exploratory factor analysis;  Green supply chain;  Performance measurements;  Validity, Factor analysis},
references={Aladwani, A.M., Palvia, P.C., Developing and validating an instrument for measuring user perceived web quality (2002) Information and Management, 39 (1). , 467-476; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Brown, T.A., Confirmatory Factor Analysis for Applied Research (2006), Guilford Press, New York; Carter, S.A., Wu, K.D., Symptoms of specific and generalized social phobia: an examination of discriminant validity and structural relations with mood and anxiety symptoms (2010) Behavior Therapy, 41 (2), pp. 254-265; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: the constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Field, A.P., Discovering Statistics Using SPSS:and Sex and Drugs and Rock 'n' Roll (2009), Sage, London; Green, K., Morton, B., New, B., Purchasing and supply policies: do they improve companies' environmental performance? (1998) Supply Chain Management: An International Journal, 3 (2), pp. 89-95; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., Multivariate Data Analysis with Readings, Prentice Hall (1995), Englewood Cliffs, New Jersey; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hu, L., Bentler, P.M., Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives (1999) Structural Equation Modeling, 6 (1), pp. 1-55; Joreskog, K.G., Sorbom, D., LISREL 8: User's Reference Guide (1999), Scientific Software Inc. Chicago; Kline, R.B., Principles and Practice of Structural Equation Modeling (2005), Guilford, New York; Krajnc, D., Mele, M., Glavi, P., Fuzzy logic model for the performance benchmarking of sugar plants by considering best available techniques (2007) Resources, Conservation and Recycling, 52 (2), pp. 314-330; Lai, K., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-456; Liang, L., Feng, F., Cook, W.D., Zhu, J., DEA models for supply chain efficiency evaluation (2006) Annals of Operations Research, 145 (1), pp. 35-49; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: an introduction (2007) Journal of Operations Management, 25 (1), pp. 1075-1082; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Nahm, A.Y., Vonderembse, M.A., Koufteros, X.A., The impact of organizational structure on time-based manufacturing and plant performance (2003) Journal of Operations Management, 21 (3), pp. 281-306; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., Development of key performance measures for the automobile green supply chain (2010), Resources, Conservation and Recycling, DOI:10.1016/j.resconrec.2010.06.003; Olugu, E.U., Wong, K.Y., Shaharoun, A.M., A comprehensive approach in assessing the performance of an automobile closed-loop supply chain (2010) Sustainability, 2 (4), pp. 871-889; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916; Sarkis, J., A strategic decision framework for green supply chain management (2003) Journal of Cleaner Production, 11 (4), pp. 397-409; Schultmann, F., Zumkeller, M., Rentz, O., Integrating spent products material into supply chains: the recycling of end-of-life-vehicles as an example, Supply Chain Management and Reverse Logistics (2004), pp. 35-59. , Dyckhoff, H, Lackes, R. and Reese, J. (eds), Springer-Verlag, Berlin; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Turnbull, P., Oliver, N., Wilkinson, B., Buyer-supplier relations in the UK automotive industry: strategic implications of the Japanese manufacturing model (1992) Strategic Management Journal, 13 (1), pp. 159-168; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Wycherley, I., Greening supply chains: the case of the body shop (1999) International Business Strategy and the Environment, 8 (2), pp. 120-127; Yang, Z., Cai, S., Zhou, Z., Zhou, N., Development and validation of an instrument to measure user perceived service quality of information presenting web portals (2005) Information and Management, 42 (4), pp. 575-589; Zhu, Q., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises" (2004) Journal of Operations Management, 22 (3), pp. 265-289; Zhu, Q., Sarkis, J., An inter-sectoral comparison of green supply chain management in China: drivers and practices (2006) Journal of Cleaner Production, 14 (5), pp. 472-486},
publisher={International Business Information Management Association, IBIMA},
isbn={9780982148952},
language={English},
abbrev_source_title={Innov. Know. Manag. Glob. Compet. Adv. - Proc. Int. Bus. Inf. Manag. Assoc. Conf. IBIMA},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Martins2011,
author={Martins, R.A. and Prancic, E.},
title={Factors which affect the development of performance measurement systems},
journal={61st Annual IIE Conference and Expo Proceedings},
year={2011},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900304481&partnerID=40&md5=1ab720733c1927614c35a36662c75412},
affiliation={Federal University of Sao Carlos, Department of Industrial Engineering, Sao Carlos, SP, Brazil; Pontifical Catholic University of Campinas, Center for Economics and Business Administration, Campinas, SP, Brazil},
abstract={Design, implementation, usage, and maintenance are the four processes of measuring performance. There are factors which affect the development of performance measurement system leading to failures or success in design and implementation processes. Product development is a very complex organizational process that has different levels of decision making, organizational structures, management frameworks, and cycle times depending on industry and product complexity. Therefore such process could be used as a proxy of organization. This paper aims to present the empirical findings from multiple case studies that have investigated factors that influence the development of performance measurement systems for product development process. The main empirical findings point out to the importance of configuration of factors such as competitive strategy, business model, management framework, organizational structure, use of performance measurement information, and PMS framework. Depending on the configuration, those factors can mediate the development of performance measurement system acting either as facilitators or blockers. Therefore, coherence between the factors is of fundamental importance.},
author_keywords={Performance measurement systems;  Performance measurement systems design;  Product development process},
keywords={Decision making;  Exhibitions;  Product development;  Structural design;  Supply chain management, Design and implementations;  Management frameworks;  Measuring performance;  Organizational process;  Organizational structures;  Performance measurement system;  Performance measurements;  Product development process, Information management},
references={Bourne, M.C.S., Neely, A.D., Mills, J.F., Platts, K.W., (2002) The Success and Failure of Performance Measurement Initiatives: Perceptions of Participating Managers International Journal of Operations and Production Management, 22 (11), pp. 1288-1300; Neely, A., Bourne, M.C.S., Why measurement initiatives fail (2000) Measuring Business Excellence, 4 (4), pp. 3-7; Franco, M., Bourne, M.C.S., Factors that play a role in "managing through measures" (2003) Management Decision, 41 (8), pp. 698-710; Leinonen, M., A survey on performance measurement system design and implementation (2001) International Business and Economic Research Conference, 15p. , Reno, USA; De Waal, A.A., Behavioral factors important for the successful implementation and use of performance management systems (2003) Management Decision, 41 (8), pp. 688-697; Cuganesan, S., Donovan, J., Organisational factors and performance measurement practices: An examination of Australian organizations (2009) Proc. of the Performance Measurement Association Conference, pp. 1-22. , April 14-17, Dunedin, New Zealand; Lee, C.-L., Yang, H.-J., Organization structure, competition and performance measurement systems and their joint effects on performance (2009) Management Accounting Research, , (October 20, 2010), doi:10.1016/j.mar.2010.10.003; Clark, K.B., Fujimoto, T., (1991) Product Development Performance: Strategy, Organization and Management in the World Auto Industry, , Harvard Business School Press, Boston; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Franco, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerly, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Turner, T., Creighton, S., Nudurupati, S., Bititci, U.S., Implementing a web enabled performance measurement system (2004) Measuring Business Excellence, 8 (2), pp. 40-54; Bititci, U.S., Mendibil, M., Nudurupati, S., Turner, T., Garengo, P., Dynamics of performance measurement and organizational culture (2006) International Journal of Operations and Production Management, 26 (12), pp. 1325-1350; Martins, R.A., The use of performance measurement information as a driver in designing a performance measurement system (2002) Proc. of the Third Performance Measurement and Management Conference, pp. 371-378. , July 17-19, Boston, USA; Bryman, A., (1989) Research Methods and Organization Studies, , Unwin Hyman. London; Yin, R.K., (1994) Case Study Research: Design and Methods, , Sage Publications, Newbury Park; Wheelwright, S.C., Clark, K.B., (1992) Revolutionizing Product Development: Quantum Leaps in Speed, Efficiency and Quality, , Free Press, New York; Rozenfeld, H., Amaral, D.C., Proposta de uma tipologia de processos de desenvolvimento de produto visando a construcao de modelos de referencia (1999) Proc of i Congresso Brasileiro de Gestao de Desenvolvimento de Produto, , Belo Horizonte, Brazil, August 1st; Prancic, E., (2009) Modelo para Concepcao de Sistema de Medicao de Desempenho do Processo de Desenvolvimento de Produto, , Ph.D. dissertation, The Federal University of São Carlos, Brazil; Zarifian, P., (1990) La Nouvelle Productivite, , Paris: L'Harmattan},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={Annu. IIE Conf. Expo Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Banomyong2011244,
author={Banomyong, R. and Supatn, N.},
title={Supply chain assessment tool development in Thailand: An SME perspective},
journal={International Journal of Procurement Management},
year={2011},
volume={4},
number={3},
pages={244-258},
doi={10.1504/IJPM.2011.040368},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84878661626&doi=10.1504%2fIJPM.2011.040368&partnerID=40&md5=09ebefe1155adbcbe6c94031c90b2c82},
affiliation={Department of International Business, Logistics and Transport, Faculty of Commerce and Accountancy, Thammasat University-Thailand, 2 Phrachan Road, Phranakorn, Bangkok 10200, Thailand; Department of Management, School of Management, Assumption University-Thailand, 88 Moo 8 Bangna-Trad Km. 26, Bangsaothong, Samutprakarn 10540, Thailand},
abstract={The purpose of this paper is to present a supply chain performance assessment tool to measure the performance of firm's key supply chain activities under different performance dimensions. Although some assessment tools have been utilised in Thailand, such as the SCOR model, they were discovered as too complicated and too difficult to use, especially when dealing with SMEs. This tool was developed based on an extensive literature review related to logistics and supply chain performance measurement. The nine key internal supply chain activities as proposed by Grant et al. in 2006 constituted the backbone of the assessment framework while performance was measured based on three dimensions; cost, time and reliability. The tool was pilot tested on 25 local SMEs. The results were then compared to existing performance benchmark as well as with a top Thai company in order to see if there were any gaps in the supply chain performance of the trial group. Copyright © 2011 Inderscience Enterprises Ltd.},
author_keywords={Assessment tool;  Benchmarking;  SME;  Supply chain performance;  Thailand},
references={Banomyong, R., (2007) Logistics Development Study of the North South Economic Corridor, , Asian Development Bank, Manila; Beamon, B.M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218. , DOI 10.1080/00207540110053156; Byrne, P., Markham, W.J., (1991) Improving Quality and Productivity in the Logistics Process-Achieving: Customer Satisfaction Breakthroughs, , Council of Logistics Management, Oak Brook, IL; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) International Journal of Logistics Management, 6 (1), pp. 61-74; Chan, F., Qi, H., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Chan, H.K., Lau, H.C.W., Ip, R.W.L., An AHP approach in benchmarking logistics performance of the postal industry (2006) Benchmarking, 13 (6), pp. 636-661. , DOI 10.1108/14635770610709031; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than just a new name for logistics (1997) International Journal of Logistics Management, 8 (1), pp. 1-13; (2004) Supply Chain Management Process Standards, , CSCMP, Council of Supply Chain Management Professionals, Oak Brook, IL; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Foggin, J., Mentzer, J., Monroe, C., A supply chain diagnostic tool (2004) International Journal of Physical Distribution & Logistics Management, 34 (10), pp. 827-855; Grant, D.B., Lambert, D.M., Stock, J.R., Ellram, L.M., (2006) Fundamentals of Logistics Management, , European Edition, McGraw Hill, Maidenhead, UK; Griffis, S.E., Cooper, M.C., Goldsby, T.J., Closs, D.J., Performance measurement: Measure selection based upon firm goals and information reporting needs (2004) Journal of Business Logistics, 25 (2), pp. 95-118; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; http://www.benchmarkingsuccess.com, (accessed on 10 June 2008); http://www.ie.me.titech.ac.jp/lab/enkawa/lsc/LSC_Eng430.pdf, (accessed 10 June 2008); http://www.supply-chain.org/cs/root/scor_tools_resources/scor_model/ scor_model, (accessed on 10 June 2008); Lambert, D.M., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lambert, D.M., Stock, J.R., Ellram, L.M., (1998) Fundamentals of Logistics Management, , International Edition, McGraw Hill, NY; Mentzer, J.T., Konrad, B.P., An efficiency/effectiveness approach to logistics performance analysis (1991) Journal of Business Logistics, 12 (1), pp. 33-62; Min, S., Mentzer, J., Developing and measuring supply chain management concepts (2004) Journal of Business Logistics, 25 (1), pp. 63-99; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , DOI 10.1108/17410400610653219; Stern, L., El-Ansary, A., Coughlan, A.T., (1996) Marketing Channels, , Fifth Edition, Prentice Hall, Englewood Cliffs, NJ; (2002) A Case Study of Benchmarking on Logistics and Supply Chain in Thailand, , Thailand Productivity Institute, Thailand Productivity Press, Bangkok; Udin, Z., Khan, M., Zairi, M., A collaborative supply chain management framework (2006) Business Process Management Journal, 12 (3), pp. 361-376; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381. , DOI 10.1108/02635570710734271},
correspondence_address1={Banomyong, R.; Department of International Business, 2 Phrachan Road, Phranakorn, Bangkok 10200, Thailand; email: ruth@banomyong.com},
publisher={Inderscience Publishers},
issn={17538432},
language={English},
abbrev_source_title={Int. J. Procure. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lauras2011354,
author={Lauras, M. and Lamothe, J. and Pingaud, H.},
title={A business process oriented method to design supply chain performance measurement systems},
journal={International Journal of Business Performance Management},
year={2011},
volume={12},
number={4},
pages={354-376},
doi={10.1504/IJBPM.2011.042013},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-80051995488&doi=10.1504%2fIJBPM.2011.042013&partnerID=40&md5=380041cd9c86da5d02774b4992ca9fe1},
affiliation={Department of Industrial Engineering, Université de Toulouse, Campus Jarlard, Route de Teillet, 81013 Albi Cedex 9, France; Department of Industrial Organisation, Logistics and Technology, Toulouse Business School, Université de Toulouse, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France},
abstract={While performance management forms an integral part of the concept of supply chain management, designing a consistent and powerful performance measurement system (PMS) at the level of an enterprise network is still proving very problematic. In this paper, a business process oriented approach is proposed to tackle problems of heterogeneity between partners. Special attention is paid to the representation of both decisional and operational activities, taking into account the links between them. Based on this framework, a method is applied to select, implement and publish the key performance indicators (KPI). We propose a classification of KPI into three categories related to management abilities: ambition, reality and facility. These classes form a consistent set to support decision making and process control at a network level. A business case is used to show the potential of this approach. Copyright © 2011 Inderscience Enterprises Ltd.},
author_keywords={Business process management;  Control theory;  Enterprise modelling;  Performance measurement system;  PMS;  Supply chain},
references={Akkermans, H.A., Van Oorschot, K.E., Relevance assumed: A case study of balanced scorecard development using system dynamics (2005) Journal of the Operational Research Society, 56 (8), pp. 931-941. , DOI 10.1057/palgrave.jors.2601923; Akyuz, G.A., Erkan, T.E., Supply chain performance measurement: A literature review (2010) International Journal of Production Research, 48 (17), pp. 5137-5155. , Vl; Alfaro, J.J., Rodriguez-Rodriguez, R., Verdecho, M.J., Ortiz, A., Business process interoperability and collaborative performance measurement (2009) International Journal of Computer Integrated Manufacturing, 22 (9), pp. 877-889; Al-Mudimigh, A.S., Zairi, M., Ahmed, A.M.M., Extending the concept of supply chain: The effective management of value chains (2004) International Journal of Production Economics, (87), pp. 309-320; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Bolstorff, P., How does SCOR measure up? (2002) Supply Chain Technology News, pp. 22-25. , http://www.totalsupplychain.com; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24. , DOI 10.1504/IJBPM.2003.002097; Bowersox, D.J., Closs, D.J., Logistical management (1996) The Integrated Supply Chain Process, , McGRaw-Hill; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing System, 14 (3), pp. 179-190; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131. , DOI 10.1504/IJBPM.2006.009032; Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution & Logistics Management, (24), pp. 17-28; Cokins, G., (2001) Activity-based Cost Management: An Executive's Guide', p. 384. , 2nd ed., John Wiley Sons Inc. Publisher; Ducq, Y., Vallespir, B., Doumeingts, G., Coherence analysis methods for production systems by performance aggregation (2001) International Journal of Production Economics, 69 (1), pp. 23-37. , DOI 10.1016/S0925-5273(00)00054-2; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, , Article in Press; Forrester, J.W., (1968) Principles of Systems, p. 391. , 2nd ed. Productivity Press, Portland, OR; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (1-2), pp. 44-45; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, (87), pp. 333-347; Gunasekaran, A., Williams, H.J., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533. , DOI 10.1016/j.technovation.2003.09.010, PII S0166497203001767; Jacot, J.H., (1990) A Propos de l'Évaluation Économique des Systèmes Intégrés de Production, , ECOSIP - Gestion industrielle et mesure économique, Economica; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston; Kleijnen, J.P.C., Smits, M.T., Performance in supply chain management (2003) Journal of the Operational Research Society, (54), pp. 507-514; Kueng, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Quality Management, 11 (1), pp. 67-85; Lee, H.L., Creating value through supply chain integration (2000) Supply Chain Management Review; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operation Research, (156), pp. 267-286; Lohman, F.A.B., (1999) The Effectiveness of Management Information, , PhD Thesis, Delft University; Martin, P.R., Patterson, J.W., On measuring company performance within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; Neely, A.D., Adams, C., Kennerley, M., The performance prism: The scorecard of measuring and managing business success (2002) Pearson Education, p. 377; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, (15), pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431. , DOI 10.1016/S0925-5273(96)00080-1, PII S0925527396000801; Neubert, G., Pichot, L., Performance measurement for the supply chain: Methodology to define a supply chain dashboard (2002) ACS'02 - SCM Conference, , October, Poland; Smith, P.C., Goddard, M., Performance management and operational research: A marriage made in heaven? (2002) Journal of the Operational Research Society, 53 (3), pp. 247-255. , DOI 10.1057/palgrave/jors/2601279, Performance management; Stadtler, H., Kilger, C., (2000) Supply Chain Management and Advanced Planning, p. 371. , Edition Springer-Verlag; (2003) Supply Chain Operations Reference-Models, p. 278. , Supply Chain Council, SCOR version 6.0, Edition Supply Chain Council; Vitale, M., Mavrinac, S.C., Hauser, M., New process/financial scorecard: A strategic performance measurement system (1994) Planning Review, 22 (4), pp. 12-16},
correspondence_address1={Lauras, M.; Department of Industrial Engineering, Route de Teillet, 81013 Albi Cedex 9, France; email: matthieu.lauras@mines-albi.fr},
publisher={Inderscience Publishers},
issn={13684892},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Panayides2011430,
author={Panayides, P.M. and Polyviou, M.},
title={The effect of ports' logistics attributes and services on the business and supply chain performance of shipping firms},
journal={International Journal of Shipping and Transport Logistics},
year={2011},
volume={3},
number={4},
pages={430-453},
doi={10.1504/IJSTL.2011.041136},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960204703&doi=10.1504%2fIJSTL.2011.041136&partnerID=40&md5=9a685f04b1239169499d6e7066f66b7c},
affiliation={Department of Commerce, Finance and Shipping, Cyprus University of Technology, 140 Agiou Andreou Street, Limassol 3603, Cyprus},
abstract={There has been widespread discussion about the transformation of ports from cargo loading and unloading facilities into integrated parts of supply chains. The contemporary view is that ports are logistics players in supply chains and their activities and performance affect the other supply chain members. Previous studies mainly focused on how a port's performance is affected by that port's characteristics or services. There are no studies that investigate how the performance of a firm which operates through a specific port is affected by that port's characteristics. Hence, this is the first paper that addresses whether logistics-related attributes and services provided by a port enhance the business and supply chain performance of shipping firms. The paper begins with an introduction and it examines the literature on port selection factors, the transformation of ports into logistics hubs and business and supply chain performance measures. We later present the conceptual model, which relates ports' logistics attributes or services to the shipping firms' performance, and to conclude, we discuss the preliminary results of the study and some implications and opportunities for further research. Copyright © 2011 Inderscience Enterprises Ltd.},
author_keywords={Business performance;  Liner shipping;  Ports' logistics services;  Supply chain performance},
references={Anderson, E.W., Fornell, C., Lehmann, D.R., Customer satisfaction, market share, and profitability: Findings from Sweden (1994) Journal of Marketing, 58 (3), pp. 53-66. , July; Anderson, J.C., Gerbing, D.W., Structural equation modelling in practice: A review and recommended two-step approach (1988) Psychological Bulletin, 103 (3), pp. 411-423; Bacidore, J.M., Boquist, J.A., Milbourn, T.T., Thakor, A.V., The search for the best financial performance measure (1997) Financial Analysts Journal, 53 (3), pp. 11-20; Banker, R.D., Potter, G., Srinivasan, D., An empirical investigation of an incentive plan that includes nonfinancial performance measures (2000) Accounting Review, 75 (1), pp. 65-92; Bentler, P.M., Bonett, D.G., Significant test and goodness of fit in the analysis of covariance structures (1980) Psychological Bulletin, 88, pp. 588-606; Bettis, R., Performance differences in related and unrelated diversified firms (1981) Strategic Management Journal, 2 (4), pp. 379-393; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Bichou, K., Gray, R., A logistics and supply chain management approach to port performance measurement (2004) Maritime Policy and Management, 31 (1), pp. 47-67. , DOI 10.1080/0308883032000174454; Boone, C., Hendricks, W., Top management team diversity and firm performance: Moderators of functional-background and locus-of-control diversity (2009) Management Science, 55 (2), pp. 165-180; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bullinger, H.-J., Kuhner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15 SPEC.), pp. 3533-3543. , DOI 10.1080/00207540210161669; Buzzell, R.D., Gale, B.T., Sultan, R.G.M., Market share: A key to profitability (1975) Harvard Business Review, 53 (1), pp. 97-106; Byrne, B.N., (2008) Structural Equation Modelling with AMOS, , 2nd ed. Psychology Press/Routledge Academic, NYC; Capon, N., Hulbert, J.M., Farley, J.U., Martin, L.E., Corporate diversity and economic performance: The impact of market specialization (1988) Strategic Management Journal, 9 (1), pp. 61-74; Carbone, V., De Martino, M., The changing role of ports in supply-chain management: An empirical analysis (2003) Maritime Policy and Management, 30 (4), pp. 305-320. , DOI 10.1080/0308883032000145618; Carmeli, A., Strategic human capital and the performance of public sector organizations (2004) Scandinavian Journal of Management, 20 (4), pp. 375-392; Chang, S.J., Hong, J., How much does the business group matter in Korea? (2002) Strategic Management Journal, 23 (3), pp. 265-274. , DOI 10.1002/smj.224; Christensen, H.K., Montgomery, C.A., Corporate economic performance: Diversification strategy versus market structure (1981) Strategic Management Journal, 2 (4), pp. 327-343; Day, G.S., Wensley, R., Assessing advantage: A framework for diagnosing competitive superiority (1988) Journal of Marketing, 52, pp. 1-20. , April; Gomez-Mejia, L., Structure and process of diversification, compensation strategy, and firm performance (1992) Strategic Management Journal, 13 (5), pp. 381-397; Grant, R.M., Jammine, A.P., Thomas, H., Diversity, diversification, and profitability among British manufacturing companies, 1972-1984 (1988) Academy of Management Journal, 31 (4), pp. 771-801; Griffith, J.M., The true value of EVA® (2004) Journal of Applied Finance, 14 (2), pp. 25-29; Grosso, M., Monteiro, F., Relevant strategic criteria when choosing a container port - The case of port of Genoa (2008) Proceedings of the European Transport Conference, pp. 1-20. , The Netherlands; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Hansen, G.S., Wernerfelt, B., Determinants of firm performance: The relative importance of economic and organizational factors (1989) Strategic Management Journal, 10 (5), pp. 399-411; Hattie, J., Methodology review: Assessing unidimensionality of tests and items (1985) Applied Psychological Measurement, 9, pp. 139-164; Hawawini, G., Subramanian, V., Verdin, P., Is performance driven by industry- or firm-specific factors? A new look at the evidence (2003) Strategic Management Journal, 24 (1), pp. 1-16; Hitt, M.A., Hoskisson, R.E., Hicheon, K., International diversification: Effects on innovation and firm performance in product-diversified firms (1997) The Academy of Management Journal, 40 (4), pp. 767-798; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Hult, G.T.M., Ketchen Jr., D.J., Slater, S.F., Market orientation and performance: An integration of disparate approaches (2005) Strategic Management Journal, 26 (12), pp. 1173-1181. , DOI 10.1002/smj.494; Hult, G.T.M., Ketchen Jr., D.J., Cavusgil, S.T., Calantone, R.J., Knowledge as a strategic resource in supply chains (2006) Journal of Operations Management, 24 (5), pp. 458-475. , DOI 10.1016/j.jom.2005.11.009, PII S0272696305001531; Jaworski, B., Kohli, A., Market orientation: Antecedents and consequences (1993) Journal of Marketing, 57, pp. 53-70. , July; Joreskog, K.G., Sorbom, D., (1993) LISREL 8: Structural Equation Modelling with the SIMPLIS Command Language, , Scientific Software, Inc. Chicago, IL; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) The Harvard Business Review, pp. 71-79. , January/February; Keebler, J.S., Manrodt, K.B., Dutshe, D.A., Ledyard, D.M., (1999) Keeping Score: Measuring the Business Value of Logistics in the Supply Chain, , University of Tennessee and the Council of Logistics Management, Chicago, IL; Lockamy III, A., McCormack, K., Linking SCOR planning practices to supply chain performance (2004) International Journal of Operations and Production Management, 24 (12), pp. 1192-1218; Magala, M., Sammons, A., A new approach to choice modelling (2008) Maritime Economics and Logistics, 10 (1-2), pp. 9-34; Malchow, M., Kanafani, A., A disaggregate analysis of factors influencing port selection (2001) Maritime Policy and Management, 28 (3), pp. 265-277. , DOI 10.1080/03088830110060840, Maritime Industry into the Millennium; Malchow, M.B., Kanafani, A., A disaggregate analysis of port selection (2004) Transportation Research Part E: Logistics and Transportation Review, 40 (4), pp. 317-337. , DOI 10.1016/j.tre.2003.05.001, PII S1366554503000772; Marlow, P.B., Paixao, A.C., Measuring lean ports performance (2003) International Journal of Transport Management, 1, pp. 189-202; McDonald, R.P., The dimensionality of tests and items (1981) British Journal of Mathematical and Statistical Psychology, 34, pp. 100-117; McGahan, A.M., Porter, M.E., How much does industry matter, really? (1997) Strategic Management Journal, 18 (SPEC. ISSUE), pp. 15-30; Miller, D., Droge, C., Psychological and traditional determinants of structure (1986) Administrative Science Quarterly, 31 (4), pp. 539-560; Morgan, N.A., Vorhies, D.W., Mason, C.H., Marketing orientation, marketing capabilities, and firm performance (2009) Strategic Management Journal, 30 (8), pp. 909-920; Murphy, P.R., Daley, J.M., Dalenberg, D.R., Selecting links and nodes in international transportation: An intermediary's perspective (1991) Transportation Journal, 31 (2), pp. 33-40; Murphy, P.R., Daley, J.M., Dalenberg, D.R., Port selection criteria: An application of a transportation research framework (1992) Logistics & Transportation Review, 28 (3), pp. 237-255; Nadkarni, S., Narayanan, V.K., Strategic schemas, strategic flexibility, and firm performance: The moderating role of industry clockspeed (2007) Strategic Management Journal, 28 (3), pp. 243-270; Nir, A.-S., Lin, K., Liang, G.-S., Port choice behaviour - From the perspective of the shipper (2003) Maritime Policy and Management, 30 (2), pp. 165-173. , DOI 10.1080/0308883032000069262; Notteboom, T.E., Rodrigue, J.-P., Port regionalization: Towards a new phase in port development (2005) Maritime Policy and Management, 32 (3), pp. 297-313. , DOI 10.1080/03088830500139885; Paixao, A.C., Marlow, P.B., Fourth generation ports - A question of agility? (2003) International Journal of Physical Distribution and Materials Management, 33 (4), pp. 355-376; Palepu, K., Diversification strategy, profit performance, and the entropy measure (1985) Strategic Management Journal, 6 (3), pp. 239-255; Panayides, P.M., Maritime logistics and global supply chains: Towards a research agenda (2006) Maritime Economics and Logistics, 8 (1), pp. 3-18. , DOI 10.1057/palgrave.mel.9100147, PII 9100147; Panayides, P.M., Song, D.W., Evaluating the integration of seaport container terminals in supply chains (2008) International Journal of Physical Distribution and Logistics Management, 38 (7), pp. 562-584; Pettit, S.J., Beresford, A.K.C., Port development: From gateways to logistics hubs (2009) Maritime Policy and Management, 36 (3), pp. 253-267; Pohlen, T.L., Coleman, B.J., Evaluating internal operations and supply chain performance using EVA and ABC (2005) SAM Advanced Management Journal, 70 (2), pp. 45-58; Porter, M., (1980) Competitive Strategy, , The Free Press, New York; Porter, M., (1985) Competitive Advantage, , The Free Press, New York; Quaresma Dias, J.C., Calado, J.M.F., Mendonca, M.C., The role of European 'Ro-Ro' port terminals in the automotive supply chain management (2010) Journal of Transport Geography, 18, pp. 116-124; Rajagopalan, N., Strategic orientations, incentive plan adoptions, and firm performance: Evidence from electric utility firms (1977) Strategic Management Journal, 18 (10), pp. 761-785; Robins, J.A., Wiersema, M.F., The measurement of corporate portfolio strategy: Analysis of the content validity of related diversification indexes (2003) Strategic Management Journal, 24 (1), pp. 39-59; Robinson, R., Ports as elements in value-driven chain systems: The new paradigm (2002) Maritime Policy & Management, 25, pp. 21-40; Robinson, R., Port-oriented landside logistics in Australian ports: A strategic framework (2006) Maritime Economics and Logistics, 8 (1), pp. 40-59. , DOI 10.1057/palgrave.mel.9100149, PII 9100149; Rumelt, R., How much does industry matter? (1991) Strategic Management Journal, 12 (3), pp. 167-185; Rumelt, R.P., (1974) Strategy, Structure and Economic Performance, , Division of Research, Graduate School of Business Administration, Harvard University, Boston; Scherer, F.M., Ross, D., (1990) Industrial Market Structure and Economic Performance, , Houghton Mifflin, Boston; Schmalensee, R., Do markets differ much (1985) The American Economic Review, 75 (3), pp. 341-351; Schumacker, R.E., Lomax, R.G., (2004) A Beginner's Guide to Structural Equation Modelling, , 2nd ed. Lawrence Erlbaum Associates, Mahwah, NJ; Short, J.C., Ketchen Jr., D.J., Palmer, T.B., Hult, G.T.M., Firm, strategic group, and industry influences on performance (2007) Strategic Management Journal, 28 (2), pp. 147-167. , DOI 10.1002/smj.574; Singh, J.V., Performance, slack, and risk taking in organizational decision making (1986) Academy of Management Journal, 29, pp. 562-585; Singh, M., Nejadmalayeri, A., Mathur, I., Performance impact of business group affiliation: An analysis of the diversification-performance link in a developing economy (2007) Journal of Business Research, 60 (4), pp. 339-347; Slack, B., Containerization, inter-port competition, and port selection (1985) MARIT. POLICY & MANAGE., 12 (4), pp. 293-303. , Oct.-Dec. 1985; Supply-chain operations reference-model (2002) Overview Version 5.0, , http://www.supplychain.org/slides/SCOR5.0OverviewBooklet.pdf, available at; Tang, L.C., Low, J.M.W., Lam, S.W., Understanding port choice behaviour - A network perspective (2008) Networks and Spatial Economics, , Springer, 10.1007/s11067-008-9081-8; Tanriverdi, H., Lee, C.H., Within-industry diversification and firm performance in the presence of network externalities: Evidence from the software industry (2008) Academy of Management Journal, 51 (2), pp. 381-397; Tongzon, J., Port choice determinants in a competitive environment (2002) IAME Panama 2002 Conference Proceedings, , Panama; Tongzon, J.L., Sawant, L., Port choice in a competitive environment: From the shipping lines' perspective (2007) Applied Economics, 39 (4), pp. 477-492. , DOI 10.1080/00036840500438871, PII 773248952; Ugboma, C., Ugboma, O., Ogwude, I.C., An analytic hierarchy process (AHP) approach to port selection decisions - Empirical evidence from Nigerian ports (2006) Maritime Economics and Logistics, 8 (3), pp. 251-266. , DOI 10.1057/palgrave.mel.9100160, PII 9100160; Wang, T.-F., Cullinane, K., The efficiency of European container terminals and implications for supply chain management (2006) Maritime Economics and Logistics, 8 (1), pp. 82-99. , DOI 10.1057/palgrave.mel.9100151, PII 9100151; Youndt, M.A., Snell, S.A., Dean, J.W., Lepak, D.P., Human resource management: Manufacturing strategy, and firm performance (1996) Academy of Management Journal, 39 (4), pp. 836-866},
correspondence_address1={Panayides, P.M.; Department of Commerce, 140 Agiou Andreou Street, Limassol 3603, Cyprus; email: photis.panayides@cut.ac.cy},
publisher={Inderscience Publishers},
issn={17566517},
language={English},
abbrev_source_title={Int. J. Shipp. Transp. Logist.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Alemayehu2011467,
author={Alemayehu, W. and Vom Brocke, J.},
title={Sustainability performance measurement - The case of Ethiopian airlines},
journal={Lecture Notes in Business Information Processing},
year={2011},
volume={66 LNBIP},
pages={467-478},
doi={10.1007/978-3-642-20511-8_43},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957520263&doi=10.1007%2f978-3-642-20511-8_43&partnerID=40&md5=e3f3e39a8514f2d659121df1702a2e6e},
affiliation={Ababa University, Addis Ababa, Ethiopia; Department of Business Process Management, Institute of Information Systems, University of Liechtenstein, Liechtenstein},
abstract={This paper presents the findings of an investigation of sustainable performance measurement practices at Ethiopian Airlines. Evidence was gathered through structured interviews conducted with key informants of the airline and document reviews. Reports were assessed with respect to sustainable performance measurement system. The paper demonstrates that, despite the availability of economic performance indicators, there are no measurements concerning the social and environmental performance. However there is some operational practice related to social and environmental responsibility of the airline. The research contributes values to two areas: First, it shares some experience in measuring sustainable performance of the airline or their practical involvement in social and environmental responsibility. Second, it tries to help decision makers - based on the example of Ethiopian Airline -to better understand the level of maturity of their sustainable performance measurement and, thus, to improve decision making through a "sustainability lens". © 2011 Springer-Verlag.},
keywords={Air transportation;  Benchmarking;  Decision making;  Enterprise resource management;  Environmental impact;  Environmental management;  Supply chain management, Decision makers;  Document review;  Economic performance;  Environmental performance;  Environmental responsibility;  Ethiopian Airlines;  Operational practices;  Performance measurements;  Sustainable performance, Sustainable development},
references={Adams, C., Neely, A., (2006) Using the Performance Prism to Boost the Success of Mergers & Acquisitions, , http://www.accenture.com/Global/Reserarch_and_Insights/Outlook/ By_Alphabet/, New York Using the Performance Prism to Boost the Success of Mergers & Acquisitions (retrieved April 2010); (2010) Economics: Annual Revenue and Earnings, Selected Years, , http://www.airlines.org, retrieved April 19, 2010; Bansal, P., The corporate challenges of sustainable development (2002) Academy of Management Executive, 16, p. 2; (2000) British Airways Social and Environmental Report 2000, , British Airways: BA, London; (1987) Our Common Future, WCED, , Brundtland: Oxford University Press, Oxford; Clarkson, M., A Stakeholder Framework for Analyzing and Evaluating Corporate Social Performance (1995) Academy of Management Review, 20 (1), pp. 17-92; (2004) Dow Jones Sustainability Indexes (DJSI)-Annual Review, , http://www.sustainability-indexes.com/htmle/assessment/review2003.html; (2004) Dow Jones Sustainability World Indexes: Corporate Sustainability Sector Overview, 2003 and 2004; Cyert, R.M., March, J.G., (1963) A Behavioral Theory of the Firm, , Prentice-Hall, Englewood Cliffs; Dobbie, L., Hooper, P.D., (2001) Airline Environmental Reporting 2001 Survey, , International Air Transport Association, Geneva; Donaldson, T., Preston, L.E., The Stakeholder Theory of the Corporation: Concept, Evidence, and Implications (1995) The Academy of Management Review, 20 (1), pp. 65-91; Elkington, J., Towards the sustainable corporation: Win-win-win business strategies for Sustainable Corporation: Win-Win-Win Business Strategies for Sustainable Development (1994) California Management Review, 36 (2); Elkington, J., (1997) Cannibals with Forks: The Triple Bottom Line of 21st Century Business, , Capstone, London; Elkington, J., Enter the triple bottom line (2004) The Triple Bottom Line - Does It All Add Up?, pp. 1-16. , Henriques, A., Richardson, J. (eds.) Earthscan, London; Enquist, B., Johnsson, M., Skålén, P., Adoption of Corporate Social Responsibility - Incorporating a Stakeholder Perspective (2006) Qualitative Research in Accounting & Management, 3 (3), pp. 188-207; (2010) Environmental and Social Performance, , http://www.globalreporting.org, GRI, Boston, USA; Epstein, M.J., Roy, M.-J., Improving sustainability performance: Specifying, implementing and measuring key principles (2003) Journal of General Management, 29 (1), pp. 15-31; Epstein, M.J., (2008) Making Sustainability Work: Best Practices in Managing and Measuring Social and Environmental Impacts, , Greenleaf, Sheffield; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The Sustainability Balanced Scorecard - A Tool for Value-Oriented Sustainability Management in Strategy-Focused Organizations (2001) Conference Proceedings of the 2001 Eco-Management and Auditing Conference, pp. 83-90. , ERP Environment, Shipley; (2006) Sustainability Reporting Guidelines, , GRI - Global Reporting Initiative: RG 3.0; (2000) Sustainability Reporting Guidelines on Economic, UNEP/SustainAbility (2000) the Global Reporters, , GRI - Global Reporting Initiative: SustainAbility Ltd., London; Hailemariam, G., Vom Brocke, J., What is Sustainability in Business Process Management? A Theoretical Framework and its Application in the Public Sector of Ethiopia Proceedings of the First International Workshop on Business Process Management and Sustainability at the 8th Business Process Management Confernece, Hoboken (2010); Hardjono, T.W., Van Marrewijk, M., The Social Dimensions of Business Excellence (2001) Corporate Environmental Strategy, 8 (3), pp. 223-233; (2000) International Air Transport Association: Environmental Review, , IATA, Montreal and Geneva October; (2000) 2000 Environmental Management - Environmental Performance Evaluation, , International Standards Organization ISO 14031: European Committee for Standardization, Brussels; (1999) Environmental Performance Evaluation - Guideline and General Principles, Geneva, , ISO (International Organization for Standardization) ISO 14031; Johnson, M., (2007) Stakeholder Dialogue for Sustainable Service, , Doctoral dissertation, Karlstad, Karlstad University Studies; Kaplan, R., Norton, D., (1997) Balanced Scorecard: Strategien Erfolgreich Umsetzen, , Schäffer-Poeschel, Stuttgart; Kolk, A., (2000) The Economics of Environmental Management, , Prentice Hall, London; Laszlo, C., (2003) The Sustainable Company: How to Create Lasting Value Through Social and Environmental Performance, , Island Press, Washington DC; How to Make the Canadian Airline Industry More Competitive (2006) Economic Note, , http://www.iedm.org/uploaded/pdf/nov06_en.pdf, Montreal Economic Institute: retrieved April 2010; Neely, A., Adams, C., Perspectives on Performance: The performance prism (2000) Handbook of Performance Measurement, , Bouine, London; Perrini, F., Tencati, A., Sustainability and stakeholder management: The need for new corporate performance evaluation and reporting systems (2006) Business Strategy & the Environment, 15 (5), pp. 296-308; Schaltegger, S., Burritt, R., (2000) Contemporary Environmental Accounting: Issues, Concepts and Practice, , Greenleaf, Sheffield; Schaltegger, S., Wagner, M., Integrative management of sustainability performance, measurement and reporting (2006) Int. J. Accounting, Auditing and Performance Evaluation, 3 (1), pp. 1-19; Seidel, S., Recker, J., Pimmer, C., Vom Brocke, J., Enablers and Barriers to the Organizational Adoption of Sustainable Business Practices 16th Americas Conference on Information Systems (2010), , Paper presented at the; (1996) Selamta, the In-Flight Magazine of Ethiopian Airlines, 14 (1); (2001) Selamta, the In-Flight Magazine of Ethiopian Airlines, 18 (2); (2004) Selamta, the In-Flight Magazine of Ethiopian Airlines, 21 (2); Sauvante, M., The triple bottom line: A boardroom guide (2002) Director's Monthly, 25 (11), pp. 1-6; Vom Brocke, J., Sonnenberg, C., Simons, A., Value-oriented Information Systems Design: The Concept of Potentials Modeling and its Application to Service-oriented Architectures (2009) Business & Information Systems Engineering, 1 (3), pp. 223-233; Waddock, S., Bodwell, C., (2007) Total Responsibility Management: The Manual, , Greenleaf Publishing, Sheffield},
correspondence_address1={Alemayehu, W.; Ababa University, Addis Ababa, Ethiopia; email: wub_ale@yahoo.com},
publisher={Springer Verlag},
issn={18651348},
isbn={9783642205101},
language={English},
abbrev_source_title={Lect. Notes Bus. Inf. Process.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Turhan2011298,
author={Turhan, D. and Vayvay, Ö.},
title={A performance-based decision-making tool for supply chain reengineering},
journal={International Journal of Business Excellence},
year={2011},
volume={4},
number={3},
pages={298-320},
doi={10.1504/IJBEX.2011.040107},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956278961&doi=10.1504%2fIJBEX.2011.040107&partnerID=40&md5=dcd2ab88f3ec5a6015a20fe607ad7fc0},
affiliation={Faculty of Engineering, Ind. Eng. Dep., Marmara University Göztepe Campus, 34722 Kadikoy, Istanbul, Turkey},
abstract={The purpose of this paper is to present a decomposition-based decision-making tool incorporating supply chain balanced scorecard performance measures for supply chain reengineering. Following a literature review on supply chain performance measures and reengineering models, various dimensions and approaches to supply chain reengineering are explored. Based on the insights gained, using the axiomatic design technique, a performance-based supply chain reengineering model is developed. In line with literature, the study conveys that there is still a large room for improvement for developing balanced supply chain measures that address different parts of the supply chain. This paper focuses on supply chain reengineering models where no conclusive work exists and is expected to fill a gap in the area and be instrumental to both researchers and practitioners by providing a direction for coherent decision-making on supply chain reengineering studies. Copyright © 2011 Inderscience Enterprises Ltd.},
author_keywords={Axiomatic design;  Decomposition;  Performance measurement;  Reengineering;  Supply chain management},
references={Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Brewer, P.T., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Burgess, R., Avoiding supply chain management failure: Lessons from business process re-engineering (1998) International Journal of Logistics Management, 9 (1), pp. 15-23; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance measurement: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521; Chen, S.-J., Huang, E., A systematic approach for supply chain improvement using design structure matrix (2007) Journal of Intelligent Manufacturing, 18 (2), pp. 285-299. , DOI 10.1007/s10845-007-0022-z; Cheng, L.C., Grimm, C.M., The application of empirical strategic management research to supply chain management (2006) Journal of Business Logistics, 27 (1), pp. 1-55; Cochran, D.S., Arinez, J.F., Duda, J.W., Linck, J., A decomposition approach for manufacturing system design (2002) Journal of Manufacturing Systems, 20 (6), pp. 371-389; Cuthbertson, R., Piotrowicz, W., Supply chain best practices-identification and categorization of measures and benefits (2008) International Journal of Productivity and Performance Management, 57 (5), pp. 389-404; Davenport, T.H., Short, J.E., The new industrial engineering: Information technology and business process redesign (1990) Sloan Management Review, 31 (4), pp. 11-27; Diaz, M.R., Rodriguez, T.F.E., Redesigning the supply chain: Reengineering, outsourcing, and relational capabilities (2006) Business Process Management, 12 (4), pp. 483-502; Duda, J.W., (2000) A Decomposition-based Approach to Linking Strategy, Performance Measurement, and Manufacturing System design, , PhD thesis, Massachusetts Institute of Technology, Cambridge, MA; Evans, G.N., Towill, D.R., Naim, M.M., Business process reengineering the supply chain (1995) Production Planning and Control, 6 (3), pp. 38-42; Fowler, A., Operations management and systemic modeling as frameworks for BPR (1998) International Journal of Operations and Production Management, 18 (9-10), pp. 1028-1056; Gorla, N., Chinta, R., Chu, T.W., An enhanced business process re-engineering model for supply chain management and a case study (2007) Journal of Information Technology Case and Application Research, 9 (2), pp. 5-27; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Van Hoek, R.I., "Measuring the immeasurable" - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192. , DOI 10.1108/13598549810244232; Kaplan, R.S., Norton, D.P., (1996) Translating Strategy Into Action: The Balanced Scorecard, , Harvard Business School Press, Boston, Massachusetts; Kumar, A., Ozdamar, L., Zhang, C.N., Supply chain redesign in the healthcare industry of Singapore (2008) Supply Chain Management: An International Journal, 13 (2), pp. 95-103; Kumar, S., Chandra, C., Stoerzinger, M., Serve your supply chain, not operations-a case study (2001) Industrial Management and Data Systems, 101 (8-9), pp. 414-425; McLaughlin, J., Motwani, J., Madan, M., Gunasekaran, A., Using information technology to improve downstream supply chain operations: A case study (2003) Business Process Management Journal, 9 (1), pp. 69-80; Melynk, S.A., Davis, E.W., Spekman, R.E., Sandor, J., Outcome-driven supply chains (2010) Sloan Management Review, 51 (2), pp. 34-38; Mohanty, R.P., Deshmukh, S.G., Reengineering of a supply chain management system: A case study (2000) Production Planning and Control, 11 (1), pp. 90-104. , DOI 10.1080/095372800232522; Nissen, M.E., Intelligent tool for process redesign: Manufacturing supply-chain applications (2000) International Journal of Flexible Manufacturing Systems, 12 (4), pp. 321-339. , DOI 10.1023/A:1008178217369; Ozcelik, Y., Do business process reengineering projects pay off? Evidence from the United States (2010) International Journal of Project Management, 28, pp. 7-13; Schnetzler, M.J., Sennheiser, A., Schonsleben, P., A decomposition-based approach for the development of a supply chain strategy (2007) International Journal of Production Economics, 105 (1), pp. 21-42. , DOI 10.1016/j.ijpe.2006.02.004, PII S0925527306000600; Segars, A.H., Harkness, W.J., Kettinger, W.J., Process management and supply-chain integration at the Bose Corporation (2001) Interfaces, 31 (3), pp. 102-114; Simatupang, T.M., Sridharan, R., Supply chain discontent (2005) Business Process Management Journal, 11 (4), pp. 349-369. , DOI 10.1108/14637150510609390; Simatupang, T.M., Sridharan, R., Design for supply chain collaboration (2008) Business Process Management, 14 (3), pp. 401-418; Sridharan, U.V., Caines, W.R., Patterson, C.C., Implementation of supply chain management and its impact on the value of firms (2005) Supply Chain Management, 10 (4), pp. 313-318. , DOI 10.1108/13598540510612785; Stock, J.R., A research view of supply chain management: Development and topics for exploration (2009) Orion, 25 (2), pp. 147-160; Suh, N.P., (1990) The Principles of Design, , Oxford University Press, New York; Trienekens, J.H., Hvolby, H.-H., Models for supply chain reengineering (2001) Production Planning and Control, 12 (3 SPEC.), pp. 254-264. , DOI 10.1080/095372801300107761; Trkman, P., Stemberger, M.I., Jaklic, J., Groznik, A., Process approach to supply chain integration (2007) Supply Chain Management: An International Journal, 12 (2), pp. 116-128; Rao Tummala, V.M., Phillips, C.L.M., Johnson, M., Assessing supply chain management success factors: A case study (2006) Supply Chain Management, 11 (2), pp. 179-192. , DOI 10.1108/13598540610652573; Varma, S., Wadhwa, S., Deshmukh, S.G., Implementing supply chain management in a firm: Issues and remedies (2006) Asia Pacific Journal of Marketing and Logistics, 18 (3), pp. 223-243; Zhang, L.L., Jaio, R.J., Ma, Q., Accountability-based order fulfillment process reengineering towards supply chain management: A case study at a semiconductor equipment manufacturer (2010) Journal of Manufacturing Technology Management, 21 (2), pp. 287-305},
correspondence_address1={Vayvay, Ö.; Faculty of Engineering, , 34722 Kadikoy, Istanbul, Turkey; email: ozalp@marmara.edu.tr},
publisher={Inderscience Publishers},
issn={17560047},
language={English},
abbrev_source_title={Int. J. Bus. Excellence},
document_type={Article},
source={Scopus},
}

@ARTICLE{Stefanović2011167,
author={Stefanović, N. and Stefanović, D.},
title={Supply chain performance measurement system based on scorecards and web portals},
journal={Computer Science and Information Systems},
year={2011},
volume={8},
number={1},
pages={167-192},
doi={10.2298/CSIS090608018S},
note={cited By 28},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79251612739&doi=10.2298%2fCSIS090608018S&partnerID=40&md5=feb5657ba4abfaa5e27b0ed8ede273ee},
affiliation={Faculty of Science, University of Kragujevac, Radoja Domanovica 12, 34000 Kragujevac, Serbia},
abstract={Successful supply chain management becomes essential for the ultimate success of corporations. Companies today seek an effective performance measurement (PM) system to maximize the bottom line. Unfortunately, performance measurement in the supply-chain field has not kept pace with today's world of interdependent business relationships. What companies need is a new PM system that unifies different business elements, concepts, technologies and tools. In this paper, the architecture of such a pervasive PM system is introduced. The main system elements such as process model, metrics and data warehouse are described. Finally, a specialized PM web portal which enables proactive performance monitoring and fosters the improvement and optimization is presented.},
author_keywords={Performance;  Scorecard;  Supply chain;  Web portal},
references={Parker, B., EPM: From Concept to Implementation (2002) AMR Research's Spring Executive Conference: Creating the Real-Time Enterprise, pp. 29-31. , May; Mittlender, D., Pervasive business intelligence: Enhancing key performance indicators (2005), DM Review, August; Vriens, D., Information and communications technology for competitive intelligence (2004), Idea Group Publishing; Bello, D.C., Gilliland, D.I., The Effects of Output Controls, Process Controls, and Flexibility on Export Channel Performance (1997) Journal of Marketing, 61, p. 22. , Winter; Stank, T.P., Lackey, C.W., Enhancing Performance Through Logistical Capabilities in Mexican Maquiladora Firms (1997) Journal of Business Logistics, 18 (1), pp. 91-123; Foster, T.A., It Pays to Measure Performance: Logistics Performance Compensation Programs (1991) Chilton's Distribution, p. 90. , September; Lee, H.L., Billington, C., Managing Supply chain Inventory: Pitfalls and Opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lappide, L., What About Measuring Supply Chain Performance (2002) ASCET, 3. , Montgomery Research; Gintic, Measuring supply chain performance using a SCOR-based approach (2002), March; Kaplan, R., Norton, D., The balanced scorecard (1996), Harvard Business School Press, Boston; Hammer, M., The Agenda (2001), Chapter6: Measure Like You Mean It, Crown Business, New York; Kaplan, R., Norton, D., Strategy-focused organization how balanced scorecard companies thrive in the new business environment (2006), Boston, MA: Harvard Business School Press; Leahy, T., The balanced scorecard meets BPM (2003) Business Finance, 33. , June; Stefanovic, D., Majstorovic, V., Stefanovic, N., Methodology for Process Integration in Supply Networks (2005) 38th CIRP Manufacturing Systems Seminar, pp. 16-18. , Brazil, May; Stefanovic, D., Stefanovic, N., Methodology for Modeling and Analysis of Supply Networks (2008) Journal of Intelligent Manufacturing, 19 (4), pp. 485-503. , Springer; Stefanovic, N., Radenkovic, B., Stefanovic, D., Designing OLAP Multidimensional Systems For Supply Chain Management (2007) International Journal of Pure and Applied Mathematics, pp. 1311-8080. , IJPAM, ISSN; Stefanovic, N., Radenkovic, B., Stefanovic, D., Supply chain intelligence (2008) Intelligent Production Machines and Systems, pp. 292-297. , D.T. Pham, D.T., Eldukhri, E.E., Soroka, A.J. (eds.), Whittles Publishing, Whittles Publishing; Stefanovic, N., Stefanovic, D., Misic, M., Application of Business Intelligence for Business Process Management (2008) Artificial Intelligence In Theory and Practice II, 276, pp. 445-449. , Bramer, M. (ed.), IFIP State of the Art Series, Springer Boston; Stefanovic, N., Stefanovic, D., Methodology for BPM in Supply Networks (2006) 5th CIRP International Seminar On Intelligent Computation In Manufacturing Engineering, pp. 25-28. , Ischia, Italy, July},
correspondence_address1={Stefanović, N.; Faculty of Science, Radoja Domanovica 12, 34000 Kragujevac, Serbia; email: nenads@kg.ac.rs},
issn={18200214},
language={English},
abbrev_source_title={Comp. Sci. Info. Sys.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Zhang20101271,
author={Zhang, W.-H. and Hou, L.-L. and Qiu, W.-H.},
title={Assessing supply chain performance based on problem discovery and analysis},
journal={Proceedings - 2010 IEEE 17th International Conference on Industrial Engineering and Engineering Management, IE and EM2010},
year={2010},
pages={1271-1275},
doi={10.1109/ICIEEM.2010.5645999},
art_number={5645999},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650589644&doi=10.1109%2fICIEEM.2010.5645999&partnerID=40&md5=139ee9d6d26826c057298efa0b4f58a2},
affiliation={School of Economics and Management, Beihang University, Beijing, China; School of Public Administration, Beihang University, Beijing, China},
abstract={Although great achievements have been made in the study of supply-chain performance measurement (SCPM), there are still lots of disputes and pronounced criticisms about current researches. In order to shed light on these problems, especially for these problems which fail to consider strategies or fail to give sufficient consideration of implementation costs of SCPM, we summarize researches on conceptual models of SCPM in this paper. With these conceptual models as useful tools to locate problems and seek right solutions, we propose assessing supply chain performance from the perspective of strategies, tactics and operations in order to align SCPM with strategies and using the idea of problem discovery and analysis in order to save the implementation costs of SCPM. © 2010 IEEE.},
author_keywords={Conceptual models;  Strategies;  Supply chain;  Supply-chain performance measurement (SCPM)},
keywords={Conceptual model;  Current researches;  Implementation cost;  Performance measurements;  Strategies;  Supply chain performance, Industrial engineering;  Supply chain management;  Supply chains, Cost benefit analysis},
references={Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-99; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62. , DOI 10.1016/j.cie.2007.04.001, PII S0360835207000617; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Production and Operations Management, 21 (1-2), pp. 71-87; Mentzer, J.T., Knorad, B.P., An efficiency/effectiveness approach to logistics performance analysis (1991) Journal of Business Logistics, 12 (1), pp. 33-61; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Lai, K., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E, 38 (6), pp. 439-456; Lai, K., Ngai, E.W.T., Cheng, T.C.E., An empirical study of supply chain performance in transport logistics (2004) International Journal of Production Economics, 87 (3), pp. 321-331; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Li, D., O'Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 59 (1-3), pp. 147-157; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B.M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218; Persson, F., Olhager, J., Performance simulation of supply chain designs (2002) International Journal of Production Economics, 77 (3), pp. 231-245; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing System, 14 (3), pp. 179-180; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-117; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, J.P., Designing supply chains: Towards theory development (2006) International Journal of Production Economics, 100 (2), pp. 223-238; Webster, M., Supply system structure, management and performance: A conceptual model (2002) International Journal of Management Reviews, 4 (4), pp. 353-369},
correspondence_address1={Zhang, W.-H.; School of Economics and Management, , Beijing, China; email: zhwhjessica@sina.com},
isbn={9781424464814},
language={English},
abbrev_source_title={Proc. - IEEE Int. Conf. Ind. Eng. Eng. Manage., IE EM},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Asdecker201046,
author={Asdecker, B. and Heigoldt, M.},
title={Supply Chain Performance Measurement - Theoretical Concept and Practical Integration [Supply chain performance measurement - Theoretisches konzept und praktische umsetzung]},
journal={Productivity Management},
year={2010},
volume={15},
number={4},
pages={46-48},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650444566&partnerID=40&md5=88ea7ae63867905adb8bf65131247e73},
affiliation={Otto-Friedrich-Universität Bamberg, Lehrstuhl für Allgemeine Betriebs wirtschaftslehre, Insb. Produktion und Logistik, Feldkirchenstr. 21 D-96052 Bamberg, Germany; Weltweiten Supply Chain Controlling bei Bosch Power Tools, Germany},
abstract={Supply chain management requires the interorganisational supervision and accounting of all conducted activities, which is undertaken within the scope of supply chain controlling. The concept of supply chain controlling employs several tools such as process mapping, interorganisational activity-based costing, and supply chain performance measurement. This paper focuses on supply chain performance measurement and pursues two goals. First, it substantiates the buzz word "supply chain performance measurement". Second, it presents the current development stage in business practice. © GITO-Verlag.},
author_keywords={Supply chain balanced scorecard;  Supply chain controlling;  Supply chain performance indicators;  Supply chain performance measurement;  Supply chain performance measurement system},
references={Ackermann, I., Using the Balanced Scorecard for Supply Chain Management - Prerequisites, Integration Issues, and Performance Measures (2003) Strategy and Organization in Supply Chains, pp. 289-304. , Seuring, S./Müller, M./Goldbach, M./Schneidewind, U. (Hrsg.), Heidelberg; Affeld, D., Mit Best Practice im Supply Chain Management (SCM) zur Optimierung der Wertschöpfungskette (2002) Supply Network Management - Mit Best Practice Der Konkurrenz Voraus, pp. 13-30. , Voegele, A./Zeuch, M. (Hrsg.), Wiesbaden; Amtsblatt Der Europäischen Union, 46, pp. 36-41. , Europäische Union: Empfehlung der Kommission vom 6. Mai 2003 betreffend die Definition der Kleinstunternehmen sowie der kleinen und mittleren Unternehmen (2003/361/EG), Jg., Nr. L124; Göpfert, I., Neher, A., Supply Chain Controlling - Wissenschaftliche Konzeptionen und praktische Umsetzungen Logistik Management, 4 (3), pp. 34-44. , Jg., Nr; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , Jg., Nr; Richert, J.P., (2006) Performance Measurement in Supply Chains - Balanced Scorecard in Wertschöpfungsnetzwerken, , Wiesbaden; Sucky, E., (2004) Koordination in Supply Chains - Spieltheoretische Ansätze Zur Ermittlung Integrierter Bestell- Und Produktionspolitiken, , Wiesbaden; Thonemann, U., Behrenbeck, K., Diederichs, R., Großpietsch, J., Küpper, J., Leopoldseder, M., (2003) Supply Chain Champions - Was Sie Tun Und Wie Sie Einer Werden, , Wiesbaden; Weber, J., (2002) Logistik- Und Supply Chain Controlling, 5. , Aufl., Stuttgart},
correspondence_address1={Asdecker, B.; Otto-Friedrich-Universität Bamberg, Feldkirchenstr. 21, D-96052 Bamberg, Germany; email: bjoern.asdecker@uni-bamberg.de},
issn={18688519},
language={English; German},
abbrev_source_title={Prod. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Netland2010307,
author={Netland, T.H. and Spjelkavik, I.},
title={Making PROFIT at the intermodal terminal - A research agenda},
journal={IFIP Advances in Information and Communication Technology},
year={2010},
volume={338 AICT},
pages={307-314},
doi={10.1007/978-3-642-16358-6_39},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649959160&doi=10.1007%2f978-3-642-16358-6_39&partnerID=40&md5=bcfeee33da15a4e0fcd0ff4eea04f617},
affiliation={Department of Industrial Management, SINTEF Technology and Society, S.P. Andersens veg 5, 7465 Trondheim, Norway},
abstract={Intermodality has been a hot topic in the logistics sector for several decades, but the expected diffusion into business is still limited. The key to increased intermodalism lies at the intermodal terminal. This paper puts forward four literature-based propositions that should be part of the future research agenda for intermodal terminals. In order to realise the intermodal terminal of the future, there is a need to: (P1) develop an effective operative terminal system, (P2) develop a holistic performance measurement system for the terminal, (P3) develop cooperation models for the network actors, and (P4) develop new value increasing services at the terminal. © 2010 IFIP International Federation for Information Processing.},
author_keywords={Intermodality;  logistics;  terminal},
keywords={Cooperation model;  Intermodal terminals;  Intermodality;  Performance measurement system;  Research agenda;  terminal;  Terminal systems, Management;  Profitability, Supply chain management},
references={PROFIT Web Page, , http://www.sintef.no/profit; Facing the challenge: The intermodal freight terminal of the future (1986) Conference on Intermodal Freight Terminal Design, New Orleans. State-of-the-art Report No. 4, Transportation Research Board, pp. iii. , US Transportation Research Board: National Research Council, USA; (2002) Benchmarking Intermodal Freight Transport, Paris, , OECD; Chatterjee, L., Lakshmanan, T.R., Inermodal freight transport in the United States (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Taniguchi, E., Nemoto, T., Intermodal freight transport in urban areas in Japan (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Andersen, J., (2008) New Service Network Design Models for Intermodal Freight Transportation, 41. , philosophia doctor thesis, Norwegian University of Science and Technology, Thesis number 2008: Trondheim, Norway; Kim, K.W., Günther, H.-O., (2007) Container Terminals and Cargo Systems - Design, Operations Management and Logistics Control Issues, , Springer, Heidelberg; Steenken, D., Voss, S., Stahlbock, R., Container terminal operation and operations research - A classification and literature review (2004) OR Spectrum, 26 (1), pp. 3-49. , DOI 10.1007/s00291-003-0157-z; Macharis, C., Bontekoning, Y.M., Opportunities for or in intermodal freight transport research: A review (2004) European Journal of Operational Research, 153, pp. 400-416; Lowe, D., (2005) Intermodal Freight Transport, , Elsevier/Butterworth-Heinemann, Oxford/UK; Woxenius, J., Bärthel, F., Intermodal road-rail transport in the European Union (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Halseth, A., Strategier og rammebetingelser for intermodal transport - Oppsummeringsrapport (2004) ECON Pöyry Forskningsrapport 2004-014; Fagerhaug, T., Olsson, N., PEMRO Arbeidspakke 1.2. State of the art innenfor prestasjonsmåling (2005) Report SINTEF Technology and Society; Meyer, M.D., Measuring System Performance: Key to Establishing Operations as a Core Agency Mission. Transportation Planning and Analysis (2002) Transportation Research Record, (1817), pp. 155-162; Humphreys, I., Francis, G., Traditional Airport Performance Indicators: A Critical Perspective. Issues, problems, and performance measures in airports and airspace (2000) Transportation Research Record, (1703), pp. 24-30; Gosling, Aviation system performance measures for state transportation planning. Issues, problems and performance measures in airports and airspace (2000) Transportation Research Record, (1703), pp. 7-15; Konings, R., Priemus, H., Nijkamp, P., The Future of Intermodal Freight Transport; an overview (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Tsamboulas, D., Development strategies for intermodal transport in Europe (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Stone, B., Critical success factors: Interconnectivity and interoperability (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Notteboom, T., Bundling freight flows and hinterland network development (2008) The Future of Intermodal Freight Transport, p. 66. , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Wiegmans, B., Nijkamp, P., Rietvald, P., Conatiner ternminal hanling quality (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Janic, M., An assessment of the performance of the European long intermodal freight trains (LIFTS) (2008) Transportation Research Part A: Policy and Practice, 42 (10), pp. 1326-1339; Metz, D., Journey quality as the focus of future transport policy (2005) Transport Policy, , article in Press; Bustinduy, J., More quality in regional transport (1995) Public Transport 1995, 51st International Congress. Paris 1995, , International Commision on Regional Transport; Kreutzberger, E., The impacts of innovative technical concepts for load unit exchange on the design of intermodal freight bundling networks (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Risjenbrij, J., Container handling in mainports: A dilemma about future scales (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin; Franke, K.-P., A technical approach to the Agile Port System (2008) The Future of Intermodal Freight Transport, , Konings, R., Priemus, H., Nijkamp, P. (eds.) MPG Nooks Ltd., Bodmin},
correspondence_address1={Netland, T. H.; Department of Industrial Management, S.P. Andersens veg 5, 7465 Trondheim, Norway; email: torbjorn.netland@sintef.no},
issn={18684238},
isbn={3642163572; 9783642163579},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Morris2010492,
author={Morris, A. and Malony, A.D. and Shende, S. and Huck, K.},
title={Design and implementation of a hybrid parallel performance measurement system},
journal={Proceedings of the International Conference on Parallel Processing},
year={2010},
pages={492-501},
doi={10.1109/ICPP.2010.57},
art_number={5599195},
note={cited By 17},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649556551&doi=10.1109%2fICPP.2010.57&partnerID=40&md5=dcb6fb66d41d012d1b1494da49868336},
affiliation={Performance Research Laboratory, Department of Computer and Information Science, University of Oregon, Eugene, OR 97403, United States; Barcelona Supercomputing Center, Centro Nacional de Supercomputación (BSC/CNS), C/ Jordi Girona 29, Barcelona 08034, Spain},
abstract={Modern parallel performance measurement systems collect performance information either through probes inserted in the application code or via statistical sampling. Probe-based techniques measure performance metrics directly using calls to a measurement library that execute as part of the application. In contrast, sampling-based systems interrupt program execution to sample metrics for statistical analysis of performance. Although both measurement approaches are represented by robust tool frameworks in the performance community, each has its strengths and weaknesses. In this paper, we investigate the creation of a hybrid measurement system, the goal being to exploit the strengths of both systems and mitigate their weaknesses. We show how such a system can be used to provide the application programmer with a more complete analysis of their application. Simple example and application codes are used to demonstrate its capabilities. We also show how the hybrid techniques can be combined to provide real cross-language performance evaluation of an uninstrumented run for mixed compiled/interpreted execution environments (e.g., Python and C/C++/Fortran). © 2010 IEEE.},
author_keywords={Analysis;  Measurement;  Parallel;  Performance;  Profiling;  Sampling;  Tracing},
keywords={Analysis;  Parallel;  Performance;  Profiling;  Tracing, Probes;  Sampling;  Supply chain management;  Switching circuits, Measurements},
references={TAU: Tuning and Analysis Utilities, , http://www.cs.uoregon.edu/research/paracomp/tau/; Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor-Crummey, J., Tallent, N., Hpctoolkit: Tools for performance analysis of optimized parallel programs (2010) Concurrency and Computation: Practice and Experience; Browne, S., Dongarra, J., Garner, N., Ho, G., Mucci, P., A portable programming interface for performance evaluation on modern processors (2000) International Journal of High Performance Computing Applications, 14 (3), pp. 189-204; Perfsuite, , http://perfsuite.ncsa.uiuc.edu/, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign. [Online]. Available; Servat, H., Llort, G., Giménez, J., Labarta, J., Detailed performance analysis using coarse grain sampling (2009) PROPER 2009; Brunst, H., Kranzlmüller, D., Nagel, W.E., Tools for scalable parallel program analysis - Vampir NG and DeWiz (2004) Distributed and Parallel Systems, Cluster and Grid Computing, 777; Harrison, R.J., Fann, G.I., Yanai, T., Beylkin, G., Multiresolution quantum chemistry in multiwavelet bases (2003) International Conference on Computational Science, 2660, pp. 103-110. , ser. Lecture Notes in Computer Science, P. M. A. Sloot, D. Abramson, A. V. Bogdanov, J. Dongarra, A. Y. Zomaya, and Y. E. Gorbachev, Eds. Springer; Mortensen, J.J., Hansen, L.B., Jacobsen, K.W., Real-space grid implementation of the projector augmented wave method (2005) Phys. Rev. B, 71 (3), p. 035109. , Jan; Rosner, R., Flash code: Studying astrophysical thermonuclear flashes (2000) Computing in Science and Engineering, 2, pp. 33-41; Graham, S., Kessler, P., McKusick, M., Gprof: A call graph execution profiler (1982) SIGPLAN '82 Symposium on Compiler Construction, pp. 120-126. , June; Kufrin, R., PerfSuite: An accessible, open source performance analysis environment for linux (2005) Sixth International Conference on Linux Clusters (LCI); Schulz, M., Galarowicz, J., Hachfeld, W., Open - Speedshop: Open source performance analysis for linux clusters (2006) SC '06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, p. 14; StackWalkerAPI, , http://www.paradyn.org/html/stackwalker1.1-features.html, [Online]. Available; Dean, J., Hicks, J.E., Waldspurger, C.A., Weihl, W.E., Chrysos, G., ProfileMe: Hardware support for instruction-level profiling on outof- order processors (1997) MICRO 30: Proceedings of the 30th Annual ACM/IEEE International Symposium on Microarchitecture, pp. 292-302; Advanced micro devices AMD CodeAnalyst Performance Analyzer, , http://developer.amd.com/cpu/codeanalyst; McCurdy, C., Vetter, J., Memphis: Finding and fixing NUMarelated performance problems on multi-core platforms (2010) Proceedings of the IEEE International Symposium on Performance Analysis of Systems and Software; Wolf, F., Wylie, B., Ábrahám, E., Becker, D., Frings, W., Fürlinger, K., Geimer, M., Szebenyi, Z., Usage of the SCALASCA toolset for scalable performance analysis of large-scale parallel applications (2008) Proc. of the 2nd HLRS Parallel Tools Workshop, pp. 157-167. , Springer, July; Chung, I.-H., Seelam, S., Mohr, B., Labarta, J., Tools for scalable performance analysis on petascale systems (2009) IPDPS '09: Proceedings of the 2009 IEEE International Symposium on Parallel& Distributed Processing, pp. 1-3},
correspondence_address1={Morris, A.; Performance Research Laboratory, , Eugene, OR 97403, United States; email: amorris@cs.uoregon.edu},
issn={01903918},
isbn={9780769541563},
coden={PCPAD},
language={English},
abbrev_source_title={Proc. Int. Conf. Parallel Process.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Duarte2010,
author={Duarte, S. and Machado, V.C.},
title={Performance evaluation for lean supply chain: A balanced scorecard framework},
journal={Proceedings of APMS 2010 - International Conference on Advances in Production Management Systems},
year={2010},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894142283&partnerID=40&md5=e1de0ffe719f88226e5317e44aaf6d9e},
affiliation={UNIDEMI, Departamento de Engenharia Mecânica e Industrial, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal},
abstract={There are new business paradigms as lean and green to develop improvements on the supply chain performance. The performance evaluation has become an important subject getting competitive advantages in organizations and their supply chains. To get a balanced performance measurement system a balanced scorecard (BSC) may be develop. This study is motivated by the lack of evidence on the supply chain performance measurement; it pretends to explore the effect of lean and green supply chain through a BSC framework. To achieve this purpose a four perspectives BSC was developed based on cause-and-effect relationships. To identify how to develop the BSC for incorporating lean and green supply chain performance, a brief literature review is presented. This paper proposes a conceptual model to explore how lean and green supply chain performance may be studied, using a BSC perspective.},
author_keywords={Balanced scorecard;  Green;  Lean;  Supply Chain},
references={Bhagwat, R., Sharma, M.K., An application of the integrated aHP-PGP model for performance measurement of supply chain management (2009) Production Planning &Control, 20, pp. 678-690; Sharma, M.K., Bhagwat, R., An integrated bSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11, pp. 57-68; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-348; Folan, P., Browne, J., A review of performance measurement:Towards performance management (2005) Computers in Industry, 56, pp. 663-680; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Production and Operations Management, 21, pp. 71-87; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management:A systematic approach to analyzing iterative kPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard, Translating Strategy into Action, , Harvard Business School Press, Boston, USA; Chia, A., Goh, M., Hum, S., Performance measurement in supply chain entities:Balanced scorecard perspective (2009) Benchmarking: An International Journal, 16, pp. 605-620; Hubbard, G., Measuring organizational performance:Beyond the triple bottom line (2009) Business Strategy and the Environment, 19, pp. 177-191; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management:A balanced scorecard approach (2007) Computers &Industrial Engineering, 53, pp. 43-62; Stenzel, J., (2007) Lean Accounting: Best Practices for Sustainable Integration, , John Wiley &Sons, Inc., Hoboken, New Jersey; Simpson, D.F., Power, D.J., Use the supply relationship to develop lean and green suppliers (2005) Supply Chain Management: An International Journal, 10, pp. 60-68; http://www.epa.gov/lean/, United States Environmental Protection Agency (EPA): "The Lean and Environmental toolkit" (2007); Sidiropoulos, M., Mouzakitis, Y., Adamides, E., Goutsos, S., Applying sustainable indicators to corporate strategy:The eco-balanced scorecard (2004) Environmental Research, Engineering and Management, 1, pp. 28-33; Hsu, Y., Liu, C., Environmental performance evaluation and strategy management using balanced scorecard (2009) Environmental Monitoring and Assessment, , December, Springer Science; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21, pp. 75-93; Bhasin, S., Lean and performance measurement (2008) Journal of Manufacturing Technology Management, 19, pp. 670-684; Zhu, Q., Sarkis, J., Lai, K., Green supply chain management:Pressures, practices and performance within the chinese automobile industry (2007) Journal of Cleaner Production, 15, pp. 1041-1052; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12, pp. 330-353; Gordon, P.J., (2001) Lean and Green: Profit for Your Workplace and Environmental, , Berrett-Koehler publications, USA; Nawrocka, D., Brorson, T., Lindhqvist, T., ISO 14001 in environmental supply chain practices (2009) Journal of Cleaner Production, 17, pp. 1435-1443; Pettersen, J., Defining lean production:Some conceptual and practical issues (2009) The TQM Journal, 21, pp. 127-142},
correspondence_address1={UNIDEMI, , 2829-516 Caparica, Portugal},
isbn={9788864930077},
language={English},
abbrev_source_title={Proc. APMS - Int. Conf. Adv. Prod. Manage. Syst.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Verdecho201056,
author={Verdecho, M.J. and Alfaro-Saiz, J.J. and Rodríguez-Rodríguez, R.},
title={COL-PMS: A collaborative performance measurement system},
journal={IFIP Advances in Information and Communication Technology},
year={2010},
volume={322 AICT},
pages={56-63},
doi={10.1007/978-3-642-14341-0_7},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884621386&doi=10.1007%2f978-3-642-14341-0_7&partnerID=40&md5=8ee64626cd1a15a7382327a26a032440},
affiliation={Department of Business Organization, CIGIP (Research Centre on Production Management and Engineering), Universidad Politécnica de Valencia, Camino de Vera, s/n, 46022, Valencia, Spain},
abstract={Enterprises look for new strategies to increase/maintain their competitiveness. One of these strategies consists of establishing collaboration relationships with other enterprises. Collaborating enterprises need tools and methods that facilitate the management their performance. Performance Measurement Systems developed in the literature have overlooked the development of frameworks that include the social side of collaboration relationships under an integrated and structured approach. The purpose of this paper is to introduce the COLlaborative Performance Measurement System (COL-PMS) framework which overcomes this gap. © IFIP International Federation for Information Processing 2010.},
author_keywords={Collaboration;  Interorganizational relationships;  Performance measurement system},
keywords={Collaboration;  Collaborative performance;  Inter-organizational relationships;  Performance measurement system;  Structured approach;  Tools and methods;  Collaboration;  Collaborative performance;  Inter-organizational relationships;  Performance measurement system;  Structured approach;  Tools and methods, Automation;  Competition;  Industry;  Manufacture;  Automation;  Competition;  Manufacture, Supply chain management;  Supply chain management},
references={Simatupang, T.M., Wright, A.C., Sridharan, R., Applying the theory of constraints to supply chain collaboration (2004) Supply Chain Management: An International Journal, 9 (1), pp. 57-70; Mentzer, J., Min, S., Zacharia, Z.G., The nature of interfirm partnering in supply chain management (2001) Journal of Retailing, 76 (4), pp. 549-568; Neely, A., Adams, C., Kennerley, M., The performance prism: The scorecard for measuring and managing business success (2002) Financial Times Prentice Hall, , London; Harland, C.M., Supply chain management: Relationships, chains and networks (1996) British Journal of Management, 7, pp. 63-80; Burgess, K., Singh, P., A proposed integrated framework for analysing supply chains (2006) Supply Chain Management: An International Journal, 11 (4), pp. 337-344; Zaklad, A., McKnight, R., Kosansky, A., Piermarini, J., The social side of the supply chain (2004) Industrial Engineer, 36 (2), pp. 40-44; Staughton, R., Johnston, R., Operational performance gaps in business relationships (2005) International Journal of Operations & Production Management, 25 (4), pp. 320-332; Mohr, J., Spekman, R., Characteristics of partnership success: Partnership attributes, communication (1994) Strategic Management Journal, 15 (2), pp. 135-152; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing System, 14 (3), pp. 179-190; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production Planning and Control, 16 (6), pp. 531-544; Chalmeta, R., Grangel, R., Performance measurement systems for virtual enterprise integration (2005) International Journal of Computer Integrated Manufacturing, 18 (1), pp. 73-84; Alfaro, J.J., Ortiz, A., Rodríguez, R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; Bititci, U.S., Turner, T., Mackay, D., Kearney, D., Parung, J., Walters, D., Managing synergy in collaborative enterprises (2007) Production Planning & Control, 18 (6), pp. 454-465; Romero, D., Galeano, N., Molina, A., A conceptual model for virtual breeding environments value system (2007) Establishing the Foundation of Collaborative Networks, , Camarihna-Matos, L., Afsarmanesh, H., Novais, P., Analide, C. (eds.) Springer, Boston; Kaplan, R.S., Norton, D.P., Strategy maps: Converting intangible assets into tangible outcomes (2004) Harvard Business School Publishing Corporation, , Boston; Westphal, I., Thoben, K.D., Seifert, M., Measuring collaboration performance in virtual organizations (2007) Establishing the Foundation of Collaborative Networks, , Camarihna-Matos, L., Afsarmanesh, H., Novais, P., Analide, C. (eds.) Springer, Boston; Boddy, D., Macbeth, D., Wagner, B., Implementing collaboration between organizations: An empirical study of supply chain partnering (2000) Journal of Management Studies, 37 (7), pp. 1003-1018; Handfield, R.B., Bechtel, C., Trust, power, dependence, and economics: Can SCM research borrow paradigms? (2004) International Journal of Integrated Supply Chain Management, 1 (1), pp. 3-32; Lejeune, M.A., Yakova, N., On characterizing the 4 C's in supply chain management (2005) Journal of Operations Management, 23 (1), pp. 81-100},
correspondence_address1={Department of Business Organization, Camino de Vera, s/n, 46022, Valencia, Spain},
issn={18684238},
isbn={3642143407; 9783642143403},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Cocca20101946,
author={Cocca, P. and Marciano, F. and Alberti, M.},
title={Towards an integrated use of video technology and performance measurement to improve workplace safety: A preliminary framework},
journal={Reliability, Risk and Safety: Back to the Future},
year={2010},
pages={1946-1954},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861674563&partnerID=40&md5=ae4d7c194566bb1699e9a892e0782944},
affiliation={University of Brescia, Brescia, Italy},
abstract={The objective of this paper is to analyze the suitability of video technology as a means to improve safety at work in manufacturing companies. A literature review is performed in order to find examples of the use of video data for safety reasons and identify possible advantages and risks connected to the use of video recording or real-time monitoring in the workplace. The need of integration of video technology with other tools, like performance measurement systems, is assessed and a safety performance measurement system, focusing on safe behaviours and conditions monitoring, is developed. The measurement system is intended to support video analysis, data recording and safety performance assessment. A preliminary framework for the use of video technology and performance measurement for safety reasons is proposed to support companies in identifying and correcting unsafe behaviours or conditions, thus fostering the process of continuous improvement of their safety performance. ©2010 Taylor & Francis Group.},
keywords={Continuous improvements;  Literature reviews;  Manufacturing companies;  Measurement system;  Performance measurement system;  Performance measurements;  Real time monitoring;  Safety at work;  Safety performance;  Use of video;  Video analysis;  Video technologies;  Workplace safety, Industry;  Reliability;  Safety engineering;  Supply chain management;  Video recording, Technology},
references={Aubert, D., Bouchafa, S., Détection de stationnarités anormales dans les couloirs du metro (1999) Recherche-Transports-Sécurit, 62 (2), pp. 56-70; Barling, J., Hutchinson, I., Commitment vs. control-based safety practices, safety reputation, and perceived safety climate (2000) Canadian Journal of Administrative Sciences, 17 (1), pp. 76-84; Bomer, T., Vision based protective devices (VBPD)- A vision becomes reality (2003) Proceedings of 3rd Int. Conf. SIAS 2003, pp. 7-11. , Nancy, France; Boult, E., Micheals, R.J., Gao, X., Eckmann, M., Into the Woods: Visual surveillance of non cooperative and camouflaged targets in complex outdoor settings (2001) Proceedings of the IEEE, 89 (10), pp. 1382-1402; Cheng, X., Wu, J., Yuan, X., Zhou, H., Principles for a video fire detection system (1999) Fire Safety Journal, 33, pp. 57-69; Collins, R.T., Lipton, A.J., Kanade, T., A system for video surveillance and monitoring (2000) VSAM Final Report, , Carnegie Mellon University; Conche, F., Tight, M., Use of CCTV to determine road accident factors in urban areas (2006) Accident Analysis and Prevention, 38 (6), pp. 1197-1207. , DOI 10.1016/j.aap.2006.05.008, PII S0001457506000868; Cucchiara, R., Multimedia surveillance systems (2005) VSSN 05: Proceedings of the Third ACM International Workshop on Video Surveillance & Sensor Networks, , New York: ACM; Cucchiara, R., Grana, C., Prati, A., Vezzani, R., Computer vision system for in-house video surveillance (2005) IEE Proceedings: Vision, Image and Signal Processing, 152 (2), pp. 242-249. , DOI 10.1049/ip-vis:20041215; Cucchiara, R., Prati, A., Vezzani, R., A multi-camera vision system for fall detection and alarm generation (2007) Expert Systems, 24 (5), pp. 334-345. , DOI 10.1111/j.1468-0394.2007.00438.x; David, G.C., Ergonomic methods for assessing exposure to risk factors for work-related musculoskeletal disorders (2005) Occupational Medicine, 55 (3), pp. 190-199. , DOI 10.1093/occmed/kqi082; Diehl, C.P., (2000) Towards Efficient Collaborative Classification for Distributed Video Surveillance, , PhD thesis, Carnegie Mellon University; Fuller, C.W., An assessment of the relationship between behaviour and injury in the workplace: A case study in professional football (2005) Safety Science, 43 (4), pp. 213-224. , DOI 10.1016/j.ssci.2005.05.002, PII S0925753505000184; Gavrila, D.M., The visual analysis of human movement: A survey (1999) Computer Vision and Image Understanding, 73, pp. 82-98; Guerlain, S., Turrentine, B., Adams, R., Calland, J.F., Using video data for the analysis and training of medical personnel (2004) Cognition Technology and Work, 6 (3), pp. 131-138; Horne, M., Future video accident recorder (1999) Proceedings of the International Symposium on Transportation Recorders, , May 3-5, Arlington, Virginia; Khoudour, L., Bruyelle, J.L., Detection de chutes de personnes sur les voies et d'intrusions en tunnels dans les transports publics (1999) Recherche-Transports-Sécurit, 62 (2), pp. 92-102; Kumar, P., Ranganath, S., Weimin, H., Sengupta, K., Framework for real-time behavior interpretation from traffic video (2005) IEEE Transactions on Intelligent Transportation Systems, 6 (1), pp. 43-53. , DOI 10.1109/TITS.2004.838219; Laureshyn, A., Ardö, H., Automated video analysis as a tool for analysing road safety behaviour (2006) Its World Congress, , London; Mackenzie, C.F., Hu, F.-M., Xiao, Y., Seagull, F.J., Video acquisition and audio system network (VAASNET©) for analysis of workplace safety performance (2003) Biomedical Instrumentation and Technology, 37 (4), pp. 285-291; Mackenzie, C.F., Xiao, Y., Horst, R., Video task analysis in high performance teams (2004) Cognition Technology and Work, 6 (3), pp. 139-147; Mackenzie, C.F., Xiao, Y., Hu, P., Seagull, F.J., Hammond, C., Bochicchio, G., Chiu, W., Dutton, R., Video clips as a data source for safety performance (2002) Proceedings of the Human Factors and Ergonomics Society 46th Annual Meeting, pp. 1414-1417; Malkhamah, S., Tight, M., Montgomery, F., The development of an automatic method of safety monitoring at Pelican crossings (2005) Accident Analysis and Prevention, 37 (5), pp. 938-946. , DOI 10.1016/j.aap.2005.04.012, PII S0001457505000758; Marbach, G., Loepfe, M., Brupbacher, T., An image processing technique for fire detection in video images (2006) Fire Safety Journal, 41, pp. 285-289; McGehee, D.V., Raby, M., Carney, C., Lee, J.D., Reyes, M.L., Extending parental mentoring using an event-triggered video intervention in rural teen drivers (2007) Journal of Safety Research, 38 (2), pp. 215-227. , DOI 10.1016/j.jsr.2007.02.009, PII S0022437507000321; McSween, T.E., (1995) The Values-Based Safety Process: Improving your Safety Culture with Behavior-Based Safety, , New York: John Wiley & Sons; Nieto, M., Public video surveillance: Is it an effective crime prevention tool? (1997) California Research Bureau Reports, , Sacramento: California State Library; Oberti, F., Stringa, E., Vernazza, G., Performance evaluation criterion for characterizing video-surveillance systems (2001) Real-Time Imaging, 7 (5), pp. 457-471; Racicot, B.M., Wogalter, M.S., Effects of video warning signs and social modeling on behavioral compliance (1995) Accid. Anal. and Prev, 27 (1), pp. 57-64; Roetting, M., Huang, Y.-H., McDevitt, J.R., Melton, D., When technology tells you how you drive - Truck drivers' attitudes towards feedback by technology (2003) Transportation Research Part F: Traffic Psychology and Behaviour, 6 (4), pp. 275-287. , DOI 10.1016/j.trf.2003.09.001, PII S1369847803000494; Rosen, G., Andersson, I.-M., Walsh, P.T., Clark, R.D.R., Saamanen, A., Heinonen, K., Riipinen, H., Paakkonen, R., A review of video exposure monitoring as an occupational hygiene tool (2005) Annals of Occupational Hygiene, 49 (3), pp. 201-217. , DOI 10.1093/annhyg/meh110; Sedky, M.H., Moniri, M., Chibelushi, C.C., Classification of smart video surveillance systems for commercial applications (2005) Advanced Video and Signal Based Surveillance, 2005, AVSS 2005, IEEE Conference on, pp. 638-643; Templer, J., Archea, J., Cohen, H.H., Study of factors associated with risk of work-related stairway falls (1985) Journal of Safety Research, 16 (4), pp. 183-196. , DOI 10.1016/0022-4375(85)90005-2; Teicher, J., O'Rourke, A., Pyman, A., Privacy and surveillance in the workplace (2005) Monash Business Review, 1 (1), pp. 1-8; Velastin, S.A., Boghossian, B.A., Vicencio-Silva, M.A., A motion-based image processing system for detecting potentially dangerous situations in underground railway stations (2006) Transportation Research Part C: Emerging Technologies, 14 (2), pp. 96-113. , DOI 10.1016/j.trc.2006.05.006, PII S0968090X06000362; Versavel, J., Road safety through video detection (1999) IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp. 753-757; Xu, F., McGlothlin, J.D., Video exposure assessments of solvent exposures in university pharmaceutical laboratories-A pilot study (2003) Chemical Health and Safety, 10 (6), pp. 23-28},
correspondence_address1={Cocca, P.; University of Brescia, Brescia, Italy},
isbn={9780415604277},
language={English},
abbrev_source_title={Reliab., Risk Saf.: Back Future},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pidun201099,
author={Pidun, T. and Felden, C.},
title={An overview of models for business process analysis - Beyond performance measurement with KPIs},
journal={Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOC},
year={2010},
pages={99-106},
doi={10.1109/EDOCW.2010.48},
art_number={5628989},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951926874&doi=10.1109%2fEDOCW.2010.48&partnerID=40&md5=101247332d5bb3862ed24d4683434483},
affiliation={Department of Information Sciences, TU Bergakademie Freiberg, Freiberg, Germany},
abstract={Almost all of today's performance measurement systems (PMS) rely on the usage of numeric indicators to quantify success or failure, normally referred to as key performance indicators (KPIs). This restriction makes it difficult to describe hard-measurable or business processes that are rather qualitative in nature. So, other existing models for process analysis containing convenient solutions and indicators have to be assessed and validated in order to offer them to stakeholders to thoroughly assess business processes. In this article, various existing models and frameworks for performance measurement were reviewed in an empirical investigation, categorized in systems and put into context to relevant theories. The result of the research is an overview of models for business process analysis that also incorporates indirect process performance aspects like soft goals, complexity, maturity or dependencies. © 2010 IEEE.},
author_keywords={KPI;  Ontologies;  PMS;  Process performance;  Success factors},
keywords={Business Process;  Business process analysis;  Empirical investigation;  Indirect process;  Key performance indicators;  KPI;  Numeric indicators;  Performance measurement system;  Performance measurements;  PMS;  Process analysis;  Process performance;  Soft goals;  Success factors, Benchmarking;  Supply chain management, Ontology},
references={(2009) Balanced Scorecard Usage Survey 2009, , http://www.2gc.co.uk/pdf/2GC-RP-BSCuseSurvey2009-091007.pdf, 2CG Ltd, last download 27 Feb 2010; Smith, M., Erwin, J., (2007) Role & Responsibility Charting (RACI), , http://www.pmforum.org/library/tips/pdf_files/RACI_R_Web3_1.pdf, last access 3 June 2010; Baetge, J., Hesse, A., (2008) Best Practices Bei SD-KPIs, , http://www.sd-m.de/files/ Baetge_Hesse_Best_Practices_SD_KPIs_Hrsg_Deloitte.pdf, last Download 25 Sep 2009; Bloch, M., Hoyos-Gomez, A., How CIOs should think about business value (2009) McKinsey on Business Technology - Innovations in IT Management (, (15). , http://www.mckinsey.de/downloads/publikation/mck_on_bt/2009/ mck_on_bt_15_how_CIOs.pdf, last Download 25 Sep 2009; Bucher, T., Winter, R., Realisierungsformen des Geschäftsprozessmanagements (2007) EOrganisation: Service Prozess- Market-Engineering, 1 (1), pp. 695-713. , Oberweis Andreas (Ed.). Edition, Karlsruhe: Universitätsverlag Karlsruhe; Eckerson, W., Performance management strategies (2010) TDWI Best Practices Report First Quarter 2009, , http://www.tdwi.org/research/display.aspx?ID=9390, last Download 17 Jan 2010; Denyer, D., Tranfield, R., Producing a systematic review (2009) The Sage Handbook of Organizational Research Methods, pp. 671-689. , D. Buchanan and A. Bryman (Eds.), Sage Publications; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116. , DOI 10.1108/01443579510083622; Gehre, A., Katranuschkov, P., Scherer, R., Managing virtual organization processes by semantic web ontologies (2007) CIB 24th W78 Conference Maribor - Bringing ITC Knowledge to Work, 1, pp. 177-182. , D. Rebolj (Ed.). Edition, University Library Maribor; Kaplan, R., Norton, D., Using the balanced scorecard as strategic management system (1996) Harvard Business Review, pp. 75-85. , January-February 1996, Boston: Harvard Business Publishing; Gruber, T., A translation approach to portable Ontology specifications (1993) Knowledge Acquisition, 5 (2), pp. 199-220. , Stanford University Academic Press; Gregor, S., Jones, D., The anatomy of a design theory (2007) Journal of the Association for Information Systems, 8 (5), pp. 312-335; Hammer, M., Der große prozess-check (2007) Harvard Business Manager, 5 (2007), pp. 35-52; Kueng, P., Krahn, A.J.W., Building a Process Performance Measurement System: Some Early Experiences (1999) Journal of Scientific and Industrial Research, 58 (3), pp. 149-159; Hevner, A., Ram, S., March, S., Design science in information systems research (2004) MIS Quarterly, 1 (28), pp. 75-105; Huth, C., (2007) Konzeption Eines Lean-Six-Sigma-Vorgehensmodelles, , Diploma Thesis, Technische Universität Dresden, unpublished; Jensen, M., Meckling, W., (1976) Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure, , http://papers.ssrn.com/abstract=94043, last Download 23 Sep 2009; Jenz, D., (2003) Ontology-based Business Process Management, , http://www.bpiresearch.com/Resources/WP_BPMVision.pdf, last Download 3 Mar 2009; Kneuper, R., (2003) CMMI: Verbesserung von Softwareprozessen Mit Capability Maturity Model Integration, 1. , Edition, Heidelberg: d.punkt; Kramberg, V., (2008) Zielorientierte Geschäftsprozesse Mit WS-BPEL, , Diploma Thesis, Universität Stuttgart; Moll, A., Rundum erneuert - EFQM-Modell und Radar-Logik in revidierter Version 2010 (2009) Qualität und Zuverlässigkeit (QZ), 11 (2009), pp. 13-15; Philip, S., Visionen vom Erfolg vergessen (2009) IT&Produktion, 12 (2009), p. 11. , Marburg : TeDo Verlag; Porter, M., (1998) Competitive Strategy: Techniques for Analyzing Industries and Competitors, 62. , Edition, New York: The Free Press; (2009) European Corporate Performance Management: How Do You Manage Your Business?, , http://www.pwc.com/gx/en/finance-consulting-services/pdf/eurocpm-survey. pdf, last Download 25 Sep 2009; Premkumar, G., Ramamurthy, K., Saunders, C.S., Information processing view of organizations: An exploratory examination of fit in the context of interorganizational relationships (2005) Journal of Management Information Systems, 22 (1), pp. 257-294; Rummler, G., Brache, A., (1995) Improving Performance - How to Manage the White Space on the Organization Chart, 2. , Edition, Hoboken: Wiley; Samiresh, J., Momotko, M., Ruggaber, R., (2006) Manifesto for the Proposal of Nessi Working Group on Business Process Management, , http://www.nessieurope.eu/Nessi/LinkClick.aspx?fileticket=3zqrf6byVko= &tabid=257&mid=767, last Download 2 May 2009; Töpfer, A., Günther, S., Mehrere wege zu verschwendungsfreien prozessen und null-fehler- qualität: einführung und überblick über die beiträge (2009) Lean Six Sigma, 1, pp. 3-23. , A. Töpfer (Ed.), Edition, Berlin: Springer; Zellner, G., (2003) Leistungsprozesse im Kundenbeziehungsmanagement, , Dissertation, Universität St. Gallen; Neely, A., Adams, C., Kennerly, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , FT Prentice Hall; Lynch, R., Cross, K., (1991) Measure Up: The Essential Guide to Measuring Business Performance, , Mandarin; Dixon, J., Nanni, A., Vollmann, T., (1990) The New Performance Challenge: Measuring Operations for World-class Competition, , Business One Irwin; Keegan, D., Eiler, R., Jones, C., Are your performance measures obsolete? (1989) Management Accounting, ABI/INFORM, 70 (12), pp. 45-50. , June, ABI/Inform Global, 1989; Fitzgerald, L., Moon, P., (1996) Performance Measurement in Service Industries: Making It Work, , The Chartered Institute of Management Accountants; Lonnqvist, A., (2004) Measurement of Intangible Success Factors: Case Studies on the Design, Implementation and Use of Measures, , Dissertation, Tampere University of Technology, Publication 485; Latintnen, E., Framework for small business performance measurement (1996) Proceedings of the University of Vaasa, Research Papers Business Administration, 77. , University of Vaasa; Sink, D., Tuttle, T., (1989) Planning and Measurement in Your Organisation of the Future, pp. 170-184. , Chapter 5, Industrial Engiuneering and Management Press; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Edvinsson, L., Developing intellectual capital at Skandia (1997) Long Range Planning, 30 (3), pp. 366-373. , June, Elsevier, 1997},
correspondence_address1={Pidun, T.; Department of Information Sciences, , Freiberg, Germany; email: pidun@extern.tu-freiberg.de},
issn={15417719},
isbn={9780769541648},
language={English},
abbrev_source_title={Proc IEE Int. Enterprise Distrib. Obj. Comput. Workshop EDOC},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pungchompoo2010235,
author={Pungchompoo, S. and Sopadang, A.},
title={A supply chain performance measurement improved with integrated methods FDSM, MOO and DEA Part I: A conceptual of performance measurement framework in Thailand: Frozen shrimp chains},
journal={IEEM2010 - IEEE International Conference on Industrial Engineering and Engineering Management},
year={2010},
pages={235-239},
doi={10.1109/IEEM.2010.5674460},
art_number={5674460},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78751680006&doi=10.1109%2fIEEM.2010.5674460&partnerID=40&md5=d2f991f53c0e453fdd5127b83c22b036},
affiliation={Department of Industrial Engineering, Chiang Mai University, Chiang Mai, Thailand},
abstract={Supply chain performance measurement has become a popular topic. However, the existing performance measure model still lacks providing interaction aspects of key performance indicators (KPIs) and measuring. The aim of this paper is therefore to propose the conceptual of performance measurement. It can be categorized into the five dimensions which are represented in term of Cost, Flexibility, Responsiveness, Quality, and Innovativeness. For next research study, after the data collection process, researchers will calculate and analyze data by using the fuzzy design structure matrix (FDSM) method for grouping KPIs in term of the interaction among KPIs. ©2010 IEEE.},
author_keywords={Performance measurement;  Supply chain},
keywords={Data collection process;  Fuzzy design structure matrixes;  Innovativeness;  Integrated method;  Key performance indicators;  Performance measure;  Performance measurement frameworks;  Performance measurements;  Research studies;  Supply chain performance;  Thailand, Benchmarking;  Industrial engineering;  Supply chain management, Supply chains},
references={Cibaa, A., (2007) Measuring Supply Chain "Performance Measures: Prioritizing Performance Measures, , Master. Thesis, Dept. Business Administration and Social Sciences, Lulea Univ. of Technology, 2007; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Neely, M.G., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3-4), pp. 275-292; Chen, F.J., Huang, E., A systematic approach for supply chain improvement using design structure matrix (2007) Journal of Intell. Manuf., 18, pp. 285-299; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Performance measurement in supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Kristof, G.M., (2005) Planning Business Important Using Analytical Hierarchy Process (AHP) and Design Structure Matrix (DSM), , Master Thesis, Dept. Industrial and Management Engineering, Univ. Montana State; Cai, J., Liu, X., Xiao, Z., Liu, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2009) Decision Support Systems, 46, pp. 512-521; Van Der Vorst, J.G.A.J., Performance measurement in agri-food supply -chain networks (2006) Quantifying the Agri-Food Supply Chain, pp. 13-24. , Netherlands, Springer, Ch. 2; Aramyan, L.H., (2007) Measureing Supply Chain Performance in the Argi-food Sector, , Ph. D. dissertation, Univ. Wageningen, Netherlands; Sen, P., Yang, J.B., (1998) Multi Criteria Decision Support in Engineering Desigh, , Springer; Bhagwat, R., Shama, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computer & Industrial Engineering, 53, pp. 43-62; Adisak, T., (2007) A Performance Measurement Method for Supply Chain: An Integrated Multiple Criteria Decision Analysis Approach, , Ph. D. dissertation, School of Management ,Univ. Asian Institute of technology, Thailand; Wong, V.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management & Data Systems, 107 (3), pp. 361-381; Kwang, Y.L., Mohamed, A., Sharkawi, E., (2008) Modern Heuristic Optimization Techniques: Theory and Applications to Power Systems, pp. 189-205. , Willey - Interscience, New Jersey; Ko, Y.T., A dynamic planning for new product development management (2010) Journal of the Chinese of Industrial Engineering, 27 (2), pp. 103-120},
correspondence_address1={Pungchompoo, S.; Department of Industrial Engineering, , Chiang Mai, Thailand; email: siriratt_dao@hotmail.com},
isbn={9781424485031},
language={English},
abbrev_source_title={IEEM - IEEE Int. Conf. Ind. Eng. Eng. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Hsieh2010,
author={Hsieh, C.-L. and Laio, S.-H.},
title={A multiobjective evolutionary approach for an integrated location-inventory problem in vendor-managed inventory systems},
journal={40th International Conference on Computers and Industrial Engineering: Soft Computing Techniques for Advanced Manufacturing and Service Systems, CIE40 2010},
year={2010},
doi={10.1109/ICCIE.2010.5668373},
art_number={5668373},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651415912&doi=10.1109%2fICCIE.2010.5668373&partnerID=40&md5=f7fd58217ae779d849b08f807a4ffb41},
affiliation={Dept. of Industrial Management and Enterprise Information, Aletheia University, Taipei, Taiwan; Dept. of Management Sciences and Decision Making, Tamkang University, Taipei, Taiwan},
abstract={In this paper, we propose an integrated model to incorporate inventory control decisions - such as economic order quantity, safety stock and inventory replenishment decisions - into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk pooling phenomenon to explore how important supply chain parameters affect the cost savings to be realized from collaborative initiatives such as vendor-managed inventory (VMI). Multiobjective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (responsive level). A multiobjective location-inventory model which permits a comprehensive trade-off evaluation for multi-objective optimization is initially presented. More specifically, a multiobjective evolutionary algorithm is developed to determine the optimal facility location portfolio and inventory control parameters in order to reach best compromise of these conflicting criteria. An experimental study using practical data was then illustrated for the possibility of the proposed approach. Computational results have presented promise solutions in solving a practical-size problem with 50 buyers and 15 potential DCs and proved to be an innovative and efficient approach for so called difficult-to-solve problems.},
author_keywords={Multi-objective evolutionary algorithm;  Supply chain integration;  Vendor-managed inventory},
keywords={Computational results;  Control parameters;  Cost saving;  Customer service levels;  Distribution network;  Economic order quantity;  Evolutionary approach;  Experimental studies;  Facility location models;  Fill rate;  Integrated models;  Inventory models;  Inventory problem;  Inventory replenishment;  Multi objective;  Multi objective evolutionary algorithms;  Multiobjective decision;  Optimal facility location;  Performance measurement system;  Risk pooling;  Safety stock;  Simultaneous model;  Stochastic demand;  Supply chain integration;  Supply chain parameters;  Vendor managed Inventory;  Vendor-Managed Inventory systems, Cost benefit analysis;  Decision making;  Decision theory;  Distributed parameter networks;  Evolutionary algorithms;  Facilities;  Industrial engineering;  Inventory control;  Problem solving;  Soft computing;  Stochastic models;  Supply chain management;  Supply chains, Multiobjective optimization},
references={Waller, M., Johnson, M.E., Davis, T., Vendor-managed inventory in the retail supply chain (1999) J. of Business Logistics, 20, pp. 183-203; Cetinkaya, S., Lee, C.Y., Stock replenishment and shipment scheduling for vendor-managed inventory systems (2000) Management Science, 46, pp. 217-232; Yao, Y.L., Evers, P.T., Dresner, M.E., Supply chain integration in vendor-managed inventory (2007) Decision Support Systems, 43, pp. 663-674; Daskin, M.S., Coullard, C.R., Shen, Z.M., An inventory-location model: Formulation, solution algorithm and computational results (2002) Annals of Operations Research, 110, pp. 83-106; Schaffer, J.D., Multiple objective optimization with vector evaluated genetic algorithms (1985) Proceedings of the First International Conference on Genetic Algorithms, Hillsdale, NJ, pp. 93-100; Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multi-objective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6, pp. 181-197; Eppen, G., Effects of centralization on expected costs in a multilocation newsboy problem (1979) Management Science, 25, pp. 498-501},
correspondence_address1={Hsieh, C.-L.; Dept. of Industrial Management and Enterprise Information, , Taipei, Taiwan; email: hsiehcl@email.au.edu.tw},
isbn={9781424472956},
language={English},
abbrev_source_title={Int. Conf. Comput. Ind. Eng.: Soft Comput. Tech. Adv. Manuf. Serv. Syst., CIE40},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Rongier2010189,
author={Rongier, C. and Gourc, D. and Lauras, M. and Galasso, F.},
title={Towards a performance measurement system to control disaster response},
journal={IFIP Advances in Information and Communication Technology},
year={2010},
volume={336 AICT},
pages={189-196},
doi={10.1007/978-3-642-15961-9_22},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78449313692&doi=10.1007%2f978-3-642-15961-9_22&partnerID=40&md5=d612ca17e1ccf3a88a477e8b5f540a7b},
affiliation={Université de Toulouse, Mines Albi, Centre Génie Industriel, Route de Teillet, 81000 Albi, France},
abstract={During a crisis, the main goal for decision-makers consists in restoring a stabilized nominal mode. The stakeholders have to face an important pressure and drastic constraints of response time and coordination. This study proposes a method assisting these stakeholders in their choices while carrying out a performance evaluation of the activities run during the crisis response process. Currently, in crises, the performance evaluation is only used subsequently due to difficulties in gathering and aggregating information into trustable performance indicators. The interest of this paper is to present this method which permits to obtain a relevant decision support system. Decision-makers will use it to resolve the crisis based on performance assessment. A case study about crisis management within the French Red Cross non-governmental organization is developed in order to explain how performance indicators can on the one hand support crisis response management and on the other hand improve the collaboration of stakeholders. © 2010 IFIP.},
author_keywords={disaster management;  humanitarian organization process;  indicators;  performance assessment},
keywords={Crisis management;  Crisis response;  Decision makers;  Disaster management;  Disaster response;  humanitarian organization process;  indicators;  Nongovernmental organizations;  Performance assessment;  Performance evaluation;  Performance indicators;  Performance measurement system;  Response time, Artificial intelligence;  Benchmarking;  Decision support systems;  Decision theory;  Disaster prevention;  Disasters;  Virtual corporation, Supply chain management},
references={Balcik, B., (2008) Relief Chain Planning and Management: Modeling and Analyzing Humanitarian Logistic Problems, , PhD thesis, Université de Washington; Neely, A.D., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approach be adopted? (1996) International Journal of Production Economics; Kueng, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Quality Management; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems literature review (2003) International Journal of Business Performance Management; (2003) Supply Chain Operations Reference-models - SCOR, p. 278. , S.C.C. Supply Chain Council: version 6.0, Edition Supply Chain Council; Bolstorff, P., How does SCOR measure up? (2002) Supply Chain Technology News, pp. 22-25. , http://www.totalsupplychain.com; Vitale, M., Mavrinac, S.C., Hauser, M., New process/financial scorecard: A strategic performance measurement system (1994) Planning Review, 22 (4), pp. 12-16; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131; (1989) Cross and Lynch; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston; Lorino, P., (2003) Méthodes et Pratiques de la Performance, , Editions d'Organisation; Marcon, E., Sénéchal, O., Burlat, P., Evaluation des performances des systèmes de production, sous la direction de Tahon, C. Hermès (2003) Concepts Pour la Performance des Systèmes de Production; Humez, V., (2008) Proposition d'Un Outil d'Aide à la Décision Pour la Gestion des Commandes en Cas de Pénurie: Une Approche Par la Performance, , PhD thesis, INP Toulouse; Durieux-Paris, S., Genin, P., Thierry, C., Document de synthèse du projet (2007) GdR Macs, Prise de Décision Dans la Chaîne Logistique en Monde Incertain; Tang, C.S., Perspectives in supply chain risk management (2006) International Journal of Production Economics, 103, pp. 451-488; Gunasekaran, A., Bulent, K., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research; Mukhopadhyay, A.K., Crisis and disaster management turbulence and aftermath (2005) New Age International; Jacques, J.M., Gatot, L., (1996) De l'Incident à la Catastrophe: Un Modèle Organisationnel; Alexander, A.O., Coppola, D.P., (2002) Introduction to International Disaster Management, , Butterworth-Heinemann, Butterworths; Porter, M., (1985) Competitive Advantage, , The free press, New York},
correspondence_address1={Rongier, C.; Université de Toulouse, Route de Teillet, 81000 Albi, France; email: carine.rongier@mines-albi.fr},
issn={18684238},
isbn={3642159605; 9783642159602},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Papakiriakopoulos20101297,
author={Papakiriakopoulos, D. and Pramatari, K.},
title={Collaborative performance measurement in supply chain},
journal={Industrial Management and Data Systems},
year={2010},
volume={110},
number={9},
pages={1297-1318},
doi={10.1108/02635571011087400},
note={cited By 76},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049494602&doi=10.1108%2f02635571011087400&partnerID=40&md5=90bf34e20a9e88b1aaad4e32fdc54461},
affiliation={ELTRUN, Department of Management Science and Technology, Athens University of Economics and Business, Athens, Greece},
abstract={Purpose - The objective of this paper is to demonstrate the challenges when developing a common performance measurement system (PMS) in the context of a collaborative supply chain. Design/methodology/approach - The paper utilizes qualitative and quantitative data from a case study. The qualitative data refer to the assessment of collaborative performance measures based on interviews with experts, while the quantitative data demonstrate the use of two performance measures in a collaborative supply chain network. Findings - The development of a collaborative PMS is a challenging task. Through the systematic study of two significant performance measures for a supply chain, it was found that the one could not be supported due to reliability restrictions, while the other requires the development of a complex information system. Based on these, a discussion of specific challenges follows. Research limitations/implications - The paper has the general case study limitations. Practical implications - Companies operating in supply chain networks need to synchronize existing business processes and data before the design of a new PMS. Selecting the measures and the measurement method is not a trivial task. Important challenges reveal when dealing with, underlying data, business processes and the evaluation method of a PMS in supply chains. Originality/value - The management control function usually focuses on the design and development of PMSs for a single organization. Limited knowledge exists when more than two companies require the development of a PMS for a jointly agreed business process. © Emerald Group Publishing Limited.},
author_keywords={Inventory;  Partnership;  Performance measurement (quality);  Supply chain management},
keywords={Business Process;  Collaborative performance;  Complex information;  Design and Development;  Design/methodology/approach;  Evaluation Method;  Inventory;  Management control;  Measurement methods;  Partnership;  Performance measure;  Performance measurement (quality);  Performance measurement system;  Qualitative data;  Quantitative data;  Supply chain network;  Systematic study, Research;  Supply chains, Supply chain management},
references={Ahn, H., Applying the balanced scorecard concept: An experience report (2001) Long Range Planning, 34 (4), pp. 441-461; Akintoye, A., McIntosh, G., Fitzgerald, E., A survey of supply chain collaboration and management in the UK construction industry (2000) European Journal of Purchasing & Supply Management, 6 (3), pp. 159-168; Anderson, S., Young, M., The impact of contextual and process factors on the evaluation of activity-based costing systems (1999) Accounting Organizations and Society, 24 (7), pp. 525-559; Azzone, G., Masella, C., Bertele, U., Design of performance measures for time-based companies (1991) International Journal of Operations & Production Management, 11 (3), pp. 77-85; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B., Ware, T., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Bititci, U., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Bourland, K., Powell, S., Pyke, D., Exploiting timely demand information to reduce inventories (1996) European Journal of Operational Research, 92 (2), pp. 239-253; Bourne, M., Researching performance measurement system implementation: The dynamics of success and failure (2005) Production Planning & Control, 16 (2), pp. 101-113; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bowersox, D., Closs, D., (1996) Logistical Management: The Integrated Supply Chain Process, , McGraw-Hill, New York, NY; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-94; Busi, M., Bititci, U., Collaborative performance management: Present gaps and future research (2006) International Journal of Productivity and Performance Management, 55 (1), pp. 7-25; Cachon, G., Fisher, M., Supply chain inventory management and the value of shared information (2000) Management Science, 46 (8), pp. 1032-1048; Chan, F.T., Qi, H.J., Chan, H.K., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642. , DOI 10.1108/00251740310495568; Chen, M., Yang, T., Li, H., Evaluating the supply chain performance of IT-based inter-enterprise collaboration (2007) Information & Management, 44 (6), pp. 524-534; Chu, W., Lee, C., Strategic information sharing in a supply chain (2006) European Journal of Operational Research, 174 (3), pp. 1567-1579; Corsten, D., Kumar, N., Do suppliers benefit from collaborative relationships with large retailers? An empirical investigation of efficient consumer response adoption (2005) Journal of Marketing, 69 (3), pp. 80-94; Croson, R., Donohue, K., Impact of POS data sharing on supply chain management: An experimental study (2003) Production and Operations Management, 12 (1), pp. 1-11; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics, and measures (2001) International Journal of Operations & Production Management, 21 (2), pp. 46-70; Dixon, J., Nanni, A., Vollman, T., (1990) The New Performance Challenge - Measuring Operations for World-Class Competition, , Dow Jones, Irwin, Homewood, IL; Eccles, R., The performance measurement manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers and bridges to effective supply chain management (2008) Supply Chain Management: An International Journal, 13 (1), pp. 35-48; Fernandes, K., Raja, V., Whalley, A., Lessons from implementing the balanced scorecard in a small and medium size manufacturing organization (2006) Technovation, 26 (6), pp. 623-634; Fisher, M., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Fliedner, G., CPFR: An emerging supply chain tool (2003) Industrial Management & Data Systems, 103 (1), pp. 14-21; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Fu, Y., Piplani, R., Supply-side collaboration and its value in supply chains (2004) European Journal of Operational Research, 152 (1), pp. 281-288; Gruen, T., Corsten, D., Bharadwaj, S., (2002) Retail Out-of-Stocks: A Worldwide Examination of Extent Causes and Consumer Response, , Grocery Manufacturers of America, Washington, DC; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Hadaya, P., Cassivi, L., The role of joint collaboration planning actions in a demand-driven supply chain (2007) Industrial Management & Data Systems, 107 (7), pp. 954-978; Hofman, D., The hierarchy of supply chain metrics (2004) Supply Chain Management Review, 8 (6), pp. 28-37; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Holmstrom, J., Framling, K., Kaipia, R., Saranen, J., Collaborative planning forecasting and replenishment: New solutions needed for mass collaboration (2002) Supply Chain Management: An International Journal, 7 (3), pp. 136-145; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement systems (2001) International Journal of Operations & Production Management, 21 (8), pp. 1096-1115; Kang, Y., Gershwin, S., Information inaccuracy in inventory systems (2005) IIE Transactions, 37 (9), pp. 843-859; Kaplan, R., Norton, D., (1995) Translating Strategy into Action: The Balanced Scorecard, , Harvard Business School Press, Boston, MA; Karaesmen, F., Liberopoulos, G., Dallery, Y., The value of advance demand information in production/inventory systems (2004) Annals of Operations Research, 126 (1), pp. 135-157; Kehoe, D., Boughton, N., New paradigms in planning and control across manufacturing supply chains (2001) International Journal of Operations & Production Management, 21 (5), pp. 582-593; Kelepouris, T., Pramatari, K., Doukidis, G., RFID-enabled traceability in the food supply chain (2007) Industrial Management & Data Systems, 107 (2), pp. 183-200; Kleijnen, J., Smits, M., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Lambert, D., Cooper, M., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Law, K., Helo, P., Kanchana, R., Phusavat, K., Managing supply chains: Lessons learned and future challenges (2009) Industrial Management & Data Systems, 109 (8), pp. 1137-1152; Lee, D., Park, J., RFID-based traceability in the supply chain (2008) Industrial Management & Data Systems, 108 (6), pp. 713-725; Lee, H., Whang, S., Decentralised multi-echelon supply chains: Incentives and information (1999) Management Science, 54 (5), pp. 633-640; Lee, H., So, K., Tang, C., The value of information sharing in a two-level supply chain (2000) Management Science, 46 (5), pp. 626-643; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, Financial Times, , Prentice-Hall, London; Neely, A., Gregory, M., Platts, K., Performance measurement system design - A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Otley, D., Performance management: A framework for management control system research (1999) Management Accounting Research, 10 (4), pp. 363-382; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Papakiriakopoulos, D., Pramatari, K., Doukidis, G., A decision support system for detecting products missing from the shelf based on heuristic rules (2009) Decision Support Systems, 46 (3), pp. 685-694; Petersen, K., Ragatz, G., Monczka, R., An examination of collaborative planning effectiveness and supply chain performance (2005) The Journal of Supply Chain Management, 41 (2), pp. 14-25; Phusavat, K., Anussornnitisarn, P., Helo, P., Dwight, R., Performance measurement: Roles and challenges (2009) Industrial Management & Data Systems, 109 (5), pp. 646-664; Pramatari, K., Miliotis, P., Impact of collaborative store ordering on shelf availability (2008) Supply Chain Management - An International Journal, 13 (1), pp. 49-61; Saura, I., Deltoro, M., Taulet, A., The value of B2B relationships (2009) Industrial Management & Data Systems, 109 (5), pp. 593-609; Searcy, C., Karapetrovic, S., McCartney, D., Application of a systems approach to sustainable development performance measurement (2008) International Journal of Productivity and Performance Management, 57 (2), pp. 182-197; Seifert, D., (2002) Collaborative Planning, Forecasting, and Replenishment - How to Create A Supply Chain Advantage, , Galileo Press GmbH, Bonn; Simatupang, T., Shidharan, R., The collaborative supply chain (2003) The International Journal of Logistics Management, 13 (1), pp. 15-30; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2008) Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies, , McGraw-Hill, Boston, MA; Simons, R., (2000) Performance Measurement and Control Systems for Implementing Strategy: Text & Cases, , Prentice-Hall, Upper Saddle River, NJ; Speakman, R., Kammouff, J., Myhr, N., An empirical investigation into supply chain management: A perspective on partnerships (1998) Supply Chain Management, 3 (2), pp. 53-67; Van Hoek, R., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Wagner, S., Kaufmann, L., Overcoming the main barriers in initiating and using purchasing - BSCs (2004) Journal of Purchasing & Supply Management, 10 (6), pp. 269-281; Yu, Z., Yan, H., Cheng, E., Benefits of information sharing with supply chain partnerships (2001) Industrial Management & Data Systems, 101 (3), pp. 114-121; Zaheer, A., McEvily, B., Perrone, V., Does trust matter? Exploring the effects of interorganizational and interpersonal trust on performance source (1998) Organization Science, 9 (2), pp. 141-159; Slack, N., (1991) The Manufacturing Advantage, , Mercury Books, London},
correspondence_address1={Papakiriakopoulos, D.; ELTRUN, , Athens, Greece; email: dpap@aueb.gr},
issn={02635577},
coden={IMDSD},
language={English},
abbrev_source_title={Ind Manage Data Sys},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wang2010413,
author={Wang, C.-H. and Lu, I.-Y. and Chen, C.-B.},
title={Integrating hierarchical balanced scorecard with non-additive fuzzy integral for evaluating high technology firm performance},
journal={International Journal of Production Economics},
year={2010},
volume={128},
number={1},
pages={413-426},
doi={10.1016/j.ijpe.2010.07.042},
note={cited By 34},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957763021&doi=10.1016%2fj.ijpe.2010.07.042&partnerID=40&md5=02d17e6e99f916e6acf0d6b5acdd3d16},
affiliation={Department of Bio-industry and Agribusiness Administration, National Chiayi University, Chiayi, Taiwan, No. 580, Sinmin Road, Chiayi City 600, Taiwan; Department of Business Administration, National Sun Yat-sen University, Taiwan, No. 70, Lienhai Road, Kaohsiung 80424, Taiwan; Department of Business Administration, National Sun Yat-sen University, No. 1, Section 2, Da Hsueh Road, Shoufeng, Hualien 97401, Taiwan},
abstract={Efficient and accurate performance measurement systems serve as a useful tool enabling managers to control, monitor and improve high technology firm processes, productivity and performance. A model is developed for measuring the acceptable performance of high tech firms based on the interaction financial, customers, internal business process and learning and growth perspective. The HBSC structure integrated with non-additive fuzzy integral for designing, developing and implementing high technology firms relevant to performance measurement was employed to overcome interaction among the various perspectives. Sixteen samples from eight high tech firms are used throughout the study to explain how the execution of the model works. Utilizing the proposed model, the fuzzy assessment of the decision-maker and the interaction among various evaluation criteria can be a focus of the evaluation of the aggregation performance, thus ensuring more effective and accurate performance evaluation and decision-making. In the light of this empirical evidence, the results provide guidance to high tech firms performance measurement in both identification appropriate metrics and overcoming key implementation obstacles for improving firm-operating efficiency and hence assistance for future strategic adjustment. © 2010 Elsevier B.V.All rights reserved.},
author_keywords={Fuzzy measure;  Hierarchical balanced scorecard;  Non-additive fuzzy integral;  Performance measurement;  Performance measurement system balanced scorecard},
keywords={Balanced scorecards;  Fuzzy measures;  Non-additive fuzzy integral;  Performance measurement system;  Performance measurements, Decision making;  Fuzzy systems;  Industry;  Integral equations;  Supply chain management;  Technology, Measurements},
references={Amir, E., Lev, B., Value-relevance of nonfinancial information: The wireless communications industry (1996) Journal of Accounting and Economics, 22, pp. 3-30; Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A stakeholder approach to strategic performance measurement (1997) Sloan Management Review, 38 (3), pp. 25-37; Abran, A., Buglione, L., A multidimensional performance model for consolidating balanced scorecards (2003) Advances in Engineering Software, 34, pp. 339-349; Ayag, Z., A fuzzy AHP-based simulation approach to concept evaluation in a NPD emvironment (2005) IIE Transactions, 37, pp. 827-842; Banker, R.D., Chang, H., Janakiraman, S.N., Konstans, C., A balanced scorecard analysis of performance metrics (2004) European Journal of Operational Research, 154, pp. 423-436; Bozarth, C.C., A conceptual model of manufacturing focus (1993) International Journal of Operations and Production Management, 13, pp. 81-93; Barclay, C., Osei-Bryson, K.M., Project performance development framework: An approach for developing performance criteria and measures for information systems (IS) projects (2010) International Journal of Production Economics, 124, pp. 272-292; Chakravarthy, B.S., Measuring strategic performance (1986) Strategic Management Journal, 7, pp. 437-458; Cameron, K., Whetten, D., (1983) Organizational Effectiveness: A Comparison of Multiple Models, , Academic Press London; Chen, M.J., The applications of fuzzy multiple attribute decision making in stock selection (1996) Journal of Management Science, 13, pp. 227-248; Chen, S.J., Hwang, C.L., (1992) Fuzzy Multiple Attribute Decision Making Methods and Applications., , Springer-Verlag Berlin, Germany; Chen, B.C., Klein, C.M., An efficient approach to solving fuzzy MADM problems (1997) Fuzzy Sets and Systems, 88, pp. 51-67; Chan, Y.C.L., An analytic hierarchy framework for evaluating balanced scorecards of healthcare organizations (2006) Canadian Journal of Administrative Sciences, 23, pp. 85-104; Chiou, H.K., Tzeng, G.H., Fuzzy multicriteria decision making approach for industrial green engineering (2002) Environmental Management, 30, pp. 816-830; Dubois, D., Prade, H., (1980) Fuzzy Sets and Systems: Theory and Applications, , Academic Press New York; De Haas, M., Kleingeld, A., Multilevel design of performance measurement systems: Enhancing strategic dialogue throughout the organization (1999) Management Accounting Research, 10, pp. 233-261; De Ron, A.J., Sustainable production: The ultimate result of a continuous improvement (1998) International Journal of Production Economics, 5657, pp. 99-110; De Lima, E.P., Da Costa, S.E.G., De Faria, A.R., Taking operations strategy into practice: Developing a process for defining priorities and performance measures (2009) International Journal of Production Economics, 122, pp. 403-418; Ecceles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69, pp. 131-137; Evans, J.R., An exploratory study of performance measurement systems and relationships with performance results (2004) Journal of Operations Management, 22, pp. 219-232; Fisher, J., Use on nonfinancial performance measures (1992) Journal of Cost Management, 6, pp. 31-38; Fortuin, L., Performance indicatorswhy where and how? (1988) European Journal of Operational Research, 34, pp. 1-9; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48, pp. 207-225; Grabisch, M., Fuzzy integral in multicriteria decision making (1995) Fuzzy Sets and Systems, 69, pp. 279-298; Grabisch, M., The application of fuzzy integrals in multicriteria decision making (1996) European Journal of Operational Research, 89, pp. 445-456; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press Boston; Kaplan, R.S., Norton, D.P., (2001) The Strategy Focused Organization How Balanced Scorecard Companies Thrive in the New Business Environment, , Harvard Business School Press Boston; Karsak, E.E., Kuzgunkaya, O., A fuzzy multiple objective programming approach for the selection of a flexible manufacturing system (2002) International Journal of Production Economics, 79, pp. 101-111; Keeney, R.L., Raiffa, H., (1976) Decision with Multiple Objectives: Preferences and Value Tradeoffs, , John Wiley and Sons New York; Leszczyski K.Penczek, Grochulski, P., Sugeno, W., Fuzzy measure and fuzzy clustering (1985) Fuzzy Sets and Systems, 15, pp. 147-158; Lee, A.H.I., Chen, W.C., Chang, C.J., A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan (2008) Expert Systems with Applications, 34, pp. 96-107; (2004) Annual Report of Industrial Production, , MOEA Department of Statistics, Ministry of Economic Affairs, Taiwan; Marichal, J., Roubens, M., Determination of weights of interacting criteria from a reference set (2000) European Journal of Operational Research, 124, pp. 641-650; Niven, P.R., (2002) Balanced Scorecard Step by Step: Maximizing Performance and Maintaining Results, , John Wiley and Sons New York; Nørreklit, H., The balanced scorecard: What is the score? A rhetorical analysis of the balanced scorecard (2003) Accounting Organizations and Society, 28, pp. 591-619; Neely, A., Mills, J., Platts, K., Gregory, M., Richard, H., Realizing strategy through measurement (1994) International Journal of Operational and Production Management, 14, pp. 104-152; Olve, N.G., Roy, J., Wetter, M., (1997) Performance Drivers: A Practical Guide to Using the Balanced Scorecard, , John Wiley and Sons New York; Onisawa, T., Sugeno, M., Nishiwaki, Y., Kawai, H., Harima, Y., Fuzzy measure analysis of public attribute towards the use of nuclear energy (1986) Fuzzy Sets and Systems, 20, pp. 259-289; Petrovic, R., Petrovic, D., Multicriteria ranking of inventory replenishment policies in the presence of uncertainty in customer demand (1996) International Journal of Production Economics, 45, pp. 439-446; Quezad, C., Cordova, F.M., Palominos, P., Godoy, K., Ross, J., Method for identifying strategic objectives in strategy maps (2009) International Journal of Production Economics, 122, pp. 492-500; Ravi, V., Shankar, R., Tiwari, M.K., Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach (2005) Computers and Industrial Engineering, 48, pp. 327-356; Schmidberger, S., Bals, L., Hartmann, E., Jahns, C., Ground handling services at European hub airports: Development of a performance measurement system for benchmarking (2009) International Journal of Production Economics, 117, pp. 104-111; Sugeno, M., (1974) Theory of Fuzzy Integrals and Its Applications, , Ph.D. Dissertation, Tokyo Institute of Technology, Japan; Sugeno, M., Terano, T., A model of learning on fuzzy information (1977) Kybernetes, 6, pp. 157-166; Sattler, L., Sohoni, V., Participative management: An empirical study of the semiconductor manufacturing industry (1999) IEEE Transactions on Engineering Management, 46, pp. 387-398; Sandstrm, J., Toivanen, J., The problem of managing product development engineers: Can the balanced scorecard be an answer? (2002) International Journal of Production Economics, 78, pp. 79-80; Terano, T., Asai, K., Sugeno, M., (1992) Fuzzy Systems Theory and Its Applications, , Academic Press Professional, Inc. San Diego, CA, USA; Tahani, H., Keller, M.J., Information fusion in computer vision using the fuzzy integral (1990) IEEE Transactions on Systems, Man and Cybernetics, 20, pp. 733-741; Watson, D.J.H., Contingency formulations of organization structure: Implications for management accounting (1975) Managerial Accounting: The Behavioral Foundations, pp. 65-80; Ward, P.T., Duray, R., Manufacturing strategy in context: Environment, competitive strategy and manufacturing strategy (2000) Journal of Operations Management, 18, pp. 123-138; Wang, W., Genetic algorithms for determining fuzzy measures from data (1998) Journal of Intelligent and Fuzzy Systems, 6 (2), pp. 171-183; Youngblood, A.D., Collins, T.R., Addressing balanced scorecard trade-off issues between performance metrics using multi-attribute utility theory (2003) Engineering Management Journal, 15, pp. 11-17; Zadeh, L.A., The concept of a linguistic variables and its application to approximate reasoning, Parts IIII (1975) Information Sciences, 8 (3), pp. 199-249; Zirger, B.J., Hartley, J.L., The effect of product acceleration techniques on product development time (1996) IEEE Transactions on Engineering Management, 43 (2), pp. 143-152},
correspondence_address1={Wang, C.-H.; Department of Bio-industry and Agribusiness Administration, No. 580, Sinmin Road, Chiayi City 600, Taiwan; email: chwang@mail.ncyu.edu.tw},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Cheng2010435,
author={Cheng, J.C.P. and Law, K.H. and Bjornsson, H. and Jones, A. and Sriram, R.D.},
title={Modeling and monitoring of construction supply chains},
journal={Advanced Engineering Informatics},
year={2010},
volume={24},
number={4},
pages={435-455},
doi={10.1016/j.aei.2010.06.009},
note={cited By 62},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957269503&doi=10.1016%2fj.aei.2010.06.009&partnerID=40&md5=9335526a70b05d5fca0c3a501be70ca6},
affiliation={Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong; Engineering Informatics Group, Department of Civil and Environmental Engineering, Stanford University, 473 Via Ortega, Stanford, CA 94305-4020, United States; Chalmers University of Technology, School of Technology Management and Economics, Gothenburg, Sweden; National Institute of Standards and Technology, 100 Bureau Drive, Stop 8263, Gaithersburg, MD 20899-8263, United States},
abstract={The planning and management of supply chains require properly specifying the participating members and the relationships among them. Construction supply chains usually consist of numerous participants and are complex in structure. Representing construction supply chains using a network model can help understand the complexity, support re-configuration, identify the bottlenecks, and prioritize company's resources, as well as add values to the management of construction projects. Using a case example on the mechanical, electrical and plumbing (MEP) processes in a construction project, this paper demonstrates the modeling of construction supply chains using the Supply Chain Operations Reference (SCOR) framework developed by the Supply Chain Council (SCC). The SCOR modeling framework provides a structured and systematic way to model and decompose a supply chain from conceptual representation to process element specification. The SCOR framework is commonly used by corporations for strategic planning of their supply chains. This paper further presents a model-based service oriented framework that leverages the SCOR models for performance monitoring of construction supply chains. In the supply chain management and monitoring framework each supply chain process element is implemented as a discrete web service component. The framework is built on a service oriented collaborative system, namely SC Collaborator, that we have developed using web service technology, open standards, and open source technologies. © 2010 Elsevier Ltd. All rights reserved.},
author_keywords={Construction supply chain;  Model-based approach;  Service oriented architecture;  Supply chain performance measurement;  Web services},
keywords={Collaborative systems;  Commonly used;  Construction projects;  Construction supply chain;  Model-based;  Model-based approach;  Modeling frameworks;  Network models;  Open Standards;  Open-source technology;  Performance monitoring;  Process elements;  SCOR models;  Service Oriented;  Supply chain operations references;  Supply chain performance measurement;  Supply chain process;  Web service components;  Web service technology, Construction industry;  Information services;  Monitoring;  Network architecture;  Planning;  Project management;  Service oriented architecture (SOA);  Supply chain management;  Web services, Supply chains},
references={(2006) Apache Axis, , Apache Software Foundation; (2008) Apache Orchestration Director Engine (ODE), , Apache Software Foundation; (2008) Apache Struts, , Apache Software Foundation; (2007) Apache Tomcat 5.5, , Apache Software Foundation; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations and Production Management, 17 (5-6), pp. 522-535; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manufact. Technol., 21 (7), pp. 534-548; Cheng, J.C.P., Law, K.H., Bjornsson, H., Jones, A., Sriram, R.D., A service oriented framework for construction supply chain integration (2010) Automat. Construct., 19 (2), pp. 245-260; Cheung, S.O., Suen, H.C.H., Cheung, K.K.W., PPMS: A web-based construction project performance monitoring system (2004) Automat. Construct., 13 (3), pp. 361-376; Dainty, A.R.J., Briscoe, G.H., Millett, S.J., New perspectives on construction supply chain integration (2001) Supply Chain Management, 6 (4), pp. 163-173. , DOI 10.1108/13598540110402700; (2009) Eclipse Foundation, BPEL Visual Designer, Version 0.4.0; (2008) BPMN Modeler, Version 0.8.0, , Eclipse Foundation; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840. , DOI 10.1080/00207540600806513, PII 778108914; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Product. Econ., 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Hausman, W., Supply chain performance metrics (2004) The Practice of Supply Chain Management: Where Theory and Application Converge, pp. 61-73. , T.P. Harrison, H.L. Lee, J.J. Neale (Eds.); Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Manage.: Int. J., 9 (1), pp. 23-29; Kagioglou, M., Cooper, R., Aouad, G., Performance management in construction: A conceptual framework (2001) Construct. Manage. Econ., 19 (1), pp. 85-95; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) J. Oper. Res. Soc., 54 (5), pp. 507-514; Lambert, D.M., (2008) Supply Chain Management: Processes, Partnerships, Performance, , Supply Chain Management Institute; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Ind. Market. Manage., 29 (1), pp. 65-83; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int. J. Logistics Manage., 12 (1), pp. 1-20; (2008) Liferay, , Liferay Open Source Enterprise Portal System; O'Brien, W.J., London, K., Vrijhoef, R., (2002) Construction Supply Chain Modeling: A Research Review and Interdisciplinary Research Agenda, In: The 10th Annual Conference of the International Group for Lean Construction (IGLC-10), , Gramado Brazil; Oakland, J., Marosszeky, M., (2006) Total Quality in the Construction Supply Chain, , Elsevier Oxford, Great Britain; (2008) Business Process Modeling Notation (BPMN), , Object Management Group (OMG) Version 1.1; (2005) Unified Modeling Language (UML), , Object Management Group (OMG) Version 2.0; (2007) Web Services Business Process Execution Language (WS-BPEL), , Organization for the Advancement of Structured Information Standards (OASIS) Version 2.0; (2008) Red Hat, Hibernate Framework; Ross, D.F., (1998) Competing Through Supply Chain Management: Creating Market-winning Strategies Through Supply Chain Partnerships, , Kluwer Academic Publishers; (2007) MySQL 5.0, , Sun Microsystems; (2004) Customer Chain Operations Reference (CCOR), , Supply Chain Council Model, Version 1.0; (2006) Design Chain Operations Reference (DCOR) Model, , Supply Chain Council Version 1.0; (2008) Supply Chain Operations Reference (SCOR), , Supply Chain Council Model, Version 9.0; Tommelein, I.D., Walsh, K.D., Hershauer, J.C., (2003) Improving Capital Projects Supply Chain Performance, Construction Industry Institute (CII), Project Team 172, , Austin Texas; (1981) Integrated Computer-aided Manufacturing (ICAM) Architecture Part II, Vol. IV - Function Modelling Manual (IDEF0), , US Air Force Air Force Materials Laboratory, Wright-Patterson AFB, Ohio 45433; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381. , DOI 10.1108/02635570710734271; (2000) Simple Object Access Protocol (SOAP), , World Wide Web Consortium (W3C) Version 1.1, May; (2004) Web Services Description Language (WSDL), , World Wide Web Consortium (W3C) Version 2.0; Yu, I., Kim, K., Jung, Y., Chin, S., Comparable performance measurement system for construction companies (2007) Journal of Management in Engineering, 23 (3), pp. 131-139. , DOI 10.1061/(ASCE)0742-597X(2007)23:3(131)},
correspondence_address1={Cheng, J. C. P.; Department of Civil and Environmental Engineering, Clear Water Bay, Kowloon, Hong Kong; email: cejcheng@ust.hk},
issn={14740346},
language={English},
abbrev_source_title={Adv. Eng. Inf.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Rattanachai2010463,
author={Rattanachai, A. and Wasusri, T. and Srilaong, V. and Tanprasert, K. and Kanlayanarat, S.},
title={Key performance indicators in baby corn supply chain in Thailand},
journal={Acta Horticulturae},
year={2010},
volume={875},
pages={463-468},
doi={10.17660/ActaHortic.2010.875.60},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650892249&doi=10.17660%2fActaHortic.2010.875.60&partnerID=40&md5=7f50b8f8497b0d193c3a10d1539c3df7},
affiliation={Division of Postharvest Technology, School of Bioresources and Technology, King Mongkut's University of Technology, Thonburi, Bangkok, 10140, Thailand; Department of Logistic Management, Graduate School of Management and Innovation, King Mongkut's University of Technology, Thonburi, Bangkok, 10140, Thailand; Department of Printing and Packaging Technology, Faculty of Industrial Education and Technology, King Mongkut's University of Technology, Thonburi, Bangkok, 10140, Thailand},
abstract={Key performance indicators (KPI) are financial and non-financial measures or metrics used to help an organization to define and evaluate how successful it is, typically in terms of making progress towards its long-term goals. KPI therefore reflects the performance of its organization. The keys to selecting KPI lie in selecting indicators that are: a) quantifiable, and b) critical to the organization's goals. Nowadays, competition between organizations has moved to competition between supply chains. To have KPI to monitor overall supply chains is crucial. The supply chain operation reference (SCOR) model provides guidance on the types of indicators that decision makers can use to develop a balanced approach towards measuring the performance of the whole supply chain. The SCOR model advocates a set of supply chain performance indicators as a combination of reliability measures (e.g. delivery, fill rate, perfect order fulfillment), cost measures (e.g. total supply chain management cost, cost of good sold), flexibility measures (e.g. supply chain response time, product flexibility), responsiveness measures (e.g. order fulfillment lead time), and asset measures (e.g. cash to cash cycle time, inventory days of supply). Agri-food products comprise majority of Thai exports. Thailand is the largest exporter of baby corn in the world. In 2007, the production area is around 90,000 hectares and values about 1,166 million Baht. The demand of baby corn continues to increase. Therefore, this paper aims to develop a conceptual model for key performance indicators for Thai baby corn supply chains. Finally, we propose the measurement of the performance of baby corn supply chains to consist of six main categories namely: reliability; cost; flexibility; responsiveness asset; and product quality. These categories are identified as key performance indicators of the baby corn supply chain performance measurement system.},
author_keywords={Baby corn;  Key performance indicators (KPI);  Supply chain management;  Supply-chain operations reference model (SCOR)},
keywords={Zea mays},
references={Alessio, B., Mario, G.C.A.C., Francesco, M., Andrea, T., (2007) Patterns and Technologies for Enabling Supply Chain Traceability Trough Collaborative E-business, Information and Software Technology, 50, pp. 342-359; Aramyan, L., Ondersteijn, C., Van Kooten, O., Oude Lansink, A., Performance indicators in agri-food production chains (2006) Quantifying the Agri-food Supply Chain, pp. 47-64. , C. J. Ondersteijn, J. H. Wijnands, R. B. Huirne and O. van Kooten eds., Springer, Dordrecht; Aramyan, L.H., Oude Lansink, A.G.J.M., Van Der Vorst, J.G.A.J., Van Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, pp. 304-315; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteriadecision-ma king methodology (2003) Int. J. Production Economics, 91, pp. 1-15; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research, Part E: Logistics and Transportation Review, 38 (6), pp. 439-456; Li, D., O'Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 59 (1-3), pp. 147-157; Luning, P.A., Marcelis, W.J., Jongen, W.M.F., (2002) Food Quality Management: A Techno Managerial Approach, Wageningen Academic Publishers, Wageningen; Munir Ahmada, M., Dhafrb, N., (2002) Establishing and Improving Manufacturing Performance Measures, Robotics and Computer Integrated Manufacturing, 18, pp. 171-176; (2008) Agricultural Statistics of Thailand 2007, p. 60. , Office of Agricultural Economics, Bangkok; Samuel, H.H., Sunil, K.S., Harshal, K., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48, pp. 377-394; Council, S., (2004) Supply Chain Operations Reference Model: SCOR, p. 295. , version 6.1, Pennsylvania, USA; Van Der Vorst, J.G.A.J., (2000) Effective Food Supply Chains. Generating, Modelling and Evaluating Supply Chain Scenarios, , PhD thesis, Wageningen University, Wageningen},
correspondence_address1={Rattanachai, A.; Division of Postharvest Technology, , Thonburi, Bangkok, 10140, Thailand},
publisher={International Society for Horticultural Science},
issn={05677572},
isbn={9789066055636},
language={English},
abbrev_source_title={Acta Hortic.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Muchiri20105905,
author={Muchiri, P.N. and Pintelon, L. and Martin, H. and De Meyer, A.-M.},
title={Empirical analysis of maintenance performance measurement in Belgian industries},
journal={International Journal of Production Research},
year={2010},
volume={48},
number={20},
pages={5905-5924},
doi={10.1080/00207540903160766},
note={cited By 91},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955691762&doi=10.1080%2f00207540903160766&partnerID=40&md5=98bc1a545f241c5164842a854f3da8cc},
affiliation={Centre for Industrial Management (CIB), Katholieke Universiteit Leuven, Celestijnenlaan, 3001 Heverlee, Belgium; Open Universiteit Nederland, 6401 DL Heerlen, Netherlands; ICTS/ICT for Research, Katholieke Universiteit Leuven, Willem de Croylaan 52a, 3001 Heverlee, Belgium},
abstract={Performance measurement is a fundamental instrument of management. For maintenance management, one of the key issues is to ensure the maintenance activities planned and executed have given the expected results. This can be facilitated by effective use of rigorously defined key performance indicators (KPI) that are able to measure important aspects of maintenance function. In this paper, an industrial survey was carried out to explore the use of performance measurement in maintenance management. Based on survey responses, analyses were performed on popularly used KPI, how these KPI are sourced or chosen; the influence of manufacturing environment and maintenance objectives on KPI choice and effective use of these KPI in decision support and performance improvement. It was found that maintenance performance measurement is dominated by lagging indicators (equipment, maintenance cost and safety performance). There is lesser use of leading (maintenance work process) indicators. The results showed no direct correlations between the maintenance objectives pursued and the KPI used. Further analysis showed that only a minority of the companies have a high percentage of decisions and changes triggered by KPI use and only a few are satisfied with their performance measurement systems. Correlation analysis showed a strong positive linear relationship between degree of satisfaction and process changes/decisions triggered by KPI use, with the least satisfied people having the least decisions and changes triggered by KPI use. The results indicate some ineffectiveness of performance measurement systems in driving performance improvement in industries. © 2010 Taylor & Francis.},
author_keywords={key performance indicators (KPI);  maintenance;  performance measurement},
keywords={Correlation analysis;  Decision supports;  Degree of satisfaction;  Driving performance;  Empirical analysis;  Industrial surveys;  Key issues;  Key performance indicators;  Lagging indicators;  Linear relationships;  Maintenance activity;  Maintenance cost;  Maintenance management;  Maintenance objectives;  Maintenance performance;  Maintenance work;  Manufacturing environments;  Performance improvements;  Performance measurement system;  Performance measurements;  Safety performance, Automobile drivers;  Decision support systems;  Industry;  Maintenance;  Measurements;  Supply chain management;  Surveys, Benchmarking},
references={Arts, R.H.P.M., Knapp, G.M., Mann, L., Some aspects of measuring maintenance performance in process industry (1998) Journal of Quality in Maintenance Engineering, 4 (1), pp. 6-11; Agresti, A., (2002) Categorical Data Analysis, , Canada: John Wiley & Sons; (2007) Jaarboek Annuaire, , BEMAS, Brussels, Belgium: Belgian Maintenance Association; (1984) Glossary of Maintenance Terms in Terotechnology, , BSI, BS 3811. London: British Standard Institution (BSI); Campbell, J.D., (1995) Uptime, Strategies for Excellence in Maintenance Management, , Portland, OR: Productivity Press; Coetzee, L.J., (1997) Maintenance, , South Africa: Maintenance Publishers; Dekker, R., Application of maintenance optimisation models. A review and analysis (1996) Reliability Engineering and System Safety, 51, pp. 229-240; Dillman, D.A., (2000) Mail and Internet Surveys. The Tailored Design Method, , 2nd ed. New York: Wiley; Dwight, R., Concepts for measuring maintenance performance (1995) New Developments in Maintenance. An International View, , H Martin, ed. . Eindhoven: IFRIM; Dwight, R., (1999) Frameworks for Measuring the Performance of the Maintenance System in A Capital Intensive Organization, , Doctoral Report. Australia: University of Wollongong; Dwight, R., Searching for real maintenance performance measures (1999) Journal of Quality in Maintenance Engineering, 5 (3), pp. 258-275; Fleischer, J., Weismann, U., Niggeschmidt, S., Calculation and optimisation model for costs and effects of availability relevant service elements (2006) Proceedings of 13th CIRP International Conference on Life Cycle Engineering (LCE2006), pp. 675-680. , 31 May-2 June 2006, Leuven, Belgium. Leuven: Acco; Huang, S., Dismukes, J.P., Manufacturing productivity improvement using effectiveness metrics and simulation analysis (2003) International Journal of Production Research, 41 (3), pp. 513-527; Kaplan, R.S., Measuring manufacturing performance. A new challenge for managerial accounting research (1983) The Accounting Review, 58 (4), pp. 686-705; Kelly, A., (1989) Maintenance and Its Management, , Conference communication, London; Kelly, A., (1998) Maintenance Strategy, Business Centred Maintenance, , Oxford: Reed Educational and Professional Publishing; Liebetrau, A.M., Measures of association (1983) Quantitative Applications in Social Sciences: Series No. 32, , Newbury Park, CA: Sage Publications; Madu, C., Reliability and Quality Interface (1999) International Journal of Quality & Reliability Management, 16 (7), pp. 691-698; Madu, C., Competing through maintenance strategies (2000) International Journal of Quality and Reliability Management, 17 (9), pp. 937-948; (1995) Maintenance Engineering Society of Australia Capability Assurance. A Generic Model of Maintenance, , MESA, Australia: Maintenance Engineering Society of Australia (MESA); Muchiri, P.N., Pintelon, L., Performance measurement using overall equipment effectiveness (OEE). Literature review and practical application (2008) International Journal of Production Research, 46 (13), pp. 3517-3535; (2008) NACE - European Standard Industrial Classification, , NACE, European Union (EU); Nakajima, S., (1988) Introduction to TPM, Total Productive Maintenance, , Cambridge, MA: Productivity Press; Neely, A., Gregory, M., Platts, K., Performance measurement system design. A literature review and research agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Parida, A., Chattopadhyay, G., Development of a multi-criteria hierarchical framework for maintenance performance measurement (MPM) (2007) Journal of Quality in Maintenance Engineering, 13 (3), pp. 241-258; Phillips, L.W., Assessing measurement error in key informants reports. A methodological note on organisational analysis in marketing research (1981) Journal of Marketing Research, 18, pp. 395-415; Pintelon, L., Gelders, L., Puyvelde, F.V., (1997) Maintenance Management, , Leuven, Belgium: Acco; Pintelon, L., Vanpuyvelde, F., (2006) Maintenance Decision Making, , Leuven, Belgium: Acco; Robson, C.R., (2002) Real World Research, , 2nd ed. Oxford: Blackwell; Saunders, M., Lewis, P., Thornhill, A., (2007) Research Methods for Business Students, , Harlow, Essex, England: Pearson Educational Limited; Tsang, A.H.C., Measuring maintenance performance. A holistic approach (1999) International Journal of Operations and Production Management, 19 (7), pp. 691-715; Visser, J.K., Pretorious, M.W., The development of a performance measurement system for maintenance (2003) SA Journal of Industrial Engineering, 14 (1), pp. 83-97; Weber, A., Thomas, R., (2006) Key Performance Indicators. Measuring and Managing the Maintenance Function, , Ontario: Ivara Corporation; White, P.G., A survey and taxonomy of strategy related performance measures for manufacturing (1996) International Journal of Operations and Production Management, 16 (3), pp. 42-61},
correspondence_address1={Pintelon, L.; Centre for Industrial Management (CIB), Celestijnenlaan, 3001 Heverlee, Belgium; email: liliane.pintelon@cib.kuleuven.be},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{ArzuAkyuz20105137,
author={Arzu Akyuz, G. and Erman Erkan, T.},
title={Supply chain performance measurement: A literature review},
journal={International Journal of Production Research},
year={2010},
volume={48},
number={17},
pages={5137-5155},
doi={10.1080/00207540903089536},
note={cited By 268},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954454289&doi=10.1080%2f00207540903089536&partnerID=40&md5=25ff68fe28082e1be88f43fbd887c6b0},
affiliation={Department of Industrial Engineering, Atilim University, Kizilcasar Mahallesi, 06836 Incek Gölbasi, Ankara, Turkey},
abstract={This paper is intended to provide a critical literature review on supply chain performance measurement. The study aims at revealing the basic research methodologies/approaches followed, problem areas and requirements for the performance management of the new supply chain era. The review study covers articles coming from major journals related with the topic, including a taxonomy study and detailed investigation as to the methodologies, approaches and findings of these works. The methodology followed during the conduct of this research includes starting with a broad base of articles lying at the intersection of supply chain, information technology (IT), performance measurement and business process management topics and then screening the list to have a focus on supply chain performance measurement. Findings reveal that performance measurement in the new supply era is still an open area of research. Further need of research is identified regarding framework development, empirical cross-industry research and adoption of performance measurement systems for the requirements of the new era, to include the development of partnership, collaboration, agility, flexibility, information productivity and business excellence metrics. The contribution of this study lies in the taxonomy study, detailed description and treatment of methodologies followed and in shedding light on future research. © 2010 Taylor & Francis.},
author_keywords={maturity;  metrics;  performance measurement;  supply chain},
keywords={Basic research;  Business excellence;  Business process management;  Cross industry;  Framework development;  Information productivity;  Literature reviews;  maturity;  Performance management;  Performance measurement system;  Performance measurements;  Problem areas;  Shedding light;  Supply chain performance, Enterprise resource management;  Information technology;  Measurements;  Research;  Supply chains;  Taxonomies, Supply chain management},
references={Akyüz, G.A., Rehan, M., Requirements for forming an 'e-supply chain' (2009) International Journal of Production Research, 47 (12), pp. 3265-3287; Bendoly, E., Kaefer, F., Business technology complementaries: Impacts of presence and strategic timing of ERP on B2B e-commerce technology inefficiencies (2004) Omega. The International Journal of Management Science, 32 (5), pp. 395-405; Bernardes, E., Zsidisin, G.A., An examination of strategic supply management benefits and performance implications (2008) Journal of Purchasing and Supply Management, 14 (4), pp. 209-219; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hirearchy process (2007) Production Planning & Control, 18 (8), pp. 666-680; Bolloju, N., Turban, E., Organisational assimilation of web services technology: A research framework (2007) Journal of Organisational Computing and Electronic Commerce, 17 (1), pp. 29-52; Buttermann, G., Germain, R., Iyer, K.N.S., Contingency theory 'fit as gestalt': An application to supply chain management (2008) Transportation Research PART e, 44 (6), pp. 955-969; Cai, J., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment (2008) Decision Support Systems; Geiger, S.W., Ritchie, W.J., Marlin, D., Strategy/structure fit and firm performance (2006) Organisational Development Journal, 24 (2), pp. 10-22; Giménez, C., Lourenço, H.R., E-supply chain management: Review, implications and directions for future research (2004) Working Paper No. 17, , Institut d'estudis Territorrials; Gunasekaran, A., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Ngai, E.W.T., Information systems in supply chain integration and management (2004) European Journal of Operations Research, 159 (2), pp. 269-295; Gunasekaran, A., Williams, H.J., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Ho, C., Measuring system performance of an ERP-based supply chain (2007) International Journal of Production Research, 45 (6), pp. 1255-1277; Hwang, Y., Lin, Y., Lyu Jr., J., The performance evalutation of SCOR sourcing process - The case study of Taiwans TFT-LCD industry (2008) International Journal of Production Economics, 115 (2), pp. 411-423; Kanji, G.K., Wong, A., Business excellence model for supply chain management (1999) Total Quality Management, 10 (8), pp. 1147-1168; Kaplan, R.S., Norton, D.P., Putting balanced scorecard to work (1993) Harvard Business Review, 71 (5), pp. 134-142. , September-October; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-86. , January-February; Kaplan, R.S., Norton, D.P., Measuring the strategic readiness of intangible assets (2004) Harvard Business Review, 82 (2), pp. 52-63. , February; Kelle, P., Akbulut, A., The role of ERP tools in supply chain information sharing, cooperation and cost optimisation (2005) International Journal of Production Economics, 93-94, pp. 41-52; Lockamy, A., McCormack, K., Linking the SCOR plannning practices to supply chain performance (2004) International Journal of Operations and Production Management, 24 (11-12), pp. 1192-1218; Martin, P.R., Patterson, J.W., On measuring company performance within a supply chain (2009) International Journal of Production Research, 47 (9), pp. 2449-2460; McCormack, K., Lockamy, A., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278; McCormack, K., Ladeira, M.B., Oliviera, M.P., Supply chain maturity and performance in Brazil (2008) Supply Chain Management: An International Journal, 13 (4), pp. 272-282; Meixell, M.J., Gargeya, V.B., Global supply chain design: A literature review and critique (2005) Transportaion Research Part e, 41 (6), pp. 531-550; Pant, S., Sethi, R., Bhandari, M., Making sense of the e-supply chain landscape: An implementation framework (2003) International Journal of Information Management, 23 (3), pp. 201-221; Perea, E., Dynamic modeling and classical control theory for supply chain management (2000) Computers and Chemical Engineering, 24 (2), pp. 1143-1149; Puigjaner, L., Lainez, J.M., Capturing dynamics in integrated supply chain management (2008) Computers and Chemical Engineering, 32 (11), pp. 2582-2605; Robinson, C.J., Malhotra, M.K., Defining the concept of supply chain quality management and its relevance to academic and industrial practice (2005) International Journal of Production Economics, 96 (3), pp. 315-337; Stadtler, H., Supply chain management and advanced planning: Basics, overview and challenges (2005) European Journal of Operations Research, 163 (3), pp. 575-588; Stock, G.N., Greis, N.P., Kasarda, J.D., Enterprise logistics and supply chain structure: Role of fit (2000) Journal of Operations Management, 18 (5), pp. 531-547; Swafford, P.M., Ghosh, S., Murthy, N., Achieving supply chain agility through IT integration and flexibility (2008) International Journal of Production Economics, 116 (2), pp. 288-297; Vonderembrese, M.A., Designing supply chains: Towards theory development (2006) International Journal of Production Economics, 100 (2), pp. 223-238; Wouters, M., A developmental approach to performance measures - Results from a longitudinal case study (2009) European Management Journal, 27 (1), pp. 64-78; Wouters, M., Wilderom, C., Developing performance measurement systems as enabling formalization: A longitudinal field study of a logistics department (2008) Accounting, Organizations and Society, 33 (4-5), pp. 488-516; Yao, K., Liu, C., An integrated approach for measuring supply chain performance (2006) Journal of Modern Accounting and Auditing, 2 (10), p. 17},
correspondence_address1={Arzu Akyuz, G.; Department of Industrial Engineering, , 06836 Incek Gölbasi, Ankara, Turkey; email: arzuakyuz@atilim.edu.tr},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Review},
source={Scopus},
}

@ARTICLE{Seetharaman201091,
author={Seetharaman, A. and Raj, J.R. and Saravanan, A.S.},
title={The changing role of accounting in the health care industry},
journal={Research Journal of Business Management},
year={2010},
volume={4},
number={2},
pages={91-102},
doi={10.3923/rjbm.2010.91.102},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955817289&doi=10.3923%2frjbm.2010.91.102&partnerID=40&md5=c2ab085d5c935931e23a52c361bc8b67},
affiliation={Global MBA Unit, SP Jain Center of Management, Singapore; Faculty of Management, Multimedia University, Malaysia},
abstract={The aim of this study is to undertake a diagnostic investigation of the changing role of accounting in the health care industry. The health care industry has historically viewed itself as being operationally different from other businesses. Health care industry is a growing industry. For the last century in all countries, the operational cost of health care has steadily risen, usually faster than the consumer price index, absorbing a larger proportion of the Gross National Product (GNP). The purpose of this study is to find out the role of accounting in the health care sector in terms of cost saving and improvement in quality management by an integrated information system. This study helps to suggest solutions or methods that have been used in other similar industries and has introduced the importance of Supply Chain Management (SCM), the role of Information System (IS), latest trend of Electronic health (E-health) and an integrated approach to healthcare Total Quality Management (TQM). Understanding the appropriate performance measurement system for health care processes are essential to implement the most suitable and effective system or method to reduce the escalating operational costs in health care organization. © 2010 Academic Journals Inc.},
author_keywords={Accounting;  Health care;  Logistic management;  Medical care},
references={Creedon, C., Save time and money with supply chain automation (2006) Mater. Manage. Health Care, 15, pp. 37-38; (2001) Crossing the Quality Chasm: A New Health System for the 21st Century, , CQHCA, CQHCA., Institute of Medicine, Washington; Danese, P., The extended VMI for coordinating the whole supply network (2006) J. Manuf. Technol. Manage., 17, pp. 888-907; Davis, K., Otwell, R., Barber, J., Managing costs through clinical quality improvement (2004) Healthcare Fin. Manage., 58, pp. 76-84; Dey, P.K., Hariharan, S., Integrated approach to healthcare quality management: A case study (2006) TQM Magazine, 18, pp. 583-605; Dixon, L., (1997) JIT 11 Creating the ultimate customer/supplier partnership, , 82nd Annual International Conference of the Institute of Supply Management; Fadlalla, A., Wickramsinghe, N., An mtegrative framework for HIPAA-comphantl IQ healthcare information system (2004) Int. J. Health Care Qual. Assurance, 17, pp. 65-74; Gonzalez, M.E., Quesada, G., Urrutia, I., Gavidia, J.V., Conceptual design of an e-health strategy for the Spanish health care system (2006) Int. J. Health Care Qual. Assurance Inc. LeadershHealth Serv., 19, pp. 146-157; Gordon, J.R., Capturing the benefits of web-based connectivity (2005) Healthcare Financial Manage, 59, pp. 78-82; Ismail, M., Seetharaman, A., Foo, Y.B., The pitfalls of internet financial reporting and their prevention (2007) J. 21st Century Accounting, 17, pp. 1-19; Jarrett, P.G., Logistic in the health care industry (1998) Int. J. Phys. Distribution Logistics Manage., 28, pp. 741-772; Kulaja, J., Lillrank, P., Kronstrom, V., Peltokorpi, A., Time-based management of Patient processes (2006) J. Health Org. Manage., 20, pp. 512-524; Millard, E., (2003) Broadband nation, , https://www.technewsworld.com/story/31299.html, August 9, E-Business Special Report; Ovretveit, J., What are the best strategies for ensuring quality in hospitals (1997) Health Econ, 6, pp. 109-116; Ovretveit, J., Total quality management in European healthcare (2000) Int. J. Health Care Qual. Assurance, 13, pp. 74-79; Russon, N., Linking the health care continuum with technology (1997) Assurance Incorporating Leadership Health Services, 10, pp. 7-9; Seetharaman, A., Subramanian, T., Marrhandan, G., loo, C.Y., The integration of ERP with SCM and CRM applications and data integrity in ERP (2006) J. Electronic Inform. Plann., 33, pp. 117-132; Seetharaman, A., Sreemvasan, J., Boon, L., Critical success factors of total quality management (2006) Qual. Quantity, 40, pp. 675-695; Seetharaman, A., Sreenivasan, J., Bathamenadan, R., Sudha, R., The impact of just-in-time on costing (2007) Int. J. Manage. Enterprise Deve., 4, pp. 635-661; Smichi-Levi., D., Kaminsky, P., (2000) Designing and Managing the Supply Chain: Concepts and Strategies and Cases, , McGraw Hill, New York; Smith, A.D., Manna, D.R., Exploring the trust factor in e-medicine (2004) Online Inform. Rev., 28, pp. 346-355; Spencer, M.S., Rogers, D.S., Daugherty, P.J., Just-m-time systems and external logistics suppliers (1994) Int. J. Operation Prod. Manage., 14, pp. 60-75; Stalk G., Jr., Time the next source of competitive advantage (1988) Harvard Business Rev, 66, pp. 41-51; Waller, M., Johnson, M.E., Davis, T., Vendor managed inventory in the retail supply chain (1999) J. Bus. Logistics, 20, pp. 183-203; Webster, R., Williams, P., An evaluation of the NHS Direct online health information e-mail enquiry service (2005) Aslib Proc. New Inform. Perspectives, 57, pp. 48-62; Whitson, D., Applying just-in-time systems in health care (1997) IIE Solutions, 29, pp. 32-37; Williams, P., Nicholas, D., Huntington, P., Gunter, B., Doc. com: Reviewing the literature on remote health information provision (2002) Aslib Proc, 54, pp. 127-141},
correspondence_address1={Seetharaman, A.; Global MBA Unit, Singapore},
issn={18191932},
language={English},
abbrev_source_title={Res. J. Bus. Manage.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Maidin2010281,
author={Maidin, S.S. and Arshad, N.H.},
title={Information technology governance practices in Malaysian public sector},
journal={2010 International Conference on Financial Theory and Engineering, ICFTE 2010},
year={2010},
pages={281-285},
doi={10.1109/ICFTE.2010.5499381},
art_number={5499381},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955285231&doi=10.1109%2fICFTE.2010.5499381&partnerID=40&md5=1c6675817065d8171094406cf5a1dac6},
affiliation={Information and Communication Technology Department, International Islamic University Malaysia, Petaling Jaya Selangor, Malaysia; Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam Selangor, Malaysia},
abstract={Organizations need to establish effective IT governance practices in order to stay competitive. The aim of this research is to determine the extent of IT governance practices in Malaysian public sector and to determine factors contributes towards IT governance practices. Questionnaire was constructed and distributed to IT management level personnel in public agencies in Klang Valley and Putrajaya. From the findings, it can be noted that 65.7 percent (65.7%) of the respondents has practiced IT governance in their organizations. There were several factors being identified as contributors towards IT governance practices such as senior management involvement in IT, corporate performance measurement systems, corporate communications systems, risk management, strategic alignment, value delivery, ethics/culture of compliance and resource management. Thus, the findings and discussion in this paper could assist Malaysian public sector to incorporate effective IT governance practices in their daily operation. © 2009 IEEE.},
author_keywords={Corporate communication systems;  Corporate performance measurement system;  Ethics/culture of compliance;  IT governance;  IT governance practices;  IT strategic alignment;  Resource management;  Risk management;  Senior management involvement;  Value delivery},
keywords={Corporate communications;  Corporate performance;  IT governance;  Resource management;  Senior management;  Strategic alignment;  Value delivery, Alignment;  Communication systems;  Measurements;  Natural resources management;  Resource allocation;  Risk management;  Security of data;  Supply chain management, Information technology},
references={Gunton, T., (1993) A Dictionary of Information Technology and Computer Science, , 2nd ed., TJ Press, Cornwall; Keen, P.G.W., (1994) Every Manager's Guide to Information Technology: A Glossary of Key Terms and Concepts for Today's Business Leader, , 2nd ed., Harvard Business School Press, Boston, MA; Hoepleman, J.P., Mayer, R., Wagner, J., (1997) Elsevier's Dictionary of Information Technology, , Elsevier Science,Amsterdam; Wang, S., Designing information systems for e-commerce (2001) Industrial Management and Data Systems, 101 (6), pp. 304-315; Yang, Y.H., Software quality management and ISO 9000 implementation (2001) Industrial Management & Data Systems, 101 (7), pp. 329-338; Lian, L.W., (2007), http://bizedge.com/cms/content.jsp?id=com.tms.cms.article. Article_43dbb1d8-cb73c03a-39060b00-85916308, Feb; Leitheiser, R.L., Wetherbe, J.C., Service support level: An organized approach to end-user computing (1986) MIS Quarterly, 10 (3), pp. 337-339; Arshad, N.H., (2003) An Approach to the Development of Framework for Software Risk Management, , Phd. Dissertation; UKM; (2004) Sweden Chapter: FoU-kommitténs COBIT-undersökning, , www.isaca.se, (In Swedish). Available online at Feb; Doughty, K., Grieco, F., IT Governance: Pass or Fail? (2005) The Information Systems Control Journal, 2; (2005) Global IT Project Management Survey -How Committed Are You?, , KPMG KPMG International; (2003) Board Briefing on IT Governance, , www.itgi.org, ITGI 2nd Edition. Available at Feb; (2005) Information Risks: Whose Business Are They, , www.itgi.org, Available at Feb; Read, T.J., Discussion of Director Responsibility for IT Governance: A Perspective on Strategy (2004) International Journal of Accounting Information Systems, 5, pp. 105-107. , 2004 Elsevier Inc; (1999) Risk Management, AS/NZS, 3360, p. 1999. , Standards Australia Standards Australia, Strathfield; Engming, L., Hsieh, C.T., Seven deadly risk factors of software development projects (1994) Journal of Systems Management, 36 (6), pp. 38-42; Irani, Z., Love, P.E.D., The propagation of technology management taxonomies for evaluating information systems (2001) Journal of Management Information Systems, 17 (3), pp. 161-177; Fuerst, W.L., Cheney, P.H., Factors affecting the perceived utilization of computer-based decision support systems in the oil industry (1982) Decision Sciences, 13 (3), pp. 443-469; Thomsett, R., (1995) Project Pathology: Causes, Patterns and Symptoms of Project Failure - Training Notes Project Risk Management, , Thomsett Company, London; King, J., Sketchy plans, politics stall software development (1994) Computer World, 29 (24), p. 81; Clancy, T., (1995) Chaos - IT Development Projects, , www.standishgroup.com/msie.htm, available at: Feb; Strassman, P., (1997) The Squandered Computer, , The Information Economics Press, Conn; Jiang, J.J., Klein, G., Software project risks and development focus (2001) Project Management Journal, 32 (1), pp. 3-9; Willcocks, L., Graeser, J., (2001) Delivering IT and E-business Value, Computer Weekly Series, , Butterworth and Heinemann, Oxford; Willcocks, L., (1996) Investing in Information Systems: Evaluation and Management, , Thomson Business Press Chapman & Hall, London; (1995) Chaos (Application Project Success and Failures), , http://www.standishgroup.com/chaos.html, Standish Group January 1995 Feb; Vaswani, R., (2003) Determinants of Effective Information Technology (IT) Governance, , Unpublished Thesis. School of Business, University of Queensland, Australia; Weill, P., Ross, J.W., (2004) IT Governance: How Top Performers Manage IT Decision Rights for Superior Results, , Boston, Massachusetts: Harvard Business School Press; Ali, S., Green, P., Determinants of Effective Information Technology Governance: A Study of IT Intensity (2005) Proceedings of International IT Governance Conference, Auckland, New Zealand, 2005; Ali, S., Green, P., (2007) ITGI Mechanisms in Public Sector Organisations: An Australian Context, , IGI Publishing; (2006) Enterprise Value: Governance of IT Investments, the Val IT Framework, , www.itgi.org, Available at Feb; Karimi, J., Bhattacherjee, A., Gupta, Y.P., Somers, T.M., The effect of MIS steering committees on information technology management sophistication (2000) Journal of Management Information Systems, 17 (FALL 2), pp. 207-230; McKeen, J.D., Guimaraes, T., Selecting MIS projects by steering committee (1985) Communications of the ACM, 28 (12), pp. 1344-1352; Beyer, J.M., Nino, D., Ethics and Cultures in International Business (1999) Journal of Management Inquiry, 8 (3), pp. 287-297; (2001) Board Briefing on IT Governance, , www.itgi.org, Available online at Feb; (2008) IT Governance Global Status Report-2008, , www.itgi.org, Available at Feb},
correspondence_address1={Maidin, S. S.; Information and Communication Technology Department, , Petaling Jaya Selangor, Malaysia; email: sitisara@iiu.edu.my},
isbn={9781424477586},
language={English},
abbrev_source_title={2010 International Conference on Financial Theory and Engineering, ICFTE 2010},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dossi2010498,
author={Dossi, A. and Patelli, L.},
title={You learn from what you measure: Financial and non-financial performance measures in multinational companies},
journal={Long Range Planning},
year={2010},
volume={43},
number={4},
pages={498-526},
doi={10.1016/j.lrp.2010.01.002},
note={cited By 84},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955269973&doi=10.1016%2fj.lrp.2010.01.002&partnerID=40&md5=8692cbd38ace3c9176183f2fa5bd1341},
affiliation={Management Accounting, Bocconi University, Italy; Accounting at Benedictine College, United States},
abstract={According to recent studies in international management, relationships between headquarters and subsidiaries in the global economy are characterised simultaneously by elements of organisational interdependence and local autonomy. These characteristics challenge the traditional design and use of performance measurement systems (PMSs). Contemporary approaches to PMSs emphasise the role of performance indicators for strategy implementation and advocate the adoption of non-financial indicators in addition to traditional financial metrics. We investigated the factors associated with the inclusion of non-financial indicators in PMSs used in relationships between headquarters and subsidiaries. Our empirical study is based on 141 questionnaires collected from Italian subsidiaries of foreign companies and 13 interviews with respondents. Non-financial performance indicators contained in PMSs refer equally to customer, internal processes and people measurement perspectives. Furthermore, we find that the inclusion of non-financial indicators is positively associated with relative performance evaluation, interactive use of PMSs, subsidiary size, headquarters nationality and subsidiary participation in the design of PMSs. Information gathered through interviews provides explanations of our results, and suggests that the inclusion of non-financial perspectives in PMSs contributes to the strategic alignment of international organisations by supporting learning and dialogue within relationships between headquarters and subsidiaries. © 2010 Elsevier Ltd.},
keywords={Empirical studies;  Financial indicator;  Financial metrics;  Financial performance;  Foreign companies;  Global economies;  International management;  Multinational companies;  Performance indicators;  Performance measurement system;  Relative performance;  Strategic alignment, Benchmarking;  International cooperation;  Supply chain management, Finance, corporate strategy;  finance;  firm size;  industrial performance;  international organization;  management;  multinational enterprise;  participatory approach},
references={(2003) Smart Globalization: Designing Global Strategies, Creating Global Networks, , Jossey-Bass, San Francisco, A.K. Gupta, D.E. Westney (Eds.); Bouquet, C., Birkinshaw, J., Weight versus voice: how foreign subsidiaries gain attention from corporate headquarters Academy of Management Journal, 51 (3), pp. 577-601. , in press; Dent, J.F., Global competition: challenges for management accounting and control (1996) Management Accounting Research, 7, pp. 247-269; Simon, R., (1995) Levers of Control, , For an extensive explanation of interactive use of PMS, Harvard Business School Press, Cambridge, MA; Kaplan, R.S., Norton, D.P., (1996) The Balance Scorecard: Strategy into Action, , For a description of the fundamental aspects of the balanced scorecard see, Harvard Business School Press, Boston; Ahn, H., Applying the Balanced Scorecard Concept: An Experience Report (2001) Long Range Planning, 34 (4), pp. 441-461. , For case studies about the implementation of the balanced scorecard see; Braam, G.J.M., Nijssen, E.J., Performance effects of using the balanced scorecard: A note on the Dutch experience (2004) Long Range Planning, 37 (4), pp. 335-349; Butler, A., Letza, S.R., Neale, B., Linking the balanced scorecard to strategy (1997) Long Range Planning, 30 (2), pp. 242-253; Papalexandris, A., Ioannou, G., Prastacos, G.P., Implementing the Balanced Scorecard in Greece: A software firm's experience (2004) Long Range Planning, 37 (4), pp. 347-362; Goold, M., Campbell, A., Managing the diversified corporation: The tensions facing the chief executive (1988) Long Range Planning, 21 (4), pp. 12-24. , For an explanation of the traditional approach to planning and control systems, see the seminal study; Ocasio, W., Joseph, J., Rise and fall - or transformation?: The evolution of strategic planning at the General Electric Company, 1940-2006 (2008) Long Range Planning, 41 (3), pp. 248-272. , For a more recent study on the role of planning and control systems within modern organisations see; Werner, S., Recent Developments in International Management Research: A Review of 20 Top Management Journals (2002) Journal of Management, 28 (3), pp. 277-305; Nohria, N., Ghoshal, S., (1997) The differentiated network: Organizing multinational corporations for value creation, , Jossey Bass Publishers, San Francisco; Malnight, T.W., The transition from decentralized to network-based MNC structures: An evolutionary perspective (1996) Journal of International Business Studies,, pp. 43-65. , first quarter; Birkinshaw, J., Hood, N., Multinational subsidiary development: Capability evolution and charter change in foreign-owned subsidiary companies (1998) Academy of Management Review, 23 (4), pp. 773-795; O'Donnell, S., Managing Foreign Subsidiary: Agents of Headquarters, or an Interdependent Network? (2000) Strategic Management Journal, 21 (6), pp. 525-548; Paterson, S.L., Brock, D.M., The development of subsidiary management research (2002) International Business Review, 11 (2), pp. 139-163; Jarillo, J.C., Martinez, J.I., Different roles for subsidiaries: The case of multinational corporations (1990) Strategic Management Journal, 11 (7), pp. 501-512; Frost, T.S., Birkinshaw, J., Ensign, P.C., Centers of excellence in multinational corporations (2002) Strategic Management Journal, 23 (11), pp. 997-1018; Delany, E., Strategic development of the multinational subsidiary through subsidiary initiative-taking (2000) Long Range Planning, 33 (2), pp. 220-244; Muralidharan, R., Hamilton, R.D., Aligning multinational control systems (1999) Long Range Planning, 32 (3), pp. 352-361; Dossi, A., Patelli, L., The decision-influencing use of performance measurement systems in relationships between headquarters and subsidiaries (2008) Management Accounting Research, 19 (1), pp. 126-148; Behn, B., Riley, R., Using non-financial information to predict financial performance: The case of the US airline industry (1999) Journal of Accounting, Auditing, and Finance, 14, pp. 29-56; Stewart, T.A., (1997) Intellectual Capital, , Doubleday/Currency Publishers, Inc; Rees, W., Sutcliffe, C., Quantitative non-financial information and income measures: The case of long-term contracts (1994) Journal of Business Finance and Accounting, pp. 331-347. , April; Kaplan, R.S., Atkinson, A., (1998) Advanced Management Accounting, , Prentice Hall, Upper Saddle River, NJ, In addition to the limitations of financial performance metrics, Brancato (Brancato, C.K., 1995, New Corporate Performance Measures, Research Report, The Conference Board, Report Number R-1118, New York, NY) underlines two additional factors explaining the emergence of non-financial indicators. First, the increase in the competitive pressure perceived by the; Kaplan, R.S., Norton, D.P., (2001) The Strategy Focused Organization. How Balanced Scorecard Companies Thrive in the New Business Environment, , Harvard Business School Press, Boston; Langfield-Smith, K., Management Control Systems and Strategy: A Critical Review (1997) Accounting, Organizations and Society, 22 (2), pp. 207-232; Henri, J.F., Management control systems and strategy: A resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558. , Examples of empirical studies on the choice of performance indicators are the following; Malina, M., Selto, F.H., Choice and Change of Measures in Performance Measurement Models (2004) Management Accounting Research, 15 (4), pp. 441-469; Ittner, C.D., Larcker, D.F., Determinants of Performance Measure Choices in Worker Incentive Plans (2002) Journal of Labor Economics, 20 (2), pp. S58-S90; Datar, S., Kulp, S., Lambert, R., Balancing Performance Measures (2001) Journal of Accounting Research, 39 (1), pp. 75-92; Janakiraman, S., Lambert, R., Larcker, D., An Empirical Investigation of the Relative Performance Evaluation Hypothesis (1992) Journal of Accounting Research, 30 (1), pp. 53-69; Bushman, R.M., Indjejikian, R.J., Smith, A., Aggregate Performance Measures in Business Unit Manager Compensation: The Role of Intrafirm Interdependencies (1995) Journal of Accounting Research, 33, pp. 101-128. , (Studies on Managerial Accounting); Andersson, U., Forsgren, M., In search of centre of excellence: network embeddedness and subsidiary roles in multinational corporations (2000) Management International Review, 40, pp. 329-350; Chenhall, R.H., Management control systems design within its organizational context: Findings from contingency-based research and directions for the future (2003) Accounting, Organizations and Society, 28 (2-3), pp. 127-168; Chow, C.W., Kato, Y., Shields, M.D., National culture and the preference for management controls: An exploratory study of the firm-labor market interface (1994) Accounting, Organizations and Society, 19 (4-5), pp. 381-400; Matejka, M., Merchant, K.A., Van der Stede, W.A., (2005), Performance Measure Choice and Target Setting in Loss-Making Firms. Working Paper; Merchant, K.A., Van der Stede, W., Zheng, L., Disciplinary constraints on the advancement of knowledge: The case of organizational incentive systems (2003) Accounting, Organizations and Society, 28 (2-3), pp. 251-286; Mediobanca, (2005) Dati cumulative di 2007 società italiane (Cumulative data of 2007 Italian companies), , Mediobanca; On average, respondents had been in their current position for seven years; Van der Stede, W., Young, M., Chen, C., Assessing the quality of evidence in empirical management accounting research: The Case of Survey Studies (2005) Accounting, Organizations and Society, 30, pp. 655-684; Both authors were present at every interview, which were held in person or via conference-call. Two research assistants were present at the structured interviews and they gathered secondary data from three subsidiaries involved in the interviews. All interviews were recorded and transcribed; Our questionnaire asks respondents to state whether the subsidiary is required by the headquarters to report performance results through a PMS on a regular basis. All 144 respondents stated that within their subsidiary there was a PMS employed in the relationship with the headquarters. Further, our questionnaire asks respondents to mark which indicators are contained in the PMS. Results of pilot testing, the inclusion of the two above questions in our questionnaire, and the outcomes of the validity assessments based on our interviews make us confident that our variable captures non-financial indicators formally and regularly reported from subsidiaries to headquarters for business performance reviews; Demers, E.A., Shackell, M., Widener, S., The Juxtaposition of Social Surveillance Controls with Traditional Organizational Design Components (2007) Contemporary Accounting Research, 25 (2), pp. 605-638. , For an example of a study adopting a similar methodology, see; Hofstede, G., (1991) Cultures and Organizations: Software of the Mind, , McGraw-Hill, London, UK; Birkinshaw, J., Entrepreneurship in multinational corporations: The characteristics of subsidiary initiatives (1997) Strategic Management Journal, 18 (3), pp. 207-229; Abernethy, M.A., Brownell, P., Management control systems in research and development the role of accounting, behavior and personnel controls (1999) Accounting, Organizations and Society, 22 (3-4), pp. 233-248; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost - The Rise and Fall of Management Accounting, , Harvard Business School Press, Boston, MA; Johnson, G., Managing strategic change - strategy, culture and action (1992) Long Range Planning, 25 (1), pp. 28-36. , For the effects of corporate culture on managerial acctions see; Carmeli, A., Schaubroeck, J., Organisational Crisis-Preparedness: The Importance of Learning from Failures (2008) Long Range Planning, 41 (2), pp. 177-196; Miller, S., Hickson, D., Wilson, D., From strategy to action: Involvement and influence in top level decisions (2008) Long Range Planning, 41 (6), pp. 606-628; Vilà, J., Canales, J.I., Can Strategic Planning Make Strategy More Relevant and Build Commitment Over Time? The Case of RACC (2008) Long Range Planning, 41 (3), pp. 273-290; Goold, M., Campbell, A., Luchs, K., Strategies and styles revisited: 'Strategic control' - is it tenable? (1993) Long Range Planning, 26 (5), pp. 54-61; Raisch, S., Balanced Structures: Designing Organizations for Profitable Growth (2008) Long Range Planning, 41 (5), pp. 483-508; Ittner, C.D., Larcker, D.F., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, pp. 88-95. , November; Merchant, K.A., Measuring General Managers' Performances: Market, Accounting and Combination-of-Measures Systems (2006) Accounting, Auditing and Accountability Journal, 19 (6), pp. 893-917; Patelli, L., (2007) Behavioral responses to measurement diversity in individual incentive plans: Role conflict, role ambiguity, and model-of-man, , Erasmus Research Institute of Management, Working Paper, October},
correspondence_address1={Dossi, A.; Management Accounting, Italy},
issn={00246301},
language={English},
abbrev_source_title={Long Range Plann.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Guo2010272,
author={Guo, W. and Lu, H. and Hu, M. and Chen, K.},
title={The research on supply chain performance modeling},
journal={INC2010 - The International Conference on Networked Computing, Proceeding},
year={2010},
pages={272-275},
art_number={5484819},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954805412&partnerID=40&md5=3658ca594b7bde2026a810cf85f72ee0},
affiliation={School of Management, Tianjin University, Tianjin, China},
abstract={The traditional supply chain performance evaluation studies focus on measuring performance results in order to find the status quo and gaps of supply chain, but it ignores the performance factors relationships and the reasons of performance results, which makes the lack of targeted improvement measures. this paper focus on how to analyze the relationships between performance interrelated factors, find the causes of the problems, and proposes "3+1" integrated supply chain performance measures. The steps of "3+1" integrated supply chain performance analysis method are summarized. Finally, a case verifies the application value of our approach. Based on the results of analysis, the paper proposed some constructive suggestions for supply chain performance modeling in the future.},
author_keywords={"3+1" analysis method;  Performance model;  Supply chain},
keywords={Analysis method;  Improvement measure;  Integrated supply chain;  Measuring performance;  Performance factors;  Performance Model;  Supply chain performance, Supply chain management, Supply chains},
references={(2006), http://www.supply-chain.org[EB/OL]; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance [J] (1992) Harvard Business Review, pp. 71-79. , Jan.- Feb; Beamon, B.M., Measuring supply chain performance[J] (1999) International Journal of Operations and Production Management, 19 (3-4), pp. 275-292; Xianhao, X., Shihua, M., A study on the performance evaluating index based on process of a supply chain[J] (2000) Journal of South-Central University for Nationalities(Nat. Sci), 2, pp. 58-62; Ke, C., (2009) The Research and Practice on Manufacturing Supply Chain Performance Modeling and Analysis[D], , Doctor Degree Thesis; Jackson, J.E., (1992) A User's Guide to Principal Components[M], , Newyork: A Wiley- Interseienee Publication; Jolliffe, I.T., (1986) Principal Component Analysis[M], , NewYork:Springer; Gefen, D., Straub, D., Boudreau, M., Structural equation modeling and regression: Guidelines for research practice[J] (2000) Comunications of the Association for Information System, 4 (7); Tomarken, A.J., Waller, N.G., Structural equation modeling: Strengths, limitations and misconceptions[J] (2005) Annual Review of Clinical Psychology, (1), pp. 31-65; Tao, H., (2006) Study on PLS Algorithm of Structural Equation Models[D], , Master Degree Thesis; Chen, S.M., Ke, J.S., Chang, J.F., Knowledge representation using fuzzy petri nets[J] (1990) IEEE Transactionon Knowledge and Data Engineering, 2 (3), pp. 311-319; Hao, J., Jinzhou, L., Ningsheng, F., Research on fuzzy petri net and its applications in weighted imprecise knowledge representing and reasoning[J] (2000) Journal of Computer Research and Development, 37 (8), pp. 918-923},
correspondence_address1={Guo, W.; School of Management, , Tianjin, China},
isbn={9788988678206},
language={English},
abbrev_source_title={INC - Int. Conf. Networked Comput., Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Manikas2010653,
author={Manikas, I. and Terry, L.A.},
title={A case study assessment of the operational performance of a multiple fresh produce distribution centre in the UK},
journal={British Food Journal},
year={2010},
volume={112},
number={6},
pages={653-667},
doi={10.1108/00070701011052736},
note={cited By 15},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952942143&doi=10.1108%2f00070701011052736&partnerID=40&md5=d95c50498436f3b05efa194142ad5314},
affiliation={School of Agricultural Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece; Postharvest Group, Plant Science Laboratory, Cranfield University, Cranfield, United Kingdom},
abstract={Purpose: The aim of this paper is to evaluate current operational status of fresh produce distribution centres in the UK and to identify the nature and magnitude of the main logistical problems within them. Design/methodology/approach: A critical evaluation of space and time utilisation efficiency was achieved by on-site studying operations in a multiple produce handling and short-term storage facility in Kent, UK. The objective was to measure operational performance of distribution centres for agricultural perishables in terms of throughput and space utilisation. Findings: The inefficient utilisation of storage space within cold rooms is identified and quantified accurately, whilst the quality control task is recognised as the most time-consuming task and a critical cause for hindering the product flow. Research limitations/implications: Despite their importance, distribution centres for fresh fruit and vegetables have received little attention in the distribution and performance management literature. Given the lack of robust performance measurement systems reported, the measurement of operational performance in distribution centres for agricultural products was a challenge. Practical implications: The measurement and improvement of the operational performance in each linkage of the fresh produce supply chain, such is the distribution centre can lead to achieving higher levels of service at substantially reduced costs. Originality/value: A small number of publications are found in the literature providing information on physical distribution of agricultural perishables, and how the key features of perishability and voluminosity of the produce affect the distribution efficiency. The paper provides a step towards the improvement of the fresh produce distribution industry operational performance by evaluating the current operational status of a leading multiple produce distribution centre in the UK. © Emerald Group Publishing Limited.},
author_keywords={Distribution;  Fresh foods;  Performance management;  Storage;  United Kingdom},
references={An evaluation of supply chain management and how it relates to perishable produce distribution/marketing (1996), www.jiscmail.ac.uk/files/SCM-FOOD/supply, Anon, Food Industry perspectives, discussion paper series: number 12, Wye College, University of London, London, available at: (accessed 5 October 2001); Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19, pp. 275-292; Broekmeulen, R.A.C.M., Operations management of distribution centres for vegetables and fruits (1998) International Transactions in Operational Research, 5, pp. 501-508; Brynzér, H., Johansson, M.I., Storage location assignment: Using the product structure to reduce order picking times (1996) International Journal of Production Economics, 46, pp. 595-603; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) The International Journal of Logistics Management, 6, pp. 61-73; Caron, F., Marchet, G., Perego, A., Routing policies and COI-based storage policies in picker-to-part systems (1998) International Journal of Production Research, 36 (3), pp. 713-732; Davenport, T.H., (1993) Process Innovation, , Harvard Business School Press, Boston, MA; de Koster, R., van der Poort, E., Routing order pickers in a warehouse: A comparison between optimal and heuristic solutions (1998) IIIE Transactions, 30, pp. 469-480; Drury, J., Towards more efficient order picking (1988), IMM Monograph No. 1, Institute of Materials Management, Cranfield; Fearne, A., Hughes, D., Success factors in the fresh produce supply chain: Insights from the UK (1999) Supply Chain Management, 4, pp. 120-128; Hackman, S.T., Frazelle, E.H., Griffin, P.M., Griffin, S.O., Vlasta, D.A., Benchmarking warehousing and distribution operations: An input-output approach (2001) Journal of Productivity Analysis, 16, pp. 79-100; Johnson, J., Wood, F.D., Wardlow, D.L., Murphy Jr., P.R., (1998) Contemporary Logistics, pp. 335-362. , Prentice-Hall, Upper Saddle River, NJ, 7th ed; Kuo, C.H., Dunn, K.D., Randhawa, S.U., A case study assessment of performance measurement in distribution centres (1999) Industrial Management & Data Systems, 2, pp. 54-63; Mans, J., Order picking costs the most (1998) Dairy Foods, 11, p. 49; Neely, A., Gregory, M., Platts, K., Performance measurement system design; a literature review and research agenda (1995) International Journal of Operations & Productions Management, 15, pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20, pp. 1119-1145; Paull, R.E., Effect of temperature and relative humidity on fresh commodity quality (1999) Postharvest Biology and Technology, 15, pp. 263-277; Rouwenhorst, B., Reuter, B., Stockrahm, V., van Houtum, G.J., Mantel, R.J., Zijm, W.H.M., Warehouse design and control: Framework and literature review (2000) European Journal of Operational Research, 122, pp. 515-533; Schwichtenberg, K., Supply chain measuring is the key to success (2000) Materials Management and Distribution, , www.mmdonline.com, available at: (accessed 11 January 2002); Thompson, A.K., (1996) Post Harvest Technology of Fruit and Vegetables, p. 410. , Blackwell Science, London; van Amstel, R.P., D'hert, G., Performance indicators in distribution (1996) The International Journal of Logistics Management, 7, pp. 73-82; van den Berg, J.P., Zijm, J.P., Models for warehouse management: Classification and examples (1999) International Journal of Production Economics, 59, pp. 519-528; van der Vorst, J.G.A.J., Beulens, A.J.M., A research model for the redesign of food supply chains (1999) International Journal of Logistics: Research and Applications, 2 (2), pp. 161-174; Voss, C., Tsikriktsis, N., Frolich, M., Case research in operations management (2002) International Journal of Operations & Production Management, 22 (2), pp. 195-219; Edwards, T., Performance indicators (2001), http://nt.headland.co.uk/cgi-bin/iolt/forum/messages, Institute of Logistics and Transport, available at: (accessed 11 January 2002); John, P., Marchal, J., Ripening and biochemistry of the fruit (1995) Bananas and Plantains, pp. 134-145. , Gowen, S. (Ed.), Chapman & Hall, London; Margulis, R., NAWGA Conference tracks important food warehouse and distribution needs (1997) Food Distributors International News Room, , www.fdi.org/newsrm, available at: (15 January 2002); New, C., The use of throughput efficiency as a key performance measure for the new manufacturing era (1993) The International Journal of Logistics Management, 4, pp. 95-104},
correspondence_address1={Manikas, I.; School of Agricultural Sciences, , Thessaloniki, Greece; email: imanikas@agro.auth.gr},
issn={0007070X},
language={English},
abbrev_source_title={Br. Food J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Mikušová2010983,
author={Mikušová, M. and Janečková, V.},
title={Developing and implementing successful key performance indicators},
journal={World Academy of Science, Engineering and Technology},
year={2010},
volume={66},
pages={983-995},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951500298&partnerID=40&md5=68c120cb8987c2f773908df7075284be},
affiliation={VSB-Technical University Ostrava, Faculty of Economics, Department of Management, Sokolska tr. 33, Ostrava, Czech Republic},
abstract={Measurement and the following evaluation of performance represent important part of management. The paper focuses on indicators as the basic elements of performance measurement system. It emphasizes a necessity of searching requirements for quality indicators so that they can become part of the useful system. It introduces standpoints for a systematic dividing of indicators so that they have as high as possible informative value of background sources for searching, analysis, designing and using of indicators. It draws attention to requirements for indicators' quality and at the same it deals with some dangers decreasing indicator's informative value. It submits a draft of questions that should be answered at the construction of indicator. It is obvious that particular indicators need to be defined exactly to stimulate the desired behavior in order to attain expected results. In the enclosure a concrete example of the defined indicator in the concrete conditions of a small firm is given. The authors of the paper pay attention to the fact that a quality indicator makes it possible to get to the basic causes of the problem and include the established facts into the company information system. At the same time they emphasize that developing of a quality indicator is a prerequisite for the utilization of the system of measurement in management.},
author_keywords={Firm;  Indicator;  Measurement;  Performance},
keywords={Basic elements;  Established facts;  Firm;  Indicator;  Key performance indicators;  Performance;  Performance measurement system;  Quality indicators;  Small firm, Benchmarking;  Supply chain management, Industry},
references={(2002) Balanced Scorecard V Praxi, 1. , Horváth & Partners (Hrsg.), vyd. Praha: Profess Consulting; Fibírová, J., (2001) Reporting, Moderní Metoda Hodnocení Výkonnosti Uvnitř Firmy, 1. , vyd. Praha: Grada Publishing; Horváthová, P., (2008) Týmy a Týmová Spolupráce, 1. , vyd. Praha: Aspi - Wolters Kluwer; Pande, P.S., Neuman, R.P., Cavanagh, R.R., (2002) Zavádíme Metodu Six Sigma, 1. , vyd. Brno: TwinsCom; Učeň, P., (2001) Metriky V Informatice. Jak Objektivně Zjistit Přínosy Informačního Systému, 1. , vyd. Praha: Grada Publishing; Eschenbach, R., (2000) Controlling, 1. , vydání. Praha: ASPI Publishing; Simmons, R., (2000) Performance Measurement and Control Systems For Implementing Strategy, , UK: Prentice Hall; Kaplan, R.S., Norton, D.S., (2001) Balanced Scorecard. Strategický System Měření Výkonnosti Podniku, 2. , vyd. Praha: Management Press; Kaplan, R.S., Norton, D.S., (2001) The Strategy-focused Organization, How Balanced Scorecard Companies Thrive in the New Business Environment, , Boston: Harvard Business School; Schneiderman, A.M., Metrics for the Order Fulfillment Process. Part 1 (1996) Journal of Cost Management, pp. 30-42. , Summer; Schneiderman, A.M., Metrics for the Order Fulfillment Process. Part 2 (1996) Journal of Cost Management, pp. 36-17; Schneiderman, A.M., Measurement, the Bridge between the Hard and Soft Sides (1998) Journal of Strategic Performance Measurement, 2 (2), pp. 14-21. , April/May; Schneiderman, A.M., Are there Limits to TQM? (1998) Strategy + Business, (11), pp. 35-46. , second quarter; Schneiderman, A.M., Why Balanced Scorecards Fail (1999) Journal of Strategic Performance Measurement, , special ed January; Schneiderman, A.M., Time to Unbalanced Your Scorecard (2001) Strategy + Business, (24). , Third Quarter; Jensen, J., Sage, P.B., A system management approach for improvement of organization performance measurement systems (2000) Information, Knowledge, Systems Management, 2, pp. 33-61; Neely, A., Adams, C., Crowe, P., The Performance Prism in Practice (2001) Measuring Business Excellence, 5 (2), pp. 6-12; Neely, A., Recent Development in Planning and Budgeting (2001) Performance Measurement Association Newsletter, 1 (4), pp. 14-15; (2002) Business Performance Measurement: Theory and Practice, , A. Neely (editor), First edition. Cambridge University Press; Porth, J., Kathuria, R., Joshi, M.P., Performance impact of the fit between manufacturing priorities of general managers and manufacturing managers (1998) The Journal of Business and Economic Studies, 4 (1), pp. 13-35; Stein, P., Measurements for business (2001) Quality Progress, 34 (2), pp. 29-32; Lefkowith, D., What gets measured gets done turning strategic plans into real world results (2001) Management Quaterly, 42 (2), pp. 20-24; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism. the Scorecard For Measuring and Managing Business Success, , London: Prentice Hall; Mikusova, M., Metrics: Business Performance Measurement System Elements (2009) Proc. of the 11th Int. Conf. of SGBED. Striving For Competitive Advantage and Sustainability, pp. 1317-1323. , New Challenges of Globalization, Bratislava; Mikoška, P., Briš, P., Řízení kontroly kvality v obchodním řetězci v kontextu současného globálního tržního prostředí (2007) Znalostní Ekonomika, Trendy Rozvoje Vzdělávání, Vědy a Praxe, pp. 13-21; Vokounová, D., Globálna solidarita - vnímanie študentov z vybraných krajín EÚ (2008) Veda a Výskum Pre Prax, pp. 93-103; Vozňáková, I., Janovská, K., Jak měřit ekonomické výsledky? (2009) MMPrůmyslové spektrum, 9 (8), p. 97. , 2009; Mateiciuc, A., K pojetí lidského potenciálu organizace (2004) Výsledky Vědecké Práce Studentů Doktorského Studia MEKON, 2004, pp. 36-45; Mateiciuc, A., (2005) Návrh systému hodnocení rizik při strategickém manažerském rozhodování, , Dissertation Thesis; Borbás, L., Regional connections and export orientation of SMEs after EU accession (2008) EU Working Papers, 1, pp. 93-10. , 2008; Mikušová, M., Knowledge in Enterprise: The Role and Performance Measurement (2008) Lex Et Scientia, 2 (xv), pp. 283-291. , 2008; Horváthová, P., The Use of Operations Management Procedures in Order to Increase Organizations Competitiveness Under the Condition of Growing Pressures of Globalization (2010) Global Business and Management Research, 2 (1). , on-line},
correspondence_address1={Mikušová, M.; VSB-Technical University Ostrava, , Sokolska tr. 33, Ostrava, Czech Republic; email: marie.mikusova@vsb.cz},
issn={2010376X},
language={English},
abbrev_source_title={World Acad. Sci. Eng. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Shaw2010320,
author={Shaw, S. and Grant, D.B. and Mangan, J.},
title={Developing environmental supply chain performance measures},
journal={Benchmarking: An International Journal},
year={2010},
volume={17},
number={3},
pages={320-339},
doi={10.1108/14635771011049326},
note={cited By 136},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952681063&doi=10.1108%2f14635771011049326&partnerID=40&md5=e4a018fcd5cea84d9d173eec2e206109},
affiliation={Logistics Institute, University of Hull, Hull, United Kingdom; School of Marine Science and Technology, Newcastle University, Newcastle, United Kingdom},
abstract={Purpose – The purpose of this paper is to review extant literature and present a proposed research agenda to examine whether environmental, i.e. green performance measures, can be integrated within an existing supply chain performance framework, explore what a meaningful industryrecognised environmental measure should look like, and understand the direct benefits of incorporating environmental measures within a supply chain performance framework. Design/methodology/approach – The paper is based on an extensive literature review in four key areas: performance management, supply chain performance management, environmental management and benchmarking. Findings – The literature suggests there is an opportunity to explore the relationship between the environment and logistics and that environmental supply chain performance measurement (SCPM) should enable organisations to more effectively benchmark their supply chain environmental performance. A framework incorporating these notions and a research agenda for empirical study are also presented. Practical implications – The paper provides direction for practitioners on measuring the environmental impact of their supply chains in the context of their overall business performance. The proposed research agenda integrates an environmental measure into an extant supply chain performance framework to provide practitioners with a more holistic view of their supply chain performance in relation to competitors. Originality/value – There has been limited research conducted in this area. This paper provides insights into developing a green SCPM framework. © 2010, Emerald Group Publishing Limited},
author_keywords={Balanced scorecard;  Benchmarking;  Environmental management;  Performance measures;  Supply chain management},
references={Argyris, C., (1952) The Impact of Budgets on People, , Controllership Foundation, New York, NY; Azzone, G., Masella, C., Bertele, U., Design of performance measures for time-based companies (1991) International Journal of Operations & Production Management, 11, pp. 77-85; Barber, E., How to measure the ‘value’ in value chains (2008) International Journal of Physical Distribution & Logistics Management, 38, pp. 685-698; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19, p. 275; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18, pp. 666-680; Boks, C., Stevels, A., Theory and practice of environmental benchmarking in a major consumer electronics company (2003) Benchmarking: An International Journal, 10, pp. 120-135; Braithwaite, A., Knivett, D., Evaluating a supply chain's carbon footprint – a methodology & case example of carbon-to-serve (2008) Logistics Research Network Conference Proceedings, pp. 323-328. , University of Liverpool; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5, p. 48; Brown, M.G., (1996) Keeping Score, , Quality Resources, New York, NY; Camp, R., (1989) Benchmarking: The Search For Industry Best Practices That Lead to Superior Performance, , ASQC Quality Press, Milwaukee, WI; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1995) International Journal of Logistics Management, 5, pp. 11-28; Tesco vs. Wal-Mart vs carbon emissions (2008), available at: www.carboncommentary.com (accessed May 26, 2008); About the carbon trust (2008), available at: www.carbontrust.co.uk (accessed May 26, 2008); Christopher, M., (2005) Logistics & Supply Chain Management, 3rd ed., , Prentice-Hall Financial Times, Harlow; Implementing the climate change act 2008 (2008), available at: www.defra.gov.uk/environment/climatechange/uk/legislation/ (accessed April 11, 2009); Environmental key performance indicators, reporting guidelines for UK business (2006), available at: www.defra.gov.uk (accessed April 4, 2009); UK climate change sustainable developement indicators (2007), available at: www.defra.gov.uk/environment/climatechange/uk/ukccp/pdf/ukccp06-all.pdf (accessed May 26, 2008); Climate change, international action (2009), available at: www.defra.gov.uk/environment/climatechange/internat/index.htm (accessed April 4, 2009); Ditz, D., Ranganathan, J., (1997) Measuring Up: Towards a Common Framework for Tracking Corporate Environmental Performance, , World Resources Institute, Washington, DC; What is EMAS? (2009), available at: http://ec.europa.eu/environment/emas/index_en.htm (accessed June 18, 2009); Epstein, M.J., Weisner, P.S., Good neighbours: implementing social and environmental strategies with the BSC (2001), Balanced Scorecard Report, Reprint Number B0105C 33, Harvard Business School, Cambridge, MA; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Business, , CIMA, London; Francis, F., Get the gain without the pain (2008) Supply Chain Management Review, 12, p. 22; About the project (2008), available at: www.greenlogistics.org (accessed May 26, 2008); GRI Reporting Framework Overview, available at: www.globalreporting.org/ReportingFramework/ReportingFrameworkOverview/ (accessed 31 May 2009). (2006); About GRI (2009), available at: www.globalreporting.org (accessed May 31, 2009); Gunasekaran, A., Benchmarking tools and practices for twenty-first century competitiveness (2001) Benchmarking: An International Journal, 8, pp. 86-87; Gunasekaran, A., Kobu, B., Performance measures and metrics in Logistics & Supply Chain Management: a review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45, pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Hendricks, K., Menor, L., Wiedman, C., The balanced scorecard: to adopt or not to adopt? (2009), available at: www.iveybusinessjournal.com/view-article.asp?intArticle_ID=527 (accessed April 4, 2009); Hervani, A.M., Helms, M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12, pp. 330-353; Hoffman, D., Getting to world-class supply chain measurement (2006) Supply Chain Management Review, 10, pp. 18-24; Hooper, D., Greenall, A., Exploring the potential for environmental performance benchmarking in the airline sector (2005) Benchmarking: An International Journal, 12, pp. 151-165; House of carbon (2008), available at: www.ibm.com/ibm/environment/ (accessed May 26, 2008); Climate change 2007, the physical science basis (2007), available at: www.ipcc.ch/ipccreports/assessments-reports.htm (accessed March 27, 2009); The ISO 2005 survey (2005), available at: www.iso.org (accessed June 7, 2009); ISO 14001:2004, environmental management systems – requirements with guidance for use (2009), available at: www.iso.org (accessed June 7, 2009); Jasch, C., Environmental performance evaluation and indicators (2000) Journal of Cleaner Production, 8, pp. 79-88; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: the Rise and Fall of Management Accounting, , Harvard Business School Press, Cambridge, MA; Johnson, S., Application of the balanced scorecard approach (1998) Corporate Environmental Strategy, 5, pp. 34-41; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kaplan, R.S., Norton, D.P., (2001) The Strategy-Focused Organisation, , Harvard Business School Press, Cambridge, MA; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50; Klassen, R.D., McLaughlin, C.P., The impact of environmental management on firm performance, management science (1996) INFORMS: Institute for Operations Research, 42, pp. 1199-1214; Krajnc, D., Glavič, P., How to compare companies on relevant dimensions of sustainability (2005) Ecological Economics, 55, pp. 551-563; Lee, H., Aligning supply chain strategies with product uncertainties (2002) California Management Review, 44, pp. 105-119; Lee, H.L., Billington, C., Supply chain management: pitfalls and opportunities (1992) Sloan Management Review, 33, pp. 65-73; Lynch, R.L., Cross, K.F., (1991) Measure Up, , Blackwell, Cambridge, MA; McIntyre, K., Smith, H.A., Henham, A., Pretlove, J., Logistics performance measurement and greening supply chains: diverging mindsets (1998) International Journal of Logistics Management, 9, pp. 57-68; Matthews, D., Environmental management systems for internal corporate benchmarking (2003) Benchmarking: An International Journal, 10, pp. 95-113; Menachof, D.A., Gibson, B.J., Hanna, J.B., Whiteing, A.E., An analysis of the value of supply chain management periodicals (2009) International Journal of Physical Distribution & Logistics Management, 39, pp. 145-163; Morgan, C., Structure, speed and salience: performance measurement in the supply chain (2004) Business Process Management Journal, 10, pp. 522-536; MORI's corporate social responsibility survey (2003), available at: www.ipsos-mori.com (accessed April 11, 2009); Neely, A.D., The evolution of performance measurement, developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25, pp. 1264-1277; Neely, A.D., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5, pp. 6-12; Neely, A.D., Adams, C., Kennerley, M., (2002) Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationships, , Prentice-Hall Financial Times, Harlow; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15, pp. 80-116; Paranjape, B., Rossiter, M., Pantono, V., Performance measurement systems: successes, failures and the future – a review (2006) Measuring Business Excellence, 10, pp. 4-14; Ridgway, V.F., Dysfunctional consequences of performance measurements (1956) Administrative Science Quarterly, 1, pp. 240-247; Rothenberg, S., Schenk, B., Maxwell, J., Lessons from benchmarking environmental performance at automobile assembly plants (2005) Benchmarking: An International Journal, 12, pp. 5-16; Sarkis, J., Corporate environmental benchmarking (2003) Benchmarking: An International Journal, 10, pp. 91-94; Saunders, M., Lewis, P., Thornhill, A., (2007) Research Methods for Business Students, 4th ed., , Prentice-Hall, Harlow; Walton, S.V., Handfield, R.B., Melkyn, S.A., The green supply chain: integrating suppliers into environmental management processes (1998) International Journal of Purchasing & Materials Management, 34, pp. 2-11; Zingales, F., O'Rourke, A., Orssatto, R.J., Environment and socio-related balance scorecard: exploration of critical issues (2002), Working Paper 2002/47/CMER, Center for Management of Environmental Resources, INSEAD, Fountainebleau},
issn={14635771},
language={English},
abbrev_source_title={Benchmarking},
document_type={Review},
source={Scopus},
}

@ARTICLE{Bigliardi2010249,
author={Bigliardi, B. and Bottani, E.},
title={Performance measurement in the food supply chain: a balanced scorecard approach},
journal={Facilities},
year={2010},
volume={28},
number={5-6},
pages={249-260},
doi={10.1108/02632771011031493},
note={cited By 98},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952362262&doi=10.1108%2f02632771011031493&partnerID=40&md5=7d441742ebe9934f2cfb53c5f001872a},
affiliation={Department of Industrial Engineering – University of Parma, Parma, Italy},
abstract={Purpose – The primary objective of this paper is to develop a balanced scorecard (BSC) model that is designed and delimited for performance measurement in the food supply chain. Design/methodology/approach – The research methodology is a combination of literature analysis, Delphi technique and case studybased research. Starting from the literature concerning performance measurement and metrics, the food industry and the BSC model, the relevant financial and nonfinancial indicators, suitable to be used for companies belonging to the food industry, were first identified. Indicators were submitted to a panel of experts, which operated following the Delphi technique, to gather possible suggestions or amendments. In its final form, the resulting BSC model was tested on two companies operating in the food industry, for a final validation. Findings – Results of the case studies show that the companies examined have a similar view for three of the four perspectives of the BSC, which can be thus considered as validated. Conversely, some diverging results were observed concerning the learning and growth perspective. Research limitations/implications – By focusing on the specific context of the food supply chain, this study completes previous works, which proposed a generic BSC model for supply chain management. On the other hand, the fact that a specific industry field was examined could be seen as a limitation of the work as the results presented are not suitable to be generalized or extended to other contexts, although some extrapolations can be made. Originality/value – The paper provides a structured performance measurement system tailored for the food supply chain. The BSC model developed could serve as a reference for the food industry, to establish applicable performance appraisal indicators, and it is believed that both researchers and practitioners would benefit from the tool developed. © 2010, Emerald Group Publishing Limited},
author_keywords={Balanced scorecard;  Food industry;  Performance management;  Supply chain management},
references={Ahumada, O., Villalobos, J.R., Application of planning models in the agri-food supply chain: a review (2009) European Journal of Operational Research, 195, pp. 1-20; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Brewer, P.C., Sfeh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21, pp. 75-93; Bullinger, H.J., Kuèhner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40, pp. 3533-3543; Cardemil-Katuranic, G., Shadbolt, N., The Balanced Scorecard as a spontaneous framework in an agricultural hybrid cooperative under strategic change: a case study in the New Zealand kiwifruit industry (2006), available at: www.ifama.org/tamu/iama/conferences/2006conference/SymposiumFinal/1132_Paper.pdf (accessed August 2009); Cavinato, J.L., Total cost value model for supply chain competitiveness (1992) Journal of Business Logistics, 13, pp. 285-291; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: more than a new name for logistics (1997) The International Journal of Logistics Management, 8, pp. 1-13; Creswell, J.W., (1994) Research Design: Qualitative & Quantitative Approaches, , Sage Publications, Thousand Oaks, CA; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Horvath, P., Unternehmenscontrolling-Erneuerung des Controllings (1996) Neue Organisationsformen im Unternehmen Ein Handbuch fur das moderne Management, pp. 937-939. , in Bullinger, H.J. and Warnecke, H.J. (Eds), Springer, Berlin; (2009), available at: www.globalgap.org/cms/front_content.php?idcat=2 (accessed August 2009); (2009), available at: www.leafuk.org/leafuk/ (accessed August 2009); Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71, pp. 134-147; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74, pp. 75-85; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: part 1 (2001) Accounting Horizons, 15, pp. 87-104; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: part 2 (2001) Accounting Horizons, 15, pp. 147-160; Kraft, K., Are product and process innovations independent of each other? (1990) Applied Economics, 22, pp. 1029-1038; Lee, H.L., Billington, C., Managing supply chain inventory: pitfalls and opportunities (1992) Sloan Management Review, 33, pp. 65-73; Levy, D.L., Lean production in an international supply chain (1997) Sloan Management Review, 38, pp. 94-102; Linstone, H.A., Turoff, M., (1975) The Delphi Method – Techniques and Applications, , Addison-Wesley, London; Little, D., Kenworthy, J., Jarvis, P., Porter, K., Scheduling across the supply chain (1995) Logistics Information Management, 8, pp. 42-48; Mapes, J., New, C., Szwejczewski, M., Performance trade-offs in manufacturing plants (1997) International Journal of Operations & Production Management, 17, pp. 1020-1033; Novich, N., Distribution strategy: are you thinking small enough? (1990) Sloan Management Review, 32, pp. 71-77; Park, J.H., Lee, J.K., Yoo, Y.S., A framework for designing the balanced supply chain scorecard (2005) European Journal of Information Systems, 14, pp. 335-346; Rouse, P., Putterill, M., An integral framework for performance measurement (2003) Management Decision, 41, pp. 791-805; Rowe, G., Wright, G., The Delphi technique as a forecasting tool: issues and analysis (1999) International Journal of Forecasting, 15, pp. 353-375; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , Pitman Publishing, London; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8, pp. 38-44; Thomas, D.J., Griffin, P.M., Co-ordinated supply chain management (1996) European Journal of Operational Research, 94, pp. 1-15; Settori industriali e servizi (2007), available at: www.upi.pr.it (accessed July 2009); Yin, R.K., (1984) Case Study Research: Design and Methods, , Sage Publications, Beverly Hills, CA},
issn={02632772},
language={English},
abbrev_source_title={Facilities},
document_type={Article},
source={Scopus},
}

@ARTICLE{Schramm2010394,
author={Schramm, E. and Staben, N.},
title={Indicators as reliable database for communicating sustainability performances in the field of water supply [Kennzahlen als glaubwürdige Datenbasis für die Kommunikation von Nachhaltigkeitsleistungen der Wasserversorgung]},
journal={GWF, Wasser - Abwasser},
year={2010},
volume={151},
number={4},
pages={394-405},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649470603&partnerID=40&md5=c55b6a24318e68003eb53a8417b6930e},
affiliation={Institut für Sozial- Ökologische Forschung (ISOE) GmbH, Hamburger Allee 45, D-60486 Frankfurt a. M, Germany; IWW Rheinisch-Westfälisches Institut für Wasser Beratungsund Entwicklungsgesellschaft MbH, Moritzstraße 26, D-45476 Mülheim an der Ruhr, Germany},
abstract={Sustainability constitutes a significant aim for water supply companies and their public services. But so far this has not been sufficiently conveyed to employers, customers, owners, consumers and other stakeholders. The IWA system of performance indicators has been upgraded by adding further key data and indicators concerning sustainability. The resulting performance measurement system can be used as a referential framework for measuring sustainability. Thus the particularities of water supply can be adequately accounted for. Key data and indicators pointing out achievements in this field can then be structured according to the topics concerned. Emerging topic modules will enable the development of focal points for individual companies and will highlight special groups of topics for tailor-made sustainability communication geared towards special target groups.},
keywords={Emerging topics;  Focal points;  Key datum;  Performance indicators;  Performance measurement system;  Public services;  Target group, Benchmarking;  Supply chain management;  Water supply, Sustainable development, database;  measurement method;  performance assessment;  public service;  stakeholder;  sustainability;  water management;  water supply},
references={(2006) Stadtwerke Düsseldorf: Nachhaltigkeltsberlcht 2005/2006, , Düsseldorf; Bundesregierung: Der Bericht der Bundesregierung zur Modernisierungsstrategie für die deutsche Wasserwirtschaft (2007) Schlaglichter der Wirtschaftspolitik, pp. 28-32. , Bundesministerium für Wirtschaft und Technologie (Hrsg.) Monatsbericht August Berlin 2007; Klewes, P.K., Hedges, F., Global Stakeholder Report 2003. Geteilte Werte? (2003) Die Erste Weltweite Stakeholder-Befragung Zum Non-financial Reporting, , www.pleon-kohtes-klewes.de, Bonn/ London; Hauff, V., (1987) Unsere Gemeinsame Zukunft, , Der Brundtland-Bericht der Weltkommission für Umwelt und Entwicklung Eggenkamp, Greven; Biebeler, H., (2004) Institut der Deutschen Wirtschaft: Ergebnisse der Unternehmensbefragung Zum Nachhaltigen Wirtschaften, , http://www.iwkieln.de/data/pdf/content/st1104nachhaltigkeits- unternehmensbefragung2004.pdf; Minsch, J., Eberle, A., Meier, B., Schneidewind, U., (1996) Mut Zum Ökologischen Umbau - Innovationsstrategien für Unternehmen, Politik und Akteurnetze., , Birkhäuser, Verlag Basel; (2007) Naturschutz und Reaktorsicherheit: Nachhaltigkeitsberichterstattung: Empfehlungen für Eine Gute Unternehmenspraxis, , Bundesministerium für Umwelt Berlin; Clausen, J., Loew, Th., (2005) Mehr Glaubwürdigkeit Durch Testate? Internationale Analyse des Nutzens von Testaten in der Umwelt- und Nachhaltigkeitsberlchterstattung, , http://www.ranking-nachhaltigkeltsberichte.de; Huck, S., Glaubwürdigkeit - Erfolgsfaktor für die Unternehmenskommunikation (2006) Ergebnisse Einer Qualitativen Befragung von Kommunikationsverantwortlichen, 6. , Kommunikation & Management; Hirner, W., Merkel, W., (2005) Kennzahlen für Benchmarking in der Wasserversorgung. Handbuch Zur Erweiterten Deutsehen Fassung des IWA-Kennzahlensystems Mit Definitionen, , Erklärungsfaktoren und Interpretationshilfen. Bonn; Lux, A., Benchmarking in der Wasserwirtschaft - Möglichkeiten und Grenzen einer Erweiterung des Benchmarking um ökologische und soziale Aspekte (2005) NetWORKSPapers, 17; Scheele, U., Nachhaltigkeitsmessung und Nachhaltigkeitsberichterstattung in der Wasserversorgung ausgewählter Länder (2007) ISOE-Materialien Soziale Ökologie, 26; Gebauer, J., Rotter, M., Praxis der Nachhaltigkeltsberichterstattung in deutschen Großunternehmen (2009) Befragungsergebnisse im Rahmen des IÖW/future-Rankings 2009, , Berlin/ Münster; Fischbach, K., (2007) Nachhaltigkeltsberichterstattung von Unternehmen: Analyse der Global Reporting Initiative (GRI), , Hamburg; Wasser, V., (2009) Nachhaltigkeitsbericht 2008, , Berlin; Nachhaltigkeitsleistungen erfolgreich kommunizieren - Eine Handreichung für die Trinkwasserversorgung (2007) ISOE-Materialien Soziale Ökologie, 25. , ISOE/IWW/ARSU; Isenmann, R., Marx Gómez, J., Intemetbasierte Nachhaltigkeltsberichterstattung (2008) Maßgeschneiderte Stakeholder-Kommunikation Mit IT, , Berlin; (2007) Kommunale Wasserwerke Leipzig: Gesellschaftliche Verantwortung, , Bericht 2006/2007. Leipzig; (2008) Oldenburgisch-Ostfriesischer Wasserverband: Nachhaltigkeitsbericht, , http://www.oowv-nachhaltig.de},
correspondence_address1={Schramm, E.; Institut für Sozial- Ökologische Forschung (ISOE) GmbH, Hamburger Allee 45, D-60486 Frankfurt a. M, Germany; email: schramm@isoe.de},
issn={00163651},
language={German},
abbrev_source_title={GWF Wasser Abwasser},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bardy201095,
author={Bardy, R.},
title={Comparative supply chain performance: Measuring cross-cultural effects. the example of the Bratislava regional automotive manufacturing cluster},
journal={Knowledge and Process Management},
year={2010},
volume={17},
number={2},
pages={95-110},
doi={10.1002/kpm.345},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952948731&doi=10.1002%2fkpm.345&partnerID=40&md5=edb78c9e83a68b5aedace6a8057a61c5},
affiliation={Florida Gulf Coast University, Fort Myers, United States; BASF SE, Ludwigshafen, Germany},
abstract={This paper examines how performance in a supply network is biased through cultural difference. Based on a benchmark study for the Bratislava automotive cluster, the review covers the specific situation of a cross border setting where top performers (high-class original equipment manufacturers, "OEMs," like VW, Peugeot, Porsche, Kia) in a dual economy environment interact with suppliers from a highly developed country and with local logistics service enterprises. The main findings were derived from a benchmarking questionnaire and were corroborated in subsequent interviews, and they can very well be generalized: specific knowledge required for interactions between buyers and sellers cannot be built and utilized effectively if acceptance, adaptation and integration are obstructed by cultural differences. The biggest threats to fully exploiting the opportunities of cross-border supply chains, are lack of trust and of ability or willingness to cooperate and insufficient power of decision-making; lack of application skills ranks second. © 2010 John Wiley & Sons, Ltd.},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 275-292; Beaverstock, J., 'Managing across borders': Knowledge management and expatriation in professional legal service firms (2004) Journal of Economic Geography, 4, pp. 157-179; Belzowski, B.M., Working with knowledge in the automotive supply chain (2002) Office For the Study of Automotive Transportation, University of Michigan Transportation Research Institute, , Ann Arbor, Michigan; Carr, N.G., (2004) Does it Matter?: Information Technology and The Corrosion of Competitive Advantage, , HBS Press: Boston; Cedillo Campos, M.G., Sánchez-Garza, J., Ramírez, C.S., The new relational schemas of inter-firms cooperation: The case of the Coahuila automobile cluster in Mexico (2006) International Journal of Automotive Technology and Management, 6, pp. 406ff; von Corswant, F., Wynstra, F., Wetzels, M., (2003) In Chains? Automotive Suppliers and Their Product Development Activities, , April, ERIM Report Series Reference No. ERS-2003-027-LIS; Cox, A., The power perspective in procurement and supply management (2001) The Journal of Supply Chain Management, 37 (2), pp. 4-7; Kim, D., (2008) How Does Dynamic Relationship Among KPIs Affect SCM Costs? Proceedings of The 19th Annual Conference of The Production and Operations Management Society, , La Jolla, California, U.S.A, May; Davenport, T., Marchand, D., Is KM just good information management? (1999) OT the Financial Times Mastering Series, Part Six: Knowledge Management, March, 8, pp. 2-3; Dehing, B., Richardson, V.J., Zmud, R.W., The financial performance effects of IT-based supply chain management systems in manufacturing firms (2007) Journal of Operations Management, 25, pp. 806-824; Desouza, C.K., Chattaraj, A., Kraft, G., Supply chain perspectives to knowledge management: Research propositions (2003) Journal of Knowledge Management, 7, pp. 129-138; de Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Dornier, P.P., Ernst, R., Fender, M., Kouvelis, P., (1998) Global Operations and Logistics, , Text and Cases. Wiley, New York; Druml, M., A model of efficiency Automotive Manufacturing, Production Control and Logistics Issue 04/2006, , http://www.automotivedigital.com; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review March, 1, pp. 105-116; Forslund, H., Jonsson, P., The impact of forecast information quality on supply chain performance (2007) International Journal of Operations & Production Management, 27, pp. 90-107; Flynn, B.B., World class manufacturing project overview and selected results (1997) International Journal of Operations & Production Management, 17, pp. 671-685; Gilmour, P., Benchmarking supply chain operations (1999) International Journal of Physical Distribution & Logistics Management, 29 (4), pp. 259-266; Gilsing, V., (2006) The Dynamics of Innovation and Interfirm Networks, , Edward Elgar Publishing: Cheltenham; Gleich, R., Henke, M., Quitt, A., Sommer, L., New approaches in performance measurement: Methods for specification and operationalisation within the context of supply management (2009) International Journal of Business Excellence, 2, pp. 105-123; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Hafeez, K., Rodiguez-Falcon, E., Abdelmeguid, H., Malak, N., Knowledge Management in Supply Chains (2000) Proceedings of The International Conference On Systems Thinking In Management (ICSTM 2000), pp. 8-10. , Geelong, Australia, November; Hammer, A., (2006) Enabling Successful Supply Chain Management Coordination, Collaboration, and Integration For Competitive Advantage, , Mannheim; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Upper Saddle River: N.J; Hausman, W.H., Supply chain performance metrics (2004) The Practice of Supply Chain Management, pp. 61-73. , Where Theory and Application Converge, Harrison TP, Hau LL, John JN (eds). Springer: US; Hong, P., Jeong, J., Supply chain management practices of SMEs: From a business growth perspective (2006) Journal of Enterprise Information Management, 19, pp. 292-302. , http://www.globalservicesmedia.com/content/general/MediaKit2007.pdf; Johannessen, J.-A., Olsen, B., Olaisen, J., Aspects of innovation theory based on knowledge-management (1999) International Journal of Information Management, 19, pp. 121-139; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of The Operational Research Society, 54, pp. 1-8; Knight, L., Harland, C., Managing supply networks: Organizational roles in network management (2005) European Management Journal, 23, pp. 281-292; Knolmayer, G., Mertens, P., Zeier, A., Supply Chain Management Based on SAP Systems (2002) Order Management In Manufacturing Companies, , Heidelberg; Koh, S.C.L., Gunasekaran, A., Performance prediction using supply chain uncertainty modelling (2006) International Journal of Services and Operations Management, 2, pp. 279-293; Lambert, D., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12, pp. 1-19; Laugen, B.T., Acur, N., Boer, H., Frick, J., Best manufacturing practices. What do the best-performing do (2005) International Journal of Operations & Production Management, 25 (2), pp. 131-150; Lockamy, A., McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management, 9, pp. 272-278; Maheshwari, B., Kumar, V., Kumar, U., Optimizing success in supply chain partnerships (2006) Journal of Enterprise Information Management, 19, pp. 277-291; Marren, P., Where did all the knowledge go? (2003) Journal of Busines Strategy, 24 (3), pp. 5-7; McDonald, M., Do your marketing strategies create or destroy shareholder value? (2006) The Journal of DatabaseMarketing & Customer Strategy Management, 13 (99), pp. 102-107; McElroy, M.W., (2003) The New Knowledge Management, p. 2003. , Burlington, Mass; Midler, C., "Projectification" of the firm the Renault case (1995) Scandinavian Journal of Management, 11, pp. 363-375; Migliori, C., The European IP market needs a revolution (2006) Intellectual Asset Management, pp. 55-58. , http://www.wipo.int/sme/en/best_prac-tices/pdf/european_ip_market.pdf, Also available at; Montgomery, J.D., The logic of role theory: Role conflict and stability of the self-concept (2005) The Journal of Mathematical Sociology, 29, pp. 33-71; Pellegrini, L., Martini, A.A., Barriers and levers towards knowledge management configurations: A case study-based approach (2005) Journal of Manufacturing Technology Management, 16, pp. 670ff; Presutti Jr., W.D., Mawhinney, J.R., The supply chain finance link (2007) Supply Chain Management Review September, 1, pp. 32-38; Radosevic, S., Yoruk, D.E., Industrial networks in central and eastern European countries at the firm level (2004) The Emerging Industrial Structure of The Wider Europe, pp. 57-76. , McGowan F, Radosevic S, von Tunzelmann N (eds). Routledge: London and New York; Radosevic, S., The electronic Industry in CEE (2004) The Emerging Industrial Structure of The Wider Europe, , McGowan F, Radosevic S, von Tunzelmann N (eds), Routledge: London and New York; Rampersad, H.K., Learning and unlearning in accordance with organizational change (2004) Organization Development Journal, 22 (4), pp. 43-460; Rothstein, J.S., Creating Lean Industrial Relations: General Motors in Silao (2004) Mexico. Competition & Change, 8, pp. 203-221; Senge, P.M., (1990) The Fifth Discipline: The Art and Practice of The Learning Organization, , New York, Doubleday Currency; Shen, T., (2005) Linking Supply Chain Practices to Operational and Financial Performance, , MIT Supply Chain, Project Working Paper; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55, pp. 242-258; Simatupang, T.M., Sridharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmarking, 11, pp. 9-30; Stevens, G.C., Integrating the supply chain (1989) International Journal of Physical Distribution and Materials Management, 19 (8), pp. 3-8; Swan, J., Scarbrough, H., Preston, J., (1999) Knowledge Management. a Literature Review, p. 80. , In Issues in People Management. Institute of Personnel and Development: Leicester, Warwick; (2005) Results and Findings of The 10th Annual Study, , Third Party Logistics Report, supplychainmagazine.fr/FORUM/ETUDES/3PL_Report_14p.pdf; Thompson, D.M., Advanced E-Commerce within the UK Automotive Supply Chain (2000) Proceedings of The 9th World- Wide Research Symposium On Purchasing and Supply Chain Management, pp. 750-760. , London, Canada; van der Vorst, J.G.A.J., van Dijk, S.J., Beulens, A.J.M., Supply chain design in the food industry (2001) The International Journal On Logistics Management, 12, pp. 73-85; van Hoek, R., Measuring the unmeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management, 3, pp. 187-192; van Landeghem, R., Persoons, K., Benchmarking of logistical operations based on a causal model (2001) International Journal of Operations & Productions, 21, pp. 254-266; Volpato, G., Stocchetti, A., Knowledge Management in the automotive supply chain: Exploring suppliers' point of view (2007) International Journal of Automotive Technology and Management, 7, pp. 184-199; von Lewinski, H., van Wassenhore, L.N., The link between supply chain and financial performance (Growth) (2003) Supply Chain Management Review},
correspondence_address1={Bardy, R.; Florida Gulf Coast University, , Fort Myers, United States; email: rbardy@t-online.de},
issn={10924604},
language={English},
abbrev_source_title={Knowl. Process Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Pei201073,
author={Pei, Y.L. and Amekudzi, A.A. and Meyer, M.D. and Barrella, E.M. and Ross, C.L.},
title={Performance measurement frameworks and development of effective sustainable transport strategies and indicators},
journal={Transportation Research Record},
year={2010},
number={2163},
pages={73-80},
doi={10.3141/2163-08},
note={cited By 44},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651296659&doi=10.3141%2f2163-08&partnerID=40&md5=64b8d3cbd82e2b1329677389dc6690bd},
affiliation={Transportation Systems Program, School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0355, United States; Center for Quality Growth and Regional Development, Georgia Institute of Technology, 760 Spring Street, Atlanta, GA 30308-1028, United States},
abstract={This paper identifies seven attributes of robust performance measurement systems by analyzing five performance measurement frameworks and their use of transportation system performance indicators. The attributes are then used to examine three case studies from Europe and the United States to demonstrate the value of performance measurement frameworks for developing and improving sustainable transportation strategies and indicators. The case studies point to important considerations in formulating a robust sustainable transportation strategy at different levels of governance and also indicate the importance of ensuring alignment in an agency's vision, objectives, and monitoring systems. The characteristics of an effective framework for the development of sustainable transportation strategies include a comprehensive sustainability objective, a good connection to the goals and objectives of an agency, and vertical and horizontal integration. In addition, a framework should capture the interactions among variables, reflect stakeholder perspectives, and consider the capabilities and constraints of the agency and should be flexible to foster self-learning.},
keywords={Horizontal integrations;  Monitoring system;  Performance measurement frameworks;  Robust performance;  Self-learning;  Sustainable transport;  Sustainable transportation;  Transportation system, Benchmarking;  Planning;  Supply chain management, Transportation},
references={Jeon, C., Amekudzi, A., Guensler, R., Evaluating transport system sustainability: Atlanta metropolitan region (2007) 86th Annual Meeting of the Transportation Research Board, , Washington, D.C; (1987) Our Common Future., , World Commission on Environment and Development, Oxford University Press, Oxford, United Kingdom; Centre for Sustainable Transportation., , http://www.centreforsustainabletransportation.org/sustainabletransport. htm, About Sustainable Transportation. Definition, Accessed June 4, 2009; Nichols, J., Garrick, N., Atkinson-Palombo, C., Framework for developing indicators of sustainability for transportation planning (2009) 88th Annual Meeting of the Transportation Research Board, , Washington, D.C; Hart, M., (2006) Guide to Sustainable Community Indicators., , Hart Environmental Data, North Andover, Mass; Jeon, C., Amekudzi, A., Addressing sustainability in transportation systems: Definitions, indicators, and metrics (2005) Journal of Infrastructure Systems, 11 (1), pp. 31-50; California Transportation Plan: 2025., , http://www.dot.ca.gov/hq/tpp/offices/osp/ctp2025_files/CTP_2006.pdf, Accessed June 9, 2009, California Department of Transportation; Zietsman, J., Knowles, W., Ramani, T., Lee, J.S., Bochner, B., Sustainability enhancement tool for state DOTs using performance measurement (2008) 87th Annual Meeting of the Transportation Research Board, , Washington, D.C; Sustainable transportation indicators subcommittee. Sustainable transportation indicators: A recommended research program for developing sustainable transportation indicators and data (2009) 88th Annual Meeting of the Transportation Research Board, , Washington, D.C; (2002) Using the Pressure-State-Response Model to Develop Indicators of Sustainability., , Organisation for Economic Co-Operation and Development, Paris; Kristensen, P., (2004) The DPSIR Framework., , Workshop on a Comprehensive/Detailed Assessment of the Vulnerability of Water Resources to Environmental Change in Africa Using River Basin Approach, Sept. 27 to 29, Nairobi, Kenya; Pearce, A.R., Vanegas, J.A., Defining sustainability for built environment systems (2002) International Journal of Environmental Technology and Management, 2 (1), pp. 94-113; Gudmundsson, H., (2000) Indicators for Performance Measures for Transportation, Environment and Sustainability in North America., , Report from a German Marshall Fund Fellowship 2000 Individual Study Tour, October 2000. Research Notes Report 148. Ministry of Environment and Energy, National Environmental, Research Institute of Denmark; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Figge, F., Hahn, T., Schaltegger, S., Wagner, M., The sustainability balanced scorecard-theory and application of a tool for value based sustainability management (2002) Proc., Greening of Industry Network Conference, , Gothenburg, Sweden; Poister, T., Performance measurement in transportation agencies: State of the practice (2007) Handbook of Transportation Policy and Administration, pp. 485-504. , CRC Press, Boca Raton, Fla; Wholey, J.S., Hatry, H.P., Newcomer, K.E., (2007) The Handbook of Practical Program Evaluation., , Jossey-Bass, San Francisco, Calif; Doyle, D., Performance measure initiative at the texas department of transportation (1998) Transportation Research Record 1649, pp. 124-128. , TRB, National Research Council, Washington, D.C; Neely, A.D., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success., , Prentice- Hall Financial Times, London; Neely, A.D., Adams, C., The Performance Prism., , http://www.performance-measurement.net/news-detail.asp?nID=31, Accessed July 22, 2009; A Sustainable Europe for A Better World: A European Union Strategy for Sustainable Development., , http://europa.eu/eur-lex/en/com/cnc/2001/com2001_0264en01.pdf, Accessed June 20, 2009; Review of the EU Sustainable Development Strategy- Renewed Strategy., , http://register.consilium.europa.eu/pdf/en/06/st10/st10917.en06.pdf, Accessed June 20, 2009; (2005) Progress on EU Sustainable Development Strategy, , http://ec.europa.eu/sustainable/docs/sds_progress_report.pdf/, Final Report, ECORYS Nederland BV, Accessed June 22, 2009; (2009) Sustainable Development Indicators, , http://epp.eurostat.ec.europa.eu/portal/page/portal/sdi/introduction, Accessed June 23, 2009; (2001) European Transport Policy for 2010: Time to Decide, , http://ec.europa.eu/transport/white_paper/documents/doc/ lb_com_2001_0370_en.pdf, White Paper, Accessed June 24, 2009; (2005) Assessment of the Contribution of the TEN and Other Transport Policy Measures to the Midterm., , http://ec.europa.eu/transport/strategies/2001_white_paper_en.htm, Transport and Mobility Leuven, Accessed June 23, 2009; (2005) Securing the Future: Delivering UK Sustainable Development Strategy., , http://www.defra.gov.uk/sustainable/government/publications/uk-strategy/ documents/SecFut_complete.pdf, United Kingdom Government, Accessed June 12, 2009; (2005) One Future-Different Paths: The UK's Shared Framework for Sustainable Development., , http://www.defra.gov.uk/sustainable/government/documents/SDFramework.pdf, United Kingdom Government, Accessed June 28, 2009; (2008) Sustainable Development in Your Pocket., , http://www.defra.gov.uk/sustainable/government/progress/documents/ SDIYP2008_a6.pdf, United Kingdom Government, Accessed June 27, 2009; (2008) Delivering A Sustainable Transport System: Main Report., , http://www.dft.gov.uk/about/strategy/transportstrategy/dasts/dastsreport. pdf, Department for Transport, United Kingdom, Accessed June 27, 2009; Department for Transport Sustainable Development Action Plan April 2009-March 2010, , http://www.dft.gov.uk/about/howthedftworks/sda/secsusdevactplan09/ sustainactplan.pdf, Department for Transport, United Kingdom, Accessed July 2, 2009; Strategic Plan 2009 Through 2013, , ftp://ftp.dot.state.tx.us/pub/txdot-info/lao/public-strategic-plan2009. pdf, Texas Department of Transportation, Accessed July 6, 2009; Ramani, T., Zietsman, J., Eisele, W., Rosa, D., Spillane, D., Bochner, B., (2009) Developing Sustainable Transportation Performance Measures for TxDOT's Strategic Plan: Technical Report., , http://tti.tamu.edu/documents/0-5541-1.pdf, Accessed July 10, 2009},
correspondence_address1={Pei, Y. L.; Transportation Systems Program, , Atlanta, GA 30332-0355, United States; email: peiyilin@gatech.edu},
issn={03611981},
coden={TRRED},
language={English},
abbrev_source_title={Transp Res Rec},
document_type={Article},
source={Scopus},
}

@ARTICLE{Carvalho2010304,
author={Carvalho, H. and Azevedo, S.G. and Cruz-Machado, V.},
title={Supply chain performance management: Lean and green paradigms},
journal={International Journal of Business Performance and Supply Chain Modelling},
year={2010},
volume={2},
number={3-4},
pages={304-333},
doi={10.1504/IJBPSCM.2010.036204},
note={cited By 78},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983570693&doi=10.1504%2fIJBPSCM.2010.036204&partnerID=40&md5=c1d5e01936252dc669fffa947f67e071},
affiliation={UNIDEMI - Department of Mechanical and Industrial Engineering, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Campus Universitário, Caparica, 2829-516, Portugal; NECE - Research Unit, Department of Management and Economics, University of Beira Interior, Pólo IV - Edifício Ernesto Cruz, Covilhã, 6200-209, Portugal},
abstract={Supply chain management (SCM) is crucial for increasing organisational effectiveness, enhancing competitiveness, customer service and profitability. Actually, the lean and green philosophies have been adopted in the SCM context, but nearly always separately and with little understanding of their influence on supply chain performance. Accordingly, this paper intends to propose a conceptual model that explores the relationships between lean and green practices and supply chain performance. A set of lean and green SCM practices and a performance measurement system are suggested. The proposed performance measures intend to evaluate the practices influence on operational, economic and environmental supply chain's performance. Copyright © 2010 Inderscience Enterprises Ltd.},
author_keywords={Green paradigm;  Lean paradigm;  Performance measurement;  SCM;  Supply chain management},
references={Agyapong-Kodua, K., Ajaefobi, J.O., Weston, R.H., Modelling dynamic value streams in support of process design and evaluation (2009) International Journal of Computer Integrated Manufacturing, 22 (5), pp. 411-427; Ahmad, S., Schroeder, R., Mallick, S., The relationship among modularity, functional coordination, and mass customization: Implications for competitiveness (2010) European Journal of Innovation Management, 13 (1), pp. 46-61; Anand, G., Kodali, R., A conceptual framework for lean supply chain and its implementation (2008) International Journal of Value Chain Management, 2, pp. 313-357; Anand, G., Kodali, R., Development of a framework for implementation of lean manufacturing systems (2010) International Journal of Management Practice, 4 (1), pp. 95-116; Aramyan, L., Meuwissen, M., Lansink, A., Van Der Vorst, J., Van Kooten, O., Van Der Lans, I., The perceived impact of quality assurance systems on tomato supply chain performance (2009) Total Quality Management & Business Excellence, 20 (6), pp. 633-653; Askariazad, M., Wanous, M., A proposed value model for prioritising supply chain performance measures (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 115-128; Barreto, L., Anderson, H., Anglin, A., Tomovic, C., Product lifecycle management in support of green manufacturing: Addressing the challenges of global climate change (2010) International Journal of Manufacturing Technology and Management, 19 (3-4), pp. 294-305; Bayraktar, E., Demirbag, M., Koh, S., Tatoglu, E., Zaim, H., A causal analysis of the impact of information systems and supply chain management practices on operational performance: Evidence from manufacturing SMEs in Turkey (2009) International Journal of Production Economics, 122 (1), pp. 133-149; Berry, W., Bruun, P., Ward, P., Lean manufacturing: A mapping of competitive, initiatives, practices, and operational performance in Danish manufacturers (2002) Proceedings of 9th International Conference, European Operations Management Association, 2002, , Copenhagen, Denmark; Biddle, J., Best Practices in Lean, 2005. , http://www.sap.com/usa/industries/machinery/pdf/Best_Practices_in_Lean.pdf, AberdeenGroup White Paper (accessed on 20 June 2009); Bowen, F.E., Cousine, P.D., Lamming, R.C., Faruk, A.C., Horses for courses: Explaining the gap between the theory and practice of green supply (2001) Greener Management International, (35), pp. 41-59; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-94; Brown, K., Mitchell, T., A comparison of just-in-time and batch manufacturing: The role of performance obstacles (1991) Academy of Management Journal, 34 (4), pp. 906-917; Cagnazzo, L., Taticchi, P., Brun, A., The role of performance measurement systems to support quality improvement initiatives at supply chain level (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 163-185; Chaharsooghi, S., Heydari, J., LT variance or LT mean reduction in supply chain management: Which one has a higher impact on SC performance? (2010) International Journal of Production Economics, 124 (2), pp. 475-481; Chan, F., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chan, F., Qi, H., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3-4), pp. 209-223; Chanintrakul, P., Mondragon, A.E.C., Lalwani, C., Wong, C.Y., Reverse logistics network design: A state-of-the-art literature review (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (1), pp. 61-81; Cheng, J.-H., Yeh, C.-H., Tu, C.-W., Trust and knowledge sharing in green supply chains (2008) Supply Chain Management, 13 (4), pp. 283-295; Chin, W., Peterson, R., Brown, S., Structural equation modeling in marketing: Some practical reminders (2008) Journal of Marketing Theory and Practice, 16 (4), pp. 287-298; Crute, V., Wickham, C., Graves, A., Exploring competitive advantage through lean implementation in the aerospace supply chain (2008) International Federation for Information Processing, N257, pp. 357-364. , Springer, Great Britain; Cumbo, D., Kline, D., Bumgardner, M., Benchmarking performance measurement and lean manufacturing in the rough mill (2006) Forest Products Journal, 56 (6), pp. 25-30; Darnall, N., Jolley, G.J., Handfield, R., Environmental management systems and green supply chain management: Complements for sustainability (2008) Business Strategy and the Environment, 17 (1), pp. 30-45; Doolen, T., Hacker, M., A review of lean assessment in organizations: An exploratory study of lean practices by electronics manufacturers (2005) Journal of Manufacturing Systems, 24 (1), pp. 55-67; Flynn, B., Huo, B., Zhao, X., The impact of supply chain integration on performance: A contingency and configuration approach (2010) Journal of Operations Management, 28 (1), pp. 58-71; Goldsby, T., Griffis, S., Roath, A., Modeling lean, agile and leagile supply chain strategies (2006) Journal of Business Logistics, 27 (1), pp. 57-80; Gonzalez, P., Sarkis, J., Adenso-Diaz, B., Environmental management system certification and its influence on corporate practices: Evidence from the automotive industry (2008) International Journal of Operations & Production Management, 28 (11), pp. 1021-1041; Green, K.W., Inman, R.A., Using a just-in-time selling strategy to strengthen supply chain linkages (2005) International Journal of Production Research, 43 (16), pp. 3437-3453; Gregorio, D., Musteen, D., Thomas, D., Offshore outsourcing as a source of international competitiveness for SMEs (2009) Journal of International Business Studies: Part Special Issue: Offshoring Administrative and Technical, 40 (6), pp. 969-988; Gunasekaran, C.P., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gurumurthy, A., Kodali, R., Application of benchmarking for assessing the lean manufacturing implementation (2009) Benchmarking: An International Journal, 16 (2), pp. 274-308; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Hines, P., Holweg, M., Rich, N., Learning to evolve a review of contemporary lean thinking (2004) International Journal of Operations & Production Management, 24 (10), pp. 994-1011; Holt, D., Ghobadian, A., An empirical study of green supply chain management practices amongst UK manufacturers (2009) Journal of Manufacturing Technology Management, 20 (7), pp. 933-956; Holweg, M., Disney, S.M., Hines, P., Naim, M.M., Towards responsive vehicle supply: A simulation-based investigation into automotive scheduling systems (2005) Journal of Operations Management, 23 (5), pp. 507-530; Hu, A.H., Hsu, C.W., Empirical study in the critical factors of green supply chain management (GSCM) practice in the Taiwanese electrical and electronics industries (2006) Proceeding of 2006 IEEE International Conference on Management of Innovation and Technology, pp. 853-857. , 21-23 June 2006, Singapore, China; Huang, Y.A., Matthews, H.S., Seeking opportunities to reduce life cycle impacts of consumer goods-an economy-wide assessment (2008) Proceedings of the 2008 IEEE International Symposium on Electronics and the Environment, pp. 1-6. , IEEE Computer Society; Hugo, A., Pistikopoulos, E., Environmentally conscious long-range planning and design of supply chain networks (2005) Journal of Cleaner Production, 13 (15), pp. 1471-1491; Hutchison, P., Farris, M., Fleischman, G., Supply chain cash-to-cash: A strategy for the 21st century (2009) Strategic Finance, 91 (1), pp. 41-48; Jiang, B., Frazier, G.V., Prater, E.L., Outsourcing effects on firms' operational performance, an empirical study (2006) International Journal of Operations and Production Management, 26 (12), pp. 1280-1300; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) International Journal of Production Economics, 101 (1), pp. 99-108; Kannan, V.R., Tan, K.C., Just in time, total quality management, and supply chain management: Understanding their linkages and impact on business performance (2005) Omega, 33 (2), pp. 153-162; Khidir, T., Zailani, S., Going green in supply chain towards environmental sustainability (2009) Global Journal of Environmental Research, 3 (3), pp. 246-251; King, A., Lenox, M., Lean and green? An empirical examination of the relationship between lean production and environmental performance (2001) Production and Operations Management, 10 (3), pp. 244-256; Koufteros, X., Babbar, S., Kaighobadi, M., A paradigm for examining second-order factor models employing structural equation modelling (2009) International Journal of Production Economics, 120 (2), pp. 633-652; Kumar, M., Vrat, P., Shankar, R., A fuzzy goal programming approach for vendor selection problem in a supply chain (2004) Computers & Industrial Engineering, 46 (1), pp. 69-85; Lambert, D., Pohlen, L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lancaster, C., Stevens, J., Jennings, J., Corporate liquidity and the significance of earnings versus cash flow (1998) Journal of Applied Business Research, 14 (4), pp. 27-38; Lee, C.W., Kwon, I.-W.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer (2007) Supply Chain Management: An International Journal, 12 (6), pp. 444-452; Lin, C.-H., Ho, Y.-H., RFID technology adoption and supply chain performance: An empirical study in China's logistics industry (2009) Supply Chain Management, 14 (5), pp. 369-378; Linton, J.D., Klassen, R., Jayaraman, V., Sustainable supply chains: An introduction (2007) Journal of Operations Management, 25 (6), pp. 1075-1082; Lippman, S., Supply chain environmental management (2001) Environmental Quality Management, 11 (2), pp. 11-14; Luh, Y.-P., Chu, S.-H., Pan, C.-C., Data management of green product development with generic modularized product architecture (2010) Computers in Industry, 61 (3), pp. 223-229; Mahidhar, V., (2005) Designing the Lean Enterprise Performance Measurement Systems, , Master thesis, Massachusetts Institute of Technology; McKone, K.E., Schroeder, R.G., Cua, K.O., The impact of total productive maintenance practices on manufacturing performance (2001) Journal of Operations Management, 19 (1), pp. 39-58; Melton, T., The benefits of lean manufacturing what lean thinking has to offer the process industries (2005) Chemical Engineering Research and Design, 83 (6), pp. 662-673; Min, H., Galle, W.P., Green purchasing practices of US firms (2001) International Journal of Operations & Production Management, 21 (9), pp. 1222-1238; Min, H., Zhou, G., Supply chain modeling: Past, present and future (2002) Computers and Industrial Engineering, 43 (1-2), pp. 231-249; Nair, S.R., Menon, C.G., An environmental marketing system - A proposed model based on Indian experience' (2008) Business Strategy and the Environment, 17 (8), pp. 467-479; Nawrocka, D., Brorson, T., Lindhqvist, T., ISO 14001 in environmental supply chain practices (2009) Journal of Cleaner Production, 17 (16), pp. 1435-1443; Ohno, T., (1998) The Toyota Production System, , Productivity Press, Portland; Pal, R., Busing, M.E., Teaching operations management in an integrated format: Student perception and faculty experience (2008) International Journal of Production Economics, 115 (2), pp. 594-610; Parveen, M., Rao, T.V.V.L.N., An integrated approach to design and analysis of lean manufacturing system: A perspective of lean supply chain (2009) International Journal of Services and Operations Management, 5 (2), pp. 175-208; Paulraj, A., Environmental motivations: A classification scheme and its impact on environmental strategies and practices (2009) Business Strategy and the Environment, 18 (7), pp. 453-468; Pochampally, K.K., Gupta, S.M., Govindan, K., Metrics for performance measurement of a reverse/closed-loop supply chain (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (1), pp. 8-32; Pohlen, L., Coleman, B., Evaluating internal operations and supply chain performance using EVA and ABC (2005) S.A.M. Advanced Management Journal, 70 (2), pp. 45-59; Ramaswamy, N.R., Selladurai, V., Gunasekaran, A., Just-in-time implementation in small and medium enterprises (2002) Work Study, 51 (2), pp. 85-90; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) International Journal of Operations and Production Management, 25 (9), pp. 898-916; Reichhart, A., Holweg, M., Lean distribution: Concepts, contributions, conflicts (2007) International Journal of Production Research, 45 (16), pp. 3699-3722; Rothenberg, S., Pil, F., Maxwell, J., Lean, green, and the quest for superior environmental performance (2001) Production and Operations Management, 10 (3), pp. 228-243; Routroy, S., Antecedents and drivers for green supply chain management implementation in manufacturing environment (2009) ICFAI Journal of Supply Chain Management, 6 (1), pp. 20-35; Sawhney, R., Teparakul, P., Bagchi, A., Li, X., En-lean: A framework to align lean and green manufacturing in the metal cutting supply chain (2007) International Journal of Enterprise Network Management, 1 (3), pp. 238-260; Seidel, R.H.A., Shahbazpour, M., Seidel, M.C., Establishing sustainable manufacturing practices in SMEs (2007) Proceedings of the 2nd International Conference on Sustainability Engineering and Science, , 21-23 February 2007, Auckland, NZ; Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) Journal of Cleaner Production, 16 (15), pp. 1699-1710; Shah, R., Ward, P., Lean manufacturing: Context, practice bundles and performance (2003) Journal of Operations Management, 21 (2), pp. 129-149; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Sterman, J., (2000) Business Dynamics: Systems Thinking and Modeling for A Complex World, , MGraw-Hill, New York; Stewart, D., Grout, J., The human side of mistake-proofing (2001) Production and Operations Management, 10 (4), pp. 440-459; Stokes, S., Tohamy, N., 7 traits of a green supply chain (2009) Supply Chain Management Review, 13 (7), p. 8; Treville, S., Antonakis, J., Could lean production job design, be intrinsically motivating? Contextual, configurational and levels-of-analysis issues (2006) Journal of Operations Management, 24 (2), pp. 99-123; Tsai, W., Hung, S., A fuzzy goal programming approach for green supply chain optimisation (2009) International Journal of Production Research, 47 (18), pp. 4991-5017; Tsoulfas, G.T., Pappis, C.P., Environmental principles applicable to supply chains design and operation (2006) Journal of Cleaner Production, 14 (18), pp. 1593-1602; (2000) The Lean and Green Supply Chain: A Practical Guide for Materials Managers and Supply Chain Managers to Reduce Costs and Improve Environmental Performance, , http://www.epa.gov/oppt/library/pubs/archive/acct-archive/pubs/lean.pdf, Washington, D.C. (accessed on 01 September 2009); Vachon, S., Green supply chain practices and the selection of environmental technologies (2007) International Journal of Production Research, 45 (18-19), pp. 4357-4379; Vachon, S., Klassen, R., Extending green practices across the supply chain: The impact of upstream and downstream integration (2006) International Journal of Operations and Production Management, 26 (7), pp. 795-821; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: The role of collaboration in the supply chain (2008) International Journal of Production Economics, 111 (2), pp. 299-315; Venkat, K., Wakeland, W., Is lean necessarily green? (2006) Proceedings of the 50th Annual Meeting of the ISSS (International Society for the Systems Sciences), , 9-14 July 2006, Sonoma State University, Rohnert Park, California, USA; Wadhwa, S., Mishra, M., Chan, F., Ducq, Y., Effects of information transparency and cooperation on supply chain performance: A simulation study (2010) International Journal of Production Research, 48 (1), pp. 145-166; Wang, G., Huang, S.H., Dismukes, J.P., Product-driven supply chain selection using integrated multi-criteria decision-making methodology (2004) International Journal of Production Economics, 91 (1), pp. 1-15; Whicker, L., Bernon, M., Templar, S., Mena, C., Understanding the relationships between time and cost to improve supply chain performance (2009) International Journal of Production Economics, 121 (2), pp. 641-650; Wilson, M., Roy, R., Enabling lean procurement: A consolidation model for small- and medium-sized enterprises (2009) Journal of Manufacturing Technology Management, 20 (6), pp. 817-833; Wilson, M.C., The impact of transportation disruptions on supply chain performance (2007) Transportation Research Part E: Logistics and Transportation Review, 43 (4), pp. 295-320; Wincel, P., (2005) Lean Supply Chain Management: A Handbook for Strategic Procurement, , The Free Press, New York, NY; Womack, J., Jones, D., Roos, D., (1991) The Machine That Change the World, , HarperCollins Publishers, New York; Wong, W.P., Performance evaluation of supply chain in stochastic environment: Using a simulation based DEA framework (2009) International Journal of Business Performance and Supply Chain Modelling, 1 (2-3), pp. 203-228; Yang, C., Su, Y.-F., The relationship between benefits of ERP systems implementation and its impacts on firm performance of SCM (2009) Journal of Enterprise Information Management, 22 (6), pp. 722-752; Zhou, Z., Cheng, S., Hua, B., Supply chain optimization of continuous process industries with sustainability considerations (2000) Computers and Chemical Engineering, 24 (2-7), pp. 1151-1158; Zhu, Q., Sarkis, J., Geng, Y., Green supply chain management in China: Pressures, practices and performance (2005) International Journal of Operations and Production Management, 25 (5-6), pp. 449-469; Zhu, Q., Sarkis, J., Lai, K., Confirmation of a measurement model for green supply chain management practices implementation (2008) International Journal of Production Economics, 111 (2), pp. 261-273},
correspondence_address1={Carvalho, H.; UNIDEMI - Department of Mechanical and Industrial Engineering, Portugal; email: hmlc@fct.unl.pt},
publisher={Inderscience Publishers},
issn={17589401},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Supply Chain Model.},
document_type={Article},
source={Scopus},
}

@ARTICLE{NoAuthor20101,
title={9th IFIP WG 5.5 International Conference on Balanced Automation Systems for Future Manufacturing Networks, BASYS 2010},
journal={IFIP Advances in Information and Communication Technology},
year={2010},
volume={322},
pages={1-308},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943637826&partnerID=40&md5=5919889b89bf15f369fe8c219d71a9dd},
abstract={The proceedings contain 35 papers. The special focus in this conference is on Co-innovation Networks in Industry, Industry Ecosystems and Emergent Business Models. The topics include: Cold supply chain processes in a fruit-and-vegetable collaborative network; enterprise architecture framework with early business/ICT alignment for extended enterprises; evaluation of collaborative enterprises networks; ICT for sustainable manufacturing; research issues on customer-oriented and eco-friendly networks for healthy fashionable goods; a multi-criteria approach to select suppliers based on performance; a collaborative performance measurement system; forest fire evolution prediction using a hybrid intelligent system; modelling human resource systems in support of efficient production; open services ecosystem supporting collaborative networks; service entities in manufacturing networks and understanding social capital in collaborative networks; a framework for developing a web-based optimization decision support system for intra/inter-organizational decision-making processes.},
editor={Ortiz A., Franco R.D., Gasquet P.G.},
publisher={Springer New York LLC},
issn={18684238},
isbn={3642143407; 9783642143403; 9783642143403},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Review},
source={Scopus},
}

@CONFERENCE{Ugazio2010469,
author={Ugazio, E. and Pigni, F.},
title={Measuring RFId benefits in the supply chain},
journal={Information Systems: People, Organizations, Institutions, and Technologies - ItAIS: The Italian Association for Information Systems},
year={2010},
pages={469-476},
doi={10.1007/978-3-7908-2148-2_54},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901660530&doi=10.1007%2f978-3-7908-2148-2_54&partnerID=40&md5=b46312c62c164bf5ddbe7ddff3dae8f5},
affiliation={Università Carlo Cattaneo, LIUC, Castellanza (VA), Italy},
abstract={RFId systems show great potentials enhancing supply chain performances. Some of the major retailers in the World (Wall Mart, Metro Group, Tesco) made sounding campaigns promoting the benefits they were able to attain. Despite the increase in the number of researches on RFId benefits, a study investigating their measurement in the supply chain context is still lacking. This paper aims at identifying and measure RFId benefits by designing three tools: (1) an RFId oriented Performance Measurement System for the identification of all RFId related supply chain performance indicators on the base of the SCOR Model. (2) A benefits-processes-measures matrix linking the benefits identified in literature with SCOR model process to identify supply chain measures impacted by the RFId system implementation. (3) A reference framework summarizing benefits measures. © Springer Physica-Verlag 2010.},
keywords={Information systems;  Supply chain management, Performance measurement system;  Reference frameworks;  RFID systems;  SCOR models;  Supply chain performance;  Wall marts, Supply chains},
references={Laubacher, R., Kothari, S.P., Malone, T.W., Subirana, B., (2006) What Is RFID Worth to Your Company? Measuring Performance at the Activity Level. A Research and Education Initiative at the MIT Sloan School of Management, pp. 1-30; Leong, K.S., Ng, M.L., Engels, D.W., EPC Network Architecture (2004) Auto-ID Labs Research Workshop, Zurich, CH, , Presented at; Smith, A.D., Exploring radio frequency identification technology and its impact on business systems (2005) Information Management and Computer Security, 13 (1), pp. 16-28; Hou, J.-L., Huang, C.-H., Quantitative performance evaluation of RFID applications in the supply chain of the printing industry (2006) Industrial Management and Data Systems, 106 (1), pp. 96-120. , DOI 10.1108/02635570610641013; Hodges, S., McFarlane, D., Radio Frequency Identification: Technology, applications and impact (2005) White Paper Series -Edition 1, , Auto-ID-Labs; Chuang, M., Shaw, W.H., (2005) How RFID Will Impact Supply Chain Networks, pp. 231-235. , IEEE Computer Society; Veeramani, D., Tang, J., Gutierrez, A., A framework for assessing the value of RFID implementation by tier-one suppliers to major retailers (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 55-70; Jeong, B.-K., Lu, Y., The impact of radio frequency identification (RFID) investment announcements on the market value of the firm (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 41-54; Chopra, S., Sodhi, M.S., Looking for the Bang from the RFId Buck (2007) Supply Chain Management Review, 11, pp. 34-41; Seddon, P.B., Graeser, V., Willcocks, L.P., Measuring organizational IS effectiveness: An overview and update of senior management perspectives (2002) Data Base for Advances in Information Systems, 33 (2), pp. 11-28; Curtin, J., Kauffman, R.J., Riggings, F.J., Making the "Most" out of RFID Technology:A research agenda for the study of the adoption, usage and impact of RFID (2007) Information Technology and Management, 8 (1), pp. 87-110; Mari, L., Lazzarotti, V., Manzini, R., Measurement in soft systems: Epistemological framework and a case study (2008) Measurement, 42, pp. 241-253; Pigni, F., Astuti, S., Buonanno, G., An evaluation framework for RFId Adoption (2004) AICA 2004 XLII Annual Congress. Benevento, Italy; Bertelè, U., (2006) RFId Alla Prova dei Fatti I Risultati 2006 dell'Osservatorio RFId, , Politecnico di Milano; Van De Wijngaert, L., Versendaal, J., Business IT Alignment and technology adoption; The case of RFID in the logistics domain (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 71-80; Xiao, Y., Yu, S., Wu, K., Ni, Q., Janecek, C., Nordstad, J., Radio frequency identification: Technologies, applications, and research issues (2007) Wireless Communications and Mobile Computing, 7 (4), pp. 457-472. , DOI 10.1002/wcm.365; Loebbecke, C., Huyskens, C., A Competitive Perspective on Standard-Making: Kaufhof's RFID Project in Fashion Retailing (2008) Electronic Markets, 18 (1), pp. 30-38; Zhang, T., Ouyang, Y., He, Y., Traceable air baggage handling system based on RFID tags in the airport (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 106-115; Spivey Overby, C., (2004) Revealing RFID's Benefits in Consumer Goods, , Forrester Research, Cambridge, MA; Koh, C.E., Kim, H.J., Kim, E.Y., The impact of RFID in retail industry: Issues and critical success factors (2006) Journal of Shopping Center Research, 13 (1), pp. 101-117; Tellkamp, C., (2006) The Impact of Auto-ID Technology on Process Performance - RFID in the FMCG Supply Chain, , Dissertation of University of St. Gallen, Graduate school of business Administration; Munoz, A., Clements, M.D., Disruptions in information flow: A revenue costing supply chain dilemma (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 30-40; McFarlane, D., Sheffi, Y., The impact of automatic identification on supply chain operations (2003) The International Journal of Logistics Management, 14 (1), pp. 1-17; Michael, K., McCathie, L., The Pros and Cons of RFID in Supply Chain Management (2005) International Conference on Mobile Business; Hilger, A., Ghijsen, P., Semeijn, J., Antecedents of logistics performance and economic performance: The case of Radio Frequency Identification (RFID) (2007) International Journal of Business Research, 7 (6), pp. 57-66; Neely, A., The evolution of performance measurement research. Developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258. , DOI 10.1108/17410400610653219; Tangen, S., Analysing the requirements of performance measurement systems (2005) Measuring Businness Excellence, 9 (4), pp. 46-54},
publisher={Physica-Verlag},
isbn={9783790821475},
language={English},
abbrev_source_title={Inf. Syst.: People, Organ., Inst., Technol. - ItAIS: Ital. Assoc. Inf. Syst.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Martins2010,
author={Martins, R.A. and Leal, J.S.},
title={How to promote the employee ownership of performance measurement systems},
journal={IIE Annual Conference and Expo 2010 Proceedings},
year={2010},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901037195&partnerID=40&md5=9bb8998bb1e9be3c48dc511e4561a2f4},
affiliation={Department of Industrial Engineering, Federal University of Sao Carlos, Sao Carlos, SP, Brazil},
abstract={There is a gap in the literature on how to maintain performance measurement systems (PMS's) relevant. During the 1990's, the research efforts on this field focused mainly on the development of new PMS frameworks. Currently, the Balanced Scorecard supremacy has facilitated the development of empirical research, but the majority of studies employ the case study method. This paper aims to present the empirical findings of an action research on how to promote employee ownership of PMS in a large Brazilian manufacturing company. The involvement of employees during the development/review decreased the ambiguity of performance measurement and increased their ownership of the system. Changes were made in the PMS design, supporting infrastructure, key organizational capabilities, and use of PMS. The major empirical finding is the improvement of the use of a performance measurement system by managers and employees in terms of actions taken to solve non-conformities and fill gaps in performance.},
author_keywords={Performance measurement systems;  Performance measures;  Use of performance measurement systems},
keywords={Human resource management;  Industrial research, Balanced scorecards;  Empirical findings;  Empirical research;  Manufacturing companies;  Organizational capabilities;  Performance measure;  Performance measurement system;  Performance measurements, Supply chain management},
references={Santos, S.P., Belton, V., Howick, S., Adding value to performance measurement by using system dynamics and multicriteria analysis (2002) International Journal of Operations & Production Management, 22 (11), pp. 1246-1272; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerly, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Gregory, M.J., Integrated performance measurement: A review of current practice and emerging trends (1993) International Journal of Operations & Production Management, 30 (1), pp. 281-296; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations & Production Management, 20 (5), pp. 520-533; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) , International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Vakkuri, J., Meklin, P., Ambiguity in performance measurement: A theoretical approach to organisational uses of performance measurement (2006) Financial Accountability & Management, 22 (3), pp. 235-250; Daniels, R.C., Burns, N.D., A framework for proactive performance measurement system introduction (1997) International Journal of Operations & Production Management, 17 (1), pp. 100-116; Coughlan, P., Coghlan, D., Action research for operations management (2002) International Journal of Operations & Production Management, 22 (2), pp. 220-240; Ariizumi, Y., (2005) Five Empowering Principles of Action Research that Lead to Successful Personal and Professional Development, , University Press of America},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo Proc.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{DeLima2010,
author={De Lima, E.P. and Da Costa, S.E.G. and Munik, J. and Angelis, J.J.},
title={Operations performance measurement systems roles},
journal={IIE Annual Conference and Expo 2010 Proceedings},
year={2010},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901020551&partnerID=40&md5=b95b3a40a11ef64d69520e582d0686fe},
affiliation={Industrial and Systems Eng. Graduate Program, Pontifical Catholic University of Parana, Parana Curitiba, Brazil; Industrial and Systems Eng. Graduate Program, Pontifical Catholic University of Parana Curitiba, Brazil; Operations Management Group, Warwick Business School, Coventry, United Kingdom},
abstract={A new competitive arena for enterprises' strategy development is evolving, forcing them to redesign their management systems. Particularly, organisations are focusing on performance measurement systems revision. The present research is based on case studies that are used to investigate performance measurement system uses. Companies were selected based on their experience in performance measurement and cover manufacturing and service operations. Results confirmed the traditional use of measurement systems, but they also pointed out a proactive use for measurement systems. Conclusions show an expanded set of performance measurement system roles that are used to challenge the established operations management structures and processes.},
author_keywords={Operations strategy;  Performance measurement;  Strategic management},
keywords={Industry;  Measurements;  Planning, Management systems;  Operations management;  Operations strategies;  Performance measurement system;  Performance measurements;  Service operations;  Strategic management;  Strategy development, Supply chain management},
references={Kathuria, R., Joshi, M.P., Porth, S.J., Organizational alignment and performance: Past, present and future (2007) Management Decision, 45 (3), pp. 503-517; Fernandes, K.J., Raja, V., Whalley, A., Lessons from implementing the balanced scorecard in a small and medium size manufacturing organization (2006) Technovation, 26 (5-6), pp. 623-634; Neely, A.D., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Marr, B., Schiuma, G., Business performance measurement - past, present and future (2003) Management Decision, 41 (8), pp. 680-687; Phusavat, K., Anussornnitisarn, P., Helo, P., Dwight, R., Performance measurement: Roles and challenges (2009) Industrial Management & Data Systems, 109 (5), pp. 646-664; Herzog, N.V., Tonchia, S., Polajnar, A., Linkages between manufacturing strategy, benchmarking, performance measurement and business process reengineering (2009) Computers & Industrial Engineering, 57 (3), pp. 963-975; Folan, P., Browne, J., Jagdev, H., Performance: Its meaning and content for today's business research (2007) Computers in Industry, 58 (7), pp. 605-620; Henry, J.F., Management control systems and strategy: A resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Joshi, M.P., Kathuria, R., Porth, S.J., Alignment of strategic priorities and performance: An integration of operations and strategic management perspectives (2003) Journal of Operations Management, 21 (3), pp. 353-369; Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), pp. 217-223; Platts, K.W., Integrated manufacturing: A strategic approach (1995) Integrated Manufacturing Systems, 6 (3), pp. 18-23; Leong, G.K., Snyder, D.L., Ward, P.T., Research in the process and content of manufacturing strategy (1990) OMEGA International Journal of Management Science, 20 (1), pp. 23-36; Slack, N., The flexibility of manufacturing systems (1987) International Journal of Operations & Production Management, 7 (4), pp. 35-45; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing: A Model for American Companies, , Productivity Press, Cambridge; Blenkinsop, S., Davis, L., The road to continuous improvement (1991) Insight, 4 (3), pp. 23-26; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures and measurement in an organizational context: A framework and direction for future research (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Clarke, P., Non-financial measures of performance in management (1995) Accountancy Ireland, 27 (2), pp. 22-24; Grady, M.W., Performance measurement: Implementing strategy (1991) Management Accounting, 72 (12), pp. 49-53; Manoochehri, G., The road to manufacturing excellence: Using performance measures to become world-class (1999) Industrial Management, 41 (2), pp. 7-13; Bhimani, A., Performance measures in UK manufacturing companies: The state of play (1993) Management Accounting, 71 (11), pp. 20-22; Santori, P.R., Anderson, A.D., Manufacturing performance in the 1990s: Measuring for excellence (1987) Journal of Accountancy, 164 (5), pp. 141-147; Tsang, A.H.C., Jardine, A.K.S., Kolodny, H., Measuring maintenance performance: A holistic approach (1999) International Journal of Operations & Production Management, 19 (7), pp. 691-715; Drucker, P.E., The emerging theory of manufacturing (1990) Harvard Business Review, 68 (3), pp. 94-102; Mcnair, C.J., Mosconi, W., Measuring performance in an advanced manufacturing environment (1987) Management Accounting, 69 (1), pp. 28-31; De Pinheiro, L.E., Da Gouvea, C.S.E., Jannis, J.J., The strategic management of operations system performance (2007) International Journal of Business Performance Management, 9 (4), pp. 108-132; De Pinheiro, L.E., Da Gouvea, C.S.E., Jannis, J.J., Strategic performance measurement systems: A discussion about their roles (2009) Measuring Business Excellence, 13 (3), pp. 39-48; De Pinheiro, L.E., Da Gouvêa, C.S.E., Angelis, J.J., Garbuio, P.A.R., A consensual analysis about strategic performance measurement systems roles (2009) Proc. of the 20th International Conference on Production Research, pp. 1-6. , August 2-6, Shanghai, China; De Pinheiro, L.E., Da Gouvêa, C.S.E., Angelis, J.J., Pepino, S., Reassessing performance measurement systems roles (2009) Proc. of the 16th International EurOMA Conference, pp. 1-10. , June 14- 17, Gothenburg, Sweden; Slack, N., Chambers, S., Johnston, R., (2007) Operations Management, , 5th edition, Prentice Hall, New Jersey; Neely, A.D., Richards, H., Mills, J.F., Platts, K.W., Bourne, M.C.S., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152},
publisher={Institute of Industrial Engineers},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo Proc.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Markovic2010257,
author={Markovic, N.},
title={Business performance measurement and information evolution model},
journal={4th European Conference on Information Management and Evaluation, ECIME 2010},
year={2010},
pages={257-264},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900809089&partnerID=40&md5=3e84d7dfd0ea160c3cd2a56d97b3868e},
affiliation={Belgrade Business School, Belgrade, Serbia},
abstract={In the core of new management philosophies lies the question of evaluation of efficiency and inevitably Performance Measurement. All organizations should have specific strategy and measures derived from strategy. Data and information should be seen as business resources. The performance measurement could be seen as an information system. While analyzing reasons for the introduction, implementation and evaluation of performance measurement, Neely points out the power provided by IT to be a key driver behind the performance measurement development. It is not by chance that the apparition of data warehouse and analytical tools was called information technology renaissance. There is several performance measurement Maturity Models, which describes the development of a performance measurement system over time, following an evolutionary pattern through four or five maturity levels which are characterized by the progressive development along different dimensions. Information Evolution Model, maturity model for Business Intelligence, includes five evolutionary stages (operational, consolidation, integration, optimization and innovation). Each level is a natural and necessary precursor to the next higher level. Characteristic at each evolutionary level are remarkably predictable across four dimensions. Instead of designing an advanced system that fulfils all types of requirements, maturity models encourages organizations to focus on how to fulfill the requirements, within the level that their existing PMS belongs to. This research analyzes PMS characteristics and explores their development during time. The introduced concept of PMS three-level Maturity Model allows companies to keep and improve their existing PMS. Organizations have their own unique need for a PMS, meaning that some of them will be satisfied with simple ones. Using the concept of PMS levels, they can choose how advanced the final result of its PMS should it be.},
author_keywords={Information Evolution Model;  maturity level;  Performance Measurement Systems},
keywords={Data warehouses;  Information management;  Information technology;  Philosophical aspects;  Supply chain management, Business performance;  Data and information;  Evolution modeling;  Evolutionary stage;  Management philosophy;  Maturity levels;  Performance measurement system;  Performance measurements, Societies and institutions},
references={Banker, R.D., Konstans, C., Mashruwala, R., A Contextual Study of Links Between Employee Satisfaction, Employee Turnover, Customer Satisfaction and Financial Performance (2000), Working Paper, University of Texas-Dallas; Bititci, U.S., Dynamics of Performance Measurement Systems (2000) International Journal of Operations and Production Management, 19 (12), pp. 1318-1334; Bourne, M., Franco, M., Wilkes, J., Corporate Performance Management (2003) Measuring Business Excellence, 7, pp. 15-21; Brignall, S., The unbalanced scorecard: a social and environmental critique (2002), in Neely, A., Walters, A., Austin, R. (Eds), Performance Measurement and Management: Research and Action, Cranfield School of Management, Cranfield; Cavalcante, I., Martins, R.A., Case Study on the Dimensions of Performance Measurement Systems Maturity (2009) Proceedings of the 2009 Industrial Engineering Research Conference; Cocca, P., Alberti, M., A framework to assess performance measurement systems in SMEs (2010) International Journal of Productivity and Performance Management, 59 (2), pp. 186-200; Davis, J., Miller, G., Russell, A., (2006), Information Revolution: Using the Information Evolution Model to Grow Your Business, John Wiley Sons Inc; Eccles, R.G., The performanse Measurment Manifesto (1991) Harvard Business Review, January-February, pp. 131-137; Ehms, K., Langen, M., Holistic Development of KM with KMMM (2002), Siemens AG / Corporate Technology Knowledge Management and Business Transformation; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards A Definition Of A Business Performance Measurement System (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Frigo, M.L., Krumwiede, K.R., Balanced scorecards: A rising trend in strategic performance measurement (1999) Journal of Strategic Performance Measurement, pp. 42-48; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations and Production Management, 16 (8), pp. 63-80; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the weighting of performance measures: evidence from a balanced scorecard (2003) The Accounting Review, 78 (3), pp. 725-758; Kanji, G.K., Sustainable growth and business excellence (2005) Total Qual Manag Bus Excell, 16 (8-9), pp. 1069-1078; Kaplan, R.S., Norton, D.P., The balanced scorecard: measures that drive performance (1992) Harvard Business Review, pp. 138-148; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, pp. 75-85; Kaplan, R.S., Norton, D.P., Strategy Maps: Converting Intangible Assets into Tangible Outcomes (2004) Harvard Business School Press, Cambridge, MA; Kennerley, M., Neely, A., A Framework of the Factors Affecting the Evolution of Performance Measurement Systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245. , Wiley, New York, A. Gorbman (Ed.); Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Malina, M.A., Selto, F.H., Communicating and controlling strategy: An empirical study of the effectiveness of the balanced scorecard (2001) Journal of Management Accounting Research, 13, pp. 47-90; Marr, B., Schiuma, G., Neely, A., The dynamics of value creation: mapping your intellectual performance drivers (2004) Journal of Intellectual Capital, 5 (2), pp. 312-325; Marr, B., Schiuma, G., Business performance measurement, Past, present and future (2003) Management Decision, 41 (8), pp. 680-687; (1996), NAPA Information Management Performance Measures: Developing Performance Measures and Management Controls for Migration Systems, Data Standards, and Process Improvement. Report by Panes of the National Academy of Public Administration for U.S. Department; Neely, A., Gregory, M.J., Platts, K., Performance Measurement System Design: a Literature Review and Research Agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., The performance measurement revolution: why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Neely, A., Does the balanced scorecard work: an empirical investigation (2004), pp. 763-770. , In: A. Neely, M. Kennerly and A. Waters (eds.), Performance measurement and management: public and private, Centre for Business Performance, Cranfield University, Cranfield:; Norreklit, H., The balanced on the balanced scorecard: a critical analysis of some of the assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Olsen, E.O., Zhou, H., Lee, D.M.S., Ng, Y.E., Chong, C.C., Padunchwit, P., Performance measurement system and relationships with performance results (2007) International Journal of Productivity and Performance Management, 56 (7), pp. 559-582; Risher, H., Fay, C., (1995), The Performance Imperative: Strategies for Enhancing Workforce Effectiveness, San Francisco: Jossey-Bass Publishers; Simons, R., (2000), Performance Measurement & Control Systems for Implementing Strategy, Prentice Hall, Upper Saddle River, New Jersey 07458; Van Aken, E., Letens, G., Coleman, G.D., Farris, J.A., Van Goubergen, D., Assessing Maturity and Effectiveness of Enterprise Performance Measurement Systems (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 400-418; Waal, A., Kourtit, K., Nijkamp, P., The relationship between the level of completeness of a strategic performance management system and perceived advantages and disadvantages (2009) International Journal of Operations & Production Management, 29 (12), pp. 1242-1265; Wettstein, T., Kueng, P., A Maturity Model For Performance Measurement Systems (2002) Management Information Systems, pp. 113-122},
correspondence_address1={Markovic, N.; Belgrade Business School, Belgrade, Serbia; email: nenad.markovic@bbs.edu.rs},
publisher={Academic Conferences Limited},
language={English},
abbrev_source_title={Eur. Conf. on Inf. Manag. and Eval.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Sekkat2010107,
author={Sekkat, S. and Paris, J.L. and Kouiss, K. and Saadi, J. and Deshayes, L.},
title={Developing integrated Performance Measurement System - Using MDA approach},
journal={4th Int. Conference on Integrated Modeling and Analysis in Applied Control and Automation, IMAACA 2010, Held at the International Mediterranean and Latin American Modeling Multiconference, I3M 2010},
year={2010},
pages={107-113},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899423126&partnerID=40&md5=7725a2f61636e3fe5c3a6d2c80cb7d3d},
affiliation={LIMOS, IFMA Campus de Clermont-Ferrand/Les Cézeaux, BP 265, 63175 Aubière Cedex, France; ERCSSP, ENSAM Marjane II Beni M'hammed, BP 4024, Meknes Alismailia, Meknes, Morocco; LAP, ENSEM Casablanca, OASIS, Route d' EL JADIDA, Casablanca, Morocco; Manbet Technology, 5 Avenue Abou Taeib Al Moutanabi, Morocco},
abstract={In an industrial context defined by more acute competition, performance measurement becomes a control tool. The Manufacturing Execution System (MES) software achieves control and execution of production functions, as Performances analysis. However, to choose the adequate performance indicators and to implement them is a difficult problem. Indeed, the enterprises need methods to specify and to install their Performance Measurement System (PMS). In this paper, we propose a methodology of performance indicators implementation. We use the Component Oriented Programming with a Model- Driven Architecture (MDA), to generate applications' final code. This method facilitates the design of Performances Measurement System and its implementation.},
author_keywords={Component Oriented Programming COP;  Manufacturing Execution Systems MES;  Model-driven;  Performances Measurement Systems PMS},
keywords={Benchmarking;  Measurements;  Supply chain management, Component oriented programming;  Manufacturing Execution System;  Measurement system;  Model-driven;  Performance indicators;  Performance measurement system;  Performance measurements;  Performances analysis, Software architecture},
references={Chiron, F., (2009) Contribution À la Flexibilité et À la Rapidité de Conception des Systèmes Automatisées Avec L'utilisation D'UML, , Thèse de Doctorat en Informatique ; université Blaise Pascal-Clermont; Curtis, D., Stone, C., Bradley, M., (2009) IIOP: OMG's Internet Inter-ORB Protocol A Brief Description. Object Management Group (OMG, , http://www.omg.org, [accessed 10 May 2010]; Halang, W.A., Sanz, R., Babuska, R., Roth, H., Information and communication technology embraces control (2006) Status Report Prepared by the IFAC: Coordinating Committee on Computers, Cognition and Communication. Annual Reviews in Control, 30, pp. 31-400; Enterprise/Control System Integration-Part 3: Activity Models of Manufacturing Operations Management. The Instrumentation, Systems, and Automation Society, , ISA. 2000. ANSI/ISA 95.00 03-2000; Kettani, N., Mignet, D., Paré, P., Rosenthal-Sabroux, C., (1999) De Merise À UML, , Editions Eyrolles; Kouiss, K., Chiron, F., David, S., Thomas, M., A Component based development of user manufacturing systems (2006) IDMME 2006, , Grenoble France; Krassi, B.A., (2002) Distributed Computing and Automation: Industrial Applications, , http://www.automationit.hut.fi/julkaisut/documents/seminars/sem_s02/ Krassi_paper.pdf; (2000) Control Definition and MES to Control Data Flow Possibilities, pp. 1-7. , http://www.mesa.org, MESA International. White paper. N : 3; (1998) OPC Overview. Version 1.0. OPC Foundation, , www.opc.org, [accessed 10 May 2010]; Yang, C.O., Guan, T.Y., Lin, J.S., Developing a computer shop floor control model for CIM systems-using object modelling technique (2000) Computers in Industry, 41, pp. 213-238},
publisher={Caltek s.r.l.},
language={English},
abbrev_source_title={Int. Conf. Integr. Model. Anal. Appl. Control Autom., IMAACA, Held Int. Mediterr. Lat. Am. Model. Multiconference, I3M},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Dobrić2010616,
author={Dobrić, V. and Poledica, A. and Petrović, B.},
title={Supply chain performance measurement using logical aggregation},
journal={Computational Intelligence Foundations and Applications - Proceedings of the 9th International FLINS Conference, FLINS 2010},
year={2010},
pages={616-621},
doi={10.1142/9789814324700_0093},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899121211&doi=10.1142%2f9789814324700_0093&partnerID=40&md5=ffc7ca5d398143fcdee351f120350a99},
affiliation={Faculty of Organizational Sciences, University of Belgrade, Jove Ilića 154, Belgrade, 11000, Serbia},
abstract={In modern economies, supply chain management has become strategic factor for realization of organizational objectives. In the era of globalization and outsourcing, organizations are integr0ating individual functions into supply chain processes in order to effectively manage their operations. Effective supply chain management requires various kinds of supply chain performance measures. This paper presents an approach to performance measurement in a supply chain based on the use of logical aggregation operator. © 2010 by World Scientific Publishing Co. Pte. Ltd.},
keywords={Artificial intelligence;  Computation theory;  Fuzzy logic;  Mathematical operators, Aggregation operator;  Performance measurements;  Strategic factors;  Supply chain performance;  Supply chain performance measurements;  Supply chain process, Supply chain management},
references={Neely, A., Gregory, M., Platts, K., (1995) Int. Jour. Oper. Prod. Man, 15, p. 4; Sink, D.S., Tuttle, T.C., (1989) Planning and Measurement in Your Organization of the Future, , IE Press Norcoss, GA; Rolstandas, A., (1995) Performance Measurement: A Business Process Benchmarking Approach, , Chapman & Hall. New York; Gunasekaran, A., Patel, C., Ronaldmcgaughey, E., (2004) Int. J. Production Economics, 87, p. 2; Radojević, D., (2008) Mathware & Soft Computing, 15, p. 125; Radojević, D., (2000) Yugoslav Journal of Operational Research, 10, p. 185},
editor={Ruan D., Kerre E.E., Ruan D., Chen G., Li T., Xu Y.},
publisher={World Scientific Publishing Co. Pte Ltd},
isbn={9814324698; 9789814324694},
language={English},
abbrev_source_title={Comput. Intel. Found. Appl. - Proc. Int. FLINS Conf., FLINS},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Espinoza2010700,
author={Espinoza, O. and Bond, B.H. and Kline, E.},
title={Supply chain measures of performance for wood products manufacturing},
journal={Forest Products Journal},
year={2010},
volume={60},
number={7-8},
pages={700-708},
doi={10.13073/0015-7473-60.7.700},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960145221&doi=10.13073%2f0015-7473-60.7.700&partnerID=40&md5=8917b82e52adc44d4589169d7632ec84},
affiliation={Dept. of Wood Sci. and Forest Products, Virginia Polytechnic Inst., State Univ., Blacksburg, United States},
abstract={This article reports the results from a supply chain study in which a performance measurement system for a wood products value stream was developed. We build on findings from a previous study in which the need for supply chain metrics of performance was identified. A five-step method to develop performance measures is suggested as an improvement alternative. Examples of specific metrics for quality and time performance are provided. This approach facilitates collaboration between supply chain partners and provides information that allows a more efficient focus on improvement projects. Supply chain measures of performance are important for seeing beyond a single entity and aligning strategy in the supply chain. Companies that want to integrate their suppliers and customers in their improvement efforts can benefit from the information presented here, because a common set of performance measures is essential in evaluating progress toward a goal. © Forest Products Society 2010.},
keywords={Wood products, Measures of performance;  Metrics of performance;  Performance measure;  Performance measurement system;  Step method;  Supply chain partners;  Value streams, Supply chains, Operating Costs;  Performance;  Quality Control;  Suppliers;  Supply Chain Management;  Wood Products},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292. , DOI 10.1108/01443579910249714; Beaudoin, D., LeBel, L., Frayret, J.-M., Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis (2007) Canadian Journal of Forest Research, 37 (1), pp. 128-140. , DOI 10.1139/X06-223; Berry, D., Evans, G.N., Mason-Jones, R., Towill, D.R., The BPR SCOPE concept in leveraging improved supply chain performance (1999) Bus. Process Manag. J., 5 (3), pp. 254-274; Blanchard, D., What's working for U.S. manufacturers (2006) IndustryWeek, 255 (10), pp. 49-51; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1288-1310; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Log., 21 (1), pp. 75-93; Buehlmann, U., What does the wood industry have to gain by leveraging the supply chain? (2004) Manufacturing Competitiveness of the Forest Products Industry: Competing in Today's Global Manufacturing and Consumer Marketplace, , Presented at November 3-5, 2004, New Orleans; Carlsson, D., Rönnqvist, M., Supply chain management in forestry - Case studies at södra cell AB (2005) Eur. J. Oper. Res., 163 (3), pp. 589-616; Chan, F.T., Qi, H.J., Chan, H.K., Lau, H.C., Ip, R.W., A conceptual model of performance measurement for supply chains (2003) Manag. Decis., 41 (7), pp. 635-642. , DOI 10.1108/00251740310495568; Chung, Y., Hsu, Y., Yen, T., Using the Six Sigma system approach to reduce core process times at a manufacturing plant (2008) Int. J. Manag., 25 (3), pp. 431-438; (2010) Supply Chain Management - Terms and Glossary, , http://cscmp.org/digital/glossary/glossary.asp, Accessed February 12, 2010; D'Amours, S., Frayret, J.-M., Rousseau, A., (2004) From the Forest to the Client - Why Have Integrated Management of the Value Creation Network, , http://www.forac.ulaval.ca/fileadmin/docs/Publications/ GestionIntegreeRCV.pdf, Accessed May 2007. (In French.); D'Amours, S., Rönnqvist, M., Weintraub, A., Using operational research for supply chain planning in the forest products industry (2008) INFOR, 46 (4), pp. 265-281; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management and Business Excellence, 14 (3), pp. 355-366. , DOI 10.1080/1478336032000046652; Espinoza, O.A., (2009) Quality Measurement in the Wood Products Supply Chain, , Doctoral dissertation. Virginia Polytechnic Institute and State University, Blacksburg; Espinoza, O.A., Bond, B.H., Kline, E., Quality measurement in the wood products supply chain (2010) Forest Prod. J., 60 (3), pp. 249-257; Fontenot, G., Behara, R., Gresham, A., Six Sigma in customer satisfaction (1994) Qual. Prog., 27, pp. 73-76; Fontenot, R.J., Vlosky, R.P., Wilson, E.J., Wilson, D.T., A model of buyer-seller relationship structure effects on firm performance (1998) Am. Mark. Assoc. Conf. Proc., 9, pp. 206-207; Frayret, J.-M., D'Amours, S., Rousseau, A., Harvey, S., Gaudreault, J., Agent-based supply-chain planning in the forest products industry (2007) International Journal of Flexible Manufacturing Systems, 19 (4), pp. 358-391. , DOI 10.1007/s10696-008-9034-z; Fynes, B., Voss, C., De Burca, S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), pp. 339-354. , DOI 10.1016/j.ijpe.2004.05.008, PII S0925527304002956, Quality in Supply Chain Management and Logistics; Goldsby, T.J., Martichenko, R., (2005) Lean Six Sigma Logistics: Strategic Development to Operational Success, p. 282. , J. Ross Publishing, Boca Raton, Forida; Graves, S., Six Sigma rolled throughput yield (2001) Qual. Engineer, 14 (2), pp. 257-266; Jones, D., Womack, J., (2002) Seeing the Whole - Mapping the Extended Value Stream, pp. 3-5. , The Lean Enterprise Institute, Brookline, Massachussetts; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harv. Bus. Rev., 70 (1), pp. 71-79; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Ind. Mark. Manag., 29 (1), pp. 5-83; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int. J. Log. Manage., 12 (1), pp. 1-19; Lapide, L., True measures of supply chain performance (2000) Supply Chain Manag. Rev., pp. 25-28. , July/August; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) Journal of Operations Management, 23 (6), pp. 618-641. , DOI 10.1016/j.jom.2005.01.002, PII S0272696305000501; Mason-Jones, R., Towill, D.R., Information enrichment: Designing the supply chain for competitive advantage (1997) Supply Chain Management, 2 (4), pp. 137-148. , DOI 10.1108/13598549710191304; Novack, R.A., Thomas, D.J., The challenges of implementing the perfect order concept (2004) Transportation Journal, 43 (1), pp. 5-16; Oliva, R., Sterman, J.D., Cutting corners and working overtime: Quality erosion in the service industry (2001) Management Science, 47 (7), pp. 894-914; Petersen, K.J., Handfield, R.B., Ragatz, G.L., Supplier integration into new product development: Coordinating product, process and supply chain design (2005) Journal of Operations Management, 23 (3-4), pp. 371-388. , DOI 10.1016/j.jom.2004.07.009, PII S0272696304001056, Coordinating Product Design, Process Design and Supply Chain Design Decisions; Raisinghani, M.S., Ette, H., Pierce, R., Cannon, G., Daripaly, P., Six Sigma: Concepts, tools, and applications (2005) Industrial Management and Data Systems, 105 (4), pp. 491-505. , DOI 10.1108/02635570510592389; Rosenzweig, E.D., Roth, A.V., Dean, J.W., The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufacturers (2003) J. Oper. Manag., 21 (4), pp. 437-456; Sasmohapatra, S., Future marketing drivers for the forest products industry (2009) BioResources, 4 (4), pp. 1263-1266; Simatupang, T.M., Sridharan, R., Design for supply chain collaboration (2008) Bus. Process Manag., 14 (3), pp. 401-418; Simpson, J.T., Wren, B.M., Buyer-seller relationships in the wood products industry (1997) Journal of Business Research, 39 (1), pp. 45-51. , PII S0148296396001506; Tan, K.-C., Kannan, V.R., Handfield, R.B., Ghosh, S., Supply chain management: An empirical study of its impact on performance (1999) International Journal of Operations and Production Management, 19 (10), pp. 1034-1052. , DOI 10.1108/01443579910287064; Towill, D.R., Time compression and supply chain management - A guided tour (1996) Supply Chain Management, 1 (1), pp. 15-27; Van Aken, E.M., Coleman, G.D., Building better measurement (2002) Ind. Manage., 44 (4), pp. 28-33; Vlosky, R.P., Wilson, E.J., Cohen, D.H., Fontenot, R., Johnston, W.J., Kozak, R.A., Lawson, D., Wren, B.M., Partnerships versus typical relationships between wood products distributors and their manufacturer suppliers (1998) Forest Products Journal, 48 (3), pp. 27-35; Wang, F.-K., Du, T.C., Li, E.Y., Applying Six-Sigma to supplier development (2004) Total Quality Management and Business Excellence, 15 (9-10), pp. 1217-1229. , DOI 10.1080/1478336042000255596},
correspondence_address1={Espinoza, O.; Dept. of Wood Sci. and Forest Products, , Blacksburg, United States; email: omar.espinoza@vt.edu},
publisher={Forest Products Society},
issn={00157473},
coden={FPJOA},
language={English},
abbrev_source_title={For Prod J},
document_type={Review},
source={Scopus},
}

@ARTICLE{Olugu2010871,
author={Olugu, E.U. and Wong, K.Y. and Shaharoun, A.M.},
title={A comprehensive approach in assessing the performance of an automobile closed-loop supply chain},
journal={Sustainability},
year={2010},
volume={2},
number={4},
pages={871-889},
doi={10.3390/su2040871},
note={cited By 49},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79958026918&doi=10.3390%2fsu2040871&partnerID=40&md5=7756c5245f8793fc5d805e56ac9fc3d2},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia; Business and Advanced Technology Centre, Universiti Teknologi Malaysia, International Campus, Kuala Lumpur, Malaysia},
abstract={The ecological issues arising from manufacturing operations have led to the focus on environmental sustainability in manufacturing. This can be addressed adequately using a closed-loop supply chain (CLSC). To attain an effective and efficient CLSC, it is necessary to imbibe a holistic performance measurement approach. In order to achieve this, there is a need to adopt a specific approach for a particular product rather than being generic. Since sustainability has direct environmental footprints that involve organizational stakeholders, suppliers, customers and the society at large, complexities surrounding supply chain performance measurement have multiplied. In this study, a suitable approach has been proposed for CLSC performance measurement in the automotive industry, based on reviewed literature. It is believed that this approach will result in increased effectiveness and efficiency in CLSC performance measurement. © 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.},
author_keywords={Automobile supply chain;  Closed-loop supply chain;  Performance measurement},
references={Clift, R., Metrics for supply chain sustainability (2004) J. Clean Technol. Environ. Policy, 5, pp. 240-247; Koplin, J., Seuring, S., Mesterharm, M., Incorporating sustainability into supply management in the automotive industry: the case of the Volkswagen AG (2007) J. Clean Prod., 15, pp. 1053-1062; Matos, S., Hall, J., Integrating sustainable development in the supply chain: the case of life cycle assessment in oil, gas and agricultural biotechnology (2007) J. Oper. Manage., 25, pp. 1083-1102; Kemp, R., Technology and the transition to environmental sustainability (1994) Futures, 26, pp. 1023-1046; (1987) Our Common Future, , World Commission on Environmental and Development (WCED). Oxford University Press: New York, NY, USA; Beamon, B.M., Environmental and sustainability ethics in supply chain management (2005) Sci. Eng. Ethics, 11, pp. 221-234; Seuring, S., Muller, M., From a literature review to a conceptual framework for sustainable supply chain management (2008) J. Clean. Prod., 16, pp. 1699-1710; Linton, J.D., Klassen, R., Jaraman, V., Sustainable supply chains: an introduction (2007) J. Oper. Manage., 25, pp. 1075-1082; Schultmann, F., Zumkeller, M., Rentz, O., Modeling reverse logistic tasks within closed-loop supply chains: an example from the automotive industry (2006) Eur. J. Oper. Res., 171, pp. 1033-1050; Well, P., Seitz, M., Business models and closed-loop supply chains: a typology (2006) Suppl. Chain Manag., 10, pp. 249-251; Beamon, B.M., Designing the green supply chain (1999) Logist. Info. Manage., 12, pp. 332-342; Green, K., Morton, B., New, S., Green purchasing and supply policies: do they improve companies' environmental performance? (1998) Suppl. Chain Manag., 3, pp. 89-95; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice Hall, Upper Saddle River: New Jersey, NJ, USA; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management. Benchmark (2005) Int. J, 12, pp. 330-353; Noci, G., Designing green vendor rating system for the assessment of a supplier's environmental performance (1997) Eur. J. Purch. Suppl. Manage., 3, pp. 103-114; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental performance indicators for integrated supply chains: the case of Xerox Ltd (1998) Suppl. Chain Manage., 3, pp. 149-156; Van Hoek, R.I., From reversed logistics to green supply chains (1999) Suppl. Chain Manage., 4, pp. 129-134; Zhu, Q., Sarki, J., Lai, K., Green supply chain management implications for closing the loop (2008) Transp. Res. Part E, 44, pp. 1-18; Trowbridge, P., A case study of green supply chain management at advanced micro devices (2006) Greening the Supply Chain, pp. 307-322. , Sarki, J., Ed.; Springer-Verlag: London, UK; Goodland, R., The case that the world has reached limits (1991) Environmentally Sustainable Economic Development: Building on Brundtland, , Goodland, R., Daly, H., El Serafy, S., Eds.; World Bank: Washington, DC, USA; Spengler, T., Stolting, W., Ploog, M., Recovery planning in closed loop supply chain: an activity analysis based approach (2004) Supply Chain Management and Reverse Logistics, pp. 61-89. , Dyckhoff, H., Lackes, R., Reese, J., Eds; Springer-Verlag: Berlin, Germany; Guide Jr., V.D.R., van Wassenhove, L., Closed-loop supply chains (2002) Quantitative Approaches to Distribution Logistics and Supply Chain Management, pp. 47-60. , Klose, A., Speranza, M.G., van Wassenhove, L, Eds.; Springer: Berlin, Germany; Fleischmann, M., Dekker, R., van der Laan, E., van Numen, J., van Wassenhove, L., Ruwaard, J., Quantitative models for reverse logistics: a review (1997) Eur. J. Oper. Res., 103, pp. 1-17; French, M.L., LaForge, R.L., Closed-loop supply chains in process industries: an empirical study of producer re-use issues (2006) J. Oper. Manage., 24, pp. 271-286; Guide Jr., V.D.R., van Wassenhove, L.N., (2000) Closed-Loop Supply Chains, , Working Paper 2000/75/TM; INSEAD: Fontainebleau, France; Solvang, W.D., Roman, E., Deng, Z., Solvang, B., A framework for holistic greening of value chains (2006) Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management, 207, pp. 350-355. , Springer: Boston, MA, USA; Rao, P., Greening of the supply chain: a new initiative in South East Asia (2002) Int. J. Oper. Prod. Manage., 22, pp. 632-655; Dyckhoff, H., Souren, R., Keilen, J., The expansion of supply chains to close loop systems: a conceptual framework and the automotive industry's point of view (2004) Suppy Chain Management and Reverse Logistics, pp. 13-34. , Dyckhoff, H., Lackes, R., Reese, J., Eds; Springer-Verlag: Berlin, Germany; (2000) Official Journal of the European Union, L Series, pp. 34-42. , CEC Directive 2000/53/EC of the European Parliament and of the Council of 18 September 2000 on End-of Life Vehicles. 21 October; von Ahsen, A., Environmental management in automotive supply chains: an empirical analysis (2006) Greening the Supply Chain, pp. 293-306. , Sarki, J., Ed.; Springer-Verlag: London, UK; Johnson, P.F., Managing value in reverse logistics systems (1998) Transp. Res. Part E: Logist. Transp. Rev., 34, pp. 217-227; Autry, C.W., Formalization of reverse logistics programs: a strategy for managing liberalized returns (2005) Ind. Market. Manage., 34, pp. 749-757; Nunes, K.R.A., Mahler, C.F., Valle, R.A., Reverse logistics in the Brazilian construction industry (2009) J. Environ. Manage., 90, pp. 3717-3720; Efendigil, T., Onut, S., Kongar, E., A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness (2008) Comput. Ind. Eng., 54, pp. 269-287; Pun, K.F., Determinants of environmentally responsible operations: a review (2006) Int. J. Quali. Reliab. Manage., 23, pp. 279-297; (2003) Position on R1 and D10 Criteria in the EU Waste Framework Directive, , www.iswa.org, International Solid Waste Association (ISWA) (assessed on 1 December 2009); Tsoulfas, G.T., Pappis, C.P., A model for supply chains environmental performance analysis and decision making (2008) J. Clean. Prod., 16, pp. 1647-1657; Beamon, B.M., Sustainability and the future of supply chain management (2008) Oper. Supp. Chain Manage., 1, pp. 4-18; Ostlin, J., Sundin, E., Bjorkman, M., Importance of closed-loop supply chain relationships for product remanufacturing (2008) Int. J. Prod. Econ., 115, pp. 336-348; Bhagwat, M., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Comput. Ind. Eng., 53, pp. 43-62; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (2005) Int. J. Oper. Prod. Manage., 25, pp. 1228-1263; Srivastava, S.K., Green supply chain management: a state-of-the-art literature review (2007) Int. J. Manage. Rev., 9, pp. 53-80; Kannan, G., Sasikumar, P., Devika, K., A genetic algorithm approach for solving a closed loop supply chain model: a case of a battery recycling (2010) Appl. Math. Model., 34, pp. 655-670; Yang, G., Wang, Z., Li, X., The optimization of the closed-loop supply chain network (2009) Transp. Res. Part E: Logist. Transp. Rev., 45, pp. 16-28; Amaro, A.C.S., Barbosa-Povoa, A.P.D.F., Optimal planning of closed loop supply chain: a discrete versus a continuous time formulation (2007) Comput. Aided Chem. Eng., 24, pp. 673-678; Hammond, D., Beullens, P., Closed-loop supply chain network equilibrium under legislation (2007) Eur. J. Oper. Res., 183, pp. 895-908; Vlachos, D., Georgiadis, P., Iakovou, E., A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chain (2007) Comput. Oper. Res., 34, pp. 367-394; Amaro, A.C.S., Barbosa-Póvoa, A.P.F.D., The effect of uncertainty on the optimal closed-loop supply chain planning under different partnership structure (2009) Comput. Chem. Eng., 33, pp. 2144-2158; Flapper, S.D., Introduction to feature cluster on closed-loop supply chains (2008) Eur. J. Oper. Res., 191, pp. 1205-1217; Salema, M.I.G., Barbosa-Povoa, A.P., Novais, A.Q., Simaultaneous design and planning of supply chains with reverse flows: a generic modeling framework (2010) Eur. J. Oper. Res., 203, pp. 336-349; Guide Jr., V.D.R., Jayaraman, V., Linton, J.D., Building contingency planning for closed-loop supply chains with product recovery (2003) J. Oper. Manage., 1, pp. 259-279; Chung, S.L., Wee, H.M., Yang, P.C., Optimal policy for a closed-loop supply chain inventory system with remanufacturing (2008) Math. Comput. Mod., 48, pp. 867-881; Kumar, S., Malegeant, P., Strategic alliance in a closed-loop supply chain, a case of manufacturer and eco-nonprofit organization (2006) Technovation, 26, pp. 1127-1135; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2006) Int. J. Prod. Perform. Manage., 55, pp. 242-258; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain (2001) Int. J. Oper. Prod. Manage., 21, pp. 71-87; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manage., 19, pp. 275-292; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Suppl. Chain Manage., 8, pp. 209-223; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol., 21, pp. 534-548; Schonsleben, P., (2004) Integral Logistics Management: Planning and Control of Comprehensive Supply Chains, , St Lucie Press: Boca Raton, FL, USA; Hieber, R., (2002) Supply Chain Management: A Collaborative Performance Measurement Approach, , VDF: Zurich, Switzerland; Artz, K.W., Buyer-supplier performance: the role of asset specificity, reciprocal investments and relational exchange (1999) Br. J. Manage., 10, pp. 113-126; Windischer, A., Grote, G., Success factors for collaborative planning (2003) Strategy and Organization in Supply Chain, pp. 131-146. , Seuring, S., Muller, M., Goldbach, M., Eds.; Physica: Heidelberg, Germany; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) Int. J. Purch. Mater. Manage., 30, pp. 13-18; Maloni, M.J., Benton, W.C., Supply chain partnerships: opportunities for operations research (1997) Eur. J. Oper. Res., 101, pp. 419-429; van der Vorst, J., Beulens, A., Identifying sources of uncertainty to generate supply chain redesign strategies (2001) Int. J. Phys. Distrib. Logist. Manage., 32, pp. 409-430; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, J.P., Designing supply chains: towards theory development (2006) Int. J. Prod. Econ., 100, pp. 223-238; Zhu, Q., Sarkis, J., Lai, K., Initiatives and outcomes of green supply chain management implementation by Chinese manufacturers (2007) J. Environ. Manage., 85, pp. 179-189; Rao, P., Holt, D., Do green supply chains lead to competitiveness and economic performance? (2005) Int. J. Oper. Prod. Manage., 25, pp. 898-916; Lettice, F., Wyatt, C., Evans, S., Buyer-supplier partnerships during product design and development in the global automotive sector: who invests, in what and when? (2009) Int. J. Prod. Econ, , (in press); Zhu, Q., Sarkis, J., Lai, K., Green supply chain management: pressures, practices and performance within the Chinese automobile industry (2007) J. Clean Prod., 15, pp. 1041-1052; Cruz-Rivera, R., Ertel, J., Reverse logistics network design for the collection of end-of-life vehicles in Mexico (2009) Eur. J. Oper. Res., 196, pp. 930-939; Olugu, E.U., Wong, K.Y., Supply chain performance evaluation: trends and challenges (2009) Am. J. Eng. Appl. Sci., 2, pp. 202-211; Wong, W.P., Wong, K.Y., Supply chain performance measurement system using DEA modeling (2007) Ind. Manage. Data Syst., 107, pp. 361-381; Anderson, K., McAdam, R., A critique of benchmarking and performance measurement (2004) Benchmark. Int. J., 11, pp. 465-483; Mentzer, J.T., Meyers, M.B., Stank, T.P., (2007) Handbook of Global Supply Chain Management, p. 585. , Sage Publications: Thousand Oaks, CA, USA; Kaplan, R.S., Norton, P.D., The balanced scorecard: measures that drives performance (1992) Harv. Bus. Rev., 70, pp. 71-79; Vachon, S., Klassen, R.D., Environmental management and manufacturing performance: the role of collaboration in the supply chain (2008) Int. J. Prod. Econ., 111, pp. 299-315},
correspondence_address1={Olugu, E.U.; Department of Manufacturing and Industrial Engineering, , 81310 UTM Skudai, Malaysia; email: euolugu3@siswa.utm.my},
publisher={MDPI},
issn={20711050},
language={English},
abbrev_source_title={Sustainability},
document_type={Article},
source={Scopus},
}

@ARTICLE{Legnani2010660,
author={Legnani, E. and Cavalieri, S.},
title={Exploring the causal relationships of KPIs in after sales service systems},
journal={IFIP Advances in Information and Communication Technology},
year={2010},
volume={338 AICT},
pages={660-668},
doi={10.1007/978-3-642-16358-6_82},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649975354&doi=10.1007%2f978-3-642-16358-6_82&partnerID=40&md5=9b37d70fdb0b318ff6d1fe3fe21198e7},
affiliation={CELS - Research Center on Logistics and After-Sales Service, Department of Industrial Engineering, University of Bergamo, Viale Marconi, 5, 24044 Dalmine, Italy},
abstract={A plethora of research and industrial contributions emphasizes the economic and strategic role of services in adding further value to a product throughout its lifelong journey with the customer. However, there is still a limited comprehension of the dynamics underlying After-Sales (AS) processes along the whole service network - which usually encompasses a manufacturer, spare parts wholesalers/retailers and technical assistance centres - till the final user. AS can be no more considered as a mere corporate function, but rather as a series of interconnected activities involving more independent organizations, each one having different objectives and perspectives to be properly aligned. Starting from previous contributions of the same authors on this research topic, aim of the paper is to examine AS as a complex system of interlinked processes, to elaborate a proposal of the main Key Performance Indicators (KPIs) which can take into account the various perspectives of the different actors involved, and, as a main result, to explore the most relevant causal relationships among these KPIs. © 2010 IFIP International Federation for Information Processing.},
author_keywords={After-Sales Service System;  Performance Measurement System;  Product-Services;  System Dynamics;  Systems Thinking},
keywords={Benchmarking;  Industrial research;  Supply chain management;  System theory, After-sales services;  Performance measurement system;  Product service;  System Dynamics;  Systems thinking, Sales},
references={Angerhofer, B.J., Angelides, M.C., System dynamics modelling in supply chain management: Research review (2000) Proc. of the 2000 Winter Simulation Conference, pp. 342-351; Camerinelli, E., Cantu, A., (2006) Linking Supply Chain Measures with Financial Performance: A Framework, , Technical Report, Supply Chain Council Research; Crespo, M.A., Blanchar, C., A decision support system for evaluating operations investments in high-technology business (2006) Decision Support Systems, 41, pp. 472-487; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement systems in the after-sales service: An integrated framework (2007) International Journal Business Performance Management, 9 (2), pp. 145-171; Heskjett, J., Jones, T., Loveman, G., Sasser, W., Schlesinger, L., Putting the service-profit chain at work (1994) Harvard Business Review, , March-April; (2007) InCoCo's 2007, , http://www.incoco.net/, EU-funded project; Kingman-Brundage, J., George, W.R., Bowen, D.E., "Service logic": Achieving service system integration (1995) International Journal of Service Industry Management, 6 (4), p. 20; Legnani, E., Cavalieri, S., Measuring the performance of after-sales service processes. A hierarchical approach (2008) Proc. of APMS 2008, International Conference on Innovations in Network, Espoo, Finland, pp. 359-368; Oliva, R., Kallenberg, R., Managing the transition from products to services (2003) International Journal of Service Industry Management, 14 (2), pp. 160-172; Potts, G.W., Exploiting your product's service life cycle (1988) Harvard Business Review, 66 (5), pp. 32-35; Rust, R.T., Metters, R., Mathematical models of service (1996) European Journal of Operational Research, 91, pp. 427-439; Santos, S.P., Belton, V., Howick, S., Adding value to performance measurement by using system dynamics and multicriteria analysis (2002) International Journal of Operations & Production Management, 22 (11), pp. 1246-1272; Sawhney, M., Balasubramanian, S., Krishnan, V.V., Creating growth with services (2004) MIT Sloan Management Review; Schneider, B., Bowen, D.E., (1995) Winning the Service Game, , Harvard Business School Press, Boston; Spohrer, J., Maglio, P.P., Bailey, J., Gruhl, D., Steps toward a science of service systems (2007) Computer, 40 (1), pp. 71-77; Sterman, J., (2000) Business Dynamics: System Thinking and Modelling for a Complex World, , McGraw-Hill, New York; (2008) Supply Chain Operations Reference Model (SCOR), , http://www.supply-chain.org, version 9.0; Wise, R., Baumgartner, P., Go downstream - The new profit imperative in manufacturing (1999) Harvard Business Review, 77, pp. 133-141; Zeithaml, V.A., Parasuraman, A., Berry, L.L., (1990) Delivering Quality Service: Balancing Customer Perceptions and Expectations, , Free Press, New York},
correspondence_address1={Legnani, E.; CELS - Research Center on Logistics and After-Sales Service, Viale Marconi, 5, 24044 Dalmine, Italy; email: elena.legnani@unibg.it},
publisher={Springer New York LLC},
issn={18684238},
isbn={3642163572; 9783642163579},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Nurmi2010239,
author={Nurmi, A. and Moyaux, T. and Botta-Genoulaz, V.},
title={Towards semantic performance measurement systems for supply Chain management},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2010},
volume={6428 LNCS},
pages={239-248},
doi={10.1007/978-3-642-16961-8_41},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649495057&doi=10.1007%2f978-3-642-16961-8_41&partnerID=40&md5=9ac58873425357ec40d664d75236bb5f},
affiliation={INSA-Lyon, LIESP, Université de Lyon, Villeurbanne Cedex 69621, France},
abstract={The literature on Supply Chain Management (SCM) supports the integration of key business processes, including the performance management process, in order to increase the performance within and between the organisations. Nevertheless, the lack of proper shared performance metrics, frameworks and technology in order to design, implement and manage Performance Measurement Systems (PMSs), have been identified as problems for achieving integration. Semantics-based technologies, especially ontologies, have been suggested to address such problems. The purpose of this paper is to discuss how ontologies may be used to raise interoperability and shared understanding in inter-organisational PMSs from syntactic to semantic level by introducing the concept of Semantic PMS. We also suggest future research issues for approaching collaborative performance management problems. © 2010 Springer-Verlag Berlin Heidelberg.},
author_keywords={Business Process Management;  Ontology Engineering;  Performance Measurement Systems;  SCOR;  Supply Chain Management},
keywords={Administrative data processing;  Computer supported cooperative work;  Enterprise resource management;  Ontology;  Semantics;  Peer to peer networks, Business process management;  Collaborative performance;  Ontology engineering;  Performance management;  Performance measurement system;  SCOR;  Shared understanding;  Supply chain managements (SCM), Supply chain management},
references={Mentzer, J.T., De Witt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22, pp. 1-25; Childerhouse, P., Towill, D.R., Simplified material flow holds the key to supply chain integration (2003) Omega, 31, pp. 17-27; Fugate, B., Sahin, F., Mentzer, J.T., Supply chain management coordination mechanisms (2006) Journal of Business Logistics, 27, pp. 129-161; Towill, D.R., The seamless supply chain - The predators strategic advantage (1997) International Journal of the Techniques of Manufacturing, 13, pp. 37-56; Swaminathan, J., Smith, S., Sadeh, N., Modeling supply chain dynamics: A multiagent approach (1998) Decision Sciences, 29, pp. 607-632; Esper, T., Ellinger, A., Stank, T., Flint, D., Moon, M., Demand and supply integration: A conceptual framework of value creation through knowledge management (2009) Journal of the Academy of Marketing Science, pp. 5-18; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12, pp. 1-19; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55, pp. 242-258; Forslund, H., Jonsson, P., Obstacles to supply chain integration of the performance management process in buyer-supplier dyads (2009) International Journal of Operations and Production Management, 29, pp. 77-95; Ye, Y., Yang, D., Jiang, Z., Tong, L., Ontology-based semantic models for supply chain management (2008) The International Journal of Advanced Manufacturing Technology, 37, pp. 1250-1260; Daconta, M.C., Smith, K.T.X., Obrst, L.J., (2003) The Semantic Web: A Guide to the Future of XML, Web Services, and Knowledge Management, , John Wiley & Sons Inc., Chichester; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 25, pp. 1228-1263; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173, pp. 211-225; Jüttner, U., Christopher, M., Baker, S., Demand chain management-integrating marketing and supply chain management (2007) Industrial Marketing Management, 36, pp. 377-392; Min, S., Mentzer, J., Ladd, R., A market orientation in supply chain management (2007) Journal of the Academy of Marketing Science, 35, pp. 507-522; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Neely, A., (2008) Business Performance Measurement: Unifying Theory and Integrating Practice, , Cambridge University Press, Cambridge; Gunasekaran, A., Ngai, E.W.T., Information systems in supply chain integration and management (2004) European Journal of Operational Research, 159, pp. 269-295; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17, pp. 522-534; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27, pp. 784-801; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19, pp. 275-292; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56, pp. 663-680; Jeschonowski, D., Schmitz, J., Wallenburg, C., Weber, J., Management control systems in logistics and supply chain management: A literature review (2009) Logistics Research, 1, pp. 113-127; Bititci, U., Mendibil, K., Nudurupati, S.S., Garengo, P., Dynamics of performance measurement and organizational culture (2007) International Journal of Operations and Production Management, 26, pp. 1325-1350; Leukel, J., Kirn, S., A supply chain management approach to logistics ontologies in information systems (2008) Proceedings of the 11th International Conference on Business Information Systems (BIS 2008), pp. 95-105. , Springer LNBIP, Innsbruck; (2008), Supply-Chain Council: Supply Chain Operations Reference Model. Version 9.0; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48, pp. 377-394; Dong, J., Ding, H., Ren, C., Wang, W., IBM SmartSCOR - A SCOR based supply chain transformation platform through simulation and optimization techniques (2006) Proceedings of the 2006 Winter Simulation Conference, Winter Simulation Conference, pp. 650-659. , Monterey; Fayez, M., An automated methodology for a comprehensive definition of the supply chain using generic ontological components (2005) The Department of Industrial Engineering and Management Systems, p. 230. , University of Central Florida Orlando; Gruber, T.R., Toward principles for the design of ontologies used for knowledge sharing (1995) International Journal of Human-Computer Studies, 43, pp. 907-928; Guarino, N., Formal ontology and information systems (1998) Proceedings of FOIS 1998, pp. 3-15. , IOS Press, Amsterdam; Gruber, T.R., A translation approach to portable ontology specifications (1993) Knowledge Acquisition, 5, pp. 199-220; Uschold, M., Gruninger, M., Ontologies: Principles, methods and applications (1996) The Knowledge Engineering Review, 11, pp. 93-136; Hepp, M., Leymann, F., Domingue, J., Wahler, A., Fensel, D., Semantic business process management: A vision towards using semantic web services for business process management (2005) Proceedings of the IEEE International Conference on E-Business Engineering, pp. 535-540. , IEEE Computer Society, Washington; Sycara, K., Paolucci, M., Ankolekar, A., Srinivasan, N., Automated discovery, interaction and composition of semantic web services (2003) Web Semantics: Science, Services and Agents on the World Wide Web, 1, pp. 27-46; Van Der Aalst, W., Hofstede, T.A., Weske, M., Business process management: A survey (2003) LNCS, 2678, pp. 1-12. , van der Aalst, W. M. P., ter Hofstede, A. H. M., Weske, M. eds., BPM 2003. Springer, Heidelberg; Pedrinaci, C., Domingue, J., Brelage, C., Van Lessen, T., Karastoyanova, D., Leymann, F., Semantic business process management: Scaling up the management of business processes (2008) Proceedings of the 2008 IEEE International Conference on Semantic Computing, pp. 546-553. , IEEE Computer Society, Santa Clara; Wetzstein, B., Ma, Z., Leymann, F., Towards measuring key performance indicators of semantic business processes (2008) Business Information Systems, pp. 227-238; Chandra, C., Supply chain workflow modeling using ontologies (2008) Collaborative Engineering, pp. 61-87. , Springer, Boston; Chandra, C., Tumanyan, A., Ontology driven knowledge design and development for supply chain management (2004) Proceedings 13th Annual Industrial Engineering Research Conference, , Houston; Chandra, C., Tumanyan, A., Organization and problem ontology for supply chain information support system (2007) Data & Knowledge Engineering, 61, pp. 263-280; Smirnov, A.V., Chandra, C., Ontology-based knowledge management for cooperative supply chain configuration (2000) Proceedings of the American Association of Artificial Intelligence Spring Symposium, pp. 85-92. , AAAI Press, Menlo Park; Hellingrath, B., Witthaut, M., Böhle, C., Brügger, S., An organizational knowledge ontology for automotive supply chains (2009) Holonic and Multi-Agent Systems for Manufacturing, pp. 37-46; Zimmermann, R., Käs, S., Butscher, R., Bodendorf, F., An ontology for agent-based monitoring of fulfillment processes (2005) Ontologies for Agents: Theory and Experiences, pp. 323-345; Fayez, M., Rabelo, L., Mollaghasemi, M., Ontologies for supply chain simulation modeling (2005) Proceedings of the 37th Conference on Winter Simulation, Winter Simulation, Conference, Orlando, pp. 2364-2370; Cope, D., Fayez, M., Mollaghasemi, M., Kaylani, A., Supply chain simulation modeling made easy: An innovative approach (2007) Proceedings of the 39th Conference on Winter Simulation: 40 Years! the Best Is Yet to Come, pp. 1887-1896. , IEEE Press, Washington; Cope, D., Automatic generation of supply chain simultion models from SCOR based ontologies (2008) Department of Industrial Engineering and Management Systems, , University of Central Florida Florida; Uschold, M., Gruninger, M., Ontologies and semantics for seamless connectivity (2004) SIGMOD Rec., 33, pp. 58-64; Hepp, M., Roman, D., An ontology framework for semantic business process management (2007) Proceedings of Wirtschaftsinformatik, pp. 423-440. , Universitätsverlag, Karlsruhe},
correspondence_address1={Nurmi, A.; INSA-Lyon, , Villeurbanne Cedex 69621, France; email: artturi.nurmi@insa-lyon.fr},
publisher={Springer Verlag},
issn={03029743},
isbn={3642169600; 9783642169601},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Espinoza2010249,
author={Espinoza, O.A. and Bond, B.H. and Kline, E.},
title={Quality measurement in the wood products supply chain},
journal={Forest Products Journal},
year={2010},
volume={60},
number={3},
pages={249-257},
doi={10.13073/0015-7473-60.3.249},
note={cited By 10},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78149252370&doi=10.13073%2f0015-7473-60.3.249&partnerID=40&md5=d7f6f61023a0c780b3899fe2509b1994},
affiliation={Dept. of Wood Sci. and Forest Products, Virginia Polytechnic Inst, State Univ., Blacksburg, United States},
abstract={In this article we report the first in-depth investigation of quality measurement practices from a supply chain perspective. Quality measurement in a wood products supply chain was studied in great detail with the objective of increasing the understanding of quality performance measurement practices in a secondary wood products supply chain. Opportunities for improvement were also identified. A single-case study was used as the main research approach, with 30 interviews and observation as major data collection methods. Findings revealed a high degree of internal integration in the focal company, made possible in great part by a continuous improvement effort that expands all of its facilities. Opportunities for improvement were found in external integration, particularly regarding supplier quality management. A disconnect was identified between supply chain members in regard to quality information; particularly, there was a lack of true supply chain quality measures reflecting the contribution of each entity to the overall quality. Results from this research highlight the importance of adopting a systems perspective when designing a supply chain performance measurement system. Poor quality at any point in the supply chain is detrimental to customer satisfaction, hurts profitability, and eventually translates into higher costs for downstream business segments and for the final customer; the end result is a decline in competitiveness of the entire system. ©Forest Products Society 2010.},
keywords={Competition;  Customer satisfaction;  Quality management;  Wood products, Business segments;  Continuous improvements;  Data collection method;  Quality information;  Quality measurements;  Quality performance;  Research approach;  Supply chain performance measurements, Supply chains, Competition;  Profitability;  Quality Control;  Supply Chain Management;  Technical Services;  Wood Products},
references={Abdulmalek, F.A., Rajgopal, J., Analyzing the benefits of lean manufacturing and value stream mapping via simulation: A process sector case study (2007) Int. J. Prod. Econ., 107 (1), pp. 223-236; Six Sigma, lean come together (2008) Ind. Week, 257 (3), p. 23. , Anonymous; Aryee, G., Naim, M.M., Lalwani, C., Supply chain integration using a maturity scale (2008) J. Manuf. Technol. Manag., 19 (5), pp. 559-575; Barki, H., Pinsonneault, A., A model of organizational integration, implementation effort, and performance (2005) Organ. Sci., 16 (2), pp. 165-179; Batson, R.G., A survey of best practices in automotive supplier development (2008) Int. J. Automat. Technol. Manag., 8 (2), pp. 129-144; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Manag., 19 (3-4), pp. 275-292; Beaudoin, D., LeBel, L., Frayret, J.-M., Tactical supply chain planning in the forest products industry through optimization and scenario-based analysis (2007) Can. J. Forest Res., 37 (1), pp. 128-140; Berry, D., Evans, G.N., Mason-Jones, R., Towill, D.R., The BPR SCOPE concept in leveraging improved supply chain performance (1999) Bus. Process Manag. J., 5 (3), pp. 254-274; Blanchard, D., What's working for U.S. manufacturers (2006) Ind. Week, 255 (10), pp. 49-51; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) Int. J. Oper. Prod. Manag., 22 (11), pp. 1288-1310; Buehlmann, U., What does the wood industry have to gain by leveraging the supply chain? (2004) Manufacturing Competitiveness of the Forest Products Industry: Competing in Today's Global Manufacturing and Consumer Marketplace, , November 3-5, 2000, New Orleans; Forest Products Society, Madison, Wisconsin; Carlsson, D., Rönnqvist, M., Supply chain management in forestry-Case studies at Södra Cell AB (2005) Eur. J. Oper. Res., 163 (3), pp. 589-616; Carr, A.S., Pearson, J.N., Strategically managed buyer-supplier relationships and performance outcomes (1999) J. Oper. Manag., 17 (5), pp. 497-519. , Pearson; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Advanced Manuf. Technol., 21 (7), pp. 534-548; Chen, C.-C., Yeh, T.-M., Yang, C.-C., Customer-focused rating system of supplier quality performance (2004) J. Manuf. Technol. Manag., 15 (7), pp. 599-606; Closs, D.J., Savitskie, K., Internal and external logistics information technology integration (2003) Int. J. Logistics Manag., 14 (1), pp. 63-76; Cumbo, D., Kline, D.E., Bumgardner, M., Benchmarking performance measurement and lean manufacturing in the rough mill (2006) Forest Prod. J., 56 (6), pp. 25-30; D'Amours, S., Frayret, J.-M., Rousseau, A., (2004) From the Forest to the Client- Why Have Integrated Management of the Value Creation Network, , http://www.forac.ulaval.ca/fileadmin/docs/Publications/ GestionIntegreeRCV.pdf, Accessed November 1, 2006. (In French.); D'Amours, S., Rönnqvist, M., Weintraub, A., Using operational research for supply chain planning in the forest products industry (2008) INFOR, 46 (4), pp. 265-281; Espinoza, O.A., (2009) Quality Measurement in the Wood Products Supply Chain, , Doctoral dissertation. Virginia Polytechnic Institute and State University, Blacksburg; Fontenot, R.J., Vlosky, R.P., Wilson, E.J., Wilson, D.T., A model of buyer-seller relationship structure effects on firm performance (1998) Am. Mark. Assoc. Conf Proc., 9, pp. 206-207; Ford, D.N., Sterman, J.D., The liar's club: Concealing rework in concurrent development (2003) Concurr. Eng. Res. Appl., 11 (3), pp. 211-219; Fram, E.H., Purchasing partnerships: The buyer's view (1995) Mark. Manag., 4 (1), p. 49; Frayret, J.M., D'Amours, S., Rousseau, A., Harvey, S., Gaudreault, J., Agent-based supply-chain planning in the forest products industry (2007) Int. J. Flexible Manuf. Syst., 19, pp. 358-391. , (Compendex); Fynes, B., Voss, C., De Burea, S., The impact of supply chain relationship quality on quality performance (2005) Int. J. Prod. Econ., 96 (3), pp. 339-354; George, M.L., (2002) Lean Six Sigma: Combining Six Sigma Quality with Lean Speed, 14, p. 322. , McGraw-Hill, New York; Germain, R., Iyer, K.N.S., The interaction of internal and downstream integration and its association with performance (2006) J. Bus. Logistics, 27 (2), pp. 29-52; Goldsby, T.J., Lean six sigma logistics: Strategic development to operational success (2005) J. Ross Pub., 17, p. 282. , R. Martichenko (Trans.). Boca Raton, Florida; Hahn, C.K., Watts, C.A., Kim, K.Y., The supplier development program: A conceptual model (1990) J. Purch. Mater. Manag., 26 (2), pp. 2-7; Hassan, M.M.D., Engineering supply chains as systems (2006) Syst. Eng., 9 (1), pp. 73-89; Jones, D., Womack, J., Seeing the whole-mapping the extended value stream (2002) The Lean Enterprise Inst., pp. 3-5. , Brookline, Massachusetts; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Ind. Mark. Manag., 29 (1), pp. 65-83; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int. J. Logistics Manag., 12 (1), pp. 1-19; Lee, C.W., Kwon, I.-W.G., Severance, D., Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer (2007) Supply Chain Manag., 12 (6), pp. 444-452; Levy, P., Bessant, J., Sang, B., Lamming, R., Developing integration through total quality supply chain management (1995) Integr. Manuf. Syst., 6 (3), pp. 4-12; Li, S.H., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain management practices (2005) J. Oper. Manag., 23 (6), pp. 618-641. , (Review.); Lo, V.H.Y., Yeung, A., Managing quality effectively in supply chain: A preliminary study (2006) Supply Chain Manag., 11 (3), pp. 208-215; Mason-Jones, R., Towill, D.R., Information enrichment: Designing the supply chain for competitive advantage (1997) Supply Chain Manag., 2 (4), pp. 137-148; Minahan, T.A., 5 Strategies for high performance procurement (2005) Supply Chain Manag. Rev., 9 (6), pp. 46-54; Muralidharan, C., Anantharaman, N., Deshmukh, S.G., A multi-criteria group decisionmaking model for supplier rating (2002) J. Supply Chain Manag., 38 (4), pp. 22-33; Neely, A., The performance measurement revolution: Why now and what next? (1999) Int. J. Oper. Prod. Manag., 19 (2), pp. 205-228; New, S.J., A framework for analysing supply chain improvement (1996) Int. J. Oper. Prod. Manag., 16 (4), pp. 19-34; Oliva, R., Sterman, J.D., Cutting corners and working overtime: Quality erosion in the service industry (2001) Manag. Sci., 47 (7), pp. 894-914; Pagell, M., Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics (2004) J. Oper. Manag., 22 (5), pp. 459-487; Petersen, K.J., Handfield, R.B., Ragatz, G.L., Supplier integration into new product development: Coordinating product, process and supply chain design (2005) J. Oper. Manag., 23 (3-4), pp. 371-388; Raisinghani, M.S., Ette, H., Pierce, R., Cannon, G., Daripaly, P., Six Sigma: Concepts, tools, and applications (2005) Ind. Manag. Data Syst., 105 (3-4), pp. 491-505; Repenning, N.P., Understanding fire fighting in new product development (2001) J. Prod. Innov. Manag., 18 (5), pp. 285-300; Rodrigues, A.M., Stank, T.P., Lynch, D.F., Linking strategy, structure, process, and performance in integrated logistics (2004) J. Bus. Logistics, 25 (2), pp. 65-94; Rogers, K.W., Purdy, L., Safayeni, F., Duimering, P.R., A supplier development program: Rational process or institutional image construction? (2007) J. Oper. Manag., 25 (2), pp. 556-572; Rosenzweig, E.D., Roth, A.V., Dean, J.W., The influence of an integration strategy on competitive capabilities and business performance: An exploratory study of consumer products manufac-turers (2003) J. Oper. Manag., 21 (4), pp. 437-456; Sanders, N.R., Premus, R., Modeling the relationship betwee firm IT capability, collaboration, and performance (2005) J. Bus. Logistics, 26 (1), pp. 1-23; Sasmohapatra, S., Future marketing drivers for the forest products industry (2009) BioResources, 4 (4), pp. 1263-1266; Simpson, J.T., Wren, B.M., Buyer-seller relationships in the wood products industry (1997) J. Bus. Res., 39 (1), pp. 45-51; Stank, T.P., Keller, S.B., Daugherty, P.J., Supply chain collaboration and logistical service performance (2001) J. Bus. Logistics, 22 (1), pp. 29-48; Tan, K.-C., Kannan, V.R., Handfield, R.B., Ghosh, S., Supply chain management: An empirical study of its impact on performance (1999) Int. J. Oper. Prod. Manag., 19 (10), pp. 1034-1052; Teigen, K.H., Yerkes-Dodson: A law for all seasons (1994) Theory Psychol., 4, pp. 525-547; Towill, D.R., Time compression and supply chain management- A guided tour (1996) Supply Chain Manag., 1 (1), pp. 15-27; Van-Aken, E.M., Coleman, G.D., Building better measurement (2002) Ind. Manag., 44 (4), pp. 28-33; Vickery, S.K., Jayaram, J., Droge, C., Calantone, R., The effects of an integrative supply chain strategy on customer service and financial performance: An analysis of direct versus indirect relationships (2003) J. Oper. Manag., 21 (5), pp. 523-539; Vlosky, R.P., Wilson, E.J., Cohen, D.H., Fontenot, R., Johnston, W.J., Kozak, R.A., Lawson, D., Wren, B.M., Partnerships versus typical relationships between wood products distributors and their manufacturer suppliers (1998) Forest Prod. J., 48 (3), pp. 27-35; Womack, J.P., Value stream mapping (2006) Manuf. Eng., 136 (5), pp. 145-156},
correspondence_address1={Espinoza, O. A.; Dept. of Wood Sci. and Forest Products, , Blacksburg, United States; email: oespin04@vt.edu},
publisher={Forest Products Society},
issn={00157473},
coden={FPJOA},
language={English},
abbrev_source_title={For Prod J},
document_type={Article},
source={Scopus},
}

@ARTICLE{Liao20106,
author={Liao, Y. and Hong, P. and Rao, S.S.},
title={Supply management, supply flexibility and performance outcomes: An empirical investigation of manufacturing firms},
journal={Journal of Supply Chain Management},
year={2010},
volume={46},
number={3},
pages={6-22},
doi={10.1111/j.1745-493X.2010.03195.x},
note={cited By 179},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77955025404&doi=10.1111%2fj.1745-493X.2010.03195.x&partnerID=40&md5=5ef6e5b8a5a0548c5fb19b423d83613e},
affiliation={Meredith College, United States; University of Toledo, United States; University of Toledo (Emeritus), United States},
abstract={Increasingly, the global market environment is becoming more turbulent, complex and uncertain. Literature has explored the importance of supply management and its direct impact on performance. However, the nature of strategic supply management and its impact on supply flexibility needs further clarification in the context of the use of supplier resources and supplier network coordination. This research presents a model of supply management, supply flexibility and supply chain performance. In this study, we examine the relationships between supply management and supply flexibility, and extend the concept of supply flexibility in terms of supplier flexibility and supply network flexibility on relevant supply chain performance measures. Data for the study were collected from 201 manufacturing firms, and the measurement scales of supply flexibility were tested and validated using structural equation modeling. The results indicate strong, positive and direct relationships between supply management practices and supply flexibility, and between supply flexibility and supply chain performance. Concluding theoretical and managerial implications are discussed. © 2010 Institute for Supply Management, Inc.},
author_keywords={Supply chain performance;  Supply flexibility;  Supply management practice},
references={Azadegan, A., Dooley, K.J., Carter, P.L., Carter, J.R., Supplier Innovativeness and the Role of Interorganizational Learning in Enhancing Manufacturer Capabilities (2008) Journal of Supply Chain Management, 44 (4), pp. 14-35; Bagozzi, R.P., Yi, Y., Phillips, L.W., Assessing Construct Validity in Organizational Research (1991) Administrative Science Quarterly, 36 (3), pp. 421-458; Baier, C., Hartmann, E., Moster, R., Strategic Alignment and Purchasing Efficacy (2008) Journal of Supply Chain Management, 44 (4), pp. 36-52. , An Exploratory Analysis of their Impact on Financial Performance; Bailey, P., Farmer, D., Jones, D., Jones, D., (2005) Purchasing Principles and Management, , Prentice Hall, London; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Catalan, M., Kotzab, M., Assessing the Responsiveness in the Danish Mobile Phone Supply Chain (2003) International Journal of Physical Distribution & Logistics Management, 33 (8), pp. 668-685; Choi, T.Y., Dooley, K.J., Supply Networks (2009) Journal of Supply Chain Management, 45 (3), pp. 25-26. , Theories and Models; Choi, T.Y., Hartley, J.L., An Exploration of Supplier Selection Practices Across the Supply Chain (1996) Journal of Operations Management, 14 (4), pp. 333-343; Christopher, M., The Agile Supply Chain Competing in Volatile Markets (2000) Industrial Marketing Management, 29, pp. 37-44; Christopher, M., Lowson, R., Peck, H., Creating Agile Supply Chains in the Fashion Industry (2004) International Journal of Retail & Distribution Management, 32 (8), pp. 367-376; Claycomb, C., Frankwick, G.L., A Contingency Perspective of Communication, Conflict Resolution, and Buyer Search Effort in Buyer–Supplier Relationships (2004) Journal of Supply Chain Management, 40 (1), pp. 18-34; Conner, K.R., Prahalad, C.K., A Resource-Based Theory of the Firm (1996) Organization Science, 7 (5), pp. 477-501. , Knowledge Versus Opportunism; Duclos, L.K., Vokurka, R.J., Lummus, R.R., A Conceptual Model of Supply Chain Flexibility (2003) Industrial Management & Data Systems, 103 (6), pp. 446-456; Fine, C.H., Clockspeed-Based Strategies for Supply Chain Design (2000) Production and Operations Management, 9, pp. 213-221; Garavelli, A.C., Flexibility Configurations for the Supply Chain Management (2003) International Journal of Production Economics, 85 (2), pp. 141-153; González-Benito, J., A Theory of Purchasing's Contribution to Business Performance (2007) Journal of Operations Management, 25 (4), pp. 901-917; Gosain, S., Malhotra, A., Sawy, O.A., Coordinating for Flexibility in e-Business Supply Chains (2005) Journal of Management Information Systems, 21 (3), pp. 7-45; Gulati, R., Social Structure and Alliance Formation Patterns (1995) Administrative Science Quarterly, 40 (4), pp. 619-652. , A Longitudinal Analysis; Gunasekaran, A., Agile Manufacturing (1999) International Journal of Production Economics, 62, pp. 87-105. , A Framework for Research and Development; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hahn, C.K., Watts, C.A., Kim, K.Y., The Supplier Development Program (1990) International Journal of Purchasing and Materials Management, 26 (2), pp. 2-7. , A Conceptual Model; Harland, C.M., Caldwell, N.D., Powell, P., Zhang, J., Barriers to Supply Chain Information Integration (2007) Journal of Operations Management, 25, pp. 1234-1254. , SMEs Adrift of Elands; Hartley, J.L., Choi, T.Y., Supplier Development (1996) Business Horizons, 39 (4), pp. 37-44. , Customers as a Catalyst of Process Change; Hausman, W.H., (2005) Supply Chain Performance Metrics. The Practice of Supply Chain Management, , Springer Science & Business Media, Inc., New York; Hines, P., International and Localization of the Kyoryoku Kai (1994) International Journal of Logistics Management, 5 (1), pp. 67-72. , The Spread of Best Practice Supplier Development; Holcomb, T.R., Hitt, M.A., Toward a Model of Strategic Outsourcing (2007) Journal of Operations Management, 25, pp. 464-481; Hong, P., Jeong, J., Supply Chain Management Practices of SMEs (2006) Journal of Enterprise Information Management, 19 (3), pp. 292-302. , From a Business Growth Perspective; Humphreys, P.K., Li, W.L., Chan, L.Y., The Impact of Supplier Development on Buyer-Supplier Performance (2004) OMEGA, 32 (2), pp. 131-143; Inkpen, A.C., Tsang, E.W.K., Social Capital, Networks, and Knowledge Transfer (2005) Academy of Management Review, 30 (1), pp. 146-165; Ittner, C.D., Larcker, D.F., Nagar, V., Rajan, M.V., Supplier Selection, Monitoring Practices, and Firm Performance (1999) Journal of Accounting and Public Policy, 18, pp. 253-281; Kei, M., Tan, B.C.Y., Wei, K., Saarinen, T., Tuunainen, V., Wassenaar, A., A Cross-Cultural Study on Escalation of Commitment Behavior in Software Projects (2000) MIS Quarterly, 24, pp. 299-325; Ketchen, D.J., Hult, G.T.M., Bridging Organization Theory and Supply Chain Management (2007) Journal of Operations Management, 25, pp. 573-580. , The Case of Best Value Supply Chains; Krause, D.R., Ellram, L.M., Success Factors in Supplier Development (1997) European Journal of Physical Distribution & Logistics Management, 3 (1), pp. 21-31; Kumar, V., Fantazy, K.A., Kumar, U., Boyle, T.A., Implementation and Management Framework for Supply Chain Flexibility (2006) Journal of Enterprise Information Management, 19 (3), pp. 303-319; Lau, R.S.M., Strategic Flexibility (1996) SAM Advanced Management Journal, 61 (2), pp. 11-15. , A New Reality for World-class Manufacturing; Lavie, D., The Competitive Advantage of Interconnected Firms (2006) Academy of Management Review, 31 (3), pp. 638-658. , An Extension of the Resource-Based View; Lee, H.L., The Triple-A Supply Chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Lee, K., Lim, G., Tan, S., Dealing with Resource Disadvantage Generic Strategies for SMEs (1999) Small Business Economics, 12 (4), pp. 299-311; Li, S.H., Ragu-Nathan, B., Ragu-Natha, T.S., Rao, S.S., The Impact of Supply Chain Management Practices on Competitive Advantage and Organizational Performance (2006) OMEGA, 34, pp. 107-124; Lummus, R.R., Duclos, L.K., Vokurka, R.J., Supply Chain Flexibility (2003) Global Journal of Flexible Systems Management, 4 (4), pp. 1-13. , Building a New Model; Macbeth, D.K., Ferguson, N., (1994) Partnership Sourcing, , Prentice Hall, London; Madhavan, R., Koka, B.R., Prescott, J.E., Networks in Transition (1998) Strategic Management Journal, 19 (5), pp. 439-459. , How Industry Events (Re) Shape Interfirm Relationships; Maguire, S., Koh, S.C.L., The Adoption of e-Business and Knowledge Management in SMEs (2007) Benchmarking: An International Journal, 14 (1), pp. 37-58; Mason, S.J., Cole, M.H., Ulrey, B.T., Yan, L., Improving Electronics Manufacturing Supply Chain Agility through Outsourcing (2002) International Journal of Physical Distribution & Logistics Management, 32 (7), pp. 610-620; McCutcheon, D.M., Grant, R.A., Hartley, J., Determinants of New Product Designers' Satisfaction with Suppliers' Contributions (1997) Journal of Engineering & Technology Management, 14 (3-4), pp. 273-290; McKone-Sweet, K., Lee, Y.T., Development and Analysis of a Supply Chain Strategy Taxonomy (2009) Journal of Supply Chain Management, 45 (3), pp. 3-24; Mentzer, J.T., Foggin, J.H., Golicic, S.L., Collaboration (2000) Supply Chain Management Review, 4 (4), pp. 52-57. , The Enablers, ImpedimentsBenefits; Miles, R.E., Snow, C.C., Organization Theory and Supply Chain Management (2007) Journal of Operations Management, 25, pp. 459-463. , An Evolving Research Perspective; Narasimhan, R., Talluri, S., Das, A., Exploring Flexibility and Execution Competencies of Manufacturing Firms (2004) Journal of Operations Management, (22), pp. 91-106; Novack, R.A., Simco, S.W., The Industrial Procurement Process (1991) Journal of Business Logistics, 12 (1), pp. 145-167. , A Supply Chain Perspective; O’leary-Kelly, S.W., Vokurka, R.J., The Empirical Assessment of Construct Validity (1998) Journal of Operations Management, 16 (4), pp. 387-405; Otto, A., Kotzab, H., Does Supply Chain Management Really Pay? Six Perspectives to Measure the Performance of Managing a Supply Chain (2003) European Journal of Operations Research, 144, pp. 306-320; Pedersen, E.R., The Many and the Few (2009) Supply Chain Management: An International Journal, 14 (2), pp. 109-116. , Rounding Up the SMEs that Manage CSR in the Supply Chain; Podsakoff, P.M., Organ, D.W., Self-Reports in Organizational Research (1986) Journal of Management, 12, pp. 69-82. , Problems and Prospects; Pujawan, I.N., Assessing Supply Chain Flexibility (2004) International Journal of Integrated Supply Management, 1 (1), pp. 79-97. , A Conceptual Framework and Case Study; Robinson, J.P., Shaver, P.R., Wrightsman, L.S., (1991) Measures of Personality and Social Psychological Attitudes, , Harcourt Brace Jovanovich, San Diego, CA; Sanchez, A.M., Perez, M.P., Supply Chain Flexibility and Firm Performance (2005) International Journal of Operations & Production Management, 25 (7), pp. 681-700; Sanchez, R., Strategic Flexibility in Product Competition (1995) Strategic Management Journal, 16 (5), pp. 135-159; Scannell, T.V., Vickery, S.K., Droge, C.L., Upstream Supply Chain Management and Competitive Performance in the Automotive Supply Industry (2000) Journal of Business Logistics, 21 (1), pp. 23-48; Schroeder, R.G., Bates, K.A., Junttila, M.A., A Resource-Based View of Manufacturing Strategy and the Relationship to Manufacturing Performance (2002) Strategic Management Journal, 23, pp. 105-117; Simatupang, T.M., Wright, A.C., Sridharan, R., The Knowledge of Coordination for Supply Chain Integration (2002) Business Process Management Journal, 8 (3), pp. 289-308; Singh, N., Sushil, J., Flexibility in Product Development for Success in Dynamic Market Environment (2004) Global Journal of Flexible Systems Management, 5 (1), pp. 23-34; Skilton, P.F., Robinson, J.L., Traceability and Normal Accident Theory (2009) Journal of Supply Chain Management, 45 (3), pp. 40-53. , How Does Supply Network Complexity Influence the Traceability of Adverse Events?; Stevenson, M., Spring, M., Flexibility from a Supply Chain Perspective (2007) International Journal of Operations & Production Management, 27 (7), pp. 685-713. , Definition and Review; Tsai, W., Social Capital, Strategic Relatedness and the Formation of Intraorganizational Linkages (2000) Strategic Management Journal, 21 (9), pp. 925-939; Vaaland, T.I., Heide, M., Can the SME Survive the Supply Chain Challenges? (2007) Supply Chain Management an International Journal, 12 (1), pp. 20-31; Van Hoek, R.I., Harrison, A., Christopher, M., Measuring Agile Capabilities in the Supply Chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 126-147; Volberda, H.W., Toward the Flexible Form (1996) Organization Science, 7 (4), pp. 359-374. , How to Remain Vital in Hyper-competitive Environments; Vonderembse, M.A., Tracey, M., The Impact of Supplier Selection Criteria and Supplier Involvement on Manufacturing Performance (1999) Journal of Supply Chain Management, 35 (3), pp. 33-39; Watts, C.A., Hahn, C.K., Supplier Development Program (1993) International Journal of Purchasing and Materials Management, 29 (2), pp. 11-17. , An Empirical Analysis; Wong, K.Y., Critical Success Factors for Implementing Knowledge Management in Small and Medium Enterprises (2005) Industrial Management & Data Systems, 105 (3), pp. 261-279; Yao, Y., Dresner, M., Palmer, J.W., Impact of Boundary-Spanning Information Technology and Position in Chain on Firm Performance (2009) Journal of Supply Chain Management, 45 (4), pp. 3-17},
issn={15232409},
language={English},
abbrev_source_title={J. Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Tezza201075,
author={Tezza, R. and Bornia, A.C. and Vey, I.H.},
title={Performance measurement systems: A review and classification of literature [Sistemas de medição de desempenho: Uma revisão e classificação da literatura]},
journal={Gestao e Producao},
year={2010},
volume={17},
number={1},
pages={75-93},
doi={10.1590/s0104-530x2010000100007},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953655168&doi=10.1590%2fs0104-530x2010000100007&partnerID=40&md5=3ab987b3e37858e976324328c26c7c16},
affiliation={Programa de Pós Graduação em Engenharia de Produção, Universidade Federal de Santa Catarina, Campus Universitário - Trindade, CP 476, CEP 88040-900, Florianópolis - SC, Brazil},
abstract={The purpose of this article is to classify Performance Measurement Systems (PMS) according to their approaches. In this paper 140 approaches were reviewed in the period 1980 to 2007. After the review, the approaches were classified on the basis of five criteria: on the scope, the type of approach, the focus, the level of measurement and on the degree of complexity. It appears that the environment in which PMS is most widespread is the corporate environment, however, several approaches are currently focused on the supply chain, services and individuals. Most of the approaches discussed were classified as practice, with no financial focus, and with different levels of measurement and degrees of complexity. The contributions of this work focus on generating a better and systematic knowledge on the subject of performance measurement systems and the identification of future trends within this topic.},
author_keywords={Classification;  Performance Measurement Systems;  Review},
references={Åhlström, P., Karlsson, C., Change processes towards lean production: The role of the management accounting system (1996) International Journal of Operations & Production Management, 16 (1), pp. 42-56; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Engineering Costs and Production Economics, 17, pp. 253-262; Antonioni, D., Designing an effective 360-degree appraisal feedback process (1996) Organizational Dynamics, 25 (2), pp. 24-38; Anthony, R.N., (1965) Planing and Control Systems: A Framework for Analysis, p. 180. , Cambridge: Harvard University Press; Atkinson, A., Strategic performance measurement and incentive compensation (1998) European Management Journal, 16 (5), pp. 552-561; Azzone, G., Masella, C., Bertelè, U., Design of performance measures for time-based companies (1991) International Journal of Operations & Production Management, 11 (3), pp. 77-85; Ballou, R.H., (1998) Business Logistics Management: Planning, Organizing, and Controlling the Supply Chain, , 4 ed. New York: Prentice Hall; Barker, B., Value-adding performance measurement: A time-based approach (1993) International Journal of Operations & Production Management, 13 (5), pp. 33-40; Beamon, B.M., Measuring supply chain performance (1999) International Journal Operations & Production Management, 19 (3), pp. 275-292; Bititci, U.S., Modelling of performance measurement systems in manufacturing enterprises (1995) International Journal of Production Economics, 42 (2), pp. 137-147; Bititci, U.S., Carrie, A.S., Mcdevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-534; Bititci, U.S., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Bititci, U.S., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations & Production Management, 26 (12), pp. 1325-1350; Bourne, M., Designing implementing and updating performance measurement systems (2000) International Journal of Operations Management, 20 (7), pp. 754-771; Bowersox, D.J., Closs, D.C., (1996) Logistical Management: The Integrated Supply Chain Process, , New York: The McGraw-Hill Companies, (McGraw-Hill Series in Marketing); Boyd, L.H., Cox, J.F., A cause-and-effect approach to analyzing performance measures (1997) Production and Inventory Management Journal, 38 (3), pp. 25-32; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), pp. 48-56; Brignall, T.J., Performance measurement in service businesses (1991) Management Accounting, 69 (10), pp. 34-36; Brown, D.M., Lecturer, S., Measuring corporate performance (1994) Long Range Planning, 27 (4), pp. 89-98; Brown, M.G., (1996) Keeping Score: Using the Right Metrics to Drive World-Class Performance, , New York: Quality Resources; Campos, V.F., (1992) TQC: Controle da Qualidade Total (No Estilo Japonês), , Belo Horizonte: Fundação Cristiano Otoni; Chin, W.W., Lee, M.K.O., On the formation of end-user computing satisfaction: A proposed model and measurement instrument (2000) International Conference on Information Systems, 21, pp. 553-563. , Brisbane. Proceedings⋯ Brisbane: Association for Information Systems 2000; Christopher, M.L., (1992) Logistics and Supply Chain Management, , London: Pitman Publishing; Cliville, V., Berrah, L., Mauris, G., Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method (2007) International Journal Production Economics, 105 (1), pp. 171-189; Cravens, K., Piercy, N., Cravens, D., Assessing the performance of strategic alliances: Matching metrics to strategies (2000) European Management Journal, 18 (5), pp. 529-541; Crawford, K.M., Cox, J.F., Designing performance measurement systems for just-in-time operations (1990) International Journal of Production Research, 28 (1), pp. 2025-2036; Cross, K.F., Lynch, R.L., Managing the corporate warriors (1990) Quality Progress, 23 (4), pp. 54-59; Daniels, R.C., Burns, N.D., A framework for proactive performance measurement system introduction (1997) International Journal of Operations & Production Management, 17 (1), pp. 100-116; De Toni, A., Tonchia, S., Lean organization, management by process and performance measurement (1996) International Journal of Operations & Production Management, 16 (2), pp. 221-236; De Toni, A., Tonchia, S., Performance measurement systems (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Devise, O., Pierreval, H., Indicators for measuring performances of morphology and material handling systems in flexible manufacturing systems (2000) International Journal Production Economics, 64 (1-3), pp. 209-218; Díaz, M.S., Gil, M.J.Á., Machuca, J.A.D., Performance measurement systems, competitive priorities, and advanced manufacturing technology (2005) International Journal of Operations & Production Management, 25 (8), pp. 781-799; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge: Measuring Operations for World Class Competition, , Homewood: Irwin Professional Publishing; Dornier, P.P., (1998) Global Operations and Logistics: Text and Cases, , New York: Wiley; Dreyer, D.E., Performance measurement: A practitioner's perspective (2000) Supply Chain Management Review, 4 (4), pp. 63-68; Driva, H., Pawar, K.S., Menon, U., Measuring product development performance in manufacturing organizations (2000) International Journal of Production Economics, 63 (2), pp. 147-159; Drucker, P.F., The information executives truly need (1995) Harvard Business Review, 73 (1), pp. 54-62; Durán, A.O., Sistemática para medição de desempenho em áreas de desempenho de produtos (2001) Congresso Brasileiro de Gestão de Desenvolvimento de Produto, 3. , Florianópolis. Anais⋯ Florianópolis: Universidade Federal de Santa Catarina, 2001; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Eccles, R.G., Pyburn, P.J., Creating a comprehensive system to measure performance (1992) Management Accounting, 74 (4), pp. 41-44; Edwards, M.R., Ewen, A.J., (1996) 360° Feedback: The New Model for Employee Assessment & Perfomence Improvement, , New York: American Management Association; Ellinger, A.E., Daugherty, P.J., The relationship between marketing/logistics interdepartmental integration and performance in US manufacturing firms: An empirical study (2000) Journal of Business Logistics, 21 (1), pp. 1-22; Figge, F., The sustainability balanced scorecard: Linking sustainability management to business strategy (2002) Business Strategy and the Environment, 11 (5), pp. 269-284; Filippini, R., Operations management research: Some reflections on evolution, models and empirical studies in OM (1997) International Journal of Operations and Production Management, 17 (7), pp. 655-670; Flapper, S.D.P., Fortuin, L., Toop, P.P.M., Towards consistent performance management systems (1996) International Journal of Operations & Production Management, 16 (7), pp. 27-37; Fleury, A.L., (2000) Um Modelo de Organização de Negócios em Mercados Eletrônicos, , Florianópolis, Dissertação (Mestrado) - Universidade Federal de Santa Catarina- UFSC; Fortuin, L., Performance indicators: Why, where and how? (1988) European Journal of Operational Research, 34 (1), pp. 1-9; Gibb, S., (2002) Learning and Development: Process, Practices and Perspectives at Work, , London: Palgrave; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Globerson, S., Issues in developing a performance criteria system for in organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Godinho Filho, M., Fernandes, F.C.F., Um sistema para classificar e codificar os trabalhos relacionados com o controle da produção e o controle da qualidade (2003) Revista Gestão & Produção, 10 (1), pp. 89-107; Gold, B., Foundations of strategic planning for productivity improvement (1985) Interfaces, 15 (3), pp. 15-30; Gomes, C.F., Yasin, M.M., Lisboa, J.V., A literature review of manufacturing performance measures (2004) Journal of Manufacturing Technology Management, 15 (6), pp. 511-530; Grady, M.W., Performance measurement: Implementing strategy (1991) Management Accounting, 73 (1), pp. 49-53; Griffin, A., Best practice for customer satisfaction in manufacturing firms (1995) Sloan Management Review, 36 (2), pp. 87-98; Griffin, A., Page, A.L., PDMA success measurement project: Recommended measures for product development success e failure (1996) Journal of Product Innovation Management, 13 (6), pp. 478-496; Guimarães, T.A., Gestão do desempenho em organizações públicas descentralizadas (1998) Congreso Internacional del Clad, 3. , Madri. Anais⋯; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Harrington, H.J., (1993) Aperfeiçoando Processos Empresariais, , São Paulo: Makron Books; Hendricks, J.A., Performance measures for a JIT manufacturer: The role of IE (1994) Industrial Engineering, 24 (1), pp. 26-29; Heredia, J.A., Natarajan, R., A methodology framework for developing an integrated performance measurement system (1997) Managing Service Operations: Lessons from the Service and the Manufacturing Sectors, pp. 59-64. , In: RIBEIRA J.; PRATS M. J. (Eds.), Barcelona: University of Navarra; Hubbard, G., Measuring sustainable organizational performance: Win for you, win for me (2006) Monash Business Review, 2 (3), pp. 25-29; Fitz-Enz, J., The mythology of measuring staff performance (1993) Canadian Business Review, 20 (1), pp. 17-20; Henri, J.F., Organizational culture and performance measurement systems (2006) Accounting Organizations and Society, 31 (1), pp. 77-103; Hiromoto, T., Another hidden edge: Japanese management accounting (1988) Harvard Business Review, 66 (4), pp. 22-26; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Hronec, S.M., (1994) Sinais Vitais, , São Paulo: Makron Books; Ittner, C.D., Larrcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting Organizations and Society, 28 (7-8), pp. 715-741; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Boston: Harvard Business School Press; Juran, J.M., (1988) Planning for Quality, p. 341. , New York: The Free Press; Kaplan, R.S., Measuring manufacturing performance: A new challenge for managerial accounting research (1983) The Accounting Review, 58 (4), pp. 686-705; Kaplan, R.S., Yesterday's accounting undermines production (1984) Harvard Business Review, 62 (4), pp. 95-101; Kaplan, R.S., Cooper, R., (1998) Cost and effect, , Boston: Harvard Business School Press; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (5), pp. 134-147; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Kaydos, W., (1991) Measuring Managing and Maximizing Performance, , Portland: Productivity Press; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsoletes? (1989) Management Accounting, 70 (1), pp. 45-50; Kelly, J., Gennard, J., (2001) Power and Influence in the Boardroom, , London: Routledge; Kennerley, K., Neely, A., Measuring performance in a chaging business environment (2003) International Journal of Production & Operations Management, 23 (2), pp. 213-229; Kochhar, A., Zhang, Y., A framework for performance measurement in virtual enterprises (2002) International Workshop on Performance Measurement, 2, pp. 2-11. , Hanover. Proceedings⋯ Hanover: IFIP WG5.7, 2002; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lambert, D., Stock, J.R., Ellram, L.M., (1998) Fundamentals of Logistics Management, , Boston: McGraw-Hill; Leachman, C., Pegels, C.C., Shin, S.K., Manufacturing performance: Evaluation and determinants (2005) International Journal of Operations & Production Management, 25 (9), pp. 851-874; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41 (1-3), pp. 23-35; Lee, N., Measuring the performance of public sector organizations: A case study on public schools in Malaysia (2006) Measuring Business Excellence, 10 (4), pp. 50-64; Lin, B., Chen, J., Corporate technology portfolios and R&D performance measures: A study of technology intensive firms (2005) R&D Management, 35 (2), pp. 157-170; London, M., Beatty, R.W., 360-degree feedback as a competitive advantage (1993) Human Resource Management, 32 (2-3), pp. 353-374; Lorino, P., (1996) Le Contrôle de Gestion Stratégique: La Gestion par les Activités, , Paris: Dunod; Martins, R.A., The use of performance measurement information as a driver in designing a performace measurement system (2002) Performance Measurement and Management Conference, 3, pp. 371-378. , In: NEELY, A.; WALTERS, A.; AUSTIN, R. Performance measurement and management: Research and action, Boston. Proceedings⋯ Boston: Performance Measurement Association, 2002; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing: A Model for American Companies, , Portland: Productivity Press; Maskell, B., Performance measures of world class manufacturing (1989) Management Accounting, 67 (5), pp. 32-33; Maskell, B., Baggaley, B., (2004) Practical Lean Accounting, , Productivity Press, New York, NY; Mcadam, R., Bannister, A., Business performance measurement and change management within a TQM framework A (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 88-107; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations & Production Management, 20 (5), pp. 520-533; Meyer, C., How the right measures help teams excel (1994) Harvard Business Review, 72 (3), pp. 95-103; Miles, I., Innovation in services (1996) The Handbook of Industrial Innovation, , In: DODGSON, M.; ROTHWELL, R. (Eds), Cheltenham: Edward Elgar; Mishra, P.K., Pandey, P.C., Simulation studies of flexible manufacturing systems using statistical design of experiments (1989) Computers & Industrial Engineering, 16 (1), pp. 65-74; Moreira, D.A., (1996) Dimensão do Desempenho em Manufatura e serviço, , São Paulo: Pioneira; Najmi, M., Kehoe, D.F., The role of performance measurement systems in promoting quality development beyond ISO 9000 (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 159-172; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-12; Neely, A., Realizing strategy through measurement (1994) International Journal of Operations & Production Management, 14 (3), pp. 140-152; Neely, A., Developing and testing a process for performance measurement system design (1996) Manufacturing Strategy: Operations Strategy in a Global Context, pp. 471-476. , In: VOSS C. A. (Ed.), London: London Business School; Neely, A., Adams, C., (2000) Perspectives on Performance: The Performance Prism, , http://www.inphase.com/downloads/performance_prism.pdf, Cranfield: University of Cranfield, Disponivel em; www.netgenesis.com/emetrics, NET GENESIS GROUP. E-Metrics. Cambridge 2000 Disponível em: Acesso em: 12 de janeiro de 2008; Ostrenga, M., (1994) Guia da Ernst & Young Para a Gestão Total de Custos, , Rio de Janeiro: Record; Parasuraman, A., Zeithaml, V.A., Berry, L.L., A conceptual model of service quality and its implications for future research (1985) Journal of Marketing, 49 (4), pp. 41-50; Parasuraman, A., Zeithaml, V.A., Berry, L.L., SERVQUAL: Multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; Parasuraman, A., Zeithaml, V.A., Malhotra, A., E-S-Qual: A multiple-item scale for assessing electronic service quality (2005) Journal of Service Research, 7 (3), pp. 213-233; Park, K.S., Park, K., Measurement of multiperiod aggregative efficiency (2007) European Journal of Operational Research, 193 (2), pp. 567-580; Petri, S.M., (2005) Modelo Para Apoiar a Avaliação das Abordagens de Gestão de Desempenho e Sugerir Aperfeiçoamentos: Sob a Ótica Construtivista, , Florianópolis, Tese (Doutorado) - Universidade Federal de Santa Catarina - UFSC; Rey, M.F., Indicadores de desempenho logístico (1999) Revista Logmam, 30 (10), pp. 86-90; Robert, K., Tools and concepts for sustainable development: How do they relate to a general framework for sustainable development, and to each other (2000) Journal of Cleaner Production, 8 (3), pp. 243-254; Robinson, H.S., Insights from research business performance measurement practices in construction engineering organizations (2005) Measuring Business Excellence, 9 (1), pp. 13-22; Rose, K.H., A performance measurement model (1995) Quality Progress, 28 (2), pp. 63-66; Rummler, G.A., Brache, A.P., (1994) Melhores Desempenhos das Empresas, , São Paulo: Makron Books; Samson, D., Terziovski, M., The relationship between total quality management practices and operational performance (1999) Journal of Operations Management, 17 (4), pp. 393-409; Santori, P.R., Anderson, A.D., Manufacturing performance in the 1990s: Measuring for excellence (1987) Journal of Accountancy, 164 (5), pp. 141-147; Schneiderman, A.M., Why balanced scorecards fail (1999) Journal of Strategic Performance Measurement, pp. 6-11. , n. special edition; Searcy, C., Mccartney, D., Karapetrovic, S., Sustainable development indicators for the transmission system of an electric utility (2007) Corporate Social Responsibility and Environmental Management, 14 (3), pp. 135-151; Senge, P.M., (1990) The Fifth Discipline: The Art & Practice of the Learning Organization, , New York: Doubleday & Co; Sharma, M.K., Bhagwat, R., Performance measurements in the implementation of information systems in small and medium-sized enterprises: A framework and empirical analysis (2006) Measuring Business Excellence, 10 (4), pp. 8-21; Simons, R., Control in an age of empowerment (1995) Harvard Business Review, 73 (2), pp. 80-88; Sinclair, D., Zairi, M., Effective process management through performance measurement: Part 1 (1995) Business Process Re-engineering & Management Journal, 1 (1), pp. 75-88; Sink, D.S., The role of measurement in achieving world class quality and productivity management (1991) Industrial Engineering, 21 (6), pp. 23-28; Sink, D.S., Tuttle, T.C., (1993) Planning and Measuring for Performance, , Rio de Janeiro: Quality Mark; Slack, N., (1991) The Manufacturing Advantage, , London: Mercury Books; Slack, N., (1998) Operations Management Instructors Manual: Operations Management, , London: Financial Times Prentice Hall; Son, Y.H., Park, C.S., Economic measure of productivity, quality and flexibility in advanced manufacturing systems (1987) Journal Manufacturing Systems, 6 (3), pp. 193-207; Stainer, A., Logistics: A productivity and performance perspective (1997) Supply Chain Management, 2 (2), pp. 53-62; Stank, T.P., Supply chain integration: Tales from the trenches (2001) Supply Chain Management Review, 5 (3), pp. 62-69; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Sullivan, E., OPTIM: Linking cost, time, and quality (1986) Quality Progress, 19 (4), pp. 52-55; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64 (1-3), pp. 231-241; Ukko, J., Tenhunen, J., Ranatanen, H., Performance measurement impacts on management and leadership: Perspectives of management and employees (2007) International Journal of Production Economics, 110 (1-2), pp. 39-51; (1997) How to Measure Performance: A Handbook of Techniques and Tools Prepared by the Training Resources and Data Exchange (TRADE) Performance-Based Management Special Interest Group (PBM-SIG), , USA-DoE (UNITED STATES DEPARTMENT OF ENERGY); Van Drogelen, I.C.K., Cook, A., Measurements systems for R&D processes (1997) R&D Management, 27 (4), pp. 345-357; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (1), pp. 15-29; Van Schalkwyk, J.C., Total quality manangement and the performance measurement barrier (1998) The TQM Magazine, 10 (10), pp. 124-131; Villas, M.V., Van Aduard Macedo-Soares, T.D.L., Russo, G.M., Bibliographical research method for business administration studies: A model based on scientific journal ranking (2008) Brazilian Administration Review, 5 (2), pp. 139-159; Vitale, M., Mavrinac, S.C., Hauser, M., New process/financial scorecard: A strategic performance measurement system (1994) Planning Review, 22 (4), pp. 12-16; Vokurka, R., Fliedner, G., Measuring operating performance: A specific case study (1995) Production and Inventory Management Journal, 36 (1), pp. 38-43; Wing, B.W., Reducing conflict in balanced scorecard evaluations (2007) Accounting, Organizations and Society, 32 (4-5), pp. 363-377; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60 (1), pp. 53-60; Zarifian, P., Soares, R.M.S.N., As novas abordagens da produtividade (1990) Gestão da Empresa, Automação e Competitividade: Novos Padrões de Organização e Relações de Trabalho, , Brasília: IPEA},
correspondence_address1={Tezza, R.; Programa de Pós Graduação em Engenharia de Produção, Campus Universitário - Trindade, CP 476, CEP 88040-900, Florianópolis - SC, Brazil; email: rafaeltezza@yahoo.com.br},
publisher={Brazilian Institute for Information in Science and Technology},
issn={0104530X},
language={Portuguese},
abbrev_source_title={Gestao Prod.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Jochem2010410,
author={Jochem, R. and Menrath, M. and Landgraf, K.},
title={Implementing a quality-based performance measurement system A case study approach},
journal={TQM Journal},
year={2010},
volume={22},
number={4},
pages={410-422},
doi={10.1108/17542731011053334},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953559990&doi=10.1108%2f17542731011053334&partnerID=40&md5=fe2330b8cff09959783c01e203969171},
affiliation={Department of Mechanical Engineering, Institute of Industrial Science/Ergonomics (IfA), University of Kassel, Kassel, Germany; Bühler Management AG, Uzwil, Switzerland; Department of Quality Management, University of Kassel, Kassel, Germany},
abstract={Purpose - The performance measurement system reflects the corporate strategy and overall business objectives of an organization. The alignment of the performance indicators with the company's critical operating factors, and the processes having bottle-necks alongside the value chain, could result in a very compact, target-oriented, and quality-based performance measurement system. This paper aims to investigate this issue. Design/methodology/approach - The quality-based performance measurement system is developed by integrating the existing performance indicators of an organization with those of best-in-class management practices. The organizational processes included range from business leadership to the operational level. The key performance indicators (KPIs) depict the existing status transparently and comprehensively, whereas later on they can be used to control the processes with the pre-defined values/measures. The KPIs of a process are documented and standardized once they are implemented. An action plan is then developed to write down the prerequisite for the new KPIs and to prioritize the existing ones for the sake of improvement. Findings - It is affirmed that most of the employees had found the performance measurement system a very useful tool to tightly control the flow of their processes. It is very important to mention that this system does not mean giving way to another flood of information; instead it is designed to support the company's decision-making process through target-oriented indicators that could be compared with the existing best practices. Originality/value - The concept described is aimed at developing and implementing a quality-based performance measurement system within an organization. This system is developed through comparing the existing organizational practices with those having been labelled "Best-in-class" and it provides an alternative to the Balanced Score Card approach. © Emerald Group Publishing Limited.},
author_keywords={Germany;  Performance measurement (quality);  Process management;  Quality indicators;  Vehicle components},
keywords={Action plan;  Balanced scorecards;  Best practice;  Business objectives;  Case study approach;  Corporate strategies;  Decision making process;  Design/methodology/approach;  Germany;  Key performance indicators;  Management practices;  Operational level;  Organizational practices;  Organizational process;  Performance indicators;  Performance measurement (quality);  Performance measurement system;  Process management;  Value chains;  Vehicle components, Benchmarking;  Decision making;  Ground vehicle parts and equipment;  Measurements;  Supply chain management;  Vehicle parts and equipment, Process control},
references={Becker, J., Kugeler, M., Rosemann, M., Prozessmanagement (2005) Ein Leitfaden zur Prozessorientierten Organisationsgestaltung, , 5th ed., Springer-Verlag, Berlin and Heidelberg; Binner, H.F., Handbuch der prozessorientierten Arbeitsorganisation (2005) Methoden und Werkzeuge zur Umsetzung, , 2nd ed., Carl Hanser Verlag, München and Wien; Weber, J., Kümmer, S., Großklaus, A., Nippel, H., Warnke, D., (1997) Methodik und Generierung von Logistik-Kennzahlen, (4), pp. 438-454. , Betriebswirtschaftliche Forschung und Praxis; Wicht, J., (2001) Entwicklung Eines Strategischen Kennzahlensystems für die Logistik Eines Internationalen Handelsunternehmens, , Eul-Verlag, Lohmar; Weber, J., (1995) Kennzahlen fü r die Logistik, , Schäffer-Poeschel, Stuttgart; Weber, J., Schäffer, U., Balanced scorecard and controlling (2000) Implementierung - Nutzen fü r Manager und Controller - Erfahrungen in deutschen Unternehmen, , 2nd updated ed., Betriebswirtschaftlicher Verlag Dr Th. Gabler GmbH, Wiesbaden},
correspondence_address1={Landgraf, K.; Department of Mechanical Engineering, , Kassel, Germany; email: landgraf@uni-kassel.de},
issn={17542731},
language={English},
abbrev_source_title={TQM J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lin2010387,
author={Lin, L.-C. and Li, T.-S.},
title={An integrated framework for supply chain performance measurement using six-sigma metrics},
journal={Software Quality Journal},
year={2010},
volume={18},
number={3},
pages={387-406},
doi={10.1007/s11219-010-9099-2},
note={cited By 48},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953477146&doi=10.1007%2fs11219-010-9099-2&partnerID=40&md5=18c48ef5d71d2a453bf5e98af05f65b7},
affiliation={Department of Logistics Management, National Kaohsiung First University of Science and Technology, No. 2, Jhuoyue Rd., Nanzih District, Kaohsiung 81164, Taiwan, Taiwan; Graduate Institute of Management, National Kaohsiung First University of Science and Technology, No 7 Aly 5 Ln 2, Paohua Rd., Rente Township, Tainan County 71751, Taiwan, Taiwan},
abstract={Developing a sound performance measurement model is a critical task for a supply chain and its members in order to examine their current status and identify improvement opportunities for steering their future direction. This research proposes an integrated framework for supply chain performance measurement. It adopts the six-sigma metrics and includes three components (i.e., team structure measurement, supply chain process measurement, and output measurement) to provide a more complete coverage of performance requisites, which has not been adequately addressed in relevant literatures before. Also, a case is applied to demonstrate the feasibility of the model. Complying with the application of this framework, the whole chain or individual members in a supply chain can advance the performance in a more effective way. © 2010 Springer Science+Business Media, LLC.},
author_keywords={Performance measurement;  Six-sigma;  Supply chain},
keywords={Process monitoring, Complete coverages;  Integrated frameworks;  Performance measurements;  Six-Sigma;  Supply chain performance measurements;  Supply chain process;  Team structures;  Three component, Supply chains},
references={Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management using the analytical hierarchy process (2007) Production Planning & Control, 18 (8), pp. 666-680; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computer & Industrial Engineering, 53 (3), pp. 43-62; Champion, M.A., Medsker, G.J., Higgs, A.C., Relations between work group characteristics and effectiveness: Implications for designing effective workgroups (1993) Personnel Psychology, 46 (4), pp. 823-847; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International, 8 (3), pp. 209-223; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: The constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-150; Clegg, C.W., Wall, T.D., Pepper, K., Stride, C., Woods, D., Morrison, D., Cordery, J., Kogi, K., An International survey of the use and effectiveness of modern manufacturing practices (2002) Human Factors & Ergonomics in Manufacturing, 12 (2), pp. 171-191; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management & Business Excellence, 14 (3), pp. 355-366; Decenzo, D.A., Robbins, S.P., (1996) Human Resource Management, , NY: Wiley; Ferguson, B.R., Implementing supply chain management (2000) Production and Inventory Management Journal March, 22, pp. 64-67; Fontenot, G., Behara, R., Gresham, A., Six sigma in customer satisfaction (1994) Quality Progress December, 27 (12), pp. 73-76; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-646; Green, K.W., Whitten, D., Inman, R.A., The impact of timely information on organizational performance in a supply chain (2007) Production Planning & Control, 18 (4), pp. 274-282; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hara, M., Kaizen Forever': Japanese OEMs and suppliers see manufacturing efficiency as key to future competitiveness (2002) Automotive Industries, 182 (10), pp. 44-46; Harry, M.J., (1997) The Vision of Six-Sigma-A Roadmap for Breakthrough, 1. , 5th edn., Phoenix: Tri Star Publishing; Huan, S.H., Sheoran, S.K., Wang, G.E., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Janz, T., Harel, G., Performance appraisal for TQM: A team approach (1993) Total Quality Management, 4 (3), pp. 275-281; Johnson, A., Six sigma in R&D (2002) Research-Technology Management, 45 (2), pp. 12-16; Kanji, G.K., Focus of excellence in Kanji's business excellence model (2001) Total Quality Management, 12 (2), pp. 259-272; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measure obsolete? (1989) Management Accounting, 70 (12), pp. 45-50; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operation & Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operation & Production Management, 23 (2), pp. 213-219; Lamber, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lamber, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issue and research opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-20; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Li, S., Rao, S.S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain practices (2005) Journal of Operations Management, 23 (6), pp. 618-641; Lynch, R.L., Cross, K.F., (1995) Measure up!: Yardsticks for Continual Improvement, , 2nd edn., Oxford: Blackwell publishing; Neely, A., Gregory, M., Platts, K., Performance measurement systems design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Pande, P.S., Neuman, R.P., Cavanagh, R.R., (2000) The Six Sigma Way: How GE Motorola, and Other Top Companies are Honing Their Performance, , NY: McGraw Hill Professional (The McGraw-Hill Companies); Saaty, T.L., (1980) Analytical Hierarchy Process: Planning, Priority Setting, Resource Allocation, , NY: McGraw-Hill Professional (The McGraw-Hill Companies); (1999) Overview of the SCOR Model V2.0, , http://www.supply-chain.org, SCC (Supply-Chain Council); Sharma, M.K., Bhagwat, R., An integrated BSC-AHP approach for supply chain management evaluation (2007) Measuring Business Excellence, 11 (3), pp. 57-68; Sharma, M.K., Bhagwat, R., Dangayach, G.S., Performance measurement of information systems in small and medium sized enterprises: A strategic perspective (2008) Production Planning & Control, 19 (1), pp. 12-24; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Stephens, S., Supply chain operations reference model vision 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-476; Storey, R., (1989) Team Building: A Manual for Leaders and Trainers, , London: British Association for Commercial and Industrial Education, BACIE; Thomas, J., Why your supply chain doesn't work (1999) Logistics Management and Distribution Report, 38 (6), pp. 42-44; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60; Wang, F.K., Du, T.C., Li, E.Y., Applying six-sigma to supplier development (2004) Total Quality Management & Business Excellence, 15 (9-10), pp. 1217-1229; Webster, M., Supply system structure, management and performance: A conceptual model (2002) International Journal of Management Reviews, 4 (4), pp. 353-369; Wyper, B., Harrison, A., Deployment of six sigma methodology in human resource: A case study (2000) Total Quality Management, 11 (4-6), pp. 720-727; Zigon, J., Oil company learns to measure work-team performance (1994) Personnel Journal, 73 (11), pp. 46-48},
correspondence_address1={Li, T.-S.; Graduate Institute of Management, No 7 Aly 5 Ln 2, Paohua Rd., Rente Township, Tainan County 71751, Taiwan, Taiwan; email: wjvali@anet.net.tw},
publisher={Kluwer Academic Publishers},
issn={09639314},
language={English},
abbrev_source_title={Softw. Qual. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cagnazzo2010163,
author={Cagnazzo, L. and Taticchi, P. and Brun, A.},
title={The role of performance measurement systems to support quality improvement initiatives at supply chain level},
journal={International Journal of Productivity and Performance Management},
year={2010},
volume={59},
number={2},
pages={163-185},
doi={10.1108/17410401011014249},
note={cited By 57},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-74349083272&doi=10.1108%2f17410401011014249&partnerID=40&md5=d1229dbf0b15ac69d9b5e71d836220f2},
affiliation={Department of Industrial Engineering, University of Perugia, Perugia, Italy; Department of Electronic and Information Engineering, University of Perugia, Perugia, Italy; Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Milan, Italy},
abstract={Purpose: The purpose of this paper is to discuss which are the most important critical success factors (CSFs) to be attained for determining whether a group of companies in a supply chain (SC) is prepared to undergo a qualitative improvement initiative (QII). By answering four critical research questions, the paper focuses on the role of performance measurement systems (PMSs) as a CSF to support QII and discusses which PMS is more appropriate for the different SC context existing in the new economy. Design/methodology/approach: The methodology adopted in this research relies on different approaches. In order to evaluate the readiness of a company in the SC to start a QII, the authors propose a checklist of CSFs defined through a literature review. Further, to evaluate the impact of PMSs to support QII at the SC level, the authors review PMS literature and discuss the characteristics of the various PMS available. Last, in order to select the best PMS to support the QII of companies belonging to different SC, the authors propose a classification matrix based on the SC characteristics. Findings: The main findings of this research is the understanding of the PMS role in supporting QII at SC level. Research limitations/implications: The research presented in this paper needs to be validated through the use of case studies, in order to optimize the methodology proposed. Practical implications: This paper offers to academics, managers and practitioners a structured methodology to select the right PMS to support QII in specific SC contexts. Originality/value: Few researches are available exploring the correlation between PMS and QII, especially in particular collaborative environments such as SCs. As a consequence, the methodology and findings of this paper add value to the existing body of knowledge and offer good insights for addressing future research. © Emerald Group Publishing Limited.},
author_keywords={Critical success factors;  Performance measures;  Qualitative methods;  Supply chain management},
references={Adam Jr., E.E., Corbett, L.M., Flores, B.E., Harrison, N.J., Lee, T.S., Rho, B.-H., Ribera, J., Westbrook, R., An international study of quality improvement approach and firm performance (1997) International Journal of Operations & Production Management, 17 (9), pp. 842-873; Anthony, J., Banuelas, R., Key ingredients for the effective implementation of Six Sigma program (2002) Measuring Business Excellence, 6 (4), pp. 20-27; Antony, J., Leung, K., Knowles, G., Gosh, S., Critical success factors of TQM implementation in Hong Kong industries (2002) International Journal of Quality & Reliability Management, 19 (5), pp. 551-566; Azzone, G., (2000) Innovare il sistema di controllo di gestione, , ETAS; Barut, M., Faisst, W., Kanet, J.J., Measuring supply chain coupling: An information system perspective (2002) European Journal of Purchasing & Supply Management, 8, pp. 161-171; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Benedetto, A.R., Adapting manufacturing-based Six Sigma methodology to the service environment of a radiology film library (2003) Journal of Healthcare Management, 48 (4), p. 263; Black, S.A., Porter, L.J., Identification of the critical factors of TQM (1996) Decision Sciences, (Winter); Bradley, P., Jordan, P., (1996) ENAPS Business Model, , ENAPS WP3.2 Final Deliverable, CIMRU, University College Galway; Browne, J., Jackson, S., (1995) AMBITE: Advanced Manufacturing Business Implementation Tool for Europe, , CIMRU, University of Galway; Brun, A., Fan, X., Is your non-profit organization ready to implement Six Sigma? (2009) Proceedings of the 5th International Working Conference, Total Quality Management - Advanced and Intelligent Approaches. Belgrade, Serbia; Brun, A., Salama, K.F., Gerosa, M., (2005) Selecting Supply Chain Performance Measurement Systems: A Reference Framework, (4), pp. 5-24. , Finanza, M. & P; Burt, D.N., Dobler, D.W., Starling, S.L., (2003) World Class Supply Management, , Irwin McGraw-Hill, Boston, MA; Calvò-Armengol, A., Bargaining power in communication networks (2001) Mathematical Social Sciences, 41, pp. 69-87; Carmignani, G., Supply chain and quality management, the definition of a standard to implement a process management system in a supply chain (2009) Business Process Management Journal, 15 (3), pp. 395-407; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Choi, T.Y., Eboch, K., The TQM paradox: Relations among TQM practices, plant performance, and customer satisfaction (1998) Journal of Operations Management, 17 (1), pp. 59-75; Choi, T.Y., Rungtusanatham, M., Comparison of quality management practices: Across the supply chain and industries (1999) Journal of Supply Chain Management, 35 (1), pp. 20-27; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1989) National Productivity Review, 9 (1), pp. 23-33; Dixon, J.R., Nanni, A.J., Vollman, T.E., (1990) The New Performance Challenge: Measuring Operations for World Class Competition, , Dow-Jones Irwin, Homewood, IL; Easton, G.S., Jarrell, S.L., The effects of total quality management on corporate performance: An empirical investigation (1998) Journal of Business, 71 (2), pp. 253-307; Epstein, M., Manzoni, J., Implementing corporate strategy: From Tableaux de Bord to Balanced Scorecard (1998) European Management Journal, 16 (2), pp. 190-203; (1999) The EFQM Excellence Model, , www.efqm.org/model_awards/model/excellence_model.htm, European Foundation for Quality Management (EFQM), EFQM, Brussels; Fawcett, S.E., Magnan, G.M., (2001) Achieving World-Class Supply Chain Alignment: Benefits, Barriers, and Bridges, , CAPS Research, Tempe, AZ; Flynn, B.B., Schroeder, R.G., Sakakibara, S., The relationship between quality management practices and performance: Synthesis of findings from the world class manufacturing project (1996) Advances in the Management of Organizational Quality, 1, pp. 140-184; Forker, L.B., Mendez, D., Hershauer, J.C., Total quality management in the supply chain: What is its impact on performance (1997) International Journal of Production Research, 35 (6), pp. 1681-1701; Fynes, B., Voss, C., A path analytic model of quality practices, quality performance and business performance (2001) Production and Operations Management, 10 (4), pp. 494-513; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice Hall, Upper Saddle River, NJ; Hendricks, K.B., Singhal, V.R., Does implementing an effective TQM program actually improve operating performance? Empirical evidence from firms that have won quality awards (1997) Management Science, 43 (9), pp. 1258-1274; Hensley, R.L., Dobie, K., Assessing readiness for six sigma in a service setting (2005) Managing Service Quality, 15 (1), pp. 82-101; Hieber, R., (2001) Supply Chain Management, , BWI ETH, Zurich; Joseph, I.N., Rajendran, C., Kamalanabhan, T.J., An instrument for measuring total quality management implementation in manufacturing-based business units in India (1999) International Journals of Production Research, 37 (10), pp. 2201-2215; Kanji, G.K., Wong, A., Business excellence model for supply chain management (1999) Total Quality Management, 10 (8), pp. 1147-1168; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California Management Review, 39 (1), pp. 53-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting (US), 70 (12), pp. 45-50; Krause, D.R., Supplier development: Current practices and outcomes (1997) International Journal of Purchasing and Materials Management, 33 (2), pp. 12-19; Kuei, C., Madu, C.N., Identifying critical success factors for supply chain quality management (2001) Asia Pacific Management Review, 6 (4), pp. 409-423; Kuei, C., Madu, C.N., Lin, C., The relationship between supply chain quality management practices and organizational performance (2001) International Journal of Quality & Reliability Management, 18 (8), pp. 864-872; Kwak, Y.H., Anbari, F.T., Benefits, obstacles, and future of Six Sigma approach (2006) Technovation, 26, pp. 708-715; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Millen, R., Sohal, A., Moss, S., Quality management in the logistics function: An empirical study (1999) International Journal of Quality & Reliability Management, 16 (2), pp. 166-180; Millen, R.A., Maggard, M., The change in quality management practices: 1995 versus 1991 (1991) Total Quality Management, 8 (4), pp. 173-179; Neely, A.D., Adams, C.A., (2000) Perspectives on Performance: The Performance Prism, , Centre for Business Performance, Cranfield School of Management; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144, pp. 306-320; Read, W.F., Miller, M.S., The state of quality in logistics (1991) International Journal of Physical Distribution & Logistics Management, 21 (6), pp. 32-47; Romano, P., Vinelli, A., Quality management in a supply chain perspective, strategies and operative choices in a textile-apparel network (2001) International Journal of Operations & Production Management, 21, pp. 446-460; Rothlein, C., Mangiameli, P., Ebrahimpour, M., Quality in US manufacturing industries: An empirical study (2002) Quality Management Journal, 9 (3), pp. 48-66; Salvador, F., Forza, C., Rungtusanatham, M., Choi, T.Y., Supply chain interactions and time related performance: An operations management perspective (2001) International Journal of Operations & Production Management, 21 (4), pp. 461-475; Samson, D., Terziovski, M., The relationship between total quality management practices and operational performance (1999) Journal of Operations Management, 17 (4), pp. 393-409; Saraph, J.V., Benson, G., Schroeder, R.G., An instrument for measuring the critical factors of quality management (1989) Decision Sciences, 20 (4), pp. 810-829; (1992) TOPP: A Productivity Program for Manufacturing Industry, , SINTEF, NTNF/NTH; Smith, L., Wilson, R., Burke, T., Enlisting lean Six Sigma in the army acquisition process (2008) Improving Processes, (March-April); Styles, C., Ambler, T., The impact of relational variables on export performance: An empirical investigation in Australia and the UK (2000) Australian Journal of Management, 25 (3), pp. 261-281; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64, pp. 231-241; Tamimi, N., A second-order factor analysis of critical TQM factors (1998) International Journal of Quality Science, 3 (1), pp. 71-79; Taticchi, P., Cagnazzo, L., Botarelli, M., Sameh, M., Performance measurement: Discussion of the literature available for large companies and SMEs (2009) Proceedings: PMA Conference, Performance Measurement: Theory and Practice, University of Otago, , New Zealand, 14-17 April 2009; Tracey, M., Vonderembse, M.A., Building supply chains: A key to enhancing manufacturing performance (2000) Mid American Journal of Business, 15 (2), pp. 11-20; Unahabhokha, C., Platts, K., Tan, K.H., A model for predictive performance measurement system (2004) IGLS 2004 Proceedings, pp. 425-439; Van Hoek, R.I., Measuring the unmeasurable- measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Wong, A., Tjosvold, D., Wong, W.Y.L., Liu, C.K., Relationships for quality improvement in the Hong Kong-China supply chain (1999) International Journal of Quality & Reliability Management, 16 (1), pp. 24-41},
correspondence_address1={Cagnazzo, L.; Department of Industrial Engineering, , Perugia, Italy; email: cagnazzo@mach.ing.unipg.it},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lauras2010342,
author={Lauras, M. and Marques, G. and Gourc, D.},
title={Towards a multi-dimensional project Performance Measurement System},
journal={Decision Support Systems},
year={2010},
volume={48},
number={2},
pages={342-353},
doi={10.1016/j.dss.2009.09.002},
note={cited By 69},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-71649097609&doi=10.1016%2fj.dss.2009.09.002&partnerID=40&md5=f3edcb0b859343852c7de0b8fa260abf},
affiliation={Université Toulouse / Mines Albi, Centre Génie Industriel, Campus Jarlard, Route de Teillet, 81013 Albi, France; Toulouse Business School/Department of Industrial Organisation, Logistics and Technology, 20 Boulevard Lascrosses, 31068 Toulouse Cedex 7, France},
abstract={This paper discusses the difficulty of controlling a complex project caused by the great number of performance indicators. The problem studied is how to allow project managers to better control the performance of their projects. From a literature review we noted several critical aspects to this problem: there are many dimensions for evaluating project performance (cost, time, quality, risk, etc.); performance factors should be able to be relevantly aggregated for controlling the project, but no formalized tool exists to do this. We suggest a method to facilitate project performance analysis via a multi-criteria approach. The method focuses on three particular axes for the analysis of project performance: project task, performance indicator categories, and a breakdown of the performance triptych (Effectiveness, Efficiency, Relevance). Finally, the MACBETH method is used to aggregate performance expressions. An application case study examining a real project management situation is included to illustrate the implementation. © 2009 Elsevier B.V. All rights reserved.},
author_keywords={Decision Support Systems;  Multiple criteria analysis;  Performance Measurement System;  Project management;  Project performance},
keywords={Aggregate performance;  Complex projects;  Literature reviews;  MACBETH;  Multi-criteria approach;  Multiple criteria analysis;  Performance factors;  Performance indicators;  Performance measurement system;  Project managers;  Project performance;  Real projects, Aggregates;  Artificial intelligence;  Benchmarking;  Decision support systems;  Decision theory;  Project management;  Supply chain management;  Waste disposal, Decision making},
references={Arnott, D., Pervan, G., Eight key issues for the decision support systems discipline (2008) Decision Support Systems, 44, pp. 657-672; Atkinson, R., Project management: cost, time and quality, two best guesses and a phenomenon, its time to accept other success criteria (1999) International Journal of Project Management, 17 (6), pp. 337-342; Bagwat, R., Sharma, M.K., Performance measurement of supply chain management: a balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Bana e Costa, C.A., Chagas, P.M., A career choice problem: an example of how to use MACBETH to build a quantitative value model based on qualitative value judgments (2004) European Journal of Operation Research, 153, pp. 323-331; Bana e Costa, C.A., De Corte, J.M., Vansnick, J.C., (2004) "MACBETH", LSE OR Working Paper 03.56, , London School of Economics, London; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Chen, Y., Marc Kilgour, D., Hipel, K.W., Screening in multiple criteria decision analysis (2008) Decision Support Systems, 45, pp. 278-290; Cheung, S.O., Suen Henry, C.H., Cheung, K.W., PPMS: a web based construction project performance monitoring system (2004) Automation in construction, 13, pp. 361-376; Clivillé, V., (2004) Approche systémique et méthode multicritère pour la définition d'un système d'indicateurs de performance, , PhD Thesis, Université de Savoie; Clivillé, V., Berrah, L., Mauris, G., Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method (2007) International Journal of Production Economics, 105, pp. 171-189; Dweiri, F.T., Kablan, M.M., Using fuzzy decision-making for the evaluation of the project management internal efficiency (2005) Decision Support System, 42 (2), pp. 712-726; Figueira, J., Greco, S., Ehrgott, M., (2005) Multiple Criteria Decision Analysis: State of the Art Surveys, , Springer 1045 pp; Ghasemzadeh, F., Archer, N.P., Project portfolio selection through decision support (2000) Decision Support Systems, 29, pp. 73-88; Grey, S., Practical Risk Assessment for Project Management (1995) John Wiley & Sons Editor; Hansen, T.A., Riis, J.O., Exploratory performance assessment (1999) International Journal Business Peformance Management, 1 (2), pp. 113-133; Hwang, H.S., Web-based multi-attribute analysis model for engineering project evaluation (2004) Computers & Industrial Engineering, 46, pp. 669-678; Hyväri, I., Project management effectiveness in project oriented business organizations (2006) International Journal of Project Management, 24 (3), pp. 216-225; International Organization for Standardization (ISO), (2003) ISO 10006:2003 standard: quality management systems-guidelines for quality management in projects, 32; Jacot, J.H., A propos de l'évaluation économique des systèmes intégrés de production, in ECOSIP, Gestion industrielle et mesure économique, Paris: (1990) Economica, pp. 61-70; Korhonen, P., Multiple criteria decision support-a review (1992) European Journal of Operational Research, 63, pp. 361-375; Lauras, M., Gourc, D., A multi-criteria approach to a more effective management of projects (2007) International Conference on Industrial Engineering and Systems Management; IESM; May 30-June 2, Beijing; Martinez, E.C., Duje, D., Perez, A., On performance modeling of project-oriented production (1997) Computers & Industrial Engineering, 32, pp. 509-527; Milosevic, D., Patanakul, P., Standardized project management may increase development project success (2005) International Journal of Project Management, 23, pp. 181-192; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of operations & Production Management, 15 (4), pp. 80-116; Neely, A.D., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: should process based approach be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431; (1996) A Guide to the Project Management Body of Knowledge, , PMI Standards Committee, PMI Publishing Division; Pritchard, C.L., (1997) Risk Management, , Concepts and Guidance. ESI International Editor; Roseneau Milton, D., Githens, G.D., Successful Project Management: A Step by Step Approach with Practical Examples (2005) John Wiley & Sons Editor, , 4th edition; Roubens, M., Rusinowska, A., de Swart, H., Using MACBETH to determine utilities of governments to parties in coalition formation (2004) European Journal of Operation Research, 172 (2), pp. 588-603; Roy, B., (1996) Multicriteria Methodology for Decision Analysis, , Kluwer Academic Publishers; Swink, M., Talluri, S., Pandejpong, T., Faster, better, cheaper: a study of NPD project efficiency and performance tradeoffs (2005) Journal of Operations Management, 24, pp. 542-562; Westerveld, E., The Project Excellence Model: linking success criteria and critical success factors (2003) International Journal of Project Management, 21, pp. 411-418; Xiaoyi Dai, C., Wells, W.G., An exploration of project management office features and their relationship to project performance (2004) International Journal of Project Management, 22, pp. 523-532; Yu Angus, G., Flett, P.D., Bowers, J.A., Developing a value-centred proposal for assessing project success (2005) International Journal of Project Management, 23, pp. 428-436; Zwikael, O., Shimizu, K., Globerson, S., Cultural differences in project management capabilities: a field study (2005) International Journal of Project Management, 23, pp. 454-462},
correspondence_address1={Marques, G.; Université Toulouse / Mines Albi, Campus Jarlard, Route de Teillet, 81013 Albi, France; email: guillaume.marques@enstimac.fr},
issn={01679236},
coden={DSSYD},
language={English},
abbrev_source_title={Decis Support Syst},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Chearskul2009299,
author={Chearskul, P.},
title={Development and assessment of a performance measurement use survey},
journal={30th Annual National Conference of the American Society for Engineering Management 2009, ASEM 2009},
year={2009},
pages={299-308},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879854337&partnerID=40&md5=a6bf67d76eb3bafcf75ed142d8d0d8b1},
affiliation={Department of Industrial and Systems Engineering, Virginia Tech, United States},
abstract={Researchers have argued that the use phase is the most vital phase for performance measurement systems to succeed, and the variability in actual performance measurement use practices reported in previous studies may explain why some organizations find it difficult to extract benefits from their performance measurement systems. Nonetheless, little work has been conducted to increase understanding of the processes underlying use of performance measurement systems. This paper describes a framework to define use practices associated with PM system use and proposes an associated survey questionnaire to measure these practices. A comprehensive review of the performance measurement systems literature was conducted and used to develop the overall framework and inform the development of the survey questionnaire. This paper presents the framework, corresponding performance measurement system use variables, and the proposed survey questionnaire items for each variable. This paper also reports results from a structured content validity study with PM experts. Copyright© (2009) by the American Society for Engineering Management.},
author_keywords={Content validity study;  Performance measurement system use;  Performance review process},
keywords={Content validity study;  Performance measurement system;  Performance measurements;  Performance reviews;  PM systems;  Use Phase, Supply chain management, Surveys},
references={Apigian, C.H., Ragu-Nathan, B.S., Ragu-Nathan, T.S., Kunnathur, A., Internet technology: The strategic imperative (2005) Journal of Electronic Commerce Research, 6 (2), pp. 123-145; Bauer, J., Tanner, S.J., Neely, A., Developing a performance measurement audit template: A benchmarking study (2004) Measuring Business Excellence, 8 (4), pp. 17-25; Bourne, M., Kennerley, M., Franco-Santos, M., Managing through measures: A study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), p. 373; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Brennan, R.L., Prediger, D.J., Coefficient kappa: Some uses, misuses, and alternatives (1981) Educational and Psychological, Measurement, 41 (3), pp. 687-699; Bunton, W., Bryd, L., Nivens, L., (1997) Serving the American Public: Best Practices in Performance Measurement, , Washington, DC: National Performance Review:Benchmarking study team; Chearskul, P., The relationship between performance measurement system use and organizational outcomes (2007) Proceedings from the 28th American Society for Engineering Management Conference, , Chattanooga Tennessee (November 7-10); Chearskul, P., Glover, W.J., Defining and assessing key practices in the effective use of performance measurement systems: A case study (2008) Proceedings from the 29th American Society for Engineering Management National Conference, , West Point, N.Y. (12-15 November); Cohen, J., A coefficient of agreement for nominal scales (1960) Educational and Psychological Measurement, 20 (1), pp. 37-46; De Waal, A.A., Behavioral factors important for the successful implementation and use of performance management systems (2003) Management Decision, 41 (8), p. 688; De Waal, A.A., Counet, H., Lesson learned from the balance scorecard (2006) Proceedings from the Performance Measurement and Management: Public and Private, , London, UK; Elg, M., The process of constructing performance measurement (2007) The TQM Magazine, 19 (3), pp. 217-228; Farris, J.A., Van Aken, E.M., Letens, G., Chearskul, P., Coleman, G.D., Improving the performance review process: A structured approach and case application (2008) International Journal of Operations & Production Management, , [Submited]; Fleiss, J.L., Measuring nominal scale agreement among many raters (1971) Psychological Bulletin, 76 (5), pp. 378-382; Franco-Santos, M., Bourne, M., An examination of the literature relating to issues affecting how companies manage through measures (2005) Production Planning & Control, 16 (2), p. 114; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Franco, M., Bourne, M., Factors that play a role in managing through measures (2003) Management Decision, 41 (8), pp. 698-710; Grant, J.S., Davis, L.L., Selection and use of content experts for instrument development (1997) Research in Nursing & Health, 20 (3), pp. 269-274; Henri, J.-F., (2006) Organizational Culture and Performance Measurement Systems, 31 (1), pp. 77-103. , Accounting, Organizations and Society; Hinkin, T.R., A brief tutorial on the development of measures for use in survey questionnaires (1998) Organizational Research Methods, 1 (1), pp. 104-121; Hoque, Z., A contingency model of the association between strategy, environmental uncertainty and performance measurement: Impact on organizational performance (2004) International Business Review, 13 (4), pp. 485-502; Ittner, C.D., Does measuring intangibles for management purposes improve performance? A review of the evidence (2008) Accounting and Business Research, 38 (3), p. 261; Ittner, C.D., Larcker, D.F., Randall, T., (2003) Performance Implications of Strategic Performance Measurement in Financial Services Firms, 28 (7-8), pp. 715-741. , Accounting, Organizations and Society; Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangible Assests into Tangible Outcomes, , Boston, MA: Harvard Business School Publishing Corporation; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston, MA: Harvard Business School Press; Kaplan, R.S., Norton, D.P., Mastering the management system (2008) Harvard Business Review, 86 (1), p. 62; Kaydos, W.J., (1999) Operational Performance Measurement: Increasing Total Productivity, , BocaRaton, FL: CRC Press; Kuwaiti, M.E., Performance measurement process: Definition and ownership (2004) International Journal of Operations & Production Management, 24 (1-2), p. 55; Lynn, M.R., Determination and quantification of content validity (1986) Nursing Research, 35 (6), pp. 382-385; Marr, B., (2006) Strategic Performance Management: Leveraging and Measuring your Intangible Value, , Drivers: Elsevier; Marr, B., Business performance measurement: An overview of the current state of use in the USA (2005) Measuring Business Excellence, 9 (3), p. 56; Martinez, V., Kennerley, M., Neely, A., Impact of PMS on business performance: A methodological approach (2004) Proceedings from the British Academy of Management Conference, , St Andrews, (30 Aug - 1 Sep 2004); Martinez, V., Kennerley, M., Impact of performance management reviews: Evidence fron an energy supplier (2005) Proceedings from the EUROMA Conference: Operations and Global Competitiveness, , Budapest, Hungry, (June 19-22); Meekings, A., Effective review meetings: The counter-intuitive key to successful performance measurement (2005) International Journal of Productivity and Performance Management, 54 (3), pp. 212-220; Meekings, A., Povey, S., Plumbed-in performance improvement: Accelerating improvement and adaptation in organizations (2007) Measuring Business Excellence, 11 (1), p. 46; Moore, G.C., Benbasat, I., Development of an instrument to measure the perceptions of adopting an information technology innovation (1991) Information Systems Research, 2 (3), pp. 192-222; Nahm, A., Solís-Galván, L.E., Rao, S., Ragu-Nathan, T.S., The Q-sort method: Assessing reliability and construct validity of questionnaire items at a pre-testing stage (2002) Journal of Modern Applied Statistical Methods, 1 (1), pp. 114-125; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Adams, C., Kennerley, M., (2002) Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationships, , London: Prentice Hall Financial Times; Neely, A., Bourne, M., Why measurement initiatives fail (2000) Measuring Business Excellence, 4 (4); Neely, A., Kennerley, M., Martinez, V., Does the balanced scorecard work: An empirical investigation (2004) Proceedings from the the 4th International Conference on Performance Measurement, , Edinburgh; Neely, A., Veronica, M., Micheli, P., (2006) Action on Information: Performance Management for the Public Sector, , London: The Advanced Institute of Management Research; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), p. 1119; Perreault Jr., W.D., Leigh, L.E., Reliability of nominal data based on qualitative judgements (1989) JMR, Journal of Marketing Research, 26 (2), p. 135; Polit, D.F., Beck, C.T., The content validity index: Are you sure you know what's being reported? critique and recommendations (2006) Research in Nursing & Health, 29 (5), pp. 489-497; Polit, D.F., Beck, C.T., Owen, S.V., Is the CVI an acceptable indicator of content validity? Appraisal and recommendations (2007) Research in Nursing & Health, 30 (4), pp. 459-467; Simons, R., (2000) Performance Measurement & Control Systems for Implementing Strategy, , New Jersey: Prentice-Hall, Inc; Spitzer, D.R., (2007) Transforming Performance Measurement: Rethinking the Way We Measure and Drive Organizational Success, , AMACOM Div American Mgmt Assn; Stratman, J.K., Roth, A.V., Enterprise resource planning (ERP) competence constructs: Two-stage multi-item scale development and validation (2002) Decision Sciences, 33 (4), p. 601; Van Aken, E.M., Letens, G., Coleman, G.D., Farris, J., Van Goubergen, D., Assessing maturity and effectiveness of enterprise performance measurement systems (2005) International Journal of Productivity and Performance Management, 54 (5-6), p. 400; Wynd, A.C., Schmidt, B., Schaefer, M.A., Two quantitative approaches for estimating content validity (2003) Western Journal of Nursing Research, 25 (5), pp. 508-518; Yaghi, B., Neely, A., Dietrich, J.R., Youell, N., (2008) Enterprise Performance Management:The U.S.A. State of the Art: Cranfield School of Management, , Fisher College of Business and Oracle},
correspondence_address1={Chearskul, P.; Department of Industrial and Systems Engineering, United States},
isbn={9781617381058},
language={English},
abbrev_source_title={Annu. Natl. Conf. Am. Soc. Eng. Manage., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Turner2009853,
author={Turner, G. and Minonne, C.},
title={Measuring the effects of knowledge management practices},
journal={Proceedings of the European Conference on Knowledge Management, ECKM},
year={2009},
volume={2},
pages={853-860},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84871053607&partnerID=40&md5=e6618aad9da20e794d987ccb226e8d5e},
affiliation={University of Nicosia, Nicosia, Cyprus; University of South Australia, Adelaide, Australia; Wissens-Management.CH, Kriens-Lucerne, Switzerland},
abstract={Successful managers focus their attention on factors that are critical in establishing and maintaining an organisation's competitive edge. The knowledge and skill of employees is one of those factors and it requires proactive management attention. Conceptually, this is achieved through Knowledge Management, a term that has existed in the mainstream of business lexicon for quite some time. Despite this, there is the conspicuous absence of a common understanding of the term that frustrates many managers. Studies have clearly established that there are three interdependent and complementary pillars that support the concept of Knowledge Management. These are Organisational Learning Management (OLM), Organisational Knowledge Management (OKM) and Intellectual Capital Management (ICM). OLM, which has so far dominated both academic and practitioner debate, concerns itself with the problem of capturing, organising and retrieving explicit knowledge, or information, and has led to the simplistic misconception that Knowledge Management only involves the capture, or downloading, of the content of employees' minds. ICM is dominated by those particularly interested in defining key performance indicators that will measure the impact and the benefits of applying knowledge management practices. If management requires measurement this is an essential task but it can only be undertaken once an organisation has clearly established the strategy-structure-process parameters to ensure it accesses, creates and embeds the knowledge that it needs... the OKM pillar of knowledge management. This paper looks more deeply at this pillar and in particular the lack of a general integrative approach to enhancing organisational performance in this key strategic area. It considers to what extent such an approach may help an organisation more effectively manage its most relevant source of competitive advantage. With a greater awareness of the various factors allied to the managing and leveraging of human oriented and system oriented knowledge assets, some proposals are put forward to assist in developing or redefining an organisation's intellectual capital reporting models in search of a planning, control and performance measurement system that accounts for the management of an organisation's intellectual assets.},
author_keywords={Competitive advantage;  Intellectual capital management;  Organisational knowledge management;  Organisational learning management;  Performance indicators},
keywords={Competitive advantage;  Intellectual capital;  Organisational knowledge;  Organisational learning;  Performance indicators, Benchmarking;  Competition;  Knowledge management;  Management;  Managers;  Supply chain management, Human resource management},
references={Andriessen, D., (2004) Making sense of intellectual capital: designing a method for the valuation of intangibles, , Elsevier Butterworth Heinemann, Oxford; Argote, L., (1999) Organizational learning: creating, retaining, and transferring knowledge, , Kluwer, Norwell; Argyris, C., (1982) Reasoning, learning, and action: individual and organizational, , Jossey-Bass, San Francisco; Argyris, C., (1990) Overcoming organizational defenses: facilitating organizational learning, , Allyn and Bacon, Boston; Argyris, C., Schön, D., (1978) Organisational learning: a theory of action perspective, , Addison-Wesley, Boston; Barney, J.B., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Cattell, A., Performance management and human resource development (2005) Wilson, J.P. (ed.), 2nd ed., Human resource development: learning and training for individuals and organisations, , Kogan Page, London; Chapman, C.S., Reflections on a contingent view of accounting (1997) Accounting, Organizations and Society, 22 (2), pp. 189-205; Daum, J.H., (2003) Intangible assets and value creation, , John Wiley & Sons, Chichester; Edvinsson, L., Malone, M.S., (1997) Intellectual capital: realizing your company's true value by finding its hidden brainpower, , Harper-Collins, New York; Fitz-enz, J., (1995) How to measure human resources management, 2nd ed, , McGraw-Hill, New York; Goh, A., Enhancing organisational performance through knowledge innovation: a proposed strategic management framework (2004) Journal of Knowledge Management Practice, 5. , October; Grant, R., Towards a knowledge-based theory of the firm (1996) Strategic Management Journal, 17, pp. 109-123; Hamel, G., Prahalad, C.K., The core competence of the organisation (1990) Harvard Business Review, pp. 79-91. , May-June; Handy, C., (1996) Intelligence - capitalism's most potent asset, pp. 8-11. , HR Monthly, December; Huber, G.P., Organizational learning: the contributing processes and the literatures (1991) Organization Sciences, 2 (1), pp. 88-115; (1998) The measurement and management of intellectual capital: an introduction, , IFAC, IFAC, New York; Lev, B., (2001) Intangibles: management, measurement and reporting, , Brooking Institute Press, Washington; Minonne, C., Towards an integrative approach for managing implicit and explicit knowledge: an exploratory study in Switzerland (2007) Doctoral Dissertation, , University of South Australia, Adelaide; Minonne, C., Towards an integrative approach for managing implicit and explicit knowledge: an exploratory study in Switzerland (2007) Conference proceedings of the 8th European Knowledge Management Conference, , Barcelona, September; Minonne, C., Wissens-Management: Wie lautet das Erfolgsrezept (2008) Wissensmanagement Magazin, 8, pp. 48-49. , November/December; Nelson, R.R., Rosenberg, N., Technical innovation and national systems (1993) Nelson, R. R. (ed.), National innovation systems: a comparative analysis, , Oxford University Press, New York and Oxford; Nonaka, I., Takeuchi, H., (1995) The knowledge creating company: how Japanese companies create the dynamics of innovation, , Oxford University Press, New York; Raub, S., Rüling, C.C., The knowledge management tussle: speech communities and rhetorical strategies in the development of knowledge management (2001) Journal of Information Technology, 16 (2), pp. 113-130; Romer, P.M., Increasing returns and long-run growth (1986) Journal of Political Economy, 94 (5), pp. 1002-1037; Romer, P.M., Endogenous technological change (1990) Journal of Political Economy, 98 (S5), pp. S71-S102; Spender, J.C., Competitive advantage from tacit knowledge? Unpacking the concept and its strategic implications (1996) Moingeon, B. and Edmondson, A. (eds.), Organisational learning and competitive advantage, , Sage Publications, London; Sveiby, K.E., Risling, A., (1986) The know-how company, , Liber, Malmo; Sveiby, K.E., (1997) The new organizational wealth: managing and measuring knowledge-based assets, , Berrett-Koehler, San Francisco; Turner, G., Using human resource accounting to bring balance to the balanced scorecard (2000) Journal of Human Resource Costing and Accounting, 5 (2), pp. 31-44; Turner, G., Accounting for human resources: Quo vadis? (2005) International Journal of Environmental, Cultural, Economic and Social Sustainability, 1 (3), pp. 11-17; Turner, G., Jackson-cox, J., If management requires measurement how may we cope with knowledge? (2002) Singapore Management Review, 24 (3), pp. 101-111; Wernerfelt, B., A resource-based view of the firm (1984) Strategic Management Journal, 5 (2), pp. 171-180},
correspondence_address1={Turner, G.; University of Nicosia, Nicosia, Cyprus; email: turner.g@unic.ac.cy},
issn={20488963},
isbn={9781906638399},
language={English},
abbrev_source_title={Proc. Eur. Conf. Knowl. Manage., ECKM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Pigni20095421,
author={Pigni, F. and Ugazio, E.},
title={Measuring RFId benefits in supply chains},
journal={15th Americas Conference on Information Systems 2009, AMCIS 2009},
year={2009},
volume={8},
pages={5421-5430},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870355947&partnerID=40&md5=ed7006d321a8983dc1a0154048e500a3},
affiliation={Università Carlo Cattaneo, LIUC, Lab#ID, Italy},
abstract={RFId systems hold great potentials in enhancing supply chain performances. Some of the major retailers in the World (Wall Mart, Metro Group, Tesco) made sounding campaigns promoting the benefits they were able to attain. Despite the growing number of researches concerning RFId benefits, a study investigating their measurement in the supply chain context is still lacking. This paper proposes to associate qualitative RFId benefits identified in literature with corresponding measures by designing three tools: (1) an RFId oriented Performance Measurement System for the identification of all RFId related supply chain performance indicators on the base of the SCOR Model. (2) A benefits-processes-measures matrix linking the benefits identified in literature with SCOR model process to identify supply chain measures impacted by the RFId system implementation. (3) A reference framework summarizing benefits measures.},
author_keywords={Benefits;  Measures;  RFId;  SCOR},
keywords={Benefits;  Corresponding measures;  Measures;  Performance measurement system;  RFID systems;  SCOR;  SCOR models;  Supply chain performance;  Wall marts, Benchmarking;  Information systems;  Radio frequency identification (RFID);  Supply chain management, Supply chains},
references={Bertelè, U., (2006) RFId alla prova dei fatti Irisultati 2006 dell'Osservatorio RFId: Politecnico di Milano; Bourne, M., Kennerley, M., Franco-Santos, M., Managing through measures:A study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), pp. 373-395; Chopra, S., Sodhi, M.S., Looking for the Bang from the RFId Buck (2007) Supply Chain Management Review; Chuang, M., Shaw, W.H., How RFID Will Impact Supply Chain Networks (2005) IEEE Computer Society, pp. 231-235; Coltman, T., Gadh, R., Michael, K., RFID and Supply Chain Management: Introduction to the Special Issue (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. III-VI; Curtin, J., Kauffman, R.J., Riggings, F.J., Making the "Most" out of RFID Technology:A research agenda for the study of the adoption, usage and impact of RFID. [Regarding IT(RFId) and Management] (2007) Information Technology and Management, 8 (1), pp. 87-110; Fosso Wamba, S., Harold Boeck, H., Enhancing Information Flow in a Retail Supply Chain Using RFID and the EPC Network: A Proof-of-Concept Approach (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 92-105; Gosselin, M., A framework to review performance measurement systems (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437; Hilger, A., Ghijsen, P., Semeijn, J., Antecedents of logistics performance and economic performance: The case of Radio Frequency Identification (RFID) (2007) International Journal of Business Research, 7 (6), pp. 57-66; Hilger, A.M., Ghijsen, P.W.T., Janjaap Semeijn, J., Antecedents of Logistics Performance and Economic Performance: The Case of Radio Frequency Identification (2007) International Journal of Business Research, 7, pp. 57-68; Hodges, S., McFarlane, D., Radio Frequency Identification: Technology, applications and impact (2005) White paper series-Edition 1, , Auto-ID-Labs; Hou, J.L., Huang, C.H., Quantitative performance evaluation of RFID applications in the supply chain of the printing industry (2006) Industrial Management Data Systems, 106, pp. 96-120; Hunt, V.D., Puglia, A., Puglia, M., History and Evolution of RFID Technology (2007) RFID-A guide to Radio Frequency Identification, pp. 25-31. , Hoboken, New Jersey: John Wiley & Sons, Inc; Jeong, B.-K., Lu, Y., The impact of radio frequency identification (RFID) investment announcements on the market value of the firm (2008) J. Theor. Appl. Electron. Commer. Res., 3 (1), pp. 41-54; Koh, C.E., Kim, H.J., Kim, E.Y., The Impact of RFID in Retail Industry: Issues and Critical Success Factors (2006) Journal of Shopping Center Research, 13 (1), pp. 101-117; Laubacher, R., Kothari, S.P., Malone, T.W., Subirana, B., What is RFID Worth to Your Company? Measuring Performance at the Activity Level (2006) A research and education initiative at the MIT Sloan School of Management, pp. 1-30; Leong, K.S., Ng, M.L., Engels, D.W., EPC Network Architecture (2004) Paper presented at the Auto-ID Labs Research Workshop, , 23-24 September 2004, Zurich, CH; Loebbecke, C., Huyskens, C., A Competitive Perspective on Standard-Making: Kaufhof's RFID Project in Fashion Retailing (2008) Electronic Markets, 18 (1), pp. 30-38; Mari, L., Lazzarotti, V., Manzini, R., Measurement in soft systems: Epistemological framework and a case study (2008) Measurement; McFarlane, D., Sheffi, Y., The Impact of Automatic Identification on Supply Chain Operations (2003) The International Journal of Logistics Management, 14 (1), pp. 1-17; Michael, K., McCathie, L., (2005) The Pros and Cons of RFID in Supply Chain Management, , Paper presented at the Mobile Business, International Conference on; Moullin, M., Eight essentials of performance measurement (2004) International Journal of Health Care Quality Assurance, 17 (3), pp. 110-112; Munoz, A., Clements, M.D., Disruptions in information flow: A revenue costing supply chain dilemma (2008) J. Theor. Appl. Electron. Commer. Res., 3 (1), pp. 30-40; Najmi, M., Rigas, J., Fan, I.S., A framework to review performance measurement systems (2005) Business Process Management Journal, 11 (2), pp. 109-122; Neely, A., The evolution of performance measurement research. Developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design literature review and research agenda (1995) Internal Journal of Operations and Production Management, 15 (4), pp. 80-116; Niederman, F., Mathieu, R.G., Roger Morley, R., Kwon, I.W., Examining RFId Applications in Supply Chain Management (2007) Communications of the ACM, 50, pp. 93-103; Pigni, F., Astuti, S., Buonanno, G., (2004) An evaluation framework for RFId Adoption, , Paper presented at the AICA 2004 XLII Annual Congress; Pigni, F., Ravarini, A., Astuti, S., Buonanno, G., Mari, L., (2006) RFId in The Fashion Industry: Exploratory Case Study and Implications, , Paper presented at the III Conference of the Italian Chapter of AIS; Rouse, R., Putterill, M., An integral framework for performance measurement (2003) Management Decision, 41 (8), pp. 791-805; Seddon, P., Greaser, V., Willcocks, L., Measuring organizational IS effectiveness: An overview and update of senior management perspectives (2002) The DATA BASE for Advances in Information Systems, 33 (2), pp. 11-28; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Smith, A.D., Exploring radio frequency identification technology and its impact on business systems (2005) Information Management & Computer Security, 13 (1), pp. 16-28; Soon, C.B., Gutiérrez, J.A., Effects of the RFID Mandate on Supply Chain Management (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 81-91; Spivey Overby, C., (2004) Revealing RFID's Benefits In Consumer Goods, , Cambridge, MA: Forrester Research; Tangen, S., Analysing the requirements of performance measurement systems (2005) Measuring Businness Excellence, 9 (4), pp. 46-54; Tangen, S., Improving the performance of a performance measure (2005) Measuring Businness Excellence, 9 (2), pp. 4-11; Tellkamp, C., The impact of Auto-ID technology on process performance-RFID in the FMCG supply chain (2006) Dissertation of University of St. Gallen, Graduate school of business Administration; Van de Wijngaert, L., Versendaal, J., Business IT Alignment and technology adoption; The case of RFID in the logistics domain (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 71-80; Veeramani, D., Tang, J., Gutierrez, A., A framework for assessing the value of RFID implementation by tierone suppliers to major retailers (2008) J. Theor. Appl. Electron. Commer. Res., 3 (1), pp. 55-70; Want, R., An Introduction to RFID Technology (2006) IEEE Pervasive Computing, 5 (1), pp. 25-33; Xiao, Y., Yu, S., Wu, K., Ni, Q., Janecek, C., Nordstad, J., Radio frequency identification: Technologies, applications, and research issues (2007) Wireless Communications and Mobile Computing, 7 (4), pp. 457-472; Zhang, T., Ouyang, Y., He, Y., Traceable air baggage handling system based on RFID tags in the airport (2008) Journal of Theoretical and Applied Electronic Commerce Research, 3 (1), pp. 106-115},
correspondence_address1={Pigni, F.; Università Carlo Cattaneo, Italy; email: fpigni@liuc.it},
isbn={9781615675814},
language={English},
abbrev_source_title={Amer. Conf. Inf. Sys., AMCIS},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ramaa2009802,
author={Ramaa, A. and Rangaswamy, T.M. and Subramanya, K.N.},
title={A review of literature on performance measurement of supply chain network},
journal={2009 2nd International Conference on Emerging Trends in Engineering and Technology, ICETET 2009},
year={2009},
pages={802-807},
doi={10.1109/ICETET.2009.18},
art_number={5395447},
note={cited By 41},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949617126&doi=10.1109%2fICETET.2009.18&partnerID=40&md5=65d0894a5125faceec5b384c60b78afc},
affiliation={Dept. of Industrial Engineering and Management, R.V. College of Engineering, Bangalore - 560059, India},
abstract={In today's competitive world, performance measurement of the supply chain is the key for effective supply chain management. The performance measurement can be utilized to describe and review the historical performance, as well as to set performance targets for the future. This paper aims to address the dearth of research in performance measurement systems and performance metrics of supply chain by reviewing the contemporary literature. The paper lists more than 60 references for further study. © 2009 IEEE.},
keywords={Historical performance;  Performance measurement system;  Performance measurements;  Performance metrics;  Performance targets;  Supply chain network, Supply chains, Supply chain management},
references={Neely, A., (1998) Measuring Business Performance, , Economist Books, London; Martinez, V., Kennerley, M., Impact of performance management reviews: Evidence from an energy supplier (2005) EUROMA Conference, Operations and Global Competitiveness, pp. 19-22. , Budapest, Hungry, June; Neely, A., Gregory, M., Platts, K., Performance measurement systems design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Kaplan, R.S., Norton, D., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79. , January-February, pp; Gunasekaran, A., Patel, C., Tittiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 2 (1-2), pp. 71-87; Schermerhorn, Chappell S (2000) - Introducing Management, New York: John Wiley & Sons, Inc; Eccles, R.G., Pyburn, P.J., Creating a comprehensive system to measure performance (1992) Management Accounting, pp. 41-44. , US, Oct, pp; Holmberg, S., A system perspective in supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Stewart, Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Kennerley, M., Neely, A., A Framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Parker, C., Performance Measurement (2000) Work Study, 49 (2), pp. 63-66; Maskell, B.H., (1992) Performance Measurement for World Class Manufacturing: A Model for American Companies, , Productivity Press, Cambridge, MA; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Kennerley, M., Neely, A., Measuring Performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Van, H., Remko, I., Measuring the Unmeasurable - Measuring and Improving Performance in the Supply Chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operation & Production Management, 19 (3), pp. 275-292; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-1310; Stephens 2001, Supply Chain Operations Reference Model Version 5.0: A New Tool to Improve Supply Chain Efficiency and Achieve Best Practice, Information Systems Frontiers, 3, No. 4, December, 2001; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Christopher, M., (2005) Logistics and Supply Chain Management: Creating value-adding networks, , FT Prentice Hall, London; Basu, R., New Criteria of performance Management: A Transition from Enterprise to Collaborative Supply chain (2001) Measuring Business Excellence, 5 (4), pp. 7-12; Landeghem Van, K.P., Benchmarking of logistical operations based on a causal model (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 254-267; Tan, T.C.L., Empirical analysis of supplier selection and involvement, customer satisfaction, and firm performance (2001) Supply Chain Management: An International Journal, 6 (4), pp. 174-188; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Review, 38 (6), pp. 439-456; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Harrison and C. New(2002), The Role of Coherent Supply Chain Strategy and Performance Management in Achieving Competitive Advantage: An International Survey , The Journal of the Operational Research Society, 53, No. 3, Part Special Issue: Performance Management (Mar., 2002), pp. 263-271; Petroni, A., Panciroli, B., Innovation as a determinant of suppliers' roles and performances: An empirical study in the food machinery industry (2002) European Journal of Purchasing & Supply Management, 8, pp. 135-149; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40 (16), pp. 4257-4269; Lummus, R., Duclos, L., Vokurka, R., Supply chain flexibility: Building a new model (2003) Global Journal of Flexible Systems Management, 4 (4), pp. 1-13; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management, 14 (2), pp. 355-366; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3-4), pp. 209-223; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of Operations Research society, 54 (5), pp. 507-514; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Melynk, S., Stewart, D.M., Swink, M., Metrics and Performance measures in operations management: Dealing with the metrics maze (2004) Journals of Operations management, 22, pp. 209-217; Lockamy, A., McCormack, K., Linking SCOR planning practices to Supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (11-12), pp. 1192-1218; Gunasekaran, A., Patel, A., Mcgaughey, R.E., A Framework for Supply Chain Performance Measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Lohman, C., Fortuin, L., Wounters, M., Designing a Performance Measurement system a case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Li, S., Rao, S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain practices (2005) Journal of Operations Management, 23, pp. 618-641; Fynes, B., Voss, C., Búrca, S.D., The impact of supply chain relationship dynamics on manufacturing performance (2005) International Journal of Operations & Production Management, 25 (1), pp. 6-19; Gunasekaran, A., Williams, H.J., Mcgaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-533; Kobu, G.A., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 1366-588X, 45 (12), pp. 2819-2840. , First published 2007; Huang, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-29; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, Issues, 13, pp. 4-6; Vereecke, A., Muylle, S., Performance improvement through supply chain collaboration in Europe (2006) International Journal of Operations and Production Management, 26 (11), pp. 1176-1198; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Koh, S.C.L., Sevkli, E., Zaim, S., Demirbag, M., Tatoglu, E., The impact of supply chain management practices on performance of SMEs (2007) Industrial Management and Data Systems, 107 (1), pp. 103-124; Hausman, W. H. (2003). Supply Chain Performance Metrics. In T. P. Harrison, H. L. Lee & J. J. Neale (Eds.), The Practice of Supply Chain Management: Where Theory and Application Converge (pp. 61-73). New York: Springer Science & Business; Kaplan, R., Norton, D., (2006) Alignment: Using the balanced scorecard to create corporate synergies, , Boston, MA: Harvard Business School Press; Marr, B., Schiuma, G., Business performance measurement ± past, present and future (2003) Management Decision, 41 (8), pp. 680-687; Tangen, S., Performance measurement: From philosophy to practice (2004) International Journal of Productivity and Performance Management, 53 (8), pp. 726-737; M Najmi, John Rigas, Ip-Shing Fan (2005), A framework to review performance measurement systems, Business Process Management Journal, Vol11, No 2, pp. 109-122; Suwignjo, Bititci, U.S., Came, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 63 (1-3), pp. 231-241; Chunhua Tian Yueting Chai Yi Liu Shouju Ren (2003), Performance management in supply chain This paper appears in: IEEE International Conference on Systems, Man and Cybernetics, Publication Date: 5-8 Oct. 2003, 5, On page(s): 4934- 4939; Ren, C., Chai, Y., Liu, Y., Active Performance Management in Supply Chains: This paper is (2004) IEEE International Conference on Systems, , presented at, Man and Cybernetics; Lockamy III, A., McCormack, K., Linking SCOR planning practices to supply chain performance An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Charan, P., Shankar, R., Baisya, R.K., Selection of Supply Chain Performance Measurement System Using AHP Approach (2007) POMS 18th Annual Conference, , Dallas, Texas, U.S.A. May 4 to May 7; Fabbe-Costes, N., Jahre, M., Supply chain integration gives better performance - the emperor's new suit? (2007) International Journal of Physical Distribution & Logistics Management, 37 (10), pp. 835-855; Tong Ren, (2008) Application of Supply Chain Performance Measurement Based on SCOR Model, This paper appears in: 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. Publication Date: 12-14 Oct. 2008 On page(s): 1-4, Location: Dalian; Z., Li, X. Xu, & Arun kumar (2007), Supply Chain Performance Evaluation from Structural and operational levels , This paper appears in: Emerging Technologies and Factory Automation, 2007. ETFA. IEEE Conference on, ieee publication Publication Date: 25-28 Sept. 2007 On page(s): 1131-1140; Kim, S.W., Effects of supply chain management practices, integration andcompetition capability on performance (2006) Supply Chain Management: An International Journal, 11 (3), pp. 241-248; Li, Z.X., Fu, J., A new Approach of Supply Network performance measurement based on Supporting level and operational level (2006) This paper appears in: Technology and Innovation Conference, 2006. ITIC 2006. International Publication Date, pp. 1537-1545. , 6-7 Nov, On pages; Neely, A., The Evolution of Performance Measurement Research: Development in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Sahay, B.S., Jatinder N.D. Gupta, Mohan,R.,(2006), Managing supply chains for competitiveness: the Indian scenario, Supply Chain Management: An International Journal, 11, No.1, pp.15-24; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E. (2002), Designing and managing the supply chain: concepts, strategies and case studies,2nded.NewYork:McGraw-Hill Higher Edn},
correspondence_address1={Ramaa, A.; Dept. of Industrial Engineering and Management, , Bangalore - 560059, India; email: ramaanadig@gmail.com},
isbn={9780769538846},
language={English},
abbrev_source_title={Int. Conf. Emerg. Trends Eng. Technol., ICETET},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Anderberg20091910,
author={Anderberg, S. and Beno, T. and Pejryd, L.},
title={A survey of metal working companies' readiness for process planning performance measurements},
journal={IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management},
year={2009},
pages={1910-1914},
doi={10.1109/IEEM.2009.5373205},
art_number={5373205},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949494794&doi=10.1109%2fIEEM.2009.5373205&partnerID=40&md5=9b07e27cb9765162062ffebac7e225dd},
affiliation={University West, Department of Engineering Science, Trollhättan, Sweden; Volvo Aero, Trollhättan, Sweden; Production Technology Centre, Innovatum AB, Trollhättan, Sweden},
abstract={The paper presents an investigation regarding the potential and the readiness for implementing performance indicators and performance measurement systems of the process planning work for metal working companies. The paper is based on a questionnaire survey distributed to process planners in the Swedish metal working industry. The main outcome of the investigation is a foundation for understanding the implementation of performance measures of the process planning work for CNC machining. The survey revealed a few strengths and short comings in the studied companies. ©2009 IEEE.},
author_keywords={Performance measurements;  Process planning},
keywords={Cnc machining;  Performance indicators;  Performance measure;  Performance measurement system;  Performance measurements;  Questionnaire surveys;  Short-comings, Benchmarking;  Industrial engineering;  Process control;  Process planning;  Supply chain management;  Surveys, Metal working},
references={Pejryd, L., Andersson, P., (2006) Information back bone systems for virtual manufacturing, a comparison of ERP and engineering based PLM systems, CIRP ISMS, , Ljubljana, Slovenia; Anderberg, S., Beno, T., Pejryd, L., Production preparation methodology in Swedish metal working industry - a State of the Art investigation (2008) Swedish Production Symposium, , Stockholm, Nov 18-20; Löfving, M., Winroth, M., Classification of competitive priorities in small and medium sized manufacturing enterprises (2008) Swedish Production Symposium, , Stockholm, Nov 18-20; Tangen, S., (2007) Evaluation and revision of performance measurement systems, , Doctoral thesis, WoxénCentrum Department of Production Engineering, Royal Institute of Technolgy, Stockholm; (1995) How to measure performance a handbook of techniques and tools, , United States Department of Energy and Oak Ridge Associated Universities; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Fortuin, L., 1988, Performance indicators - Why, where and how?, European Journal of Operational Research, 34/1-9; Kennerley, M., Neely, A., Performance measurement frameworks: A review, Neely, A (2002) Business Performance Measurement: Theory and Practice, , Cambridge University Press; Hannula, M., 2002, Total productivity measurement based on partial productivity ratios, International Journal of Production Economics, 78/57-67; Rosen, R., Using project management metrics as a tool to achieve product development objectives (2003) MDDI, , May; Jonsson, P., Lesshammar, M., Evaluation and improvement of manufacturing performance measurment systems - the role of OEE (1999) International Journal of Operations and Production Management, 19 (1), pp. 55-78; Busby, J.S., Williamson, A., The appropriate use of performance measurement in non-production activities - the case of engineering design (2000) International Journal of Operations and Production Management, 20 (3), pp. 336-358; Bond, T.C., The role of performance measurement in continuous improvement (1999) International Journal of Operations & Production Management, 19 (12), pp. 1318-1334; Roos, G., Roos, J., Measuring your Company's Intellectual Performance (1997) Long Range Planning, 30 (3), pp. 413-326; Kankanhalli, A. and Tan, B. C., 2004, A review of metrics for knowledge management systems and knowledge management initiatives, 37th Hawaii International Conference on System Sciences; Murgau, A., (2009) Variety Management for the Industrial Administration, , Doctoral thesis, Materials and Manufacturing, Chalmers University of Technology, Goteborg},
correspondence_address1={Anderberg, S.; University West, , Trollhättan, Sweden; email: staffan.anderberg@hv.se},
isbn={9781424448708},
language={English},
abbrev_source_title={IEEM - IEEE Int. Conf. Ind. Eng. Eng. Manage.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Zhenggang2009,
author={Zhenggang, L. and Xiao, L. and Dinghua, Z.},
title={A structural framework of SPMS for value chain of service supply chain by ecology analogy},
journal={Proceedings - International Conference on Management and Service Science, MASS 2009},
year={2009},
doi={10.1109/ICMSS.2009.5303407},
art_number={5303407},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-73849119726&doi=10.1109%2fICMSS.2009.5303407&partnerID=40&md5=d3243bdf193c30f481da83c428f552de},
affiliation={Management School, Hangzhou Dianzi University, Hangzhou, China},
abstract={To measure service supply chain (SSC) with penetrable value perspective, a structural framework of strategic performance measurement system (SPMS) for value chain (VC) of SSC is put forward by ecology analogy. Based on the belief that the competition and cooperation of value supply in supply chain (SC) and value demand in demand chain (DC) are the root of dynamic complexity of SSC, a general value flow model in SC and in DC are put forward respectively by analogy with the universal energy flow model. The measures extracted from the two value flow models consider main interest groups of a firm. A structural framework for firm is a self-evident structure with two fulcrums of the co-action between the firm and suppliers or between the firm and customers, which helps expressing the balancing of contradictory goals. A three levels (firm, SSC, environment) structural framework of SPMS for VC of SSC interlinks many pairs of supplier/customer and integrates spatio-temporal SSC structure and environment, which might realize the dynamic evolution of the structural framework of SPMS for firm with SSC and environment. ©2009 IEEE.},
author_keywords={Ecology analogy;  Service supply chain;  Strategic performance measurement system;  Structural framework},
keywords={Competition and cooperation;  Demand chains;  Dynamic complexity;  Dynamic evolution;  Energy flow;  Performance measurement system;  Service supply chains;  Spatio-temporal;  Structural framework;  Structural frameworks;  Value chains;  Value flow, Ecology;  Measurements;  Mobile telecommunication systems;  Supply chain management;  Supply chains, Competition},
references={Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Ind. Market. Manage, 29, pp. 65-83; Baltacioglu, T., Ada, E., Kaplan, M.D., Yurt, O., Kaplan, Y.C., A new framework for service supply chains (2007) Serv. Ind. J, 27 (2), pp. 105-124. , Mar; Ellram, L.M., Tate, W.L., Billington, C., Understanding and managing the services supply chain (2004) JSCM, 40 (4), pp. 17-32. , Fall; Neely, A., The evolution of performance measurement research (2005) Int. J. Oper. Prod. Man, 25 (12), pp. 1264-1277; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Comput. Ind. Eng, 56, pp. 663-680; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Bus. Rev, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Bus. Rev, 74 (1), pp. 75-85; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Log, 21 (1), pp. 75-93; Bullinger, H.J., Kühner, M., Hoof, A.V., Analysing supply chain performance using a balanced measurement method (2002) Int. J. Prod. Res, 40 (15), pp. 3533-3543; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) Int. J. Oper. Prod. Man, 25 (14), pp. 333-353; Juan, J.A.S., Angel, O.B., Raul, R.R., Performance measurement system for enterprise networks (2007) IJPPM, 56 (4), pp. 305-334; Beamon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Man, 19 (3), pp. 275-292; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Man, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Prod. Econ, 87, pp. 333-347; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) Int. J. Logist. Manag, 12 (1), pp. 1-19; Chan, F.T.S., Performance measurement in a supply chain (2003) Int. J. Adv. Manuf. Technol, 21, pp. 534-548; Chan, F., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Pohlen, T.L., Coleman, B.J., Evaluating internal operations and supply chain performance using EVA and ABC (2005) S.A.M. Adv. Manag. J, 70 (2), pp. 45-58; Debra, H., The hierarchy of supply chain metrics (2004) Supply Chain Manag. Rev, pp. 28-37. , Sep; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) IJPPM, 55 (3-4), pp. 242-258; Morgan, C., Supply network performance measurement: Future challenges? (2007) Int. J. Logist. Manag, 18 (2), pp. 255-273; Liu, Z.G., Li, X., Discussion on the framework of performance measurement system (PMS) for supply and demand chain (SDC) based on complex adaptive system (CAS) hypothesis (2008) Proc. of the 15th Int. Conf. on Industrial Engineering and Engineering Management, pp. 26-31. , Zhengzhou; Li, X., Gu, X.J., Liu, Z.G., SPMS for SDC should base on TA by analogy with ecological succession and energy flow model (2008) Proc. of 2008 int. conf. of production and operation management, , Xiamen; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain (1999) Int. J. Prod. Econ, 62, pp. 107-118; Treville, S.D., Shapiro, R.D., Hameri, A.P., From supply chain to demand chain: The role of lead time reduction in improving demand chain performance (2004) J. Oper. Manag, 21, pp. 613-627; Li, X., Gu, X.J., Liu, Z.G., Developing structural dimensions of service supply chain by analogy with community structure Proc. of 6th Int. conf. on Service Systems and Service Management, , in press; Heikkilä, J., From supply to demand chain management (2002) J. Oper. Manag, 20, pp. 747-767; Walters, D., Rainbird, M., The demand chain as an integral component of the value chain (2004) J. Consum. Market, 21 (7), pp. 465-475; Rainbird, M., Demand and supply chains: The value catalyst (2004) IJPDLM, 34 (3-4), pp. 230-250; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Account. Org. Soc, 30, pp. 395-422; Tomkins, C., Interdependencies, trust and information in relationships, alliances and networks (2001) Account. Org. Soc, 26, pp. 161-191; Dekker, H.C., Value chain analysis in interfirm relationships (2003) Manage. Accounting. Res, 14, pp. 1-23; Barnett, W.P., Mischke, G.A., Ocasio, W., The evolution of collective strategies among organizations (2000) Organ. Stud, 21 (2), pp. 325-354; Sorensen, J.B., Recruitment-based competition between industries: A community ecology (2004) Ind. Corp. Change, 13 (1), pp. 149-170. , Feb; Ruef, M., For whom the bell tolls: Ecological perspectives on industrial decline and resurgence (2004) Ind. Corp. Change, 13 (1), pp. 61-89. , Feb; Li, X., Gu, X.J., Liu, Z.G., The model of dominant species and keystone species in SCM (2005) Proc. of the 12th Int. Conf. on Industrial Engineering and Engineering Management, pp. 257-262; Audia, P.G., Freeman, J.H., Reynolds, P.D., Organizational foundings in community context: Instruments manufacturers and their interrelationship with other organizations (2006) Admin. Sci. Quart, 51 (3), pp. 381-419. , Sep; Odum, E.P., (1983) Basic ecology, pp. 121-130. , CBS College Publishing. USA},
correspondence_address1={Zhenggang, L.; Management School, , Hangzhou, China; email: liuzhenggang@sina.com},
isbn={9781424446391},
language={English},
abbrev_source_title={Proc. - Int. Conf. Manage. Serv. Sci., MASS},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Yang2009548,
author={Yang, J.-H.},
title={A balanced performance measurement scorecard approach for Product Service Systems},
journal={2009 International Conference on Business Intelligence and Financial Engineering, BIFE 2009},
year={2009},
pages={548-551},
doi={10.1109/BIFE.2009.129},
art_number={5208824},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-71049150119&doi=10.1109%2fBIFE.2009.129&partnerID=40&md5=22f766ddef3b4c1d7899bbae382fa81f},
affiliation={School of Economic and Management, University of Science and Technology Beijing, Beijing, China},
abstract={This paper develops a balanced performance measurement scorecard for a product-service system (PSS) from following four perspectives: customer, finance, business process integration, and learning and growth. The balanced cube framework of performance measurement for a PSS is presented first. Then, the multi-levels balanced scorecard and performance mesures is developed and supported by large enterprise in China. The balanced scorecard developed in this paper provides a useful guidance for the practical managers in evaluation and measuring of PSS in a balanced way and proposes a balanced performance measurement system to analyze a product-service system. © 2009 IEEE.},
author_keywords={Performance measurement systems;  Product service system;  The balanced scorecard},
keywords={Balanced scorecards;  Business process integration;  Multilevels;  Performance measurement system;  Performance measurement systems;  Performance measurements;  Product service system;  Product-service systems;  The balanced scorecard, Competitive intelligence;  Management science;  Supply chain management, Strategic planning},
references={Mont, O., Product service systems (Editorial) (2003) Journal of Cleaner Production,11(8)a, pp. 815-817; Arnold Tukker. Eight types of product-service system:eight ways to sustainability?experiences from suspronet.Business Strategy and the Environment Bus. Strat. Env. 13,2004, 246-260; Mont, O. PSS - a review of achievements and refining the research agenda. Editorial. J. Cleaner Prod.,14(17),2006; T S Baines, H W Lightfoot, S Evans et al. State-of-the-art in product-service systems. Proc.IMech E 22 Part B:J.Engineering Manufacture. 2007,1543-1551; Wisner, J.D., Keong Leong, G., Choon Tan, K., (2005) Principles of Supply Chain Management: A Balanced Approach, Mason, Ohio: South-Western (Thomson Corp.), pp. 432-446; Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Rajat Bhagwat, Milind Kumar Sharma.Performance measurement of supply chain management: A balanced scorecard approach.Computers & Industrial Engineering. 53 ,2007.43-62},
correspondence_address1={Yang, J.-H.; School of Economic and Management, , Beijing, China; email: yangjh@manage.ustb.edu.cn},
isbn={9780769537054},
language={English},
abbrev_source_title={Int. Conf. Bus. Intell. Financ. Eng., BIFE},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Stefanovic2009217,
author={Stefanovic, N. and Stefanovic, D.},
title={Supply Chain business intelligence: Technologies, issues and trends},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2009},
volume={5640 LNAI},
pages={217-245},
doi={10.1007/978-3-642-03226-4_12},
note={cited By 23},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350660775&doi=10.1007%2f978-3-642-03226-4_12&partnerID=40&md5=5bc0eca3be32e6ab0be37e7cf08569ce},
affiliation={Information Systems Department, Zastava Automobiles, Kragujevac, Serbia; Faculty of Science, University of Kragujevac, Serbia},
abstract={Supply chains are complex systems with silos of information that are very difficult to integrate and analyze. The best way to effectively analyze these disparate systems is the use of Business Intelligence (BI). The ability to make and then to process the right decision at the right time in collaboration with the right partners is the definition of the successful use of BI. This chapter discusses the need for Supply Chain Business Intelligence, introduces driving forces for its adoption and describes the supply chain BI architecture. The global supply chain performance measurement system based on the process reference model is described. The main cutting-edge technologies such as service-oriented architecture (SOA), business activity monitoring (BAM), web portals, data mining, and their role in BI systems are also discussed. Finally, key BI trends and technologies that will influence future systems are described. © 2009 Springer Berlin Heidelberg.},
keywords={Business Activity Monitoring;  Business intelligence;  Complex systems;  Cutting edge technology;  Disparate systems;  Driving forces;  Global supply chain;  Process reference models;  Web portal, Artificial intelligence;  Information services;  Service oriented architecture (SOA);  Supply chain management, Supply chains},
references={Martin, J., Roth, R., (2000) Supply Chain management-Direction Strategy, , ECRU Technologies, Inc; Lambert, M.D., Cooper, C.M., Pagh, D.J., Supply Chain Management: Implementation Issues and Research Opportunities (1998) The International Journal of Logistics Management, 44 (2), pp. 1-19; Gintic, (2002) Measuring supply chain performance using a SCOR-based approach, , Institute of Manufacturing Technology March; Lapide, L., (2000) What About Measuring Supply Chain Performance, p. 2. , ASCET; Supply Chain Council, Operations Reference-Model Overview Version 8.0 2006; Bolstroff, P., Rosenbaum, R., (2003) Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , Amacom, New York; Quinn, K., (2003) Establishing a Culture of Measurement-A Practical Guide to Business Intelligence, , Information Builders; Computerworld, (2005) Executive Briefings-Get Smart About, , Business Intelligence; Datamonitor, BI Trends-What to Expect in 2006 (January 2006); Decker, J., Brett, C., (2003) The Joy of SOX: Part 2-The SOX Solution Blueprint, , META Group; Betts, M., (2003) The future of business intelligence, Computerworld; Lambert, M.D., Pohlen, L.T., Supply Chain Metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Atre, S.: The Top 10 Critical Challenges for Business Intelligence Success, Computerworld, Computerworld (2003); Biere, M., (2003) Business Intelligence for the Enterprise, , Pearson Education, New Jersey; Inmon, H.W., (2002) Building the Data Warehouse, , 3rd edn. John Wiley & Sons, Chichester; Moeller, A.R., (2001) Distributed Data Warehousing Using Web Technology, , Amacom, New York; Stefanovic, N., Radenkovic, B., Stefanovic, D., Supply Chain Intelligence (2007) Intelligent Production Machines and Systems, 3. , Pham, D.T, Eldukhri, E.E, Soroka, A.J, eds, Whittles Publishing, Dunbeath; Haydock, P.M., Supply Chain Intelligence (2003) ASCET, 5, pp. 15-21; Shobrys, D., (2003) Supply Chain Management and Business Intelligence, Supply Chain Consultants; Curt, H., (2002) Supply Chain Intelligence: Applying, , Business Intelligence to Enhance Operational Efficiencies, Wipro; Wolfe, M.E., Wadewitz, R.T., Combe, G.C.: E-gistics, Bear, Stearns & Co. Inc. (2000); Srinivasa, P.R., Saurabh, S., (2001) Business Intelligence and Logistics, Wipro; Ferguson, M., (2007) Developing a Service-Oriented Architecture (SOA) for Business Intelligence, BeyeNetwork; White, C., (2007) What Do SOA and ESB Mean in Business Intelligence, BeyeNetwork; Everett, D., (2003) Web Services and Business Intelligence, , Hyperion, New York; Berenson, H., (2005) Why Consider a Service-Oriented Database Architecture for Scalability and Availability, , Microsoft; Microsoft, What is BAM? (March 2006), http://msdn2.microsoft.com/enus/ library/aa560139.aspx; Ferguson, M., Building Intelligent Agents Using Business Activity Monitoring (2005) DMReview Magazine, , Dec; Stefanovic, N., Stefanovic, D., Methodology for BPM in Supply Networks (2006) 5th CIRP International Seminar on Intelligent Computation in Manufacturing Engineering, , Ischia, Italy; Hand, D., Mannila, H., Smyth, P., (2001) Principles of Data Mining, , MIT Press, Cambridge; Tang, Z.H., MacLennan, J., (2005) Data Mining With SQL Server 2005, , Wiley, Indianopolis; Larose, T.D., (2005) Discovering Knowledge in Data, , John Wiley & Sons, Chichester; Berry, M.J.A., Linoff, G.S., (2004) Data Mining Techniques for Marketing, Sales, and Customer relationship Management, , John Wiley & Sons, Chichester; Beal, B.: Application Vendors to Dig Into Data Mining (Jan. 2005), http://searchcrm. techtarget.com/originalContent/0,289142,sid11-gci10473 47,00.html; Bisconti, K., Integrating BI Tools into the Enterprise Portal (2005) DMReview Magazine, , Aug; (2006) Athena IT Solutions, BI as a Smart Investment, , http://www.athenasolutions.com/bi-brief/june03-issue3.html; McKnight, W., Humphrey, S., Building Business Intelligence: Rafting Into the Business Intelligence Future (2004) DMReview Magazine, , Oct; (2007) Common Warehouse Metamodel, CWM, , http://www.omg.org/technology/documents/formal/cwm.htm; Computerworld, (2004) The Future of Business Intelligence, , June; Linthicum, D., Gartner Sees $19.3 Billion SaaS Market by 2011, , http://www.intelligententerprise.com/blog/archives/2007/08/gartner-sees-19.html, August 2007; Flex News Desk, Business Intelligence in the world of Rich Internet Applications (2007), http://java.sys-con.com/read/280900.htm; Ames, B., (2007) Web 2.0 tools inspire data-sharing software, , http://www.infoworld.com/article/07/04/18/HNweb2datasharingtools-1.html; Michalewicz, Z., Schmidt, M., Michalewicz, M., Chiriac, C., (2006) Adaptive Business Intelligence, , Springer, Heidelberg; Stefanovic, D., Stefanovic, N., Methodology for modeling and analysis of supply networks (2008) Journal of Intelligent Manufacturing, 19 (4), pp. 485-503},
correspondence_address1={Stefanovic, N.; Information Systems Department, , Kragujevac, Serbia},
editor={Bramer M.},
issn={03029743},
isbn={3642032257; 9783642032257},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Yaoli200989,
author={Yaoli, C.},
title={Construction of supply chain quality performance measurement system based on the excellent performance criteria},
journal={Proceedings - 2009 IITA International Conference on Services Science, Management and Engineering, SSME 2009},
year={2009},
pages={89-91},
doi={10.1109/SSME.2009.49},
art_number={5233339},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350539571&doi=10.1109%2fSSME.2009.49&partnerID=40&md5=32b5e07d7fd9f9ba9a8a41c48f9b0c8f},
affiliation={School of Economics and Management, ZhengZhou University of Light Industry, ZhengZhou, China},
abstract={Performance measures are critical to the success of supply chain. As to the study on supply chain performance and supply chain quality performance measurement, the present researches are mainly about single corporate and less about the whole supply chain quality performance. Based on the excellent performance criteria of the United States Malcolm Baldrige Award, this paper proposes that the Performance Excellence model may become the theoretical foundation of supply chain quality performance measurement, and then establishes the supply chain quality performance measurement system. © 2009 IEEE.},
author_keywords={Excellent performance criteria;  Performance;  Supply chain;  System},
keywords={Baldrige Award;  Excellent performance;  Excellent performance criteria;  Performance;  Performance measure;  Quality performance;  Supply chain performance;  System;  Theoretical foundations, Supply chain management, Supply chains},
references={Deloitte Consulting. (1999), Energizing the supply chain: Trends and issues in supply chain management. Retrieved June 21, 2004 from http://www.deloitte.com/dtt/research/0,2310, sid%253D2222%2526cid %253D35758,00.html; McAdam, R., McCormack, D.J., Integrating business processes for global alignment and supply chain management (2001) Business Process Management Journal, 7 (2), pp. 113-130; Lummus, R.R., Vokurka, R.J., Alber, K.L., Strategic supply chain Planning (1998) Production and Inventory Management Journal, 39 (3), pp. 49-58; Ellram, L.M., Liu, B., The financial impact of supply management (2002) Supply Chain Management Review, 6 (6), pp. 30-37; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations I Production Management, 19 (3), pp. 275-292; Chen, C.-C., Yang, C.-C., Customer-focused rating system of supplier quality performance (2004) Journal of Manufacturing Technology Management, 7, pp. 599-606; Lo, V.H.Y., Yeung, A.H.W., Yeung, A.C.L., How supply quality management improves an organization's quality performance: A study of Chinese manufacturing firms (2007) International Journal of Production Research, 45 (10), pp. 2219-2243; Fynesa, Brian, Voss, Chris, de Burca, Sean, The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96 (3), p. 339},
correspondence_address1={Yaoli, C.; School of Economics and Management, , ZhengZhou, China; email: xll308@163.com},
isbn={9780769537290},
language={English},
abbrev_source_title={Proc. IITA Int. Conf. Serv. Sci., Manage. Eng., SSME},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Ceroni2009255,
author={Ceroni, J.A. and Quezada, L.E.},
title={Development of collaborative production systems in emerging economies},
journal={International Journal of Production Economics},
year={2009},
volume={122},
number={1},
pages={255-256},
doi={10.1016/j.ijpe.2009.06.033},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349792493&doi=10.1016%2fj.ijpe.2009.06.033&partnerID=40&md5=dc7eec6eabb77d76345020d3d4863256},
affiliation={Collaborative Systems Research Group, School of Industrial Engineering, Pontifical Catholic University of Valparaiso, Av. Brasil 2241, Valparaiso, Chile; University of Santiago of Chile, Chile},
abstract={The 19th version of the International Conference of Production Research (ICPR19) took place in August 2007. Twenty-six of the submissions to ICPR19 were evaluated and finally selected to be published. Collaboration is present in the researches by Velásquez and Nof, Huang and co-workers, and Cauvin and colleagues which cover interesting aspects of collaboration among enterprises from the perspective of protocols in supply chains and assembly operations, and agent modeling for cooperative problem solving. Matsui proposes a stochastic model for evaluating the integration of production and sales function in different specialized enterprises. Garrido presented a model for assisting in the distribution network design decision, considering service level and inventory decisions. Pinheiro et al proposed a process to integrate operations strategy content to the design of the performance measurement system in the enterprise.},
references={Cauvin, A.C.A., Ferrarini, A.F.A., Tranvouez, E.T.E., Disruption management in distributed enterprises: a multiagent modelling and simulation of cooperative recovery behaviours (2009) International Journal of Production Economics, 122 (1), pp. 429-439; Ferreira, A.M.D., Laurindo, F.J.B., Outsourcing decision-making aspects considered by it departments in Brazilian companies (2009) International Journal of Production Economics, 122 (1), pp. 305-311; Fitzgerald, G., Barad, M., Papazafeiropoulou, A., Alaa, G., A framework for analyzing flexibility of generic objects (2009) International Journal of Production Economics, 122 (1), pp. 329-339; Fleury, A., Fleury, M.T.L., Understanding the strategies of late-movers in international manufacturing (2009) International Journal of Production Economics, 122 (1), pp. 340-350; Ge, L., Voß, S., ERP application in china: an overview (2009) International Journal of Production Economics, 122 (1), pp. 501-507; Gebennini, E., Gamberini, R., Manzini, R., An integrated production-distribution model for the dynamic location and allocation problem with safety stock optimization (2009) International Journal of Production Economics, 122 (1), pp. 286-304; Gomez-Padilla, A., Mishina, T., Supply contract with options (2009) International Journal of Production Economics, 122 (1), pp. 312-318; Hammami, R., Frein, Y., Hadj-Alouane, A.B., A strategic-tactical model for the supply chain design in the delocalization context: mathematical formulation and case study (2009) International Journal of Production Economics, 122 (1), pp. 351-365; Hu, G., Bidanda, B., Modeling sustainable product lifecycle decision support systems (2009) International Journal of Production Economics, 122 (1), pp. 366-375; Huang, C.-Y., Huang, T.-S., Chen, W.-L., Communication protocols for order management in collaborative manufacturing (2009) International Journal of Production Economics, 122 (1), pp. 257-268; Ishii, K., Ishimura, T., Ikeda, I., Tsuchiya, A., Nakano, M., Development of educational program for production manager leading new perspectives on manufacturing technology (2009) International Journal of Production Economics, 122 (1), pp. 469-478; Jain, V., Deshmuhk, S.G., Dynamic supply chain modelling using a new fuzzy hybrid negotiation mechanism (2009) International Journal of Production Economics, 122 (1), pp. 319-328; Kakehi, M., Yamada, T., Watanabe, I., PLM education in production design and engineering by e-learning (2009) International Journal of Production Economics, 122 (1), pp. 479-484; Kulmalaa, H.I., Ahoniemi, L., Nissinen, V., Performance through measuring leader's profiles: an empirical study (2009) International Journal of Production Economics, 122 (1), pp. 385-394; Kusiak, A., Innovation: a data-driven approach (2009) International Journal of Production Economics, 122 (1), pp. 440-448; Mao, G., Hu, B., Song, H., Exploring talent flow in Wuhan automotive industry cluster at China (2009) International Journal of Production Economics, 122 (1), pp. 395-402; Matsui, M., Enterprise modeling and integration: a stochastic management approach (2009) International Journal of Production Economics, 122 (1), pp. 485-491; Miranda, P., Garrido, R., Inventory service level optimization within distribution network design problem (2009) International Journal of Production Economics, 122 (1), pp. 276-285; Palominos, P., Quezada, L., Moncada, G., Modeling the response capability of a production system (2009) International Journal of Production Economics, 122 (1), pp. 458-468; Pedroso, M.C., Nakano, D., Knowledge and information flows in supply chains: a study on pharmaceutical companies (2009) International Journal of Production Economics, 122 (1), pp. 376-384; Pinheiro de Lima, Edson, da Costa, Sérgio Eduardo Gouvêa, de Faria, Avides Reis, 2009. Taking operations strategy into practice: developing a process for defining priorities and performance measures. International Journal of Production Economics 122 (1), 403-418; Quezada, L.E., Cordova, F.M., Palominos, P., Godoy, K., Ross, J., Method for identifying strategic objectives in strategy maps (2009) International Journal of Production Economics, 122 (1), pp. 492-500; Sakaguchi, M., Inventory model for an inventory system with time-varying demand rate (2009) International Journal of Production Economics, 122 (1), pp. 269-275; Salerno, M.S., Reconfigurable organisation to cope with unpredictable goals (2009) International Journal of Production Economics, 122 (1), pp. 419-428; Tan, K.H., Rae, R.H., Uncovering the links between regulation and performance measurement (2009) International Journal of Production Economics, 122 (1), pp. 449-457; Velásquez, J.D., Nof, S.Y., (2009) Best-matching protocols for assembly in e-work networks, International Journal of Production Economics, 122 (1), pp. 508-516},
correspondence_address1={Ceroni, J.A.; Collaborative Systems Research Group, Av. Brasil 2241, Valparaiso, Chile; email: jceroni@ucv.cl},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Editorial},
source={Scopus},
}

@ARTICLE{Li20092918,
author={Li, X. and Gu, X.J. and Liu, Z.G.},
title={A strategic performance measurement system for firms across supply and demand chains on the analogy of ecological succession},
journal={Ecological Economics},
year={2009},
volume={68},
number={12},
pages={2918-2929},
doi={10.1016/j.ecolecon.2009.06.018},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-69449089559&doi=10.1016%2fj.ecolecon.2009.06.018&partnerID=40&md5=758675195c2e26cb990966ae4f44765f},
affiliation={Institute of Contemporary Manufacturing Engineering, Zhejiang University, 310027 Hangzhou, China; Management School, Hangzhou Dianzi University, 310018 Hangzhou, China},
abstract={To answer the call for a new business that integrates economic, biological and human systems, this paper develops a strategic performance measurement system (SPMS) for firms across supply and demand chains (SDC) by analogy with ecological succession. Based on the explanation that SDC can be viewed as community, it develops SDC (monetary value) structure by analogy with community (trophic) structure. As energy flow in ecology follows the first and second laws of thermodynamics, monetary value flow in business follows two laws that are similar to the laws of thermodynamics. Based on these laws, as well as throughput accounting and traditional cost accounting, it puts forward a general monetary value flow model in SDC (i.e. in demand chain and in supply chain respectively). Based on the value flow model in SDC, it conceives an SDC evolution model with a case study on the maturity of Toyota Motor Corporation. Based on these two models, it develops an SPMS for firms across SDC with procedural and structural frameworks. The discussion about monetary value flows in business from nature to the final consumers or converse gives a chance to coordinate business with nature. The SPMS that rationally integrates effective evaluation of tiers in SDC and practical product development plans in firm will help firms create a sustainable commerce (e.g. product-service system). © 2009 Elsevier B.V. All rights reserved.},
author_keywords={Ecology of commerce;  Monetary value flow model;  Organizational ecology;  SDC evolution model;  Strategic performance measurement system (SPMS);  Supply and demand chains (SDC)},
keywords={accountability;  commercialization;  cost-benefit analysis;  demand analysis;  demand-side management;  ecological impact;  energy flow;  evolution;  exchange rate;  firm ownership;  strategic approach;  structural adjustment;  thermodynamics;  trophic structure},
references={Ackoff, R.L., (1999) Ackoff'S best: his classic writings on management, , John Wiley & Sons, NY; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Boone, C., Witteloostuijn, A.V., Industrial organization and organizational ecology: the potentials for cross-fertilization (1995) Organization Studies, 16 (2), pp. 265-298; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bullinger, H.J., Kühner, M., Hoof, A.V., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Chan, F.T.S., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chan, F., Qi, H.J., Feasibility of performance measurement system for supply chain: a process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: an exploratory study (2005) Accounting, Organizations and Society, 30, pp. 395-422; Cilliers, P., (1998) Complexity & postmodernism, , Routledge, London; Cohen, S., Roussel, J., (2004) Strategic supply chain management: the five disciplines for top performance, , McGraw-Hill Professional, NY; de Treville, S.D., Shapiro, R.D., Hameri, A.P., From supply chain to demand chain: the role of lead time reduction in improving demand chain performance (2004) Journal of Operations Management, 21, pp. 613-627; Dugdale, D., Jones, T.C., Throughput accounting: transforming practices? (1998) British Accounting Review, 30, pp. 203-220; Encyclopedia Britannica, 2009. Species abundance. http://www.britannica.com/EBchecked/topic/558668/species-abundance; Faber, M., Manstetten, R., Proops, J.L.R., (1996) Ecological Economics: Concepts and Methods, , Edward Elgar, Cheltenham; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Folan, P., Browne, J., A review of performance measurement: towards performance management (2005) Computers in Industry, 56, pp. 663-680; Goldratt, E.M., Cox, J., (1993) The Goal. 2nd ed., , Gower, London; Goldratt, E.M., Cox, J., (2004) The Goal. 3rd ed., , The North River Press; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hannan, M.T., Freeman, J., The population ecology of organizations (1977) American Journal of Sociology, 82 (5), pp. 929-964; Hannan, M.T., Carroll, G.R., Pólos, L., The organizational niche (2003) Sociological Theory, 21 (4), pp. 309-340; Hawken, P., (1993) The ecology of commerce: a declaration of sustainability, , HarperCollins Publishers, NY; Hergert, M., Morris, D., Accounting data for value chain analysis (1989) Strategic Management Journal, 10, pp. 175-188; Juan, J.A.S., Angel, O.B., Raul, R.R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; Kaplan, R.S., Yesterday's accounting undermines production (1984) Harvard Business Review, 62 (4), pp. 95-101; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Klug, W.S., Cummings, M.R., (1997) Concepts of genetics. 5th ed., , Prentice-Hall, Upper Saddle River; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Li, X., Gu, X.J., Liu, Z.G., The model of dominant species and keystone species in SCM (2005) The Proceeding of the 12th International Conference on Industrial Engineering and Engineering Management, 1, pp. 257-262; Lincoln, J.R., Ahmadjian, C.L., Mason, E., Organizational learning and purchase-supply relations in Japan: Hitachi, Matsushita, and Toyota compared (1998) California Management Review, 40 (3), pp. 241-264; Liu, Z.G., Li, X., Discussion on the framework of performance measurement system (PMS) for supply and demand chain (SDC) based on complex adaptive system (CAS) hypothesis (2008) The Proceeding of the 15th International Conference on Industrial Engineering and Engineering Management, Zhengzhou, A, pp. 26-31; Lockamy III, A., McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278; Matutinović, I., Organizational patterns of economies: an ecological perspective (2002) Ecological Economics, 40 (3), pp. 421-440; Mitchell, R.K., Agle, B.R., Wood, D.J., Toward a theory of stakeholder identification and salience: defining the principle of who and what really counts (1997) Academy of Management Review, 22, pp. 853-886; Molles, M.C., (2002) Ecology: Concepts and Applications. 2nd ed., , McGraw-Hill; Mont, O., Clarifying the concept of product-service system (2002) Journal of Cleaner Production, 10 (3), pp. 237-245; Morgan, C., Supply network performance measurement: future challenges? (2007) The International Journal of Logistics Management, 18 (2), pp. 255-273; Neely, A., The evolution of performance measurement research (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Noreen, E., Smith, D., Mackey, J.T., (1995) The theory of constraints and its application to management accounting, , North River Press, NY; Odum, E.P., The strategy of ecosystem development (1969) Science, 164, pp. 262-270; Odum, E.P., (1983) Basic ecology, , CBS College Publishing; Pawar, K.S., Beltagui, A., Riedel, J.C.K.H., The PSO triangle: designing product, service and organisation to create value (2009) International Journal of Operations & Production Management, 29 (5), pp. 468-493; Pohlen, T.L., Coleman, B.J., Evaluating internal operations and supply chain performance using EVA and ABC (2005) S.A.M. Advanced Management Journal, 70 (2), pp. 45-58; Porter, M.E., (1985) Competition advantage, , Free Press, NY; Pólos, L., Hannan, M.T., Carroll, G.R., Foundations of a theory of social forms (2002) Industrial and Corporate Change, 11 (1), pp. 85-115; Rainbird, M., Demand and supply chains: the value catalyst (2004) International Journal of Physical Distribution & Logistics Management, 34 (3-4), pp. 230-250; Shepherd, C., Günter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Thrane, S., The complexity of management accounting change: bifurcation and oscillation in schizophrenic inter-organisational systems (2007) Management Accounting Research, 18, pp. 248-272; Walters, D., Rainbird, M., The demand chain as an integral component of the value chain (2004) Journal of Consumer Marketing, 21 (7), pp. 465-475; Webster, M., Supply system structure, management and performance: a conceptual model (2002) International Journal of Management Reviews, 4 (4), pp. 353-369},
correspondence_address1={Li, X.; Institute of Contemporary Manufacturing Engineering, , 310027 Hangzhou, China; email: lixiao189@yahoo.com.cn},
issn={09218009},
coden={ECECE},
language={English},
abbrev_source_title={Ecol. Econ.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yang200911369,
author={Yang, C.-L. and Chuang, S.-P. and Huang, R.-H.},
title={Manufacturing evaluation system based on AHP/ANP approach for wafer fabricating industry},
journal={Expert Systems with Applications},
year={2009},
volume={36},
number={8},
pages={11369-11377},
doi={10.1016/j.eswa.2009.03.023},
note={cited By 91},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349221714&doi=10.1016%2fj.eswa.2009.03.023&partnerID=40&md5=2c222334e6e71b475f564592a3f9c620},
affiliation={Department of Business Administration, Fu Jen Catholic University, No. 510, Chung-Cheng Road, Hsinchuang City, Taipei Hsien 24205, Taiwan; Department of Information Management, Huafan University, No. 1, Huafan Road, Shihding Township, Taipei County 22301, Taiwan; Graduate Institute of Management, Fu Jen Catholic University, No. 510, Chung-Cheng Road, Hsinchuang City, Taipei, Hsien 24205, Taiwan},
abstract={The critical role played by manufacturing performance measurement systems in achieving competitive success is increasingly recognized. Manufacturing success may depend on the compatibility between a performance measurement system in operation at subordinate organizational levels and an organization's global goals. Therefore, developing an integrated performance measurement model is significant for strategy management. This study proposes an integrated process that allows manufacturing systems to construct performance measurement model. Performance criteria from the literature and an expert questionnaire were utilized prior to building the performance measurement model. The analytical hierarchy process (AHP) and the analytical network process (ANP) are utilized to determine the weight of each criterion when generating the performance model for manufacturing systems. © 2009 Elsevier Ltd. All rights reserved.},
author_keywords={Analytic hierarchy process;  Analytical network process;  Manufacturing system;  Performance measurement},
keywords={Analytical hierarchy process;  Analytical network process;  Competitive success;  Fabricating industry;  Integrated process;  Manufacturing evaluation;  Manufacturing performance;  Manufacturing system;  Organizational levels;  Performance criterion;  Performance measurement;  Performance measurement system;  Performance measurements;  Performance Model;  Strategy management, Analytic hierarchy process;  Hierarchical systems;  Measurements;  Supply chain management, Manufacture},
references={Adbel-Maksoud, A., Manufacturing in the UK: Contemporary characteristics and performance indicators (2004) Journal of Manufacturing Technology Management, 15 (2), pp. 155-171; Agarwal, A., Shankar, R., Tiwari, M.K., Modeling the metrics of lean, agile and leagile supply chain: An ANP-based approach (2006) European Journal of Operational Research, 173, pp. 211-225; Ahmad, M.M., Dhafr, N., Establishing and improving manufacturing performance measures (2002) Robotics and Computer Integrated Manufacturing, 18, pp. 171-176; Chenhall, R.H., Strategies of manufacturing flexibility, manufacturing performance measures and organizational performance: An empirical investigation (1996) Integrated Manufacturing System, 7 (5), pp. 25-32; Chung, S.H., Lee, A.H.I., Pearn, W.L., Analytic network approach for product mix in semiconductor fabricator (2005) International Journal of Production Economics, 96, pp. 15-36; Ertuǧrul, I., Karakaşoǧlu, N., Performance evaluation of Turkish cement firms with fuzzy analytic hierarchy process and TOPSIS methods (2007) Expert Systems with Applications, 36 (1), pp. 702-715; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system (1997) International Journal of Production Economics, 48, pp. 207-225; Lee, A.H.I., Chen, W.C., Chang, C.J., A fuzzy AHP and BSC approach for evaluating performance of IT department in the manufacturing industry in Taiwan (2008) Expert Systems with Applications, 34 (1), pp. 96-107; Leong, G.K., Snyder, D.L., Ward, P.T., Research in the process and content of manufacturing strategy (1990) OMEGA, 18 (2), pp. 109-122; Liu, S.T., Slacks-based efficiency measures for predicting bank performance (2008) Expert Systems with Applications, 36 (2 PART 2), pp. 2813-2818; Önüt, S., Kara, S.S., Işik, E., Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company (2008) Expert Systems with Applications, 36 (2 PART 2), pp. 3887-3895; Saaty, R.W., (2003) Decision making in complex environments the analytic hierarchy process, , McGraw-Hill, New York; Saaty, T.L., (2005) Theory and applications of the analytic network process: Decision making with benefits, opportunities, costs, and risks, , RWS, PA; Wang, W.K., Huang, H.C., Lai, M.C., Design of a knowledge-based performance evaluation system: A case of high-tech state-owned enterprises in an emerging economy (2008) Expert Systems with Applications, 34 (3), pp. 1795-1803; Wu, W.Y., Shih, H.A., Chan, H.C., The analytic network process for partner selection criteria in strategic alliances (2008) Expert Systems with Applications, 36 (3 PART 1), pp. 4646-4653; Yuan, F.C., Chiu, C.C., A hierarchical design of case-based reasoning in the balanced scorecard application (2007) Expert Systems with Applications, 36 (1), pp. 333-342; Yurdakul, M., Measuring a manufacturing system's performance using Saaty's system with feedback approach (2002) Integrated Manufacturing System, 13 (1), pp. 25-34},
correspondence_address1={Yang, C.-L.; Department of Business Administration, No. 510, Chung-Cheng Road, Hsinchuang City, Taipei Hsien 24205, Taiwan; email: 051125@mail.fju.edu.tw},
issn={09574174},
coden={ESAPE},
language={English},
abbrev_source_title={Expert Sys Appl},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lin2009369,
author={Lin, C.-Y. and Ho, Y.-H.},
title={RFID technology adoption and supply chain performance: An empirical study in China's logistics industry},
journal={Supply Chain Management},
year={2009},
volume={14},
number={5},
pages={369-378},
doi={10.1108/13598540910980288},
note={cited By 103},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349630941&doi=10.1108%2f13598540910980288&partnerID=40&md5=822ded8d58a07190efa7b1a85f34c2fb},
affiliation={Department of International Business, Chang Jung Christian University, Tainan, Taiwan},
abstract={Purpose - The purpose of this paper is to study the factors influencing the adoption of RFID technology and the relation between RFID technology adoption and supply chain performance for logistics companies. Design/methodology/approach - Determinants of RFID technology adoption are analyzed from the technological, organizational and environmental perspectives. Supply chain performance measurements consist of financial and non-financial indices. Data were collected by means of delivering questionnaires to logistics service providers in China. A total of 574 logistics companies were analyzed. Findings - Logistics companies' willingness to adopt RFID technology are significantly influenced by the explicitness and accumulation of technology, organizational encouragement for innovation, quality of human resources, and governmental support. There is a positive association between the willingness to adopt RFID technology and supply chain performance for logistics service providers. Research limitations/implications - As a single-country and single-industry study, the current research may be limited in its generalizability. Practical implications - The growth of China's economy hinges to a large extent on the ability of the logistics industry to operate efficiently and effectively in the global supply chain system. China's logistics service providers need to adopt more efficient logistics technologies to provide better supply chain services for their customers. This paper provides some suggestions for governmental officials, logistics companies, and RFID technology providers in advancing the application of RFID in supply chains. Originality/value - This paper may be the first empirical research in analyzing the adoption of RFID technology and its association with supply chain performance in the logistics industry, especially for China's logistics industry. © Emerald Group Publishing Limited.},
author_keywords={China;  Communication technologies;  Distribution management;  Innovation;  Supply chain management},
references={Amabile, T.M., A model of creativity and innovation in organization (1988) Research in Organizational Behavior, 10, pp. 123-67. , in Staw, B.M. and Cummings, L.L. (Eds), Aldine Publishing Company, Chicago, IL; Angeles, R., RFID technologies: Supply-chain applications and implementation issues (2005) Information System Management, 22 (1), pp. 51-65; Angeles, R., RFID mandate compliance: Absorptive capacity attributes as differentiating dynamic capabilities (2008) International Journal of Value Chain Management, 2 (3), pp. 287-312; Ajzen, I., From intentions to actions: A theory of planned behavior (1985) Action-Control: From Cognition to Behavior, pp. 11-39. , in Kuhl, J. and Beckman, J. (Eds), Springer, Heidelberg; Attaran, M., RFID: An enabler of supply chain operations (2007) Supply Chain Management: An International Journal, 12 (4), pp. 249-57; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-92; Bowersox, D.J., Closs, D.J., (1996) Logistical Management: The Integrated Supply Chain Process, , McGraw-Hill, New York, NY; Brewer, P., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Capon, N., Farley, J.U., Hoenig, S., Determinants of financial performance: A meta-analysis (1990) Management Science, 36 (10), pp. 1143-59; Chapman, R.L., Soosay, C., Kandampully, J., Innovation in logistic services and the new business model: A conceptual framework (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 630-50; Chau, P.Y.K., Tam, K.Y., Factors affecting the adoption of open systems: An exploratory study (1997) MIS Quarterly, 21 (1), pp. 1-24; Chow, H.K.H., Choy, K.L., Lee, W.B., Chan, F.T.S., Integration of web-based and RFID technology in visualizing logistics operations - a case study (2007) Supply Chain Management: An International Journal, 12 (3), pp. 221-34; Damanpour, F., Organizational innovation: A meta-analysis of effects of determinants and moderators (1991) Academy of Management Journal, 34 (3), pp. 555-90; Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology (1989) MIS Quarterly, 13 (3), pp. 319-40; Davis, H.E., Luehlfing, M.S., Radio frequency identification: The wave of the future (2004) Journal of Accountancy, 198 (5), pp. 43-9; Delfmann, W., Albers, S., Gehring, M., The impact of electronic commerce on logistics service providers (2002) International Journal of Physical Distribution & Logistics Management, 32 (3), pp. 203-22; Donovan, J., RF identification tags: Show me the money (2003) Electronic Engineering Times, 12, p. 41. , May; Dutta, A., Whang, S., Radiofrequency identification applications in private and public sector operations: Introduction to the special issue (2007) Production and Operations Management, 16 (5), pp. 523-4; Grant, R.M., Prospering in dynamically-competitive environments: Organizational capability as knowledge integration (1996) Organization Science, 7 (4), pp. 375-87; Grant, R.M., The resource-based theory of competitive advantage (1991) California Management Review, 33 (3), pp. 114-35; Groenwegen, P., Vergragt, P., Environmental issues as threats and opportunities for technological innovation (1991) Technology Analysis and Strategic Management, 3 (1), pp. 43-55; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-47; Hart, S.L., A natural resource-based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1014; Harvey, M., Innovation and competition in UK supermarkets (2000) Supply Chain Management: An International Journal, 5 (1), pp. 15-21; Jiang, B., How international firms are coping with supply chain issues in China (2002) Supply Chain Management: An International Journal, 7 (4), pp. 184-8; Karkkainen, M., Increasing efficiency in the supply chain for short shelf life goods using RFID tagging (2003) International Journal of Retail & Distribution Management, 31 (10), pp. 529-36; Kemp, R., An economic analysis of cleaner technology: Theory and evidence (1993) Environmental Strategies for Industry, pp. 79-113. , in Fischer, K. and Schot, J. (Eds), Island, Press, Washington, DC; Kimberly, J.R., Evanisko, M.J., Organizational innovation: The influence of individual, organizational, and contextual factors on hospital adoption of technological and administrative innovations (1981) Academy of Management Journal, 24 (4), pp. 689-713; King, N., Anderson, N.R., (1995) Innovation and Change in Organizations, , Routledge, London; Lai, F., Hutchinson, J., Zhang, G., Radio frequency identification (RFID) in China: Opportunities and challenges (2005) International Journal of Retail & Distribution Management, 33 (12), pp. 905-16; Lai, K.H., Service capability and performance of logistics service providers (2004) Transportation Research Part E, 40 (5), pp. 385-99; Recent development of the logistics industry in China (2004-2005) (2005) China Distribution & Trading, , www.lifunggroup.com/research/china_dis02.htm, Li & Fung Research Centre, June 27, available at:; Li-Hua, R., Khalil, T.M., Technology management in China: A global perspective and challenging issues (2006) Technology Management in China, 1 (1), pp. 9-26; Lieb, R., Miller, J., The use of third-party logistics services by large US manufacturers: The 2000 survey (2002) International Journal of Logistics: Research and Applications, 5 (1), pp. 1-12; McAfee, A., The impact of enterprise information technology adoption on operational performance: An empirical investigation (2002) Production and Operations Management, 11 (1), pp. 33-53; Miles, R.E., Snow, C.C., Organizational Strategy (1978) Structure, and Process, , McGraw-Hill, New York, NY; Murphy, P.R., Poist, R.F., Third-party logistics: Some user versus provider perspective (2000) Journal of Business Logistics, 21 (1), pp. 121-31; Nunnally, J.C., (1978) Psychometric Theory, , McGraw-Hill, New York, NY; Prater, E., Frazier, G.V., Reyes, P.M., Future impacts of RFID on e-supply chains in grocery retailing (2005) Supply Chain Management: An International Journal, 10 (2), pp. 134-42; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-90; Russell, D.M., Hoag, A.M., People and information technology in the supply chain: Social and organizational influences on adoption (2004) International Journal of Physical Distribution & Logistics Management, 34 (1-2), pp. 102-22. , Vol, Nos; Russo, M.V., Fouts, P.A., A resource-based perspective on corporate environmental performance and profitability (1997) Academy of Management Journal, 40 (3), pp. 534-59; Scupola, A., The adoption of Internet commerce by SMEs in the South of Italy: An environmental, technological and organizational perspective (2003) Journal of Global Information Technology Management, 6 (1), pp. 52-71; Shrivastava, P., Ecocentric management for a risk society (1995) Academy of Management Review, 20 (1), pp. 118-37; Simonin, B.L., Transfer of marketing know-how in international strategic alliances: An empirical investigation of the role and antecedents of knowledge ambiguity (1999) Journal of International Business Studies, 30 (3), pp. 463-90; Smith, A.D., Exploring radio frequency identification technology and its impact on business systems (2005) Information Management & Computer Security, 13 (1), pp. 16-28; Spencer, J.W., Firms' knowledge-sharing strategies in the global innovation system: Empirical evidence from the flat panel display industry (2003) Strategic Management Journal, 24 (3), pp. 217-33; Srivastava, B., Radio frequency ID technology: The next revolution in SCM (2004) Business Horizons, 47 (6), pp. 60-8; Ta, H.P., Choo, H.L., Sum, C.C., Transportation concerns of foreign firms in China (2000) International Journal of Physical Distribution & Logistics Management, 30 (1), pp. 35-53; Teece, D.J., Firm organization, industrial structure, and technological innovation (1996) Journal of Economic Behavior and Organization, 31 (2), pp. 193-224; Tornatzky, L.G., Fleischer, M., (1990) The Process of Technological Innovation, Lexington Books, , Lexington, MA; Tsai, W., Ghoshal, S., Social capital and value creation: The role of intra-firm networks (1998) Academy of Management Journal, 41 (4), pp. 464-76; Wang, Q., Lai, F., Zhao, X., The impact of information technology on the financial performance of third-party logistics firms in China (2008) Supply Chain Management: An International Journal, 13 (2), pp. 138-50; Zhao, M., Droge, C., Stank, T.P., The effects of logistics capabilities on firm performance: Customer-focused versus information-focused capabilities (2001) Journal of Business Logistics, 22 (2), pp. 91-107; Zhu, K., Weyant, J.P., Strategic decisions of new technology adoption under asymmetric information: A game-theoretic model (2003) Decision Sciences, 34 (4), pp. 643-75},
correspondence_address1={Lin, C.-Y.; Department of International Business, , Tainan, Taiwan; email: jylin@mail.cjcu.edu.tw},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Thakkar2009702,
author={Thakkar, J. and Kanda, A. and Deshmukh, S.G.},
title={Supply chain performance measurement framework for small and medium scale enterprises},
journal={Benchmarking},
year={2009},
volume={16},
number={5},
pages={702-723},
doi={10.1108/14635770910987878},
note={cited By 113},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350215605&doi=10.1108%2f14635770910987878&partnerID=40&md5=b2ccb269834c002369bc105d0fad9107},
affiliation={Mechanical Engineering Department, A.D. Patel Institute of Technology, Vitthal Udyognagar, India; Mechanical Engineering Department, Indian Institute of Technology Delhi, New Delhi, India; IIITM Gwalior, Gwalior, India},
abstract={Purpose - The purpose of this paper is to propose an integrated supply chain performance measurement framework for the case of small and medium scale enterprises (SMEs) using set of qualitative and quantitative insights gained during the case study research. Design/methodology/approach - This paper develops the supply chain performance measurement framework using the facts revealed through case study analysis, secondary data specific to various SME clusters in India and detailed contemporary studies reported on supply chain management in SMEs. It integrates the salient features of balanced scorecard (BSC) and supply chain operation reference (SCOR) model to deliver a comprehensive performance measurement framework for SMEs. Findings - This paper reports set of performance indicators for the supply chain processes like "source," "make," and "deliver" in SMEs. It also relates the measures with various supply chain cycles like "procurement," "manufacturing," "replenishment," and "customer order." As a result, paper delivers an integrated performance measurement framework for supply chain evaluation and planning in SMEs. It also outlines the detailed guideline for the implementation and use of the framework. Practical implications - This paper is expected that the developed framework: will help SME managers to improve the visibility of their supply chain amongst interacting partners; and will help SME firms to explain the basis of their decisions to the other supply chain partners and specifically, origingal equipment manufacturer organizations where less bargaining power exist. Originality/value - This paper integrates the spirit of two well-known contributions in the field of performance measurement namely BSC and SCOR. The proposed framework is deduced using the findings of real life case study research and hence establishes an adequate platform for its application. It would help the managers or decision makers in SMEs to gain a systematic insight into their supply chain problems and hence undertake the necessary improvements. © Emerald Group Publishing Limited.},
author_keywords={Performance measures;  Small to medium-sized enterprises;  Supply chain management},
references={Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A stakeholder's approach to strategic performance measurement (1997) Sloan Management Review, 1 (1), pp. 31-43; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-92; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: An audit and development guide (1997) The TQM Magazine, 9 (1), pp. 46-53; Brown, M.G., (2000) Winning Score: How to Design and Implement Organizational Scorecards, , Productivity Press, Cambridge, MA; Chan, F.T., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-90; Dess, G.G., Lumpkin, G.T., (2003) Strategic Management: Creating Competitive Advantages, , McGraw-Hill, New York, NY; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-47; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hammer, M., The 7 deadly sins of performance measurement and how to avoid them (2007) MIT Sloan Management Review, 48 (3), pp. 19-28; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-68; Hudson, M., Smart, A., Bourne, M., Theory and practice in SME performance measurement systems (2001) International Journal of Operations & Production Management, 21 (8), pp. 1096-115; Huin, S.F., Luong, L.H.S., Abhary, K., Internal supply chain planning detriments in small medium-sized manufacturers (2002) International Journal of Physical Distribution & Logistics Management, 32 (9), pp. 771-82; Kaplan, R.S., Atkinson, A.A., Advanced Management Accounting (1998) Prentice-Hall, , Englewood Cliffs, NJ; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, pp. 71-90. , January-February; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lockamy, A.I.I.I., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-218; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-36; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , FT Prentice-Hall, London; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-45; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Schroeder, R.G., John, C.A., Scudder, G.D., White collar productivity measurement (1986) Management Decision, 24 (5), pp. 3-7; Shamsie, J., The context of dominance: An industry-driven framework for exploiting reputation (2003) Strategic Management Journal, 24 (3), pp. 199-215; Stephens, S., Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-6; Storey, D., (1994) Understanding the Small Business Sector, , Routledge, London; Thakkar, J., Deshmukh, S.G., Gupta, A.D., Shankar, R., Development of a balanced scorecard: An integrated approach of interpretive structural modeling (ISM) and analytic network process (ANP) (2007) International Journal of Productivity and Performance Management, 56 (1), pp. 25-59; Vaaland, T.I., Heide, M., Can the SME survive the supply chain challenges? (2007) Supply Chain Management: An International Journal, 12 (1), pp. 20-31; van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 15-29; Yin, R., (2003) Case Study Research: Design and Methods, , 3rd ed., Sage, Thousand Oaks, CA},
correspondence_address1={Thakkar, J.; Mechanical Engineering Department, , Vitthal Udyognagar, India; email: thakkar_jitesh@yahoo.com},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Li2009867,
author={Li, X. and Mao, Z. and Qi, E.},
title={Research on multi-supplier performance measurement based on genetic ant colony algorithm},
journal={2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC'09},
year={2009},
pages={867-870},
doi={10.1145/1543834.1543962},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650694264&doi=10.1145%2f1543834.1543962&partnerID=40&md5=9bf2360d31079c91f4128fbec9d3ab4d},
affiliation={School of Management, Tianjin University, Tianjin, China},
abstract={With the growing maturation of the economical globalization and the fast progress of the IT industry, both the development of the global market and intellectual economy has overrun the national broad lines. However, the subsequent competition has also becoming fiercer and fiercer. Many enterprises have made more closely joint development with their partners, and built up "supply chain" with their partners to further expand supply-and-demand network. In the whole chain even the whole network Suppliers are upstream and key organizations of this chain and the net, selection of suppliers is the key for whole chain, and it plays important role for efficient operation of whole chain. Although many specialists have done research on multi-supplier selection and performance measurement system, it is still one of the most difficult problems for most manufacturing, but many subjective and objective issues exist during actual operation of supplier selection. In this paper, the improved genetic ant colony algorithm is used for research about selection of multi-supplier based on various relevant literatures about selection of suppliers at home and abroad. Via analysis for simulated examples, it is proven that this method is effective and feasible, and provides referential model and algorithm for selection of various types in supply chain. Copyright 2009 ACM.},
author_keywords={Genetic ant colony algorithm;  Performance measurement},
keywords={Actual operation;  Ant colony algorithms;  Broad line;  Economical globalization;  Genetic ant colony algorithm;  Global market;  Intellectual economy;  IT industry;  Joint development;  Performance measurement;  Supplier performance measurement;  Supplier selection, Calculations;  Competition;  Industrial research;  International trade;  Optimization;  Semiconducting germanium compounds;  Supply chain management;  Supply chains, Evolutionary algorithms},
references={Chou, S.-Y., a , Yao-Hui Chang.A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach (2007) Expert Systems with Applications, pp. 1-13; Wu Bei, Shanshan Wang,Jun Hu. An analysis of Supplier Selection in Manufacturing Supply Chain Management; Luitzen de Boer, Eva Labro, Pierangela Morlacchi A.review of methods supporting supplier selection.European Journal of Purchasing&Supply Management 2001 (7):75-89; Gye Hang Honga,b, Sang Chan Parkb, Dong Sik Janga, Hyung Min Rhoc.An effective supplier selection method for constructing a competitive supply-relationship.Expert Systems with Applications 2005(28):629-639; Araz, C., Ozkarahan, I., Supplier evaluation and management system for strategic sourcing based on a new multicriteria sorting procedure (2007) Int. J. Production Economics, 106, pp. 585-606; Bevilacquaa, M., Ciarapicab, F.E., Giacchetta, G., A fuzzy-QFD approach to supplier selection (2006) Journal of Purchasing&Supply Management, (12), pp. 14-27; Amida, S.H., Ghodsypourb, C., O'Brienc, A., Weighted Additive Fuzzy Multi- objective Model for the Supplier Selection Problem under Price Breaks in a Supply Chain (2007) International Journal of Production Economics, 2, p. 40; Sung Ho Haa, Ramayya Krishnan, A hybrid approach to supplier selection for the maintenance of a competitive supply chain. Expert Systems with Applications, 2007:1-9; Li, X., (2007) Study on Performance Measurement System and Coordinative Tactics of Automobile Manufacturing Suppliers Based on the Whole Supply-Demand Network[D], , Tianjin: Tianjin university; Wenhui, D.O.N.G., (2005) Partner Selection and Optimization Model in Global Manufacturing[J],Journal of Northeastern University(Natural Science), (5), pp. 474-477; Haibin, D.U.A.N., (2005) Ant colony algorithms:theory and applications[M], , Beijing: Science Press,in Chinese; DICKSON, G.W., An analysis of vendor selection systems and decisions[J] (1996) Journal of Purchasing, 1, pp. 5-17; Jingfeng, D.O.N.G., Multi-supplier selection problem solution based on improved ant colony algorithm[J] (2007) Computer Integrated Manufacturing Systems, 8, pp. 1639-1644},
correspondence_address1={Li, X.; School of Management, , Tianjin, China; email: lxm@tju.edu.cn},
isbn={9781605583266},
language={English},
abbrev_source_title={Proc. ACM/SIGEVO Summit Genet. Evol. Comput., GEC},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Melo2009401,
author={Melo, M.T. and Nickel, S. and Saldanha-da-Gama, F.},
title={Facility location and supply chain management - A review},
journal={European Journal of Operational Research},
year={2009},
volume={196},
number={2},
pages={401-412},
doi={10.1016/j.ejor.2008.05.007},
note={cited By 1368},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149242603&doi=10.1016%2fj.ejor.2008.05.007&partnerID=40&md5=0dc835560937abbb4c2275cfd8ea7bb1},
affiliation={Department of Business Administration, University of Applied Sciences, D 66123 Saarbrücken, Germany; Department of Optimization, Fraunhofer Institute for Industrial Mathematics (ITWM), D 67663 Kaiserslautern, Germany; Saarland University, D 66041 Saarbrücken, Germany; Department of Statistics and Operations Research, Operations Research Center, University of Lisbon, P 1749-016 Lisbon, Portugal},
abstract={Facility location decisions play a critical role in the strategic design of supply chain networks. In this paper, a literature review of facility location models in the context of supply chain management is given. We identify basic features that such models must capture to support decision-making involved in strategic supply chain planning. In particular, the integration of location decisions with other decisions relevant to the design of a supply chain network is discussed. Furthermore, aspects related to the structure of the supply chain network, including those specific to reverse logistics, are also addressed. Significant contributions to the current state-of-the-art are surveyed taking into account numerous factors. Supply chain performance measures and optimization techniques are also reviewed. Applications of facility location models to supply chain network design ranging across various industries are presented. Finally, a list of issues requiring further research are highlighted. © 2008 Elsevier B.V. All rights reserved.},
author_keywords={Facility location;  Network design;  Supply chain management},
keywords={Design;  Facilities;  Logistics;  Metropolitan area networks;  Network management;  Supply chains, Current states;  Facility location;  Facility location decisions;  Facility location models;  Literature reviews;  Location decisions;  Network design;  Optimization techniques;  Reverse logistics;  Strategic designs;  Strategic supply chain planning;  Supply chain network designs;  Supply chain networks;  Supply chain performances, Supply chain management},
references={Aghezzaf, E., Capacity planning and warehouse location in supply chains with uncertain demands (2005) Journal of the Operational Research Society, 56, pp. 453-462; Aksen, D., Altinkemer, K., A location-routing problem for the conversion to the "click-and-mortar" retailing: The static case (2008) European Journal of Operational Research, 186, pp. 554-575; Altiparmak, F., Gen, M., Lin, L., Paksoy, T., A genetic algorithm approach for multi-objective optimization of supply chain networks (2006) Computers & Industrial Engineering, 51, pp. 197-216; Ambrosino, D., Scutellà, M.G., Distribution network design: New problems and related models (2005) European Journal of Operational Research, 165, pp. 610-624; Amiri, A., Designing a distribution network in a supply chain system: Formulation and efficient solution procedure (2006) European Journal of Operational Research, 171, pp. 567-576; Avittathur, B., Shah, J., Gupta, O.K., Distribution centre location modelling for differential sales tax structure (2005) European Journal of Operational Research, 162, pp. 191-205; Barahona, F., Jensen, D., Plant location with minimum inventory (1998) Mathematical Programming, 83, pp. 101-111; Barros, A.I., Dekker, R., Scholten, V., A two-level network for recycling sand: A case study (1998) European Journal of Operational Research, 110, pp. 199-214; Bender, T., Hennes, H., Kalcsics, J., Melo, M.T., Nickel, S., Location software and interface with GIS and supply chain management (2002) Facility Location: Applications and Theory, pp. 233-274. , Drezner Z., and Hamacher H.W. (Eds), Springer, New York (Chapter 8); Bhatnagar, R., Sohal, A.S., Supply chain competitiveness: Measuring the impact of location factors, uncertainty and manufacturing practices (2005) Technovation, 25, pp. 443-456; Camm, J.D., Chorman, T.E., Dill, F.A., Evans, J.R., Sweeney, D.J., Wegryn, G.W., Blending OR/MS, judgment, and GIS: Restructuring P&G's supply chain (1997) Interfaces, 27, pp. 128-142; Canel, C., Khumawala, B.M., Multi-period international facilities location: An algorithm and application (1997) International Journal of Production Economics, 35, pp. 1891-1910; Canel, C., Khumawala, B.M., International facilities location: A heuristic procedure for the dynamic uncapacitated problem (2001) International Journal of Production Research, 39, pp. 3975-4000; Canel, C., Khumawala, B.M., Law, J., Loh, A., An algorithm for the capacitated, multi-commodity multi-period facility location problem (2001) Computers & Operations Research, 28, pp. 411-427; Carlsson, D., Rönnqvist, M., Supply chain management in forestry - Case studies at södra cell AB (2005) European Journal of Operational Research, 163, pp. 589-616; Chakravarty, A.K., Global plant capacity and product allocation with pricing decisions (2005) European Journal of Operational Research, 165, pp. 157-181; Chan, Y., Carter, W.B., Burnes, M.D., A multiple-depot, multiple-vehicle, location-routing problem with stochastically processed demands (2001) Computers & Operations Research, 28, pp. 803-826; Chopra, S., Meindl, P., (2007) Supply Chain Management: Strategy, Planning and Operations, , Prentice Hall, New Jersey; Cohen, M.A., Mallik, S., Global supply chains: Research and applications (1997) Production and Operations Management, 6, pp. 193-210; Cordeau, J.-F., Pasin, F., Solomon, M.M., An integrated model for logistics network design (2006) Annals of Operations Research, 144, pp. 59-82; Council of Supply Chain Management Professionals, <http://cscmp.org>, 2007; Dasci, A., Verter, V., A continuous model for production-distribution system design (2001) European Journal of Operational Research, 129, pp. 287-298; Daskin, M., Snyder, L., Berger, R., Facility location in supply chain design (2005) Logistics Systems: Design and Optimization, pp. 39-65. , Langevin A., and Riopel D. (Eds), Springer, New York (Chapter 2); Daskin, M.S., (1995) Network and Discrete Location: Models, Algorithms, and Applications, , Wiley, New York; Daskin, M.S., Coullard, C., Shen, Z.-J.M., An inventory-location model: Formulation, solution algorithm and computational results (2002) Annals of Operations Research, 110, pp. 83-106; Dekker, R., van Wassenhove, L.N., Indurfurth, K., (2004) Reverse Logistics, , Springer, New York; Dias, J., Captivo, M.E., Clímaco, J., Efficient primal-dual heuristic for a dynamic location problem (2007) Computers & Operations Research, 34, pp. 1800-1823; Dogan, K., Goetschalckx, M., A primal decomposition method for the integrated design of multi-period production-distribution systems (1999) IIE Transactions, 31, pp. 1027-1036; (2004) Facility Location: Applications and Theory, , Drezner Z., and Hamacher H.W. (Eds), Springer, New York; Du, F., Evans, G.W., A bi-objective reverse logistics network analysis for post-sale service (2008) Computers & Operations Research, 35, pp. 2617-2634; (2003) Supply Chain Management and Reverse Logistics, , Dyckhoff H., Lackes R., and Reese J. (Eds), Springer, New York; Erengüç, S.S., Simpson, N.C., Vakharia, A.J., Integrated production/distribution planning in supply chains: An invited review (1999) European Journal of Operational Research, 115, pp. 219-236; Erlebacher, S.J., Meller, R.D., The interaction of location and inventory in designing distribution systems (2000) IIE Transactions, 32, pp. 155-166; Eskigun, E., Uzsoy, R., Preckel, P.V., Beaujon, G., Krishnan, S., Tew, J.D., Outbound supply chain network design with mode selection, lead times and capacitated vehicle distribution centers (2005) European Journal of Operational Research, 165, pp. 182-206; EURO Winter Institute on Location and Logistics, <http://ewi2007.fc.ul.pt>, 2007; European Union WEEE Directive, <http://ec.europa.eu/environment/waste/weee/index_en.htm>, 2007; Farahani, R.Z., Asgari, N., Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study (2007) European Journal of Operational Research, 176, pp. 1839-1858; Fleischmann, B., Ferber, S., Henrich, P., Strategic planning of BMW's global production network (2006) Interfaces, 36, pp. 194-208; Fleischmann, M., Bloemhof-Ruwaard, J.M., Dekker, R., van der Laan, E., van Nunen, J.A.E.E., van Wassenhove, L.N., Quantitative models for reverse logistics: A review (1997) European Journal of Operational Research, 103, pp. 1-17; Ghiani, G., Laporte, G., Musmanno, R., (2004) Introduction to Logistics Systems Planning and Control, , Wiley, New York; Goetschalckx, M., Vidal, C.J., Dogan, K., Modeling and design of global logistics systems: A review of integrated strategic and tactical models and design algorithms (2002) European Journal of Operational Research, 143, pp. 1-18; Goh, M., Lim, J.I.S., Meng, F., A stochastic model for risk management in global supply chain networks (2007) European Journal of Operational Research, 182, pp. 164-173; Guillén, G., Mele, F.D., Bagajewicz, M.J., Espuña, A., Puigjaner, L., Multiobjective supply chain design under uncertainty (2005) Chemical Engineering Science, 60, pp. 1535-1553; Gunnarsson, H., Rönnqvist, M., Lundgren, J.T., Supply chain modelling of forest fuel (2004) European Journal of Operational Research, 158, pp. 103-123; Hinojosa, Y., Puerto, J., Fernández, F.R., A multiperiod two-echelon multi-commodity capacitated plant location problem (2000) European Journal of Operational Research, 123, pp. 271-291; Hinojosa, Y., Kalcsics, J., Nickel, S., Puerto, J., Velten, S., Dynamic supply chain design with inventory (2008) Computers & Operations Research, 35, pp. 373-391; Hugo, A., Pistikopoulos, E.N., Environmentally conscious long-range planning and design of supply chain networks (2005) Journal of Cleaner Production, 13, pp. 1471-1491; Hwang, H.-S., Design of supply-chain logistics system considering service level (2002) Computers & Industrial Engineering, 43, pp. 283-297; Jang, Y.-J., Jang, S.-Y., Chang, B.-M., Park, J., A combined model of network design and production/distribution planning for a supply network (2002) Computers & Industrial Engineering, 43, pp. 263-281; Jayaraman, V., Pirkul, H., Planning and coordination of production and distribution facilities for multiple commodities (2001) European Journal of Operational Research, 133, pp. 394-408; Jayaraman, V., Ross, A., A simulated annealing methodology to distribution network design and management (2003) European Journal of Operational Research, 144, pp. 629-645; Jayaraman, V., Guide Jr., V., Srivastava, R., A closed-loop logistics model for remanufacturing (1999) Journal of the Operational Research Society, 50, pp. 497-508; Jayaraman, V., Patterson, R.A., Rolland, E., The design of reverse distribution networks: Models and solution procedures (2003) European Journal of Operational Research, 150, pp. 128-149; Julka, N., Baines, T., Tjahjono, B., Lendermann, P., Vitanov, V., A review of multi-factor capacity expansion models for manufacturing plants: Searching for a holistic decision aid (2007) International Journal of Production Economics, 106, pp. 607-621; Karabakal, N., Günal, A., Ritchie, W., Supply-chain analysis at Volkswagen of America (2000) Interfaces, 30, pp. 46-55; Keskin, B.B., Ülster, H., Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem (2007) European Journal of Operational Research, 182, pp. 663-682; Keskin, B.B., Ülster, H., A scatter search-based heuristic to locate capacitated transshipment points (2007) Computers & Operations Research, 34, pp. 3112-3125; Klose, A., Drexl, A., Facility location models for distribution system design (2005) European Journal of Operational Research, 162, pp. 4-29; Ko, H.J., Evans, G.W., A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs (2007) Computers & Operations Research, 34, pp. 346-366; Kouvelis, P., Rosenblatt, M.J., A mathematical programming model for global supply chain management: Conceptual approach and managerial insights (2002) Applied Optimization, pp. 245-277. , Supply Chain Management: Models, Applications, and Research directions. Geunes J., Pardalos P.M., and Romeijn H.E. (Eds), Kluwer, Dordrecht (Chapter 10); Laval, C., Feyhl, M., Kakouros, S., Hewlett-Packard combined OR and expert knowledge to design its supply chains (2005) Interfaces, 35, pp. 238-247; Lebreton, B., (2007) Strategic Closed-Loop Supply Chain Management, , Springer, New York; Lee, D.-H., Dong, M., A heuristic approach to logistics network design for end-of-lease computer products recovery (2008) Transportation Research Part E: Logistics and Transportation Review, 44, pp. 455-474; Levén, E., Segerstedt, A., Polarica's wild berries: An example of a required storage capacity calculation and where to locate this inventory (2004) Supply Chain Management, 9, pp. 213-218; Lieckens, K., Vandaele, N., Reverse logistics network design with stochastic lead times (2007) Computers & Operations Research, 34, pp. 395-416; Lin, J.-R., Nozick, L.K., Turnquist, M.A., Strategic design of distribution systems with economies of scale in transportation (2006) Annals of Operations Research, 144, pp. 161-180; Listeş, O., A generic stochastic model for supply-and-return network design (2007) Computers & Operations Research, 34, pp. 417-442; Listeş, O., Dekker, R., A stochastic approach to a case study for product recovery network design (2005) European Journal of Operational Research, 160, pp. 268-287; Lowe, T.J., Wendell, R.E., Hu, G., Screening location strategies to reduce exchange rate risk (2002) European Journal of Operational Research, 136, pp. 573-590; Lu, Z., Bostel, N., A facility location model for logistics systems including reverse flows: The case of remanufacturing activities (2007) Computers & Operations Research, 34, pp. 299-323; Ma, H., Davidrajuh, R., An iterative approach for distribution chain design in agile virtual environment (2005) Industrial Management and Data Systems, 105, pp. 815-834; Marín, A., Pelegrin, B., The return plant location problem: Modelling and resolution (1998) European Journal of Operational Research, 104, pp. 375-392; Marín, A., Pelegrin, B., Applying Lagrangian relaxation to the resolution of two-stage location problems (1999) Annals of Operations Research, 86, pp. 179-198; Mazzola, J.B., Neebe, A.W., Lagrangian-relaxation-based solution procedures for a multiproduct capacitated facility location problem with choice of facility type (1999) European Journal of Operational Research, 115, pp. 285-299; Meixell, M.J., Gargeya, V.B., Global supply chain design: A literature review and critique (2005) Transportation Research Part E: Logistics and Transportation Review, 41, pp. 531-550; Melachrinoudis, E., Min, H., The dynamic relocation and phase-out of a hybrid, two-echelon plant/warehousing facility: A multiple objective approach (2000) European Journal of Operational Research, 123, pp. 1-15; Melachrinoudis, E., Min, H., Redesigning a warehouse network (2007) European Journal of Operational Research, 176, pp. 210-229; Melachrinoudis, E., Messac, A., Min, H., Consolidating a warehouse network: A physical programming approach (2005) International Journal of Production Economics, 97, pp. 1-17; Melkote, S., Daskin, M.S., Capacitated facility location/network design problems (2001) European Journal of Operational Research, 129, pp. 481-495; Melo, M.T., Nickel, S., Saldanha-da-Gama, F., Dynamic multi-commodity capacitated facility location: A mathematical modeling framework for strategic supply chain planning (2006) Computers & Operations Research, 33, pp. 181-208; M.T. Melo, S. Nickel, F. Saldanha-da-Gama, Network design decisions in supply chain planning, Technical Report 140, Fraunhofer Institute for Industrial Mathematics (ITWM), Kaiserslautern, Germany, <www.itwm.fhg.de/zentral/download/berichte/bericht140.pdf>, 2008; Min, H., Melachrinoudis, E., The relocation of a hybrid manufacturing/distribution facility from supply chain perspectives: A case study (1999) Omega, 27, pp. 75-85; Min, H., Zhou, G., Supply chain modeling: Past, present and future (2002) Computers & Industrial Engineering, 43, pp. 231-249; Min, H., Ko, C.S., Ko, H.J., The spatial and temporal consolidation of returned products in a closed-loop supply chain network (2006) Computers & Industrial Engineering, 51, pp. 309-320; Min, H., Ko, H.J., Ko, C.S., A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns (2006) Omega, 34, pp. 56-69; Miranda, P.A., Garrido, R.A., Incorporating inventory control decisions into a strategic distribution network design model with stochastic demand (2004) Transportation Research Part E: Logistics and Transportation Review, 40, pp. 183-207; Miranda, P.A., Garrido, R.A., Valid inequalities for Lagrangian relaxation in an inventory location problem with stochastic capacity (2008) Transportation Research Part E: Logistics and Transportation Review, 44, pp. 47-65; (1990) Discrete Location Theory, , Mirchandani P.B., and Francis R.L. (Eds), Wiley, New York; Mitra, S., Revenue management for remanufactured products (2007) Omega, 35, pp. 553-562; Nickel, S., Puerto, J., (2005) Location Theory: A Unified Approach, , Springer, New York; Nickel, S., Velten, S., Weimerskirch, G., Strategische supply-chain entscheidungen in der stahlindustrie - Eine fallstudie (2006) Supply Chain Management und Logistik, pp. 157-177. , Günther H.-O., Mattfeld D.C., and Suhl L. (Eds), Springer, New York (in German); Nozick, L.K., Turnquist, M.A., Integrating inventory impacts into a fixed-charge model for locating distribution centers (1998) Transportation Research Part E: Logistics and Transportation Review, 34, pp. 173-186; Owen, S.H., Daskin, M.S., Strategic facility location: A review (1998) European Journal of Operational Research, 111, pp. 423-447; Pati, R.K., Vrat, P., Kumar, P., A goal programming model for paper recycling system (2008) Omega, 36, pp. 405-417; Pirkul, H., Jayaraman, V., A multi-commodity, multi-plant, capacitated facility location problem: Formulation and efficient heuristic solution (1998) Computers & Operations Research, 25, pp. 869-878; ReVelle, C.S., Eiselt, H.A., Location analysis: A synthesis and survey (2005) European Journal of Operational Research, 165, pp. 1-19; ReVelle, C.S., Eiselt, H.A., Daskin, M.S., A bibliography for some fundamental problem categories in discrete location science (2008) European Journal of Operational Research, 184, pp. 817-848; Romeijn, H.E., Shu, J., Teo, C.-P., Designing two-echelon supply networks (2007) European Journal of Operational Research, 178, pp. 449-462; Sabri, E.H., Beamon, B.M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28, pp. 581-598; Sahin, G., Süral, H., A review of hierarchical facility location models (2007) Computers & Operations Research, 34, pp. 2310-2331; Salema, M.I., Póvoa, A.P.B., Novais, A.Q., A warehouse-based design model for reverse logistics (2006) Journal of the Operational Research Society, 57, pp. 615-629; Salema, M.I., Barbosa-Póvoa, A.P., Novais, A.Q., An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty (2007) European Journal of Operational Research, 179, pp. 1063-1077; Santoso, T., Ahmed, S., Goetschalckx, M., Shapiro, A., A stochastic programming approach for supply chain network design under uncertainty (2005) European Journal of Operational Research, 167, pp. 96-115; Schultmann, F., Engels, B., Rentz, O., Closed-loop supply chains for spent batteries (2003) Interfaces, 33, pp. 57-71; Shah, N., Pharmaceutical supply chains: Key issues and strategies for optimisation (2004) Computers & Chemical Engineering, 28, pp. 929-941; Shapiro, J.F., (2001) Modeling the Supply Chain, , Brooks/Cole-Thomson, Belmont, CA; Shapiro, J.F., Challenges of strategic supply chain planning and modeling (2004) Computers & Chemical Engineering, 28, pp. 855-861; Shen, Z.-J., Qi, L., Incorporating inventory and routing costs in strategic location models (2007) European Journal of Operational Research, 179, pp. 372-389; Shen, Z.-J.M., A profit-maximizing supply chain network design model with demand choice flexibility (2006) Operations Research Letters, 34, pp. 673-682; Shen, Z.-J.M., Coullard, C., Daskin, M.S., A joint location-inventory model (2003) Transportation Science, 37, pp. 40-55; Sheu, J.-B., Locating manufacturing and distribution centers: An integrated supply-chain based spatial interaction approach (2003) Transportation Research Part E: Logistics and Transportation Review, 39, pp. 381-397; Shu, J., Teo, C.-P., Shen, Z.-J.M., Stochastic transportation-inventory network design problem (2005) Operations Research, 53, pp. 48-60; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (1999) Designing and Managing the Supply Chain: Concepts, Strategies, and Cases, , McGraw-Hill, New York; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2004) Managing the Supply Chain: The Definitive Guide for the Business Professional, , McGraw-Hill, New York; Snyder, L.V., Facility location under uncertainty: A review (2006) IIE Transactions, 38, pp. 537-554; Snyder, L.V., Daskin, M.S., Models for reliable supply chain network design (2007) Advances in Spatial Science, pp. 257-290. , Critical Infrastructure: Reliability and Vulnerability. Murray A.T., and Grubesic T. (Eds), Springer, New York (Chapter 13); Snyder, L.V., Daskin, M.S., Teo, C.-P., The stochastic location model with risk pooling (2007) European Journal of Operational Research, 179, pp. 1221-1238; Sourirajan, K., Ozsen, L., Uzsoy, R., A single-product network design model with lead time and safety stock considerations (2007) IIE Transactions, 39, pp. 411-424; Sridharan, R., The capacitated plant location problem (1995) European Journal of Operational Research, 87, pp. 203-213; Srivastava, S.K., Network design for reverse logistics (2008) Omega, 36, pp. 535-548; Syam, S.S., A model and methodologies for the location problem with logistical components (2002) Computers & Operations Research, 29, pp. 1173-1193; Talluri, S., Baker, R.C., A multi-phase mathematical programming approach for effective supply chain design (2002) European Journal of Operational Research, 141, pp. 544-558; Teo, C.-P., Shu, J., Warehouse-retailer network design problem (2004) Operations Research, 52, pp. 396-408; Troncoso, J.J., Garrido, R.A., Forestry production and logistics planning: An analysis using mixed-integer programming (2005) Forest Policy and Economics, 7, pp. 625-633; Tüshaus, U., Wittmann, S., Strategic logistic planning by means of simple plant location: A case study (1998) Advances in Distribution Logistics, pp. 241-263. , Fleischmann B., van Neunen J.A.E.E., Speranza M.G., and Stähly P. (Eds), Springer, New York; Tuzun, D., Burke, L.I., A two-phase tabu search approach to the location routing problem (1999) European Journal of Operational Research, 116, pp. 87-99; Ulstein, N.L., Christiansen, M., Gronhaug, R., Magnussen, N., Solomon, M.M., Elkem uses optimization in redesigning its supply chain (2006) Interfaces, 36, pp. 314-325; van Ommeren, J.C.W., Bumb, A.F., Sleptchenko, A.V., Locating repair shops in a stochastic environment (2006) Computers & Operations Research, 33, pp. 1575-1594; Verter, V., Dasci, A., The plant location and flexible technology acquisition problem (2002) European Journal of Operational Research, 136, pp. 366-382; Verter, V., Dincer, M.C., Global manufacturing strategy (1995) Facility Location: A Survey of Applications and Methods, pp. 263-282. , Drezner Z. (Ed), Springer, New York (Chapter 12); Vidal, C.J., Goetschalckx, M., Strategic production-distribution models: A critical review with emphasis on global supply chain models (1997) European Journal of Operational Research, 98, pp. 1-18; Vidal, C.J., Goetschalckx, M., A global supply chain model with transfer pricing and transportation cost allocation (2001) European Journal of Operational Research, 129, pp. 134-158; Vila, D., Martel, A., Beauregard, R., Designing logistics networks in divergent process industries: A methodology and its application to the lumber industry (2006) International Journal of Production Economics, 102, pp. 358-378; Wang, Q., Batta, R., Bhadury, J., Rump, C.M., Budget constrained location problem with opening and closing of facilities (2003) Computers & Operations Research, 30, pp. 2047-2069; Wang, Z., Yao, D.-Q., Huang, P., A new location-inventory policy with reverse logistics applied to B2C e-markets of China (2007) International Journal of Production Economics, 107, pp. 350-363; Wilhelm, W., Liang, D., Rao, B., Warrier, D., Zhu, X., Bulusu, S., Design of international assembly systems and their supply chains under NAFTA (2005) Transportation Research Part E: Logistics and Transportation Review, 41, pp. 467-493; Wouda, F.H.E., van Beek, P., van der Vorst, J.G.A.J., Tacke, H., An application of mixed-integer linear programming models on the redesign of the supply network of Nutricia Dairy & Drinks Group in Hungary (2002) OR Spectrum, 24, pp. 449-465; Wu, T.-H., Low, C., Bai, J.-W., Heuristic solutions to multi-depot location-routing problems (2002) Computers & Operations Research, 29, pp. 1393-1415; Yan, H., Yu, Z., Cheng, T.C.E., A strategic model for supply chain design with logical constraints: Formulation and solution (2003) Computers & Operations Research, 30, pp. 2135-2155},
correspondence_address1={Saldanha-da-Gama, F.; Department of Statistics and Operations Research, , P 1749-016 Lisbon, Portugal; email: fsgama@fc.ul.pt},
issn={03772217},
coden={EJORD},
language={English},
abbrev_source_title={Eur J Oper Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chae2009422,
author={Chae, B.},
title={Developing key performance indicators for supply chain: An industry perspective},
journal={Supply Chain Management},
year={2009},
volume={14},
number={6},
pages={422-428},
doi={10.1108/13598540910995192},
note={cited By 173},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350338782&doi=10.1108%2f13598540910995192&partnerID=40&md5=0b48cecb61457158b354140fcde11f4c},
affiliation={Kansas State University, Manhattan, KS, United States},
abstract={Purpose - Measuring or monitoring supply chain performance reveals the gap between planning and execution and helps companies to identify potential problems and areas for improvement. However, it is recognized that developing key performance indicators (KPIs), or metrics, is very challenging and a set of practical guidelines is not readily available for companies and supply chain management (SCM) practitioners. This paper seeks to offer a practical approach to performance measurement and to present a list of essential KPIs. Design/methodology/approach - This paper offers insights from industry in the area of supply chain performance measurement and a practical approach to developing performance metrics. Findings - The experience from, and the review of, industry standards and best practices in supply chain performance measurement suggest that less is better as to developing performance metrics. Companies should focus on only a small list of KPIs which are critical for their operations management, customer service, and financial viability. Potential KPIs should be developed for each of the supply chain operations-reference (SCOR) model's four meta-processes (plan, source, make, and delivery) and need to be hierarchically grouped such as primary and secondary metrics. Practical implications - The paper offers a background of why performance measurement is necessary for SCM success, pragmatic guidelines for designing and implementing performance metrics, and critical KPIs with the definition, examples, and computation mechanism. Originality/value - Despite the importance of performance measurement for successful SCM, many companies wonder how to put performance metrics in place and make them work in practice. This paper offers an industry-oriented, practical approach to performance measurement in SCM contexts and proposes key performance metrics which can be easily adapted for different businesses. © Emerald Group Publishing Limited.},
author_keywords={Performance measurement (quality);  Quality indicators;  Supply chain management},
references={Aramyan, L., Lansink, A., Vorst, J., Kooten, O., Performance measurement in agri-food supply chains: A case study (2007) Supply Chain Management: An International Journal, 12 (4), pp. 304-15; Chan, F., Qi, H., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-23; Farris, M., Hutchison, P., Cash-to-cash: The new supply chain management practice (2002) International Journal of Physical Distribution & Logistics Management, 32 (4), pp. 288-98; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hoffman, D., (2004) The Hierarchy of Supply Chain Metrics: Diagnosing Your Supply Chain Health, , AMR Research, Boston, MA; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lapide, L., What about measuring supply chain performance? (2000) ASCET, 2 (15), pp. 287-97; Moon, M., Mentzer, J., Thomas, D., Customer demand planning at lucent technologies (2000) Industrial Marketing Management, 29 (1), pp. 19-26; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-58; Vollmann, T., Berry, B., Whybark, D., Jacobs, F., (2005) Manufacturing Planning and Control for Supply Chain Management, , McGraw-Hill, Boston, MA; Yang, H., Choi, B., Park, H., Suh, M., Chae, B., Supply chain management six sigma: A management innovation methodology at the Samsung Group (2007) Supply Chain Management: An International Journal, 12 (2), pp. 88-95},
correspondence_address1={Chae, B.; Kansas State University, Manhattan, KS, United States; email: kevinbschae@gmail.com},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Manikas2009421,
author={Manikas, I. and Terry, L.A.},
title={A case study assessment of the operational performance of a multiple fresh produce distribution centre in the UK},
journal={British Food Journal},
year={2009},
volume={111},
number={5},
pages={421-435},
doi={10.1108/00070700910957276},
note={cited By 19},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349236165&doi=10.1108%2f00070700910957276&partnerID=40&md5=db14031d42622bf0c6bbe478eb42e004},
affiliation={School of Agricultural Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece; Plant Science Laboratory, Institute of BioScience and Technology, Cranfield University, Cranfield, United Kingdom},
abstract={Purpose - The aim of this research is to evaluate the current operational status of fresh produce distribution centres in the UK and identify the nature and magnitude of the main logistical problems within them. Design/methodology/approach - A critical evaluation of space and time utilization efficiency has been achieved by studying on-site operations in a multiple produce handling and short-term storage facility in Kent, UK. The objective of this research was to measure operational performance of distribution centres for agricultural perishables in terms of through-put and space utilization. Findings - The inefficient utilization of storage space within cold rooms has been identified and quantified accurately, whilst the quality control task has been recognized as the most time-consuming task and a critical cause for hindering product flow. Practical implications - Despite their importance, distribution centres for fresh fruit and vegetables have received little attention in the distribution and performance management literature. Given the lack of robust performance measurement systems reported, the measurement of operational performance in distribution centres for agricultural products was a challenge. Originality/value - The measurement and improvement of the operational performance in each linkage of the fresh produce supply chain - such as a distribution centre - can lead in achieving higher levels of service at substantially reduced costs. A small number of publications are found in the literature providing information on physical distribution of agricultural perishables, and how the key features of perishability and voluminosity of the produce affect the distribution efficiency. In this research, a step towards the improvement of the fresh produce distribution industry operational performance has been attempted, by evaluating the current operational status of a leading multiple produce distribution centre in the UK. © Emerald Group Publishing Limited.},
author_keywords={Cold storage;  Distribution management;  Fresh foods;  Operations management;  Supply chain management;  United Kingdom},
references={Aylott, D.J., Brindle-Wood-Williams, D., (1970) Physical Distribution in Industrial and Consumer Marketing, p. 456. , Hutchinson & Co. Ltd, London; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19, pp. 275-92; Broekmeulen, R.A.C.M., Operations management of distribution centres for vegetables and fruits (1998) International Transactions in Operational Research, 5, pp. 501-8; Brynzér, H., Johansson, M.I., Storage location assignment: Using the product structure to reduce order picking times (1996) International Journal of Production Economics, 46, pp. 595-603; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) The International Journal of Logistics Management, 6, pp. 61-73; Caron, F., Marchet, G., Perego, A., Routing policies and COI-based storage policies in picker-to-part systems (1998) International Journal of Production Research, 36 (3), pp. 713-32; Davenport, T.H., (1993) Process Innovation, , Harvard Business School Press, Boston, MA; de Koster, R., van der Poort, E., Routing order pickers in a warehouse: A comparison between optimal and heuristic solutions (1998) IIIE Transactions, 30, pp. 469-80; Drury, J., Towards more efficient order picking (1988) IMM Monograph, , No. 1, The institute of Materials Management, Cranfield; Fearne, A., Hughes, D., Success factors in the fresh produce supply chain: Insights from the UK (1999) Supply Chain Management, 4, pp. 120-8; Hackman, S.T., Frazelle, E.H., Griffin, P.M., Griffin, S.O., Vlasta, D.A., Benchmarking warehousing and distribution operations: An input-output approach (2001) Journal of Productivity Analysis, 16, pp. 79-100; Johnson, J., Wood, F.D., Wardlow, D.L., Murphy Jr., P.R., (1998) Contemporary Logistics, pp. 335-62. , 7th ed., Prentice-Hall, Englewood Cliffs, NJ; Kuo, C.H., Dunn, K.D., Randhawa, S.U., A case study assessment of performance measurement in distribution centres (1999) Industrial Management & Data Systems, 2, pp. 54-63; Mans, J., Order picking costs the most (1998) Dairy Foods, 11, p. 49; Neely, A., Gregory, M., Platts, K., Performance measurement system design; a literature review and research agenda (1995) International Journal of Operations & Production Management, 15, pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement system design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-31; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20, pp. 1119-45; Paull, R.E., Effect of temperature and relative humidity on fresh commodity quality (1999) Postharvest Biology and Technology, 15, pp. 263-77; Rouwenhorst, B., Reuter, B., Stockrahm, V., van Houtum, G.J., Mantel, R.J., Zijm, W.H.M., Warehouse design and control: Framework and literature review (2000) European Journal of Operational Research, 122, pp. 515-33; Schwichtenberg, K., Supply chain measuring is the key to success (2000) Materials Management and Distribution, , www.mmdonline.com, available at: (accessed 11 January 2002); Terry, L.A., White, S.F., Tigwell, L.A., The application of biosensors to fresh produce and the wider food industry (2005) Journal of Agricultural and Food Chemistry, 53, pp. 1309-19; An evaluation of supply chain management and how it relates to perishable produce distribution/marketing (1996), www.jiscmail.ac.uk/files/SCM-FOOD/supply, University of London, Wye College, University of London, Food Industry perspectives, Discussion Paper series: No. 12, available at: (accessed 5 October 2001); van Amstel, R.P., D'hert, G., Performance indicators in distribution (1996) The International Journal of Logistics Management, 7, pp. 73-82; van den Berg, J.P., Zijm, J.P., Models for warehouse management: Classification and examples (1999) International Journal of Production Economics, 59, pp. 519-28; van der Vorst, J.G.A.J., Beulens, A.J.M., A research model for the redesign of food supply chains (1999) International Journal of Logistics; Research and Applications, 2 (2), pp. 161-74; Voss, C., Tsikriktsis, N., Frolich, M., Case research in operations management (2002) International Journal of Operations & Production Management, 22 (2), pp. 195-219; Wentworth, F., (1980) Handbook of Physical Distribution Management, , Gower Press, Aldershot; Edwards, T., (2001) Performance Indicators Institute of Logistics and Transport, , http://nt.headland.co.uk/cgi-bin/iolt/forum/messages, available at: (accessed 11 January 2002); John, P., Marchal, J., Ripening and biochemistry of the fruit (1995) Bananas and Plantains, pp. 134-45. , in Gowen, S. (Ed.), Chapman & Hall, London; Margulis, R., NAWGA Conference tracks important food warehouse and distribution needs (1997) Food Distributors International News Room, , www.fdi.org/newsrm, available at: (accessed 15 February 2002); New, C., The use of throughput efficiency as a key performance measure for the new manufacturing era (1993) The International Journal of Logistics Management, 4, pp. 95-104},
correspondence_address1={Manikas, I.; School of Agricultural Sciences, , Thessaloniki, Greece; email: imanikas@agro.auth.gr},
issn={0007070X},
language={English},
abbrev_source_title={Br. Food J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Verdecho2009193,
author={Verdecho, M.J. and Alfaro, J.J. and Rodriguez-Rodriguez, R.},
title={Foundations for collaborative performance measurement},
journal={Production Planning and Control},
year={2009},
volume={20},
number={3},
pages={193-205},
doi={10.1080/09537280902721001},
note={cited By 34},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-67651232409&doi=10.1080%2f09537280902721001&partnerID=40&md5=bb476f7b50f445937e106ba6500bdf41},
affiliation={Research Centre on Production Management and Engineering, Polytechnic University of Valencia, Valencia 46022, Spain; Business Organization, Polytechnic University of Valencia, Valencia 46022, Spain},
abstract={In the past years, companies have been establishing collaboration relationships as a means to become more competitive. This type of relationship presents some special characteristics that need to be managed properly in order to be successful. Performance measurement systems (PMSs) may support the management of such relationships by aiding to define and collect the necessary information. However, further understanding is needed to identify the elements and interactions among collaborative elements and how they can be integrated within a PMS. The aim of this article is twofold. The first purpose is to analyse the contents of works that tackle interorganisational and collaborative relationships as well as PMSs for interorganisational and collaborative environments by defining a typology. The typology comprises two dimensions: degree of relationships among collaboration elements and integration of collaboration within PMS. The second purpose is to perform a comparative study of the works for identifying main strengths and gaps for future research.},
author_keywords={Collaboration;  Performance measurement system;  Supply chain management},
keywords={Collaboration;  Collaborative environments;  Collaborative performance;  Collaborative relationships;  Comparative studies;  Performance measurement system;  Two-dimension, Oceanographic equipment;  Supply chains, Supply chain management},
references={Alarcón, F., et al., 2007. Propuesta de marco conceptual para el modelado del proceso de planificación colaborativa de operaciones en contextos de Redes de Suministro/ Distribución (RdS/D), In: A. García Sánchez and M. Ortega Mier, eds. Proceedings of the international conference on industrial engineering and industrial management - xi congreso de ingeniería de organización. Madrid: Departamento de Ingeniería de Organización, Administración de Empresas y Estadística - ETSII, 873-882; Alfaro, J.J., Ortiz, A., Rodríguez, R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56 (4), pp. 305-334; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Anthony, T., (2000) Supply chain collaboration: Success in the new internet economy, , http://anthony.ASCET.com, online, ASCET. Available from:, Accessed 13 February 2008; Aviv, Y., The effect of collaborative forecasting on supply chain performance (2001) Management Science, 47 (10), pp. 1326-1343; Barrat, M., Understanding the meaning of collaboration in the supply chain (2004) Supply Chain Management: An International Journal, 9 (1), pp. 30-42; Beth, C., Supply chain challenges: Building relationships (2003) Harvard Business Review, 81 (7), pp. 64-73; Bititci, U.S., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Bititci, U., Managing synergy in collaborative enterprises (2007) Production Planning & Control, 18 (6), pp. 454-465; Birnbirg, J.C., Control in interfirm co-operative relationships (1998) Journal of Management Studies, 25 (4), pp. 421-428; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers and Industrial Engineering, 53 (1), pp. 43-62; Blanc, S., Ducq, Y., Vallespir, B., Evolution management towards interoperable supply chains using performance measurement (2007) Computers in Industry, 58 (7), pp. 720-732; Boddy, D., Macbeth, D., Wagner, B., Implementing collaboration between organizations: An empirical study of supply chain partnering (2000) Journal of Management Studies, 37 (7), pp. 1003-1018; Bowersox, D.J., Closs, D.J., Stank, T.P., How to master cross-enterprise collaboration (2003) Supply Chain Management Review, 7 (4), pp. 18-27; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Bullington, K.E., Bullington, S.F., Stronger supply chain relationships: Learning from research on strong families (2005) Supply Chain Management: An International Journal, 10 (3), pp. 192-197; Burgess, K., Singh, P., A proposed integrated framework for analysing supply chains (2006) Supply Chain Management: An International Journal, 11 (4), pp. 337-344; Busi, M., Bititci, U.S., Collaborative performance management: Present gaps and future research (2006) International Journal of Productivity and Performance Management, 55 (1), pp. 7-25; Caputo, A.C., An integrated framework for e-supply network analysis (2005) Supply Chain Management: An International Journal, 10 (2), pp. 84-95; Chalmeta, R., Grangel, R., Performance measurement systems for virtual enterprise integration (2005) International Journal of Computer Integrated Manufacturing, 18 (1), pp. 73-84; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing System, 14 (3), pp. 179-190; Chapman, R.L., Corso, M., From continuous improvement to collaborative innovation: The next challenge in supply chain management (2005) Production Planning & Control, 16 (4), pp. 339-344; Chen, D., Daclin, N., Barriers Driven Methodology for Enterprise Interoperability (2007) Establishing the foundation of collaborative networks, pp. 453-460. , L. Camarihna-Matos, L.H. Afsarmanesh, P. Novais and C. Analide, eds, NY: Springer; Danese, P., Collaboration forms, information and communication technologies, and coordination mechanisms in CPFR (2006) International Journal of Production Research, 44 (16), pp. 3207-3226; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers, and bridges to effective supply chain management (2008) Supply Chain Management: An International Journal, 13 (1), pp. 35-48; Fawcett, S.E., Information sharing and supply chain performance: The role of connectivity and willingness (2007) Supply Chain Management: An International Journal, 12 (5), pp. 358-368; Fiske, A.P., Relativity within Moose ('Mossi') culture: Four incommensurable models for social relationships (1990) Ethos, 18 (2), pp. 180-204; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production Planning & Control, 16 (6), pp. 531-544; Francisco, R.D., Azevedo, A., Dynamic performance management in business networks environments (2007) Digital enterprise technology. Perspectives and future challenges, pp. 401-409. , P.F. Cunha and P.G. Maropoulos, eds, NY: Springer; Gabel, J.E., Pilnick, S., (2002) The shadow organization in logistics: The real world of cultural change and supply chain efficiency, , Illinois: Council of Logistics Management; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement systems in the after-sales service: An integrated framework (2007) International Journal of Business Performance Management, 9 (2), pp. 145-171; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Management: An International Journal, 12 (6), pp. 400-411; Gruat La Forme, F.A., Botta Genoulaz, V., Campagne, J.P., A framework to analyse collaborative performance (2007) Computers in Industry, 58 (7), pp. 687-697; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Handfield, R.B., Bechtel, C., Trust, power, dependence, and economics: Can SCM research borrow paradigms? (2004) International Journal of Integrated Supply Chain Management, 1 (1), pp. 3-32; Harland, C.M., Supply chain management: Relationships, chains and networks (1996) British Journal of Management, 7 (SPEC. ISSUE), pp. 63-80; Horvath, L., Collaboration: The key to value creation in supply chain management (2001) Supply Chain Management: An International Journal, 6 (5), pp. 205-207; Jagdev, H.S., Thoben, K.D., Anatomy of enterprise collaborations (2001) Production Planning & Control, 12 (5), pp. 421-436; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., (2004) Strategy maps: Converting intangible assets into tangible outcomes, , Boston: Harvard Business School Publishing Corporation; Kampstra, R.P., Ashayeri, J., Gattorna, J.L., Realities of supply chain collaboration (2006) The International Journal of Logistics Management, 17 (3), pp. 312-330; Lejeune, M.A., Yakova, N., On characterizing the 4 C's in supply chain management (2005) Journal of Operations Management, 23 (1), pp. 81-100; Lockamy, A., McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-278; Manrodt, K.B., Fitzgerald, M., Seven propositions for successful collaboration (2001) Supply Chain Management Review, 5 (4), pp. 66-72; Matopoulos, A., A conceptual framework for supply chain collaboration: Empirical evidence from the agri-food industry (2007) Supply Chain Management: An International Journal, 12 (3), pp. 177-186; McCarter, M.W., Fawcett, S.E., Magnan, G.M., The effect of people on the supply chain world: Some over-lookedissues (2005) HumanSystems Management, 24 (3), pp. 197-208; McLaren, T., Head, M., Yuan, Y., Supply chain collaboration alternatives: Understanding the expected costs and benefits (2002) Supply Chain Management: An International Journal, 12 (4), pp. 348-364; Mentzer, J., Managing supply chain collaboration (2001) Supply Chain Management, pp. 83-84. , May; Mentzer, J., Min, S., Zacharia, Z.G., The nature of interfirm partnering in supply chain management (2001) Journal of Retailing, 76 (4), pp. 549-568; Min, S., Supply chain collaboration: What's happening? (2005) The International Journal of Logistics Management, 16 (2), pp. 237-256; Mohr, J.J., Spekman, R.E., Perfecting partnerships (1996) Marketing Management, 4 (4), pp. 34-43; Neely, A., (2002) Business performance measurement: Theory and practice, , Cambridge: Cambridge University Press; Rodriguez, R., 2007. Desarrollo de una metodología y arquitectura para la identificación y análisis de relaciones entre los indicadores de rendimiento de una organización. Aplicación a una empresa del sector textil. Thesis (PhD). Polytecnic University of Valencia; Romero, D., Galeano, N., Molina, A., A conceptual model for virtual breeding environments value system (2007) Establishing the foundation of collaborative networks, pp. 43-52. , L. Camarihna-Matos, H. Afsarmanesh, P. Novais and C. Analide, eds, Boston: Springer; Sabath, R.E., Fontanella, J., The unfulfilled promise of supply chain collaboration (2002) Supply Chain Management Review, 6 (4), pp. 24-29; Sahay, B.S., Supply chain collaboration: The key to value creation (2003) Work Study, 52 (2), pp. 76-83; Simatupang, T.M., Sridharan, R., An integrative framework for supply chain collaboration (2005) The International Journal of Logistics Management, 16 (2), pp. 257-274; Simatupang, T.M., Wright, A.C., Sridharan, R., Applying the theory of constraints to supply chain collaboration (2004) Supply Chain Management: An International Journal, 9 (1), pp. 57-70; Soosay, C.A., Hyland, P.W., Ferrer, M., Supply chain collaboration: Capabilities for continuous innovation (2008) Supply Chain Management: An International Journal, 13 (2), pp. 160-169; Spekman, R.B., Kamauff, J.W., Myhr, N., An empirical investigation into supply chain management. A perspective on partnerships (1998) International Journal of Physical Distribution & Logistics Management, 28 (8), pp. 630-650; Staughton, R., Johnston, R., Operational performance gaps in business relationships (2005) International Journal of Operations & Production Management, 25 (4), pp. 320-332; Westphal, I., Thoben, K.-D., Seifert, M., Measuring collaboration performance in virtual organizations (2007) Establishing the foundation of collaborative networks, pp. 33-42. , L. Camarihna-Matos, H. Afsarmanesh, P. Novais and C. Analide, eds, Boston: Springer; Wilson, D.T., (1995) An integrated model of buyer-seller relationships, , Working paper, Institute for the Study of Business Markets, The Pennsylvania State University, State College, PA, May; Zaklad, A., McKnight, R., Kosansky, A., Piermarini, J., The social side of the supply chain (2004) Industrial Engineer, 36 (2), pp. 40-44},
correspondence_address1={Verdecho, M. J.; Research Centre on Production Management and Engineering, , Valencia 46022, Spain; email: mverdecho@omp.upv.es},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Li2009200,
author={Li, X. and Mao, Z. and Qi, E.},
title={A cross-country comparison on supplier performance measurement system in manufacturing industry},
journal={2008 IEEE Symposium on Advanced Management of Information for Globalized Enterprises, AMIGE 2008 - Proceedings},
year={2009},
pages={200-203},
doi={10.1109/AMIGE.2008.ECP.48},
art_number={4721490},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-61649100808&doi=10.1109%2fAMIGE.2008.ECP.48&partnerID=40&md5=894cdc1d0408370a859de9eef06ebbee},
affiliation={Tianjin University Science Park, School of Management, Tianjin University, Tianjin, China; School of Mechanical Engineering, Tianjin University, Tianjin, China; School of Management, Tianjin University, Tianjin, China},
abstract={Although many specialists have done research on supplier performance measurement system, it is still one of the most difficult problems for most manufacturing enterprises to choose reasonable measurement indexes and establish a supplier performance measurement system that adapts to enterprises' development demands. This paper provides an optimum basis for manufacturing enterprises in establishing a modern supplier performance measurement system with reasonable indexes, through analyzing and comparing 202 correlative Chinese and foreign research papers from 1966. Meanwhile, this paper makes the specific analysis on current measurement systems and puts forward the demands of suppliers' collaborative ability under new environment of supply chain or supply network. This paper also suggests new thinking and methods to solve such a problem mentioned above. ©2008 IEEE.},
author_keywords={Collaborative ability;  Cross-country comparison;  Performance measurement},
keywords={Competition;  Electric current measurement;  Industrial research;  Supply chains, Collaborative ability;  Cross-country comparison;  Manufacturing enterprise;  Manufacturing industries;  On currents;  Performance measurement;  Research papers;  Supplier performance measurements;  Supply networks, Measurements},
references={Chai, Y., Yi, L., Supply-Demand, A., (2001) Chain Management, , Tsinghua University Press, Beijing; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, pp. 53-60. , Emerald Group Publishing Ltd, Sweden; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21, pp. 46-70. , Emerald Group Publishing Limited, UK; Dickson, G.W., An Analysis of Vender Selection System and Decisions (1966) Journal of Purchasing, pp. 5-17. , Edward Elgar Publishing Ltd, Netherlands; Hatherall, D.A., Purchasing in the pharmaceutical industry Unpublished, Thesis, Department of Management Science, Lancaster University, U.K; Weber, C.A., Current, J.R., Benton, W.C., Vendor Selection Criteria and Methods (1991) European Journal of Operational Research, pp. 2-18. , Edward Elgar Publishing Ltd, Netherlands; Yahya, S., Kingsman, B., Modeling a multi-objective allocation problem in a government sponsored entrepreneur development program (2002) European Journal of Operational Research, pp. 430-448. , Edward Elgar Publishing Ltd, Netherlands},
correspondence_address1={Li, X.; Tianjin University Science Park, , Tianjin, China; email: lxm@tju.edu.cn},
isbn={9781424429721},
language={English},
abbrev_source_title={IEEE Symp. Adv. Manage. Inf. Globalized Enterp., AMIGE - Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dreyer2009147,
author={Dreyer, H.C. and Alfnes, E. and Strandhagen, J.O. and Thomassen, M.K.},
title={Global supply chain control systems: A conceptual framework for the global control centre},
journal={Production Planning and Control},
year={2009},
volume={20},
number={2},
pages={147-157},
doi={10.1080/09537280802705013},
note={cited By 28},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-61449173971&doi=10.1080%2f09537280802705013&partnerID=40&md5=c9c17f6762fd36eb7dfcb49e10de4857},
affiliation={Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway},
abstract={The operation of global supply chains is challenging due to the complexity in product and information flows, diversity in sites, localisation and processes and the information processing needed for coordination and control. In order to be competitive, manufacturing supply networks should strive to use information to create transparent and visible demand patterns and to create an efficient balance of supply and demand by an integrated and coordinated manufacturing planning and control system. In this article, a framework for developing global control centres (GCCs) is presented that includes a global control model, performance measurement system, information and communications technology (ICT) and organisation of roles and responsibilities. The framework has been further tested in a company for realising a GCC. The main challenges for realisation include control issues, information handling and ICT and management of change and organisational resistance.},
author_keywords={Case study;  Global operations management;  Global supply chain control;  Information and communications technology;  Manufacturing planning and control systems},
keywords={Competition;  Control system analysis;  Empowerment of personnel;  Manufacture;  Planning;  Supply chains, Case study;  Global operations management;  Global supply chain control;  Information and communications technology;  Manufacturing planning and control systems, Supply chain management},
references={Alfnes, E., (2005) Enterprise reengineering: A strategic framework and methodology, , Doctoral Thesis PhD, Norwegian University of Science and Technology; Atkinson, A., (1995) Management accounting, , Englewood Cliffs, NJ: Prentice Hall; Busi, M., (2005) An integrated framework for collaborative enterprise performance management, , Doctoral Thesis PhD, Norwegian University of Science and Technology; Chase, R.B., Jacobs, F.R., Aquilano, N.J., (2004) Operations management for competitive advantage, , 10th ed. Boston: McGraw-Hill; Chen, F., Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information (2000) Management Science, 46 (3), pp. 436-443; Child, J., Faulkner, D., (1998) Strategies of co-operation: Managing alliances networks and joint ventures, , New York: Oxford University Press; Eckerson, W., (2005) Performance dashboards: Measuring, monitoring, and managing your business, , Hoboken, NJ: John Wiley & Sons, Inc; Eisenhardt, K.M., Building theories from case study research (1989) Academy of Management Review, 14 (4), pp. 532-550; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, pp. 105-116. , March-April; Forrester, J.W., (1961) Industrial dynamics, , Cambridge, MA: MIT Press; Holweg, M., Supply chain collaboration: Making sense of the strategy continuum (2005) European Management Journal, 23 (2), pp. 170-181; Jonsson, P., (2008) Logistics and supply chain management, , Berkshire: McGraw-Hill; Jonsson, P., Lindau, R., The supply chain planning studio: Utilising the synergetic power of teams and information visibility (2002) Proceedings of the annual conference for nordic researchers in logistics, NOFOMA 2002, pp. 115-130. , 13-14 June, Trondheim, Norway. Trondheim: Norwegian University of Science and Technology NTNU; Kristensen, K., Røyrvik, J., Sivertsen, O.I., Applications of the physical designing network in extended teams (2005) Proceedings of the 11th international conference on concurrent enterprising, , 20-22 June, Munich, Germany. Nottingham: Centre for Concurrent Enterprising, Nottingham University Business School; Lee, H.L., Padmanabhan, V., Whang, S., Information distortion in a supply chain: The bullwhip effect (1997) Management Science, 43 (4), pp. 546-558; Liff, S., Posey, P., (2004) Seeing is believing: How the new art of visual management can boost performance throughout your organisation, , New York: AMACOM; Ling, C., Goddard, W., (1988) Orchestrating success: Improve control of the business with sales and operations planning, , New York: John Wiley & Sons; Niven, P., (2002) Balanced scorecard step by step: Maximising performance and maintaining results, , New York: John Wiley & Sons; Olhager, J., Wikner, J., Production planning and control tools (2000) Production Planning & Control, 11 (3), pp. 210-222; Reason, P., Bradbury, H., (2006) Handbook of action research, , London: Sage Publications Ltd; Rudberg, M., Olhager, J., Manufacturing networks and supply chains: An operations strategy perspective (2003) Omega, 31 (1), pp. 29-39; Rudberg, M., West, M.B., Global operations strategy: Coordinating manufacturing networks (2008) Omega, 36, pp. 91-106; Shi, Y. and Gregory, M., 1998. International manufacturing networks-to develop global competitive capabilities. Journal of Operations Management, 16 (2, 3), 195-214; Strandhagen, O., Alfnes, E., Dreyer, H.C., Supply chain control dashboards (2006) Conference proceedings production and operations management society (POMS), , 28 April-1 May, Boston [CD-ROM; Teasley, S., How does radical collocation help a team succeed?: Computer supported cooperative work (2000) Proceedings of the 2000 ACM conference on Computer supported cooperative work, pp. 339-346. , 2-6 December, Philadelphia, ACM Press; Vollmann, T.E., (2005) Manufacturing planning and control for supply chain management, , New York: McGraw-Hill; Wortmann, J.C., Information systems for supply chain management (2000) Proceedings of the 4th international conference on chain management in agribusiness and the food industry, pp. 3-9. , 25-26 May, Wageningen, The Netherlands; Yin, R.K., 1994. Case study research: design and methods. Thousand Oaks, CA: Sage Publications, Inc},
correspondence_address1={Dreyer, H. C.; Department of Production and Quality Engineering, , Trondheim, Norway; email: heidi.c.dreyer@ntnu.no},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Rodriguez2009104,
author={Rodriguez, R.R. and Saiz, J.J.A. and Bas, A.O.},
title={Quantitative relationships between key performance indicators for supporting decision-making processes},
journal={Computers in Industry},
year={2009},
volume={60},
number={2},
pages={104-113},
doi={10.1016/j.compind.2008.09.002},
note={cited By 88},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58849132814&doi=10.1016%2fj.compind.2008.09.002&partnerID=40&md5=2d9b18e13ecd9c384bd8f5282d3bee0d},
affiliation={Research Centre on Production Management and Engineering, Polytechnic University of Valencia, Camino Vera S/N, Edif. 8G, Acceso D Planta 1 Valencia, 46022, Spain},
abstract={Performance measurement systems (PMS) are tools widely used by enterprises for managing and making strategy-based decisions. A PMS defines a group of strategic objectives and associated performance indicators (KPIs) that provide information as to whether the upstream objectives are being reached or not, but with no further information about the causes. Up to now, if an objective is not being reached managers do not have further information regarding the causes; in terms of accurate information they are limited to the associated KPI. However, regarding the decisions to be made: What would they be based on? How and where to dig to find cause-effect relationships? And, even more difficult: How to make it objective? This study presents a unique proposal able to objectively - not based neither on experience nor subjective judgments - identify and quantify relationships between performance elements defined within a PMS, offering additional information to managers to make cross-enterprise decisions. Finally, the paper presents the main results obtained from applying the proposal to a real world enterprise and future research lines. © 2008 Elsevier B.V. All rights reserved.},
author_keywords={Decision making;  Information analysis;  Performance measurement;  Relationships},
keywords={Benchmarking;  Information analysis;  Oceanographic equipment;  Supply chain management, Cause-effect relationships;  Decision making;  Decision-making process;  Key performance indicators;  Performance indicators;  Performance measurement;  Performance Measurement Systems;  Relationships;  Strategic objectives, Managers},
references={Kaplan, R.S., Norton, D.P., The balanced scorecard. Measures that drive performance (1992) Harvard Business Review, (January-February), pp. 71-79; Itner, D., Larker, D., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, (November), pp. 91-105; J.J. Alfaro, PMS IE-GIP, Doctoral Thesis, Universidad Politécnica de Valencia, 2003; Merchant, K.A., Measuring general managers' performances market, accounting and combination-of-measures systems (2006) Accounting, Auditing and Accountability, 19 (6), pp. 893-917; C. Wolf, P. Harmon, The State of Business Process Management, Business Process Trends, 2006 June, Accessed 10th March 2007, Available at www.bptrends.com; Jagdev, H.S., Brennan, A., Browne, J., (2003) Strategic Decision Making in Modern Manufacturing, , Springer, New York; Coughlan, P., Coughlan, D., Action research: action research for operations management (2002) International Journal of Operation and Productions Management, 22 (2), pp. 220-240; Cross, K.F., Lynch, R.L., The SMART way to sustain and define success (1988) National Productivity Review, 8 (1), pp. 23-33; Atkinson, A.A., Watherhouse, J.H., Wells, R.B., A stakeholder approach to strategic performance measurement (1997) Sloan Management Review, 38 (3), pp. 25-37; Neely, A.D., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5 (2), pp. 6-12; Alfaro, J.J., Ortiz, A., Rodriguez, R.R., Performance measurement system for enterprise networks (2007) International Journal of Productivity and Performance Management, 56, pp. 305-334; Da Silveira, G.J.C., Improving trade-offs in manufacturing: method and illustration (2005) International Journal Production Economics, 95 (1), pp. 27-38; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64, pp. 231-241; Saaty, T.L., (1980) The Analytic Hierarchy Process, , McGraw-Hill, New York; Youngblood, A.D., Collins, T.R., Addressing balanced scorecard trade-off issues between performance metrics using multi-attribute utility theory (2003) Engineering Management Journal, 15 (1), pp. 11-18; D. Cardona Siado, A. Garcia, Modelo de Indicadores para el despliegue de la Estrategia de Calidad, 2005, Accessed 14th June 2007, Available at http://hdl.handle.net/1992/300; Godet, M., (1993) From Anticipation to Action-A Handbook of Strategic Prospective, , UNESCO, Paris; K. Bauer, KPI: reduction the correlation way, DM Review (February issue, 2005) 1-3; Fraser, J., Beyond KPIs: impacting supply chain performance (2005) Manufacturing Business Technology, pp. 34-35; Lange, I., Schneider, O., Schnetzler, M., Jones, L., (2007) IFIP International Federation for Information Processing, Volume 246, Advances in Production Managment Systems, pp. 379-386. , Olhager J., and Persson F. (Eds), Springer, Boston; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1995) Multivariate Data Analysis, , Prentice Hall International Inc., New York; Jackson, J.E., (2003) A User's Guide to Principal Components, , Hoboken, New Jersey; Nelson, P.P.C., Taylor, P.A., MacGregor, J.F., Missing data methods in PCA and PLS: score calculations with incomplete observations (1993) Chemometrics and Intelligent Laboratory Systems, 35, pp. 45-65; Wold, S., Esbensen, K., Geladi, P., Principal component analysis (1987) Chemometrics and Intelligent Laboratory Systems, 4, pp. 37-52; Geladi, P., Kowalski, B., Partial least-squares regression: a tutorial (1986) Analytica Chimica Acta, 185, pp. 1-32; Martens, H., Martens, M., (2001) Multivariate Analysis of Quality, , John Wiley & Sons Ltd., New York; Rodriguez, R., Alfaro, J.J., Ortiz, A., Relationships among key performance indicators within the performance measurement system context: literature review (2006) PMA Conference, pp. 689-701. , London; Patel, B., Chaussalet, T.J., Millard, P.H., Balancing the NHS balanced scorecard (2008) European Journal of Operational Research, 185, pp. 905-914; Kaiser, H.F., The application of electronic computers to factor analysis (1960) Educational and Psychological Measurement, 20, pp. 141-151; Aluja, M., Marineau, D., (1999) Practical Application of the Principal Component Analysis, , Palais, Paris},
correspondence_address1={Rodriguez, R.R.; Research Centre on Production Management and Engineering, Camino Vera S/N, Edif. 8G, Acceso D Planta 1 Valencia, 46022, Spain; email: raurodro@upvnet.upv.es},
issn={01663615},
coden={CINUD},
language={English},
abbrev_source_title={Comput Ind},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wouters200964,
author={Wouters, M.},
title={A developmental approach to performance measures-Results from a longitudinal case study},
journal={European Management Journal},
year={2009},
volume={27},
number={1},
pages={64-78},
doi={10.1016/j.emj.2008.06.006},
note={cited By 60},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-57549113337&doi=10.1016%2fj.emj.2008.06.006&partnerID=40&md5=eecfd78b1170bf8334211c1f74bc345b},
affiliation={University of Twente, P.O. Box 217, 7500 AE Enschede, Netherlands},
abstract={Performance measurement systems (PMS) serve different functions. These are formal devices for control, and for the formulation and communication of strategy, and as such PMS primarily serve higher-level managers. But we can also aspire PMS to support operational managers, to motivate and enable these managers to improve operations. Building on Adler and Borys [Adler, P.S., Borys, B., 1996. Two types of bureaucracy: enabling and coercive. Administrative Science Quarterly 41(March), 61-89] and Ahrens and Chapman [Ahrens, T.A., Chapman, C.S., 2004. Accounting for flexibility and efficiency: a field study of management control systems in a restaurant chain. Contemporary Accounting Research 21(2), 271-301], we use the term enabling PMS. This study reports on a developmental approach for such PMS, based on a longitudinal case study, with action research. The company has made enormous investments in operations, and it therefore needed PMS to facilitate improvement of processes and to measure the actual realization of the benefits from their investments. The challenge was to develop a performance measurement system as an enabler of performance improvement, rather than merely as a control device. The company adopted a developmental approach to performance measurement, which was based on the following principles: (1) experienced-based, (2) allowing experimentation, (3) building on employees' professionalism, (4) transparency and employee ownership, and (5) outside facilitators. This resulted in extended set of new and well-founded measures, it has enhanced employees' beliefs in the PMS and their commitment to performance improvement, and it has created organizational learning concerning performance measurement. © 2008 Elsevier Ltd. All rights reserved.},
author_keywords={Organizational behavior;  Performance measurement;  Process improvement;  Supply chain management},
references={Abdel-Maksoud, A., Dugdale, D., Luther, R., Non-financial performance measurement in manufacturing companies (2005) The British Accounting Review, 37, pp. 261-297; Abernethy, M.A., Bouwens, J., Lent, L.v., Determinants of control system design in divisionalized firms (2004) The Accounting Review, 79 (3), pp. 545-570; Abernethy, M.A., Lillis, A.M., The impact of manufacturing flexibility on management control system design (1995) Accounting, Organizations and Society, 20 (4), pp. 241-258; Abrahamson, E., Change without pain (2000) Harvard Business Review, 78 (4), pp. 75-79; Adler, P.S., Borys, B., Two types of bureaucracy: Enabling and coercive (1996) Administrative Science Quarterly, 41 (March), pp. 61-89; Ahrens, T.A., Chapman, C.S., Accounting for flexibility and efficiency: A field study of management control systems in a restaurant chain (2004) Contemporary Accounting Research, 21 (2), pp. 271-301; Ahrens, T.A., Chapman, C.S., Doing qualitative field research in management accounting: Positioning data to contribute to theory (2006) Accounting, Organizations and Society, 31, pp. 819-841; Alcouffe, S., Berland, N., Levant, Y., Actor-networks and the diffusion of management accounting innovations: A comparative study (2008) Management Accounting Research, 19, pp. 1-17; Andrews, B.H., Carpenter, J.J., Gowen, T.L., A new approach to performance measurement and goals setting (2001) Interfaces, 31 (3), pp. 44-54; Baer, M., Frese, M., Innovation is not enough: Climates for initiative and psychological safety, process innovations, and firm performance (2003) Journal of Organizational Behavior, 24, pp. 45-68; Bass, B.M., Avolio, B.J., Jung, D.I., Berson, Y., Predicting unit performance by assessing transformational and transactional leadership (2003) Journal of Applied Psychology, 88, pp. 207-218; Bontis, N., Dragonetti, N.C., Jacobsen, K., Roos, G., The knowledge toolbox: A review of the tools available to measure and manage intangible resources (1999) European Management Journal, 17 (4), pp. 391-402; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Caldwell, S.D., Herold, D.M., Fedor, D.B., Toward an understanding of the relationship among organizational change, individual differences, and changes in person-environment fit: A cross-level study (2004) Journal of Applied Psychology, 89, pp. 868-882; Carlile, P.R., A pragmatic view of knowledge and boundaries: Boundary objects in new product development (2002) Organization Science, 13 (4), pp. 442-455; Carlile, P.R., Transferring, translating, and transforming: An integrative framework for managing knowledge across boundaries (2004) Organization Science, 15 (5), pp. 555-568; GChenhall, R.H., Reliance on manufacturing performance measures, total quality management and organizational performance (1997) Management Accounting Research, 8, pp. 187-206; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, l earning and strategic outcomes: An exploratory study (2005) Accounting, Organizations and Society, 30, pp. 395-422; Chenhall, R.H., Langfield-Smith, K., and benefits of management accounting practices: an Australian study (1998) Management Accounting Research, 9 (1), pp. 1-19; Chenhall, R.H., Langfield-Smith, K., Multiple perspectives of performance measures (2007) European Management Journal, 25 (4), pp. 266-282; Coughlan, P., Coghlan, D., Action research for operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 220-240; D'Adderio, L., Crafting the virtual prototype: How firms integrate knowledge and capabilities across organisational boundaries (2001) Research Policy, 30, pp. 1409-1424; Davila, T., Wouters, M., Management accounting in the manufacturing sector: Managing costs at the design and production stages (2006) Handbook of management accounting research, pp. 831-858. , Chapman C.S., Hopwood A.G., and Shields M.D. (Eds), Elsevier, Oxford vol. 2, Chapter 15; Dechow, N., Mouritsen, J., Enterprise resource planning systems, management control and the quest for integration (2005) Accounting, Organizations and Society, 30 (7-8), pp. 691-733; Drew, S.A., Kaye, R., Engaging boards in corporate direction-setting: Strategic scorecards (2007) European Management Journal, 25 (5), pp. 359-369; Dubois, A., Gadde, L.-E., Systemic combining: An abductive approach to case research (2002) Journal of Business Research, 55, pp. 553-560; Edmondson, A., Psychological safety and learning behavior in work teams (1999) Administrative Science Quarterly, 44, pp. 350-383; Epstein, M., Manzoni, J.-F., Implementing corporate strategy: From tableaux de bord to balanced scorecards (1998) European Management Journal, 16 (2), pp. 190-203; Evans, J.R., An exploratory study of performance measurement systems and relationships with performance results (2004) Journal of Operations Management, 22, pp. 219-232; Fullerton, R.R., McWatters, C.S., The role of performance measures and incentive systems in relation to the degree of JIT implementation (2002) Accounting, Organizations and Society, 27, pp. 711-735; Gold, J., Rodgers, H., Smith, V., What is the future for the human resource development professional? A UK perspective (2003) Human Resource Development International, 6, pp. 437-456; Groote, X., Loch, C., Van Wassenhove, L., Yücesan, E., Measuring management quality in the factory (1996) European Management Journal, 14 (6), pp. 540-554; Haas, M.d., Algera, J.A., Demonstrating the effect of the strategic dialogue: Participation in designing the management control system (2002) Management Accounting Research, 13, pp. 41-69; Hausman, W.H., Supply chain performance metrics (2003) The practice of supply chain management. Where theory and application converge, pp. 1-73. , Harrison T., Lee H.L., and Neale J.J. (Eds), Kluwer Academic Publishers, Norwell, Massachusetts; Hyvönen, J., Strategy, performance measurement techniques and information technology of the firm and their links to organizational performance (2007) Management Accounting Research, 18, pp. 343-366; Ittner, C.D., Larcker, D.F., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, 81 (11), pp. 88-95; Jönsson, S., Lukka, K., There and back again: Doing interventionist research in management accounting (2007) Handbook of management accounting research, , Chapman C.S., Hopwood A.G., and Shields M.D. (Eds), Elsevier; Kaplan, R.S., Norton, D.P., The balanced scorecard-Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., How to implement a new strategy without disrupting your organization (2006) Harvard Business Review, 84 (3), pp. 100-109; Kasanen, E., Lukka, K., Siitonen, A., The constructive approach in management accounting research (1993) Journal of Management Accounting Research, 5, pp. 243-264; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Labro, E., Tuomela, T.-S., On bringing more action into management accounting research: Process considerations based on two constructive case studies (2003) European Accounting Review, 12 (3), pp. 409-442; Levina, N., Vaast, E., The emergence of boundary spanning competence in practice: Implications for implementation and use of information systems (2005) MIS Quarterly, 29 (2), pp. 335-363; Libby, T., Waterhouse, J.H., Predicting change in management accounting systems (1996) Journal of Management Accounting Research, 8, pp. 137-150; Lillis, A.M., Managing multiple dimensions of manufacturing performance-An exploratory study (2002) Accounting, Organizations and Society, 27, pp. 497-529; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system-A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Löning, H., Besson, M., Can distribution channels explain differences in marketing and sales performance measurement systems? (2002) European Management Journal, 20 (6), pp. 597-609; Lowe, A., Jones, A., Emergent strategy and the measurement of performance: The formulation of performance indicators at the microlevel (2004) Organization Studies, 25 (8), pp. 1313-1337; Llonch, J., Eusebio, R., Ambler, T., Measures of marketing success: A comparison between Spain and the UK (2002) European Management Journal, 20 (4), pp. 414-422; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-217; Moers, F., Performance measure properties and delegation (2006) The Accounting Review, 81, pp. 897-924; Mooraj, S., Oyon, D., Hostettler, D., The balanced scorecard: A necessary good or an unnecessary evil? (1999) European Management Journal, 17 (5), pp. 481-491; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Otley, D.T., Berry, A.J., Case study research in management accounting and control (1994) Management Accounting Research, 5, pp. 45-65; Papalexandris, A., Ioannou, G., Prastacos, G., Soderquist, K.E., An integrated methodology for putting the balanced scorecard into action (2005) European Management Journal, 23 (2), pp. 214-227; Perera, S., Harrison, G., Poole, M., Customer-focused manufacturing strategy and the use of operations-based non-financial performance measures: A research note (1997) Accounting, Organizations and Society, 22, pp. 557-572; Quattrone, P., Hopper, T., What is IT? SAP, accounting, and visibility in a multinational organization (2006) Information and Organization, 16 (3), pp. 212-250; Simons, R., Strategic orientation and top management attention to control systems (1991) Strategic Management Journal, 12 (1), pp. 49-62; Simons, R., Control in an age of empowerment (1995) Harvard Business Review, 73 (2), pp. 80-88; Smith, F.O., KPIs made easy (2007) Control Engineering, (January), pp. 42-46; Sorge, A., Witteloostuijn van, A., The (non)sense of organizational change: An essay about universal management hypes, sick consultancy metaphors, and healthy organization theories (2004) Organization Studies, 25 (7), pp. 1205-1231; Soltani, E., Van der Meer, R., Williams, T.M., A contrast of HRM and TQM approaches to performance management: Some evidence (2005) British Journal of Management, 16, pp. 211-230; Székely, F., Knirsch, M., Responsible leadership and corporate social responsibility: Metrics for sustainable performance (2005) European Management Journal, 23 (6), pp. 628-647; Thomke, S., Managing experimentation in the design of new products (1998) Management Science, 44 (6), pp. 743-762; Thomke, S., von Hippel, E., Franke, R., Modes of experimentation: An innovation process-and competitive-variable (1998) Research Policy, 27, pp. 315-332; Tidd, J., Bodley, K., The influence of project novelty on the new product development process (2002) R&D Management, 32 (2), pp. 127-138; Townley, B., Cooper, D.J., Oakes, L., Performance measures and the rationalization of organizations (2003) Organization Studies, 24 (7), pp. 1045-1071; Tuomela, T.-S., The interplay of different levers of control: A case study of introducing a new performance measurement system (2005) Management Accounting Research, 16, pp. 293-320; Van de Ven, A., Johnson, P.E., Knowledge for theory and practice (2006) Academy of Management Review, 31 (4), pp. 802-821; Vike, C., Weyerhaeuser's data management model gathers supply chain data (2007) Pulp and Paper, (February), pp. 36-38; Wouters, M., Sportel, M., The role of existing measures in developing and implementing performance measurement systems (2005) International Journal of Operations and Production Management, 25 (11), pp. 1062-1082; Wouters, M., Wilderom, C., Developing performance measurement systems as enabling formalization: A longitudinal field study of a logistics department (2008) Accounting, Organizations and Society, 33 (4-5), pp. 488-516; Wilderom, C., Wouters, M. and Van Brussel, J. (2007) Balanced leadership, professionalism, and team trust predict positive attitudes toward performance measurement. In: George T. Solomon (Editor), Proceedings of the Sixty-Sixth Annual Meeting of the Academy of Management (CD), Best Conference Papers (ISSN 1543-8643); Walsh, J.P., Tushman, M.L., Kimberly, J.R., Starbuck, B., Ashford, S., On the relationship between research and practice (2007) Journal of Management Inquiry, 16 (2), pp. 128-154; West, J., Iansiti, M., Experience, experimentation, and the accumulation of knowledge: The evolution of R&D in the semiconductor industry (2003) Research Policy, 32, pp. 809-825; Wouters, M., Wynstra, F., Anderson, J.C., The adoption of total cost of ownership for sourcing decisions-A structural equations analysis (2005) Accounting, Organizations and Society, 30 (2), pp. 167-191; Zollo, M., Winter, S.G., Deliberate learning and the evolution of dynamic capabilities (2002) Organization Science, 13 (3), pp. 339-351},
correspondence_address1={Wouters, M.; University of Twente, P.O. Box 217, 7500 AE Enschede, Netherlands; email: m.j.f.wouters@utwente.nl},
issn={02632373},
language={English},
abbrev_source_title={Eur. Manage. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sharahi200921,
author={Sharahi, S. and Abedian, M.},
title={Performance Measurement},
journal={Contributions to Management Science},
year={2009},
pages={21-42},
doi={10.1007/978-3-7908-2156-7_2},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053445721&doi=10.1007%2f978-3-7908-2156-7_2&partnerID=40&md5=18a6857fc9b6c006c54ea29ba3a6a582},
affiliation={Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran},
abstract={As supply chains continue to replace individual firms as the economic engine for creating value during the twenty-first century, understanding the relationship between supply chain management (SCM) practices and supply chain performance (SCP) becomes increasingly important. Performance measurement is the process of quantifying the effectiveness and efficiency of action. Effectiveness is the extent to which a customer’s requirements are met and efficiency measures how economically a firm’s resources are utilized when providing a pre-specified level of customer satisfaction. Performance measurement systems are described as the overall set of metrics used to quantify both the efficiency and effectiveness of action (Shepherd and Gunter 2006). © Springer-Verlag Berlin Heidelberg 2009.},
author_keywords={Customer Satisfaction;  Delivery Performance;  Lead Time;  Supply Chain;  Supply Chain Management},
references={Bolstorff, P., (2006) Balancing Your Value Chain Metrics, , http://www.scelimited.com/sitebuildercontent/sitebuilderfiles/balancingyourvaluechainmetrics.pdf, Accessed 11 Nov 2007; Forslund, H., Jonsson, P., Dyadic integration of the process management process (2007) Int J Phys Distrib Logist Manage, 37 (7), pp. 546-567; Garelli, S., (1999) World Competitiveness Yearbook, , IMD International, Lausanne; Giannakis, M., Performance measurement of supplier relationships (2007) Supply Chain Manage Int J, 12 (6), pp. 400-411; Gunasekaran, A., Patel, C., McGaughey, R.A., A framework for supply chain performance measurement (2004) Int J Prod Econ, 87, pp. 333-347; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Comput Ind Eng, 48, pp. 377-394; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transp Res Part E, 38, pp. 439-456; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) Eur J Oper Res, 144, pp. 306-320; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2006) Int J Prod Perform Manage, 55 (3-4), pp. 242-258; Simatupang, T.M., Sridharan, R., The collaboration index: A measure for supply chain collaboration (2005) Int J Phys Distrib Logist Manage, 35 (1), pp. 44-62},
correspondence_address1={Sharahi, S.; Department of Industrial Engineering, Iran; email: s.sharahi@aut.ac.ir},
publisher={Springer},
issn={14311941},
language={English},
abbrev_source_title={Contrib. Manag. Sci.},
document_type={Book Chapter},
source={Scopus},
}

@ARTICLE{Askariazad2009115,
author={Askariazad, M. and Wanous, M.},
title={A proposed value model for prioritising supply chain performance measures},
journal={International Journal of Business Performance and Supply Chain Modelling},
year={2009},
volume={1},
number={2-3},
pages={115-128},
doi={10.1504/IJBPSCM.2009.030637},
note={cited By 28},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995391051&doi=10.1504%2fIJBPSCM.2009.030637&partnerID=40&md5=5a711eeb9e56d53bc3748cf1ce3e84a7},
affiliation={Operations and Supply Chain Systems Group (OASIS), Elliott Jaques Building, Brunel University, Uxbridge, Middlesex, UB8 3PH, United Kingdom},
abstract={The purpose of this paper is to outline a new holistic approach for identifying and prioritising supply chain performance measures according to their importance in the evaluation of value-added activities in the entire supply chain. Analytic hierarchy process (AHP) technique was used for pairwise comparisons of main supply chain functions, processes and criteria. A questionnaire consisting of comparison tables of all functions was designed based on the AHP methodology and structured interviews were conducted with expert managers of four large Iranian companies. Total weights of each function and its processes in generating value towards the entire supply chain were calculated and their priorities were identified. Total weight of each criterion in evaluating the performance of supply chain was also calculated and a short list of the most important, practical and strategic performance measures is proposed to help managers and practitioners in analysing their supply chains. Copyright © 2009 Inderscience Enterprises Ltd.},
author_keywords={AHP;  Analytic hierarchy process;  Performance measures;  PM;  SC;  Structured interviews;  Supply chain;  Value-added functions},
references={Beamon, B.M., Performance measures in supply chain management (1996) Proceedings of the 1996 Conference on Agile and Intelligent Manufacturing Systems, , 2-3 October, Rensselaer Polytechnic Institute, Troy, New York, NY; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19, pp. 275-292; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Christopher, M., (1992) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , Financial Times/Pitman Publishing, London; Cohen, S., Roussel, J., (2005) Strategic Supply Chain Management the Five Disciplines for Top Performance, , McGraw-Hill, New York, NY; Gammelgaard, B., Vesth, H., The SCOR model - A critical review (2004) Proceedings of Operations Management As A Change Agent Conference, pp. 233-241. , INSEAD; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and application (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Handfield, R.B., Nichols, E.L., Jr., (2002) Supply Chain Redesign: Transforming Supply Chains into Integrated Value Systems, , Financial Times Prentice-Hall, Inc., New Jersey; Ho, W., Integrated analytic hierarchy process and its applications - A literature review (2007) European Journal of Operational Research, 186, pp. 211-228; Kannan, G., Noorul Haq, A., Sasi Kumar, P., An application of the AHP and FAHP in the selection of collecting centre location for the reverse logistics multi criteria decision-making supply chain model (2008) International Journal of Management and Decision Making, 9 (4), pp. 350-365; Kasi, V., Systematic assessment of SCOR for modelling supply chains (2005) Proceeding of the 38th Annual Hawaii International Conference on System Science, p. 87; Lummus, R., Vokurka, J., Defining supply chain management: A historical perspective and practical guidelines (1999) Industrial Management & Data Systems, 99 (1), pp. 11-17; Neely, A., Gregory, M., Platts, K., Performance measurement systems design (1995) International Journal of Operations & Production Management, 15, pp. 80-116; Noorul Haq, A., Kannan, G., Fuzzy analytical hierarchy process for evaluating and selecting a vendor in a supply chain model (2006) International Journal of Advanced Manufacturing Technology, 29, pp. 826-835; O'Neil, H., Sackett, P., The extended manufacturing enterprise paradigm (1994) Management Decision, 32 (8), pp. 42-49; Saaty, T.S., The analytical hierarchy process - What it is and how it used (1987) Math Modelling, 9 (3-5), pp. 49-65; Saaty, T.S., Highlights and critical points in the theory and application of the analytic hierarchy process (1994) European Journal of Operational Research, 74, pp. 426-447; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Theeranuphattana, A., Tang, J.C.S., A conceptual model of performance measurement for supply chains: Alternative considerations (2008) Journal of Manufacturing Technology Management, 19 (1), pp. 125-148; Yilmaz, Y., Bititci, U., Performance measurement in the value chain: Manufacturing V. Tourism (2006) International Journal of Productivity and Performance Management, 55 (5), pp. 371-389},
correspondence_address1={Askariazad, M.; Operations and Supply Chain Systems Group (OASIS), United Kingdom; email: mohammad.askariazad@brunel.ac.uk},
publisher={Inderscience Publishers},
issn={17589401},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Supply Chain Model.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{NoAuthor2009,
title={Knowledge Management and Innovation in Advancing Economies Analyses & Solutions - Proceedings of the 13th International Business Information Management Association Conference},
journal={Knowledge Management and Innovation in Advancing Economies: Analyses and Solutions - Proceedings of the 13th International Business Information Management Association Conference, IBIMA 2009},
year={2009},
volume={3},
page_count={2008},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84905102114&partnerID=40&md5=5a765645372e25a1fe254b81a9917da5},
abstract={This proceedings contains 172 papers. Knowledge Management and Innovation in Advancing Economies: Analyses & Solutions book is a compilation of research efforts, case studies, and viewpoints of researchers and Technology managers on timely issues and practical solutions related to information technology in modern organizations. This book is a valuable source to advance research in information systems area and to transform organizations in the global business world. The key terms of this proceedings include green supply chain performance measurement, fostering group decision support system, financial analysis, e-government metadata standard, conventional construction method, mobile data services, Supply Chain Technology Management (SCTM), green strategy mix, data warehouse model, debt management.},
publisher={International Business Information Management Association, IBIMA},
isbn={9780982148921},
language={English},
abbrev_source_title={Knowl. Manag. Innov. Adv. Econ. Anal. Solut. - Proc. Int. Bus. Inf. Manag. Assoc. Conf. IBIMA},
document_type={Conference Review},
source={Scopus},
}

@CONFERENCE{Katic2009935,
author={Katic, D. and Bevanda, L.},
title={Project scorecard in construction projects},
journal={Annals of DAAAM and Proceedings of the International DAAAM Symposium},
year={2009},
pages={935-936},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904357002&partnerID=40&md5=20e1999a575681d12e935689e3f22606},
abstract={This paper describes performance measurement methodology of construction projects from construction company view. Traditional performance indicators (time, cost, quality) are not sufficient for balanced view of project success or fail. They are lagging indicators because they constitute results of project execution and do not show drivers performance indicators of construction projects. Project scorecard (PSC) is methodology of translating the project plans and goals in criteria success through financial and nonfinancial indicators in performance measurement system. For each defined project goals it is need to determine critical success factors and appropriate key performance indicators. PSC is founded on requests of the projects technical specifications and project management. Contribution of PSC application is in efficiency execution construction company strategy through their projects.},
author_keywords={Construction projects;  Performance measurement;  Project scorecard},
keywords={Benchmarking;  Construction industry;  Engineering education;  Manufacture;  Supply chain management, Construction projects;  Critical success factor;  Key performance indicators;  Performance indicators;  Performance measurement system;  Performance measurements;  Project scorecard;  Technical specifications, Project management},
references={Beatham, S., Anumba, C., Thorpe, T., KPIs: A critical appraisal of their use construction (2004) An International Journal, 11 (1), pp. 93-117; Cox, R.F., Issa, R.A., Ahrens, D., Management's perception of key performance indicators for construction (2003) Journal of Construction Management and Engineering, pp. 142-151; Kagioglou, M., Cooper, R., Aouad, G., Performance management in construction: A conceptual framework (2001) Construction Management and Economics, 19, pp. 85-95; Kaplan, R.S., Norton, D.P., (2004) The Strategy Map: Guide to Aligning Intangible Assets, 32 (5), pp. 10-17. , Harvard Business School Press; Letza, S.R., The design and implementation of the balanced business scorecard: An analysis of three companies in practice (1996) Business Process Re-engineering & Management Journal, 2 (3), pp. 54-76; Nelly, A., The evolution of performance measurement research (2005) International Journal of Productivity and Performance Management, 25 (12), pp. 1264-1277; Nelly, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations and Production Management, 17 (11), pp. 1131-1152; Niven, P., (2002) Balanced Scorecard Step-by-step-Maximizing Performance and Maintaining Results, , John Wiley&Sons, New York; Chan A, P.C., Chan A, P.L., Key performance indicators for measuring construction success (2004) An International Journal, 11 (2), pp. 203-221; Vukomanovic, M., (2006) The Key Performance Indicators in Project Oriented Construction, , Master work, Zagreb; Vukomanovic, M., Radujkovic, M., Burcar Dunovic, I., Performance management models in construction companies (2008) Gradjevinar, 60, pp. 771-778},
publisher={Danube Adria Association for Automation and Manufacturing, DAAAM},
issn={17269679},
isbn={9783901509704},
language={English},
abbrev_source_title={Annals DAAAM Proc. Int. DAAAM Symp.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ducq20092053,
author={Ducq, Y. and Berrah, L.},
title={Supply chain performance measurement: Management models, performance indicators and interoperability},
journal={IFAC Proceedings Volumes (IFAC-PapersOnline)},
year={2009},
volume={13},
number={PART 1},
pages={2053-2058},
doi={10.3182/20090603-3-RU-2001.0498},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960930204&doi=10.3182%2f20090603-3-RU-2001.0498&partnerID=40&md5=339949eb399958bbd07ab1f1eefeb73a},
affiliation={IMS-LAPS/GRAI, University of Bordeaux, UMR 5218 CNRS, 351 Cours de la Libération, 33405 Talence, France; LISTIC, University of Savoie, BP 806, 74016 Annecy Cedex, France},
abstract={Complex Supply Chains cannot be controlled without a coherent decision system and a coherent set of performance indicators. So, first of all, this paper presents a decision system model using GRAI method. This model is presented at the global level (i.e. the whole supply chain level) and at the local level (i.e. the level of each company of the chain). Then, performance indicators are proposed at both levels, using ECOGRAI Method. But in order to collect and aggregate such performance indicators at the global level, it is necessary for the various companies to use compatible decision support system. This leads to an interoperability problem which is highlighted in the last part of this paper, insisting on solutions to solve this problem. © 2009 IFAC.},
author_keywords={GRAI model;  Interoperability;  Performance measurement system - PMS;  SCOR model;  Supply chain management - SCM},
keywords={Benchmarking;  Decision support systems;  Interoperability, Complex supply chain;  Decision systems;  Management Model;  Performance indicators;  Performance measurement system;  SCOR models;  Supply chain performance measurements, Supply chain management},
references={Les indicateurs de performance en logistique (2008) AFNOR, , AFNOR FD X50-605 (in french); Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301. , DOI 10.1016/j.dss.2004.12.005, PII S0167923604003070; Ayers, J.B., (2000) Handbook of Supply Chain Management, , APICS Series on Resource management; Bana E Costa, C.A., De Corte, J.M., Vansnick, J.C., Measuring supply chain coupling: An information system perspective (2004) Europea. Journal of Purchasing & Supply Management, 8, pp. 161-171; Barut, M., Faisst, W., Kanet, J.J., Measuring supply chain coupling: An information system perspective (2002) European Journal of Purchasing Supply Management, 8, pp. 161-171; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , PII S0925527398000796; Berrah, L., Mauris, G., Haurat, A., Foulloy, L., Global vision and performance indicators for an industrial improvement approach (2000) Computers in Industry, 43, pp. 211-225; Berrah, L., Mauris, G., Vernadat, F., Information aggregation in industrial performance measurement: Rationales, issues and definitions (2004) International Journal of Production Research, 42 (20), pp. 4271-4293. , DOI 10.1080/00207540410001716534; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations and Production Management, 17 (5), pp. 522-534. , DOI 10.1108/01443579710167230; Blanc, S., (2006) Contribution À la Caractérisation et L'évaluation de L'interopérabilité pour les Entreprises Collaboratives, 1. , Thèse de doctorat de l'Université Bordeaux (in french); Breuil, D., Malhéné, N., Intégration des PME dans la supply chain (2000) Revue Rencontres, , IUT Bordeaux, 4; Brimson, J.A., (1991) Activity Accounting, , John Wiley and Sons. New York; Chan, F.T., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, (21), pp. 534-548; Cliville, V., Berrah, L., Mauris, G., Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method (2007) International Journal of Production Economics, 105 (1), pp. 171-189. , DOI 10.1016/j.ijpe.2006.03.002, PII S0925527306000739; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1989) National Productivity Review, 8 (1), pp. 23-33; Doumeingts, G., Ducq, Y., Enterprise modelling techniques to improve efficiency of enterprises (2001) International Journal of Production Planning and Control, 12; Ducq, Y., (1999) Contribution À Une Méthode D'analyse de la Cohérence des Systèmes de Production dans le Cadre du Modèle GRAI, , Thèse de doctorat. Université de Bordeaux (in french); Ducq, Y., Vallespir, B., Definition and aggregation of a performance measurement system in three aeronautical workshops using the ECOGRAI method (2005) Production Planning and Control, 16 (2), pp. 163-177. , DOI 10.1080/09537280512331333066, PII U53495118015; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680. , DOI 10.1016/j.compind.2005.03.001, PII S0166361505000412; Fortuin Leonard, PERFORANCE INDICATORS - WHY, WHERE AND HOW? (1988) European Journal of Operational Research, 34 (1), pp. 1-9. , DOI 10.1016/0377-2217(88)90449-3; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) Int. J. of Production Economics, 48; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, pp. 75-85; Krantz, D.H., Luce, R.D., Suppes, P., Tversky, A., Foundations of measurement (1971) Additive and Polynomial Representations, 1. , Academic Press; Kueng, P., Krahn, A.J.W., Building a Process Performance Measurement System: Some Early Experiences (1999) Journal of Scientific and Industrial Research, 58 (3), pp. 149-159; Lee, H.L., Padmanabhan, V., Wang, S., Information distortion in a supply chain: The bull-whip effect (1997) Management Science, 43; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156, pp. 267-286; Medori, D., The development and implementation of an integrated performance measurement framework (1998) Performance Measurement-theory and Practice: International Conference of European Operations Management Association, pp. 313-318; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228. , DOI 10.1108/01443579910247437; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism - The Scorecard for Measuring and Managing Business Success, , Edition Prentice Hall; Ravelomanantsoa, M., Ducq, Y., Vallespir, B., A generic framework for performance indicator system methods (2006) 5th International Conference on Theory and Practice in Performance Measurement and Management; Supply chain council (2008) Model SCOR. Version 8; Villa, A., Introducing some supply chain management problems (2001) International Journal of Production Economics, 73 (1), pp. 1-4. , DOI 10.1016/S0925-5273(01)00090-1, PII S0925527301000901; Woomack, J.P., Jones, D.T., (2003) Lean Thinking, , Mackays of Chatham Ltd, Chatham Kent},
correspondence_address1={Ducq, Y.; IMS-LAPS/GRAI, 351 Cours de la Libération, 33405 Talence, France; email: yves.ducq@laps.ims-bordeaux.fr},
publisher={IFAC Secretariat},
issn={14746670},
isbn={9783902661432},
language={English},
abbrev_source_title={IFAC Proc. Vol. (IFAC-PapersOnline)},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Olugu200940,
author={Olugu, E.U. and Wong, K.Y. and Shaharoun, A.M.},
title={A conceptual framework for green supply chain performance measurement in the automotive industry},
journal={Knowledge Management and Innovation in Advancing Economies: Analyses and Solutions - Proceedings of the 13th International Business Information Management Association Conference, IBIMA 2009},
year={2009},
volume={3},
pages={40-50},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956203250&partnerID=40&md5=2ddaaacc91633e1e075fae5e3f101f5c},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia; Business and Advanced Technology Centre, Universiti Teknologi Malaysia, International Campus, 54100 Kuala Lumpur, Malaysia},
abstract={It is an established fact that measuring the performance of a supply chain is a key strategy for achieving efficient supply chain management. Due to increasingly intensified focus on green manufacturing, measuring a supply chain without consideration on the environment is no longer adequate. Thus, there is a need to redefine supply chain performance measurement to embed environmental aspects. Measuring the green supply chain performance will indicate the effectiveness of greening exercises adopted by the supply chain echelons. Since the automotive industry constitutes a vast amount of wastes in the form of frames of end-of-life vehicles (ELV), which lead to landfill issues, it becomes imperative to focus on green supply chain performance measurement in the automotive supply chain. This study is part of an ongoing research on green supply chain performance measurement. Literatures in the area of supply chain performance measurement have been extensively reviewed. Green supply chain management literatures were reviewed as well. Different frameworks for supply chain performance measurement were considered. In addition, the disposal of end-of-life vehicles was thoroughly reviewed and studied. Based on the review and analysis, a framework for green supply chain performance measurement was developed for the automotive industry. The framework developed in this study should assist any automotive manufacturer to evaluate how green its supply chain is. It is also a useful tool to guide manufacturers in their quest for a green supply chain as well as green manufacturing.},
author_keywords={Framework;  Green supply chain;  Supply chain performance measurement},
keywords={Automotive industry;  Knowledge management;  Supply chain management, Automotive manufacturers;  Automotive supply chains;  Conceptual frameworks;  Environmental aspects;  Framework;  Green supply chain;  Green supply chain management;  Supply chain performance measurements, Manufacture},
references={Trowbridge, P., A case study of green supply chain management at advanced micro devices (2003) Greening the Supply Chain, pp. 307-322. , in, J. Sarki (ed), Greenleaf, Sheffield; Goodland, R., The Case that the World Has Reached Limits (1991) Environmentally Sustainable Economic Development: Building on Brundtland, , in, R. Goodland, H. Daly, and S. El Serafy (eds), World Bank, Washington D.C; Directive 2000/53/EC of the European Parliament and of the Council, September 2000 on end-of life vehicles (2000) Official Journal of the European Communities, pp. 34-42. , EC, L269. 21 October; Fox, M.S., Barbyceanu, M., Teigen, R., Agent-oriented Supply Chain Management (2000) International Journal of Flexible Manufacturing Systems, 12 (1), pp. 165-188; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Sabri, E.H., Beamon, M., A Multi-Objective Approach to Simultaneous Strategic and Operational Planning in Supply Chain Design (2000) Omega: an International Journal of Management Science, 28 (1), pp. 581-598; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A Conceptual Model of Performance Measurement for Supply Chains (2003) Management Decisions, 41 (7), pp. 635-642; Wong, W.P., Wong, K.Y., Supply Chain Performance Measurement System Using DEA Modeling (2007) Industrial Management and Data Systems, 107 (3), pp. 361-381; Gunasekaran, A., Patel, C., McCaughey, R.E., A Framework for Supply Chain Performance Measurement (2004) International Journal of Production Economics, 87 (1), pp. 333-347; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: International Journal, 8 (1), pp. 209-223; Mentzer, J.T., Meyers, M.B., Stank, T.P., Handbook of Global Supply Chain Management (2007), p. 585. , Sage Publications Inc., CA; Lai, K., Ngai, E.W.T., Cheng, T.C.E., Measures for Evaluating Supply Chain Performance in Transportation Logistics (2002) Transportation Research Part E, 38 (1), pp. 439-456; Liang, L., Feng, F., Cook, W.D., Zhu, J., DEA Models for Supply Chain Efficiency Evaluation (2006) Annals of Operation Research, 145 (1), pp. 35-49; Green, K., Morton, B., New, S., Green Purchasing and Supply Policies: Do they Improve Companies' Environmental Performance? (1998) Supply Chain Management: An International Journal, 3 (2), pp. 89-95; Hervani, A.A., Helms, M.M., Sarkis, J., Performance Measurement for Green Supply Chain Management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Rao, P., Greening of the Supply Chain: a New Initiative in South East Asia (2002) International Journal of Operations and Production Management, 22 (6), pp. 632-655; Dyckhoff, H., Souren, R., Keilen, J., The Expansion of Supply Chains to close loop Systems: a Conceptual Framework and the Automotive Industry's Point of View (2004) Supply Chain Management and Reverse Logistics, pp. 13-34. , in, H. Dyckhoff, R. Lackes, J. Reese (eds), Springer-Verlag, Berlin; Beamon, B.M., Environmental and Sustainability Ethics in Supply Chain Management (2005) Science and Engineering Ethics, 11 (1), pp. 221-234; Solvang, W.D., Roman, E., Deng, Z., Solvang, B., A Framework for Holistic Greening of Value Chains (2006) Manufacturing, and Management, 207 (1), pp. 350-355; Van Hoek, R.I., van Hoek, From reversed logistics to green supply chains (1999) Supply Chain Management, 4 (3), pp. 129-134; Pun, K.F., Determinants of Environmentally Responsible Operations: a Review (2006) International Journal of Quality and Reliability Management, 23 (3), pp. 279-297; Cruz-Rivera, R., Ertel, J., Reverse Logistics Network Design for the Collection of End-of-Life Vehicles in Mexico (2009) European Journal of Operational Research, 196 (1), pp. 930-939; Tsoulfas, G.T., Pappis, C.P., A Model for Supply Chains Environmental Performance Analysis and Decision Making (2008) Journal of Cleaner Production, 1 (1), pp. 1-11; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A Literature Review and Research Agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Bhagwat, M., Sharma, M.K., Performance Measurement of Supply Chain Management: A Balanced Scorecard Approach (2007) Computers & Industrial Engineering, 53 (1), pp. 43-62; Sroufe, R.P., A Framework for Strategic Environmental Sourcing (2006) Green Supply Chain Management, pp. 3-23. , in, J. Sarkis (ed), Springer, U.K; Beamon, B.M., Sustainability and the Future of Supply Chain Management (2008) Operations and Supply Chain Management, 1 (1), pp. 4-18; Vonderembse, M.A., Uppal, M., Huang, S.H., Dismukes, J.P., Designing Supply chains: Towards Theory Development (2006) International Journal of Production Economics, 100 (2), pp. 223-238; Olugu, E.U., Wong, K.Y., Supply Chain Performance Evaluation: Trends and Challenges (2009) American Journal of Engineering and Applied Sciences, 2 (1), pp. 202-211; Handfield, R.B., Nichols, E.L., Introduction to Supply Chain Management (1999), Prentice Hall, Upper Saddle River, New Jersey},
publisher={International Business Information Management Association, IBIMA},
isbn={9780982148921},
language={English},
abbrev_source_title={Knowl. Manag. Innov. Adv. Econ. Anal. Solut. - Proc. Int. Bus. Inf. Manag. Assoc. Conf. IBIMA},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Olugu2009130,
author={Olugu, E.U. and Wong, K.Y. and Shaharoun, A.M.},
title={Proposed performance measurement areas for an automobile green supply chain},
journal={Knowledge Management and Innovation in Advancing Economies: Analyses and Solutions - Proceedings of the 13th International Business Information Management Association Conference, IBIMA 2009},
year={2009},
volume={3},
pages={130-138},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79956194312&partnerID=40&md5=0c51b44fca72487cda7f4ad617b97cda},
affiliation={Department of Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Malaysia; Business and Advanced Technology Centre, Universiti Teknologi Malaysia, International Campus, 54100 Kuala Lumpur, Malaysia},
abstract={The focus on environmental management and operations has shifted from local optimization of environmental factors to cross boundary consideration of the entire supply chain involving the acquisition of raw materials, manufacturing and/or assembly, distribution, consumption and reversed logistics of the end-of-life-vehicles. Thus, supply chain performance measurement which is an integral part of supply chain management has shifted from the traditional supply chain to the green supply chain which is the contemporary issue in supply chain management. This study provides a set of performance measurement areas for the assessment of a green supply chain in the automotive industry. Since greening of the supply chain is a continuing process aimed at achieving perfection over a period of time, these key performance measures will serve as pointers in performance measurement. This in-turn will compliment in overcoming the challenges faced in the measurement of the efficiency and effectiveness of the green supply chain in the automotive industry. Finally, conclusions and directions for further studies are proposed to enhance research in the field of supply chain performance measurement.},
author_keywords={Green supply chain;  Performance measurement;  Performance measures;  Reversed logistics},
keywords={Environmental management;  Knowledge management;  Supply chain management, Contemporary issues;  Environmental factors;  Green supply chain;  Integral part;  Local optimizations;  Performance measure;  Performance measurements;  Supply chain performance measurements, Automotive industry},
references={Solvang, W.D., Roman, E., Deng, Z., Solvang, B., A Framework for Holistic Greening of Value Chains (2006) Manufacturing, and Management, 207 (1), pp. 350-355; Van Hoek, R.I.V.H., From reversed logistics to green supply chains (1999) Supply Chain Management, 4 (3), pp. 129-134; Zhu, Q., Sarki, J., An inter-sectoral comparison of green supply chain management in China:Drivers and practices (2006) Journal of Cleaner Production, 14 (1), pp. 472-486; Trowbridge, P., A case study of green supply chain management at advanced micro devices (2003) Greening the Supply Chain, pp. 307-322. , in, J. Sarki (ed), Greenleaf, Sheffield; Salam, M.A., Corporate Social Responsibility in Purchasing and Supply Chain (2008) Journal of Business Ethics, , DOI 10.1007/s10551-008-9733-0; Walan, S., Handfield, R., Melnyk, S., The green supply chain: integrating suppliers into environmental management process (1998) International Journal of Purchasing, 34 (2), pp. 2-10; Handfield, R.B., Nichols, E.L., Introduction to Supply Chain Management (1999), Prentice Hall, Upper Saddle River, New Jersey; Preuss, L., In Dirty Chains: Purchasing and Greener Manufacturing (2001) Journal of Business Ethics, 34 (1), pp. 345-359; Zhu, Q., Sarkis, J., Lai, K., Initiatives and Outcomes of Green Supply Chain Management Implementation by Chinese Manufacturers (2007) Journal of Environmental Management, 85 (1), pp. 179-189; Rao, P., Holt, D., Do Green Supply Chains Lead to Competitiveness and Economic Performance? (2005) International Journal of Operations & Production Management, 25 (9), pp. 898-916; Dyckhoff, H., Souren, R., Keilen, J., The Expansion of Supply Chains to close loop Systems: a Conceptual Framework and the Automotive Industry's Point of View (2004) Supply Chain Management and Reverse Logistics, pp. 13-34. , in, H. Dyckhoff, R. Lackes, J. Reese (eds), Springer-Verlag, Berlin; Schultmann, F., Zumkeller, M., Rentz, O., Integrating Spent Products Material into Supply chains: The recycling of end-of-life-vehicles as an example (2004) Supply Chain Management and Reverse Logistics, , H. Dyckhoff, R. Lackes, J. Reese (eds), Springer-Verlag Berlin, Germany; Hervani, A.A., Helms, M.M., Sarkis, J., Performance Measurement for Green Supply Chain Management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-353; Cruz-Rivera, R., Ertel, J., Reverse Logistics Network Design for the Collection of End-of-Life Vehicles in Mexico (2009) European Journal of Operational Research, 196 (1), pp. 930-939; Olugu, E.U., Wong, K.Y., Supply Chain Performance Evaluation: Trends and Challenges (2009) American Journal of Engineering and Applied Sciences, 2 (1), pp. 202-211; Spengler, T., Stolting, W., Ploog, M., Recovery planning in closed loop supply chain:An activity analysis based approach (2004) Supply Chain Management and Reverse Logistics, , H. Dyckhoff, R. Lackes, J. Reese (eds), Springer-Verlag Berlin, Germany; Inderfurth, K., Product Recovery Behavior in a closed loop supply chain (2004) Supply Chain Management and Reverse Logistics, , H. Dyckhoff, R. Lackes, J. Reese (eds), Springer-Verlag Berlin, Germany; Steven, M., Network in reveresd logistics (2004); Inderfurth, K., Product Recovery Behavior in a closed loop supply chain (2004) Supply Chain Management and Reverse Logistics, , H. Dyckhoff, R. Lackes, J. Reese (eds), Springer-Verlag Berlin, Germany, 2004; Directive 2000/53/EC of the European Parliament and of the Council of 18 September 2000 on end-of life vehicles (2000) Journal of the European Communities, 269 (21), pp. 34-42. , EC; Sector based public policy in the Asia-Pacific Region (1999), US-AEP, USAEP; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A Literature Review and Research Agenda (2005) International Journal of Operations and Production Management, 25 (12), pp. 1228-1263; Scipioni, A.A., Mazzi, A., Zuliani, F., Mason, M., The ISO 14031 standard to guide the urban sustainability measurement process: an Italian experience (2008) Journal of Cleaner Production, 16 (12), pp. 1247-1257; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Anderson, K., McAdam, R., A Critique of Benchmarking and Performance Measurement (2004) Benchmarking: An International Journal, 11 (5), pp. 465-483; Mentzer, J.T., Meyers, M.B., Stank, T.P., Handbook of Global Supply Chain Management (2007), p. 585. , Sage Publications Inc., CA; Kaplan, R.S., Norton, P.D., The Balanced Scorecard: Measures that Drives Performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Tsoulfas, G.T., Pappis, C.P., A Model for Supply Chains Environmental Performance Analysis and Decision Making (2008) Journal of Cleaner Production, 1 (1), pp. 1-11; Rao, P., Greening of the Supply Chain: a New Initiative in South East Asia (2002) International Journal of Operations and Production Management, 22 (6), pp. 632-655; McIntyre, K., Smith, H., Henham, A., Pretlove, J., Environmental Performance Indicators for Integrated Supply Chains: The Case of Xerox Ltd (1998) Supply Chain Management, 3 (3), pp. 149-156},
publisher={International Business Information Management Association, IBIMA},
isbn={9780982148921},
language={English},
abbrev_source_title={Knowl. Manag. Innov. Adv. Econ. Anal. Solut. - Proc. Int. Bus. Inf. Manag. Assoc. Conf. IBIMA},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Saghaei20091234,
author={Saghaei, A. and Ghasemi, R.},
title={Using structural equation modeling in causal relationship design for balanced-scorecards' strategic map},
journal={World Academy of Science, Engineering and Technology},
year={2009},
volume={37},
pages={1234-1240},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651533319&partnerID=40&md5=1eecaf93b3149f889252a4188f3b8b72},
affiliation={Department of industrial engineering, Research Branch of Azad University},
abstract={Through 1980s, management accounting researchers described the increasing irrelevance of traditional control and performance measurement systems. The Balanced Scorecard (BSC) is a critical business tool for a lot of organizations. It is a performance measurement system which translates mission and strategy into objectives. Strategy map approach is a development variant of BSC in which some necessary causal relations must be established. To recognize these relations, experts usually use experience. It is also possible to utilize regression for the same purpose. Structural Equation Modeling (SEM), which is one of the most powerful methods of multivariate data analysis, obtains more appropriate results than traditional methods such as regression. In the present paper, we propose SEM for the first time to identify the relations between objectives in the strategy map, and a test to measure the importance of relations. In SEM, factor analysis and test of hypotheses are done in the same analysis. SEM is known to be better than other techniques at supporting analysis and reporting. Our approach provides a framework which permits the experts to design the strategy map by applying a comprehensive and scientific method together with their experience. Therefore this scheme is a more reliable method in comparison with the previously established methods. © 2008 WASET.ORG.},
author_keywords={BSC;  SEM;  Strategy map},
keywords={Balanced scorecards;  BSC;  Causal relations;  Causal relationships;  Critical business;  Factor analysis;  Management accounting;  Multivariate data analysis;  Performance measurement system;  Scientific method;  SEM;  Strategic map;  Strategy map;  Structural equation modeling;  Supporting analysis;  Test of hypothesis, Multivariant analysis;  Supply chain management, Strategic planning},
references={Dearden, J., The case against ROI control (1969) Harvard Business Review May-June, pp. 124-135; Lawrie, G., Cobbold, I., Third-generation balanced scorecard: Evolution of an effective strategic control tool (2004) International Journal of Productivity and Performance Management, 53 (7), pp. 611-623; Kaplan, R.S., Norton, D.P., The Balanced Scorecard: Measures that Drive Performance (1992) Harvard Business Review, 70 (1), pp. 71-79. , January-February; Rigby, D., Management tools and techniques: A survey (2001) California Manage. Rev, 43 (2), pp. 139-160; Rigby, D.K., Management tools survey 2003: Usage up as companies strive to make headway in tough times (2003) Strategy & Leadership, 31 (5); Niven, P.R., (2006) Balanced Scorecard Step by Step: Maximizing Performance and Maintaining Results, , 2nd edition, Wiley, New York, NY; Kaplan, R.S., Norton, D.P., Putting the Balanced Scorecard to Work (1993) Harvard Business Review, 71 (5), pp. 134-142. , September- October; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Boston: Harvard Business School Press; Kaplan, R.S., Norton, D.P., Using the Balanced Scorecard as a Strategic Management System (1996) Harvard Business Review, 74 (1), pp. 75-85. , January-February; Kaplan, R.S., Norton, D.P., (2001) The Strategy-Focused Organization, , Harvard Business School Press, Harvard; Kaplan, R.S., Norton, D.P., (2001) Transforming the Balanced Scorecard from performance measurement to strategic management, , Business School Press, Boston, MA; Achterbergh, J., Beeres, R., Vriens, D., Does the balanced scorecard support organizational viability? (2003) Kybernetes, 32 (9-10), pp. 1387-1404; Evans, N., Assessing the balanced scorecard as a management tool for hotels (2005) International Journal of Contemporary Hospitality Management, 17 (5), pp. 376-390; Malmi, T., Balanced scorecards in Finnish companies: A research note (2001) Management Accounting Research, 12, pp. 207-220; Speckbacher, G., Bischof, J., Pfeiffer, T., A descriptive analysis of the implementation of balanced scorecard in German- speaking countries (2003) Management Accounting Research, 14, pp. 361-387; Atkinson, A.A., Balakrishnan, R., Booth, P., Cote, J.M., Groot, T., Malmi, T., Roberts, H., Wu, A., New directions in management accounting research (1997) Journal of Management Accounting Research, 9, pp. 79-108; Norreklit, H., The balance on the Balanced Scorecard; a critical analysis of some of its assumptions (2000) Management Accounting Research, 11, pp. 65-88; Hoque, Z., James, W., Linking Balanced Scorecard Measures to Size and Market Factors: Impact on Organizational Performance (2000) Journal of Management Accounting Research, 12, pp. 1-17; Laitinen, E.K., Towards a microeconomic approach of the balanced scorecard (2004) Managerial Finance, 30 (6), pp. 1-27; Otley, D., Performance Management: A Framework for Management Control Systems Research (1999) Management Accounting Research, 10, pp. 363-382; Ittner, C.D., Larcker, D.F., Innovations in Performance Measurement: Trends & Research Implications (1998) Journal of Management Accounting Research, 10, pp. 205-238; Othman, R., Balanced scorecard and causal model development: Preliminary findings (2006) Management Decision, 44 (5); Davis, S., Albright, T., An investigation of the effect of balanced scorecard implementation on financial performance (2004) Management Accounting Research, 15, pp. 135-153; Bruno, H., Resources that drive performance: An empirical investigation (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 340-354; Shook, C., Ketchen, D., Hult, J., Kacmar, K., An assessment of the use of SEM in strategic management research (2004) Strategic Management Journal, 25, pp. 397-404; Evans, N., Assessing the balanced scorecard as a management tool for hotels (2005) International Journal of Contemporary Hospitality Management, 17 (5), pp. 376-390; Cheng, E.W.L., SEM being more effective than multiple regression in parsimonious model testing for management model testing (2001) Journal of management development, 20 (7); Gefen, D., Straub, D.W., Boudreau, M.-C., (2000) Structural Equation Modeling and regression: Guidelines for research practice, CAIS, 4, p. 7. , Article; Loehlin, J.C., (1998) Latent Variable Models: An Introduction to Factor, Path, and Structural Analysis, , 3rd ed. Mahwah, N.J, Lawrence Erlbaum Associates; Schumacker, R.E., Lomax, R.G., (2004) A beginner's guide to structural equation modeling, , Second edition. Mahwah, NJ: Lawrence Erlbaum Associates; Joreskog, K.G., Factor analysis by Minres (2003) To the memory of Harry Harman and Henry Kaiser; Anderson, J.C., Gerbing, D.W., Structural equation modeling in practice: A review and recommended two-step approach (1988) Psychological Bulletin, 103 (3), p. 411; Joreskog, K.G., Sorbom, D., (1984) LISREL VI: Analysis of linear structural relationships by the method of maximum likelihood, , Chicago, National Educational Resources; Browne, M.W., Cudeck, R., Alternative ways of assessing model fit (1993) Testing structural equation models, pp. 136-162. , K. A. Bollen & J. S. Long Eds, Thousand Oaks, CA: Sage; MacCallum, R., Specification searches in covariance structure modeling (1986) Psychological Bulletin, 100, pp. 107-120; Donkor, S.(2003): Performance Measurement in the eCommerce Industry, A Professional Master Project Submitted to the Faculty of Worcester Polytechnic Institute in Partial fulfillment of the requirements for the Degree of Professional Master of Science in Financial Mathematics},
correspondence_address1={Saghaei, A.; Department of industrial engineering, },
issn={2010376X},
language={English},
abbrev_source_title={World Acad. Sci. Eng. Technol.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Carr200945,
author={Carr, J.E. and Hasan, M.},
title={An empirical study of performance measurement systems in New Zealand manufacturing companies},
journal={Australian Journal of Mechanical Engineering},
year={2009},
volume={7},
number={1},
pages={45-52},
doi={10.1080/14484846.2009.11464577},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952697852&doi=10.1080%2f14484846.2009.11464577&partnerID=40&md5=a5b69264a9e5ae20f70ffbb88b7300ec},
affiliation={School of Mechanical and Manufacturing Engineering, The University of New South Wales, Sydney, Australia},
abstract={In the current dynamic business environment performance measurement (PM) is increasingly important. Although fi nancial measures have been the primary focus in the past these measures have outdated and are considered by some writers to be misleading and even harmful. Much more emphasis is being placed on non-fi nancial measures in approaches such as the Balanced Scored and Integrated Performance Measurement Systems. To date there have been few empirical studies of the characteristics and indicators of the PM systems being used. This project surveyed 219 manufacturing companies to investigate the use of performance measurement system (PMS) in New Zealand. The low response rate does not allow any general conclusions to be drawn but there are indications that New Zealand manufacturers are adopting with an increasing emphasis on non-fi nancial measures.},
keywords={Supply chain management;  Surveys, Current dynamics;  Empirical studies;  Manufacturing companies;  New zealand;  Performance measurement system;  Performance measurements;  PM systems;  Response rate, Manufacture},
references={Beischel, M., Smith, K., Linking the shop floor to the top floor (1991) Management Accounting, October, pp. 25-29; Chenhall, R., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting, Organizations and Society, 30, pp. 395-422; Eccles, R., The performance measurement manifesto (1991) Harvard Business review, pp. 131-137. , January-February; Gosselin, M., An empirical study of performance measurement in manufacturing firms (2005) International Journal of Productivity and Performance Management, 54 (5-6), pp. 419-437; Grady, M., Performance measurement: Implementing strategy (1991) Management Accounting, pp. 49-53. , June; Kennerly, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Maskell, B., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Massachusetts; Parker, L.D., Back to the future: The broadening accounting trajectory (2001) British Accounting Review, 33 (4), pp. 421-453; Yu, J., Cooper, H.A., Quantitative review of research design effects on response rates to questionnaires (1983) Journal of Marketing Research, pp. 36-44. , February},
correspondence_address1={Carr, J. E.; School of Mechanical and Manufacturing Engineering, , Sydney, Australia; email: m.hasan@unsw.edu.au},
publisher={Engineers Media},
issn={14484846},
language={English},
abbrev_source_title={Aust. J. Mech. Eng.},
document_type={Article},
source={Scopus},
}

@ARTICLE{VanDerLaan200922,
author={Van Der Laan, E.A. and De Brito, M.P. and Vergunst, D.A.},
title={Performance measurement in humanitarian supply chains},
journal={International Journal of Risk Assessment and Management},
year={2009},
volume={13},
number={1},
pages={22-45},
doi={10.1504/IJRAM.2009.026388},
note={cited By 42},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-67650494620&doi=10.1504%2fIJRAM.2009.026388&partnerID=40&md5=717f7786ec94f3a0e7158a5e845c528e},
affiliation={Rotterdam School of Management, Erasmus University, P.O. Box 1738, 3000DR Rotterdam, Netherlands; OTB/TBM-Delft University of Technology, P.O. Box 5030, 2600 GA Delft, Netherlands},
abstract={Recent humanitarian disasters, such as the Asian tsunami and Katrina, have pointed out the importance of supply chain management in dealing with the complex emergency situations and risks that humanitarian organisations are faced with. Although performance measurement is known to be crucial for performance improvement, little insight exists in how effective performance indicators can be selected in the humanitarian context. This paper adds to this insight through the use of an extensive literature review to identify necessary conditions for an effective performance measurement system. Subsequently, we conduct a case study at the Dutch filial of Médecins Sans Frontières to investigate whether these conditions are met or not. It appears that the biggest challenges lie in data accuracy and the fact that the current set of performance indicators is not geared towards future improvement. As MSF is known for its emergency logistics performance, it is not unthinkable that other humanitarian supply chains struggle with the same issues.},
author_keywords={Case study;  Humanitarian supply chain management;  Performance measures},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations &Production Management, 19 (3-4), pp. 281-294; Beamon, B.M., Humanitarian relief chains: Issues and challenges (2004) 34th International Conference on Computers and Industrial Engineering San Francisco, pp. 14-16. , CA, USA, November, pp; Beamon, B.M., Kotleba, S.A., Inventory modelling for complex emergencies in humanitarian relief operations (2006) International Journal of Logistics: Research and Applications, 9 (1), pp. 1-18; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Beck, R., Oliver, R., Selecting key performance indicators for strategy (2004) PTQ Winter, pp. 125-129; Blansjaar, M., (1998) MSF, Internal document; Bortolotti, D., (2004) Hope in Hell: Inside the World of Doctors Without Borders, , New York; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Chow, G., Henrikssen, L.E., Heaver, T.D., Strategy, structure and performance: A framework for logistics research (1995) Logistics and Transportation Review, 31 (4), pp. 199-285; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, , 2nd ed, Financial Times/Prentice-Hall, London; Christopher, M., The agile supply chain: Competing in volatile markets (2000) Industrial Marketing Management, 29, pp. 37-44; Cohen, S., Roussell, J., (2004) Strategic Supply-Chain Management: The 5 Disciplines for Top Performance, , McGraw-Hill; Conner, D.R., (1998) Managing at the Speed of Change: How Resilient Managers Succeed and Prosper Where Others Fail, , John Wiley & Sons, Chichester; Davidson, A.L., (2006) Key performance indicators in humanitarian logistics, , MIT Master's thesis; De Brito, M.P., van der Laan, E.A., Vergunst, D., Humanitarian logistics and performance (2007) HUMLOG Conference, , November, Cranfield; Dongen, van, A., (2005) Supply Chain Performance Management, , MSF-H Internal document; Easterly, W., (2006) The White Man's Burden, , Penguin Press, New York; Fenton, G., Coordination in the great lakes (2003) Forced Migration Review, 18, pp. 23-24; Fisher, M., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; (1999) Monitoring and Evaluating Logistics, , FSU Guideline , 2nd edition; (1999) A Practical Format Guide for Logistics Reporting Co-days Edition, , FSU Guideline; (2005) Logistics and the Effective Delivery of Humanitarian Relief, , www.fritzinstitute.org, Fritz Institute , Fritz Institute, available at; World disasters report: Focus on information on disasters (2005) International Federation of Red Cross, , IFRC; Jeffreys, A., Giving voice to silent emergencies (2002) Humanitarian Exchange, pp. 2-4. , March, pp; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-78; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-229; Kovacs, G., Spens, K.M., Humanitarian logistics in disaster relief operations (2007) International Journal of Physical Distribution & Logistics Management, 37 (2), pp. 99-114; Krauth, E., Moonen, H., Popova, V., Schut, M.C., Performance measurement and control and logistics service providing (2005) International Conference on Enterprise Information Systems, pp. 239-247; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Low, J., Siesfeld, T., Measures that matter (1998) Strategy and Leadership, 26 (2), pp. 24-29. , March-April; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22, pp. 209-217; (2005) MSF Financial Report, , MSF; Harmonizing donor practices for effective aid delivery (2003) DAC Guidelines and Reference Series, , OECD; Oloruntoba, R., Gray, R., Humanitarian aid: An agile supply chain? (2006) Supply Chain Management: An International Journal, 11 (2), pp. 115-120; (1999) Humanitarian Assistance in Disaster Situations: A Guide for Effective Aid, , PAHO , Washington DC; Paylor, D. (2002) 'Improve supply chain performance', White paper, Corvu Managing Business Performance, University of Cranfield, UK; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Sheffi, Y., (2005) Resilient Enterprise: Overcoming Vulnerability for Competitive Advantage, , MIT Press; Silver, E.A., Peterson, R., Pyke, D.F., (1998) Inventory Management and Production Planning and Scheduling, , 3rd ed, John Wiley & Sons, New York; Sürie, R., Christopher, M., Wager, H., (2002) Supply Chain Analysis in Supply Chain Management and Advanced Planning, Stadler, Hartmut and C, pp. 29-43. , Kilger Eds, 2nd ed, pp, Springer Verlag, Heidelberg; Tatham, P., Kovacs, G., An investigation into the application of the military sea-basing concept to the provision of immediate relief in a rapid onset disaster (2007) Proceedings of the POMS 2007 Conference, , Gupta, S and J. Coelho Eds, Dallas, USA; Thomas, A., Fritz, L., Disaster relief (2006) Inc. Harvard Business Review, 84 (11), pp. 114-126; Thomas, A.S., Kopczak, L.R., (2005) From Logistics to Supply Chain Management: The Path Forwards in the Humanitarian Sector, , www.fritzinstitute.org, Fritz Institute, available at; Van der Laan, E.A., De Brito, M.P., van Fenema, P.C., Vermaesen, S., Managing information cycles for intra-organizational co-ordination of humanitarian logistics (2008) International Journal of Services Technology and Management, , submitted; Van Wassenhove, L.N., Humanitarian aid logistics: Supply chain management in high gear (2006) Journal of the Operational Research Society, 57, pp. 475-489; Yin, R.K., (2003) Case Study Research: Design and Methods, , Newbury Park, CA, Sage},
correspondence_address1={Van Der Laan, E. A.; Rotterdam School of Management, P.O. Box 1738, 3000DR Rotterdam, Netherlands; email: elaan@rsm.nl},
publisher={Inderscience Publishers},
issn={14668297},
language={English},
abbrev_source_title={Int. J. Risk Assess. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Huang2009471,
author={Huang, L.},
title={A study of supply chain performance measurement based on the SCOR-model and the BP neural network},
journal={Journal of Information and Computational Science},
year={2009},
volume={6},
number={1},
pages={471-479},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-66149119773&partnerID=40&md5=a17631835e70441ef989fe5f78e80e61},
affiliation={School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, China},
abstract={Based on the famous Supply Chain Operations Reference-model (SCOR) proposed by the International Supply-Chain Council and methodology of Back-Propagation Neural Network (BP NN), this paper begins with a study on how to measure Supply Chain Performance. This paper divides into 4 sections: The first is to review the current international researches of Supply Chain Performance Measurement, and to emphasize significance of those researches. The second is to introduce the related knowledge about the SCOR-model and its metrics system. The third is to elaborate the basic principle of BP NN. The last is to apply BP NN in Supply Chain Performance Measurement, and to design a new network topology structure so-called BP NN group for improving operation efficiency of BP NNs. © 2009 Binary Information Press.},
author_keywords={BP neural network;  Performance measurement;  SCOR-model;  Supply chain},
keywords={Back propagation neural networks;  Basic principles;  BP neural network;  BP neural networks;  International researches;  Network topology structure;  Operation efficiencies;  Performance measurement;  SCOR-model;  Supply chain operations reference models;  Supply chain performance, Electric network topology;  Neural networks;  Supply chain management;  Supply chains, Backpropagation},
references={Christopher, M., (1992) Logistics and Supply Chain Management, , London: Pitman Publishing House; Sarkis, J., Talluri, S., Evaluating and selecting e-commerce software and communication systems for a supply chain (2004) European Journal of Operational Research, pp. 318-329; (2008) Supply-Chain Council (SCC), , www.supply-chain.org; (2005) Neural Network Theory and Realization in Matlab 7, pp. 165-178. , FeiSi Science Research Center, Beijing: Publishing House of Electronics, Industry; Roiger, R.J., Geatz, M.W., (2008) Data Mining, , Pearson Education, Inc. publishing as Addison-Wesley; Haykin, S., Neural Networks: A comprehensive foundation (2004), World publishing house; Helen, L.R., Does your supply chain deliver value (2003), pp. 41-58. , Transportation & Distribution, Mar},
correspondence_address1={Huang, L.; School of Information Management, , Nanchang 330013, China; email: huanglijuan66s@126.com},
issn={15487741},
language={English},
abbrev_source_title={J. Inf. Comput. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Brun2009336,
author={Brun, A. and Fahmy Salama, K. and Gerosa, M.},
title={Selecting performance measurement systems: Matching a supply chain's requirements},
journal={European Journal of Industrial Engineering},
year={2009},
volume={3},
number={3},
pages={336-362},
doi={10.1504/EJIE.2009.025051},
note={cited By 12},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-65649099788&doi=10.1504%2fEJIE.2009.025051&partnerID=40&md5=df238552d15401f177645e963a05ac5c},
affiliation={Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Via G. Colombo, 40 20133 Milano, Italy},
abstract={The purpose of the paper is to provide a framework for the selection of the right Performance Measurement System (PMS) for different Supply Chain (SC) typologies. The literature on PMSs belonging to the class of 'nonfinancial and SC-oriented frameworks' is critically reviewed. Frameworks are classified according to four drivers. The developed model identifies the features of a PMS to match with SC requirements. The model is tested on ten case studies, confirming a lack of SC orientation in PMSs and suggesting guidelines to effectively reposition the features of PMSs according to SCs' features. The paper represents a useful tool for companies to re-align PMSs to their strategic requirements, which nowadays go beyond the single company's boundaries. It also fulfils the lack in the literature of driving the selection of SC-oriented PMSs, which tend to be difficult to implement or generic. Copyright © 2009, Inderscience Publishers.},
author_keywords={Bargaining power balance;  BPB;  Completeness;  Evaluation;  Implementation;  Objectivity;  Performance measurement systems;  PMSs;  SCC;  Strategic impact;  Supply chain;  Supply chain coupling},
keywords={Industrial engineering, Chain coupling;  Completeness;  Evaluation;  Implementation;  Objectivity;  Performance measurement system;  PMSs;  Power balance;  Strategic impacts, Supply chains},
references={Bailou, R.H., Stephen, M.G., Ashok, M., New managerial challenges from supply chain opportunities (2000) Industrial Marketing Management, 29 (1), pp. 7-18; Barut, M., Faisst, W., Kanet, J.J., Measuring supply chain coupling: An information system perspective (2002) European Journal of Purchasing & Supply Management, 8, pp. 161-171; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Browne, J., Devlin, J., Rolstadas, A., Andersen, B., (1997) Performance Measurement: The ENAPS Approach, , ENAPS; Browne, J., Jackson, S., AMBITE: Advanced manufacturing business implementation tool for Europe (1995) AMBITE 12 monthly report, CIMRU, , University College Galway; Calvò-Armengol, A., Bargaining power in communication networks (2001) Mathematical Social Sciences, 41, pp. 69-87; Caridi, M., Crippa, R., Sianesi, A., Measuring supply chain's performance: An advanced approach and a case study (2002) Proceedings of RIRL 2002, pp. 775-785. , Lisbon, 13-15 October, pp; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Clemons, E., Gauging the power play in the new economy (2000) Financial Times, , 13 June; Cooper, M.C., Ellram, L.M., Characteristics of supply chain management and implications for purchasing and logistics strategy (1993) International Journal of Logistics Management, 4 (2), pp. 13-22; Dixon, J.R., Nanni, A.J., Vollman, T.E., (1990) The New Performance Challenge: Measuring Operations For World Class Competition, , Homewood, IL: Dow-Jones Irwin; Epstein, M., Manzoni, J., Implementing corporate strategy: From Tableaux de Bord to balanced scorecard (1998) European Management Journal, 16 (2), pp. 190-203; (1999) The EFQM excellence model, , http://www.efqm.org/model_awards/model/excellence_model.htm, EFQM, Bruxelles; Fawcett, S.E., Magnan, G.M., (2001) Achieving World-Class Supply Chain Alignment: Benefits, Barriers, and Bridges, , CAPS Research; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) Int. J. Production Economics, 48, pp. 207-255; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J. Production Economics, 87, pp. 333-347; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Upper Saddle River, NJ: Prentice Hall; Harland, C., Brenchley, R., Walker, H., Risk in supply networks (2003) Journal of Purchasing & Supply Management, 9, pp. 51-62; Hieber, R., (2001) Supply Chain Management, , BWI ETH, Zurich; Ittner, C.D., Larcker, D.F., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, pp. 88-95. , November-December, pp; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California Management Review, 39 (1), pp. 53-79; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting (US), 70 (12), pp. 45-50; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lynch, R.L., Cross, K.F., (1991) Measure Up - The Essential Guide to Measuring Business Performance, , Mandarin, London; Neely, A., Adams, C., (2000) Perspectives on performance: The performance prism, , http://www.som.cranfield.ac.uk/som/cbp/current%20site/prismarticle.pdf, Centre for Business Performance, Cranfield School of Management; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144, pp. 306-320; Ritchie, B., Brindley, C., Disintermediation, disintegration and risk in the SME global supply chain (2000) Management Decision, 38 (8), pp. 575-583; Romano, P., Co-ordination and integration mechanisms to manage logistics processes across supply networks (2003) Journal of Purchasing & Supply Management, 9, pp. 119-134; (1992) TOPP: A Productivity Program for Manufacturing Industry NTNF/NTH, , SINTEF , Trondheim, Norway; Supply-Chain Council (2005) Supply Chain Operations Reference Model Version 7.0, Supply-Chain Council, Pittsburgh, Pennsylvania; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) Int. J. Production Economics, 64, pp. 231-241; Unahabhokha, C., Platts, K., Tan, K.H., A model for predictive performance measurement system (2004) IGLS 2004 Proceeding, pp. 425-439; Van Hoek, R.I., Measuring the unmeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192},
correspondence_address1={Gerosa, M.; Department of Management, Via G. Colombo 40, 20133 Milano, Italy; email: marco.gerosa@polimi.it},
publisher={Inderscience Publishers},
issn={17515254},
language={English},
abbrev_source_title={Eur. J. Ind. Eng.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Charan2009614,
author={Charan, P. and Shankar, R. and Baisya, R.K.},
title={Modelling the barriers of Supply Chain Performance Measurement System implementation in the Indian automobile supply chain},
journal={International Journal of Logistics Systems and Management},
year={2009},
volume={5},
number={6},
pages={614-630},
doi={10.1504/IJLSM.2009.024794},
note={cited By 29},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-65549117051&doi=10.1504%2fIJLSM.2009.024794&partnerID=40&md5=1015089ec65d8ec33d389d677445a012},
affiliation={Department of Management Studies, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India},
abstract={This paper determines the key barriers of Supply Chain Performance Measurement System (SCPMS) implementation, which the top management should focus so as to improve the effectiveness and efficiency of Supply Chain (SC). To analyse the interaction among the key barriers, Interpretive Structural Modelling (ISM) based approach has been employed. ISM aims at understanding the mutual influences so that those barriers which are at the root of few more barriers (called driving barriers) and those which are most influenced by the others (called dependent barriers) are identified. A key finding of this modelling is that lack of awareness about Performance Measurement System (PMS) in SC is a very significant barrier. Finally, the implications for managers and policymakers are discussed. Copyright © 2009, Inderscience Publishers.},
author_keywords={Barriers;  Interpretive structural modeling;  ISM;  Performance measurement system;  PMS;  SC;  SCM;  SCPMS;  Supply chain;  Supply chain management;  Supply chain performance measurement system},
references={Agarwal, A., Shankar, R., Tiwari, M.K., Modeling agility of supply chain (2007) Industrial Marketing Management, 36 (4), pp. 443-457; Austin, J.E., (1990) Managing in Developing Countries, , Free Press, New York, NY; Beamon, M.B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bender, P.S., Debunking five supply chain myths (2000) Supply Chain Management Review, 4 (1), pp. 52-58; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations and Production Management, 25 (4), pp. 333-353; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), p. 48; Caputo, A.C., Cucchiella, F., Fratocchi, L., Pelagagge, P.M., Scacchia, F., Analysis and evaluation of e-supply chain performances (2004) Industrial Management and Data Systems, 104 (7), pp. 546-557; Chandra, C., Kumar, S., Supply chain management in theory and practice: A passing fad or a fundamental change? (2000) Industrial Management and Data Systems, 100 (3), pp. 100-114; Childerhouse, P., Hermiz, R., Mason-Jones, R., Popp, A., Towill, D.R., Information flow in automotive supply chains - identifying and learning to overcome barriers to change (2003) Industrial Management and Data Systems, 103 (7), pp. 491-502; Christopher, M., Towill, D.R., An integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution and Logistics Management, 31 (4), pp. 235-246; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-14; Duperrin, J.C., Godet, M., (1973) Methode de Hierarhisation des Elements d'un Systeme Economique du, pp. R-45-R-51. , CEA, Paris; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Comp. Indus, 56, pp. 663-680; Fynes, B., Voss, C., Burca, D.S., The impact of supply chain relationship quality on quality performance (2005) International Journal of Production Economics, 96, pp. 339-354; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., James Williams, H., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25, pp. 523-533; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Hofman, D., Getting to world-class supply chain measurement (2006) Supply Chain Management Review, pp. 18-24. , October, pp; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Jharkharia, S., Shankar, R., IT-enablement of supply chains: Understanding the barriers (2005) The Journal of Enterprise Information Management, 18 (1), pp. 11-27; Jharkharia, S., Shankar, R., Supply chain management: Some sectoral dissimilarities in the Indian manufacturing industry (2006) Supply Chain Management: An International Journal, 11 (4), pp. 345-352; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kerr, S., The best-laid incentive plans (2003) Harvard Business Review, pp. 27-37. , January, pp; Kilpatrick, J., Factor, R., Logistics in Canada survey: Tracking year 2000 supply chain issues and trends (2000) Materials Management and Distribution, 45 (1), pp. 16-20; LaLonde, B.J., The gap creep (2000) Supply Chain Management Review, 3 (4), pp. 7-9; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156, pp. 267-286; Mandal, A., Deshmukh, S.G., Vendor selection using interpretive structural modeling (ISM) (1994) International Journal Operations and Production Management, 14 (6), pp. 52-59; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Inc, Portland, OR; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533; Moberg, C.R., Speh, T.W., Freese, T., SCM: Making the vision a reality (2003) Supply Chain Management Review, pp. 34-39. , September-October, pp; Monczka, R.M., Morgan, J.P., What's wrong with supply chain management? (1997) Purchasing, 16 (1), pp. 69-72. , Vols, and 122; (2006) The National Strategy for Manufacturing, p. 49. , National Manufacturing Competitiveness Council, Government of India , March, p; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Parker, C., Performance measurement (2000) Work Study, 49 (2), pp. 63-66; Ren, C., Chai, Y., Liu, Y., Active performance management in supply chains (2004) IEEE International Conference on Systems, 7, pp. 6036-6041. , Man and Cybernetics, October, pp; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Sage, A.P., (1977) Interpretive Structural Modeling: Methodology for Large-Scale Systems, pp. 91-164. , McGraw-Hill, New York, NY, pp; Schroeder, R.G., John, C.A., Scudder, G.D., White collar productivity measurement (1986) Management Decision, 24 (5), pp. 3-7; Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Toni, A.D., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 46-70; Warfield, J.W., Developing interconnected matrices in structural modeling (1974) IEEE Transactions on Systems, Man and Cybernetics, 4 (1), pp. 51-81; Welch, J., Wietfeldt, P., How to leverage your systems investment (2005) Supply Chain Management Review, pp. 24-30. , November, pp},
correspondence_address1={Charan, P.; Department of Management Studies, Hauz Khas, New Delhi 110016, India; email: parikshitcharan@yahoo.co.in},
publisher={Inderscience Publishers},
issn={17427967},
language={English},
abbrev_source_title={Int. J. Logist. Syst. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Hofmann200968,
author={Hofmann, E. and Locker, A.},
title={Value-based performance measurement in supply chains: A case study from the packaging industry},
journal={Production Planning and Control},
year={2009},
volume={20},
number={1},
pages={68-81},
doi={10.1080/09537280802685272},
note={cited By 51},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-61449264857&doi=10.1080%2f09537280802685272&partnerID=40&md5=fc63d4d2507dc55eb7e36871ad4426a8},
affiliation={Chair for Logistics Management, University of St Gallen, St Gallen, Switzerland; Soltar-The Supply Chain Experts, Zurich, Switzerland},
abstract={This article investigates the development of a value-based performance measurement concept in supply chains on the basis of a case study from the packaging industry. The value-based view offers a direct link between operating supply chain activities and shareholder value creation expressed in the economic value added (EVA). The present article differs from previous examinations of performance measurement since it consequently matches the operative key performance indicators directly via value drivers to the ultimate measure of the value generation of a company. In addition, the article identifies different gaps that require further research in the area of managerial accounting and performance measurement in supply chains.},
author_keywords={Case study;  Economic value added;  Supply chain management;  Supply chain performance measurement;  Value-based management},
keywords={Benchmarking;  Shareholders;  Supply chains;  Value engineering, Case study;  Direct links;  Economic value added;  Key performance indicators;  Managerial accountings;  Packaging industries;  Performance measurements;  Shareholder values;  Supply chain performance measurement;  Value-based management, Supply chain management},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bourne, M., Researching performance measurement system implementation: The dynamics of success and failure (2005) Production Planning & Control, 16 (2), pp. 101-113; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Chan, F.T.S., Chan, H.K., Qi, H.J.A., Review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2), pp. 110-131; Chan, F.T.S., Qi, H.J.A., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (3), pp. 209-223; Christopher, M., (1998) Logistics and supply chain management. Strategies for reducing cost and improving service, , London: FT Press; Christopher, M., Ryals, L., Supply chain strategy: Its impact on shareholder value (1999) International Journal of Logistics Management, 10 (1), pp. 1-10; Coyle, J.J., Bardi, E.J., Langley Jr, J., (2003) The management of business logistics, , 7th ed. New York: South-Western College Publishing; Ellram, L., Liu, B., The financial impact of supply management (2002) Supply Chain Management Review, 6 (11-12), pp. 30-37; Fawcett, S.E., Magnan, G.M., McCarter, M.W., Benefits, barriers, and bridges to effective supply chain management (2008) Supply Chain Management, 13 (1), pp. 35-48; Fawcett, S.E., Information sharing and supply chain performance: The role of connectivity and willingness (2007) Supply Chain Management, 12 (5), pp. 358-368; Global Supply Chain Forum, 2007. Mission, approach, membership and funding [online]. Available from: http://www.fisher.osu.edu/centers/scm/ about-the-forum/mission [Accessed 16 July 2007]; Green, K.W., Whitten, D., Inman, R.A., The impact of timely information on organisational performance in a supply chain (2007) Production Planning & Control, 18 (4), pp. 274-282; Gunasekaran, A., Kobu, B., Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995-2004) for research and applications (2007) International Journal of Production Research, 45 (12), pp. 2819-2840; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Handfield, R.B., Nichols Jr, E.L., (1999) Introduction to supply chain management, , Upper Saddle River: Prentice-Hall; Hofmann, E., Quantifying and setting off network performance (2006) International Journal of Networking and Virtual Organisations, 3 (3), pp. 317-339; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Kaipia, R., Holmström, J., Hellström, M., Measuring the benefit of changing the value offering in grocery supply chains (2007) Production Planning & Control, 18 (2), pp. 131-141; Kajüter, P., Kulmala, H.I., Open-book accounting in networks. Potential achievements and reasons for failures (2005) Management Accounting Research, 16 (2), pp. 179-204; Kaplan, R.S., Innovative action research: Creating new management theory and practice (1998) Journal of Management Accounting Research, 10, pp. 89-118; Kaplan, R.S., Norton, R., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kasanen, E., Lukka, K., Siitonen, A., The constructive approach in management accounting research (1993) Journal of Management Accounting Research, 5, pp. 243-264; Ketokivi, M., Heikkilä, J., A strategic management system for manufacturing: Linking action to performance (2003) Production Planning & Control, 14 (6), pp. 487-496; Kippenberg, T., The Stern Stewart approach (1996) The Antidote, 1 (3), pp. 20-22; Knight, J.A., (1998) Value-based management, , New York: McGraw-Hill; Kouvelis, P., Chambers, C., Wang, H., Supply chain management research and production and operations management: Review, trends, and opportunities (2006) Production & Operations Management, 15 (3), pp. 449-469; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-19; Lambert, D.M., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (11-12), pp. 1192-1218; Ma, J., Modelling the effect of custom and stock orders on supply-chain performance (2004) Production Planning & Control, 15 (3), pp. 282-291; Mangan, J., Lalwani, C., Gardner, B., Combining quantitative and qualitative methodologies in logistics research (2004) International Journal of Physical Distribution & Logistics Management, 34 (7), pp. 565-578; Meredith, J., Theory building through conceptual methods (1993) International Journal of Operations and Production Management, 13 (5), pp. 3-11; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Naslund, D., Logistics needs qualitative research: Especially action research (2002) International Journal of Physical Distribution & Logistics Management, 32 (5), pp. 321-338; Pohlen, T.L., Goldsby, T.J., VMI and SMI programs. How economic value added can help sell the change (2003) International Journal of Physical Distribution & Logistics Management, 33 (7), pp. 565-581; Porter, M., (1985) Competitive advantage: Creating and sustaining superior performance, , New York: Free Press; Rappaport, A., (1998) Creating shareholder value, , 2nd ed. New York: The Free Press; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Sachan, A., Datta, S., Review of supply chain management and logistics research (2005) International Journal of Physical Distribution & Logistics Management, 35 (9), pp. 664-704; Schnetzler, M.J., Sennheiser, A., Schönsleben, P., A decomposition-based approach for the development of a supply chain strategy (2007) International Journal of Production Economics, 105 (1), pp. 21-42; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity & Performance Management, 55 (3), pp. 242-258; Singhal, V.R., Hendricks, K.B., How supply chain glitches torpedo shareholder value (2002) Supply Chain Management Review, 6 (1), pp. 18-24; Spremann, K., (2005) Modern finance, , 2nd ed. München: Oldenbourg; Stern, J.M., Shiely, J.S., Ross, I., (2001) The EVA challenge, , New York: John Wiley & Sons; Stewart, G.B., (1999) The quest for value, , 2nd ed. New York: Harper Collins; Storey, J., Supply chain management: Theory, practice and future challenges (2006) International Journal of Operations & Production Management, 26 (7), pp. 754-774; Towill, D.R., Childerhouse, P., Enabling the seamless supply chain by exploiting the four smooth material flow controls (2006) Production Planning & Control, 17 (8), pp. 756-768; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations & Production Management, 22 (2), pp. 195-219; Walters, D., The implication of shareholder value planning and management for logistics decision making (1999) International Journal of Physical Distribution & Logistics, 29 (4), pp. 240-258; Weiers, R.M., Introduction to business statistics (2005) Belmont: Thomson Brooks/Cole, , 5th ed; Young, S.D., O'Byrne, S.F., (2001) EVA and value-based management. A practical guide to implementation, , New York: McGraw-Hill},
correspondence_address1={Hofmann, E.; Chair for Logistics Management, , St Gallen, Switzerland; email: erik.hofmann@unisg.ch},
issn={09537287},
coden={PPCOE},
language={English},
abbrev_source_title={Prod Plann Control},
document_type={Article},
source={Scopus},
}

@ARTICLE{Schmidberger2009104,
author={Schmidberger, S. and Bals, L. and Hartmann, E. and Jahns, C.},
title={Ground handling services at European hub airports: Development of a performance measurement system for benchmarking},
journal={International Journal of Production Economics},
year={2009},
volume={117},
number={1},
pages={104-116},
doi={10.1016/j.ijpe.2008.10.006},
note={cited By 51},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049192818&doi=10.1016%2fj.ijpe.2008.10.006&partnerID=40&md5=ee5819ce2c0dde4ba85564a4b1af6dc3},
affiliation={EBS European Business School, Söhnleinstr. 8, 65201 Wiesbaden-Schierstein, Germany; Centre for Strategic Management and Globalization, Copenhagen Business School, Porcelænshaven 24 B, 2000 Frederiksberg, Denmark},
abstract={The liberalization of ground handling in Europe forces airports to assess their performance relative to their competitors in order to remain competitive and sustain long-term competitive advantage. Together with main EU hub airports, action research was conducted for one year to develop a holistic performance measurement system (PMS) for ramp services. The resulting PMS entails a process-based perspective and reflects the supply chain of airport logistics. As the findings of an ex-post validation suggest, the system represents a suitable basis for competitive benchmarking activities. © 2008 Elsevier B.V. All rights reserved.},
author_keywords={Action research;  Benchmarking;  European union (EU);  Ground handling;  Hub airports},
keywords={Benchmarking;  Competition;  Oceanographic equipment;  Supply chains, Action research;  Airport logistics;  Benchmarking activities;  Competitive advantages;  European union (EU);  Europeans;  Ground handling;  Ground handling services;  Hub airports;  Performance Measurement Systems, Airports},
references={Abbott, M., Wu, S., Total factor productivity and efficiency of Australian airports (2002) The Australian Economic Review, 35 (3), pp. 244-260; Adler, N., Berechman, J., Measuring airport quality from the airlines' viewpoint: An application of data envelopment analysis (2001) Transport Policy, 8, pp. 171-181; Air Transport Association, 2006. ATA Economic Report 2006. Air Transport Association of America, Washington, DC; Albers, S., Koch, B., Ruff, C., Strategic alliances between airlines and airports (2005) Journal of Air Transport Management, 11 (2), pp. 49-58; Altrichter, H., Kemmis, S., McTaggart, R., Zuber-Skerritt, O., The concept of action research (2002) The Learning Organization, 9 (3), pp. 125-131; Alvesson, M., Leadership studies: From procedure and abstraction to reflexity and situation (1996) Leadership Quarterly, 7 (4), pp. 455-485; Andersen, B., Benchmarking (1995) Performance Management: A Business Process Benchmarking Approach, , Rolstadas A. (Ed), Chapman & Hall, London; Ashford, N., Stanton, M., Moore, C., (1995) Airport Operations, , Wiley, New York; ATRS, 2003. Airport Benchmarking Report. Air Transport Research Society, University of British Columbia, Vancouver; Ballé, M., (1995) The Business Process Re-engineering Action Kit, , Kogan Page, London; Bernabai, C., Airports: An integral part of the air traffic management system (2001) Air and Space Europe, 3 (1-2), pp. 25-27; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Bredrup, H., Background for performance management (1995) Performance Management: A Business Process Benchmarking Approach, pp. 61-87. , Rolstadas A. (Ed), Chapman & Hall, London; Brown, M., (1996) Keeping Score: Using the Right Metrics to Drive World Class Performance, , Productivity Press, New York; Burrell, G., Morgan, G., Sociological paradigms and organisational analysis: Elements of the sociology of corporate life (1985) The International Journal of Logistics Management, 8 (1), pp. 1-14; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; Chan, F., Chan, H., Lau, H., Ip, R., An AHP approach in benchmarking logistics performance of postal industry (2006) Benchmarking: An International Journal, 13 (6), pp. 636-661; Checkland, P., (1985) Systems Thinking, Systems Practices, , Wiley, Chichester; Checkland, P., Holwell, S., (1998) Information, Systems and Information Systems: Making Sense of the Field, , Wiley, Chichester; Chow, G., Heaver, T., Henriksson, L., Logistics performance: Definition and measurement systems (1994) International Journal of Physical Distribution and Logistics Management, 24 (1), pp. 17-28; Civil Aviation Authority, 2000. The use of benchmarking in airport reviews. Civil Aviation Authority, London; Doganis, R., (2001) The Airline Business in the Twenty-First Century, , Routledge, London; Doganis, R., (2002) Flying off Course: The Economics of International Airlines, , Routledge, London; Doganis, R., Graham, A., Lobbenberg, A., 1995. The economic performance of European airports (No. 3). Department of Air Transport Research, Cranfield University, Bedford, UK; Dunn, S., Seaker, R., Stenger, A., Young, R., 1993. An assessment of logistics research paradigms. Working Paper, The Pennsylvania State University, State College, Pennsylvania; Durst, S., Binder, M., Improving efficiency through internal benchmarking (2006) International Journal of Business Performance Management, 8 (4), pp. 290-306; European Commission, 2003. Ground handling market at community airports (revision on the directive 96/67/EC). Retrieved 05.04.2008 from 〈http://ec.europa.eu/transport/air_portal/consultation/2003_06_01_en.htm〉; Fahrni, F., Völker, R., Bodmer, C., (2002) Erfolgreiches Benchmarking in Forschung und Entwicklung, Beschaffung und Logistik, , Hanser, München; Fan, Y., Strategic outsourcing: Evidence from British companies (2000) Marketing Intelligence and Planning, 18 (4), pp. 213-219; Francis, G., Humphreys, I., Fry, J., The benchmarking of airport performance (2002) Journal of Air Transport Management, 8 (4), pp. 239-247; Francis, G., Humphreys, I., Fry, J., An international survey of the nature and prevalence of quality management systems in airports (2003) TQM and Business Excellence, 14 (7), pp. 819-829; Francis, G., Humphreys, I., Fry, J., The nature and prevalence of the use of performance measurement techniques by airlines (2005) Journal of Air Transport Management, 11 (4), pp. 207-217; Fuhr, J., 2006. (De)regulation of European ramp handling market: Lessons to be learned from an institutional perspective? Working Paper, Center for Network Industries and Infrastructure CNI, Berlin; Fuhr, J., Beckers, T., Vertical governance between airlines and airports: A transaction cost analysis (2006) Review of Network Economics, 5 (4), pp. 386-412; Garvens, M., The airport of the future against backdrop of dramatic changes in the aviation sector (2005) Strategic Management in the Aviation Industry, , Delfmann W., Baum H., Auerbach S., and Albers S. (Eds), Ashgate, Aldershot; Gillen, D., Lall, A., 1997. Developing measures of airport productivity and performance: An application of data envelopment analysis. Paper presented at the Aviation Transport Research Group Conference, Vancouver; Glaeson, J., Barnum, D., Toward valid measures of public sector productivity: Performance measures in urban transit (1986) Management Science, 28 (4), pp. 379-386; Gonnord, C., Lawson, F., Airports: A precious resource of the aviation network (2000) Air and Space Europe, 2 (5), pp. 33-39; Graham, B., Guyer, C., Environmental sustainability, airport capacity and European air transport liberalization: Irreconcilable goals? (1999) Journal of Transport Geography, 7, pp. 165-180; Gregory, M.J., Integrated performance measurement: A review of current practice and emerging trends (1993) International Journal of Production Economics, 30-31, pp. 281-296; Hafeez, K., Zhang, Y., Malak, N., Determining key capabilities of a firm using analytic hierarchy process (2002) International Journal of Production Economics, 76, pp. 39-51; Hales, D., Chakravorty, S., Implementation of Deming's style of quality management: An action research study in a plastics company (2006) International Journal of Production Economics, 103, pp. 131-148; Hameria, A.-P., Paatela, A., Supply network dynamics as a source of new business (2005) International Journal of Production Economics, 98, pp. 41-55; Hegendorfer, H., Morris, P., Competition hots up for top airport ratings (2000) Airport World, 5 (3), pp. 26-29; Hegendorfer, H., Tyler, C., Who's top in passenger satisfaction (1999) Airport World, 4 (3), pp. 39-41; Hill, P., Tangibles, intangibles and services: A new taxonomy for the classification of output (1999) Canadian Journal of Economics, 32 (2), pp. 426-446; Hinton, M., Francis, G., Holloway, J., Best practice benchmarking in the UK (2000) Benchmarking: An International Journal, 7 (1), pp. 52-61; Humphreys, I., Francis, G., 2000a. A critical perspective on traditional airport performance indicators. Paper presented at the 79th Annual Transportation Research Board Meeting, Washington, DC; Humphreys, I., Francis, G., The past present and future performance measurement of airports: The impact of changing ownership patterns (2000) Performance Measurement 2000: Past, Present and Future, pp. 251-258. , Neely A. (Ed), Cranfield School of Management, Bedford; Jarrar, Y., Zairi, M., Future trends in benchmarking for competitive advantage: A global survey (2001) Total Quality Management, 12, pp. 906-912; Kakabadse, A., Kakabadse, N., Trends in outsourcing: Contrasting USA and Europe (2002) European Management Journal, 20 (2), pp. 189-198; Kaplan, R., Norton, D., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R., Norton, D., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (5), pp. 134-147; Kaplan, R., Norton, D., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harvard Business School Press, Boston, MA; Kaplan, R., Norton, D., Linking the balanced scorcard to strategy (1996) California Management Review, 39 (1), pp. 53-79; Kaplan, R., Norton, D., Strategic learning and balanced scorecard (1996) Strategy and Leadership, 24 (5), pp. 18-24; Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Kaplan, R., Norton, D., Transforming the balanced scorecard from performance measurement to strategic management: part I (2001) Accounting Horizons, 15 (1), pp. 87-104; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations and Production Management, 22 (11), pp. 1222-1245; Korpela, J., Lehmusvaara, A., Tuominem, M., An analytic approach to supply chain development (2001) International Journal of Production Economics, 71, pp. 145-155; Kyrö, P., Benchmarking as an action research process (2004) Benchmarking: An International Journal, 11 (1), pp. 52-73; Lai, K.-H., Ngai, E., Cheng, T., An empirical study of supply chain performance in transport logistics (2004) International Journal of Production Economics, (87), pp. 312-331; Lawson, P., Performance management: An overview (1995) The Performance Management Handbook, pp. 1-13. , Walters M. (Ed), Institute of Personnel & Development, London; Leyk, J., Kopp, J., Unternehmensplanung bei Logistikdienstleistern mit Advanced Budgeting (2004) Controlling für Logistikdienstleister: Konzepte, Instrumente, Anwendungsbeispiele, Trends, pp. 361-377. , Schneider C. (Ed), Deutscher Verkehrsverlag, Hamburg; Martin, J., Roman, C., An application of DEA to measure the efficiency of Spanish airports prior to privatization (2001) Journal of Air Transport Management, 7 (3), pp. 149-157; McIvor, R., Outsourcing: Insights from the telecommunications Industry (2003) Supply Chain Management: An International Journal, 8 (4), pp. 380-394; McIvor, R., Humphreys, P., McAleer, W., A strategic model for the formulation of an effective make or buy decision (1997) Management Decision, 35 (2), pp. 169-178; Mentzer, J., Kahn, K., A framework of logistics research (1995) Journal of Business Logistics, 6 (1), pp. 231-250; Mentzer, J., Konrad, B., An efficiency/effectiveness approach to logistics performance analysis (1991) Journal of Business Logistics, 12 (1), pp. 33-61; Miles, M., Huberman, A., (1994) Qualitative Data Analysis, , Sage Publications, Thousand Oaks; Müller, J., Kamp, V., Niemeier, H., 2005. Can we learn from benchmarking studies of airports and where do we want to go from here? Paper presented at the German Aviation Research Society Workshop on Benchmarking, Wien; Murillo-Melchor, C., An analysis of technical efficiency and productivity changes in Spanish airports using the malmquist index (1999) International Journal of Transport Economics, 26 (2), pp. 271-292; Näslund, D., Logistics needs qualitative research: Especially action research (2002) International Journal of Physical Distribution and Logistics Management, 32 (5), pp. 321-338; Neely, A., Gregory, J., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Performance measurement design: Should process based approaches be adopted? (1996) International Journal of Production Economics, 46-47, pp. 423-431; Neely, A., Mills, J., Platts, K., Huw, R., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Ottosson, S., Participation action research: A key to improved knowledge of management (2003) Technovation, 23 (2), pp. 87-94; Oum, T., Yu, C., Fu, X., A comparative analysis of productivity performance of the world's major airports: Summary report of the ATRS global airport benchmarking research report-2002 (2003) Journal of Air Transport Management, 9 (5), pp. 285-297; Parker, D., The performance of BAA before and after privatization (1999) Journal of Transport Economics and Policy, 33, pp. 133-145; Pels, E., Nijkamp, P., Rietveld, P., Inefficiency and scale economics of European airport operations (2003) Transportation Research Part E, 39, pp. 341-361; Quinn, J., Hilmer, F., Strategic outsourcing (1994) Sloan Management Review, 35 (4), pp. 43-55; Rafele, C., Logistic service measurement: a reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Rozemeijer, F., 2000. Creating corporate advantage in purchasing. Unpublished Dissertation, Universiteits Drukkerij, Technische Universität Eindhoven, Eindhoven; Sandström, J., Toivanen, J., The problem of managing product development engineers: Can the balanced scorecrad be an answer? (2002) International Journal of Production Economics, 78, pp. 79-90; Sarkis, J., An analysis of the operational efficiency of major airports in the United States (2000) Journal of Operations Management, 18, pp. 335-351; SAS, 2005. The SAS Group's Annual Report and Sustainability Report. Scandinavian Airlines, Stockholm; Schein, E., (1969) Process Consultation, , Addison-Wesley, Reading, MA; Schmitz, J., Platts, K., Supplier performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89, pp. 231-243; Schwolgin, A., Stand und Entwicklungsperspektiven des Controllings von Logistikdienstleistern (2004) Controlling für Logistikdienstleister: Konzepte, Instrumente, Anwendungsbeispiele, Trends, pp. 17-50. , Schneider C. (Ed), Deutscher Verkehrsverlag, Hamburg; SH&E, 2002. Study on the quality. London: SH&E; Silverman, D., (1993) Interpreting Qualitative Data: Methods for Analysing Talk, Text, and Interaction, , Sage Publications, London; Silverman, D., (2004) Doing Qualitative Research. second ed, , Sage Publications, Thousand Oaks; Smith, M., Smith, D., Implementing strategically aligned performance measurement in small firms (2007) International Journal of Production Economics, 106, pp. 393-408; Soames, T., Ground handling liberalization (1997) Journal of Air Transport Management, 3 (2), pp. 83-94; Stainer, A., Logistics: A productivity and performance perspective (1997) Supply Chain Management, 2 (2), pp. 53-62; Templin, C., 2005. Deregulation of ground handling on six European airports. Paper presented at the German Aviation Research Society Workshop, Bremen; TRL, 2003. Airport performance indicators. Research Report, Transport Research Laboratory, Wokingham; Tyler, C., Pleasing the passenger (2000) Airport World, 5 (3), pp. 19-22; Ukko, J., Tenhunen, J., Rantanen, H., Performance measurement impacts on management and leadership: Perspectives of management and employees (2007) International Journal of Production Economics, 110, pp. 39-51; Ulrich, H., Anwendungsorientierte Wissenschaft (1982) Die Unternehmung, 36 (1), pp. 1-10; Upham, P., Mills, J., Environmental and operational sustainability of airports: Core indicators and stakeholder communication (2005) Benchmarking: An International Journal, 12 (2), pp. 166-179; Wahler, J., Cox, J., Competitive factors and performance measurement: Applying the theory of constraints to meet customer needs (1994) International Journal of Production Economics, 37, pp. 229-240; Wegelius-Lehtonen, T., Performance measurement in construction logistics (2001) International Journal of Production Economics, 69, pp. 107-116; Willcocks, L., Lacity, M., Fitzgerald, G., Information technology outsourcing in Europe and the USA: Assessment issues (1995) International Journal of Information Management, 15 (5), pp. 333-351; Wyld, D., Jones, M., Totten, J., Where is my suitcase? RFID and airline customer service (2005) Marketing Intelligence and Planning, 23 (4), pp. 382-394; Yee, C., Tan, K., Platts, K., Managing 'downstream' supply network: A process and tool (2006) International Journal of Production Economics, 104, pp. 722-735; Yeh, C.-H., Kuo, Y.-L., Evaluating passenger services of Asia-Pacific international airports (2003) Transportation Research E, 39, pp. 35-48; Yin, R., (2003) Case Study Research: Design and Methods. third ed, , Sage Publications, Thousands Oaks; Zairi, M., Benchmarking: The best tool for measuring competitiveness (1994) Benchmarking for Quality Management and Technology, 1 (1), pp. 11-24; Zeithaml, V., Parasuraman, A., Berry, L., Problems and strategies in services marketing (1985) Journal of Marketing, 49, pp. 33-46; Zuber-Skerritt, O., Action learning and action research: Paradigm, praxis and programs (2001) Effective Change Management Using Action Research and Action Learning: Concepts, Frameworks, Processes and Applications, , Sankaran S., Dick B., Passfield R., and Swepson P. (Eds), Souther Cross University Press, Lismore, Australia},
correspondence_address1={Bals, L.; Centre for Strategic Management and Globalization, Porcelænshaven 24 B, 2000 Frederiksberg, Denmark; email: lydia.bals@web.de},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zimmermann200963,
author={Zimmermann, K. and Seuring, S.},
title={Two case studies on developing, implementing and evaluating a balanced scorecard in distribution channel dyads},
journal={International Journal of Logistics Research and Applications},
year={2009},
volume={12},
number={1},
pages={63-81},
doi={10.1080/13675560802141697},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-55449108125&doi=10.1080%2f13675560802141697&partnerID=40&md5=000cf577786424095bc58ff763184c28},
affiliation={Multiversum Consulting, Hamburg, Germany; Department of International Management, Faculty of Organic Agricultural Sciences, University of Kassel, Kassel, Germany},
abstract={Supply chain management demands the development and implementation of performance measures across organisations. Although this has been discussed a lot, reviewing the literature reveals that only a few cases have been presented, where the development and implementation of performance management systems spanning at least two companies in a supply chain have been presented. This paper discusses the application of the balanced scorecard (BSC) across supply chains and presents two case studies where such inter-organisational BSCs have been developed. This paper is built on case study research in two distribution dyads, one in the automotive and one in the chemical industry. In both cases, the respective companies cooperated in the development and implementation of the joint BSC. Hence, it is outlined how the traditional criteria describing the BSC can be transferred to the dyad level. In each case, a producer and a retailer developed and implemented joint BSCs to manage the distribution function, where a set of indicators across the dimensions of the BSC was implemented. The research offers a starting point for further empirical research on how cross company performance measures are applied. Of course, dyads are only the smallest inter-organisational unit, so further research on longer supply chains is needed. Still, the two cases offer insights towards both the development of such joint performance measurement systems as well as practical guidance on how companies can cooperate using the BSC.},
author_keywords={Automotive industry;  Balanced scorecard;  Case studies;  Chemical industry;  Distribution;  Performance measurement;  Supply chain management},
references={Beamon, B.M., Measuring supply chain performance (1989) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2006) International Journal of Operations and Production Management, 22 (11), pp. 1288-1310; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-94; Burgess, K., Singh, P.J., Koroglu, R., Supply chain management: A structured literature review and implications for future research (2006) International Journal of Operations and Production Management, 26 (7), pp. 703-729; Chen, I.J., Paulraj, A., Towards a theory of supply chain management - The constructs and measurement (2004) Journal of Operations Management, 22 (2), pp. 119-150; Christiansen, P.E., Rohde, C., Hald, K.S., Differences in supply chain performance across inter-organisational communication levels - Case studies from Denmark (2003) Global Journal of Flexible Systems Management, 4 (4), pp. 23-31; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: More than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-14; Cullen, J., Berry, A.J., Seal, W., Dunlop, A., Ahmed, M., Marson, J., Interfirm supply chains: The contribution of management accounting (1999) Management Accounting (British), 77 (6), pp. 30-33; De Toni, A., Tonchia, S., Performance measurement systems - Models, characteristics and measures (2001) International Journal of Operations and Production Management, 21 (12), pp. 46-70; Eisenhardt, K.M., Building theory from case study research (1989) Academy of Management Review, 14 (4), pp. 532-550; Fawcett, S.E., Cooper, M.B., Logistics performance measurement and customer success (1998) Industrial Marketing Management, 27 (4), pp. 341-357; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (12), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , PrenticeHall, New Jersey; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy Into Action, , Harvard Business School Press, Boston; Kaplan, R.S., Norton, D.P., Why does business need a balanced scorecard? (1997) Journal of Cost Management, 11 (3), pp. 5-10; Kuo, C.-H., Dunn, K.D., Randhawa, S.U., A case study assessment of performance measurement in distribution centres (1999) Industrial Management and Data Systems, 99 (2), pp. 54-63; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; McCarthy, T.M., Golicic, S.L., Implementing collaborative forecasting to improve supply chain performance (2002) International Journal of Physical Distribution and Logistics Management, 32 (6), pp. 431-454; Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-26; Mentzer, J.T., Min, S., Bobbitt, L.M., Towards a unified theory of logistics (2004) International Journal of Physical Distribution and Logistics Management, 34 (8), pp. 606-627; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Bourne, M., Why measurement initiatives fail (2000) Measuring Business Excellence, 4 (4), pp. 3-7; Neely, A., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Neely, A., The evolution of performance measurement research-developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing the supply chain (2003) European Journal of Operational Research, 144 (2), pp. 306-320; Perona, M., Miragliotta, G., Complexity management and supply chain performance assessment - A field study and a conceptual framework (2004) International Journal of Production Economics, 90 (1), pp. 103-115; Schmitz, J., Platts, K.W., Roles of supplier performance measurement: Indication from a study in the automotive industry (2003) Management Decision, 41 (8), pp. 711-721; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89 (2), pp. 231-243; Seuring, S.A., Assessing the rigor of case study research in supply chain management (2008) Supply Chain Management: An International Journal, 13 (2), pp. 128-137; Sheperd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (34), pp. 242-258; Stuart, I., Mc Cutcheon, D., Handfield, R., McLachlin, R., Samson, D., Effective case research in operations management: A process perspective (2002) Journal of Operations Management, 20 (5), pp. 419-433; van Hoek, R.I., Measuring the un-measurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management: an International Journal, 3 (4), pp. 187-192; van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations and Production Management, 21 (12), pp. 15-29; Voss, C., Tsikriktsis, N., Frohlich, M., Case research in operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 195-219; Yin, R., (2003) Case Study Research-Design and Methods, , Sage, Thousand Oaks},
correspondence_address1={Seuring, S.; Department of International Management, , Kassel, Germany},
publisher={Taylor and Francis Ltd.},
issn={13675567},
language={English},
abbrev_source_title={Int. J. Logist. Res. Applic.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Zhou2008,
author={Zhou, M. and Huo, J.-Z.},
title={Measure product-driven supply chain performance using MEOWA},
journal={2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008},
year={2008},
doi={10.1109/WiCom.2008.1489},
art_number={4679397},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049117610&doi=10.1109%2fWiCom.2008.1489&partnerID=40&md5=83b0177c6e0411ed9e79d3e7e86fbbc1},
affiliation={School of Economics and Management, Tongji University, Shanghai, China},
abstract={We hold that supply chain strategy should match with product characteristics; moreover performance measurement system should be designed to reflect the match, which increases the complexity of the aggregation and assessment process. This paper describes the differences between agile and lean supply chain characteristics driven by different product strategies. Then, a method of Maximum Entropy OWA (MEOWA) operator with fuzzy linguistic quantifier is presented to satisfy the complexity during the match of supply chain and product strategies. The method presented in this paper is applicable and easy to be applied. © 2008 IEEE.},
author_keywords={Fuzzy linguistic quantifier;  MEOWA;  Product characteristics;  Supply chain performance},
keywords={Linguistics;  Mobile computing;  Supply chains;  Wireless networks;  Wireless telecommunication systems, Assessment processes;  Fuzzy linguistic quantifier;  Maximum entropies;  MEOWA;  Performance Measurement Systems;  Product characteristics;  Product strategies;  Supply chain performance;  Supply chain strategies, Supply chain management},
references={Ronald, A.D., Strategy selection and performance measurement choice when profit drivers are uncertain (2004) Management Science, 50 (12), pp. 1624-1637; Thonemann, U.W., Improving supply-chain performance by sharing advance demand information (2002) European Journal of Operational research, 142 (1), pp. 81-107; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-239; Kim, S.W., Organizational structures and the performance of supply chain management (2007) International Journal of Production Economics, 106 (2), pp. 323-345; Fisher, L.M., What is the right supply chain for your product (1997) Harvard Business Review, pp. 105-116. , March-April; Zadeh, L.A., Fuzzy sets (1965) Journal of Information and Control, 8, pp. 338-353; Yager, R.R., On ordered weighted averaging aggregation operators in multi-criteria decision making (1988) IEEE Transactions on Systems, Man and Cybernetics, 18 (1), pp. 183-190; Yager, R.R., Quantifier guided aggregation using OWA operators (1996) International Journal of Intelligent Systems, 11 (1), pp. 49-73; Filev, D.P., Yager, R.R., On the issue of obtaining OWA operator weights (1998) Fuzzy Sets and Systems, 94 (2), pp. 157-169; Filev, D.P., Yager, R.R., Induced ordered weighted averaging operators (1999) IEEE Transactions on Systems, Man and Cybernetics, Part B, 29 (2), pp. 141-150; O'Hagan, M., Aggregating template or rule antecedents in real-time expert systems with fuzzy set logic (1988) Proc 22nd Annual IEEE Asilomar Conference on Signals, Systems and Computers, pp. 681-689. , Pacific Grove, CA; Filev, D.P., Yager, R.R., Analytic properties of maximum entropy OWA operators (1995) Information Sciences, 85, pp. 11-27; L.A. Zadeh, A concept of a linguistic variable and its applications to approximate reasoning. Part 1, Information Science, 1975, 8:199-249, Part 2, Information Science, 1975, 8: 301-357, Part 3, Inform. Sci., 1975, 9:43-80; R.C. Wang, S.J. Chuu, Group decision-making using a fuzzy linguistic approach for evaluating the flexibility in a manufacturing system, European Journal of Operational Research, 2004, 154(3): 563-572; Chang, S.L., Wang, R.C., Wang, S.Y., Applying fuzzy linguistic quantifier to select supply chain partners at different phases of product life cycle (2006) International Journal of Production Economics, 100 (2), pp. 348-359; Liu, X.W., Preference Representation with Geometric OWA Operator (2004) System Engineering, 22 (9), pp. 82-86; Zadeh, L.A., A computational approach to fuzzy quantifiers in natural languages (1983) Comp. Math. Appl, 9, pp. 149-184; Samuel, H.H., Uppal, M., Shi, J., A product driven approach to manufacturing supply chain selection (2002) Supply Chain Management, 7 (3-4), pp. 189-199},
correspondence_address1={Zhou, M.; School of Economics and Management, , Shanghai, China; email: zhoumin@hotmail.com},
isbn={9781424421084},
language={English},
abbrev_source_title={Int. Conf. Wirel. Commun., Netw. Mob. Comput., WiCOM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Cui2008,
author={Cui, S. and Zhang, H.},
title={Time scorecard: An integrative performance measurement framework for time-based companies},
journal={2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008},
year={2008},
doi={10.1109/WiCom.2008.2242},
art_number={4680431},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049116314&doi=10.1109%2fWiCom.2008.2242&partnerID=40&md5=0cb1b55d60eb19f5f2c612bfb37d2c0b},
affiliation={Huazhong University of Science and Technology, Wuhan, China},
abstract={Time-based competition (TBC) has received much attention in the literature and in industry. With time being the most important competitive factor, many companies take time-based strategy ( TBS ) to gain competitive advantage and long-term competitiveness. New strategy needs urgently new strategy management tool-performance measurement system to manage the implement of the TBS. So the Balance Scorecard (BSC), as a traditional strategy management tool, should also evolve with the change of competitive environment and company's strategy. While rigorous research has been conducted in relation to the TBC and performance measurement systems, little research has been done regarding application of performance measurement systems to manage TBS. The purpose of this paper is to present an integrative performance measurement framework for time-based companies. The contemporary performance measurement frameworks are reviewed, mainly including BSC. In addition, since time is of human essence and a better understanding of the nature of time helps enhance managerial effectives, the dimensions of time for TBC is then discussed. Critical Success Factors(CSFs) of time-based companies are presented. According to the dimensions of time and CSFs of time-based companies, a Time Scorecard (TSC) is developed and a theoretical model for using the TSC to manage time effectively is put forward. The TSC helps implement TBS on six perspectives (financial, customer, internal process, learning, supplier and leadership) from three interrelated aspects (including vertical time, negative time and zero time) in two dimensions of time(vertical time and horizontal time). The TSC presented in this paper is not only a conceptual model, but also a measurement model. Furthermore, this approach has the potential to give a deeper understanding of which areas should be monitor in the time-based companies and help companies to develop, cascade and implement the ZT strategy. Finally, since the cause-and-effect relationship exists in these six perspectives and in the three aspects of time, an organization can research the true reason through the vertical and horizontal analysis if the any item has deviation. © 2008 IEEE.},
author_keywords={Balance scorecard;  Framework;  Performance measurement;  Time;  Time scorecard;  Time-based company;  Time-based competition;  Time-basedstrategy;  Zero time;  Zero time company},
keywords={Enterprise resource planning;  Measurements;  Mobile computing;  Statistical methods;  Supply chain management;  Wireless networks;  Wireless telecommunication systems, Balance scorecard;  Framework;  Performance measurement;  Time;  Time scorecard;  Time-based company;  Time-based competition;  Time-basedstrategy;  Zero time;  Zero time company, Competition},
references={Stalk, G.J., Time-the next source of competitive advantage (1988) Harvard Business Review, 66 (4), pp. 41-51; Davis, P.S., Dibrell, C.C., Janz, B.D., The impact of time on the strategy-performance relationship implications for managers (2002) Industrial Marketing Management, 31, pp. 339-347; Merrils, R., How northern telecom competes on time (1989) Harvard Business Review, pp. 108-114; Stonich, P.J., Time: The next strategic frontier (1990) Planning Review, 18 (6), pp. 4-7,46-48; Droge, C., Jayaram, J., Vickery, S.K., The effects of internal versus external integration practices on time-based performance and overall firm performance (2004) Journal of Operation Management, (22), pp. 557-573; Azzone, G., Masella, C., Bertele, U., Design of performance measures for time-based companies (1991) International Journal of Operations & Production Management, 11 (3), pp. 77-85; Dirks, P.V.V., Management control and the production environment: A review (2005) International Journal of Production Economics, 93-94, pp. 263-272; Hum, S.H., Sim, H.H., Time-based competition: Literature review and implications for modelling (1996) International Journal of Operations & Production Management, 16 (1), pp. 75-90; Chung, C.H., Balancing the two dimensions of time for time-based competition (1999) Journal of Managerial Issues, 11 (3). , Fall; Yeh, R.T., Pearlson, K.E., Kozmetsky, G., (1998) Zero time a conceptual architecture for 21st Century enterprises, , Zero Time White Paper, September 15; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Bassioni, H.A., Price, A.D.F., Hassan, T.M., Performance measurement in construction (2004) Journal of Management in Engineering, pp. 42-45; Marr, B., Scored for life (1989) Financial Management, 79 (4), p. 30; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part I (2001) Accounting Horizons, 15 (1), pp. 87-104; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part II (2001) Accounting Horizons, 15 (2), pp. 147-160; Schneiderman, A., Why balanced scorecards fail (1999) Journal of strategic performance measurement, pp. 6-11. , January; Neely, A., Bourne, M., Why measurement initiatives fail (1999) Journal of strategic performance measurement, p. 6. , January; Kanji, G.K., SA, P.M.E., Kanji's business scorecard (2001) Total Quality Management, 12 (7-8), pp. 898-905; Epstein, M., Manzoni, J.F., from tableaux de bord to balanced scorecards (1998) European Management Journal, 16 (2), pp. 190-203. , Implementing corporate strategy:, April; Raymond, Y., Keri son, P., George, K., (2000) Zero time: Providing instant customer value-every time, all the time, p. 13. , John Wiley & Sons, Inc; Kanji, G.K., Performance measurement system (2002) Total Quality Management, 13 (5), pp. 715-728},
correspondence_address1={Cui, S.; Huazhong University of Science and Technology, Wuhan, China; email: cuisong1999@126.com},
isbn={9781424421084},
language={English},
abbrev_source_title={Int. Conf. Wirel. Commun., Netw. Mob. Comput., WiCOM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ren2008,
author={Ren, T.},
title={Application of supply chain performance measurement based on SCOR model},
journal={2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008},
year={2008},
doi={10.1109/WiCom.2008.1490},
art_number={4679398},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049083605&doi=10.1109%2fWiCom.2008.1490&partnerID=40&md5=70c4b7fca3bae0b08ede0e9fb3880fa6},
affiliation={School of Manufacture Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan Province, 621010, China},
abstract={This paper is based on the framework and proposes an approach of SCM performance measurement via the balanced score card (BSC) in manufacturing industry. The findings of this case study in this paper will benefit companies seeking to create a competitive advantage in the marketplace. © 2008 IEEE.},
author_keywords={Balance scorecard;  Performance measurement;  SCOR model;  Supply chain management},
keywords={Competition;  Mobile computing;  Supply chains;  Wireless networks;  Wireless telecommunication systems, Balance scorecard;  Balanced score cards;  Case studies;  Competitive advantages;  Manufacturing industries;  Performance measurement;  SCOR model;  Supply chain performances, Supply chain management},
references={Harrison, A., Van Hoek, R., (2001) Logistics Management and Strategy, , Prentice Hall, UK; Lambert, D.M., Stock, J.R., Ellram, L.M., (1998) Fundamentals of Logistics Management, , Irwin/McGraw-Hill, Boston; Tarn, J.M., Yen, D.C., Beaumont, M., Exploring the rationales for ERP and SCM integration (2002) Industrial Management & Data System, 102 (1-2), pp. 26-34; Larson, J.C., Rogers, D.S., Supply Chain Management: Definition, Growth, and Approach (1998) Journal of Marketing Theory and Practice, 6 (4), pp. 1-5; Cachon, G., Fischer, M., Supply chain and the value of shared information (2000) Management Science, 46 (8), pp. 1032-1048; Hvolby, H.H., Thorstenson, A., Indicators for performance measurement in small and medium-sized enterprises (2001) Proceedings of the Institution of Mechanical Engineering, pp. 1143-1146; Mayer, R.J., (1994) IDEF0 Function Modeling, , Knowledge Based Systems, Inc; Sink, D.S., Turtle, T.C., (1989) Planning and Measurement in your organization of the future, , Industrial Engineering Press, Norcross, GA; Stephens, S., Supply Chain Operations Reference Model Vision 5.0: A New Tool to Improve Supply Chain Efficiency and Achieve Best Practice (2001) Information System Frontiers, 3 (4), pp. 471-476; Stank, T.P., Keller, S.B., Closs, D.J., Performance Benefits of Supply Chain Logistical Integration (2001) Transportation Journal, 41 (2-3), p. 3246; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Amaratunga, D., Bldry, D., A conceptual framework, to measure facilities management performance (2003) Property Management, 21 (2), pp. 171-189; Min, S., Mentzer, J.T., Developing and Measuring Supply Chain Management Concepts (2004) Journal of Business Logistics, 25 (1), pp. 63-72; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54, pp. 507-514; http://www.supply-chain.org, URL},
correspondence_address1={Ren, T.; School of Manufacture Science and Engineering, , Mianyang, Sichuan Province, 621010, China; email: Rentong64@163.com},
isbn={9781424421084},
language={English},
abbrev_source_title={Int. Conf. Wirel. Commun., Netw. Mob. Comput., WiCOM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Seo20081018,
author={Seo, I.W. and Lee, D.H. and Park, I.S.},
title={The empirical study about constructing and application of performance measurement system based on an integrated DEA approach},
journal={Proceedings - 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008},
year={2008},
volume={1},
pages={1018-1024},
doi={10.1109/ICCIT.2008.334},
art_number={4682166},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-57849160997&doi=10.1109%2fICCIT.2008.334&partnerID=40&md5=55f4c22a4ba0f45ed9684ebe9d55daef},
affiliation={Korea Research Institute of Standards and Science, Yuseong Gu, Taejeon, South Korea; Information and Communications University, Yuseong Gu, Taejeon, South Korea},
abstract={This paper tries to construct performance management system (PMS) of public research institute. Traditional PMS approach focuses on deriving financial measures, revealing the limit of developing quantitative management tool, because research institute's performance is intangible and complex. BSC approach comes into spotlight as a performance measuring tool since it balanced the financial and nonfinancial aspects. According to the BSC framework, performance indicators (KPT) are defined as quantitative measure. The principle of data envelope analysis (DEA) is applied to calculate the relative efficiency between decision units. To provide more precise comparison, weight is restricted by the judging matrices resulting from pair-wise comparison of analytic hierarchy process (AHP). An empirical study is conducted to verify the reliability of proposed research model. The weight restriction, which is determined on the basis of experts survey, 4 input vectors and 5 output vectors produce DEA-AR score. The result provides additional information about input resource ratio, which can be used as quantitative PMS tool. © 2008 IEEE.},
keywords={Analytic hierarchy process;  Data envelopment analysis;  Hierarchical systems;  Information technology;  Oceanographic equipment;  Statistical methods;  Supply chain management;  Vectors, Analytic hierarchies;  Data envelope analyses;  Decision units;  Empirical studies;  Financial measures;  Input vectors;  Output vectors;  Pair-wise comparisons;  Performance indicators;  Performance management systems;  Performance Measurement Systems;  Performance measuring;  Public research institutes;  Quantitative managements;  Quantitative measures;  Relative efficiencies;  Research institutes;  Research models;  Resource ratios;  Weight restrictions, Decision theory},
references={Albert H.C. Tsang, Andrew K.S. Jardine and Harvey Kolodny, Measuring maintenance performance: a holistic approach International, Journal of Operations & Production Management, 19 No. 7, 1999, pp. 691-715; Bach, N., Calais, P., Calais, M., Marketing residential grid-connected PV system using a Balanced Scorecard as a marketing tool (2001) Renewable Energy, pp. 211-216; Baker, N., Freeland, J., Recent advances in R&D benefit measurement and project selection methods (1975) Management Science, pp. 1164-1175; Banker, A balanced scorecard analysis of performance metrics (2004) European Journal of Operational Research, 154, pp. 423-436; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, pp. 429-444; Charnes, A., Cooper, W.W., Wei, Q.L., Huang, Z.M., Cone ratio data envelopment analysis and multi-objective programming (1989) International Journal of Systems Sciences, pp. 1099-1118; Cooper, W.W., Li, S., Seiford, L.M., Tone, K., Thrall, R.M., Zhu, J., Sensitivity and stability analysis in DEA: Some recent developments (2001) Journal of Productivity Analysis, 15, pp. 217-246; Cooper, W.W., Seiford, L.M., Tone, K., (2000) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, , Kluwer Academic Publishers, Boston; Jing-yuan, G.U.O., Jia, L.I.U., Li, Q.I.U., Research on Supply Chain performance Evaluation Based on DEA/AHP model Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing; Eilat, H., Golany, B., Shtub, A., R&D project evaluation: An integrated DEA and balanced scorecard approach (2006) International journal of Management Science, 5; Ukko, J., Tenhunen, J., Rantanen, H., Performance measurement impacts on management and leadership: Perspectives of management and employees (2007) Int. J. Production Economics, 110, pp. 39-51; Kaplan, R.S., Strategic Performance Measurement and Management in Nonprofit Organizations (2001) Nonprofit Management & Leadership, 11 (3), pp. 353-437; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, pp. 71-79. , January-February, p; Kaplan, R.S., Norton, D.P., Using the Balanced Scorecard as a Strategic Management System (1996) Harvard Business Review, pp. 75-85. , January-February, pp; Bryant, L., Jones, D.A., Widener, S.K., Managing Value Creation within the Firm: An Examination of Multiple Performance Measures (2004) Journal of Management Accounting Research, 16, pp. 107-131; Alexandras, P., George, I., Gregory, P., Eric, K.S., An Integrated Methodology for Putting the Balanced Scorecard into Action (2005) European Management Journal, 23 (2), pp. 214-227; Fare, R., Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries (1994) The American Economic Review, 84 (1), pp. 66-83; Satty, T.L., How to make a Decision: The Analytic Hierarchy Process (1980) European Journal of Operational Research, 48, pp. 9-26; Sinuany-Stern, Z., Mehrez, A., Hadad, Y., An AHP/DEA methodology for ranking decision making units (2000) International Transaction in Operational Research, 7 (2), pp. 109-124; Tsang, A.H.C., Measuring maintenance performance: A holistic approach (1999) International Journal of Operations & Production Management, 19 (7), pp. 691-715; Mao-sheng, Y.A.N.G., Tao, L.I., Shu, B.A.I., Performance evaluation of supply chain based on DEA method (2005) Journal of Xi'an University of Engineering Science and Technology, 19 (2), pp. 180-182},
correspondence_address1={Seo, I. W.; Korea Research Institute of Standards and Science, Yuseong Gu, Taejeon, South Korea; email: veny@kriss.re.kr},
isbn={9780769534077},
language={English},
abbrev_source_title={Proc. Int. Conf. Convergence Hybrid Inf. Technol., ICCIT},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Su2008301,
author={Su, Y. and Jin, Z. and Yang, L.},
title={System dynamics modeling for strategic management of green supply chain},
journal={Web-Based Green Products Life Cycle Management Systems: Reverse Supply Chain Utilization},
year={2008},
pages={301-333},
doi={10.4018/978-1-60566-114-8.ch014},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900040324&doi=10.4018%2f978-1-60566-114-8.ch014&partnerID=40&md5=b8fbe135081b8ef6a57f7afecb2aebbc},
affiliation={Institute of Scientific and Technical Information of China, Beijing, China; Department of Business Strategy and Policy, Tsinghua University, Beijing, China; South China University of Technology, Panyu, Guangzhou, China; School of Economics and Management, Tsinghua University, China},
abstract={Environmental issues are rapidly emerging as one of the most important topics in strategic manufacturing decisions. Perusal of the literature has shown many models to support executives in the assessment of a company's environmental performance. Unfortunately, none of these identifies operating guidelines on how the systems should be adapted to support the deployment of different types of green supplychain strategies. This chapter seeks to investigate how system dynamics modeling can be supportive for management of feasible green supply-chain strategies. Besides conceptual considerations, we base our arguments on the development of efficient performance measurement systems for remanufacturing facilities in reverse supply chains, taking into account not only economic but also environmental issues. The behavior of the green supply-chain management under study is analyzed through a simulation model based on the principles of the system dynamics methodology. The simulation model can be helpful for green strategic management as an experimental tool, which can be used to evaluate alternative longterm strategies ("what-if" analysis) using total supply chain profit as measure of strategy effectiveness. Validation and numerical experimentation further illustrate the applicability of the developed methodology, while providing additional intuitively sound insights. © 2009, IGI Global.},
references={Angell, L.C., Klassen, R.D., Integrating environmental issues into the mainstream: An agenda for research in operations management (1999) Journal of Operations Management, 17 (5), pp. 575-598; Ashayeri, J., Heuts, R., Jansen, A., Inventory management of repairable service parts for personal computers (1996) International Journal of Operational and Production Management, 16, pp. 74-97; Barros, A.I., Dekker, R., Scholten, V., (1998) A Two-level Network For Recycling Sand: A Casestudy, 110, pp. 199-214; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-342; Bloemhof-Ruwaard, J.M., Solomon, M., van Wassenhove, L.N., The capacitated distribution and waste disposal problem (1996) European Journal of Operational Research, 88, pp. 490-503; Bloemhof-Ruwaard, J.M., van Wassenhove, L.N., Gabel, H.L., Weaver, P.M., An environmental life cycle optimization model for the european pulp and paper industry (1996) Omega, 20, pp. 615-629; Boothroyd, G., Alting, L., Design for assembly and disassembly (1992) Ann. CIRP, 41, pp. 625-636; Bowen, F.E., Cousins, P.D., Lamming, R.C., Faruk, A.C., Horses for courses (2001) Greener Management International, 35, pp. 41-60; Bras, B., McIntosh, M.W., Product, process, and organizational design for remanufacture: An overview of research (1999) Robot. Comput.-Integr. Mfg, 15, pp. 167-178; Burgelman, R.A., McKinney, W., Managing the strategic dynamics of acquisition integration: Lessons from hp and compaq (2006) California Management Review, 48 (3), pp. 6-27; Carter, C.R., Ellram, L.M., Reverse logistics: A review of the literature and framework for future investigation (1998) Journal of Business Logistics, 19, pp. 85-102; Chinander, K.R., Aligning accountability and awareness for environmental performance in operations (2001) Production and Operations Management, 10, pp. 276-291; Chouinard, M., D'Amours, S., At-Kadi, D., (2005) Integration of Reverse Logistics Activitieswithin a Supply Chain Information System, 56, pp. 105-124; Crossland, P., Smith, F.L., Value creation in fine arts: A system dynamics model of inverse demand and information cascades (2002) Strategic Management Journal, 23 (5), pp. 417-434; de Brito, M.P., Flapper, S.D.P., Dekker, R., (2003) Reverse Logistics: A Review of Case Studies B2--reverse Logistics: A Review of Case Studies, , The Netherlands, Erasmus University Rotterdam; de Koster, M.B.M., de Brito, M.P., van de Vendel, M., How to organise return handling: An exploratory study with nine retailer warehouses (2002) International Journal of Retail & Distribution Management, 30, pp. 407-421; Dekker, R., Fleischmann, M., Inderfurth, K., van Wassenhove, L.N., (2004) Reverse Logistics: Quantitative Models For Closed-loop Supply Chains B2--reverse Logistics: Quantitative Models For Closed-loop Supply Chains, , Berlin, SpringerVerlag; Depass, D., Green manufacturing gets a push (2006) Knight Ridder Tribune Business News, p. 1; Dickinson, D.A., Draper, C.W., Saminathan, M., Sohn, J.E., Williams, G., Green product manufacturing (1995) At&T Technical Journal, 74 (6), pp. 26-35; Duhaime, R., Riopel, D., Langevin, A., Value analysis and optimization of reusable containers at canada post (2000) Interfaces, 30, pp. 3-15; Dyckhoff, H., Lackes, R., Reese, J., (2003) Supply Chain Management and Reverse Logistics B2: Supply Chain Management and Reverse Logistics, , Berlin, Springer-Verlag; Ferrer, G., The economics of personal computer remanufacturing (1997) Resources, Conservation and Recycling, 21, pp. 79-108; Ferrer, G., The economics of tire remanufacturing (1997) Resources, Conservation and Recycling, 19, pp. 221-255; Ferrer, G., Ayres, R.U., The impact of remanufacturing in the economy (2000) Ecological Economics, 32, pp. 413-429; Ferrer, G., Whybark, D.C., Material planning for a remanufacturing facility (2001) Production and Operations Management, 10, pp. 112-124; Flapper, S.D.P., van Nunen, J.E.E., van Wassenhove, L.N., (2005) Managing Closed-loop Supply Chains B2: Managing Closed-loop Supply Chains, , Berlin, Springer-Verlag; Fleischmann, M., Beullens, P., Bloemhof-Ruwaard, J.M., van Wassenhove, L.N., The impact of product recovery on logistics network design (2001) Production & Operations Management, 10, pp. 156-173; Fleischmann, M., Krikke, H.R., Dekker, R., Flapper, S.D.P., A characterization of logistics networks for product recovery (2000) Omega, 28, pp. 653-666; Fleischmann, M., Kuik, R., Dekker, R., Controlling inventories with stochastic item returns: A basic model (2002) European Journal of Operational Research, 138, pp. 63-75; Fleischmann, M., van Wassenhove, L.N., van Nunen, J.A.E.E., van der Laan, E., Dekker, R., Bloemhof-Ruwaard, J.M., Quantitative models for reverse logistics: A review (1997) European Journal of Operational Research, 103, pp. 1-17; Fleishmann, B., Klose, A., (2005) Distribution Logistics B2: Distribution Logistics, , Berlin, Springer-Verlag; Forrester, J.W., (1999) Industrial Dynamics, , Waltham, MA: Pegasus Communications; Forrester, J.W., System dynamics: A personal view of the first fifty years [dagger] (2007) System Dynamics Review, 23 (2-3), p. 345; Forrester, J.W., System dynamics: The next fifty years [dagger] (2007) System Dynamics Review, 23 (2-3), p. 359; Friedman, F.B., The changing role of the environmental manager (1992) Business Horizons, 35 (2), p. 25; Gatenby, D.A., Foo, G., Design for x (dfx): Key to competitive, profitable products (1990) AT&T Technical Journal, 69, pp. 2-15; Glantschnig, W.J., Green design: An introduction to issues and challenges (1994) IEEE Transactions On Components, Packaging and Manufacturing Technology-Part A, 17, pp. 508-513; Goggin, K., Browne, J., Towards a taxonomy of resource recovery from end-of-life products (2000) Computers In Industry, 42, pp. 177-191; Goldsby, T.J., Closs, D.J., Using activitybased costing to reengineer the reverse logistics channel (2000) International Journal of Physical Distribution & Logistics Management, 30, pp. 500-514; Graedel, T.E., Material substitution: A resource supply perspective (2002) Resources, Conservation and Recycling, 34, pp. 107-115; Guide, V.D.R., Scheduling with priority dispatching rules and drum-buffer-rope in a recoverable manufacturing system (1997) International Journal Production Economics, 53, pp. 101-116; Guide Jr., V.D.R., Production planning and control for remanufacturing: Industry practice and research needs (2000) Journal of Operations Management, 18, pp. 467-483; Guide, V.D.R., Jayaraman, V., Srivastava, R., Repairable inventory theory: Models and applications (1997) European Journal of Operational Research, 102, pp. 1-20; Guide, V.D.R., Jayaraman, V., Srivastava, R., Production planning and control for remanufacturing: A state-of-the-art survey (1999) Robot. Comput.-Integr. Mfg, 15, pp. 221-230; Guide, V.D.R., Spencer, M.S., Srivastava, R., Are production systems ready for the green revolution? (1996) Production and Inventory Management Journal, Fourth Quarter, pp. 70-78; Guide, V.D.R., Spencer, M.S., Srivastava, R., An evaluation of capacity planning techniques in a remanufacturing environment (1997) International Journal of Production Research, 35, pp. 67-82; Guide Jr., V.D.R., van Wassenhove, L.N., Managing product returns for remanufacturing (2001) Production and Operations Management, 10, pp. 142-155; Guide Jr., V.D.R., van Wassenhove, L.N., The reverse supply chain (2002) Harvard Business Review, 80 (2), pp. 25-26; Gungor, A., Gupta, S.M., Issues in environmentally conscious manufacturing and product recovery: A survey (1999) Computers & Industrial Engineering, 36 (4), pp. 811-853; Gupta, A., Approximate solution of a singlebase multi-indentured repairable-item inventory system (1993) Journal of the Operational Research Society, 44, pp. 701-710; Gupta, M., Sharma, K., Environmental operations management: An opportunity for improvement (1996) Production and Inventory Management Journal, 37 (3), pp. 40-46; Haas, D.A., Murphy, F.H., Compensating for non-homogeneity in decision-making units in data envelopment analysis (2002) European Journal of Operational Research, 144, pp. 530-554; He, C.H., Gao, Y.H., Yang, S.H., Edwards, D.W., Optimization of the process for recovering caprolactam from wastewater in a pulsed-sieve-plate column using green design methodologies (2004) Journal of Loss Prevention In the Process Industries, 17, pp. 195-204; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking, 12 (4), pp. 330-353; Hilmola, O.-P., Helo, P., Ojala, L., The value of product development lead time in software startup (2003) System Dynamics Review, 19 (1), pp. 75-82; Hoek R.I.V, From reversed logistics to green supply chains (1999) Supply Chain Management, 4 (3), p. 129; Hu, T.L., Sheu, J.B., Huang, K.H., Reverse logistics cost minimization model for the treatment of hazardous wastes (2002) Transportation research, Part E: Logistics and Transportation Review, 38, pp. 457-473; Hui, I.K., Chan, A.H.S., Pun, K.F., A study of the environmental management system implementation practices (2001) Journal of Cleaner Production, 9, pp. 269-276; Inderfurth, K., de Kok, A.G., Flapper, S.D.P., Product recovery in stochastic remanufacturing system with multiple reuse options (2001) European Journal of Operational Research, 133, pp. 130-152; Inderfurth, K., van der Laan, E., Leadtime effects and policy improvement for stochastic inventory control with remanufacturing (2001) International Journal of Production Economics, 71, pp. 381-390; Jahre, M., Household waste collection as a reverse channel: A theoretical perspective (1995) International Journal Physical Distribution and Logistics Management, 25, pp. 39-46; Jayaraman, V., Guide, V.D.R., Srivastava, R., A closed-loop logistics model for remanufacturing (1999) Journal of the Operational Research Society, 50, pp. 497-508; Jayaraman, V., Patterson, R., The design of reverse distribution networks: Models and solution procedures (2003) European Journal of Operational Research, 150, pp. 128-149. , Rolland, E; Jayaraman, V., Srivastava, R., A service logistics model for simultaneous siting of facilities and multiple levels of equipment (1995) Computers and Operations Research, 22, pp. 191-204; Jayaraman, V., Srivastava, R., Benton, W.C., A joint optimization of product variety and ordering approach (1998) Computers & Operations Research, 25, pp. 557-566; Johnson, P.F., Managing value in reverse logistics systems (1998) Logistics and Transportation Review, 34, pp. 217-227; Johnson, M.R., Wang, M.H., Planning product disassembly of material recovery opportunities (1995) International Journal of Production Research, 33, pp. 3119-3142; Kainuma, Y., Tawara, N., A multiple attribute utility theory approach to lean and green supply chain management (2006) International Journal of Production Economics, 101, pp. 99-108; Kaiser, B., Eagan, P., Shaner, H., Solutions to health care waste: Lifecycle thinking and "green" purchasing (2001) Environmental Health Perspectives, 109, pp. 205-208; Khoo, H., Spedding, T.A., Bainbridge, I.D.M.R., Taplinct, Creating a green supply chain (2001) Greener Management International, 35, pp. 71-88; Kiesmuller, G.P., Scherer, C.W., Computational issues in a stochastic finite horizon one product recovery inventory model (2003) European Journal of Operational Research, 146, pp. 553-579; Kiesmuller, G.P., van der Laan, E.A., An inventory model with dependent product demands and returns (2001) International Journal of Production Economics, 72, pp. 73-87; Klassen, R., Plant level environmental management orientation: The influence of management views and plant characteristics (2001) Production and Operations Management, 10, pp. 257-275; Klausner, M., Hendrickson, C.T., Reverse logistics strategy for product take-back (2000) Interfaces, 30, pp. 156-165; Krikke, H., Bloemhof-Ruwaard, J., van Wassenhove, L.N., Concurrent product and closed-loop supply chain design with an application to refrigerators (2003) International Journal Prod. Res, 41, pp. 3689-3719; Krikke, H.R., Harten, A.V., Schuur, P.C., On a medium term product recovery and disposal strategy for durable assembly products (1998) International Journal Prod. Res, 36, pp. 111-139; Krikke, H.R., van Harten, A., Schuur, P.C., Business case oce: Reverse logistic network re-design for copiers (1999) OR Spectrum, 21, pp. 381-409; Krikke, H.R., van Harten, A., Schuur, P.C., Business case roteb: Recovery strategies for monitors (1999) Computers & Industrial Engineering, 36, pp. 739-757; Kroll, E., Beardsley, B., Parulian, A., A methodology to evaluate ease of disassembly for product recycling (1996) IIE Transactions, 28, pp. 837-845; Kroon, L., Vrijens, G., Returnable containers: An example of reverse logistics (1995) International Journal of Physical Distribution & Logistics Management, 25, pp. 56-68; Krumwiede, D.W., Sheu, C., A model for reverse logistics entry by third-party providers (2002) Omega, 30, pp. 322-333; Laperiere, L., El Maraghy, H.A., Planning of products assembly and disassembly (1992) Annals of the CIRP, 41, pp. 5-9; Lee, J.J., O'Callaghan, P., Critical review of life cycle analysis and assessment techniques and their application to com-mercial activities (1995) Conservation and Recycling, 13, pp. 37-56. , Allen, D; Linton, J.D., Johnston, D.A., A decision support system for the planning of remanufacturing at nortel (2000) Interfaces, 30, pp. 17-31; Listes, O., Dekker, R., A stochastic approach to a case study for product recovery network design (2005) European Journal of Operational Research, 160, pp. 268-287; Louwers, D., Kip, B.J., Peters, E., Souren, F., Flapper, S.W.P., A facility location allocation model for reusing carpet materials (1999) Computers & Industrial Engineering, 36, pp. 855-869; Madu, C.N., Kuei, C., Madu, I.E., A hierarchic metric approach for integration of green issues in manufacturing: A paper recycling application (2002) Journal of Environmental Management, 64, pp. 261-272; Majumder, P., Groenevelt, H., Summer, Competition in remanufacturing (2001) Production and Operations Management, 10, pp. 125-141; Marin, A., Pelegrin, B., The return plant location problem: Modeling and resolution (1998) European Journal of Operational Research, 104, pp. 375-392; Marx-Gomez, J., Rantenstrauch, C., Nurnberger, A., Kruse, R., Neuro-fuzzy approach to forecast returns of scrapped product to recycling and remanufacturing (2002) Knowledge-Based Systems, 15, pp. 119-128; Meade, L., Sarkis, J., A conceptual model for selecting and evaluating third-party reverse logistics providers (2002) Supp. Chain Manag. Int. J, 7, pp. 283-295; Melissen, F.W., de Ron, A.J., Defining recovery practices: Definitions and terminology (1999) International Journal On Environmentally Conscious Manufacturing and Design, 8, pp. 1-18; Min, H., Galle, W.P., Green purchasing practices of U.S. Firms (2001) International Journal of Operations and Production Management, 21, pp. 1222-1238; Min, H., Ko, J.H., Ko, S.C., A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns (2006) Omega, 34, pp. 56-69; Minner, S., Strategic safety stocks in reverse logistics supply chains (2001) International Journal of Production Economics, 71, pp. 417-428; Moore, K.E., Gungor, A., Gupta, S.M., A petri net approach to disassembly process planning (1998) Comp. Indus. Engin, 35, pp. 165-168; Moore, K.E., Gungor, A.K., Gupta, S.M., Petri net approach to disassembly process planning for products with complex and/or precedence relationships (2001) European Journal of Operational Research, 135, pp. 428-449; Mostard, J., Teunter, R., The newsboy problem with resalable returns: A single period model and case study (2006) European Journal of Operational Research, 169, pp. 81-96; Mourao, M.C., Amado, L., Heuristics method for a mixed capacitated arc routing problem: A refuse collection application (2005) European Journal of Operational Research, 160, pp. 139-153; Mukhopadhyay, S.K., Setoputro, R., Optimal return policy and modular design for build-to-order products (2005) Journal of Operations Management, 23, pp. 496-506; Nagel, C., Meyer, P., Caught between ecology and economy: End-of-life aspects of environmentally conscious manufacturing (1999) Comput. Ind. Eng, 36, pp. 781-792; Nagurney, A., Reverse supply chain management and electronic waste recycling: A multitiered network equilibrium framework for e-cycling (2005) Transportation Re-search Part E: Logistics and Transportation Review, 41, pp. 1-28; Nasr, N., Environmentally conscious manufacturing (1997) Careers and The Engineer, pp. 26-27; Navinchandra, D., Design for environmentally (1991) Desig Theor Methodol, 31, pp. 99-125; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Nimse, P., Vijayan, A., Kumar, A., Varadarajan, C., A review of green product databases (2007) Environmental Progress, 26 (2), pp. 131-137; Owen, J.V., Environmentally conscious manufacturing (1993) Manufacturing Engineering, 111 (4), p. 44; Pochampally, K.K., Gupta, S.M., Strategic planning of a reverse supply chain network (2005) International Journal of Integrated Supply Management (IJISM), 1 (4), pp. 421-441; Porter, M.E., van der Linde, C., Green and competitive: Ending the stalemate (1995) Harvard Business Review, 73 (5), pp. 120-134; Porter, M.E., van der Linde, C., Toward a new conception of the environment: Competitiveness relationship (1995) Journal of Economic Perspectives, 9 (4), pp. 97-118; Ravi, V., Shankar, R., Tiwari, M.K., Analyzing alternatives in reverse logistics for endof-life computers: Anp and balanced scorecard approach (2005) Computers and Industrial Engineering, 48, pp. 327-356; Rentmeester, K., Green manufacturing (2005) Marketplace, 17 (4), p. 26; Richter, K., The eoq repair and waste disposal model with variable setup numbers (1996) European Journal of Operational Research, 95, pp. 313-324; Richter, K., Dobos, I., Analysis of the eoq repair and waste disposal problem with integer setup (1999) International Journal of Production Economics, 59, pp. 463-467; Richter, K., Sombrutzki, M., Remanu-facturing planning for the reverse wagner/whitin models (2000) European Journal of Operational Research, 121, pp. 304-315; Richter, K., Weber, J., The reverse wagner/whitin model with variable manufacturing and remanufacturing cost (2001) International Journal of Production Economics, 71, pp. 447-456; Ritchie, L., Burnes, B., Whittle, P., Hey, R., The benefits of reverse logistics: The case of the manchester royal infirmary pharmacy (2000) Supply Chain Management, 5, pp. 226-233; Sarkis, J., Evaluating environmentally conscious business practices (1998) European Journal of Operational Research, 107, pp. 159-174; Sarkis, J., A methodological framework for evaluating environmentally conscious manufacturing programs (1999) Comput. Ind. Eng, 36, pp. 793-810; Sarkis, J., (2001) Greener Manufacturing and Operations: From Design to Delivery and Back B2: Greener Manufacturing and Operations: From Design to Delivery and Back, , Sheffield: Greenleaf; Sarkis, J., Cordeiro, J.J., An empirical evaluation of environmental efficiencies and firm performance: Pollution prevention versus end-ofpipe practice (2001) European Journal of Operational Research, 135, pp. 102-113; Savaskan, R.C., Bhattacharya, S., van Wassenhove, L.N., Closed loop supply chain models with product remanufacturing (2004) Management Science, 50, pp. 239-252; Scherpereel, C., van Koppen, C.S.A., Heering, G.B.F., Selecting environmental performance indicators (2001) Greener Management International, 33, pp. 97-114; Scheuring, J., Bras, B.A., Lee, K.M., Significance of design for disassembly on integrated disassembly and assembly processes (1994) International Journal of Environmentally Conscious Design and Manufacturing, 3, pp. 21-23; Seliger, G., Zussman, E., Kriwet, A., Integration of recycling considerations into product design: A system approach (1994) Information and Collaboration Models of Integration-based On the NATO Research Workshop On Integration, , In S. Y. Nof (Ed.), Il Crocco, Italy. Dordrecht, The Netherlands: Kluwer Academic; Sheu, J.B., Chou, Y.H., Hu, C.C., An integrated logistics operational model for green supply chain management (2005) Transportation Research Part E, 41, pp. 287-313; Shih, L.H., Reverse logistics system planning for recycling electrical appliances and computers in taiwan (2001) Resour. Conserv. Recycl, 32, pp. 55-72; Spicer, A.J., Johnson, M.R., Thirdparty demanufacturing as a solution for extended producer responsibility (2004) Journal of Cleaner Production, 12, pp. 37-45; Srivastava, S.K., Green supply-chain management: A state-of-the-art literature review (2007) International Journal of Management Reviews, 9 (1), pp. 53-80; Srivastava, S.K., Srivastava, R.K., Profit driven reverse logistics (2005) International Journal of Business Research, 4, pp. 53-61; Srivastava, S.K., Srivastava, R.K., Managing product returns for reverse logistics (2006) International Journal of Physical Distribution and Logistics Management (Special Issue On 3PL, 4PL and Reverse Logistics), 36, pp. 524-546; Swenseth, S., Godfrey, M., Incorporating transportation costs into inventory replenishment decisions (2002) International Journal of Production Economics, 77, pp. 113-130; Taleb, K.N., Gupta, S.M., Disassembly of multiple product structures (1997) Comput. Ind. Engng, 32, pp. 949-961; Tan, A.W.K., Yu, W.S., Kumar, A., Improving the performance of a computer company in supporting its reverse logistics operations in the asia-pacific region (2002) International Journal of Physical Distribution & Logistics Management, 33, pp. 59-74; Theyel, G., Customer and supplier relations for environmental performance (2001) Greener Management International, 35, pp. 61-69; Van der Laan, E., Dekker, R., Salomon, M., Product remanufacturing and disposal: A numerical comparison of alternative control strategies (1996) International Journal of Production Economics, 45, pp. 489-498; Van der Laan, E., Dekker, R., Salomon, M., Ridder, A., An (s, q) inventory model with remanufacturing and disposal (1996) International Journal of Production Economics, 46, pp. 339-350; van der Laan, E., Salomon, M., Pro-duction planning and inventory control with remanufacturing and disposal (1997) European Journal of Operational Research, 102, pp. 264-278; van der Laan, E., Salomon, M., Dekker, R., An investigation of lead-time effects in manufacturing/remanufacturing systems under simple push and pull control strategies (1999) European Journal of Operational Research, 115, pp. 195-214; van der Laan, E., Salomon, M., Dekker, R., van Wassenhove, L., Inventory control in hybrid systems with remanufacturing (1999) Manage-ment Science, 45, pp. 733-747; van Hoek, R.I., From reversed logistics to green supply chains (1999) Supply Chain Manage-ment, 4, pp. 129-135; Vincent, M., China's manufacturing capa-bility steadily ramping, going 'green' (2006) Connector Specifier, 22 (5), pp. 1-2; Vlachos, D., Georgiadis, P., Iakovou, E., A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop sup-ply chains (2007) Computers & Operations Research, 34 (2), p. 367; Vlachos, D., Tagaras, G., An inventory system with two supply modes and capacity constraints (2001) International Journal of Production Economics, 72, pp. 41-58; Wagner, M., Schaltegger, S., Wehrmeyer, W., The relationship between the environmental and economic performance of firms: What does theory propose and what does empirical evidence tell us? (2001) Greener Management International, 34, pp. 95-108; Warren, J.P., Rhodes, E., Carter, R., A total product system concept (2001) Greener Manage-ment International, 35, pp. 89-104; White, C.D., Masanet, E., Rosen, C.M., Beck-Man, S.L., Product recovery with some byte: An overview of management challenges and environmental consequences in reverse manu-facturing for the computer industry (2003) Journal of Cleaner Production, 11, pp. 445-458; Zhang, H.C., Kuo, T.C., Lu, H., Huang, S.H., Environmentally conscious design and manufacturing: A state-of-the-art survey (1997) Journal Mfg Systems, 16, pp. 352-371; Zhu, Q.H., Geng, Y., Spring, Integrating environmental issues into supplier selection and manage management: A study of large and medium-sized stateowned enterprises in china (2002) Greener Management International, 35, pp. 27-40; Zhu, Q.H., Sarkis, J., Relationships between operational practices and performance among early adopters of green supply chain management practices in chinese manufacturing enterprises (2004) Journal of Operations Management, 22 (3), pp. 265-289},
correspondence_address1={Institute of Scientific and Technical Information of China, Beijing, China},
publisher={IGI Global},
issn={12345678},
isbn={9781605661148},
language={English},
abbrev_source_title={Web-Based Gn. Prod. Life Cycle Mgmt. Syst.: Reverse Supp. Chain Utiliz.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Peters2008149,
author={Peters, M. and Odenthal, B. and Schlick, C.M.},
title={Design, implementation and evaluation of a performance measurement system for virtual enterprises in the aerospace industry},
journal={Strategies and Tactics in Supply Chain Event Management},
year={2008},
pages={149-165},
doi={10.1007/978-3-540-73766-7_10},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84892314136&doi=10.1007%2f978-3-540-73766-7_10&partnerID=40&md5=63c6807cb85227d7baec3c36016b899e},
affiliation={Institute of Industrial Engineering and Ergonomics of RWTH Aachen, Bergdriesch 27, 52425 Aachen, Germany},
abstract={Requirements towards suppliers in the aerospace industry are currently changing significantly. Aircraft manufacturers expect their suppliers not only to deliver components and modules but also to develop, manufacture and integrate complex systems independently. Furthermore, suppliers are expected to provide logistic support over the complete life cycle of their system, especially during their operation, e.g. with airlines. Thus, first-tier-suppliers must on the one hand offer the required competencies for the complex process of system integration, and on the other hand possess a sufficient financial capacity to carry the risks of product development. These requirements are met only by few companies, leading to a drastic reduction of potential first-tier suppliers. Companies that do not meet the mentioned requirements have to re-position within the supply-chain and become suppliers of subsystems or sub-subsystems for manufacturers of systems. © 2008 Springer-Verlag Berlin Heidelberg.},
references={(2005) Positionspapier des Bundesverband der Deutschen Luft- und Raumfahrtin-dustrie EV. (BDLI) 2005, , BDLI; Ijioui, R., Emmerich, H., Ceyp, M., Hägele, K., Diercks, W., Supply Chain Event Management in der Luftfahrtindustrie (2006) Supply Chain Management, 1, pp. 41-46. , 2006; Jehle, E., Schulze Im Hove, A., Performance measurement in supply chains with the supply chain balanced scorecard (2005) Conference Proceedings, , NOFOMA 2005, Ko-penhagen, 9th-10th June 2005; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Management, 20, pp. 1119-1145; Peters, M., Odenthal, B., Hinrichsen, S., Schlick, C., Kennzahlensysteme in Virtuellen Unternehmen der Luftfahrtzulieferindustrie (2006) Virtu-elle Organisationen und Neue Medien 2006, , Meißner K, Engelien M eds.:, Dresden: TUDpress, 2006; Samtleben, M., Müller, A., Hess, T., Unterstützung der Balanced Scorecard durch In-formationstechnologie: Eine Bestandsaufnahme für den deutschsprachigen Raum (2005) ZfCM Controlling & Management, 49, pp. 400-407; Stausberg, M., (2003) Kennzahlen im Managementsystem, , WEKA, Augsburg; (2005) Supply-chain Operations Reference-model - Overview of SCOR Version 7.0, , Supply-Chain Council, Washington},
correspondence_address1={Peters, M.; Institute of Industrial Engineering and Ergonomics of RWTH Aachen, Bergdriesch 27, 52425 Aachen, Germany},
publisher={Springer Berlin Heidelberg},
isbn={9783540737650},
language={English},
abbrev_source_title={Strategies and Tactics in Supp. Chain Event Mgmt.},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Yu20081079,
author={Yu, M. and Zhang, C. and Yang, J.},
title={On operation mode of Beijing olympics logistics analysis of Beijing olympic logistics SCOR model and performance measurement system},
journal={38th International Conference on Computers and Industrial Engineering 2008},
year={2008},
volume={2},
pages={1079-1089},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888222031&partnerID=40&md5=76573c91dc2bf904c6e8124047088085},
affiliation={Business School, China University of Political Science and Law, Beijing, China; Beijing Foreign Studies University, China},
abstract={Olympic logistics could be regarded as complicated system engineering: The relationship among the sports equipment manufacturer, the Olympic Games partners and the sponsors, there has the essential buying-selling relations for the product or the service; Regarding the participative personnel, audience and tourists, there has the material supply-sales relations. Therefore the viewpoint how to analyze operation mode on the basis of the supply chain and construct performance measurement system must play vital role in Olympic logistics. This thesis creatively applies the SCOR model in the analysis of Beijing Olympic logistics, constructs Olympic logistics SCOR model from the corporate planning, the core flow level, the disposition level and the decomposition level and so on. Also the author selects some appropriate performance measurement indicator about the core flow level and instructs measurement systems by using the comparison - Analytic hierarchy process. Copyright© (2008) by Computers & Industrial Engineering.},
author_keywords={Olympic logistics;  Operation pattern;  Performance measurement system;  SCOR model},
keywords={Complicated systems;  Corporate planning;  Decomposition level;  Olympics;  Operation patterns;  Performance measurement indicators;  Performance measurement system;  SCOR models, Industrial engineering;  Supply chains, Supply chain management},
references={Mascaritolo, J., Logistics at the 1996 olympic games (1996) American Logistics Annual Meeting Collection in 1996; Sowinski, L.L., Going for the (Logistics) gold (2000) International Trade; Wisner, J.D., Leong, G.K., Tan, K.-C., (2003) Principles of Supply Chain Management-A Balanced Approach, pp. 440-449. , Thomson South-Western; Hongyi, R., (2002) Research Electronic Commerce in Olympic Games Application Pattern; Wenjie, Z., The implicate opportunities in olympic logistics (2004) China Storage and Transport; Xianping, S., Na, Z., Depth management for olympic logistic (2004) Economist, 10; Lijuan, W., (2005) On the Operation Mode of Olympic Logistics, , Beijing Wuzi University; Jinsheng, S., Wei, G., (2006) Modern Olympic Logistics Technology and Management, (1). , China Railway Publishing House; Qiao, L., (2007) Analysis of Beijing Olympic Logistics Supply Chain, , Tianjing University; Huixia, Z., (2004) Logistics Supply Chain Management, pp. 17-19. , Tsinghua University Press; Wenjie, Z., (2007) Beijing Olympic Games Logistics System Planning, pp. 27-30. , Chinese Commodity Publishing house},
correspondence_address1={Business School, , Beijing, China},
isbn={9781627486828},
language={English},
abbrev_source_title={Int. Conf. Comput. Ind. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Chearskul2008175,
author={Chearskul, P. and Glover, W.J.},
title={Defining and assessing key practices in the effective use of performance measurement systems: A case Study},
journal={29th Annual National Conference of the American Society for Engineering Management 2008, ASEM 2008},
year={2008},
pages={175-184},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880058653&partnerID=40&md5=845add67aba4055d7895fca89041f047},
affiliation={Department of Industrial and Systems Engineering, Virginia Tech, United States},
abstract={It has been suggested that the use phase is the most vital phase for successful performance measurement systems. For a performance measurement system to lead to improved organizational performance, the performance information must be effectively reviewed by organization members. The purpose of this paper is to describe and illustrate a case application of an assessment tool to define and measure key practices in the performance review process: monitoring, analyzing and interpreting, decision-making, taking-action, and communicating and managing emergent and existing knowledge. These key performance measurement use practices were defined based on a literature review drawing from performance measurement, management information systems and organizational learning. This paper presents a framework for assessing the use of a performance measurement system and the pilot application of this framework to assess the organizational review process. Lastly, a future research agenda is discussed. Copyright © (2008) by the American Society for Engineering Management.},
author_keywords={Performance analysis;  Performance measurement system use;  Performance review process},
keywords={Literature reviews;  Organizational learning;  Organizational performance;  Performance analysis;  Performance measurement system;  Performance measurements;  Performance reviews;  Pilot applications, Engineering;  Industrial engineering, Supply chain management},
references={Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), p. 217; Assiri, A., Zairi, M., Eid, R., How to profit from the balanced scorecard (2006) Industrial Management + Data Systems, 106 (7), p. 937; Bauer, J., Tanner, S.J., Neely, A., Developing a performance measurement audit template -€ a benchmarking study (2004) Measuring Business Excellence, 8 (4), pp. 17-25; Bisbe, J., Otley, D., The effects of the interactive use of management control systems on product innovation (2004) Accounting, Organizations and Society, 29 (8), pp. 709-737; Bititci, U.S., Turner, U.T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Bourne, M., Kennerley, M., Santos, M.F., Managing through measures: A study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), p. 373; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bourne, M., Neely, A., Mills, J., Platts, K., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), p. 1; Brown, M.G., (2000) Winning Score: How to Design and Implement Organizational Scorecards, , Productivity Press; Chearskul, P., The relationship between performance measurement system use and organizational outcomes (2007) Proceedings from the 28th American Society for Engineering Management Conference, , Chattanooga Tennessee, (November 7-10); De Waal, A.A., (2002) Quest for Balance : The Human Element in Performance Management Systems, , New York: J. Wiley; De Waal André, A., Counet, H., Lesson learned from the balance scorecard (2006) Proceedings from the Performance Measurement and Management, , Public and Private, London, UK; Dervitsiotis, K.N., The design of performance measurement systems for management learning (2004) Total Quality Management & Business Excellence, 15 (4), p. 457; Doll, W.J., Torkzadeh, G., Developing a multidimensional measure of system-use in an organizational context (1998) Information & Management, 33 (4), pp. 171-185; Farris, J., Aken, E.V., Groesbeck, R.L., (2004) Assessment of AT&T FASC Performance Review Process, , Unpublished Report, Virginia Tech; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Hass, S., Burnaby, P., Bierstaker, J.L., The use of performance measures as an integral part of an entity's strategic plan (2005) Managerial Auditing Journal, 20 (2), pp. 179-186; Henri, J.-F., Management control systems and strategy: A resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558; Henri, J.-F., Organizational culture and performance measurement systems (2006) Accounting, Organizations and Society, 31 (1), pp. 77-103; Ho, S.-J.K., Chan, Y.-C.L., Performance measurement and the implementation of balanced scorecards in municipal governments (2002) The Journal of Government Financial Management, 51 (4), p. 8; Hoque, Z., A contingency model of the association between strategy, environmental uncertainty and performance measurement: Impact on organizational performance (2004) International Business Review, 13 (4), pp. 485-502; Hoque, Z., James, W., Linking balanced scorecard measures to size and market factors: Impact on organizational performance (2000) Journal of Management Accounting Research, 12, p. 1; Ittner, C.D., Larcker, D.F., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, 81 (11), pp. 88-95; Ittner, C.D., Larcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting, Organizations and Society, 28 (7-8), pp. 715-741; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), p. 71; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), p. 75; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), p. 213; Marr, B., (2006) Strategic Performance Management: Leveraging and Measuring your Intangible Value Drivers, , Elsevier; Marr, B., Business performance measurement: An overview of the current state of use in the USA (2005) Measuring Business Excellence, 9 (3), p. 56; Marr, B., (2006) Strategic Performance Management, , Oxford, UK: Elsevier Ltd; Martinez, V., Kennerley, M., Impact of performance management reviews: Evidence fron an energy supplier (2005) Proceedings from the EUROMA Conference: Operations and Global Competitiveness, , Budapest, Hungry, June 19-22); Martinez, V., Kennerley, M., Performance review: A definition analysis (2006) Proceedings from the Performance Measurement and Management: Public and Private, , London, UK; Martins, R., A. Use of performance measurement systems: Some thought towards a comprehensive approach (2000) Performance Measurement 2000: Past, Present, and Future, , A. Neely (Ed.), Crandield: Centre for Business Performance; Meekings, A., Unlocking the potential of performance measurement: A practical implementation guide (1995) Public Money & Management, 15 (4), p. 5; Meekings, A., Effective review meetings: The counter-intuitive key to successful performance measurement (2005) International Journal of Productivity and Performance Management, 54 (3), pp. 212-220; Najmi, M., Rigas, J., Fan, I.-S., A framework to review performance measurement systems (2005) Business Process Management Journal, 11 (2), pp. 109-122; (1999) Balancing Measures: Best Practices in Performance Management, , National Partnership for Reinventing Government ,Washington, DC; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Adams, C., Kennerley, M., (2002) Performance Prism: The Scorecard for Measuring and Managing Stakeholder Relationships, , London: Prentice Hall Financial Times; Neely, A., Bourne, M., Why measurement initiatives fail (2000) Measuring Business Excellence, 4 (4); Neely, A., Kennerley, M., Martinez, V., Does the balanced scorecard work: An empirical investigation (2004) Proceedings from the the 4th International Conference on Performance Measurement, , Edinburgh; Neely, A., Micheli, P., Martinez, Veronica., (2006) Action on Information: Performance Management for the Public Sector, , London: The Advanced Institute of Management Research; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), p. 1119; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Pollanen, R.M., Performance measurement in municipalities: Empirical evidence in Canadian context (2005) International Journal of Public Sector Management, 18 (1), pp. 4-24; Rungtusanatham, M., Anderson, J.C., Dooley, K.J., Conceptualizing organizational implementation and practice of statistical process control (1997) Journal of Quality Management, 2 (1), pp. 113-137; Simons, R., (1995) Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal, , Harvard Business School Press; Simons, R., (2000) Performance Measurement & Control Systems for Implementing Strategy, , New Jersey: Prentice-Hall, Inc; Speckbacher, G., Bischof, J., Pfeiffer, T., A descriptive analysis on the implementation of Balanced Scorecards in German-speaking countries (2003) Management Accounting Research, 14 (4), pp. 361-388; Ukko, J., Tenhunen, J., Rantanen, H., Performance measurement impacts on management and leadership: Perspectives of management and employees (2007) International Journal of Production Economics, 110 (1-2), p. 39; Widener, S.K., An empirical analysis of the levers of control framework (2007) Accounting, Organizations and Society, 32 (7-8), p. 757},
correspondence_address1={Department of Industrial and Systems Engineering, United States},
language={English},
abbrev_source_title={Annu. Natl. Conf. Am. Soc. Eng. Manage., ASEM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Barth2008769,
author={Barth, K.B. and Formoso, C.T.},
title={Improvement of performance measurement systems using production management dashboards},
journal={Proceedings of IGLC16: 16th Annual Conference of the International Group for Lean Construction},
year={2008},
pages={769-780},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856630725&partnerID=40&md5=f5ff3b9f5cbedaabdafd4395803bd3d1},
affiliation={Federal University of Rio Grande Do sul, Building Innovation Research Unit, 99 Osvaldo Aranha Av., Porto Alegre-RS, Brazil; UFRGS, NORIE, Brazil},
abstract={The issue of performance measurement has been increasingly discussed by the academic community and professionals of most industries. The large amount of publications about this theme indicates its importance for business management and also the existence of gaps in knowledge. In Brazil, there has been an increasing interest from construction companies in improving their performance measurement systems. However, the characteristics of this industry make the development and implementation of performance measurement systems a relatively complex task. Previous research has pointed out the need for improving the use of performance measurement for decision making, by properly processing and analyzing existing data. One way of improving performance measurement systems is the development of performance dashboards, which allow a set of performance indicators to be monitored through a single interface. This paper proposes a set of guidelines for the conception and assessment of production management dashboards, as a mechanism for improving performance measurement systems for production management in construction. It is based on two action research empirical studies, which involved the development and implementation of production management dashboards in two different construction companies. The main contributions of this research study are: (a) criteria to assess the effectiveness of dashboards; (b) mechanisms for involving different management levels on the use of dashboards; and (c) guidelines for implementing dashboards.},
author_keywords={Dashboard;  Measurement systems;  Performance measurement;  Process transparency;  Production management},
keywords={Academic community;  Action research;  Business management;  Complex task;  Construction companies;  Dashboard;  Empirical studies;  Improving performance;  Management level;  Measurement systems;  Performance indicators;  Performance measurement system;  Performance measurements;  Production management;  Research studies, Benchmarking;  Construction industry;  Research, Supply chain management},
references={Beatham, S., Anumba, C., Thorpe, T., Hedges, I., An integrated business improvement system (IBIS) for construction (2005) Measuring Business Excellence, 9 (2), pp. 42-55. , DOI 10.1108/13683040510602876; Beatham, S., Anumba, C., Thorpe, T., Hedges, I., KPIs: A critical appraisal of their use in construction (2004) Benchmarking, 11 (1), pp. 93-117. , DOI 10.1108/14635770410520320; Bourguignon, A., Malleret, V., Nørreklit, H., (2001) Balanced Scorecard Versus French Tableau de Bord: Beyond Dispute, A Cultural and Ideological Perspective, , mar; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., The success and failure of performance measurement initiatives: Perception of participating managers (2002) International Journal of Operation & Production Mgmt., 22 (11), pp. 1288-1310; Busi, M., Bititci, U.S., Collaborative performance management: Present gaps and future research (2006) International Journal of Productivity and Performance Management, 55 (1), pp. 7-25. , DOI 10.1108/17410400610635471; Chiapello, E., Lebas, M., The tableau de Bord a French approach to management information (1996) 19th Annual Meeting of the European Accounting Association, , Communication presented at the, Bergen (Norway), 2-4th May; Costa, D.B., Formoso, C.T., Kagioglou, M., Alarcon, L.F., Caldas, C.H., Benchmarking initiatives in the construction industry: Lessons learned and improvement opportunities (2006) Journal of Management in Engineering, 22 (4), pp. 158-167. , DOI 10.1061/(ASCE)0742-597X(2006)22:4(158); Eckerson, W.W., (2005) Performance Dashboards: Measuring, Monitoring, and Managing Your Business, 320p. , 1 ed., John Wiley Trade; Eden, C., Huxham, C., Action research for management research (1996) British Journal of Management, 7, pp. 75-86; Few, S., Intelligent dashboard design (2005) DM Review Magazine, pp. 12-14. , United States; Galsworth, G.D., (1997) Visual Systems: Harnessing the Power of A Visual Workplace, p. 320. , New York, AMACOM; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48 (3), pp. 207-225; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations & Production Mgmt, 16 (8), pp. 63-80; Grief, M., (1991) The Visual Factory: Building Participation Through Shared Information, , Portland: Productivity Press; Kaplan, R.S., Norton, D.P., (2004) Strategy Maps: Converting Intangible Assets into Tangible Outcomes, , Harvard Business School Press; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations and Production Management, 23 (2), pp. 213-229. , DOI 10.1108/01443570310458465; Krauss, M., Marketing dashboards drive better decisions (2005) Marketing News, , out; Lantelme, E.M.V., Formoso, C.T., Improving performance though measurement: The application of lean production and organisational learning principles (2000) 8th International IGLC Conference, , Brighton; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 1-3 (41), pp. 23-35. , Amsterdam; Manoochehri, G., Overcoming obstacles to developing effective performance measures (1999) Work Study, 48 (6), pp. 223-229. , London; Malik, S., (2005) Enterprise Dashboards: Design and Best Practices for It, 240p. , John wiley trade; Middleton, A., By the dashboard light (2005) Marketing, 10 (15), p. 14. , Toronto, 2 p., apr; Mohamed, S., Benchmarking and improving construction productivity (1996) Benchmarking for Quality Management & Technology., 3 (3), pp. 50-58; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations and Production Management, 19 (2), pp. 205-228. , DOI 10.1108/01443579910247437; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277. , DOI 10.1108/01443570510633648; Neely, A., Bourne, M., Why measurement initiatives fail (2000) Measuring Business Excellence., 4 (4), pp. 3-6; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152. , Bradford; Neely, A., Najjar, M.A., Management learning not management control: The true role of performance measurement? (2006) California Management Review., 48 (3); Orts, D., Dashboard implementation methodology (2005) DM Review Magazine, pp. 16-17. , United States, jun; Schiemann, W.A., Lingle, J.H., (1999) Bullseye!: Hitting Your Strategic Targets Through Highimpact Measurement, , New York: The Free Press; Shermach, K., Driving performance (2005) Sales and Marketing Management, 157 (12), p. 18; Samson, M., Lema, N.M., Development of construction contractors performance measurement framework (2002) 1st International Conference of Creating A Sustainable Construction Industry in Developing Countries, , Paper presented at the, Cape Town, 11-13 November; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement system: An interdisciplinary review (1999) International Journal of Production Economics, 60-61, pp. 53-60. , Amsterdam, apr},
correspondence_address1={Barth, K.B.; Federal University of Rio Grande Do sul, 99 Osvaldo Aranha Av., Porto Alegre-RS, Brazil; email: kbertotto@gmail.com},
isbn={9781905732456},
language={English},
abbrev_source_title={Proc. IGLC: Annu. Conf. Int. Group Lean Constr.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Tonanont2008764,
author={Tonanont, A. and Yimsiri, S. and Jitpitaklert, W. and Rogers, K.J.},
title={Performance evaluation in reverse logistics with data envelopment analysis},
journal={IIE Annual Conference and Expo 2008},
year={2008},
pages={764-769},
note={cited By 18},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-64249091413&partnerID=40&md5=ae730745d773dde1a7ada4fad3f504e8},
affiliation={Department of Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, Arlington, TX 76019, United States},
abstract={Due to the increasing value of reverse logistics in recent years, many enterprises pay more attention to their reverse supply chain. Performance measurement is one of the most important tools that help them identify problems and improve their existing reverse logistics strategies. In this article, the authors apply the concept of Balanced Scorecard (BSC), Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) together to create a new performance measurement framework to analyze the performance of reverse channels. This research applies this new methodology with real reverse logistics data from electronics companies proposed in the form of a case study.},
author_keywords={Data Envelopment Analysis;  Performance evaluation;  Performance measurement;  Reverse Logistics},
keywords={Analytic hierarchies;  Balanced score cards;  Electronics companies;  Performance evaluation;  Performance measurement;  Performance measurement frameworks;  Reverse channels;  Reverse Logistics;  Reverse supply chains, Analytic hierarchy process;  Data envelopment analysis;  Hierarchical systems;  Linear programming;  Supply chains, Logistics},
references={Stock, J., Speh, T., Many Happy (Product) Returns (2002) Harvard Business Review, 80 (7), pp. 16-17; Rogers, D.S., (2005) Reverse logistics executive council, , http://www.rlec.org/glossary.html, 25 July, Internet on-line; Rogers, D. S., R. S. Tibben-Lembke, et al. (1999). Going backwards reverse logistics trends and practices. [Reno], University of Nevada, Reno, Center for Logistics Management; Li, W., Research on the Performance Evaluation of Logistics Activities (2005) China-USA Business Review, 4 (4); Durtsche, J.S.K.A.D., Logistics Performance Measurement and the 3PL Value Proposition (2001) Logistics Quarterly, 7 (2), pp. 10-11-22. , and; Beamon, Benita M. (1999). Designing the Green Supply Chain, Logistics Information Management, 12, No. 4, pp. 332-342. Awarded most outstanding paper of 1998 in Logistics Information Management; Kaplan, R.S., Norton, D.P., The balanced scorecard-Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Charnes, A., Cooper, W.W., Rhodes, E., (1978) Measuring the Efficiency of Decision Making Units, 2, pp. 429-444. , EJOR; Hamdan, A. (2005). A methodology to establish warehouse productivity measures and warehouse efficiency using DEA, Ph.D., The University of Texas at Arlington, United States -- Texas; Ramanathan, R., Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process (2006) Computers & Operations Research, 33 (5), pp. 1289-1307; Estellita Lins, M.P., Moreira da Silva, A.C., Lovell, C.A.K., Avoiding infeasibility in DEA models with weight restrictions (2007) European Journal of Operational Research, 181 (2), pp. 956-966; Li, X.B., Reeves, G.R., A multiple criteria approach to data envelopment analysis (1999) European Journal of Operational Research, 115, pp. 507-517; Yellepeddi, S. S. (2006). A methodology for evaluating the performance of reverse supply chains in consumer electronics industry, Ph.D., The University of Texas at Arlington, United States -- Texas; Dyson, R.G., Allen, R., Camanho, A.S., Podinovski, V.V., Sarrico, C.S., Shale, E.A., Pitfalls and protocols in DEA (2001) European Journal of Operational Research, 132, pp. 245-259},
correspondence_address1={Tonanont, A.; Department of Industrial and Manufacturing Systems Engineering, , Arlington, TX 76019, United States},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Kodrowski20081136,
author={Kodrowski, J. and Youngblood, A.},
title={Evaluation of performance measurement systems for a market-leading, multi-national organization},
journal={IIE Annual Conference and Expo 2008},
year={2008},
pages={1136-1141},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-63849337251&partnerID=40&md5=e2a6db581b52d01001877bf411d19dd0},
affiliation={Bredero Shaw Singapore Pte Ltd Singapore, Singapore 596748, Singapore; Department of Industrial Systems Engineering and Engineering Management, University of Alabama in Huntsville, Huntsville, AL 35899, United States},
abstract={Performance measurement systems for a market-leading, multi-national organization are reviewed. The question of whether or not those systems can support continued attainment of strategic goals is explored. This paper considers current performance measurement systems that align with strategic goals. The case study organization is presented with the existing performance measurement systems employed. A gap analysis is performed comparing the performance measurement systems in the case study organization to other published systems in similar organizations. Particularly, the balanced scorecard system is utilized in the gap analysis. Strengths and weaknesses for the organization's performance measurement system are identified and recommendations for improvement are made.},
author_keywords={Balanced scorecard;  Engineering management;  Performance measurement},
keywords={Balanced scorecard;  Current performance;  Engineering management;  Gap analysis;  Performance measurement;  Performance measurement systems;  Strategic goals, Supply chain management, Measurements},
references={Abdel-Maksoud, A., Dugdale, D., Luther, R., Non-financial performance measurement in manufacturing companies (2005) The British Accounting Review, 37 (36), p. 261; Chakravarthy, B.S., Measuring Strategic Performance (1986) Strategic Management Journal, 437 (5), p. 7; Franceschini, F., Galetto, M., Cecconi, P., A Worldwide Analysis of ISO 9000 Standard Diffusion (2006) Benchmarking, 13 (4), pp. 523-541; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) Int. J. Production Economics, 48 (18), p. 207; Jick, T.A., Peiperl, M.A., (2003) Managing Change, , McGraw Hill. New York; Kaplan, R.S., Norton, D.P., The Balanced Scorecard - Measures That Drive Performance (1992) Harvard Business Review, 70 (1), p. 71; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (5), p. 134; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California Management Review, 39 (1), pp. 53-79; Kaplan, R.S., Norton David, P., Building a strategy-focused organization (2001) Ivey Business Journal, 65 (5), p. 12; Kaplan, R.S., Norton David, P., Transforming the balanced scorecard from performance measurement to strategic management: Part I (2001) Accounting Horizons, 15 (1), p. 87; Kaplan, R.S., Norton David, P., Transforming the balanced scorecard from performance measurement to strategic management: Part II (2001) Accounting Horizons, 15 (2), p. 147; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (9), p. 209; Stewart, I.J., Measuring the Performance of Foreign Subsidiaries (1989) Business, 48 (4), p. 39; Stoneham, P., BP Amoco: Integrating Competitive and Financial Strategy. Part One: Strategic Planning in the Oil Industry (2000) European Management Journal, 18 (4). , 411-419},
correspondence_address1={Kodrowski, J.; Bredero Shaw Singapore Pte Ltd Singapore, Singapore 596748, Singapore},
language={English},
abbrev_source_title={IIE Annu. Conf. Expo},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Xiongwei2008410,
author={Xiongwei, Z. and Feicheng, M. and Xueying, W.},
title={Information sharing in multi-level supply chain with demand uncertainty},
journal={Proceedings - 2008 International Seminar on Future Information Technology and Management Engineering, FITME 2008},
year={2008},
pages={410-413},
doi={10.1109/FITME.2008.40},
art_number={4746522},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-62949115851&doi=10.1109%2fFITME.2008.40&partnerID=40&md5=909860b354f1ca2e509dbf92e199a466},
affiliation={School of Information Management, Wuhan University, China},
abstract={Previous research indicated the effectiveness of information sharing on supply chain through analyzing simplified two-level supply chain model to counteract the bullwhip effect, describing growing variation upstream in a supply chain. In this paper, we present a simulation study that investigates the effectiveness of information sharing under demand uncertainty scenarios within a complex multi-level supply chain model. In our research, a computer model is developed to simulate the complex multi-level supply chain model. We build a new supply chain performance measure mechanism. The simulation data and results analysis show both distributors and suppliers gain significantly from information sharing except retailers. Further more, the improved mechanism is feasible on supply chain performance measure. © 2008 IEEE.},
keywords={Information dissemination;  Information retrieval;  Information technology;  Supply chain management;  Supply chains, Bullwhip effects;  Computer models;  Demand uncertainties;  Improved mechanisms;  Information sharing;  Multi levels;  Simulation datum;  Simulation studies;  Supply chain models;  Supply chain performance, Competition},
references={F. Chen, Z. Drezner,J.K, & D. Simchi-Levi, Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting,lead time, and information, Management Science,46(4), 2000,pp.436-443; Gavirneni, S., Information flows in capacitated supply chains with fixed ordering costs (2002) Management Science, 48 (5), pp. 664-651; Lee, H., Padmanabhan, P., Whang, S., The bull-whip effect in supply chains (1997) Sloan management Review, 8 (3), pp. 93-102; Lee, H., Padmanabhan, P., Whang, S., Information distortion in a supply chain: The bullwhip effect (1997) Management Science, 43 (4), pp. 546-558; Huang, Z., Gangopadhyay, A., A simulation study of supply chain management to measure the impact of information sharing (2004) Information Resource Management Journal, 17 (3), pp. 20-31. , PP; Huang, Z., Gangopadhyay, A., Gupta, J.N.D., Sharing information among supply chain partners to counteract the bullwhip effect (2004) Proceedings of the DSI Annual Meeting, pp. 2101-2106; Huang, Z., Gangopadhyay, A., Information sharing in supply chain management with demand uncertainty (2005) Advanced Topics in Information Resource Management, 5, pp. 45-45. , Hertfordshire:Idea Group; Tan, G.W., Wang, B., The relationship between product nature, demand patterns and information sharing strategies (2001) Proceedings of 22nd ICIS, pp. 543-549. , New Orleans, USA; Hunag, M.X., Pan, Q., Cheng, Y.M., An incentive mechanism of information sharing in supply chain (2007) Proceedings of 14nd ICMS&E, pp. 711-716. , Harbin, P.R. China; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Prodution Economy, 55, pp. 281-294; Gunasekaran, A., Patel, C., Tittiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operation and Production management, 21, pp. 71-87; J.P.C. Kleijnen, M.T.Smits, Performance metrics in supply chain management, Journal of Operational Research Society, 11(11), 2003,pp.1-8; Yan, L., Woo, T.G., Information sharing in a supply chain with dynamic consumer demand pattern (2004) Proceedings of the 37th Hawaii International Conference on System Sciences, pp. 1-10},
correspondence_address1={Xiongwei, Z.; School of Information Management, China; email: daweycs@126.com},
isbn={9780769534800},
language={English},
abbrev_source_title={Proc. - Int. Semin. Future Inf. Technol. Manage. Eng., FITME},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Tok2008216,
author={Tok, A. and Jeng, S.-T. and Liu, H. and Ritchie, S.G.},
title={Design and initial implementation of an inductive signature-based real-time traffic performance measurement system},
journal={IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
year={2008},
pages={216-221},
doi={10.1109/ITSC.2008.4732703},
art_number={4732703},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-60749092757&doi=10.1109%2fITSC.2008.4732703&partnerID=40&md5=50a66e5dcec2ed986b968afb897f07c7},
affiliation={Institute of Transportation Studies, University of California, Irvine, CA 92697, United States; Institute of Transportation Studies, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, United States},
abstract={The need for accurate, comprehensive and timely traffic surveillance information is critical to ensure optimal traffic operations and management for advanced traffic management systems (ATMS). This paper describes an on-going study that involves the design and implementation of the section-based freeway Real-Time Traffic Performance Measurement System (RTPMS)-an advanced surveillance system based on inductive vehicle signature technologies. Unlike traffic performance measurement systems that depend on point measures, RTPMS provides section-based travel time measures via matching of inductive vehicle signatures obtained at two adjacent detector station locations. Hence, the performance measures account for traffic conditions spanning an entire section, not just at a local detector station. In addition, each re-identified vehicle is also classified in RTPMS, yielding detailed section-based performance measures of different vehicle classes. This gives the ability to obtain more accurate travel statistics and vehicle exposure rates, such as those of commercial vehicles. © 2008 IEEE.},
keywords={Advanced traffic management systems;  Commercial vehicles;  Detector stations;  Exposure rates;  Inductive vehicle signatures;  Performance measures;  Real-time traffics;  Surveillance systems;  Traffic conditions;  Traffic operations;  Traffic performance;  Traffic surveillances;  Travel time, Highway traffic control;  Intelligent vehicle highway systems;  Motor transportation;  Supply chain management;  Taxicabs;  Trucks;  Vehicle locating systems;  Vehicles, Traffic surveys},
references={Jeng, S.-T., Ritchie, S.G., A new inductive signature data compression and transformation method for on-line vehicle reidentification, (2006) Proceedings of the 85th Annual Meeting of the Transportation Research Board, , CD-ROM, Washington, D.C; Jeng, S.-T., Ritchie, S.G., Real-time vehicle classification using Inductive loop signature data Transportation Research Record, , to be published; Chen, C., Petty, K., Varaiya, P., Jia, Z., Freeway performance measurement system: Mining loop detector data (2001) Transportation Research Record, (1748), pp. 96-102. , TRB, National Research Council, Washington D.C; Coifman, B., Improved velocity estimation using single loop detectors (2001) Transportation Research, 35 A (10), pp. 863-880; C. Sun, S. G. Ritchie, K. Tsai, and R. Jayakrishnan, Use of vehicle; signature analysis and lexicographic optimization for vehicle reidentification on freeways, Transportation Research, 7C, no. 4, 1999, pp. 167-185; Ritchie, S.G., Park, S., Oh, C., Jeng, S.-T., Tok, A., Anonymous vehicle tracking for real-time freeway and arterial street performance measurement, California PATH Research Report, , UCB-ITS-PRR-2005-9, 2005; Jeng, S.-T., Ritchie, S.G., Tok, A., Freeway performance measurements based on vehicle reidentification (2006) Proceedings of the 86th Annual Meeting of the Transportation Research Board, , CD-ROM, Washington, D.C; Google.com, , http://code.google.com/apis/maps/documentation, Google Maps API Concepts, June 12, 2008},
correspondence_address1={Tok, A.; Institute of Transportation Studies, , Irvine, CA 92697, United States; email: sjeng@uci.edu},
language={English},
abbrev_source_title={IEEE Conf Intell Transport Syst Proc ITSC},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Jiang20082141,
author={Jiang, H. and Ruan, J.},
title={Analysis of influencing factors on performance measurement of the supply chain based on SCOR-model and AHM},
journal={Proceedings of 2008 IEEE International Conference on Service Operations and Logistics, and Informatics, IEEE/SOLI 2008},
year={2008},
volume={2},
pages={2141-2146},
doi={10.1109/SOLI.2008.4682888},
art_number={4682888},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049188338&doi=10.1109%2fSOLI.2008.4682888&partnerID=40&md5=ad37c2dc62eff339ef9b722b2673f891},
affiliation={School of Economics and Management, Hebei University of Engineering, Handan, China},
abstract={The performance measurement of the supply chain is the key of effective supply chain management, which is not only helpful to describe and analyze the historical performance and status, but also can be used to set performance targets and determine the future focus. The paper applies Analytic Hierarchical Model (AHM) and bases on the performance measurement system of supply chain in the SCOR-model to analyze the factors that influence the whole performance of the supply chain. This can provide the basis for researchers to study how to improve the operation performance of the supply chain and for enterprises to implement performance measurement of the supply chain. ©2008 IEEE.},
author_keywords={Analytic hierarchical model;  Performance measurement;  SCOR-model;  The supply chain},
keywords={Hierarchical systems;  Logistics;  Modal analysis;  Public key cryptography;  Supply chains, Analytic hierarchical model;  Historical performances;  Influencing factors;  Operation performances;  Performance measurement;  Performance Measurement Systems;  Performance targets;  SCOR-model;  The supply chain, Supply chain management},
references={Stadtler, H., Kilger, C., Supply Chain Management and Advanced Planning, pp. 53-54. , Germany: Springer, pp; Otto, A., Kotzab, H., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144, p. 306320; Otto, A., Kotzab, H., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E, 38, pp. 439-456; Li, W., Zhang, L., Meng, W., A Reanalysis of AHM in Enterprise Funding Decision-making Journal of Hebei Institute of Architectural Science and Technology, 21 (4), pp. 107-110. , in Chinese; Liu, Z., Analytic Hierarchical Model and Its Application Journal of Hebei Institute of Architectural Science and Technology, 20 (3), pp. 77-80; Cai, H., Cheng, Q., The Application of Attribute Hierarchical Model to Decision Making of Stock-Selection Mathematics in Practice and Theory, 35 (3), pp. 55-58},
correspondence_address1={Jiang, H.; School of Economics and Management, , Handan, China; email: hdjianghua@126.com},
isbn={9781424420131},
language={English},
abbrev_source_title={Proc. IEEE Int. Conf. Serv. Oper. Logist., Informatics, IEEE/SOLI},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ran2008142,
author={Ran, W. and Wang, M.},
title={Develop adaptive workplace E-learning environments by using performance measurement systems},
journal={ICEIS 2008 - 10th International Conference on Enterprise Information Systems, Proceedings},
year={2008},
volume={SAIC},
pages={142-147},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-58049134630&partnerID=40&md5=5415fd6bfcd5410261cddb286b3dc508},
affiliation={Division of Information and Technology Studies, Faculty of Education, University of Hong Kong, Hong Kong, Hong Kong},
abstract={Workplace learning is an environmental contextual and dynamic procedure. Needs and desires in workplace learning arise from actions and practices in working environment; learning contents consist of explicit and tacit knowledge dynamically created and intertwined with working and practicing. The development of an effective workplace E-learning system is faced with several problems: 1) How to specify and update learning needs and desires in contextual and dynamic workplace settings? 2) How to activate and formalize knowledge sharing and contribution procedure for collecting knowledge emerged during practices in working communities? 3) How to organize and store knowledge pieces in a way that reflects workplace learning needs and supports adaptive learning content delivery? 4) How to incessantly update and adjust learning contents to keep up with the changing working context? In order to solve these problems, we propose an adaptive workplace learning model, in which the performance measurement result is used as an indication of working proficiency, a reflection of learning needs, and a sign of the level and quality of knowledge shared and contributed for achieving specific performance, with a view to organizing learning contents and effectively guiding learning and knowledge sharing process.},
author_keywords={Adaptive E-learning;  Performance measurement system;  Workplace learning},
keywords={Adaptive E-learning;  Adaptive learnings;  E-learning systems;  Knowledge sharing;  Learning contents;  Performance measurement system;  Performance measurements;  Tacit knowledges;  Working environments;  Workplace learning, E-learning;  Information systems;  Internet;  Learning systems;  Multimedia systems;  Statistical methods;  Supply chain management, Education},
references={Brookfield, S., Uncovering Assumptions: The Key to Reflective Practice (1992) Adult Learning, 3 (4), pp. 13-18; Brubaker, R., Cooper, F., Beyond 'identity' (2000) Theory and Society, 29, pp. 1-47; Brusilovsky, P. 2003. Developing adaptive educational hypermedia systems: From design models to authoring tools. In Murray, T., Blessing, S., Ainsworth, S (eds.). Authoring tools for advanced technology learning environment. Dordrecht: Kluwer Academic Publishers; Constantine, K., Reconceptualizing the learning transfer conceptual framework: Empirical validation of a new systemic model (2004) International Journal of Training & Development, 8 (3); Derouin, R.E., Fritzsche, B.A., SALAS, E., E-Learning in Organizations (2005) Journal of Management, 31, p. 920; Desimone, R.L., Werner, J.M., Harris, D.M., (2002) Human Resource Development, , 3rd ed. Orlando, FL: Harcourt, Inc; Eraut, M., Non-formal Learning and Tacit Knowledge in Professional Work (2000) British Journal of Educational Psychology, 70, pp. 113-136; Fenwick, T., Tides of Change: New Themes and Questions in Workplace Learning (2001) New Directions for Adult and Continuing Education, 92, pp. 3-17; Win. Kontoghiorghes, G 2002. Predicting motivation to learn and motivation to transfer learning back to the job in a service organization: a new systemic model for training effectiveness. Performance Improvement Quarterly, 15:114-129; Lave, J., Wenger, E., (1991) Situated Learning: Legitimate Peripheral Participation, , Cambridge: Cambridge University Press; Learning Technology Standards Committee of the IEEE. 2002. Draft Standard for Learning Object Metadata. IEEE 1484.12.1-2002 ed. New York, Institute of Electrical and Electronics Engineers; Megginson, D., Planned and Emergent Learning (1996) Management Learning, 27 (4), pp. 411-428; Modritscher, F., Gare A-Barrios, V. M. & G T1, C 2004. The Past, the Present and the Future of adaptive E-Learning. Proceedings of ICL. Villach, Austria; Nonaka, I., A Dynamic Theory of Knowledge Creation (1994) Organization Science, 5 (1), pp. 14-37; (1991) Action Learning in Practice, , Pedler, M, ed, 2nd ed. Aldershot: Gower; Paramythis, A., Loidl-Reisinger, S., Adaptive Learning Environments and e-Learning Standards (2004) Electronic Journal on e-Learning, 2 (1), pp. 181-194; Rigano, D., Edwards, J., Incorporating Reflection into Work Practice (1998) Management Learning, 29 (4), pp. 431-446; Robinson, H.S., Anumba, G.J., Carrillo, P.M., Al-Ghassani, A.M., Business performance measurement practices in construction engineering organisations (2005) Measuring Business Excellence, 9 (1), pp. 13-22; Shute, V., Towle, B., Adaptive E-Learning (2003) Educational Psychologist, 38 (2), pp. 105-114; Slizyte, A. & Bakanauskiene, I. 2007. Designing performance measurement system in organization. Organizacij Vadyba: Sisteminiai Tyrimai, 43; Stephanie, S., Signs of the times (2005) Training Magazine; Tyre, M.J., Hippel, E.V., The Situated Nature of Adaptive Learning in Organizations (1997) Organization Science, 8 (1), pp. 71-82; Wang, M., Wang, H., Xu, D., The Design of Intelligent Workflow Monitoring with Agent Technology (2005) Knowledge-based Systems, 18 (6), pp. 257-266; Wang, M., Wang, H., From Process Logic to Business Logic - A Cognitive Approach to Business Process Management (2006) Information & Management, 43 (2), pp. 179-193; Winch, A., Ingram, H., Re-defining the focus of workplace learning (2002) International Journal of Contemporary Hospitality Management, 14, pp. 361-367},
correspondence_address1={Ran, W.; Division of Information and Technology Studies, , Hong Kong, Hong Kong; email: ranwj@hku.hk},
isbn={9789898111395},
language={English},
abbrev_source_title={ICEIS - Int. Conf. Enterp. Inf. Syst., Proc.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Lam2008187,
author={Lam, A.C.Y. and Kwok, S.K. and Lee, W.B.},
title={A knowledge-based performance measurement system for improving resource utilization},
journal={ICEIS 2008 - Proceedings of the 10th International Conference on Enterprise Information Systems},
year={2008},
volume={AIDSS},
pages={187-192},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-55349106185&partnerID=40&md5=cf12b5b964225263134838c18c1e8a55},
affiliation={Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong},
abstract={In current manufacturing industry, there are various challenges including short product life cycle, process automation and global competition. It is critical for the manufacturing companies to ensure effective utilization of production assets for overall business success. In order to focus scarce resources on areas that have the greatest impact on productivity, performance evaluation on the resource allocation is necessary to assist companies improving the resource utilization and accomplishing their objectives. In this paper, a knowledge-based performance measurement system (KPMS) is designed to evaluate the resource allocation decisions and provide recommendations to improve the performance and physical asset utilization. The framework of the proposed system, which is constructed by rule-based reasoning, case-based reasoning and a mathematical model, is introduced. By integrating the mathematical model with knowledge rules, performance indicators that are associated with the achievement of company objectives can be determined to quantify the performance of the resource allocation. Moreover, case-based reasoning technique is adopted to evaluate the performance and reuse the experience in past cases to provide recommendations for improvement.},
author_keywords={Knowledge-based system;  Performance measurement;  Resource utilization},
keywords={Business success;  Company objectives;  Global competition;  Knowledge rules;  Manufacturing companies;  Manufacturing industries;  Performance evaluation;  Performance indicators;  Performance measurement system;  Performance measurements;  Physical assets;  Process automation;  Production assets;  Resource allocation decision;  Resource utilizations;  Rule based reasoning;  Scarce resources;  Short product, Artificial intelligence;  Benchmarking;  Competition;  Decision support systems;  Decision theory;  Knowledge based systems;  Mathematical models;  Measurements;  Resource allocation;  Supply chain management, Case based reasoning},
references={Ahmad, M.M., Dhafr, N., Establishing and improving manufacturing performance measures (2002) Robotics and Computer Integrated Manufacturing, 18, pp. 171-176; Chen, C.C., An objective-oriented and product-line-based manufacturing performance measurement (2008) International Journal of Production Economics, 112, pp. 380-390; Lee, J., Ni, J., Djurdjanovic, D., Qiu, H., Liao, H.T., Intelligent prognostics tools and e-maintenance (2006) Computers in Industry, 57, pp. 476-489; Mitchell, J.S., Carlson, J., Equipment asset management - what are the real requirements? (2001) Reliability Magazine, pp. 4-14. , October, pp; Wang, W.M., Cheung, C.F., Lee, W.B., Kwok, S.K., A knowledge-based infotronics supervisory system for intelligent service automation (2006) Proceedings of the Institution of Mechanical Engineers - Part B -Engineering Manufacture, 220 (5), pp. 745-758; Wen, W., Chen, Y.H., Chen, I.C., A knowledge-based decision support system for measuring enterprise performance (2008) Knowledge-based systems, 21, pp. 148-163; Yurdakul, M., Measuring a manufacturing system's performance using Saaty's system with feedback approach (2002) Integrated manufacturing systems, 13 (1), pp. 25-34},
correspondence_address1={Lam, A. C. Y.; Department of Industrial and Systems Engineering, , Hung Hom, Hong Kong; email: 06900222r@polyu.edu.hk},
isbn={9789898111388; 9789898111371},
language={English},
abbrev_source_title={ICEIS - Proc. Int. Conf. Enterp. Inf. Syst.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Choy20081998,
author={Choy, K.L. and Chow, H.K.H. and Tan, K.H. and Chan, C.-K. and Mok, E.C.M. and Wang, Q.},
title={Leveraging the supply chain flexibility of third party logistics - Hybrid knowledge-based system approach},
journal={Expert Systems with Applications},
year={2008},
volume={35},
number={4},
pages={1998-2016},
doi={10.1016/j.eswa.2007.08.084},
note={cited By 61},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-48749115315&doi=10.1016%2fj.eswa.2007.08.084&partnerID=40&md5=957baae9a377be0eabc31ddd0d56fc3c},
affiliation={Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong; Nottingham University Business School, Jubilee Campus, Nottingham, NG8 1GL, United Kingdom; Department of Applied Mathematics, Hong Kong Polytechnic University, Hung Hom, Hong Kong; Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hung Hom, Hong Kong},
abstract={Nowadays, intensified globalization and consequent competitive pressures have reemphasized the importance of third party logistics (3PL) in managing logistics processes as well as customer and supplier relationships within the supply chain management (SCM). However, 3PL providers and their upstream and downstream parties within the whole chain have usually interacted as disconnected entities in the logistics performance management process. Despite the increased interest in using and improving logistics information systems, the number of researchers investigating the design and implementation of this process in close detail is still very small. This paper proposes an intelligent performance measurement system (K-LPMS) for measuring the performance of 3PL providers and their upstream and downstream supply chain partners. With the help of K-LPMS, 3PL providers are capable to fulfil different customer's supply chain service requirement through accessing the capability of each SC partners and reconfigure SC network. This paper describes the structure of K-LPMS and its features. The results of testing and applying the tool in companies are presented. This paper concludes by discussing the implications of this research for managers, and identifying directions for future research. © 2007 Elsevier Ltd. All rights reserved.},
author_keywords={3PL;  Knowledge-based system;  Operations management;  Supply chain flexibility;  Supply chain management},
keywords={Customer satisfaction;  Digital signal processing;  Industrial management;  Information management;  Knowledge based systems;  Knowledge management;  Light emission;  Logistics;  Luminescence;  Management;  Military operations;  Outsourcing;  Process engineering;  Research;  Rivers;  Supply chains, 3PL;  3PL providers;  Future research;  Information systems;  Knowledge-based system;  Logistics processes;  Operations management;  Performance management;  Performance Measurement Systems;  Supplier relationships;  Supply chain flexibility;  Supply chain partners;  Third-party logistics, Supply chain management},
references={Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), pp. 217-223; Barad, M., Sapir, D.E., Flexibility in logistic systems - modeling and performance evaluation (2003) International Journal of Production Economics, 85, pp. 155-170; Beamon, B., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Bello, D.C., Gilliland, D.I., The effects of output controls, process controls and flexibility on export channel performance (1997) Journal of Marketing, 61 (4), pp. 22-38; Billesbach, T.J., Hayen, R., Long-term impact of just-in-time on inventory performance measures (1994) Production and Inventory Management Journal, 35 (1), pp. 62-67; Bowersox, D.J., Closs, D.J., (1996) Logistical management: The integrated supply chain process, , McGraw-Hill, New York; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Caprice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) International Journal of Logistics Management, 5 (2), pp. 11-28; Caprice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) International Journal of Logistics Management, 6 (1), pp. 61-74; Chan, F., Qi, H., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chow, H.K.H., Choy, K.L., Lee, W.B., Chan, F.T.S., Design of a knowledge-based logistics strategy system (2005) Expert Systems with Applications, 29, pp. 272-290; Chow, H.K.H., Choy, K.L., Lee, W.B., Lau, K.C., Design of a RFID case-based resource management system for warehouse operations (2006) Expert Systems with Applications, 30 (4), pp. 561-576; Christopher, M., Juttner, U., Developing strategic partnerships in the supply chain: A practitioner perspective (2000) European Journal of Logistics Management, 6, pp. 117-127; Dougherty, P.J., Pittman, P.H., Utilization of time-based strategies (1995) International Journal of Operations and Production Management, 15 (2), pp. 54-60; Farley, G.A., Discovering supply chain management: A roundtable discussion (1997) APICS - The Performance Advantage, 7 (1), pp. 38-39; Fawcett, S.E., Clinton, S.R., Enhancing logistics performance to improve the competitiveness of manufacturing organizations (1996) Production and Inventory Management Journal, 37 (1), pp. 40-46; Fawcett, S., Smith, S., Cooper, M., Strategic intent, measurement capability, and operational success: Making the connection (1997) International Journal of Physical Distribution and Logistics, 27 (7), pp. 410-421; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56, pp. 663-680; Fuller, J.B., O'Conor, J., Rawlinson, R., Tailored logistics: The next advantage (1993) Harvard Business Review, (May-June), pp. 87-97; Garvin, D.A., Building a learning organization (1993) Harvard Business Review, 71 (4), pp. 78-81; Gosain, S., Malhotra, A., Elsawy, O.A., Coordinating for flexibility in e-business supply chains (2004) Journal of Management Information Systems, 21 (3), pp. 7-45; Handfield, R.B., Nichols, E.L., (1999) Introduction to supply chain management, , Prentice Hall, Upper Saddle River, NJ; Harland, C.M., Lamming, R.C., Cousins, P.D., Developing the concept of supply strategy (1999) International Journal of Operations and Production Management, 19 (7), pp. 650-673; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics, 30 (10), pp. 847-868; Huang, S.H., Sheoran, S.K., Keskar, H., Computer-assisted supply chain configuration based on supply chain operations (SCOR) model (2005) Computers and Industrial Engineering, 48, pp. 377-394; Hult, G.S.M., Ketchen Jr., D.J., Slater, S.F., Information processing, knowledge development, and strategic supply chain performance (2004) Academy of Management Journal, 47 (2), pp. 241-253; Lieb, R.C., Millen, R.A., Wassenhove, L.V., Third-party logistics services: A perspective of experienced American and European manufactures (1993) International Journal of Physical Distribution and Logistics Management, 6 (23), pp. 35-44; Lieb, R.C., Randel, H.L., A comparison of the use of third-party logistics services by large American manufacturers, 1991, 1994, and 1995 (1996) Journal of Business Logistics, 17 (1), pp. 305-321; Maskell, B., Performance measurement for world class manufacturing (1992) Corporate Controller (COP), (January-February), pp. 44-48; Maskell, P., (1992) Nyetableringer Industrien, , CBS Press, Copenhagen; Mello, A. (2001), Sept 11 attacks reveal supply-chain vulnerabilities, ZDNet Update (October 29, 2001) available at echupdate.zdnet.com/techupdate/main/0,141179,2817153,00.html; Naylor, J.B., Naim, M.M., Berry, D., Leagility - integrating the lean and agile manufacturing paradigms in the total supply chain (1999) International Journal of Production Economics, 62 (1-2), pp. 107-118; Odette, (2001), Logistics evaluation - A guide to logistics improvement, Version 2, Release 2, Organization for data exchange by tele-transmission in Europe, London; Ohmae, K., Getting back to strategy (1988) Harvard Business Review, 66, pp. 149-156; Pardalos, P.M., Tsitsiringos, V.K., (2002) E-commerce and supply chain, , Kluwer Academic Publishers, Netherlands; Rabinovich, E., Windle, R., Dresner, M., Corsi, T., Outsourcing of integrated logistics functions (1999) International Journal of Physical Distribution and Logistics Management, 29 (6), pp. 353-373; Radjou, N. (2002), Building an adaptive supply network, supply-chain, World North America, April 22-24, New Orleans, LA; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89, pp. 231-243; Serve, M., Yen, D.C., Wang, J.-C., Lin, B., B2B-enhanced supply chain process: Toward building virtual enterprises (2002) Business Process Management Journal, 8 (3), pp. 245-253; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Designing and managing the supply chain. 2nd Ed., , Mcgraw-Hill, Irwin; Sohail, M.S., Sohal, A.S., The use of third party logistics services: A Malaysian perspective (2003) Technovation, 23, pp. 401-408; Stanley, E.F., Cooper, M.B., Logistics performance measurement and customer success (1998) Industrial Marketing Management, 27, pp. 341-357; Tan, K., Lim, C., Platts, K., Koay, H., Managing manufacturing technology investments: An intelligent learning system approach (2006) International Journal of Computer Integrated Manufacturing, 19 (1), pp. 4-13; Van Hoek, R., Measuring the un-measurable - measuring and improving performance in the supply chain (1998) Supply Chain Management, (4), pp. 187-192; Young-Ybarra, C., Wiersema, M., Strategic flexibility in information technology alliances: The influence of transaction cost economics and social exchange theory (1999) Organization Science, 10 (4), pp. 439-459},
correspondence_address1={Choy, K.L.; Department of Industrial and Systems Engineering, , Hung Hom, Hong Kong; email: mfklchoy@inet.polyu.edu.hk},
issn={09574174},
coden={ESAPE},
language={English},
abbrev_source_title={Expert Sys Appl},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Johnsson20081224,
author={Johnsson, S. and Norström, C. and Wall, A.},
title={PMEX - A performance measurement evaluation matrix for the development of complex products and systems},
journal={PICMET: Portland International Center for Management of Engineering and Technology, Proceedings},
year={2008},
pages={1224-1234},
doi={10.1109/PICMET.2008.4599732},
art_number={4599732},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-52649148061&doi=10.1109%2fPICMET.2008.4599732&partnerID=40&md5=47aef1cc5df23e6544abaf4e2a317df4},
affiliation={Mälardalen University, School of Innovation, Design and Engineering, Sweden; ABB Corporate Research, Västerås, Sweden},
abstract={A key aspect in a sustainable economy is to be able to do more with less by making better use of resources. Within the development of complex products and systems, a continuous need to improve performance exists, i.e., making better use of a company's resources. In this improvement process it is important to measure the performance of the product development process. Previous research mainly focuses on the design and implementation of new performance measurement systems, not on evaluating the measures currently used. The research question in this paper is how to evaluate a company's performance measurement systems from a manager's perspective. To answer this question, a performance measurement evaluation matrix (PMEX) is developed. The PMEX has the different phases of the Stage-Gate process as one dimension and important success factors in the development of complex products and systems as the other dimension. Furthermore, a multiple case study has been conducted as a first verification of the PMEX. The first results of the study indicate that the PMEX enables managers to overview what is and what is not measured. The PMEX can therefore function as a conceptual tool in the discussions for setting the scope of the performance measurement system. © 2008 PICMET.},
keywords={Chlorine compounds;  Managers;  Matrix algebra;  Product development;  Project management;  Research;  Statistical methods;  Supply chain management;  Technology;  Uncertain systems, Complex products;  Evaluation matrices;  Gate process;  Multiple-case study;  One-dimension;  Performance measurement;  Performance Measurement Systems;  Portland;  Product development process;  Success factors;  Sustainable economy;  Technology managements, Industrial management},
references={Goffin, K., Mitchell, R., (2005) Innovation Management: Strategy and Implementation Using the Pentathlon Framework, , London: Palgrave MacMillan; Hobday, M., Product complexity, innovation and industrial organization (1998) Research Policy, 26, pp. 689-710; Paranjape, B., Rossiter, M., Pantano, V., Performance measurement systems: Successes, failures and future - a review (2006) Measuring Business Excellence, 10, p. 12; Arbnor, I., Bjerke, B., (1997) Methodology for Creating Business Knowledge, , USA: Sage Publications; Katz, D., Kahn, R.L., (1978) The Social Psychology of Organizations, , 2ed. New York: John-Wiley & Sons; Senge, P.M., (1990) The Fifth Discipline, the Art & Practice of Learning Organizations, , New York: Currency Doubleday; Bremser, W.G., Barsky, N.P., Utilizing the balanced scorecard for R&D performance measurement (2004) R&D Management, 34, pp. 229-238; Jiménez-Zarco, A.I., Martínez-Ruiz, M.P., González-Benito, O., Performance measurement system (PMS) integration into new product innovation: A literature review and conceptual framework (2006) Academy of Marketing Science Review, 2006; Cooper, R.G., Stage-Gate Systems: A New Tool for Managing New Products (1990) Business Horizons, 33, pp. 44-55; Ulrich, K.T., Eppinger, S.D., (2003) Product design and development, , 3ed. Singapore: McGraw-Hill Education; Rush, H., Managing innovation in complex product systems (CoPS) (1997) EPSRC Technology Management Initiative (Engineering & Physical Sciences Research Council), IEE Colloquium on, p. 4; R. G. Cooper and E. J. Kleinschmidt, www.stage-gate.com. 2008: Stage-Gate Inc, 2008; Cooper, R.G., (2001) Winning at new products: Accelerating the process from idea to launch, , Cambridge, MA: Perseus Books; Griffin, A., PDMA research on new product development practices: Updating trends and benchmarking best practices (1997) The Journal of Product Innovation Management, 14, pp. 429-458; Ettlie, E.J., Elsenbach, M.J., Modified Stage-Gate® Regimes in New Product Development (2007) The Journal of Product Innovation Management, 24, pp. 20-20; Neely, A., (2007) Business performance measurement, , 2 ed. Cambridge: Cambridge University Press; Neely, A., (1998) Measuring Business Performance, , London: Profile books Ltd; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25, pp. 1264-1277; R. S. Kaplan and D. P. Norton, The Balanced Scorecard measures that drive performance, Harvard Business Review, No. January-February, 1992; Driva, H., Pawar Kulwant, S., Menon, U., Performance evaluation of new product development from a company perspective (2001) Integrated Manufacturing Systems, 12, pp. 368-378; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20, pp. 754-754; Kerssens-van Drongelen, I.C., Nixon, B., Pearson, A., Performance measurement in industrial R&D (2000) International Journal of Management Reviews, 2, pp. 111-143; Neely, A., Adams, C., Crowe, P., The performance prism in practice (2001) Measuring Business Excellence, 5, pp. 6-12; Pun Kit, F., White Anthony, S., A performance measurement paradigm for integrating strategy formulation: A review of systems and frameworks (2005) International Journal of Management Reviews, 7, p. 22; Nixon, B., Conference report: Performance measurements for R&D (1997) R & D Management, 27, pp. 87-90; Syamil, A., Doll William, J., Apigian Charles, H., Process performance in product development: Measure and impacts (2004) European Journal of Innovation Management, 7, pp. 205-217; Johnsson, S., Cederblad, L., Norström, C., Wall, A., Productivity framework for innovative product development (2007) 5th International Symposium on Management of Technology, pp. 1655-1659. , Hangzhou, China; V. Tang, B. Liu, B. A. Kellam, K. N. Otto, and W. Seering, P, Enabling factors in successful product development, in International conference on engineering design Melburne, Australia, 2005; Ernst, H., Success Factors of New Product Development: A Review of the Empirical Literature (2002) International Journal of Management Reviews, 4, pp. 1-40; Cooper, R.G., Kleinschmidt, E.J., Winning business in product development: The critical success factors (2007) Research Technology Management, 50, pp. 52-66; Adams, R., Bessant, J., Phelps, R., Innovation management measurement: A review (2006) International Journal of Management Reviews, 8, pp. 21-47; Bessant, J., Tidd, J., (2007) Innovation and entrepreneurship, , Chichester: John Wiley & Sons Ltd; Christensen, C.M., (2003) The Innovator's Dilemma: The Revolutionary Book that Will Change the Way You Do Business, , New York: Collins; Schilling, M.A., (2006) Strategic management of technical innovation, , 2 ed, McGraw-Hill; Foos, T., Schum, G., Rothenberg, S., Tacit knowledge transfer and the knowledge disconnect (2006) Journal of Knowledge Management, 10, pp. 6-18},
correspondence_address1={Johnsson, S.; Mälardalen University, Sweden},
isbn={1890843172; 9781890843175},
language={English},
abbrev_source_title={PICMET - Portland Int. Cent. Manage. Eng. Technol. Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Zhou20081940,
author={Zhou, J. and Li, Q. and Xiao, T.-Y. and Folan, P.},
title={Performance measurement of inter-organizational integration in the networked manufacturing environment},
journal={Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS},
year={2008},
volume={14},
number={10},
pages={1940-1950},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-56549093252&partnerID=40&md5=614df662f0751d690354a5dc0449a213},
affiliation={Department of Automation, Tsinghua University, Beijing 100084, China; Department of University Affairs, National University of Defense Technology, Changsha 410073, China; Research Unit of Computer Integrated Manufacturing, National University of Ireland, Galway, Ireland},
abstract={To enhance inter-organizational integration performance in the networked manufacturing environment, based upon the conceptual framework of economic view, an integrated and complete solution was proposed for the performance measurement of intra-organizational and inter-organizational integration, which realized the related performance analysis and management from enterprise network level to enterprise node level and under the enterprise node level as well. A concrete intra-organizational modeling method was presented for economic view at first, which was correlated to the relevant models of other views in Computer Integrated Manufacturing (CIM) architecture. Then, a performance measurement method of inter-organizational integration was carefully examined and discussed from three aspects, which were the required essentials to construct a complete performance measurement system.},
author_keywords={Computer integrated manufacuturing;  Economic view;  Inter-organizational integration;  Networked manufacturing;  Performance measurement},
keywords={Computer aided manufacturing;  Computer architecture;  Manufacture;  Supply chain management, Complete solutions;  Conceptual frameworks;  Economic view;  Economic views;  Enterprise networks;  Inter-organizational integration;  Modeling methods;  Networked manufacturing;  Networked manufacturing environments;  Performance analyses;  Performance measurement;  Performance Measurement Systems;  Performance measurements, Computer integrated manufacturing},
references={Folan, P., Browne, J., A review of performance measurement (2005) Towards performance management, Computers in Industry, 56 (3), pp. 663-680; Li, Q., Chen, Y., (2007) Modeling and Analysis of Enterprise and Information System, , Beijing: China Higher Education Press, in Chinese; Browne, J., Sackett, P.J., Wortmann, J.C., Future manufacturing systems-towards the extended enterprise (1995) Computers in Industry, 25 (3), pp. 235-254; Browne, J., Zhang, J., Extended and virtual enterprises-similarities and differences (1999) International Journal of Agile Management Systems, 1 (1), pp. 30-36; Xu, X., Zhan, D., Ye, D., Building of agile virtual enterprises and its integrated-support environment (1998) Computer Integrated Manufacturing Systems, 4 (1), pp. 9-13. , in Chinese; Zhen, T., Qi, G., Wu, Z., Research on the general technology of agile manufacturing (1999) Computer Integrated Manufacturing Systems, 5 (3), pp. 1-10. , in Chinese; Chen, Y.L., Tseng, M.M., A stair-like CIM system architecture (1997) IEEE Transactions on CPMT, Part C, 20 (2), pp. 101-110; Wang, Q., Study on the enterprise performance modelling method supporting the research of economic view (2006), Beijing: Tsinghua University, in Chinese; Ma, N., Studies on the economic view of the stair-like CIM system architecture (2003), Beijing: Tsinghua University, in Chinese; Zadeh, L.A., Toward a generalized theory of uncertainty (GTU) - An outline (2005) Information Sciences, 172 (1-2), pp. 1-40; Zadeh, L.A., Generalized theory of uncertainty (GTU) - Principal concepts and ideas (2006) Computational Statistics and Data Analysis, 51 (1), pp. 15-46; Kueng, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Quality Management, 11 (1), pp. 67-85; Zhou, J., Li, Q., Xu, D.F., Fuzzy performance modeling aligned with process and organization model of integrated system in manufacturing (2006) Lecture Notes in Computer Science, 4223, pp. 1000-1009; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics, 30 (10), pp. 847-868; Kleijnen, J., Smits, M., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Kaplan, R., Norton, D., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Bradley, P., A performance measurement approach to the re-engineering of manufacturing enterprises (1996), Galway, Ireland: CIMRU, NUI Galway; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations & Production Management, 20 (5), pp. 520-533},
correspondence_address1={Zhou, J.; Department of Automation, , Beijing 100084, China; email: zhoujiandragon@gmail.com},
issn={10065911},
coden={JJZXF},
language={Chinese},
abbrev_source_title={Jisuanji Jicheng Zhizao Xitong},
document_type={Article},
source={Scopus},
}

@ARTICLE{Charan2008512,
author={Charan, P. and Shankar, R. and Baisya, R.K.},
title={Analysis of interactions among the variables of supply chain performance measurement system implementation},
journal={Business Process Management Journal},
year={2008},
volume={14},
number={4},
pages={512-529},
doi={10.1108/14637150810888055},
note={cited By 119},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-49349095530&doi=10.1108%2f14637150810888055&partnerID=40&md5=ccde8d80a1eac19bf5a85f46a50ccce7},
affiliation={Department of Management Studies, Indian Institute of Technology Delhi, New Delhi, India},
abstract={Purpose - The purpose of this paper is to determine the key supply chain performance measurement system (SCPMS) implementation variables, on which the top management should focus, so as to improve the effectiveness and efficiency of supply chain (SC). Design/methodology/approach - In this paper, an interpretive structural modeling (ISM)-based approach has been employed to model the SCPMS implementation variables. These variables have been categorized under "enablers" and "results." The enablers are the variables that help boost the SCPMS implementation variables, while results variables are the outcome of good SCPMS implementation. Findings - The paper highlights the variables associated with implementation of SCPMS. A key finding of this modeling is that awareness about performance measurement system (PMS) in SC is a very significant enabler. For better results, top management should focus on improving the high-driving power enablers such as awareness of PMS in SC, commitment by the top management, consistency with strategic goals, funding for PMS implementation, and effective information systems. Originality/value - In this paper, an interpretation of SCPMS implementation variables in terms of their driving and dependence powers has been carried out. Those variables possessing higher driving power in the ISM need to be taken care of on a priority basis because there are a few other dependent variables being affected by them. © Emerald Group Publishing Limited.},
author_keywords={India;  Interpretive programs;  Modelling;  Performance measures;  Supply chain management},
references={Andersen, B., Pettersen, P.-G., (1996) The Benchmarking Handbook, , Chapman & Hall New York, NY; Austin, J.E., (1990) Managing in Developing Countries, , Free Press New York, NY; Balsmeier, P.W., Voisin, W., Supply chain management: A time - Based strategy (1996) Industrial Management, 38 (5), pp. 24-7; Beamon, B., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55, pp. 281-94; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-92; Bechtel, C., Jayaram, J., Supply chain management: A strategic perspective (1997) The International Journal of Logistics Management, 8 (1), pp. 15-34; Bender, P.S., Debunking five supply chain myths (2000) Supply Chain Management Review, 4 (1), pp. 52-8; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), p. 48; Chen, I.J., Paulraj, A., Towards a theory of supply chain management: The constructs and measurements (2004) Journal of Operations Management, 22 (2), pp. 119-50; Christopher, M., Towill, D.R., An integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution & Logistics Management, 31 (4), pp. 235-46; Fawcett, S., Smith, S., Cooper, M., Strategic intent, measurement capability, and operational success: Making the connection (1997) International Journal of Physical Distribution & Logistics Management, 27 (7), pp. 410-21; Ghalayini, A.M., James, S.N., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Gilmour, P., Benchmarking supply chain operations (1998) Benchmarking for Quality Management & Technology, 5 (4), pp. 283-90; Guiffrida, A.L., Nagi, R., Cost characterizations of supply chain delivery performance (2005) International Journal of Production Economics, 102, pp. 22-36; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International. Journal of Production Economics, 87, pp. 333-47; Hervani, A.A., Helms, M.M., Sarkis, J., Performance measurement for green supply chain management (2005) Benchmarking: An International Journal, 12 (4), pp. 330-53; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-68; Jharkharia, S., Shankar, R., IT-enablement of supply chains: Understanding the barriers (2005) The Journal of Enterprise Information Management, 18 (1), pp. 11-27; Jharkharia, S., Shankar, R., Supply chain management: Some sectoral dissimilarities in the Indian manufacturing industry (2006) Supply Chain Management: An International Journal, 11 (4), pp. 345-52; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-9; Kilpatrick, J., Factor, R., Logistics in Canada survey: Tracking year 2000 supply chain issues and trends (2000) Materials Management and Distribution, 45 (1), pp. 16-20; Mandal, A., Deshmukh, S.G., Vendor selection using interpretive structural modeling (ISM) (1994) International Journal Operations & Production Management, 14 (6), pp. 52-9; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Inc. Portland, OR; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-36; Novack, R.A., Langley Jr., C.J., Rinehart, L.M., (1995) Creating Logistics Value: Themes for the Future, , Council of Logistics Management Oak Brook, IL; Ravi, V., Shankar, R., Analysis of interactions among the barriers of reverse logistics (2005) Technological Forecasting & Social Change, 72 (8), pp. 1011-29; Saad, M., Patel, B., An investigation of supply chain performance measurement in the Indian automotive sector (2006) Benchmarking: An International Journal, 13 (1-2), pp. 36-53; Sage, A.P., (1977) Interpretive Structural Modeling: Methodology for Large-Scale Systems, , McGraw-Hill New York, NY; Schroeder, R.G., John, C.A., Scudder, G.D., White collar productivity measurement (1986) Management Decision, 24 (5), pp. 3-7; Shepherd, C., Günter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-58; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Towill, D.R., The seamless chain - The predator's strategic advantage (1997) International Journal of Technology Management, 13 (1), pp. 37-56; Van Hoek, R.I., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-92; Warfield, J.W., Developing interconnected matrices in structural modeling (1974) IEEE Transactions on Systems, Man and Cybernetics, 4 (1), pp. 51-81; Zaire, M., Whymark, J., The transfer of best practice: How to build a culture of benchmarking and continuous learning - Part 1 (2000) Benchmarking: An International Journal, 7 (1), pp. 62-79},
correspondence_address1={Shankar, R.; Department of Management Studies, , New Delhi, India; email: ravi1@dms.iitd.ernet.in},
issn={14637154},
language={English},
abbrev_source_title={Bus. Process Manage. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zheng2008312,
author={Zheng, P. and Lai, K.K.},
title={A rough set approach on supply chain dynamic performance measurement},
journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
year={2008},
volume={4953 LNAI},
pages={312-322},
doi={10.1007/978-3-540-78582-8_32},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-47249164303&doi=10.1007%2f978-3-540-78582-8_32&partnerID=40&md5=3269b07c3a4d92d8f7dfad81d9ad962b},
affiliation={College of Business Administration, Hunan University, Changsha 410082, China; Department of Management Science, City University of Hong Kong, Hong Kong, Hong Kong},
abstract={Most of the times, traditional supply chain performance measurement is a static method. However, in the real world, the supply chain is a dynamic system, which needs dynamic performance measurement methods. For the sake of integrative performance measurement of agile virtual enterprise, the traditional Balanced Scorecard is extended into 5 dimensions. According to it, incorporated with the Rough Set theory, the decision table of dynamic performance measurement is constructed. The decision rule set of performance measurement prediction is obtained by attribute reduct and value reduct of decision table. Finally, a calculation example of performance measurement is provided, which shows that the suggested evaluation method is feasible and efficient for dynamic performance measurement and forecasts. Thus, it supplies reasonable analysis and policy making tools for supply chain management. © 2008 Springer-Verlag Berlin Heidelberg.},
author_keywords={Agile Virtual Enterprise (AVE);  Attribute reduct;  Dynamic balanced scorecard;  Performance measurement;  Rough Set (RS)},
keywords={Decision tables;  Decision theory;  Dynamic programming;  Forecasting;  Fuzzy sets;  Measurements;  Mechanisms;  Multi agent systems;  Rough set theory;  Set theory;  Supply chain management;  Supply chains, Agile/Virtual enterprises;  Attribute reduct;  Balanced score card (BSC);  Chain management;  Decision rules;  Dynamic performances;  Dynamic systems;  Evaluation methods;  Heidelberg (CO);  International symposium;  Multi agent systems (MAS);  Performance measurement (quality);  Policy making;  Real world;  Rough sets;  Static methods;  Supply chain performance, Industrial management},
references={Van Hoek, R.I., Measuring the Unmeasurable-Measuring and Improving Performance in the Supply Chain [J] (1998) Supply Chain Management, 3, pp. 187-192; Kaplan, R.S., Norton, D.P., (1992) The balanced scorecard-measures that drive performance, pp. 71-79. , Harvard Business Review January-February; Kaplan, R.S., Norton, D.P., Putting the Balanced Scorecard to Work (1996) Harvard Business Review, 9-10. , Boston; Kaplan, R.S., Norton, D.P., Using the Balanced Scorecard as a Strategic Management System (1996) Harvard Business Review, 3-4. , Boston; Kaplan, R.S., Norton, D.P., (2001) The Strategy Focused Organization: How Balanced Scorecard Companies Thrive in the New Competitive Environment, p. 2. , Harvard Business School Press, Boston; Brewer, P.C., Speh, T.W., Using the Balanced Scorecard to Measure Supply Chain Performance (2000) Journal of Business Logistics, 21 (1); Ma, S.H., Li, H.Y., Lin, Y., Study on application of Balanced Scorecard to performance measurement of supply chain (2002) Industry Engineering and Management, pp. 5-10; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study [J] (2004) European Journal of Operational Research, 156, pp. 267-286; Gijerdrum, J., Shah, N., A Combined Optimization and Agent2based Approach to Supply Chain Modeling and Performance Assessment [J] (2001) Production Planning and Control, 12, pp. 81-88; Beamon, B.M., Measuring Supply Chain Performance [J] (1999) International Journal of Operations & Production Management, 19, pp. 275-292; Pawlak, Z., (1991) Rough set-theoretical aspects of reasoning about data [M], , Kluwer Academic Publishers, Boston, MA; Skowron, Rauszer, C.: The discernibility matrices and functions in information systems [A]. In: Slowinski R. Intelligence decision support-handbook of application and advances of the rough sets theory [C], pp. 331-362. Kluwer Academic Publishers, Dordrecht (1992); Rosetta [EB/OL]. Knowledge Systems Group, Dept. of Computer and Info. Science, Norwegian University of Science and Technology, Trondheim, Norway and Group of Logic, Inst. of Mathematics, University of Warsaw, Poland http://rosetta.lcb.uu.se/general/; Rough Analysis [EB/OL]. Enrique Alvarez, , http://www.lsi.upc.es/∼ealvarez/rough.html, August 1998},
correspondence_address1={Zheng, P.; College of Business Administration, , Changsha 410082, China; email: zphnu@yahoo.com.cn},
issn={03029743},
isbn={3540785817; 9783540785811},
language={English},
abbrev_source_title={Lect. Notes Comput. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{McCormack2008272,
author={McCormack, K. and Ladeira, M.B. and Valadares De Oliveira, M.P.},
title={Supply chain maturity and performance in Brazil},
journal={Supply Chain Management},
year={2008},
volume={13},
number={4},
pages={272-282},
doi={10.1108/13598540810882161},
note={cited By 120},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-46249122956&doi=10.1108%2f13598540810882161&partnerID=40&md5=51cf00f3203c661e0c6db0ee250884c9},
affiliation={North Carolina State University, Fuquay Varina, NC, United States; CEPEAD/UFMG - NIPE-LOG/UFMG, Rua Curitiba, Centro, Belo Horizonte/MG, Brazil; CEPEAD/UFMG, Rua Oliveira, Cruzeiro, Belo Horizonte/MG, Brazil},
abstract={Purpose - The purpose of the paper is to investigate the relationship between supply chain maturity and performance, with specific references both to the business process orientation maturity model and to the supply chain operation reference model. Design/methodology/approach - Quantitative, survey based research was carried out with 478 Brazilian companies. Statistical analysis combined the use of descriptive statistics and structural equation modeling. Findings - Empirical results indicate a strong and positive statistical relationship between supply chain maturity and performance. The results also suggest that the deliver process maturity has a higher impact on overall performance than the other supply chain processes. Research limitations/implications - Quantifying supply chain maturity and performance is an opportunity for a company to align its performance measurements and process improvement actions with its broader policies and strategies. The use of this approach has been validated in several previous research studies in organizational self-assessment and business management. Practical implications - Maturity models are valuable frameworks for corporate leadership. This study provides solid statistical evidence that a company that has achieved a higher maturity level and implemented the maturity factors also has achieved superior performance. It also validates the application of these specific maturity factors in South America, specifically Brazil. Originality/value - This paper confirms and expands upon earlier research suggesting higher levels of process maturity were related to superior performance. This paper also examines the evolution of performance measurement systems, moving from a traditional approach to a more process oriented perspective by reporting on the origins of maturity models and presenting the main empirical contributions through the use of the business process maturity model and supply chain operation reference model. © Emerald Group Publishing Limited.},
author_keywords={Brazil;  Performance management;  Supply chain management},
references={Anderson, D.R., Sweeney, D.J., Williams, T., (2002) Essentials of Statistics for Business and Economics, , Thomson Learning Belmont, CA; Bolstorff, P., Rosenbaum, R., (2003) Supply Chain Excellence: A Handbook for Dramatic Improvement Using the SCOR Model, , AMACOM New York, NY; Chan, F.T.S., Qi, H.F., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 18 (3), pp. 209-23; Coyle, J.J., Bardi, E., Langley, C.J., (2003) The Management of Business Logistics: A Supply Chain Perspective, , Thomson Learning Belmont, CA; Day, G.S., The capabilities of market driven organizations (1994) Journal of Marketing, pp. 37-52; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (12), pp. 46-70; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Gupta, N., Shaw, J.D., Delery, J.E., Correlates of response outcomes among organizational key informants (2000) Organizational Research Methods, 3 (4), pp. 323-47. , October; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (2005) Mutlivariate Data Analysis, , 6th ed. Prentice-Hall Upper Saddle River, NJ; Huan, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-9; Jöreskog, K.G., Wold, H., The ML and PLS techniques for modeling with latent variables: Historical and comparative aspects (1982) Systems under Indirect Observation: Causality, Structure, Prediction, 1, pp. 263-70. , Wold, H. Jöreskog, K. North-Holland Amsterdam; Kumar, N., Stern, L.W., Conducting interorganizational research using key informants (1993) Academy of Management Journal, 36 (6), pp. 1633-51; Kumar, N., Anderson, J.C., Stern, L.W., Conducting organizational research using organizational key informants (1993) Academy of Management Journal, 363, pp. 1633-51; Lockamy, A., McCormack, K., The development of a supply chain management process maturity model using the concepts of business process orientation (2004) Supply Chain Management: An International Journal, 9 (4), pp. 272-8; McCormack, K., Johnson, W., (2002) Supply Chain Networks and BPO: Advanced Strategies and Best Practices for Obtaining Superior Results, , CRC Press Boca Raton, CA; McCormack, K., Johnson, W., Walker, W., (2003) Supply Chain Networks and Business Process Orientation: Advanced Strategies and Best Practices, , APICS series on resource management CRC Press LLC Boca Raton, FL; Mentzer, J.T., Dewitt, W., Keebler, J.S., Min, S., Nix, N., Smith, C.D., Zach, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Poirier, C.C., Quinn, F.J., How are we doing: A survey of supply chain progress (2004) Supply Chain Management Review; Scc, (2007) SCOR 8.0 Overview Booklet, Supply-Chain Council, , available at: http://supply-chain.org/page.ww?section= SCOR+Model&name=SCOR+Model (accessed May 28); Seidler, J., On using informants: A technique for collecting quantitative data and controlling for measurement error in organizational analysis (1974) American Sociological Review, 39, pp. 816-31; Teo, W.F., Dale, B.G., Self-assessment: Methods, management and process (1997) Proceedings of the Institute for Mechanical Engineers, , Vol. 211 Part B; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-92; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Pmi, (2003) Organizational Project Management Maturity Model (OPM3), , s.l., Project Management Institute available at: PMI.Bookstore.org; Poirier, C.C., Quinn, F.J., A survey of supply chain progress (2003) Supply Chain Management Review; Urdang, L., Flexner, S.B., (1968) The Randon House Dictionary of the English Language, , College edition Random House New York, NY},
correspondence_address1={Ladeira, M. B.; CEPEAD/UFMG - NIPE-LOG/UFMG, Rua Curitiba, Centro, Belo Horizonte/MG, Brazil; email: marcelobronzo@cepead.face.ufmg.b},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gellynck200847,
author={Gellynck, X. and Molnár, A. and Aramyan, L.},
title={Supply chain performance measurement: The case of the traditional food sector in the EU},
journal={Journal on Chain and Network Science},
year={2008},
volume={8},
number={1},
pages={47-56},
doi={10.3920/JCNS2008.x088},
note={cited By 24},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-46049096347&doi=10.3920%2fJCNS2008.x088&partnerID=40&md5=bc0671b5cc0e5fb5689886d923360966},
affiliation={Ghent University, Faculty of Bioscience Engineering, Department of Agricultural Economics, Coupure Links 653, B-9000 Ghent, Belgium; Agricultural Economics Research Institute (LEI), P.O. Box 2970, 2502 LS The Hague, Netherlands},
abstract={The objective of this study is to develop a sound measurement instrument of traditional food supply chain performance integrating the perspectives of different stakeholders. Therefore first, stakeholders' goals are generalized via focus groups and individual interviews. Second, stakeholders' goals are consolidated, based on their specificity and conflicting/divergent nature and as a result, supply chain goals are developed. Third, potential performance indicators are listed, supporting the developed supply chain goals. Fourth, key performance indicators (KPIs) are selected. As a result, five main supply chain goals are formulated (traditionalism, efficiency, responsiveness, quality and chain balance) together with their supporting performance indicators. Future research should focus on validating the instrument in other food and non-food sectors.},
author_keywords={Performance measurement instrument;  Stakeholders' perspective;  Traditional food supply chain},
references={Akkermans, H.A., Bogerd, P., Yucesan, E., Van Wassenhove, L.N., The impact of ERP on supply chain management: Exploratory findings from a European Delphi study (2003) European Journal of Operational Research, 146 (2), pp. 284-301; Aramyan, L.H., (2007) Measuring supply chain performance in the agri-food sector, pp. 0-144. , Wageningen, Wageningen University; Bagozzi, R.P., Marketing as Exchange (1975) Journal of Marketing, 39 (4), pp. 32-39; Beamon, B.M., 1996. Performance measures in supply chain management. In: R.P. Institute (Ed.) Proceedings of the 1996 Conference on Agile and Intelligent Manufacturing Systems, Troy, New York; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bensaou, M., Interorganizational Cooperation: The Role of Information Technology. An Empirical Comparison of U.S. and Japanese Supplier Relations (1997) Information Systems Research, 8 (2), pp. 107-124; Bensaou, M., Venkatraman, N., Configurations of Interorganizational Relationships: A Comparison between U.S. and Japanese Automakers (1995) Management Science, 41 (9), pp. 1471-1492; Bérard, L., Marchenay, P., Localized products in France: Definition, protection and value-adding (2007) Anthropologz of Food, 2. , Match; Bowersox, D.J., Closs, D.J., Stank, T.P., Ten Mega-Trends That Will Revolutionize Supply Chain Logistrics (2000) Journal of Business Logistics, 21 (2), pp. 1-15; Brosius, F., 2002. SPSS 11. Bonn, MITP-Verlag; Chen, I.J., Paulraj, A., Understanding Supply Chain Management: Critical Research and a Theoretical Framework (2004) International Journal of Production Research, 42 (1), pp. 131-163. , p; Christopher, M., (1998) Logistics and supply chain management: Strategies for reducing cost and improving service, , London, Financial times; Claro, D.P., Hagelaar, G., Omta, O., The determinants of relational governance and performance: How to manage business relationships? (2003) Industrial Marketing Management, 32 (8), pp. 703-716; Cohen, S., Roussel, J., Strategic Supply Chain Management (2005) The five discipline for top performance, , McGraw-Hill; Collins, A., Henchion, M., Reilly, P., Logistics customer service: Performance of Irish food exporters (2001) International Journal of Retail & Distribution Management, 29, pp. 6-15; Cox, A., Power, value and supply chain management (1999) Supply Chain Management: An International Journal, 4 (4), pp. 167-175; Dransfield, S.B., Fisher, N.I., Vogel, N.J., Using Statistics and Statistical Thinking to Improve Organisational Performance (1999) International Statistical Review/Revue Internationale de Statistique, 67 (2), pp. 99-122; Felföldi, J., A zöldség termékpálya SWOT-mátrixa (2007) Ágazatspecifikus innováció alapuló projektek generálása a zöldség termékpályán, , Felföldi, J. and E. Szabó editors, Debreceni Egyetem, Debrecen; Fox, R., Reinventing the gastronomic identity of Croatian tourist destinations (2007) International Journal of Hospitality Management, 26 (3), pp. 546-559; Gellynck, X., Vermeire, B., Viaene, J., Innovation in food firms: Contribution of regional networks within the international business context (2007) Entrepreneurship and Regional Development, 19 (3), p. 209; Gerbens-Leenes, P.W., Moll, H.C., Schoot Uiterkamp, A.J.M., Design and development of a measuring method for environmental sustainability in food production systems (2003) Ecological Economics, 46 (2), pp. 231-248; Giraud, G., Organic and origin-labeled food products in Europe: Labels for consumers or from producers? (2002) Ecolabels and the Greening of the Food Market, pp. 41-49. , Lockeretz, W, editor, Tufts University, Boston, pp; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hasler, C.M., (2002) Functional Foods: Benefits, 132, pp. 3772-3781. , Concerns and Challenges, A Position Paper from the American Council on Science and Health; Hines, P., Lamming, R., Jones, D., Cousins, P., Rich, N., (2000) Value Stream Management, , Financial Times Prentice Hall; Ho, S.-C., Evolution Versus Tradition in Marketing Systems: The Hong Kong Food-Retailing Experience (2005) Journal of Public Policy & Marketing, 24 (1), pp. 90-99; Ittner, C.D., Larcker, D.E., Coming Up Short on Nonfinancial Performance Measurement (2003) Harvard Business Review, 81 (11), pp. 88-95; Jordana, J., Traditional foods: Challenges facing the European food industry (2000) Food Research International, 33 (3-4), pp. 147-152; Kaplan, R.S., Measuring Manufacturing Performance: A New Challenge for Managerial Accounting Research (1983) Accounting Review, 58 (4), p. 686; Keeble, J.J., Topiol, S., Berkeley, S., Using Indicators to Measure Sustainability Performance at a Corporate and Project Level (2003) Journal of Business Ethics, 44 (2-3), pp. 149-158. , p; Kozár, L., A gabona bázison alapuló, energetikai felhasználású etanol el állítás ökonómiai kérdései. (2001) Agrártudományi Közlemények, 2. , Debreceni Egyetem; Lambert, D.M., Cooper, M.C., Issues in Supply Chain Management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1999) Sloan Management Review, 33, pp. 65-73; Li, D., O'Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 59 (1-3), pp. 147-157; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., Realizing Strategy through Measurement (1994) International Journal of Operations & Production Management, 14 (3), pp. 140-152; Pearson, M., Samali, A., Offsite Solution Delivery Centers Increasingly Important to High-performance Supply Chains (2005) Outlook Point of View, Vol, , January; Roos, G., Jacobsen, K., Management in a complex stakeholder organization; a case study of the application of the IC-process to a branch of the commonwealth public service. Monash Mt (1999) Eliza Business Review, 2. , 1 p; Theodoras, D., Laios, L., Socrates, M., Improving customer service performance within a food supplier-retailer conext (2005) International Journal of Retail & Distribution Management, 33 (5), pp. 353-370; Trichopoulou, A., Vasilopoulou, E., Georga, K., Soukara, S., Dilis, V., Traditional foods: Why and how to sustain them (2006) Trends Food Sci Tech, 17, pp. 498-504; Van de Ven, A.H., A Framework For Organization Assessment (1976) Academy of Management Review, 1 (1), pp. 64-78; Van der Vorst, J., (2000) Effective food supply chains: Generating, modeling and evaluating supply chain scenarios, , Wageningen, Wageningen University 305 p; Van der Vorst, J.G.A.J., (2006) Performance Measurement in Agri-Food Supply-Chain Networks, pp. 14-24. , Logistics and Operations Research Group, Wageningen University; Walsh, P., Finding key performance drivers: Some new tools (1996) Total Quality Management, 7 (5), pp. 509-519; Williamson, O.E., The Economics of Organization: The Transaction Cost Approach (1981) The American Journal of Sociology, 87 (3), pp. 548-577; Yamamoto, H., Fujino, J., Yamaji, K., Evaluation of bioenergy potential with a multi-regional global-land-use-and-energy model (2001) Biomass and Bioenergy, 21 (3), pp. 185-203; Young, N.L., Bradely, C.S., Blanchette, V., Wakefield, C.D., Barnard, D., Wu, J.K.M., Mccusker, P.J., Development of a health-related quality of life measure for boys with haemophilia: The Canadian Haemophilia Outcomes - Kids Life Assessment Tool (CHO-KLAT) (2004) Haemophilia, 10 (1), pp. 34-43},
correspondence_address1={Molnár, A.; Ghent University, Coupure Links 653, B-9000 Ghent, Belgium; email: Adrienn.Molnar@Ugent.be},
issn={15691829},
language={English},
abbrev_source_title={J. Chain Netw. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zheng2008,
author={Zheng, P. and Lai, K.K.},
title={Research on supply chain dynamic balanced scorecard based on fuzzy evaluation and Markov forecast techniques},
journal={Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice},
year={2008},
volume={28},
number={4},
pages={57-64+80},
note={cited By 5},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-44449118027&partnerID=40&md5=2451b7c5e7cec88ac7cd760f981980b6},
affiliation={College of Business Administration, Hunan University, Changsha 410082, China; Faculty of Business, City University of Hong Kong, Hong Kong, Hong Kong},
abstract={Most of the traditional supply chain performance measurement is static method. However, in the real world, the supply chain is a dynamic system, which needs dynamic performance measurement methods to evaluate. For the sake of integrative performance measurement of agile virtual enterprise, the dimension of the traditional Balanced Scorecard is extended into 5. Then Analytical Hierarchy Process (AHP) is utilized to analyze the weights of key performance indices so that the indices with minor weights are abandoned. Based on it, the measure results of supply chain of all past periods are obtained by fuzzy evaluation method. Furthermore, incorporated with Markov forecast technique, the future performance measurement and development trend of dynamic supply chain is presented. Finally, a calculation example of performance measurement is illustrated which shows that the mentioned evaluation method is scientific and feasible. The evaluation result can provide the guide for effective analysis and decision-making of supply chain management.},
author_keywords={Analytical hierarchy process (AHP);  Dynamic balanced scorecard;  Fuzzy evaluation;  Markov forecast;  Performance measurement},
keywords={Analytic hierarchy process;  Decision making;  Dynamical systems;  Fuzzy sets;  Markov processes;  Supply chains;  Virtual corporation, Agile virtual enterprise;  Dynamic balanced scorecard;  Fuzzy evaluation;  Markov forecast;  Performance measurement, Supply chain management},
references={Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, Jan.-Feb., pp. 71-79; Kaplan, R.S., Norton, D.P., (1993) Putting the Balanced Scorecard to Work, (9-10). , Harvard Business Review; Kaplan, R.S., Norton, D.P., (1996) Using the Balanced Scorecard as a Strategic Management System, (3-4). , Boston Harvard Business Review; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) International Journal of Logistics Management, 9 (2), pp. 1-19; Shi, L.P., Cai, X., The application of balanced scorecard measures that drive performance based on the supply chain enterprise (2003) Journal of Harbin University of Commerce (Social Science Edition), (2), pp. 69-70; Sun, S.M., Zhao, X.N., Feng, M., Strategic performance measurement design of sustainable development (2004) Management Review, (6), pp. 59-62; Sun, S.M., Luo, N., Design of supply chain based on balanced scorecard system at three levels in six perspectives (2005) Journal of Northeastern University (Social Science), 7 (5), pp. 350-353; Kaplan, R.S., Norton, D.P., (2001) The Strategy Focused Organization: How Balanced Scorecard Companies Thrive in the New Competitive Environment, , Boston, Harvard Business School Press; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1); Huo, J.Z., Sui, M.G., Liu, Z.Y., Construction of integrated supply chain performance measurement system (2002) Journal of Tongji University (Natural Science), 30 (4), pp. 495-499; Gijerdrum, J., Shah, N., A combined optimization and agent2based approach to supply chain modeling and performance assessment (2001) Production Planning and Control, 12, pp. 81-88; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19, pp. 275-292; Wang, H., Ma, D., Scale evaluation and new scale methods (1993) Systems Engineering - Theory and Practice, 13 (5), pp. 24-26; Satty, T.L., How to make a decision: The analytic hierarchy process (1990) European Journal of Operational Research; Liu, P.Y., Wu, M.D., (2000) Fuzzy Theory and Its Application, , Changsha: National University of Defense Technology Press; Nola, A.D., Sessa, S., Pedrycz, W., (1989) Fuzzy Relation Equations and Their Applications to Knowledge Engineering, , Kluwer Academic Publishers; Bolch, G., Greiner, S., Meer, H.D., (2006) Queuing Networks and Markov Chains - Modeling and Performance Evaluation with Computer Science Applications, , 2nd Edition, A John Wiley and Sons, Inc. Publication; Nagatanit, Helbingd, Stability analysis and stabilization strategies for linear supply chains (2004) Physica A, 35 (3-4), pp. 644-660; Zhang, W., Lin, J., Analysis on stability of dynamic supply chain (2006) Computer Integrated Manufacturing Systems, 12 (1), pp. 123-126},
correspondence_address1={Zheng, P.; College of Business Administration, , Changsha 410082, China},
issn={10006788},
coden={XGLSE},
language={Chinese},
abbrev_source_title={Xitong Gongcheng Lilum yu Shijian},
document_type={Article},
source={Scopus},
}

@ARTICLE{Longo2008570,
author={Longo, F. and Mirabelli, G.},
title={An advanced supply chain management tool based on modeling and simulation},
journal={Computers and Industrial Engineering},
year={2008},
volume={54},
number={3},
pages={570-588},
doi={10.1016/j.cie.2007.09.008},
note={cited By 143},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-38849130019&doi=10.1016%2fj.cie.2007.09.008&partnerID=40&md5=2441fe73fe33ff225fcc438684eacf0c},
affiliation={Mechanical Department, University of Calabria, Via P. Bucci, Cubo 44C, 87036 Rende, CS, Italy},
abstract={The paper presents an advanced modeling approach and a simulation model for supporting supply chain management. The first objective is to develop a flexible, time-efficient and parametric supply chain simulator starting from a discrete event simulation package. To this end we propose and advanced modeling approach. The second objective is to provide a decision making tool for supply chain management. The simulator is a decision making tool capable of analyzing different supply chain scenarios by using an approach based on multiple performance measures and user-defined set of input parameters. Our simulator capabilities as decision making tool are strongly amplified if Design of Experiment (DOE) and Analysis of Variance (ANOVA) are respectively used for experiments planning and simulation results analysis. With regard to supply chain decision making process, we propose an application example for a better understanding of tool potentials. The application example considers a specific supply chain scenario and analyzes the effects of inventory control policies, lead times, customers' demand intensity and variability, on three different supply chain performance measures. © 2007 Elsevier Ltd. All rights reserved.},
author_keywords={Decision making tool;  Modeling;  simulation;  Supply chain management},
keywords={Analysis of variance (ANOVA);  Computer simulation;  Decision making;  Design of experiments;  Inventory control, Decision making tools;  Demand intensity;  Simulation package, Supply chain management},
references={Axsäter, S., Optimal policies for serial inventory systems under fill rate constraints (2003) Management Science, 49 (2), pp. 247-253; Baganha, M.P., Cohen, M., The stabilizing effect of inventory in supply chains (1998) Operations Research, 46 (3), pp. S72-S73; Banks, J., (1998) Handbook of simulation, , Wiley Interscience Publication; Beamon, B.M., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bruzzone, A. G., Longo, F., Massei, M., & Saetta, S. (2006). The vulnerability of supply chain as key factor in supply chain management. In Bruzzone, A. G., Williams, E. (Eds.). Proceedings of summer computer simulation conference, Calgary, Canada (pp. 181-186); Chan, F.T.S., Chan, H.K., Simulation modeling for comparative evaluation of supply chain management strategies (2005) Journal of Advanced Manufacturing Technology, 25, pp. 998-1006; Fleisch, E., Tellkamp, C., Inventory inaccuracy and supply chain performance: a simulation study of a retail supply chain (2005) International Journal of Production Economics, 95, pp. 373-385; Ganeshan, R., Boone, T., Stenger, A.J., The impact of inventory and how planning parameters on supply chain performance: An exploratory study (2001) International Journal of Production Economics, 71, pp. 111-118; Graves, S.C., Willems, S.P., Optimizing the supply chain configuration for new product (2005) Management Science, 51 (8), pp. 1165-1180; Ingalls, R. G. (1998). The value of simulation in modeling supply chain. In Proceedings of the 1998 winter simulation conference, Washington DC (pp. 1371-1375); Lee, Y.H., Cho, M.K., Kim, S.J., Kim, Y.B., Supply chain simulation with discrete continuous combined modeling (2002) Computer and Industrial Engineering, 43, pp. 375-392; Longo, F., Bruzzone, A. G. (2005). Modeling and simulation applied to security systems. In Bruzzone, A. G., Williams, E. (Eds.). Proceedings of summer computer simulation conference, Philadelphia, USA (pp. 183-188); Longo, F., Mirabelli, G., Papoff, E. (2005). Modeling analysis and simulation of a supply chain devoted to support pharmaceutical business retail. In Proceedings of international conference on production research, Salerno, Italy; Moinzadeh, K., A multi-echelon inventory system with information exchange (2002) Management Science, 48 (3), pp. 414-426; Persson, F., Olhager, J., Performance simulation of supply chain designs (2002) International Journal of Production Economics, 77, pp. 231-245; Tagaras, G., Vlachos, D., A periodic review inventory system with emergency replenishments (2001) Management Science, 47 (3), pp. 415-429; Thor, C.G., Measures of success - Creating a high performing organization (1994) Essex junction, , Oliver Wight, VT; Viswanadham, N., (1999) Analysis and design of manufacturing enterprises, , Kluwer Academic Publishers; Zhang, C., Zhang, C., Design and simulation of demand information sharing in a supply chain (2007) Simulation Modeling Practice and Theory, 15, pp. 32-46},
correspondence_address1={Longo, F.; Mechanical Department, Via P. Bucci, Cubo 44C, 87036 Rende, CS, Italy; email: f.longo@unical.it},
issn={03608352},
coden={CINDD},
language={English},
abbrev_source_title={Comput Ind Eng},
document_type={Article},
source={Scopus},
}

@ARTICLE{Cousins2008238,
author={Cousins, P.D. and Lawson, B. and Squire, B.},
title={Performance measurement in strategic buyer-supplier relationships: The mediating role of socialization mechanisms},
journal={International Journal of Operations and Production Management},
year={2008},
volume={28},
number={3},
pages={238-258},
doi={10.1108/01443570810856170},
note={cited By 141},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-40249108473&doi=10.1108%2f01443570810856170&partnerID=40&md5=7cb7a2768943ee04317ed4797857050e},
affiliation={Manchester Business School, University of Manchester, Manchester, United Kingdom; School of Management and Economics, Queen's University Belfast, Belfast, United Kingdom},
abstract={Purpose - Close links between buyers and suppliers are increasingly cited as a critical differentiator of high and low performers in global supply chains. While the application of performance measures to manage supplier relationships has been well-identified and encouraged in the literature, comparatively little research exists on the inter-organizational socialization mechanisms that underlie the flow of learning and information within supply chains. The authors aim to develop a model positing that socialization mechanisms play an important role in mediating the relationship between supplier performance measures and performance outcomes. Design/methodology/approach - A structural equation model, using a sample of 142 manufacturing and service firms based in the UK, tests this hypothesised model. Findings - The theoretical framework was supported, with results indicating that socialization mechanisms fully mediate the effects of supplier performance measures (communication and operational-based) on firm performance. Practical implications - This study provides additional insights for purchasing managers seeking to improve the management of their strategic supplier relationships. The authors find that monitoring supplier performance is not of itself sufficient, rather, it is the process of socializing the buyer and supplier that is critical to success. Originality/value - As far as the authors are aware, no previous supply chain research has examined how supplier performance measurement systems, socialization mechanisms, and firm performance are related. The paper makes a significant contribution to this literature embedding an established theoretical construct (socialization) into the supply chain literature. © Emerald Group Publishing Limited.},
author_keywords={Linear structure equation modelling;  Performance measurement (quality);  Relationship marketing;  Suppliers},
references={Abernethy, M.A., Lillis, A.M., The impact of manufacturing flexibility on management control system design (1995) Accounting, Organizations and Society, 20 (4), pp. 241-58; Anderson, J.C., Gerbing, D.W., Structural equation modeling in practice: A review and recommended two-step approach (1988) Psychological Bulletin, 103, pp. 411-23; Ansari, S.L., An integrated approach to control system design (1977) Accounting Organisations and Society, 2 (2), pp. 101-12; Araujo, L., Dubois, A., Gadde, L.E., Managing interfaces with suppliers (1999) Industrial Marketing Management, 28 (5), pp. 497-506; Arbuckle, J.L., (2003) AMOS 5.0: Update to the AMOS User's Guide, , SPSS Chicago, IL; Arbuckle, J.L., (2006) AMOS User's Guide 7.0, , Amos Development Corporation Spring House, PA; Armstrong, S.J., Overton, T.S., Estimating non-response bias in mail surveys (1977) Journal of Marketing Research, 14, pp. 396-402; Bagozzi, R.P., Dholakia, U.M., Antecedents and purchase consequences of customer participation in small group brand communities (2006) International Journal of Research in Marketing, 23 (1), p. 45; Bagozzi, R.P., Yi, Y., On the evaluation of structural models (1988) Journal of the Academy of Marketing Science, 16 (1), pp. 74-94; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17 (1), pp. 99-120; Baron, R.M., Kenny, D.A., The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations (1986) Journal of Personality and Social Psychology, 51 (6), pp. 1173-82; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-88; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-13; Berry, A.J., (1995) Control in Networks. Third Management Control Symposium, , Imperial College London; Bessant, J., Kaplinsky, R., Lamming, R., Putting supply chain learning into practice (2003) International Journal of Operations & Production Management, 23 (2), pp. 167-84; Bollen, K.A., (1989) Structural Equations with Latent Variables, , Wiley New York, NY; Cannon, J., Perreault, W.D.J., Buyer-seller relationships in business markets (1999) Journal of Marketing Research, 36 (4), pp. 439-60; Chen, I.J., Paulraj, A., Lado, A.A., Strategic purchasing, supply management, and firm performance (2004) Journal of Operations Management, 22 (5), pp. 505-23; Chenhall, R.H., Reliance on manufacturing performance measures, total quality management and organizational performance (1997) Management Accounting Research, 8, pp. 187-206; Chenhall, R.H., Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: An exploratory study (2005) Accounting Organisations and Society, 30, pp. 395-422; Chenhall, R.H., Langfield-Smith, K., The relationship between strategic priorities, management techniques and management accounting: An empirical investigation using a systems approach (1998) Accounting, Organizations and Society, 23 (3), pp. 243-64; Chenhall, R.H., Morris, D., Organic decision and communication processes and management accounting systems in entrepreneurial and conservative business organizations (1995) Omega, International Journal of Management Science, 5, pp. 485-97; Choi, T.Y., Hong, Y., Unveiling the structure of supply networks: Case studies of Honda, Acura, and DaimlerChrysler (2002) Journal of Operations Management, 20 (5), pp. 469-93; Chung, S.A., Singh, H., Lee, G.M., Complementarity, status similarity and social capital as drivers of alliance formation (2000) Strategic Management Journal, 21 (1), pp. 1-22; Cousins, P.D., Supply base rationalisation: Myth or reality (1999) European Journal of Purchasing & Supply Management, 5 (1), pp. 15-25; Cousins, P.D., Menguc, B., The implications of socialization and integration in supply chain management (2006) Journal of Operations Management, 24 (5), pp. 604-20; Cousins, P.D., Handfield, R.B., Lawson, B., Petersen, K.J., Creating supply chain relational capital: The impact of formal and informal socialization processes (2006) Journal of Operations Management, 24 (6), pp. 851-63; Cross, K.F., Lynch, R.L., For good measure (1992) CMA Magazine, 66, pp. 20-3; Davis, S., Albright, T., An investigation of the effect of balanced scorecard implementation on financial performance (2004) Management Accounting Research, 15, pp. 135-53; Dumond, E.J., Value management: An underlying framework (2000) International Journal of Operations & Production Management, 20 (9), pp. 1062-75; Duschek, S., Inter-firm resources and sustained competitive advantage (2004) Management Revue, 15 (1), pp. 53-73; Dwyer, R.F., Schurr, P.H., Oh, S., Developing buyer-seller relationships (1987) Journal of Marketing, 51, pp. 11-27; Dyer, J.H., (2000) Collaborative Advantage: Winning Through Extended Enterprise Supplier Networks, , Oxford University Press Oxford; Dyer, J.H., Chu, W., The determinants of trust in supplier-automaker relationships in the US, Japan, and Korea (2000) Journal of International Business Studies, 31 (2), pp. 259-85; Dyer, J.H., Nobeoka, K., Creating and managing a high-performance knowledge-sharing network: The Toyota case (2000) Strategic Management Journal, 21 (3), pp. 345-67; Dyer, J.H., Singh, H., The relational view: Cooperative strategy and sources of interorganizational competitive advantage (1998) Academy of Management Review, 23 (4), pp. 660-79; Eisenhardt, K.M., Schoonhoven, C.B., Resource-based view of strategic alliance formation: Strategic and social effects in entrepreneurial firms (1996) Organization Science, 7 (2), pp. 136-50; Ellram, L., Hendrick, T.E., Partnering characteristics: A dyadic perspective (1995) Journal of Business Logistics, 16 (1), pp. 41-56; Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error (1981) Journal of Marketing Research, 18 (1), pp. 39-50; Fullerton, R.R., McWatters, C.S., Fawson, C., An examination of the relationships between JIT and financial performance (2003) Journal of Operations Management, 21, pp. 1-22; Gelderman, C.J., Van Wheele, A., Handling measurement issues and strategic directions in Kraljic's purchasing portfolio model (2003) Journal of Purchasing & Supply Management, 9, pp. 207-16; Gerbing, D.W., Anderson, J.C., Monte Carlo evaluations of goodness of fit indices for structural equation models (1992) Sociological Methods & Research, 21 (2), pp. 132-60; Granovetter, M.S., Economic action and social structure: The problem of embeddedness (1985) American Journal of Sociology, 9 (3), pp. 481-510; Gulati, R., Nohria, N., Zaheer, A., Strategic networks (2000) Strategic Management Journal, 21 (3), pp. 203-15; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-47; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Gupta, A.K., Govindarajan, V., Knowledge flows within multinational corporations (2000) Strategic Management Journal, 21, pp. 473-96; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1998) Multivariate Data Analysis, , 5th ed. Prentice-Hall Englewood Cliffs, NJ; Handfield, R.B., Bechtel, C., The role of trust and relationship structure in improving supply chain responsiveness (2002) Industrial Marketing Management, 31 (4), pp. 367-82; Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (12), pp. 15-29; Holmbeck, G.N., Toward terminological, conceptual, and statistical clarity in the study of mediators and moderators: Examples from the child-clinical and pediatric psychology literatures (1997) Journal of Consulting and Clinical Psychology, 65 (4), pp. 599-610; Hoque, Z., James, W., Linking the balanced scorecard measures to size and market factors: Impact on organizational performance (2000) Journal of Management Accounting Research, 12, pp. 1-17; Hoyle, R., Smith, G., Formulating clinical research hypotheses as structural equation models: A conceptual overview (1994) Journal of Consulting and Clinical Psychology, 62 (3), pp. 429-40; Hu, L.-T., Bentler, P.M., Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives (1999) Structural Equation Modeling, 6 (1), pp. 1-55; Hyvönen, J., Strategy, performance measurement techniques and information technology of the firm and their links to organizational performance (2007) Management Accounting Research, 18 (3), pp. 343-66; Jones, G.R., Socialization tactics, self-efficacy, and newcomers' adjustments to organizations (1986) Academy of Management Journal, 29 (2), pp. 262-79; Jöreskog, K.G., Sörbom, D., (1993) LISREL 8: Analysis of Linear Structural Relationships by Maximum Likelihood, Instrument Variables and Least Squares Methods, , 8th ed. Scientific Software Melbourne; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, pp. 71-9. , January-February; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California Management Review, 39 (1), pp. 53-79; Kraimer, M.L., Organizational goals and values: A socialization model (1997) Human Resource Management Review, 7 (4), pp. 425-47; Krause, D.R., Scannell, T.V., Calantone, R.J., A structural analysis of the effectiveness of buying firms' strategies to improve supplier performance (2000) Decision Sciences, 31 (1), pp. 33-55; Langfield-Smith, K., Management control systems and strategy: A critical review (1997) Accounting, Organizations and Society, 22 (2), pp. 207-32; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-12; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Liker, J.K., Choi, T.Y., Building deep supplier relationships (2004) Harvard Business Review, , December 1; Lipset, S.M., Social structure and social change (1975) Approaches to the Study of Social Structure, , Blau, P.M. The Free Press New York, NY; Iii, L.A., McCormck, K., Linking SCOR planning practices to supply chain performance (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-218; Louis, M.R., Surprise and sense making: What newcomers experience in enterprising unfamiliar organizational settings (1980) Administrative Science Quarterly, 25, pp. 226-48; Mahama, H., Management control systems, cooperation and performance in strategic supply relationships: A survey in the mines (2006) Management Accounting Research, 17 (3), pp. 315-39; Maloni, M.J., Benton, W.C., Supply chain partnerships: Opportunities for operations research (1997) European Journal of Operational Research, 101 (3), pp. 419-29; Marsh, H.W., Balla, J.R., Hau, K.-T., An evaluation of incremental fit indices: A clarification of mathematical and empirical properties (1996) Advanced Structural Equation Modelling: Issues and Techniques, pp. 315-45. , Marcoulides, G. Schumacker, R. Lawrence Erlbaum Associates Mahwah, NJ; Mathews, J.A., Competitive dynamics and economic learning: An extended resource-based view (2003) Industrial and Corporate Change, 12 (1), pp. 115-45; Mohr, J., Spekman, R., Characteristics of partnership success: Partnership attributes, communication behaviour and conflict resolution techniques (1994) Strategic Management Journal, 15 (2), pp. 135-52; Monczka, R.M., Trent, R.J., Handfield, R.B., (2000) Purchasing and Supply Chain Management, , South-Western Mason, OH; Moschis, G.P., Churchill, G.A., Consumer socialization: A theoretical and empirical analysis (1978) Journal of Marketing Research, 15, pp. 599-609; Mouritsen, J., Hansen, A., Hansen, C.O., Inter-organizational controls and organizational competencies: Episodes around target cost management/functional analysis and open book accounting (2001) Management Accounting Research, 12 (2), pp. 221-44; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1999) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nonaka, I., Takeuchi, H., (1995) The Knowledge-creating Company: How Japanese Companies Create the Dynamics of Innovation, , Oxford University Press Oxford; Nunnally, J.C., (1978) Psychometric Theory, , 2nd ed. Tata McGraw-Hill New Delhi; O'Donnell, S.W., Managing foreign subsidiaries: Agents of headquarters, or an independent network? (2000) Strategic Management Journal, 21, pp. 525-48; Olsen, R., Ellram, L., Buyer-supplier relationships: Alternative research approaches (1997) European Journal of Purchasing & Supply Management, 4 (3), pp. 221-31; Prahinski, C., Benton, W.C., Supplier evaluations: Communication strategies to improve supplier performance (2004) Journal of Operations Management, 22, pp. 39-62; Said, A.A., Hassabelnaby, H.R., Wier, B., An empirical investigation of the performance consequences of nonfinancial measures (2003) Journal of Management Accounting Research, 15, pp. 193-223; Sako, M., (1992) Prices, Quality and Trust: Buyer Supplier Relationships in Britain and Japan, , 1st ed. Cambridge University Press Cambridge; Schmitz, J., Platts, K.W., Roles of supplier performance measurement: Indication from a study in the automotive industry (2003) Management Decision, 41 (8), pp. 711-21; Schroeder, R.G., John, C.A., Scudder, G.D., White collar productivity measurement (1986) Management Decision, 24 (5), pp. 3-7; Sim, K.L., Killough, L.N., The performance effects of complementarities between manufacturing practices and management accounting systems (1998) Journal of Management Accounting Research, 10, pp. 325-46; Simons, R., (1995) Levers of Control: How Managers Use Innovative Control Systems to Drive Strategic Renewal, , Harvard Business School Press Cambridge, MA; Sparks, J.R., Hunt, S.D., Marketing researcher ethical sensitivity: Conceptualization, measurement and exploratory investigation (1998) Journal of Marketing, 62 (2), pp. 92-109; Spekman, R.E., Strategic supplier selection - Understanding long-term buyer relationships (1988) Business Horizons, 31 (4), pp. 75-81; Stuart, F.I., Deckert, P., McCutheon, D., Kunst, R., A leveraged learning network (1998) Sloan Management Review, 39 (4), pp. 81-94; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40 (16), pp. 4257-69; Tan, K.C., Lyman, S.B., Wisner, J.D., Supply chain management: A strategic perspective (2002) International Journal of Operations & Production Management, 22 (6), pp. 614-31; Van Maanen, J., Breaking in: Socialization to work (1976) Handbook of Work, Organization and Society, pp. 67-130. , Dubin, R. Rand McNally Chicago, IL; (1979) Toward A Theory of Organizational Socialization, , van Maanen, J. Schein, E.H. JAI Press Inc. Greenwich, CT; Widener, S.K., Associations between strategic resource importance and performance measure use: The impact on firm performance (2006) Management Accounting Research, 17 (4), pp. 433-57},
correspondence_address1={Cousins, P.D.; Manchester Business School, , Manchester, United Kingdom; email: paul.cousins@mbs.ac.uk},
issn={01443577},
language={English},
abbrev_source_title={Int. J. Oper. Prod. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wong200825,
author={Wong, W.P. and Wong, K.Y.},
title={A review on benchmarking of supply chain performance measures},
journal={Benchmarking},
year={2008},
volume={15},
number={1},
pages={25-51},
doi={10.1108/14635770810854335},
note={cited By 138},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-39149129030&doi=10.1108%2f14635770810854335&partnerID=40&md5=675d22166af470eeca7068c3d162efbf},
affiliation={Department of Industrial and Systems Engineering, National University of Singapore, Singapore, Singapore; Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Malaysia},
abstract={Purpose - The purpose of this paper is to redress the imbalances in the past literature of supply chain benchmarking and enhance data envelopment analysis (DEA) modeling approach in supply chain benchmarking. Design/methodology/approach - The paper is sorted into sections: benchmarking, definitions, concepts and literature survey, supply chain benchmarking, problems in supply chain benchmarking, existing tools used in benchmarking, problems in existing tools and motivation of using DEA as a supply chain benchmarking tool. A range of recently published (1995-2004) works is referred and aims to provide insights into possible new research focus in supply chain benchmarking. Findings - Provides information about new trends in supply chain benchmarking and what are the existing problems. Recognizes the lack of mathematical modeling tool in addressing this issue. Research limitations/implications - Supply chain benchmarking is gearing towards a holistic approach. DEA is deemed to be an appropriate benchmarking tool with modifications in existing model. Practical implications - Provide insights to managers on the use of DEA in decision making at strategic level. Originality/value - This paper fulfils an identified information/resources need and offers insights to the future research focus in supply chain benchmarking. © Emerald Group Publishing Limited.},
author_keywords={Benchmarking;  Data analysis;  Performance measures;  Supply chain management},
references={Ahmed, P.K., Benchmarking innovation best practice (1998) Benchmarking for Quality Management & Technology, 5 (1), pp. 45-58; Ahmed, P.K., Rafiq, M., Integrated benchmarking: A holistic examination of select techniques for benchmarking analysis (1998) Benchmarking: An International Journal, 5 (3), pp. 225-42; Arntzen, B.C., Brown, G.G., Harrison, T.P., Trafton, L., Global supply chain management at Digital Equipment Corporation (1995) Interfaces, 25, pp. 69-93; Athanassopoulos, A.D., Ballantine, J.A., Ratio and frontier analysis for assessing corporate performance: Evidence from the grocery industry in the UK (1995) Journal of the Operational Research Society, 46 (4), pp. 427-40; Atkinson, A.B., Stiglitz, J.E., A new view of technological change (1969) Economic Journal, 79, pp. 573-8; Ball, A., Benchmarking in local government under a central government agenda (2000) Benchmarking: An International Journal, 7 (1), pp. 20-34; Ballou, R.H., Gilbert, S.M., Mukherjee, A., New managerial challenges from supply chain opportunities (2000) Industrial Marketing Management, 29, pp. 7-18; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Management Science, 30 (9), pp. 1078-92; Barr, R.S., Seiford, L.M., Benchmarking with data envelopment analysis (1994) ORSA/TIMS Joint National Meeting, , paper presented at the October, Detroit, MI; Basnet, C., Corner, J., Wiense, J., Tan, K., Benchmarking supply chain management practice in New Zealand (2003) Supply Chain Management: An International Journal, 8 (1), pp. 57-64; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-92; Bean, A.S., Russo, M.J., Whiteley, R.L., Benchmarking your R&D: Results from IRI/CIMS annual R&D survey for FY'96 (1998) Research Technology Management, 41 (1), pp. 21-30; Bell, R.A., Morey, R.C., Increasing the efficiency of corporate travel management through macro benchmarking (1995) Journal of Travel Research, 33 (3), pp. 11-20; Bendell, T., Boulter, L., Kelly, J., (1993) Benchmarking for Competitive Advantage, , Pitman Publishing London; Bhutta, K.S., Faizul, H., Benchmarking - Best practices: An integrated approach (1999) Benchmarking: An International Journal, 6 (3), pp. 254-68; Blossom, A.P., Bradley, J.R., (1998) Nine Mistakes Managers Make in Using Performance Measures to Motivate Employees, , working paper S.C. Johnson Graduate School of Management, Cornell University Ithaca, NY; Blumberg, D.R., Strategic benchmarking of service and logistic support operations (1994) Journal of Business Logistics, 15 (2), pp. 89-119; Bogan, C., Callahan, D., Benchmarking in rapid time (2001) Industrial Management, 43 (2), pp. 28-33; Bowersox, D.J., Integrated supply chain management: A strategic imperative (1997) Council of Logistics Management Annual Conference, , paper presented, Chicago, IL October 5-8; Bowersox, D.J., Closs, D.J., Keller, S.B., How supply chain competency leads to business success (2000) Supply Chain Management Review, 4 (4), pp. 70-8; Boyson, S., Corsi, T.M., Dresner, M.E., Harrington, L.H., (1999) Logistics and the Extended Enterprise: Benchmarks and Best Practices for the Manufacturing Professional, , Wiley New York, NY; Camp, R.C., (1989) Benchmarking the Search for Industry Best Practices That Lead to Superior Performance, , Quality Resources White Plains, NY; Camp, R.C., (1995) Business Process Benchmarking: Finding and Implementing Best Practices, , ASQC Quality Press Milwaukee, WI; Carr, A.S., Smeltzer, L.R., The relationship among purchasing benchmarking, strategic purchasing, firm performance and firm size (1999) Journal of Supply Chain Management, 35 (4), pp. 51-60; Chan, J.W.K., Burn, N.D., Benchmarking manufacturing planning and control (MPC) systems - An empirical study of Hong Kong supply chains (2002) Benchmarking: An International Journal, 9 (3), pp. 256-77; Charnes, A., Cooper, W.W., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2, pp. 429-44; Charnes, A., Cooper, W.W., Lewin, A., Seiford, L.M., (1994) Data Envelopment Analysis: Theory, Method, and Applications, , Kluwer Academic Publishers Boston, MA; Chopra, S., Meindl, P., (2001) Supply Chain Management: Strategy, Planning, Operation, , Prentice-Hall Englewood Cliffs, NJ; Christopher, M., (1998) Logistics and Supply Chain Management, , Pitman Publishing London; Collier, D.A., Storbeck, J.E., (1993) A data envelopment approach to benchmarking in the telecommunications industry, , working paper Ohio State Faculty of Management Science, Ohio State University Columbus, OH; Cox, A.W., (1997) Business Success: A Way of Thinking about Strategy, Critical Supply, Chain Assets and Operational Best Practices, , Earlsgate Press Boston, MA; Dale, B.G., Benchmarking on total quality management adoption: A positioning model (1996) Benchmarking for Quality Management & Technology, 3 (1), pp. 28-37; Dattakumar, R., Jagadeesh, R., A review of literature on benchmarking (2003) Benchmarking: An International Journal, 10 (3), pp. 176-209; Davis, P., The burgeoning of benchmarking in British local government (1998) Benchmarking for Quality Management & Technology, 5 (4), pp. 260-70; Davis, T., Effective supply chain management (1993) Sloan Management Review, 34, pp. 35-46; Detoro, I., The 10 pitfalls of benchmarking (1995) Quality Progress, 28 (1), pp. 61-3; Dorsch, J.J., Yasin, M.M., A framework for benchmarking in the public sector - Literature review and directions for future research (1998) International Journal of Public Sector Management, 11 (23), pp. 91-115; Drew, S.A.W., From knowledge to action: The impact of benchmarking on organizational performance (1997) Long Range Planning, 30 (3), pp. 427-41; Ellram, L.M., (2002) Strategic Cost Management in the Supply Chain: A Purchasing and Supply Management Perspective, , CAPS Research Tempe, AZ; Eyrich, H.G., Benchmarking to become the best of breed (1991) Manufacturing Systems, 9 (4), pp. 40-7; Farrell, M.J., The measurement of productive efficiency (1957) Journal of the Royal Statistical Society, 120 (3), pp. 253-90. , Series A (General); Featherman, D.W., (2000) Using influence diagrams as a benchmarking tool, , www.em.doe.gov/bch.feath.html; Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75, pp. 105-16; Freeman, C., The economics of technical change (1994) Cambridge Journal of Economics, 18, pp. 463-514; Fuller, C.W., Key performance indicators for benchmarking health and safety management in intra- and inter-company comparisons (1997) Benchmarking for Quality Management & Technology, 4 (3), pp. 165-74; Fuller, C.W., Modelling continuous improvement and benchmarking process through the use of cost benefit curves (2000) Benchmarking: An International Journal, 7 (1), pp. 35-51; Garvin, D.A., Building a learning organization (1993) Harvard Business Review, 71 (4), pp. 78-92; Geary, S., Zonnenberg, J.P., What is means to be best in class (2000) Supply Chain Management Review, 4 (3), pp. 42-8; Gilmour, P., Benchmarking supply chain operations (1999) Benchmarking for Quality Management & Technology, 5 (4), pp. 283-90; Golany, B., An interactive MOLP procedure for the extension of DEA to effectiveness analysis (1988) The Journal of the Operational Research Society, 39 (8), pp. 725-34; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1), pp. 71-87; Heib, R., Daneva, M., (1995) Benchmarks and benchmarking: A definitional analysis, , working paper Institute for Information Systems, University of Saarlandes Saarbruecken; Imai, K., Yamazaki, A., (1992) Dynamics of the Japanese industrial system from a Schumpeterian perspective, , discussion paper presented at the International J.A. Schummpeter Society Conference, Kyoto; Jones, R., The role of benchmarking within the cultural reform journey of an award-winning Australian local authority (1999) Benchmarking: An International Journal, 6 (4), pp. 338-49; Kahn, K.B., Benchmarking sales forecasting performance measures (1999) Journal of Business Forecasting Methods & Systems, 17 (4), pp. 19-23; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, , September/October; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard, Business Review, 74 (1), pp. 75-85; Kleinhans, S., Merle, C., Doumeingts, G., Determination of what to benchmark: A customer-oriented methodology (1995) Benchmarking Theory and Practice, pp. 267-76. , Tolstadas, A. Chapman & Hall London; Koopman, T.C., An analysis of production as an efficient combination of activities (1951) Activity Analysis of Production and Allocation, Cowles Commission for Research in Economics, , Koopmans, T.C. Monograph No. 13 Wiley New York, NY; Kopczak, L.R., Logistics partnerships and supply chain restructuring: Survey results from the US computer industry (1997) Production and Operations Management, 6 (3), pp. 226-47; Korpela, J., Tuominen, M., Benchmarking logistics performance with an application of the analytic hierarchy process (1996) IEEE Transactions on Engineering Management, 43 (3), pp. 323-33; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) International Journal of Logistics Management, 12, pp. 1-19; Lapide, L., True measures of supply chain performance (2001) Supply Chain Management Review, 4 (3), pp. 25-38; McAdam, R., Kelly, M., A business excellence approach to generic benchmarking in SMEs (2002) Benchmarking: An International Journal, 9 (1), pp. 7-27; McNair, C.J., Leibfried, K.H., (1992) Benchmarking: A Tool for Continuous Improvement, , Oliver Wight Publications Essex Junction, VT; McNamee, P., Greenan, L., The competitive analysis model: A new approach to strategic development for small businesses (1999) Benchmarking: An International Journal, 6 (2), pp. 125-39; Magan, G.M., Fawcett, S.E., Birou, L.M., Benchmarking manufacturing practice using the product life cycle (1999) Benchmarking: An International Journal, 6 (3), pp. 239-53; Marti, J.M.V., ICBS - Intellectual capital benchmarking system (2001) Journal of Intellectual Capital, 2 (2), pp. 148-64; Maskell, B.H., (1991) Productivity Measurement for World Class Manufacturing, , Productivity Press Inc. Cambridge, MA; Mentzer, J.T., (2000) Supply Chain Management, , Sage Thousand Oaks, CA; Metnzer, J.T., Min, S., Zacharia, Z.G., The nature of interfirm partnering in supply chain management (2000) Journal of Retailing, 76 (4), pp. 549-68; Metters, R.D., Frei, F.X., Vargas, V.A., Measurement of multiple sites in service firms with data envelopment analysis (1999) Production and Operations Management, 8 (3), pp. 264-81; Millen, R., Benchmarking Australian firms' usage of contract logistics services: A comparison with American and western European practice (1997) Benchmarking for Quality Management & Technology, 4 (1), pp. 34-46; Moseng, B., Productivity measurement: Methods and tools developed in TOPP (1995) Benchmarking Theory and Practice, pp. 248-60. , Rolstadas, A. Chapman & Hall London; Partovi, F.Y., Determining what to benchmark: An analytic hierarchy process approach (1994) International Journal of Operations & Production Management, 14 (6), pp. 25-39; Paula, K., Revising the concept and forms of benchmarking (2003) Benchmarking: An International Journal, 10 (3), pp. 210-25; Poirier, C.C., (1999) Advanced Supply Chain Management, , Berret-Koehler Publishers San Francisco, CA; Polese, W.T., Measuring the success of collaboration across the virtual supply chain through performance measurement systems and benchmarking (2002) Supply Chain World Conference and Exposition, , paper presented, New Orleans, LA, April 23; Pulat, M.B., Benchmarking is more than organized tourism: Implementing benchmarks (1994) Industrial Engineering, , March; Putzger, I., All the ducks in a row (1998) World Trade, 11 (9), pp. 54-6; Ramabadron, R., Dean, J.W., Evans, J.R., Benchmarking and project management: A review and organizational model (1997) Benchmarking for Quality Management & Technology, 4 (1), pp. 47-58; Ramdas, K., Spekman, R.E., Chain or shackles: Understanding what drives supply-chain performance (2000) Interfaces, 30 (4), pp. 3-21; Rickards, R., Setting benchmarks and evaluating balanced scorecards with data envelopment analysis (2003) Benchmarking: An International Journal, 10 (3), pp. 226-45; Rigby, D., Management tools and techniques: A survey (2001) California Management Review, 43 (2), pp. 139-60; Samuel, H.H., Sunl, K.S., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-9; Sarkis, J., Benchmarking for agility (2001) Benchmarking: An International Journal, 8 (2), pp. 88-107; Sarkis, J., Talluri, S., Efficiency valuation and internal benchmarking for business process improvement (1996) Journal of Engineering Valuation and Cost Analysis, 1 (1), pp. 43-54; Schefcyzk, M., Industrial benchmarking: A case study of performance analysis techniques (1993) International Journal of Production Economics, 32 (1), pp. 1-11; Seiford, L.M., Data envelopment analysis: The evolution of the state of the art (1978-1995) (1994) Journal of Productivity Analysis, 7 (23), pp. 99-137; Sheridan, J.H., Where benchmarkers go wrong (1993) Industry Week, 242 (6), pp. 28-34; Sherman, H.D., Ladino, G., Managing bank productivity using data envelopment analysis (DEA) (1995) Interfaces, 25 (2), pp. 60-73; Simatupang, T.M., Sridharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmarking: An International Journal, 11 (1), pp. 9-30; Simatupang, T.M., Sridharan, R., Benchmarking supply chain collaboration - An empirical study (2004) Benchmarking: An International Journal, 11 (5), pp. 484-503; Spendolini, M.J., The benchmarking process (1992) Compensation & Benefits Review, 24 (5), pp. 21-9; Spronk, J., Post, T., Performance benchmarking using interactive data envelopment analysis (1999) European Journal of Operational Research, 115 (3), pp. 472-87; Stank, T.P., Crum, M.R., Arango, M., Benefits of interfirm coordination in food industry supply chain (1999) Journal of Business Logistics, 20 (2), pp. 21-41; Stevens, J., Integrating the supply chain (1989) International Journal of Physical Distribution & Materials Management, 19 (8), pp. 3-8; Stewart, G., Supply-chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply chain management (1997) Logistics Information Management, 10 (2), pp. 62-7; Talluri, S., A benchmarking method for business-process reengineering and improvement (2000) International Journal of Flexible Manufacturing Systems, 12 (4), pp. 291-304; Talluri, S., Sarkis, J., A computational geometry approach for benchmarking (2001) International Journal of Operations & Production Management, 21 (1), pp. 210-22; Thompson, I., Cox, A., Don't imitate, innovate (1997) Supply Management, pp. 40-3. , October; Van Landeghem, R., Persoons, L., Benchmarking a logistical operations based on causal model (2001) International Journal of Operations & Production Management, 21 (12), pp. 254-66; Voss, C.A., Blackmon, K., Benchmarking and operational performance: Some empirical results (1997) Benchmarking for Quality Management & Technology, 4 (4), pp. 273-85; Vromen, J.J., (1995) Economic Evolution: An Enquiry into the Foundations of New Institutional Economics, , Routledge New York, NY; Wiesendanger, B., Benchmarking for beginners (1992) Sales & Marketing Management, 144 (14), pp. 59-64; Yasin, M.M., The theory and practice of benchmarking: Then and now (2002) Benchmarking: An International Journal, 9 (3), pp. 217-43; Zairi, M., Youssef, M., A review of key publications on benchmarking: Part i (1995) Benchmarking for Quality Management & Technology, 2 (1), pp. 65-72; Zairi, M., Youssef, M., A review of key publications on benchmarking: Part II (1996) Benchmarking for Quality Management & Technology, 3 (1), pp. 45-9; Zhu, J., Seiford, L.M., Cook, W.D., Models for performance benchmarking: Measuring the effect of e-business activities on banking performance (2004) Omega, the International Journal of Management Science, 32, pp. 313-22; Ahmad, P.K., Zairi, M., Supply chain partnerships - Theory and example of best practice (1999) Benchmarking: An International Journal, 6 (1), pp. 78-95; Carter, P.J., Monczka, R.M., Mosconi, T., Looking at the future of supply measurement (2005) Supply Chain Management Review, 9 (9), pp. 27-9; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-48; Chan, F.T.S., Chan, H.K., The future trend on system-wide modeling in supply chain studies (2005) International Journal of Advanced Manufacturing Technology, 25, pp. 820-32; Chan, F.T.S., Qi, H.J., A conceptual model of performance measurement for supply chains (2003) Journal of Management Decision, 41 (7), pp. 635-42; Easton, L., Murphy, D.J., Pearson, J.N., Purchasing performance evaluation: With data envelopment analysis (2002) European Journal of Purchasing & Supply Management, 8, pp. 123-34; Forker, L.B., An analytical method for benchmarking best peer suppliers (2001) International Journal of Operations & Production Management, 21 (1), pp. 195-209; Gardner, C., Harrity, C., Vitasek, K., A better way to benchmark (2005) Supply Chain Management Review, 9 (3), pp. 20-9; Golany, B., Roll, Y., Rybak, D., Measuring efficiency of power plants in Israel by data envelopment analysis (1994) IEEE Transactions on Engineering Management, 41 (3), pp. 291-301; Hawdon, D., Hodson, M., The use of data envelopment analysis in benchmarking (1992) The Business Economist, 27 (3), pp. 23-39; Homburg, C., Using data envelopment analysis to benchmark activities (2001) International Journal of Production Economics, 73, pp. 51-8; Munir, M.A., Nasreddin, D., Establishing and improving manufacturing performance measures (2002) Robotic and Computer Integrated Manufacturing, 18, pp. 171-6; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Gregory, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-45; Parlar, M., Weng, Z.K., Designing a firm's coordinated manufacturing and supply decisions with short product life cycles (1997) Management Science, 43 (10), pp. 1329-44; Ross, A., Droge, C., An integrated benchmarking approach to distribution center performance using DEA modeling (2002) Journal of Operations Management, 20, pp. 19-32; Seiford, L.M., Thrall, R.M., Recent developments in DEA - The mathematical programming approach to frontier analysis (1990) Journal of Econometrics, 46, pp. 7-38; Suhaiza, Z., Premkumar, R., Supply chain integration and performance: US versus East Asian companies (2005) Supply Chain Management: An International Journal, 10 (5), pp. 379-93; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40, pp. 4257-69; Thierry, P., Jaap, S., Performance benchmarking using interactive data envelopment analysis (1999) European Journal of Operational Research, 115, pp. 472-87; Thomas, D.J., Griffin, P.M., Coordinated supply chain management (1996) European Journal of Operational Research, 94, pp. 1-15},
correspondence_address1={Wong, W.P.; Department of Industrial and Systems Engineering, , Singapore, Singapore},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Review},
source={Scopus},
}

@ARTICLE{Beamon20084,
author={Beamon, B.M. and Balcik, B.},
title={Performance measurement in humanitarian relief chains},
journal={International Journal of Public Sector Management},
year={2008},
volume={21},
number={1},
pages={4-25},
doi={10.1108/09513550810846087},
note={cited By 430},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-38549102015&doi=10.1108%2f09513550810846087&partnerID=40&md5=fc771e409377a4e64fa41c45c1f1c426},
affiliation={University of Washington, Seattle, Washington, United States},
abstract={Purpose – The purpose of this paper is to compare performance measurement in the humanitarian relief chain with performance measurement in the commercial supply chain, develop performance metrics for the humanitarian relief chain, and present a framework that can be used as a basis for a performance measurement system in the relief sector. Design/methodology/approach – The performance measurement analysis is developed through extensions on an existing performance measurement framework. Details regarding relief chain system were obtained through off-site and on-site interviews with relief professionals from World Vision International. Findings – The paper finds that this work yielded: a comparison of performance measurement in the humanitarian relief chain with performance measurement in the commercial supply chain, new performance metrics for the humanitarian relief chain, and a performance measurement framework for the relief chain. Research limitations/implications – The paper shows that future work includes performance measurement in community involvement and empowerment, performance measurement in community development, performance measurement in the combined relief and development mission, and understanding the role and impacts of cooperation and coordination in the relief chain. Practical implications – This paper provides a practical procedure for developing effective performance measurement systems for relief logistics processes. Originality/value – The paper presents to humanitarian relief professionals a new approach to performance measurement for relief logistics and to researchers in supply chain performance a comparison and contrast between performance measurement for relief and performance measurement in the commercial chain, new performance metrics for the relief chain, and implications for modern, quick-response supply chains. © 2008, Emerald Group Publishing Limited},
author_keywords={Performance measures;  Supply chain management},
references={Aboum, T.A., Chole, E., Manibe, K., Minear, L., Mohammed, A., Sebstad, J., Weiss, T.G., A critical review of Operation Lifeline Sudan, Humanitarian and War Project (2007), Feinstein International Famine Center, Tufts University, Medford, MA, available at: hwproject.tufts.edu/publications/electronic/e_croo.html (accessed April 27, 2007); Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision Support Systems, 42 (1), pp. 283-301; Ballou, R.H., (2004) Business Logistics/Supply Chain Management: Planning, Organizing, and Controlling the Supply Chain, , Pearson/Prentice-Hall, Upper Saddle River, NJ; Baruch, Y., Ramalho, N., Communalities and distinctions in the measurement of organizational performance and effectiveness across for-profit and nonprofit sectors (2006) Nonprofit and Voluntary Sector Quarterly, 35 (1), pp. 39-65; Beamon, B.M., Performance measures in supply chain management (1996), Proceedings of the 1996 Conference on Agile and Intelligent Manufacturing Systems, Rensselaer Polytechnic Institute, Troy, New York, NY, October 2-3; Beamon, B.M., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3/4), pp. 275-292; Beamon, B.M., Humanitarian relief chains: issues and challenges (2004), Proceedings of the 34th International Conference on Computers and Industrial Engineering, San Francisco, CA, November 14-16; Beamon, B.M., Kotleba, S.K., Inventory management support systems for emergency humanitarian relief operations in South Sudan (2006) The International Journal of Logistics Management, 17 (2), pp. 187-212; Boland, T., Fowler, A., A systems perspective of performance measurement in public sector organisations (2000) International Journal of Public Sector Management, 13 (5), pp. 417-446; Brooks, A.C., Can nonprofit management help answer public management's big questions? (2002) Public Administration Review, 62 (3), pp. 259-266; Buckmaster, N., Associations between outcome measurement, accountability and learning for non-profit organizations (1999) International Journal of Public Sector Management, 12 (2), pp. 186-197; Byman, D., Lesser, I.O., Pirnie, B.R., Benard, C., Waxman, M., (2000) Strengthening the Partnership: Improving Military Coordination with Relief Agencies and Allies in Humanitarian Operations, , www.rand.org/pubs/monograph_reports/MR1185/, Rand Corporation, Santa Monica, CA, available at: (accessed April 29, 2007); Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3/4), pp. 209-223; Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: definition and measurement (1994) International Journal of Physical Distribution & Logistics Management, 24 (1), pp. 17-28; Davidson, A.L., Key performance indicators in humanitarian logistics (2006), http://ctl.mit.edu/index.pl?id=6146&isa=Item&field_name=item_attachment_file&op=download_file, Master of Engineering in Logistics thesis, Massachusetts Institute of Technology, Cambridge, MA, available at: (accessed April 23, 2007); Fisher, M.L., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Folan, P., Browne, J., A review of performance measurement: towards performance management (2005) Computers in Industry, 56 (7), pp. 663-680; Freeman, R.E., (1984) Strategic Management: A Stakeholder Approach, , Pitman Publishing, Boston, MA; Henderson, D.A., Chase, B.W., Woodson, B.M., Performance measures for NPOs (2002) Journal of Accountancy, 193 (1), pp. 63-68. , www.aicpa.org/PUBS/JOFA/jan2002/hender.htm, available at: (accessed April 28, 2007); Hilhorst, D., Being good at doing good? Quality and accountability of humanitarian NGOs (2002) Disasters, 26 (3), pp. 193-212; Kanter, R.M., Summers, D.V., Doing well while doing good: dilemmas of performance measurement in nonprofit organizations and the need for multiple-constituency approach (1987) The Nonprofit Sector: A Research Handbook, pp. 154-166. , in Powell, W.W. (Ed.), Yale University Press, New Haven, CT; Kaplan, R.S., Strategic performance measurement and management in nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 353-370; Kaplan, R., Norton, D., The Balanced Scorecard – measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Lai, K., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E, 38 (6), pp. 439-456; Lee, H.L., Aligning supply chain strategies with product uncertainties (2002) California Management Review, 44 (3), pp. 105-119; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Letts, C.W., Ryan, W.P., Grossman, A., (1999) High Performance Nonprofit Organizations: Managing Upstream for Greater Impact, , John Wiley & Sons, New York, NY; Lindenberg, M., Bryant, C., (2001) Going Global: Transforming Relief and Development NGOs, , Kumarian Press, Bloomfield, CT; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations & Production Management, 20 (5/6), pp. 520-533; Micheli, P., Kennerly, M., Performance measurement frameworks in public and non-profit sectors (2005) Production Planning and Control, 16 (2), pp. 125-134; Moore, M.H., Managing for value: organizational strategy in for-profit, nonprofit, and governmental organizations (2000) Nonprofit and Voluntary Sector Quarterly, 29 (1), pp. 183-204; Neely, A., The evolution of performance measurement research: developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116. , reprinted in International Journal of Operations & Production Management, Vol. 25 No. 12, 2005, 1228-63; Oloruntoba, R., Gray, R., Humanitarian aid: an agile supply chain? (2006) Supply Chain Management, 11 (2), pp. 115-120; O'Neill, M., Young, D.R., Educating managers of nonprofit organizations (1988) Educating Managers of Nonprofit Organizations, pp. 1-21. , in O'Neill, M. and Young, D.R. (Eds), Praeger Publishers, New York, NY; Oster, S.M., (1995) Strategic Management for Nonprofit Organizations: Theory and Cases, , Oxford University Press, New York, NY; Oster, S.M., Massarsky, C.W., Beinhacker, S.L., (2004) Generating and Sustaining Nonprofit Earned Income: A Guide to Successful Enterprise Strategies, , Jossey-Bass, San Francisco, CA; Parhizgari, A.M., Gilbert, G.R., Measures of organizational effectiveness: private and public sector performance (2004) Omega, 32 (3), pp. 221-229; Persson, F., Olhager, J., Performance simulation of supply chain designs (2002) International Journal of Production Economics, 77 (3), pp. 231-245; Poister, T.H., (2003) Measuring Performance in Public and Nonprofit Organizations, , Jossey-Bass, San Francisco, CA; Rouse, P., Putterill, M., An integral framework for performance measurement (2003) Management Decision, 41 (8), pp. 791-805; Sawhill, J.C., Williamson, D., Mission impossible? Measuring success in nonprofit organizations (2001) Nonprofit Management and Leadership, 11 (3), pp. 371-386; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: indications from a study in the automotive industry (2004) International Journal of Production Economics, 89 (2), pp. 231-243; Seaman, J., Malnutrition in emergencies: how can we do better and where do the responsibilities lie? (1999) Disasters, 23 (4), pp. 306-315; Shepherd, C., Gunter, H., Measuring supply chain performance: current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3/4), pp. 242-258; Slack, N., Flexibility as a manufacturing objective (1983) International Journal of Operations & Production Management, 3 (3), pp. 4-13; Slack, N., (1991) The Manufacturing Advantage, , Mercury Books, London; Sowa, J.E., Selden, S.C., Sandfort, J.R., No longer unmeasurable? A multidimensional integrated model of nonprofit organizational effectiveness (2004) Nonprofit and Voluntary Sector Quarterly, 33 (4), pp. 711-728; Speckbacher, G., The economics of performance management in nonprofit organizations (2003) Nonprofit Management and Leadership, 13 (3), pp. 267-281; Thomas, A.S., (2007) Humanitarian Logistics: Enabling Disaster Response, , www.fritzinstitute.org/PDFs/WhitePaper/EnablingDisaster, Fritz Institute, San Francisco, CA, available at: Response.pdf (accessed April 27, 2007); Thomas, A.S., Kopczak, L.R., (2005) From Logistics to Supply Chain Management: The Path Forward in the Humanitarian Sector, , www.fritzinstitute.org/PDFs/WhitePaper/FromLogisticsto.pdf, Fritz Institute, San Francisco, CA, available at: (accessed April 20, 2007); Van Wassenhove, L.N., Humanitarian aid logistics: supply chain management in high gear (2006) Journal of Operational Research Society, 57 (5), pp. 475-489; Vladeck, B.C., The practical differences in managing nonprofits: a practitioner's perspective (1988) Educating Managers of Nonprofit Organizations, pp. 71-81. , in O'Neill, M. and Young, D.R. (Eds), Praeger Publishers, New York, NY; Wisner, J.D., Fawcett, S.E., Linking firm strategy to operating decisions through performance measurement (1991) Production and Inventory Management Journal, 32 (3), pp. 5-11; Zimmermann, J.A.M., Stevens, B.W., The use of performance measurement in South Carolina nonprofits (2006) Nonprofit Management and Leadership, 16 (3), pp. 315-327; Thomas, M.U., Supply chain reliability for contingency operations (2002), pp. 61-67. , Proceedings of the Annual Reliability and Maintainability Symposium},
correspondence_address1={Beamon, B.M.; University of Washington, Seattle, Washington, United States},
issn={09513558},
language={English},
abbrev_source_title={Int. J. Public Sect. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yaibuathet2008322,
author={Yaibuathet, K. and Enkawa, T. and Suzuki, S.},
title={Effect of industry type and ownership status on supply chain operational performance in a developing country},
journal={International Journal of Integrated Supply Management},
year={2008},
volume={4},
number={3-4},
pages={322-354},
doi={10.1504/IJISM.2008.020758},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-54349091158&doi=10.1504%2fIJISM.2008.020758&partnerID=40&md5=54e3ba8d1ed86681bfbc7ae07c39ee61},
affiliation={Department of Industrial Engineering and Management, Graduate School of Decision Science and Technology, Tokyo Institute of Technology, 2-12-1 O-Okayama, Meguro-ku, Tokyo 152-8552, Japan},
abstract={This research investigates the Supply Chain Operational Performance (SCOP) of Thailand's manufacturing sector through the use of a supply chain management Logistics Scorecard (LSC). The LSC was developed as a self-evaluation tool for companies and focuses on four key areas. Over 150 companies from five industries in Thailand participated in this study by providing their LSC self-evaluation data. This data was analysed to identify determinants of SCOP in terms of industry type and ownership status. The results indicate that different industries have focused on different areas of SCOP. Furthermore, it was observed that multinational companies in Thailand significantly outperform domestic companies in terms of SCOP level in several industry sectors. Also, dissimilarity between multinational and local companies was found in terms of their approach to supply chain management. The findings of this research may be useful as guidelines for investigating and enhancing SCOP in other newly industrialised or developing countries. © 2008 Inderscience Enterprises Ltd.},
author_keywords={Developing country;  Multinational company;  SCM logistics scorecard;  Supply chain performance measurement},
references={Aiken, B.J., Harrison, A.E., Do domestics firms benefit from direct foreign investment? Evidence from Venezuela (1999) American Economics Reviews, 89 (3), pp. 605-618; Arashida, K., Enkawa, T., Hamasaki, A., Suzuki, S., Developing the SCM logistics scorecard and analysing its relation to the managerial performance (2004) Journal of Japan Industrial Management Association, 55 (2), pp. 95-103. , in Japanese; Barbosa, N., Louri, H., Corporate performance: Does ownership matter? A comparison of foreign- and domestic-owned firms in Greece and Portugal (2005) Review of Industrial Organization, 27, pp. 73-102; Boardman, A.E., Shapiro, D.M., Vining, A.R., The role of agency cost in explaining the superior performance of foreign MNE subsidiaries (1997) International Business Review, 6, pp. 295-317; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chibber, P.K., Majundar, S.K., Foreign ownership and profitability: Property rights, controls and performance of firms in Indian industry (1999) Journal of Laws and Economics, 42, pp. 209-238; Childerhouse, P., Disney, S., Naim, M.M., A quick scan method for supply chain diagnostics (1999) Proceedings of the 4th International Symposium on Logistics, pp. 755-760. , 11-14 July, Florence, Italy, pp; Closs, D.J., Mollenkopf, D.A., A global supply chain framework (2004) Industrial Marketing Management, 33, pp. 37-44; Driffield, N., Girma, S., Regional foreign direct investment and wages spillover, plant level evidence from the UK Electronics Industry (2003) Oxford Bulletin of Economics and Statistics, 65 (4), pp. 453-474; (1998) Efficient Consumer Response (ECR) Scorecard, , Electronic Commerce Council of Canada ECCC, Electronic Commerce Council of Canada; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategy (2001) Journal of Operations Management, 19, pp. 185-200; Hamasaki, A., Arashida, K., Enkawa, T., A study on relationship between logistics performance and financial outcome using SCM logistics scorecard and some consideration on role of logistics department (2004) Japan Logistics Society Magazine, 12, pp. 89-94. , in Japanese; Hayashi, C., Discussion of Tanaka's paper' (1979) Environmental Health Perspectives, 32, pp. 125-129; Ito, K., Are foreign multinationals more efficient? Plant productivity in the Thai automobile industry (2002) Working Paper Series, 19. , July, International Center for Study of East Asian Development, Kitakyushu; Ito, K., Foreign ownership and plant productivity in the Thai automobile industry in 1996 and 1998: A conditional quantile analysis (2004) Journal of Asian Economics, 15, pp. 321-353; Jones, C., Moving beyond ERP: Making the missing link (1998) Logistics Focus, 6 (7), pp. 2-7; Kim, J., Charles, W., Factor Analysis: Statistical Methods and Practical Issues (1978) Quantitative Applications in the Social Science Series, 14. , Thousand Oaks, CA: Sage Publications; Kim, W.S., Lyn, E.O., FDI theories and the performance of foreign multinationals operating in US (1990) Journal of International Business Studies, 21 (1), pp. 41-54; (1999) KSA Millennium Readiness Profile, , Kurt Salmon Associates (KSA, Kurt Salmon Associates (KSA) Available online; Kuwaiti, M.E., Kay, J.M., The role of performance measurement in business process re-engineering (2000) International Journal of Operations and Production Management, 20 (12), pp. 1411-1426; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) OMEGA: The International Journal of Management Science, 34, pp. 107-124; Lou, Y., Tan, J., A comparison of multinational and domestic firms in an emerging market: A strategic choice perspective (1998) Journal of International Management, 4 (1), pp. 21-40; Majundar, S.K., The impact of size and age on firm-level performance: Some evidence from Indian industry (1997) Review of Industrial Organization, 12, pp. 231-241; Naim, M.M., Childerhouse, P., Disney, S.M., Towill, D.R., A supply chain diagnostic methodology: Determine the vector of changes (2002) Computers and Industrial Engineering, 43 (1-2), pp. 135-157; Okamoto, Y., Sjoholm, F., Productivity in the Indonesian automotive industry (2000) Asian Economic Bulletin, 17 (1), pp. 60-73; (1996) QRLC Measurements for Excellent, American Apparel Manufacturers Association (AAMA), , Quick Response Leadership Committee QRLC; Ramstatter, E.D. (2002) 'Does technology differ in local plants and foreign multinationals in Thai manufacturing? Evidence from translog production functions for 1996 and 1998', Working Paper Series, 2002-2004, The International Center for Study of East Asian Development, Kitakyushu; Ramstetter, E.R., Labor productivity, wages, nationality, and foreign ownership shares in Thai manufacturing, 1996-2000 (2004) Journal of Asian Economics, 14, pp. 861-884; Rosenzweig, E.D., Roth, A.V., Dean Jr., J.W., The influence of an integration strategy on competitiveness capabilities and business performance: An exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21, pp. 437-456; Santos, J.R.A., Cronbach's alpha: A tool for assessing the reliability of scales' (1999) Journal of Extension, 37 (2), pp. 1-5; Shin, H., Collier, D.A., Wilson, D.D., Supply management orientation and supplier/buyer performance (2000) Journal of Operations Management, 18, pp. 317-333; Stank, T.P., Keller, S.B., Closs, D.J., Performance benefits of supply chain logistical integration (2001) Transportation Journal, 41 (2-3), pp. 32-46; Stevens, J.P., (1996) Applied Multivariate Statistics for the Social Sciences, , 3rd edition, Mahway, NJ: Lawrence Erlbaum Associates, Inc; Supply Chain Council (2001) Supply Chain Operation Reference-Model: SCOR version 5.0, Supply Chain Council, Inc; Suzuki, S., Enkawa, T., Yaibuathet, K., Identifying the factors which determine the operational performance of SCM and analyzing their impacts on financial indexes (2005) Proceedings of 1st International Conference on Operations and Supply Chain Management, pp. H1-H8. , Bali, Indonesia, pp; Swink, M., Narasimhan, R., Wang, C., Managing beyond the factory walls: Effects of four types of strategic integration on manufacturing plant performance (2007) Journal of Operations Management, 25 (1), pp. 148-164; (2001) The Quick Scan Handbook, , http://www.cf.ac.uk/carbs/lom/lsdg/quickscanhand.html, The Logistics Systems Dynamics Group, Cardiff University , Available at; Wilmore, L., The comparative performance of foreign and domestics firms in Brazil (1986) World Development, 14 (4), pp. 489-502; Yaibuathet, K., Enkawa, T., Suzuki, S., Supply chain operational performance and its influential factors: Cross national analysis (2007) Journal of Japan Industrial Management Association, 57 (6), pp. 473-482; Yaibuathet, K., Enkawa, T., Suzuki, S., Impact of information technology and SCM organizational strategy on corporate financial performance and its cross national comparison (2007) International Journal of Information Systems for Logistics and Management, 3 (1), pp. 13-24},
correspondence_address1={Yaibuathet, K.; Department of Industrial Engineering and Management, 2-12-1 O-Okayama, Meguro-ku, Tokyo 152-8552, Japan; email: kyaibuathet@yahoo.com.au},
publisher={Inderscience Enterprises Ltd.},
issn={14775360},
language={English},
abbrev_source_title={Int. J. Integr. Supply Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{DeLeeuw200856,
author={De Leeuw, S. and Beekman, L.},
title={Supply chain-oriented performance measurement for automotive spare parts},
journal={International Journal of Automotive Technology and Management},
year={2008},
volume={8},
number={1},
pages={56-70},
doi={10.1504/IJATM.2008.018768},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-45949083967&doi=10.1504%2fIJATM.2008.018768&partnerID=40&md5=60466049f043dd23a51647ba10a3bd2d},
affiliation={Department of Economics and Business Sciences Information and Logistics, Vrije Universiteit Amsterdam, De Boelelaan 1105/3A-33, 1081 HV Amsterdam, Netherlands; Windesheim Honours College, Praubstraat 17, 8011 PN Zwolle, Netherlands; AP-Logistics B.V., s-Gravenlandseweg 379, 3125 BJ Schiedam, Netherlands; Department of Logistics, Vrije Universiteit Amsterdam; Department of Studies for the Study Area of Trade, Transport and Logistics, Windesheim Honours College, Zwolle, Netherlands; AP-Logistics B.V.},
abstract={Literature provides a number of conceptual frameworks and discussions on performance measurement in supply chains. However, most of these frameworks focus on a single link of a supply chain. Furthermore, there is a lack of empirical analysis and case studies on performance metrics and measurements in a supply chain context. This article provides the results of an empirical study into performance measurement in the automotive spare parts supply chain, with a focus on the dependent channel. We have applied a supply chain performance measurement model called the LogistiQual model. We will discuss the types of measures used by companies in the automotive spare parts supply chain and will argue that in the automotive spare parts supply chain performance is, to a certain extent, measured with a supply chain perspective but not completely. Copyright © 2008 Inderscience Enterprises Ltd.},
author_keywords={Automotive;  Logistics;  Performance management;  Performance measurement;  Spare parts;  Supply chain management},
keywords={Automotive engineering;  Logistics;  Management, Automotive;  Conceptual frameworks;  Performance management;  Performance measurements;  Performance metrics;  Spare parts;  Spare parts supply;  Supply chain performance measurements, Supply chain management},
references={(2002) Global Study on Spare Parts Logistics, , Barkawi and Partners GmbH , Munich; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bhagwat, R., Sharma, M.K., Performance measurement of supply chain management: A balanced scorecard approach (2007) Computers & Industrial Engineering, 53, pp. 43-62; Bijl, J., Mordret, H., Multrier, B., Nieuwhuys, S., Pitot, N., The evolution of the European automotive spare pans distribution market (2000) Supply Chain Forum -An International Journal, 1, pp. 70-79; Chan, F.T.S., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Chieux, T., Guillaneuf, V., (2005) The European Aftermarket; The Market and Prospects for Authorised and Independent Repairers, , Solihull, UK: ICDP; Chieux, T., Guillaneuf, V., Whiteman, J., (2005) Aftermarket Parts: Changing Channels Under 1400/02, , Solihull, UK: ICDP; Cohen, M.A., Agrawal, N., Agrawal, V., (2005) Achieving Breakthrough Service Delivery Through Dynamic Asset Deployment Strategies, , Philadelphia, USA: MCA Solutions Inc; Cohen, M.A., Cull, C., Lee, H.L., Willen, D., Saturn's supply-chain innovation: High value in after-sales service' (2000) Sloan Management Review, 41 (4), pp. 93-101; De Toni, A., Tonchia, S., Performance measurement systems - models, characteristics and measures (2001) International Journal of Operations & Productions Management, 21 (1-2), pp. 46-70; Eisenhardt, K.H., Building theories from case study research (1999) The Academy of Management Review, 14 (4), pp. 532-550; Fisher, M.L., What is the right supply chain type for you? (1997) Harvard Business Review, 75 (2), pp. 105-117; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Computers in Industry, 56, pp. 663-680; Fortuin, L., Martin, H., Control of service parts (1999) International Journal of Operations & Production Management, 19 (9), pp. 950-971; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Grimaldi, S., Rafele, C., Current applications of a reference framework for supply chain performance measurement (2007) International Journal of Business Performance Management, 9 (2), pp. 206-225; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Hausman, W., Supply chain performance metrics (2003) Kluwer's International Series, pp. 61-73. , T.P. Harrison, H.L. Lee and J.J. Neale Eds, The Practice of Supply Chain Management; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Holweg, M., Pil, F.K., (2004) The Second Century, , Cambridge: MIT Press; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the weighting of performance measures: Evidence from the balanced scorecard (2003) The Accounting Review, 78 (3), pp. 725-758; Johnston, W.J., Leach, M.P., Liu, A.H., Theory testing using case studies in business-to-business research (1999) Industrial Marketing Management, 28 (3), pp. 201-213; Kaplan, S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, pp. 71-79. , January-February, pp; Lai, K.H., Ngia, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E, 38, pp. 439-456; Lambert, D.M., Garcia-Dastague, S.J., Croxton, K.L., An evaluation of process-oriented supply chain management frameworks (2005) Journal of Business Logistics, 26 (1), pp. 25-51; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lapide, L., What about measuring supply chain performance? (2000) ASCET, 2, pp. 287-297. , 15 April, pp; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Mentzer, J.T., de Witt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain management (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Neely, A., The evolution of performance measurement research; developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Parasuraman, A., Zeithaml, V.A., Berry, L.L., A conceptual model of service quality and its implications for future research (1985) Journal of Marketing, 49 (4), pp. 41-50; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Saccani, N., Songini, L., Gaiardelli, P., The role and performance measurement of after-sales in the durable consumer goods industries: An empirical study (2006) International Journal of Productivity and Performance Management, 55 (4), pp. 259-283; Short, J.E., Venkatraman, N., Beyond process business redesign: Redefining Baxter's business network' (1992) Sloan Management Review, 34 (1), pp. 7-21; Van Hoek, R.I., Measuring the unmeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Yin, R., (1994) Case Study Research: Design and Methods, , Beverly Hills, CA: Sage Publishing},
correspondence_address1={De Leeuw, S.; Department of Economics and Business Sciences Information and Logistics, De Boelelaan 1105/3A-33, 1081 HV Amsterdam, Netherlands; email: sleeuw@feweb.vu.nl},
publisher={Inderscience Publishers},
issn={14709511},
language={English},
abbrev_source_title={Int. J. Automot. Technol. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wong2008162,
author={Wong, W.P. and Jaruphongsa, J. and Lee, L.H.},
title={Supply chain performance measurement system: A Monte Carlo DEA-based approach},
journal={International Journal of Industrial and Systems Engineering},
year={2008},
volume={3},
number={2},
pages={162-188},
doi={10.1504/IJISE.2008.016743},
note={cited By 60},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-39149114149&doi=10.1504%2fIJISE.2008.016743&partnerID=40&md5=e802efc03f9ee2ac33bb2aaa501cc356},
affiliation={Department of Industrial and Systems Engineering, National University of Singapore, Singapore, Singapore},
abstract={A supply chain operates in a dynamic platform and its performance efficiency measurement requires intensive data collection. The task of collecting data in a supply chain is not trivial and it often faces with uncertainties. This paper develops a simple tool to measure supply chain performance in the real environment, which is stochastic. Firstly, it introduced the Data Envelopment Analysis (DEA) supply chain model to measure the supply chain performance. Next, it enhanced the model with Monte Carlo (random sampling) methodology to cater for efficiency measurement in stochastic environment. Monte Carlo approximations to stochastic DEA have not been practically used in empirical analysis, despite being an important tool to make statistical inferences on the efficiency point estimator. This method proves to be a cost saving and efficient way to handle uncertainties and could be used in other relevant field other than supply chain, to measure efficiency. Copyright © 2008 Inderscience Enterprises Ltd.},
author_keywords={Data Envelopment Analysis;  DEA;  Monte Carlo;  Stochastic data;  Supply chain efficiency},
keywords={Data envelopment analysis;  Monte Carlo methods;  Random processes;  Sampling;  Stochastic models, Empirical analysis;  Point estimator;  Random sampling, Supply chains},
references={Banker, R.D., Gadh, W.M., Gorr, W.L., A Monte Carlo comparison of two production frontier estimation methods: Correlated ordinary least squares and data envelopment analysis (1993) European Journal of Operational Research, 69 (3), pp. 332-343; Castelli, L., Presenti, R., Ukovich, W., DEA-like models for the efficiency evaluation of hierarchically structured units (2004) European Journal of Operational Research, 154 (2), pp. 465-476; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-444; Chen, Y., Zhu, J., Measuring information technology's indirect impact on firm performance' (2004) Information Technology and Management Journal, 5 (1), pp. 9-22; Chen, Y., Liang, L., Yang, F., A DEA game model approach to supply chain efficiency (2006) Annals of Operations Research, 145 (1), pp. 5-13; Cheung, K.L., Hausman, W.H., An exact performance evaluation for the supplier in a two-echelon inventory system (2000) Operations Research, 48 (4), pp. 646-653; Chou, T., Hsu, L., Yeh, Y., Ho, C., Towards a framework of the performance evaluation of SMUs' industry portals' (2005) Industrial Management and Data Systems, 105 (4), pp. 527-544; Cooper, W.W., Seiford, L.M., Tone, K., (2006) Introduction to Data Envelopment Analysis and its Uses: With DEA-Solver Software and References, , New York: Springer; Easton, L., Murphy, D.J., Pearson, J.N., Purchasing performance evaluation: With data envelopment analysis (2002) European Journal of Purchasing and Supply Management, 8 (3), pp. 123-134; Fare, R., Grosskopf, S., Network DEA (2000) Socio-Economic Planning Sciences, 34 (1), pp. 35-49; Gentle, J.E., (2003) Random Number Generation and Monte Carlo Methods, , 2nd edition, New York, NY: Springer-Verlag; Golany, B., Hackman, S.T., Passy, U., An efficiency measurement framework for multi-stage production systems (2006) Annals of Operations Research, 145 (1), pp. 51-68; Humphreys, P., Huang, G., Cadden, T., A web-based supplier evaluation tool for the product development process (2005) Industrial Management and Data Systems, 105 (2), pp. 147-163; Lahdelma, R., Salminen, P., Stochastic multicriteria acceptability analysis using the data envelopment model (2006) European Journal of Operational Research, 170 (1), pp. 241-252; Land, K.C., Lovell, C.A.K., Thore, S., Chance-constrained data envelopment analysis (1993) Managerial and Decision Economics, 14 (6), pp. 541-554; Olesen, O.B., Peterson, N.C., Chance constrained efficiency evaluation (1995) Management Science, 41 (3), pp. 442-457; Parlar, M., Weng, Z.K., Designing a firm's coordinated manufacturing and supply decisions with short product life cycles' (1997) Management Science, 43 (10), pp. 1329-1344; Rao, M.P., A performance measurement system using a profit-linked multi-factor measurement model (2006) Industrial Management and Data Systems, 106 (3), pp. 362-379; Rickards, R., Setting benchmarks and evaluating balanced scorecards with data envelopment analysis (2003) Benchmarking: An International Journal, 10 (3), pp. 226-245; Sarkis, J., Talluri, S., Efficiency valuation and internal benchmarking for business process improvement (1996) Journal of Engineering Valuation and Cost Analysis, 1 (1), pp. 43-54; Seiford, L.M., Zhu, J., Profitability and marketability of the top 55 US commercial banks (1999) Management Science, 45 (9), pp. 1270-1288; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Designing And Managing The Supply Chain - Concepts, Strategies and Case Studies, , New York, NY: Mc Graw Hill Publishing; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply chain management (1997) Logistics Information Management, 10 (2), pp. 62-67; Thomas, D.J., Griffin, P.M., Coordinated supply chain management (1996) European Journal of Operational Research, 94 (1), pp. 1-15; Troutt, M.D., Ambrose, P., Chan, C.K., Optimal throughput for multistage input-output processes (2001) International Journal of Operations and Production Management, 21 (1), pp. 148-158; Weber, C.A., Desai, A., Determinants of paths to vendor market efficiency using parallel coordinates representation: A negotiation tool for buyers (1996) European Journal of Operational Research, 90 (1), pp. 142-155; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2008) Benchmarking: An International Journal, 15 (1), pp. XXXX; Zhang, Y., Bartels, R., The effect of sample size on the mean efficiency in DEA with an application to electricity distribution in Australia, Sweden and New Zealand (1998) Journal of Productivity Analysis, 9 (3), pp. 187-204},
correspondence_address1={Wong, W.P.; Department of Industrial and Systems Engineering, , Singapore, Singapore; email: g0500743@nus.edu.sg},
publisher={Inderscience Publishers},
issn={17485037},
language={English},
abbrev_source_title={Int. J. Ind. Syst. Eng.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Chearskul2007215,
author={Chearskul, P.},
title={The relationship between performance measurement system use and organizational outcomes},
journal={28th Annual National Conference of the American Society for Engineering Management 2007 - Innovation Management: Innovation in a Flattened World, ASEM 2007},
year={2007},
pages={215-224},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880058121&partnerID=40&md5=3a5d5d8716a3177dd3ae57770e67ee8d},
affiliation={Virginia Tech, United States},
abstract={Current empirical studies offer mixed results as to whether the adoption of a structured, strategicallyfocused performance measurement system leads to improved organizational performance. It has been suggested that the use phase is the most vital phase for performance measurement systems to succeed, and different practices for using measurement systems may explain why some organizations are satisfied with their performance measurement system while others are not. The purpose of this paper is to propose a conceptual framework to explore the relationship between practices associated with use of performance measurement systems (e.g. review of performance information, decision-making based on performance information, etc.), intermediates outcomes (e.g. shared view of performance by organizational members, learning behaviors, etc.) and organizational performance outcomes. The framework contributes to the study of the impact of performance measurement system use on organizational outcomes. This paper presents a literatures review and defines a future research agenda for the relationship between key performance measurement use practices and their influence on organizational performance.},
author_keywords={Organizational outcomes;  Organizational performance;  Performance measurement system;  Use},
keywords={Conceptual frameworks;  Empirical studies;  Measurement system;  Organizational outcomes;  Organizational performance;  Performance measurement system;  Performance measurements;  Use, Innovation, Supply chain management},
references={Akgun, A.E., Lynn, G.S., Byrne, J.C., Organizational learning: A socio-cognitive framework (2003) Human Relations, 56 (7), p. 839; Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), p. 217; Bititci, U.S., Mendibil, K., Nudurupati, S., Garengo, P., Turner, T., Dynamics of performance measurement and organisational culture (2006) International Journal of Operations & Production Management, 26 (12), p. 1325; Bititci, U.S., Turner, U.T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations & Production Management, 20 (6), pp. 692-704; Bourne, M., Kennerley, M., Franco-Santos, M., Managing through measures: A study of impact on performance (2005) Journal of Manufacturing Technology Management, 16 (4), p. 373; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Buhovac, A.R., Slapnicar, S., The role of balanced, strategic, cascaded and aligned performance measurement in enhancing firm performance (2007) Economic and Business Review for Central and South - Eastern Europe, 9 (1), p. 47; Chan, Y.-C.L., Preformance measurement and adoption of balanced scorecard: A survey of municipal governments in the usa and Canada (2004) The International Journal of Public Sector Management, 17 (2-3), p. 204; Chenhall, R.H., Management control systems design within its organizational context: Findings from contingency-based research and directions for the future (2003) Accounting, Organizations and Society, 28 (2-3), pp. 127-168; De Waal, A.A., Counet, H., Lesson learned from the balance scorecard (2006) Proceedings from the Performance Measurement and Management: Public and Private, , London, UK; Franco-Santos, M., Bourne, M., An examination of the literature relating to issues affecting how companies manage through measures (2005) Production Planning & Control, 16 (2), p. 114; Franco-Santos, M., Kennerley, M., Micheli, P., Martinez, V., Mason, S., Marr, B., Gray, D., Neely, A., Towards a definition of a business performance measurement system (2007) International Journal of Operations & Production Management, 27 (8), pp. 784-801; Franco, M., Bourne, M., Factors that play a role in "managing through measures (2003) Management Decision, 41 (8), pp. 698-710; Garengo, P., Bititci, U., Towards a contingency approach to performance measurement: An empirical study in scottish smes (2007) International Journal of Operations & Production Management, 27 (8), pp. 802-825; Henri, J.-F., Management control systems and strategy: A resource-based perspective (2006) Accounting, Organizations and Society, 31 (6), pp. 529-558; Henri, J.-F., Organizational culture and performance measurement systems (2006) Accounting, Organizations and Society, 31 (1), pp. 77-103; Hoque, Z., A contingency model of the association between strategy, environmental uncertainty and performance measurement: Impact on organizational performance (2004) International Business Review, 13 (4), pp. 485-502; Hoque, Z., James, W., Linking balanced scorecard measures to size and market factors: Impact on organizational performance (2000) Journal of Management Accounting Research, 12, p. 1; Ittner, C.D., Larcker, D.F., Rajan, M.V., The choice of performance measures in annual bonus contracts (1997) The Accounting Review, 72 (2), p. 231; Ittner, C.D., Larcker, D.F., Randall, T., Performance implications of strategic performance measurement in financial services firms (2003) Accounting, Organizations and Society, 28 (7-8), pp. 715-741; Kaplan, R.S., Norton, D.P., The balanced scorecard - measures that drive performance (1992) Harvard Business Review, 70 (1), p. 71; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), p. 75; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), p. 213; Malmi, T., Balanced scorecards in finnish companies: A research note (2001) Management Accounting Research, 12 (2), pp. 207-220; Marr, B., Business performance measurement: An overview of the current state of use in the usa (2005) Measuring Business Excellence, 9 (3), p. 56; Marr, B., Bourne, M., Kennerley, M., Franco, M., Wilcox, M., Adams, C., Mason, S., (2004) Business Performance Management: Current State of the Art, , (Survey Report), Cranfield University, Cranfield; Martinez, V., Kennerley, M., Impact of performance management reviews: Evidence fron an energy supplier (2005) Proceedings from the EUROMA Conference: Operations and Global Competitiveness, , Budapest, Hungry (June 19- 22); Martinez, V., Kennerley, M., Performance measurement systems: Mix effects (2005) Proceedings from the EURAM Conference, , Munich, Germany (May 4-7); Martinez, V., Kennerley, M., Performance review: A definition analysis (2006) Procesedings from the Performance Measurement and Management: Public and Private, , London, UK; Mausolff, C., Learning from feedback in performance measurement systems (2004) Public Performance & Management Review, 28 (1), p. 9; Meekings, A., Effective review meetings: The counter-intuitive key to successful performance measurement (2005) International Journal of Productivity and Performance Management, 54 (3), pp. 212-220; Neely, A., The evolution of performance measurement research: Developments in the last decade and a research agenda for the next (2005) International Journal of Operations & Production Management, 25 (12), pp. 1264-1277; Neely, A., Veronica, M., Micheli, P., Action on information: Performance management for the public sector (2006) The Advanced Institute of Management Research, , London; Norreklit, H., The balance on the balanced scorecard a critical analysis of some of its assumptions (2000) Management Accounting Research, 11 (1), pp. 65-88; Smith, P.C., Outcome-related performance indicators and organisational control in the public sector (1995) Performance Measurement and Evaluation, , Holloway, Jacky, Jenny Lewis and Geoff Mallory (Eds.), Sage; Speckbacher, G., Bischof, J., Pfeiffer, T., A descriptive analysis on the implementation of balanced scorecards in german-speaking countries (2003) Management Accounting Research, 14 (4), pp. 361-388; Tangen, S., Improving the performance of a performance measure (2005) Measuring Business Excellence, 9 (2), p. 4; Tapinos, E., Dyson, R.G., Meadows, M., Examing the relationship between organisational direction and performance measurement within the strategic development process (2006) From the Performance Measurement and Management: Public and Private, pp. 761-768. , Proceedings, London, UK (July 25-28); Ukko, J., Tenhunen, J., Rantanen, H., The effects of performance measurement on leadership and the quality of worklife: Perspective of management and employees (2006) Proceedings from the Performance Measurement and Management: Public and Private, pp. 793-800. , London, UK (July 25- 28); Vakkuri, J., Meklin, P., Ambiguity in performance measurement: A theoretical approach to organisational uses of performance measurement (2006) Financial Accountability and Management, 22 (3), pp. 235-250},
correspondence_address1={Chearskul, P.; Virginia TechUnited States},
language={English},
abbrev_source_title={Annu. Natl. Conf. Am. Soc. Eng. Manage. - Innov. Manage.: Innov. Flattened World, ASEM},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Moon2007141,
author={Moon, H.-G. and Yu, J.-H. and Kim, C.-D.},
title={Performance indicators based on TFV theory},
journal={Lean Construction: A New Paradigm for Managing Capital Projects - 15th IGLC Conference},
year={2007},
pages={141-146},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84866059806&partnerID=40&md5=1f811eea2e02925b96c7683c43c35081},
affiliation={Department of Architecture Engineering, Kwangwoon University, 447-1 Wolgye- Dong, Nowon-Ku, Seoul, South Korea},
abstract={In order to obtain good project performance, the performance of construction work processes should be improved. Traditionally, good project performance means a project is on budget and on time with expected quality. Thus, most project managers have used project performance indicators concerning time, cost, quality, safety, profitability, etc. However, these performance indicators are result-oriented and they cannot measure the performance of construction work processes, which we call as process-oriented performance. As process-oriented performance indicators for construction works, this paper suggests the use of three concepts: reliability, efficiency and effectiveness. These are derived from the TFV theory. The following three statements are the basis for establishing indicators related to those three TFV concepts: a "Transformation should be efficient", "flow should be reliable", and "value of a work relies on the effective completion of the preceding work". This paper proposes three TFV based indicators, establishes a logical relation model between the indicators and other traditional indicators, and tests the validity of those indicators. Finally, this paper suggests how to use these indicators to build a process-oriented performance measurement system for construction projects.},
author_keywords={Effectiveness;  Efficiency;  Performance measurement;  Reliability;  TFV},
keywords={Construction projects;  Construction works;  Effectiveness;  Logical relations;  Performance indicators;  Performance measurement system;  Performance measurements;  Process-oriented;  Project managers;  Project performance;  TFV, Benchmarking;  Efficiency;  Profitability;  Reliability;  Supply chain management, Project management},
references={Ahmad, I.U., Sein, M.K., Implementing TQM principles in construction projects; Difficulties and remedies (1997) Int. Conf. on Leadership and Total Quality Management in Construction and Building, , CI Primier Conference, Singapore; (2002) The Performance Prism, , Andy Neely Mike Kennerley Chris Adams; Bassioni, H.A., Price, A.D.F., Hassan, T.M., Performance management in construction (2004) Journal of Management in Engineering, 20 (2), pp. 42-50. , April 2004; Formoso, C.T., Lantelme, E.M.V., A performance measurement system for construction companies in Brazil (2000) International Project Management Journal, Finland, 6 (3), pp. 54-60; Howell, G., Laufer, A., Ballard, G., Interaction between sub cycles: One key to improved methods (1993) J. Constr. Engrg. and Mgmt., 119 (4), pp. 714-728. , ASCE, New York, NY; Koskela, L., (1992) Application of the New Production Philosophy to Construction, , Technical Report # 72, Centre for Integrated Facility Engineering, Department of Civil Engineering, Stanford University, CA; Koskela, L., Management of production in construction: A theoretical view (1999) Proceedings of the 7th Conference of the International Group for Lean Construction, , Berkeley, California, USA; Koskela, L., (2000) An Exploration Towards A Production Theory and Its Application to Construction, , VTT Publications, 408, VTT Building Technology, Espoo; Koskela, L., Howell, G., Ballard, G., Tommelein, I.D., The foundation of lean construction (2002) Design and Construction: Building in Value, , Edited by Best, T. and de Valence, G. Butterworth-Heinemann, New York, NY; Lantelme, E., Formoso, C.T., (1999) Improving Performance Through Measurement: The Application of Lean Production and Organizational Learning Principles, , paper presented at the 8th Conference of International Group for Lean Construction, Sussex University, Brighton; Alarcón, L.F., Ashley, D.B., Modelling project performance for decision making (1996) Journal of Construction Engineering and Management, 122 (3), pp. 265-273. , September 1996; Cross, L., (1991) Measure Up!-Yardsticks for Continuous Improvement, , Blackwell Business, Oxford; Cross, L., (1991) Measure Up!-Yardsticks for Continuous Improvement, , business, Oxford; Robert, F.C., Raja, R.A., Ahrens, D., Management's perception of key performance indicators for construction (2003) Journal of Construction Engineering and Management, 129 (2), pp. 142-151. , March/April 2003; Schiemann, W.A., Lingle, J.H., (1999) Bullseye-hitting Your Strategic Targets Through High-impact Measurement, , New York: The Free Press},
correspondence_address1={Moon, H.-G.; Department of Architecture Engineering, 447-1 Wolgye- Dong, Nowon-Ku, Seoul, South Korea; email: gsman@kw.ac.kr},
language={English},
abbrev_source_title={Lean Constr.: New Paradigm Managing Cap. Proj. - IGLC Conf.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Magali2007525,
author={Magali, M. and Ricardo, C.},
title={Performance measurement systems implementation in small and medium-sized enterprises},
journal={ICEIS 2007 - 9th International Conference on Enterprise Information Systems, Proceedings},
year={2007},
volume={ISAS},
pages={525-528},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349576687&partnerID=40&md5=d3eef95f42ba3f6da4195baf575552f3},
affiliation={Grupo IRIS, Universitat Jaume I, Campus Riu Sec, 12071, Castellon, Spain},
abstract={This paper describes the PMS-IRIS methodology for designing and implementing performance measurement systems in small and medium-sized enterprises (SME). Performance Measurement Systems enable enterprises to evaluate the efficiency and effectiveness of their decisions and operations through a set of indicators related to the vision and strategy of the company. The methodology embraces activities of project planning, strategy design, definition of indicators, process improvement, monitoring, and the design of the computer system required to support the implementation of a performance measurement system.},
author_keywords={Business performance;  Methodology;  Performance measurement},
keywords={Business performance;  Methodology;  Performance measurement;  Performance measurement system;  Process Improvement;  Project planning;  Small- and medium-sized enterprise, Supply chain management, Information systems},
references={Ahn, H., Applying the Balanced Scorecard concept: An experience report (2001) Long Range Planning, 34, pp. 441-461; Bititci, U., Turner, 1. Begemann, C., 2000. Dynamics of performance measurement systems. International Journal of Operations & Production Management, 20 (6), 692-704; Chalmeta, R., Campos, C., Grangel, R., Reference architectures for enterprise integration (2001) Journal of Systems and Software, 57, pp. 175-191; Chand, D., Hachey, G., Hunton, J., Owhos, V., Vasudevan, S., A balanced scorecard based framework for assessing the strategic impacts of ERP systems (2005) Computer in Industry, 56, pp. 558-572; Chris, M., (1999) Enterprise Modelling with UML: Designing Successful Software Through Business Analysis, , Addison-Wesley, UK; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge: Measuring Operations for World-class Competition, , Dow Jones Irwin, II; Fitzgerald, L., Johnson, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, , CIMA, London; Kaplan, R., Norton, D., (1996) The Balanced Scorecard: Translating strategy into action, , Harvard Business School Press, Boston, USA; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156, pp. 267-286; Lynch, R., Cross, K., (1991) Measure up: The Essential Guide to Measuring Business Performance, , London: Mandarin; Medori, D., The development and implementation of an integrated performance measurement framework (1998) Proceedings of Performance Measurement -Theory and Practice: International Conference, 2, pp. 639-646. , University of Cambridge; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Gregory, M., Richards, H., Platts, K., Bourne, M., (1996) Getting the Measure of Your Business, Works Management, , Cambridge; Rom, A., Rohde, C., Enterprise resource planning systems, strategic enterprise management systems and management accounting (2006) Journal of Enterprise Information Management, 19 (1), pp. 50-66},
correspondence_address1={Magali, M.; Grupo IRIS, Campus Riu Sec, 12071, Castellon, Spain; email: magali@csd.uo.edu.cu},
language={English},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Xu20071308,
author={Xu, L.X.X. and Ma, B. and Lim, R.},
title={AHP based supply chain performance measurement system},
journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA},
year={2007},
pages={1308-1315},
doi={10.1109/EFTA.2007.4416932},
art_number={4416932},
note={cited By 16},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-47849115430&doi=10.1109%2fEFTA.2007.4416932&partnerID=40&md5=5a0674d49726cb37ca14e57ebcdf1643},
affiliation={Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075, Singapore},
abstract={With the trend towards globalizations, intensifying competition and an increasing emphasis on customer orientation, many companies have realized the importance of supply chain management for their survival and competitiveness. It is a well recognized fact that it is the performance of the supply chain that actually determines the winner, since measuring supply chain performance can facilitate a greater understanding of the supply chain, positively influence supply chain players ' behavior, and improve its overall performance. However companies are facing many challenges to measure their supply chains as there are currently relatively few attempts to systematically collate metrics, integrate quantitative and qualitative measurements and link supply chain strategy with performance measurements. To overcome these challenges, this paper proposes a supply chain performance measurement system (SCPMS). © 2007 IEEE.},
keywords={Customer orientations;  Emerging technologies;  International conferences;  Overall performance;  Performance measurements;  Qualitative measurements;  Supply chain performance;  Supply chain strategy, Competition;  Factory automation;  Industrial economics;  Industrial engineering;  Industrial management;  Outsourcing;  Semiconductor quantum dots;  Supply chains;  Systems analysis;  Technology, Supply chain management},
references={Shepherd, C., Gunter, H., Measuring supply chain performance: Current research and future directions (2006) International Journal of Productivity and Performance Management, 55 (3-4), pp. 242-258; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-163; Hausman, Warren H., Supply chain performance metrics, a chapter in The practice of supply chain management, edited by Corey Billington, Terry H., Hau L., and Hohn N., Kluwer Academic Publishiers, 2002; Laura, X., Performance Metrics Design Framework for Software Focused Products and Services Supply Chain (2005) INDIN05, IEEE International Conference on Industrial Informatics, , CD-ROM; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; (1996) Supply-chain Operations Reference-model, , Supply-chain Council , USA; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, p. 209. , 8, 3/4, ABI/INFORM Global; Stefan, H., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (ISS.10), p. 847; Yusuf, Y.Y., Ren, J., Burns, N.D., A method or evalutiing enterprersie agility - an empirical study (2001) Proceedings of the 16 th International Conference on Productions Research, , 29 July, 3 August, Prague, Czech Republic; Lin, C.T., Chiu, H., Chu, P.Y., Agility index in the supply chain (2006) International Journal of Production Economics, pp. 285-299; Christopher, M., Towill, D.R., Supply chain migration from lean and functional to agile and customized Supply Chain Management, 5, pp. 206-213; Naylor, J.B., Naim, M.M., Berry, D., Leagility: Integrating the lean and agile manufacturing paradigm in the total supply chain (1997) International Journal of Production Economics, 62, pp. 1007-1018},
correspondence_address1={Xu, L. X. X.; Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075, Singapore; email: xxxu@simtech.a-star.edu.sg},
isbn={1424408261; 9781424408269},
coden={85ROA},
language={English},
abbrev_source_title={IEEE Symp Emerging Technol Fact Autom ETFA},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Li20071131,
author={Li, Z. and Xu, X. and Kumar, A.},
title={Supply chain performance evaluation from structural and operational levels},
journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA},
year={2007},
pages={1131-1140},
doi={10.1109/EFTA.2007.4416909},
art_number={4416909},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-47849096739&doi=10.1109%2fEFTA.2007.4416909&partnerID=40&md5=6a4b06fd65e1c85971213b7ba86a728a},
affiliation={Planning and Operations Management Group, Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 639798, Singapore; School of Mechanical and Production Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore},
abstract={Performance measurement can be defined as the process of quantifying the efficiency and effectiveness of organisations and actions. This paper postulates a comprehensive supply chain performance measurement approach which evaluates a supply chain both from structure and operational levels. The paper first elaborates on why supply chain should be evaluated from these two levels, and then performance indices used in structure and operational level evaluation are identified and related calculation model is proposed. The paper also discusses the relationships of the indices, and suggests that simulation could be applied to build supply chain models both on structure and process levels for evaluation purpose. © 2007 IEEE.},
keywords={Calculation models;  Emerging technologies;  International conferences;  Operational level;  Performance indexes;  Performance measurement;  Process levels;  Supply chain modelling;  Supply chain performance, Factory automation;  Industrial engineering;  Semiconductor quantum dots;  Supply chain management;  Supply chains;  Systems analysis;  Technology, Industrial management},
references={Kumar, A., Ozdamar, L., Ng, C.P., Procurement Performance Measurement system in the health care industry (2005) International Journal of Health Care Quality Assurance, 18 (2), pp. 152-166; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4); Teague, J., Eilon, S., Productivity measurement: A brief survey (1973) Applied Economics, 5, pp. 133-145; Edosomwan, J.A., (1985) Integrating Productivity and Quality Management, , Marcel Dekker, New York; Riggs, J.L., Felix, G.H., (1983) Productivity by Objectives: Results-Oriented Solutions to the Productivity Puzzle, , Prentice-Hall, Englewood Cliffs, NJ; Ghalayini, A.M., Noble, J.S., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Maskell, B.H., (1992) A Model for American Companies, , Performance Measurement for World Class Manufacturing:, Productivity Press, Cambridge, MA; Lockamy III, A., Smith, W.I., Target costing for supply chain management: Criteria and selection (2000) Industrial Management and Data Systems, 100 (5), pp. 210-218; Kaplan, R.S., Norton, D., The balanced scorecard -measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79. , January-February; Bockerstette, J.A., Shell, R.L., (1993) Time-based Manufacturing, , Institute of Industrial Engineers and McGraw-Hill. Norcross, GA; Krupka, D.C., Time as a primary system metric, in Heim. J.A. and Compton, W.D (Eds) Manufacturing Systems: Foundations of World-Class Practice. National Academy of Engineering, Washington, DC, 1992, pp 167-172; Stalk, G., Hout, T.J., (1990) Competing against Time - How Time-based Competition is Reshaping Global Markets, , Free Press. New York; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1999) national Productivity Review, 8 (1), pp. 23-33; Dixon, J.R., Nanni, A.J., Vollman, T.E., (1990) The New Performance Challenge: Measuring operations for World Class Competition, , Dow-Jones-Irwin, Homewood, IL; Gregory, M.J., Integrated performance measurement: A review of current practice and emerging trends (1993) International Journal of Production Economics, 30-31, pp. 281-296; Goldratt, Eliyahu, M., (1986) The Goal, , The North River Press; Goldratt, Eliyahu.M., Fox, J., The Goal, North River Press, Inc. 1992; Spencer, M.S., Cox, J., Optimum production technology (OPT) and the Theory of Constraints: Analysis and generlogy (1995) International Journal of Production Research, 33, pp. 1495-1540; Lockamy III, A., Spencer, M.S., Performance measurement in a theory of constraints environment (1997) International Journal of Production Research, 36 (8), pp. 2045-2060; Cohen, M.A., Lee, H.L., Resource deployment analysis of global manufacturing and distribution network (1989) Journal of Manufacturing and Operation management, 2, pp. 81-104; Arntzen, B.C., Brown, G.G., Harrison, T.P., Global supply chain management at digital equipment corporation (1995) Interfaces, 25, pp. 69-93; Altiok, T., Ranjan, R., Multi-stage, pull-type production/inventory systems (1995) IIE Transactions, 27, pp. 43-48; Lee. L. Hua, Billington. C., Carter, B., Hewlett-Packard gains control of inventory and service through design for localisation, interface 23(4), 1993; Voudouris, V.T., Mathematical programming techniques to de-bottleneck the supply chain of fine chemical industries (1996) Computers and Chemical Engineering, 20, pp. 1269-1274; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operation & Production Management, 19 (3), pp. 275-292; Cooper, R., Slagmulder, R., (1997) Target costing and value engineering, , Productivity Press, Portland, OR; Camp, R.C., (1989) Benchmarking: The Search for Industry Best Practices that Lead to Superior Performance, , ASQC Quality Press, Milwaukee, WI; Port, O., Smith, G., Beg, borrow - and benchmark (1992) Business Week, pp. 74-75. , 30 November, pp; Gilmour, P., Benchmarking supply chain operations (1999) International Journal of Physical Distribution and Logistics management, 5 (4), pp. 259-266; Malone, T.W., Smith, A., modelling the performance of organisation structures (1987) Operation Research, 36 (3), pp. 421-436; Li, Z. and Kumar, A., Supply Chain Network Scenario Design and Evaluation, International Journal of Logistics, 8 No. 2, June of 2005, pp107-123; (2006) Supply Chain Operations Reference-Model (SCOR), , www.supply-chain.com, Web site},
correspondence_address1={Li, Z.; Planning and Operations Management Group, 71 Nanyang Drive, Singapore 639798, Singapore; email: zpli@simtech.a-star.edu.sg},
isbn={1424408261; 9781424408269},
coden={85ROA},
language={English},
abbrev_source_title={IEEE Symp Emerging Technol Fact Autom ETFA},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Ma2007234,
author={Ma, B. and Xu, L.X.X. and Lim, R.},
title={Modeling supply chain's reconfigurability using fuzzy logic},
journal={IEEE International Conference on Emerging Technologies and Factory Automation, ETFA},
year={2007},
pages={234-241},
doi={10.1109/EFTA.2007.4416774},
art_number={4416774},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-47849085135&doi=10.1109%2fEFTA.2007.4416774&partnerID=40&md5=e5fea65bfc55668bba9ea5ad094ae765},
affiliation={Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075, Singapore},
abstract={To survive and gain a competitive advantage in today's ever-fast moving business environment, a company needs to constantly adapt its supply chain based on the changing market demands. Achieving an adaptive supply chain has therefore become crucial to its business success. For a company to adopt adaptive supply chain as its strategy, the most important metric to assess is reconfigurability - which is rather difficult to measure quantitatively like good, normal or poor and includes a number of second-level metrics. Therefore one of the key R&D challenges is how to model, measure and assess the reconfigurability of adaptive supply chain in a rational and flexible way so that such model could be adaptable to the changing needs of the company in various industry domains. This paper presents a fuzzy logic model for assessing the supply chain's reconfigurability, which could be further customized based on different measurement metrics, end-users ' domain knowledge and self-defined business rules. This enables the reconfigurability metric to be measured and implemented, and expands the dimension of supply chain performance measurement. © 2007 IEEE.},
keywords={Business environments;  Business rules;  Business successes;  Competitive advantages;  Do-mains;  Domain knowledges;  Flexible ways;  Fuzzy logic models;  Market demands;  Reconfigurability;  Supply chain performances, Factory automation;  Fuzzy logic;  Fuzzy sets;  Fuzzy systems;  Semiconductor quantum dots;  Supply chain management;  Supply chains;  Systems analysis, Competition},
references={Porter, M.E., (1998) Competitive strategy: Techniques for analyzing industries and competitors: with a new introduction, , The Free Press, USA; Heinrich, C., Betts, B., (2003) Adapt or Die: Transforming your supply chain into an adaptive business network, , John Wiley & Sons Inc, Canada; Princeton WordNet-Online English dictionary (WordNet 2.0), Princeton University, USA, htrp://www.wordnet-online.com/adaptability.shtml; Pavlou, P.A., It-Enabled Dynamic Capabilities in New Product Development: Building a Competitive Advantage in Turbulent Environments (2004), ICIS MIS Doctoral Dissertation Award, dissertation completed at Anderson Graduate School of Management, University of Southern California, USA; Hsiao-Lan Wei and Eric T.G.Wang, Creating Strategic Value from Supply Chain Visibility - the Dynamic Capabilities View, Proceedings of the 40th Hawaii International Conference on System Sciences, 2007; Slack, N., (1991) The Manufacturing Advantage, , Mercury Books, London; http://www.supply-chain.org, Supply Chain Council, 2007, Supply Chain Operations Reference Model Version 8.0, Supply Chain Council website; Xu, L., Xia, X., Supply Chain Modelling and Improvement in Telecom Industry: A Case Study (2006) 4th International IEEE Conference on Industrial Informatics, , Singapore, August; Xu, L., Xia, X., Ma, B., Lim, R., AHP Based Supply Chain Performance Measurement System (2007) 12th International IEEE Conference on, , Emerging Technologies and Factory Automation. Patras, Greece, September; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Beamon, B.M., Performance Analysis of Conjoined Supply Chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218; Fazel Zarandi, M.H., Ismail, B.T., Saghiri, S., Supply Chain: Crisp and Fuzzy Aspects (2002) Internal Journal of Apply Math Computer Science, 12 (3), pp. 423-435; Zadeh, L. A., Fuzzy sets, Information and Control, 8, pp. 338-353, 1965; Klir, G., St, U.H., Clair and Bo Yuan (1997) Fuzzy Set Theory Foundations and Applications; Wang, J., Shu, Y.-F., Fuzzy Decision Modeling for Supply Chain Management (2005) Fuzzy Sets and Systems, 150 (1), pp. 107-127. , Feb; Wang, J., Shu, Y.-F., A Possible Decision Model for New Product Supply Chain Design (2007) European Journal of Operational Research, 177, pp. 1044-1061; Mamdani, E.H., Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller (1975) International Journal of Man-Machine Studies, 7 (1), pp. 1-13},
correspondence_address1={Ma, B.; Singapore Institute of Manufacturing Technology, 71 Nanyang Drive, Singapore 638075, Singapore; email: bma@simtech.a-star.edu.sg},
isbn={1424408261; 9781424408269},
coden={85ROA},
language={English},
abbrev_source_title={IEEE Symp Emerging Technol Fact Autom ETFA},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Roper20071434,
author={Roper, E. and Utley, D. and Youngblood, A.},
title={A proposed method for developing a team performance measurement system},
journal={IIE Annual Conference and Expo 2007 - Industrial Engineering's Critical Role in a Flat World - Conference Proceedings},
year={2007},
pages={1434-1439},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-44949229521&partnerID=40&md5=323b644ae6b47fec83791522d894f4a9},
affiliation={Department of Industrial and Systems Engineering and Engineering Management, University of Alabama in Huntsville, Huntsville, AL 35899, United States},
abstract={Many theories involving effective team performance have been popularized in the literature. Several of these theories have suggested measurement techniques for evaluation of teams. Some of these performance measurement systems measure individual's contributions such as skills, attitudes, and output but often the way they measure individuals is counter-productive to the team-process effort. In reality, a contradiction may exist between metrics for individuals and actual team performance. Measuring the performance of the team as a whole provides information to help with training, coaching, and rewarding. This paper will provide details of a suggested system for measuring team performance.},
author_keywords={Balanced scorecard;  Team measurement;  Team performance;  Teaming, performance measurement},
keywords={Automation;  Computer networks;  Exhibitions;  Industrial engineering;  Measurements;  Multitasking;  Production control;  Project management;  Statistical methods;  Supply chain management;  Systems engineering;  Technology, (e ,3e) process;  annual conference;  conference proceedings;  Individual (PSS 544-7);  Measurement techniques;  Metrics (CO);  Performance measurement system (PMS);  Team performance, Industrial management},
references={Maddox, T., Roper, E., Shetty, S., Utley, D., Determining the IPT Design Winner by Investigating Comparative Team Performance Relative to Teaming Theories (2006) Proceedings of the 27th Annual ASEM National Conference; Larson, C., LaFasto, F., Teamwork: What Must Go Right/What Can Go Wrong (1989) Sage Publications; Gordon, J., Work Teams How Far They Have Come? (1992) Training, pp. 59-65. , October; Galvin, T., Industry Report Training Magazine's 22nd Annual Comprehensive Analysis of Employer-Sponsored Training in the United States (2003) Training, pp. 21-45. , October; Bugental, D.B., Do all social processes follow the same rules? An argument for domain-specificity (1995) annual meetings of the Society for Personality and Social Psychology, , Presented at the, New York, June; Caporael, L.R., Brewer, M.B., Hierarchical evolutionary theory: There is an alternative, and its not creationism (1995) Sychological Inquiry, 6, pp. 31-80; Benfield, Michael P.J, 2006, Final Research Report: A Computer-based Capability to Support the Training and Certification of Multi Organization Teams 8. Zigon, Jack, How to Measure Team Performance, by Zigon Performance Group, Version 2.2, Revised July 1, 1999; Kaplan, R.S., Norton, D.P., The Balanced Scorecard-Measures the Drive Performance (1998) Harvard Business Review on Measuring Corporate Performance, p. 123; Tata, J. and Prasad, S. Team Self-Management, Organizational Structure, and Judgments of Team Effectiveness. In Journal of Managerial Issues 16.2, Summer, p248-268, 2004; Wang, J.-C., Corporate Strategic Management and Business Re-Engineering Effort Analyzed by the Balanced Scorecard Model (2006) The Journal of the American Academy of Business, 10 (1), pp. 102-109. , September; Kaplan, R.S., Norton, D.P., Using the Balanced Scorecard as a Strategic Management System (1996) Harvard Business Review, p. 76. , January-February; Senior, B., Team Performance; Using Repertory Grid Technique to gain a view from the inside (1997) Team Performance Management, 3 (1); Balancing Measures: Best Practices in Performance Management, , http://govinfo.library.unt.edu/npr/library/papers/bkgrd/balmeasure.html, August 1999},
correspondence_address1={Roper, E.; Department of Industrial and Systems Engineering and Engineering Management, , Huntsville, AL 35899, United States},
language={English},
abbrev_source_title={IIE Ann. Conf. Expo. - Ind. Engg. Crit. Role Flat World - Conf. Proc.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{C.Boehm2007757,
author={C. Boehm, A. and P. Leone, H. and P. Henning, G.},
title={Industrial supply chains: Performance measures, metrics and benchmarks},
journal={Computer Aided Chemical Engineering},
year={2007},
volume={24},
pages={757-762},
doi={10.1016/S1570-7946(07)80149-0},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-43049124475&doi=10.1016%2fS1570-7946%2807%2980149-0&partnerID=40&md5=bebef780e208fc8ee7357b8cad1a8507},
affiliation={Instituto de Desarrollo Tecnologica (Universidad Nacional, Litoral-Consejo Nacional de Investigaciones Cientificas, Gueemes 3450, Santa Fe, S3002GJC, Argentina; Instituto de Desarrollo Tecnologica (Universidad Nacional, Litoral-Consejo Nacional de Investigaciones Cientificas, Gueemes 3450, Santa Fe, S3002GLN, Argentina},
abstract={Industrial organizations are moving toward more integrated supply chains (SCs) to remain competitive. To be effectively designed and managed, these SCs need to be measured and evaluated in a consistent way. The formal definition of different metrics, benchmarks and performance related concepts will facilitate the measurement process and enable the effective communication among the SC stakeholders. For this reason, it is important to acquire a common and unified understanding of the SC associated performance, process and structure concepts. With this intention an extension of SCOntology [1] is proposed. © 2007 Elsevier B.V. All rights reserved.},
author_keywords={Metrics;  Ontology.;  Performance Indicators;  Supply Chain},
references={Gonnet, S., Vegetti, M., Leone, H., Henning, G., SCOntology: A Formal Approach Towards a Unified and Integrated View of the Supply Chain (2006) Adaptive Technologies and Business Integration: Social, Managerial and Organizational Dimension, , Cunha M., Cortes B., and Putnik G. (Eds), Idea Group Publishing; SCOR: www.supply-chain.org/page.ww?section=SCOR+Model&name=SCOR+Model; (2003) The Practice of Supply Chain Management, , Hausman W., Billington C., Harrison T., Lee H., and Neale J. (Eds), Kluwer, Boston; Lambert, D., Pohlen, T., (2001) International Journal of Logistics Management, 12 (1), p. 1; Kleijnen, J., Smits, M., (2003) Journal of the Operational Research Society, 54 (5), p. 507},
correspondence_address1={C. Boehm, A.; Instituto de Desarrollo y Diseno (Universidad Tecnologica Nacional), Avellaneda 3657, Santa Fe, S3002GJC, Argentina; email: abohm@ceride.gov.ar},
editor={Plesu V., Agachi P.S.},
issn={15707946},
isbn={9780444531575},
language={English},
abbrev_source_title={Comput. Aided Chem. Eng.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Kang20074224,
author={Kang, S. and Peng, Y.},
title={Performance measurement for SCM based on balanced score card and self-adaptive RBF neural network},
journal={International Conference on Transportation Engineering 2007, ICTE 2007},
year={2007},
pages={4224-4229},
doi={10.1061/40932(246)690},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-37749036734&doi=10.1061%2f40932%28246%29690&partnerID=40&md5=fe41d7363be950c643dc97261fab73e4},
affiliation={School of Computer Science and Information Technology, Chongqing Technology and Business University, Chongqing, 400067, China},
abstract={According to the four perspective of BSC thinking, this article has selected new SCPM index system, which almost reflected multiple requests that meet the needs of customer and enterprises management. Meanwhile, based on typical and uniformity training samples selected by scientific uniform design method(UDM), the corresponding self-adaptive RBFNN model has been established. The experiments show the results between self-adaptive RBFNN evaluation and experts fuzzy comprehensive evaluation(FCE) are very close, the generalization of self-adaptive RBFNN with UDM is more better than that of the general RBFNN with Monte-Carlo method. The designed evaluation method realizes non-linear approaching ability of evaluation, meantime conquers the capability limitation of general RBFNN and BP neural network, and avoids the subjectivity and uncertainty of traditional FCE. Copyright ASCE 2007.},
author_keywords={Balanced score card (BSC);  Nearest neighbor- clustering algorithm[NNCA];  Self-adaptive radial basis function neural network (RBFNN);  Supply chain performance measurement (SCPM);  Uniform design method(UDM)},
keywords={Adaptive systems;  Enterprise resource planning;  Monte Carlo methods;  Radial basis function networks;  Uncertainty analysis, Balanced score card (BSC);  Nearest neighbor clustering algorithm (NNCA);  Self-adaptive radial basis function neural network (RBFNN);  Supply chain performance measurement (SCPM);  Uniform design method(UDM), Supply chain management},
references={FANG Kaitai. Uniform Design and its Application[J], Application of Statistics and Management, 1994 ,13(1) :57-63 (in Chinese); FANG Chengwu,etc,Supply Chain Performance Evaluation Based on Customer' s Satisfaction,J. of Anhui University of Technology,2005.10:412-415(in Chinese); Joel D.Wisner. Supply Chain Management[M]. Machinery Industry Press 2006.1:312 (in Chinese); Rui, S.U.N., Haiyan, W.A.N.G., (2005) The Study of E-commerce Supply Chain Alliance's Performance Evaluation Based on BP Neural Network[J], Management review, (12), pp. 22-27. , in Chinese; Qiyi, T.A.N.G., Minguang, F.E.N.G., Data, D.P.S., (2002) Processing System for Practical Statistics(M), (5). , Science Press,, in Chinese; ZHU Mingxing, ZHANG Delong. Study on the Algorithms of Selecting the Radial Basis Function Center[J].Journal of Anhui University,2000(3):72:78(in Chinese)},
correspondence_address1={Kang, S.; School of Computer Science and Information Technology, , Chongqing, 400067, China; email: china_ksy@163.com},
isbn={9780784409329},
language={English},
abbrev_source_title={Int. Conf. Transport. Eng.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Lange2007379,
author={Lange, I. and Schneider, O. and Schnetzler, M. and Jones, L.},
title={Understanding the interdependences among performance indicators in the domain of industrial services},
journal={IFIP International Federation for Information Processing},
year={2007},
volume={246},
pages={379-386},
doi={10.1007/978-0-387-74157-4_45},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-37549007590&doi=10.1007%2f978-0-387-74157-4_45&partnerID=40&md5=b64923687928c19872b18f1782bd4dce},
affiliation={Center for Enterprise Sciences (BWI), ETH Zurich, Zurich 8092, Switzerland; Ventana Systems UK Ltd., Oxton Merseyside CH43 5RD, United Kingdom},
abstract={Within the context of the EU-Project InCoCo-'S', one of the key aims is to standardize integrative industrial service processes in order to facilitate transparency on service operation performance and the resulting customer benefit. Therefore the Service Performance Measurement System (SPMS) has been developed in order to quantify both the efficiency and effectiveness of industrial service operation activities and to support the measurement of customers' benefit through industrial service activities. But performance indicators are only a measurable expression of the underlying system performance, a system which is ordinarily complex in nature. It follows, therefore, that it would be beneficial to understand the interdependences between performance indicators in order to better utilize them in evaluating the options for improvements in system performance and the monitoring of an often complex system. Based on a comprehensive literature review and making best use of the tools and expertise available to the InCoCo-S consortium, a process to develop an understanding of the interdependences between performance indicators was created and executed. The results provide both the service provider and the manufacturing customer with an insight into those performance indicators to be targeted for improvement actions and those better suited to monitoring. © 2007 International Federation for Information Processing.},
author_keywords={Industrial service operations;  Interdependencies among performance indicators;  Interdependency matrix;  Performance indicators;  Service performance measurement system},
keywords={Benchmarking;  Industry;  Management;  Sales, Customer benefits;  Industrial services;  Literature reviews;  Measurement system;  Performance indicators;  Service operations;  Service performance;  Underlying systems, Supply chain management},
references={Lange, I., Schneider, O., Larsson, A., Minkus, A., Logistics controlling concept for benchmarking service delivery performance (2006) Proceedings of the 11th World Congress for TQM, , December 4-6, Wellington, New Zealand; Osadsky, P., Fischer, T., Garg, A., Lange, L., Schaedlich, B., Schneider, O., Montorio, M., Puetz, F., (2006) Innovation, Coordination and Collaboration in Service Driven Manufacturing Supply Chains, , www.incoco.net, Brochure to EU-funded project InCoCo-'S; Neely, A., The evolution of performance measurement research (2005) International Journal of Operations & Production Management, 12, pp. 1264-1277. , S; Vester, F., (1999) Neuland des Denkens: Vom technokratischen zum kybernetischen Zeitalter, , 11. Aufl, Munchen; http://www.supply-chain.org, Supply Chain Council 2006, Supply chain operations reference model SCOR Version 8.0. Pittsburgh: Supply Chain Council; Lange, I.; Schnetzler, M.; Schneider, O.; Osadsky, P., (2007): Design of a Performance Measurement System. In: Proceedings of the 2nd International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV), 22.-24. July 2007, Toronto; Ulrich, H., Probst, J.B., (1988) Anleitung zum ganzheitlichen Denken und Handeln: Ein Brevier für Führungskräfte, , Bern, Haupt; NincV, A., (2004) Systemik: Vernetztes Denken in komplexen Situationen, , 4. Aufl, Verlag Industrielle Organisation, Zürich; Schnetzler, M.; Sennheiser, A., (2003): Identification of performance improvement strategies in production networks. In: Cunningham, P. et al.: Building the knowledge economy - issues, applications, case studies. Amsterdam: IOS Press/Omsa, 2003, pp. 306-312},
correspondence_address1={Lange, I.; ETH Zurich, , Zurich 8092, Switzerland; email: ilange@ethz.ch},
editor={Olhager, Persson},
issn={15715736},
isbn={9780387741567},
language={English},
abbrev_source_title={IFIP Int. Fed. Inf. Process.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dreyer2007161,
author={Dreyer, H.C. and Bakås, O. and Alfnes, E. and Strandhagen, O. and Kollberg, M.},
title={Global supply chain control: A conceptual framework for the global control centre (GCC)},
journal={IFIP International Federation for Information Processing},
year={2007},
volume={246},
pages={161-170},
doi={10.1007/978-0-387-74157-4_19},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-37549006380&doi=10.1007%2f978-0-387-74157-4_19&partnerID=40&md5=f70f5cb78f4a95ea02ba6abc508fcad4},
affiliation={Department of Production and Quality Engineering, Norwegian University of Science and Technology, 7491 Trondheim, Norway; Department of Operations Management, SINTEF Technology and Society, 4765 Trondheim, Norway},
abstract={The operation of global manufacturing network is challenging due to the complexity in product and information flow, diversity in sites, localization and processes and the information processing needed for control. Thus information technology has to be developed to cope with this complexity and to develop decision support for controlling the network. In this paper the concept of the Global Control Centre for manufacturing activity is developed based on research on a Norwegian supplier of fish hooks. The main elements of the GCC is found to be the global control model, performance measurement system, ICT solutions and the organization and the physical environment. In order to realize the GCC the main challenges are ICT investments and standardization, and the management of change and organizational resistance. The findings presented in this paper are not yet collectively implemented and tested and must therefore only be viewed as conceptual proposals. © 2007 International Federation for Information Processing.},
author_keywords={Case study;  Global operations;  Operations management;  Supply chain control},
keywords={Complex networks;  Data processing;  Decision support systems;  Empowerment of personnel;  Industrial research;  Information technology;  Manufacture;  Supply chains, Conceptual frameworks;  Global Manufacturing Networks;  Global operations;  Management of change;  Manufacturing activities;  Operations management;  Physical environments;  Supply chain control, Industrial management},
references={Beardsley, S.C., Johnson, B.C., Manyika, J.M., Competitive advantage from better interactions (2006) The McKinsey Quarterly, (2); Busi, M., Dreyer, H.C., Collaboration or business as usual? Suggested matrix to categorize and develop collaboration (2004) Proceedings of the 16th Annual Conference for Nordic Researchers in Logistics, NOFOMA 2004, , Linköping, Sweden; Chen, F., Drezner, Z., Ryan, J., Simichi-Levi, D., Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information (2000) Management Science, 46 (3), pp. 436-443; Chopra, S., Meindl, P., (2007) Supply Chain Management - Strategy, Planning, & Operation, , 3rd Ed, Pearson Prentice Hall, New Jersey; Cooper, M.C., Gardner, J.T., Strategic Supply Chain Mapping Approaches (2003) Journal of Business Logistics, 24 (2), pp. 37-64; CollaborationΩork. The 2006 report on new working environments and practices (2006) 1ST and European Commission publication, , European Commission , October; Forrester, J.W., (1961) Industrial Dynamics, , MIT Press, Cambridge, MA; Frost, Sullivan, Meetings Around the World: The impact of Collaboration on Business Performance (2006) Frost & Sullivan white paper sponsored by Verizon Business & Microsoft, , www.verizonbusiness.com/us/resources/conferencing/impactcollab.pdf; Hansen, M.T., Nohria, N., How to Build Collaborative Advantage (2006) Sloan Management Review, , special monograph for World Economic Forum, Davos, Switzerland, January; Hedgepeth, W.O., (2007) RFID Metrics - Decision Making Tools for Today's Supply Chains, , CRC Press Taylor & Francis Group, Boca Raton, Florida; Holweg, M., Disney, S., Holmström, J., Smaros, J., Supply Chain Collaboration: Making Sense of the Strategy Continuum (2005) European Management Journal, 23 (2), p. 170; Jonsson, P., Lindau, R., The supply chain planning studio - utilising the synergetic power of teams and information visibility (2002) Proceedings of the Annual Conference for Nordic Researchers in Logistics, , NOFOMA 2002, Trondheim, Norway; Kristensen, K., Royrvik, J., Sivertsen, O.I., Applications of the Physual Designing Network in Extended Teams (2005) Proceedings of the 11th International Conference on Concurrent Enterprising, , Munich, Germany; Lee, H.L., Padmanabhan, V., Whang, S., Information Distortion in a Supply Chain: The Bullwhip Effect (1997) Management Science, 43 (4), pp. 546-558; Liff, S., Posey, P., (2004) Seeing is believing - how the new art of visual management can boost performance throughout your organisation, , Amacom, New York; Niven, P., (2002) Balanced scorecard step by step: Maximising performance and maintaining results, p. 116. , John Wiley & Sons, p; Oljeindustriens Landsforening - The Norwegian Oil Industry Association e-Drift på norsk sokkel, , www.olf.no/hms/aktuelt/?19031, OLF:, e-Operations on the Norwegian continental shelf, thematic report from OLF, available at; Silberberger, H., cBusiness (2003) Erfolgreiche Internetstrategien durch Collaborative Business, , Spinger, Berlin; Strandhagen, O., Alfnes, E., Dreyer, H.C., Supply Chain Control Dashboards (2006) Conference proceedings Production and Operations Management Society (POMS), , Boston, US; Sanders, N.R., Premus, R., Modelling the Relationship between Firm IT Capability, Collaboration, and Performance (2005) Journal of Business Logistics, 26 (1), pp. 1-23; Teasley, S., Covi, L., Krishnan, M.S., Olson, J.S., How Does Radical Collocation help a Team Succeed? (2000) Proceedings of the 2000 ACM conference on Computer supported cooperative work, , Computer Supported Cooperative Work, Philadelphia, Pennsylvania, US; Thoben, K.-D., Jagdev, H., Eschenbaecher, J., Emerging concepts in E-business and Extended Products (2003) Advanced Information Processing Series, , Gasos, J, Thoben, K.-D, Eds, E-Business Applications, Technologies for Tomorrow's Solutions;, Springer},
correspondence_address1={Dreyer, H.C.; Department of Production and Quality Engineering, , 7491 Trondheim, Norway; email: Heidi.C.Dreyer@sintef.no},
editor={Olhager, Persson},
issn={15715736},
isbn={9780387741567},
language={English},
abbrev_source_title={IFIP Int. Fed. Inf. Process.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Berrah2007709,
author={Berrah, L. and Clivillé, V.},
title={Towards an aggregation performance measurement system model in a supply chain context},
journal={Computers in Industry},
year={2007},
volume={58},
number={7},
pages={709-719},
doi={10.1016/j.compind.2007.05.012},
note={cited By 82},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34447538190&doi=10.1016%2fj.compind.2007.05.012&partnerID=40&md5=305b90eeb1456256504902a46f1d19bd},
affiliation={Laboratoire d'Informatique, Systemes, Traitement de l'Information et de la Connaissance (LISTIC), Polytech' Savoie, Université de Savoie, BP 806, 74016 Annecy cedex, France},
abstract={This study deals with the supply chain (SC) performance formalization. We propose to build performance measurement systems (PMSs) by linking an overall performance expression to elementary ones. The overall performance is associated to a global objective whose break-down provides elementary objectives. Elementary performances are thus aggregated in a corollary way. The problem in the design of such PMS's, by the break-down/aggregation model, concerns both the coherent elementary performance expressions and the definition of the links between them. Some answers are proposed in the literature, such as the weighted mean aggregation operator, to handle hierarchical links, the Choquet integral operator, for taking interactions into account. As global frameworks, the AHP or MACBETH methodologies are suggested. By considering the SCOR model break-down, we propose to extend here the proposed approaches for expressing the overall performance of a SC. An aggregation methodology, based on the Choquet integral operator and MACBETH framework, is thus adopted. © 2007 Elsevier B.V. All rights reserved.},
author_keywords={Choquet integral;  MACBETH methodology;  Performance aggregation;  SCOR model;  Supply chain},
keywords={Choquet integral;  MACBETH methodology;  Performance aggregation;  Performance measurement systems (PMS);  SCOR model, Administrative data processing;  Integral equations;  Mathematical models;  Mathematical operators;  Personnel, Supply chain management},
references={Deming, E.W., (1982) Quality, Productivity and Competitive Position, , The MIT Press; Fortuin, L., Performance indicators-why, where and how (1988) European Journal of Operational Research, 34, pp. 1-9; Bititci, U.S., Modelling of performance measurement systems in manufacturing enterprises (1995) International Journal Production Economics, 42, pp. 137-147; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competiveness (1997) International Journal of Operations & Production Management, 15, pp. 80-116; Globerson, S., Issues in developing a performance criteria system for an organisation (1985) International Journal of production research, 23 (4), pp. 639-646; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, pp. 71-79; Neely, A., The performance measurement revolution: why now and what next (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-228; Melnyk, S.A., Stewart, D.M., Swin, M., Metrics and performance measurement in operations management: dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-218; Berrah, L., Mauris, G., Haurat, A., Foulloy, L., Global vision and performance indicators for an industrial improvement approach (2000) Computers in Industry, 43 (3), pp. 211-225; Clivillé, V., Berrah, L., Mauris, G., Quantitative expression and aggregation of performance measurements based on the MACBETH multi-criteria method (2007) International Journal of Production Economics, 105 (1), pp. 171-189; Grabot, B., Objective satisfaction assessment using neural nets for balancing multiple objectives (1998) International Journal of Production Research, 36, pp. 2377-2395; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1988) National Productivity Review, 8 (1), pp. 23-33; Brimson, J.A., (1991) Activity Accounting, , Editions John Wiley and Sons, New York; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, pp. 75-85; Kueng, P., Krahn, A.J., Building a process performance measurement system: some early experiences (1999) Journal of Scientific & Industrial Research, 58, pp. 149-159; Geneste, L., Grabot, B., Letouzey, A., Scheduling uncertain orders in the customers-subcontractor context (2003) European Journal of Operational Research, 147, pp. 297-311; Azzone, G., Masella, C., Bertele, U., Design of performance measures for time based companies (1991) International Journal of Operations & Production Management, 11 (3), pp. 77-85; Chandler, A.D., (1977) The Visible Hand: The Managerial Revolution in American Business, , Harvard University Press; Berrah, L., Mauris, G., Vernadat, F., Information aggregation in industrial performance measurement: rationales, issues and definitions (2004) International Journal of Production Research, 42 (20), pp. 4271-4293; Ducq, Y., Vallespir, B., Doumeingts, G., Coherence analysis methods for production systems by performance aggregation (2001) International Journal of Production Economics, 69 (1), pp. 239-252; Suwignjo, P., Bititci, U.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64, pp. 231-241; Saaty, T., The analytic hierarchy and the analytic network processes for the measurement of intangible criteria and for decision making (2004) MCDA. Multiple Criteria Decision Analysis, , Figueira J., Greco S., and Ehrgott M. (Eds), Kluwer Academic Publishers pp. 345-407; Grabisch, M., Labreuche, C., Fuzzy measures and integrals (2004) MCDA. Multiple Criteria Decision Analysis, pp. 563-608. , Figueira J., Greco S., and Ehrgott M. (Eds), Kluwer Academic Publishers; Bana e Costa, C.A., De Corte, J.M., Vansnick, J.C., On the mathematical foundations of MACBETH (2004) MCDA. Multiple Criteria Decision Analysis, pp. 409-442. , Figueira J., Greco S., and Ehrgott M. (Eds), Kluwer Academic Publishers; Choo, E.U., Schoner, B., Wedley, W.C., Interpretation of criteria weights in multicriteria decision making (1999) Computers & Industrial Engineering, 37 (3), pp. 527-541; Marichal, J.L., An axiomatic approach of the discrete Choquet integral as a tool to aggregate interacting criteria (2000) IEEE Transactions on Fuzzy Systems, 8 (6), pp. 800-807; Barut, M., Faisst, W., Kanet, J.J., Measuring supply chain coupling: an information system perspective (2002) European Journal of Purchasing & Supply Management, (8), pp. 161-171; Kranz, D.H., Luce, R.D., Suppes, P., Tversky, A., (1971) Foundations of measurement: Additive and Polynomial Representations, , Academic Press; Ayers, J.B., (2000) Handbook of Supply Chain Management, , APICS Series on Resource management, Alexandria, Virginia; Villa, A., Introducing some supply chain management problems (2001) International Journal of Production Economics, 73, pp. 1-4; Beamon, B., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2006) Decision support system, 42, pp. 283-301; Folan, P., Browne, J., A review of performance measurement: towards performance management (2005) Computers in Industry, 56, pp. 663-680; Gunasekaran, A., Patelb, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, (87), pp. 333-347; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: a case study (2004) European Journal of Operational Research, (156), pp. 267-286; Chan, F.T., Performance Measurement in a Supply Chain (2003) International Journal of Advanced Manufacturing Technology, (21), pp. 534-548; Bana e Costa, C.A., Vansnick, J.C., Applications of the MACBETH approach in the framework of an additive aggregation model (1997) Journal of Multi-Criteria Decision Analysis, 6 (2), pp. 107-114; Vansnick, J.C., Strength of preference: theorical and practical aspects (1984) European Journal of Operational Research, pp. 367-381; Grabisch, M., K-ordered discrete fuzzy measures and their representation (1997) Fuzzy sets and Systems, 92, pp. 167-189},
correspondence_address1={Berrah, L.; Laboratoire d'Informatique, Systemes, Traitement de l'Information et de la Connaissance (LISTIC), BP 806, 74016 Annecy cedex, France; email: lamia.berrah@univ-savoie.fr},
issn={01663615},
coden={CINUD},
language={English},
abbrev_source_title={Comput Ind},
document_type={Article},
source={Scopus},
}

@ARTICLE{Blanc2007720,
author={Blanc, S. and Ducq, Y. and Vallespir, B.},
title={Evolution management towards interoperable supply chains using performance measurement},
journal={Computers in Industry},
year={2007},
volume={58},
number={7},
pages={720-732},
doi={10.1016/j.compind.2007.05.011},
note={cited By 36},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34447514173&doi=10.1016%2fj.compind.2007.05.011&partnerID=40&md5=3e575b74b30186c1cdaa4610d86e9cbf},
affiliation={LAPS/GRAI, University Bordeaux 1, UMR 5131 CNRS, 351 cours de la Libération, 33405 Talence Cedex, France},
abstract={Today, the enterprise must cooperate to survive in an increasingly competitive context. This co-operation is carried out by organisations in networks through the supply chain (SC). In order to do so, enterprises must be interoperable. In order to reach interoperability, the system must eradicate heterogeneity. This paper aims at presenting the problems of heterogeneity from two different points of view: semantic and organisational. In the first part, the paper exposes the problems of heterogeneity from the two points of view. This part insists on the weak alien problem resolution for the semantics. In the second part, the synchronisation and standardisation of practices of business processes are presented in order to solve a part of the problem of organisation heterogeneity. The third part presents the use of the performance measurement system to manage the evolution of the enterprises towards interoperability, i.e. the implementation and exploitation of the principles exposed previously. Then, the last part presents an application of the ECOGRAI method and implementation using a software tool in the frame of interoperability between manufacturing and maintenance companies. © 2007 Elsevier B.V. All rights reserved.},
author_keywords={ECOGRAI method;  Enterprise modelling;  Interoperability;  PBview software tool;  Performance measurement system},
keywords={ECOGRAI method;  Enterprise modeling;  PBview software tool;  Performance measurement systems, Administrative data processing;  Computer aided software engineering;  Interoperability;  Problem solving;  Semantics;  Standardization, Supply chain management},
references={Chalmeta, R., Grangel, R., Performance measurement systems for virtual enterprise integration (2005) International Journal of Computer Integrated Manufacturing, 18 (1), pp. 73-84; Doumeingts, G., Ducq, Y., Enterprise modelling techniques to improve efficiency of enterprises (2001) International Journal of Production Planning & Control, 12 (2), pp. 146-163; Ducq, Y., Vallespir, B., Definition and aggregation of performance measurement system in three aeronautical workshops using the ECOGRAI method (2005) International Journal of Production Planning & Control, 16 (2), pp. 163-177; Ducq, Y., Vallespir, B., Doumeingts, G., Coherence analysis method for production system by performance aggregation (2001) International journal of Production Economics, (69), pp. 23-37; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard, , Harvard Business School Press; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism - The Scorecard for Measuring and Managing Business Success, , Edition Prentice Hall 394 p; },
correspondence_address1={Blanc, S.; LAPS/GRAI, 351 cours de la Libération, 33405 Talence Cedex, France; email: firstname.name@laps.u-bordeaux1.fr},
issn={01663615},
coden={CINUD},
language={English},
abbrev_source_title={Comput Ind},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gaiardelli2007698,
author={Gaiardelli, P. and Saccani, N. and Songini, L.},
title={Performance measurement of the after-sales service network-Evidence from the automotive industry},
journal={Computers in Industry},
year={2007},
volume={58},
number={7},
pages={698-708},
doi={10.1016/j.compind.2007.05.008},
note={cited By 112},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34447313649&doi=10.1016%2fj.compind.2007.05.008&partnerID=40&md5=9cde1162745587ded157ea4aaf5704ff},
affiliation={Department of Industrial Engineering, Università di Bergamo, Viale Marconi 5, 24044 Dalmine, Italy; Department of Mechanical and Industrial Engineering, Università di Brescia, Via Branze 38, 25123 Brescia, Italy; Department of Accounting and Finance, Università Commerciale Luigi Bocconi, Via Sarfatti 25, 20136 Milano, Italy},
abstract={The after-sales activities are nowadays acknowledged as a relevant source of revenue, profit and competitive advantage in most manufacturing industries. Top and middle management, therefore, should focus on the definition of a structured business performance measurement system for the after-sales business. In addition, since many actors are involved along the after-sale service supply chain, an integrated and multi-attribute set of measures needs to be designed consistently at every level of the supply chain. Nonetheless, little attention was devoted by scientific and managerial literature to this topic. The paper aims at filling this gap, and proposes an integrated framework for the after-sales network performance measurement, and provides an empirical application to two automotive case companies and their official service network. The cases show that performance measurement systems of different supply chain actors should be aligned in order to achieve strategic consistency. In particular, the performance of different actors at the process level of the framework concurs in determining the after-sales service overall performance towards the final customer. In addition, linkages at other levels (mainly the business and activity ones) may be needed or helpful in ensuring consistency between strategic and operational objectives, inside the organisations and thus for the whole supply chain. © 2007 Elsevier B.V. All rights reserved.},
author_keywords={After-sales service network;  Automotive industry;  Case studies;  Performance measurement systems;  Reference model},
keywords={After-sales service network;  Case studies;  Operational objectives;  Performance measurement systems;  Reference model, Automotive industry;  Competitive intelligence;  Mathematical models;  Production engineering, Industrial management},
references={Wise, R., Baumgartner, P., Go downstream-the new profit imperative in manufacturing (1999) Harvard Business Review, 77, pp. 133-141; Levitt, T., After the sale is over... (1983) Harvard Business Review, 61 (5), pp. 87-93; Bundschuh, R.G., Dezvane, T.M., How to make after sale services pay off (2003) The McKinsey Quarterly, 4, pp. 116-127; Alexander, W.L., Dayal, S., Dempsey, J.J., Vander Ark, J., The secret life of factory service centers (2002) The McKinsey Quarterly, 3, pp. 106-115; Gallagher, T., Mitchke, M.D., Rogers, M.C., Profiting from spare parts (2005) The McKinsey Quarterly.; Armistead, C., Clark, G., (1992) Customer Service Support Implementing Effective Strategies, , FT Books, Pitman Publishing, London; Cohen, M., Whang, S., Competing in product and service: a product life-cycle model (1997) Management Science, 43 (4), pp. 535-545; Thoben, K.D., Jagdev, H., Eschenbaecher, J., Extended products: evolving traditional product concepts (2001) Proceedings of the 7th International Conference on Concurrent Enterprising: Engineering the Knowledge Economy Through Co-operation, pp. 429-439. , Bremen, Germany, June 27-29; Lele, M., After-sales service-necessary evil or strategic opportunity? (1997) Managing Service Quality, 7 (3), pp. 141-145; Neely, A., The evolution of performance measurement research (2005) International Journal of Operations and Production Management, 25 (12), pp. 1264-1277; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, , CIMA, London; Kaplan, R.S., Norton, D.P., The balanced scorecard. Measures that drive performance (1992) Harvard Business Review; Lynch, R.L., Cross, K.F., (1991) Measure Up-The Essential Guide to Measuring Business Performance, , Mandarin, London; Neely, A., Adams, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , FT Prentice Hall, London; Banker, R.D., Potter, G., Srinivasan, D., An empirical investigation of an incentive plan that includes non-financial performance measures (2000) The Accounting Review, 75 (1), pp. 65-92; Ittner, C.D., Larcker, D.F., Coming up short on non-financial performance measurement (2003) Harvard Business Review, pp. 88-95; Porter, M.E., (1985) Competitive Advantage, , Free Press, New York; Mentzer, J.T., De Witt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D., Zacharia, Z.G., Defining supply chain integration (2001) Journal of Business Logistics, 22 (2), pp. 1-25; Harland, C.M., Lamming, R.C., Sheng, J., Johnsen, T.E., A taxonomy of supply networks (2001) Journal of Supply Chain Management, 37 (4), pp. 21-27; Hertz, S., Supply chain myopia and overlapping supply chains (2006) Journal of Business and Industrial Marketing, 21 (4), pp. 208-217; Helper, S., Strategy and irreversibility in supplier relations: the case of the U.S. automobile industry (1991) Business History Review, pp. 781-824; Bensaou, M., Venkatraman, N., Configurations of interorganisational relationships: a comparison between U.S. and Japanese automakers (1995) Management Sciences, 41 (9), pp. 1469-1487; Dyer, J.H., How Chrysler created an American keiretsu (1996) Harvard Business Review, 4, pp. 42-56; Bensaou, M., Portfolios of buyer-supplier relationships (1999) Sloan Management Review, pp. 35-44; Cox, A., Understanding buyer and supplier power: a framework for procurement and supply competence (2001) Journal of Supply Chain Management, 37, pp. 8-15; Cousins, P.D., Crone, M.J., Strategic models for the development of obligation based inter-firm relationships (2003) International Journal of Production and Operations Management, 23, pp. 1447-1474; Caniels, M.C., Gelderman, C.J., Purchasing strategies in the Kraljic matrix. A power and dependence perspective (2006) Journal of Purchasing & Supply Management, 11, pp. 141-155; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Chan, F., Performance Measurement in a Supply Chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Spekman, R.E., Carraway, R., Making the transition to collaborative buyer-seller relationship (2006) Industrial Marketing Management, 35 (1), pp. 10-19; Brewer, P., Speh, T., Using the Balanced Scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Lambert, D., Pohlen, T., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Folan, P., Browne, J., Development of an extended enterprise performance measurement system (2005) Production Planning & Control, 16 (6), pp. 531-544; Bititci, U., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-353; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Storey, J., Emberson, C., Godsell, J., Harrison, A., Supply chain management: theory, practice and future challenges (2006) International Journal of Operations and Production Management, 26 (7), pp. 754-774; Gaiardelli, P., Saccani, N., Songini, L., Performance measurement systems in the after-sales service: an integrated framework (2007) International Journal of Business Performance Measurement, 9 (2), pp. 145-171; Fabrychy, W.J., Blanchard, B.S., (1991) Life-Cycle Cost and Economic Analysis, , Prentice Hall, Englewood Cliffs, NJ; Shields, M.D., Young, S.M., Managing product life cycle costs: an organisational model (1991) Cost Management, pp. 39-52; Cooper, R., Slagmulder, R., (1999) Supply Chain Development for the Lean Enterprise: Interorganisational Cost Management, , Productivity Press, Portland; Cooper, R., Slagmulder, R., Strategic cost management: expanding scope and boundaries (2003) Journal of Cost Management; Ellram, L.M., Total cost of ownership. An analysis approach for purchasing (1995) International Journal of Physical Distribution and Logistics Management, 25 (8), pp. 4-23; Armistead, C., Clark, G., A framework for formulating after-sales support strategy (1991) International Journal of Operations and Production Management, 11, pp. 111-124; Frambach, R.T., Wels-Lips, I., Gundlach, A., Proactive product service strategies (1997) Industrial Marketing Management, 26, pp. 341-352; Mathieu, V., Service strategies within the manufacturing sector: benefits, costs and partnership (2001) International Journal of Service Industry Management, 12 (5), pp. 451-475; Lele, M., How service needs influence product strategy (1996) Sloan Management Review, pp. 63-70; Goffin, K., Customer support-a cross-industry study of distribution channels and strategies (1999) International Journal of Physical Distribution and Logistics Management, 29, pp. 374-397; Loomba, A.P., Linkages between product distribution and service support functions (1996) International Journal of Physical Distribution and Logistics Management, 26, pp. 4-22; Loomba, A.P., Product distribution and service support strategy linkages (1998) International Journal of Physical Distribution and Logistics Management, 28 (2), pp. 143-216; Agnihothri, S., Sivasubramaniam, N., Simmons, D., Leveraging technology to improve field service (2002) International Journal of Service Industry Management, 13 (1), pp. 47-68; Papadopoulos, H.T., A field service support system using a Queueing Network Model and the priority MVA algorithm (1996) Omega, 24, pp. 95-200; Hopp, W.J., Zhang, R.Q., Spearman, M.L., An easily implementable hierarchical heuristic for a two-echelon spare parts distribution system (1999) IEEE Transactions, 31, pp. 977-988; Zhang, R.Q., Hopp, W.J., Supatgiat, C., Spreadsheet implementable inventory control for a distribution center (2001) Journal of Heuristics, 7, pp. 185-203; Huiskonen, J., Maintenance spare parts logistics: special characteristics and strategic choices (2001) International Journal of Production Economics, 71, pp. 125-133; Kennedy, W.J., Pattersonb, J.W., Fredendall, L.D., An overview of recent literature on spare parts inventories (2002) International Journal of Production Economics, 76, pp. 201-215; Cohen, M., Lee, H.L., Out of touch with customer needs? Spare parts and after sales service (1990) Sloan Management Review, 31, pp. 55-66; Mathe, H., Shapiro, R., (1993) Integrating Service Strategy Into the Manufacturing Company, , Chapman and Hall, London; Cohen, M., Cull, C., Lee, H.L., Willen, D., Saturn's supply-chain innovation: high value in after-sales service (2000) Sloan Management Review, 41, pp. 93-101; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard. Translating Strategy into Action, , Harvard Business School Press, Boston; Fitzgerald, L., Moon, P., (1996) Performance Measurement in Service Industries: Making it Work, , CIMA, London; Bohmann, E., Rosenberg, J.H., Stenbrink, P., Overhauwling, European auto distribution (2003) The McKinsey Quaterly, 1, pp. 134-142; Voss, C., Tsikriktsis, N., Frolich, M., Case research in operations management (2002) International Journal of Operations and Production Management, 22 (2), pp. 195-219},
correspondence_address1={Gaiardelli, P.; Department of Industrial Engineering, Viale Marconi 5, 24044 Dalmine, Italy; email: paolo.gaiardelli@unibg.it},
issn={01663615},
coden={CINUD},
language={English},
abbrev_source_title={Comput Ind},
document_type={Article},
source={Scopus},
}

@ARTICLE{Morgan2007255,
author={Morgan, C.},
title={Supply network performance measurement: Future challenges?},
journal={The International Journal of Logistics Management},
year={2007},
volume={18},
number={2},
pages={255-273},
doi={10.1108/09574090710816968},
note={cited By 46},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986059618&doi=10.1108%2f09574090710816968&partnerID=40&md5=b4081b6b08b77891d43cc45897eb9b09},
affiliation={Cranfield School of Management, Cranfield University, Cranfield, United Kingdom},
abstract={This reflective paper aims to identify and discuss key areas for the development of, and research into, supply network performance measurement systems. The paper traces the broad history of performance measurement before identifying the specific issues that performance measurement has raised in the supply network environment from both local and global managerial perspectives. Having laid this foundation, the paper continues to examine the likely future challenges that managers of supply networks face. Four outstanding challenges are identified and discussed: the response to the increasing environmental challenges; the effect of the lean/agile debate on the development of supply networks and their measurement systems; the need to develop performance measurement systems to break through the “dyadic relationship” barrier and make management of the supply network a more realistic aspiration; and challenges faced in making performance measurement a multi-cultural phenomenon and through this make management of international supply chains more effective. The paper identifies some of the outstanding issues in current and future network performance measurement. © 2007, Emerald Group Publishing Limited},
author_keywords={Integration;  Performance management;  Supply and demand;  Supply chain management},
references={Aitken, J., Childerhouse, P., Christopher, M., Towill, D., Designing and managing multiple pipelines (2005) Journal of Business Logistics, 26 (2), pp. 73-95; Bititci, U., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25, pp. 333-351; Checkland, P.B., (1981) Systems Thinking: Systems Practice, , Wiley, Chichester; Christopher, M.G., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, 2nd ed., , Financial Times – Pitman, London; Christopher, M., Peck, H., (2003) Marketing Logistics, 2nd ed., , Butterworth-Heinemann, Oxford; Clott, C., Perspectives on global outsourcing and the changing nature of work (2004) Business & Society Review, 109 (2), pp. 153-170; Defee, C.C., Stank, T.P., Applying the strategy-structure-performance paradigm to the supply chain environment (2005) International Journal of Logistics Management, 16 (1), pp. 28-50; Deming, W.E., (1939) Statistical Methods from the Viewpoint of Quality Control, , Dover Publications, New York, NY; Donselaars, K., Kokke, K.V., Allessie, M., Performance measurement in the transportation & distribution sector (1998) International Journal of Production, Distribution and Logistics Management, 26 (6), pp. 434-450; Emery, M., Purser, R., (1996) The Search Conference: A Powerful Method for Planning Organizational Change and Community Action, , Jossy-Bass, San Francisco, CA; Ford, H., (1926) Today and Tomorrow, p. 80. , William Heinemann Ltd, London; Gilbreth, F., Gilbreth, L., (1921) Time and Motion Study as Fundamental Factors in Planning and Control, , The Mountainside Press, Shafts burg, NJ; Glaister, K.W., Buckley, P.J., Measures of performance in UK international alliances (1998) Organization Studies, 19 (1), pp. 89-119; Goldratt, E.M., Cox, J., (1984) The Goal, , Gower, Aldershot; Harrison, A., van Hoek, R., (2005) Logistics Management and Strategy, 2nd ed., p. 189. , FT Prentice-Hall (Pearson), Harlow; Hofstede, G., (2001) Cultures Consequences, Comparing Values, Behaviours, Institutions, and Organizations across Nations, , Sage, Thousand Oaks, CA; Hughes, J., Ralf, M., Michels, W., (1998) Transform Your Supply System: Releasing Value in Business, p. 96. , Thompson Business Press, London; Jacobs, D., The promise of demand chain management in fashion (2006) Journal of Fashion Marketing & Management, 10 (1), pp. 84-97; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, p. 12. , HBS Press, Boston, MA; Juran, J., (1988) Juran on Planning for Quality, , The Free Press, New York, NY; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 20-25; Kendra, K., Taplin, L., Project success: a cultural framework (2004) Project Management Journal, 35 (1), pp. 30-45; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Linder, J.C., Transformational outsourcing (2004) MIT Sloan Management Review, 45 (2), pp. 52-58; Morash, E.A., Dröge, C.L.M., Vickery, S.K., Strategic logistics capabilities for competitive advantage and firm success (1996) Journal of Business Logistics, 17 (1), pp. 1-22; Morgan, C., Is there a crisis in supply chain performance measurement? (2005) Supply Chain Practice, 7 (2), pp. 68-79; Morgan, C., Dewhurst, A., Multiple retailers supplier performance: an exploratory investigation into using SPC techniques (2007) International Journal of Production Economics, 109. , July; Novak, R.A., Thomas, D.J., The challenges of implementing the perfect order concept (2004) Transportation Journal, 43 (1), pp. 5-16; Phusavat, K., Chaisakul, S., Takala, J., (2004) Managing Outsourcing Activities: Roles of Performance Measurement, , D.I.E., Kasetstart University, Bangkok; Rushton, A., Oxley, J., Croucher, P., (2000) The Handbook of Logistics and Distribution Management, 2nd ed., pp. 44-47. , Kogan Page, London; Saeed, K.A., Malhotra, M.K., Grover, V., Examining the impact of inter-organizational systems on process efficiency and sourcing leverage in buyer-supplier dyads (2005) Decision Sciences, 36 (3), pp. 365-396. , August; (2002), www.sarbanes-oxley-guidelines.com (accessed 13 February 2007); Shewart, W.A., (1931) Economic Control of Quality of Manufactured Product, , American Society of Quality Control, Milwaulkee; Simraturpang, T.M., Sridharan, R., An integrative framework for supply chain collaboration (2005) International Journal of Logistics Management, 16 (2), pp. 257-274; Srikanth, M.L., Cavallaro, H.E., (1987) Regaining Competitiveness: Putting the Goal to Work, , North River Press, Great Barving, MA; Stevens, G., Integrating the supply network (1989) International Journal of Physical Distribution & Materials Management, 19, pp. 3-8; (2006), available at: www.supply-chain.org (accessed 28 June); Taylor, F.W., (1911) The Principles of Scientific Management, pp. 5-29. , Harper Bros., New York, NY; Taylor, J., Fielden, D., (1993) Performance by Design: Socio Technical Systems in North America, , Prentice-Hall, Upper Saddle River, NJ; Trompenaars, F., (1997) Riding the Waves of Culture: Understanding Cultural Diversity in Business, , Nicholas Brearly, London; van Hoek, R., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (1), p. 15; van Hoek, R., Chatman, R., Wilding, R., Managers in supply network management: the critical dimension (2002) An International Journal, 7, pp. 119-125; Walters, D., Effectiveness and efficiency: the role of demand chain management (2006) International Journal of Logistics Management, 17 (1), pp. 75-94; Wang, C., Regan, A.C., (2003) Reducing Risks in Logistics Outsourcing, , G.S.M, University of California, Berkley, CA; (2007), available at: www.businesslink.gov.uk/bdotg/action/detail (accessed 13 February); Yilmaz, Y., Bititci, U., Performance measurement in the value chain: manufacturing v. tourism (2006) International Journal of Productivity and Performance Management, 55 (5), pp. 371-391; (2007), summarised at www.paragonrouting.com/wtd and www.dft.gov.uk/freight/rtd (accessed 17 February)},
correspondence_address1={Morgan, C.; Cranfield School of Management, , Cranfield, United Kingdom},
issn={09574093},
language={English},
abbrev_source_title={Int. J. Logist. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bhagwat200743,
author={Bhagwat, R. and Sharma, M.K.},
title={Performance measurement of supply chain management: A balanced scorecard approach},
journal={Computers and Industrial Engineering},
year={2007},
volume={53},
number={1},
pages={43-62},
doi={10.1016/j.cie.2007.04.001},
note={cited By 501},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34447249471&doi=10.1016%2fj.cie.2007.04.001&partnerID=40&md5=19d9e25750f391ff76332ebc875668f3},
affiliation={Department of Mechanical Engineering, M.B.M. Engineering College, Faculty of Engineering and Architecture, Jodhpur, Rajasthan, India; Department of Production and Industrial Engineering, M.B.M. Engineering College, Faculty of Engineering and Architecture, Jodhpur, Rajasthan, India},
abstract={This paper develops a balanced scorecard for supply chain management (SCM) that measures and evaluates day-to-day business operations from following four perspectives: finance, customer, internal business process, and learning and growth. Balanced scorecard has been developed based on extensive review of literature on SCM performance measures, supported by three case studies, each illustrating ways in which BSC was developed and applied in small and medium sized enterprises (SMEs) in India. The paper further suggests that a balanced SCM scorecard can be the foundation for a strategic SCM system provided that certain development guidelines are properly followed, appropriate metrics are evaluated, and key implementation obstacles are overcome. The balanced scorecard developed in this paper provides a useful guidance for the practical managers in evaluation and measuring of SCM in a balanced way and proposes a balanced performance measurement system to map and analyze supply chains. While suggesting balanced scorecard, different SCM performance metrics have been reviewed and distributed into four perspectives. This helps managers to evaluate SCM performance in a much-balanced way from all angles of business. © 2007 Elsevier Ltd. All rights reserved.},
author_keywords={Balanced scorecard;  Case studies;  Framework;  Metrics;  Performance measurement;  Supply chain management},
keywords={Balanced scorecard;  Business operations;  Performance measurement;  Small and medium sized enterprises (SME), Administrative data processing;  Managers, Supply chain management},
references={Barua, A., Lee, S.C.H., Whinston, A.B., The calculus of reengineering (1996) Information Systems Research, 7 (4), pp. 409-428; Bhagwat, R., Sharma, M.K., Management of information system in Indian SMEs: An exploratory study (2006) International Journal of Enterprise and Network Management, 1 (1), pp. 99-125; Bititici, U.S., Nudurupati, S.S., Using performance measurement to derive continuous improvement (2002) Manufacturing Engineer, 81 (5), pp. 230-235; Bititici, U.S., Cavalieri, S., Cieminski, G., Implementation of performance measurement systems: Private and public sectors (2005) Editorial, Production Planning and Control, 16 (2), pp. 99-100; Bower, J.L., Hout, T.M., Fast cycle capability for competitive power (1988) Harvard Business Review, pp. 110-118; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Chan, F.T.S., Qi, H.J., A fuzzy basis channel-spanning performance measurement method for supply chain management (2002) Proceedings of The Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 216, pp. 1155-1167; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management - An International Journal, 8 (3), pp. 209-223; Chan, F.T.S., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-642; Chan, F.T.S., Chan, H.K., Qi, H.J., A review of performance measurement systems for supply chain management (2006) International Journal of Business Performance Management, 8 (2-3), pp. 110-131; Christopher, M., (1992) Logistics and supply chain management, , Pitman Publishing, London; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1989) National Production Review, 8 (1), pp. 23-33; Dixion, J.R., Nanni, A.J., Vollmann, T.E., (1990) The new performance challenge-measuring operation for world-class competition, , Dow Jones-Irwin, Homewood, USA; Dobler, D.W., Burt, D.N., (1996) Purchasing and Supply Management, , The McGraw-Hill Companies, New York, NY; Ellram, L.M., A managerial guide for the development and implementation of purchasing partnerships (1991) International Journal of Purchasing and Materials Management, 27 (3), pp. 2-8; Fisher, L.M., What is the right supply chain for your product? (1997) Harvard Business Review, pp. 105-116; Gelders, L., Mannaert, P., Maes, J., Manufacturing strategy performance indicators and improvement programs (1994) International Journal of Production Research, 32 (4), pp. 797-805; Ghalayinin, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48, pp. 207-225; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) International Journal of Purchasing and Materials Management, 30 (4), pp. 13-18; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Production and Operations Management, 21 (1-2), pp. 71-87; Gunasekaran, A., Patel, C., Ronald, E., McGaughey, R., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-348; Hudson, M., Lean, J., Smart, P.A., Improving control through effective performance measurement in SMEs (2001) Production Planning and Control, 12 (8), pp. 804-813; Hvolby, H.H., Trienkens, J., Supply chain planning opportunities for small and medium sized companies (2002) Computers in Industry, 49, pp. 3-8; Kaplan, R., Norton, D., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-99; Kaplan, R., Norton, D., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Kueng, P., Process performance measurement system: a tool to support process-based organizations (2000) Total Quality Management, 11 (1), pp. 67-85; Landeros, R., Reck, R., Plank, R.E., Maintaining buyer-supplier partnerships (1995) International Journal of Purchasing and Materials Management, 31 (3), pp. 3-11; Letza, S.R., The design and implementation of the balanced business scorecard: An analysis of three companies in practice (1996) Business Process Re-engineering and Management Journal, 2 (3), pp. 54-76; Levy, D.L., Lean production in an international supply chain (1997) Sloan Management Review, Winter, pp. 94-102; Malmi, T., Balanced scorecards in Finnish companies: A research note (2001) Management Accounting Research, 12, pp. 207-220; Maloni, M.J., Benton, W.C., Supply chain partnerships: Opportunities for operations research (1997) European Journal of Operations Research, 101, pp. 419-429; Mapes, J., New, C., Szwejczewski, M., Performance trade-offs in manufacturing plants (1997) International Journal of Operations and Production Management, 17 (10), pp. 1020-1033; Martinsons, M., Davison, R., Tse, D., The balanced scorecard: A foundation for the strategic management of information systems (1999) Decision Support Systems, 25, pp. 71-88; Maskell, B.H., (1991) Performance measurement for World Class Manufacturing, , Productivity Press, Portland, OR; Mason-Jones, R., Towill, D.R., Enlightening supplies (1997) Manufacturing Engineering, 3, pp. 156-160; McBeth, D.K., Ferguson, N., (1994) Partnership sourcing: An integrated supply chain management approach, , Pitman Publishing, London; New, S.J., A framework for analyzing supply chain improvement (1996) International Journal of Operations and Production Management, 16 (4), pp. 19-34; Norrekilt, H., The balance on the balanced scorecard - A critical analysis of some of its assumptions (2000) Management Accounting Research, 11, pp. 65-68; Novich, N., Distribution strategy: Are you thinking small enough? (1990) Sloan Management of Review, Fall, pp. 71-77; Pyke, D.F., Cohen, M.A., Multiproduct integrated production-distribution systems (1994) European Journal of Operational Research, 74, pp. 18-49; Rushton, A., Oxley, J., (1991) Handbook of logistics and distribution management, , Kogan Page, London; Schonberger, R.J., (1990) Building a chain of customers, , The Free Press, London; Sharma, M.K., Bhagwat, R., Practice of information systems: Evidence from select Indian SMEs (2006) Journal of Manufacturing Technology Management, 17 (2), pp. 199-223; Sharma, M.K., Bhagwat, R., Dangayach, G.S., Practice of performance measurement: Experience from Indian SMEs (2005) International Journal of Globalization and Small Business, 1 (2), pp. 183-213; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , Pitman Publishing, London; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Thomas, D.J., Griffin, P.M., Co-ordinated supply chain management (1996) European Journal of Operations Research, 94 (3), pp. 1-15; Toni, A.D., Nissimbeni, G., Tonchia, S., New trends in supply environment (1994) Logistics Information Management, 7 (4), pp. 41-50; Towill, D.R., The seamless supply chain - The predator's strategic advantage (1997) International Journal of Technology Management, 14, pp. 37-55; Turner, T.J., Bititici, U.S., Nudurupati, S.S., Implementation and impact of performance measures in two SMEs in Central Scotland (2005) Production Planning and Control, 16 (2), pp. 135-151; Vitale, M., Mavrinac, S.C., Hauser, M., New process/financial scorecard: A strategic performance measurement system (1994) Planning Review, 22 (4), pp. 12-16; Wagner, B.A., Fillis, I., Johansson, U., E-business and E-supply chain strategy in small and medium sized businesses (SMEs) (2003) Supply Chain Management - An International Journal, 8 (4), pp. 343-354; Wild, R., (1995) Production and operations management, , Cassell Educational Limited, London},
correspondence_address1={Sharma, M.K.; Department of Production and Industrial Engineering, , Jodhpur, Rajasthan, India; email: milindksharma@rediffmail.com},
issn={03608352},
coden={CINDD},
language={English},
abbrev_source_title={Comput Ind Eng},
document_type={Article},
source={Scopus},
}

@ARTICLE{Aramyan2007304,
author={Aramyan, L.H. and Lansink, A.G.J.M.O. and Van Der Vorst, J.G.A.J. and Kooten, O.V.},
title={Performance measurement in agri-food supply chains: A case study},
journal={Supply Chain Management},
year={2007},
volume={12},
number={4},
pages={304-315},
doi={10.1108/13598540710759826},
note={cited By 276},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250314138&doi=10.1108%2f13598540710759826&partnerID=40&md5=27f684acbe311ffba73729f1b603a912},
affiliation={Wageningen University, Wageningen, Netherlands},
abstract={Purpose - Measurement of the performance of entire supply chains is an important issue because it allows for "tracking and tracing" of efficacy and efficiency failures and leads to more informed decision making with regard to chain design. However, the choice of appropriate supply chain performance indicators is rather complicated due to the presence of multiple inputs and multiple outputs in the system. Therefore, this paper aims to evaluate the usefulness of a novel conceptual model for supply chain performance measurement in an agri-food supply chain. Design/methodology/approach - A conceptual model for integrated supply chain performance measurement is evaluated in a Dutch-German tomato supply chain by means of a case study approach. Findings - The proposed conceptual framework is found to be useful for measuring performance of the tomato supply chain. From the case study it is concluded that four main categories of performance measures (i.e. efficiency, flexibility, responsiveness, and food quality) are identified as key performance components of the tomato supply chain performance measurement system. Originality/value - This research evaluates a novel concept for measuring the performance of agri-food supply chains. This concept is the first step in developing an integrated performance measurement system that contains financial as well as non-financial indicators combined with the specific characteristics of agri-food supply chains. Based on a case study in the tomato supply chain, this concept is found to have potential. © Emerald Group Publishing Limited.},
author_keywords={Case study;  Food industry;  Performance measurement (quality);  Supply chain management},
references={Aramyan, L., Ondersteijn, C., Van Kooten, O., Oude Lansink, A., Performance indicators in agri-food production chains (2006) Quantifying the Agri-food Supply Chain, pp. 47-64. , Ondersteijn, C.J. Wijnands, J.H. Huirne, R.B. van Kooten, O. Springer Dordrecht; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-94; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-92; Beamon, B.M., Designing the green supply chain (1999) Logistics Information Management, 12 (4), pp. 332-42; Berry, L., (2006) On Great Service: A Framework for Action, , The Free Press New York, NY; Bititci, U.S., Carrie, A.S., McDevitt, L., Integrated performance measurement systems: A development guide (1997) International Journal of Operations & Production Management, 17 (5), pp. 522-34; Bowersox, D.J., Closs, D.J., (1996) Logistical Management: The Integrated Supply Chain Process, , McGraw-Hill New York, NY; Bunte, F., Mulder, M., Van Tongeren, F., De Vlieger, K., (1998) Meting van de 'Performance' van Agrarische Productiekolommen, , Research Report No. 163, Dutch Agricultural Economics Institute, The Hague; Christopher, M.G., (1998) Logistics and Supply Chain Management: Strategies for Reducing Costs and Improving Services, , Pitman London; Churchill, G.A., (1999) Marketing Research: Methodological Foundations, , The Dryden Press Orlando, FL; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Hobbs, J.E., A transaction cost approach to supply chain managment (1996) Supply Chain Management, 1 (2), pp. 15-27; Kano, N., Seraku, N., Takahashi, F., Tsuji, S., Attractive quality and must-be quality (1984) The Journal of the Japanese Society for Quality Control, 14, pp. 39-48; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research, Part E: Logistics and Transportation Review, 38 (6), pp. 439-56; En Tuinbouwcijfers, L., (2004), Centraal Bureau voor de Staitistiek (CBS), LEI, Wageningen University and Research Center, Wageningen; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33, pp. 65-73; Li, D., O'Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 59 (13), pp. 147-57; Luning, P.A., Marcelis, W.J., Jongen, W.M.F., (2002) Food Quality Management: A Techno Managerial Approach, , Wageningen Academic Publishers Wageningen; Persson, F., Olhager, J., Performance simulation of supply chain designs (2002) International Journal of Production Economics, 77 (3), pp. 231-45; Rosenau, M.D., Griffin, A., Castellion, G.A., Anschuetz, N.F., (1996) The PDMA Handbook of New Product Development, , Wiley New York, NY; Simons, R., (2000) Performance Measurement and Control Systems for Implementing Strategy, , Prentice-Hall Englewood Cliffs, NJ; Chain Council, S., (2004), available at: www.supply-chain.org; Valeeva, N.I., (2005) Cost-effectiveness of Improving Food Safety in the Dairy Production Chain, , PhD thesis, Wageningen University, Wageningen; Van Der Spiegel, M., (2004) Measuring Effectiveness of Food Quality Management, , PhD thesis, Wageningen University, Wageningen; Van Der Vorst, J.G.A.J., (2000) Effective Food Supply Chains. Generating, Modelling and Evaluating Supply Chain Scenarios, , PhD thesis, Wageningen University, Wageningen; Van Der Vorst, J.G.A.J., Performance measurement in agri-food supply chain networks. An overview (2005) Quantifying the Agri-food Supply Chain, pp. 13-24. , Ondersteijn, C.J. Wijnands, J.H. Huirne, R.B. van Kooten, O. Springer Dordrecht; Van Hoek, R.I., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-92; Wijnands, J.H., Ondersteijn, C., (2006) Quantifying the Agri-food Supply Chain, , Springer Dordrecht; Womack, J., Jones, D., (2002) Seeing the Whole: Mapping the Extended Value Stream, , Lean Enterprise Institute Cambridge, MA; Yin, R.K., (2003) Case Study Research, Design and Methods, , 3rd ed. Sage Publications London},
correspondence_address1={Aramyan, L.H.; Wageningen University, Wageningen, Netherlands; email: Lusine.Aramyan@wur.nl},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Choy2007352,
author={Choy, K.L. and Chow, H.K.H. and Lee, W.B. and Chan, F.T.S.},
title={Development of performance measurement system in managing supplier relationship for maintenance logistics providers},
journal={Benchmarking},
year={2007},
volume={14},
number={3},
pages={352-368},
doi={10.1108/14635770710753149},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34249733045&doi=10.1108%2f14635770710753149&partnerID=40&md5=210d08e536b8373e3706719119f3042f},
affiliation={Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong; Department of Industrial and Manufacturing Systems Engineering, University of Hong Kong, Hong Kong, Hong Kong},
abstract={Purpose: To develop a performance measurement system (PMS) in the application of supplier relationship management operated under a supply chain benchmarking framework. Acting as a monitoring tool for evaluating the performance of maintenance logistics providers against the defined performance levels stated in the contract, and facilitating the application of benchmarking approach in maintenance logistics activities. Design/methodology/approach: A six tiers collaborative management model is designed in building the PMS, by which information sharing of performance history of suppliers is made possible. By following the work flow of the PMS, performance of suppliers is benchmarked with the best-in-class supplier, resulting in the identification of the most appropriate supplier for the particular requirement. Findings: PMS helps a company and its suppliers to understand the performance gap between its service levels with the best-in-class practice. The resulting performance gap provides valuable information in the formulating of a new supply chain and strategic plan in solving problems and challenges in aviation industry. By means of PMS, a company can make decisions with the basis of a good relationship with its business partners, especially in the maintenance logistics area. Research limitations/implications: The design of PMS must take into consideration of the data sources, the duration of taking the required data, and the focal point on collecting information. Moreover, findings from the study have to be revised every two years. Originality/value: By applying PMS in one of the leading airlines in Hong Kong, suppliers' deficiencies in the logistics performance are identified easily. Moreover, current operational service level is effectively enhanced and the combination of the best-in-class supplier service package is accurately selected. © Emerald Group Publishing Limited.},
author_keywords={Airlines;  Benchmarking;  Hong Kong;  Performance measures;  Supplier relations},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-92; Bogan, C., Callahan, D., Benchmarking in rapid time (2001) Industrial Management, 78 (6), pp. 169-76; Bowersox, D.J., (1990) Logisticsal Excellence, , Digital Press Burlington, MA; Boyson, S., Corsi, T.M., Dresner, M.E., Harrington, L.H., (1999) Logistics and the Extended Enterprise: Benchmarks and Best Practices for the Manufacturing Professional, , Wiley New York, NY; Bullinger, H., Kuhner, M., Van Hoof, A., Analyzing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-43; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement systems (1995) International Journal of Logistics Management, 6 (1), pp. 61-74; Cassell, C., Nadin, S., Gray, M.O., The use and effectiveness of benchmarking in SMEs (2001) Benchmarking: An International Journal, 8 (3), pp. 212-22; Chan, J.W.K., Burns, N.D., Benchmarking manufacturing planning and control (MPC) systems: An empirical study of Hong Kong supply chains (2002) Benchmarking: An International Journal, 9 (3), pp. 256-77; Choy, K.L., Lee, W.B., Task allocation using case-based reasoning for distributed manufacturing systems (2000) Logistics Information Management, 13 (3), pp. 167-76; Choy, K.L., Lee, W.B., Multi-agent based virtual enterprise supply chain network for order management (2001) Journal of Industrial Engineering Research, 2 (2), pp. 126-41; Choy, K.L., Lee, W.B., A generic tool for the selection and management of supplier relationships in an outsourced manufacturing environment - The application of case based reasoning (2002) Logistics Information Management, 15 (4), pp. 235-53; Choy, K.L., Lee, W.B., On the development of a case-based supplier management tool for multi-national manufacturers (2002) Measuring Business Excellence, 6 (1), pp. 15-22; Choy, K.L., Lee, W.B., Lo, V., An intelligent supplier management tool for benchmarking suppliers in outsource manufacturing (2002) Expert Systems with Applications, 22 (3), pp. 213-24; Christopher, M., (1998) Logistics and Supply Chain Management, , Financial Times Management London; Garvin, D.A., Building a learning organization (1993) Harvard Business Review, 71 (4), pp. 78-81; Gentry, J.J., Carrier involvement in buyer-seller strategic partnerships (1996) International Journal of Physical Distribution & Logistics Management, 26 (3), pp. 14-25; Gilmour, P., A strategic audit framework to improve SC performance (1999) Journal of Business & Industrial Marketing, 14, pp. 355-63; Gopal, K.K., Performance measurement system (2002) Total Quality Management, 13 (5), pp. 715-28; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Hanman, S., Benchmarking your firm's performance with best practice (1997) International Journal of Logistics Management, 8 (2), pp. 1-18; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lau, H.C.W., Lee, W.B., Lau, P.K.H., Development of an intelligent decision support system for benchmarking assessment of business partners (2001) Benchmarking: An International Journal, 8 (5), pp. 376-95; Meyer, H., Many happy returns (1999) The Journal of Business Strategy, 30 (2), pp. 27-31; Simatupang, T.M., Sridharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmarking: An International Journal, 11 (1), pp. 9-30; Sundaram, R.M., A comparative study of three different SCM approaches (2002) International Journal of Physical Distribution & Logistics Management, 32 (7), pp. 532-55; Talluri, S., A benchmarking method for business-process re-engineering and improvement (2000) International Journal of Flexible Manufacturing Systems, 12 (4), pp. 291-304; Tibben-Lembke, R.S., Rogers, D.S., The impact of reverse logistics on total cost of ownership (1998) Journal of Marketing Theory and Practice, 6, pp. 51-60; Vogt, J.J., (2002) Business Logistics Management: Theory and Practice, , Oxford University Press New York, NY},
correspondence_address1={Choy, K.L.; Department of Industrial and Systems Engineering, , Kowloon, Hong Kong; email: mfklchoyt@inet.polyu.edu.hk},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Yaibuathet2007473,
author={Yaibuathet, K. and Enkawa, T. and Suzuki, S.},
title={Supply chain operational performance and its influential factors: Cross national analysis},
journal={Journal of Japan Industrial Management Association},
year={2007},
volume={57},
number={6},
pages={473-482},
note={cited By 11},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34248549117&partnerID=40&md5=7a376eba2806a4fc0d9cc55c2737efa1},
affiliation={Department of Industrial Engineering and Management, Tokyo Institute of Technology, Japan},
abstract={This research paper aims to investigate operational performance and potential factors that constitute efficient supply chain operational performance in different countries, namely, Japan, Thailand and China. By analyzing the supply chain performance, the supply chain management (SCM) logistics scorecard (LSC) has been utilized as a self-evaluation tool for participating companies in the above-mentioned countries. The LSC focused on four decisive areas, namely, company strategy, planning and execution capability, logistics performance, and IT utilization. The number of participating companies from each country was 205 from Japan, 105 from Thailand and 107 from China. The scores in each assessment area were compared among countries. Subsequently, in this research study, a factor analysis was conducted using the result of the LSC in order to identify significant factors that represent the operational performance of SCM in each country. The results of the factor analysis indicate that the structures for generating successful SCM in Japanese, Thai and Chinese industries are not the same due to different operating environments.},
author_keywords={Cross national analysis;  Factor analysis;  SCM Logistics Scorecard (LSC);  Supply chain performance measurement},
keywords={Industrial economics;  Operations research;  Strategic planning, Cross national analysis;  Factor analysis;  SCM Logistics Scorecard (LSC);  Supply chain performance measurement, Supply chain management},
references={Closs, D.J., Mollenkopf, D.A., A global supply chain framework (2004) Industrial Marketing Management, 33, pp. 37-44; Frohlich, M.T., Westbrook, R., Arcs of integration: An international study of supply chain strategy (2001) Journal of Operations Management, 19, pp. 185-200; Rosenzweig, E.D., Roth, A.V., Dean Jr., J.W., The influence of an integration strategy on competitiveness capabilities and business performance: An exploratory study of consumer products manufacturers (2003) Journal of Operations Management, 21, pp. 437-456; Shin, H., Collier, D.A., Wilson, D.D., Supply management orientation and supplier/ buyer performance (2000) Journal of Operations Management, 18, pp. 317-333; Chan, F.T.S., Performance Measurement in a Supply Chain (2003) The International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Stank, T.P., Keller, S.B., Closs, D.J., Performance benefits of supply chain logistical intergration (2001) Transportation Journal, 41 (2-3), pp. 32-46; Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., Rao, S.S., The impact of supply chain management practices on competitive advantage and organizational performance (2006) OMEGA: The international Journal of Management Science, 34, pp. 107-124; Swink, M., Narasimhan, R., Wang, C., Managing beyond the factory walls: Effects of four types of strategic integration on manufacturing plant performance (2006) Article in Press, to be pulished in Journal of Operations Management; Arashida, K., Enkawa, T., Hatnasaki, A., Suzuki, S., Developing the SCM logistics scorecard and analyzing its relation to the managerial performance (2004) Journal of Japan Industrial Management Association, 55 (2), pp. 95-103; Hamasaki, A., Arashida, K., Enkawa, T., A study on the relationship between logistics performance and financial outcome using SCM logistics scorecard and some consideration on the role of the logistics department (2004) Japan Logistics Society Magazine, 12, pp. 89-94; Suzuki, S., Enkawa, T., Yaibuathet, K., Identifying the factors which determine the operational performance of SCM and analysing their impacts on financial indexes (2005) Proceeding of 1st International Conference on Operations and Supply Chain Management, pp. H1-H8. , Bali, Indonesia, pp; (1996) QRLC Measurements for Excellent, , QRLC, Quick Response Leadership Committee, American Apparel Manufacturers Association AAMA; (1999) Kurt Salmon Associates, KSA Millinium Readiness Profile, , http://www.cf.ac.uk/carbs/lom/lsdg/ quickscanhand.html, KSA, Kurt Salmon Associates (KSA, ) Available online; The Logistics Systems Dynamics Group, Cardiff University, The Quick Scan Handbook; Childerhouse, P., Disney, S., Naim, M.M., A quick scan method for supply chain diagnostics (1999) Proceedings of the Fourth International Symposium on Logistics, pp. 755-760. , Florence, Italy 11-14 July, pp; Naim, M.M., Childerhouse, P., Disney, S.M., Towill, D.R., A supply chain diagnostic methodology: Determine the vector of changes (2002) Computers and Industrial Engineering, 43 (1-2), pp. 135-157; Supply Chain Council, Supply Chain Operation Reference-Model. SCOR version 5.0, Supply Chain Council, Inc, 2001; (1998) Efficient Consumer Response (ECR) Scorecard, , ECCC, Electronic Commerce Council of Canada, Electronic Commerce Council of Canada; Suzuki, S., Simplified Benchmarking Tool: LSC (2006) Logi-Biz, 6 (2), pp. 16-30; Clark, T., Yuxing, D., Corporate Governance in China: Explosive Growth and New Patterns of Ownership (1998) Long Range Planning, 31 (2), pp. 239-251; Hallihan, A., Sackett, P., Williams, G.M., JIT manufacturing: The evolution to an implementation model founded in current practice (1997) International Journal of Production Research, 35 (4), pp. 901-920; Hofstede, G., Hofstede, G.J., What is different is dangerous: Cultures and Organizations Software of the Mind (2005) Intercultural Cooperation and Its Importance for Survival, pp. 163-205. , McGraw-Hill, USA, pp},
correspondence_address1={Yaibuathet, K.; Department of Industrial Engineering and Management, Japan; email: korrakot@ie.me.titech.ac.jp},
issn={13422618},
language={English},
abbrev_source_title={J. Jpn Ind. Manage. Assoc.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Saiz2007305,
author={Saiz, J.J.A. and Bas, A.O. and Rodríguez, R.R.},
title={Performance measurement system for enterprise networks},
journal={International Journal of Productivity and Performance Management},
year={2007},
volume={56},
number={4},
pages={305-334},
doi={10.1108/17410400710745324},
note={cited By 54},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34247389229&doi=10.1108%2f17410400710745324&partnerID=40&md5=cc6d5db6719ab202eb0b6307507ef468},
affiliation={Department of Business Organisation, Financial Economy and Accounting, Polytechnic University of Valencia, Valencia, Spain},
abstract={Purpose - The purpose of this research paper is to present a performance measurement system for enterprise networks (PMS-EN) developed within the context of a European project's context. Design/methodology/approach - This research follows a constructivist approach, based on the following activities: recompilation, analysis and study of scientific knowledge, acquisition of main postulates and construction of initial framework. Findings - This paper describes a performance measurement system called PMS-EN that can be used for managing performance in the enterprise networks context. After analysing different frameworks relating to such a thematic, and once their main weaknesses were identified, it was decided to develop a PMS that will overcome these difficulties and, at the same time, provide enterprises operating within these environments, with a simple, efficient, robust and useful framework. Research limitations/implications - This PMS has been built upon the framework called PMS IE-GIP, but thought could be given to building it upon other frameworks existing within the literature. Practical implications - Even though this proposal has been successfully applied to an enterprises network, it should be tested further by implementing it to other enterprises networks, with the main objective of enriching and extending it to other sectors. Originality/value - This paper has described a new performance measurement system called PMS-EN that can be used for managing performance in the enterprise networks context. It provides enterprises operating in this context a simple, efficient, robust and useful framework. Additionally, it is an efficient and effective tool for creating, managing and monitoring performance measures, in an analytical and graphical way, at both global and individual levels of the enterprise networks, which is lacking in most of the existing frameworks. © Emerald Group Publishing Limited.},
author_keywords={Organizational structures;  Performance management systems;  Performance measures;  Supply chain management},
references={Alfaro, J.J., Ortiz, A., Poler, R., Franco, R.D., Performance measurement for e-business enterprise (2002) International Journal of Business Performance Management, 4 (234), pp. 296-315; Altiok, T., Ranjan, R., Multi-stage, pull-type production/inventory systems (1995) IIE Transactions, 27, pp. 190-200; Arntzen, B.C., Brown, G.G., Harrison, T.P., Trafton, L.L., Global supply chain management at Digital Equipment Corporation (1995) Interfaces, 25, pp. 69-93; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-94; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-92; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) International Journal of Operations & Production Management, 25 (4), pp. 333-53; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-94; Bullinger, H.J., Kühner, M., Hoof, A.V., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15), pp. 3533-43; Chan, F., Qi, H.J., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Integrated Manufacturing Systems;, 14 (3), pp. 179-90; Chan, F., Qi, H.J., Chan, H.K., Lau, H., Ip, R., A conceptual model of performance measurement for supply chains (2003) Management Decision, 41 (7), pp. 635-42; Christy, D.P., Grout, J.R., Safeguarding supply chain relationships (1994) International Journal of Production Economics, 36, pp. 233-42; Cohen, M.A., Lee, H.L., Strategic analysis of integrated production-distribution systems: Models and methods (1988) Operations Research, 36 (2), pp. 216-28; Cohen, M.A., Lee, H.L., Resource deployment analysis of global manufacturing and distribution networks (1989) Journal of Manufacturing and Operations Management, 2, pp. 81-104; Cohen, M.A., Moon, S., Impact of production scale economies, manufacturing complexity, and transportation costs on supply chain facility networks (1990) Journal of Manufacturing and Operations Management, 3, pp. 269-92; Cook, R.L., Rogowski, R.A., Applying JIT principles to continuous process manufacturing supply chains (1996) Production and Inventory Management Journal, 37, pp. 12-17; Coughlan, P., Coughlan, D., Action research: Action research for operations management (2002) International Journal of Operation & Productions Management, 22 (2), pp. 220-40; Davis, T., Effective supply chain management (1993) Sloan Management Review, 34 (4), pp. 35-46; Gehlen De Leao, A., A business logistics approach to design performance evaluation systems (2002) Papers from the Third International Conference on Performance Measurement and Management (PMA 2002); Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Hammami, A., Burlat, P., Campagne, J.P., Evaluating orders allocation within networks of firms (2003) International Journal of Production Economics, 86 (3), pp. 233-49; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice-Hall Upper Saddle River, NJ; Hausman, W.H., Supply chain performance metrics (2003) The Practice of Supply Chain Management: Where Theory and Application Converge, , Harrison, T.P. Lee, H.L. Neale, J.J. Kluwer Academic Publishers Boston, MA; Ishii, K., Takahashi, K., Muramatsu, R., Integrated production, inventory and distribution systems (1988) International Journal of Production Research, 26 (3), pp. 473-82; Kasanen, E., Lukka, K., Siitonen, A., The constructive approach in management accounting research (1993) Journal of Management Accounting Research, 5, pp. 243-64; Lee, H.I., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lee, H.I., Billington, C., Material management in decentralized supply chains (1993) Operations Research, 41 (5), pp. 835-47; Lee, H.L., Feitzinger, E., Product configuration and postponement for supply chain efficiency (1995) Fourth Industrial Engineering Research Conference Proceedings, pp. 43-8. , Institute of Industrial Engineers Atlanta, GA; Leseure, M., Shaw, N., Chapman, G., Performance measurement in organisational networks: An exploratory case study (2001) International Journal of Business Performance Management, 3 (1), pp. 30-46; Newhart, D.D., Stott, K., Vasko, F.J., Consolidating product sizes to minimize inventory levels for a multi-stage production and distribution systems (1993) Journal of the Operational Research Society, 44 (7), pp. 637-44; Ortiz, A., Lario, F.C., Ros, L., Enterprise integration - Business process integrated management: A proposal for a methodology to develop enterprise integration programs (1999) Computers in Industry, 40 (23), pp. 155-71; Pyke, D.F., Cohen, M.A., Performance characteristics of stochastic integrated production- distribution systems (1993) European Journal of Operational Research, 68 (1), pp. 23-48; Pyke, D.F., Cohen, M.A., Multi-product integrated production-distribution systems (1994) European Journal of Operational Research, 74 (1), pp. 18-49; Towill, D.R., Supply chain dynamics (1991) International Journal of Computer Integrated Manufacturing, 4 (4), pp. 197-208; Towill, D.R., Naim, M.M., Wikner, J., Industrial dynamics simulation models in the design of supply chains (1992) International Journal of Physical Distribution and Logistics Management, 22 (5), pp. 3-13; Tzafestas, S., Kapsiotis, G., Coordinated control of manufacturing/supply chains using multi-level techniques (1994) Computer Integrated Manufacturing Systems, 7 (3), pp. 206-12; Voudouris, V.T., Mathematical programming techniques to debottleneck the supply chain of fine chemical industries (1996) Computers and Chemical Engineering, 20, pp. 1269-74. , Suppl. Pt. B; Wikner, J., Towill, D., Naim, M., Smoothing supply chain dynamics (1991) International Journal of Production Economics, 22 (3), pp. 231-48; Zhao, F., Measuring inter-organizational partnerships: The challenge of cultural discrepancy (2002) Third International Conference on Performance Measurement and Management (PMA 2002), , Papers from the, July; Barad, M., Sapirb, D., Flexibility in logistic systems - Modelling and performance evaluation (2003) International Journal of Productions Economics, 85 (2), pp. 155-70; Beamon, B.M., Chen, V., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (17), pp. 3195-218; Christopher, M., (1994) Logistics and Supply Chain Management, , Richard D. Irwin, Inc./Financial Times New York, NY; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long-term strategic impact of purchasing partnerships (1994) International Journal of Purchasing and Materials Management;, 30 (4), pp. 13-18; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-68; Johnson, J.B., Randolph, S., Brief: Making alliances work. Using a computer-based management system to integrate the supply chain (1995) Journal of Petroleum Technology, 47 (6), pp. 512-3; Maloni, M.J., Benton, W.C., Supply chain partnerships opportunities for operations research (1997) European Journal of Operations Research, 101, pp. 419-29; Roth, N.G., Collaborative knowledge networks: Reflections from a performance measurement, complexity and knowledge perspective (2003) Processes and Foundations for Virtual Organizations: IFIP TC5/WG5.5, , Camarinha-Matos, L.M. Afsarmanesh, H. Kluwer Academic Publishers Amsterdam; Toni, A.D., Nissimbeni, G., Tonchia, S., New trends in supply environment (1994) Logistics Information Management;, 7 (4), pp. 1-15; Van Hoek, R.I., 'Measuring the unmeasurable' - Measuring and improving performance in the supply chain (1998) International Journal of Supply Chain Management, 3 (4), pp. 187-92},
correspondence_address1={Saiz, J.J.A.; Department of Business Organisation, Financial Economy and Accounting, , Valencia, Spain; email: jalfaro@omp.upv.es},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wong2007361,
author={Wong, W.P. and Wong, K.Y.},
title={Supply chain performance measurement system using DEA modeling},
journal={Industrial Management and Data Systems},
year={2007},
volume={107},
number={3},
pages={361-381},
doi={10.1108/02635570710734271},
note={cited By 172},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33947128605&doi=10.1108%2f02635570710734271&partnerID=40&md5=a3a0b291aaa905228468f14fc6068841},
affiliation={Department of Industrial and Systems Engineering, National University of Singapore, Singapore, Singapore; Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia},
abstract={Purpose - This paper aims to illustrate the use of data envelopment analysis (DEA) in measuring internal supply chain performance. Design/methodology/approach - Two DEA models were developed - the technical efficiency model and the cost efficiency model. The models are further enhanced with scenario analysis to derive more meaningful business insights for managers in making resources planning decisions. Findings - The information obtained from the DEA models helps managers to identify the inefficient operations and take the right remedial actions for continuous improvement. More importantly, the opportunity cost (forgone profit) calculated serves as a good reference to managers to make efficient decisions on resource allocations. Research limitations/implications - Results are based on the deterministic data set. Future enhancement of the study would be to look into the possibility of modeling DEA in a stochastic supply chain environment (non-deterministic) due to the fact that supply chain operates in a dynamic environment. Practical implications - The proposed DEA-based approach provides useful managerial implications in the measurement of supply chain efficiency. The study proves the usefulness of DEA as a decision-making tool in supply chain. Originality/value - This paper provides useful insights into the use of DEA as a modeling tool to aid managerial decision making in measuring supply chain efficiency. © Emerald Group Publishing Limited.},
author_keywords={Data analysis;  Modelling;  Performance measures;  Process efficiency;  Supply chain management},
keywords={Cost effectiveness;  Data envelopment analysis;  Data reduction;  Decision making;  Managers;  Mathematical models;  Resource allocation, Modelling;  Performance measures;  Process efficiency;  Resources planning decisions, Supply chain management},
references={Ali, A.I., Seiford, L.M., The mathematical programming approach to efficiency analysis (1993) The Measurement of Productive Efficiency, pp. 120-59. , Fried, H.O. Lovell, C.A.K. Schmidt, S.S. Oxford University Press New York, NY; Athanassopoulos, A.D., Ballantine, J.A., Ratio and frontier analysis for assessing corporate performance: Evidence from the grocery industry in the UK (1995) Journal of the Operational Research Society, 46 (4), pp. 427-40; Baccarini, D., Salm, G., Love, P., Management of risks in information technology projects (2004) Industrial Management & Data Systems, 104 (4), pp. 286-95; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Management Science, 30 (9), pp. 1078-92; Barr, R.S., Seiford, L.M., (1994) Benchmarking with Data Envelopment Analysis, , paper presented at the ORSA/TIMS Joint National Meeting, Detroit, MI, 23 October; Bell, R.A., Morey, R.C., Increasing the efficiency of corporate travel management through macro benchmarking (1995) Journal of Travel Research, 33 (3), pp. 11-20; Chan, A., Kwok, W., Duffy, V., Using AHP for determining priority in a safety management system (2004) Industrial Management & Data Systems, 104 (5), pp. 430-45; Charnes, A., Cooper, W.W., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2 (6), pp. 429-44; Charnes, A., Cooper, W.W., Lewin, A.Y., Seiford, L.M., (1994) Data Envelopment Analysis: Theory, Methodology and Applications, , Kluwer Academic Publishers Boston, MA; Chou, T., Hsu, L., Yeh, Y., Ho, C., Towards a framework of the performance evaluation of SMUs' industry portals (2005) Industrial Management & Data Systems, 105 (4), pp. 527-44; Christopher, M., (1998) Logistics and Supply Chain Management, , Pitman Publishing London; Collier, D.A., Storbeck, J.E., (1993) A Data Envelopment Approach to Benchmarking in the Telecommunications Industry, , working paper Ohio State Faculty of Management Science, Ohio State University Columbus, OH; Dey, P., Ogunlana, S., Selection and application of risk management tools and techniques for build-operate-transfer projects (2004) Industrial Management & Data Systems, 104 (4), pp. 334-46; Farrell, M.J., The measurement of productive efficiency (1957) Journal of the Royal Statistical Society, 120 (3), pp. 253-90; Ferrier, G., Lovell, C.A.K., Measuring cost efficiency in banking: Econometric and linear programming evidence (1990) Journal of Econometrics, 46 (1), pp. 229-45; Helo, P., Managing agility and productivity in the electronics industry (2005) Industrial Management & Data Systems, 104 (7), pp. 567-77; Humphreys, P., Huang, G., Cadden, T., A web-based supplier evaluation tool for the product development process (2005) Industrial Management & Data Systems, 105 (2), pp. 147-63; Lovell, C.A.K., Production frontiers and productive efficiency (1993) The Measurement of Productive Efficiency, pp. 3-67. , Fried, H.O. Lovell, C.A.K. Schmidt, S.S. Oxford University Press New York, NY; Rao, M.P., A performance measurement system using a profit-linked multi-factor measurement model (2006) Industrial Management & Data Systems, 106 (3), pp. 362-79; Rickards, R., Setting benchmarks and evaluating balanced scorecards with data envelopment analysis (2003) Benchmarking: An International Journal, 10 (3), pp. 226-45; Ross, D.F., (1998) Competing Through Supply Chain Management, , Kluwer Academic Publishers Boston, MA; Sarkis, J., A comparative analysis of DEA as a discrete alternative multiple criteria decision tool (2000) European Journal of Operational Research, 123 (3), pp. 543-57; Sarkis, J., Talluri, S., Efficiency valuation and internal benchmarking for business process improvement (1996) Journal of Engineering Valuation and Cost Analysis, 1 (1), pp. 43-54; Schefcyzk, M., Industrial benchmarking: A case study of performance analysis techniques (1993) International Journal of Production Economics, 32 (1), pp. 1-11; Seiford, L.M., Data envelopment analysis: The evolution of the state of the art (1978-1995) (1996) Journal of Productivity Analysis, 7 (2), pp. 99-138; Seiford, L.M., Thrall, R.M., Recent developments in DEA: The mathematical approach to frontier analysis (1990) Journal of Econometrics, 46 (1), pp. 7-38; Seydel, J., Data envelopment analysis for decision support (2006) Industrial Management & Data Systems, 106 (1), pp. 81-95; Sherman, H.D., Ladino, G., Managing bank productivity using data envelopment analysis (DEA) (1995) Interfaces, 25 (2), pp. 60-73; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Designing and Managing the Supply Chain - Concepts, Strategies and Case Studies, , McGraw-Hill Publishing New York, NY; Stevens, J., Integrating the supply chain (1989) International Journal of Physical Distribution & Materials Management, 19 (8), pp. 3-8; Stewart, G., Supply-chain operations reference model (SCOR): The first cross-industry framework for integrated supply chain management (1997) Logistics Information Management, 10 (2), pp. 62-7; Takala, J., Suwansaranyum, U., Phusavat, K., A proposed white-collar workforce performance measurement framework (2006) Industrial Management & Data Systems, 106 (5), pp. 644-62; Wagner, W.P., Chung, Q.B., Baratz, T., Implementing corporate intranets: Lessons learned from two high-tech firms (2002) Industrial Management & Data Systems, 102 (3), pp. 140-5; Wong, W.P., Wong, K.Y., A review on benchmarking of supply chain performance measures (2006) Benchmarking: An International Journal, 15 (1); Yee, C., Tan, K., A process and tool for supply network analysis (2004) Industrial Management & Data Systems, 104 (4), pp. 355-63; Zhu, J., Multi-factor performance measure model with an application to Fortune 500 companies (2000) European Journal of Operational Research, 123 (1), pp. 105-24},
correspondence_address1={Wong, W.P.; Department of Industrial and Systems Engineering, , Singapore, Singapore; email: g0500743@nus.edu.sg},
issn={02635577},
coden={IMDSD},
language={English},
abbrev_source_title={Ind Manage Data Sys},
document_type={Article},
source={Scopus},
}

@ARTICLE{Wickramatillake200752,
author={Wickramatillake, C.D. and Koh, S.C.L. and Gunasekaran, A. and Arunachalam, S.},
title={Measuring performance within the supply chain of a large scale project},
journal={Supply Chain Management},
year={2007},
volume={12},
number={1},
pages={52-59},
doi={10.1108/13598540710724338},
note={cited By 43},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33846451111&doi=10.1108%2f13598540710724338&partnerID=40&md5=21f068fd6708475c25697d7fbcbca16b},
affiliation={British Airports Authority, T5 Project Team, Vanderlande Industries Ltd., Longford, United Kingdom; Management School, University of Sheffield, Sheffield, United Kingdom; Management Department, University of Massachusetts-Dartmouth, North Dartmouth, MA, United States; School of Computing and Technology, University of East London, Barking, United Kingdom},
abstract={Purpose - The purpose of this paper is to explore the performance measurement methodology used by a real case company, namely Vanderlande Industries Ltd, for cost and progress capture for in-house production, resale material and third party supplied material, in an effort to measure performance of the supply chain of a large scale project - the €6bn London Heathrow Airport Terminal 5 construction project. Design/methodology/approach - The case study approach was adopted, which includes ethnography. This research is not about testing a theory; it is about gaining insight to the actual functioning and supply chain performance measuring of a large-scale project. In that sense, the experiences reported could be shared with those dealing with performance measurement of large-scale projects. Findings - Eight key areas of concerns are found relating to supply chain performance measurement of a large-scale project: performance measurement requirements, with forecasting and progress reporting not owned by suppliers; lack of detailed planning causing regular changes to baseline; detailed work breakdown structure causing unnecessary complications to performance analysis; organisational structure; performance measurement tool; data capture; timing of progress and cost capture affecting the analysing process; and scope change and traceability. Originality/value - The paper is targeted at senior and middle managers involved in large cost reimbursable projects. Managers should manage more carefully the eight key areas of concerns identified. Recommended solutions targeting each area of concerns from the lessons learnt in the €6bn London Heathrow Airport Terminal 5 construction project give a basis for improved project management and supply chain performance measurement for large-scale projects. © Emerald Group Publishing Limited.},
author_keywords={Performance measures;  Project management;  Supply chain management},
references={Berends, T.C., Cost plus incentive fee contracting - Experiences and structuring (2000) International Journal of Project Management, 18 (3), pp. 165-71; Bryman, A., (1995) Research Methods and Organization Studies, , Routledge London; Bubshait, A.A., Incentive/disincentive contracts and its effects on industrial projects (2003) International Journal of Project Management, 21 (1), pp. 63-70; Cadilhon, J.J., Fearne, A.P., Tam, P.T.G., Moustier, P., Poole, N.D., Collaborative commerce or just common sense? Insights from vegetable supply chains in Ho Chi Minh City (2005) Supply Chain Management: An International Journal, 10 (3), pp. 147-9; Cetindamar, D., Catay, B., Basmaci, O.S., Competition through collaboration: Insights from an initiative in the Turkish textile supply chain (2005) Supply Chain Management: An International Journal, 10 (4), pp. 238-40; Eden, C., Williams, T., Ackermann, F., Howick, S., The role of feedback dynamics in disruption and delay on the nature of disruption and delay (D&D) in major projects (2000) Journal of the Operational Research Society, 51, pp. 291-300; Harland, C., Brenchley, R., Walker, H., Risk in supply networks (2003) Journal of Purchasing and Supply Management, 9 (2), pp. 51-62; Howick, S., Eden, C., The impact of disruption and delay when compressing large projects: Going for incentives? (2001) Journal of the Operational Research Society, 52, pp. 26-34; Hwarng, H.B., Chong, C.S.P., Xie, N., Burgess, T.F., Modelling a complex supply chain: Understanding the effect of simplified assumptions (2005) International Journal of Production Research, 43 (13), pp. 2829-72; Love, P.E.D., Irani, Z., Edwards, D.J., A seamless supply chain management model for construction (2004) Supply Chain Management: An International Journal, 9 (1), pp. 43-56; Papadakis, I.S., Financial performance of supply chains after disruptions: An event study (2006) Supply Chain Management: An International Journal, 11 (1), pp. 25-33; Proverbs, D., Holt, G.D., Reducing construction costs: European best practice supply chain implications (2000) European Journal of Purchasing and Supply Chain Management, 6 (34), pp. 149-58; Puschmann, T., Alt, R., Successful use of e-procurement in supply chains (2005) Supply Chain Management: An International Journal, 10 (2), pp. 122-33; Teng, S.G., Jaramillo, H., Integrating the US textile and apparel supply chain with small companies in South America (2006) Supply Chain Management: An International Journal, 11 (1), pp. 44-55; Wang, G., Huang, S.H., Dismukes, J.P., Manufacturing supply chain design and evaluation (2005) International Journal of Advanced Manufacturing Technology, 25 (12), pp. 93-100; Wickramatillake, C.D., Co-ordinating multi-plans within a large scale project (2004) Proceedings of the 3rd SENET Project Management Conference, Bratislava, 22-24 September; Wong, C.Y., Arlbjorn, J.S., Johansen, J., Supply chain management practices in toy supply chains (2005) Supply Chain Management: An International Journal, 10 (5), pp. 367-78; Yin, R.K., (1994) Case Study Research: Design and Methods, 5. , 2nd ed. Applied Social Research Methods Series London},
correspondence_address1={Gunasekaran, A.; Management Department, , North Dartmouth, MA, United States; email: agunasekaran@urnassd.edu},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Rivera2007241,
author={Rivera, L. and Wan, H.-D. and Chen, F.F. and Lee, W.M.},
title={Beyond Partnerships: The Power of Lean Supply Chains},
journal={Springer Series in Advanced Manufacturing},
year={2007},
pages={241-268},
doi={10.1007/978-1-84628-607-0_10},
note={cited By 14},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151532938&doi=10.1007%2f978-1-84628-607-0_10&partnerID=40&md5=c3bbe9f8319a1916a0c7c6950620bc4b},
affiliation={Grado Department of Industrial and Systems Engineering, Virginia Polytechnic Institute and State University, United States},
abstract={Managing an integrated supply chain has become a necessary capability for competing in the global market. Cooperating with business partners prevents duplicating efforts and allows a company to focus on its core competencies. Hence, collaborating companies together have the potential to form a strong supply chain. At the individual company level, practitioners of lean concepts have demonstrated dramatic improvements in productivity within their manufacturing facilities. However, the potential benefits of lean implementation are limited to four walls of companies when business partners are excluded from integrating into a leaner overall value stream. The flow from a lean supplier to a lean customer may not be lean if the two parties are not synchronized. As a result, the supply chain formed within lean companies may not be lean after all, due to lack of cooperation and synchronization among participating companies. Extending the value stream from a lean company to its partners allows the company to widen the pursuit for perfection to the whole supply network. Furthermore, applying lean concepts to a supply chain opens new windows of opportunity for supply chain participants to collectively achieve higher levels of competitiveness. Thus, the integration of supply chain management and lean thinking covers both local and overall leanness, which leads to a truly lean supply chain. This chapter provides an overview of the lean supply chain, the including concepts, approaches, challenges, and future development. In the first part of the chapter, basic concepts and impacts of lean thinking will be introduced, followed by the issues of applying lean thinking in supply chain management. Then, an integrated framework of a lean supply chain will be presented. The second part of the chapter covers the building blocks of lean supply chains, including lean logistics, information technology, performance measurement and improvement, and collaboration beyond partnerships. Subsequently, the challenges for building or maintaining a sustainable lean supply chain will be discussed. Finally, discussions of potential developments that can further improve lean supply chains will be summarized in the concluding section. © 2007, Springer-Verlag London Limited.},
author_keywords={Lean Manufacturing;  Performance Measurement System;  Supply Chain;  Supply Chain Management;  Vendor Manage Inventory},
references={Alshawi, S., Lotistics in the Internet age: Toward a holistic information and process picture (2001) Logistics Information Management, 14, pp. 235-241; Baudin, M., (2004) Lean Logistics: The Nuts and Bolts of Delivering Materials and Goods, , Productivity Press, New York, NY; Bell, S., (2006) Lean Enterprise Systems: Using IT for Continuous Improvement, , Wiley, Hoboken, NJ; Cagliano, R., Caniato, F., Spina, G., The link between supply chain integration and manufacturing improvement programmes (2006) International Journal of Operations and Production Management, 26, pp. 282-299; Cook, R.L., Gibson, B., Maccurdy, D., A lean approach to cross docking, Supply Chain Management Review (2005) March, pp. 54-59; Cooper, R., Slagmulder, R., Supply Chain Development for the Lean Enterprise, Interorganizatonal Cost Management (1999) Productivity, , Portland, OR; Fooks, J.H., (1993) Profiles for Performance: Total Quality Methods for Reducing Cycle Time, , Addison-Wesley, Reading, MA; Friedman, T.L., The World Is Flat: A Brief History of the Twenty-First Century, Farra,r Straus and Giroux (2005) New York; Gilligan, E., Lean logistics: Not a fad diet, The (2004) Journal of Commerce. May, pp. 18-20; Groesbeck, R., (2005) Class Notes for the Course in Production Systems Improvement, , Virginia Tech, Blacksburg, VA; Jones, D.T., Womack, J.P., (2002) Seeing the Whole: Mapping the Extended Value Stream, , Cambridge, MA; Lamming, R., Squaring lean supply with supply chain management (1996) International Journal of Operations and Production Management, 16, pp. 183-196; Levy, D.L., Lean production in an international supply chain, Sloan Management Review (1997) Winter, pp. 94-102; Liker, J., Becoming Lean: Inside Stories of U.S. Manufacturers (1998) Productivity Press, , Portland, OR; Liker, J.K., Wu, Y.C., Japanese automakers, U.S. suppliers and supply-chain superiority (2000) Sloan Management Review. Fall, pp. 81-93; Monden, Y., (1983) Toyota Production System, , Industrial Engineering and Management Press, Norcross, GA; Papatheodorou, Y., The price of leanness (2005) Industrial Management, 47, pp. 8-13; Phelps, T., Smith, M., Hoenes, T., Building a lean supply chain (2004) Manufacturing Engineering, 132, pp. 107-116; Power, D., Supply chain management integration and implementation: A literature review (2005) Supply Chain Management: An International Journal, 10, pp. 252-263; Reeve, J.M., The financial advantages of the lean supply chain, Supply Chain Management Review (2002) March/April, pp. 42-49; Rivera, L., Inter-enterprise Cost-time Profiling, Dissertation Research Proposal (2005) Virginia Tech, , Blacksburg, VA; Rivera, L., Chen, F.F., (2006) Cost-Time Profiling: Putting Monetary Measures onto Value Stream Maps, , Orlando, FL; Rother, M., Shook, J., (1998) Learning to See – Value Stream Mapping to Add Value and Eliminate Muda, , Brookline, MA; Schonberger, R.J., Lean extended: It’s much more (and less) than you think, Industrial Engineer (2005) December, pp. 26-31; Tapping, D., Shuker, T., Luyster, T., (2002) Value Stream Management, , Productivity Press, New York; Taylor, D., (2004) Supply Chains: A Manager’s Guide, , Addison-Wesley, Boston, MA; Vitasek, K., Manrodt, K.B., Abbott, J., What makes a lean supply chain, Supply Chain Management Review (2005) October, pp. 39-45; Womack, J., Lean thinking: Where have we been and where are we going? Forming & Fabricating, Lean Manufacturing Special Insert (2002) September, p. L2; Womack, J.P., Jones, D.T., (1996) Lean Thinking, , Simon and Schuster, New York; Womack, J.P., Jones, D.T., (2005) Lean Solutions, , Free Press, New York},
publisher={Springer Nature},
issn={18605168},
language={English},
abbrev_source_title={Springer Ser. Adv. Manuf.},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Cavalieri2007159,
author={Cavalieri, S. and Valckenaers, P. and Bititci, U. and Bandinelli, R. and Mendibil, K.},
title={Performance measurement systems in manufacturing: State-of-the-art and future trends},
journal={Advanced Manufacturing - an ICT and Systems Perspective},
year={2007},
pages={159-178},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85056069022&partnerID=40&md5=624a8d9b15c2cff73f3931d42bcf2a8f},
affiliation={University of Bergamo, Dalmine, BG, Italy; K.U.Leuven, Leuven, Belgium; University of Strathclyde, Glasgow, United Kingdom; Università di Firenze, Italy},
abstract={Several research activities and industrial projects have been undertaken in the area of performance measurement systems. The main reason for this is acknowledgement by practitioners and researchers that to improve it is first necessary to understand the present position and circumstances. The rising strategies, policies and methodologies of the last two decades, even from distant fields, such as Business Process Re-engineering, Strategic Benchmarking, Total Quality Management, Balanced Scorecard, Six Sigma, Total Productive Maintenance, have strongly highlighted the need, before acting, to assess the performance of the processes to be controlled, whichever the level they belong to, whether at supply chain or extended enterprise stage or, at the other extreme, at single production or simple machine level. Indeed, what is often missing in the industrial practice is a sound alignment among the performance metrics, designed and adopted at the different levels of the decision making processes of a manufacturing company. This gap is mainly owing to the fact that a consistent and effective Performance Measurement System (PMS) requires, in its development, the consistent contribution of different expertise, encompassing the complex, multifaceted and interdisciplinary nature of an industrial company. This is also what the Special Interest Group on Benchmarking and Performance Measurement, established within the IMS-NoE, has experienced during its activities and discussed in its meetings and workshops. Having started mainly with the purpose to focus on a particularly important, yet specific issue, how to evaluate the level of quality and effectiveness of production schedules which is quite compelling in the scheduling industrial and research community, the Group progressively extended its scope, gathering also contributions from experts working in the area of strategic management and supply chain management. This paper provides a synthesis of the various contributions gathered from SIG4 members, assessing in particular the state-of-the-art of PMS in the industrial environment, understanding the current needs from industry and the main gaps, and providing a vision on future challenges and research directions. © 2007 Taylor & Francis Group, London.},
author_keywords={Benchmarking;  Extended enterprise;  Future challenges;  Manufacturing;  Performance management;  Performance measurement system;  Scheduling;  State-of-the-art;  Supply chain management},
references={Ali, S., Maciejewski, A.A., Siegel, H.J., Kim, J., Definition of a Robustness Metric for Resource Allocation (2003) Proceedings of the 17Th International Parallel and Distributed Processing Symposium (IPDPS 2003), , Nice, France, 22–26 April; Process analysis tools for process improvement (1998) TQM Magazine, 10 (5), pp. 342-354; Beamon, M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19, pp. 275-292; Beer, S., (1985) Diagnosing the System for Organisations, , Wiley, Chichester, England; Bititci, U.S., Carrie, A.S., (1998) Integrated Performance Measurement Systems: Structures and Relationships, , EPSRC Final Research Report, Grant No. GR/K 48174, Swindon; Bititci, U.S., Mendibil, K., Martinez, V., Albores, P., Measuring and Managing Performance in Extended Enterprises (2005) International Journal of Operations and Production Management, 25 (4), pp. 333-353; Bititci, U.S., Modelling of Performance Measurement Systems in Manufacturing Enterprises (1995) Int. J. Production Economics, 42, pp. 137-147; Bititci, U.S., Martinez, V., Albores, P., Mendibil, K., Creating and sustaining competitive advantage in collaborative systems: The what and the how (2003) International Journal of Production Planning and Control, 14 (5), pp. 410-424; Bititci, U.S., Mendibil, K., Nudurapati, S., Turner, T., Garengo, P., The Interplay Between Performance Measurement, Organisational Culture and Management Styles (2004) Measuring Business Excellence, 8 (3), pp. 28-41; Bodner, D.A., Reveliotis, S.A., Virtual factories: An object-oriented simulation-based framework for real-time FMS control (1997) Emerging Technologies and Factory Automation Proceedings, ETFA ’97, pp. 208-213; Bongaerts, L., Indrayadi, Y., van Brussel, H., Valckenaers, P., Predictability of Hierarchical, Heterarchical and Holonic Control (1999) Proceedings of the 2Nd International Workshop on Intelligent Manufacturing Systems 1999, pp. 167-176. , Leuven, Belgium, 22–24 September; Booth, A.W., Object Oriented Modeling for Flexible Manufacturing Systems (2000) The Journal of Flexible Manufacturing Systems, pp. 301-314; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations and Production Management, 22 (11), pp. 1288-1310; Brandl, D., Business-To-Manufacturing (B2M) collaboration between business and manufacturing using ISA95 (2002) Proceedings of the ISA/SEE Conference, , Nice, France; Brennan, R.W., Performance comparison and analysis of reactive and planning-based control architectures for manufacturing (2000) Robotics and Computer Integrated Manufacturing, 16, pp. 191-200; Browne, J., Sackettand, P., Worthman, H., Industry requirements and associated research issues in extended enterprises (1995) Integrated Manufacturing Systems Engineering, , P. Ladet and F. Bernadat, Chapman and Hall, London; Bruner, R., Spekman, R.E., The dark side of alliances: Lessons from Volvo-Renault (1998) European Management Journal, 16 (2), pp. 136-150; Cavalieri, S., Taisch, M., Garetti, M., Macchi, M., An experimental benchmarking of two multi-agent systems for production scheduling and control (2000) Computers in Industry, 43, pp. 139-152; (2004) A Roadmap Towards the Collaborative Enterprise – CE Vision 2010, , CE-NET Consortium (ISBN 0 85358 130 4); Chakravarty, A.K., Jain, H.K., Liu, J.J., Object-Oriented Domain Analysis for flexible Manufacturing Systems (1997) Integrated Computer Aided Engineering, 4, pp. 290-309; Chen, D., Vernadat, F., Standardisation on enterprise modelling and integration: Achievements, on-going works and future perspectives (2001) Proceedings of 10Th IFAC Symposium on Information Control Problems in Manufacturing, INCOM’01, , Vienna, Austria; Choi, K.H., Kim, S.C., Yook, S.H., Multi-agent hybrid shopfloor control systems (2000) In: Int. J. Production Research, 38 (17), pp. 4193-4203; Cross, K.F., Lynch, R.L., The SMART way to define and sustain success (1988) National Productivity Review, 9 (1), pp. 1988-1989; Daniels, R., Kouvelis, P., Robust Scheduling to Hedge Against Processing Time Uncertainty in Single Stage Production (1995) Management Science, 41 (2), pp. 363-376; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge – Measuring Operations for World-Class Competition, , Dow Jones-Irwin, Homewood, Illinois, 1990; Doumeingts, G., Vallespir, B., Chen, D., Decision modelling GRAI Grid (1998) Handbook on Architecture for Information Systems, , P. Bernus, K. Mertins, G. Schmidt (Eds.)) Springer-Verlag; Drago, W.A., When strategic alliances make sense (1997) Industrial Management & Data Systems, 97 (2), pp. 53-57; Drummond, M., (1995) Scheduling Benchmarks and Related Resources, , http://ic-www.arc.nasa.gov/ic/projects/xfr/papers/benchmark-article.html; (1998) Self-Assessment Guidelines for Companies, , European Foundation for Quality Management, Brussels, Belgium; Fitzgerald, L., Johnston, R., Brignall, S., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, , CIMA Publishing; Fox, B., Ringer, M., (1995) Planning and Scheduling Benchmarks. Benchmarks Secretary of the AAAI Special Interest Group in Manufacturing, , http://www.neosoft.com/; Franco, M., Bourne, M., Factors that play a role in managing through measures (2003) Management Decision, 41 (8), pp. 698-710; Gibb, S., (2002) Learning and Development; Process, Practices and Perspectives at Work, , Palgrave; Gören, S., (2002) Robustness and Stability for Scheduling Policies in a Single Machine Environment, , M.Sc. thesis, Bilkent University, Ankara, Turkey; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21, pp. 71-87; Hanks, S., Pollack, M.E., Cohen, P., Benchmarks, testbeds, controlled experimentation, and the design of agent architectures (1993) AI Magazine, 14 (4), pp. 17-42; Harland, C.M., Lamming, R.C., Cousins, P.D., Developing the concept of supply strategy (1999) International Journal of Operations & Production Management, 19 (7), pp. 650-673; Jensen, M.T., (2001) Robust and Flexible Scheduling with Evolutionary Computation, , Ph.D. thesis, Department of Computer Science, University of Aarhus, Denmark; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost – the Rise and Fall of Management Accounting, , Harvard Business School Press, Boston MA; Kaplan, R.S., (1990) Measures for Manufacturing Excellence, , Harvard Business School Press, Boston MA 1990; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard – Translating Strategy into Action, , Harvard Business School Press Boston, MA, USA; Kelly, J., Gennard, J., (2001) Power and Influence in the Boardroom, , London: Routledge; Kempf, K., Uzsoy, R., Smith, S., Gary, K., Evaluation and comparison of production schedules (2000) Computers in Industry, 42, pp. 203-220; King, R.E., Kym, K.S., AgvTalk: An object-oriented simulator for Agv system (1995) Computers & Industrial Engineering, 28 (3), pp. 575-592; Kochhar, A., Zhang, Y., A framework for performance measurement in virtual enterprises (2002) Proceedings of the 2Nd International Workshop on Performance Measurement, pp. 2-11. , 6–7 June 2002, Hanover, Germany, ISBN 3-00-009491-1; Kochhar, A., Kennerley, M., Davies, A., (1996) Improvingyour Business through Effective Manufacturing Planning and Control, , Workbook produced by researchers at UMIST as part of an EPSRC Funded research programme; Krause, O., (1999) Performance Management, , Global Production Management edited by Mertins, K., Krause, O. and Schallock, B., Kluwer Academic Publishers, ISBN 0-7923-8605-1; Kueng, P., Meier, A., Wettstein, T., Computer-based Performance Measurement in SMEs: Is there any option (2000) Proceedings of the International Conference on Systems Thinking in Management, pp. 318-323. , Greelong, Australia, 8–10; Lambert, D.M., Knemeyer, A.M., (2004) We’re in This Together, Harvard Business Review, pp. 114-122. , December; Lipnack, J., Stamps, J., (1997) Virtual Teams: Reaching across Space, Time and Organizations with Technology, , John Wiley & Sons, New York; Maskell, B., Performance measures of world class manufacturing (1989) The Journal of Applied Manufacturing System, pp. 19-26. , Winter 1992; Mendibil, K., (2003) Designing Effective Team-Based Performance Measurement Systems: An Integrated Approach, , PhD thesis, University of Strathclyde, Glasgow; Mignon, D.J., Honkomp, S.J., Reklaitis, G.V., Framework for Investigating Schedule Robustness Under Uncertainty (1995) Computers in Chemical Engineering, 19, pp. S615-S620; Neely, A.D., Adams, C., Kennerley, M., (2002) The Performance Prism, the Scorecard for Measuring and Managing Business Success, , FT Prentice-Hall, London; Neely, A.D., Gregory, M.J., Platts, K.W., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A.D., Mills, J., Gregory, M.J., Richards, H., Platts, K., Bourne, M., (1996) Getting the Measure of Your Business, , University of Cambridge, Manufacturing Engineering Group, Mill Lane, Cambridge; Neely, A.D., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance Measurement System Design: Developing and Testing a Process Based Approach (2000) International Journal of Operations and Production Management, 20 (10), pp. 1119-1145; Nudurupati, S.S., Bititci, U.S., Impact of IT enabled Performance Measurement on Business and Management (2003) Proceedings of Euroma and POMS Joint International Conference, , Como Lake, Italy, 16–18 June 2003; O’Donnel, F., Duffy, A.H.B., Modelling Design Development Performance (2002) International Journal of Operations and Production Management, 22 (11), p. 2002; Oliver, N., Design and Development Benchmarking (1996) 5Th Operations Strategy and Performance Measurement Workshop, , Loughborough University, 8 May 1996; Wight, O., (1993) The Oliver Wight ABCD Check List, , 4th Edition, John Wiley & Sons, Inc, New York; Ouelhadj, D., Cowling, P., Petrovic, S., Utility and Stability Measures for Agent-Based Dynamic Scheduling of Steel Continuous Casting (2003) Proceedings of the IEEE International Conference on Robotics and Automation, , Taipei, Taiwan, 14–19 September; Parunak, V.D., MASCOT: A Virtual Factory for Research and Development in Manufacturing Scheduling and Control (1993) ITI Tech Memo; Russell, R., The Role of Performance Measurement in Manufacturing Excellence (1992) BPICS Conference, , Birmingham, UK; Scott, T., Mannion, R., Marshall, M., Davies, H., Does organisational culture influence health care performance? A review of the evidence (2003) Journal of Health Services Research and Policy, 8 (2), pp. 105-117; Shen, W., Norrie, D.H., Agent based systems for intelligent manufacturing: A state of the art survey (1999) Knowledge and Information Systems, 1, pp. 129-156; Shewchuk, J.P., Moodie, C.L., A Framework for Classifying Flexibility types in Manufacturing (1997) Computers in Industry, 33, pp. 261-269; Suwignjo, P., Bititci, U.S., Carrie, A.S., Quantitative Models for Performance Measurement Systems (2000) International Journal of Production Economics, 64, pp. 231-241. , March 2000; Venkatesh, K., Zhou, M., Object Oriented Design of FMS Control Software Based on Object Modeling Technique Diagrams and Petri Nets (1998) Journal of Manufacturing Systems, 17 (2); White, G.P., A Survey and Taxonomy of Strategy-Related Performance Measures for Manufacturing (1996) International Journal of Operations & Production Management, 16 (3), pp. 42-61; Wilson, A., The Use of Performance Information in the Management of Service Delivery (2000) Marketing Intelligence & Planning, 18 (7), pp. 127-134; Wu, B., Object-oriented systems analysis and definition of manufacturing operations (1995) Int. J. Production Research, 33 (4), pp. 955-974; Yeung, W.H.R., Moore, C.L., Object-oriented modelling and control of flexible conveyor systems for automated assembly (1996) Mechatronics, 6 (7), pp. 799-815; Zhang, D., Zhang, H.C., An object-oriented integration test-bed for process planning and production scheduling (1997) Integrated Computer-Aided Engineering, 4 (4). , Wiley Interscience; Zineldin, M., Bredenlow, T., Strategic alliances: Synergies and challenges (2003) International Journal of Physical Distribution & Logistic Management, 33 (5), pp. 449-464},
publisher={CRC Press},
isbn={9781439828328; 9780415429122},
language={English},
abbrev_source_title={Advanced Manufacturing - an ICT and Systems Perspective},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Yu20072874,
author={Yu, T. and Dao, H. and Dong, B.L.},
title={Supply chain coordination with service level constraint},
journal={2007 IEEE International Conference on Control and Automation, ICCA},
year={2007},
pages={2874-2878},
doi={10.1109/ICCA.2007.4376887},
art_number={4376887},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-44349146582&doi=10.1109%2fICCA.2007.4376887&partnerID=40&md5=e77f232a32e81e3613ff9e347e9748bc},
affiliation={Research Institute of Automation, East China University of Science and Technology, Shanghai, China},
abstract={The coordination issue is investigated in a supply chain with service level constraint. The supply chain consists of one risk neutral supplier and one risk averse retailer. Both inventory model and coordination model are established and the supply chain performance measure is presented for coordination under the Pareto optimal criterion. Sequentially, the practicability of cost sharing contracts is examined to show that it could not achieve the complete coordination but Pareto improvement. Furthermore, the difference is demonstrated between service level constraint and downside risk constraint so that a strong/weak risk aversion definition is proposed. Since the strong risk aversion is indicated to be one of difficulties in supply chain coordination, it is suggested avoiding these scenarios by policies. © 2007 IEEE.},
author_keywords={Linear demand model;  Nash equilibrium;  Price and service competition;  Supply chain coordination;  Wholesale contracts},
keywords={Competition;  Constraint theory;  Contracts;  Costs;  Mathematical models;  Risk analysis, Linear demand models;  Nash equilibrium;  Price and service competition;  Supply chain coordination;  Wholesale contracts, Supply chain management},
references={Khouja, M., The single-period (news-vendor) problem: Literature review and suggestions for future research, Omega, Int (1999) J. Mgmt. Sci, 27, pp. 537-553; Lau, H.S., The Newsboy Problem under Alternative Optimization Objectives (1980) Journal of the Operations Research Society, 31 (1), pp. 525-535; Eeckhoudt, L., Gollier, C., Schlesinger, H., The Risk-Averse (and Prudent) Newsboy (1995) Management Science, 41 (5), pp. 786-794; Chen, F., Federgruen, A., (2000) Mean-Variance Analysis of Basic Inventory Models, , working paper, Columbia University, New York; Agrawal, V., Seshadri, S., Impact of Uncertainty and Risk Aversion on Price and Order Quantity in the Newsvendor Problem (2000) Manufacturing Service Operations Management, 2 (4), pp. 410-423; Vasermanis, E.K., Nechval, N.A., Nechval, K.N., New Objective in Inventory Control Problem (2002) Transport and Telecommunication, 3 (1), pp. 68-74; Brown, A., Tang, C., (2004) The Impact of Alternative Performance Measures on Single-Period Inventory Policy, , http://repositories.cdlib.org/anderson/dotm/CT35, Available in; Tsay, A., S. Nahmias and N. Agrawal. (1999), Modeling supply chain contracts: a review, Tayur, S., Ganeshan, R., and Magazine, M. Quantitative Models for Supply Chain Management. Kiuwer, Boston, MA; Cachon, G., (2003) Supply chain coordination with contracts, , Handbooks in Operations Research and Management Science: Supply Chain Management. Edited by Steve Graves and Ton de Kok. North Holland; Gan, X., Sethi, S., Yan, H., Coordination of Supply Chains with Risk-Averse Agents (2004) Production and Operations Management, 13 (2), pp. 135-149; Suo, H., Wang, J., Jin, Y., Coordinating a loss-averse newsvendor with vendor managed inventory (2004) proceeding of 2004 IEEE International Conference on Systems, Man and Cybernetics, 7, pp. 6026-6030. , Hague, Oct. 10-13; Gan, X., Sethi, S., Yan, H., Channel Coordination with a Risk-Neutral Supplier and a Downside-Risk-Averse Retailer (2005) Production and Operations Management, 14 (1), pp. 80-89; Robbins, T., Supply Chain Coordination Limitations and Impediments (2005) proceeding of Society for Marketing Advances (SMA) Conference, , San Antonio, Nov 2-5},
correspondence_address1={Yu, T.; Research Institute of Automation, , Shanghai, China; email: m0336003@163.com},
publisher={Institute of Electrical and Electronics Engineers Inc.},
isbn={1424408180; 9781424408184},
language={English},
abbrev_source_title={IEEE Int. Conf. Control Autom. ICCA},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Dobson2007138,
author={Dobson, P. and Myles, J. and Jackson, P.},
title={Making the case for critical realism: Examining the implementation of automated performance management systems},
journal={Information Resources Management Journal},
year={2007},
volume={20},
number={2},
pages={138-152},
doi={10.4018/irmj.2007040109},
note={cited By 35},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-37549054187&doi=10.4018%2firmj.2007040109&partnerID=40&md5=6ea1353f8a45f9bc999da4e08495ceef},
affiliation={Edith Cowan University, Australia},
abstract={This article seeks to address the dearth of practical examples of research in the area by proposing that critical realism be adopted as the underlying research philosophy for enterprise systems evaluation. We address some of the implications of adopting such an approach by discussing the evaluation and implementation of a number of automated performance measurement systems (APMS). Such systems are a recent evolution within the context of enterprise information systems. They collect operational data from integrated systems to generate values for key performance indicators, which are delivered directly to senior management. The creation and delivery of these data are fully automated, precluding manual intervention by middle or line management. Whilst these systems appear to be a logical progression in the exploitation of the available rich, real-time data, the statistics for APMS projects are disappointing. An understanding of the reasons is elusive and little researched. We describe how critical realism can provide a useful "underlabourer" for such research, by "clearing the ground a little ... removing some of the rubbish that lies in the way of knowledge" (Locke, 1894, p. 14). The implications of such an underlabouring role are investigated. Whilst the research is still underway, the article indicates how a critical realist foundation is assisting the research process.},
author_keywords={Automated performance measurement;  Critical realism;  Enterprise systems implementation},
keywords={Automation;  Benchmarking;  Enterprise resource planning;  Information systems;  Real time systems;  Supply chain management, Automated performance managements;  Critical realisms;  Enterprise information system;  Enterprise system;  Key performance indicators;  Manual intervention;  Performance measurement system;  Performance measurements, Information management},
references={Aastrup, J., (2002) Networks Producing Intermodal Transport, , Unpublished doctoral dissertation, Copenhagen Business School; Archer, M., (1995) Realist Social Theory: The Morphogenetic Approach, , Cambridge: Cambridge University Press; Backström, T., Van Eijnatten, F.M., Kira, M., A Complexity Perspective on Sustainable Work Systems (2002) Creating Sustainable Work Systems: Emerging Perspectives and Practice, , P. Docherty, J. Forslin, & A. B. Shani, (Eds.). London: Routledge; Bhaskar, R., (1978) A Realist Theory of Science, , Sussex: Harvester Press; Bhaskar, R., (1979) The Possibility of Naturalism, , Hemel, Hempstead: Harvester Wheatsheaf; Bhaskar, R., (1986) Scientific Realism and Human Emancipation, , Verso: London; Bhaskar, R., (1989) Reclaiming Reality: A Critical Introduction to Contemporary Philosophy, , London: Verso; Bhaskar, R., (1991) Philosophy and the Idea of Freedom, , Oxford: Blackwell; Bourne, M., Neely, A., Implementing performance measurement systems: A literature review (2003) International Journal of Business Performance Management, 5 (1), pp. 1-24; Carlsson, S., Advancing information systems evaluation (research): A critical realist approach (2003) Electronic Journal of Information Systems Evaluation, 6 (2), pp. 11-20; Collier, A., (1994) Critical Realism: An Introduction to the Philosophy of Roy Bhaskar, , London: Verso; Delone, W.H., McLean, E.R., Information systems success: The quest for the dependent variable (1992) Information Systems Research, 3 (1), pp. 60-95; Delone, W.H., McLean, E.R., Information systems success revisited (2002) Proceedings of the 35th Hawaii International Conference on System Sciences, , Hawaii; Dobson, P., The philosophy of critical realism - An opportunity for information systems research (2001) Information Systems Frontiers; Dobson, P., Critical realism and IS research- Why bother with philosophy? (2002) Information Research, , http://InformationR.net/ir/, January. Retrieved from; Fitzgerald, B., Howcroft, D., Towards dissolution of the IS research debate: From polarization to polarity (1998) Journal of Information Technology, 13, pp. 313-326; (1999) Critical Realism in Economics: Development and Debate, , Fleetwood, S. (Ed.) London: Routledge; Hedström, P., Swedberg, R., Social mechanisms: An introductory essay (1998) Social Mechanisms: An Analytical Approach to Social Theory, pp. 1-31. , P. Hedström & R. Swedberg (Eds.). Cambridge University Press, New York; Iivari, J., Hirschheim, R., Klein, H.K., A paradigmatic analysis contrasting information systems development approaches and methodologies (1998) Information Systems Research, 9 (2), pp. 164-193; Kanter, R.M., When a thousand flowers bloom: Structural, collective, and social conditions for innovation in organizations (1996) Knowledge Management and Organizational Design, , P. S. Myers (Ed.). Newton, MA: Butterworth-Heinemann; Krueger, R.A., (1988) Focus Groups: A Practical Guide for Applied Research, , Newbury Park, CA: Sage Publications; Lawson, T., (1997) Economics and Reality, , Routledge: London; Layder, D., (1993) New Strategies in Social Research: An Introduction and Guide, , Cambridge: Polity Press; Liyanage, J.P., Kumar, U., Towards a valuebased view on operations and maintenance performance management (2003) Journal of Quality in Maintenance Engineering, 9 (4), pp. 1355-2511; Locke, J., (1894) An Essay Concerning Human Understanding, 1. , A. C. Fraser (Ed.). Oxford: Clarendon; Manicas, P., Accounting as a human science (1993) Accounting, Organizations and Society, 18, pp. 147-161; Martin, D.D., Technological change and manual wiork (1988) Employment in Britain, pp. 102-127. , D. Gallie (Ed.). Oxford: Basil Balckwell; Mingers, J., Combining IS research methods: Towards a pluralist methodology (2001) Information Systems Research, 12 (3), pp. 240-259; Mingers, J., Realizing information systems: Critical realism as an underpinning philosophy for information systems (2002) Proceedings Twenty-Third International Conference on Information Systems, pp. 295-303; Mutch, A., Critical realism, managers and information (1999) British Journal of Management, 10, pp. 323-333; Mutch, A., Managers and information: Agency and structure (2000) Information Systems Review, 1; Mutch, A., Actors and networks or agents and structures: Towards a realist view of information systems (2002) Organization, 9 (3), pp. 477-496; Outhwaite, W., (1987) New Philosophies of Social Science: Realism, Hermeneutics, and Critical Theory, , New York: St. Martin's Press; Pawson, R., Greenhalgh, T., Harvey, G., Walshe, K., (2004) Realist Synthesis: An Introduction (RMP Methods Paper 2/2004), , Manchester, ESRC Research Methods Programme, University of Manchester; Pawson, R., Greenhalgh, T., Harvey, G., Walshe, K., Realist review - A new method of systematic review designed for complex policy interventions (2005) Journal of Health Services Research & Policy, 10 (1), pp. 21-34; Pratt, A., Putting critical realism to work: The practical implications for geographical research (1995) Progress in Human Geography, 19 (1), pp. 61-74; Robey, D., Markus, M.L., Ritual in information systems design (1984) MIS Quarterly, 8, pp. 5-15; Ryan, S., Porter, S., Breaking the boundaries between nursing and sociology: A critical realist ethnography of the theory-practice gap (1996) Journal of Advanced Nursing, 24, pp. 413-420; Sayer, A., Realism in geography (1985) The Future of Geography, pp. 159-173. , R. J. Johnston (Ed.). London: Methuen; Sayer, A., (2000) Realism and Social Science, , London: Sage Publications Ltd; Sayer, R.A., (1992) Method in Social Science: A Realist Approach, , Routledge: London; Scarbrough, H., Corbett, J.M., (1992) Technology and Organization: Power, Meaning and Design, , Routledge: London; Smith, M.L., (2005) Overcoming Theory-practice Inconsistencies: Critical Realism and Information Systems Research, , Unpublished manuscript, Department of Information Systems, London School of Economics and Political Science, working paper 134; Spasser, M.A., Realist activity theory for digital library evaluation: Conceptual framework and case study (2002) Computer Supported Cooperative Work, 11, pp. 81-110; Stones, R., (1996) Sociological Reasoning: Towards A Past-modern Sociology, , MacMillan; Tsang, E., Kwan, K., Replication and theory development in organizational science: A critical realist perspective (1999) Academy of Management Review, 24 (4), pp. 759-780; Wad, P., Critical realism and comparative sociology (2001) Draft Paper for the IACR Conference, , August 17-19; Wainwright, S.P., A new paradigm for nursing: The potential of realism (1997) Journal of Advanced Nursing, 26, pp. 1262-1271; Wilson, F., Flogging a dead horse: The implications of epistemological relativism within information systems methodological practice (1999) European Journal of Information Systems, 8 (3), pp. 161-169; Wixom, B.H., Watson, H.J., An empirical investigation of the factors affecting data warehousing success (2001) MIS Quarterly, 25 (1), pp. 17-41; Yin, R.K., (1989) Case Study Research: Design and Methods, 5. , Beverley Hills, CA: SAGE Publications Ltd},
correspondence_address1={Dobson, P.; Edith Cowan UniversityAustralia},
publisher={IGI Publishing},
issn={10401628},
language={English},
abbrev_source_title={Inf. Resour. Manage. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Bendoly2007257,
author={Bendoly, E. and Rosenzweig, E.D. and Stratman, J.K.},
title={Performance metric portfolios: A framework and empirical analysis},
journal={Production and Operations Management},
year={2007},
volume={16},
number={2},
pages={257-276},
doi={10.1111/j.1937-5956.2007.tb00179.x},
note={cited By 56},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34547324240&doi=10.1111%2fj.1937-5956.2007.tb00179.x&partnerID=40&md5=412bd5ac76fdf6c2787143ac06156bde},
affiliation={Goizueta Business School, Emory University, 1300 Clifton Road, Atlanta, GA 30322-2710, United States; David Eccles School of Business, University of Utah, 1645 E. Campus Center Drive, Salt Lake City, UT 84112-9304, United States},
abstract={The widespread adoption of supply chain management principles suggests that managers recognize the importance of evaluating operational decisions holistically. However, it is often difficult to link specific operational practices to strategic level outcomes and in turn to corporate financial results. This presents problems for both managers and academic researchers attempting to justify the often high cost of operational improvement initiatives in terms of objective accounting metrics. This study provides evidence that it is possible to demonstrate linkages between carefully chosen portfolios of tactical, strategic, and financial metrics. Survey data from 118 manufacturers are used to evaluate hypotheses linking multilevel metrics of performance across three well-established strategic foci. We present portfolios of metrics drawn from the literature and from the Supply Chain Council's supply-chain operations reference model and related design and customer chain models. Our analysis suggests that metric portfolios in which tactical metrics are designed to match strategic-level metrics, based on alignment with a specific strategic focus, provide clearer mechanisms for understanding performance linkages. © 2007 Production and Operations Management Society.},
author_keywords={Empirical research;  Metrics;  Operations strategy;  Performance measurement systems},
references={Albert, M.B., Avery, D., Narin, F., McAllister, P., Direct validation of citation counts as indicators of industrially important patents (1991) Research Policy, 20 (3), pp. 251-259; Anderson, J.C., Gerbing, D.W., Predicting the performance of measures in a confirmatory factor analysis with a pretest assessment of their substantive validities (1991) Journal of Applied Psychology, 76 (5), pp. 732-741; Anderson, E.G., Joglekar, N.R., A hierarchical product development planning framework (2005) Production and Operations Management, 14 (3), pp. 344-361; Baron, R.M., Kenny, D.A., The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations (1986) Journal of Personality and Social Psychology, 51 (6), pp. 1173-1182; Bollen, K.A., (1989) Structural Equations with Latent Variables, , Wiley, New York; Boyer, K., Swink, M., Rosenzweig, E.D., Operations strategy research in the POMS journal (2005) Production and Operations Management, 14 (4), pp. 442-449; Boyer, K.K., Ward, P.T., Leong, G.K., Approaches to the factory of the future: An empirical taxonomy (1996) Journal of Operations Management, 14 (4), pp. 297-313; Buhman, C., Kekre, S., Singhal, J., Interdisciplinary and interorganizational research: Establishing the science of enterprise networks (2005) Production and Operations Management, 14 (4), pp. 493-513; Churchill, G., A paradigm for developing better measures of marketing constructs (1979) Journal of Marketing Research, 16 (1), pp. 64-73; Cooper, R., Kaplan, R.S., Measure costs right: Make the right decisions (1988) Harvard Business Review, 66 (5), pp. 96-103; Dean Jr., J.W., Snell, S.A., The strategic use of integrated manufacturing: An empirical investigation (1996) Strategic Management Journal, 17 (6), pp. 459-480; Dillman, D., (2000) Mail and Internet Surveys: The Tailored Design Method, , John Wiley & Sons, Inc, New York; Dixon, J.R., Nanni Jr., A.J., Vollman, T.E., (1990) The new performance challenge: Measuring operations for world-class competition, , Dow Jones-Irwin, Homewood, IL; Duray, R., Ward, P.T., Milligan, G., Berry, W.L., Approaches to mass customization: Configurations and empirical validation (2000) Journal of Operations Management, 18 (6), pp. 605-625; Epstein, M.J., Westbrook, R.A., Linking actions to profit in strategic decision making (2001) Sloan Management Review, 42 (3), pp. 39-49; Evans, J.R., An exploratory study of performance measurement systems and relationships with performance results (2004) Journal of Operations Management, 22 (3), pp. 219-232; Ferdows, K., De Meyer, A., Lasting improvements in manufacturing performance: In search of a new theory (1990) Journal of Operations Management, 9 (2), pp. 168-184; Frohlich, M., Dixon, J.R., A taxonomy of manufacturing strategies revisited (2001) Journal of Operations Management, 19 (5), pp. 541-558; Garcia, R., Calantone, R., Levine, R., The role of knowledge in resource allocation to exploration versus exploitation in technologically- oriented organizations (2003) Decision Sciences, 34 (2), pp. 323-349; Giffi, C., Roth, A.V., Seal, G., (1990) Competing in World-Class Manufacturing: America's 21st Century Challenge, , Business One Irwin, Homewood, IL; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Gupta, Y., Lonial, S., Exploring linkages between manufacturing strategy, business strategy, and organizational strategy (1998) Production and Operations Management, 7 (3), pp. 243-264; Hair, J.F., Anderson, R.E., Tatham, R.L., Black, W.C., (1995) Multivariate Data Analysis, , Prentice-Hall, Englewood Cliffs, New Jersey; Hambrick, D.C., Some tests of the effectiveness and functional attributes of Miles and Snow's strategic types (1983) Academy of Management Journal, 26 (1), pp. 5-26; Hambrick, D.C., Fredrickson, J.W., Are you sure you have a strategy? (2001) Academy of Management Executive, 15 (4), pp. 48-59; Harman, H., (1967) Modern Factor Analysis, , Second edition. University of Chicago Press, Chicago, IL; Hatcher, L., (1994) A Step-By-Step Approach to Using the SAS System for Factor Analysis and Structural Equation Modeling, , SAS Institute Inc, Cary, NC; Hax, A., Candea, D., (1984) Production and Inventory Management, , Prentice-Hall, Englewood Cliffs, NJ; Hayes, R.H., Wheelwright, S.C., (1984) Restoring Our Competitive Edge: Competing Through Manufacturing, , John Wiley and Sons, New York, NY; Heskett, J.L., Jones, T.O., Loveman, G.W., Sasser Jr., E., Schlesinger, L.A., Putting the service-profit chain to work (1994) Harvard Business Review, 72 (2), pp. 164-174; Hill, T., (1989) Manufacturing Strategy: Text and Cases, , MacMillan, London; Ittner, C.D., Larcker, D.F., Coming up short on nonfinancial performance measurement (2003) Harvard Business Review, 81 (11), pp. 88-95; Ittner, C.D., Larcker, D.F., Are nonfinancial measures leading indicators of financial performance? An analysis of customer satisfaction (1998) Journal of Accounting Research, 36 (3), pp. 1-35; Ittner, C.D., Larcker, D.F., Meyer, M.W., Subjectivity and the weighting of performance measures: Evidence from a balanced scorecard (2003) The Accounting Review, 78 (3), pp. 725-758; Johnson, D., (1998) Applied Multivariate Methods for Data Analysts, , Duxbury Press, Pacific Grove, California; Kaplan, R.S., Norton, D.P., Measuring the strategic readiness of intangible assets (2004) Harvard Business Review, 82 (2), pp. 52-63; Kaplan, R.S., Norton, D.P., Transforming the balanced scorecard from performance measurement to strategic management: Part II (2001) Accounting Horizons, 15 (2), pp. 147-160; Kaplan, R.S., Norton, D.P., Having trouble with your strategy? Then map it (2000) Harvard Business Review, 78 (5), pp. 167-176; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-85; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (5), pp. 134-142; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kathuria, R., Competitive priorities and managerial performance: A taxonomy of small manufacturers (2000) Journal of Operations Management, 18 (6), pp. 627-641; Ketokivi, M., Elaborating the contingency theory of organizations: The case of manufacturing flexibility strategies (2006) Production and Operations Management, 15 (2), pp. 215-228; Khurana, A., Lin, T., Rosenzweig, E.D., Roth, A.V., Measuring manufacturing strategies of industrial suppliers: What kind of balanced scorecard to use (1998) Proceedings of the 1998 Decision Sciences Institute Annual Meeting, , Las Vegas, NV; Kim, J.S., Arnold, P., Operationalizing manufacturing strategy: An exploratory study of constructs and linkage (1996) International Journal of Operations & Production Management, 16 (12), pp. 45-73; Kleindorfer, P., Singhal, K., Van Wassenhove, L., Sustainable operations management (2005) Production and Operations Management, 14 (4), pp. 482-492; Koufteros, X.A., Vonderembse, M.A., Doll, W.J., Examining the competitive capabilities of manufacturing firms (2002) Structural Equation Modeling, 9 (2), pp. 256-282; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Magretta, J., Stone, N., (2002) What Management is: How it Works and why it's Everyone's Business, , Free Press, New York, NY; Malhotra, M.K., Steele, D.C., Grover, V., Important strategic and tactical manufacturing issues in the 1990s (1994) Decision Sciences, 25 (2), pp. 189-214; Mallick, D.N., Schroeder, R.G., An integrated framework for measuring product development performance in high technology industries (2005) Production and Operations Management, 14 (2), pp. 142-158; Melnyk, S.A., Stewart, D.M., Swink, M., Metrics and performance measurement in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22 (3), pp. 209-217; Menor, L., Roth, A.V., What is a good measurement scale? Tutorial on new item and scale development in OM research (2003) Ivey Business School, University of Western Ontario, London, Ontario, Canada, working paper; Miles, R.E., Snow, C.C., (1978) Organizational Strategy, Structure, and Process, , McGraw Hill, New York; Miller, J.G., Roth, A.V., A taxonomy of manufacturing strategies (1994) Management Science, 40 (3), pp. 285-304; Moore, G.C., Benbasat, I., Development of an instrument to measure the perceptions of adopting an information technology innovation (1991) Information Systems Research, 2 (2), pp. 192-222; Narasimhan, R., Das, A., The impact of purchasing integration and practices on manufacturing performance (2001) Journal of Operations Management, 19 (5), pp. 593-609; Narasimhan, R., Jayaram, J., Carter, J., An empirical examination of the underlying dimensions of purchasing competence (2001) Production and Operations Management, 10 (1), pp. 1-15; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nerkar, A., Old is gold? The value of temporal exploration in the creation of new knowledge (2003) Management Science, 49 (2), pp. 211-229; Parente, D., Across the manufacturing-marketing interface: Classification of significant research (1998) International Journal of Production Economics, 18 (12), pp. 1205-1222; Porter, M.E., (1985) Competitive Advantage, , The Free Press, New York; Richardson, P.R., Taylor, A.J., Gordon, J., A strategic approach to evaluating manufacturing performance (1985) Interfaces, 15 (6), pp. 15-27; Roth, A.V., Jackson, W., Strategic determinants of service quality and performance: Evidence from the banking industry (1995) Management Science, 41 (11), pp. 1720-1733; Roth, A.V., Miller, J.G., Manufacturing strategy, manufacturing strength, managerial success, and economic outcomes (1990) Manufacturing Strategy, pp. 97-108. , J. Ettlie, M. Burstein, and A. Feigenbaum Eds, Kluwer Academic, Boston, MA, pp; Sabherwal, R., Chan, Y.E., Alignment between business and IS strategies: A study of prospectors, analyzers, and defenders (2001) Information Systems Research, 12 (1), pp. 11-33; (1989) SAS/STAT User's Guide, 1. , SAS Institute, Inc, Version 6, Fourth Edition, SAS Institute, Inc, Cary, NC; Skinner, W., Manufacturing-Missing link in corporate strategy (1969) Harvard Business Review, 47 (3), pp. 136-145; Slater, S.F., Olson, E.M., Reddy, V.K., Strategy-based performance measurement (1997) Business Horizons, 40 (4), pp. 37-44; Sousa, R., Voss, C., Quality management: Universal or context dependent? (2001) Production and Operations Management, 10 (4), pp. 383-404; Sroufe, R., Effects of environmental management systems on environmental management practices and operations (2003) Production and Operations Management, 12 (3), pp. 416-431; Stratman, J.K., Roth, A.V., Enterprise resource planning (ERP) competence constructs: Two-stage multi-item scale development and validation (2002) Decision Sciences, 33 (4), pp. 601-628; Treacy, M., Wiersema, F., Customer intimacy and other value disciplines (1993) Harvard Business Review, 71 (1), pp. 84-93; Tsikriktsis, N., Lanzolla, G., Frohlich, M., Adoption of e-processes by service firms: An empirical study of antecedents (2004) Production and Operations Management, 13 (3), pp. 216-229; Upton, D.M., The management of manufacturing flexibility (1994) California Management Review, 36 (2), pp. 72-89; Venkatraman, N., The concept of fit in strategy research: Toward verbal and statistical correspondence (1989) Academy of Management Review, 14 (3), pp. 423-444; Vickery, S.K., Droge, C., Markland, R.E., Strategic production competence: Convergent, discriminant, and predictive validity (1994) Production and Operations Management, 3 (4), pp. 308-318; Vickery, S.K., Droge, C., Markland, R.E., Production competence and business strategy: Do they affect business performance? (1993) Decision Sciences, 24 (2), pp. 435-455; Wacker, J.G., A theoretical model of manufacturing lead times and their relationship to manufacturing goal hierarchy (1996) Decision Sciences, 27 (3), pp. 483-517; Wheelwright, S., Manufacturing strategy: Defining the missing link (1984) Strategic Management Journal, 5 (1), pp. 77-91; Wheelwright, S., Clark, K., (1992) Revolutionizing Product Development, , Free Press, New York, NY; White, G.P., A survey and taxonomy of strategy related performance measures for manufacturing (1996) International Journal of Operations & Production Management, 16 (3), pp. 42-61; White, G.P., A meta-analysis model of manufacturing capabilities (1996) Journal of Operations Management, 14 (3), pp. 315-331; Zhang, Q., Vonderembse, M.A., Lim, J.S., Manufacturing flexibility: Defining and analyzing relationships among competence, capability, and customer satisfaction (2003) Journal of Operations Management, 21 (2), pp. 173-191},
correspondence_address1={Bendoly, E.; Goizueta Business School, 1300 Clifton Road, Atlanta, GA 30322-2710, United States; email: Elliot_Bendoly@bus.emory.edu},
publisher={Florida International University},
issn={10591478},
language={English},
abbrev_source_title={Prod. Oper. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Grimaldi2007206,
author={Grimaldi, S. and Rafele, C.},
title={Current applications of a reference framework for the supply chain performance measurement},
journal={International Journal of Business Performance Management},
year={2007},
volume={9},
number={2},
pages={206-225},
doi={10.1504/IJBPM.2007.011863},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33847065018&doi=10.1504%2fIJBPM.2007.011863&partnerID=40&md5=c9aa17b22428d52c9c2cf64179ca2bb8},
affiliation={Politecnico di Torino, Department of Production Systems and Business Economics, Corso Duca degli Abruzzi 24, Torino, Italy},
abstract={Several methods and indicators are used to measure logistic performances; thus sometimes there are various values for the same performance. This paper starts examining the structure for a supply chain performance measurement according to the service quality requirements, and determines an ordered framework for logistic indicators called LogistiQual, applying the various aspects of service quality management, which is included in the Parasuraman-Zeithaml-Berry (PZB) model, to logistics. By adapting the PZB model, the logistic performance classification for a manufacturing supply chain could be identified by macroclasses and divided into subclasses, and finally specific indicators for each subclass determined. The second step of this work is to enlarge the influence of the model on the supply chain. In this sense, the LogistiQual is replicated in the Self and in the Source LogistiQual. Finally, two validations of the framework are shown. Firstly, a general process analysis performed by SCOR 6.0 is considered. Secondly, the LogistiQual is tested within companies in many industrial sectors. Copyright © 2007 Inderscience Enterprises Ltd.},
author_keywords={Customer service;  Indicators;  Logistics;  Performance measurement;  Service quality evaluation;  Supply chain},
references={Beamon, B., 'Performance measures in supply chain management' (1996) Proceeding of the Conference on Agile and Intelligent Manufacturing Systems Rensselaer Polytechnic Institute, , Troy, NY; Beamon, B., 'Supply chain and analysis models and methods' (1998) International Journal of Production Economics, 55, pp. 281-294; Beamon, B., 'Measuring supply chain performance' (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Brewer, P., Speh, T., 'Using the balanced scorecard to measure supply chain performance' (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Caplice, C., Sheffi, Y., 'A review and evaluation of logistics performance measurement system' (1995) The International Journal of Logistics Management, 6 (1), pp. 61-74; Cokins, G., (1999) Understanding Activity-Based Costing and Supply Chain Management, 1. , ABC Technologies, ASCET, 15 April; Cooper, R., 'Implementing an activity-based cost system' (1990) Journal of Cost Management, (4), pp. 33-42. , Spring; De Marco, M., Salvo, V., Lanzani, W., (1999) Balanced Scorecard: Dalla Teoria Alla Pratica, , FrancoAngeli, Milano; Ellram, L.M., 'Activity-based costing and total cost of ownership: A critical linkage' (1995) Journal of Cost Management, 8 (4), pp. 22-30; English, J.R., (1999) Quality Monitoring of Logistics Systems, pp. 1-7. , draft publication; Franceschini, F., Rafele, C., 'Quality evaluation in logistic services' (2000) International Journal of Agile Management Systems, 2 (1), pp. 49-53; Gaw, B., 'Make success measurable' (2003) Manufacturing Basics and Best Practices Bulletin, Internet Journal, , http://bbasicsllc.com/Manufacturing-Best-Practices-Bulletin-Archives.htm; Kaplan, R.S., Norton, D.P., (1996) Translating Strategy Into Action: The Balanced Scorecard, , Boston, MA: Harvard Business School Press; Isaia, E., Rafele, C., 'Un modello di valutazione delle performance logistiche' (2001) Logistica Management, pp. 99-108. , September; Ittner, C.D., Larcker, D.F., 'Coming up short on nonfinancial performance measurement' (2003) Harvard Business Review, pp. 2-10. , November; Lambert, D.M., Pohlen, T.L., 'Supply chain metrics' (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lee, H.L., Amaral, J., 'Continuous and sustainable improvement through supply chain performance management' (2002) Global Supply Chain Management Forum, , Stanford, October; Lohman, C., Fortuin, L., Wouters, M., 'Designing a performance measurement system: A case study' (2004) European Journal of Operational Research, (156), pp. 267-286; Parasuraman, A., Zeithmal, V.A., Berry, L.L., 'A conceptual model of service quality and its implications for future research' (1985) Journal of Marketing, 49, pp. 41-50; Parasuraman, A., Zeithmal, V.A., Berry, L.L., 'ServQual: A multiple-item scale for measuring consumer perceptions of service quality' (1988) Journal of Retailing, 64 (1), pp. 12-40; Rafele, C., 'Logistic service measurement: A reference framework' (2004) International Journal of Integrated Manufacturing Systems, 15 (4), pp. 280-290; Rafele, C., Grimaldi, S., 'A measurement model for logistics service' (2003) 3rd International Workshop Performance Measurements, , Bergamo, 19-20 June; Rossetto, S., (2001) Manuale Di Economia E Organizzazione D'Impresa - Teorie E Tecniche, , UTET, Torino; Shapiro, (1985) Logistics Strategy: Cases and Concepts, , St. Paul, MN: West Publishing; Stancari, A., (2003) Il Tableau De Bord Logistico-produttivo, , Amministrazione & Finanza, Milano; Stank, Goldsby, Vickery, 'Effect of service supplier performance on satisfaction and loyalty of store managers in the fast food industry' (1999) Journal of Operations Management, 17 (2), pp. 429-447; (2003) Supply Chain Operations Reference Model SCOR Version 6.0, , Supply Chain Council Inc. Pittsburg},
correspondence_address1={Rafele, C.; Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, Italy; email: carlo.rafele@polito.it},
publisher={Inderscience Enterprises Ltd.},
issn={13684892},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Stone2007240,
author={Stone, J. and Love, D.},
title={Modelling the relationship between local logistics management decisions and overall supply chain performance: A research agenda},
journal={International Journal of Business Performance Management},
year={2007},
volume={9},
number={2},
pages={240-252},
doi={10.1504/IJBPM.2007.011865},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33847012525&doi=10.1504%2fIJBPM.2007.011865&partnerID=40&md5=40d389c1c7f54e6ca12dc70f60cd2e0e},
affiliation={Industrial Management and Design Research Group, Aston University, School of Engineering and Applied Science, Aston Triangle, Birmingham B4 7ET, United Kingdom; Technology and Operations Management Research Group, Aston University, Aston Business School, Aston Triangle, Birmingham B4 7ET, United Kingdom},
abstract={Supply chains are advocated widely as being the new units for commercial competition and developments have made the sharing of supply chain wide information increasingly common. Most organisations however still make operational decisions intended to maximise local organisational performance. With improved information sharing a holistic focus for operational decisions should now be possible. The development of a pan supply chain performance framework requires an examination of the conditions under which holistic-decisions provide benefits to either the individual enterprise or the complete supply chain. This paper presents the background and supporting methodology for a study of the impact of an overall supply chain performance metric framework upon local logistics decisions and the conditions under which such a framework would improve overall supply chain performance. The methodology concludes a simulation approach using a functionally extended Gensym's e-SCOR model, together with case based triangulation, to be optimum. Copyright © 2007 Inderscience Enterprises Ltd.},
author_keywords={Supply chain management;  Supply chain performance measurement;  Supply chain simulation},
references={Barnett, C.M., Miller, C.J., 'Analysis of the virtual enterprise using distributed supply chain modelling and simulation: An application of e-SCOR' (2000) Proceedings of the 2000 Winter Simulation Conference; Beamon, B.M., 'Measuring supply chain performance' (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Bechtel, C., Jayaram, J., 'Supply chain management: A strategic perspective' (1997) The International Journal of Logistics Management, 8 (1), pp. 15-34; Buxmann, P., Ahsen, A., Diaz, L.M., Wolf, K., (2002) 'Supply Chain Management Software: A Survey for the SCM Software Market in the European Automotive Industry Research Report, , http://www.wiwi.tu-freiberg.de/wi/publications/rp/scm_survey.pdf, (accessed 5 October 2004); Campbell, A., Goold, M., Alexander, M., 'Corporate strategy: The quest for parenting advantage' (1995) Harvard Business Review, 73 (2), p. 123; Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman, London; Clarke, M., 'Virtual logistics - An introduction and overview of the concepts' (1998) International Journal of Physical Distribution and Logistics Management, 28 (7), pp. 486-507; Fearne, A., Hughes, D., 'Success factors in the fresh produce supply chain' (2000) British Food Journal, 102 (10), pp. 760-772; Garland, C., Heaver, T.D., Henriksson, L.E., 'Logistics performance: Definition and measurement' (1994) International Journal of Physical Distribution and Logistics Management, 24 (1), pp. 17-28; (2004) E-SCOR Overview, , http://www.gensym.com/supplychain/, Gensym, (accessed 5 October); Gunasekaran, A., Patel, C., Tirtiroglu, E., 'Performance measures and metrics in a supply chain environment' (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Hermann, C.F., 'Validation problems in games and simulations with special reference to models of international politics' (1967) Behavioral Science, 12, pp. 216-231; Kehoe, D., Boughton, N., 'New paradigms in planning and control across manufacturing supply chains' (2001) International Journal of Operations and Production Management, 21 (5-6), pp. 582-593; Lamming, R., 'Squaring lean supply with supply chain management' (1996) International Journal of Operations and Production Management, 16 (2), pp. 183-196; Lee, H.L., Billington, C., 'Managing supply chain inventory: Pitfalls and opportunies' (1992) Sloan Management Review, 33, pp. 65-73; Lee, H.L., Padmanabhan, V., Whang, S., 'The bullwhip effect in supply chains' (1997) Sloan Management Review, 38 (3). , Spring; Maskel, B., 'Performance measures for world class manufacturing' (1989) Management Accounting, 5, p. 32. , London; 'Performance indicators in logistics' (1992) Logistics Information Management, 5 (3), pp. 35-40. , NEVEM Workgroup; Porter, M.E., (1998) Competitive Advantage, Creating and Sustaining Superior Performance, , New York, London, Free Press; (2002) Supply-Chain Operations Reference Model, SCOR Version 5.0, , Supply Chain Council, Supply Chain Council, Pittsburg, USA; Towill, D.R., 'Industrial dynamics modelling of supply chains' (1996) International Journal of Physical Distribution and Logistics Management, 26 (2), pp. 23-42; van Hoek, R.L., 'Measuring the unmeasurable - Measuring and improving performance in the supply chain' (1998) Supply Chain Management, 3 (4), pp. 187-192; van Landeghem, R., Persoons, K., 'Benchmarking of logistical operations based on a causal mode' (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 254-266; Webster, M., 'Subcontracting within the supply chain for electronics assembly manufacture' (1997) International Journal of Operations and Production Management, 17 (9), pp. 827-841},
correspondence_address1={Stone, J.; Industrial Management and Design Research Group, Aston Triangle, Birmingham B4 7ET, United Kingdom; email: j.stone@aston.ac.uk},
publisher={Inderscience Enterprises Ltd.},
issn={13684892},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@BOOK{Busi2006313,
author={Busi, M.},
title={Monitoring supply chain flows through improved performance measurement of extended processes},
journal={Supply Chain Management: Issues in the New Era of Collaboration and Competition},
year={2006},
pages={313-354},
doi={10.4018/978-1-59904-231-2.ch012},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84900278642&doi=10.4018%2f978-1-59904-231-2.ch012&partnerID=40&md5=aff60e14ce466359d5e6790d944aa8d5},
affiliation={SINTEF Industrial Management, Norwegian University of Science and Technology, Norway; University of Strathclyde, United Kingdom},
abstract={Increasing strategic importance of logistics-related processes demands a higher integration of performance management and supply chain management. Despite the increasing focus on supply chain management and business collaboration, studies in the area of performance management still narrowly look at the single enterprise and its "within-enterprise" processes. Most of these existing studies are losing relevance in today's industrial dynamics, where business models, such as that of the supply chain, are merely the tip of an emerging trend in new organization alliances, boundary redefinition, and market structures. This chapter goes beyond existing work to develop a better understanding of the issue of performance management from a supply chain management perspective. The goal is to demonstrate how today's manufacturing systems and processes could be measured and managed in the context of the extended business of which they are part-back through the supplier chain and forward into the distribution and customer chain. The chapter's intended major outcomes for its readers are: a clearer understanding of the concept of supply chain management through performance, and a process for designing a supply chain performance measurement system. Additional information emerges throughout the chapter. © 2007, Idea Group Inc.},
references={Ahmad, M.M., Dhafr, N., Establishing and improving manufacturing performance measures (2002) Robotics and Computer-Integrated Manufacturing, 18 (3-4), pp. 171-176; Amaratunga, D., Baldry, D., Moving from performance measurement to performance management (2002) Facilities, 20 (5-6), pp. 217-223; Andersen, B., Fagerhaug, T., (2002) Performance Measurement Explained: Designing and Implementing Your State-of-the-art System, , Milwaukee, WI: ASQ Press; Anderson, J.C., Cleveland, G., Schroeder, R.G., Operations strategy: A literature review (1989) Journal of Operations Management, 8 (2), pp. 133-158; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bititci, U.S., Carrie, A.S., McDevitt, L.G., Integrated performance measurement systems: A development guide (1997) International Journal of Operations and Production Management, 17 (6), pp. 522-535; Bititci, U.S., Martinez, V., Albores, P., Parung, J., Creating and managing value in collaborative networks (2004) International Journal of Physical Distribution & Logistics Management, 34 (3-4), pp. 251-268; Bowersox, D.J., Daugherty, P.J., Logistics paradigms: The impact of information technology (1995) Journal of Business Logistics, 16 (1), pp. 65-80; Bredrup, H., (1995) Performance Measurement In a Changing Competitive Industrial Environment: Breaking the Financial Paradigm, , Trondheim, Norway: Norwegian Institute of Technology; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Browne, J., Zhang, J., Extended and virtual enterprises: Similarities and differences (1999) International Journal of Agile Management Systems, 1 (1), pp. 30-36; Bungay, S., Goold, M., Creating a strategic control system (1991) Long Range Planning, 24 (3), pp. 32-39; Burgess, T.F., Gules, H.K., Tekin, M., Supply-chain collaboration and success in technology implementation (1997) Integrated Manufacturing Systems, 8 (5), pp. 323-332; Busi, M., Alfnes, E., Bolseth, S., Strandhagen, J.O., The mobile extended manufacturing enterprise (2003) One World? One View of OM? the Challenges of Integrating Research &amp; Practice, Proceedings of the 10th Annual EurOMA Conference, pp. 351-360. , June 16-18, In G. Spina June et al. (Eds.), Cernobbio, Lake Como, Italy, Padova: Servizi Grafici Editoriali; Busi, M., Strandhagen, J.O., Monitoring extended enterprise operations using KPIs and a performance dashboard (2004) Proceedings of the 2nd World POM Conference On POM, , Cancun, Mexico; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement system (1995) The International Journal of Logistics Management, 6 (1), pp. 61-74; Child, J., Faulkner, D., (1998) Strategies of Co-operation: Managing Alliances, Networks, and Joint Ventures, , Oxford, UK: Oxford University Press; Christopher, M., (1992) Logistics & Supply Chain Management, , London, Pitsman; Christopher, M., (1998) Logistics and Supply Chain Management, , London, Financial Times Prentice Hall; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge: Measuring Operations For World-class Competition, , Homewood, IL: Dow Jones-Irwin; Eccles, R.G., January-February).The performance measurement manifesto (1991) Harvard Business Review, pp. 131-137; Euske, K.J., Lebas, M.J., McNair, C.J., Performance management in an international setting (1993) Proceedings of the 16th Annual Congress of the European Accounting Association, , April 28-30, Turku, Finland; Fawcett, S.E., Magnan, G.M., The rhetoric and reality of supply chain integration (2002) International Journal of Physical Distribution and Logistics Management, 32 (5), pp. 339-361; Fine, C.H., Hax, A.C., Manufacturing strategy: A methodology and an illustration (1985) Interfaces, 15 (6), pp. 28-46; Franco, M., Bourne, M., Business performance measurement systems: A systematic Review (2003) One World? One View of OM? the Challenges of Integrating Research &amp; Practice, Proceedings of the 10 th Annual EurOMA Conference, pp. 451-460. , June 16-18, In G. Spina June et al. (Eds.), Cernobbio, Lake Como, Italy, Padova: Servizi Grafici Editoriali; Ghalayini, A.M., Noble, J.S., Crowe, T.J., An integrated dynamic performance measurement system for improving manufacturing competitiveness (1997) International Journal of Production Economics, 48, pp. 207-225; Goldratt, E.M., Cox, J., (1986) The Goal: A Process of Ongoing Improvement, , New York, North River Press; Hayes, R.H., Abernathy, W.J., Managing our way to economic decline (1980) Harvard Business Review, pp. 67-77. , July-August; Hayes, R.H., Wheelwright, S.C., Link manufacturing process and product life cycles (1979) Harvard Business Review, pp. 133-140. , January-February; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution & Logistics, 30 (10), pp. 847-868; Hronec, S.M., (1993) Vital Signs: Using Quality, Time, and Performance Measurements to Chart Your Company's Future, , New York: AMACOM/American Management Association; Ireland, R., Bruce, R., CPFR: Only the beginning of collaboration (2000) Supply Chain Management Review, pp. 80-88. , September-October; Jagdev, H.S., Thoben, K.D., Anatomy of enterprise collaborations (2001) Production Planning & Control, 12 (5), pp. 437-451; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Boston, Harvard Business School Press; Kald, M., Nilsson, F., Performance measurement at Nordic companies (2000) European Management Journal, 18 (1), pp. 113-127; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, , January-February; Kaplan, R.S., Norton, D.P., September-October).Having trouble with your strategy?Then map it (2000) Harvard Business Review, pp. 167-176; Kathuria, R., Partovi, F.Y., Aligning work force management practices with competitive priorities and process technology: A conceptual examination (2000) The Journal of High Technology Management Research, 11 (2), pp. 215-234; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 45-50; Kleijnen, J., Smits, M., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-514; Konsynski, B.R., Strategic control in the extended enterprise (1993) IBM Systems Journal, 32 (1), pp. 111-142; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 1-19; Lambert, D.M., Pholen, T.L., Supply chain matrix (2001) International Journal of Logistics Management, 12 (1), pp. 1-19; Lee, H.L., Creating value through supply chain integration Supply Chain Management Review, , September/October; Lee, H.L., Whang, S., E-business and supply chain integration (2001) Stanford Global Supply Chain Management Forum, , November, SGSCMF-W2-2001; Lee, W.B., Cheung, C.F., Lau, H.C.W., Choy, K.L., Development of a Web-based enterprise collaborative platform for networked enterprises (2003) Business Process Management Journal, 9 (1), pp. 46-58; Lummus, R.R., Vokurka, R.J., Managing the demand chain through managing the information flow: Capturing 'moments of information.' (1999) Production and Inventory Management Journal, pp. 16-20. , First Quarter; Lupton, G., Dooley, L., Product life cycle performance indicators (2003) Proceedings of the 20 th International Manufacturers Conference, pp. 591-597. , IMC 20, Cork, Ireland; Macaulay, S., Cook, S., Performance management as the key to customer service (1994) Industrial and Commercial Training, 26 (11), pp. 3-8; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533; Moullin, M., (2002) Delivering Excellence In Health and Social Care, , Buckingham, UK: Open University Press; Neely, A.D., (1998) Performance Measurement: Why, What and How?, , London: Economics Books; Neely, A.D., Mills, J., Gregory, M., Platts, K., Performance measurement system design- A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Otley, D., Performance management: A framework for management control systems research (1999) Management Accounting Research, 10 (4), pp. 363-382; Patterson, K.A., Grimm, C.M., Thomas, M.C., Adopting new technologies for supply chain management (2003) Transportation Research Part E, 39, pp. 95-121; Perrin, B., Effective use and misuse of performance measurement (1998) American Journal of Evaluation, 19 (3), pp. 367-379; Rafele, C., Logistic service measurement: A reference framework (2004) Journal of Manufacturing Technology Management, 15 (3), pp. 280-290; Rouse, P., Putterill, M., An integral framework for performance measurement (2003) Management Decision, 41 (8), pp. 791-805; Russell, R., Taylor, I.I.I.B., (1998) Operations Management-Focusing On Quality and Competitiveness, , Upper Saddle River, NJ: Prentice Hall; Sabath, R., Fontanella, J., July-August.The unfulfilled promise of supply chain collaboration (2002) Supply Chain Management Review, pp. 24-29; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89, pp. 231-243; Simatupang, T.M., Sridharan, R., The collaborative supply chain (2002) The International Journal of Logistics Management, 13 (1), pp. 15-30; Sinclair, D., Zairi, M., Effective process management through performance measurement, Part III - An integrated model of total quality-based performance measurement (1995) Business Process Re-Engineering, 1 (3), pp. 50-65; Stank, T.P., Dougherty, P.J., Autry, C.W., Collaborative planning: Supporting automatic replenishment programs (1999) Supply Chain Management, 4 (2), pp. 75-85; Thorpe, R., The characteristics of performance management research, implication and challenges (2004) International Journal of Productivity and Performance Management, 53 (4), pp. 334-344; Waggoner D.Neely.A.Kennerley, M., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, pp. 60-61. , 53-60; Wholey, J., Formative and summative evaluation: Related issues in performance measurement (1996) Evaluation Practice, 17 (2), pp. 145-149; Williamson, O.E., (1985) The Economic Institutions of Capitalism, , New York: The Free Press, Macmillian; Winston, J., Performance indicators-Promises unmet: A response to Perrin (1999) American Journal of Evaluation, 20 (1), pp. 95-99; Wognum, P.M., Faber, E.C.C., Infrastructures for collaboration in virtual organisations (2002) International Journal of Networking and Virtual Organizations, 1 (1); Yeniyurt, S., A literature review and integrative performance measurement framework for multinational companies (2003) Marketing Intelligence & Planning, 21 (3), pp. 134-142; Zairi, M., (1994) Measuring Performance For Business Results, , London, Chapman & Hall; Zimmermann, K., Using the balanced scorecard for interorganisational performance management of supply chains-A case study (2001) Cost Management In Supply Chains, pp. 399-415. , In S. Seuring & M. Goldbach (Eds), Heidelberg: Physica},
correspondence_address1={SINTEF Industrial Management, Norway},
publisher={IGI Global},
isbn={9781599042312},
language={English},
abbrev_source_title={Supply Chain Management: Issues in the New Era of Collaboration and Competition},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Sakthong2006888,
author={Sakthong, O. and Somboonwiwat, T.},
title={An Integrated supply chain performance measurement},
journal={36th International Conference on Computers and Industrial Engineering, ICC and IE 2006},
year={2006},
pages={888-898},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886873508&partnerID=40&md5=ce0b995e74147bf0c27fe72a14cf714f},
affiliation={Production Engineering Department, King Mongkut's University of Technology Thonburi, 126 Prachautid Rd., Bangmod Tungkhru Bangkok 10140, Thailand},
abstract={The changes in customer requirements, complexities of business operations, new developments in technology, globalization, and other driving forces, all affect management of the supply chain in complicated ways. The supply chain performance measurement (SCPM) can play an important role in the control of all activities in the chain. Past research in SCPM can be divided into two categories; inter-organization level and intra-organization level, of which the details are presented under the topics of 'how to design a performance measurement system and dimension of metrics in the structure' or 'the assessment technique of measurement with any specific technique'. Various past researches have not presented the connection between the levels of synchronous operations along the chain. Hence, a performance measurement system is required to link between levels to identify problems and causes. The linkage between any levels along the chain can be of different dimensions of metrics in each level of the SCPM, which affects to a confinement for problem solving. These problems may be caused from the result of measurements in the dimension of metrics in the same level, or the same dimension of metrics but in different levels, or both together. Thus, this paper offers an integrated performance measurement system between intra-organization and inter-organization, with the same dimension of metrics (time, cost, quality and flexibility) for depicting the linkage of the level in each dimension of metrics, and proposes a technique for measuring assessments from the past researches. The purpose of this paper is to provide an academic and practical understanding of a new format of SCPM to solve problems which occur as a result of the dimension of metrics in assessment measurements from different structures.},
author_keywords={Assessment technique;  Inter-organization level;  Intraorganization level;  Performance metric;  Supply chain performance measurement},
keywords={Assessment technique;  Inter-organization;  Intraorganization level;  Performance metrices;  Supply chain performance measurements, Chains;  Industrial engineering;  Research;  Supply chains, Problem solving},
references={Neely, E., Gregory, M., Platt, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Dobler, D.W., Burt, D.N., (1996) Purchasing and Supply Chain Management, , The Mcgraw-Hill Co; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-535; Fynes, B., Voss, C., De Burca, S., (2005) The Impact of Supply Chain Relationship Quality on Quality Performance; Shin, H., Collier, D.A., Wilson, D.D., Supply management orientation and supplier/buyer performance (2000) Journal of Operation Management, 18, pp. 317-333; Lin, C., Chow, W.S., Madu, C.N., Kuei, C.H., Yu, P.P., A structural equation model of supply chain quality management and organizational performance (2004) International Journal of Production Economics, 96, pp. 355-365; Tracey, M., Tan, C.L., Empirical analysis of supplier selection and involvement, customer satisfaction, and firm performance (2001) Supply Chain Management: International Journal, 6 (4), pp. 174-188; Gunarsekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Van Hoek, R.I., Measuring the unmeasurable - measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Gunarsekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2003) International Journal of Production Economics, 87, pp. 333-347; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistic Management, 30 (10), pp. 847-868; Folan, P., Browne, J., A review of performance measurement: Toward performance management (2005) Computers in Industry, 56, pp. 663-680; Kaplan, R., Norton, D., The balance scorecard-measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Bradley, P., (1996) A Performance Measurement Approach to the Reengineering of Manufacturing Enterprises, , Ph. D. Thesis, Ireland, CIMRU, NUI Galway; Lebas, M.J., Performance measurement and performance measurement (1995) International Journal Production Economics, 43, pp. 23-35; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chains system (1998) International Journal of Physical Distribution & Logistic Management, 28 (9-10), pp. 704-715; Yee, C.L., Huatan, K., A process and tool for supply network analysis (2004) Industrial Management & Data Systems, 104 (4), pp. 355-363; Pham, T.T., Quantitative approach to using e-commerce data to monitor and control the performance of a supply chain (2004) International Conference on E-Technology, , e-Commerce and e- Service (EEE'04); Gilmour, P., Benchmarking supply chain operation (1999) International Journal of Physical Distribution & Logistic Management, 5 (4), pp. 283-290; Neely, A., Adam, C., Kennerley, M., (2002) The Performance Prism: The Scorecard for Measuring and Managing Business Success, , Financial Time Prentice Hall; Lambert, D.M., Garcia-Dastugue, S.J., An evaluation of process-oriented supply chain management frameworks (2005) Journal of Business Logistics, 26 (1), pp. 25-51; Liu, F., Ding, F.Y., Lall, V., Using data envelopment analysis to compare suppliers for supplier selection and performance improvement (2000) Supply Chain Management: An International Journal, 5 (3), pp. 143-150; Cebi, F., Bayraktar, D., An integrated approach for supplier selection (2003) Logistic Information Management, 16 (6), pp. 395-400; Dahel, N.E., Vendor selection and order quantity allocation i volume discount environment (2003) Supply Chain Management: An International Journal, 8 (4), pp. 335-342; Chen, C.C., Yeh, T.M., Yang, C.C., Customer-focused rating system of supplier quality performance (2004) Journal of Manufacturing Technology Management, 15 (7), pp. 599-606; Lau, H.C.W., Pang, W.K., Wong, C.W.Y., Methodology for monitoring supply chain performance: A fuzzy logic approach (2002) Logistic Information Management, 15 (4), pp. 271-280; Bruzzone, A., Orsani, A., (2003) AI and Simulation-Based Techniques for the Assessment of Supply Chain Logistic Performance, , ANSS'03 IEEE 2003; Ma, H., Deng, Z., Deng, W.D., An online approach for distribution benchmarking (2004) Benchmarking: An International Journal, 11 (4), pp. 385-402; Kannan, V.R., Tan, K.C., Attitude of us and european managers to supplier selection and assessment and implications for business performance (2003) Benchmarking: An International Journal, 10 (5), pp. 472-489; Edward, R., Ruth, B., Selecting supplier in the automotive industry: Comparing international logistics costs (2005) Asia Pacific Journal of Marketing and Logistics, 17, pp. 61-69; Beamon, B.M., Measuring supply chain performance (1999) Journal of Operations & Production Management, 19 (3), pp. 275-292; Tian, C., Chai, Y., Yi, L., Ren, S., (2003) Performance Management in Supply Chain, pp. 4934-4939. , IEEE 2003; Angerhofer, B.J., Angelides, M.C., A model and a performance measurement system for collaborative supply chains (2005) Decision Support System; Lockamy, A., (1991) A Study of Operational and Strategic Performance Measurement Systems in Selected World Class Manufacturing Firms: An Examination of Linkages for Competitive Advantage, , Ph.D. thesis, University of Georgia; Bowersox, D.J., Closs, D.J., Stank, T.P., How supply chain competency leads to business success (2000) Supply Chain Management Review, 4 (4), pp. 70-78; Hausman, W.H., Supply chain performance metrics (2000) The Practice of Supply Chain Management; Medori, D., Steeple, D., A framework for auditing and enchancing performance measurement systems (2000) International Journal of Operation and Production Management, 20 (5), pp. 520-533; Horris, C.L., Neural networks in supply chain management (1995) Engineering Management Conference IEEE 1995, pp. 347-352; Haoxun, C., Lionel, A., Feng, C., Karim, L., Modeling and performance evaluation of supply chain using batch deterministic and stochastic petri nets (2005) IEEE Transaction on Automation Science and Engineering, 2 (2), pp. 132-144; Lockamg III, A., McCormark, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (12), pp. 1192-1218; Simatupang, T.M., Wright, A.C., Sriharan, R., Applying the theory of constraints to supply chain collaboration (2004) Supply Chain Management: An International Journal, 9 (1), pp. 57-70; Fawcett, S., Smith, S., Cooper, M., Strategic intent, measurement capacity and operational success: Making the connection (1997) International Journal of Physical Distribution and Logistics, 27 (7), pp. 410-421; Carlos, F.G., Mahmoud, M.Y., Joao, V.L., An examination of manufacturing organizations' performance evaluation-analysis, implications and a framework for future research (2004) International Journal of Operations & Production Management, 24 (5), pp. 488-513; Coyle, J.J., Bardi, E.J., Langley Jr., C.J., (2003) The Management of Business Logistics-A Supply Chain Perspective, 1. , (7th edition). Thompson com; Mark, W., Mike, B., Prediction performance (2003) Management Decision, 41 (8), pp. 806-816; Chan, T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: International Journal, 8 (3), pp. 209-223; Chen, C.T., Lin, C.T., Huang, S.F., A fuzzy approach for supplier evaluation and selection in supply chain management (2005) International Journal Production Economics; Chan, F.T.S., Kumar, N., Global supplier development considering risk factor using fuzzy extended ahp-based approach (2005) The International Journal of Management Science; Ohdar, R., Ray, P.K., Performance measurement and evaluation of suppliers: An evolutionary fuzzy-based approach (2004) Journal of Manufacturing Technology Management, 15 (8), pp. 723-734; Fain, V., Tiwari, M.K., Chan, F.T.S., Evaluation of the performance using an evoluationary fuzzy-base approach (2004) Journal of Manufacturing Technology Management, 15 (8), pp. 735-744; Kim, B., Oh, H., The impact of decision-making sharing between supplier and manufacturer on their collaboration performance (2005) Supply Chain Management: International Journal, 10 (3), pp. 223-236; Simatupang, T.M., Sriharan, R., A benchmarking scheme for supply chain collaboration (2004) Benchmarking: An International Journal, 11 (1), pp. 9-30; Zarandi, M.H.F., Turksen, I.B., Saghiri, S., Supply chain: Crisp and fuzzy aspects (2002) International Journal Application Mathematic Science, 12 (3), pp. 423-435; Schmitz, J., Platts, K.W., Supplier logistic performance measurement: Indications from a study in the automotive industry (2004) International Journal Productions Economics, 89, pp. 231-243; Choy, K.L., Lee, W.B., Lo, V., An intelligent supplier management too for benchmarking suppliers in out source manufacturing (2002) Expert Systems with Applications, 22, pp. 213-224; Choy, K.L., Lee, W.B., Lo, V., Design of an intelligent supplier relationship management system: A hybrid case based neural network approach (2003) Expert Systems with Applications, 24, pp. 225-237},
correspondence_address1={Production Engineering Department, 126 Prachautid Rd., Bangmod Tungkhru Bangkok 10140, Thailand},
language={English},
abbrev_source_title={Int. Conf. Comput. Ind. Eng., ICC IE},
document_type={Conference Paper},
source={Scopus},
}

@BOOK{Blanc2006621,
author={Blanc, S.-A. and Ducq, Y. and Vallespir, B.},
title={Interoperability Problems in Supply Chains Context},
journal={Information Control Problems in Manufacturing 2006},
year={2006},
pages={621-626},
doi={10.1016/B978-008044654-7/50236-3},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883908685&doi=10.1016%2fB978-008044654-7%2f50236-3&partnerID=40&md5=e8e8f74a0775bc3d472ba548f99fb743},
affiliation={Laboratoire d'Automatique, LAPS/GRAI, UMR 5131 CNRS, ENSEIRB, Université Bordeaux 1, 33405 Talence Cedex, France},
abstract={Today, the enterprises must cooperate to survive in a context increasingly competing. This cooperation is carried out using organization in network through supply chain. The general problematic is the interoperability in the context of supply chain. To solve the problem it is necessary to define precisely the interoperability. This chapter presents the problems of interoperability and heterogeneity and presents a method to solve heterogeneity problems by using an evolution management method. These solutions are mainly based on enterprises modeling and semantic heterogeneity techniques. By using Performance Measurement System appropriate for the evolution management and exploitation of the systems, the enterprises of supply chains can evolve towards interoperability. In the future, the research works will be about the characterization of the interoperability and the definition of the interoperability of an enterprise compared to another. © 2006 Copyright © 2006 Elsevier Ltd. All rights reserved.},
references={ATHENA Annex I&-quot;Description of work", sixth framework program (2003) European Integrated Project ATHENA, , 11 november 2003; Chalmeta, R., Grangel, R., Performance measurement systems for virtual enterprise integration (2005) Computer Integrated Manufacturing, 18 (1), pp. 73-84. , January-February 2005; Doumeingts, G., Ducq, Y., Enterprise modelling techniques to improve efficiency of enterprises (2001) Production Planning & Control, 12 (2), pp. 146-163. , 2001; Ducq, Y., Vallespir, B., Definition and aggregation of performance measurement system in three aeronautical workshops using the ECOGRAI method (2005) Production Planning & Control, 16 (2), pp. 163-177. , 2005; (1990) IEEE standard computer dictionary: a compilation of IEEE standard computer glossaries, , IEEE; Kleinhans, S., (1999) Intégration de la modélisation d'entreprise dans une démarche de stratégie industrielle, , October 29th, 1999, Thèse de l'Université Bordeaux 1; Malhene, N., (2000) Gestion du processus d'évolution des systèmes industriels-Conduite et méthode, , 2000, GEM, thèse de l'Université Bordeaux 1, Janvier; Sansonnet, J.-P., Approches de l'hétérogénéité sémantique entre agents informationnels, , http://www.limsi.fr/Individu/jps/research/weakaliens/weakaliens.htm; Valencia, E., (2000) Hétérogénéité Sémantique entre Agents modélisés en Logique de Descriptions, , December 8, 2000, Thèse de doctorat de l'Université de Paris XI; Valencia, E., Sansonnet, J.-P., Simplicial Representation for Description Logics Databases (2000) International Conference on Description Logic DL 2000},
correspondence_address1={Blanc, S.-A.; Laboratoire d'Automatique, , 33405 Talence Cedex, France; email: severine.blanc@giiaps.u-bordeauxl.fr},
publisher={Elsevier Ltd},
isbn={9780080446547},
language={English},
abbrev_source_title={Inf. Cont. Prob. in Manufact. 2006},
document_type={Book Chapter},
source={Scopus},
}

@BOOK{Papakiriakopoulos2006211,
author={Papakiriakopoulos, D.},
title={Performance measurement in supply chain networks the case of fast moving consumer goods},
journal={Managing Dynamic Networks: Organizational Perspectives of Technology Enabled Inter-firm Collaboration},
year={2006},
pages={211-237},
doi={10.1007/3-540-32884-X_8},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84879303528&doi=10.1007%2f3-540-32884-X_8&partnerID=40&md5=1e2606fea51468f5c21da76dea55d4ab},
affiliation={Athens University of Economics and Business, Athens, Greece},
abstract={In the area of Management Control Systems research efforts focus primarily on the development of performance measurement systems for a single organization. However, collaboration practices create common areas of interest among organizations. To this end, integrated performance measures are required. In a collaborative context facilitated by an IT infrastructure, organizations have the opportunity to develop a common performance measurement system. This chapter briefly presents some widely discussed theoretical issues in the area of performance measurement as well as some practical considerations. Based on case study research in the area of Fast Moving Consumer Goods, I present a concise method to realize which performance measures are available on the top of the IT infrastructure. At the same time, the chapter addresses the challenges and problems of a single performance measure, showing that the measurement method is important although sometimes complex. © 2006 Springer-Verlag Berlin Heidelberg.},
author_keywords={collaboration;  Performance measurement;  store replenishment process},
references={Antony, R., Govindarajan, V., (2001) Management Control Systems, , 10th Edition, McGraw-Hill Irwin, New York; (2002) European CPFR Insights, , http://www.ecrnet.org, Accenture, ECR Europe Publications; Almassy, E., Baatz, B., Strategic Alliances: 455 electronic executives say rugged individualism is fading (1992) Electronic Business, pp. 38-42. , March; Anderson, S., Young, M., The impact of contextual and process factors on the evaluation of activity-based costing systems (1999) Accounting Organizations and Society, 24 (7), pp. 525-559; Argyris, C., The dilemma of implementing controls: The case of managerial accounting (1990) Accounting, Organizations and Society, 15 (6), pp. 503-511; Austin, J., (2000) The Collaboration Challenge, , Jossey-Bass Publishers, San Francisco; Beamon, B., Ware, T., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Becker, J., Rosemann, M., Schuette, R., Guidelines of good modeling (1995) Wirtschaftsinformatik, 37 (4), pp. 435-445; Biemans, W., Brand, M., Reverse Marketing: A synergy of purchasing and relationship marketing (1995) International Journal of Purchasing and Materials Management, 31 (3), pp. 29-37; Bititci, U., Turner, T., Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Productions Management, 20 (6), pp. 692-704; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Burke, M., Rupinski, M., Dunlap, W., Davison, H., Do situational variables act as substantive causes of relationships between individual differences variables? Two largescale tests of 'common cause' models (1996) Personnel Psychology, 49 (3), pp. 573-598; Chiu, M.-L., An organizational view of design communication in design collaboration (2002) Design Studies, 23 (2), pp. 187-210; Choi, T., Liker, J., Bring Japanese continuous improvement approaches to U. S. manufacturing: The roles of process orientation and communications (1995) Decision Sciences, 26 (5), pp. 589-620; Churchman, W., Why measure (1959) Measurement: Definition and Theories, , W. Churchman, and P. Ratoosh, eds., John Wiley & Sons, New York; Collins, F., Managerial accounting systems and organizational control: A role perspective (1982) Accounting, Organizations and Society, 7 (2), pp. 107-122; Cooper, R., Implementing an activity-based cost system (1990) Journal of Cost Management, 4 (1), pp. 69-78; Cravens, D., Piercy, N., Relationship marketing and collaborative networks in service organizations (1994) International Journal of Service Industry Management, 5 (3), pp. 39-53; Cravens, K., Piercy, N., Cravens, D., Assessing the performance of strategic alliances: Matching metrics to strategies (2000) European Management Journal, 18 (5), pp. 529-541; Daniel, S., Reitsperger, W., Linking quality strategy with management control systems: Empirical evidence from Japanese industry (1991) Accounting, Organizations and Society, 16 (7), pp. 601-618; Daniels, C., Rosen, A., (1994) Performance Management: Improving Quality and Productivity Through Positive Reinforcement, , 4th ed., Performance Management Publications, Tucker, GA; Dekker, H., Control of inter-organizational relationships: Evidence on appropriation concerns and coordination requirements (2004) Accounting, Organizations and Society, 29 (1), pp. 27-49; Dixon, J., Nanni, A., Vollman, T., (1990) The New Performance Challenge - Measuring Operations for World-class Competition, , Irwin, Homewood, IL; Dutta, S., Manzoni, J., (1999) Process Re-engineering, Organizational Change and Performance Improvement, , McGraw-Hill; Eccles, J., The performance management manifesto (1991) Harvard Business Review, 69 (1), pp. 131-137; Euske, J., (1984) Management Control: Planning, Control, Measurement, and Evaluation, , Addison-Wesley, Reading, MA; Germain, R., Dröge, C., Effect of just-in-time purchasing relationships on organizational design, purchasing department configuration, and firm performance (1997) Industrial Marketing Management, 26 (2), pp. 115-125; Govindarajan, V., Fisher, J., Strategy, control systems and resource sharing: Effects on business-unit performance (1990) Academy of Management Journal, 33 (2), pp. 259-285; Gruen, T., Corsten, D., Bharadwaj, S., (2002) Retail Out-of-stocks: A Worldwide Examination of Extent Causes and Consumer Response, , The Food Institute Forum CIES, FMI, GMA; Gulati, R., Network location and learning: The influence of network resources and firm capabilities on alliance formation (1999) Strategic Management Journal, 20 (5), pp. 397-420; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environment (2001) International Journal of Operation & Production Management, 21 (1), pp. 71-87; Gunasekaran, A., Patel, C., McGaughey, R., A framework for supply chain performance improvement (2004) International Journal of Production Economics, 87 (3), pp. 333-347; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Holmstrom, J., Framling, K., Kaipia, R., Saranen, J., Collaborative planning forecasting and replenishment: New solutions needed for mass collaboration (2002) Supply Chain Management, 7 (3), pp. 136-145; Jarillo, C., On strategic networks (1988) Strategic Management Journal, 9 (1), pp. 31-41; Jeans, M., Morrow, M., Performance measurement in the UK industry (1990) Journal of Cost Management for the Manufacturing Industry, 3, pp. 51-64; Jermier, J., Critical perspective on organizational control (1998) Administrative Science Quarterly, 43 (2), pp. 235-256; (1994) Performance Measurement: Applying Value Chain Analysis to the Grocery Industry, , Joint Industry ECR Project, Report published by ECR; Kang, Y., Gershwin, S., (2003) Information Inaccuracy in Inventory Systems, , http://cell1.mit.edu/papers/kang-gershwinautoid04.pdf, Auto-ID Center, University of Washington, Last Accessed 18 November 2004; Kaplan, R., Norton, D., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Kaplan, R., Norton, D., (1995) Translating Strategy Into Action: The Balanced Scorecard, , Boston, MA, Harvard Business School Press; Kaplan, R., Management Accounting (1984-1994): Development of new practice and theory (1994) Management Accounting Research, 5, pp. 247-260; Kennerley, M., Neely, A., Performance measurement frameworks: A review (2000) Proceedings of Performance Measurement 2000: Past Present and Future Conference, pp. 291-298. , Robinson College, Cambridge, 19-21 July; Krumwiede, K., The implementation stages of activity-based costing and the impact of contextual and organizational factors (1998) Journal of Management Accounting Research, 10, pp. 240-277; Kueng, P., Process performance measurement system: A tool to support process-based organizations (2000) Total Quality Management, 11 (1), pp. 67-85; Lambert, D., Cooper, M., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Lambert, D., Pohlen, T., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1); Langfield, K., Management control systems and strategy: A critical review (1997) Accounting, Organizations and Society, 22, pp. 207-232; Neely, A., Gregory, M., Platts, K., Performance measurement system design - A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Nohria, N., Garcia-Pont, C., Global strategic linkages and industry structure (1991) Strategic Management Journal, 12, pp. 105-124; Olian, D., Rynes, L., Making total quality work: Aligning organizational processes, performance measures, and stakeholders (1991) Human Resource Management, 30 (3), pp. 303-333; Otley, D., Budget use and managerial performance (1978) Journal of Accounting Research, 16 (1), pp. 122-149; Otley, D., Performance management: A framework for management control system research (1999) Management Accounting Research, 10, pp. 363-382; Ouchi, W., Johnson, J., Types of organizational control and their relationship to emotional well-being (1978) Administrative Science Quarterly, 23, pp. 293-317; Ouchi, W., A conceptual framework for the design of organizational control mechanisms (1978) Management Science, 25 (9), pp. 833-848; Powell, W., Neither Market nor Hierarchy: Network forms of organization (1990) Research in Organization Behavior, 12, pp. 295-336; Reichlestein, S., Providing managerial incentives: Cash flows versus accrual accounting (2000) Journal of Accounting Research, 38 (2), pp. 243-269; Ring, S., Van De Ven, H., Structuring cooperative relationships between organizations (1992) Strategic Management Journal, 13, pp. 483-498; Berger, R., (2002) Full Shelf Satisfaction. Reducing the Out-of-stocks in the Grocery Channel, , Grocery Manufacturers of America GMA; Schneiderman, A.M., Metrics for the order fulfillment process (part 1) (1996) Journal of Cost Management, 10 (2), pp. 30-42; Seifert, D., (2002) Collaborative Planning Forecasting and Replenishment, , Galileo Press, Bonn; Sieger, M., Manage your numbers to match your strategy (1992) Management Review, 8 (2), pp. 46-48; Simons, R., (1995) Levers of Control: How Managers use Innovative Control Systems to Drive Strategic Renewal, , Harvard Business School Press; Simons, R., (2000) Performance Measurement and Control Systems for Implementing Strategy: Text & Cases, , Prentice Hall, Upper Saddle River, NJ; Speakman, R., Kammouff, J., Myhr, N., An empirical investigation into supply chain management: A perspective on partnerships (1998) Supply Chain Management, 3 (2), pp. 53-67; Stewart, G., Supply-chain operations reference model (SCOR): The fisrt cross-industry framework for integrated supply-chain management, of their supply chain, and to support (1997) Logistics Information Management, 10 (2), pp. 62-67; Tarr, J., Developing performance measurement systems that support continuous improvement (1995) Hospital Material Management Quarterly, 17 (3), pp. 59-67; Thor, G., Performance measurement in a research organization (1991) National Productivity Review, pp. 499-507; Van Hoek, R., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Vyas, N., Shelburn, W., Rogers, D., An analysis of strategic alliances: Forms, functions and framework (1995) Journal of Business and Industrial Marketing, 10 (3), pp. 47-60; White, G.P., A survey and taxonomy of strategy-related performance measures for manufacturing (1996) International Journal of Operations & Production Management, 16 (3), pp. 42-61; White, M., Flores, B., Goal setting in the management of operations (1987) International Journal of Operations & Production Management, 7 (6), pp. 5-16; Willcocks, L., Lester, S., (1999) Beyond the IT Productivity Paradox, , John Wiley & Sons, Chichester; Zairi, M., (1994) Measuring Performance for Business Results, , Chapman & Hall, London},
correspondence_address1={Papakiriakopoulos, D.; Athens University of Economics and Business, Athens, Greece},
publisher={Springer Berlin Heidelberg},
isbn={354025367X; 9783540253679},
language={English},
abbrev_source_title={Mng. Dyn. Netw.: Org. Perspect. of Technol. Enabled Inter-firm Collaboration},
document_type={Book Chapter},
source={Scopus},
}

@CONFERENCE{Mao20061537,
author={Mao, Z. and Li, X. and Jia, F.},
title={A new approach of supply network performance measurement based on supporting level and operational level},
journal={IET Conference Publications},
year={2006},
number={524},
pages={1537-1545},
doi={10.1049/cp:20061011},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250643607&doi=10.1049%2fcp%3a20061011&partnerID=40&md5=3de443f148a694b85a76960db341ab50},
affiliation={Centre for Logistics and Supply Chain Management, School of Management, Cranfield University, MK43 0AL, United Kingdom; School of Management, Tianjin University, 300072, China},
abstract={The successful application of advanced manufacturing technologies (AMT) has brought remarkable effects and benefits to most companies after many years of accumulated practical experiences. However, along with unceasing improvement of people' s living standard and increasing aggravation of market competition, competitions between companies are more and more intensive and growing up to competitions between supply networks. How to realize the target of "reducing cost and improving service together" on the level of networks becomes the very knotty problem for every company and their supply networks. As the effective instrument for aligning node companies within their network, AMT accomplishes sharing information and coordinative operational among companies through its hard technologies (such as robotics, computers and other scientific technologies) and soft technologies (managerial technologies such as TQM, JIT, VMI, E-Business, etc.). They also play a very important role in the coordination of the relationship between node enterprises. As the method of organization development, performance measurement's effect is not only to evaluate current performance, but to guide future development direction of the whole supply network. Therefore, the investments which could bring potential benefits to supply networks, such as AMT, must be taken into account when build up performance measurement systems. This paper establishes an equitable measurement system including supporting evaluation level (consisting of HITS Human, Institution, Technology and Surroundings) and operational evaluation level (consisting of TQFS - Time, Quality, Finance and Service) to break through the traditional measurement methods which only concentrate on current performance, while pay more attention to supply networks' further development. Moreover, this paper introduces the evaluation of relationship among node companies into measurement system which is cardinal significant for the supply networks' coordinative development as well.},
author_keywords={HITS;  Operational level;  Performance measurement;  Supply network;  Supporting level;  TQFS},
keywords={Electronic commerce;  Finance;  Quality control;  Sustainable development;  Technology transfer, Operational levels;  Performance measurements;  Supply networks;  Supporting levels, Supply chains},
references={Burgess, T.F., Gules, H.K., Buyer-supplier relationships in firms adopting advanced manufacturing technology: An empirical analysis of the implementation of hard and soft technologies (1998) Journal of Engineering and Technology Management, 15, pp. 127-152; Andraski, J.C., Foundations for a successful continuous replenishment programme (1994) International Journal of Logistics Management, 5 (1), pp. 1-8; Van Hoek, R.L., Postponement in European supply chains, , Cranfield University working paper, 2000; John Gattorna, G., (2003) Handbook of supply chain management, , 5th Edition. Gower Publishing Company; Christopher, M., (1998) Logistics and supply chain management: Strategies for reducing cost and improving service, , 2nd ed. London: Financial Times Management; Shucheng, M., Tang, X., Characteristics & strategies of quality management in supply chain (2001) Computer Integrated Manufacturing System (CIMS), 7 (9), pp. 32-35; Chopra, S., Meindl, P., (2001) Supply chain management: Strategy, planning and operations, , Prentice-Hall; Lambert, D.M., Stock, J.R., Ellram, L.M., (1998) Fundamentals of logistics management, , Irwin/McGraw-Hill, Boston, MA; Ferguson, B.R., Implementing supply chain management (2000) Production and Inventory Management Journal, 41 (2), p. 64},
correspondence_address1={Mao, Z.; Centre for Logistics and Supply Chain Management, , MK43 0AL, United Kingdom; email: zhao.mao@cranfield.ac.uk},
isbn={0863416969; 9780863416965},
language={English},
abbrev_source_title={IET Conf Publ},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Reiner20065065,
author={Reiner, G. and Hofmann, P.},
title={Efficiency analysis of supply chain processes},
journal={International Journal of Production Research},
year={2006},
volume={44},
number={23},
pages={5065-5087},
doi={10.1080/00207540500515123},
note={cited By 49},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750317284&doi=10.1080%2f00207540500515123&partnerID=40&md5=ca97f2a0fd33a1c58466b01870ab90ca},
affiliation={Department of Information Systems and Operations, Vienna University of Economics and Business Administration, Nordbergstraße 15, 1090 Vienna, Austria; SAP Research, SAP Labs. Palo Alto, 3410 Hillnew Avenue, Palo Alto, CA, United States},
abstract={We present an integrated benchmarking approach. To analyse the performance of inter-organizational (supply chain) processes at company level we combine dependency analysis and data envelopment analysis (DEA). DEA has been proven to be a powerful tool for benchmarking processes and for identifying the most efficient ones. Before using DEA analysis the inputs and outputs as well as the relevant dependencies have to be identified. To support the determination of input and output variables we propose to use dependency analysis. We illustrate the application of this integrated approach by analysing the results of an empirical benchmarking study of 65 European and North American companies. The study shows that make-to-stock is still the predominating manufacturing strategy of the analysed industries. Therefore, we utilize different DEA models with weight restrictions only for companies with a make-to-stock strategy. The results support our basic hypotheses that efficient supply chains lead to high financial performance. Furthermore they indicate improvement potential for the benchmarked supply chain processes.},
author_keywords={Benchmarking;  DEA;  Supply chain performance measurement;  TETRAD},
keywords={Benchmarking;  Data reduction;  Finance;  Inventory control;  Mathematical models;  Societies and institutions, Data envelopment analysis (DEA);  Supply chain performance measurement;  Supply chain processes, Production engineering},
references={Banker, R., Charnes, A., Cooper, W., Some models for estimating technical scale inefficiencies in data envelopment analysis (1984) Manage. Sci., 30 (9), pp. 1078-1092; Boussofiane, A., Dyson, R.G., Thanassoulis, E., Applied data envelopment analysis (1991) Euro. J. Op. Res., 51 (1), pp. 1-15; Castillo, E., Gutierrez, J.M., Hadi, A.S., (1997) Expert Systems and Probabilistic Network Models, , Springer: New York; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) Euro. J. Op. Res., 2 (6), pp. 429-444; Christopher, M., (1992) Logistics and Supply Chain Management - Strategies for Reducing Costs and Improving Services, , Pitman Publishing: London; Cooper, W.W., Park, K.S., Yu, G., IDEA and AR-IDEA: Models for dealing with imprecise data in DEA (1999) Manage. Sci., 45 (4), pp. 597-607; Cooper, W.W., Seiford, L.M., Tone, K., (2000) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software, , Kluwer Academic: Boston; Dahlgaard, J.J., Kristensen, K., Kanji, G.K., (1998) Fundamentals of Total Quality Management - Process Analysis and Improvement, , Chapman & Hall: London; Dyson, R.G., Allen, R., Camanho, A.S., Podinovski, V.V., Sarrico, C.S., Shale, E.A., Pitfalls and protocols in DEA (2001) Euro. J. Op. Res., 132 (2), pp. 245-259; EFQM Excellence Model, , http://www.efqm.org, accessed 17 December 2004; Großpietsch, J., (2003) Erfolgsfaktoren im Supply Chain Management der Konsumgüterindustrie, , Doctoral thesis, University of Münster (Westphalia); Hammel, T., Kuettner, D., Phelps, T., The re-engineering of Hewlett-Packard's CD-RW supply chain (2002) Supply Chain Manage.: An International Journal, 7 (3), pp. 113-118; Homburg, C., Using data envelopment analysis to benchmark activities (2001) Int. J. Prod. Econ., 73 (1), pp. 51-58; Jammernegg, W., Kischka, P., Dynamic, customer-oriented improvement of supply networks (2005) Euro. J. Op. Res., 167 (2), pp. 413-426; http://www.quality.nist.gov, MBNQA, Baldrige National Quality Program accessed 5 January 2005; Meyr, H., Rohde, J., Stadtler, H., Basics for modeling (2002) Supply Chain Management and Advanced Planning - Concepts, Models, Software and Case Studies, 2nd Ed., pp. 45-70. , edited by H. Stadtler and C. Kilger, (Springer: Berlin); Narasimhan, R., Talluri, S., Mendez, D., Supplier evaluation and rationalisation via data envelopment analysis: An empiric examination (2001) J. Supply Chain Manage., 37 (3), pp. 28-37; Olhager, J., Strategic positioning of the order penetration point (2003) Int. J. Prod. Econ., 85 (3), pp. 319-329; Post, T., Performance evaluation in stochastic environments using mean-variance data envelopment analysis (2001) Op. Res., 49 (2), pp. 281-292; Ramanathan, R., (2003) An Introduction to Data Envelopment Analysis - A Tool for Performance Measurement, , Sage: London; Reiner, G., Customer-oriented improvement and evaluation of supply chain processes supported by simulation models (2005) Int. J. Prod. Econ., 96 (3), pp. 381-395; Roll, Y., Cook, W.D., Golany, B., Controlling factor weights in data envelopment analysis (2004) IIE Transactions, 23 (1), pp. 2-9; Ross, A., Droge, C., An analysis of operations efficiency in large-scale distribution systems (2004) J. Op. Manage., 21 (6), pp. 673-688; Pittiglio, Rabin, Todd and McGrath (PRTM)/SAP Benchmarking Study 2002-2003: Supply Chain Planning, , http://www.sap.com/company/press/press.asp?pressID=2425, http://www.sap.com/company/press/pdf/PRTM_SCM_Benchmarking.pdf (accessed 6 April 2005); Scheel, H., (2000) EMS: Efficiency Measurement System, User's Manual, Version 1.3, , Dortmund; Scheel, H., Undesirable outputs in efficiency valuations (2001) Euro. J. Op. Res., 132 (2), pp. 400-410; Scheines, R., Spirtes, P., Glymour, C., Meek, C., (1994) TETRAD II: Toots for Discovery, , Lawrence Erlbaum Associates: New York; http://www.supply-chain.org, SCOR, (accessed 22 August 2005); Spirtes, P., Scheines, R., Glymour, C.N., (1993) Causation, Prediction and Search, , (Springer: New York); Sudit, E.F., Productivity measurement in industrial operations (1995) Euro. J. Op. Res., 85 (3), pp. 435-453; The TETRAD Project: Causal Models and Statistical Data, , http://www.phil.cmu.edu/projects/tetrad/index.html, TETRAD-Project, (accessed 17 December 2004); Talluri, S., A benchmarking method for business-process reengineering and improvement (2000) Int. J. Flex. Manuf. Syst., 12 (4), pp. 291-304; Talluri, S., Baker, R.C., A multi-phase mathematical programming approach for effective supply chain design (2002) Euro. J. Op. Res., 141 (3), pp. 544-558; Talluri, S., Sarkis, J., A methodology for monitoring system performance (2002) Int. J. Prod. Res., 40 (7), pp. 1567-1582; Tavares, G., (2002) A Bibliography of Data Envelopment Analysis (1978-2001), , RUTCOR Research Report, RRR 01-02, January, Piscataway, New Jersey; Van Der Vorst, J.G.A.J., Beulens, A.J.M., De Wit, W., Van Beek, P., Supply chain management in food chains: Improving performance by reducing uncertainty (1998) Int. Trans. in Op. Res., 5 (6), pp. 487-499},
correspondence_address1={Reiner, G.; Department of Information Systems and Operations, Nordbergstraße 15, 1090 Vienna, Austria; email: gerald.reiner@wu-wien.ac.at},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Hwang2006255,
author={Hwang, M.-H. and Rau, H.},
title={Development of strategies for shortening supply chain and demand chain},
journal={Human Systems Management},
year={2006},
volume={25},
number={4},
pages={255-263},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33750905879&partnerID=40&md5=3ef7ec8b530024202f6ea03ba82f9d11},
affiliation={Department of Information Management, Diwan College of Management, Taiwan; Department of Industrial Engineering, Chung Yuan Christian University, Taiwan},
abstract={Supply chain management (SCM) recently has attracted a lot of attention from industrial practitioners and academics worldwide. Moreover, shortening a supply chain has been a main issue in supply chain management. Shortening supply chain means reducing the process time required for supply chain activities, which are logistics, business, information, cash flow and design process, etc. This study proposes methods of shortening supply chain based on the activities listed above together with their classification and descriptions. To analyze the cause and effect between those solutions and methods of shortening supply chain, this study uses the cause and effect diagram as a supporting method. Additionally, to understand the efficiency of these methods, this study builds up a supply chain performance measure to evaluate the efficiency of methods of shortening supply chain. © 2006 - IOS Press and the authors. All rights reserved.},
author_keywords={Competitive strategy;  Demand chain;  Supply chain},
keywords={Competition;  Graphic methods;  Information management;  Logic design, Competitive strategy;  Demand chain;  Supply chain management (SCM), Strategic planning},
references={New, S.J., Payne, P., Research frameworks in logistics: Three models, seven dinners and a survey (1995) International Journal of Physical Distribution and Logistics Management, 25 (10), pp. 60-77; Scott, C., Westbrook, R., New strategic tools for supply chain management (1991) International Journal of Physical Distribution & Logistics, 21 (1), pp. 23-33; Tan, K.C., Supply chain management: Practices, concerns, and performance issues (2002) Journal of Supply Chain Management, 38 (1), pp. 42-52; Min, H., Zhou, G., Supply chain modeling: Past, present and future (2002) Computer & Industrial Engineering, 43, pp. 231-249; Chan, F.T.S., Performance measurement in a supply chain (2003) The International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measure and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54, pp. 507-514; Weihrich, H., The SWOT matrix-A tool for situational analysis (1982) Long Rang Planning, 15 (2), p. 60; Porter, M., (1985) Competitive Advantage: Creating and Sustaining Superior Performance, , The Free Press, New York; Cook, R.L., Rogowski, R.A., Applying JIT principles to continuous process manufacturing supply chains (1996) Production and Inventory Management Journal, pp. 12-16. , First Quarter; Hammel, T.R., Cpim, C.P.M., Lopczak, L.R., Tightening the supply chain (1993) Production and Inventory Management Journal, pp. 63-70. , Second Quarter; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2003) Designing and Managing the Supply Chain, 2 Edn, , McGraw-Hill; Chopra, S., Meindl, P., (2000) Supply Chain Management: Strategy, Planning and Operation, , Prentice-Hall, Upper Saddle River, NJ; Lin, F.R., Tan, G.W., Shaw, M.J., Modeling supply-chain networks by a multi-agent system (1998) IEEE Proc. 31st Annual Hawaii International Conference on System Sciences, pp. 105-114; Wisner, J.D., A structural equation model of supply chain management strategies and firm performance (2003) Journal of Business Logistics, 24 (1), pp. 1-24; Korpela, J., Lehmusvaara, A., Tuominen, M., An analytic approach to supply chain development (2001) International Journal of Production Economics, 71, pp. 145-155; Andreas, O., Herbert, K., Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain (2003) European Journal of Operational Research, 144, pp. 306-320; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87, pp. 333-347; Edward, A.M., Supply chain strategies, capabilities, and performance (2001) Transportation Journal, 41 (1), pp. 37-54; Brace, G., Market powertrain: An imperative to co-operation (1989) Proceedings of the Commission of European Communities Partnership between Small and Large Films Conference, , Graham and Trotman, London; Vollmann, T.E., Cordon, C., Raabe, H., From supply chain management to demand chain management (1995) IMD Perspectives for Managers, (NOVEMBER); Tim, W., Roger, M., Bruce, E., Demand chain management theory: Constraints and development from global aerospace supply webs (2002) Journal of Operations Management, 20, pp. 691-706},
correspondence_address1={Rau, H.; Department of Industrial Engineering, Taiwan; email: hsinrau@cycu.edu.tw},
issn={01672533},
coden={HSMAD},
language={English},
abbrev_source_title={Hum Syst Manage},
document_type={Article},
source={Scopus},
}

@ARTICLE{Angerhofer2006283,
author={Angerhofer, B.J. and Angelides, M.C.},
title={A model and a performance measurement system for collaborative supply chains},
journal={Decision Support Systems},
year={2006},
volume={42},
number={1},
pages={283-301},
doi={10.1016/j.dss.2004.12.005},
note={cited By 161},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33747785214&doi=10.1016%2fj.dss.2004.12.005&partnerID=40&md5=7cf2ecb3fc5d4f90a754d76f9415b08c},
affiliation={Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, United Kingdom},
abstract={Modeling the constituents of a collaborative supply chain, the key parameters they influence, and the appropriate performance measures in a decision support environment enables prior understanding of the impact on the performance of a collaborative supply chain as a result of changes in the constituents and key parameters. In turn, this allows pinpointing of those areas where the actual supply chain can be improved and hence manage the chain's performance. This paper shows how the constituents, key parameters and performance indicators are modelled into the environment and through a case study illustrates how the decision support environment may be used to improve the performance of a collaborative supply chain by pinpointing areas for improvement. © 2005 Elsevier B.V. All rights reserved.},
author_keywords={Collaborative supply chain;  Performance measurement},
keywords={Decision theory;  Mathematical models;  Measurement theory;  Parameter estimation, Collaborative supply chain;  Performance indicators;  Performance measurement, Decision support systems},
references={Akkermans, H.A., Renga: a systems approach to facilitating inter-organizational network development (2001) System Dynamics Review, 17 (3); Akkermans, H.A., Bogerd, P., Vos, B., Virtuous and vicious cycles on the road towards international supply chain management (1999) International Journal of Operations and Production Management, 19 (5-6); Anderson, D.L., Lee, H.L., (1999) Synchronised supply chains: the new frontier; Anderson Jr., E.G., Fine, C.H., Parker, G.G., Upstream volatility in the supply chain: the machine tool industry as a case study (2000) Production and Operations Management, 9 (3); APQC, (2000) Supply chain performance metrics survey, , American Productivity and Quality Center; Baourakis, G., Stroe, M., The optimization of the distribution system in the context of supply chain management development (2002) Financial engineering, e-commerce and supply chain, , Pardalos P.M., and Tsitsiringos V.K. (Eds), Kluwer, Dordrecht; Barlas, Y., Aksogan, A., (1996) Product diversification and quick response order strategies in supply chain management, , International System Dynamics Conference, Cambridge, (MA), USA; Beamon, B.M., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Beamon, B.M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-3218; Berger, P.D., Gerstenfeld, A., Zeng, A.Z., How many suppliers are best? A decision-analysis approach (2004) Omega, 32, pp. 9-15; Berry, D., Evans, G.N., Mason-Jones, R., Towill, D.R., The BPR SCOPE concept in leveraging improved supply chain performance (1999) Business Process Management Journal, 5 (3), pp. 254-274; Checkland, P., (1981) Systems thinking, systems practice, , John Wiley & Sons; Childerhouse, P., Aitken, J., Towill, D.R., Analysis and design of focused demand chains (2002) Journal of Operations Management, 20 (6), pp. 675-689; Dejonckheere, J., Disney, S.M., Lambrecht, M.R., Towill, D.R., Transfer function analysis of forecasting induced bullwhip in supply chains (2002) International Journal of Production Economics, 78 (2), pp. 133-144; Dejonckheere, J., Disney, S.M., Lambrecht, M.R., Towill, D.R., The impact of information enrichment on the bullwhip effect in supply chains: a control engineering perspective (2004) European Journal of Operational Research, 152, pp. 727-750; Disney, S.M., Naim, M.M., Towill, D.R., Dynamic simulation modelling for lean logistics (1997) International Journal of Physical Distribution and Logistics Management, 27 (3-4), pp. 174-196; Elliott, G., Starkings, S., (1998) Business information systems: systems, theory and practice, , Longman, London; Evans, G.N., Naim, M.M., (1994) The dynamics of capacity constrained supply chains, international system dynamics conference, , Stirling, Scotland; Evans, G.N., Naim, M.M., Towill, D.R., Application of a simulation methodology to the redesign of a logistical control system (1998) International Journal of Production Economics, 56-57, pp. 157-168; Forrester, J.W., Industrial dynamics: a major breakthrough for decision makers (1958) Harvard Business Review, 36 (4), pp. 37-66; Graves, S.C., Rinnooy, K.A.H.G., Zipkin, P.H., Logistics of production and inventory (1993) Handbooks in operations research and management science, , Nemhauser G.L., and Rinnooy K.A.H.G. (Eds), Elsevier, Amsterdam; Hafeez, K., Griffiths, M., Griffiths, J., Naim, M.M., Systems design of a two-echelon steel industry supply chain (1996) International Journal of Production Economics, 45, pp. 121-130; Hammant, J., Disney, S.M., Childerhouse, P., Naim, M.M., Modelling the consequences of a strategic supply chain initiative of an automotive aftermarket operation (1999) International Journal of Physical Distribution and Logistics Management, 29 (9), pp. 535-550; Harrison, A., New, C., The role of coherent supply chain strategy and performance management in achieving competitive advantage: an international survey (2002) Journal of the Operational Research Society, 53, pp. 263-271; Hieber, R., Collaborative performance measurement in logistics networks-the model, approach and assigned KPIs (2002) Logistik Management, 2 (2), pp. 13-21; Johansson, H.J., McHugh, P., Pendlebury, A.J., Wheeler, W.A., (1993) Business process re-engineering, , John Wiley & Sons, Chichester; Kelley, G.G., A case study of an ERP implementation: promises, phantoms, and prugatory (2002) New directions in supply-chain management: technology, strategy, and implementation, , Boone T., and Ganeshan R. (Eds), AMACOM, New York, NY; König, U.H., (1997) Simulating multidimensional supply chains-a Vensim based model, , Industrieseminar der Universität Mannheim, Mannheim; Lee, H.L., Padmanabhan, V., Whang, S., The bullwhip effect in supply chains (1997) Sloan Management Review, 38 (3), pp. 93-102; Lewis, J.C., Naim, M.M., Towill, D.R., An integrated approach to re-engineering material and logistics control (1997) International Journal of Physical Distribution and Logistics Management, 27 (3-4), pp. 197-209; Mason-Jones, R., Towill, D.R., Total cycle time compression and the agile supply chain-the integrated supply chain logistics (1999) International Journal of Production Economics, 62 (1), pp. 61-73; Mavrommati, A., Migdalas, A., From logistics to collaborative logistics-a theoretical approach (2002) Financial engineering, e-commerce and supply chain, , Pardalos P.M., and Tsitsiringos V.K. (Eds), Kluwer, Dordrecht; Parker, G.G., Anderson Jr., E.G., Supply chain integration: putting humpty dumpty back together again (2002) New directions in supply-chain management: technology, strategy, and implementation, , Boone T., and Ganeshan R. (Eds), AMACOM, New York, NY; Poirier, C.C., (2003) The supply chain manager's problem-solver, , St. Lucie Press, Boca Raton, FL; Porter, M.E., (1985) Competitive advantage: creating and sustaining superior performance, , Free Press, New York, NY; Richardson, G.P., Pugh III, A.L., (1981) Introduction to system dynamics modeling, , Productivity Press, Portland, OR; Ross, D.F., (2000) Competing through supply chain management: creating market-winning strategies through supply chain partnerships, , Kluwer, Chicago, IL; Sankar, R., Inventory management across the retail supply chain (1998) Supply Chain Management Review, Winter, 1998, pp. 56-63; SCC, (2001) Supply-chain operations reference-model V5.0, , Supply-Chain Council; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2000) Designing and managing the supply chain: concepts, strategies, and case studies, , McGraw-Hill, Boston, MA; Sterman, J.D., Misperceptions of feedback in dynamic decision making (1989) Organizational Behavior and Human Decision Processes, 43, pp. 301-335; Sterman, J.D., (2000) Business dynamics: systems thinking and modeling for a complex world, , McGraw-Hill, Boston, MA; Szuprowicz, B.O., (2000) Supply chain management for e-business infrastructures, , Computer Technology Research Corporation, Charleston, SC; Towill, D.R., Industrial dynamics modelling of supply chains (1996) Logistics Information Management, 9 (4), pp. 43-56; Towill, D.R., Time compression and supply chain management-a guided tour (1996) Supply Chain Management, 1, pp. 15-27; Towill, D.R., The seamless supply chain-the predator's strategic advantage (1997) International Journal of Transport Management Special Issue on Strategic Cost Management, pp. 37-56; Towill, D.R., Naim, M.M., Partnership sourcing helps smooth supply chain dynamics (1993) Journal of the Institute of Purchasing and Supply Management, pp. 38-42; Vos, B., Redesigning international manufacturing and logistics structures (1997) International Journal of Physical Distribution and Logistics Management, 27 (7), pp. 377-394; Warburton, R.D.H., Disney, S.M., Towill, D.R., Hodgson, J.P.E., Further insights into the stability of supply chains (2004) International Journal of Production Research, 42 (3), pp. 639-648},
correspondence_address1={Angelides, M.C.; Department of Information Systems and Computing, , Uxbridge, Middlesex UB8 3PH, United Kingdom; email: angelidesm@acm.org},
issn={01679236},
coden={DSSYD},
language={English},
abbrev_source_title={Decis Support Syst},
document_type={Article},
source={Scopus},
}

@ARTICLE{Thron2006596,
author={Thron, T. and Nagy, G. and Wassan, N.},
title={The impact of various levels of collaborative engagement on global and individual supply chain performance},
journal={International Journal of Physical Distribution and Logistics Management},
year={2006},
volume={36},
number={8},
pages={596-620},
doi={10.1108/09600030610702880},
note={cited By 27},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33748306045&doi=10.1108%2f09600030610702880&partnerID=40&md5=b2ead4f8da8ec40a49e55850a2240964},
affiliation={Kent Business School, University of Kent, Canterbury, United Kingdom},
abstract={Purpose - Most collaborative SCM research has focused on the ideal situation of a manufacturer engaging with all its downstream partners. In view of extensive entry costs, lack of trust or simply non-suitability of electronic data processing systems this, however, provides only limited support to actual problems of many companies. The paper seeks to investigate various common supply chain performance measures to show what impact increasing adoption of collaborative replenishment between manufacturer and several major customers has on each market participant. Design/methodology/approach - The study uses discrete event simulation to evaluate various adjustments within the distribution frameworks of two food-manufacturers and their major customers. Findings - The analysis suggests that manufacturer and customers can substantially benefit from even a partial increase in demand visibility. This nevertheless can be costly since favouring some customers due to sharing a collaborative replenishment system, while others often seem to experience heavier delivery delays and declining service-level. Research limitations/implications - The research focused on the delivery framework of the two involved manufacturers and the chosen products. Hence, research findings may differ and need to be modified before drawing conclusions for different products, companies or industries. Practical implications - The analysis aims to help practitioners to identify possible opportunities and threats within an expanding collaborative SC replenishment system. Originality/value - Investigating heterogeneous delivery frameworks within an emerging collaboration system has not been addressed much within prior SCM research. It aims to help mainly small- or medium-sized enterprises to reveal possible advantages and drawbacks within the process of emerging with a varying number of customers from a traditional predetermined reorder-point into a collaborative VMI/CPFR system. © Emerald Group Publishing Limited.},
author_keywords={Demand forecasting;  Information exchange;  Supply chain management},
references={Albino, V., Garavelli, A.C., Limited flexibility in cellular manufacturing systems: A simulation study (1999) International Journal of Production Economics, 6061, pp. 447-455; Barratt, M., Understanding the meaning of collaboration in the supply chain (2004) Supply Chain Management: An International Journal, 9 (1), pp. 30-42; Borenstein, D., Implementation of an object-oriented tool for the simulation of manufacturing systems (2000) International Journal of Production Research, 38 (9), pp. 2125-2142; Brooks, R., Robinson, S., Lewis, C., (2001) Simulation. Inventory Control, , Palgrave New York, NY; Cachon, G., Fisher, M., Campbell Soup's continuous replenishment program: Evaluation and enhanced inventory decision rules (1997) Production and Operations Management, 6 (3), pp. 266-276; Cachon, G., Fisher, M., Supply chain inventory management and the value of shared information (2000) Management Science, 46 (8), pp. 1032-1048; Caprihan, R., Wadhwa, S., Impact of routing flexibility on the performance of an FMS - A simulation study (1997) International Journal of Flexible Manufacturing Systems, 9 (3), pp. 273-298; Chen, F., Echelon reorder points, installation reorder points, and the value of decentralized demand information (1998) Management Science, 44 (12), pp. 221-234; Christopher, M., Towill, D.R., Developing market specific supply chain strategies (2002) International Journal of Logistics Management, 13 (1), pp. 1-14; (2000) Small and Medium Enterprises (SME) Trends Report, pp. 10-12. , Confederation of British Industry London July 2003; Daugherty, P.J., Myers, M.B., Autry, C.W., Automatic replenishment programs: An empirical examination (1999) Journal of Business Logistics, 20 (2), pp. 63-82; Fraza, V., Streamlining the channel (1998) Industrial Distribution, 87 (9), pp. 73-74; Garg, S., Vrat, P., Kanda, A., Equipment flexibility vs inventory: A simulation study of manufacturing systems (2001) International Journal of Production Economics, 70 (2), pp. 125-143; Gupta, Y.P., Goyal, S., Flexibility trade-offs in a random flexible manufacturing system: A simulation study (1992) International Journal of Production Research, 30 (3), pp. 527-557; Holmström, J., Business process innovation in the supply chain: A case study of implementing vendor managed inventory (1998) European Journal of Purchasing & Supply Management, 4 (23), pp. 127-131; Holmström, J., Fraemling, K., Kaipa, R., Saranen, J., Collaborative planning forecasting and replenishment: New solutions needed for mass collaboration (2002) Supply Chain Management: An International Journal, 7 (3), pp. 136-145; Hugos, M., (2003) Essentials of Supply Chain Management, , Wiley New York, NY; Ireland, R., Bruce, R., CPFR: Only the beginning of collaboration (2000) Supply Chain Management Review, 4 (4), pp. 80-88; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29 (1), pp. 65-83; Lau, H.C.W., Lee, W.B., On a responsive supply chain information system (2000) International Journal of Physical Distribution & Logistics Management, 30 (78), pp. 598-610; Law, A.M., Kelton, W.D., (2000) Simulation Modeling and Analysis, , 3rd ed. McGraw-Hill New York, NY; Lee, H.L., So, K.C., Tang, C.S., The value of information sharing in a two-level supply chain (2000) Management Science, 46 (5), pp. 626-643; Lin, F., Huang, S., Lin, S., Effects of information sharing on supply chain performance in electronic commerce (2002) IEEE Transactions on Engineering Management, 49 (3), pp. 258-268; McCarthy, S., Golocic, S., Implementing collaborative forecasting to improve supply chain performance (2002) International Journal of Physical Distribution & Logistics Management, 32 (6), pp. 431-454; Maloni, M.J., Benton, W.C., Supply chain partnerships: Opportunities for operations research (1997) European Journal of Operational Research, 101, pp. 419-429; Moberg, C.R., Speh, T.W., Freese, T.L., SCM: Making the vision a reality (2003) Supply Chain Management Review, 7 (5), pp. 34-39; Nandkeolyar, U., Christy, D.P., An investigation of the effect of machine flexibility and number of part families (1992) International Journal of Production Research, 30 (3), pp. 513-526; Nesheim, T., Externalisation of the core: Antecedents of collaborative relationships with suppliers (2001) European Journal of Purchasing and Supply, 7 (4), pp. 217-225; Pidd, M., (2004) Computer Simulation in Management Science, , Wiley Chichester; Quayle, M., A study of supply chain management practice in UK industrial SMEs (2003) Supply Chain Management: An International Journal, 8 (1), pp. 79-86; Sabath, R., Fontanella, J., The unfulfilled promise of supply chain collaboration (2002) Supply Chain Management Review, 6 (4), pp. 24-29; Shapiro, J.F., (2001) Modeling the Supply Chain, , Duxbury Press Pacific Grove, CA; Simchi-Levi, D., Kaminsky, P., (2002) Designing and Managing the Supply Chain, , McGraw-Hill Higher Education New York, NY; Corp, S., (2005) Simul8 Professional R12 Process Simulation Software Package - The Leading PC Simulation Software, , www.simul8.com, Boston, MA; Smros, J., Lehtonen, J.M., Appelqvist, P., Holmström, J., The impact of increasing demand visibility on production and inventory control efficiency (2003) International Journal of Physical Distribution & Logistics Management, 33 (4), pp. 336-354; Stank, T.P., Crum, M., Arango, M., Benefits of inter-firm co-ordination in food industry supply chains (1999) Journal of Business Logistics, 20 (2), pp. 21-41; Tang, M., Gattorna, J., Developing an aligned supply chain operating strategy (2003) Gower Handbook of Supply Chain Management, , Gattorna, J. 5th ed. Gower London; Thron, T., (2004) Opportunities and Vulnerabilities of Demand Forecasting Approaches Prior to and After Implementation into Computer Simulation Models, , working paper Kent Business School Kent; Towill, D.R., Naim, M.M., Wikner, J., Industrial dynamics simulation models in the design of supply chains (1992) International Journal of Physical Distribution & Logistics Management, 22 (5), pp. 3-14; (1998) Collaborative Planning Forecasting and Replenishment (CPFR), , www.vics.org/standards/, Voluntary Interindustry Commerce Standards Association; (1999) The Roadmap to CPFR: The Case Studies, , Voluntary Interindustry Commerce Standards Association, available at: www.vics.org/standards/cpfrroadmapcase studies/; Waller, M., Johnson, M.E., Davis, T., Vendor-managed inventory in the retail supply chain (1999) Journal of Business Logistics, 20 (1), pp. 183-203; Hambuch, P., Collaborative planning, forecasting and replenishment - Collaboration along the total supply chain (2004) Business Briefing: Global Purchasing and Supply Chain Strategies 2004, , International Federation of Purchasing and Materials Management (IFPMM)},
correspondence_address1={Thron, T.; Kent Business School, , Canterbury, United Kingdom; email: T.Thron@kent.ac.uk},
issn={09600035},
language={English},
abbrev_source_title={Int. J. Phys. Distrib. Logist. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Qi20061472,
author={Qi, F.-Z. and Zhong, L.-Y. and Pan, X.-H.},
title={Performance measurement process and method in agile supply chain},
journal={Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS},
year={2006},
volume={12},
number={9},
pages={1472-1476},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33751580617&partnerID=40&md5=623b08fd5ff1869f8972588cec73ef5a},
affiliation={Sch. of Management, Zhejiang Univ., Hangzhou 310058, China; Sch. of Business and Administration, Zhejiang Univ. of Tech., Hangzhou 310023, China; Sch. of Info. Management and Eng., Shanghai Univ. of Finance and Economics, Shanghai 200433, China},
abstract={Based on operation characteristics of agile supply chain, the structure of performance measurement index system in agile supply chain management was studied. A hierarchal measurement index system was established which was applicable to make decision for strategy layer of agile supply chain. Measurement benchmark issue was discussed and a process framework for agile supply chain performance measurement based on benchmarking was proposed, including the construction of internal and external benchmark databases. An Analytic Hierarchy Process (AHP) algorithm was suggested to calculate evaluation weights of measurement indexes, and an improved fuzzy comprehensive evaluation algorithm was presented to solve the complicated performance measurement problem in the Web-based performance measurement prototype system. Implementation of the process framework indicated that it was an effective approach to performance measurement in agile supply chain.},
author_keywords={Agile supply chain;  Benchmarking;  Measurement index system;  Performance measurement;  Process framework},
keywords={Database systems;  Evaluation;  Measurements;  Performance, Agile supply chain;  Analytic hierarchy process (AHP);  Measurement index system;  Performance measurement;  Process framework, Benchmarking},
references={SCOR model (2003), http://www.supplychain.org, PRTM; Gao, P., Huang, P., Zhang, C., Supply chain performance measurement and metrics selection based on SCOR (2004) Industrial Engineering and Management, 9 (3), pp. 49-52. , in Chinese; Begemann, C., Dynamics of performance measurement systems (2000) International Journal of Operations and Production Management, 20 (6), pp. 692-704; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal Production Economics, 89 (2), pp. 231-243; Gunasekaran, A., Patel, C., Mcgaughey, R.E., A framework for supply chain performance measurement (2004) International Journal Production Economics, 87 (3), pp. 333-347; Chen, Z., Performance measures system for supply and demand coordination based on agile supply chain (2004) Computer Integrated Manufacturing Systems, 10 (1), pp. 99-105. , in Chinese; Bourne, M., Mills, J., Wilcox, M., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Cohen, M.A., Zheng, Y.S., Agrawal, V., Service parts logistics: A benchmark analysis (1997) IIE Transactions on Logistics, 29 (8), pp. 627-639; Elmuti, D., The perceived impact of the benchmarking process on organizational effectiveness (1998) Production and Inventory Management Journal, 39 (3), pp. 6-11; Cox, A., Thompson, I., On the appropriateness of benchmarking (1998) Journal of General Management, 23 (3), pp. 1-20; Supply-chain practice maturity model and performance assessment (2003), http://www.pmgbenchmarking.com, The Performance Measurement Group; Kaplan, R.S., Norton, D.P., (1997) The Balanced Scorecard: Translating Strategy into Action, , Boston, Mass., USA: Harvard Business School Press},
correspondence_address1={Qi, F.-Z.; Sch. of Management, , Hangzhou 310058, China; email: fz_qi@zjut.edu.cn},
issn={10065911},
coden={JJZXF},
language={Chinese},
abbrev_source_title={Jisuanji Jicheng Zhizao Xitong},
document_type={Article},
source={Scopus},
}

@ARTICLE{Liang200635,
author={Liang, L. and Yang, F. and Cook, W.D. and Zhu, J.},
title={DEA models for supply chain efficiency evaluation},
journal={Annals of Operations Research},
year={2006},
volume={145},
number={1},
pages={35-49},
doi={10.1007/s10479-006-0026-7},
note={cited By 393},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33747822841&doi=10.1007%2fs10479-006-0026-7&partnerID=40&md5=27b93c8b7cae72619c1644059d6c741f},
affiliation={School of Business, University of Science and Technology of China, He Fei, An Hui Province, 230026, China; Schulich School of Business, York University, Toronto, Ont. M3J 1P3, Canada; Department of Management, Worcester Polytechnic Institute, Worcester, MA 01609, United States},
abstract={An appropriate performance measurement system is an important requirement for the effective management of a supply chain. Two hurdles are present in measuring the performance of a supply chain and its members. One is the existence of multiple measures that characterize the performance of chain members, and for which data must be acquired; the other is the existence of conflicts between the members of the chain with respect to specific measures. Conventional data envelopment analysis (DEA) cannot be employed directly to measure the performance of supply chain and its members, because of the existence of the intermediate measures connecting the supply chain members. In this paper it is shown that a supply chain can be deemed as efficient while its members may be inefficient in DEA-terms. The current study develops several DEA-based approaches for characterizing and measuring supply chain efficiency when intermediate measures are incorporated into the performance evaluation. The models are illustrated in a seller-buyer supply chain context, when the relationship between the seller and buyer is treated first as one of leader-follower, and second as one that is cooperative. In the leader-follower structure, the leader is first evaluated, and then the follower is evaluated using information related to the leader's efficiency. In the cooperative structure, the joint efficiency which is modelled as the average of the seller's and buyer's efficiency scores is maximized, and both supply chain members are evaluated simultaneously. Non-linear programming problems are developed to solve these new supply chain efficiency models. It is shown that these DEA-based non-linear programs can be treated as parametric linear programming problems, and best solutions can be obtained via a heuristic technique. The approaches are demonstrated with a numerical example.},
author_keywords={Best practice;  Buyer;  Data envelopment analysis (DEA);  Efficiency;  Performance;  Seller;  Supply chain},
references={Altiok, T., Ranjan, R., Multi-stage, pull-type production/inventory systems (1995) IIE Transactions, 27, pp. 190-200; Camm, J.D., Chorman, T.E., Dull, F.A., Evans, J.R., Sweeney, D.J., Wegryn, G.W., Blending OR/MS, judgment, and GIS: Restructuring P&G's supply chain (1997) Interfaces, 27 (1), pp. 128-142; Chen, Y., Liang, L., Yang, F., A DEA game model approach to supply chain efficiency (2006) Annals of Operations Research, (THIS ISSUE); Chen, Y., Zhu, J., Measuring information technology's indirect impact on firm performance (2004) Information Technology and Management Journal, 5 (1-2), pp. 9-22; Cheung, K.L., Hansman, W.H., An exact performance evaluation for the supplier in a two-echelon inventory system (2000) Operations Research, 48, pp. 646-653; Cohen, M.A., Lee, H.L., Resource deployment analysis of global manufacturing and distribution networks (1989) Journal of Manufacturing and Operations Management, 2, pp. 81-104; Färe, R., Grosskopf, S., Network DBA (2000) Socio-economic Planning Sciences, 34, pp. 35-49; Forker, L.B., Mendez, D., Hershauer, J., Total quality management in the supply chain: What is its impact on performance? (1997) International Journal of Production Research, 35, pp. 1681-1702; Golany, B., Hackman, S.T., Passy, U., An efficiency measurement framework for multi-stage production systems (2003) Working Paper; Huang, Z., Franchising cooperation through chance cross-constrained games (2000) Naval Research Logistics, 47, pp. 669-685; Ishii, K., Takahashi, K., Muramatsu, R., Integrated production, inventory and distribution systems (1988) International Journal of Production Research, 26 (3), pp. 473-482; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Lee, H.L., Billington, C., Material management in decentralized supply chains (1993) Operations Research, 41, pp. 835-847; Li, S.X., Huang, Z., Ashley, A., Seller-buyer system co-operation in a monopolistic market (1995) Journal of the Operational Research Society, 46, pp. 1456-1470; Li, S.X., Huang, Z., Ashley, A., Improving buyer-seller system cooperation through inventory control (1996) International Journal of Production Economics, 43, pp. 37-46; Newhart, D.D., Stott, K.L., Vasko, F.J., Consolidating production sizes to minimize inventory levels for a multi-stage production and distribution systems (1993) Journal of the Operational Research Society, 44 (7), pp. 637-644; Porter, M.E., Consumer behavior, retailer power and market performance in consumer goods industries (1974) Review of Econom. Statist., 56, pp. 419-436; Ross, D.F., (1998) Competing Through Supply Chain Management, , Boston: Kluwer Academic Publishers; Seiford, L.M., Zhu, J., Profitability and marketability of the top 55 US commercial banks (1999) Management Science, 45 (9), pp. 1270-1288; Simaan, M., Cruz, J.B., On the Stackelberg strategy in nonzero-sum games (1973) Journal of Optimization Theory and Applications, 11, pp. 533-555; Tayur, S., Ganeshan, R., Magazine, M., (1998) Quantitative Models for Supply Chain Management, , Boston: Kluwer Academic Publishers; Voudouris, V.T., Mathematical programming techniques to debottleneck the supply chain of fine chemical industries (1996) Computers and Chemical Engineering, 20, pp. S1269-S1274; Zhu, J., (2003) Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, , Boston: Kluwer Academic Publishers},
correspondence_address1={Zhu, J.; Department of Management, , Worcester, MA 01609, United States; email: jzhu@wpi.edu},
issn={02545330},
language={English},
abbrev_source_title={Ann. Oper. Res.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Zheng2006161,
author={Zheng, H.-W. and Zuo, X.-F.},
title={Framework for the supply chain performance measurement},
journal={Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology},
year={2006},
volume={26},
number={SUPPL.},
pages={161-165},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33748449484&partnerID=40&md5=76bf1c3800934393582a2160f0232ef0},
affiliation={School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China},
abstract={Based on Vensim software, a simulation model for the single supplier-single manufacture -single retailer supply chain was proposed. The simulation result was evaluated with the linear weighted average method. The simulation result showed the supply chain with high quality products, dissatisfactory service, low delivery speed and great redundant delivery capabilities. The framework incorporates the idea of real time, which is to make the base for determining the quantity relationship between the on-going process and the final result, and provides a way to foresee the potential problems during the supply chain operation.},
author_keywords={Metrics system;  Performance measurement;  Supply chain},
keywords={Computer simulation;  Computer software;  Evaluation;  Manufacture;  Metric system;  Real time systems;  Speed, Linear weighted average method;  Performance measurement;  Software Vensim;  Supply chain, Marketing},
references={Wang, C., Zha, J., Li, J., Research status and developing trends of the performance measurement of supply chain (2003) Journal of Industrial Engineering/Engineering Management, (3), pp. 27-30. , Chinese source; Zha, D., Chen, X., Wang, N., Performance measurement system in supply chain environment (2003) Industrial Engineering Journal, 6 (4), pp. 28-30. , Chinese source; Li, Q., Song, G., Zhang, S., Performance measuring indicators of supply chains (2003) China Mechanical Engineering, 14 (10), pp. 881-884. , Chinese source; Ma, J., Zhang, C., Research on performance test index oriented to supply chain (2003) Logistics Technology, (1), pp. 37-39. , Chinese source; Huo, J., Ma, X., Process-based supply chain performance evaluation (2005) Commercial Research, (5), pp. 1-4. , Chinese source; Zhang, L., Han, Y., Chen, J., A review: The application of system dynamics in supply chain management (2005) Systems Engineering, 23 (6), pp. 8-15. , Chinese source},
correspondence_address1={Zheng, H.-W.; School of Management and Economics, , Beijing 100081, China; email: zhwbb@bit.edu.cn},
issn={10010645},
coden={BLXUE},
language={Chinese},
abbrev_source_title={Beijing Ligong Daxue Xuebao},
document_type={Article},
source={Scopus},
}

@ARTICLE{Folan2006301,
author={Folan, P. and Higgins, P. and Browne, J.},
title={A communications framework for extended enterprise performance measurement},
journal={International Journal of Computer Integrated Manufacturing},
year={2006},
volume={19},
number={4},
pages={301-314},
doi={10.1080/09511920500340858},
note={cited By 13},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33646921824&doi=10.1080%2f09511920500340858&partnerID=40&md5=0248770aaa37b0dda1d963ce8daf215e},
affiliation={Computer Integrated Manufacturing Research Unit (CIMRU), National University of Ireland, Galway, Ireland},
abstract={Inter-organizational performance measurement communication is a neglected facet of the performance measurement literature. Performance measurement (PM) is undergoing a transformation in today's business environment, consistent with the development of inter-organizational paradigms, such as the virtual enterprise, extended enterprise (EE), and supply chain management. In this paper the communications issue is discussed using an EE mindset, and the concepts behind a communications framework are presented. This utilizes the concept of an EE host - the member of the EE with responsibilities for formulating, detailing and distributing information concerning the EE performance measurement system to other EE nodes. The framework design is based upon standardized intranets located at each partner in the EE. This provides the necessary elements to enable different organizations in the EE to maintain PM databases locally, while ensuring the existence of a centrally located database maintained by the EE host. A case study is presented that focuses upon the implementation of the EE performance measurement system at the EE host.},
author_keywords={Communication;  Extended enterprise;  Extranet;  Performance measurement},
keywords={Communication;  Database systems;  Electronic communities;  Industrial management;  Performance;  Societies and institutions, Extended enterprise;  Extranet;  Performance measurement;  Supply chain management, Local area networks},
references={Addyman, T., The internet: International information highway (1994) New Library World, 95, pp. 4-9; Au, R., Looi, M., Ashley, P., Automated cross-organisational trust establishment on extranets (2001) Proceedings of the Workshop on Information Technology for Virtual Enterprises, pp. 3-11. , Queensland, Australia, 29-30 January; Backhouse, C., Burns, N., Agile value chains for manufacturing -implications for performance measures (1999) Int. J. Agile Manage. Sys., 1, pp. 76-82; Bafoutsou, G., Mentzas, G., Review and functional classification of collaborative systems (2002) Int. J. Inform. Manage., 22, pp. 281-305; Basu, R., New criteria of performance management: A transition from enterprise to collaborative supply chain (2001) Measuring Business Excellence, 5, pp. 7-12; Beaumon, B.M., Measuring supply chain performance (1999) Int. J. Oper. Prod. Man., 19, pp. 275-292; Bititci, U., Mendibil, K., Martinez, V., Albores, P., Measuring and managing performance in extended enterprises (2005) Int. J. Oper. Prod. Man., 25, pp. 333-353; Bititci, U.S., Martinez, V., Albores, P., Mendibil, K., Creating and sustaining competitive advantage in collaborative systems: The what and the how (2003) Prod. Plan. Control, 14, pp. 410-424; Brewer, P., Spek, T., Using the balanced scorecard to measure supply chain performance (2000) J. Bus. Log., 21, pp. 75-93; Browne, J., Guest editorial: Extended enterprises, intranet and SMEs (1996) Prod. Plan. Control, 8, p. 207; Browne, J., Sackett, P., Wortmann, J.C., Future manufacturing systems -towards the extended enterprise (1995) Comput. Ind., 25, pp. 235-254; Browne, J., Zhang, J., Extended and virtual enterprises - Similarities and differences (1999) Int. J. Agile. Manage. Syst., 1, pp. 30-36; Cagliano, R., Caniato, F., Spina, G., E-business strategy: How companies are shaping their supply chain through the internet (2003) Int. J. Oper. Prod. Manage., 23, pp. 1142-1162; Chalmeta, R., Grangel, R., Performance measurement systems for virtual enterprise integration (2005) Int. J. Comput. Integ. Manuf., 18, pp. 73-84; Chan, F., Qi, H., Feasibility of performance measurement system for supply chain: A process-based approach and measures (2003) Int. Manuf. Sys., 14, pp. 179-190; Childe, S.J., The extended enterprise - A concept of co-operation (1998) Prod. Plan. Control., 9, pp. 320-327; Clegg, B., The extended enterprise: A matrix framework for effective strategic operations management (2003) Proceedings of the 20th International Manufacturing Conference IMC 20, pp. 739-746. , Cork, Ireland, 3-5 September; Cullen, P.A., Contracting, co-operative relations and extended enterprises (2000) Technovation, 20, pp. 363-372; Damsgaard, J., Truex, D., Binary trading relations and the limits of EDI standards: The procrustean bed of standards (2000) Ear. J. Info. Syst., 9, pp. 173-188; Dreyer, D., Performance measurement: A practioner's perspective (2000) Supply Chain Manage. Rev., pp. 62-68. , September-October; Folan, P., Browne, J., A review of performance measurement: Towards performance management (2005) Comput. Ind., 56, pp. 663-680; Folan, P., Browne, J., The development of an extended enterprise performance measurement system (2005) Prod. Plan. Control, 16, pp. 531-544; Folan, P., Jagdev, H., Browne, J., Providing for inter-organisational performance measurement (2005) Proceedings of the 4th International Workshop on Performance Measurement IFIP WG 5.7, pp. 1-13. , Bordeaux, France, 27-28 June; Graf, F., Providing security for eLearning (2002) Comput. Graph., 26, pp. 355-365; Gulledge, T., E-Hubs: The critical role of first-tier suppliers (2003) E-Business Applications: Technologies for Tomorrow's Solutions, pp. 1-1. , edited by J. Gasos and K. Thoben, (Springer-Verlag: Berlin); Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) Int. J. Oper. Prod. Man., 21, pp. 71-87; Holmberg, S., A systems perspective on supply chain measurements (2000) Int. J. Phys. Distrib. Log., 30, pp. 847-868; Jagdev, H., Browne, J., The extended enterprise - A context for manufacturing (1998) Prod. Plan. Control, 9, pp. 216-229; Jagdev, H., Thoben, K., Anatomy of enterprise collaborations (2001) Prod. Plan. Control, 12, pp. 437-451; Kleijnen, J., Smits, M., Performance metrics in supply chain management (2003) J. Oper. Res. Soc., 54, pp. 507-514; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) Eur. J. Opl. Res., 156, pp. 267-286; Pakstas, A., Towards electronic commerce via science park multi-extranets (1999) Comput. Communs, 22, pp. 1351-1363; Park, K., Favrel, J., Virtual enterprises - Information system and networking solution (1999) Comput. Ind. Eng., 37, pp. 441-444; Riggins, F., Rhee, H., Developing the learning network using extranets (1998) Proceedings of the 31st Annual Hawaii International Conference on System Sciences, 6, pp. 339-349. , 6-9 January Kohala Coast, Hawaii; Serve, M., Yen, D., Wang, J.C., Lin, B., B2B-enhanced supply chain process: Toward building virtual enterprises (2002) Bus. Process Manage. J.1, 8, pp. 245-253; Sridhar, S., Decision support using the intranet (1998) Dec. Support Syst., 23, pp. 19-28; Van Hoek, R., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Manage., 3, pp. 187-192; Vlosky, R., Fontenot, R., Blalock, L., Extranets: Impacts on business practices and relationships (2000) J. Bus. Ind. Market., 15, pp. 438-457; Whicker, L., Walton, J., Logistics and the virtual enterprise (1996) Logis. Focus, 4, pp. 7-10},
correspondence_address1={Folan, P.; Computer Integrated Manufacturing Research Unit (CIMRU), , Galway, Ireland; email: paul.folan@nuigaway.ie},
issn={0951192X},
coden={ICIME},
language={English},
abbrev_source_title={Int J Computer Integr Manuf},
document_type={Article},
source={Scopus},
}

@ARTICLE{Shepherd2006242,
author={Shepherd, C. and Günter, H.},
title={Measuring supply chain performance: Current research and future directions},
journal={International Journal of Productivity and Performance Management},
year={2006},
volume={55},
number={3-4},
pages={242-258},
doi={10.1108/17410400610653219},
note={cited By 402},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33645668377&doi=10.1108%2f17410400610653219&partnerID=40&md5=c7c5332e045ea3dd2295a44e8ae6abd8},
affiliation={Institute of Work Psychology, University of Sheffield, Sheffield, United Kingdom; ETH Zurich, Organization Work and Technology Group, Zurich, Switzerland; Institute of Work Psychology, University of Sheffield, United Kingdom; Organization Work and Technology Group, ETH Zurich, Switzerland},
abstract={Purpose - This paper aims to go some way towards addressing the dearth of research into performance measurement systems and metrics of supply chains by critically reviewing the contemporary literature and suggesting possible avenues for future research. Design/methodology/approach - The article provides a taxonomy of performance measures followed by a critical evaluation of measurement systems designed to evaluate the performance of supply chains. Findings - The paper argues that despite considerable advances in the literature in recent years, a number of important problems have not yet received adequate attention, including: the factors influencing the successful implementation of performance measurement systems for supply chains; the forces shaping their evolution over time; and, the problem of their ongoing maintenance. Originality/value - The paper provides a taxonomy of measures and outlines specific implications for future research. © Emerald Group Publishing Limited.},
author_keywords={Performance measures;  Supply chain management},
references={Artz, K.W., Buyer-supplier performance: The role of asset specificity, reciprocal investments and relational exchange (1999) British Journal of Management, 10, pp. 113-26; Baiman, S., Fischer, P.E., Rajan, M.V., Performance measurement and design in supply chains (2001) Management Science, 47 (1), pp. 173-88; Balasubramanian, P., Tewary, A.K., Design of supply chains: Unrealistic expectations on collaboration (2005) Sadhana-Academy Proceedings in Engineering Sciences, 30, pp. 463-73; Basnet, C., Corner, J., Wisner, J., Tan, K.C., Benchmarking supply chain management practice in New Zealand (2003) Supply Chain Management: An International Journal, 8 (1), pp. 57-64; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-94; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (34), pp. 275-92; Beamon, B.M., Chen, V.C.P., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-218; Billig, M., (1996) Arguing and Thinking: A Rhetorical Approach to Social Psychology, , Cambridge University Press Cambridge; Bititci, U., Cavalieri, S., Von Cieminski, G., Implementation of performance measurement systems: Private and public sectors (2005) Production Planning & Control, 16 (2), pp. 99-100; Bourne, M., Neely, A., Platts, K., Mills, J., The success and failure of performance measurement initiatives: Perceptions of participating managers (2002) International Journal of Operations & Production Management, 22 (11), pp. 1288-310; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-71; Bradford, K.D., Stringfellow, A., Weitz, B.A., Managing conflict to improve the effectiveness of retail networks (2004) Journal of Retailing, 80 (3), pp. 181-95; Cachon, G.P., Lariviere, M.A., Capacity choice and allocation: Strategic behavior and supply chain performance (1999) Management Science, 45 (8), pp. 1091-108; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-48; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management: An International Journal, 8 (34), pp. 209-23; Chen, H.X., Amodeo, L., Chu, F., Labadi, K., Modelling the performance evaluation of supply chains using batch deterministic and stochastic Petri nets (2005) IEEE Transactions on Automated Science and Engineering, 2 (2), pp. 132-44; Chen, I.J., Paulraj, A., Understanding supply chain management: Critical research and a theoretical framework (2004) International Journal of Production Research, 42 (1), pp. 131-63; Chiang, W.Y.K., Monahan, G.E., Managing inventories in a two-echelon dual-channel supply chain (2005) European Journal of Operational Research, 162 (2), pp. 325-41; Choy, K.L., Lee, W.B., An intelligent supplier relationship management system for selecting and benchmarking suppliers (2003) International Journal of Technology Management, 26 (7), pp. 717-42; Clegg, C.W., Wall, T.D., Pepper, K., Stride, C., Woods, D., Morrison, D., Cordery, J., Kogi, K., An international survey of the use and effectiveness of modern manufacturing practices (2002) Human Factors & Ergonomics in Manufacturing, 12, pp. 171-91; Cox, A., Benchmarking: A dead end for supply-chain management? (2000) Sloan Management Review, 41 (4), p. 5; Dasgupta, T., Using the six-sigma metric to measure and improve the performance of a supply chain (2003) Total Quality Management & Business Excellence, 14 (3), pp. 355-66; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (12), pp. 46-70; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge: Measuring Operations for World-class Competition, , Dow Jones-Irwin Homewood, IL; Dyapur, K.R., Patnaik, K.K., Transaction oriented computing (HIVE computing) using GRAM-Soft (2005) Computational Science, 3516, pp. 879-82; Ellinger, A.E., Improving marketing/logistics cross functional collaboration in the supply chain (2000) Industrial Marketing Management, 29, pp. 85-96; Ferguson, B.R., (2000) Implementing Supply Chain Management; Flynn, B.B., Flynn, E.J., Synergies between supply chain management and quality management: Emerging implications (2005) International Journal of Production Research, 43 (16), pp. 3421-36; Fynes, B., De Burca, S., Voss, C., Supply chain relationship quality: The competitive environment and performance (2005) International Journal of Production Research, 43 (16), pp. 3303-20; Globerson, S., Issues in developing a performance criteria system for an organization (1985) International Journal of Production Research, 23 (4), pp. 639-46; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) International Journal of Purchasing & Materials Management, 30 (4), pp. 13-18; Green, K.W., Inman, R.A., Using a just-in-time selling strategy to strengthen supply chain linkages (2005) International Journal of Production Research, 43 (16), pp. 3437-53; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-47; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Gunasekaran, A., Williams, H.J., McGaughey, R.E., Performance measurement and costing system in new enterprise (2005) Technovation, 25 (5), pp. 523-33; Handfield, R.B., Nichols, E.L., (1999) Introduction to Supply Chain Management, , Prentice-Hall Englewood Cliffs, NJ; Harrison, A., New, C., The role of coherent supply chain strategy and performance management in achieving competitive advantage: An international survey (2002) Journal of the Operational Research Society, 53 (3), pp. 263-71; Hieber, R., (2002) Supply Chain Management: A Collaborative Performance Measurement Approach, , VDF Zurich; Holmberg, S., A system perspective in supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-68; Huang, S.H., Sheoran, S.K., Wang, G., A review and analysis of supply chain operations reference (SCOR) model (2004) Supply Chain Management: An International Journal, 9 (1), pp. 23-9; Huang, S.H., Sheoran, S.K., Keskar, H., Computer assisted supply chain configuration based on supply chain operations reference (SCOR) model (2005) Computers & Industrial Engineering, 48 (2), pp. 377-94; Hwarng, H.B., Chong, C.S.P., Hie, N., Burgess, T.F., Modelling a complex supply chain: Understanding the effect of simplified assumptions (2005) International Journal of Production Research, 43 (13), pp. 2829-72; Kaplan, R.S., Norton, D.P., The balanced scorecard: Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-9; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) Management Accounting, pp. 134-47; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-45; Kennerley, M., Neely, A., Measuring performance in a changing business environment (2003) International Journal of Operations & Production Management, 23 (2), pp. 213-29; Kleijnen, J.P.C., Smits, M.T., Performance metrics in supply chain management (2003) Journal of the Operational Research Society, 54 (5), pp. 507-14; Krajewski, L., Wei, J.C., Tang, L.L., Responding to schedule changes in build-to-order supply chains (2005) Journal of Operations Management, 23 (5), pp. 452-69; Lai, K.H., Ngai, E.W.T., Cheng, T.C.E., Measures for evaluating supply chain performance in transport logistics (2002) Transportation Research Part E: Logistics and Transportation Review, 38 (6), pp. 439-56; Lambert, D.M., Pohlen, R.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-13; Li, G., Yan, H., Wang, S.Y., Xia, Y.S., Comparative analysis on value of information sharing in supply chains (2005) Supply Chain Management: An International Journal, 10 (1), pp. 34-46; Li, S., Subba Rao, S., Ragu-Nathan, T.S., Ragu-Nathan, B., Development and validation of a measurement instrument for studying supply chain practices (2005) Journal of Operations Management, 23, pp. 618-41; Lin, F.R., Sung, Y.W., Lo, Y.P., Effects of trust mechanisms on supply-chain performance: A multi-agent simulation study (2005) International Journal of Electronic Commerce, 9 (4), pp. 91-112; Lockamy, A., McCormack, K., Linking SCOR planning practices to supply chain performance: An exploratory study (2004) International Journal of Operations & Production Management, 24 (1112), pp. 1192-218; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156 (2), pp. 267-86; Lowe, A., Jones, A., Emergent strategy and the measurement of performance: The formulation of performance indicators at the microlevel (2004) Organization Studies, 25 (8), pp. 1313-37; Lummus, R.R., Duclos, L.K., Vokurka, R.J., The impact of marketing initiatives on the supply chain (2003) Supply Chain Management: An International Journal, 8 (34), pp. 317-23; Maloni, M.J., Benton, W.C., Supply chain partnerships: Opportunities for operations research (1997) European Journal of Operations Research, 101, pp. 419-29; Melnyk, S., Stewart, D.M., Swink, M., Metrics and performance measures in operations management: Dealing with the metrics maze (2004) Journal of Operations Management, 22, pp. 209-17; Morgan, C., Structure, speed and salience: Performance measurement in the supply chain (2004) Business Process Management Journal, 10 (5), pp. 522-36; Neely, A., Gregory, M., Platts, K., Performance measurement systems design: A literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerley, M., Performance measurement system design: Developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (910), pp. 1119-45; Nudurupati, S.S., Bititci, U.S., Implementation and impact of IT-supported performance measurement systems (2005) Production Planning & Control, 16 (2), pp. 152-62; Parker, S., Axtell, C.M., Seeing another viewpoint: Outcomes and antecedents of employee perspective taking activity (2001) Academy of Management Journal, 44 (6), pp. 1085-100; Ramdas, K., Spekman, R.E., Chain or shackles? Understanding what drives supply-chain performance (2000) Interfaces, 30 (4), pp. 3-21; Reiner, G., Customer-oriented improvement and evaluation of supply chain processes supported by simulation models (2005) International Journal of Production Economics, 96 (3), pp. 381-95; Schmitz, J., Platts, K.W., Supplier logistics performance measurement: Indications from a study in the automotive industry (2004) International Journal of Production Economics, 89 (2), pp. 231-43; Schönsleben, P., (2004) Integral Logistics Management: Planning and Control of Comprehensive Supply Chains, , St Lucie Press Boca Raton, FL; Smith, M.F., Lancioni, R.A., Oliva, T.A., The effects of management inertia on the supply chain performance of produce-to-stock firms (2005) Industrial Marketing Management, 34 (6), pp. 614-28; Soltani, E., Van Der Meer, R., Williams, T.M., A contrast of HRM and TQM approaches to performance management: Some evidence (2005) British Journal of Management, 16, pp. 211-30; Sperka, M., Zur Entwicklung eines Fräbogens der Kommunikation in Organisationen (1997) Zeitschrift fur Arbeits-und Organizationspsychologie, 41 (4), pp. 182-90; Stephens, S., Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice (2001) Information Systems Frontiers, 3 (4), pp. 471-6; Talluri, S., Sarkis, J., A model for performance monitoring of suppliers (2002) International Journal of Production Research, 40 (16), pp. 4257-69; Thomas, J., Why your supply chain doesn't work (1999) Logistics Management and Distribution Report, 38 (6), pp. 42-4; Tranfield, D., Denyer, D., Smart, P., Towards a methodology for developing evidence-informed management knowledge by means of systematic review (2003) British Journal of Management, 14, pp. 207-22; Ulusoy, G., An assessment of supply chain and innovation management practices in the manufacturing industries in Turkey (2003) International Journal of Production Economics, 86 (3), pp. 251-70; Van Der Vorst, J., Beulens, A., Identifying sources of uncertainty to generate supply chain redesign strategies (2001) International Journal of Physical Distribution & Logistics Management, 32 (6), pp. 409-30; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (12), pp. 15-29; Van Landeghem, R., Persoons, K., Benchmarking of logistical operations based on a causal model (2001) International Journal of Operations & Production Management, 21 (12), pp. 254-66; Van Veen-Dirks, D., Management control and production environment (2005) International Journal of Production Economics, 93 (4), pp. 263-72; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organisational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60 (1), pp. 53-60; Wang, F.K., Du, T.C., Li, E.Y., Applying six-sigma to supplier development (2004) Total Quality Management & Business Excellence, 15 (910), pp. 1217-29; Wang, G., Huang, S.H., Dismukes, J.P., Manufacturing supply chain design and evaluation (2005) International Journal of Advanced Manufacturing Technology, 25, pp. 93-100; Webster, M., Supply system structure, management and performance: A conceptual model (2002) International Journal of Management Reviews, 4 (4), pp. 353-69; Windischer, A., (2003), "Kooperatives Planen", dissertation, University of Zurich, Zurich; Windischer, A., Grote, G., Success factors for collaborative planning (2003) Strategy and Organization in Supply Chain, pp. 131-46. , Seuring, S. Muller, M. Goldbach, M. Physica Heidelberg; Wood, S.J., Stride, C., Wall, T.D., Clegg, C.W., Revisiting the use and effectiveness of modern manufacturing practices (2004) Human Factors & Ergonomics in Manufacturing, 14 (4), pp. 415-32; Wu, J.H., Quantity flexibility contracts under Bayesian updating (2005) Computers & Operations Research, 32 (5), pp. 1267-88},
correspondence_address1={Shepherd, C.; Institute of Work Psychology, , Sheffield, United Kingdom; email: c.shepherd@sheffield.ac.uk},
issn={17410401},
language={English},
abbrev_source_title={Int. J. Product. Perform. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Walley200693,
author={Walley, P. and Silvester, K. and Mountford, S.},
title={Health-care process improvement decisions: A systems perspective},
journal={International Journal of Health Care Quality Assurance},
year={2006},
volume={19},
number={1},
pages={93-104},
doi={10.1108/09526860610642618},
note={cited By 20},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33644602311&doi=10.1108%2f09526860610642618&partnerID=40&md5=d046a2c48ebe9cda3cd379de41299294},
affiliation={University of Warwick, Coventry, United Kingdom; West Midlands South Strategic Health Authority, Redditch, United Kingdom; George Eliot Hospital, Nuneaton, United Kingdom; West Midlands South Strategic Health Authority, Redditch, Worcestershire, United Kingdom; George Eliot Hospital, Nuneaton, Warwickshire, United Kingdom},
abstract={Purpose - The paper seeks to investigate decision-making processes within hospital improvement activity, to understand how performance measurement systems influence decisions and potentially lead to unsuccessful or unsustainable process changes. Design/methodology/approach - A longitudinal study over a 33-month period investigates key events, decisions and outcomes at one medium-sized hospital in the UK. Process improvement events are monitored using process control methods and by direct observation. The authors took a systems perspective of the health-care processes, ensuring that the impacts of decisions across the health-care supply chain were appropriately interpreted. Findings - The research uncovers the ways in which measurement systems disguise failed decisions and encourage managers to take a low-risk approach of "symptomatic relief" when trying to improve performance metrics. This prevents many managers from trying higher risk, sustainable process improvement changes. The behaviour of the health-care system is not understood by many managers and this leads to poor analysis of problem situations. Practical implications - Measurement using time-series methodologies, such as statistical process control are vital for a better understanding of the systems impact of changes. Senior managers must also be aware of the behavioural influence of similar performance measurement systems that discourage sustainable improvement. There is a risk that such experiences will tarnish the reputation of performance management as a discipline. Originality/value - Recommends process control measures as a way of creating an organization memory of how decisions affect performance - something that is currently lacking. © Emerald Group Publishing Limited.},
author_keywords={Case studies;  National Health Service;  Performance measurement (qualtiy);  Process management;  United Kingdom},
keywords={emergency health service;  health care quality;  health services research;  hospital administrator;  human;  longitudinal study;  national health service;  nonbiological model;  organization;  organization and management;  public hospital;  review;  risk;  system analysis;  total quality management;  United Kingdom;  utilization review, Decision Making, Organizational;  Emergency Service, Hospital;  England;  Health Services Research;  Hospital Administrators;  Hospitals, Public;  Humans;  Longitudinal Studies;  Models, Organizational;  Organizational Case Studies;  Organizational Innovation;  Process Assessment (Health Care);  Risk;  State Medicine;  Systems Analysis;  Total Quality Management},
references={Boland, T., Fowler, A., A systems perspective of performance management in public sector organizations (2000) The International Journal of Public Sector Management, 13 (5), pp. 417-46; Carey, R.G., (2002) Improving Healthcare with Control Charts, , ASQ Quality Press Milwaukee, WI; Deming, W.E., (1986) Out of the Crisis, , Cambridge University Press Cambridge; Forrester, J., (1961) Industrial Dynamics, , MIT Press Cambridge, MA; Inamadar, N., Kaplan, R.S., Applying the balanced scorecard in healthcare provider organisations (2002) Journal of Healthcare Management, 47 (3), pp. 170-95; Langley, G.J., Nolan, K.M., Nolan, T.M., Norman, C.L., Provost, L.P., (1996) The Improvement Guide. a Practical Approach to Enhancing Organizational Performance, , Jossey-Bass San Francisco, CA; Norman, R., Gregory, R., Paradoxes and pendulum swings: Performance management in New Zealand's public sector (2003) Australian Journal of Public Administration, 62 (4), pp. 35-49; Radnor, Z., Lovell, B., Defining, justifying and implementing the balanced scorecard in the National Health Service (2004) International Journal of Medical Marketing, 3 (3), pp. 174-88; Ritter-Teitel, J., Sail smoother with systems thinking (2002) Nursing Management, 33 (11), pp. 35-7; Senge, P., (1994) The Fifth Discipline: The Art and Practice of the Learning Organisation, , Bantam Doubleday London; Walley, P., Gowland, B., Completing the circle: From P-D to PDSA (2004) International Journal of Health Care Quality Assurance, 17 (6), pp. 349-58; Wheeler, D.J., (2000) Understanding Variation: The Key to Managing Chaos, , SPC Press Knoxville, TN; Wheeler, D.J., (2003) Making Sense of Data, , SPC Press Knoxville, TN},
correspondence_address1={Walley, P.; University of Warwick, Coventry, United Kingdom; email: paul.walley@wbs.ac.uk},
issn={09526862},
pubmed_id={16548403},
language={English},
abbrev_source_title={Int. J. Health Care Qual. Assur.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Saad200636,
author={Saad, M. and Patel, B.},
title={An investigation of supply chain performance measurement in the Indian automotive sector},
journal={Benchmarking},
year={2006},
volume={13},
number={1-2},
pages={36-53},
doi={10.1108/14635770610644565},
note={cited By 125},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33644532895&doi=10.1108%2f14635770610644565&partnerID=40&md5=29091f272f12db9f11bd4f1812f10f87},
affiliation={Bristol Business School, University of the West of England, Bristol, United Kingdom; KITS, Ramtek, Nagpur, India},
abstract={Purpose - The purpose of this paper is to investigate the relevance of the concept of supply chain performance in developing countries. The research also attempts to identify performance measure sets for supply chain performance in the context of a developing nation. Design/methodology/approach - The research focuses on supply chain practices in the Indian automobile sector. It identifies and discusses the main motives and determinants for the adoption and implementation of supply chain management concepts. It reviews the relevance of the main models to measure the performance of supply chain in developing countries. The research is based on a combination of qualitative and quantitative methods. Primary data were collected through semi-structured interviews and an exploratory survey. Performance measure sets were identified through factor analysis. Findings - This research proposes that the concept of supply chain performance is not fully embraced by the Indian automobile sector and highlights the difficulties associated with its implementation. Research limitations/implications - Further research involving other sectors and industries needs to be undertaken in order to gain an in-depth understanding of the key factors associated with the implementation of supply chain performance practices in India. This could also help develop a generic model to measure supply chain performance. Originality/value - The paper provides an attempt to adopt the concept of supply chain performance to the Indian context and culture. © Emerald Group Publishing Limited.},
author_keywords={Automotive industry;  Developing countries;  India;  Performance measures;  Supply chain management},
references={Akintoye, A., McIntosh, G., Fitzgerald, E., A survey of supply chain collaboration and management in the UK construction industry. Supply chain management in construction - Special issue (2000) European Journal of Purchasing and Supply Management, 6, pp. 159-168; Alaa, M., Ghalayini, James, S.N., The changing basis of performance measurement (1996) International Journal of Operations & Production Management, 16 (8), pp. 63-80; Ali, F., Smith, G., Saker, J., Developing buyer-supplier relationships in the automobile industry, a study of Jaguar and Nippondenson (1997) European Journal of Purchasing and Supply Management, 3 (1), pp. 33-42; Andersen, B., Randmael, S., Fagerhaug, T., (1998), final report from the bench marking activity SMARTMAN document, Trondheim; Austin, J.E., (1990) Managing in Developing Countries, , Free Press New York, NY; Azzone, G., Maseela, C., Bertele, U., Design of performance measures for time based companies (1991) International Journal of Operations & Productions Management, 11 (3), pp. 77-85; Barnett, V., (1991) Sample Survey Principles and Methods, , Edward Arnold London; Benita, B.M., Measuring supply chain performance (1999) International Journal of Operations & Productions Management, 19 (3), pp. 275-92; Betchel, C., Jayaram, J., Supply chain management: A strategic perspective (1997) International Journal of Logistics Management, 8 (1), pp. 15-34; Brown, M.G., (1996) Keeping Score: Using the Right Metrics to Drive World-Class Performance, , Quality Resources New York, NY; Burgess, R., Avoiding supply chain management failure: Lessons from business process reengineering (1988) International Journal of Logistics Management, 9, pp. 15-23; Carter, J.R., Narasimhan, R., Purchasing in international market place: Implications for operations (1990) Journal of Purchasing & Materials Management, 26 (3), pp. 2-11; Christopher, M., (1998) Logistics and Supply Chain Management - Strategies for Reducing Cost and Improving Service, , Financial Times, Prentice-Hall Englewood Cliffs, NJ; Christopher, M., Juttner, U., Developing strategic partnerships in the supply chain: A practitioner perspective (2000) European Journal of Purchasing & Supply Chain Management, 2 (6), pp. 117-27; Christopher, M., Towill, An integrated model for the design of agile supply chains (2001) International Journal of Physical Distribution & Logistics Management, 31 (1), pp. 235-46; Dixon, J.R., Nanni, A.J., Vollman, T.E., (1990) The New Performance Challenge; Measuring Operations for World Calls Competition, , Dow-Jones Irwin Homewood, IL; (2002), documents and reports 1997-2002 on Indian automobile industry provided by ACMA and SIAM; Dubois, A., Gadde, L., Supply strategy and network effects - Purchasing behaviour in the construction industry (2000) European Journal of Purchasing & Supply Chain Management, (6), pp. 207-15. , Supply Chain Management in Construction - special issue; Edum-Fotwe, F.T., Thorpe, A., McCaffer, R., Information procurement practices of key actors in construction supply chains (2001) European Journal of Purchasing & Supply Management, (7), pp. 155-64; Fitzgerald, L., Johnston, R., Brignall, T.J., Silvestro, R., Voss, C., (1991) Performance Measurement in Service Businesses, , The Chartered Institute of Management Accountants London; Gilmour, P., Bechmarking supply chain operations (1998) Bechmarking for Quality Management and Technology, 5 (4), pp. 283-90; Gunasekaran, A., Patel, C., Tirtirogulu, E., Performance measures and metrics in supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Harland, C.M., Supply chain management relationships (1996) Chains, Networks, British Academy of Management, 7; Harland, C.M., Lamming, R.C., Cousins, P.D., Developing the concept of supply strategy (1999) International Journal of Operations & Production Management, 19 (7), pp. 650-73; Holti, R., (1997) Adapting Supply Chain for Construction, , Workshop Report CPN727 Construction Productivity Network, CIRIA; Houlihan, J.B., International supply chain management (1985) International Journal of Physical Distribution & Materials Management, 3 (4), pp. 22-8; Hum, S., Industrial progress and the strategic significance of JIT and TQC for developing countries (1990) International Journal of Operations & Production Management, 11 (5); Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Harvard Business School Press Boston, MA; Kaplan, R.S., Norton, D.P., The balance score card; Measures that drive performance (1992) Harvard Business Review, pp. 71-9; Kosela, L., Management of production construction: A theoretical view (1999) Proceedings of the 7th Annual Conference of the International Group for Lean Construction IGLC-7, Berkeley, pp. 241-52; Lamming, R., (1993) Beyond Partnership: Strategies for Innovation and Lean Supply, , Prentice-Hall New York, NY; Madu, C.N., Quality management in developing economies (1997) International Journal of Quality Science, 2 (4), pp. 272-91; Mitchell, V., Assessing the reliability and validity of the questionnaires: An empirical example (1996) Journal of Applied Management Studies, 5 (2), pp. 199-207; Mowery, D.C., (1988) International Collaborative Ventures in US Manufacturing, , Ballinger Cambridge, MA; Neely, A., (1998) Measuring Business Performance - Why, What and How, , Economist Books London; Neely, A., Bourne, M., Kennerly, M., Performance measurement system design (2000) International Journal of Operations & Productions Management, 20 (10); New, S., Ramsay, J., A critical appraisal of aspects of the lean approach (1997) European Journal of Purchasing & Supply Management, 3 (2), pp. 93-102; Remko, I.V.H., Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-92; Saad, M., Jones, M., James, P., A review of the progress towards the adoption of supply chain management (SCM) relationships in construction (2002) European Journal of Purchasing & Supply Management, 8, pp. 173-83; Saad, M., Patel, P., Issues and challenges in the supply chain performance measurement in developing nations: A case of Indian automobile sector (2002) EUROMA 2002 Conference Proceedings, 2, pp. 1429-40; Savage, C., (1990) Fifth Generation Management, , Digital Press Burlington, MA; Slack, N., Chambers, S., Johnston, R., (2001) Operations Management, , 3rd ed. Financial Times, Prentice-Hall Eaglewood cliff, NJ; Spekman, R.E., Kamauff Jr., J.W., Myhr, N., An empirical investigation into supply chain management: A perspective on partnerships (1998) International Journal of Physical Distribution & Logistics Management, 28 (8), pp. 630-50; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Vollman, T., Cordon, C., Raabe, H., (1997) Supply Chain Management in Mastering Management, pp. 316-22. , FT Pitman London; Von Hipple, E., Co-operation between rivals: Informal know how trading (1986) Research Policy, 16 (5), pp. 21-8; Vrijhoef, R., Koskela, L., The four roles of supply chain management in construction (2000) European Journal of Purchasing & Supply Management, 6, pp. 169-78. , Supply Chain Management in Construction - special issue; Whipple, J.M., Frankel, R., Strategic alliance success factors (2000) The Journal of Supply Chain Management, 36 (3), pp. 21-8; Wong, A., Fung, P., Total quality management in the construction industry in Hong Kong: A supply chain management perspective (1999) Total Quality Management, 10 (2), pp. 199-208; Zaire, M., Whymark, J., The transfer of best practice: How to build a culture of benchmarking and continuous learning - Part 1 (2000) Benchmarking An International Journal, 7 (1), pp. 62-79},
correspondence_address1={Saad, M.; Bristol Business School, , Bristol, United Kingdom; email: mohammed.saad@uwe.ac.uk},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Blanc2006,
author={Blanc, S. and Ducq, Y. and Vallespir, B.},
title={Interoperability problems in supply chains context},
journal={IFAC Proceedings Volumes (IFAC-PapersOnline)},
year={2006},
volume={12},
number={PART 1},
page_count={6},
doi={10.3182/20060517-3-fr-2903.00331},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961198057&doi=10.3182%2f20060517-3-fr-2903.00331&partnerID=40&md5=b81a5beb96ffadc3a5e4fb98fa6087e3},
affiliation={Laboratoire d'Automatique, Productique et Signal, UMR 5131 CNRS, Université Bordeaux 1, 351 cours de la Libération, 33405 Talence Cedex, France},
abstract={Today, the enterprises must cooperate to survive in a context increasingly competing. This cooperation is carried out using organization in network through supply chain. In order to do so, the enterprises must be interoperable. For this, the system must eradicate the heterogeneity. In a first part, the paper exposes the problems of heterogeneity. In a second part is presented the synchronisation and standardisation of practices of business processes in order to solve a part of the problem of organisational heterogeneity. Then, the last part presents a method to solve heterogeneity problems by using an evolution management method: GEM. In parallel, two performance measurement systems are used. Copyright © 2006 IFAC.},
author_keywords={Communication environment;  Enterprise modelling;  Semantic problems;  System management},
keywords={Semantics, Business Process;  Communication environments;  Enterprise modelling;  Evolution managements;  Organisational;  Performance measurement system;  Semantic problems;  System management, Supply chains},
references={(2003) Annex I - "Description of Work", Sixth Framework Program, , ATHENA, European Integrated Project ATHENA, 11 november 2003; Chalmeta, R., Grangel, R., Performance measurement systems for virtual enterprise integration (2005) International Journal of Computer Integrated Manufacturing, 18 (1), pp. 73-84. , DOI 10.1080/0951192042000213164; Doumeingts, G., Ducq, Y., Enterprise modelling techniques to improve efficiency of enterprises (2001) Production Planning and Control, 12 (2 SPEC.), pp. 146-163. , DOI 10.1080/09537280150501257; Ducq, Y., Vallespir, B., Definition and aggregation of a performance measurement system in three aeronautical workshops using the ECOGRAI method (2005) Production Planning and Control, 16 (2), pp. 163-177. , DOI 10.1080/09537280512331333066, PII U53495118015; (1990) IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer Glossaries, , IEEE; Kleinhans, S., (1999) Intégration de la Modélisation d'Entreprise Dans Une Démarche de Stratégie Industrielle, , Thèse de l'Université Bordeaux 1, October 29th, 1999; Malhene, N., (2000) Gestion du Processus d'Évolution des Systèmes Industriels - Conduite et Méthode, GEM, , thèse de l'Université Bordeaux 1, Janvier 2000; Sansonnet, J.-P., (2004) Approches de l'Hétérogénéité Sémantique Entre Agents Informationnels, , http://www.limsi.fr/Individu/jps/research/weakaliens/weakaliens.htm, 2004; Valencia, E., (2000) Hétérogénéité Sémantique Entre Agents Modélisés en Logique de Descriptions, , Thèse de doctorat de l'Université de Paris XI, December 8, 2000; Valencia, E., Sansonnet, J.-P., Simplicial Representation for Description Logics Databases International Conference on Description Logic DL 2000, Aachen Germany, August 2000},
correspondence_address1={Blanc, S.; Laboratoire d'Automatique, 351 cours de la Libération, 33405 Talence Cedex, France; email: severine.blanc@laps.u-bordeaux1.fr},
publisher={IFAC Secretariat},
issn={14746670},
isbn={9783902661043},
language={English},
abbrev_source_title={IFAC Proc. Vol. (IFAC-PapersOnline)},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Rafele2006,
author={Rafele, C. and Cagliano, A.C.},
title={Performance measurement in supply chain supported by system dynamics},
journal={IFAC Proceedings Volumes (IFAC-PapersOnline)},
year={2006},
volume={12},
number={PART 1},
page_count={6},
doi={10.3182/20060517-3-fr-2903.00293},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961191136&doi=10.3182%2f20060517-3-fr-2903.00293&partnerID=40&md5=ba34c451c0afae1f3f63d4c2dfd3ccdf},
affiliation={Department of Production Systems and Business Economics, Politecnico di Torino, corso Duca degli Abruzzi 24, 10129 Turin, Italy},
abstract={An effective performance measurement system has to capture not only the performance of a company, but also how this is linked to the partners' in a supply chain. Starting from a well-established model for logistic performance evaluation, LOGISTIQUAL, this paper tries to extend its validity beyond the boundary of a single organisation. Particularly, it aims at discussing the use of System Dynamics to connect the performance of a company and its suppliers', through homogeneous indicators. This work is the basis for developing a System Dynamics model to evaluate quantitatively the links between logistic performance at different supply chain echelons. Copyright © 2006 IFAC.},
author_keywords={Complex systems;  Performance evaluation;  Supply chain management;  System analysis;  System dynamics},
keywords={Large scale systems;  Petroleum reservoir evaluation;  Supply chain management;  Systems analysis, Effective performance;  Logistic performance;  Performance evaluation;  Performance measurements;  System Dynamics;  System dynamics model, System theory},
references={Akkermans, H., Vos, B., Amplification in service supply chains: An exploratory case study from the telecom industry (2003) Production and Operations Management, 12 (2), pp. 204-223. , Designing and Managing Service Operations; Beamon, B.M., Measuring supply chain performance (1999) Int. J. of Operations and Production Management, 19, pp. 275-292; Borshchev, A., Filippov, A., From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools (2004) Proc. from the 22nd Int. Conference of the System Dynamics Society. Oxford, England; Brewer, P.C., Speh, T.W., Using the Balanced Scorecard to measure supply chain performance (2000) J. of Business Logistics, 21, pp. 75-93; Bullinger, H.-J., Kuhner, M., Van Hoof, A., Analysing supply chain performance using a balanced measurement method (2002) International Journal of Production Research, 40 (15 SPEC.), pp. 3533-3543. , DOI 10.1080/00207540210161669; Christopher, M., The Agile Supply Chain: Competing in Volatile Markets (2000) Industrial Marketing Management, 29; Forrester, J.W., (1961) Industrial Dynamics, , The MIT Press, Cambridge, Massachusetts; Grimaldi, S., Rafele, C., Current applications of a reference framework for the supply chain performance measurement (2004) Proc. from Int. Workshop on Performance and Risk Measurement: Operations Logistics and Supply Chain. Bocconi University, Milan (Italy), 2004, pp. 217-231; Grimaldi, S., Rafele, C., Current applications of a reference framework for the supply chain performance measurement (2006) Int. J. of Business Performance Management, , For publication; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) Int. J.of Production Economics, 87, pp. 333-347; Kaplan, R.S., Norton, D.P., (1996) The Balanced Scorecard: Translating Strategy into Action, , Harward Business School Press, Boston, Massachusetts; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) The Int. J. of Logistics Management, 12, pp. 1-19; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European J. of Operational Research, 156, pp. 267-286; Neo, H.-Y., Xie, M., Tsui, K.-L., Service quality analysis: Case study of a 3PL company (2004) Int. J. Logistics Systems and Management, 1, pp. 64-80; Panov, S.A., Shiryaev, V.I., Manufacturing supply chain adaptive management under changing demand conditions (2003) Proc. from the 21st Int. Conference of the System Dynamics Society. New York, USA; Parasuraman, A., Zeithaml, V.A., Berry, L.L., A Conceptual Model of Service Quality and its Implications for Future Research (1985) J. of Marketing, 49, pp. 41-50; Parasuraman, A., Zeithaml, V.A., Berry, L.L., Servqual: A Multiple - Item Scale for Measuring Consumer Perceptions of Service Quality (1988) J. of Retailing, 64, pp. 12-40; Rafele, C., Logistic service measurement: A reference framework (2004) J. of Manufacturing Technology Management, 15, pp. 280-290; Roberts, E.B., Systems Analysis of Apparel Company Problems (1999) Managerial Applications of System Dynamics, pp. 227-243. , E.B. Roberts, Ed.. Pegasus Communications Inc., Waltham, Massachusetts; Schieritz, N., Größler, A., Emergent Structures in Supply Chains-A Study Integrating Agent-Based and System Dynamics Modeling (2003) Proc. from the 36th Annual Hawaii International Conference on System Sciences; Stank, T.P., Goldsby, T.J., Vickery, S.K., Effect of service supplier performance on satisfaction and loyalty of store managers in the fast food industry (1999) J. of Operations Management, 17, pp. 429-447; Sterman, J.D., (2000) Business Dynamics: Systems Thinking and Modeling for A Complex World, , McGraw - Hill, USA},
correspondence_address1={Rafele, C.; Department of Production Systems and Business Economics, corso Duca degli Abruzzi 24, 10129 Turin, Italy},
publisher={IFAC Secretariat},
issn={14746670},
isbn={9783902661043},
language={English},
abbrev_source_title={IFAC Proc. Vol. (IFAC-PapersOnline)},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Aramyan2006267,
author={Aramyan, L.H. and Ondersteijn, C.J.M. and Lansink, A.G.J.M.O. and Van Kooten, O. and Wijnands, J.H.M.},
title={Analyzing greenhouse firm performance across different marketing channels},
journal={Agribusiness},
year={2006},
volume={22},
number={2},
pages={267-280},
doi={10.1002/agr.20084},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79959633496&doi=10.1002%2fagr.20084&partnerID=40&md5=e8b5aa3369d9ea4632cacfe314b0f128},
affiliation={Social Sciences Group, Business Economics, Wageningen University, Wageningen, Netherlands; Alfa-Accountants en Adviseurs, Gorinchem, Netherlands; Horticultural Production Chains Group, Plant Sciences, Wageningen University, Wageningen, Netherlands; Agricultural Economics Research Institute (LEI), The Hague, Netherlands},
abstract={In recent years, supply chain performance measures have been in the spotlight of many authors. Many performance measures have been used. In this paper, efficiency was measured as an indicator of performance in the vegetable supply chain. The study consisted of two stages. In the first, data envelopment analysis (DEA) evaluated technical and scale efficiency of the Dutch vegetable sector relative to the type of final distribution (mixed marketing channels versus traditional auctions). In the second stage, a truncated regression model (TRM) was applied to explain grower efficiency from a managerial point of view. The results of the first stage showed that growers who sell their products through auctions are less efficient. The second stage revealed that variables such as firm size, age, ownership structure, and distribution channel strategy significantly contribute to the explanation of technical efficiency of the growers, while the variables firm location, firm size, and firm age affect their scale. [EconLit citations: Q130, L 100, and D440] © 2006 Wiley Periodicals, Inc. Agribusiness 22: 267-280, 2006.},
references={Alvarez, A., Gonzalez, E., Using cross-section data to adjust technical efficiency indexes estimated with panel data (1999) American Journal of Agricultural Economics, 81, pp. 894-901; Amara, N., Traore, N., Landry, R., Romain, R., Technical efficiency and farmers’ attitudes toward technological innovation: The case of the potato farmers in Quebec (1999) Canadian Journal of Agricultural Economics, 47, pp. 31-43; Banker, R.D., Charnes, A., Cooper, W.W., Some models for estimating technical and scale inefficiencies in data envelopment analysis (1984) Management Science, 30, pp. 1078-1092; Beamon, M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Bezlepkina, I., Oude Lansink, A., (2003) Debts, subsidies and performance of Russian agricultural enterprises, 20, pp. 67-90. , A. Balmann & A. Lissitsa (Eds.), Proceedings of the International Workshop on Large farm management, Halle (Saale), Germany: AgriMedia; Bijman, J., (2002) Essays on agricultural co-operatives. Governance structure in fruit and vegetable chains, , PhD thesis, Erasmus University, Rotterdam, The Netherlands; Boonekamp, G.F.J., (2002) Significant changes in the European greenhouse vegetable industry, , Paper presented at the Canadian Greenhouse Conference, Toronto, Ontario, Canada; Bowersox, D.J., Cooper, M.B., (1992) Strategic marketing channel management, , New York, McGraw-Hill; Bremmer, J., (2004) Strategic decision making and firm development in Dutch greenhouse horticulture, , PhD thesis, Wageningen University, The Netherlands; Burki, A., Terrel, D., Measuring production efficiency of small firms in Pakistan (1998) World Development, 26, pp. 155-169; Chambers, R.G., Cooper, W., Fare, R., Jaenicke, E., Lichtenberg, E., Using dominance in forming bounds on DEA model: The case of experimental agricultural data (1998) Journal of Econometrics, 85, pp. 189-203; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision-making units (1978) European Journal of Operational Research, 2, pp. 429-444; Christopher, M., The agile supply chain (2000) Industrial Marketing Management, 29, pp. 37-44; Coelli, T., Rao, D.S.P., Battese, G.E., (1998) An introduction to efficiency and productivity analysis, , Norvell, MA, Kluwer Academic Publisher; Cox, T.L., Wohlegenant, M.K., Prices and quality effects in cross-sectional demand analysis (1986) American Journal of Agricultural Economics, 68, pp. 908-919; Färe, R., Grosskopf, S., (2000) Reference guide to ONFRONT, , Lund, Sweden, Measurement and Quality I Lund AB; Färe, R., Grosskopf, S., Lovell, C.A.K., (1994) Production frontiers, , London, Cambridge University Press; Farell, M.J., The measurement of productive efficiency (1957) Journal of the Royal Statistical Society, 3, pp. 253-290; Fraser, I., Cordina, D., An application of data envelopment analysis to irrigated dairy farms in Northern Victoria, Australia (1999) Agricultural Systems, 59, pp. 267-282; Hendrikse, G.W.J., (2004) Restructuring agricultural cooperatives, , Rotterdam, Erasmus University Press; Hendrikse, G.W.J., Bijman, W.J.J., (2001) On the emergence of growers’ associations: Self-selection versus market power, , Working paper ERIM Report Series Research in Management. ERS-2001-34-ORG; Johnston, J., DiNardo, J., (1997) Econometric methods, , 4th, New York, McGraw-Hill International Edition; Li, D., O’Brien, C., Integrated decision modelling of supply chain efficiency (1999) International Journal of Production Economics, 59, pp. 147-157; Macdonald, M., (1998) Marketing plans. How to prepare them, how to use them, , 4th, London, Butterworth Heinemann; Ondersteijn, C., Giesen, G., Huirne, R., Identification of farmer characteristics and farm strategies explaining changes in environmental management and environmental and economic performance of dairy farms (2003) Agricultural Systems, 78, pp. 31-55; Oude Lansink, A., Silva, E., CO2 and energy efficiency of different heating technologies in the Dutch glasshouse industry (2003) Environmental and Resource Economics, 21, pp. 395-407; Simar, L., Wilson, P., A general methodology for bootstrapping in nonparametric frontier models (2000) Journal of Applied Statistics, 27, pp. 779-802; Shipley, D., Colin, E., Edgett, S., Meeting source selection criteria: Direct versus distribution channels (1991) Industrial Marketing Management, 20, pp. 297-303; Stern, L.W., El-Ansary, A.I., (1977) Marketing channels, , Englewood Cliffs, NJ, Prentice-Hall Inc; Veall, R., Zimmermann, F., Goodness of fit measures in the tobit model (1994) Oxford Bulletin of Economics and Statistics, 56, pp. 485-499; Wilson, P., Hadley, D., Measuring and explaining technical efficiency in UK potato production (1998) Journal of Agricultural Economics, 49, pp. 294-305},
issn={07424477},
language={English},
abbrev_source_title={Agribusiness},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Chang20061392,
author={Chang, L. and Hu, J. and Li, L.},
title={Flexible supply chain performance measurement},
journal={Proceedings - ICSSSM'06: 2006 International Conference on Service Systems and Service Management},
year={2006},
volume={2},
pages={1392-1396},
doi={10.1109/ICSSSM.2006.320715},
art_number={4114694},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-40649121572&doi=10.1109%2fICSSSM.2006.320715&partnerID=40&md5=a9e7f5c15c51b6823cbe37b926b96f05},
affiliation={School of Economics and Management, Tsinghua University, Beijing, 100084, China; School of Business Administration, Northeastern University, Shenyang, 110006, China},
abstract={In this research, Considering demand order period uncertainty in a supply chain Stackelberg game, a multi- objective decision analysis is adopted to allow use of a performance measurement that includes cost, customer service levels, order flexibility. This measurement provides more comprehensive measurement of supply chain performance through three measures approaches, and identifies factors affecting performance, their relationships and the trade-off effects among them. A solution algorithm with the thoughts of Genetic Algorithms for Stackelberg game was very successful in generating solutions in short computation times, and simulation works are carried out in Liaoyang Petroleum Company. The results obtained confirmed the significance of cost, customer service, order flexibility. © 2006 IEEE.},
author_keywords={Flexible;  Genetic algorithm;  Performance measure;  Stackelberg game;  Supply chain},
keywords={Computation theory;  Customer satisfaction;  Decision theory;  Game theory;  Genetic algorithms, Objective decision analysis;  Performance measurement;  Stackelberg game, Supply chain management},
correspondence_address1={Chang, L.; School of Economics and Management, , Beijing, 100084, China; email: changlf@em.tsinghua.edu.cn},
publisher={IEEE Computer Society},
isbn={1424404517; 9781424404513},
language={English},
abbrev_source_title={Int. Conf. Services Systems Services Manage. Proc. ICSSSM},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Byrne2006126,
author={Byrne, P.J. and Heavey, C.},
title={Simulation model of a vertically integrated supply chain: A case study},
journal={International Journal of Industrial Engineering : Theory Applications and Practice},
year={2006},
volume={13},
number={2},
pages={126-135},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-34047189706&partnerID=40&md5=666cd61fbee7e34cbdc90a5f0a5833d9},
affiliation={Enterprise Research Centre, University of Limerick, Limerick, Ireland},
abstract={This paper reports on the successful development and use of simulation for the analysis of a vertically integrated supply chain. Simulation has only recently been applied to the analysis of industrial supply chains and this model differs from existing work in this area in that the manufacturing function is modelled in detail. This is contrast to the logistical models developed using simple lead times to represent manufacturing. This paper reports on the scale of system being analysed, the type of data required to populate such a model, such as product routings, standard times, work centre capacities and shift cycles and the level of detail included in the study and the performance outputs from the model. Using this model experiments were carried out to analyse the effect of stocking policies, production controls, changing demand trends and the effect of forecasting and information sharing on supply chain performance measures. One such experiment to determine the effective trade-off from operating three different finish stocking policies is outlined in detail. These experiments provide management with a useful tool for decision support in relation to both strategic and production strategies. Significance: To managers this paper presents a case study on the successful use of simulation for the analysis of a supply chain. To simulation practitioners it gives details on the development of the supply chain simulation model. ©INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING.},
author_keywords={Decision support systems;  Manufacturing detail;  Supply chain simulation},
keywords={Decision support systems;  Industrial applications;  Industrial engineering;  Mathematical models;  Supply chain management, Information sharing;  Manufacturing details;  Supply chain simulation, Computer simulation},
references={Axsäter, S., Simple solution procedures for a class of two echelon inventory problems (1990) Operations Research, 38 (1), pp. 64-69; Bagchi, S., Buckley, S.J., Ettl, M., Lin, G.Y., Experience using the IBM supply chain simulator (1998) Proceedings of the 1998 Winter Simulation Conference, pp. 1387-1394; Changchien, S.W., Shen, H.-Y., Supply chain reengineering using a core process analysis matrix and object-orientated simulation (2002) Information & Management, 39, pp. 345-358; Chen, F.R., Echelon reorder points, installation reorder points, and the value of centralized demand information (1998) Management Science, 44 (12), pp. S221-S234; Chen, F.R., Decentralized supply chains subject to information delays (1999) Management Science, 45 (8), pp. 1076-1090; Chen, F.R., Song, J.S., Optimal policies for multiechelon inventory problems with Markov-modulated demand (2001) Operations Research, 49 (2), pp. 226-234; Christopher, M., (1998) Logistics and Supply Chain Management. Strategies for reducing cost and improving service, , England, Financial Times Professional Limited; Clark, A.J., Scarf, H., Optimal policies for a multi-echelon inventory problem (1960) Management Science, 6, pp. 475-490; Federgruen, A., Zipkin, P., Computational issues in an infinite-horizon, multiechelon inventory model (1984) Operations Research, 32 (4), pp. 818-836; Feigin, G. E. (1999). Inventory planning in large assembly supply chains. Quantitative Models for supply chain management. S. Tayur, R. Ganeshan and M. Magazine. Boston/Dordrecht/London, Kluwer Academic Press: 761-788; Gachon, G. and M. Fisher (1998). Campbell Soup's continuous replenishment program: Evaluation and enhanced inventory decision rules. Global Supply chain and technology management, POMS series in technology and operations management. H. L. S. M. Lee. (1998), Miami, Florida. 1: 130-140; Gachon, G.P., Managing supply chain demand variability with scheduled ordering policies (1999) Management Science, 45 (6), pp. 843-856; Gachon, G.P., Exact evaluation of batch-ordering inventory policies in two-echelon supply chains with periodic review (2001) Operations Research, 49 (1), pp. 79-98; Gachon, G.P., Fisher, M., Supply chain inventory management and the value of shared information (2000) Management Science, 46 (8), pp. 1032-1048; Gallego, G., New bounds and heuristics for (Q, r) policies (1998) Management Science, 44 (2), pp. 219-233; Ganeshan, R., Boone, T., Stenger, A.J., The impact of inventory and flow planning parameters on supply chain performance: An exploratory study (2001) International Journal of Production Economics, 71, pp. 111-118; Grupp, K. (1998). Mit Supply Chain Management globale Transparenz in der Distribution, PPS Management. Supply Chain Management and Advanced Planning. H. Stadtler and C. Kilger. (2000), Berlin, Springer. 3, No 2: 50-52; Ingalls, R., The value of simulation in modelling supply chains (1998) Proceedings of the 1998 Winter Simulation Conference, pp. 1371-1375; Ingalls, R., Kasales, C., CSCAT: The COMPAQ supply chain analysis tool (1999) Proceedings of the 1999 Winter Simulation Conference, pp. 1201-1206; Law, A.M., Kelton, W.D., (2000) Simulation Modelling and Analysis, , New York, McGraw-Hill; Lee, H.L., Billington, C., The evoloution of suppl-chain-integration models in practice at Hewlett Packard (1995) Interfaces, 25 (5), pp. 42-63; Lee, H.L., Billington, C., The Evolution of Supply-Chain-Management Models and Practice at Hewlett-Packard (1995) Interfaces, 25 (5), pp. 42-63; Lin, G., Ettl, M., Buckley, S., Bagchi, S., Yao, D.D., Naccarto, B.L., Allan, R., Koenig, L., Extended-enterprise supply-chain management at IBM personal systems group and other divisions (2000) Interfaces, 30 (1), pp. 7-25; Petrovic, D., Roy, R., Petrovic, R., Modelling and simulation of a supply chain in an uncertain environment (1998) Euro. J. of Operational Research, 109, pp. 299-309; Rosling, K., Optimal inventory policies for assembly systems under random demands (1989) Operations Research, 37 (4), pp. 565-579; Schary, P.B., Skjøtt-Larsen, T., (1995) Managing the global supply chain, , Copenhagen, Handelshøjskolens forlag; Distribution, Munksgaard International; Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E., (2000) Design and Managing the Supply Chain, , Boston, London, McGraw-Hill},
correspondence_address1={Byrne, P.J.; Enterprise Research Centre, , Limerick, Ireland},
publisher={University of Cincinnati},
issn={10724761},
coden={IJIEF},
language={English},
abbrev_source_title={Int J Ind Eng Theory Appl Pract},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chan2006110,
author={Chan, F.T.S. and Chan, H.K. and Qi, H.J.},
title={A review of performance measurement systems for supply chain management},
journal={International Journal of Business Performance Management},
year={2006},
volume={8},
number={2-3},
pages={110-131},
doi={10.1504/IJBPM.2006.009032},
note={cited By 48},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33646173373&doi=10.1504%2fIJBPM.2006.009032&partnerID=40&md5=6644e4efc07cfeab519b056deab68f0a},
affiliation={Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong},
abstract={Manufacturers have been exploring innovative strategies to achieve and sustain their competitive advantages by facing a new era of intensive global competition. Such a strategy is Supply Chain Management (SCM), which has gained a tremendous amount of attention from both researchers and practitioners since the last decade. Well-designed supply chain systems can substantially improve efficiency and product quality, and eventually enhance customer satisfaction and profitability. There is still a lack of integration between Performance Measurement Systems (PMSs) and practical requirements for supply chains. Performance Measurement (PM) is a vital task in SCM because it can help to monitor the progress, identify weak areas, enhance motivation, and strengthen accountability. The methodologies of business PM have been evolving for decades; numerous theories and models of PMS, ranging form design to implementation, have been proposed. However, the vast majority of PM models have concentrated on single business organisations or on partial performance areas. A commonly accepted PMS for supply chains is hardly available. This paper sets out to discuss and review recent development of PMSs, in particular those in relation to SCM, and to suggest further research direction in supply chain PMSs. Copyright © 2006 Inderscience Enterprises Ltd.},
author_keywords={Monitoring;  Performance measurement system;  Review;  Supply chain management},
references={Adams, S.M., Sarkis, J., Liles, D., 'The development of strategic performance metrics' (1995) Engineering Management Journal, 7 (1), pp. 24-32; Beamon, B.M., 'Performance measures in supply chain management' (1996) Proceedings of the 1996 Conference on Agile and Intelligent Manufacturing Systems, , Rensselaer Polytechnic Institute, Troy, New York, 2-3 October; Beamon, B.M., 'Supply chain design and analysis: Models and methods' (1998) International Journal of Production Economics, 55, pp. 281-294; Beamon, B.M., 'Measuring supply chain performance' (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Beamon, B.M., Ware, T.M., 'A process quality model for the analysis, improvement and control of supply chain systems' (1998) Logistics Information Management, 11 (2), pp. 105-113; Bevan, S., Thompson, M., 'Performance measurement at the crossroads' (1991) Personnel Management, pp. 36-39. , November; Bititci, U.S., 'Modeling of performance measurement systems in manufacturing enterprises' (1995) International Journal of Production Economics, 42, pp. 137-147; Bititci, U.S., Suwignjo, P., Carrie, A.S., 'Strategy management through quantitative modelling of performance measurement systems' (2001) International Journal of Production Economics, 69 (1), pp. 15-22; Chandra, C., Kumar, S., 'Supply chain management in theory and practice: A passing fad or a fundamental change?' (2000) Industrial Management and Data Systems, 100 (3), pp. 100-113; Cook, T.J., Vansant, J., Stewart, L., Adrian, J., 'Performance measurement: Lessons learned for development management' (1995) World Development, 28 (8), pp. 1303-1315; Cooper, M.C., Lambert, D.M., Pagh, J.D., 'Supply chain management: More than a new name for logistics' (1997) International Journal of Logistics Management, 8 (1), pp. 1-14; Cross, K.F., Lynch, R.L., 'The SMART way to define and sustain success' (1989) National Production Review, 8 (1), pp. 23-33; Dixon, J.R., Nanni, A.J., Vollmann, T.E., (1990) The New Performance Challenge - Measuring Operation for World-Class Competition, , Homewood, USA: Dow Jones-Irwin; Eccles, R.G., Pyburn, P.J., 'Creating a comprehensive system to measure performance' (1992) Management Accounting, pp. 41-44. , October; Fry, T.D., Cox, J.F., 'Manufacturing performance: Local versus global measures' (1989) Production and Inventory Management Journal, pp. 52-56. , 2nd Quarter; Fry, T.D., Karwan, K.R., Baker, W.M., 'Performance measurement systems and time-based manufacturing' (1993) Production Planning and Control, 42 (2), pp. 102-111; Ghalayini, A.M., Noble, J.S., Crowe, T.J., 'An integrated dynamic performance measurement system for improving manufacturing competitiveness' (1997) International Journal of Production Economics, 48, pp. 207-225; Gunasekaran, A., Patel, C., Tirtiroglu, E., 'Performance measurement and metrics in a supply chain environment' (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Harrington, H.J., (1991) Business Process Improvement, , New York: McGraw-Hill; Hazell, M., Morrow, M., 'Performance measurement and benchmarking' (1992) Management Accounting, pp. 44-45. , December; Van Hoek, R.I., 'Measuring the unmeasurable - Measuring and improving performance in the supply chain' (1998) Supply Chain Management: An International Journal, 3 (4), pp. 187-192; Holmberg, S., 'A system perspective on supply chain measurement' (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Kaplan, R.S., Norton, D.P., 'The balanced scorecard - Measures that drive performance' (1992) Harvard Business Review, pp. 71-79. , January-February; Kueng, P., 'Process performance measurement system: A tool to support process-based organizations' (2000) Total Quality Management, 11 (1), pp. 67-85; Kutucuoglu, K.Y., Hamali, J., Irani, Z., Sharp, J.M., 'A framework for managing maintenance using performance measurement systems' (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 173-194; Lambert, D.M., Cooper, M.C., 'Issues in supply chain management' (2000) Industrial Marketing Management, 29, pp. 65-83; Lambert, D.M., Cooper, M.C., Pagh, J.D., 'Supply chain management: Implementation issues and research opportunities' (1998) The International Journal of Logistics Management, 9 (2), pp. 1-19; Lebas, M.J., 'Performance measurement and performance management' (1995) International Journal of Production Economics, 41, pp. 23-35; Lockamy, A., 'A study of operational and strategic performance measurement systems in selected world-class manufacturing firms: An examination of linkage for competitive advantage' (1991), PhD Thesis, University of Georgia; Lockamy, A., Cox, J.F., (1994) Reengineering Performance Measurement: How to Align Systems to Improve Processes, Products and Profits, , Burr Ridge, Irwin; Lohman, C., Fortuin, L., Wouters, M., 'Designing a performance measurement system: A case study' (2004) European Journal of Operational Research, 156 (2), pp. 267-286; Lummus, R.R., Vokurka, R.J., 'Defining supply chain management: A historical perspective and practical guidelines' (1999) Industrial Management and Data Systems, 99 (3), pp. 11-17; Macleod, M., Bowden, R., Bevan, N., Curson, I., 'The MUSiC performance measurement method' (1997) Behavior and Information Technology, 16 (4-5), pp. 279-293; Maskell, B.H., 'Performance measurement for world class manufacturing' (1989) Management Accounting, pp. 32-33. , May; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing: A Model for American Companies, , Portland, Oregon: Productivity Press; Medori, D., Steeple, D., 'A framework for auditing and enhancing performance measurement systems' (2000) International Journal of Operations and Production Management, 20 (5), pp. 520-533; Moore, N., 'Supply chain management' (1998) Work Study, 47 (5), pp. 172-174; Narasimhan, R., Jayanth, J., 'Causal linkages in supply chain management: An exploratory study of North American manufacturing firms' (1998) Decision Science, 29 (3), pp. 579-605; Neely, A., Gregory, M., Platts, K., 'Performance measurement system design; a literature review and research agenda' (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Neely, A., Mills, J., Platts, K., Gregory, M., Richards, H., 'Performance measurement system design: Should process based approach be adopted?' (1996) International Journal of Production Economics, 46-47, pp. 423-431; Neely, A., Richards, H., Mills, J., Platts, K., Bourne, M., 'Designing performance measures: A structured approach' (1997) International Journal of Operations and Production Management, 17 (11), pp. 1131-1152; Parker, C., 'Performance measurement' (2000) Work Study, 49 (2), pp. 63-66; Pawar, K., Driva, H., 'Performance measurement for product design and development in a manufacturing environment' (1999) International Journal of Production Economics, 60-61, pp. 61-68; Polakoff, J.C., 'How to design a performance measurement program' (1992) Corporate Controller, 4 (3), pp. 49-53; Pursell, R.B., 'Administrating divisional incentive compensation' (1980) Compensation Review, 12, pp. 15-20; Rolstandas, A., (1995) Performance Measurement: A Business Process Benchmarking Approach, , New York, USA: Chapman and Hall; Rose, K.H., 'A performance measurement method' (1995) Quality Progress, 28 (2), pp. 63-66; Ross, D.F., (1997) Competing Through Supply Chain Management: Creating Market - Winning Strategies Through Supply Chain Partnerships, , New York, NY, USA: International Thomson Publishing; Sarkis, J., 'Quantitative models for performance measurement systems - Alternate considerations' (2003) International Journal of Production Economics, 86 (1), pp. 81-90; Sink, D.S., 'The role of measurement in achieving world class quality and productivity management' (1991) Industrial Engineering, , June; Sink, D.S., Tuttle, T.C., De Vries, S.J., 'Productivity measurement and evaluation: What is available?' (1984) National Productivity Review, 4 (3), pp. 265-387; Stainer, A., Heap, J., 'Performance measurement - A management services perspective' (1996) Management Services, 40 (7), pp. 10-12; Tan, K.C., 'A framework of supply chain management literature' (2001) European Journal of Purchasing and Supply Management, 7, pp. 39-48; Taylor, L., Convey, S., 'Making performance measurement meaningful to the performers' (1993) Canadian Manager, 18 (3), pp. 22-24; Vitale, M., Mavrinac, S.C., Hauser, M., 'New process/financial scorecard: A strategic performance measurement system' (1994) Planning Review, 22 (4), pp. 12-16; Waggoner, D.B., Neely, A.D., Kennerley, M.P., 'The forces that shape organizational performance measurement systems: An interdisciplinary review' (1999) International Journal of Production Economics, 60, pp. 53-60},
correspondence_address1={Chan, F.T.S.; Department of Industrial and Manufacturing Systems Engineering, Pokfulam Road, Hong Kong, Hong Kong; email: ftschan@hkucc.hku.hk},
publisher={Inderscience Enterprises Ltd.},
issn={13684892},
language={English},
abbrev_source_title={Int. J. Bus. Perform. Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Young200642,
author={Young, J.},
title={Pushing the envelope},
journal={Industrial Fabric Products Review},
year={2006},
volume={91},
number={1},
pages={42-43},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33645505112&partnerID=40&md5=c41acb711650e18a22e4ca6d64a4ee23},
abstract={A proper performance measurement system and a business self-evaluation model can allow companies to assess their strengths and weaknesses, a key step towards competitiveness. One of the most important metrics that a company management should look while evaluating performance is the order-to-delivery cycle. Supply chain performance is significant to the overall internal and external performance measurement system that allows companies to measure how well the business is functioning. It is also important for a company to measure how good it is as a customer towards its suppliers and distributors. The goal is to eliminate the problems by minimizing unpredictability that can generate significant performance improvement. The effective use of performance measurement and having the right metrics and analytical tools would help the management to follow the trends and patterns in the market and compete in the marketplace accordingly.},
keywords={competitiveness;  management;  market;  performance assessment;  textile industry},
correspondence_address1={Young, J., Indianapolis, United States},
issn={00198307},
language={English},
abbrev_source_title={Ind. Fabr. Prod. Rev.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Erensal2005631,
author={Erensal, Y.C. and Vayvay, Ö.},
title={Designing a strategic supply chain performance measurement system: A conceptual framework with a holistic approach},
journal={Proceedings of the 35th International Conference on Computers and Industrial Engineering, ICC and IE 2005},
year={2005},
pages={631-636},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84886872520&partnerID=40&md5=3f03a9084604aec3dd473aa56426da6c},
affiliation={Industrial Engineering Department, Engineering Faculty, Dogus University, Acibadem Zeamet Sokak, 34722 Kadikoy, Istanbul, Turkey; Industrial Engineering Department, Engineering Faculty, Marmara University, Göztepe Campus Kuyubasi, Istanbul, Turkey},
abstract={The purpose of the presented article is to build a comprehensive and applicable supply chain performance measurement framework within holistic approach. Special emphasis has been given to analyzing and constructing of performance measurement framework in respect to company supply chain strategy. In this regard, attention has been called to the alignment between performance measures and the organizational objectives and strategies.},
author_keywords={Balanced scorecard;  Logistics;  Performance measurement;  Supply chain},
keywords={Balanced scorecards;  Conceptual frameworks;  Holistic approach;  Performance measure;  Performance measurement frameworks;  Performance measurements;  Supply chain performance measurements;  Supply chain strategy, Industrial engineering;  Logistics, Supply chains},
references={Chopra, S., (2004) Supply Chain Management, , Prentice Hall; Mcgaughey, R.E., Gunasekaran, A., Patelb, C., A framework for supply chain performance measurement (2004) Int. J. Production Economics, 87, pp. 333-347. , 2004; Hausman, W.H., (2002) Supply Chain Performance Metrics, Forthcoming in the Practice of Supply Chain Management, , Eds. Billington C. Harrison T. Lee H. Neale J. Stanford University Press; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (5), pp. 134-142. , 1993; Kaplan, R.S., Norton, D.P., Linking the balanced scorecard to strategy (1996) California Management Review, 39 (1); Kaplan, R.S., Norton, D.P., Why does dusiness need a balanced scorecard? (1997) Journal of Cost Management, 11 (3), pp. 5-10. , 1997; Kaplan, R.S., Norton, D.P., (1997) The Balanced Scorecard: Translating Strategy into Action, , Schaeffer-Poeschel, Stuttgart; Keebler, J.S., Manrodt, K.B., Durtshe, D.A., Ledyard, D.M., (1999) Keeping Score Measuring the Business Value of Logistics in the SC, , Council of Logistics Management, Oak Brook, IL; Kleijnen, J., Peterson, R.R., Smits, M.T., Performance of supply chains, under review (2002) J. of the ORS; Klingebiel, N., Performance management - Performance measurement (1998) Zeitschrift Für Planung, 9 (1), pp. 1-15. , 1998; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) J. of Logistics Management, 12 (1), pp. 1-19. , Int; Levi, S.D., Kaminsky, P., Levi, E.S., (2003) Designing and Managing the Supply Chain, , Mc-Graw Hill; Littler, K., Aisthorpe, K.P., Hudson, R., A new approach to linking strategy formulation and strategy implementation: An example from the uk banking sector (2000) International Journal of Information Management, 20, pp. 411-428. , 2000; Zimmermann, K., Using the balanced scorecard for interorganizational performance management of supply chains - A case study (2004) Cost Management in Supply Chains, Physica, pp. 399-415. , Seuring, S./Goldbach, M. (Eds.). Heidelberg},
correspondence_address1={Erensal, Y.C.; Industrial Engineering Department, Acibadem Zeamet Sokak, 34722 Kadikoy, Istanbul, Turkey; email: yerensal@dogus.edu.tr},
isbn={9755612653; 9789755612652},
language={English},
abbrev_source_title={Proc. Int. Conf. Comput. Ind. Eng., ICC IE},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Perrin2005,
author={Perrin, B. and Pervan, G.},
title={IT outsourcing relationship management and performance measurement system effectiveness},
journal={ACIS 2005 Proceedings - 16th Australasian Conference on Information Systems},
year={2005},
note={cited By 6},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869139438&partnerID=40&md5=d7762c0217bc9691880e76c189ecacca},
affiliation={Curtin University of Technology, WA, Australia},
abstract={Managing the relationship between the client and the vendor is of critical importance to the success of the IT outsourcing arrangement. This paper develops and explores a conceptual model based on relational exchange theory that identifies a number of attributes or dimensions that contribute to the overall success of (a) the relationship, (b) performance measurement system effectiveness and (c) the outsourcing contract. This research supports the proposition that successful management of the relationship positively influences the success of public sector IT outsourcing contracts and the effectiveness of the performance measurement system used. An effective relationship complements the performance measurement system effectiveness. © 2005.},
author_keywords={It outsourcing;  Performance measurement systems;  Public sector IT outsourcing;  Relational exchange theory},
keywords={Conceptual model;  IT outsourcing;  Performance measurement system;  Public sector;  Relational exchange theory;  Relationship management;  Research support;  Successful management, Information systems;  Supply chain management, Outsourcing},
references={Alborz, S., Seddon, P., Scheepers, R., A Model for Studying IT Outsourcing Relationships (2003) Paper Presented At the 7th Pacific Asia Conference On Information Systems, , 10 - 13 July 2003, Adelaide, South Australia; Anderson, J., Narus, J., A Model of Distributor Firm and Manufacturer Firm Working Partnerships (1990) Journal of Marketing, 54 (1), pp. 42-58; (2001) Contract Management: Better Practice Guide, , http://www.anao.gov.au/publications/betterpracticeguide, Australian National Audit Office (ANAO), Retrieved 10/6/03, from; Chiles, T., McMackin, J., Integrating variable risk preferences, trust and transaction cost economics (1996) Academy of Management Review, 21, pp. 73-99; Dwyer, F.R., Schurr, P.H., Sejo, O., Developing Buyer-Seller Relationships (1987) Journal of Marketing, 51, pp. 11-27; Earl, M.J., The risks of outsourcing IT (1996) Sloan Management Review, 37 (3), p. 26; Eisenhardt, K.M., Building Theories From Case Study Research (1989) Academy of Management. the Academy of Management Review, 14 (4), p. 532; Goles, T., (2001) The Impact of the Client-Vendor Relationship On Information Systems Outsourcing Success, , Unpublished PhD Thesis, University of Houston, Houston; Grover, V., Cheon, M., Teng, J., The effect of service quality and partnership on the outsourcing of information systems functions (1996) Journal of Management Information Systems, 12 (4), pp. 89-117; Hurley, M.A., Costa, C., (2001) The Blurring Boundary of the Organisation: Outsourcing Comes of Age, , Melbourne, Vic.: KPMG; Kern, T., (1997) The Gestalt of An Information Technology Outsourcing Relationship: An Exploratory Analysis, , Paper presented at the Eighteenth International Conference on Information Systems, Atlanta, Georgia; Kern, T., Willcocks, L., Contracts, Control and 'Presentation' in IT Outsourcing: Research in Thirteen UK Organisations (2000) Journal of Global Information Management, 8 (4), pp. 15-42; Kern, T., Willcocks, L., Exploring information technology outsourcing relationships: Theory and practice (2000) Journal of Strategic Information Systems, 9, pp. 321-350; Kern, T., Willcocks, L., Exploring relationships in information technology outsourcing: The interaction approach (2002) European Journal of Information Systems, 11 (1), pp. 3-19; Kim, S., Chung Y., S., Critical Success Factors For IS Outsourcing Implementation From an Interorganisational Relationship Perspective (2003) The Journal of Computer Information Systems, 43 (4), pp. 81-90; Klepper, R., Outsourcing Relationships (1994) Managing Information Technology With Outsourcing: Idea Group Publishing, , In In Khosrowpour, M (ed), Harisburg, PA; Klepper, R., The Management of Partnering Development in IS Outsourcing (1999) Strategic Sourcing of Information Systems: Perspectives and Practices, pp. 305-326. , Chichester: John Wiley; Lacity, M., Hirschheim, R., (1995) Beyond the Information Systems Outsourcing Bandwagon: The Insourcing Response, , New York Chichester: John Wiley & Sons; Langfield-Smith, K., Stringer, C., Smith, D.A., (2000) Managing the Outsourcing Relationship, , Sydney: UNSW Press; Lee, J., Huynh, M., Chi-Wai, K., Pi, S., (2000) The Evolution of Outsourcing Research: What is The Next Issue?, , Paper presented at the 33rd Hawaii International Conference on System Sciences, Hawaii; Lee, J., Kim, Y., Effect of Partnership Quality on IS Outsourcing Success: Conceptual Framework and Empirical Validation (1999) Journal of Management Information Systems, 15 (4), pp. 29-61; Macneil, I., (1980) The New Social Contract: An Inquiry Into Modern Contractual Relations, , New Haven, CT, Yale University Press; McFarlan, F.W., Nolan, R.L., How to manage an IT outsourcing alliance (1995) Sloan Management Review, 36 (2), p. 9; Mohr, J., Spekman, R., Characteristics of Partnership Success: Partnership Attributes, Communication Behavior, and Conflict Resolution Techniques (1994) Strategic Management Journal, 15 (2), pp. 135-152; Mulgan, R., Contracting Out and Accountability (1997) Australian Journal of Public Administration, 56 (4), pp. 106-116; Poppo, L., Zenger, T., Do Formal Contracts and Relational Governance Function as Substitutes or Complements? (2002) Strategic Management Journal, pp. 707-725. , 23(August); (2002) The Contract Process Guidelines, , http:/www.ssc.wa.gov.au/pol_guide/policies/contract.html, State Supply Commission, W. A., Retrieved 20/4/04, from; Willcocks, L., Lacity, M., Fitzgerald, G., Information technology outsourcing in Europe and the USA: Assessment issues (1995) International Journal of Information Management, 15 (5), p. 333; Yin, R., (1994) Case Study Research: Design and Methods, , 2nd ed.). Thousand Oaks, California: Sage Publications, Inc},
correspondence_address1={Perrin, B.; Curtin University of Technology, WA, Australia; email: brian.perrin@cbs.curtin.edu.au},
language={English},
abbrev_source_title={ACIS Proc.- Aus. Conf. Inf. Syst},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Laura2005176,
author={Laura, X.X.X. and William, L. and Chai, L.S. and Li, Z.},
title={Performance metrics design framework for software focused supply chain},
journal={2005 3rd IEEE International Conference on Industrial Informatics, INDIN},
year={2005},
volume={2005},
pages={176-180},
doi={10.1109/INDIN.2005.1560372},
art_number={1560372},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845332020&doi=10.1109%2fINDIN.2005.1560372&partnerID=40&md5=007fe2e81cc1c1687498dd26b03103e7},
affiliation={Singapore Institute of Manufacturing Technology, Singapore, Singapore},
abstract={Supply chain performance measurement plays an important role in supply chain management. It helps companies to improve their bottom lines and provides the basis for companies to make business decisions. Software focused supply chain has been identified as a different type of supply chain from 'traditional' supply chain. It deals with products which the software constitutes the major innovation and value of the products. Very limited researches have been done on software focused supply chain. Singapore Institute of Manufacturing Technology initiated a research project on software focused supply chain management. A lot of research challenges have been raised and studied through the project. This paper focuses on how to measure software focused supply chain performance. In this paper, we review current supply chain performance measurement systems. Characteristics of software focused supply chain have been identified. A performance metrics design framework for software focused supply chain is proposed. Resource, visibility and agility are the three dimensions of the proposed framework. © 2005 IEEE.},
author_keywords={Performance measurement;  Software focused supply chain},
keywords={Administrative data processing;  Computer software;  Decision theory;  Inventory control, Performance measurement;  Performance metrics;  Software focused supply chain;  Supply chain management, Industrial management},
references={Andreas, N., Christer, L., Key issues for a telecom supplier in the transition towards a SW-focused supply chain (2003) Proceedings of the 8th International Symposium on Logistics, pp. 17-27; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Christopher, M., The agile supply chain: Competing in volatile markets (2002) White Paper in Cranfield School of Management, , UK; Chan, F.T.S., Qi, H.J., An innovative performance measurement method for supply chain management (2003) Supply Chain Management, 8 (3-4), p. 209. , ABI/INFORM Global; Hausman, W.H., Supply chain performance metrics (2002) The Practice of Supply Chain Management, , a chapter edited by Corey Billington, Terry Harrison, Hau Lee, and John Neale; Kluwer Academic Publishiers; Jacquet, J.P., From software metrics to software measurement methods: A process model (1997) Software Engineering Standards Symposium and Forum, 'Emerging International Standards', ISESS 97, pp. 128-135. , Third IEEE International; Kulik, P., A practical approach to software metrics (2000) IT Professional, 2 (1), pp. 38-42. , Jan-Feb; Van Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 126-147; (1996) Supply-chain Operations Referencemodel, , Supply-chain Council, USA; Stefan, H., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), p. 847},
correspondence_address1={Laura, X.X.X.; Singapore Institute of Manufacturing Technology, Singapore, Singapore; email: xxxu@SIMTech.a-star.edu.sg},
isbn={0780390946; 9780780390942},
language={English},
abbrev_source_title={Int. Conf. Indust. Info.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Wu200591,
author={Wu, L. and Song, Y.},
title={Measuring integrated supply chain performance},
journal={2005 International Conference on Services Systems and Services Management, Proceedings of ICSSSM'05},
year={2005},
volume={1},
pages={91-96},
doi={10.1109/ICSSSM.2005.1499441},
art_number={1499441},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745265676&doi=10.1109%2fICSSSM.2005.1499441&partnerID=40&md5=38004229f38d0a36753fb8e1c7dbd29c},
affiliation={Management School, Dalian Universeity of Technology, Dalian, Liaoning 116023, China},
abstract={Combining the traditional enterprise performance measurement method with the nature and the characteristics of integrat ed supply chain, this research determines the scope and contents of integrated supply chain performance measrement system. In line with the individual enterprise performance measurement method and theory, integrated supply chain performance measurement systems should reflect the strategy of integrated supply chain. At the same time, it should be able to check the efficiency of the strategy. According to the layer upon layer stratigic objects of integrated supply chain, five key performance aspect(flnance, business processes, customer, environment, core enterprise ability) are found. These five key performance fields are significant for the formation and improvement of integrated supply chain © 2005 IEEE.},
author_keywords={Indicator system Performance measurement;  Integrated supply chain},
keywords={Finance;  Measurement theory;  Strategic planning, Enterprise performance measurement;  Indicator system Performance measurement;  Integrated supply chain, Industrial economics},
references={Barker, R.C., Value chain development: An account of some implementation problems (1996) International Journal of Operations & Production Management, 16 (10), pp. 23-36; Beamon, B.M., Performance measures in supply chain management (1996) Proceedings of the 1996 Conference on Agile and Intelligent Manufacturing Systems, , Rensselaer Polytechnic Institute, Troy, New York, NY. 2-3 October; Christy, D.P., Grout, J.R., Safeguarding supply chain relationships (1994) International Journal of Production Economics, 36, pp. 233-242; Cohen, M.A., Lee, H.L., Strategic analysis of integrated production-distribution systems: Models and methods (1988) Operations Research, 36 (2), pp. 216-228; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294. , August 15; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; New, S.J., A framework for analysing supply chain improvement (1996) International Journal of Operations & Production Management, 16 (4), pp. 19-34; Wikner, J., Towill, D.R., Naim, M., Smoothing supply chain dynamics (1991) International Journal of Production Economics, 22 (3), pp. 231-248},
correspondence_address1={Wu, L.; Management School, , Dalian, Liaoning 116023, China; email: wlwdut@163.com},
isbn={0780389719; 9780780389717; 0780389719; 9780780389717},
language={English},
abbrev_source_title={Int. Conf. Services Systems Services Manage. Proc. ICSSSM},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Swinehart2005533,
author={Swinehart, K.D. and Smith, A.E.},
title={Internal supply chain performance measurement: A health care continuous improvement implementation},
journal={International Journal of Health Care Quality Assurance},
year={2005},
volume={18},
number={7},
pages={533-542},
doi={10.1108/09526860510627210},
note={cited By 30},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-27644435015&doi=10.1108%2f09526860510627210&partnerID=40&md5=e5b1edad6e3d39ad43c155efc95217c4},
affiliation={Department of Management and Marketing, East Tennessee State University, Johnson City, TN, United States; School of Business, Francis Marion University, Florence, SC, United States},
abstract={Purpose - The purpose of this article is to present one example of how the strategies of total quality management (TQM) and continuous improvement are being used by US health care providers to meet the challenges of the future. Design/methodology/approach - This article presents an application utilizing the strategies of TQM and continual and rapid improvement in the area of assessing internal customer satisfaction in the health care arena. Satisfaction information concerning internal processes is critically important to the health care provider, and this article presents the development and application of an instrument designed to provide timely and relevant internal customer satisfaction information to individual health care providers. This provides information on problem identification and improvement opportunities for a world-class continuous improvement program. Findings - The article finds that customer satisfaction is increasingly being recognized as an appropriate measure for determining how well a particular organization is accomplishing its mission and, while customer satisfaction surveys provide valuable information and may be used to improve the entire operation, they provide limited insight into the details of the inner workings of each cost center. Each of the measures discussed in this article is potentially equally insightful and may provide more directly usable information when applied to internal customers. Originality/value - This article provides useful information on providing customer satisfaction in the health care arena. © Emerald Group Publishing Limited.},
author_keywords={Continuous improvement;  Customer satisfaction;  Health services;  United States of America},
keywords={article;  health care facility;  health services research;  methodology;  organization and management;  total quality management;  United States, Efficiency, Organizational;  Health Facilities;  Organizational Case Studies;  Total Quality Management;  United States},
references={Davis, E., Luthans, F., The healthcare cost crisis: Causes and containment (1990) Personnel, (FEBRUARY), pp. 24-30; Eubanks, P., TQM/CQI: The CEO experience (1992) Hospitals, , June 5; Hayes, R.H., Wheelwright, S.C., Dark, K.B., (1988) Dynamic Manufacturing: Creating the Learning Organization, , The Free Press New York, NY; Hospitals, Networks, H., (1993) The Quality March: National Survey Profiles Improvement Activities, 67 (23). , December 5; Imai, M., (1986) Kaizen: The Key to Japan's Competitive Success, , Random House New York, NY; (2000) Comprehensive Accreditation Manual for Hospitals (CAMH), , JCAHO Oakbrook Terrace, IL; Koska, M.T., Clinical quality initiatives: The search for meaningful and accurate measures (1992) Hospitals, , March 5; Levit, K., Cowan, C., Lazenby, H., Sensenig, A., McDonnell, P., Stiller, I., Martin, A., Health spending in 1998: Signals of change (2000) Health Affairs, (JANUARY-FEBRUARY); Lynn, G.F., TQM delivers as promised (1992) Modern Healthcare, , March 23; Health care spending may double (1998) (The) New York Times: On the Web, , http://econ.bu.edii/obrieii/ec387/healtharticles/HCEdoiibling.htm, September 14; O'Connor, S., Lanning, J.A., The new healthcare quality: Value, outcomes, and continuous improvement (1991) Clinical Laboratory Management Review, (JULY-AUGUST); Shaller, D.V., Sharpe, R.S., Rubin, R.D., A national action plan to meet health care quality information needs in the age of managed care (1998) Journal of the American Medical Association, 279 (16), pp. 1254-8; Kpmg, (1998), "Consumerism in health care: new voices", summary findings from KPMG's consumerism in health care research study, January, available at: www.us.kpmg/ps/hcls/publications/specmrt/docs/w0199.pdf; Schonberger, R.J., (1986) World Class Manufacturing: The Lessons of Simplicity Applied, , The Free Press New York, NY},
correspondence_address1={Swinehart, K.D.; Department of Management and Marketing, , Johnson City, TN, United States},
issn={09526862},
pubmed_id={16335618},
language={English},
abbrev_source_title={Int. J. Health Care Qual. Assur.},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Park2005161,
author={Park, S. and Suresh, N.C.},
title={An investigation of the roles of electronic marketplace in the supply chain},
journal={Proceedings of the Annual Hawaii International Conference on System Sciences},
year={2005},
pages={161},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-27544449609&partnerID=40&md5=5b941e113ae4690e062efe5fe712c351},
abstract={Electronic marketplace (EM) has been considered lately as an alternative coordination mechanism between suppliers and buyers in a supply chain. In this paper an appropriate model for EM-based supply chain is developed in order to investigate the impact of EM on two important supply chain performance measures - total supply chain cost per sale and customer fill rate. We adopt a simulation modeling approach and conduct experiments with key factors which include auction-based coordination mechanism. In addition, supply chain effects such as demand pooling, matching and aggregation effects in procurement of parts, etc. are also considered. The results indicate that EM could bring operational benefits even though strategic and situational factors that change the magnitude of benefits may occasionally prevent EM from being successful in a supply chain.},
keywords={Computer simulation;  Industrial economics;  Information technology;  Mathematical models, Coordination mechanisms;  Demand pooling;  Electronic marketplace;  Supply chain management, Electronics industry},
editor={Spraque, Jr. R.H.},
issn={15301605},
language={English},
abbrev_source_title={Proc. Annu. Hawaii Int. Conf. Syst. Sci.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Flynn20053421,
author={Flynn, B.B. and Flynn, E.J.},
title={Synergies between supply chain management and quality management: Emerging implications},
journal={International Journal of Production Research},
year={2005},
volume={43},
number={16},
pages={3421-3436},
doi={10.1080/00207540500118076},
note={cited By 217},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-27844528021&doi=10.1080%2f00207540500118076&partnerID=40&md5=bdf99a841a23b4270643b55d2d8f464c},
affiliation={Babcock Graduate School of Management, Wake Forest University, Reynolda Station, PO Box 7659, Winston-Salem, NC 27109-7659, United States},
abstract={This paper examines the potential that quality management offers for improving supply chain management performance. Based on the theoretical and descriptive literature, four themes related to this topic are extracted. These are related to the pursuit of supply chain and quality goals simultaneously, leading to the development of cumulative capabilities, the relationship between quality management practices and supply chain performance measures and the relationship between a specific set of quality management practices known as co-makership and supply chain performance measures. Hypotheses were developed and tested using an existing database of information from 164 plants in the machinery, electronics and transportation components industries in the USA, Germany, Italy, Japan and England. There was strong support for all four hypotheses, indicating that there is a relationship between quality management and supply chain management. Practical implications and guidelines for managers focus upon leveraging this relationship as a competitive weapon in the increasingly complex global environment. © 2005 Taylor & Francis Group Ltd.},
author_keywords={Co-makership;  Quality;  Supply chain management;  Total quality management (TQM)},
keywords={Database systems;  Large scale systems;  Total quality management, Co-makership;  Quality;  Supply chain management, Enterprise resource planning},
references={Anderson, J., Rungtusanatham, M., Schroeder, R.G., A theory of total quality management underlying the Deming management method (1994) Acad. of Manage. Rev., 3, pp. 472-509; Bessant, J., (1990) Managing Advanced Manufacturing Technology: The Challenge of the Fifth Wave, , Basil Blackwell: London; Bevan, J., What is co-makership? (1987) Int. J. Quality & Reliability Manage., 4 (3), pp. 47-56; Bevan, J., Co-makership (1989) Management Decision, 27 (3), pp. 50-54; Boyer, K.K., Lewis, M.W., Competitive priorities: Investigating the need for trade-offs in operations strategy (2002) J. Op. Manage., 11 (1), pp. 9-20; Carr, A.S., Pearson, J.N., Strategically managed buyer-supplier relationships and performance outcomes (1999) J. Op. Manage., 17 (5), pp. 497-519; Carter, J.R., Narasimhan, R., Purchasing and supply chain management: Future directions and trends (1996) International Journal of Purchasing and Materials Management, 32 (4), pp. 2-12; Carter, P., Carter, J.R., Monczka, R.M., Slaight, T.H., Swan, A.J., The future of purchasing and supply: A ten year forecast (2000) J. Supply Chain Manage., 36 (1), pp. 14-26; Chen, C.C., Yang, C.C., Cost-effectiveness based performance evaluation for suppliers and operations (2002) Quality Manage. J., 9 (4), pp. 59-73; Choi, T.Y., Rungtusanatham, M., Comparison of quality management practices: Across the supply chain and industries (1999) J. Supply Chain Manage., 35 (1), pp. 20-27; Crosby, L., LeMay, S.A., Empirical determination of shipper requirements for motor carrier services: SERVQUAL, direct questioning and policy capturing methods (1998) J. Business Logist., 19 (1), pp. 139-153; Dean, J.W., Snell, S.A., Integrated manufacturing and job design: Moderating effects of organizational inertia (1991) Acad. of Manage. J., 34, pp. 776-804; Detouzos, M.L., Lester, R.K., (1989) Made in America, , MIT Press: Cambridge, MA; Ferdows, K., DeMeyer, A., Lasting improvements in manufacturing performance: In search of the new theory (1990) J. Op. Manage., 9, pp. 168-184; Ferdows, K., Lewis, M.A., Machuca, J.A.D., Rapid-fire fulfillment (2004) Harvard Business Review, 82 (11), pp. 104-110; Flynn, B.B., Flynn, E.J., An exploratory study of the nature of cumulative capabilities (2004) J. Op. Manage., 22, pp. 439-457; Flynn, B.B., Sakakibara, S., Schroeder, R.G., Relationship between JIT and TQM: Practices and performance (1995) Academy of Management Journal, 38 (5), pp. 1325-1360; Flynn, B.B., Saladin, B., Further evidence on the validity of the theoretical models underlying the Baldrige criteria (2001) J. Op. Manage., 19 (6), pp. 617-652; Flynn, B.B., Schroeder, R.G., World class manufacturing project: Overview and selected results (1997) Int. J. Op. & Prod. Manage., 17 (718), pp. 671-686; Flynn, B.B., Schroeder, R.G., Sakakibara, S., The impact of quality management practices on performance and competitive advantage (1995) Decision Sciences, 26 (5), pp. 659-692; Forker, L., Ruch, W., Hershauer, J.C., Examining supplier improvement efforts from both sides (1999) J. Supply Chain Manage., 35 (3), pp. 40-50; Hayes, R.H., Wheelwright, S.C., (1984) Restoring Our Competitive Advantage: Competing Through Manufacturing, , John Wiley & Sons: New York, NY; Heide, J.B., Intel-organizational governance in marketing channels (1994) J. Marketing, 58 (1), pp. 71-86; Heiko, L., Production lead time minimisation and just-in-time (1990) Op. Manage. Rev., 3 (3-4), pp. 19-23; Hill, C., National institutional structures, transaction cost economising and competitive advantage: The case of Japan (1995) Organizational Science, 1, pp. 119-131; Joshi, A.W., Stump, R.L., Determinants of commitment and opportunism: Integrating and extending insights from transactions cost analysis and relational exchange theory (1999) Canadian Journal of Administration Sciences, 16 (4), pp. 334-352; Kanji, G.K., Wong, A., Business excellence model for supply chain management (1999) Total Quality Management, 10 (8), pp. 1147-1168; Kuei, C.H., Madu, C.N., Lin, C., The relationship between supply chain quality management practices and organizational performance (2001) Int. J. Quality & Reliability Manage., 18 (8-9), pp. 864-872; Lambert, D.M., Knemeyer, A.M., We're in this together (2004) Harvard Business Review, 82 (12), pp. 114-122; Lee, G.L., Oakes, I.K., Templates for change with supply chain rationalisation (1996) Int. J. Op. & Prod. Manage., 16 (2), pp. 197-208; Lee, H.L., The triple-A supply chain (2004) Harvard Business Review, 82 (10), pp. 102-112; Levy, P., Bessant, J., Sarg, B., Lamming, R., Developing integration through total quality supply chain management (1995) Integ. Manuf. Syst., 6 (3), pp. 4-13; Liker, J.K., Choi, T.Y., Building deep supplier relationships (2004) Harvard Business Review, 82 (12), pp. 104-113; Mefford, R.M., The productivity nexus of new inventory and quality control techniques (1989) Eng. Costs & Prod. Econ., 17, pp. 21-28; Mehra, S., Agrawal, S.P., Total quality as a new global competitive strategy (2003) Int. J. Quality & Reliability Manage., 20 (8-9), pp. 1009-1025; Mehra, S., Hoffman, J.M., Sirias, P., TQM as a management strategy for the next millennia (2001) Int. J. Op. & Prod. Manage., 21 (5-6), pp. 855-876; Mehra, S., Inman, R.A., Purchasing management and business competitiveness in the coming decade (2004) Prod. Planning & Control, 15 (7), pp. 710-718; Meredith, J., Roth, A., Operations management in the USA (1998) Int. J. Op. & Prod. Manage., 18 (7), pp. 668-683; Muralidharan, C., Antharaman, N., A multi-criteria group decision-making model for supplier rating (2002) J. Supply Chain Manage., 38 (4), pp. 22-33; Narayanan, V.G., Raman, A., Aligning incentives in supply chains (2004) Harvard Business Review, 82 (11), pp. 94-102; Noble, M.A., Manufacturing strategy: Testing the cumulative model in a multiple country context (1995) Decision Sciences, 26 (5), pp. 693-721; Pfeffer, J., Salancik, G.R., (1978) The External Control of Organizations, , Harper and Row: New York, NY; Poulymenakou, A., Tsironis, L., Quality and electronic commerce: A partnership for growth (2003) The TQM Magazine, 15 (3), pp. 137-151; Samson, D., Terziovski, M., The relationship between total quality management practices and operational performance (1999) J. Op. Manage., 17 (4), pp. 393-409; Schmenner, R.W., Swink, M.L., On theory in operations management (1998) J. Op. Manage., 17 (1), pp. 97-113; Slone, R.E., Leading a supply chain turnaround (2004) Harvard Business Review, 82 (10), pp. 114-121; Tan, K.C., Kannan, V.R., Handfield, R.B., Ghoshi, S., Supply chain management: An empirical study of its impact on performance (1999) Int. J. Op. & Prod. Manage., 19 (10), pp. 1034-1048; Trent, R.J., Monczka, R.M., Achieving world-class supplier quality (1999) Total Quality Management, 10 (6), pp. 927-938},
correspondence_address1={Flynn, B.B.; Babcock Graduate School of Management, PO Box 7659, Winston-Salem, NC 27109-7659, United States; email: Barb.flynn@mba.wfu.edu},
issn={00207543},
coden={IJPRB},
language={English},
abbrev_source_title={Int J Prod Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Hervani2005330,
author={Hervani, A.A. and Helms, M.M. and Sarkis, J.},
title={Performance measurement for green supply chain management},
journal={Benchmarking},
year={2005},
volume={12},
number={4},
pages={330-353},
doi={10.1108/14635770510609015},
note={cited By 1009},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-22944491753&doi=10.1108%2f14635770510609015&partnerID=40&md5=006c68c1272d7c6b67252c37cba4d54b},
affiliation={Division of Business Administration, Dalton State College, Dalton, GA, United States; Graduate School of Management, Clark University, Worcester, MA, United States},
abstract={Purpose - To introduce and provide an overview of the various issues related to environmental (green) supply chain management performance measurement. Design/methodology/approach - The work relies on experiences, case studies and other literature related to performance measurement in environmental supply chains. It seeks to integrate works in supply chain management, environmental management, and performance management into one framework. A systems framework forms the discussion outline with a focus on controls/pressures, inputs, tools, and outputs as major categories for evaluation and review. Findings - Provides an integrative framework for study, design and evaluation of green supply chain management performance tools. The findings also identify a number of issues that need to still be addressed. Research limitations/implications - We have only one design of the issues in which numerous categorizations could be provided. There is limited research in this area and new and current models/developments can provide additional insight. Implications of the work is that these gaps exist and that significantly more work needs to be completed in this field. Practical implications - A very useful source of information for practitioners that seek to implement these systems within and between organizations. Also, the paper provides numerous areas which researchers could complete additional research and develop research agendas. Originality/value - This paper provides some of the very first insights into development of a green supply chain management performance measurement system. Typically performance measurement systems are internally and business focused, we expand on these issues by considering inter-organizational and environmental issues within a business context. © Emerald Group Publishing Limited.},
author_keywords={Environmental management;  Performance measurement (quality);  Supply chain management},
references={Adams, S., Sarkis, J., Liles, D., The development of strategic performance metrics (1995) Engineering Management Journal, 7 (1), pp. 24-32; Allenby, B., Supporting environmental quality: Developing an infrastructure for design (1993) Total Quality Environmental Management, 2 (3), pp. 303-8; Altiok, T., Ranjan, R., Multi-stage, pull-type production/inventory systems (1995) IIE Transactions, 27, pp. 190-200; Ammenberg, J., (2001) How do Standardized Environmental Management Systems Affect Environmental Performance and Business?, , Department of Physics and Measurement Technology, Linköping University Linköping; Andrews, R.N.L., Do EMSs improve performance? (2003) Final Report of the National Database on Environmental Management Systems Department of Public Policy, , University of North Carolina at Chapel Hill Chapel Hill, NC available at: http://ndems.cas.unc.edu/; Angel Del Brio, J., Junquera, B., A review of the literature on environmental innovation management in SMEs: Implications for public policies (2003) Technovation, 23 (12), pp. 939-48; Arntzen, B.C., Brown, G.G., Harrison, T.P., Trafton, L.L., Global supply chain management at digital equipment corporation (1995) INTERFACES, 25, pp. 69-93; Bansal, P., Roth, K., Why companies go green: A model of ecological responsiveness (2000) Academy of Management Journal, 43 (4), pp. 713-48; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-92; Beamon, B., Chen, V., Performance analysis of conjoined supply chains (2001) International Journal of Production Research, 39 (14), pp. 3195-218; Bennet, J., Martin, P., ISO 14031 and the future of environmental performance evaluation (1998) Greener Management International, 21, pp. 71-86; Bowen, F.E., Cousins, P.D., Lamming, R.C., Faruk, A.C., Horses for courses: Explaining the gap between the theory and practice of green supply (2001) Greener Management International, 35, pp. 41-60; Bowen, F.E., Cousins, P.D., Lamming, R.C., Faruk, A.C., The role of supply management capabilities in green supply (2001) Production and Operations Management, 10 (2), pp. 174-89; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5 (2), p. 48; Carr, A.S., Pearson, J.N., Strategically managed buyer-supplier relationships and performance outcomes (1999) Journal of Operations Management., p. 17; Carr, A.S., Smeltzer, L.R., An empirically based operational definition of strategic purchasing (1997) European Journal of Purchasing and Supply Management, 34, p. 199; Carr, A.S., Smeltzer, L.R., The relationship among purchasing benchmarking strategic purchasing, firm performance, and firm size (1999) Journal of Supply Chain Management, 35 (4), pp. 51-61; Charnes, A., Cooper, W.W., Rhodes, E., Measuring the efficiency of decision making units (1978) European Journal of Operational Research, 2, pp. 429-44; Chen, I., Paulraj, A., Strategic supply management and supply chain performance (2002) Decision Sciences Institute 2002 Annual Meeting Proceedings; Christopher, M., (1994) Logistics and Supply Chain Management, , Irwin, R D. Financial Times New York, NY; Clayton, A., Spinardi, G., Williams, R., (1999) Policies for Cleaner Technology: A New Agenda for Government and Industry, , Earthscan London; Cohen, M.A., Lee, H.L., Resource deployment analysis of global manufacturing and distribution networks (1989) Journal of Manufacturing and Operations Management, 2, pp. 81-104; Cohen, W., Levinthal, D., Absorptive capacity: A new perspective on learning and innovation (1990) Administrative Science Quarterly, 35, pp. 128-52; Cohen, W., Moon, S., Impact of production scale economies, manufacturing complexity, and transportation costs on supply chain facility networks (1990) Journal of Manufacturing and Operations Management, 3, pp. 269-92; Cook, R.L., Rogowski, R.A., Applying JIT principles to continuous process manufacturing supply chains (1996) Production and Inventory Management Journal, pp. 12-17. , First Quarter; Cramer, J., Experiences with integrated chain management in Dutch industry (1996) Business Strategy and the Environment, 5, pp. 38-47; Davis, T., Effective supply chain management (1993) Sloan Management Review, pp. 35-46; Deming, W.E., (1986) Out of the Crisis, , MIT/CAES Boston, MA; Drumwright, M.E., Socially responsible organizational buying: Environmental concern as a noneconomic buying criterion (1994) Journal of Marketing, 58 (3), pp. 1-19; Ellram, L.M., Ready, K.J., Environmental purchasing: Benchmarking our German counterparts (1998) International Journal of Purchasing and Materials Management, pp. 28-38. , fall; Epstein, M.J., Wisner, P.S., Good neighbors: Implementing social and environmental strategies with BSC (2001) Balanced Scorecard Report, 3 (3). , Reprint Number B0105C Harvard Business School Publishing Cambridge, MA; Faruk, A.C., Lamming, R.C., Cousins, P.D., Bowen, F.E., Analyzing, mapping, and managing environmental impacts along supply chains (2002) Journal of Industrial Ecology, 5 (2), pp. 13-36; Florida, R., Atlas, M., Cline, M., (2000) What Makes Companies Green? Organizational and Geographic Factors in the Adoption of Environmental Practice, , research supported by the NSF Division of Geography and Rational Science and Environmentally Conscious Manufacturing Program; Florida, R., Davidson, D., Why do firms adopt environmental practices (and do they make a difference)? (2001) Regulating from the Inside: Can Environmental Management Systems Achieve Policy Goals?, p. 82. , Coglianese, C. Nash, J. Resources for the Future Press Washington, DC; Greffen, C., Rothenberg, S., Suppliers and environmental innovation (2000) International Journal of Operations & Production Management, 20 (2), pp. 166-86; Gunasekaran, A., Patel, C., McGaughey, R.E., A framework for supply chain performance measurement (2004) International Journal of Production Economics, 87 (3), pp. 333-47; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (12), pp. 71-87; Gupta, M., Environmental management and its impact on the operations function (1995) International Journal of Operations & Production Management, 15 (8), pp. 34-51; Hall, J., Environmental supply chain dynamics (2000) Journal of Cleaner Production, 8 (6); Hamschmidt, J., Economic and ecological impacts of environmental management systems in companies: Experiences from Switzerland (2000) EURO Environment, , Aalborg; Handfield, R., Walton, S.V., Sroufe, R., Melnyk, S.A., Applying environmental criteria to supplier assessment: A study in the application of the analytical hierarchy process (2002) European Journal of Operational Research, 141, pp. 70-87; Harris, J., New paths to business value: Linking environment, health and safety performance to strategic sourcing (2004) Greening the Supply Chain, , Sarkis, J. Rao, P. Greenleaf Publishing Sheffield; Hart, S., A natural-resource-based view of the firm (1995) Academy of Management Review, 20 (4), pp. 986-1014; Harvey, B., Schaefer, A., Managing relationships with environmental stakeholders: A study of UK water and electricity utilities (2001) Journal of Business Ethics, 30 (3), pp. 243-52; Hemmelskamp, J., (1999) The Influence of Environmental Policy on Innovative Behavior - An Econometric Study, , IPTS Seville; Henriques, I., Sadorsky, P., The determinants of an environmentally responsive firm: An empirical approach (1996) Journal of Environmental Economics and Management, 30, pp. 381-95; Henriques, I., Sadorsky, P., The relationship between environmental commitment and managerial perceptions of stakeholder importance (1999) Academy of Management Journal, 42 (1), pp. 87-99; Kanji, G.K., A new business excellence model from an old Indian philosophy (2003) Abinngdon, 14 (9), p. 1071; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (1), pp. 71-80; King, A.A., Lenox, M.J., Lean and green? An empirical examination of the relationship between lean production and environmental performance (2001) Production and Operations Management, 10 (3), pp. 244-56; Klassen, R.D., McLaughlin, C.P., (1996) The Impact of Environmental Management of Firm Performance Management Science, 42 (8), pp. 1199-215; Korhonen, J., Two paths to industrial ecology: Applying the product-based and geographical approaches (2002) Journal of Environmental Planning and Management, 45 (10), pp. 39-51; Korhonen, J., Niutanen, V., Material and energy flows of a local forest industry system in Finland (2003) Sustainable Development, 11 (3), pp. 121-30; Lee, H.L., Billington, C., Material management in decentralized supply chains (1993) Operations Research, 41 (5), pp. 835-47; Lee, H.L., Feitzinger, E., Product configuration and postponement for supply chain Efficiency (1995) Institute of Industrial Engineers, Fourth Industrial Engineering Research Conference Proceedings, pp. 43-8; Lokkegaard, K.E., ISO 14031 used as a tool in ISO 14001 or as an alternative for simple EMS (1999) Greener Management International, pp. 79-89; Malaman, R., (1996) Technological Innovation for Sustainable Development - Generation and Diffusion of Cleaner Technologies in Italian Firms, , paper from a research project carried out by Instituto per la Richercha Sociale; Nagel, M., Environmental quality in the supply chain of an original equipment manufacturer (2004) Greening the Supply Chain, , Sarkis, J. Rao, P. Greenleaf Publishing Sheffield; Nicoll, A.D., Integrating logistics strategies (1994) Annual International Conference Proceedings, pp. 590-4. , American Production and Inventory Control Society; Osterman, P., How common is workplace transformation and who adopts it? (1994) Industrial and Labor Relations Review, 47 (2); Pineda-Henson, P., Culaba, A.B., Mendoza, G.A., Evaluating environmental performance of pulp and paper manufacturing using the analytic hierarchy process and life cycle assessment (2002) Journal of Industrial Ecology, 6 (1), pp. 15-28; Porter, M.E., America's green strategy (1991) Scientific American, 264 (4), p. 168; Porter, M., Van Der Linde, C., Green and competitive: Ending the stalemate (1995) Harvard Business Review, pp. 120-34; Porter, M., Van Der Linde, C., Toward a new conception of the environment-competitiveness relationship (1995) Journal of Economic Perspectives, 9 (1), pp. 97-118; Putnam, D., ISO 14031: Environmental performance evaluation (2002) Draft Submitted to the Confederation of Indian Industry for Publication in Their Journal, pp. 1-10; Russo, M.V., (2001) Institutional Changes and Theories of Organizational Strategy: ISO 14001 and Toxic Emissions in the Electronics Industry, p. 45. , unpublished paper Department of Management, University of Oregon Eugene, OR; Saaty, T.L., (1980) The Analytical Hierarchy Process, , McGraw-Hill New York, NY; Sarkis, J., Evaluating environmentally conscious business practices (1998) European Journal of Operational Research, 107 (1), pp. 159-74; Sarkis, J., A strategic decision making framework for green supply chain management (2003) Journal of Cleaner Production, 11 (4), pp. 397-409; Sarkis, J., Kitazawa, S., The relationship between ISO 14001 and continuous source reduction programs (2000) International Journal of Operations & Production Management, 20 (2), pp. 225-48; Sarkis, J., Rasheed, A., Greening the manufacturing function (1995) Business Horizons, 38 (5), pp. 17-27; Sarkis, J., Talluri, S., Ecoefficiency measurement using data envelopment analysis: Research and practitioner issues (2004) Journal of Environmental Assessment Policy and Management, 6 (1), pp. 91-123; Sharma, S., Managerial interpretations and organizational context as predictors of firm choice of environmental strategies (2000) Academy of Management Journal, 43, pp. 681-97; Sink, S.D., Tuttle, T.C., The performance management question in the organization of the future (1990) Industrial Management, 32 (1), p. 4; Teece, D., Pisano, G., The dynamic capabilities of firms: An introduction (1994) Industrial Corporate Change, 3, pp. 537-56; Towill, D.R., Naim, M.M., Wikner, J., Industrial dynamics simulation models in the design of supply chains (1992) International Journal of Physical Distribution & Logistics Management, 22 (5), pp. 3-13; Trowbridge, P., A case study of green supply-chain management at advanced micro devices (2001) Greener Management International, 37, pp. 121-35; Tzafestas, S., Kapsiotis, G., Coordinated control of manufacturing/supply chains using multi-level techniques (1994) Computer Integrated Manufacturing Systems, 7 (3), pp. 206-12; Wikner, J., Towill, D.R., Naim, M., Smoothing supply chain dynamics (1991) International Journal of Production Economics, 22 (3), pp. 231-48; Winsemius, P., Guntram, U., Responding to the environmental challenge (1992) Business Horizons, 35 (2), pp. 12-20; Winter, S., Knowledge and competence as strategic assets (1987) The Competitive Challenge: Strategies for Industrial Innovation and Renewal, , Teece, D. Harper & Row New York, NY; Womack, J., Jones, D., Ross, D., (1990) The Machine That Changed the World, , Rawson and Associates New York, NY; Zingales, F., O'Rourke, A., Orssatto, R.J., Environment and socio-related balanced scorecard: Exploration of critical issues (2002) Working Paper 2002/47/CMER Center for the Management of Environmental Resources, , INSEAD, Fontainebleau, France},
correspondence_address1={Hervani, A.A.; Division of Business Administration, , Dalton, GA, United States},
issn={14635771},
coden={BQMTF},
language={English},
abbrev_source_title={Benchmarking},
document_type={Article},
source={Scopus},
}

@ARTICLE{Kumar2005152,
author={Kumar, A. and Ozdamar, L. and Ng, C.P.},
title={Procurement performance measurement system in the health care industry},
journal={International Journal of Health Care Quality Assurance},
year={2005},
volume={18},
number={2},
pages={152-166},
doi={10.1108/09526860510588179},
note={cited By 61},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-19944375112&doi=10.1108%2f09526860510588179&partnerID=40&md5=6cd10773154ef0f5ab96c41c60f5cddd},
affiliation={Systems and Engineering Management Division, School of Mechanical and Production Engineering, Nanyang Technological University, Singapore, Singapore; Carmel Manufacturing Pvt. Ltd., Singapore, Singapore},
abstract={Purpose - The rising operating cost of providing healthcare is of concern to health care providers. As such, measurement of procurement performance will enable competitive advantage and provide a framework for continuous improvement. The objective of this paper is to develop a procurement performance measurement system. Design/methodology/approach - The paper reviews the existing literature in procurement performance measurement to identify the key areas of purchasing performance. By studying the three components in the supply chain collectively with the resources, procedures and output, a model is been developed. Additionally, a balanced scorecard is proposed by establishing a set of generic measures and six perspectives. A case study conducted at the Singapore Hospital applies the conceptual model to describe the purchasing department and the activities within and outside the department. Findings - The results indicate that the material management department has already made a good start in measuring the procurement process through the implementation of the balanced scorecard. Research limitations/implications - There are many data that are collected but not properly collated and utilized. Areas lacking measurement include cycle time of delivery, order processing time, effectiveness, efficiency and reliability. Though a lot of hard work was involved, the advantages of establishing a measurement system outweigh the costs and efforts involved in its implementation. Practical implications - Results of balanced scorecard measurements provide decision-makers with critical information on efficiency and effectiveness of the purchasing department's work. Originality/value - The measurement model developed could be used for any hospital procurement system. © Emerald Group Publishing Limited.},
author_keywords={Health services;  Performance measurement (quality);  Procurement},
keywords={decision making;  health care delivery;  health care industry;  health care quality;  human;  population model;  quality control;  review, Delivery of Health Care;  Efficiency, Organizational;  Equipment and Supplies;  Models, Organizational;  Singapore},
references={Alt, S., Airforce JIT deal won't fly with current rags (1997) Hospital and Materials Management, 22 (12), p. 12; Butler, T.W., Management science operations research projects in healthcare: The administrator's perspective (1995) Healthcare Management Review, (10); Cavinato, J.L., Kauffman, R.G., (1999) The Purchasing Handbook: A Guide for Purchasing and Supply Professionals, , 6th ed. McGraw-Hill New York, NY; Davenport, T., (1994) Process Innovation: Reengineering Work Through Information Technology, , Harvard Business School Press Boston, MA; Dobler, D., Burt, D., (1996) Purchasing and Supply Management, , McGraw-Hill New York, NY; Fearon, H., Dobler, D., Killen, K., (1992) The Purchasing Handbook, , McGraw-Hill New York, NY; Kotter, J.P., (1978) Organizational Dynamics, , Addison-Wesley Reading, MA; Knudsen, D., (1999) Procurement Performance Measurement System, , licentiate dissertation, Department of Design Sciences, Lund University, Lund; Lysons, K., (1996) Purchasing, , Pitman Boston, MA; Of Health, M., (2001) Annual Report, , Ministry of Health Singapore; Neely, A., The performance measurement revolution: Why now and what next? (1999) International Journal of Operations & Production Management, 19 (2), pp. 205-28; Scheuing, E.E., (1989) Purchasing Management, , Prentice-Hall Englewood Cliffs, NJ; General Hospital, S., (2003), available at: www.sgh.com.sg (accessed February 2003); Van Weele, A.J., (2000) Purchasing and Supply Chain Management, , Thomson Learning Boston, MA; Al-Shammari, M., A multi-criteria data envelopment analysis model for measuring the productive efficiency of hospitals (1999) Operations & Production Management, 19, pp. 879-90; Chilingerian, J.A., Sherman, H.D., Benchmarking physician practice patterns with DEA: A multi-stage approach for cost containment (1996) Annuals of Operations Research, 67, pp. 83-116; Chirikos, T.N., Sear, A.M., Measuring hospital efficiency: A comparison of two approaches (2000) Health Services Research, 34 (6), pp. 1389-408; Creteur, M., Closon, M.C., Pouplier, I., Pochet, Y., Organizational performance evaluation: Application to Belgian hospitals (2002) International Journal of Healthcare Technology and Management, 4 (1-2), pp. 148-79; Curtright, J.W., Stolp-Smith, S.C., Edell, E.S., Strategic performance management: Development of a performance measurement system at the Mayo Clinic (2000) Journal of Healthcare Management, 45 (1), pp. 56-68; Fowler, A., Campbell, D., Benchmarking and performance management in clinical pharmacy (2001) International Journal of Operations & Production Management, 21 (3), p. 327; Griffth, J.R., Alexander, J.A., Warden, G.L., Measuring comparative hospital performance/practitioner response (2002) Journal of Healthcare Management, 47 (1), pp. 41-57; Of Health, M., (1993) White Paper on Affordable Health Care, , Ministry of Health Singapore; Of Health, M., (2003), available at: http://app.moh.gov.sg/our/ (accessed February 2003); Monczka, R., Carter, P., (1979) Purchasing Performance Measurements, , Michigan State University Business Services East Lansing, MI; Noble, E., Klein, L., Quality assurance: The measure of quality culture in a managed care setting (2000) Total Quality Management, 45 (2), pp. 199-205; Pink, G.H., McKillop, I., Schraa, E.G., Preyra, C., Creating a balanced scorecard for a hospital system (2001) Journal of Health Care Finance, 27 (3), pp. 1-20; Stewart, L.J., Greisler, D., Feldman, K.J., Measuring primary care practice performance within an integrated delivery system: A case study (2002) Journal of Healthcare Management, 47 (4), pp. 250-62},
correspondence_address1={Kumar, A.; Systems and Engineering Management Division, , Singapore, Singapore},
issn={09526862},
pubmed_id={15974511},
language={English},
abbrev_source_title={Int. J. Health Care Qual. Assur.},
document_type={Review},
source={Scopus},
}

@ARTICLE{CliffordDefee200528,
author={Clifford Defee, C. and Stank, T.P.},
title={Applying the strategy-structure-performance paradigm to the supply chain environment},
journal={The International Journal of Logistics Management},
year={2005},
volume={16},
number={1},
pages={28-50},
doi={10.1108/09574090510617349},
note={cited By 149},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986173316&doi=10.1108%2f09574090510617349&partnerID=40&md5=2f69b8206148a6914aa364868026b7e3},
affiliation={Department of Marketing and Logistics, The University of Tennessee, Knoxville, Tennessee, United States},
abstract={Purpose – The paper extends a central paradigm of the strategy literature to the supply chain environment to foster a better understanding of the elements characterizing strategic decisions that lead to supply chain structural development and performance. Design/methodology/approach – Strategic literature is reviewed to provide a fuller explanation of the strategy-structure-performance (SSP) research stream. SSP foundational principles are linked to supply chain management concepts, and synthesized into an explanatory framework. Propositions for future research are presented based upon the framework. Findings – An iterative relationship among internal firm strategy, structure, and performance measurement systems is indicated, implying that firm supply chain strategy should be complementary with that of supply chain partners. Research limitations/implications – The paper presents a content analysis of existing research and a conceptual framework emerging from it. No data were collected nor were research propositions tested. Practical implications – The primary implication is “know your supply chain partners”. Do their strategies mesh – either as consistent or complementary – to your own firm's supply chain strategy? Strategic alignment is a necessary precursor to deployment of an effective supply chain structure. Originality/value – The paper shows that SSP theory can be extended beyond the firm to the complex supply chain environment. © 2005, Emerald Group Publishing Limited},
author_keywords={Decision making;  Strategic management;  Supply chain management},
references={Achrol, R., Changes in the theory of interorganizational relations in marketing: toward a network paradigm (1997) Journal of the Academy of Marketing Science, 25, pp. 56-71; Achrol, R., Reve, T., Steren, L.W., The environment of marketing channel dyads: a framework for comparative analysis (1983) Journal of Marketing, 47, pp. 55-67; Antia, K.D., Frazier, G.L., The severity of contract enforcement in interfirm channel relationships (2001) Journal of Marketing, 65, pp. 67-81; Atkinson, P.E., Why do companies pursue total quality management? (1986) Management Services, 30, pp. 8-12; Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A stakeholder approach to strategic performance management (1997) Sloan Management Review, 19, pp. 25-37; Barney, J., Firm resources and sustained competitive advantage (1991) Journal of Management, 17, pp. 99-120; Bartlett, C.A., Ghoshal, S., (1991) Managing Across Borders: The Transnational Solution, , Harvard Business School, Boston, MA; Bechtel, C., Jayaram, J., Supply chain management: a strategic perspective (1997) The International Journal of Logistics Management, 8, pp. 15-34; Boghossian, F.H., Build quality in; don't inspect it (1988) SAM Advanced Management Journal, 53, p. 44; Bowersox, D.J., Closs, D.J., (1996) Logistical Management: The Integrated Supply Chain Process, , McGraw-Hill, New York, NY; Bowersox, D.J., Closs, D.J., Cooper, M.B., (2002) Supply Chain Logistics Management, , McGraw-Hill, Boston, MA; Bowersox, D.J., Closs, D.J., Stank, T.P., (1999) 21st Century Logistics: Making Supply Chain Integration a Reality, , The Council of Logistics Management, Oak Brook, IL; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21, pp. 75-93; Brewer, P.C., Speh, T.W., Adapting the balanced scorecard to supply chain management (2001) Supply Chain Management Review, 5, pp. 48-56; Bucklin, L.P., Postponement, speculation and the structure of distribution channels (1965) Journal of Marketing Research, 2, pp. 26-31; Cannon, J.P., Perreault, W.D., Jr., Buyer-seller relationships in business markets (1999) Journal of Marketing Research, 36, pp. 439-460; Carter, C.R., Ellram, L.M., Thirty-five years of The Journal of Supply Chain Management: where have we been and where are we going? (2003) Journal of Supply Chain Management: A Global Review of Purchasing and Supply, 39, pp. 27-39; Chandler, A.D., Jr., (1962) Strategy and Structure, , The MIT Press, Cambridge, MA; Channon, D.F., The strategy and structure of British enterprise (1973), unpublished dissertation, Graduate School of Business Administration; Chow, G., Henrikssen, L.E., Heaver, T.D., Strategy, structure and performance: a framework for logistics research (1995) Logistics and Transportation Review, 31, pp. 199-285; Choy, K.L., Lee, W.B., Lo, V., An intelligent supplier relationship management system for selecting and benchmarking suppliers (2003) International Journal of Technology Management, 26, pp. 717-742; Christensen, H.K., Montgomery, C.A., Corporate economic performance: diversification strategy versus market structure (1981) Strategic Management Journal, 2, pp. 327-343; Christopher, M.L., (1992) Logistics and Supply Chain Management, , Pittman, London; Christopher, M., Ryals, L., Supply chain strategy: its impact on shareholder value (1999) The International Journal of Logistics Management, 10, pp. 1-10; Christopher, M., Towill, D.R., Developing market specific supply chain strategies (2002) The International Journal of Logistics Management, 13, pp. 1-14; Cooper, M.C., Ellram, L.M., Characteristics of supply chain management and the implications for purchasing and logistics strategy (1993) The International Journal of Logistics Management, 4, p. 10; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: more than a new name for logistics (1997) The International Journal of Logistics Management, 8, pp. 1-14; Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D.M., Rogers, D.S., The supply chain management processes (2001) The International Journal of Logistics Management, 12, pp. 13-36; Dalton, D.R., Todor, W.D., Spendolini, M.J., Fielding, G.J., Porter, L.W., Organization structure and performance: a critical review (1980) Academy of Management Review, 5, pp. 49-64; Day, G.S., The capabilities of market-driven organizations (1994) Journal of Marketing, 58, pp. 37-52; Doney, P., Cannon, J., An examination of the nature of trust in buyer-seller relationships (1997) Journal of Marketing, 61, pp. 35-51; Droge, C., Germain, G., The impact of the centralized structuring of logistics activities on span of control, formalization and performance (1989) Journal of the Academy of Marketing Science, 17, pp. 83-89; Duarte, M., Davies, G., Testing the conflict-performance assumption in business-to-business relationships (2003) Industrial Marketing Management, 32, pp. 91-99; Dyas, G.P., Thanheiser, H.T., (1976) The Emerging European Enterprise: Strategy and Structure in French and German Industry, , Macmillian, London; Egelhoff, W.G., Strategy and structure in multinational-corporations – a revision of the Stopford and Wells model (1988) Strategic Management Journal, 9, pp. 1-14; Eisenhardt, K.M., Martin, J.A., Dynamic capabilities: what are they? (2000) Strategic Management Journal, 21, pp. 1105-1121; Ellram, L.M., Partnering pitfalls and success factors (1995) International Journal of Purchasing and Materials Management, 31, pp. 36-44; Ellram, L.M., Cooper, M.C., Supply chain management, partnerships and the shipper-third party relationship (1990) The International Journal of Logistics Management, 1, pp. 1-10; Emerson, R., Power-dependence relations (1962) American Sociological Review, 27, pp. 31-41; Fawcett, S.E., Stanely, L.L., Smith, S.R., Developing a logistics capability to improve the performance of international operations (1997) Journal of Business Logistics, 18, pp. 101-127; Ferdows, K., Lewis, M.A., Machuca, J.A.D., Rapid-fire fulfillment (2004) Harvard Business Review, 82, pp. 104-110; Fombrun, C.J., Structural dynamics within and between organizations (1986) Administrative Science Quarterly, 31, pp. 403-421; Franko, L.G., (1976) The European Multinationals: A Renewed Challenge to American and British Big Business, , Greylock Publishing, Stamford, CT; Frazier, G.L., Organizing and managing channels of distribution (1999) Journal of the Academy of Marketing Science, 27, pp. 226-240; Galbraith, J.R., Kazanjian, R.K., (1986) Strategy Implementation: Structure, Systems, and Process, , West Publishing Company, St Paul, MN; Galbraith, J.R., Nathanson, D.A., (1978) Strategy Implementation: The Role of Structure and Process, , West Publishing Company, St Paul, MN; Galunic, D.C., Eisenhardt, K.M., Renewing the strategy-structure-performance paradigm (1994) Research in Organizational Behavior, 16, pp. 215-255; García-Dastugue, S.J., Lambert, D.M., Internet-enabled coordination in the supply chain (2003) Industrial Marketing Management, 32, pp. 251-263; Gruen, T.W., Relationship marketing: the route to marketing efficiency and effectiveness (1997) Business Horizons, 40, pp. 32-38; Halley, A., Nollet, J., The supply chain: the weak link for some preferred suppliers (2002) The Journal of Supply Chain Management: A Global Review of Purchasing and Supply, 38, pp. 39-47; Handfeld, R.B., A resource dependence perspective of just-in-time purchasing (1993) Journal of Operations Management, 11, pp. 289-311; Heide, J.B., Interorganizational governance in marketing channels (1994) Journal of Marketing, 58, pp. 71-85; Hofer, C., Schendel, D., (1978) Strategy Formulation: Analytical Concepts, , West Publishing, St Paul, MN; Holmberg, S., A systems perspective on supply chain measurements (2000) International Journal of Physical Distribution and Logistics Management, 30, pp. 847-868; Hoskisson, R.E., Multidivisional structure and performance: the contingency of diversification strategy (1987) Academy of Management Journal, 30, pp. 625-644; Hunt, S.D., (1991) Modern Marketing Theory: Critical Issues in Philosophy of Marketing Science, , South-Western Publishing, Cincinnati, OH; Hunt, S.D., Morgan, R.M., The comparative advantage theory of competition (1995) Journal of Marketing, 59, pp. 1-15; Hutt, M.D., Stafford, E.R., Defining the social network of a strategic alliance (2000) Sloan Management Review, 41, pp. 51-62; Iyer, G., Villas-Boas, J.M., A bargaining theory of distribution channels (2003) Journal of Marketing Research, 40, pp. 80-100; Johnson, J.D., Donahue, W.A., Atkin, C.K., Differences between formal and informal communications channels (1994) The Journal of Business Communication, 31, pp. 111-122; Kaplan, R.S., Norton, D.P., The balanced scorecard – measures that divide performance (1992) Harvard Business Review, 70, pp. 71-79; Kogut, B., Joint ventures and the option to expand and acquire (1991) Management Science, 37, pp. 19-33; Lambert, D.M., (2004) Supply Chain Management Processes, Partnerships, Performance, , Supply Chain Management Institute, Sarasota, FL; Lambert, D.M., Pohlen, T.L., Supply chain metrics (2001) The International Journal of Logistics Management, 12, pp. 1-19; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9, pp. 1-19; Lowson, R.H., The nature of an operations strategy: combining strategic decisions from the resource-based and market-driven viewpoints (2003) Management Decision, 41, pp. 538-549; Lubatkin, M., Rogers, R.C., Diversification, systematic risk, and shareholder return: a capital market extension of Rumelt's 1974 study (1989) Academy of Management Journal, 32, pp. 454-465; Lynch, D.F., Keller, S.B., Ozment, J., The effects of logistics capabilities and strategy on firm performance (2000) Journal of Business Logistics, 21, pp. 47-68; Maloni, M., Benton, W.C., Power influences in the supply chain (2000) Journal of Business Logistics, 21, pp. 49-73; Mentzer, J.T., (2001) Supply Chain Management, , Sage, Thousand Oaks, CA; Mentzer, J.T., (2004) Fundamentals of Supply Chain Management: Twelve Drivers of Competitive Advantage, , Sage, Thousand Oaks, CA; Mentzer, J.T., Konrad, B.P., An efficiency/effectiveness approach to logistics performance analysis (1991) Journal of Business Logistics, 12, pp. 33-61; Miles, R.E., Snow, C.C., (1978) Organizational Strategy, Structure and Process, , McGraw-Hill, New York, NY; Miles, R.E., Snow, C.C., Fit, failure and the hall of fame (1984) California Management Review, 26, pp. 10-28; Miller, D., Relating Porter's business strategies to environment and structure: analysis and performance implications (1988) Academy of Management Journal, 31, pp. 280-308; Mohr, J., Nevin, J.R., Communication strategies in marketing channels: a theoretical perspective (1990) Journal of Marketing, 54, pp. 36-51; Mollenkopf, D., Gibson, A., Ozanne, L., The integration of marketing and logistics functions: an empirical examination of New Zealand firms (2000) Journal of Business Logistics, 21, pp. 89-112; Nault, B.R., Information technology and organization design: locating decisions and information (1998) Management Science, 44, pp. 1321-1335; Pagell, M., Understanding the factors that enable and inhibit the integration of operations, purchasing and logistics (2004) Journal of Operations Management, 22, pp. 459-487; Porter, M.E., (1980) Competitive Strategy, , The Free Press, New York, NY; Porter, M.E., (1985) Competitive Advantage, , The Free Press, New York, NY; Prahalad, C.K., Hamel, G., The core competence of the corporation (1990) Harvard Business Review, 68, pp. 79-91; Rabin, S., The real-time enterprise, the real-time supply chain (2003) Information Systems Management, 20, pp. 58-62; Rinehart, L.M., Page, J.T., Jr., The development and test of a model of transaction negotiation (1992) Journal of Marketing, 56, pp. 18-32; Rinehart, L.M., Eckert, J.A., Handfeld, R.B., Page, T.J., Jr., Atkin, T., An assessment of supplier-customer relationships (2004) Journal of Business Logistics, 25, pp. 25-63; Ring, P.S., Van de Ven, A.H., Developmental processes in cooperative interorganizational relationships (1994) Academy of Management Review, 19, pp. 90-118; Rodrigues, A.M., Stank, T.P., Lynch, D., Linking strategy, structure, process and performance in integrated logistics (2004) Journal of Business Logistics, 25, pp. 65-94; Rumelt, R.P., (1974) Strategy, Structure, and Economic Performance, , Harvard University Press, Cambridge, MA; Schonberger, R.J., The transfer of Japanese manufacturing management approaches to US industry (1982) Academy of Management Review, 7, pp. 479-487; Slone, R.E., Leading a supply chain turnaround (2004) Harvard Business Review, 82, pp. 114-121; Stank, T.P., Traichal, P.A., Logistics strategy, organizational design, and performance in a cross-boarder environment (1998) Logistics and Transportation Review, 31, pp. 75-86; Stank, T.P., Keller, S.B., Closs, D.J., Performance benefits of supply chain logistical integration (2001) Transportation Journal, 41, pp. 32-46; Stevens, G.C., Integration of the supply chain (1989) International Journal of Physical Distribution & Logistics Management, 19, pp. 3-8; Stimpert, J.L., Duhaime, I.M., Seeing the big picture: the influence of industry, diversification, and business strategy on performance (1997) Academy of Management Journal, 40, pp. 560-583; Stock, G.N., Greis, N.P., Kasarda, J.D., Logistics, strategy and structure (1999) International Journal of Operations & Production Management, 18, pp. 37-52; Stopford, J.M., Wells, L.T., (1972) Managing the Multinational Enterprise, , Basic Books, New York, NY; Sugimori, Y., Kusunoki, K., Cho, F., Uchikawa, S., Toyota production system and Kanban system materialization of just-in-time and respect-for-human system (1977) International Journal of Production Research, 15, pp. 553-564; Teece, D.J., Internal organization and economic performance: an empirical analysis of the profitability of principal firms (1981) Journal of Industrial Economics, 30, pp. 173-199; Teece, D.J., Pisano, G., Shuen, A., Dynamic capabilities and strategic management (1997) Strategic Management Journal, 18, pp. 509-533; Thompson, J.D., (1967) Organizations in Action, , McGraw-Hill, New York, NY; Varadarajan, P.R., Jayachandran, S., Marketing strategy: an assessment of the state of the field and outlook (1999) Journal of the Academy of Marketing Science, 27, pp. 120-143; Weigand, R.E., Why studying the purchasing agent is not enough (1968) Journal of Marketing, 32, pp. 41-45; Wernerfelt, B., A resource-based view of the firm (1984) Strategic Management Journal, 5, pp. 171-180; Williams, L.R., Avril, N., Dimples, I., Terence, F., Logistics integration: the effect of information technology, team composition, and corporate competitive positioning (1997) Journal of Business Logistics, 18, pp. 31-41; Williamson, O.E., (1975) Markets and Hierarchies, , The Free Press, New York, NY; Williamson, O.E., (1985) The Economic Institutions of Capitalism, , The Free Press, New York, NY; Williamson, E.A., Harrison, D.K., Jordan, M., Information systems development within supply chain management (2004) International Journal of Information Management, 24, pp. 375-385},
issn={09574093},
language={English},
abbrev_source_title={Int. J. Logist. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Gupta2005337,
author={Gupta, K.M. and Gunasekaran, A.},
title={Costing in new enterprise environment: A challenge for managerial accounting researchers and practitioners},
journal={Managerial Auditing Journal},
year={2005},
volume={20},
number={4},
pages={337-353},
doi={10.1108/02686900510592034},
note={cited By 19},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-18344381427&doi=10.1108%2f02686900510592034&partnerID=40&md5=fc528763ba84d936673e02f38f40582b},
affiliation={University of Massachusetts-Dartmouth, North Dartmouth, Massachusetts, United States},
abstract={Purpose Faced with new wealth creation paradigm, triggered by technology and relentless globalization of markets, increasing number of companies are becoming knowledge-based enterprises. This paper aims to discuss the change in enterprise environment; evolution of performance and cost measures; and the challenges for managerial accounting researchers and practitioners in developing value-based costing and performance measurement systems (PMS). Design/methodology/approach A conceptual discussion and approach are taken. Findings Internet and e-commerce have changed forever the way companies conduct their businesses. Virtual enterprise and efficient supply chain management systems will shape the future of these enterprises. Organizations are trying to become agile enterprises with the help of strategic alliances of firms and integration using information technologies. Traditional performance and cost measures are no longer suitable for developing and managing enterprises in the so-called new environment. In order to remain relevant and to add value, cost and performance measures must be designed and systematically evaluated to reduce the often-unnoticed mismatch between strategic goals and operational tactics. Research limitations/implications Suggestions are presented for future research directions in managerial accounting areas that would address the requirements of new economy enterprises. Originality/value Alerts managerial accounting researchers and practitioners to develop new costing and PMS taking into account the new enterprise environment. © 2005, Emerald Group Publishing Limited},
author_keywords={Accounting;  Accounting research;  Business environment},
references={Barker, R.C., Financial performance measurement: not a total solution (1995) Management Decision, 33 (2), pp. 31-39; Birchfield, R., (2002) Keeping Score: 10 Dumb Performance Measurements, , New Zealand Management Association, Auckland; Drickhamer, D., Next generation performance measurement (2002) Industry Week, 251 (6), p. 14; Drucker, P., Be Data Literate – know what to know (1992) Readings in Management Accounting, , 3rd ed.,, Ed. Young, S.M. (Ed.), Prentice-Hall, New York, NY; Elliott, R.K., Dinosaurs, passenger pigeons, and financial accountants (1986) World, , New York, NY; Elliott, R.K., The third wave breaks on the shores of accounting (1992) Accounting Horizons, , June; Ferrara, W.L., Cost/management accounting: the 21st century paradigm (1995) Management Accounting, pp. 30-36. , December; Fleischman, R.K., Tyson, T., Developing expertise: two episodes in early nineteenth century U.S. management accounting history (1997) Business and Economic History, Williamsburg, 26 (2), pp. 365-381; Hotler, D., 21st century management and the quest for excellence: is there anything, new under the sun? (2002) Super Vision, 63 (10), pp. 3-8; Johnson, H.T., It's time to stop overselling activity-based concepts (1992) Management Accounting, 74 (3), pp. 26-36; Johnson, H., Kaplan, R., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , HBS Press, Boston, MA; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 30-117; Neely, A., Mills, J., Platts, K., Richard, H., Gregory, M., Keenerley, M., Performance measurement system design: developing and testing a process-based approach (2000) International Journal of Operations & Production Management, 20 (10), pp. 1119-1145; Sriram, R.S., Accounting information system issues of FMS (1995) Integrated Manufacturing Systems, 6 (1), pp. 35-40; U.S. Bureau of Labor Statistics, Office of Productivity and Technology (2002) Productivity and Costs, , Third Quarter 2002, Revised 4 December, available at:, www.bls.gov/news.release/prod2.nr0.htm; Fincham, R., Roslender, R., Thinking critically about intellectual capital accounting (2001) Accounting, Auditing & Accountability Journal, 14 (2), pp. 383-398; Gupta, K., A house divided against itself: changing identity of the accounting profession in the twenty-first century (2002), working paper, Charlton College of Business, University of Massachusetts-Dartmouth, Dartmouth, MA; Kloock, J., Schiller, U., Marginal costing: cost budgeting and cost variance analysis (1997) Management Accounting Research, 8 (3), pp. 299-324; Lockamy, A., Smith, W.I., Target costing for supply chain management: criteria and selection (2001) Industrial Management & Data Systems, 100 (5), pp. 210-218; Previtts, G.J., Merino, B., (1998) A History of Accounting in the United States: The Cultural Significance of Accounting, , Ohio State University Press, Columbus, OH},
issn={02686902},
language={English},
abbrev_source_title={Manage. Audit. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Langfield-Smith200539,
author={Langfield-Smith, K. and Smith, D.},
title={Performance measures in supply chains},
journal={Australian Accounting Review},
year={2005},
volume={15},
number={35},
pages={39-51},
doi={10.1111/j.1835-2561.2005.tb00250.x},
note={cited By 9},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84983431341&doi=10.1111%2fj.1835-2561.2005.tb00250.x&partnerID=40&md5=0a0da7968375720540e14095594607c9},
affiliation={Department of Accounting and Finance, Monash University, Australia; Department of Accounting and Business Information Systems, University of Melbourne, Australia},
abstract={Interest in supply chain management (SCM) has increased as organisations seek opportunities to improve performance. Monitoring plays a key role in enhancing supply-chain performance by highlighting opportunities for improvements. However, evidence on the use of performance measures in SCM is scant. This paper explains the role of performance measures in SCM, and the difficulties in their design and implementation. The paper discusses frameworks for the development of supply chain performance measures, and presents case studies of performance measurement in Australian supply chains. © 2005 CPA Australia.},
references={(2004) The Department’s Supply Chain Management Project, , Department of Agriculture, Fisheries and Forestry, Canberra; (2002) Performance Audit Report: E-Government Electronic Procurement of Hospital Supplies, , 25 September, The Audit Office of NSW, Sydney; Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Brewer, P.C., Speh, T.W., Using The Balanced Scorecard To Measure Supply Chain Performance (2000) Journal of Business Logistics, 2 (11), pp. 75-93; Chan, F.T.S., Qi, H.J., Feasibility of Performance Measurement System for Supply Chain: A Process-Based Approach and Measures (2003) Integrated Manufacturing Systems, 14 (3), pp. 179-190; Chan, F.T.S., Qi, H.J., Chan, H.K., Lau, H.C.W., Ip, R.W.L., A Conceptual Model of Performance Measurement For Supply Chains (2003) Management Decision, 41 (7), pp. 635-642; Croxton, K.L., Garcia-Dastugue, S.J., Lambert, D.M., The Supply Chain Management Processes (2001) International Journal of Logistics Management, 12 (2), pp. 13-36; Supply Chain Environmental Management Program-Auto Textiles (2004) Department of the Environment and Heritage, Canberra, , http:www.deh.gov.auindustrycorporateeecpcase-studiestextile.html; Dekker, H.C., Value Chain Analysis in Interfirm Relationships: A Field Study (2003) Management Accounting Research, 14, pp. 1-23; Elmuti, D., The Perceived Impact of Supply Chain Management on Organizational Effectiveness (2002) The Journal of Supply Chain Management, 38 (3), pp. 49-57; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance Measures and Metrics in a Supply Chain Environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Holmberg, S., A System Perspective On Supply Chain Performance Measurement (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; (2002) Auto Textiles Supply Chain Partnership for Environmental Management, , May, Infotech Research, Melbourne; Ittner, C.D., Larcker, D.F., Nagar, V., Rajan, M.V., Supplier Selection, Monitoring Practices and Firm Performance (1999) Journal of Accounting and Public Policy, 18, pp. 253-281; Kaplan, R.S., Norton, D.P., Using the Balanced Scorecard as a Strategic Management System (1996) Harvard Business Review, pp. 75-85. , January-February; LaLonde, B., Where’s The Beef in Supply Chain Management (1997) Supply Chain Management Review, 33 (3), pp. 9-10; Lambert, D.M., Cooper, M.C., Pagh, J., Supply Chain Management: Implementation Issues and Research Opportunities (1998) International Journal of Logistics Management, 9, pp. 1-19; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) International Journal of Logistics Management, 12, pp. 1-19; Langfield-Smith, K., Greenwood, M., Developing Co-operative Buyer-Supplier Relationships: A Case Study of Toyota (1998) Journal of Management Studies, 35 (5), pp. 331-352; Morgan, C., Structure, Speed and Salience: Performance Measurement in the Supply Chain (2004) Business Process Management Journal, 10 (5), pp. 522-536; Surviving Supply Chain Integration: Strategies For Small Manufacturers (2000) Committee on Supply Chain Integration, Board on Manufacturing and Engineering Design, Commission on Engineering and Technical Systems, , National Research Council, National Academy Press, Washington, DC; (2003) Connecting Trading Partners: Business Framework to Optimise Health Supply Chains, , http:www.healthsupplychain.gov.au, Document 2, June, National Supply Chain Reform Task Force; Performance Management Guideline: Achieving World Class Supply Chain Performance in Australian Hospitals (2003) Document 5, June, National Supply Chain Reform Task Force, , http:www.healthsupplychain.gov.au; Quinn, F., The Payoff (1997) Logistics Management, 37 (11), pp. 56-62; Simpson, P.M., Siguaw, J.A., White, S.C., Measuring the Performance of Suppliers: An Analysis of Evaluation Processes (2002) The Journal of Supply Chain Management, 38 (1), pp. 29-41; Youngdahl, W.E., (2000) Global Supply Chain Management, , John Wiley, Toronto},
issn={10356908},
language={English},
abbrev_source_title={Aust. Account. Rev.},
document_type={Letter},
source={Scopus},
}

@CONFERENCE{Barroso200531,
author={Barroso, A.P. and Machado, V.H.},
title={A model for measuring supply chain performance. Case study},
journal={In Memoriam Lilian Barros - Proceedings of the International Conference on Industrial Logistics, ICIL 2005},
year={2005},
pages={31-40},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910684263&partnerID=40&md5=e83938a0444b944cb07834e8f92f9488},
affiliation={Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Lisboa, Portugal},
abstract={In today's world, supply chain management is a key strategic factor for increasing organizational effectiveness and for better realization of organizational goals such as enhanced competitiveness, better customer care and increased profitability. The era of both globalization of markets and outsourcing has begun, and many companies select supply chain and logistics to manage their operations. Most of these companies realize that, in order to evolve an efficient and effective supply chain, supply chain management needs to be assessed for its performance: "You can't manage what you do not measure". So, the performance measurement must involve a performance diagnostic on the first step, and after, if necessary, a definition of actions that permit to achieve improvements. The effectiveness of performance measurement is an issue of growing importance to industrialists and academics alike but is complicated by its multi-dimensional nature. Many organizations are investigating considerable amounts of resource implementing measures that reflect all dimensions of their performance. Consideration is being to what should be measured today, but little attention is being paid to the question of what should be measured tomorrow. Measurement systems should be dynamic. They have to be modified as circumstances change. When designing measurement systems managers need to resolve issues such as: conflicts between performance measures; the appropriate balance of internal and external measures; the linking of measures and strategy; and so on. Customers are becoming more critical about quality and customer services. Measurement systems must be focused on the customer, and ultimately, the end costumer. We consider that a focus on the end costumer is a missing link in current performance measurement systems. So, we suggest that the success critical factors identification must be based on costumer needs. Hence, the first step on the conceptual system proposed on this paper is costumer needs identification. This paper is based on a case study carried out at a medium Portuguese company. The study resulted in a simple prototype system that has the aim of quantify costumer service through different perspectives. On this paper it will be identified some performance measurement system that have different performance perspectives, several critical factors that affect its implementation. The conceptual system proposed is presented and applied.},
author_keywords={Customer service;  Performance measurement systems;  Performance metrics;  Supply chain management},
keywords={Chains;  Outsourcing;  Sales, Customer services;  Organizational effectiveness;  Organizational goals;  Performance diagnostics;  Performance measurement system;  Performance measurements;  Performance metrics;  Supply chain performance, Supply chain management},
references={Bourne, M., Mils, J., Wilcox, A., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Beamon, B.M., Suply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55 (3), pp. 281-294; Chan, F.T.S., Performance measurement in a supply chain (2003) International Journal of Advanced Manufacturing Technology, 21, pp. 534-548; Bowersox, D.J., Closs, D.J., (1996) Logistical Management. The Integrated Supply Chain Process, , McGraw-Hill, New York; http://www.erim.org/cec/papers/metrics/mandm.htm, Available 2004.07.19; Suwignjo, P., Bitici, U.S., Carrie, A.S., Quantitative models for performance measurement system (2000) International Journal of Production Economics, 64, pp. 231-241; De Toni, A., Tonchia, S., Performance measurement systems. Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Neely, A., Adams, C., Perspectives on Performance: The Performance Prism, , http://www.cranfield.ac.uk/som/cbp/prism.htm, Cranfield University, Available 2001.04.23; Lohman, C., Fortuin, L., Wouters, M., Designing a performance measurement system: A case study (2004) European Journal of Operational Research, 156, pp. 267-286; Kennerley, M., Neely, A., A framework of the factors affecting the evolution of performance measurement systems (2002) International Journal of Operations & Production Management, 22 (11), pp. 1222-1245},
correspondence_address1={Barroso, A.P.; Faculdade de Ciências e Tecnologia, Universidade Nova de LisboaPortugal},
editor={Helo P., Cancela H., Rubino G., Urquhart M.E.},
publisher={International Centre for Innovation and Industrial Logistics (ICIIL)},
isbn={9974002745},
language={English},
abbrev_source_title={Memoriam Lilian Barros - Proc. Int. Conf. Industr. Logist., ICIL},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{NoAuthor2005,
title={IFIP TC5 WG 5.5 6th IFIP Working Conference on Virtual Enterprises, PRO-VE 2005},
journal={IFIP Advances in Information and Communication Technology},
year={2005},
volume={186},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84902790347&partnerID=40&md5=51a5d6001e3485074dadadfcfc189b10},
abstract={The proceedings contain 63 papers. The special focus in this conference is on Collaborative Networks. The topics include: A holistic approach to creation and management of dynamic virtual organizations; multi-perspective challenges on collaborative networks business environments; a framework for management of virtual organization breeding environments; an approach to modeling collaborative demand and supply network; coordination of competencies development within networks of SMEs; towards a framework for creation of dynamic virtual organizations; an integrative approach for VO planning and launching; the role of enterprise modelling in virtual enterprises; towards ontology-based CNO matching applied to squads; a multi-criteria mathematical programming model for agile virtual organization creation; a multi-criteria decision support system for the formation of collaborative networks of enterprises; trust building for SMES through an E-engineering hub; a decision support approach to trust modeling in networked organizations; toward web services profiles for trust and security in virtual organisations; characterizing virtual organizations and their management; understanding and managing shared projects in SMEs networks; human supervised virtual organization management; improving client service reliability in collaborative supply chains; an approach for the ascertainment of profit shares for network participants; performance indicators based on collaboration benefits; a performance measurement system for virtual and extended enterprises; towards performance measurement in virtual organizations; perceptions of value that sustain collaborative networks; a meta-methodology prototype for collaborative networked organisations; methodology for business model definition of collaborative networked organizations; towards a meta-model for collaborative construction project management; modeling structured non-monolithic collaboration processes; quantitative models of collaborative networks; collaborative healthcare process modelling; the organization and business model of a software virtual community in China; mobile and location-aware workplaces and global value networks; E-services interoperability analysis and roadmap actions; E-business software evaluation; experiments on grid computing for VE-related applications; technology infrastructure for virtual organisation of toolmakers; legal security and credibility in agent based virtual enterprises; institutional services for dynamic virtual organizations; different view of learning and knowledge creation in collaborative networks; a new approach for E-learning in collaborative networks; towards a virtual enterprise for passenger transportation using agents and success and failure factors of collaborative networks of SME.},
publisher={Springer New York LLC},
issn={18684238},
isbn={9780387282596},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Review},
source={Scopus},
}

@CONFERENCE{Bandinelli2005271,
author={Bandinelli, R. and Cavalieri, S. and Terzi, S.},
title={Proposal of a framework for the evaluation and comparison of production schedules},
journal={IFAC Proceedings Volumes (IFAC-PapersOnline)},
year={2005},
volume={38},
number={1},
pages={271-276},
doi={10.3182/20050703-6-cz-1902.01467},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960704584&doi=10.3182%2f20050703-6-cz-1902.01467&partnerID=40&md5=cac42f798af2182ab5a113dc15bd0d68},
affiliation={Dipartimento di Energetica S. Stecco, Università di Firenze, Via Lombroso, 6/17, I-50134 Firenze, Italy; Department of Industrial Engineering, University of Bergamo, viale Marconi, 5, I-24044 Dalmine BG, Italy},
abstract={Production Planning and Control (PP&C) has been increasingly becoming a critical activity, since competition in the markets is leveraging on a multitude of factors ranging from product quality, to delivery times and pre-sales and after-sales services. Among PP&C activities, scheduling decisions are the final temporal decision-making phase where plant and supply chain managers have to act for fixing any short noticed variations and maintaining satisfying overall production system performances, "assigning scarce resources to competing activities over a given time horizon to obtain the best possible system performance". in particular, lot of work has been done in the past (and is currently on-going) on Performance Measurement for manufacturing systems at a strategic level. However, at a more operative scheduling level, a comprehensive approach seems to be still missing. in order to provide an answer to this main issue, the paper illustrates the main distinctive features of the PMS-ESS, a performance measurement system for the evaluation of production scheduling systems, and its application in a test case. Copyright © 2005 IFAC.},
author_keywords={Benchmarking;  Performance Measurement;  Scheduling systems},
keywords={Benchmarking;  Decision making;  Production control;  Scheduling;  Supply chains, After-sales services;  Critical activities;  Performance measurement system;  Performance measurements;  Production planning and control;  Production scheduling system;  Scheduling decisions;  Scheduling systems, Quality control},
references={Bititci, U.S., Modelling of performance measurement systems in manufacturing enterprises (1995) International Journal of Production Economics, 42, pp. 137-147; Bititci, U.S., Carrie, A.S., McDevitt, L.G., Integrated performance measurement systems: A development guide (1997) International Journal of Operations and Production Management, 17 (6), pp. 522-535; Bititci, U.S., Suwignjo, P., Carrie, A.S., Strategy management through quantitative modelling of performance measurement systems (2000) International Journal of Production Economics, 69, pp. 15-22; Bititci, U.S., Carrie, A.S., Turner, T., Integrated performance measurement systems: Structure and dynamics (2002) Business Performance Measurement: Theory and Practice, pp. 174-197. , Neely, A.D., ed., Cambridge University Press, Cambridge; Bongaerts, L., Indrayadi, Y., Van Brussel, H., Valckenaers, P., Predictability of hierarchical, heterarchical, and holonic control (1999) Proceedings of the 2nd International Workshop on Intelligent Manufacturing Systems, pp. 167-176. , Leuven, Belgium, 22-24 September; Cavalieri, S., Taisch, M., Garetti, M., Macchi, M., An experimental benchmarking of two multi-agent systems for production scheduling and control (2000) Computers in Industry, 43, pp. 139-152; Daniels, R., Kouvelis, P., Robust scheduling to hedge against processing time uncertainty in single stage production (1995) Management Science, 41 (2), pp. 363-376; Gören, S., (2002) Robustness and Stability for Scheduling Policies in a Single Machine Environment, , M.Sc. thesis, Bilkent University, Ankara, Turkey; (2003) Network of Excellence on Intelligent Manufacturing Systems, , www.ims-noe.org, IMS NoE, IMS-2001-00002, IST-2001-65001; Jensen, M.T., Robust and Flexible Scheduling with Evolutionary Computation, 2001, , Ph.D. thesis, Department of Computer Science, University of Aarhus, Denmark; Kaplan, R.S., Norton, D.P., The balanced scorecard-measures that drive performance (1992) Harvard Business Review, pp. 71-79. , January- February; Kempf, K., Uzsoy, R., Smith, S., Gary, K., Evaluation and comparison of production schedules (2000) Computers in Industry, 42, pp. 203-220; Kim, G., Park, C.S., Yoon, K.P., Identifying investment opportunities for advanced manufacturing system with comparativeintegrated performance measurement (1997) International Journal of Production Economics, 50, pp. 23-33; Mignon, D.J., Honkomp, S.J., Reklaitis, G.V., Framework for investigating schedule robustness under uncertainty (1995) Computers in Chemical Engineering, 19 (SUPPL.), pp. S615-S620; Ouelhadj, D., Cowling, P., Petrovic, S., Utility and stability measures for agent-based dynamic scheduling of steel continuous casting (2003) Proceedings of the IEEE International Conference on Robotics and Automation, , Taipei, Taiwan, 14-19 September; (2003) Supply Chain Operations Reference Model - SCOR Version 5.0, , Supply-Chain Council, Supply Chain Council, Pittsburgh, Pennsylvania; Terzi, S., Cavalieri, S., Bandinelli, R., Tucci, M., Experiences in distributed simulation for benchmarking production scheduling systems (2004) Proocedings of International IMS Forum 2004, , May 17th -19th 2004, Villa Erba - Cernobbio - Lake Como, Italy; Terzi, S., Brambilla, P., Cavalieri, S., Evaluation and validation of scheduling systems: Issues and trends (2003) Proceedings of the 3rd International Workshop on Performance Measurement, pp. 155-168. , Dalmine, Italy, 19-20 June},
correspondence_address1={Bandinelli, R.; Dipartimento di Energetica S. Stecco, Via Lombroso, 6/17, I-50134 Firenze, Italy; email: r.bandinelli@ing.unifi.it},
publisher={IFAC Secretariat},
issn={14746670},
isbn={008045108X; 9780080451084},
language={English},
abbrev_source_title={IFAC Proc. Vol. (IFAC-PapersOnline)},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Ma2005174,
author={Ma, X. and Sun, S.},
title={Multi-objective optimization and simulation of supply chain},
journal={Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering},
year={2005},
volume={41},
number={6},
pages={174-180},
doi={10.3901/jme.2005.06.174},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-21444442065&doi=10.3901%2fjme.2005.06.174&partnerID=40&md5=46a1e4126f408b46074baf2765a65e55},
affiliation={Ocean College, Shanghai Fisheries University, Shanghai 200090, China; System Integration and Engineering Management Institute, Northwest Polytechnical University, Xi'an 710072, China},
abstract={The optimization of supply chain is studied. A multi-objective model is constructed, which simultaneously optimizing collaborative enterprises, strategic and operational planning. Moreover, in the model, multi-objective analysis is adopted to allow use of a performance measurement system that includes cost, flexibility, customer service levels and delivery time of products. Furthermore, a improved multi-population pareto genetic algorithm is designed for the model. Finally, the model is illustrated by an example and the simulation result is obtained.},
author_keywords={Genetic algorithm;  Multi-objective;  Optimization;  Simulation;  Supply chain},
keywords={Computer simulation;  Costs;  Genetic algorithms;  Materials handling;  Mathematical models, Customer service levels;  Enterprises;  Flexibility;  Multi-objective;  Supply chain, Industrial management},
references={Ehap, H.S., Benita, M.B., A multi-objective approach to simultaneous strategic and operational planning in supply chain design (2000) Omega, 28, pp. 581-598; John, C., Charles, W., Application of facility location modeling constructs to vendor selection problems (1994) European Journal of Operations Research, 76, pp. 387-392; Ram, G.S., Managing supply chain inventories: A multiple retailer, one warehouse, multiple supplier model 1 (1999) Int. J. Production Economics, 59, pp. 341-354; Dellaert, N.P., Meio, M.T., Make-to-order policies for a stochastic lot-sizing problem using overtime (1998) International Journal of Production Economics, 20, pp. 79-97; Benita, M.B., Supply chain design and analysis: Models and methods (1998) Int J. Production Economics, 55, pp. 281-294; Chinese source; Chinese source},
correspondence_address1={Ma, X.; Ocean College, , Shanghai 200090, China; email: maxuefen@yahoo.com.cn},
publisher={Editorial Office of Chinese Journal of Mechanical Engineering},
issn={05776686},
coden={CHHKA},
language={Chinese},
abbrev_source_title={Jixie Gongcheng Xuebao},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Hamdan20041535,
author={Hamdan, A. and Rogers, K.J.},
title={Establishing and tracking performance measures for warehouse logistics operations},
journal={IIE Annual Conference and Exhibition 2004},
year={2004},
pages={1535-1540},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-30044445878&partnerID=40&md5=21a71ec7950defeca1cfaddd7740852a},
affiliation={Industrial and Manufacturing Systems Engineering, University of Texas at Arlington, P. O. Box 19017, Arlington, TX 76019-0017, United States},
abstract={The effective use of supply chain performance measures has gained much attention in recent years as companies try hard to find ways to reduce cost, increase productivity and to stay in business. However, there does not appear to be much non-proprietary information available for establishing effective logistics performance measures, especially for general warehousing and distribution facilities. In this paper, a methodology for establishing and implementing warehousing performance measures is proposed. This quantitative methodology is focused on labor productivity, since labor cost is the largest single cost component of general warehousing and distribution. It has been implemented in several logistics and distribution operations, and has demonstrated a significant impact on warehouse performance and productivity levels. These improvements with real warehousing data will be discussed.},
author_keywords={Performance Measures;  Warehouse Productivity},
keywords={Non-proprietary information;  Performance warehouse;  Warehouse productivity, Costs;  Information analysis;  Productivity;  Warehouses, Logistics},
references={Carmen, R., Conard, R., Key performance indicators: Outting the customer first (2000) Supply Chain Management Review, , November/December 2000; Dreyer, D.E., Performance measurement: A practitioner's perspective (2000) Supply Chain Management Review, , September 2000; Frazelle, E.H., (1996) World-class Warehousing: Timeless Insights for Planning, Designing, and Managing Warehouse Operations; Keebler, J.S., Durtsche, D.A., Logistics performance measurement and the 3PL value proposition (2001) Logistics Quarterly, 7 (2); Konz, S., (1981) Allowances: Developing Work Standards, , Institute of Industrial Engineers; Mentzer, J.T., (2002) Supply Chain Management, , Thousand Oaks, Sage, CA; Cooper, W.W., Seiford, L.M., Tone, K., (2000) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, and DEA Solver Software, , Kluwer Academic Publisher},
correspondence_address1={Hamdan, A.; Industrial and Manufacturing Systems Engineering, P. O. Box 19017, Arlington, TX 76019-0017, United States},
language={English},
abbrev_source_title={Annu. Conf. Exhibition},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Verwater-Lukszo20041516,
author={Verwater-Lukszo, Z. and Roeterink, H.J.H.},
title={Decision support system for planning and scheduling in batch-wise plants},
journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
year={2004},
volume={2},
pages={1516-1521},
doi={10.1109/ICSMC.2004.1399846},
note={cited By 2},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-15744385480&doi=10.1109%2fICSMC.2004.1399846&partnerID=40&md5=5b0cd9af7c24dd7f4db76aa9a93b8be1},
affiliation={Fac. on Technol., Plcy. and Mgmt., Delft University of Technology, Delft, Netherlands; Interduct, Delft University of Technology, Delft, Netherlands},
abstract={The process industry has to cope with a rigorous competition caused by more short-term dynamics in supply, more unpredictable and turbulent demand patterns, stronger requirements on product variety, delivery lead-time and quality of product. It forces company to spend efforts at improving its competitiveness and productivity. Appropriate strategies or action in the area of production planning and scheduling can contribute to survive in these conditions. The paper describes an effective performance measurement system for supporting planning and scheduling decision making in batch-wise plants contributing to the improvement of company's competitiveness and productivity. To assess possible improvement options with respect to the selected evaluation parameters a generic System Dynamics model of an internal supply chain in a batch-wise company is developed. The model applied to a specific industrial plant in the food sector results in a recommendation to reduce inventory level and to increase effective capacity utilization in the plant. © 2004 IEEE.},
author_keywords={Batch-wise industry;  Planning;  Scheduling;  System Dynamics},
keywords={Batch wise industry;  Competitiveness;  Performance measurement systems;  System dynamics, Decision making;  Industrial management;  Mathematical models;  Production engineering;  Productivity;  Scheduling;  Strategic planning, Decision support systems},
references={Heijnen, P.W., Grievink, J., Dealing with uncertainty in mid-term planning (2004) IEEE International Conference SMC, , submitted to; Schumacher, J., (2003) A Disturbance Management Approach to Improving the Performance of Batch Process Operations, , PhD Thesis, TU Delft; Roeterink, H.J.H., Improving the logistic performance in a food company: A case study (2003) International System Dynamics Conference, , 2003, New York; Bustard, D., Kawalek, P., Norris, M., (2000) System Modeling for Business Process Improvement, , Artech House, Inc, Boston; Sterman, J.D., (2000) Business Dynamics: Systems Thinking and Modeling for a Complex World, , Irwin McGraw Hill, Boston; Verwater-Lukszo, Z., (2003) Batch Processes - Cleaner and More Efficient, , Report for Economy, Ecology and Technology programme, TU Delft; Heijnen, P.W., Verwater-Lukszo, Z., Batch plant integration in need of more support (2003) FOCAPO Conference, , Florida, USA; Geenhuizen, M., Thissen, W., Uncertainty and intelligent transport system: Implication for policy (2002) International Journal of Technology, Policy and Management, 2 (1); Walker, W.E., A systematic approach to supporting policy making in the public sector (2000) Journal of Multicriteria Decision Analysis, 9 (1); Anggraeni, E., (2003) Production Planning and Scheduling Decision Making in Batch-wise Processes, , Master Thesis, Faculty Technology, Policy and Management, TU Delft},
correspondence_address1={Verwater-Lukszo, Z.; Fac. on Technol., Plcy. and Mgmt., , Delft, Netherlands; email: Z.Verwater-Lukszo@tbm.tudelft.nl},
issn={1062922X},
isbn={0780385667},
coden={PICYE},
language={English},
abbrev_source_title={Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Terzi20043827,
author={Terzi, S. and Cavalieri, S.},
title={Proposal of a performance measurement system for the evaluation of scheduling solutions},
journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
year={2004},
volume={4},
pages={3827-3832},
doi={10.1109/ICSMC.2004.1400941},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-15744378547&doi=10.1109%2fICSMC.2004.1400941&partnerID=40&md5=7ebe30aa3689112bb0165dc88be2a5dd},
affiliation={Dep. of Economics, Eng., Politecnico di Milano, Milan, Italy; Department of Industrial Engineering, University of Bergamo, Dalmine, BG, Italy},
abstract={Production Planning and Control (PP&C) is increasingly becoming a critical activity. In particular, scheduling decisions, which are the final temporal decision-making phase where industrial and supply chain managers could act for maintaining satisfactory production system performances, turn out to be more and more critical. Industrial practitioners are in trouble in understanding the best scheduling policy for their plants, also because they are not able to measure the obtained results and to compare it with other policies. In such a context, the paper illustrates the implementation of a wider Performance Measurement System for the Evaluation of Scheduling Solutions (PMS ESS). PMS ESS is currently under development in a large initiative, the Special Interest Group 4 on Benchmarking and Performance Measurement of scheduling solutions, established in the context of the IMS NoE community. © 2004 IEEE.},
author_keywords={Benchmarking;  Performance Measurement;  Plant Management;  Scheduling Evaluation},
keywords={Industrial managers;  Performance measurement;  Production scheduling systems;  Scheduling evaluation, Benchmarking;  Decision theory;  Industrial plants;  Multi agent systems;  Performance;  Production engineering;  Quality assurance, Scheduling},
references={Bongaerts, L., Indrayadi, Y., Van Brussel, H., Valckenaers, P., Predictability of hierarchical, heterarchical, and holonic control (1999) Proceedings of the 2nd International Workshop on Intelligent Manufacturing Systems, pp. 167-176. , Leuven, Belgium, 22-24 September; Cavalieri, S., Taisch, M., Garetti, M., Macchi, M., An experimental benchmarking of two multi-agent systems for production scheduling and control (2000) Computers in Industry, 43, pp. 139-152; Daniels, R., Kouvelis, P., Robust scheduling to hedge against processing time uncertainty in single stage production (1995) Management Science, 41 (2), pp. 363-376; Jensen, M.T., (2001) Robust and Flexible Scheduling with Evolutionary Computation, , Ph.D. thesis, Department of Computer Science, University of Aarhus, Denmark; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, pp. 71-79. , January-February; Kempf, K., Uzsoy, R., Smith, S., Gary, K., Evaluation and comparison of production schedules (2000) Computers in Industry, 42, pp. 203-220; (2003) Network of Excellence on Intelligent Manufacturing Systems, , www.ims-noe.org, Project reference IST-2001-65001 IMS-02006; Gören, S., (2002) Robustness and Stability for Scheduling Policies in A Single Machine Environment, , M.Sc. thesis, Bilkent University, Ankara, Turkey; Mignon, D.J., Honkomp, S.J., Reklaitis, G.V., Framework for investigating schedule robustness under uncertainty (1995) Computers in Chemical Engineering, 19 (SUPPL.), pp. S615-S620; Ouelhadj, D., Cowling, P., Petrovic, S., Utility and stability measures for agent-based dynamic scheduling of steel continuous casting (2003) Proceedings of the IEEE International Conference on Robotics and Automation, , Taipei, Taiwan, 14-19 September; Terzi, S., Brambilla, P., Cavalieri, S., Evaluation and validation of scheduling systems: Issues and trends (2003) Proceedings of the 3rd International Workshop on Performance Measurement, pp. 155-168. , Dalmine, Italy, 19-20 June; (2003) Supply Chain Operations Reference Model - SCOR Version 5.0, , Supply Chain Council, Pittsburgh, Pennsylvania; Kim, G., Park, C.S., Yoon, K.P., Identifying investment opportunities for advanced manufacturing system with comparative-integrated performance measurement (1997) International Journal of Production Economics, 50, pp. 23-33},
correspondence_address1={Terzi, S.; Dep. of Economics, Eng., , Milan, Italy; email: sergio.terzi@polimi.it},
issn={1062922X},
isbn={0780385667},
coden={PICYE},
language={English},
abbrev_source_title={Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Morgan2004522,
author={Morgan, C.},
title={Structure, speed and salience: Performance measurement in the supply chain},
journal={Business Process Management Journal},
year={2004},
volume={10},
number={5},
pages={522-536},
doi={10.1108/14637150410559207},
note={cited By 107},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986052273&doi=10.1108%2f14637150410559207&partnerID=40&md5=27ea58dc4fc2952bf56c40741a831791},
affiliation={Cranfield School of Management, Cranfield Centre for Logistics and Transport, Cranfield University, Cranfield, United Kingdom},
abstract={The supply chain presents many challenges for management and for the design of performance measurement systems. It is possibly one of the final structural areas of business in which significant savings are to be made, and it is becoming an increasingly important strategic tool as trade becomes global in perspective. To assess the problems faced in the supply chain, this paper begins with an overview of where we are in terms of the development of performance measurement theory. It then highlights two key issues that the supply chain raises in measuring performance in an intra-organisation scenario before identifying no fewer than nine preconditions necessary for effective and dynamic performance measurement within supply chains. © 2004, Emerald Group Publishing Limited},
author_keywords={Information management;  Organizational analysis;  Performance management;  Performance measurement (quality);  Supply chain management},
references={Barker, R.C., Value-adding performance measurement: a time-based approach (1993) International Journal of Operations & Production Management, 13 (5), pp. 33-40; Bowersox, D.J., Closs, D.J., Stank, T.P., (1999) 21st Century Logistics: Making Supply Chain Integration a Reality, , Council of Logistics Management, Michigan State University, East Lansing, MI; Christopher, M., (1998) Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service, 2nd ed., , Prentice Hall, Harlow; Cooper, R., (1995) When Lean Enterprises Collide, , Harvard Business School Press, Boston, MA; Cooper, R., Kaplan, R.S., How cost accounting systematically distorts product costs (1987) Accounting and Management, Field Study Perspectives, , in Burns, W.J. (Ed.), Harvard University Business Press, Boston, MA; Drucker, P., The information executives truly need (1995) Harvard Business Review, 73; Eccles, R., Pieburn, P., Creating a comprehensive system to measure performance (1992) Management Accounting UK, 70 (10), pp. 41-44; Ford, H., Crowther, S., (1926) Today and Tomorrow, , William Heinemann, London; Goldratt, E.M., Cox, J., (1984) The Goal, , North River Press, New York, NY; Hammer, M., Stanton, S.A., (1995) The Reengineering Revolution Handbook, , Harper Collins, London; Harrison, A., (1992) Just in Time in Perspective, , Prentice-Hall, London; Hughes, J., Ralf, M., Michels, W., (1998) Transform Your Supply Chain: Releasing Value in Business, , Thompson Business Press, London; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: the Rise and Fall of Management Accounting, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Putting the Balanced Scorecard to work (1993) Harvard Business Review, 71, pp. 134-142; Kaplan, R.S., Norton, R., The balanced scorecard – measures that drive performance (1992) Harvard Business Review, 70, pp. 71-79; Kaplan, R.S., Norton, R., Having trouble with your strategy? – then map it (2000) Harvard Business Review, 78, p. 67. , et seq; Luscombe, M., (1993) Integrating the Business: MRP II, a Practical Guide for Managers, , Butterworth Heineman, London; Maskell, B., Performance measurement for world class manufacturing (parts 1, 2 and 3) (1989) Management Accounting; Maskell, B., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press, Cambridge, MA; Mather, H., (1988) Competitive Manufacturing, , Prentice-Hall, Englewood Cliffs, NJ; Neely, A.D., Gregory, M.J., Platts, K.W., Performance measurement systems design (1995) International Journal of Operations & Production Management, 15 (4); Oakland, J.S., (1995) Total Quality Management, , Butterworth-Heineman, London; Stalk, G., Hout, T.M., How time based management measures performance (1990) Planning Review, pp. 26-29; Schonberger, R.J., (1986) World Class Manufacturing: The Lessons of Simplicity Applied, , Free Press, New York, NY; Suzaki, K., (1987) The New Manufacturing Challenge, , Free Press, New York, NY; Zeithaml, V.A., Parasuraman, A., Berry, L.L., (1990) Delivering Quality Service: Balancing Customer Perceptions and Expectations, , Free Press, New York, NY; Goldratt, E.M., (1990) The Theory of Constraints, , North River Press, New York, NY; Lea, R., Parker, B., The JIT spiral of continuous improvement (1989) Industrial Management & Data Systems, 4, pp. 10-13; Neely, A.D., Gregory, M.J., Platts, K.W., Richards, H., Realizing strategy through measurement (1994) International Journal of Operations of Production Management, 12 (3); Oram, M., Wells, R.S., (1995) Re-engineering's Missing Ingredient, , IPD, London; Stephens, D.W., Krebs, J.R., (1986) Foraging Theory, , Princeton University Press, NJ},
correspondence_address1={Morgan, C.; Cranfield School of Management, , Cranfield, United Kingdom},
issn={14637154},
language={English},
abbrev_source_title={Bus. Process Manage. J.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Lohman2004267,
author={Lohman, C. and Fortuin, L. and Wouters, M.},
title={Designing a performance measurement system: A case study},
journal={European Journal of Operational Research},
year={2004},
volume={156},
number={2},
pages={267-286},
doi={10.1016/S0377-2217(02)00918-9},
note={cited By 308},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-1442307877&doi=10.1016%2fS0377-2217%2802%2900918-9&partnerID=40&md5=4f7ede6598fce0c50af754f25ca13ce6},
affiliation={TLO Holland Controls bv, Noordhoek 37, 3351 LD Papendrecht, Netherlands; Faculty for Technology Management, Eindhoven University of Technology, Paviljoen F.18, NL 5600 MB Eindhoven, Netherlands; School of Technology and Management, University of Twente, P.O. Box 217, 7500 AE Enschede, Netherlands},
abstract={Performance measurement (PM) by means of local performance indicators (PIs) is developing into performance management at a company-wide scale. But how should PIs at various levels in the organization be incorporated into one system that can help managers, working at levels that range from operational to strategic? How do we convince potential users and obtain their support when starting to develop such a system? How can we aggregate PIs? How do we present results? This paper addresses these and related questions. It is based on a case study carried out at the European Operations department of Nike, a company producing and selling sportswear worldwide. The study resulted in a prototype system that basically is a balanced scorecard tailored to the needs of the company. The empirical findings differ in some ways from the literature on developing performance measurement systems (PMSs) in Operations. Discussing these differences provides new theoretical and practical insights. They relate to the role of parallel initiatives for PM, the role of standardized metrics, the continuous improvement of PMSs, and the normalization and aggregation of measures. Our findings suggest that developing PMSs should to a large extent be understood as a co-ordination effort rather than a design effort. The lessons learned cannot have universal validity, but may be helpful in similar kinds of initiatives. © 2003 Elsevier B.V. All rights reserved.},
author_keywords={Balanced scorecards;  Performance measurement;  Supply chain management},
keywords={Marketing;  Research;  Sales;  Societies and institutions;  Standardization;  Strategic planning, Balanced scorecards;  Performance measurement;  Supply chain management, Operations research},
references={Abrahamsson, M., Brege, S., Structural changes in the supply chain (1997) International Journal of Logistics Management, 8 (1), pp. 35-45; Anderson, E., Day, G.S., Rangan, V.K., Strategic channel design (1997) Sloan Management Review, 38 (3), pp. 59-69; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Engineering Costs and Production Economics, 17, pp. 253-262; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Berliner, C., Brimson, J.A., (1988) Cost Management for Today's Advanced Manufacturing, , Harvard Business School Press, Cambridge, MA; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations & Production Management, 20 (7), pp. 754-771; Bowersox, D.J., Closs, D.J., (1996) Logistical Management. The Integrated Supply Chain Process, , McGraw-Hill, New York; Ten Broeke, A.M., (1998) Prestatie - Indicatoren in de Logistiek: Aanpak en Samenhang, , Kluwer/NEVEM. (in Dutch); Buzzel, R.D., Ortmeyer, G., Channel partnerships streamline distribution (1995) Sloan Management Review, 36 (2), pp. 85-96; Chow, G., Heaver, T.D., Henriksson, L.E., Logistics performance: Definition and measurement (1994) International Journal of Physical Distribution & Logistics Management, 24 (1), pp. 17-28; Davies, G.B., Strategies for information requirements determination (1982) IBM Systems Journal, 21 (1), pp. 4-30; Eccles, R.G., The performance measurement manifesto (1991) Harvard Business Review, 69 (JANUARY-FEBRUARY), pp. 131-137; Fawcett, S.E., Cooper, M.B., Logistics performance measurement and customer success (1998) Industrial Marketing Management, 27, pp. 341-357; Fitzgerald, L., Moon, P., (1996) Performance Measurement in Service Industries: Making it Work, , Chartered Institute of Management Accountants, London; Flapper, S.D., Fortuin, L., Stoop, P.P.M., Towards consistent performance measurement systems (1996) International Journal of Operations & Production Management, 16 (7), pp. 27-37; Fortuin, L., Performance indicators - Why where and how? (1988) European Journal of Operational Research, 34, pp. 1-9; Fransoo, J.C., Wouters, M.J.F., Measuring the bullwhip effect in a supply chain (2000) Supply Chain Management, 5 (2), pp. 78-89; Hall, R.W., Johnson, H.T., Turney, P.B.B., (1990) Measuring Up: Charting Pathways to Manufacturing Excellence, , Business One Irwin, Homewood, IL; Ittner, C.D., Larcker, D., Innovations in performance measurement: Trends and research implications (1998) Journal of Management Accounting Research, 10, pp. 205-238; Johnson, H.T., Kaplan, R.S., (1987) Relevance Lost: The Rise and Fall of Management Accounting, , Harvard Business School Press, Boston, MA; Kaplan, R.S., (1990) Measures for Manufacturing Excellence, , Harvard Business School Press, Boston, MA; Kaplan, R.S., Norton, D.P., The balanced scorecard - Measures that drive performance (1992) Harvard Business Review, 70 (JANUARY-FEBRUARY), pp. 71-79; Kaplan, R.S., Norton, D.P., Putting the balanced scorecard to work (1993) Harvard Business Review, 71 (SEPTEMBER-OCTOBER), pp. 134-147; Kaplan, R.S., Norton, D.P., Using the balanced scorecard as a strategic management system (1996) Harvard Business Review, 74 (1), pp. 75-87; Kaplan, R.S., Norton, D.P., Having trouble with your strategy? then map it (2000) Harvard Business Review, 78 (5), pp. 167-178; Lampel, J., Mintzberg, H., Customizing customization (1996) Sloan Management Review, 37 (4), pp. 21-29; Lipe, M., Salterio, S., A note on the judgmental effects of the balanced scorecard's information organization (2002) Accounting, Organizations and Society, 27, pp. 531-540; Lohman, F.A.B., (1999) The Effectiveness of Management Information, , Ph.D. Thesis, Delft University; Lynch, R.L., Cross, K.F., (1995) Measure Up!: Yardsticks for Continuous Improvement, , Blackwell, Oxford; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing. A Model for American Companies, , Productivity Press, Cambridge, MA; Mccutcheon, D.M., Meredith, J.R., Conducting case study research in operations management (1993) Journal of Operations Management, 11 (3), pp. 239-256; Medori, D., Steeple, D., A framework for auditing and enhancing performance measurement systems (2000) International Journal of Operations & Production Management, 20 (5), pp. 520-533; Merchant, K., (1998) Modern Management Control Systems, , Prentice Hall; Nanni, A.J., Dixon, J.R., Vollmann, T.E., Integrated performance measurement: Management accounting to support the new manufacturing realities (1992) Journal of Management Accounting Research, 4, pp. 1-19; Neely, A.D., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Neely, A.D., Richards, A.H., Mills, J.F., Platts, K.W., Bourne, M.C.S., Designing performance measures: A structured approach (1997) International Journal of Operations & Production Management, 17 (11), pp. 1131-1152; Olve, N.G., Roy, J., Wetter, M., (1999) Performance Drivers: A Practical Guide to Using the Balanced Scorecard, , John Wiley & Sons, New York; Ormerod, R.J., Combining management consultancy and research (1996) Omega, International Journal of Management Science, 24 (1), pp. 1-12; Sanger, M., Supporting the balanced scorecard (1998) Work Study, 47 (6), pp. 197-200; Scudder, G.D., Hill, C.A., A review and classification of empirical research in operations management (1998) Journal of Operations Management, 16, pp. 91-101},
correspondence_address1={Fortuin, L.; Faculty for Technology Management, Paviljoen F.18, NL 5600 MB Eindhoven, Netherlands; email: l.fortuin@tm.tue.nl},
issn={03772217},
coden={EJORD},
language={English},
abbrev_source_title={Eur J Oper Res},
document_type={Article},
source={Scopus},
}

@ARTICLE{Rafele2004280,
author={Rafele, C.},
title={Logistic service measurement: A reference framework},
journal={Journal of Manufacturing Technology Management},
year={2004},
volume={15},
number={3},
pages={280-290},
doi={10.1108/17410380410523506},
note={cited By 88},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-2942521278&doi=10.1108%2f17410380410523506&partnerID=40&md5=e81ef90ed15324c12b76b2d722d0bb68},
affiliation={Department of Production Systems, Politecnico di Torino, Turin, Italy},
abstract={Many companies measure their logistic performances using several methods. As a consequence, various sets of indicators can be found in practice and in the literature. In particular, the same event can be measured in different ways, so that the relative performance is sometimes totally different. The purpose of this paper is to suggest a hierarchy in logistic performance measurements. A referring model for evaluating the logistic performances is also proposed in this paper, together with the definition of a reference framework for the selection of supply chain performance measurements.},
author_keywords={Customer service management;  Logistics management;  Performance measurement (quality);  Service quality assurance;  Supply chain management},
keywords={Customer satisfaction;  Logistics;  Process control;  Product design;  Quality assurance;  Raw materials;  Strategic planning, Customer service management;  Logistics management;  Service quality assurance;  Supply chain management, Industrial management},
references={Beamon, B., Performance measures in supply chain management (1996) Proceedings of the Conference on Agile and Intelligent Manufacturing Systems, , Rensselaer Polytechnic Institute, Troy, NY; Beamon, B., Supply chain and analysis models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Beamon, B., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Brewer, P., Speh, T., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Caplice, C., Sheffi, Y., A review and evaluation of logistics metrics (1994) The International Journal of Logistics Management, 5 (2), p. 11; Caplice, C., Sheffi, Y., A review and evaluation of logistics performance measurement system (1995) The International Journal of Logistics Management, 6 (1), pp. 61-74; (1998) Annual Business Meeting, , presented at the, CLM, San Francisco, CA, October; English, J.R., (1999) Quality Monitoring of Logistics Systems, pp. 1-7. , draft publication; (2000) Logistics Performance Measures, Requirements and Measuring Methods, , CENCR 13408; Franceschini, F., Rafele, C., Quality evaluation in logistic services (2000) International Journal of Agile Management Systems, 2 (1), pp. 49-53; Kaplan, R., Norton, D., (1996) The Balanced Scorecard, Translating Strategy into Action, , Harvard Business School Press, Boston, MA; Keegan, D.P., Eiler, R.G., Jones, C.R., Are your performance measures obsolete? (1989) The International Journal of Logistics Management, 71, pp. 45-50. , June; Lambert, D., Cooper, M.C., Pagh, J.D., Supply chain management: Implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9 (2), p. 2; Lockamy, A., Cox, J., (1994) Reengineering Performance Measurement, , Irwin Professional Publishing, New York, NY; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Parasuraman, A., Zeithaml, V.A., Berry, L.L., A conceptual model of service quality and its implications for future research (1985) Journal of Marketing, 49; Parasuraman, A., Zeithaml, V.A., Berry, L.L., SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality (1988) Journal of Retailing, 64 (1), pp. 12-40; Rafele, C., Frizziero, F., La misurazione delle performance logistiche (1996) Logistica, 27 (5); Shapiro, R.D., Heskett, J.L., (1985) Logistics Strategy: Cases and Concepts, , West Publishing, St Paul, MN; Trimble, D., (1996) How to Measure Success: Uncovering the Secrets of Effective Metrics, , sponsored by ProSci, Online Learning Center, ProSci, Loveland, CO},
correspondence_address1={Rafele, C.; Department of Production Systems, , Turin, Italy},
issn={1741038X},
language={English},
abbrev_source_title={J. Manuf. Technol. Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Gunasekaran2004333,
author={Gunasekaran, A. and Patel, C. and McGaughey, R.E.},
title={A framework for supply chain performance measurement},
journal={International Journal of Production Economics},
year={2004},
volume={87},
number={3},
pages={333-347},
doi={10.1016/j.ijpe.2003.08.003},
note={cited By 1447},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0346339454&doi=10.1016%2fj.ijpe.2003.08.003&partnerID=40&md5=c35241ffe1c7b81b824cece3bfb41dde},
affiliation={Dept. of Business Administration, Univ. of Illinois at Springfield, One University Plaza, Springfield, IL 62703-5407, United States; Ace InfoTech, Inc., 406 Wellington Drive, Streamwood, IL 60107, United States; Dept. of Mgmt. Information Systems, University of Central Arkansas, Conway, AR 72035-0001, United States},
abstract={Supply chain management (SCM) has been a major component of competitive strategy to enhance organizational productivity and profitability. The literature on SCM that deals with strategies and technologies for effectively managing a supply chain is quite vast. In recent years, organizational performance measurement and metrics have received much attention from researchers and practitioners. The role of these measures and metrics in the success of an organization cannot be overstated because they affect strategic, tactical and operational planning and control. Performance measurement and metrics have an important role to play in setting objectives, evaluating performance, and determining future courses of actions. Performance measurement and metrics pertaining to SCM have not received adequate attention from researchers or practitioners. We developed a framework to promote a better understanding of the importance of SCM performance measurement and metrics. Using the current literature and the results of an empirical study of selected British companies, we developed the framework presented herein, in hopes that it would stimulate more interest in this important area. © 2003 Elsevier B.V. All rights reserved.},
author_keywords={Empirical analysis;  Framework;  Metrics;  Performance measurements;  Supply chain},
keywords={Industrial economics;  Industrial research;  Performance;  Productivity;  Societies and institutions;  Strategic planning, Empirical analysis;  Metrics;  Performance measurements;  Supply chain, Industrial management},
references={Ballou, R.H., (1992) Business Logistics Management, , Englewood Cliffs, NJ: Prentice-Hall, Inc; Benjamin, R., Wigand, R., Electronic markets and virtual value chains on the information superhighway (1995) Sloan Management Review, 36 (2), pp. 62-72; Cavinato, J.L., Total cost value model for supply chain competitiveness (1992) Journal of Business Logistics, 13 (2), pp. 285-291; Christopher, M., (1992) Logistics and Supply Chain Management, , London: Pitman Publishing; De Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70; Ellram, L.M., A managerial guide for the development and implementation of purchasing partnerships (1991) International Journal of Purchasing and Materials Management, 27 (3), pp. 2-8; Fawcett, S.E., Using strategic assessment to increase the value-added capabilities of manufacturing and logistics (1995) Production and Inventory Management Journal, 36 (2), pp. 33-37; Fisher, L.M., What is the right supply chain for your product? (1997) Harvard Business Review, 75 (2), pp. 105-116; Graham, T.S., Dougherty, P.J., Dudley, W.N., The long term strategic impact of purchasing partnerships (1994) International Journal of Purchasing and Materials Management, 30 (4), pp. 13-18; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measure and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Kaplan, R.S., Norton, P.D., The balanced scoreboard-measures that drives performance (1992) Harvard Business Review, 70 (1), pp. 71-79; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, 33 (3), pp. 65-73; Levy, D.L., Lean production in an international supply chain (1997) Sloan Management Review, 38 (2), pp. 94-102; Little, D., Kenworthy, J., Jarvis, P., Porter, K., Scheduling across the supply chain (1995) Logistics Information Management, 8 (1), pp. 42-48; Macbeth, D.K., Ferguson, N., (1994) Partnership Sourcing: An Integrated Supply Chain Management Approach, , London: Pitman Publishing; Maloni, M.J., Benton, W.C., Supply chain partnerships: Opportunities for operations research (1997) European Journal of Operational Research, 101, pp. 419-429; Mapes, J., New, C., Szwejczewski, M., Performance trade-offs in manufacturing plants (1997) International Journal of Operations & Production Management, 17 (10), pp. 1020-1033; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing, , Portland, OR: Productivity Press, Inc; Naim, M.M., The Book That Changed the World (1997) Manufacturing Engineer, (FEBRUARY), pp. 13-16; Novich, N., Distribution strategy: Are you thinking small enough? (1990) Sloan Management Review, 32 (1), pp. 71-77; Rushton, A., Oxley, J., (1989) Handbook of Logistics and Distribution Management, , London: Kogan Page Ltd; Schroeder, R.G., John, C.A., Scudder, G.D., White collar productivity measurement (1986) Management Decision, 24 (5), pp. 3-7; Slack, N., Chambers, S., Harland, C., Harrison, A., Johnston, R., (1995) Operations Management, , London: Pitman Publishing; Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence (1995) Logistics Information Management, 8 (2), pp. 38-44; Thomas, D.J., Griffin, P.M., Co-ordinated supply chain management (1996) European Journal of Operational Research, 94 (3), pp. 1-15; Trent, R.J., Monczka, R.M., Effective cross-functional sourcing teams: Critical success factors (1994) International Journal of Purchasing and Materials Management, 30 (4), pp. 3-11; Van Hoek, R.I., The contribution of performance measurement to the expansion of third party logistics alliances in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 15-29; Van Hoek, R.I., Harrison, A., Christopher, M., Measuring agile capabilities in the supply chain (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 126-147},
correspondence_address1={Gunasekaran, A.; Dept. of Business Administration, One University Plaza, Springfield, IL 62703-5407, United States; email: Gunasekaran.angappa@uis.edu},
issn={09255273},
coden={IJPCE},
language={English},
abbrev_source_title={Int J Prod Econ},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{McDermott200433,
author={McDermott, A. and Lovatt, S.J. and Kowlow, S.},
title={Supply chain performance measures for producers and processors of premium beef cuts: A conjoint approach},
journal={Journal on Chain and Network Science},
year={2004},
volume={4},
number={1},
pages={33-43},
doi={10.3920/JCNS2004.x040},
note={cited By 3},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-59849084793&doi=10.3920%2fJCNS2004.x040&partnerID=40&md5=0cfc654f0e1c96c0eb9591e8054d7ad7},
affiliation={AgResearch Limited, Ruakura Research Centre, Private Bag 3123, Hamilton, New Zealand; Waikato Management School, University of Waikato, Private Bag 3105, Hamilton, New Zealand},
abstract={The performance measures important to New Zealand beef producers and processors in their selling and buying decisions were studied using a conjoint analysis methodology. 98 producers and 5 processors were asked to rank and rate various scenarios. Producers preferred situations in which they received a high price, had high payment security, a premium for quality, had a short lead-time and the processor shared some information. Processors focussed on factors that enabled them to reduce their risk and cost of supply, and ensure traceability back to farms.},
author_keywords={Beef supply chain;  Conjoint analysis;  Performance measures},
references={Amanor-Boadu, V., Cognitive Barriers to Supply Chains in Canada's AgriFood Industries (2000) Proceedings of the Fourth International Conference on Chain Management in Agribusiness and the Food Industry, , G. Ziggers, J. Trienekens, & P. Zuurbier Eds, Management Studies Group, Wageningen Agricultural University; Ben-Akiva, M., Lerman, S., (1985) Discrete Choice Analysis: Theory and Application to Travel Demand, , Cambridge, MA, MIT Press; Cochran, W.G., (1983) Planning and Analysis of Observational Studies, , New York, John Wiley & Sons; Fearne, A., Hughes, D., Success factors in the fresh produce supply chain: Insights from the UK (2000) British Food Journal, 102 (10), pp. 760-772; Kaufmann, L., Rousseeuw, P., (1990) Finding Groups in Data: An Introduction to Cluster Analysis, , Wiley: New York; Koslow, S., Conjoint Analysis (1999) The Elgar Companion to Consumer Research and Economic Psychology, , Earl, P. E. and Kemp, S. editors, Cheltenham, UK, Edward Elgar; Louviere, J.J. 1988. Analysing decision making: Metric conjoint analysis. Report No. 07-067, Sage University Paper Series on Quantitative Applications in the Social Sciences. Sage Publications Inc: Newbury Park, California; McFadden, D., Economic Models of Probabilistic Choice (1981) Structural Analysis of Discrete Data, pp. 198-272. , Manski, C. and McFadden, D, eds, Cambridge, MA, MIT Press; McFadden, D., The Choice Theory Approach to Marketing Research (1986) Marketing Science, 5, pp. 275-297; Meat New Zealand (2003), Annual Report 2002-2003; Introduction to the Personal Property Securities Register (2003) Material, , http://www.ppsr.govt.nz/pls/web/dbssiten.main, Ministry of Economic Development, downloaded from website on 12 December 2003 from; O'Keeffe, M., Establishing supply chain partnerships: Lessons from Australian agribusiness (1998) Supply Chain Management, 3 (1), pp. 5-9; Ratchford, B.T., The New Economic Theory of Consumer Behavior: An Interpretive Essay (1975) Journal of Consumer Research, 2, pp. 65-75; Ratchford, B.T., Operationalizing Economic Models of Demand for Product Characteristics (1979) Journal of Consumer Research, 6, pp. 76-85; Skytte, H., Blunch, N., Food retailer's buying behaviour: An analysis in 16 European countries (2001) Journal on Chain and Network Science, 1 (2), pp. 133-145; von Winterfeldt, D., Edwards, W., (1986) Decision Analysis and Behavioral Research, , Cambridge, Cambridge University Press},
correspondence_address1={McDermott, A.; AgResearch Limited, Ruakura Research Centre, Private Bag 3123, Hamilton, New Zealand; email: alan.mcdermott@agresearch.co.nz},
publisher={Wageningen Academic Publishers},
issn={15691829},
language={English},
abbrev_source_title={J. Chain Netw. Sci.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Campos200477,
author={Campos, J. and Taboada, C. and Chalmeta, R.},
title={Methodology for evaluation of performance of the logistics chain [Metodología para la Evaluación del Rendimiento de la Cadena Logística]},
journal={Informacion Tecnologica},
year={2004},
volume={15},
number={4},
pages={77-84},
note={cited By 4},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-4043144989&partnerID=40&md5=9b0ff6370b920f38791322e97e93edc2},
affiliation={Univ. Federal del Amazonas, Fac. de Estudios Sociales, Campus Universitario, Av. Gral. Rodrigo Otavio J.R.N. 3000, 69077-000 Manaus, AM, Brazil; Univ. Federal de Santa Catarina, Dpto. de Ing. de Producción, Trindade, Casiila Postal 476, 88040-900 Florianópolis, SC, Brazil; Univ. Jaume I, Dpto. de Lenguajes, Grupo IRIS, Campus de Riu Sec s/n, 12071 Castellón, Spain},
abstract={This paper describes a methodology, called MESIADLog, which allows for the construction of an integrated system for the evaluation of supply chain performance that takes into account certain aspects that do not wholly fit into other existing methodologies. This methodology was applied to Spanish and Brazilian firms for its validation, determining the potential that exists for conducting research in this field and generating cases which can aid in its dissemination. In this paper practical examples of its application to a Spanish ceramics company are described. It is concluded that the proposed methodology permits companies to develop their own evaluation system for the logistic chain from an integrated perspective.},
author_keywords={Logistic chain;  MESIADLog;  Performance measurement systems;  Process improvement},
keywords={Evaluation;  Inventory control;  Process control;  Research, Logistic chains;  MESIADLog;  Performance measurement systems;  Process improvement, Materials handling},
references={Beamon, B.M., Measurement supply chain performance (1999) International Journal of Operations & Production Management, 9 (3); Chalmeta, R., Grangel, R., ARDIN extension for virtual enterprise integration (2003) Journal of Systems and Software, 67, pp. 141-152; Chalmeta, R., Grangel, R., Performance measurement systems for virtual enterprise integration Journal of Computer Integrated Manufacturing, , Aceptada para su publicación (2005); Chiavenato, I., (1979) Teoria Geral da Administração, , Ed. McGraw-Hill do Brasil; Campos, J., (2004) Metodologia Para Elaboração de Sistema Integrado de Avaliação de Desempenho Logístico, , Tesis Doctoral leída en la Universidad Federal de Santa Catarina. Florianópolis, Brasil; Lockamy III, A., Quality-focused performance measurement systems: A normative model (1998) International Journal of Operations & Production Management, 18 (8), pp. 740-766; Kaplan, R.S., Norton, D.P., (1999) The Balanced Sco-recard: Translating Strategy into Action, , Harvard Business School Press; Rey, M., (2000) Medición de Desempeño en Logística y Operaciones, , Enfases Logística, Brasil; Sink, D.S., Tuttle, T.C., (1993) Planejamento e Medicao Para a Performance, , Qualitymark, Brasil; Toni, A., Tonchia, S., Performance measurement systems: Models, characteristics and measures (2001) International, Journal of Operations & Production Management, 21, pp. 46-70; Tsang, H.C., Measurement maintenance performance: A holistic approach (1999) International Journal of Operations & Production Management, 19 (7)},
correspondence_address1={Campos, J.; Univ. Federal del Amazonas, Fac. de Estudios Sociales, Campus Universitario, Av. Gral. Rodrigo Otavio J.R.N. 3000, 69077-000 Manaus, AM, Brazil; email: jorgecam@pop.com.br},
publisher={Centro de Informacion Tecnologica},
issn={07168756},
coden={ITECF},
language={Spanish},
abbrev_source_title={Inf Tecnol},
document_type={Article},
source={Scopus},
}

@CONFERENCE{Radhakrishnan20032127,
author={Radhakrishnan, A. and Sridharan, V. and Hales, D.N.},
title={Sustaining supply chain integration},
journal={Proceedings - Annual Meeting of the Decision Sciences Institute},
year={2003},
pages={2127-2131},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0442325457&partnerID=40&md5=f347dfb5a57c78cd426259ef9a804922},
affiliation={Department of Management, 101 Sirrine Hall, Clemson University, Clemson, SC 29634-1305, United States},
abstract={Supply chain integration is advocated as one of the important factors to attain competitive advantage. Supply chain integration is not a one-time accomplishment. Sustaining supply chain integration is just as important. When supply chain partners are not able to sustain integration, the relationships among initially cooperating partners slowly degenerate and culminate in open conflict. In order to prevent that, a regime of process based performance measurement system should be carefully designed, mutually agreed upon, implemented, and monitored by all partners. We propose a conceptual model for such a regime. The model incorporates three perspectives internal, cooperative, and sustainability - to emphasize the importance of establishing and monitoring boundary spanning performance measurement system that hold multiple supply chain members jointly accountable.},
author_keywords={Performance measurement system;  Supply chain integration;  Sustainability},
keywords={Performance measurement system;  Supply chain integration;  Sustainability, Competition;  Costs;  Customer satisfaction;  Distribution of goods;  Inventory control;  Investments;  Societies and institutions;  Strategic planning, Professional aspects},
references={Deming, W.E., (1986) Out Of A Crisis MIT Center for Advanced Engineering Study, , Cambridge, MA; Ramdas, K., Spekman, R.E., Chain Or Shackles: Understanding What Drives Supply-Chain Performance (2000) Interfaces, 30 (4), pp. 3-21; Fisher, M.L., What Is The Right Supply Chain For Your Product? (1997) Harvard Business Review, 75 (2), pp. 105-117; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply Chain Management: Implementation Issues And Research Opportunities (1998) International Journal of Logistics Management, 9 (2), pp. 1-19; Brewer, P.C., Speh, T.W., Using The Balanced Score Card To Measure Supply Chain Performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Narasimhan, R., Jayaram, J., Decision Sciences (1998) Causal Linkages in Supply Chain Management: An Exploratory Study of North American Manufacturing Firms, 29 (3), pp. 579-587; Lee, H., Billington, C., (1992) Materials Management In Decentralized Supply Chain", Operations Research, 41 (5), pp. 835-847; Armistead, C.G., Mapes, J., The Impact Of Supply Chain Integration On Operating Performance (1993) Logistics Information Management, 6 (4), pp. 9-14; Cachon, G.P., Fisher, M., Supply Chain Inventory Management and the Value of Shared Information (2000) Management Science, 46 (8), pp. 1032-1048; Deeter-Schmeiz, A.R., Applying Team to Logistics Processes: Information Acquisition and the Impact of Team Role Clarity and Norms (1997) Journal of Business Logistics, 18 (1), pp. 159-178; Kendall, K.E., The Significance of Information Systems Research on Emerging Technologies: Seven Information Technologies that Promise to Improve Managerial Effectiveness (1997) Decision Sciences, 28 (4), pp. 775-792; Greis, N.P., Kasarda, J.D., Enterprise Logistics in the Information Era (1997) California Management Review, 39 (3), pp. 55-78; Mason-Jones, R., Towill, D.R., Time Compression in the Supply Chain: Information Management is the Vital Ingredient (1998) Logistics Information Management, 11 (2), pp. 93-106; Vinas, T., Supply - Chain Strategist (2001) Industry Week, 250 (13), p. 62; Holmberg, S., A Systems Perspective on Supply Chain Measurements (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Kumar, K., Van Dissel, H.G., Sustainable Collaboration: Managing Conflict and Cooperation in InterOrganizational Systems (1996) MIS Quarterly, 20 (3), pp. 279-300; Maloni, M.J., Benton, W., Supply Chain Partnership pportunities from Operational Research (1997) European Journal of Operational Research, 101 (1), pp. 419-429; Croom, S., Romano, P., Giannakis, M., Supply Chain Management: An Analytical framework for Critical Literature Review (2000) European Journal of Purchasing and Supply Management, 6 (1), pp. 67-83; Ho, D.C.K., Au, K.F., Newtor, E., Empirical Research on Supply Chain Management: A Critical Review and Recommendations (2002) International Journal of Production Research, 40 (17), pp. 4415-4430; O'Keefe, M., Establishing Supply Chain Partnerships: Lessons from Australian Agribusiness (1998) International Journal of Supply Chain Management, 3 (1), pp. 5-9; Lapide, L., True Measures of Supply Chain Performance (2000) Supply Chain Management Review, 4 (3), p. 25; Bullinger, H., Kuhner, M., Van Hoof, A., Analyzing Supply Chain Performance Using Balanced Measurement System (2002) International Journal of Production Research, 40 (15), pp. 3533-3543; Eccles, R.G., Pyburn, P.J., Creating a Comprehensive System to Measure Performance (1992) Management Accounting, 74 (7), pp. 41-44; Senge, P.M., The Fifth Discipline - The Art and Practice of Learning Organizations (1992) Century Business, , London; Lambert, D.M., Pohlen, T.L., Supply Chain Metrics (2001) The International Journal of Logistics Management, 12 (1), pp. 1-19; Detoni, A., Tonchia, S., Performance Measurement Systems: Models, Characteristics and Measures (2001) International Journal of Operations and Productions Management, 21 (1-2), pp. 46-70},
correspondence_address1={Sridharan, V.; Department of Management, , Clemson, SC 29634-1305, United States; email: suhas@clemson.edu},
coden={PAMSE},
language={English},
abbrev_source_title={Proc Annu Meet Decis Sci Inst},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Park20032133,
author={Park, D.-J. and Kim, D.},
title={A development blueprint for performance measurement system design framework for an integrated supply chain},
journal={Proceedings - Annual Meeting of the Decision Sciences Institute},
year={2003},
pages={2133-2137},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0442309888&partnerID=40&md5=4e7a10d95601a52a144ea0cd0e7e1181},
affiliation={Department of Industrial Engineering, Kongju National University, Yesan Kun, Chungnam, South Korea; Center for Supply Chain Management, College of Business Administration, Marquette University, Milwaukee, WI 53201-1881, United States},
abstract={In recent years, significant attention has been given to implementing a performance measurement system as one of the key enablers for supply chain integration. And yet, there is little guidance to practitioners as to how to develop the system. This paper proposes a development blueprint for building a design framework for a performance measurement system (PMS) that supports effective supply chain integration among partners, along with the discussion of critical issues in the PMS design framework.},
author_keywords={Performance Measurement System;  Supply Chain Management},
keywords={Performance measurement system;  Supply chain management (SCM), Customer satisfaction;  Decision support systems;  Electronic commerce;  Information science;  Internet;  Online systems;  Real time systems;  Strategic planning, Professional aspects},
references={Atkinson, A.A., Waterhouse, J.H., Wells, R.B., A Stakeholder Approach to Strategic Performance Measurement (1997) Sloan Management Review, 39 (1); Beamon, B.M., Measuring Supply Chain Performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Brewer, P.C., Speh, T.W., Using the Balanced Scorecard to Measure Supply Chain Performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply Chain Management: More Than a New Name for Logistics (1997) The International Journal of Logistics Management, 8 (1); Kaplan, R.S., Norton, D.P., The Balanced Scorecard Measures That Drive Performance (1992) Harvard Business Review, , January-February; Kim, D., Mabert, V.A., Jun, M., Process Chain: Managing E-Business and Supply Chain Dynamics (2003) Technical Report, , Center for Supply Chain Management, Marquette University; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply Chain Management: Implementation Issues and Research Opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-19; Lee, H.L., Whang, S., E-Business and Supply Integration (2001) Stanford Global Supply Chain Management Forum, , November; (2001) Supply-chain Operations Reference-Model, SCOR Version 5.0., , Pittsburgh: Supply-Chain Council, Inc; Tan, K.C., Supply Chain Management: Practices, Concerns, and Performance Issues (2002) The Journal of Supply Chain Management, 38 (1), pp. 42-53},
correspondence_address1={Park, D.-J.; Department of Industrial Engineering, , Yesan Kun, Chungnam, South Korea},
coden={PAMSE},
language={English},
abbrev_source_title={Proc Annu Meet Decis Sci Inst},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Tian20034934,
author={Tian, C. and Chai, Y. and Liu, Y. and Ren, S.},
title={Performance management in supply chain},
journal={Proceedings of the IEEE International Conference on Systems, Man and Cybernetics},
year={2003},
volume={5},
pages={4934-4939},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0242468532&partnerID=40&md5=585371dd63caf3fd1918fef9f1bdd743},
affiliation={Department of Automation, Tsinghua University, Beijing 100084, China},
abstract={Appropriate performance management system is essential to the collaborative operation of supply chain. This paper presents a quantitative performance measurement system for supply chain. From time, quality, cost, and flexibility perspective, performance measures at department, enterprise and supply chain level are proposed. The comprehensive evaluation model is selected in Cobb-Douglas form to reflect the indispensability of each perspective. The key features in human evaluation are uncertainty, inconsistency and dependency, thus a modified fuzzy analytic network process is adopted as the comprehensive method.},
author_keywords={Fuzzy analytic network process;  Performance management;  Supply chain},
keywords={Evaluation;  Industrial applications;  Mathematical models;  Strategic planning;  Uncertain systems;  Vectors, Cobb-Douglas form;  Performance management;  Supply chain, Fuzzy sets},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Bowersox, D.J., Closs, D.J., Stank, T.P., How supply chain competency leads to business success (2000) Supply Chain Management Review, 4 (4), pp. 70-78; Brewer, P.C., Speh, T.W., Using the balanced scorecard to measure supply chain performance (2000) Journal of Business Logistics, 21 (1), pp. 75-93; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measures and metrics in a supply chain environment (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Mikhailov, L., Singh, M.G., Fuzzy analytic network process and its application to the development of decision support systems (2003) IEEE Transactions on Systems, Man and Cybernetics (Part C), 33 (1), pp. 33-41; Mikhailov, L., A fuzzy programming method for deriving priorities in the analytic hierarchy process (2000) Journal of Operation. Research. Society, 51, pp. 341-349; Miller, C.A., The nature and design of supply chain performance measurement systems - an empirical study (2001), Pennsylvania State University Ph.D. Thesis; Satty, T.L., (1996) Decision Making with Dependence and Feedback: The Analytic Network Process, , RWS, Pittsburgh, PA; Sink, D.S., Tuttle, T.C., (1989) Planning and Measurement in Your Organization of the Future, , Industrial Engineering and Management Press, Norcross, GA; De Toni, A., Tonchia, S., Performance measurement systems-models, characteristics and measures (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 46-70},
correspondence_address1={Tian, C.; Department of Automation, , Beijing 100084, China; email: tch00@mails.tsinghua.edu.cn},
issn={08843627},
coden={PICYE},
language={English},
abbrev_source_title={Proc IEEE Int Conf Syst Man Cybern},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Chan2003534,
author={Chan, F.T.S.},
title={Performance measurement in a supply chain},
journal={International Journal of Advanced Manufacturing Technology},
year={2003},
volume={21},
number={7},
pages={534-548},
doi={10.1007/s001700300063},
note={cited By 377},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0038046206&doi=10.1007%2fs001700300063&partnerID=40&md5=ef551971a2fe9ebd00e8cc2d8b1661a6},
affiliation={Dept. of Indust. and Mfg. Syst. Eng., University of Hong Kong, Pokfulam Road, Kowloon, Hong Kong},
abstract={The supply chain is an important element in logistics development for all industries. It can improve efficiency and effectiveness of not only product transfer, but also information sharing between the complex hierarchy of all the tiers. There is no systematic grouping of the different performance measures in the existing literatures. This paper presents the formulisation of both quantitative and qualitative performance measurements for easy representation and understanding. Apart from the common criteria such as cost and quality, five other performance measurements are defined: resource utilisation; flexibility; visibility; trust; and innovativeness. In particular, new definitions are developed for visibility, trust, and innovativeness. Details of choices of these performance measurements are listed and suggested solutions are given, with the hope that a full picture of supply chain performance measurements is developed. In addition, a multi-attribute decision-making technique, an analytic hierarchy process (AHP), is used to make decisions based on the priority of performance measures. This paper outlines the application and particularly the pairwise comparison which helps to identify easily the importance of different performance measurements. An example from the electronic industry is used to demonstrate the AHP technique.},
author_keywords={Analytic hierarchy process;  Multi-attribute decision-making;  Performance measurement;  Supply chain},
keywords={Cost effectiveness;  Customer satisfaction;  Decision making;  Electronics industry;  Performance;  Production engineering;  Resource allocation, Analytic hierarchy process;  Multi attribute decision making;  Performance measurement;  Supply chain management, Industrial management},
references={Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations and Production Management, 19 (3), pp. 275-292; Neely, A., Gregory, M., Platts, K., Performance measurement system design: A literature review and research agenda (1995) International Journal of Operations and Production Management, 15 (4), pp. 80-116; Sink, D.S., Tuttle, T.C., (1989) Planning and Measurement in Your Organization of the Future, Industrial Engineering and Management Press, , Norcross, USA; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: An interdisciplinary review (1999) International Journal of Production Economics, 60, pp. 53-60; Rolstandas, A., (1995) Performance: A Business Process Benchmarking Approach, , Chapman and Hall, New York, USA; Van Hoek, R.I., Measuring the unmeasurable - Measuring and improving performance in the supply chain (1998) Supply Chain Management, 3 (4), pp. 187-192; Bourne, M., Mills, J., Wilcox, M., Neely, A., Platts, K., Designing, implementing and updating performance measurement systems (2000) International Journal of Operations and Production Management, 20 (7), pp. 754-771; Kuwaiti, M.E., Kay, J.M., The role of performance measurement in business process re-engineering (2000) International Journal of Operations and Production Management, 20 (12), pp. 1411-1426; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environment (2001) International Journal of Operations and Production Management, 21 (1-2), pp. 71-87; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution and Logistics Management, 30 (10), pp. 847-868; Oakshott, L., (1998) Essential Quantitative Methods for Business Management and Finance, , Macmillan; Kuglin, F.A., (1998) Customer-Centered Supply Chain Management - a Link-by-Link Guide, , Amacom, New York, USA; Yucesan, E., Xaxiew, D.G., Theory and methodology: Lead times, order release mechanisms, and customer service (2000) European Journal of Operational Research, 120, pp. 118-130; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Lummus, R.R., Vokurka, R.J., Defining supply chain management: A historical perspective and practical guidelines (1999) Industrial Management and Data Systems, 99 (3), pp. 11-17; Chandra, C., Kumar, S., Supply chain management in theory and practice: A passing fad or a fundamental change? (2000) Industrial Management and Data Systems, 100 (3), pp. 100-113; Koste, L.L., Malhotra, M.K., A theoretical framework for analyzing he dimensions of manufacturing flexibility (1999) Journal of Operations Management, 18, pp. 75-93; Subramaniam, V., Lee, G.K., Ramesh, T., Hong, G.S., Wong, Y.S., Machine selection rules in a dynamic job shop (2000) International Journal of Advanced Manufacturing Technology, 16, pp. 902-908; Bordoloi, S.K., Coper, W.W., Matsuo, H., Flexibility, adaptability and efficiency in manufacturing systems (1999) International Journal of Operations and Production Management, 8 (2), pp. 133-145; Caprihan, R., Wadhwa, S., Impact of routing flexibility on the performance of an FMS - A simulation study (1997) International Journal of Flexible Manufacturing Systems, 9, pp. 273-298; Mahmoodi, F., Mosier, C.T., Morgan, J.R., The effects of scheduling rules and routing flexibility on the performance of a random flexible manufacturing system (1999) International Journal of Flexible Manufacturing Systems, 11, pp. 271-289; Wilhelm, W.E., Shin, H.M., Effectiveness of alternate operations in a flexible manufacturing system (1985) International Journal of Production Research, 23 (1), pp. 65-79; Sethi, A.K., Sethi, S.P., Flexibility in manufacturing: A survey (1990) International Journal of Flexible Manufacturing Systems, 2, pp. 289-328; Wen, H.J., Smith, C.H., Minr, E.D., Formation and dynamic routeing of part families among flexible manufacturing cells (1996) International Journal of Production Research, 34 (8), pp. 2229-2245; Shin, H., Collier, D.A., Wilson, D.D., Supply management orientation and supplier/buyer performance (2000) Journal of Operations Management, 18, pp. 317-333; Mapes, J., Szwejczewski, M., New, C., Process variability and its effect on plant performance (2000) International Journal of Operations and Production Management, 20 (7), pp. 792-808; Fransoo, F.C., Wouters, M.J.F., Measuring the bullwhip effect in the supply chain (2000) International Journal of Supply Chain Management, 5 (2), pp. 78-89; Rebstock, S.E., Kaula, R., The effectiveness of an analytic hierarchy process in group decision making: A case study (1996) International Journal of Computer Applications in Technology, 9 (2-3), pp. 95-105; Korpela, J., Tuominen, M., Benchmarking logistics performance with an application of the analytic hierarchy process (1996) IEEE Transactions on Engineering Management, 42 (3), pp. 323-333. , August; Saaty, T.L., (1980) The Analytic Hierarchy Process, , McGraw-Hill, New York; Saaty, T.L., Axiomatic foundation of the analytic hierarchy process (1986) Management Science, 32, pp. 841-855; Saaty, T.L., How to make a decision: The analytic hierarchy process (1990) European Journal of Operational Research, 48 (1), pp. 9-26; Vargas, L., An overview of the analytic hierarchy process and its applications (1990) European Journal of Operational Research, 48, pp. 2-8; Korpela, J., Lehmusvaara, A., Tuominen, M., An analytic approach to supply chain development (2001) International Journal of Production Economics, 71, pp. 145-155; (1999) Expert Choice, , Expert Choice Inc., Pittsburgh, Pennsylvania, USA; Kim, B., Rank, K., Kim, T., The perception gap among buyer and suppliers in the semiconductor industry (1999) International Journal of Supply Chain Management, 4 (5), pp. 231-241},
correspondence_address1={Chan, F.T.S.; Dept. of Indust. and Mfg. Syst. Eng., Pokfulam Road, Kowloon, Hong Kong; email: ftschan@hkucc.hku.hk},
issn={02683768},
coden={IJATE},
language={English},
abbrev_source_title={Int J Adv Manuf Technol},
document_type={Article},
source={Scopus},
}

@ARTICLE{Chan2003179,
author={Chan, F.T.S. and Qi, H.J.},
title={Feasibility of performance measurement system for supply chain: A process-based approach and measures},
journal={Integrated Manufacturing Systems},
year={2003},
volume={14},
number={3},
pages={179-190},
doi={10.1108/09576060310463145},
note={cited By 144},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0038292124&doi=10.1108%2f09576060310463145&partnerID=40&md5=38ca69f6c4bfd2fdea0f0a9bf89638ec},
affiliation={Department of Industrial and Manufacturing System Engineering, The University of Hong Kong, Hong Kong},
abstract={Nowadays more than ever, supply chain management has gained a tremendous amount of attention from both the academic and practitioner communities. It provides a revolutionary management philosophy to cope with the increasing business competition and economy globalization. However, there exist many puzzles, especially in structure mapping and analysis, and performance measurement. The objective of this paper is to propose a process-based approach to mapping and analyzing the practically complex supply chain network. Via this approach, a process-based performance measurement system is proposed, in which a method called performance of activity is used to identify the performance measures and metrics. © 2003, MCB UP Limited},
author_keywords={Model;  Performance measurement;  Supply-chain management},
keywords={Customer satisfaction;  Industrial management;  Inventory control;  Process engineering;  Product development, Supply chain management (SCM), Just in time production},
references={Beamon, B.M., Supply chain design and analysis: models and methods (1998) International Journal of Production Economics, 55, pp. 281-294; Beamon, B.M., Measuring supply chain performance (1999) International Journal of Operations & Production Management, 19 (3), pp. 275-292; Beamon, B.M., Ware, T.M., A process quality model for the analysis, improvement and control of supply chain systems (1998) Logistics Information Management, 11 (2), pp. 105-113; Bititci, U.S., Modeling of performance measurement systems in manufacturing enterprises (1995) International Journal of Production Economics, 42, pp. 137-147; Chan, S.L., Choi, C.F., A conceptual and analytical framework for business process reengineering (1997) International Journal of Production Economics, 50, pp. 211-223; Chandra, C., Kumar, S., Supply chain management in theory and practice: a passing fad or a fundamental change? (2000) Industrial Management and Data Systems, 100 (3), pp. 100-113; Changchien, S.W., Shen, H.Y., Supply chain reengineering using a core process analysis matrix and object-oriented simulation (2002) Information & Management, 39, pp. 345-358; Christopher, M., (1992) Logistics and Supply Chain Management, , Pitman Publishing, London; Cooper, M.C., Lambert, D.M., Pagh, J.D., Supply chain management: more than a new name for logistics (1997) The International Journal of Logistics Management, 8 (1), pp. 1-13; Davenport, T.H., (1993) Process Innovation – Reengineering Work through Information Technology, , Harvard Business School Press, Boston, MA; Giunipero, L.C., Brand, R.R., Purchasing's role in supply chain management (1996) The International Journal of Logistics Management, 7 (1), pp. 29-37; Gunasekaran, A., Patel, C., Tirtiroglu, E., Performance measurement and metrics in a supply chain environemt (2001) International Journal of Operations & Production Management, 21 (1-2), pp. 71-87; Holmberg, S., A system perspective on supply chain measurement (2000) International Journal of Physical Distribution & Logistics Management, 30 (10), pp. 847-868; Kanji, G.K., Wong, S., Business excellence model for supply chain management (1999) Total Quality Management, 10 (8), pp. 1147-1168; Korpela, J., Lehmusvaara, A., Tuominen, M., An analytic approach to supply chain development (2001) International Journal of Production Economics, 71, pp. 145-155; Kueng, P., Process performance measurement system: a tool to support process-based organizations (2000) Total Quality Management, 11 (1), pp. 67-85; Kuwaiti, M.E., Kay, J.M., The role of performance measurement in business process re-engineering (2000) International Journal of Operations & Production Management, 20 (12), pp. 1411-1426; Lambert, D.M., Cooper, M.C., Issues in supply chain management (2000) Industrial Marketing Management, 29, pp. 65-83; Lambert, D.M., Cooper, M.C., Pagh, J.D., Supply chain management: implementation issues and research opportunities (1998) The International Journal of Logistics Management, 9 (2), pp. 1-19; Lebas, M.J., Performance measurement and performance management (1995) International Journal of Production Economics, 41, pp. 23-35; Lummus, R.R., Vokurka, R.J., Defining supply chain management: a historical perspective and practical guidelines (1999) Industrial Management & Data Systems, 99 (3), pp. 11-17; Moore, N., Supply chain management (1998) Work Study, 47 (5), pp. 172-174; Narasimhan, R., Jayaram, J., Causal linkages in supply chain management: an exploratory study of North American manufacturing firms (1998) Decision Science, 29 (6), pp. 579-605; Neely, A., Gregory, M., Platts, K., Performance measurement system design: a literature review and research agenda (1995) International Journal of Operations & Production Management, 15 (4), pp. 80-116; Rolstandas, A., (1995) Performance Measurement: A Business Process Benchmarking Approach, , Chapman and Hall, New York, NY; Ross, D.F., (1997) Competing Through Supply Chain Management: Creating Market-winning Strategies through Supply Chain Partnerships, , International Thomson Publishing, New York, NY; Sink, D.S., Tuttle, T.C., (1989) Planning and Measurement in Your Organization of the Future, , Industrial Engineering and Management Press, Norcross, GA; Van Hoek, R.I., Measuring the unmeasurable’ – measuring and improving performance in the supply chain (1998) International Journal of Supply Chain Management, 3 (4), pp. 187-192; Waggoner, D.B., Neely, A.D., Kennerley, M.P., The forces that shape organizational performance measurement systems: an interdisciplinary review (1999) International Journal of Production Economics, 60, pp. 53-60},
issn={09576061},
language={English},
abbrev_source_title={Integr Manuf Syst},
document_type={Article},
source={Scopus},
}

@ARTICLE{NoAuthor2003,
title={IFIP TC5 / WG5.7 8th International Conference on Advances in Production Management Systems},
journal={IFIP Advances in Information and Communication Technology},
year={2003},
volume={129},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904299463&partnerID=40&md5=26b0caf03b55274ffb4fac032a740731},
abstract={The proceedings contain 43 papers. The special focus in this conference is on Collaboration in Supply Chain Planning, Collaboration in Product Innovation, ICT Solutions for Collaborative Work, Simulation and Modelling, Advances in Production Management Systems, and Advances in Applications Management Software. The topics include: Agent-based collaborative supply net management; creating synergy through the integration of advanced planning systems and configuration; dealing with constraint issues in agile discrete manufacturing companies; lean supply chain planning; supply chain integration; a case study from the German cutting tool industry; collaboration in value creating networks; product development cost estimation and optimisation in a global manufacturing environment; product family based coordination for design and production in the supply chain; technology value analysis of the ship contract design value stream; technology value analysis of the ship contract design value stream production; security integration in inter-enterprise business process engineering; discovering distributed processes in supply chains; review of B2B integration; collaborative software needs for contract manufacturers; internet-based integration for collaborative business processes; a generic framework for simulation of supply networks with bargaining agents; the path from business modelling to technology management; integration of product, process and functional orientations; balancing of hybrid assembly systems using a simulation approach; managing bottleneck resources in production networks; a study on characterization of agile manufacturing system with square array layout of machining centres; enhancing collaboration through web enabled performance measurement system; system design principles in customer-driven manufacturing; strategic manufacturing -requirements for supporting tool; analyzing maintenance collaboration in multiple overlapping SAP instances and indicators for interactive scheduling.},
publisher={Springer New York LLC},
issn={18684238},
isbn={9781475747911},
language={English},
abbrev_source_title={IFIP Advances in Information and Communication Technology},
document_type={Conference Review},
source={Scopus},
}

@ARTICLE{O'Keeffe2002296,
author={O'Keeffe, M. and Fearne, A.},
title={From commodity marketing to category management: Insights from the Waitrose category leadership program in fresh produce},
journal={Supply Chain Management},
year={2002},
volume={7},
number={5},
pages={296-301},
doi={10.1108/13598540210447737},
note={cited By 34},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-3242691319&doi=10.1108%2f13598540210447737&partnerID=40&md5=9c724b4837adeabc8e5a0ce3a807d666},
affiliation={Agribuys Australia Pty Ltd., Pymble, Australia; Centre for Food Chain Research, Imperial College, Ashford, United Kingdom},
abstract={Looks at how Waitrose deals with commodity marketing and category management by examining its category leadership strategy, which aims to reduce costs and align its network to the needs of the consumer. Seeks to shed light on how this was achieved and the key lessons to be learned from this procurement strategy. Concludes that the category leadership strategy requires a fundamental shift in the role of the retailer and supplier as well as a redesign of the performance measurement systems, but once these are achieved there are significant and guaranteed rewards.},
author_keywords={Commodities;  Management;  Marketing;  Strategy},
keywords={Costs;  Fruits;  Marketing;  Sensitivity analysis;  Standards, Commodity marketing;  Performance measurement systems;  Supply chain management, Industrial management},
correspondence_address1={O'Keeffe, M.; Agribuys Australia Pty Ltd., Pymble, NSW, Australia},
issn={13598546},
language={English},
abbrev_source_title={Supply Chain Manage.},
document_type={Review},
source={Scopus},
}

@CONFERENCE{Chen2002622,
author={Chen, I.J. and Paulraj, A.},
title={Strategic supply management and supply chain performance},
journal={Proceedings - Annual Meeting of the Decision Sciences Institute},
year={2002},
pages={622-627},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-1642458379&partnerID=40&md5=2584313669f873c65746aef58160a795},
affiliation={Department of Operations Management, College of Business Administration, Cleveland State University, Cleveland, OH 44115, United States},
abstract={This research sets forth to explore the interaction between key factors of strategic supply management including strategic purchasing, long-term relationships, communication, supplier's prompt response, buyer's responsiveness to customers, and buyer's financial performance. The constructs were developed based on an extensive literature review. They were tested for reliability, validity and unidimensionality using confirmatory factor analysis. The proposed path analytical model was tested using the structural equation modeling approach. Results of this study support previous research and provide additional insights into strategic supply management and supply chain performance. They also demonstrate the importance of customer responsiveness as a time-based supply chain performance measurement.},
keywords={Supply chain management (SCM);  Supply chain performance, Customer satisfaction;  Mathematical models;  Personnel training;  Purchasing;  Quality assurance;  Risk assessment;  Strategic planning, Industrial management},
correspondence_address1={Chen, I.J.; Department of Operations Management, , Cleveland, OH 44115, United States},
coden={PAMSE},
language={English},
abbrev_source_title={Proc Annu Meet Decis Sci Inst},
document_type={Conference Paper},
source={Scopus},
}

@CONFERENCE{Yee20021260,
author={Yee, S.-T.},
title={Establishment of product offering and production leveling principles via supply chain simulation under order-to-delivery environment},
journal={Winter Simulation Conference Proceedings},
year={2002},
volume={2},
pages={1260-1268},
note={cited By 8},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0036928311&partnerID=40&md5=d9faf9a6a4ce15211ac9313f9a853a02},
affiliation={Enterprise Systems Laboratory, Gen. Motors Res./Development Center, 30500 Mound Road, Warren, MI 48090, United States},
abstract={In support of the order-to-delivery (OTD) business initiative, a simulation framework has been developed at GM R & D. The OTD simulation program is aimed at simulating the behavior of the OTD supply chain using detailed inputs associated with demand, supply, and production processes. Customer demand variation is a key source of uncertainty in GM's supply chain. Early capture of customer demand fluctuation enables GM to effectively reduce aggregate mismatch between production and sales and appropriate time series models have been suggested to capture demand patterns based on actual data. The vehicle model and option mix with a given demand variation influences the performance of the OTD supply chain and provides a means to establish certain principles determining the extent of product offering and the scope of production leveling. Analyzing the impact of the model and option mix on primary supply chain performance measures, such as customer wait time, condition mismatch, and parts usage, capacitates reduction of the mismatch between demand and production and stabilizes supply chain operations.},
keywords={Competition;  Computer simulation;  Customer satisfaction;  Distribution of goods;  Sales, Supply chains, Process control},
references={Box, G., Luceno, A., (1997) Statistical Control by Monitoring and Feedback Adjustment, , (Wiley-interscience); Costy, T., Truss, L., Tew, J., (2000) Integrated Supply Chain Management Exploratory Project Midterm Report, , Report ESL-44, GM R & D Center; Krishnan, S., (2001) A System Level Simulation Analysis of CXD-II Distribution Process, , Report ESL-86, GM R & D Center; Morgan, L.L., Daniels, R.L., Integrating product mix and technology adoption decisions: A portfolio approach for evaluating advanced technologies in the automotive industry (2001) Journal of Operations Management, 19, pp. 219-238; Yee, S.-T., (2002) Aggregate Demand Generation for Order-to-Delivery (OTD) Simulation, , Report ESL-142, GM R & D Center; Winter, D., Interiors become extroverts (1997) Ward's Auto World, 33 (11), pp. 43-44},
correspondence_address1={Yee, S.-T.; Enterprise Systems Laboratory, 30500 Mound Road, Warren, MI 48090, United States; email: shang-tae.yee@gm.com},
editor={Yucesan E., Chen C.H., Snowdon J.L., Charnes J.M.},
issn={02750708},
coden={WSCPD},
language={English},
abbrev_source_title={Winter Simul Conf Proc},
document_type={Conference Paper},
source={Scopus},
}

@ARTICLE{Preuss2001345,
author={Preuss, L.},
title={In dirty chains? Purchasing and greener manufacturing},
journal={Journal of Business Ethics},
year={2001},
volume={34},
number={3-4},
pages={345-359},
doi={10.1023/A:1012549318786},
note={cited By 146},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0035605730&doi=10.1023%2fA%3a1012549318786&partnerID=40&md5=9f9a77749669b397a048d377438e3855},
affiliation={Heriot-Watt University, Edinburgh, United Kingdom; School of Languages, Heriot-Watt University, Edinburgh EH14 4AS, United Kingdom},
abstract={Against the backdrop of an increasing importance of the purchasing function in manufacturing companies, this paper introduces the notion of a "green multiplier effect" and suggests that purchasing could become an important agent for change regarding environmental initiatives in the supply chain. The literature offers some support for this concept. However, a study into purchasing in Scottish manufacturing companies produces a less optimistic picture, as environmental initiatives involving the supply chain are rare. Where they occur, they are mostly undertaken in a passive fashion, with the main motivation being compliance with legislation. Above-average environmental initiatives are reported from three industries: paper making, chemicals and electronics. Explanations of the discrepancy between anticipated and actual results point to the reactive nature of purchasing and to performance measurement systems that focus on economic criteria.},
author_keywords={Cleaner manufacturing;  Environmental protection;  Greener manufacturing;  Industrial buying;  Purchasing;  Supply chain management},
references={Arnold, U., (1995) Beschaffungsmanagement, , Schäffer-Poeschel, Stuttgart; (1995) How Green is my Front Door: B&q's Second Environmental Review, , B&Q, Eastleigh; Baas, L., Duffy, N., Ryan, B., Spinardi, G., Williams, R., Petro-chemical sector (1999) Cleaner Technology: A New Agenda for Government and Industry, pp. 73-90. , A Clayton, G. Spinardi and R. Williams (eds.), Earthscan, London; Bailey, P., Farmer, D., Jessop, D., Jones, D., (1998) Purchasing Principles and Management, , Financial Times Management, London; (1997) Values Report 1997, , Body Shop Plc, Littlehampton; (1997) Buying into a Green Future: Partnerships for Change, , Business in the Environment, London; (1992) Standard Industrial Classification of Economic Activity 1992, , HMSO, London; Chadwick, T., Rajagopal, S., (1995) Strategic Supply Chain Management, , Butterworth-Heinemann, Oxford; (1990) The European Electronics and Information Technology Industry: State of Play, Issues at Stake and Proposals for Action, , Commission of the European Communities, DG XIII Telecommunications, Information Industries and Innovation, Brussels; Dobilas, G., MacPherson, A., Environmental regulation and international sourcing policies of multinational firms (1997) Growth and Change, 28, pp. 7-23; Drumwright, M.E., Socially responsible organizational buying: Environmental concern as a noneconomic buying criterion (1994) Journal of Marketing, 58, pp. 1-19; Duffy, N., Ryan, B., Baas, L., Boons, F., Hansen, O.E., Spinardi, G., Fine chemicals sector (1999) Cleaner Technology: A New Agenda for Government and Industry, pp. 91-106. , A. Clayton, G. Spinardi and R. Williams (eds.), Earthscan, London; Dunn, Bradstreet, (1999) Scottish Business Register, , Dunn & Bradstreet, High Wycombe; Fiksel, J., (1996) Design for Environment: Creating Ecoefficient Products and Processes, , McGraw-Hill, New York; Green, K., Morton, B., Now, S., Purchasing and environmental management: Interactions policies and opportunities (1996) Business Strategy and the Environment, 5, pp. 188-197; Hall, J., Environmental supply chain dynamics (2000) Journal of Cleaner Production, 8, pp. 455-471; Hill, K.E., Supply-chain dynamics, environmental issues and manufacturing firms (1997) Environment and Planning, 29, pp. 1257-1274; Howes, R., Skea, J., Whelan, B., (1997) Clean and Competitive? Motivating Environmental Performance in Industry, , Earthscan, London; Lamming, R., Warhurst, A., Hampson, J., (1996) The Environment and Purchasing - Problem or Opportunity?, , Chartered Institute of Purchasing and Supply, Stamford; (1973) Pollution by the Pulp and Paper Industry: Present Situation and Trends, , OECD, Paris; Porter, M., Van Der Linde, C., Green and competitive: Ending the stalemate (1995) Harvard Business Review, 73, pp. 120-134. , September-October; Preuss, L., Should you buy your customer's values? On the transfer of moral values in industrial purchasing (2000) International Journal of Values-Based Management, 13, pp. 141-158; Russel, T., (1998) Green Purchasing: Opportunities and Innovations, , Greenleaf Publishing, Sheffield; Spinardi, G., Stauning, I., Larsen, B., Williams, R., Paper mills sector (1999) Cleaner Technology: A New Agenda for Government and Industry, pp. 186-203. , A. Clayton, G. Spinardi and R. Williams (eds.), Earthscan, London; Walton, S.V., Handfield, R.B., Melnyk, S.A., The green supply chain: Integrating suppliers into environmental management processes (1998) International Journal of Purchasing and Materials Management, 34, pp. 2-11; Welford, R., Breaking the link between quality and environment: Auditing for sustainability and life-cycle assessment (1996) Business and the Environment, pp. 208-218. , R. Welford and R. Starkey (eds.), Earthscan, London; Wood, G., Ethical issues in purchasing (1996) The Ethical Organisation: Ethical Theory and Corporate Behaviour, pp. 185-194. , A. Kitson and R. Campbell (eds.), Macmillan, Basingstoke},
correspondence_address1={Preuss, L.; School of Languages, , Edinburgh EH14 4AS, United Kingdom; email: L.Preuss@hw.ac.uk},
issn={01674544},
language={English},
abbrev_source_title={J. Bus. Ethics},
document_type={Article},
source={Scopus},
}

@ARTICLE{DacostaClaro2001321,
author={Dacosta Claro, I.},
title={Health services supply departments: Performance measurement [Los servicios de suministros de los establecimientos sanitarios: La medición de la performance]},
journal={Revista Espanola de Salud Publica},
year={2001},
volume={75},
number={4},
pages={321-335},
doi={10.1590/s1135-57272001000400006},
note={cited By 1},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034794896&doi=10.1590%2fs1135-57272001000400006&partnerID=40&md5=ca465f3dfc49a99933662ce7406979dc},
affiliation={12 Bidebarrieta, 4, 20600 Eibar, Guipúzcoa, Spain},
abstract={Performance measurement, in their different meanings - either balance scorecard or outputs measurement - have become an essential tool in today's organizations (World-Class organizations) to improve service quality and reduce costs. This paper presents a performance measurement system for the hospital supply chain. The system is organized in different levels and groups of indicators in order to show a hierarchical, coherent and integrated vision of the processes. Thus, supply services performance is measured according to (1) financial aspects, (2) customers satisfaction aspects and (3) internal aspects of the processes performed. Since the informational needs of the managers vary within the administrative structure, the performance measurement system is defined in three hierarchical levels. Firstly, the whole supply chain, with the different interrelation of activities. Secondly, the three main processes of the chain - physical management of products, purchasing and negotiation processes and the local storage units. And finally, the performance measurement of each activity involved. The system and the indicators have been evaluated with the participation of 17 health services of Quebec (Canada), however, and due to the similarities of the operation, could be equally implemented in Spanish hospitals.},
author_keywords={Health services;  Performance;  Supply chain},
keywords={article;  Canada;  health care financing;  health care management;  health care quality;  health service;  hospital purchasing;  Spain},
references={Omachonu, V.K., (1991) Total quality and productivity management in health care organizations, , American Society for Quality Control: Milwaukee (WI); Chilingerian, J.A., Evaluating physician efficiency in hospitals: A multivariate analysis of best practices (1995) Eur J Oper Res, 80, pp. 548-574; Kaplan, R.S., Norton, D.P., The balanced scorecard measures that drive performance (1992) Harv Bus Rev, JAN-FEB, pp. 71-79; Maskell, B.H., (1994) New Performance Measures, , Productivity Press: Oregon; Kaplan, R.S., Norton, D.P., (1997) The balanced scorecard: translating strategy into action, , Harvard Business School Press: Boston (MA); Donabedian, A., Criteria and standards for quality assessment and monitoring (1986) Qual Rev Bull, MAR, pp. 99-108; (1993) Creating a government that works better and costs less: report of the National Performance Review, , National Performance Review Publications; Andersson, P., Aronsson, H., Storhagen, N.G., Measuring logistics performance (1989) Eng Cost Prod Econ, 17, pp. 253-262; Plemmons, J.K., Bell, L.C., Measuring effectiveness of material management process (1995) J Manag Eng, NOV-DEC, pp. 26-32; Lambert, D.M., Adams, R.J., Emmelhainz, M.A., Supplier selection criteria in the healthcare industry: A comparison of importance and performance (1997) Int J Purch Mater Manag, JAN, pp. 16-22; Cohen, M.A., Zheng, Y.-S., Agrawal, V., Service parts logistics: A benchmark analysis (1997) IIE Trans, 29, pp. 627-641; McCampbell, A., Slaich, L.L., Purchasing efficiency and staffing benchmarks: A county government study (1995) Int J Purch Mater Manag, 4, pp. 30-36; Connor, L.R., Reengineering health care material management (1998) J Soc Health Syst, 5, pp. 74-89; Egbelu, P.J., Harmonosky, C.M., Ventura, J.A., O'Brien, W.E., Sommer III, H.J., Cost analysis of hospital material management systems (1998) J Soc Health Syst, 5, pp. 1-10; Vollman, K., Sprung, P., Posa, S., Ladin, D., Kachhal, S.K., Strategies for reducing material cost through implementation of clinical guidelines (1998) J Soc Health Syst, 5, pp. 69-73; Mintzberg, H., (1973) The nature of managerial work, pp. 31-35. , New York (NY): Harper & Row},
correspondence_address1={Dacosta Claro, I.12 Bidebarrieta, 4, 20600 Eibar, Guipúzcoa, Spain; email: dacosta@cirano.qc.ca},
publisher={Ministerio de Sanidad y Consumo},
issn={11355727},
coden={RESPF},
pubmed_id={11693070},
language={Spanish},
abbrev_source_title={Rev. Esp. Salud Publica},
document_type={Article},
source={Scopus},
}

@ARTICLE{Sabri2000581,
author={Sabri, E.H. and Beamon, B.M.},
title={A multi-objective approach to simultaneous strategic and operational planning in supply chain design},
journal={Omega},
year={2000},
volume={28},
number={5},
pages={581-598},
doi={10.1016/S0305-0483(99)00080-8},
note={cited By 451},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0034288912&doi=10.1016%2fS0305-0483%2899%2900080-8&partnerID=40&md5=93da09a3e4fb959cd37e63ff98d0882f},
affiliation={i2 Technologies, 909 E. Las Colinas Blvd., Irving, TX 75039, United States; Dept. of Industrial Engineering, Box 352650, University of Washington, Seattle, WA 98195-2650, United States},
abstract={In this research, an integrated multi-objective supply chain (SC) model is developed for use in simultaneous strategic and operational SC planning. Multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (volume or delivery). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single-measure approaches. Moreover, this model incorporates production, delivery, and demand uncertainty, and provides a multi-objective performance vector for the entire SC network. The model developed here will aid in the: (1) design of efficient, effective, and flexible supply chain systems and (2) evaluation of competing SC networks. © 2000 Elsevier Science Ltd. All rights reserved.},
author_keywords={Mathematical programming;  Multi-objective decision analysis;  Supply chain},
references={Cohen, M.A., Lee, H.L., Manufacturing strategy: Concepts and methods (1987) The Management of Productivity and Technology in Manufacturing, pp. 153-183. , Kleindorfer PR, editor. New York: Plenum Press; Geoffrion, A.M., Graves, G.W., Lee, S.J., Strategic distribution system planning: A status report (1978) Studies in Operations Management, pp. 178-314. , Hax AC, editor. Amsterdam: North-Holland; Cohen, M.A., Moon, S., Impact of production scale economics, manufacturing complexity, and transportation costs on supply chain facility networks (1990) Journal of Manufacturing and Operations Management, 3, pp. 269-292; Cohen, M.A., Moon, S., An integrated plant loading model with economics of scale and scope (1991) European Journal of Operational Research, 50, pp. 266-279; Cohen, M.A., Lee, H.L., Strategic analysis of integrated production-distribution systems: Models and methods (1988) Operations Research, 36 (2), pp. 216-228; Cohen, M.A., Lee, H.L., Resource deployment analysis of global manufacturing and distribution networks (1989) Journal of Manufacturing and Operations Management, 2, pp. 81-104; Cohen, M.A., Fisher, M., Jaikumar, R., International manufacturing and distribution networks: A normative model framework (1989) Managing International Manufacturing, pp. 67-93. , Ferdows K, editor. Amsterdam: Elsevier Science, North-Holland; Robinson, P.E., Goa, L.L., Muggenborg, S.T., Designing an integrated distribution system at Dowbrands (1993) Inc. Interfaces, 23 (3), pp. 107-117; Tzafestas, S., Kapsiotis, G., Coordinated control of manufacturing/supply chains using mult-level techniques (1994) Computer Integrated Manufacturing Systems, 7 (3), pp. 206-221; Arntzen, B.C., Brown, G.G., Harrison, T.P., Trafton, L.L., Global supply chain management at digital equipment corporation (1995) Interfaces, 25 (1), pp. 69-93; Camm, J.D., Chorman, T.E., Dill, F.A., Evans, J.R., Sweeney, D.J., Wegryn, G.W., Restructuring P&G's supply chain (1996) Interfaces, 27 (1), pp. 128-142; Voudouris, V.T., Mathematical programming techniques to debottleneck the supply chain of fine chemical industries (1996) Computers and Chemical Engineering, 20, pp. 1269-1274; Cohen, M.A., Kleindorfer, P.R., Lee, H.L., Optimal stocking policies for low usage items in multi-echelon inventory systems (1986) Naval Research Logistics Quarterly, 33, pp. 17-38; Wikner, J., Towil, D.R., Naim, M.M., Smoothing chain dynamics (1991) International Journal of Production Economics, 22, pp. 231-248; Towill, D.R., Naim, M.M., Wikner, J., Industrial dynamics simulation models in the design of supply chains (1992) International Journal of Physical Distribution and Logistics Management, 22 (5), pp. 3-13; Lee, H.L., Billington, C., Material management in decentralized supply chains (1993) Operations Research, 41 (5), pp. 835-847; Newhart, D.D., Stott, K.L., Vasko, F.J., Consolidating product sizes to minimize inventory levels for a multi-stage production and distribution system (1993) Operational Research Society, 44 (7), pp. 637-644; Pyke, D.F., Cohen, M.A., Multiproduct integrated production-distribution systems (1994) European Journal of Operational Research, 74, pp. 18-49; Pyke, D.F., Cohen, M.A., Performance characteristics of stochastic integrated production-distribution systems (1993) European Journal of Operational Research, 68, pp. 23-48; Berry, D., Naim, M.M., Quantifying the relative improvements of redesign strategies in a P.C. Supply chain (1996) International Journal of Production Economics, 46-47, pp. 181-196; Lee, H.L., Feitzinger, E., Product configuration and postponement for supply chain efficiency (1995) 4th Industrial Engineering Research Conference, pp. 43-48; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunities (1992) Sloan Management Review, pp. 65-73. , Spring; Slack, N., The flexibility of manufacturing systems (1987) International Journal of Operations and Production Management, 7 (4), pp. 35-45; Liao, C.J., Shyu, C., An analytical determination of lead-time with normal demand (1991) International Journal of Operations and Production Management, 11 (9), pp. 72-78; Goicoecha, A., Hansen, D.R., Duckstein, L., (1982) Multiobjective Decision Analysis with Engineering and Business Applications, pp. 40-55. , New York: Wiley; Silver, E.A., Peterson, R., (1985) Decision Systems for Inventory Management and Production Planning, , New York: Wiley; Johnson, M.E., Davis, T., Lee, H.L., Robustness of order reliability models with applications to order ageing (1996) International Journal of Production Research, 34 (12), pp. 3499-3514; Kruger, G.A., The supply chain approach to planning and procurement management (1997) Hewlett-packard Journal, pp. 28-38. , February},
correspondence_address1={Beamon, B.M.; Dept. of Industrial Engineering, , Seattle, WA 98195-2650, United States},
publisher={Elsevier BV},
issn={03050483},
coden={OMEGA},
language={English},
abbrev_source_title={Omega},
document_type={Article},
source={Scopus},
}

@ARTICLE{Beamon1999275,
author={Beamon, B.M.},
title={Measuring supply chain performance},
journal={International Journal of Operations and Production Management},
year={1999},
volume={19},
number={3},
pages={275-292},
doi={10.1108/01443579910249714},
note={cited By 1460},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-3543100440&doi=10.1108%2f01443579910249714&partnerID=40&md5=121cd4ee22760c982d315676d89cb3c2},
affiliation={University of Cincinnati, Cincinnati, Ohio, USA, United States},
abstract={The process of choosing appropriate supply chain performance measures is difficult due to the complexity of these systems. The paper presents an overview and evaluation of the performance measures used in supply chain models and also presents a framework for the selection of performance measurement systems for manufacturing supply chains. Three types of performance measures are identified as necessary components in any supply chain performance measurement system, and new flexibility measures for supply chains are developed.},
author_keywords={Flexibility;  Performance measurement;  Supply chain},
references={Altiok, T., Ranjan, R., Multi-stage, pull-type production/inventory systems (1995) IIE Transactions, 27, pp. 190-200; Arntzen, B.C., Brown, G.G., Harrison, T.P., Trafton, L.L., Global supply chain management at Digital Equipment Corporation (1995) INTERFACES, 25, pp. 69-93; Barker, R.C., Value chain development: An account of some implementation problems (1996) International Journal of Operations > Production Management, 16 (10), pp. 23-36; Beamon, B.M., Performance measures in supply chain management (1996) Proceedings of the 1996 Conference on Agile and Intelligent Manufacturing Systems, , Rensselaer Polytechnic Institute Troy, New York, NY; Beamon, B.M., Supply chain design and analysis: Models and methods (1998) International Journal of Production Economics; Bytheway, A., (1995) Information in the Supply Chain: Measuring Supply Chain Performance, , Cranfield School of Management Working Paper Series SWP195; Bytheway, A., (1995) A Review of Current Logistics Practice, , Cranfield School of Management Working Paper Series SWP1095; Camp, R.C., (1989) Benchmarking - The Search for Industry Best Practices That Lead to Superior Performance, , ASQS Quality Press Milwaukee, WI; Christopher, M., Logistics and Supply Chain Management (1994) Financial Times, , New York, NY; Christy, D.P., Grout, J.R., Safeguarding supply chain relationships (1994) International Journal of Production Economics, 36, pp. 233-4; Cohen, M.A., Lee, H.L., Strategic analysis of integrated production-distribution systems: Models and methods (1988) Operations Research, 36 (2), pp. 216-28; Cohen, M.A., Lee, H.L., Resource deployment analysis of global manufacturing and distribution networks (1989) Journal of Manufacturing and Operations Management, 2, pp. 81-104; Cohen, M.A., Moon, S., Impact of production scale economies, manufacturing complexity, and transportation costs on supply chain facility networks' (1990) Journal of Manufacturing and Operations Management, 3, pp. 269-92; Cook, R.L., Rogowski, R.A., Applying JIT principles to continuous process manufacturing supply chains (1996) Production and Inventory Management Journal, pp. 12-17; Corbett, L.M., Delivery windows - A new view on improving manufacturing flexibility and on-time delivery performance (1992) Production and Inventory Management Journal, 33 (3), pp. 74-9; Das, S.K., The measurement of flexibility in manufacturing systems (1996) International Journal of Flexible Manufacturing Systems, 8, pp. 67-93; Davis, T., Effective supply chain management (1993) Sloan Management Review, pp. 35-46; Gupta, Y.P., Goyal, S., Flexibility of manufacturing systems: Concepts and measurements (1989) European Journal of Operational Research, 43 (2), pp. 119-35; Ishii, K., Takahashi, K., Muramatsu, R., Integrated production, inventory and distribution systems (1988) International Journal of Production Research, 26 (3), pp. 473-82; Johnson, J.B., Randolph, S., Brief: Making alliances work - Using a computer-based management system to integrate the supply chain (1995) Journal of Petroleum Technology, 47 (6), pp. 512-13; Lamming, R., Squaring lean supply with supply chain management (1996) International Journal of Operations > Production Management, 16 (2), pp. 183-96; Lee, H.L., Billington, C., Managing supply chain inventory: Pitfalls and opportunies (1992) Sloan Management Review, 33, pp. 65-73; Lee, H.L., Billington, C., Material management in decentralized supply chains (1993) Operations Research, 41 (5), pp. 835-47; Lee, H.L., Feitzinger, E., Product configuration and postponement for supply chain efficiency (1995) Fourth Industrial Engineering Research Conference Proceedings, pp. 43-8. , Institute of Industrial Engineers; Maskell, B.H., (1991) Performance Measurement for World Class Manufacturing, , Productivity Press Portland, OR; Neely, A., Gregory, M., Platts, K., Performance measurement system design (1995) International Journal of Operations > Production Management, 15 (4), pp. 80-116; New, S.J., A framework for analysing supply chain improvement (1996) International Journal of Operations > Production Management, 16 (4), pp. 19-34; Newhart, D.D., Stott, K.L., Vasko, F.J., Consolidating product sizes to minimize inventory levels for a multi-stage production and distribution systems (1993) Journal of the Operational Research Society, 44 (7), pp. 637-44; Nicoll, A.D., Integrating logistics strategies (1994) Annual International Conference Proceedings, pp. 590-94. , American Production and Inventory Control Society; Pyke, D.F., Cohen, M.A., Performance characteristics of stochastic integrated production- distribution systems (1993) European Journal of Operational Research, 68 (1), pp. 23-48; Pyke, D.F., Cohen, M.A., Multi-product integrated production-distribution systems' (1994) European Journal of Operational Research, 74 (1), pp. 18-49; Sethi, A.K., Sethi, S.P., Flexibility in manufacturing: A survey (1990) International Journal of Flexible Manufacturing Systems, 2 (4), pp. 289-328; Shank, J.K., Govindarajan, V., Strategic cost management and the value chain (1992) Journal of Cost Management for the Manufacturing Industry, 5 (4), pp. 5-21; Slack, N., Flexibility as a manufacturing objective (1983) International Journal of Operations > Production Management, 3 (3), pp. 4-13; Slack, N., (1991) The Manufacturing Advantage, , Mercury Books London; Towill, D.R., Supply chain dynamics (1991) International Journal of Computer Integrated Manufacturing, 4 (4), pp. 197-208; Towill, D.R., Naim, M.M., Wikner, J., Ndustrial dynamics simulation models in the design of supply chains (1992) International Journal of Physical Distribution > Logistics Management, 22 (5), pp. 3-13; Tzafestas, S., Kapsiotis, G., Coordinated control of manufacturing/supply chains using multi-level techniques (1994) Computer Integrated Manufacturing Systems, 7 (3), pp. 206-12; Voudouris, V.T., Mathematical programming techniques to debottleneck the supply chain of fine chemical industries (1996) Computers and Chemical Engineering, 20, pp. S1269-74; Waters-Fuller, N., JIT purchasing and supply: A review of the literature (1995) International Journal of Operations > Production Management, 15 (9), pp. 220-36; Wikner, J., Towill, D.R., Naim, M., Smoothing supply chain dynamics (1991) International Journal of Production Economics, 22 (3), pp. 231-48},
correspondence_address1={University of Cincinnati, United States},
issn={01443577},
language={English},
abbrev_source_title={Int. J. Oper. Prod. Manage.},
document_type={Review},
source={Scopus},
}

@ARTICLE{Mcintyre199857,
author={Mcintyre, K. and Smith, H.A. and Henham, A. and Pretlove, J.},
title={Logistics Performance Measurement and Greening Supply Chains: Diverging Mindsets},
journal={The International Journal of Logistics Management},
year={1998},
volume={9},
number={1},
pages={57-68},
doi={10.1108/09574099810805744},
note={cited By 47},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84993025147&doi=10.1108%2f09574099810805744&partnerID=40&md5=697c142dba8c13ec8f7eda4498c7347d},
affiliation={Xerox Ltd., United States; University of Surrey, United Kingdom},
abstract={Although there has been considerable effort placed on measuring supply chains in order to assess their performance, these techniques have been found to be time and cost focused, aimed at coping with rapid change. This approach tends to have a short-term outlook. Work on greening supply chains is much longer-term in outlook. Is information intensive and biased towards the supply side? These two mindsets appear to be diverging, developing in conflicting directions. This is an alarming prospect for the environment, which has no place in future supply chain performance measurements, thus running the risk of being increasingly side-lined; and for performance measurements, which is unconcerned with longer-term sustainability in terms of the environment. The case is made to amalgamate the advantages of both schools of thought to allow long-term views to be represented by short-term performance measurement. © 1998, MCB UP Limited},
author_keywords={Environment;  Operating performance;  Recycling;  Supply chain},
references={(1994) Guidelines on Environmental Issues, , London, The Engineering Council, September; Shrivastava, P., Environmental Technologies and Competitive Advantage (1995) Strategic Management Journal, 16, pp. 183-200; Bansal, P., Why Firms Go Green (1995), Ph.D. Thesis, Templeton College, Oxford University; Porter, M.E., van der Linde, C., Green and Competitive: Ending the Stalemate (1995) Harvard Business Review, 73 (5), pp. 120-134. , (September–October); Hutchinson, C., Integrating Environment Policy with Business Strategy (1996) Long Range Planning, 29 (1), pp. 11-26; Eden, S.E., Using Sustainable Development — the Business Case (1994) Global Environment Change, 4 (2), pp. 160-167; Murphy, P.R., Poist, R.F., Braunschweig, C.D., Role and Relevance of Logistics to Corporate Environmentalism — an Empirical Assessment (1995) International Journal of Physical Distribution and Logistics Management, 25 (2), pp. 5-19; Dobson, A., (1990) Green Political Thought, , London: Routledge; Malcolm, R., (1994) A Guidebook to Environmental Law, , London: Sweet and Maxwell; Penman, I., Environmental Concern: Implications for Supply Chain Management (1994) Logistics and Distribution Planning — Strategies for Management, pp. 165-172. , 2nd Ed., J. Cooper, London: Kogan Page Ltd., Chapter 10; Cooper, J., Browne, M., Peters, M., Green Logistics (1992) European Logistics: Markets, Management and Strategy, pp. 270-292. , London: Blackwell Business Ltd., Chapter 13; Howie, B., Environmental Impacts on Logistics (1994) An International Review of Logistics Practice and Issues, pp. 53-55. , Ed., G. Brace, Logistics Technology International; Howie, B., Environmental Impacts on Logistics (1994) An International Review of Logistics Practice and Issues, pp. 53-55. , Ed., G. Brace, Logistics Technology International; Steger, U., Managerial Issues in Closing the Loop (1996) Business Strategy and the Environment, 5 (4), pp. 252-268. , (December Page 66); Giuntini, R., An Introduction to Reverse Logistics for Environmental Management: A New System to Support Sustainability and Profitability (1996) Total Quality Environmental Management, pp. 81-87. , Spring; Jones, J.V., Life Cycle Cost (1995) Integrated Logistics Support Handbook, pp. 181-189. , 2nd Ed., McGraw-Hill Ltd., Chapter 18; Plc, B., (1995) How Green is My Front Door?, , B&Q Plc, July; Lamming, R., Hampson, J., The Environment as a Supply Chain Management Issue (1996) British Journal of Management, , Special Issue; Hopfenbeck, W., Integrative Approach to Logistics Management (1993) The Green Management Revolution — Lessons in Excellence, pp. 139-173. , London: Prentice Hall, Chapter 10; Hopfenbeck, W., Integrative Approach to Logistics Management (1993) The Green Management Revolution — Lessons in Excellence, pp. 139-173. , London: Prentice Hall; Wu, Haw-Jan, Dunn, S.C., Environmentally Responsible Logistics Systems (1995) The International Journal of Physical Distribution and Logistics Management, 25 (2), pp. 20-38; Bebbington, J., Gray, R., Thomson, I., Accountancy and Sustainability: Tentative Findings on the Meaning and Implementation of Sustainability (1994) Fourth Interdisciplinary Perspectives on Accounting Conference, , University of Manchester, July 11–13; Milne, M.J., Sustainability, The Environment and Management Accounting (1994) Fourth Interdisciplinary Perspectives on Accounting Conference, , University of Manchester, July 11–13; Azzone, G., Noci, G., Manzini, R., Welford, R., Young, C.W., Defining Environmental Performance Indicators: An Integrated Framework (1996) Business Strategy and the Environment, 5, pp. 69-80; (1995) Consulting the Stakeholder — a Profile of IBM U. K.'s Environmental Performance; Brown, L.G., Ward, R., Titus, E., Using New LCA Performance Metrics: Getting the Most Out of Your EMS (1996) Environmental Quality Management, 6 (1), pp. 3-10; Cahan, J., Schweiger, M., Product Life Cycle: The Key to Integrating EHS into Corporate Decision Making and Operations (1994) Total Quality Environmental Management, pp. 141-150. , (Winter); Bober, M.M., Mitchell, J.W., Bell, R.H., Hart, W.L., Corporate Performance Standards at Kodak (1993) Total Quality Environmental Management, pp. 177-180. , (Winter, /94); James, P., Bennet, M., Measuring the Environmental Performance of Business (1993), Ashridge: Ashridge Research Group; James, P., Wehrmeyer, W., Environmental Performance Measurement (1994), unpublished book chapter; Epstein, M.J., Improving Environmental Management with Full Environmental Cost Accounting (1996) Environmental Quality Management, 6 (1), pp. 11-22; Haines, R.W., Environmental Performance Indicators: Balancing Compliance with Business Economics (1993) Total Quality Environmental Management, pp. 367-372. , (Summer); Time to Measure Your Environmental Progress (1996) Environment Business Magazine, 6 (2), p. 17. , (March–April); Corbett, Charles, J., Van Wassenhove, L.N., How Green is Your Manufacturing Strategy? — Exploring the Impact of Environmental Issues on Manufacturing Strategy (1991), Series paper from INSEAD, France; Caplice, C., Sheffi, Y., A Review and Evaluation of Logistics Metrics (1994) The International Journal of Logistics Management, 5 (2), pp. 11-28; New, S.J., Payne, P., Research Frameworks in Logistics: Three Models, Seven Dinners and a Survey (1995) International Journal of Physical Distribution and Logistics Management, 25 (10), pp. 60-77; Cooper, J., Griffiths, J., Managing Variety in Automotive Logistics With the Rule of Three (1994) The International Journal of Logistics Management, 5 (2), pp. 29-46; Cooper, J., The Paradox of Logistics in Europe (1991) The International Journal of Logistics Management, 2 (2), pp. 43-54; Braithwaite, A., Christopher, M., Managing the Global Pipeline (1991) The International Journal of Logistics Management, 2 (2), pp. 55-62; van Amstel, P., Marinus, J., Verstegen, M.F.G.M., Development of a Total Logistics Concept: A Method for Improving Logistics Performance (1991) The International Journal of Logistics Management, 2 (2), pp. 63-73; Chow, G., Heaver, T.D., Henriksson, L.E., Strategy, Structure and Performance: A Framework for Logistics Research (1995) Logistics and Transportation Review, 31 (4), pp. 285-307; Narus, J.A., Anderson, J.C., Rethinking Distribution: Adaptive Channels (1996) Harvard Business Review, 74 (4), pp. 112-120. , (July–August); Christopher, M., Logistics and Competitive Strategy (1993) European Management Journal, 11 (2), pp. 258-261. , (June); Cooper, J., The Professional Vision — Logistics in the 21st Century (1994) Institute of Logistics Annual Conference Proceedings, , Birmingham, Institute of Logistics; Bytheway, A., Information in the Supply Chain: Measuring Supply Chain Performance (1995) Cranfield School of Management working paper, , SWP 1/95, Cranfield University; Doyle, P., Setting Business Objectives and Measuring Performance (1994) European Management Journal, 12 (2), pp. 123-132; Hastings, S., A Strategy Evaluation Model for Management (1996) Management Decision, 34 (1), pp. 25-34; Brown, M.D., Laverick, S., Measuring Corporate Performance (1994) Long Range Planning, 27 (4), pp. 89-98},
issn={09574093},
language={English},
abbrev_source_title={Int. J. Logist. Manage.},
document_type={Article},
source={Scopus},
}

@ARTICLE{Koota1998267,
author={Koota, P. and Takala, J.},
title={Developing a performance measurement system for world-class distribution logistics by using activity-based costing and management: Case: Basic metal industries},
journal={International Journal of Technology Management},
year={1998},
volume={16},
number={1-3},
pages={267-280},
doi={10.1504/ijtm.1998.002653},
note={cited By 7},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0031703341&doi=10.1504%2fijtm.1998.002653&partnerID=40&md5=0fdeed846a3e35060d92bdc2359e15f2},
affiliation={University of Vaasa, P.O. Box 700, FIN-65101, Vaasa, Finland; University of Vaasa, Finland; Åkerlund and Rausing Oy, Finland; Tampere University of Technology, Finland; Dept. of Production Economics, University of Vaasa, Finland; Helsinki University of Technology, University of Jyvåskylå, Finland},
abstract={The performance of distribution logistics has developed into a significant element in the competitiveness of an industrial company. The basis for the development has been the globalization of the markets in the 20th century, the customer's increased demand for service and the capital intensiveness of distribution logistics. In order to have control over the performance and capability to direct the development efforts effectively, the formulation of performance needs to be measured. The previous research within the field of measuring logistics performance has presented various dimensions of performance from a certain viewpoint, e.g. physical, economic, internal and external measures. However, the use of performance measures in practise for strategic and operative business management requires a limited set of measures to ensure the top corporate performance. This paper discusses the formulation and measurement of performance in distribution logistics by using a descriptive and constructive research approach. Distribution logistics is considered as an interactive part of a company's total business process and supply chain. The following aspects of performance measurement are discussed: the development of measures, today's needs for measurement and different roles of performance measurement. Performance is approached from aspects vital to successful business management in today's environment, including both internal and external viewpoints. Discussion also reflects distribution logistics role in a company's business process on strategic and operational levels. Activity Based Costing and Management (ABC/ABM) is introduced as a tool for producing relevant information for performance measurement and an overall measurement system is developed in a participative case study in a huge and globally competitive company in basic metal industries. The system is designed to measure the operational performance of the distribution logistics which provides information also for strategic decision making. It was found that a conventional cost accounting system built mainly for the registration of costs does not provide the managers with the information needed to manage performance and to identify areas for potential improvement. With ABC/ABM the performance could be measured and analysed starting from the activity level and proceeding to larger totalities. The inefficiencies were located and the structuring of profits and costs became more visible. The system consists six of different measurements. The viewpoints represented include internal and external, physical and economic, soft and hard as well as the suppliers' performance backwards in the supply chain and the customers' performance forward. As a result, the following findings were achieved in the performance measurement system: importance of internal customerships to performance, formulation of performance in own activities, effect of distribution logistics performance on the overall profitability, level of service realised in own operation and development of the highly emphasized innovativeness in the distribution logistics. The results of the development of the distribution logistics (with its development tools) may be generally utilized, e.g. in basic metal and forest industries having bulk production.},
author_keywords={Activity based analysis;  Basic metal industries;  Distribution logistics;  Performance measurement},
keywords={Competition;  Cost effectiveness;  Decision making;  Decision theory;  Distribution of goods;  Marketing;  Performance;  Resource allocation;  Strategic planning;  Value engineering, Activity based costing and management (ABC/ABM);  World class distribution logistics, Industrial management},
references={(1995) Logistiikan Tunnusluvut ja Mittarit, , Väliraportti. Ministry of Traffic, Helsinki, Finland; Korpela, J., (1994) An Analytic Approach to Distribution Logistics Strategic Management, , Lappeenranta University of Technology publications, Lappeenranta, Finland; Bowersox, D.J., Closs, D.J., Helferich, O.K., (1986) Logistical Management, , Macmillan Publishing Company, New York, USA; Lehtola, R., (1993) Logistinen Päätöksentekomalli: Kvantitatiivinen Tapa Analysolda Yritystä ja Liiketoimintaketjua Logistisesta Näkökulmasta, , Tampere University of Technology publications, Tampere, Finland; Abrahamsson, M., Strategisk och operativ effektivitet (1993) NoFoMa 1993 Congress Publication, , Publication Turku, Finland; (1989) Performance Indicators in Logistics: Approach and Coherence, , Spinger-Verlag, Berlin; Mentzer, J.T., Konrad, B.P., An efficiency/effectiveness approach to logistics performance analysis (1991) Journal of Business Logistics, 12 (1), pp. 32-62; Novack, R., Logistics control: An approach to quality (1989) Journal of Business Logistics, 10 (2), pp. 24-43; Sartjärvi, T., (1992) Logistiikka Kilpailutekijänä. Suomen Kuljetustaloudellinen Yhdistys RY, , Keuruu, Finland; Uusi-Rauva, E., (1994) Ohjauksen Tunnusluvut ja Suoritusten Mittaus. Opetusmonisteita 2/94, , Tampere University of Technology, Tampere, Finland; Aronsson, H., Andersson, P., Storhagen, N., (1988) Materialadministriva Mått Och Mätmetoder, , Studentlitteratur, Lund, Sverige; Pär, A., Aronsson, H., Storhagen, N., Measuring logistics performance (1989) Production Economics: State-of-the-Art and Perspectives, pp. 253-262. , publication (Ed.) Grubbström, R.W, Hinterhuber,H.H, Elsevier, Netherlands; Pohlen, T.L., (1993) The Effect of Activity-Based Costing on Logistics Management, , UMI Dissertation Services, Michigan, USA; Brimson, J.A., (1991) Activity Accounting: An Activity-Based Costing Approach, , John Wiley & Sons, New York, USA; Turney, P.B.B., The ABC performance breakthrough. How to succeed with activity-based costing (1991) Cost Technology, , Hillsboro; Lumijärvi, O.-P., (1995) Toimintolaskenta Johtamisen Apuvälineenä. Tilisanomat Nr. 4, pp. 50-53. , Helsinki, Finland; Lynch, R.L., Cross, K.F., (1991) Measure Up! Yardsticks for Continuous Improvement, , Blackwell Publishers, Massachuttes},
correspondence_address1={Koota, P.; University of Vaasa, P.O. Box 700, FIN-65101, Vaasa, Finland},
publisher={Inderscience Publishers},
issn={02675730},
coden={IJTME},
language={English},
abbrev_source_title={Int J Technol Manage},
document_type={Article},
source={Scopus},
}

@CONFERENCE{NoAuthor1995,
title={Proceedings of the 1995 IEEE International Symposium on Electronics and the Environment, ISEE},
journal={IEEE International Symposium on Electronics & the Environment},
year={1995},
page_count={324},
note={cited By 0},
url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029218212&partnerID=40&md5=1df477a6cdb643f90ee678367a2cb6df},
abstract={The proceedings contains 58 papers from the IEEE International Symposium on Electronics and the Environment. Topics discussed include: environmental protection; environmental technology development; life-cycle product planning; integrated environmental performance measurement system; design for environment (DFE) methodology; alternative software technologies for life cycle management; asset recovery; cleaning technologies; electronic equipment manufacturing; supply chain management; cost models for end-of-life consumer electronics; and environmental product regulations and standards.},
keywords={Ecosystems;  Electronic equipment manufacture;  Environmental impact;  Environmental protection;  Industrial economics;  Industrial waste disposal;  Laws and legislation;  Product design;  Production control;  Recycling;  Service life;  Standards, Asset recovery;  Design for environment (DFE) methodology;  EiRev;  Environmental management system;  Life cycle product planning;  Product end of life management (PELM);  Supply chain management;  Sustainable development, Electronics industry},
editor={Anon},
publisher={IEEE, Piscataway, NJ, United States},
coden={85OPA},
language={English},
abbrev_source_title={IEEE Int Symp Electron Environ},
document_type={Conference Review},
source={Scopus},
}